Obstructive Sleep Apnea: Pathophysiology, Comorbidities, and Consequences (Sleep Disorders)

Obstructive Sleep Apnea Pathophysiology, Comorbidities, and Consequences

SLEEP DISORDERS

Advisory Board Antonio Culebras, M.D. Professor of Neurology Upstate Medical University Consultant, The Sleep Center Community General Hospital Syracuse, New York, U.S.A.

Anna Ivanenko, M.D., Ph.D. Loyola University Medical Center Department of Psychiatry and Behavioral Neuroscience Maywood, Illinois, U.S.A.

Clete A. Kushida, M.D., Ph.D., RPSGT Director, Stanford Center for Human Sleep Research Associate Professor, Stanford University Medical Center Stanford University Center of Excellence for Sleep Disorders Stanford, California, U.S.A.

Nathaniel F. Watson, M.D. University of Washington Sleep Disorders Center Harborview Medical Center Seattle, Washington, U.S.A.

1. Clinician’s Guide to Pediatric Sleep Disorders, edited by Mark A. Richardson and Norman R. Friedman 2. Sleep Disorders and Neurologic Diseases, Second Edition, edited by Antonio Culebras 3. Obstructive Sleep Apnea: Pathophysiology, Comorbidities, and Consequences, edited by Clete A. Kushida 4. Obstructive Sleep Apnea: Diagnosis and Treatment, edited by Clete A. Kushida

Obstructive Sleep Apnea Pathophysiology, Comorbidities, and Consequences

Edited by

Clete A. Kushida

Stanford University Stanford, California, USA

Cover art (middle panel) courtesy of Brian Palmer, DDS. Informa Healthcare USA, Inc. 52 Vanderbilt Avenue New York, NY 10017 © 2007 by Informa Healthcare USA, Inc. Informa Healthcare is an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-10: 0-8493-9180-6 (Hardcover) International Standard Book Number-13: 978-0-8493-9180-4 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequence of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, please access www. copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging-in-Publication Data Obstructive sleep apnea: Pathophysiology, comorbidities, and consequences / edited by Clete A. Kushida. p. ; cm. -- (Sleep disorders ; 3) Includes bibliographical references and index. ISBN-13: 978-0-8493-9180-4 (hb : alk. paper) ISBN-10: 0-8493-9180-6 (hb : alk. paper) 1. Sleep apnea syndromes. I. Kushida, Clete Anthony, 1960- II. Title: Pathophysiology, comorbidities, and consequences. III. Series: Sleep disorders (New York, N.Y.) ; 3. [DNLM: 1. Sleep Apnea, Obstructive--physiopathology. 2. Sleep Apnea, Obstructive--complications. WF 143 O139 2007] RC737.5.O267 2007 616.2’09--dc22

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Preface

When in doubt, pressurize the snout. —attributed to Philip R. Westbrook

I often thought of this mantra during my on-call nights when, as a Stanford sleep medicine fellow, I was awakened from sleep by a technologist informing me that one of the clinic patients had repetitive obstructive apneas with significant oxygen desaturations. The technologist would typically ask, can I start the patient on CPAP? Invariably, I would mutter a drowsy “yes,” often chiding myself that on the previous day I should have clearly written the respiratory thresholds for starting continuous positive airway pressure on the patient’s sleep-study order sheet. This anecdote illustrates the fact that continuous positive airway pressure has become such an important and ubiquitous treatment for obstructive sleep apnea since its development over a quarter century ago. The modern sleep specialist has new diagnostic tools and other treatments, such as upper airway surgery and oral appliances, for patients with obstructive sleep apnea; nevertheless, our field is still in its adolescence with respect to the diagnosis and treatment of obstructive sleep apnea and other sleep disorders. The reader might wonder why a neurologist is editing a two-volume set of books on obstructive sleep apnea, since it is a sleep-related breathing disorder and would therefore appear to be within the domain of pulmonary physicians. However, besides pulmonologists, neurologists, psychiatrists, internists, pediatricians, and otolaryngologists have entered the field of sleep medicine. Many clinicians now treat patients with sleep disorders on a full-time basis. Sleep medicine has truly become multidisciplinary, and a sleep clinician is expected to diagnose and treat a wide range of sleep disorders, from insomnia to restless legs syndrome, that were previously referred by internists to other specialists. It is indeed a testament to the ever-increasing knowledge base on obstructive sleep apnea that there is a need for a two-volume set of books on this topic. This book covers the pathophysiology, comorbidities, and consequences of obstructive sleep apnea, with sections exploring the features, factors, and characteristics of this disorder as well as its associations and consequences. The second volume, Obstructive Sleep Apnea: Diagnosis and Treatment, focuses on the diagnosis and treatment of obstructive sleep apnea, and includes a section on special conditions, disorders, and clinical issues. The authors and I have tried to conform the conditions and disorders described in this book to the second edition of the International Classification of Sleep Disorders: Diagnostic & Coding Manual published by the American Academy of Sleep Medicine in 2006, although some terms, such as obstructive sleep apnea syndrome and sleep-disordered breathing, have been retained in a few statements when appropriate. We have also tried to discuss new entities and findings such as complex sleep apnea, oxidative stress, cyclic alternating pattern, and adaptive servo-ventilation. However, given the rapidity with which the area of sleep medicine is advancing, it is highly conceivable that two volumes might not be sufficient to cover the topic of obstructive sleep apnea in just a few short years! iii

iv

Preface

These books could not exist without the excellent contributions of a talented group of international authors; their detailed and comprehensive works are greatly appreciated. I am deeply indebted to the renowned and true pioneers of our field of sleep, William Dement, Christian Guilleminault, Sonia Ancoli-Israel, Chris Gillin, and Allan Rechtschaffen, who served as my mentors through various stages of my career. In all of my endeavors, I can always count on my parents, Samiko and Hiroshi Kushida, to assist me; these books were no exception. I have been very fortunate to serve, along with Dr. Dement, as Principal Investigator of the multicenter, randomized, double-blind, placebo-controlled Apnea Positive Pressure Long-Term Efficacy Study, sponsored by the National Heart, Lung, and Blood Institute of the National Institutes of Health. To date, this is the largest controlled trial funded by the National Institutes of Health in the field of sleep. This book is dedicated not only to my parents but also to the marvelous core team of the Apnea Positive Pressure Long-Term Efficacy Study, consisting of William Dement, Pamela Hyde, Deborah Nichols, Eileen Leary, Tyson Holmes, Dan Bloch, as well as National Heart, Lung, and Blood Institute officials (Michael Twery and Gail Weinmann), site directors, co-ordinators, consultants, committee members, key Stanford site personnel (Chia-Yu Cardell, Rhonda Wong, Pete Silva, Jennifer Blair), Data and Safety Monitoring Board members, and other personnel without whom this project could not have functioned in such a meticulous and efficient manner. It is my sincere hope that the reader will strive to become expert in the field of sleep. Although there is always room for improvement, awareness of sleep disorders by patients, physicians, and the general public is at an all-time high. However, available funding for sleep research and the number of young investigators interested in a career in basic or clinical sleep research are areas that need enhancement. The interested reader can directly contribute to this field in several ways: applying for membership in the American Academy of Sleep Medicine or Sleep Research Society, serving on committees in these organizations, becoming board certified in sleep medicine, submitting a sleep-related grant proposal to the National Institutes of Health, and/or just simply learning more about sleep and its disorders. Lastly, etched forever in my memory is a sticker posted on the door of Mary Carskadon’s former office at Stanford that contained words to live by: “Be alert. The world needs more lerts.” Clete A. Kushida

Contents

Preface . . . . iii Contributors . . . . vii 1. Perspectives 1 Clete A. Kushida SECTION I: FEATURES, FACTORS, AND CHARACTERISTICS 2. History 11 Michael Zupancic and Peretz Lavie 3. Epidemiology Kin M. Yuen

27

4. Ontogeny 39 Timothy F. Hoban and Donald L. Bliwise 5. Phylogeny and Animal Models: An Uninhibited Survey 61 Todd D. Morgan and John E. Remmers 6. Upper Airway Anatomy 81 Avery Tung 7. Physiology and Dynamics of the Upper Airway 93 Jingtao Huang and Carole L. Marcus 8. Upper Airway Pathology 111 John G. Park and Teofilo Lee-Chiong 9. Control of Breathing in Sleep 125 Curtis A. Smith, Jerome A. Dempsey, Steven R. Barczi, and Ailiang Xie 10. Arousal from Sleep 149 Péter Halász 11. Pathogenesis 171 Anh Tu Duy Nguyen, Susie Yim, and Atul Malhotra 12. Risk Factors 197 Kannan Ramar and Christian Guilleminault 13. Familial and Genetic Factors 223 Sanjay R. Patel and Peter V. Tishler v

vi 14. The Spectrum of Sleep-Disordered Breathing 245 Adnan Habib and Barbara Phillips SECTION II: ASSOCIATIONS AND CONSEQUENCES 15. Morbidity and Mortality 259 Christine Won and Dominique Robert 16. Central and Autonomic Nervous Systems 275 Ian M. Colrain and John Trinder 17. Cardiac Arrhythmias and Congestive Heart Failure 293 Sheree Chen and T. Douglas Bradley 18. Hypertension and the Cardiovascular System 323 Rohit Budhiraja and Stuart F. Quan 19. Endocrine Function and Glucose Metabolism 337 Katherine Stamatakis and Naresh M. Punjabi 20. Obesity 355 Mark Eric Dyken, Mohsin Ali, Shekar Raman, and Kim E. Eppen 21. Mood and Behavior 377 Mark S. Aloia and Amanda Schurle Bruce 22. Sleepiness 393 Douglas B. Kirsch and Ronald D. Chervin 23. Health-Related Quality-of-Life 415 Cheryl A. Moyer, Jeffrey S. Moyer, and Ronald D. Chervin 24. Driving Risk and Accidents 443 Patricia Sagaspe and Pierre Philip 25. Economic and Societal Impact 451 Valérie Wittmann and Daniel O. Rodenstein Index . . . . 473

Contents

Contributors

Mohsin Ali State University of New York, Upstate Medical Center, Syracuse, New York, U.S.A. Mark S. Aloia Department of Medicine, National Jewish Medical and Research Center, Denver, Colorado, U.S.A. Steven R. Barczi Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A. Donald L. Bliwise Sleep Disorders Center, Department of Neurology, Emory University Medical School, Atlanta, Georgia, U.S.A. T. Douglas Bradley Department of Medicine, Toronto General Hospital of the University Health Network, Toronto, Ontario, Canada Amanda Schurle Bruce Department of Medicine, National Jewish Medical and Research Center, Denver, Colorado, U.S.A. Rohit Budhiraja Division of Pulmonary and Critical Care Medicine, Southern Arizona VA Healthcare System, Tucson, Arizona, U.S.A. Sheree Chen

Kaiser Permanente, Vallejo, California, U.S.A.

Ronald D. Chervin Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan, U.S.A. Ian M. Colrain Human Sleep Research Program, SRI International, Menlo Park, California, U.S.A. Jerome A. Dempsey The John Rankin Laboratory of Pulmonary Medicine, Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A. Mark Eric Dyken Sleep Disorders Center, Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, U.S.A. Kim E. Eppen

University of Iowa Hospitals and Clinics, Iowa City, Iowa, U.S.A.

Christian Guilleminault Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, U.S.A. Adnan Habib The Division of Pulmonary, Critical Care, and Sleep Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, U.S.A. Péter Halász Department of Neurology, National Institute of Psychiatry and Neurology, Budapest, Hungary Timothy F. Hoban The Michael S. Aldrich Sleep Disorders Center, Departments of Pediatrics and Neurology, University of Michigan, Ann Arbor, Michigan, U.S.A. vii

viii

Contributors

Jingtao Huang Sleep Center, The Childrens’ Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A. Douglas B. Kirsch Division of Sleep Medicine, Department of Internal Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts, U.S.A. Clete A. Kushida Stanford University, Stanford, California, U.S.A. Peretz Lavie

Sleep Medicine Center, Rambam Medical Center, Haifa, Israel

Teofilo Lee-Chiong National Jewish Medical and Research Center, Denver, Colorado, U.S.A. Atul Malhotra Sleep Disorders Program, Brigham and Women’s Hospital, Boston, Massachusetts, U.S.A. Carole L. Marcus Sleep Center, The Childrens’ Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A. Todd D. Morgan

Scripps Memorial Hospital, Encinitas, California, U.S.A.

Cheryl A. Moyer Global REACH, Department of Medical Education, University of Michigan Medical School, Ann Arbor, Michigan, U.S.A. Jeffrey S. Moyer Division of Head and Neck Surgery, Department of Otolaryngology, University of Michigan Hospital, Ann Arbor, Michigan, U.S.A. Anh Tu Duy Nguyen Sleep Disorders Program, Brigham and Women’s Hospital, Boston, Massachusetts, U.S.A. John G. Park

Mayo Clinic College of Medicine, Rochester, Minnesota, U.S.A.

Sanjay R. Patel Sleep and Epidemiology Research Center, Department of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A. Pierre Philip Clinique du Sommeil CHU Pellegrin, Université Bordeaux 2, CNRS UMR-5227, Bordeaux, France Barbara Phillips The Division of Pulmonary, Critical Care, and Sleep Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, U.S.A. Naresh M. Punjabi Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland, U.S.A. Stuart F. Quan Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, Arizona, U.S.A. Shekar Raman University of Iowa Hospitals and Clinics, Iowa City, Iowa, U.S.A. Kannan Ramar Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, U.S.A. John E. Remmers Respiratory Research Group, University of Calgary, Calgary, Alberta, Canada Dominique Robert Emergency and Intensive Care Department, Edouard Herriot Hospital, Lyon, France

ix

Contributors

Daniel O. Rodenstein Service de Pneumologie, Cliniques universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium Patricia Sagaspe Clinique du Sommeil CHU Pellegrin, INRETS, Bordeaux, France Curtis A. Smith The John Rankin Laboratory of Pulmonary Medicine, Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A. Katherine Stamatakis Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, U.S.A. Peter V. Tishler Partners Center for Genetics and Genomics, Channing Laboratory, Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts, U.S.A. John Trinder Department of Psychology, University of Melbourne, Parkville, Victoria, Australia Avery Tung

University of Chicago Hospitals, Chicago, Illinois, U.S.A.

Valérie Wittmann Service de Pneumologie, Cliniques universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium Christine Won Stanford University Center of Excellence for Sleep Disorders, Stanford, California, U.S.A. Ailiang Xie Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A. Susie Yim Sleep Disorders Program, Brigham and Women’s Hospital, Boston, Massachusetts, U.S.A. Kin M. Yuen Stanford University Center of Excellence for Sleep Disorders, Stanford, California, U.S.A. Michael Zupancic Department of Neurology, University of Michigan Sleep Disorders Center, Ann Arbor, Michigan, U.S.A.

1

Perspectives Clete A. Kushida Stanford University, Stanford, California, U.S.A.

There are more than a few primary, fundamental questions in the field of sleep that remain unanswered. Research into our field is still in its infancy, with only a little over 50 years since the discovery of rapid eye movement (REM) sleep that initiated the systematic, scientific exploration of sleep and its disorders. Any list of the most important questions to resolve in sleep would probably contain the following: ■ ■ ■ ■

Why do we sleep (i.e., what is the function of sleep)? Where is sleep controlled in the brain? What determines the onset of sleep? How can we cure obstructive sleep apnea?

Of these content areas, the question that has direct clinical relevance is the one regarding obstructive sleep apnea (OSA). Sleep apnea is also arguably the most important disorder of sleep, since it is the one sleep disorder that the majority of sleep clinicians spend the bulk of their time diagnosing and treating and it has serious consequences to the affected individual and society as well. Unfortunately, the current therapies either do not effectively treat this disorder or enable high patient adherence in a large proportion of patients. However, there is hope as technology and molecular procedures, such as those involving stem cells, advance and improve over time. Since Terry Young’s landmark epidemiology study published in 1993 (1) showed that a quarter of men and about 10% of women between the ages of 30 and 60 years have polysomnographic evidence of OSA, we know that it is one of the more highly prevalent diseases in the world. Although the pathogenesis of this disorder has not been conclusively demonstrated, risk factors for the development of OSA, such as obesity, craniofacial disproportion, and ventilatory control abnormalities, have been identified. The first-line treatment modality for OSA is nasal continuous positive airway pressure (CPAP), which was invented a little over 25 years ago. This consists of a portable device that provides a fan-generated continuous flow of air into the upper airway via a mask fitted over the nose; this airflow splints open the airway, thereby preventing its collapse. CPAP does have its limitations, particularly in terms of patient adherence that often stems from discomfort due to the mask. Randomized controlled clinical trials (Table 1) that evaluated adherence to both sham and active CPAP interventions and reported hours of use, have demonstrated that the mean nightly active CPAP use was 4.46 hours (2). Surprisingly, OSA patients on sham CPAP were slightly better in their mean nightly sham CPAP use (4.85 hours) (2). Despite the limitations of CPAP, it is the most commonly prescribed treatment for OSA. CPAP has been shown to decrease abnormal respiratory events and arousals; however, there is some controversy whether sleep architecture is significantly improved (3,4). Nevertheless, untreated OSA has been associated with hypertension, 1

32 52/49 23/18 20/14 21/18 32/37 20/16 29/25 27/18 66/59 24/23 20/18 15/13 53/51 16/16 21/18

4 wk 4 wk 1 wk 1 wk 1 wk 4 wk 1 wk 6 wk 35 days 6 mo 6 wk 10 days 8 wk 4 wk 9 wk 1 wk

Duration

Control — 4.6 >5 5.2 ± 1.2 >5 5.0 4.9 ± 0.3 4 ± 0.5 4.9–5.2 — 4.5 ± 2 Unknown — 4.5 ± 2.4 5.4 ± 2.2 >5

Active 3.7 ± 0.4 5.4 >5 5.6 ± 1.1 >5 5.6 5.5 ± 0.3 5 ± 0.4 5.8–5.9 4.8 ± 2.2 4.25 ± 2 Unknown 3.5 ± 2.1 4.9 ± 2.0 5.5 ± 2.0 >5

Adherence (hr)

N/A 0.035 — n.s. — n.s. n.s. n.s. n.s. N/A N/A n.s. N/A n.s. n.s. —

Diff

Placebo tablet, AHI = 49 ± 1.5, crossover Sham (1 cmH2O), O2 desaturations for OSA diagnosis Sham (2 cmH2O), AHI 56.4 vs. 44.2 Sham (2 cmH2O), AHI 45.9 vs. 35.2 Sham (2 cmH2O), AHI 53.6 vs. 41.7 Sham (1 cmH2O), O2 desaturations for OSA diagnosis Sham (2 cmH2O), AHI 4.36 vs. 56.8 Sham, AHI ≥ 30, ESS < 10 Sham (0–1 cmH2O), AHI 62.1 vs. 68.1 Sleep hygiene + weight loss, AHI 20 vs. 21 Sham, AHI 50.5 vs. 57.1 Sham (2 cmH2O), AHI 54 vs. 39 Placebo tablet, AHI 12.9 ± 6.3 Sham (1 cmH2O), O2 desaturations for OSA diagnosis Sham (3–4 cmH2O), AHI 62.5 vs. 65 Sham (2 cmH2O), AHI 53.6 vs. 41.7

Type of control and OSA criteria

Abbreviations: AHI, apnea-hypopnea index; ESS, Epworth sleepiness scale; N/A, not applicable; n.s., not significant; OSA, obstructive sleep apnea; ref, reference.

Engleman (1994) (44) Jenkinson (1999) (35) Loredo (1999) (3) Yu (1999) (38) Dimsdale (2000) (53) Hack (2000) (51) Bardwell (2001) (54) Barbe (2001) (52) Henke (2001) (55) Monasterio (2001) (56) Montserrat (2001) (57) Ziegler (2001) (58) Barnes (2002) (59) Pepperell (2002) (60) Becker (2003) (61) Profant (2003) (62)

n

Adherence Data from Controlled Trials

Primary author (year) (ref)

TABLE 1

2 Kushida

Perspectives

3

myocardial infarction, cardiac failure, stroke, cardiac dysrhythmias, increased risk for industrial and motor vehicle accidents, and sudden death (5,6). In fact, data from the Sleep Heart Health Study, a multi-center observational project funded by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH), showed a linear relationship between severity of sleep-disordered breathing (SDB) and hypertension (7,8). There is also an emerging body of evidence (9,10) that neurocognitive abilities, especially in the domains of attention, working memory, and executive function, may be impaired with OSA. The major studies on the effects of OSA on neurocognitive function are summarized in Table 2. The neurocognitive function tests administered to the OSA subjects are typically segregated into the domains of attention and psychomotor (A/P) function, learning and memory (L/M), and executive and frontallobe (E/F) function. Engleman et al. (10) reviewed some of the case-control studies in Table 2; review of these studies indicated that community-acquired subjects with mild average indices of SDB showed slight attentional and executive function impairment. Those studies with moderate and severe SDB indices revealed moderate and large impairment in all three areas of neurocognitive function. However, relatively small sample sizes and inadequate control groups handicapped these earlier studies. In addition, newer technologies, such as the Sustained Attention Metric (11–18) and functional magnetic resonance imaging (MRI) have not been used to systematically evaluate neurocognitive function in OSA patients. The etiology of the decline in neurocognitive function with OSA is unknown. The theory that the hypoxemia of OSA is responsible for this decline is controversial; prior research on OSA patients (19) and hypoxemic chronic obstructive pulmonary disease (COPD) patients (20,21) failed to find a relationship between measures of hypoxemia and neurocognitive function. However, other investigators (22) have found that OSA patients with hypoxemia were significantly more cognitively impaired than OSA patients without hypoxemia. Another theory is that the decline in neurocognitive function with OSA is related to sleepiness. This does not appear to be completely true; OSA patients performed worse in neuropsychological tests than both healthy volunteers and patients with other disorders of excessive sleepiness (19). Perhaps the most parsimonious explanation is that these OSA-related neurocognitive deficits are the result of a combination of both hypoxemia and decreased vigilance; some investigators (23,24) found that these deficits in OSA patients were associated with both of these variables. CPAP has been shown to improve neurocognitive function in a few studies with limited sample sizes. These studies have demonstrated improvements in tests of A/P function (25,26), tests of L/M (25), as well as in tests of E/F function (27). However, other investigators have detected persistent deficits in similar measures of psychomotor, short-term memory, and executive function with CPAP use, indicating that some deficits may be due to irreversible anoxic central nervous system damage (28–31). The etiology of the daytime sleepiness associated with OSA is unknown. It is widely believed that the SDB of OSA results in brief arousals from sleep, which, in turn, fragments sleep and produces daytime sleepiness. However, this hypothesis has not been adequately tested, and there is controversy regarding whether it is accurate (26,32,33). Regardless of the etiology of this sleepiness, this symptom is a primary criterion for the diagnosis of OSA. CPAP has been demonstrated to improve the sleepiness associated with OSA both in long-term

CS CC CC CS CS CS CC CC CC CH CC RCT RCT RCT RCT RCT RCT RCT RCT RCT RCT

Findley (1986) (22)

Greenberg (1987) (19) Bédard (1991) (23)

Presty (1991) (63) Cheshire (1992) (64)

Telakivi (1993) (65) Ingram (1994) (66) Naëgelé (1995) (67) Verstraeten (1996) (68) Kim (1997) (69)

Redline (1997) (70) Engleman (1997) (71) Engleman (1998) (45) Engleman (1999) (40) Hack (2000) (51) Barbé (2001) (52) Bardwell (2001) (54) Henke (2001) (55) Monasterio (2001) (56) Barnes (2002) (59) Barnes (2004) (72)

32/20 16 23 34 59 29/25 36 46 42 42 80

31 16/43 17/17 26/22 199/642

119 29

14/14 20/10

26

n

Mild–mod Mild Mod–severe Mild Mild–severe Severe Mod–severe Mod–severe Mild–mod Mild–mod Mild–mod

Mild–severe Mild–severe Severe Mild–severe Mild–severe

Mild–severe Mod–severe

Severe Mod–severe

Severe

OSA severity

Conclusions Improved in 4/8 A/P, L/M, and E/F tests for hypoxemic vs. non-hypoxemic OSA subjects Improveda in 7/14 A/P and E/F tests vs. controls Improveda in 7/9 A/P and 2/4 E/F tests; decreasea in 5/6 L/M tests (only severe cases) Improveda in A/P and L/M tests for those OSA patients with severe hypoxia Correlationa between AHI and 1/2 EF tests and IQ decrease; no correlation in 3 A/P or 1 L/M tests No correlation between hypoxia or sleepiness and 7 A/P, L/M, and E/F tests No difference in OSA vs. controls subjects ≥ 54 yrs for 1 A/P test Improveda in 1/4 A/P tests, 8/10 L/M tests, and 3/9 E/F tests vs. controls No differences in OSA vs. insomnia subjects for 6 A/P, L/M, or E/F tests Negative associationa between log AHI and psychomotor efficiency in 8 A/P, L/M, or E/F tests Improveda in 1/4 A/P tests, 0/3 L/M tests, and 1/5 E/F tests vs. controls Improveda mental flexibility Improveda in 2/3 A/P tests, 0/2 L/M tests, and 0/4 E/F tests vs. controls Improveda in 1/3 A/P tests and 1/3 E/F tests vs. controls Improved driving simulation No difference in active vs. sham CPAP groups for 8 A/P, L/M, and E/F tests Improveda in 1/2 A/P tests, 0/4 L/M tests, and 0/2 E/F tests vs. controls Improveda in 2/3 A/P tests, 3/3 L/M tests, and 2/3 E/F tests vs. controls Improveda in 0/4 A/P tests, 0/2 L/M tests, and 0/8 E/F tests vs. controls Improveda in 0/3 A/P tests, 0/2 L/M tests, and 1/4 E/F tests vs. controls Improveda in 0/2 A/P and 2/5 E/F tests vs. controls

a

a

Significance level, p < 0.05. OSA severity by average apnea-hypopnea index (AHI), with mild = 5–15 events/hr, moderate = 15–30 events/hr, and severe >30 events/hr. Abbreviations: A/P, tests of attention and psychomotor function; CC, case-control; CH, cohort; CPAP, continuous positive airway pressure; CS, case series; E/F, tests of executive and frontal lobe function; IQ, intelligence quotient; L/M, tests of learning and memory; OSA, obstructive sleep apnea; RCT, randomized control trial; ref, reference.

Study type

Review of Studies on Neurocognitive Function in OSA Patients

Primary author (year) (ref)

TABLE 2

4 Kushida

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5

studies (34) and in comparisons with subtherapeutic CPAP (35); however, in the former limited studies, this improvement did not approach the baseline levels of controls without OSA. The effects of CPAP on mood states in OSA patients are largely unknown. The results have been mixed, with some studies reporting improvement (36,37), others revealing significant improvement in both active CPAP and placebo groups (38), or finally others showing no significant effects (25,39). However, these studies were limited by their use of small sample sizes, standard and nonstandard indices of mood state, and different types of controls. Quality of life assessment has become an integral component of health outcomes research. This assessment is still in its infancy for the evaluation of sleep apnea patients. Nevertheless, a few studies with small sample sizes have documented significant improvement in quality of life indices following CPAP treatment in OSA patients (26,35,40,41). Up to the present time, there has never been a multicenter, randomized, double-blind, placebo-controlled, long-term study to systematically investigate the therapeutic effectiveness of CPAP, despite its widespread use as the primary treatment for OSA (42) and the perception by the majority of patients with OSA that it is a successful treatment for this disorder (43). To date, the efficacy of CPAP has generally been evaluated against control groups that do not meet the requirements of placebo groups since the controls are not subjected to the same instrumental constraints (44–47) as the experimental group. This lack of adequate placebocontrolled studies combined with the patient costs of CPAP, has called into question the usefulness of CPAP as a primary therapy for OSA (48,49). The NHLBI-funded, multicenter Apnea Positive Pressure Long-term Efficacy Study (APPLES), under the leadership of Dr. William C. Dement and Dr. Clete A. Kushida is designed to examine this question. Although there is some evidence as described above that OSA affects these areas of human function and that these effects may be reversed with CPAP, it has never been evaluated in a comprehensive, systematic, and well-controlled manner. The primary goal of APPLES is to test the hypothesis that CPAP therapy results in significant, stable, and longterm benefits to neurocognitive function, mood, sleepiness, and quality of life in patients with OSA. The study evaluates this hypothesis by administering a comprehensive yet novel test battery containing measures of neurocognitive function, mood, sleepiness, and quality of life on over a thousand OSA subjects in the United States assigned to either active or sham (subtherapeutic) CPAP therapy in a double-blinded and randomized manner (50). Subtherapeutic CPAP has been used successfully as a placebo in a few studies (3,35,51,52). For APPLES, the use of the active vs. sham CPAP devices will evaluate the long-term benefits of CPAP therapy for a six-month period. The major risk of the study is that the subjects randomized to the sham CPAP condition are without effective treatment for a seven-month period from the time of enrollment into the study. However, this period of subtherapeutic treatment for OSA appears brief considering that OSA remains largely undiagnosed and untreated by physicians, there is a significant delay from appearance of symptoms to seeking treatment on the part of the patient, and the majority of sleep clinics and laboratories have very lengthy waiting lists. We are also incorporating functional magnetic resonance imaging (fMRI) in APPLES, because the use of these technologies will assist us in determining if OSA-related neurocognitive deficits are associated with changes in cortical activation, and whether CPAP can reverse these changes.

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If, as anticipated, we find that CPAP usage results in improved daily function for OSA patients, the most important benefit of the study includes advancing the knowledge that CPAP can effectively treat the debilitating consequences of OSA in the areas of neurocognitive function, mood, sleepiness, and quality of life. We further anticipate providing the necessary evidence to symptomatic individuals and health care providers that CPAP has a lasting positive impact on the nature of this sleep-related breathing disorder. APPLES is designed to evaluate neurocognitive function, mood, sleepiness, and quality-of-life in OSA patients; however, there are other known consequences of OSA, which impact the cardiac, cardiovascular, and endocrine systems, as well as affecting a patient’s alertness, mental state, and driving ability. These individual and societal domains of human existence that are affected by OSA are described in this two-volume set of books on obstructive sleep apnea. In addition, these books also discuss upper airway surgery, oral appliances, and adjunctive and alternative treatments, all of which constitute the other major therapeutic approaches for OSA. It is the sincere hope of this author that these books will stimulate further research into the effects and consequences of OSA as well as its treatment, so that we can successfully diagnose and cure the majority of patients with this debilitating disease. REFERENCES 1. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328:1230–1235. 2. Gay P, Weaver T, Loube D, et al. Evaluation of positive airway pressure treatment for sleep related breathing disorders in adults. Sleep 2006; 29(3):381–401. 3. Loredo JS, Ancoli-Israel S, Dimsdale JE. Effect of continuous positive airway pressure vs placebo continuous positive airway pressure on sleep quality in obstructive sleep apnea. Chest 1999; 116:1545–1549. 4. Fietze I, Quispe-Bravo S, Hansch T, et al. Arousals and sleep stages in patients with obstructive sleep apnoea syndrome: Changes under nCPAP treatment. J Sleep Res 1997; 6(2):128–133. 5. Weiss JW, Launois SH, Anand A. Cardiorespiratory changes in sleep-disordered breathing. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine. Philadelphia: WB Saunders, 2000:859–868. 6. Findley LJ, Unverzagt ME, Suratt PM. Automobile accidents involving patients with obstructive sleep apnea. Am Rev Respir Dis 1988; 138:337. 7. Quan SF, Howard BV, Iber C, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep 1997; 20(12):1077–1085. 8. Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. JAMA 2000; 283(14): 1829–1836. 9. Décary A, Rouleau I, Montplaisir J. Cognitive deficits associated with sleep apnea syndrome: A proposed neuropsychological test battery. Sleep 2000; 23(3):369–381. 10. Engleman HM, Kingshott RN, Martin SE, et al. Cognitive function in the sleep apnea/ hypopnea syndrome (SAHS). Sleep 2000; 23(suppl 4):S102–S108. 11. Gevins A, Smith ME. Detecting transient cognitive impairment with EEG pattern recognition methods. Aviat Space Environ Med 1999; 70:1018–1024. 12. Gevins A, Smith ME, Leong H, et al. Monitoring working memory load during computerbased tasks with EEG pattern recognition methods. Hum Factors 1998; 40:79–91. 13. Gevins A, Smith ME, McEvoy L, et al. High resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb Cortex 1997; 7:374–385. 14. Gevins A, Smith ME, Le J, et al. High resolution evoked potential imaging of the cortical dynamics of human working memory. EEG Clin Neurophysiol 1996; 98:327–348.

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15. Gevins A, Leong H, Smith ME, et al. Mapping cognitive brain function with modern high-resolution electroencephalography. TINS 1995; 18:429–436. 16. Gevins AS, Bressler SL, Cutillo BA, et al. Effects of prolonged mental work on functional brain topography. Electroencephalography and Clinical Neurophysiology 1990; 76:339–350. 17. Gevins A, Smith ME. Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cereb Cortex 2000; 10(9):829–839. 18. McEvoy LK, Smith ME, Gevins A. Test-retest reliability of cognitive EEG. Clinical Neurophysiology 2000; 111(3):457–463. 19. Greenberg GD, Watson RK, Deptula D. Neuropsychological dysfunction in sleep apnea. Sleep 1987; 10(3):254–262. 20. Fix AJ, Golden CJ, Daughton D, et al. Neuropsychological deficits among patients with chronic obstructive pulmonary disease. Int J Neurosci 1982; 16:99–105. 21. Grant I, Heaton RK, McSweeny AJ, et al. Neuropsychological findings in hypoxemic chronic obstructive pulmonary disease. Arch Intern Med 1982; 142:1470–1476. 22. Findley LJ, Barth JT, Powers DC, et al. Cognitive impairment in patients with obstructive sleep apnea and associated hypoxemia. Chest 1986; 90(5):686–690. 23. Bédard M-A, Montplaisir J, Richer F, et al. Obstructive sleep apnea syndrome: pathogenesis of neuropsychological deficits. J Clin Exper Neuropsychol 1991; 13(6):950–964. 24. Valencia-Flores M, Bliwise DL, Guilleminault C, et al. Cognitive function in patients with sleep apnea after acute nocturnal nasal continuous positive airway pressure (CPAP) treatment: sleepiness and hypoxemia effects. J Clin Exp Neuropsychol 1996; 18(2):197–210. 25. Borak J, Cieslicki JK, Koziej M, et al. Effects of CPAP treatment on psychological status in patients with severe obstructive sleep apnoea. J Sleep Res 1996; 5(2):123–127. 26. Kingshott RN, Vennelle M, Hoy CJ, et al. Predictors of improvements in daytime function outcomes with CPAP therapy. Am J Respir Crit Care Med 2000; 161:866–871. 27. Feuerstein C, Naëgelé B, Pepin JL, et al. Frontal lobe-related cognitive functions in patients with sleep apnea syndrome before and after treatment. Acta Neurol Belg 1997; 97(2):96–107. 28. Montplaisir J, Bédard MA, Richer F, et al. Neurobehavioral manifestations in obstructive sleep apnea syndrome before and after treatment with continuous positive airway pressure. Sleep 1992; 15:S17–S19. 29. Bédard M-A, Montplaisir J, Malo J, et al. Persistent neuropsychological deficits and vigilance impairment in sleep apnea syndrome after treatment with continuous positive airways pressure (CPAP). J Clin Exp Neuropsychol 1993; 15(2):330–341. 30. Kotterba S, Rasche K, Widdig W, et al. Neuropsychological investigations and eventrelated potentials in obstructive sleep apnea syndrome before and during CPAP-therapy. J Neurol Sci 1998; 159:45–50. 31. Naëgelé B, Pépin J-L, Lévy P, et al. Cognitive executive dysfunction in patients with obstructive sleep apnea syndrome (OSAS) after CPAP treatment. Sleep 1998; 21(4):392–397. 32. Kingshott RN, Engleman HM, Deary IJ, et al. Does arousal frequency predict daytime function? Eur Respir J 1998; 12(6):1264–1270. 33. Martin SE, Engleman HM, Deary IJ, et al. The effect of sleep fragmentation on daytime function. Am J Respir Crit Care Med 1996; 153(4 pt 1):1328–1332. 34. Sforza E, Krieger J. Daytime sleepiness after long-term continuous positive airway pressure (CPAP) treatment in obstructive sleep apnea syndrome. J Neurol Sci 1992; 110(1-2):21–26. 35. Jenkinson C, Davies RJ, Mullins R, et al. Comparison of therapeutic and subtherapeutic nasal continuous positive airway pressure for obstructive sleep apnoea: a randomised prospective parallel trial. Lancet 1999; 353:2100–2105. 36. Derderian SS, Bridenbaugh RH, Rajagopal KR. Neuropsychologic symptoms in obstructive sleep apnea improve after treatment with nasal continuous positive airway pressure. Chest 1988; 94(5):1023–1027. 37. Yamamoto H, Akashiba T, Kosaka N, et al. Long-term effects of nasal continuous positive airway pressure on daytime sleepiness, mood and traffic accidents in patients with obstructive sleep apnoea. Respir Med 2000; 94(1):87–90. 38. Yu BH, Ancoli-Israel S, Dimsdale JE. Effect of CPAP treatment on mood states in patients with sleep apnea. J Psychiatr Res 1999; 33:427–432.

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39. Munoz A, Mayoralas LR, Barbe F, et al. Long-term effects of CPAP on daytime functioning in patients with sleep apnoea syndrome. Eur Respir J 2000; 15(4):676–681. 40. Engleman HM, Kingshott RN, Wraith PK, et al. Randomized placebo-controlled crossover trial of CPAP for mild sleep apnea/hypopnea syndrome. Am J Respir Crit Care Med 1999; 159(2):461–467. 41. Jenkinson C, Stradling J, Petersen S. Comparison of three measures of quality of life outcome in the evaluation of continuous positive airway pressure therapy for sleep apnoea. J Sleep Res 1997; 6:199–204. 42. Sullivan CE, Issa F, Berthon-Jones M, et al. Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 1981; 1:862–865. 43. Hoffstein V, Viner S, Mateika S, et al. Treatment of obstructive sleep apnea with nasal continuous positive airway pressure. Patient compliance, perception of benefits, and side effects. Am Rev Respir Dis 1992; 145(4 Pt 1):841–845. 44. Engleman HM, Martin SE, Deary IJ, et al. Effect of continuous positive airway pressure treatment on daytime function in sleep apnoea/hypopnoea syndrome. Lancet 1994; 343:572–575. 45. Engleman HM, Martin SE, Kingshott RN, Mackay TW, Deary IJ, Douglas NJ. Randomized placebo controlled trial of daytime function after continuous positive airway pressure (CPAP) therapy for the sleep apnoea/hypopnoea syndrome. Thorax 1998; 53:341–345. 46. Ballester E, Badia JR, Hernandez L, et al. Evidence of the effectiveness of CPAP in the treatment of sleep apnoea/hypopnoea syndrome. Am J Respir Crit Care Med 1999; 159:495–501. 47. Lojander J, Kajaste S, Maasilta P, et al. Cognitive function and treatment of obstructive sleep apnea syndrome. J Sleep Res 1999; 8:71–76. 48. Wright, J, Johns R, Watt I, et al. Health effects of obstructive sleep apnoea and the effectiveness of continuous positive airway pressure: a systematic review of the research evidence. BMJ 1997; 314:851–860. 49. Stradling J. Sleep apnoea and the misuse of evidence-based medicine. Lancet 1997; 349:201–202. 50. Kushida CA, Nichols DA, Quan SF. The Apnea Positive Pressure Long-term Efficacy Study (APPLES): rationale, design, methods, and procedures. J Clin Sleep Med 2006; 2(3):288–300. 51. Hack M, Davies RJ, Mullins R, et al. Randomised prospective parallel trial of therapeutic versus subtherapeutic nasal continuous positive airway pressure on simulated steering performance in patients with obstructive sleep apnoea. Thorax 2000; 55:224–231. 52. Barbe F, Mayoralas LR, Duran J, et al. Treatment with continuous positive airway pressure is not effective in patients with sleep apnea but no daytime sleepiness. A randomized, controlled trial. Ann Intern Med 2001; 134:1015–1023. 53. Dimsdale JE, Loredo JS, Profant J. Effect of continuous positive airway pressure on blood pressure: a placebo trial. Hypertension 2000; 35:144–147. 54. Bardwell WA, Ancoli-Israel S, Berry CC, et al. Neuropsychological effects of one-week continuous positive airway pressure treatment in patients with obstructive sleep apnea: a placebo-controlled study. Psychosom Med 2001; 63:579–584. 55. Henke KG, Grady JJ, Kuna ST. Effect of nasal continuous positive airway pressure on neuropsychological function in sleep apnea/hypopnea syndrome. A randomized, placebo-controlled trial. Am J Respir Crit Care Med 2001; 163:911–917. 56. Monasterio C, Vidal S, Duran J, et al. Effectiveness of continuous positive airway pressure in mild sleep apnea-hypopnea syndrome. Am J Respir Crit Care Med 2001; 164:939–943. 57. Montserrat JM, Ferrer M, Hernandez L, et al. Effectiveness of CPAP treatment in daytime function in sleep apnea syndrome: a randomized controlled study with an optimized placebo. Am J Respir Crit Care Med 2001; 164:608–613. 58. Ziegler MG, Mills PJ, Loredo JS, et al. Effect of continuous positive airway pressure and placebo treatment on sympathetic nervous activity in patients with obstructive sleep apnea. Chest 2001; 120:887–893. 59. Barnes M, Houston D, Worsnop CJ, et al. A randomized controlled trial of continuous positive airway pressure in mild obstructive sleep apnea. Am J Respir Crit Care Med 2002; 165:773–780.

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60. Pepperell JC, Ramdassingh-Dow S, Crosthwaite N, et al. Ambulatory blood pressure after therapeutic and subtherapeutic nasal continuous positive airway pressure for obstructive sleep apnoea: a randomised parallel trial. Lancet 2002; 359:204–210. 61. Becker HF, Jerrentrup A, Ploch T, et al. Effect of nasal continuous positive airway pressure treatment on blood pressure in patients with obstructive sleep apnea. Circulation 2003; 107:68–73. 62. Profant J, Ancoli-Israel S, Dimsdale JE. A randomized, controlled trial of 1 week of continuous positive airway pressure treatment on quality of life. Heart Lung 2003; 32:52–58. 63. Presty SK, Barth JT, Suratt PM, et al. Effects of nocturnal hypoxemia on neurocognitive performance in obstructive sleep apne. Am J R Crit 1991; 143:A384. 64. Cheshire K, Engleman H, Deary I, et al. Factors impairing daytime performance in patients with sleep apnea/hypopnea syndrome. Arch Intern Med 1992; 152:538–541. 65. Telakivi T, Kajaste S, Partinen M, et al. Cognitive function in obstructive sleep apnea. Sleep 1993; 16:S74–S75. 66. Ingram F, Henke KG, Levin HS, et al. Sleep apnea and vigilance performance in a communitydwelling older sample. Sleep 1994; 17(3):248–252. 67. Naëgelé B, Thouvard V, Pépin J-L, et al. Deficits of cognitive executive functions in patients with sleep apnea syndrome. Sleep 1995; 18(1):43–52. 68. Verstraeten E, Cluydts R, Verbraecken J, et al. Neuropsychological functioning and determinants of morning alertness in patients with obstructive sleep apnea syndrome. J Int Neuropsychol Soc 1996; 2:306–314. 69. Kim JC, Young T, Matthews CG, et al. Sleep-disordered breathing and neuropsychological deficits: a population-based study. Am J Respir Crit Care Med 1997; 156:1813–1819. 70. Redline S, Strauss ME, Adams N, et al. Neuropsychological function in mild sleepdisordered breathing. Sleep 1997; 20(2):160–167. 71. Engleman HM, Martin SE, Deary IJ, et al. Effect of continuous positive airway pressure treatment on daytime function in sleep apnoea/hypopnoea syndrome. Thorax 1997; 52:114–119. 72. Barnes M, McEvoy RD, Banks S, et al. Efficacy of positive airway pressure and oral appliance in mild to moderate obstructive sleep apnea. Am J Respir Crit Care Med 2004; 170:656–664.

Section I: Features, Factors, and Characteristics

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History Michael Zupancic Department of Neurology, University of Michigan Sleep Disorders Center, Ann Arbor, Michigan, U.S.A.

Peretz Lavie Sleep Medicine Center, Rambam Medical Center, Haifa, Israel

PRETWENTIETH CENTURY LITERATURE Obstructive sleep apnea (OSA) is a common disorder that affects one of four men and one of 11 women in the world (1). Though it has been described since the 1970s, it was not until 20 years later that its prevalence and importance as a public health problem was widely recognized. How has this disorder, with clear signs and symptoms, been hiding in plain view for so many years? Features of OSA have been described for more than 100 years and interestingly enough, they first appeared in fictional literature. William Shakespeare (2), for instance, was aware of the connection between obesity and sleepiness when describing the picturesque Sir John Falstaff in Henry IV (Fig. 1). Falstaff was well known for his corpulence and propensity for napping and was occasionally found, “fast asleep behind the arras, and snoring like a horse.” Charles Dickens’ character Joe, from The Pickwick Papers (1835) (3), is described as “a wonderfully fat boy” who falls asleep easily and against his will (Fig. 2). When we first meet Joe, he is standing up asleep after knocking vigorously on a door. When asked why he knocked relentlessly, Joe explains it is to prevent himself from falling asleep. Dickens’ description of Joe caught the eye of several physicians who independently coined the term “Pickwickian syndrome.” Of these physicians, the most cited is Sir William Osler (4) (Fig. 3). In the eighth edition of his celebrated textbook Principles and Practice of Medicine, published in 1914, he used the term “Pickwickian syndrome” to describe obese and sleepy patients, in homage to Dickens’ character Joe. George Catlin (5) (Fig. 4), a nineteenth-century lawyer, artist, and amateur anthropologist hinted of sleep-disordered breathing in his book The Breath of Life published in 1861. He noted that American Indians were healthier than EuropeanAmericans because he observed that they breathed through their noses when asleep. Catlin claimed that breathing through the mouth while asleep caused snoring, a feeling of tiredness in the morning, headaches upon waking, and a strong desire to carry on sleeping. Moreover, he was convinced that breathing through the mouth while asleep caused more illnesses, and even death. Catlin concluded his book by suggesting that the sides of the cribs and cradles of babies be inscribed with the words “shut your mouth” to promote health (5). Some of Catlin’s paintings also show the virtues of breathing through the nose while asleep. In 1818, John Cheyne (6) (Fig. 5), a Dublin physician, was among the first to describe sleep-disordered breathing in the medical literature. Observing a patient dying from heart disease, he noted an irregular breathing pattern and paralysis when asleep. In 1854, William Stokes (7) (Fig. 6), who also practiced in Dublin, 11

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FIGURE 1 Artist rendering of William Shakespeare’s character, Sir John Falstaff in Henry IV (c. 1596–1597).

FIGURE 2 Artist (Hablot Knight BrownePhiz) rendering of Charles Dickens’ character, Joe, in The Pickwick Papers (c. 1836–1837).

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FIGURE 3

Sir William Osler (1849–1919).

FIGURE 4 George Catlin (1796–1872).

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FIGURE 5

John Cheyne, MD (1777–1836).

described a similar case of abnormal breathing in a heart patient. Today, periodic breathing in heart failure patients is named “Cheyne-Stokes breathing” after these two sharp-eyed Dublin physicians. W. H. Broadbent (8), a physician from St. Mary’s Hospital in London, was one of the first to describe what we term today “obstructive sleep apnea” in an article published in The Lancet in 1877. He described a strange case of sleep-disordered breathing that was “similar to Cheyne-Stokes breathing.” But from the very detailed description of this patient, it is clear there was more than periodic breathing, since this patient’s snoring “ceased at regular intervals, and the pause was so long as to excite attention, and indeed alarm.” Broadbent concluded his article by declaring: “All the theories that have attempted to explain this phenomenon are inadequate and I have none of my own” (8).

FIGURE 6 William Stokes, MD (1804–1878).

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The second case of OSA described in a medical arena occurred 11 years later on February 8, 1889 by Richard Caton of Liverpool (9), who has gained his place in medical history as the discoverer of the brain’s electrical activity. He presented an interesting case to the Clinical Society of London, a case he erroneously described as one of narcolepsy. The patient, a 37-year-old poulterer, complained of great sleepiness that appeared at the same time as a marked increase in his weight. The poulterer’s sleepiness was so severe that he sank into sleep even when at work. While serving customers in his shop, he would fall asleep as he stood by the counter and would awaken to find himself holding in his hand the duck or chicken which he had been selling to a customer fifteen minutes earlier. Caton’s description of the poulterer’s sleep clearly alludes to his diagnosis: “When in sound sleep a very peculiar state of the glottis is observed, a spasmodic closure entirely suspending respiration. The thorax and abdomen are seen to heave from fruitless contractions of the inspiratory and expiratory muscles; their efforts increase in violence for about a minute or a minute and a half, the skin meantime becoming more and more cyanosed, until at last, when the condition to the onlooker is most alarming, the glottic obstruction yields, a series of long inspirations and expirations follows, and cyanosis disappears.” “This acute dyspnoeic attack does not awaken the patient . . . .” “If in the midst of the dyspnoeic attack he is forcibly aroused, the glottic spasm at once relaxes. The night nurses state that these attacks go on throughout the night” (9). Caton attributed the patient’s abnormal sleep to be the result of a toxin that acted only on the glottal muscles. Following treatment with various drugs, and after weight loss, the patient’s condition improved in both his level of daytime alertness and his sleep (9). Alexander Morrison (10) described a similar case in 1889 of a 63-year-old man who suffered from sleepiness and described cyanotic episodes when the patient slept. Though Broadbent, Caton, and Morrison accurately described the sleep of people suffering from sleep apnea, and further described debilitating sleepiness during the day, none of them claimed that the problem was a disorder unique to sleep. At the time, physicians did not ascribe much importance to excessive daytime sleepiness unless it was related to a specific illness or damage to the central nervous system. In fact, it was not until the middle of the 20th century that the scientific viewpoint of sleep began to change from that of the 2000-year-old school of thought introduced by Galen, who described sleep as a passive state created by the disconnection of the brain from the rest of the body parts and the external environment. BURWELL ET AL.’S POKER PLAYER AND THE FIRST SLEEP RECORDINGS OF PICKWICKIAN PATIENTS One of the articles most often mistakenly quoted as the first to “describe” sleep apnea is “Extreme Obesity Associated with Alveolar Hypoventilation—A Pickwickian Syndrome,” published in 1956 by Bickelmann et al. (11). This article describes an obese man whose daytime sleepiness became so severe after gaining weight that he fell asleep playing poker while holding a “full house,” which prompted him to seek medical care. Though this story is memorable, and the article correctly shows the effect sleep apnea can have on daytime sleepiness, it incorrectly identified the source of sleepiness as increased carbon dioxide secondary to lack of respiratory effort, rather than disrupted nocturnal sleep. In fact, throughout the paper there is no mention of the patient’s nocturnal sleep (11). Werner Gerardy and Dieter Herberg performed the first physiological recordings in a sleeping Pickwickian patient at the Ludolf-Krehl Clinic of the Heidelberg

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University Hospital. In 1959, Gerardy performed an electroencephalogram on an obese patient who had been admitted because of reduced work capacity and frequent migraines upon awakening. During the one-hour examination the patient fell asleep. Gerardy described the sleeping patient as having periodic breathing. During the onset of these events Gerardy noted that patient’s tongue fell back and airflow ceased, despite increased movements of the thorax; upon waking, the patient’s tongue moved forward as he resumed breathing. Additionally, Gerardy noted the cardiac arrhythmia that occurs with obstructive apneas with the heart rate decelerating during the suspension of breathing, and its dramatic acceleration with renewal of breathing. In 1960, Gerardy, Herberg, and Hans Manfred Kuhn described their findings in the first sleep recording of Pickwickian patients (12). Though they were innovative in describing the breathing and heart rate changes during obstructive apneas, their explanation for the suspension of breathing during sleep was no different from before. They believed it was secondary to high carbon dioxide from shallow and inefficient breathing, and attributed the tongue moving backward as a sign of deep sleep rather than that of obstruction (12). Two years later Daniel Drachman and Robert Gumnit (13) of the U.S. National Institutes of Health published an article that led to further advancements in the understanding of sleep apnea. They examined the daytime sleep of a female Pickwickian patient who worked as an ice cream vendor and who had become sleepier and heavier over the course of several years. When examining the patient’s sleep, they described similar findings as Gerardy et al., but were the first to note a drop in oxygen saturation level during the breathing events (13). Despite these innovations in understanding sleep in Pickwickian patients, they still believed these abnormal breathing events were secondary to elevated carbon dioxide rather than closure of the airway (13). Wolfgang Kuhl (14), a German neurologist and his department head Richard Jung helped pioneer the understanding of obstructive sleep apnea. In 1964, they presented their findings of a Pickwickian patient’s nocturnal sleep at the annual meeting of the European Neurological Society. They were the first to conclude that the daytime sleepiness of a Pickwickian patient was secondary to recurring interruptions in sleep rather than carbon dioxide poisoning. This put the understanding of the Pickwickian syndrome in an entirely different light. Kuhl’s recordings clearly showed that the syndrome originated not in shallow and inefficient breathing or heart disease, but in a disorder in the normal course of sleep. This important conclusion shifted the Pickwickian syndrome from primarily a pulmonary and internal medicine domain to the field of neurology (14). Henri Gastaut attended this conference and was greatly impressed by Kuhl’s conclusion. Gastaut was the head of the Neurobiological Research Unit in Marseilles. After attending this conference Gastaut recruited Bernard Duron, a young researcher well versed in respiratory function and neurophysiology, and they set up recording instruments to evaluate the sleep of Pickwickian patients. In addition to monitoring the patient’s respiratory efforts through thorax movements and respiratory muscle activity (as done by Kuhl), they also measured airflow with respiratory sensors at the patient’s nostrils and mouth. With this innovative monitoring technique, Duron’s recordings revealed that blockage of the upper airway during sleep was the etiology of the apneas (15). They concluded that apneas were not secondary to dysfunction of the brain’s respiratory center as proposed by Jung and Kuhl. Gastaut’s group also was the first to describe three different types of apneas: obstructive apneas, central apneas, and mixed apneas. Later, Daniel Kurtz and Jean

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Krieger of Strasbourg defined and added a further type of sleep-disordered breathing, the hypopnea (15). Elio Lugaresi (Fig. 7), a former student of Gastaut, and Giorgio Coccagna (16) were two neurologists who also were influenced by Kuhl’s lecture at the 1964 European Neurological Conference. They studied Pickwickian patients’ sleep in Bologna and came to similar conclusions as Duron and Gastaut that apneas were caused from upper airway obstruction. Additionally, Lugaresi’s group showed that systemic and pulmonary blood pressure varied greatly during and following obstructive apneas, with a drop in blood pressure at the start of an apnea and a dramatic rise in blood pressure once breathing resumed (16). FIRST TREATMENT ATTEMPTS Though weight loss was identified early as a treatment to improve nocturnal sleep and daytime function in Pickwickian patients, many patients were unable to succeed with losing the necessary weight, and they continued to suffer from daytime sleepiness. Several sleep researchers theorized that tracheostomy would be an effective treatment to bypass the sleep-related breathing obstruction. However, due to potentially serious complications, physicians were leery of being the first to perform tracheostomies on a Pickwickian patient. Chance intervened when a Pickwickian patient of Wolfgang Kuhl sank into a protracted coma as a result of what was diagnosed as carbon dioxide poisoning. This Pickwickian patient was the first to undergo tracheostomy and the patient recovered almost immediately from his coma. Additionally, he made a complete recovery from his sleep-disordered breathing and his daytime alertness became normal. On hearing about Kuhl’s case, Lugaresi et al. (17) managed to convince the surgical staff of Bologna to perform emergency tracheostomies on several Pickwickian patients. Within a short time six patients underwent the procedure and all of them showed

FIGURE 7 Elio Lugaresi, MD (center) with William C. Dement, MD, PhD (left) and Christian Guilleminault, MD (right) (c. 1983). Source: Photograph courtesy of William C. Dement, MD, PhD.

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great improvement in their sleep-disordered breathing, daytime alertness, and systemic and pulmonary blood pressures. Though tracheostomy for sleep-disordered breathing was an effective treatment for Pickwickian patients, it was rarely performed outside of Europe for several years. One of the first patients to undergo a tracheostomy was Raymond M., a pediatric patient admitted to Stanford University Medical Center for uncontrolled hypertension. After undergoing an extensive and unrevealing medical workup Raymond’s physicians asked Christian Guilleminault (Fig. 8) to evaluate an “unrelated symptom” of daytime sleepiness that his mother had noted for years. A subsequent sleep study revealed that the patient had OSA and in late 1972 he underwent a tracheostomy as encouraged by Dr. Guilleminault. Several days following treatment the patient’s daytime sleepiness and hypertension resolved. In 1973, Bill Orr and N. K. Imes began evaluating the sleep of patients hospitalized in the intensive care unit (ICU) of Oklahoma City Presbyterian Hospital. They discovered a relatively large number of patients with sleep-disordered breathing and they performed tracheostomies, with subsequent improvements in sleep and daytime function. Another successful report came from Canada when Meir Kryger saw his first case of sleep apnea in 1973. The patient was an obese man hospitalized for “epileptic attacks” that occurred during sleep. While studying the patient’s sleep, Kryger noted that the “epileptic attacks” were in fact prolonged apneas. After undergoing a tracheostomy this drowsy, obese, “epileptic” patient was cured (18). SLEEP APNEA IN THIN PERSONS Until the early 1970s, it was generally accepted that sleep-disordered breathing occurred solely in obese patients. Christian Guilleminault and William Dement (Fig. 8) at Stanford University changed this perception completely when they proved that obesity is not a prerequisite for the existence of these disorders. One patient who presented to the Stanford Sleep Laboratory was a slender man with complaints of difficulty falling asleep and frequent awakenings overnight. In the course of their

FIGURE 8 William C. Dement, MD, PhD (left) and Christian Guilleminault, MD (right) (c. 1997). Source: Photograph courtesy of William C. Dement, MD, PhD.

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recordings, Guilleminault and Dement noted that this patient’s heartbeat was irregular. Upon entering the bedroom they noted that this slender man was having apneas hundreds of times a night which were associated with the irregular heartbeat. Guilleminault and Dement decided that monitoring breathing during sleep should be done on all patients examined at the Stanford Sleep Laboratory, regardless of their diagnosis, complaint, and body size. To their great surprise, it quickly became clear that many patients referred for sleep testing due to complaints of tiredness, daytime sleepiness, and even insomnia were, in fact, suffering from breathing disorders in their sleep—often with no connection at all to their body weight. Guilleminault and Dement also showed that sleep apnea occurs in children and that treating the apnea could lower elevated blood pressure (19–21). In 1977 Guilleminault and Dement (22) published an article summarizing their sleep study findings, including breathing recordings in 250 patients referred to the Stanford Sleep Laboratory. In 35 of them, the sleep recordings showed evidence of breathing disorders [as defined as an apnea-hypopnea index (AHI) > 30], even though not one of the patients had complained of breathing problems during sleep in their prerecording interviews. The subsequently published article was the first to use the term “sleep apnea syndrome” and the authors also suggested a definition of it in accordance with their laboratory findings. This study also revealed that sleep apnea occurred more frequently in men, with only one woman among the 35 sleep apnea subjects. Another important finding was that 16 of the sleep apnea subjects had hypertension and three had pathological signs on electrocardiographic recording (22). FIRST MAJOR INTERNATIONAL CONFERENCE OF RESPIRATORY SLEEP DISORDERS In July 1977, an international group of eminent researchers and specialists in sleep and pulmonary physiology gathered at the Kroc Foundation in Santa Ynez Valley, California, to examine the accumulated knowledge on respiratory sleep disorders. Several important topics were presented which furthered the understanding of obstructive sleep apnea. One important lecturer was Eliot Phillipson (23), from Toronto, who studied breathing in normal sleeping dogs. Phillipson reported that during non-rapid eye movement (NREM) sleep in dogs, an increase in carbon dioxide concentration caused a gradual increase in their breathing rate and volume until they awakened. However, in rapid eye movement (REM) sleep, breathing was almost entirely unaffected by carbon dioxide concentration and was inherently unstable, varying moment-to-moment. Phillipson claimed that in NREM sleep, breathing was under the control of the automatic, or metabolic, breathing mechanism located in the brainstem’s respiratory center. In REM sleep, Phillipson believed that breathing was controlled by the “voluntary” respiratory center located in the frontal lobe. According to Phillipson (24,25), the wakefulness mechanism, located close to the respiratory center in the brainstem, protected a sleep apnea patient from suffocating during an apnea episode. Another important topic discussed at this conference was the exact location of the upper airway blockage and why it occurs. Elliot Weitzman (Fig. 9), a leading sleep researcher and founder of the Sleep Disorders Center at the Montefiore Medical Center in the Bronx, New York, showed by fluoroscopy that the cause of the blockage was a collapse of the side walls of the pharynx and a backward movement of the base of the tongue. This “landslide” always occurred at the end of exhalation

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FIGURE 9 Elliot D. Weitzman, M.D. (1930– 1983). Source: Photograph courtesy of William C. Dement, MD, PhD.

and before the next inhalation, presumably from the suction created from movements of the diaphragm and respiratory muscles (26). Ron Harper complemented Weitzman’s observation by noting that during an apnea episode there is a reduction of the tongue muscle’s electrical activity facilitating its backward movement. Weitzman’s collapse theory was innovative in that it suggested airway collapse was not an active process as had been previously believed. This topic was hotly debated during the conference with multiple theories concerning the etiology of airway collapse. This seminal meeting heralded the change in attitude of the medical community toward the importance of breathing disorders in sleep. SLEEP APNEA OVER THE PAST TWENTY-FIVE YEARS The 1980s were an important decade in sleep apnea research. There continued to be a greater understanding of the physiology of sleep and pathophysiology of obstructive sleep apnea. Several studies demonstrated the importance of pressure sensors in the upper airway in maintaining normal respiration during sleep. In 1982, Eliot Phillipson noted prolonged central apneas in normal subjects after applying lidocaine spray to airway receptors in the nose and pharynx. David White, Director of the Sleep Disorders Program at the Brigham and Women’s Hospital in Boston, Massachusetts, also showed the importance of airway receptors in the nose. When his group administered local anesthesia to the nostrils in normal subjects, they noted a four-fold increase in apneas (27). Dr. White’s group also made the important discovery that the muscle activity of upper airway dilator muscles was abnormal in OSA patients. Neurophysiologic studies of these pharyngeal muscles revealed greater muscle activity in OSA patients than in controls during waking hours. With sleep onset, there was a great diminution

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in tonic muscle activity of these muscles in OSA patients compared with controls (28). Additionally, White’s group demonstrated that pharyngeal receptors are abnormal in OSA patients, suggesting that damage to these receptors, perhaps by “benign snoring,” is important in the pathogenesis of OSA. In 1983, Lavie (29) published an article in Sleep, which indicated there was likely a link between OSA and hypertension. In evaluating the polysomnograms and records of industrial workers referred for complaints of daytime sleepiness and chronic fatigue, 36.3% of workers with more than 10 apneas per hour had hypertension compared to 7.4% of those without OSA. Terry Young et al. (1) brought OSA to the attention of the general medical community by examining the prevalence of asymptomatic and symptomatic OSA in civil service workers in Wisconsin. This study, published in 1993, was the first major epidemiologic study in the United States assessing OSA. It evaluated over 600 civil workers with extensive questionnaires and by polysomnography. The study’s findings were quite dramatic and revealed that the prevalence of OSA (as defined by an AHI ≥ 5 per hour) is high in the United States, occurring in 24% of men and 9% of women in the 30- to 60-year age range. When complaints of daytime sleepiness were taken into account, it was determined that 2% of women and 4% of men sampled had symptomatic OSA. Young’s study was published in the New England Journal of Medicine and gained worldwide attention. The association between OSA and cardiovascular disease is rapidly becoming more established. In 2000, data derived from the Wisconsin Cohort Study (30) showed that OSA was an independent risk factor in developing hypertension four years after the diagnosis of OSA. The Sleep Heart Health Study (31) was a large epidemiologic study conducted from October 1995 to February 1998 that examined the association of cardiovascular disease and risk factors by evaluating a multiethnic cohort of over 6400 women and men over the age of 40 years. Patients enrolled in this study underwent home polysomnography and physical exams, and completed extensive questionnaires. Data from this study published in 2001 showed that OSA is an independent risk factor in the development of cardiovascular disease, with a higher proportion of coronary artery disease, stroke, and heart failure occurring in OSA patients compared to individuals without OSA. More recently, data published in 2006 from a subgroup of this cohort demonstrated that people with severe OSA have a two- to fourfold increased risk of complex cardiac arrhythmias when compared to those without OSA (32). OSA is also associated with other significant health-related consequences and comorbidities. In a study published in Sleep in 1999, Meir Kryger et al. (33) compared health service utilization for a 10-year interval prior to diagnosis of 181 OSA patients to those of randomly selected age-, gender-, and geographically-matched controls from the general population. Although there is a slim possibility that their findings reflected risk factors that predispose to OSA (e.g., obesity, alcohol usage), they found that OSA patients used approximately twice as many health care services (as defined by physician claims and overnight stays in hospital) in the 10 years prior to their OSA diagnosis. Kryger’s group concluded that by the time patients are finally diagnosed for OSA, they have already been heavy users of health services for several years. Literature regarding OSA as a public safety issue has been published over the past decade. OSA not only results in increased drowsiness, but also negatively affects concentration and attention; qualities which are critically important for driver safety. Larry Findley (34), from The University of Virginia, published a study

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in 1998 comparing the driving records of OSA patients to those without this disease. The results of this study were profound, showing a sevenfold greater rate of automobile accidents in patients with OSA than those without OSA. In 1999, Antonio Jimenez et al. (35) published an important and influential article regarding OSA and automobile accidents in the New England Journal of Medicine. He and his colleagues examined the sleep of 102 drivers in Spain who had received medical treatment following traffic accidents and compared the results to a control group. The prevalence of sleep apnea syndrome was six times higher in the group of drivers injured in car accidents compared to the controls, suggesting OSA is a risk factor for automobile accidents. John Stradling’s group (36) from Oxford, England demonstrated that sleep apnea patients performed poorer while driving in automobile simulators as compared to controls, having problems with both staying in their lane and in noticing changes along their route, especially in limited visibility conditions. Though there is a good body of literature suggesting OSA contributes to automobile accidents, Spain (prompted by Jimenez’s article) is the only country mandating OSA patients to undergo treatment to maintain their driving privileges. SLEEP APNEA IN CHILDREN In 1976, Christian Guilleminault and William Dement (37) from Stanford were the first to describe pediatric OSA in a case series of eight patients published in the journal Pediatrics. In this article they described that the pediatric patients with OSA often suffered from hypertension, daytime somnolence, hyperactivity, learning difficulties, nocturnal enuresis, and weight abnormalities. They also noted that with treatment, which consisted of tonsillectomy/adenoidectomy often followed by tracheostomy, many of these problems improved. David Gozal (38) was one of the first to present strong evidence that OSA affects academic performance in children. While working at Tulane University and establishing a sleep laboratory for children, he evaluated 297 first-graders for sleep apnea and noted that 54 children had OSA and an additional 62 snored loudly. Knowing that OSA could affect cognitive function in children, he offered children with OSA free treatment with tonsillectomies and adenoidectomies of which 24 children/parents consented. The results of his study published in Pediatrics in 1998 were profound; the children treated surgically experienced significant improvement in overall mean grades during the second grade, one year following treatment. The children with untreated OSA had no significant academic improvement over this time period. Gozal took his hypothesis of OSA affecting scholastic performance one step further when he and Dennis Pope (39) examined the computerized databases of schools in Jefferson County, Kentucky; this began an influential study published in the journal Pediatrics in 2001. They found 1000 adolescents aged 13 and 14 years whose scholastic performance was significantly poorer than that of their peers and then selected a group of adolescents with similar sex, age, ethnic background, school attended, and street of residence, but whose school grades were high. A detailed questionnaire was sent to the parents of these 2000 adolescents and asked whether the adolescents had snored while asleep (as a measure of OSA) between the ages of two and six, whether they had undergone tonsillectomy because of snoring, and whether they snored now. Analysis of the data revealed that 13% of the poor-performance adolescents had snored loudly “almost daily” during infancy, compared

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with 5% of the high-performance adolescents. Twenty-four percent of the underperformers had undergone a tonsillectomy in infancy “due to snoring,” compared with only 7% of the high-performers. This study suggested that OSA in early childhood causes long-lasting adverse cognitive consequences. Ron Chervin (40) at the University of Michigan was one of the first to demonstrate that obstructive sleep apnea is associated with attention-deficit disorder in children. He determined this by administering extensive questionnaires regarding sleep-disordered breathing, inattention, and hyperactivity to over 800 children being evaluated in general pediatric clinics. This study, published in Pediatrics in 2002, showed that inattention and hyperactivity were associated with increased daytime sleepiness and measures of sleep-disordered breathing, specifically snoring, especially in boys younger than eight years of age. In 2005, Ron Chervin’s group (41) published an article in Sleep suggesting that OSA leads to hyperactive behavior in the pediatric population. In this study, the parents of over 200 children were surveyed about sleep-disordered breathing and hyperactivity initially, and then four years later. The results of this study showed that snoring and other features of sleep-disordered breathing are strong risk factors for the emergence of hyperactive behavior four years later. OSA is now recognized to be a common, important, and treatable disorder in children. In 2002, nearly three decades after Guilleminault and Dement first published pediatric cases of OSA, the American Academy of Pediatrics published a guideline stating that all children who snore regularly should undergo polysomnography for evaluation of OSA. CORNERSTONES OF MODERN TREATMENT FOR OBSTRUCTIVE SLEEP APNEA Colin Sullivan, an Australian physician whose specialty is respiratory medicine, pioneered the treatment of OSA by inventing and developing continuous positive airway pressure (CPAP). In 1972, Sullivan started working as a physician in Sydney. He did his doctoral studies under the physiologist David Read and his research originally focused on the etiology of crib death in children, which was believed to be related to sleep-disordered breathing. Sullivan first became interested in adult sleep apnea after seeing an overweight truck driver named Albie C., admitted through the hospital emergency room. Albie was almost unconscious, suffering from respiratory failure and high serum levels of carbon dioxide. This patient would frequently present to the hospital in a similar condition but would make a miraculous recovery after being intubated for several hours. Sullivan, an astute clinician, was the first to realize that Albie’s respiratory failure was the result of OSA. In 1981, while doodling a sketch of the upper airway on a sheet of paper, Sullivan came up with the idea of entering pressurized airflow via the nostrils to prevent obstructive apneas during sleep. Using a variable speed motor from a vacuum cleaner as an air pump, he attached it to a pipe with soft plastic tubes at its end, which were made to fit in a person’s nostrils. Sullivan then connected another tube for exhalation and made the system airtight with liquid adhesive. In June of that same year, Sullivan first used his machine on a patient with OSA. He attached the soft plastic tubes to the OSA patient’s nostrils and, while the patient slept, he adjusted the motor speed and pressure to eliminate obstructive breathing events. “The change was absolutely amazing,” Sullivan recalled years later. “Before we even had a chance to understand what had happened, the patient

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was already in REM sleep.” When the patient awoke in the morning he was completely alert for the first time in several years and remained alert throughout the day. Later that year Sullivan published a paper (42) in the Lancet describing the successful treatment of five OSA patients with CPAP. The creation of this innovative and noninvasive treatment for OSA represents a true milestone in sleep apnea history. Soon after this initial report other sleep disorder centers were creating CPAP machines and using them as an effective treatment modality. Though these first CPAP devices developed barely 25 years ago were undeniably crude, they paved the way for the lightweight, sophisticated devices that now provide automatic and bilevel pressure, as well as adaptive servo-ventilation. Today, CPAP is considered the most effective and most frequently prescribed treatment for patients with OSA. It is considered the “gold standard” treatment. Upper airway surgery as a treatment for OSA began with early work by the Japanese surgeon Shiro Fujita (43) of Detroit, Michigan, who published the first report of uvulopalatopharyngoplasty (UPPP) in 1981. In this procedure, the surgeon removes the uvula, soft palate tissue, redundant lateral pharyngeal mucosa, and tonsils in order to enlarge the upper airway. Fugita et al. showed that UPPP successfully decreased snoring, apneas, and hypopneas, and improved the clinical symptoms of many OSA patients. Though this procedure was the first successful surgical treatment for OSA, aside from tracheostomy, it had a relatively high failure rate and often did not “cure” OSA. Interestingly, though Fujita popularized this procedure for the treatment of OSA in the West, it was initially performed and invented in Japan by the surgeon T. Ikematsu (44) for the treatment of chronic snoring. Since then, surgical treatment has expanded considerably to include such techniques as mandibular osteotomy with genioglossus advancement, hyoid myotomy, and bimaxillary advancement, many of which were pioneered by Robert Riley and Nelson Powell at Stanford. With these newer techniques Riley and Powell (45) have shown that up to 95% of their OSA patients have been successfully treated with surgery. Other techniques such as osteogenic distraction, radiofrequency ablation, oral appliances, and bariatric surgery represent other treatments for OSA; even more therapeutic advances are anticipated, perhaps involving stem cells or other genetic/ molecular techniques, in the not too distant future. CONCLUSIONS Today skepticism of the existence and importance of OSA has faded considerably from the medical community. OSA is now a recognized disorder to patients and physicians alike. It is interesting that such an obvious disease had been ignored for so many years, but thanks to the diligent efforts of many dedicated sleep medicine physicians and researchers, OSA is now recognized as an important disease with serious consequences that can be successfully treated or cured. REFERENCES 1. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328(17):1230–1235. 2. Shakespeare W. The Caxton edition of the complete works of William Shakespeare/with annotations and a general introduction by Sidney Lee. London: Caxton, 1910–1914. 3. Dickens C. The Posthumous Papers of the Pickwickian Club. London: Chapman and Hall, 1837.

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4. Osler W. The Principles and Practice of Medicine: Designed for the Use of Practitioners and Students of Medicine/by William Osler. New York: D. Appleton and Co., 1894, c1892. 5. Catlin G. The Breath of Life. New York: Wiley, 1861. 6. Cheyne J. A case of apoplexy in which the fleshy part of the heart was converted into fat. Dublin Hospital Report 1818; 2:216–222. 7. Stokes W. The Diseases of the Heart and Aorta. Dublin, Ireland: Hodges & Smith, 1854. 8. Broadbent WH. On Cheyne-Stokes’ respiration in cerebral haemorrhage. The Lancet 1877; 109(2792):307–309. 9. Canton R. Case of Narcolepsy. Clin Soc Trans 1889; 22:133–137. 10. Morrison A. Somnolence with cyanosis cured by massage. Practitioner 1889; 22:133–137. 11. Bickelmann AG, Burwell CS, Robin ED, Whaley RD. Extreme obesity associated with alveolar hypoventilation -a pickwickian syndrome. Am J Med 1956; 21(5):811–818. 12. Gerardy W, Herberg D, Kuhn HM. Comparative studies on pulmonary function and the electroencephalogram in 2 patients with Pickwick’s syndrome. Z Klin Med 1960; 156:362–380. 13. Drachman DB, Gumnit RJ. Periodic alteration of consciousness in the “pickwickian” syndrome. Arch Neurol 1962; 6:63–69. 14. Jung R, Kuhl W. Neurophysiological studies of abnormal night sleep and the pickwickian syndrome. In: Akert K, Bally C, Schade JP, eds. Prog Brain Res: Sleep Mechanisms, Vol. 18, Amsterdam: Elsevier, 1965:140–159. 15. Gastaut H, Tassinari CA, Duron B. Polygraphic study of the episodic diurnal and nocturnal (hypnic and respiratory) manifestations of the pickwick syndrome. Brain Res 1966; 1:167–186. 16. Lugaresi E, Coccagna G, Mantovani M, Cirignotta F, Ambrosetto G, Baturic P. Hypersomnia with periodic breathing: Periodic apneas and alveolar hypoventilation during sleep. Bulletin of Physiopathologic Respiration (Bulletin de Physiopathologic Respiratoire) 1972; 8:1103–1113. 17. Lugaresi E, Coccagna G, Mantovani M, Brignani F. Effect of tracheostomy in hypersomnia with periodic respiration. Electroencephalogr Clin Neurophysiol 1971; 30:373–374. 18. Kryger M, Quesney LF, Holder D, Gloor P, MacLeod P. The sleep deprivation syndrome of the obese patients: A problem of periodic nocturnal upper airway obstruction. Am J Med 1974; 56:531–539. 19. Guilleminault C, Eldridge FS, Simmons B, et al. The Sleep Apnea Syndromes. Annual Review Medicine 1976; 27:465–484. 20. Guilleminault C, Liebhaber M, Navelet Y, et al. Proceedings: Sleep-induced apnea syndrome in six children. Electroencephalogr Clin Neurophysiol 1975; 39(4):432. 21. Guilleminault C, Eldridge FL, Dement WC. Insomnia with sleep apnea: A new syndrome. Science 1973; 181:856–858. 22. Guilleminault C, Dement WC. 235 cases of excessive daytime sleepiness. Diagnosis and tentative classification. J Neurol Sci 1977; 31(1):13–27. 23. Phillipson EA, Murphy E, Kozar LF. Regulation of respiration in sleeping dogs. J Appl Physiol 1976; 40:688–693. 24. Phillipson EA, Sullivan CE, Read DJ, Murphy E, Kozar LF. Ventilatory and waking responses to hypoxia in sleeping dogs. J Appl Physiol 1978; 44(4):512–20. 25. Phillipson EA. Control of breathing in sleep. Am Rev Respir Dis 1978; 118:909–939. 26. Weitzman ED, Pollak C, Borowiecki B, Burack B, Shprintzen R, Rakoff S. The hypersomnia sleep-apnea syndrome: Site and mechanism of upper airway obstruction. Trans Am Neurol Assoc 1977; 102:150–153. 27. White DP, Cadieux RJ, Lombard RM, et al. The effects of nasal anesthesia on breathing during sleep. Am Rev Respir Dis 1985; 132(5):972–975. 28. Mezzanotte WS, Tangel DJ, White DP. Waking genioglossal electromyogram in sleep apnea patients versus normal controls (a neuromuscular compensatory mechanism). J Clin Invest 1992; 89(5):1571–1579. 29. Lavie P. Incidence of sleep apnea in a presumably healthy working population: a significant relationship with excessive daytime sleepiness. Sleep 1983; 6(4):312–318. 30. Peppard PE, Young T, Palta M, et al. Prospective study of the association between sleepdisordered breathing and hypertension. N Engl J Med 2000; 342(19):1378–1384.

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31. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med 2001; 163(1):19–25. 32. Mehra R, Benjamin EJ, Shahar E, et al. Association of nocturnal arrhythmias with sleepdisordered breathing: The Sleep Heart Health Study. Am J Respir Crit Care Med 2006; 173(8):910–916. Epub 2006 Jan 19. 33. Ronald J, Delaive K, Roos L, et al. Health care utilization in the 10 years prior to diagnosis in obstructive sleep apnea syndrome patients. Sleep 1999; 22(2):225–229. 34. Findley LJ, Unverzagt ME, Suratt PM. Automobile accidents involving patients with obstructive sleep apnea. Am Rev Respir Dis 1988; 138(2):337–340. 35. Teran-Santos J, Jimenez-Gomez A, Cordero-Guervara J. The association between sleep apnea and the risk of traffic accidents. N Engl J Med 1989; 320(13):847–851. 36. Juniper M, Hack MA, George CF, et al. Steering simulation performance in patients with obstructive sleep apnoea and matched control subjects. Eur Respir J 2000; 15(3):590–595. 37. Guilleminault C, Eldridge FL, Simmons FB, et al. Sleep apnea in eight children. Pediatrics 1976; 58(1):23–30. 38. Gozal D. Sleep-disordered breathing and school performance in children. Pediatrics 1998; 102(3 Pt 1):616–620. 39. Gozal D, Pope DW Jr. Snoring during early childhood and academic performance at ages thirteen to fourteen years. Pediatrics 2001; 107(6):1394–1399. 40. Chervin RD, Archbold KH, Dillon JE, et al. Inattention, hyperactivity, and symptoms of sleep-disordered breathing.Pediatrics 2002; 109(3):449–456. 41. Chervin RD, Ruzicka DL, Archbold KH, et al. Snoring predicts hyperactivity four years later. Sleep 2005; 28(7):885–890. 42. Sullivan CE, Issa FG, Berthon-Jones M, et al. Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 1981; 1(8225):862–865. 43. Fujita S, Conway W, Zorick F, et al. Surgical correction of anatomic abnormalities in obstructive sleep apnea syndrome: uvulopalatopharyngoplasty. Otolaryngol Head Neck Surg 1981; 89(6):923–934. 44. Ikematsu T. Study of snoring, fourth report: Therapy. Nippon Jibiinkoka Gakkai Kaiho 1964; 64:434–435. 45. Riley RW, Powell NB, Guilleminault C. Obstructive sleep apnea syndrome: a review of 306 consecutively treated surgical patients. Otolaryngol Head Neck Surg 1993; 108(2):117–125.

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Epidemiology Kin M. Yuen Stanford University Center of Excellence for Sleep Disorders, Stanford, California, U.S.A.

INTRODUCTION With technological advances, remaining “connected” has become part of our lives. Remaining alert and attentive increases productivity, but what is being sacrificed in the end? It is with good reason that there is increasing interest in sleep and sleep disorders, since inattention and insufficient sleep have become the norm in our society, rather than aberrancies. In April of 2006, the National Highway Traffic Safety Administration (NHTSA) (1), found that driving while drowsy apparently increased one’s crash or near-crash risk four- to six-fold. In National Sleep Foundation’s 2005 Sleep in America poll, there was a trend to sleeping less on weekdays and weekends. Among 1506 adults surveyed, 40% reported sleeping less than seven hours weeknights, and a majority (71%) were sleeping less than eight hours weeknights (2). More importantly, 34% of those surveyed were found to be at risk of having a sleep disorder. Of the existing sleep disorders, obstructive sleep apnea (OSA) is one of the better known. OSA describes the periodic pauses in breathing and choking sensations in sleep. Earliest reports described a Pickwickian syndrome as presumed obesityrelated hypoventilation syndrome that produced daytime somnolence. Cases affecting children were published as early as the 1960s, but the term “sleep apnea syndromes” was not coined until 1978 (3), and it was not until 1981 that apnea treatment with nasal continuous positive airway pressure (CPAP) was initiated (4). Nowadays, despite the rapid advances of technology and understanding of OSA during the past decades, a cure for OSA remains elusive. However, the treatment options have dramatically increased. The quality of life for OSA-afflicted patients has correspondingly improved (5). In this chapter, the epidemiology of snoring, OSA and its comorbidities are discussed. SNORING While OSA commonly afflicts those who snore frequently, occasional snorers do not necessarily have OSA. Furthermore, amongst the snorers, while some complain of daytime symptoms, others do not. This section discusses the current known relationship between snoring and OSA. A cross-sectional study of 4648 responders (ages 20–69 years) in Northern Sweden, from 1991–2000, found that the prevalence rate of snoring, presenting as a problem, or enough to cause concern to the participants’ relatives, was 17.9% for men and 7.4% for women. Furthermore, the relatives were concerned about witnessed apneas in 11% of men, and 2.4% of women (6). Other epidemiologic studies support these data. In 1996, a large cross-sectional study found that 33% of men and 19% of women snored loudly among 5201 adults age 65 and older (7). 27

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Snoring was associated with excessive daytime sleepiness (EDS) in a large population study (8). The authors found that those who snored regularly had a higher prevalence of EDS [defined as an Epworth sleepiness scale (ESS) score > 11]: 39% of those who snored six to seven nights weekly, compared to 15% of “neversnorers.” This relationship was reported to be seen at all levels of apnea-hypopnea index (AHI) when documented by unattended polysomnography (PSG) at subjects’ homes. Recent studies from Australia as well as France suggested that truncal obesity and smoking were key determinants of loud, habitual snoring in their subjects (9,10). Of 850 French male subjects, 149 (17.5%) were habitual loud snorers. Studies have also shown that there is lower quality of life amongst those who snore, their bed partners and those with frank OSA (11,12). Whether one transitions from habitual snoring to development of OSA is still under investigation. There are likely multiple factors modifying one’s risks of developing OSA. Recent research has explored whether age, obesity, gender differences, ethnicity, and other comorbid conditions may predispose someone to become symptomatic of OSA. The following sections will discuss the prevalence of OSA, and possible contributing factors as outlined above. OBSTRUCTIVE SLEEP APNEA PREVALENCE Early epidemiological studies reported variable prevalence rates of OSA due to differences in study design and definitions of respiratory events. In an effort to standardize the definition of OSA, the American Academy of Sleep Medicine issued guidelines in 2001 (13) which further expanded the diagnostic criteria. The second edition of the International Classification of Sleep Disorders (ICSD2) includes not only PSG-determined respiratory-related arousals into the AHI, but also considers upper airway resistance as part of the continuum of OSA (14). Additionally, there is now a more detailed, stand-alone definition of pediatric OSA. In 1993, Young et al. (15) reported in their landmark article an estimated OSA prevalence rate of 2% and 4% in middle-aged women and men, respectively in the Wisconsin cohort. These individuals had an AHI ≥ 5 plus daytime sleepiness. The estimation was based on baseline evaluations of 1490 state employees 30 to 60 years of age. The subset of habitual snorers was then prospectively studied at four-year intervals with in-laboratory PSG. Of 626 subjects, 24% men, and 9% women had an AHI ≥ 5, whereas 9% men and 4% women had an AHI ≥ 15, but without daytime symptoms. Bixler et al. (16) in 1998 also reported an effect of age in the prevalence of OSA. After a telephone survey of a random sample of 4364 men (ages 20–100 years), 741 were studied by in-laboratory PSG. They found an OSA prevalence rate of 3.3%, when OSA was defined by an AHI ≥ 20 and the presence of daytime symptoms. OSA was most prevalent in the middle-aged group (45–64 years). Also, based solely on laboratory criteria, the prevalence of OSA (obstructive AHI ≥ 20) showed a similar age distribution to OSA diagnosed by laboratory and clinical criteria. The prevalence of any type of sleep apnea (central and obstructive) increased with age, with central apnea accounting more for this increase. However, “severity of sleep apnea, as indicated by both number of events and minimum oxygen saturation, decreased with age when any sleep apnea criteria were used and when controlling for body mass index (BMI).” Thus, the authors argued that the prevalence of sleep apnea tended to increase with age, but that the clinical significance (severity) of apnea decreased.

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Thereafter, Zamarron et al. (17) chose a random sample of 76 subjects, 50 to 70 years of age, from the electoral census in a small study in 1999. It was found that 28.9% of those surveyed had an AHI ≥ 5, and there were no differences between men (28%) and women (30%). However, “sleep apnea syndrome” was found more commonly in men (6.8%, five cases) than in women (zero cases) (p = 0.0521). Further studies followed which examined the specific effects of gender on OSA presentation. After performing two in-home studies in two years for 47 participants, Tischler et al. (18) found a five-year incidence rate of OSA to be 7.5% for moderately severe OSA and 16% (or less) for mild-to-moderately severe OSA. OSA criteria were: AHI of at least 10 (mild to moderate) or of at least 15 (moderate). Moreover, the incidence of OSA was determined to be independently influenced by age, sex, BMI, waist–hip ratio, and serum cholesterol concentration. The investigators concluded: “Predominance in men diminishes with increasing age, and by age 50 years, incidence rates among men and women are similar. The effect of BMI also decreases with age and may be negligible at age 60 years.” In summary, aging is associated with higher OSA prevalence; however, it is unclear if OSA in the elderly as compared to middle-aged adults represents the same disorder. Obesity alone is independently associated with more severe OSA, but may be less important with advanced age. Gender Early epidemiological and empirical data showed that OSA affects more men than women. However, after menopause, women and men seem to have equal prevalence. Thus, further population studies were conducted to evaluate the gender differences in OSA presentation and progression. Joaquin Durán et al. (19) evaluated gender differences in the prevalence of habitual snoring and OSA in the general population in Basque Country, Spain in a two-phase cross-sectional study. First, a random, stratified sample of 30- to 70-yearold participants was recruited by mail or telephone. There were 2148 subjects (76.9%) who each completed a home survey, blood pressure recording, and a portable respiratory recording in Phase I. Of these subjects, 1050 were men, and 1058 were women. Thereafter, 442 subjects diagnosed with OSA by home recordings, and 305 subjects (subgroup of 1706) who had normal results were invited to undergo in-laboratory PSG. A total of 555 subjects underwent PSG: 390 with suspected OSA, and 165 with previously normal home studies. Within these study subjects, 35% had habitual snoring, and 6% had OSA. More men presented with both problems, and there was an increased trend with age. EDS was reported in 18% of the subjects, but interestingly, was not associated with OSA. Furthermore, an AHI > 10 was found in 19% of men and 15% of women. If OSA is defined as AHI > 5, then its prevalence increased with age in both sexes, with an odds ratio (OR) of 2.2 for each 10-year increase. In 2002, the Sleep Heart Health Study (SHHS) Research Group found that among 5615 community-dwelling men and women aged between 40 and 98 years, those who reported habitual snoring, loud snoring, and frequent breathing pauses were three to four times more likely to have an AHI ≥ 15 or greater versus an AHI ≤ 15 (as detected by in-home PSG) (20). To further explore the gender differences in the prevalence of OSA, other studies examined if there is an intrinsic gender difference in upper airway collapsibility (21). Seventy-one consecutive patients with an AHI ≥ 5 events per hour were enrolled into the study. The median (range) AHI was 20 (5–132)

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events/hr. In addition, measurements of upper airway dimensions were made, using an acoustic reflectance method. OSA was much more positional and severe in men than women as indicated by the higher AHI in the supine position compared with sleeping on their sides (difference between supine and side AHI: 43.7 ± 5.2 [standard error of mean (SEM)] events/hr in men versus 10.7 ± 7.6 events/hr in women, p = 0.0015). The author concluded: “Men tend to have a larger but more collapsible airway during mandibular movement than women and this, in part, may play a role in the positional dependency and severity of OSA in men.” A retrospective tertiary university medical center-based study, which analyzed 130 randomly selected women with OSA against 130 men with OSA matched for age, body mass index, AHI (in-laboratory PSG studies), and ESS scores (22). The mean age was 48.0 years for women versus 47.6 years for men. Both women and men were morbidly obese with high BMI (40.4 kg/m2 for women vs. 40.0 kg/m2 for men), severe OSA with AHI > 30 (36.8 per hour for women vs. 36.0 per hour for men), and elevated ESS scores (12.45 for women vs. 12.84 for men). The authors discovered that women more often described their main presenting symptoms as insomnia [OR, 4.20; 95% confidence interval (CI), 1.54–14.26] and were much more likely to have a history of depression (OR, 4.60; 95% CI, 1.71–15.49) and hypothyroid disease (OR, 5.60; 95% CI, 2.14–18.57). Women presented less often with a primary complaint of witnessed apnea (OR, 0.66; 95% CI, 0.38–1.12), consumed less caffeine per day (3.3 cups in women vs. 5.2 cups in men; p = 0.0001), and admitted to less alcohol consumption (OR, 0.36; 95% CI, 0.18–0.70). There have been only a few studies remarking on possible gender differences of rapid eye movement (REM) sleep-related OSA. O’Connor et al. (23), in a retrospective study of 830 patients with OSA (diagnosed by PSG), found that the female subjects had “milder” OSA because of fewer events in non-REM (NREM) sleep. Women also had more REM sleep-related events than men. REM OSA occurred in 62% of women and 24% of men with OSA. But OSA was more common in men in the supine position than in women. The men predominated in the “severe” spectrum (AHI > 50/hour) with a male:female ratio of 7.9:1. The authors argued that these findings might reflect differences between the sexes in upper airway function during sleep in patients with OSA. International efforts have since produced the following literature in support of these theories. In Italy, 20 severely obese women exhibited more disturbances in sleep architecture and more OSA in REM sleep compared to 25 age- and weightmatched men (24). Women had significantly more wake time after sleep onset (WASO) 92.6 ± 52.4 versus 58.2 ± 45.2 min (p < 0.05), total wake time 104.8 ± 51.4 versus 67.8 ± 47.4 min (p < 0.05), and number of awakenings 15.5 ± 3.6 versus 10.2 ± 6.215 ( p < 0.001) compared to men. Women also had OSA that occurred almost exclusively during REM sleep (REM OSA) 35% versus 4%, (p < 0.05). Whether these findings persist into larger population studies remains to be seen. Nonetheless, in studies of the sleep of perimenopausal to postmenopausal women reported by a National Institutes of Health (NIH) panel, more frequent arousals and decreased sleep efficiency were noted (25). Similarly, poorer sleep quality and a higher REM sleep AHI were found in women in a retrospective study of 1010 Greek patients (844 males, 166 females) diagnosed with severe OSA (mean AHI of 42.4 ± 28.2 events/hr for men vs. 33.2 ± 27.7 events/hr for women, p < 0.001) by overnight PSG (26). In this study, OSA was five times more common in men than in women. Forty percent of men had AHI-REM sleep > AHI-NREM sleep, compared to 62% of women. Sleep quality was

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reportedly worse in female than in male patients: sleep efficiency index was lower (79.4 ± 16.1 vs. 85.1 ± 12.5%, p < 0.001), sleep onset latency was longer (27.7 ± 27.7 vs. 17.9 ± 18.1 min, p < 0.001), REM onset latency was longer (161.5 ± 76.2 vs. 145.7 ± 71.4 min, p < 0.018), and WASO was greater (42.6 ± 46.5 vs. 30.7 ± 34.9 min, p < 0.003) in women compared to men. Thus, it appears that, although men and women experience higher prevalence of OSA as they age, women might be more selectively affected by sleep fragmentation, poorer sleep quality, and more REM-related respiratory events. The mechanism involved is unclear, but seems to involve more than hormonal differences between the genders. Menopause As described in the section above, with the onset of menopause women experience a higher OSA prevalence than before menopause. Menopause has been defined as the complete cessation of menses for one year. This effect may be accounted for purely by aging as suggested by the Pennsylvania group. Bixler et al. (27), examined the effects of menopausal status on OSA in the community with a two-phase study in Southern Pennsylvania. Phase I was comprised of telephone interviews of 12,219 randomly selected women and 4364 men (age range of 20–100 years) from the general population. Phase II was comprised of 1000 women and 741 men randomly drawn from the phase I subjects. They all had one night of an in-laboratory sleep study. The authors reported a low OSA prevalence rate of 0.6% in premenopausal women, and 0.5% in postmenopausal women treated with hormonal replacement therapy (HRT), whereas the prevalence rate was 2.7% (p = 0.02) in postmenopausal women not on HRT. As compared with premenopausal women, the Wisconsin cohort also saw a higher OR ( CI) for menopausal women with an AHI ≥ 15: menopausal women on HRT, 1.5 (0.5-4.0); menopausal women without HRT, 2.8 (1.2-6.4) (28). The sample was comprised of 541 women, 30 to 60 years of age, and it was adjusted for confounders, such as age and body habitus. Current research in progress hypothesize that the fluctuations in hormonal levels, not limited only to estradiol and progesterone, months to years preceding the cessation of menses might yield further understanding of the natural progression of OSA in women (28,29). Currently, hormonal replacement is recommended on a case-by-case basis for the treatment of vasomotor dysfunction and nocturnal sleep disruptions (25). Future research into this very interesting area will undoubtedly shed more light into why the menopausal transition tips the balance towards higher OSA prevalence for women. Pre-eclampsia There have been recent reports about a possible association between pre-eclampsia and snoring/OSA in pregnancy. Franklin et al. (30), in Sweden performed a retrospective, cross-sectional study of consecutive cases of 502 women with singleton pregnancies. The prevalence rate of nightly snoring during the last week of pregnancy was 23%, while only 4% reported snoring before pregnancy. Hypertension developed in 14% of snoring women, compared with 6% of nonsnorers (p < 0.01). Significantly more of the snorers developed pre-eclampsia (10%) than nonsnorers (4%) (p < 0.05). Furthermore, infants born to habitual snorers had lower Apgar scores (≤ 7). A significantly higher percentage of infants (7.1%) were small for gestational age at birth of the infants of snoring mothers compared to that of nonsnoring

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mothers (2.6%) (p < 0.05). Because habitual snoring independently predicted hypertension (OR, 2.03; p < 0.05) and growth retardation (OR, 3.45; p < 0.01) in a logistic regression analysis after controlling for weight, age, and smoking, the authors concluded that snoring was associated with risk of pre-eclampsia. A French study, of 438 postpartum women with singleton deliveries, surveyed obstetrical history, sleep disorders during their last trimester, snoring and ESS scores (31). Forty-five percent of the patients reported habitual snoring during pregnancy. Among these, 85% were nonsnorers before pregnancy. The prevalence of pregnancy-induced hypertension (PIH) was found to be 4.5%, with two apparently independent risk factors: BMI (OR = 1.1) and an association between snoring and impaired vigilance (OR = 2.6). No statistical difference was found concerning intrauterine growth retardation (IUGR). The authors concluded that OSA symptoms were frequent during pregnancy, and snoring appeared to be linked with PIH. However, there was no conclusive PSG evidence to suggest direct relationships. Furthermore, there was a case report in 2004 of a 25-year-old woman, gravida four para two, at 37 weeks gestation in Missouri who had a documented apneic episode with maternal oxygen desaturation and concurrent fetal heart rate deceleration during a nocturnal recording (32). She subsequently delivered an infant that was small for gestational age. These findings suggest that hypoxemia during apneic events is directly linked to fetal distress, and is associated with an increased risk of IUGR. Edward et al. (33), in Australia found more autonomic pressor responses among pregnant women with pre-eclampsia and OSA than their counterparts with OSA, but no hypertension before or during pregnancy. Therefore, the authors suggested that the augmented pressor responses in pre-eclamptic women occurred as a result of maternal endothelial damage induced by the pre-eclampsia disease process. The blood pressures above baseline for control OSA patients versus pre-eclamptic OSA patients were 21 ± 2/12 ± 1 mmHg and 38 ± 5/25 ± 4 mmHg, respectively, p = 0.005/0.005. In contrast, there was no difference in heart rate responses between the two groups of subjects (34 ± 5 beats/min and 49 ± 13 beats/min above baseline in control OSA and pre-eclamptic OSA patient groups, respectively, p = 0.326). Since obesity might predispose women to OSA, a Finnish case-control study examined 11 obese pregnant women (mean prepregnancy BMI > 30 kg/m2) against 11 normal pregnant controls (mean BMI, 20–25 kg/m2) for evidence of OSA and IUGR (34). Overnight PSG was performed during early (after 12 weeks) and late (after 30 weeks) pregnancy. Obese mothers had significantly higher AHIs (1.7 vs. 0.2 events per hour; p < 0.05), 4% oxygen desaturations (5.3 vs. 0.3 events per hour; p < 0.005), and snoring times (32 vs. 1%; p < 0.001) during early compared to late pregnancy. These differences between the groups persisted in late pregnancy with further increases in snoring time in the obese mothers. Pre-eclampsia and mild OSA were diagnosed in one obese mother. Another obese mother delivered a baby showing growth retardation [weight 3 standard deviations (SD) below the mean]. Thus, the authors affirmed that OSA was more prevalent among obese mothers. Izci et al. (35), had serial reports about snoring in pregnancy in Edinburgh, Scotland. Fifty women in the third trimester of pregnancy, and 37 women with preeclampsia were recruited consecutively from the antenatal service and matched with 50 nonpregnant female controls. Upper airway dimensions were measured using acoustic reflection. Significantly more pregnant preeclamptic women (75%) reported snoring as compared to their pregnant nonpre-eclamptic counterparts

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(28%) and nonpregnant female controls (14%) (p < 0.001). Women with preeclampsia had upper airway narrowing in both upright and supine postures. Oropharyngeal junctional area in the seated position was less (p < 0.01) in pre-eclamptic women (mean ± SD, 0.9 ± 0.1 cm2) than either pregnant women (1.3 ± 0.1cm2) or nonpregnant female controls (1.1 ± 0.1 cm2). Supine oropharyngeal junctional area was less in the women with preeclampsia than in the nonpregnant female controls (0.8 ± 0.1 vs. 1.0 ± 0.1cm2; p = 0.01), but similar in women with pre-eclampsia and pregnant women (0.9 ± 0.1cm2; p > 0.3). Perhaps the smaller upper airway in pre-eclamptic women predisposes them to OSA or vice versa. A follow-up study by this group involved 167 healthy and 82 pre-eclamptic women in the third trimester of pregnancy (at about 36 weeks gestation) and 160 nonpregnant women. Subjects and their partners completed a sleep questionnaire (36). Preeclamptic women were heavier than pregnant and nonpregnant women and had higher BMIs than pregnant women before pregnancy (all p < 0.05). A significantly higher prevalence rate of snoring was found among pre-eclamptic women: 32% of control, 55% of pregnant and 85% of preeclamptic women snored (p < 0.001). Although prepregnancy snoring rates were similar amongst the groups, the prevalence of snoring was quite high: pre-eclamptic women 36%, healthy pregnant women 27%, and nonpregnant women 32% (p > 0.7). Sleepiness was reported by 15% of pre-eclamptic women, 23% of pregnant women, and 12% of nonpregnant women (p < 0.04), but nonpregnant women had significantly lower mean ESS scores than both pregnant and pre-eclamptic groups (p < 0.001). “Snoring was correlated with (p = 0.002), but explained only < 2% of, the variance in sleepiness.” Therefore, the authors concluded that “snoring and sleepiness increased in the third trimester of pregnancy, particularly in patients with preeclampsia.” However, the study suggests that sleepiness in pregnancy is predominantly the result of factors other than snoring or breathing pauses. Alternatively, the ESS might not fully reflect sleepiness in the pregnant state. Ethnicity An increased risk of OSA has been reported among African-Americans, Latinos and Asians (37,38). Craniofacial features that may contribute to higher prevalence rates amongst the minority groups have been reported in the following studies. A collaborative prospective study between Hong Kong and Vancouver of 239 consecutive patients (164 Asian and 75 white subjects) with an AHI of ≥ 5/hr on full overnight PSG revealed that a crowded posterior oropharynx and a steep thyromental plane predicted OSA across two different ethnic groups with varying degrees of obesity (39). After controlling for ethnicity, BMI and neck circumference, patients with OSA were older, had larger thyromental angles, and higher Mallampati scores than nonapneic subjects. In Malaysian population, Wong et al. (40), found that those with moderate-tosevere OSA had shorter maxillary and mandibular lengths. In their study, 34 men (11 Malay, 11 Indian and 12 Chinese), ages 27 to 52 years, were diagnosed with OSA by overnight PSG. Rhinomanometry and standardized lateral cephalometric radiographs were used to record linear and angular dimensions of their jaw structures. Malay subjects with moderate-severe OSA had a shorter maxillary and mandibular length when compared with a mild OSA reference sample (p < 0.05). The hyoid bone was located more caudally in the Chinese moderate-severe subjects (p < 0.05), and may be a useful diagnostic indicator for severity in this racial group.

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Undoubtedly, more research will clarify this new development. Another area of interest is the occurrence of OSA in children. Children Like adults, many snoring children are affected by OSA as well. Nieminen et al. (41), described the factors they found helpful in predicting OSA in Finnish children . Of the 78 snoring children who underwent overnight PSG, 29 children had a mean apnea index (AI) ≥ 1.55 (range 0–15), while 49 had a normal PSG. Parental report of nightly detectable apneic episodes correlated most with the occurrence of OSA [relative ratio (RR) 3.6, 95% CI 1.7–7.7]. Nighttime “constant snoring” (RR 1.5, CI 1.0–2.1) and restless sleep (RR 2.1, CI 1.1–4.0) were also predictive of OSA. A history of adenoidectomy (RR 1.7, CI 1.1–2.7) and tonsillar enlargement (RR 1.4, CI 1.1–1.8) also correlated significantly with OSA. The absence of excessive sleepiness was negatively correlated with OSA (RR 0.3, CI 0.1–1.0). Thus, a more thorough history in the aforementioned areas highly enhances the chances of a child being diagnosed with OSA on PSG studies. In Louisville, Kentucky, a more recent study was conducted on 122 preschoolers and 172 children in the five to seven year range to measure the predictive value of parental observations (42). The investigators found that parental report of frequent snoring based on questionnaire scores was “highly sensitive and specific for both age groups” compared to the presence of snoring on PSG. Moreover, when multiple measures of child behavior were added, the predictability value increased further. The authors concluded that the sleep questionnaire used was sufficient as a screening tool when the above domains were taken into account. A large epidemiological study in Tucson, Arizona found that “snoring, EDS, and learning problems are each highly specific, but not sensitive, for sleep-disordered breathing (SDB) in 6- to 11-year-old children” (43). This was a communitybased cohort of children 6 to 11 years of age (50% boys, 42.3% Hispanic, and 52.9% between the ages of six and eight years) using in-home monitoring. Of 480 recordings performed, boys were twice as likely as girls to have an RDI ≥ 1 (p < 0.01). Surprisingly, “witnessed apnea, ethnicity, age, obesity, and airway size (based on clinical evaluation) were not significantly different between those with SDB and without SDB.” A cross-sectional study of 829 children, eight to 11 years old (50% female, 46% Black, and 46% former preterm birth) with SDB assessed by either parental report of habitual snoring or unattended in-home overnight cardiorespiratory recordings was performed on school-aged children. Forty (5%) children were classified as having OSA (median AHI, 7.1/hr; inter-quartile range, 3.1–10.5), 122 (15%) had primary snoring without OSA, and the remaining 667 (80%) had neither snoring nor OSA. Children with relatively mild SDB, ranging from primary snoring to OSA, “had a higher prevalence of problem behaviors, with the strongest, most consistent associations for externalizing, hyperactive-type behaviors” (44,45). Another cross-sectional, population-based, cohort study evaluated thirdgraders in 27 primary schools in Hannover, Germany (46). Of 1760 eligible children, a representative sample of 1144 parents and their children (49% were girls) participated. Among these, 114 (10.1%) were habitual snorers (HS): 51 boys and 63 girls. There were 1015 (89.9%) nonsnorers: 524 boys and 491 girls. Of the habitual snorers, 80 were available during follow-up at about one year (mean interval, 13.5 months). Thirty-nine continued to snore (14 boys, 25 girls), 39 snored only occasionally, and two stopped snoring. Of the 41 “ex-habitual snorers,” four had

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ear, nose, and throat (ENT) surgery on adenoids and/or tonsils. Furthermore, this study found that independent risk factors for habitual snoring were a BMI > 90th percentile (OR, 3.5; 95% CI, 1.8–7.1), low maternal education (OR, 2.3; 95% CI, 1.1– 4.7), regular daytime mouth breathing (OR, 7.4; 95% CI, 3.5–15.6), and a higher frequency of sore throats (OR, 17.6; 95% CI, 6.4–48.8). The association of low maternal education and HS was higher in boys than girls (OR, 4.4; 95% CI, 1.5–13.6; vs. OR, 1.2; 95% CI, 0.4–3.6; respectively), while that of sore throats and HS was higher in girls than boys (OR, 52.7; 95% CI, 6.0–460.2; vs. OR, 13.3; 95% CI, 3.0–58.5, respectively). Therefore, it appeared that socioeconomic status, obesity, signs of nasal obstruction, and pharyngeal problems were independent risk factors for HS in these primary school children. More importantly, the authors suggested that the expression of HS varied considerably over time. Though a full review of this area is beyond the scope of this chapter, with more research into this expanding area of interest, our understanding of childhood snoring and sleep-disordered breathing will be more complete. It is also worth emphasizing that pediatric OSA was described alongside adult cases in the 1960s. Awareness of advances in understanding pediatric OSA furthers our understanding of adult OSA, and is essential to practicing good medicine.

NEW DIRECTIONS Metabolic Syndrome Just like the diagnosis of OSA preceded the availability of alternative treatments, clinical observations of OSA, obesity, and insulin resistance led to the study of the “metabolic syndrome.” A Chinese study of 270 consecutive subjects (197 male) without known history of diabetes mellitus in Hong Kong were evaluated by PSG, and 185 were found to have OSA with an AHI ≥ 5 (47). OSA subjects had higher levels of fasting serum insulin (p = 0.001); they were also older and more obese. Both obesity and SDB (AHI and minimum oxygen saturation) were independent determinants of insulin resistance. However, the association between OSA and insulin resistance was seen in both obese and nonobese subjects. An early study in Iowa showed some intriguing results. The authors prospectively studied 32 male patients (43 ± 2 years) with OSA who were “newly diagnosed and never treated and who were free of any other diseases” (48). There were 32 obesity-matched control male subjects (38 ± 2 years). Despite significantly higher levels of leptin in obese OSA subjects (13.7 ± 1.3 ng/mL) compared to those without OSA (9.2 ± 1.2 ng/mL) (p = 0.02), OSA subjects gained more weight the year before diagnosis (5.2 ± 1.7 kg vs. 0.5 ± 0.9 kg in OSA and obesity control subjects, respectively; p = 0.04). Thus, it appeared that the OSA subjects were resistant to the weight-reducing effects of leptin. Thirty obese patients with severe OSA (21 men, 9 women) with a mean BMI of 38.6 kg/m2 and a mean AHI of 40.5 per hour underwent overnight PSG and oral glucose tolerance testing in Italy (49). Controls included 27 weight-matched patients (12 males and 15 females; AHI, 2.15) and 20 normal subjects. The insulin sensitivity index composite value was significantly lower in OSA (1.71 ± 1.41) than in obese (3.08 ± 0.27) and in normals (6.1 ± 0.4) even after adjustments for age, BMI and waist-to-hip ratio. Therefore, obese OSA patients appeared to be insulin resistant independently of the degree and distribution of adiposity. Other important work has revealed the possible association of metabolic syndrome and cardiovascular disease in OSA. A study in Liverpool, United Kingdom

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assessed glucose, insulin, lipids, and blood pressure (BP) of 61 male subjects with OSA and 43 controls (50). “Subjects with OSA were more obese, had higher BP and fasting insulin, were more insulin resistant, had lower high-density lipoprotein (HDL) cholesterol, and an increased incidence of metabolic syndrome (87% vs. 35%, p < 0.0001).” Metabolic syndrome was 9.1 (95% CI, 2.6 – 31.2, p < 0.0001) times more likely to be present in subjects with OSA. In 2005, there was a succinct review discussing the possible mechanism of OSA and metabolic syndrome by Vgontzas et al. (51). In this article, the authors proposed that the elevated cytokines interleukin-6 (IL-6) and tumor necrosis factoralpha (TNF-alpha) were linked to disorders of EDS. Subsequent studies demonstrated that IL-6, TNF-alpha, and insulin levels were elevated in sleep apnea independently of obesity and visceral fat. Furthermore, women with the polycystic ovary syndrome (PCOS) were much more likely than controls to have sleepdisordered breathing and daytime sleepiness, suggesting a pathogenetic role of insulin resistance in OSA. Thus the combination of all these factors, along with menopausal status in women and aging, all contribute to the development of metabolic syndrome, which is highly linked with inflammatory cytokine production and cardiovascular morbidities in OSA. Additionally, recent literature regarding stroke occurrence and OSA illuminates this concept further (52). Severe OSA was associated independently with extracranial artery disease (EAD) as well as peripheral artery disease (PAD) in 395 stroke survivors. Interesting results were seen in a case-control series study in a hospital population in the United Kingdom. There were 181 patients admitted to hospital with first-ever stroke and community control subjects matched individually for age, sex, and general practitioner (53). The association between snoring alone and stroke was not statistically significant [OR (95% CI), 1.44 (0.88– 2.41)]. Daytime sleepiness was, however, significantly associated with stroke: OR 3.07 (1.65 – 6.08). Multiple logistic regression showed that hypertension, current smoking, taking alcohol regularly (negatively) and a higher ESS score were independently associated with stroke. In this study, there was more sleepiness prestroke, the cause of which is unclear, although OSA is a possible candidate. CONCLUSIONS Obviously more research is ongoing. It is still unclear how OSA serves to trigger various physiological consequences. What is clear, however, is that the cascade of events continues to cost individuals alertness, concentration, quality of life, societal economic losses, and loss of lives in the form of work-related and traffic accidents. While genetic predisposition may not be reversible, many symptoms and signs are readily recognizable, and easily treatable. It behooves all of us to alert not only the medical community, but also the world at large to the latest findings regarding OSA. REFERENCES 1. Administration NHTS. The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data: U.S. Department of Transportation; 2006 April. 2. Research WAM. 2005 Sleep in America Poll. Washington, D.C. National Sleep Foundation ; 2006. 3. Dement C, Carskadon M, Richardson G. Excessive daytime sleepiness in the sleep apnea syndromes. In: Guilleminault C, We D, eds. Sleep Apnea Syndromes. New York: Alan R Liss 1978:23–46.

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4. Sullivan CE, Issa FG, Berthon-Jones M, et al. Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 1981; 1(8225):862–865. 5. Moyer CA, Sonnad SS, Garetz SL, et al. Quality of life in obstructive sleep apnea: a systematic review of the literature. Sleep Med 2001; 2(6):477–491. 6. Larsson LG, Lindberg A, Franklin KA, et al. Gender differences in symptoms related to sleep apnea in a general population and in relation to referral to sleep clinic. Chest 2003; 124(1):204–211. 7. Enright PL NA, Wahl PW, Manolio TA, et al. Prevalence and correlates of snoring and observed apneas in 5201 older adults. Sleep 1996; 19(7):531–538. 8. Gottlieb DJ, Yao Q, Redline S, et al. Does snoring predict sleepiness independently of apnea and hypopnea frequency? Am J Resp Crit Care Med 2000; 162(4 Pt 1):1512–1517. 9. Knuiman MW, James AL, Divitini ML, et al. Correlates of habitual snoring and witnessed apnoeas in Busselton, Western Australia. Aust N Z J Public Health 2005; 29(5):412–415. 10. Teculescu D, Benamghar L, Hannhart B, et al. Habitual loud snoring. A study of prevalence and associations in 850 middle-aged French males. Respiration; international review of thoracic diseases 2006; 73(1):68–72. 11. Parish JM, Lyng PJ. Quality of life in bed partners of patients with obstructive sleep apnea or hypopnea after treatment with continuous positive airway pressure. Chest 2003; 124(3):942–947. 12. Reda M, Ullal U, Wilson JA. The quality of life impact of snoring and the effect of laser palatoplasty. Clin Otolaryngol 2000; 25(6):570–576. 13. AASM Clinical Practice Review Committee. Hypopnea in sleep-disordered breathing in adults. Sleep 2001; 24:469–470. 14. AASM. International Classification of Sleep Disorders, coding and diagnostic manual. 2nd ed. Westchester, 2005. 15. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328(17):1230–1235. 16. Bixler EO, Vgontzas AN, Ten Have T, et al. Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med 1998; 157(1):144–148. 17. Zamarron C, Gude F, Otero Y, et al. Prevalence of sleep disordered breathing and sleep apnea in 50- to 70-year-old individuals. A survey. Respiration; international review of thoracic diseases 1999; 66(4):317–322. 18. Tishler PV, Larkin EK, Schluchter MD, et al. Incidence of sleep-disordered breathing in an urban adult population: the relative importance of risk factors in the development of sleep-disordered breathing. JAMA 2003; 289(17):2230–2237. 19. Duran J, Esnaola S, Rubio R, et al. Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. Am J Respir Crit Care Med 2001; 163(3 Pt 1):685–689. 20. Young T, Shahar E, Nieto FJ, et al. Predictors of sleep-disordered breathing in community-dwelling adults: The Sleep Heart Health Study. Arch Intern Med 2002; 162(8): 893–900. 21. Mohsenin V. Effects of gender on upper airway collapsibility and severity of obstructive sleep apnea. Sleep medicine 2003; 4(6):523–529. 22. Shepertycky MR, Banno K, Kryger MH. Differences between men and women in the clinical presentation of patients diagnosed with obstructive sleep apnea syndrome. Sleep 2005; 28(3):309–314. 23. O’Connor C, Thornley KS, Hanly PJ. Gender differences in the polysomnographic features of obstructive sleep apnea. Am J Respir Crit Care Med 2000; 161(5):1465–1472. 24. Resta O, Carpanano GE, Lacedonia D, et al. Gender difference in sleep profile of severely obese patients with obstructive sleep apnea (OSA). Respir Med 2005; 99(1):91–96. 25. National Institutes of Health State-of-the-Science Conference statement: Management of menopause-related symptoms. Ann Intern Med 2005; 142(12 Pt 1):1003–1013. 26. Vagiakis E, Kapsimalis F, Lagogianni I, et al. Gender differences on polysomnographic findings in Greek subjects with obstructive sleep apnea syndrome. Sleep Med 2006; 7(5):424–430. 27. Bixler EO, Vgontzas AN, Lin HM, et al. Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med 2001; 163(3 Pt 1):608–613.

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28. Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165(9):1217–1239. 29. Eichling P, Jyotsna S. Menopause related sleep disorders. J Clin Sleep Med 2005; 1(3):291–300. 30. Franklin KA, Holmgren PA, Jonsson F, et al. Snoring, pregnancy-induced hypertension, and growth retardation of the fetus. Chest 2000; 117(1):137–141. 31. Calaora-Tournadre D RS, Meurice JC, Pourrat O, et al. Obstructive sleep apnea syndrome during pregnancy: prevalence of main symptoms and relationship with pregnancy inducedhypertension and intra-uterine growth retardation. Rev Med Intern 2006; 27(4):291–295. 32. Roush SF, Bell L. Obstructive sleep apnea in pregnancy. J Am Board Fam Pract 2004; 17(4): 292–294. 33. Edwards N, Blyton DM, Kirjavainen TT, et al. Hemodynamic responses to obstructive respiratory events during sleep are augmented in women with preeclampsia. Am J Hypertens 2001; 14(11 Pt 1):1090–1095. 34. Maasilta P, Bachour A, Teramo K, et al. Sleep-related disordered breathing during pregnancy in obese women. Chest 2001; 120(5):1448–1454. 35. Izci B, Riha RL, Martin SE, et al. The upper airway in pregnancy and pre-eclampsia. Am J Respir Crit Care Med 2003; 167(2):137–140. 36. Izci B, Martin SE, Dundas KC, et al. Sleep complaints: snoring and daytime sleepiness in pregnant and pre-eclamptic women. Sleep Med 2005; 6(2):163–169. 37. Ip MS, Lam B, Lauder IJ, et al. A community study of sleep-disordered breathing in middle-aged Chinese men in Hong Kong. Chest 2001; 119(1):62–69. 38. Scharf SM, Seiden L, DeMore J, et al. Racial differences in clinical presentation of patients with sleep-disordered breathing. Sleep & breathing = Schlaf & Atmung 2004; 8(4):173–183. 39. Lam B, Ip MS, Tench E, et al. Craniofacial profile in Asian and white subjects with obstructive sleep apnoea. Thorax 2005; 60(6):504–510. 40. Wong ML, Sandham A, Ang PK, et al. Craniofacial morphology, head posture, and nasal respiratory resistance in obstructive sleep apnoea: an inter-ethnic comparison. Eur J Orthod 2005; 27(1):91–97. 41. Nieminen P, Tolonen U, Lopponen H, et al. Snoring children: factors predicting sleep apnea. Acta Otolaryngol 1997; 529:190–194. 42. Montgomery-Downs HE, O’Brien LM, Holbrook CR, et al. Snoring and sleep-disordered breathing in young children: subjective and objective correlates. Sleep 2004; 27(1):87–94. 43. Goodwin JL, Kaemingk KL, Mulvaney SA, et al. Clinical screening of school children for polysomnography to detect sleep-disordered breathing—the Tucson Children’s Assessment of Sleep Apnea Study (TuCASA). J Clin Sleep Med 2005; 1(3):247–254. 44. Rosen CL, Storfer-Isser A, Taylor HG, et al. Increased behavioral morbidity in schoolaged children with sleep-disordered breathing. Pediatrics 2004; 114(6):1640–1648. 45. Pelayo R, Sivan Y. Increased behavioral morbidity in school-aged children with sleepdisordered breathing. Pediatrics 2005; 116(3):797–798. 46. Urschitz MS, Guenther A, Eitner S, et al. Risk factors and natural history of habitual snoring. Chest 2004; 126(3):790–800. 47. Ip MS, Lam B, Ng MM, et al. Obstructive sleep apnea is independently associated with insulin resistance. Am J Respir Crit Care Med 2002; 165(5):670–676. 48. Phillips BG, Kato M, Narkiewicz K, et al. Increases in leptin levels, sympathetic drive, and weight gain in obstructive sleep apnea. Am J Physiol 2000; 279(1):234–237. 49. Tassone F, Lanfranco F, Gianotti L, et al. Obstructive sleep apnoea syndrome impairs insulin sensitivity independently of anthropometric variables. Clin Endocrinol (Oxf) 2003; 59(3):374–379. 50. Coughlin SM, Mawdsley L, Mugarza JA, et al. Obstructive sleep apnoea is independently associated with an increased prevalence of metabolic syndrome. Eur Heart J 2004; 25(9):735–741. 51. Vgontzas AN, Bixler EO, Chrousos GP. Sleep apnea is a manifestation of the metabolic syndrome. Sleep Med Rev. 2005; 9(3):211–224. 52. Nachtmann A, Stang A, Wang YM, et al. Association of obstructive sleep apnea and stenotic artery disease in ischemic stroke patients. Atherosclerosis 2003; 169(2):301–307. 53. Davies DP, Rodgers H, Walshaw D, et al. Snoring, daytime sleepiness and stroke: a case-control study of first-ever stroke. J Sleep Res 2003; 12(4):313–318.

4

Ontogeny Timothy F. Hoban The Michael S. Aldrich Sleep Disorders Center, Departments of Pediatrics and Neurology, University of Michigan, Ann Arbor, Michigan, U.S.A.

Donald L. Bliwise Sleep Disorders Center, Department of Neurology, Emory University Medical School, Atlanta, Georgia, U.S.A.

INTRODUCTION At first glance, describing the ontogeny of obstructive sleep apnea (OSA) across the human life span would appear to be a simple and straightforward matter. It has long been recognized that OSA exhibits clear age-related changes, beginning as a childhood condition, most closely associated with adenotonsillar hypertrophy or craniofacial deformity and evolving into a disorder of adults more strongly linked to obesity, age, and male sex. Recent research, however, suggests that the obvious age-related changes in OSA, are accompanied by more complex aspects that are equally important to the understanding and diagnosis of this disorder. For example, children with OSA are more likely to have chronic partial obstruction as the primary mode of respiratory disturbance during sleep as opposed to the discrete apneas or hypopneas usually exhibited by adults (1,2), which limits the sensitivity of standard polysomnography (PSG) for diagnosis in some cases. Disturbances of learning, attention, and behavior are the most common daytime symptoms for children with OSA in contrast to the excessive somnolence demonstrated more frequently by adults (3,4). Age-related changes in the clinical presentation and epidemiology of OSA have also been identified during later adulthood, including increased prevalence for OSA in women following menopause (5), and evidence that OSA among individuals older than age 65 is less strongly associated with sleepiness and hypertension than it is for middleaged individuals (6,7). These and other data suggest that the ontogeny of OSA is in fact complex, incompletely understood, and characterized by profound changes in clinical manifestations and pathophysiology at different ages (Table 1). This chapter reviews current concepts regarding age-related changes of OSA, in broad strokes, with focus upon how clinical manifestations, prevalence, risk factors and morbidity evolve across the human lifespan. Detailed review of the epidemiology and pathophysiology of OSA is provided in other chapters of this text; however selected aspects are briefly discussed when necessary for adequate elucidation of the age-related mechanisms involved in the ontogeny of OSA. INFANCY Infants with OSA are usually described as having noisy breathing during sleep that sometimes is more strident in character than the snoring exhibited by older children and adults with OSA. Mouth breathing, excessive waking, and night sweats are also 39

Infancy

Absent or intermittent Failure-to-thrive

Unknown

Secondary symptoms Daytime sleepiness Neurobehavioral

Cardiovascular

Note: ?, questionable association; ↑, increase.

Prolonged or intermittent Arousal with obstruction Variable Sleep architecture Normal or fragmented

Pathophysiology Mode of obstruction

Neck hyperextension

Other

Symptoms during sleep Breathing Stridor or snoring Mouth breathing

Prolonged or intermittent Variable Normal to fragmented

Less frequent Normal > fragmented

Hypertension Rare cor pulmonale

More common Inattention Hyperactivity Behavioral disturbance

Restlessness Nocturia

Snoring with observed pauses

Obesity

Male > female Not identified Obese > normal

Older adults

More frequent Fragmented > normal

Intermittent > prolonged

Intermittent > prolonged More frequent Fragmented > normal

Very common Variable Subtle cognitive Limited data impairments Increased rates of motor vehicle and occupational accidents Frequent hypertension Limited data ? ↑ risk of stroke ? ↑ risk of cardiovascular morbidity

Restlessness Nocturia

Snoring with observed pauses

Obesity Male sex Menopause ? Pregnancy

Obesity Adenotonsillar hypertrophy Craniofacial anomalies Snoring with or without pauses Mouth breathing Restlessness Perspiration

Male > female 40–60 years Obese > normal

Adulthood

? Male > female Not identified Obese > normal

Adolescence

Prolonged > intermittent

Hypertension Rare cor pulmonale

Absent or intermittent Inattention Hyperactivity Behavioral disturbance

Snoring often continuous Mouth breathing Restlessness Perspiration

Male = female 3–6 years Usually normal, occasionally obese Adenotonsillar hypertrophy Craniofacial anomalies Obesity

Childhood

Ontogenetic Aspects of Obstructive Sleep Apnea at Different Ages

Physical and demographic characteristics Gender Male > female Peak age Not identified Body weight Normal or failure-to-thrive Major risk factors Craniofacial anomalies Prematurity Adenotonsillar hypertrophy

TABLE 1

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commonly seen during the sleep of affected infants, although observed pauses in respiration are only occasionally reported by parents and caregivers. Daytime symptoms during infancy are often subtle unless significant anatomic airway obstruction results in noisy breathing, mouth breathing, or labored respiration while awake. Excessive sleepiness is rarely evident except in the most severe cases, perhaps related to the fact that the frequent napping that normally occurs at this age might limit recognition of mild or moderate degrees of sleepiness. On occasion poor weight gain or failure to thrive may be the most obvious presenting symptoms of infant OSA (8–10). A specific anatomic cause of airway obstruction can be identified for the majority of infants diagnosed as having OSA (9,11–13). Craniofacial conditions (Table 2) and adenotonsillar obstruction represent the two most commonly identified causes of symptomatic obstruction in this age group. Choanal atresia, Pierre TABLE 2 Syndromes and Medical Conditions Predisposing to Obstructive Sleep Apnea in Children and Adolescents Craniofacial syndromes associated with significant mandibular or maxillary hypoplasia Apert syndrome Crouzon syndrome Goldenhar syndrome (hemifacial microsomia) Hallerman-Streiff syndrome Pierre Robin syndrome (Robin sequence) Rubinstein-Taybi syndrome Russell-Silver syndrome Treacher Collins syndrome Other syndromes featuring prominent craniofacial involvement Achondroplasia Klippel-Feil syndrome Larsen syndrome Saethre-Chotzen syndrome Stickler syndrome Velocardiofacial syndrome Conditions associated with macroglossia Beckwith-Wiedemann syndrome Down syndrome Hypothyroidism Mucopolysaccharide storage disorders (e.g., Hunter, Hurler syndromes) Conditions associated with anatomic airway obstruction Adenotonsillar hypertrophy Cleft palate and/or cleft palate repair Choanal atresia or stenosis Fetal warfarin syndrome Laryngotracheomalacia Nasal polyps or septal deviation Pfeiffer syndrome Vascular ring Neurologic disorders associated with weakness or impaired ventilatory control Cerebral palsy Cranial neuropathies (e.g., Mobius syndrome, poliomyelitis) Neuromuscular disorders (e.g., Duchenne, myotonic dystrophies) Structural brainstem lesions (e.g., Chiari malformations, syringobulbia) Conditions characterized by obesity Morbid obesity/metabolic syndrome Prader-Willi syndrome

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Robin sequence, and other conditions associated with severe micrognathia have the potential to cause particularly severe obstruction during early infancy. Although cleft palate in the absence of other anatomic defects is not universally considered to be a risk factor for infant sleep apnea, surgical closure procedures for cleft palate do appear to increase the risk for symptomatic obstruction (14–16). Obesity is only occasionally reported in association with OSA during infancy (17). Only limited data are available regarding the epidemiology of OSA during infancy, but symptomatic obstructive apnea is generally considered to be rare among infants born at term without predisposing medical conditions. There is some evidence that OSA is more frequent among infants born prematurely, with risk being highest until 43 weeks postconceptional age (18). Among 1023 term infants aged two through 27 weeks, occasional obstructive apneas were identified in 40% of subjects, primarily in babies younger than 12 weeks (19). The mean obstructive apnea index (AI) remained low at 0.1 for all age strata analyzed within this cohort, while average obstructive apnea duration declined from 5.5 seconds for younger infants (two to seven weeks) to 4.5 seconds for older ones (20–27 weeks). Other studies suggest that obstructive events are more frequent among male infants and often resolve by two months of age (20–22). The frequency with which infant OSA persists into childhood or adulthood remains unknown. It is interesting to note that the frequency of obstructive apneas is quite low in healthy term infants despite the presence of anatomic factors such as small airway size, high nasal resistance, and high chest wall compliance that would ordinarily appear to convey high risk for symptomatic obstruction. It is postulated that the presence of increased ventilatory drive and decreased airway collapsibility relative to older ages allows most normal infants to compensate for these potentially adverse influences (15). In addition, age-dependent ventilatory reflexes may provide partial explanation for the fact that the mode of sleep-related obstruction differs for infants compared to adults. Infants arouse less readily to hypercapnia than adults and do not normally compensate for increases in inspiratory resistive load during sleep (23–25). This may explain why infants are more likely to exhibit prolonged periods of obstructive hypoventilation than the cyclical, discrete apneas more commonly exhibited by adults. CHILDHOOD AND ADOLESCENCE Although symptomatic OSA is thought to be less common in children and adolescents than in adults, occasional obstructive apneas occur in normal children. Among 50 healthy children aged 1 to 18 years without symptoms of OSA, the mean obstructive AI was reported to be 0.1, with maximal durations of obstruction ranging between three and 10 seconds (26). Although this study helped define the frequency and character of obstructive events in healthy, asymptomatic subjects, and is the origin of the commonly used guideline that an obstructive AI of 0 to 1 is considered the “normal range” for pediatric PSG, the threshold at which an elevated apnea index (AI) or apnea-hypopnea index (AHI) becomes associated with clinically significant health consequences has not been established. Because childhood OSA is more commonly characterized by periods of prolonged obstructive hypoventilation with less frequent apneas and hypopneas than adults, it has been suggested that use of the AHI alone may not be sensitive for the diagnosis of childhood OSA (27). These concerns are in accordance with pediatric case series that have identified children with clinically-significant obstruction with mean AHIs below five events per hour (28).

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The prevalence of habitual snoring among children has been estimated to be 5 to 14%, with witnessed respiratory pauses reported in about 5% of children (29–34). Estimates regarding prevalence of OSA during childhood are varied, due to the lack of universally accepted diagnostic criteria for OSA in this age group, and because large population-based studies have not yet been performed. Most studies suggest a minimum prevalence of 1% to 3% (29,32,35,36). OSA during childhood demonstrates several unique epidemiologic characteristics. Case series suggest that the prevalence of OSA peaks between two and five years of age (12). Boys and girls exhibit comparable prevalence of OSA in contrast to the male preponderance seen during infancy and adulthood (29,30,32,37). Although obesity is a less frequent risk factor for OSA in children than for adults, obese children are more likely to have moderate or severe OSA than nonobese children (35,38). Limited data suggest that former premature infants and that specific ethnic groups may be at increased risk for OSA during childhood (35,39). Children and adolescents with OSA usually exhibit noisy respiration during sleep, although this may be only intermittently witnessed or the severity underestimated by parents. Snoring is often accompanied by prominent mouth breathing and sometimes by snorting or gasping noises. Witnessed respiratory pauses are less often seen in children than adults, consistent with evidence that chronic partial obstruction is more frequent than discrete obstructive apneas for many affected children. Children with OSA exhibit more frequent restlessness, diaphoresis, and enuresis than unaffected children and may be at increased risk for parasomnias (8,40,41). Unusual sleeping positions involving excessive extension of the head and neck are sometimes observed, potentially representing a compensatory mechanism for improving the patency of an obstructed airway. The daytime symptoms manifested by children with OSA are highly variable and are usually less obvious than the daytime sleepiness exhibited by affected adults. Children will sometimes report dry mouth, headache, or sore throat upon waking, but these symptoms are often mild and transient. Mouth breathing may be seen during wakefulness in children with significant adenotonsillar hypertrophy or other nasal obstruction. For preadolescent children with OSA, daytime somnolence is seldom prominent unless nocturnal obstruction is severe. Subtle or intermittent sleepiness may sometimes be seen, most often during sedentary activities such as automobile rides. In contrast, adolescents with OSA are more likely to present in an “adult” fashion where sleepiness represents a pervasive and easily recognizable symptom. Deficits involving learning, attention, and behavior represent the most common daytime symptoms of childhood OSA, and children with OSA may be initially misdiagnosed with attention deficit-hyperactivity disorder (ADHD). Significant learning disabilities, hyperactivity, and behavioral disturbances were reported in early case series of children with OSA, which documented relatively severe cases (12,13,42,43). More recent data suggest that even milder degrees of OSA may be associated with neurocognitive deficits. Neuropsychometric assessments of children with mild or moderate OSA suggest behavioral regulation and executive functioning may be affected to a greater degree than other objective measures (4,44). Neurobehavioral consequences involving similar domains have also been identified in children having snoring without OSA (45,46). Children with OSA exhibit characteristic physical features more often than affected adults. Tonsils and adenoids are largest in relation to upper airway size between two and eight years of age (47). Tonsillar size is easily verified during

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routine examination, whereas physical signs of adenoidal hypertrophy may consist only of mouth breathing or the elongated facial appearance of “adenoid facies”. Children with OSA sometimes exhibit a narrow, high-arched palate or other craniofacial abnormalities (48–50), however it is possible for the physical exam to be entirely normal in affected children. OSA in children is less frequently associated with obesity than in the adult population, however obesity still represents an identifiable risk factor for OSA throughout childhood and adolescence (51–53). In a large epidemiologic study, obesity represented the most prominent risk factor for moderate OSA, with an odds ratio of 4.5 for obese children compared to the nonobese (35). Obese adolescents may be at particularly high risk. In a series of 22 obese adolescents without specific sleep complaints, 10 (45%) had abnormal PSG and six (27%) were classified as having moderate or severe OSA (38). In this group, degree of obesity correlated significantly with AI, sleepiness, and nadir oxygen saturation. The long-term health consequences of OSA have been studied less rigorously for children than for the adult population. Hypertension has been reported in several case series of children with OSA (42,54,55), with other series reporting evidence of often asymptomatic pulmonary hypertension (56,57). Cor pulmonale now represents a rare complication of severe OSA which may resolve following treatment of obstruction (58–60). Recent reports suggest that children with OSA have increased rates of health care utilization (61) which decline substantially following adenotonsillectomy (62). The long-term effects of childhood OSA upon educational achievement, socioeconomic status, and mortality have not been established. As is the case for infants, higher arousal thresholds and failure to briskly compensate for increases in inspiratory resistive load are thought to represent the major reasons that children with OSA exhibit prolonged partial airway obstruction more often than the brief, cyclical apneas and hypopneas that characterize adult OSA (15). These mechanisms do not necessarily explain why the daytime symptoms of OSA differ so dramatically between children and adults. Differences in arousal frequency and associated disruption of sleep architecture between the two populations represent one potential mechanism that could underlie the differing clinical manifestations during wakefulness. It is also postulated that the extremely high degrees of daytime alertness exhibited by preadolescent children relative to adults (63) might modify the daytime expression of sleepiness and therefore account for the fact that inattention and hyperactivity are seen more often than overt somnolence in younger children with OSA. Since daytime alertness as measured by multiple sleep latency testing gradually declines to adult levels during adolescence (64,65), this postulated mechanism is consistent with the clinical observation that adolescents with OSA are more likely than younger children to exhibit daytime sleepiness as a prominent symptom. The differences in the pathophysiology of airway obstruction, arousal responses, and daytime symptoms between children and adults with OSA are sufficiently profound that there has been speculation that pediatric and adult OSA might represent two distinct disorders rather than a single disease with substantial age-dependent changes (15). This speculation notwithstanding, it is uncertain how often untreated childhood OSA persists into adulthood or whether the condition might improve or resolve for some children in the absence of treatment. Several case series do suggest that children successfully treated for OSA during early childhood sometimes exhibit recurrence during adolescence (66,67).

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ADULTHOOD AND OLD AGE Clinical Manifestations Adults with OSA demonstrate symptoms during sleep that are in some ways comparable to those exhibited by children. Prominent snoring is almost universal among affected patients, often accompanied by significant restlessness or perspiration during sleep. In many cases, bed partners or family members report audible pauses in respiration which often terminate with a snorting or gasping noise. Enuresis associated with OSA is less common for adults than for children, however repetitive awakenings to use the bathroom (nocturia) represent a common complaint (68,69) which may correlate with severity of obstruction (70). Insomnia as a presenting symptom of OSA is more frequently reported by women than men (71,72) The daytime symptoms of adult OSA differ considerably from those of children. Daytime sleepiness is one of the cardinal features of adult OSA as opposed to an occasionally observed symptom for affected children. Although self-reported measures of sleepiness are reported to correlate with the AHI in large epidemiologic studies of adults with OSA (73,74), many adults with OSA report little or no sleepiness, suggesting considerable variation in individual susceptibility to this symptom. Sleepiness, as an associated symptom of nonapneic snoring, was also reported in these cohorts. Fewer data are available with respect to other daytime symptoms of adult OSA. Several series have reported impairments of working memory, visual vigilance, attention span, and psychomotor efficiency in adults with OSA (75–77). Patients with OSA are also reported to have higher motor vehicle crash and occupational injury rates (78–80). Other occasionally reported daytime symptoms considered more common for adults than children include automatic behavior, hypnagogic hallucinations, and sleep drunkenness. The physical feature most strongly associated with adult OSA is obesity, in contrast to the adenotonsillar enlargement most frequently associated with childhood OSA. Physical findings in affected adults may also include septal deviation, maxillary hypoplasia, redundant pharyngeal tissue, small/retrognathic mandible, and large neck circumference (81). Age Dependence For the adult population as a whole, the prevalence for OSA of at least mild severity (AHI ≥ 5) is estimated to be 20% and the prevalence for OSA of at least moderate severity (AHI ≥ 15) is estimated to be 6.7% (27,82). Population-based studies suggest that prevalence is lowest among younger adults and increases during middle age. In a cross-sectional analysis of the Wisconsin Sleep Cohort Study (WSCS), prevalence for mild or worse OSA (AHI ≥ 5) among men was 17% at ages 30–39, rising to 25% at ages 40–49 and 31% at ages 50–60 (73). For moderate or worse OSA (AHI ≥ 15), prevalence rose from 6.2% at ages 30–39 to 11% at ages 40–49 and 9.1% at ages 50-60. Increased prevalence of OSA during middle age has been reported in other epidemiologic studies as well (83–85). Older adulthood represents a portion of the life span that is in some ways even more vulnerable to airway obstruction during sleep. As many as 24% of ambulatory, community-dwelling adults over the age of 65 demonstrate an AI in excess of five events per hour (86) and this prevalence figure is even higher (42%) in older and more infirm nursing home populations (87). For many years, the sleep apnea of elderly persons was thought to represent predominantly central, rather than

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obstructive, events, but many descriptive studies have shown that this is not the case (88). The distinction between central and obstructive apnea as the predominant subtype in elderly populations bears particular relevance for understanding the pathophysiology of sleep apnea in the elderly (see below). Elsewhere we have argued that although chronologic age per se can be a misleading construct (cf., physiologic age) when discussing OSA in aging (89), it can be a helpful framework to discuss potential similarities and differences between the OSA of middle-aged and older age groups. Figure 1 represents a hypothetical model employing arbitrarily defined chronologic age ranges that suggests that OSA in adulthood is likely to represent two separate phenomena, one age related (having a specific window of vulnerability perhaps in the middle 40s and early 50s) and one age dependent (more likely to occur with increasing age with the effect suspected to emerge in the mid-60s) (7). The reader must understand that assignment of specific chronological ages in the model is arbitrary, though undoubtedly heuristic and capable of hypothesis testing. The distinction between age-related and age-dependent conditions takes origin in research in aging processes (90), which indicates that many conditions constitute biomarkers of aging (i.e., they show age dependence). The reader is directed elsewhere to more detailed expositions of this framework (7,88,91). Here we will review selectively some literature that bears upon the model. Age dependence in OSA is demonstrable in three general domains: (i) risk factors, (ii) pathophysiology and (iii) disease outcomes. Risk Factors for OSA During Adulthood Case series and population-based studies have almost universally reported strong associations between adult OSA and most measures of excess weight, including body mass index and neck circumference (73,92–94). In a cross-sectional analysis from the WSCS, an increase of one standard deviation in the body mass index

FIGURE 1 Heuristic model suggesting sleep apnea as both an age-related and age-dependent condition with potential overlap of distributions in the 60- to 70-year-old age range. Cross-sectionally, note that the number of cases observed may remain high and increase with age, despite a presumed decrease in age-related sleep apnea. Source: From Ref. 88. Abbreviation: SA, sleep apnea.

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resulted in a threefold increase of risk for AHI ≥ 5 (73). A longitudinal analysis drawn from the same cohort suggested that a 10% weight gain over four years predicted a six-fold increase in risk for developing moderate or severe OSA (95). Specific craniofacial features may be associated with adult OSA, including retrognathia, maxillary retrusion, and altered mandibular size, although only mandibular body length has demonstrated significance for the prediction of adult OSA (96–98). Adults with OSA may have larger tongues, soft palates, and lateral pharyngeal walls than those without (99–101). It is thought that some of the soft-tissue enlargement within the upper airway of adults with OSA might result from chronic edema or inflammation caused by snoring (102,103). Adenotonsillar hypertrophy is identified much less often in adults with OSA compared to children. OSA is more frequent in men than in women at all ages during adulthood, with most population-based studies suggesting that men are at two to three times greater risk throughout the early and middle adult years (73). The prevalence of OSA in adult women appears to increase with advancing age in a manner comparable to men but female risk for OSA may additionally be impacted by such age-dependent factors as pregnancy and menopause (84). The extent to which pregnancy increases a woman’s risk for developing OSA is uncertain. Pregnancy-associated weight gain, pharyngeal edema, or vasomotor rhinitis represent factors that can potentially cause or worsen upper airway obstruction during sleep (104–106). Several series have reported increased snoring in the latter stages of pregnancy (106,107) which may resolve within several months following delivery (108). PSG-based data assessing sleep-related breathing disorders during pregnancy are scant. One small series studying women during the early third trimester of pregnancy found that 11 women with chronic snoring demonstrated abnormal esophageal pressure fluctuations indicative of increased airway resistance more often than 13 nonsnoring controls, however none of the subjects exhibited AHI ≥ 5 (108). The risk of developing OSA during pregnancy may be higher for women with obesity. In a case-control series assessing PSG changes from early-to-late pregnancy in obese women, mean AHI increased significantly (1.7 to 2.6 , p ≤ 0.05), with 1 of 11 subjects developing frank OSA (AHI > 10) (109). Improvement of pregnancy-associated OSA following delivery has been reported (110,111). Although menopause has long been suspected to be a risk factor for OSA in older adult women, population-based data have only recently provided objective support for this belief. The WSCS found that the odds ratio for AHI ≥ 5 relative to the premenopausal baseline increased to 1.2 during perimenopause and 2.6 with postmenopause after adjusting for obesity, age and other confounding factors (5). Hormone replacement therapy in postmenopausal women may be associated with reduced risk for OSA (112). Several medical and lifestyle risk factors potentially associated with OSA are age dependent insofar as they are more commonly identified in adults with OSA than in children. Hypothyroid states represent a common and independently treatable predisposing condition more commonly identified in women than in men or children (72,113,114). Although evening time consumption of alcohol has been found in many studies to have an acute deleterious effect upon respiration during sleep (115,116), the long-term effects of alcohol use upon adult OSA have not been systematically investigated. Cross-sectional studies have suggested positive associations between smoking and OSA as well (117,118), but these associations have not been confirmed via prospective assessment.

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Pathophysiology of OSA in Adults and the Elderly Age differences in the waking respiratory control system have long been known. Specifically, decreased hypercapnic ventilatory drive and diminished response to hypoxia (119) as a function of age have been demonstrated. The relevance of such changes in central and peripheral chemoreceptor sensitivity while awake, however, for age-dependent alterations during sleep is less well understood. Sleep is associated with mild CO2 retention with depressed hypercapnic response (120), and stable breathing in sleep is ensured by assumed decreases in chemoreceptor loop gain (121). Instability in breathing during sleep may result when loop gain is increased or “overshoots.” Elderly persons could be susceptible to unstable breathing during sleep because of relatively higher levels of ventilatory responsiveness during sleep, leading to periodic breathing and subsequent airway obstruction (122). There have been relatively few studies examining the ventilatory response to hypercapnia during sleep as a function of age. One early study reported absence of sleep-related decline in the hypercapnic ventilatory response in older subjects (123), consistent with increased loop gain as a function of age. In a more recent study, however, using continuous positive airway pressure (CPAP) to control for muscle activation induced by upper airway resistance (see below), Browne et al. (124) showed that the sleep-related decline in chemosensitivity to hypercapnia was preserved similarly in both young and older subjects without sleep apnea and that the magnitude of that decline was similar, arguing against the loop gain hypothesis. Krieger et al. (125) proposed that the reduced endoesophageal pressure generated during OSA in older subjects might reflect altered chemosensitivity but did not present data on hypercapnic or hypoxic ventilatory drive in those subjects. Other factors may also underlie altered chemosensitivity in the elderly in sleep. For example, frequent arousals during sleep, a characteristic feature of sleep in the elderly (126), might destabilize ventilation (127,128), by unmasking the hypocapnic apneic threshold induced by large breaths concurrent with termination of arousal (129). Studies evaluating this hypothesis have analyzed intrinsic variability in breathing during sleep as a function of age. Data on age differences are mixed. A number of early studies (130–132) failed to find age differences in the wake-tosleep transition of minute volumes during episodes of non-apneic breathing, but Hudgel et al. (133) reported that older subjects showed wide oscillations in tidal volumes during sleep that were inversely related to upper airway resistance. Browne et al. (134) reported similar coefficients of variation in tidal volumes of young and older subjects during periods of sleep without respiratory events. Taken together, these data present only a mixed picture that unstable breathing per se underlies the majority of OSA of the geriatric population, unless conditions predisposing to unstable ventilation (e.g., congestive heart failure) are also present. Increased compliance of the upper airway of elderly subjects (specifically men) in wakefulness was first noted over 20 years ago in several studies using rhinometry (135,136), and pharyngeal cross-sectional area, measured with acoustic reflection, also showed substantial decreases with aging (137), particularly when supine (138). These descriptions of upper airway structure generally have been supported by functional studies. Thurnheer et al. (139) noted that both the rapid eye movement (REM) and non-REM (NREM) sleep-related increase in total respiratory resistance was of greater absolute magnitude in middle-aged men relative to younger men. Some age differences were also seen in younger versus older women in both the REM and NREM pressure-flow relationships during latter stages of inspiration. It should be noted that any sleep-wake state instability may enhance collapsibility of the upper airway (140).

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Several studies have examined age differences in protrusion force of the major upper airway dilator, the genioglossus (GG). During waking, GG strength, but not endurance, decreased with age in both men and women (141,142); however, endurance measurements were made at submaximal pressures, which may have underestimated the force generated during sleep-related obstruction. No data were available on OSA in those subjects. A follow-up study (143) correlated waking tongue protrusion force to AHI and found a minimal relationship, but weak AHI/ age correlations decrease the relevance of this finding for understanding agedependent mechanisms. Alternative pathophysiologic investigations have examined spontaneously occurring activity among upper airway muscles. Elevated dilator activity has been recognized in OSA patients during wakefulness (144,145), and this is also seen during sleep, even when upper airway resistance is normalized with CPAP to eliminate the possible activation of negative pressure breathing (146). These findings can be interpreted as compensatory for a more compliant airway, at least among phasically active muscles such as the GG. The question then arises as to how enhanced collapsibility may be manifested in older individuals. Worsnop et al. (147) reported that the decline in ventilation at sleep onset in older relative to younger men was accompanied by a more precipitous drop in intra-muscularly recorded activity in both the GG and tensor palatini (TP), an upper airway dilator with more tonic activity throughout the respiratory cycle. Conversely, on awakening the increment in GG electromyographic (EMG) activity was greater in older subjects than in younger subjects. On the other hand, Fogel et al. (148) reported that although baseline levels of EMG activation in the GG were higher in older subjects, the magnitude of the reduction of EMG activity with sleep was comparable between age groups. In a later study, pulses of negative pressure breathing delivered during sleep via mask generated lower GG EMG responses in older relative to younger subjects (149). Animal studies of structure and function of the upper airway dilators provide a useful translational substrate for a discussion of age-dependent effects on muscle fatigue. Relative to other muscle groups (e.g., diaphragm), dilating muscles in the upper airway, such as the GG and sternohyoid (STH), are composed of a higher proportion of type II fast-twitch (fatigue resistant) fibers (150). Cantillon and Bradford (151) did not examine GG physiologically; however, they were unable to demonstrate in vitro age differences in endurance of the rat sternohyoid, though they noted that tension generated per unit of cross-sectional area in the geniohyoid (GH) was higher in older animals. Oliven et al. (152) found no age differences in the histochemical properties of upper airway dilators (GG, GH, and STH) when compared to non-dilating upper airway muscles (mylohyoid); however, when examined with a quantitative visual method sensitive to the oxidative status of muscle (succinate dehydrogenase densitometry), older animals showed substantial reductions, particularly in the GG. These changes were far more pronounced than age differences in the simple proportion of fast-twitch fibers in any of the muscle groups studied and suggest differences in GG function by age. Finally, a role for altered proprioception within the upper airway as a function of age cannot be discounted. Although recent studies of upper airway function during sleep have controlled for upper airway afferent stimulation by using nasal CPAP, the role of altered sensory input from the upper airway may play a role in the higher prevalence of OSA in old age. Reduction in both two-point discrimination (153) and detection of thermal gradients (154) have been noted in OSA, and similar deficits have been noted in the upper airway in older subjects who were not

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specifically evaluated for OSA (155,156). The longer duration of apneic events in REM and NREM sleep in older subjects has been interpreted partially as a function of reduced proprioception within the upper airway that otherwise triggers arousal and termination of breathing events (157). In summary, full consideration of the complex array of mechanisms accounting for airway obstruction in OSA suggests that the upper airway structure/function of older persons and, to a somewhat lesser extent, alterations in ventilatory control, bear more similarity than dissimilarity to those phenomena in younger adults with OSA. We should note that many of these studies have been limited to comparing men in their 20s and 50s, so some uncertainty remains regarding the applicability of the findings to women, as well as in advanced old age (i.e., over age 80). Outcomes and Comorbidities of OSA in Adults and the Elderly The morbidity associated with OSA has been examined more thoroughly for middleaged adults than for other age groups. Several avenues of investigation have suggested a strong association between OSA and hypertension in adults. Apneic episodes have been found to cause substantial but transient increases in blood pressure during sleep (158,159). Multiple population-based studies have reported significant associations between PSG-documented OSA and hypertension while controlling for potential confounding influences such as obesity, sex, and age (83,160,161). Although these cross-sectional studies could not demonstrate whether OSA predates or causes hypertension, the limited observational data available suggest that adult OSA represents an independent risk factor for the development of high blood pressure over time. A prospective study involving the WSCS found that an AHI of five to 14.9 at baseline doubled the risk for developing hypertension within four years and that baseline AHI ≥ 15 nearly tripled that risk (162). Other cardiovascular morbidity linked to adult OSA in cross-sectional studies and case series includes stroke, myocardial infarction, heart failure, cardiac dysrhythmia, and pulmonary hypertension (163–166). Because these conditions are also closely associated with hypertension—common in adults with OSA—prospective population-based studies will be required to determine whether OSA represents an independent risk factor in their pathogenesis. In contrast to data suggesting strong associations between OSA and cardiovascular morbidity in middle-aged adults, initial evidence appears weaker that putative adverse outcomes are associated with OSA in old age. Undoubtedly the study that has fostered the most widespread belief that OSA in older individuals has less clinical importance than in younger individuals is the retrospective study of He et al. (167). This classic study, although methodologically flawed because it was a nonrandomized, retrospectively conducted comparison of aggressively treated versus untreated sleep apnea patients, and showed that untreated patients had higher follow-up mortality relative to treated patients. More intriguing was the observation that this differential mortality was present only in patients under 50 years of age. Treatment had no bearing upon survival in patients over the age of 50. This study has often been cited as evidence that OSA in older patients is of less consequence than in younger patients, though it is undoubtedly confounded by selective survivorship among the older subjects. Lavie et al. (168) also reported that only younger OSA patients had elevated mortality rates relative to the age-matched general population; in older OSA patients (over age 70), mortality rates in OSA were significantly lower than for the elderly

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population as a whole. Not all clinical studies suggest, however, that OSA has decreased salience for elderly patients. A five-year follow-up among patients in the mid 60s to 70s hospitalized for coronary artery disease, some of whom were diagnosed with OSA, showed that untreated OSA was associated with mortality (169). These results were all based on clinical populations, who represent a highly biased health-care seeking sample of patients with higher prevalence of multiple diseases. As such, they cannot be used to draw valid inferences regarding mortality/morbidity associated with OSA as a function of age in the general population. Within the United States, data from the Sleep Heart Health Study (SHHS), a multi-site cohort composed of surviving members of epidemiologic studies of cardiovascular disease, have illuminated numerous issues related to age-dependent morbidity and OSA. Cross-sectional analyses of SHHS subjects have demonstrated that many morbidities associated with OSA, including hypertension (83,170), stroke (164), endothelial function (171), high-density lipoprotein cholesterol (93) and a vulnerable apolipoprotein genotype (172), diabetes (93) and even reported daytime sleepiness (74) show weaker magnitudes of relationships in the over-65 segment of the population relative to the under-65 year old population. At least one other major epidemiologic study, based in eastern Pennsylvania, has also shown weaker relationships between OSA and hypertension in older participants relative to younger subjects (160). These data argue that the OSA and associated morbidities of middle-aged populations be viewed as age-related phenomena and that of the older population as age-dependent and of far less consequence. Several comments should be offered regarding this perspective. First, because many of the elderly subjects in SHHS are survivors from previously defined cohorts, such as the Cardiovascular Health Study, they represent older persons of betterthan-expected health. Therefore, morbidities associated with OSA would be mitigated in the presence of such survival bias. Secondly, a blanket assumption of decreased associations with morbidities in older individuals with OSA may discourage attempts at intervention (e.g., CPAP or oral appliance therapy) in some individuals who may derive benefit from treatment. Finally, a closer examination of data from SHHS, in fact, does not always suggest that OSA/morbidity associations are weaker in older persons. In the analyses of hypertension for example, the fullyadjusted odds ratios for highest versus lowest quintile of AHI in the prediction of AHI showed a 64% excess risk in the < 65 population with the risk being nonsignificant for the over 65 population (83). However, when expressed as risk for the highest versus lowest quintile of cumulative percentage of sleep with SaO2 < 90%, older persons incurred a significant 44% excess risk, compared to a nonsignificant risk in the under-65 year old population (83). Similarly when individuals with concurrent evidence of cardiovascular disease were excluded from the analyses, the AHI was significantly associated with hypertension and diabetes in women both under and over the age of 65 (93), though men showed differential results by age. Taken together, these data suggest caution before attributing lack of importance to OSA in old age. Longitudinal studies of middle-aged populations who are presently being followed into old age, such as the WSCS, might be expected to provide somewhat better estimates of the risks and natural history of OSA with somewhat less influence of survivor bias. In fact, analyses from WSCS have demonstrated risks for hypertension associated with incident OSA that are somewhat higher than those based on the cross-sectional analyses from SHHS (162). Moreover, those relationships were independent of baseline age, which ranged from 30 to 60. In the Cleveland Family Study, older chronologic age at baseline was also a risk factor for incident

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OSA, which suggests age dependence, and the effect was stronger in women than in men (173). Other smaller cohort studies have portrayed an equivocal picture regarding associations between OSA and mortality in older populations. A suggestion of such relationships occurred in community-dwelling populations from northern (174) and southern (175) California and in a nursing home population (176). Confounders with chronologic age in analyses have made it difficult to tease out separate effects of OSA in such observational studies. We have argued that, to the extent that OSA represents a biomarker of aging, parceling the effects of chronologic age in such models represents an over-adjustment, because age serves as a proxy for all other competing mortality risks, which should be examined individually instead (89). Indeed when chronologic age was removed but competing morbidities were included in models in one of these studies, OSA predicted mortality at borderline significance (175). When viewed in combination, these studies suggest that agedependent OSA cannot be assumed to be of less consequence than OSA occurring in middle-aged adults. CONCLUSIONS The ontogeny of OSA exhibits clear age-related changes and yet its associations are exceedingly complex. Infants with OSA typically demonstrate noisy and/or mouth breathing, excessive waking, and night sweats, but daytime symptoms are often subtle. Craniofacial conditions and adenotonsillar obstruction are common causes of symptomatic airway obstruction in this age group, and there is evidence to indicate that OSA is more frequent in male infants and those born prematurely. For children with OSA, the apnea-hypopnea index alone may not be sensitive for the diagnosis of childhood OSA, since prolonged obstructive hypoventilation with less frequent abnormal breathing events than adults characterize this age group. Additionally, the peak prevalence of OSA in childhood is between two and five years of age with an equal gender distribution, and obese children are more likely to have more severe OSA than nonobese children. Noisy breathing, snoring with prominent mouth breathing, chronic partial obstruction, restlessness, diaphoresis, enuresis, and unusual sleeping positions are observed in children with OSA. Tonsillar and adenoidal hypertrophy as well as craniofacial abnormalities (e.g., elongated face; narrow, high-arched palate) may be observed. Daytime symptoms are highly variable in childhood OSA but most commonly present as deficits of learning, attention, and behavior. The differences between childhood and adult OSA with respect to pathophysiology, arousal responses, and daytime symptoms have prompted discussion as to whether pediatric and adult OSA are in fact two distinct disorders. Further research needs to explore this controversy, as well as the long-term health, educational, and socioeconomic consequences of childhood OSA. Adolescents with OSA are most likely to present in the adult form of OSA, which markedly differs from childhood OSA with respect to daytime sleepiness, representing a cardinal symptom in adults with OSA and only occasionally observed in affected children. Obesity, a large neck circumference, septal deviation, maxillary and mandibular deficiencies, and redundant pharyngeal tissue are common physical findings associated with OSA of adulthood. The prevalence of OSA rises from young to older adulthood, and risk factors for OSA in adults include excess body weight, specific craniofacial features, male gender (though pregnancy and menopause may increase female OSA risk), and hypothyroidism. Examination of the

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mechanisms for airway obstruction in OSA in the elderly indicate that upper airway structure/function and perhaps ventilatory control alterations are more similar than different when compared to those of younger adults with OSA. The association between cardiovascular morbidity and OSA appears to be stronger in middle-aged adults compared to older individuals, but these data may have methodological limitations. Thus, one should be cautious about attributing lack of importance to OSA in old age, and future work is clearly indicated to further explore this association. REFERENCES 1. Ward SL, Marcus CL. Obstructive sleep apnea in infants and young children. J Clin Neurophysiol 1996; 13(3):198–207. 2. Bower CM, Gungor A. Pediatric obstructive sleep apnea syndrome. Otolaryngol Clin North Am 2000; 33(1):49–75. 3. Gozal D. Sleep-disordered breathing and school performance in children. Pediatrics 1998; 102(3):616–620. 4. O’Brien LM, Mervis CB, Holbrook CR, et al. Neurobehavioral correlates of sleepdisordered breathing in children. J Sleep Res 2004; 13(2):165–172. 5. Young T, Finn L, Austin D, et al. Menopausal status and sleep-disordered breathing in the Wisconsin Sleep Cohort Study. Am J Respir Crit Care Med 2003; 167(9):1181–1185. 6. Ingram F, Henke KG, Levin HS, et al. Sleep apnea and vigilance performance in a community-dwelling older sample. Sleep 1994; 17(3):248–252. 7. Young T. Sleep-disordered breathing in older adults: is it a condition distinct from that in middle-aged adults?[comment]. Sleep 1996; 19(7):529–530. 8. Brouilette R, Hanson D, David R, et al. A diagnostic approach to suspected obstructive sleep apnea in children. J Pediatr 1984; 105(1):10–14. 9. Kahn A, Groswasser J, Sottiaux M, et al. Mechanisms of obstructive sleep apneas in infants. Biol Neonate 1994; 65(3-4):235–239. 10. Leiberman A, Tal A, Brama I, et al. Obstructive sleep apnea in young infants. Int J Pediatr Otorhinolaryngol 1988; 16(1):39–44. 11. Brouillette RT, Fernbach SK, Hunt CE. Obstructive sleep apnea in infants and children. J Pediatr 1982; 100(1):31–40. 12. Frank Y, Kravath RE, Pollak CP, et al. Obstructive sleep apnea and its therapy: clinical and polysomnographic manifestations. Pediatrics 1983; 71(5):737–742. 13. Guilleminault C, Korobkin R, Winkle R. A review of 50 children with obstructive sleep apnea syndrome. Lung 1981; 159:275–287. 14. de Serres LM, Deleyiannis FW, Eblen LE, et al. Results with sphincter pharyngoplasty and pharyngeal flap. Int J Pediatr Otorhinolaryngol 1999; 48(1):17–25. 15. Arens R, Marcus CL. Pathophysiology of upper airway obstruction: a developmental perspective. Sleep 2004; 27(5):997–1019. 16. Liao YF, Chuang ML, Chen PK, et al. Incidence and severity of obstructive sleep apnea following pharyngeal flap surgery in patients with cleft palate. Cleft Palate Craniofac J 2002; 39(3):312–316. 17. Kahn A, Mozin MJ, Rebuffat E, et al. Sleep pattern alterations and brief airway obstructions in overweight infants. Sleep 1989; 12(5):430–438. 18. Ramanathan R, Corwin MJ, Hunt CE, et al. Cardiorespiratory events recorded on home monitors: comparison of healthy infants with those at increased risk for SIDS. JAMA 2001; 285(17):2199–2207. 19. Kato I, Franco P, Groswasser J, et al. Frequency of obstructive and mixed sleep apneas in 1023 infants. Sleep 2000; 23(4):487–492. 20. Guilleminault C, Ariagno RL, Forno LS, et al. Obstructive sleep apnea and near miss for SIDS: I. Report of an infant with sudden death. Pediatrics 1979; 63(6):837–843. 21. Kahn A, Groswasser J, Rebuffat E, et al. Sleep and cardiorespiratory characteristics of infant victims of sudden death: a prospective case-control study.[see comment]. Sleep 1992; 15(4):287–292.

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152. Oliven A, Carmi N, Coleman R, et al. Age-related changes in upper airway muscles: morphological and oxidative properties. Exp Gerontol 2001; 36:1673–1686. 153. Guilleminault C, Li K, Chen NH, et al. Two-point palatal discrimination in patients with upper airway resistance syndrome, obstructive sleep apnea syndrome, and normal control subjects. Chest 2002; 122(3):866–870. 154. Larsson H, Carlsson-Nordlander B, Lindblad LE, et al. Temperature thresholds in the oropharynx of patients with obstructive sleep apnea syndrome. Am Rev Resp Dis 1992; 146:1246–1249. 155. Calhoun KH, Gibson B, Hartley L, et al. Age-related changes in oral sensation. Laryngoscope 1992; 102:109–116. 156. Aviv JE. Effects of aging on sensitivity of the pharyngeal and supraglottic areas. Am J Med 1997; 103:74S–76S. 157. Ware JC, McBrayer RH, Scott JA. Influence of sex and age on duration and frequency of sleep apnea events. Sleep 2000; 23(2):165–170. 158. Okabe S, Hida W, Kikuchi Y, et al. Role of hypoxia on increased blood pressure in patients with obstructive sleep apnoea. Thorax 1995; 50(1):28–34. 159. Morgan BJ, Dempsey JA, Pegelow DF, et al. Blood pressure perturbations caused by subclinical sleep-disordered breathing. Sleep 1998; 21(7):737–746. 160. Bixler EO, Vgontzas AN, Lin HM, et al. Association of hypertension and sleepdisordered breathing. Arch Intern Med 2000; 160(15):2289–2295. 161. Young T, Peppard P, Palta M, et al. Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med 1997; 157(15):1746–1752. 162. Peppard PE, Young T, Palta M, et al. Prospective study of the association between sleepdisordered breathing and hypertension.[see comment]. N Engl J Med 2000; 342(19): 1378–1384. 163. Grimm W, Hoffmann J, Menz V, et al. Electrophysiologic evaluation of sinus node function and atrioventricular conduction in patients with prolonged ventricular asystole during obstructive sleep apnea. Am J Cardiol 1996; 77(15):1310–1314. 164. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med. 2001; 163(1):19–25. 165. Sanner BM, Doberauer C, Konermann M, et al. Pulmonary hypertension in patients with obstructive sleep apnea syndrome. Arch Intern Med 1997; 157(21):2483–2487. 166. Wolk R, Somers VK. Cardiovascular consequences of obstructive sleep apnea. Clin Chest Med 2003; 24(2):195–205. 167. He J, Kryger MH, Zorick FJ, et al. Mortality and apnea index in obstructive sleep apnea: experience in 385 male patients. Chest 1988; 94:9–14. 168. Lavie P, Herer P, Peled R, et al. Mortality in sleep apnea patients: a multivariate analysis of risk factors. Sleep 1995; 18:149–157. 169. Peker Y, Hedner J, Kraiczi H, et al. Respiratory disturbance index: an independent predictor of mortality in coronary artery disease. Am J Respir Crit Care Med 2000; 162(1):81–86. 170. Haas DC, Foster GL, Nieto FJ, et al. Age-dependent associations between sleepdisordered breathing and hypertension. Circulation 2005; 111:614–621. 171. Nieto FJ, Herrington DM, Redline S, et al. Sleep apnea and markers of vascular endothelial function in a large community sample of older adults. Am J Respir Crit Care Med 2004; 169:354–360. 172. Gottlieb DJ, DeStefano AL, Foley DJ, et al. APOE epsilon4 is associated with obstructive sleep apnea/hypopnea: the Sleep Heart Health Study. Neurology 2004; 63(4):664–668. 173. Tishler PV, Larkin EK, Schluchter MD, et al. Incidence of sleep-disordered breathing in an urban adult population. JAMA 2003; 289:2230–2237. 174. Bliwise DL, Bliwise NG, Partinen M, et al. Sleep apnea and mortality in an aged cohort. Am J Public Health 1988; 78:544–547. 175. Ancoli-Israel S, Kripke DF, Klauber MR, et al. Morbidity, mortality, and sleep-disordered breathing in community dwelling elderly. Sleep 1996; 19:277–282. 176. Ancoli-Israel S, Klauber MR, Kripke DF, et al. Sleep apnea in female nursing home patients: increased risk of mortality. Chest 1989; 96:1054–1058.

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Phylogeny and Animal Models: An Uninhibited Survey Todd D. Morgan Scripps Memorial Hospital, Encinitas, California, U.S.A.

John E. Remmers Respiratory Reasearch Group, University of Calgary, Calgary, Alberta, Canada

INTRODUCTION This chapter examines human obstructive sleep apnea (OSA) in relation to air breathing in vertebrates. It begins by examining the origins of air breathing and showing that the neuromuscular factors that were responsible for air breathing in the earliest terrestrial vertebrates play a key role in modern sleep-disordered breathing in man. The chapter considers the evolution of the pharynx in prehuman species, documenting trends in the structure of the facial framework and pharyngeal dimensions. We show that these trends in hominids underlie human speech but predispose Homo sapiens to sleep apnea. Finally, we review animal models of obstructive sleep apnea to discover why it is a uniquely human disorder. We label our survey uninhibited to emphasize its diversity of informational sources and to acknowledge the speculative nature of our discussion. Our lack of inhibition reflects our exuberance for the topic, which we hope is shared by the reader. TERRESTRIALITY AND EVOLUTION OF THE PHARYNX Evolution underwent a major transition 400 million years ago when animal life moved from the sea onto land. For animals, this allowed access to a new, oxygenrich convective media for ventilation of gas-exchange organs. The key and most fundamental aspect of this transition was the development of a lung and the means for ventilating it. Fish meet their aerobic demands by ventilating gills with water, a dense, viscous media with a limited capacity for carrying oxygen. The early terrestrial air breathers (Dipnoi) ventilated a lung, which was homologous with the highly partitioned mammalian lung (1). However, they used an oropharyngeal force pump to inflate their lung. This differed dramatically from the pumping mechanism, which was subsequently evolved in Amniota (Fig. 1). The lung fish, the modern descendant of one of the earliest air breathers, inflates the lung by first aspirating air into the oropharynx, then opening the pharynx and compressing the oropharyngeal gas, thereby inflating the lungs (2). The modern amphibia employs the same force pump mechanism (Fig. 2). Thus, from its inception, the oropharynx plays a fundamental role in ventilating the lungs by providing the neuromuscular mechanism used for lung inflation (3). Thus, respiratory oscillators of the early tetrapods generated outputs that projected to cranial motoneurons, including hypoglossal motoneurons, and over from the hypoglossal nerve to oropharyngeal muscles (Fig. 2). Of great interest is that in mammals these same neural pathways convey rhythmic inspiratory activity to pharyngeal dilator muscles and ultimately play a key role in stabilizing the pharynx of humans during sleep. 61

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FIGURE 1 A cladogram depicting the phylogenetics of air breathing. Actinopterygii used a four-stroke mechanism to ventilate a lung that was probably not homologous with the modern lung. Dipnoi and Amphibia used a two-stroke force pump mechanism to ventilate a lung that was homologous with the modern lung. Source: From Ref. 3.

Development of an aspiration mechanism of lung inflation appeared in reptiles and was further perfected with the appearance of the diaphragm in the mammal (4). The development of the thorax allows inspiratory pump muscles to generate subatmospheric pressures that act directly on the surface of the lung. With the appearance of this method for inflating the lungs, the oropharynx was no longer used for gas pumping, and the single oropharynx of the amphibian (Fig. 3, left) was subdivided into three cavities, the nasal cavity, the oral cavity, and the pharynx in reptiles (Fig. 3, right) and in mammals (Fig. 4). In reptiles, a rudimentary soft palate separates the nasal cavity from the pharynx (Fig. 3, right). In mammals, the soft palate is well developed and partitions the pharynx from the oral cavity or nasal cavity (Fig. 4). As we shall see, the inspiratory activation of pharyngeal dilators was maintained; rather than assisting with lung inflation by creating a positive intrapharyngeal pressure, the actions of these muscles serve to prevent collapse of the nares and pharynx by subatmospheric pressures in the nose and pharynx during aspirative inspiration. What happened to the respiratory rhythmogenic process during the evolution of air breathing? Did the evolution of a vastly different ventilatory act, aspiration, require a new neural mechanism for generating tidal breathing? The answers to these questions hold interest for not only the neurobiologist. They also lend insight into motor control of the pharyngeal dilator muscles, and ultimately, to the genesis of obstructive sleep apnea. A paired, coupled oscillator drives breathing in the frog and comprises a buccal oscillator and a lung oscillator (2). The rhythmogenic mechanisms and timing of respiratory events in mammals bear a striking resemblance to those found in amphibia. As in the amphibian, paired, coupled, oscillators make up the fundamental rhythmogenic mechanism driving breathing. Too, a preinspiratory (pre-I) oscillator displays a burst of activity prior to mechanical inspiration, and it triggers activity in a second or inspiratory oscillator, the pre-Bötzinger (pre-Böt) oscillator, which activates inspiratory pump muscles (5).

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FIGURE 2 Breathing in a frog. (A) Normal buccal breathing (left) and lung breathing (right). The former consists of tidal ventilation of the oropharynx and the latter consists of a large buccal dilation, opening of the larynx and compression of the oropharyngeal gas, forcing it into the lungs. (B) Efferent neurograms from cranial nerve (CN) V and from the hypoglossal (H) nerve illustrate buccal dilation (Bd) and buccal constriction (Bc), followed by a series of lung (L) inflations. Source: From Ref. 3.

FIGURE 3 Drawings of the nose and oropharynx. (A) The common oropharynx and rudimentary nasal cavity of the amphibian (frog). (B) A partitioning of the nose from the mouth by a hard palate in the reptile (alligator). Abbreviations: E, esophagus; H, hard palate; L, larynx; N, nose; O, oral cavity; P, pharynx; T, tongue; TR, trachea. Source: From Ref. 6.

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FIGURE 4 Drawings of the upper airway configuration in mammals. (A) The linear airway of the dog. (B) The partially angulated airway of the ape with the mouth set more dorsally than in nonprimates. (C) Human infant with retroplaced mouth. (D) Human adult with severely angulated airway and retroplaced tongue. Note: The velo-epiglottal overlap is present in all except for the adult human. Source: Visible Productions, 2001.

The pre-I burst appears to activate pharyngeal dilators, particularly the genioglossus, which is innervated by the hypoglossal nerve. The mechanical action of the muscles innervated by the main branch of the hypoglossal nerve has changed during evolution. The hyoid and larynx migrate caudally and the supraglottic airway becomes angulated during evolution of mammals. Another important development that appeared with evolution of the mammalian pharynx was the appearance of the epiglottis. Unlike other pharyngeal cartilages (e.g., larynx and hyoid) the epiglottis was not derived from the original branchial arch skeletal tissue. The epiglottis is thought to play a role in infant suckling, as nonsuckling, egg-laying mammals (duck-billed platypus and anteater) lack this structure (6). During suckling the epiglottis covers most of the ventral wall of the oropharynx and extends upward above the free margin of the soft palate. This means that milk can flow from the oral cavity around the lateral margins of the epiglottis and into the esophagus without contacting the larynx (Fig. 4), thereby allowing simultaneous breathing and swallowing. In other words, air can pass from the nose into the larynx while milk is flowing from the oral cavity into the esophagus. Various authors have speculated that a mechanical linkage exists between the soft palate and the epiglottis. The evidence for this so-called “locking” of the two structures is highly inferential,

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however. Nonetheless the overlap of the soft palate and epiglottis is a unique feature of the pharynx in all suckling mammals except for the human where it is present at birth but lost in childhood. In summary, the first major advance in the evolution of air breathing was the development of the modern lung, and the second was the development of the thorax and the muscles for ventilating it using aspiration. Mammals and reptiles developed a compartmentalized cavity: the nose, which allowed olfaction and conditioning of the inspirate, the mouth for mastication, and the pharynx for both airflow and alimentation. As well, the pharynx took on the role in sound communication by modulating sound generated in the larynx. The fundamental role of the larynx, protector of the pulmonary airway, remained unchanged in all terrestrial air breathers. EVOLUTIONARY PRESSURES INFLUENCE THE PHARYNX Walking, Talking, and Breathing: What Is the Problem? The advance of hominids through intellectual development, walking, and speech are paramount to the success of Homo sapiens. In this evolution, three factors have major implications for the upper airway and for breathing during sleep. These are: development of the brain, upright posture, and oral articulation. The location and function of the aero-digestive tract in our closest ancestors is well described within skeletal remains. By considering cranial base angles, the size and attachments of the hyoid, and the landmarks from which the larynx suspends, soft tissue models can be created that predict the acquisition of speech. In this section, we consider the possibility that an anatomic model for speech in prehistoric species provides insights for understanding of OSA as a uniquely human disease. Phylogeny reveals an elegant evolution of pharynx and its added complexity of function that mirrors brain growth and development of the human intellect. For articulate speech, we need a pharynx that has the length and flexibility necessary for sound modulation as well as the neural network necessary to respond to the brain’s instructions. The vocal-tract anatomy related to speech and the origin of language is quite distinct issues; one relates to mechanical function, while the other involves reorganization of the brain. Great apes, for example, are capable of symbolic thought, but do not have the neuro-mechanical features required for articulation and speech (7). The neural origins of speech appear to arise from a region within the inferior frontal gyrus, termed “Broca’s area.” Broca’s area may be home to mirror neurons that enable mimicking behavior and may also have undetected synaptic influence over the higher functions of pharynx involving the interplay of symbolic thought, syntax, and cortical interpretation (Fig. 5 displays one version of early human phylogeny). Homo erectus, a possible human ancestor alive two million years ago, had the expanded Broca’s area required for symbolic thought, as do all Homo genus lines. However, the anatomical fossil record fails to indicate that this species was capable of language. Hominids as far back as Homo heidelbergsis (600,000 years) ago displayed enough cranial base flexion and presumptive laryngeal length to have a potential for speech and, perhaps, OSA as well. However, no cultural evidence in the fossil record supports the acquisition of speech until 40,000 years ago, around the time when Cro Magnon (CM) displaced Neandertals in the Levant (8). In hindsight, CM brought with him the ultimate trump card: the aptitude and ability to finally fulfill the role of the pharynx in the communicative expression of associated thoughts that would enhance creativity and survival. In other words, the

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FIGURE 5 (See color insert.) A proposed phylogenetic tree for hominids. A. Afarensis (reconstruction at right) is but one of many Australopithecine species known to science. Researchers disagree about exactly how these species are related to one another, but most presume that A. afarensis was a precursor to our own genus. Source: From Ref. 26.

potential for speech appears to have long existed in hominids but the actual use of language occurred relatively recently. For Homo sapiens, language became an ineluctable advantage over other species. Perhaps the length and collapsibility of the pharynx required for speech constituted a powerful countervailing disadvantage that limited the appearance of language until the ascendance of CM. Cleverness was the ultimate Homo sapiens survival strategy, and this development was intrinsically related to the development of speech and language. However, the development of language and speech as driving forces in evolution has apparently outpaced the development of compensatory reflexes or anatomic safeguards for the pharynx during sleep. While reflexes generated from pharyngeal mechanoreceptors effectively guard the patency of the pharynx driving wakefulness, these reflexes are lost or greatly suppressed during sleep (9). Thus, without a more linear airway, reinforced ventral pharynx, and the ligamentous “strutting effect” of the hyoid afforded to other vertebrates, hominids were left vulnerable to the night. As shown in Figure 4, we speculate that three features of the pharynx in Homo sapiens allowed this characteristic function of walking and talking, but severely limited the ability to breathe during sleep. These are: severely angulated airflow path (upright posture), lack of velo-epiglottal overlap (pharyngeal length) and loss of hyoidal strutting (pharyngeal compliance). Changes in the Craniofacial Relationships Comparison of the chimpanzee and man reveals striking differences in the ratio of the horizontal oral length and the vertical pharyngeal height (Fig. 6). As well, the relationship between the cranial vault and the facial framework differs strikingly among nonhuman primates, hominids and Homo sapiens (Fig. 7). The facial bones can be seen to move dorsally as one compares the great apes, Australopithecus, H. sapiens, H. neanderthalensis, and modern man. In essence, the facial structures (nasal, oral, and pharyngeal cavities) rotate dorsally in relation to the skull so that in

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FIGURE 6 Comparison of oral/pharyngeal dimensions in the chimpanzee and man from sagittal drawings. Note that in relation to the horizontal pharynx to incisor, the vertical laryngeal to oropharyngeal distance is much greater in modern man (left) than in the Neandertal (right) or chimpanzee (center). Source: Visible Productions, 2001.

Homo sapiens, they become positioned under the temporal and frontal bones rather than ventral to them (Fig. 7). This change can be quantitated as a decrease in the midsagittal angle between the line connecting the auditory meatus to the supraorbital torus and a facial skeleton strut connecting the supraorbital torus to the postorbital bar (values presented in Fig. 7). This rearrangement of the craniofacial relationship can also be assessed by the cranial base angle (CBA), the angle between precordal and postcordal planes. This is measured as the angle between a line connecting the centre of the sella turcica to the basion and the line from the centre of the sella turcica to the foramen cecum (Fig. 8, left). A decrease in the CBA is referred to as “flexing” of the cranial base. Leiberman and McCarthy (12) measured the CBA in mature Pan troglodytes and Homo sapiens to be 156° and 134°, respectively. This indicates flexion of the cranial base in humans compared to nonhuman primates, allowing less space for the facial framework. Also of interest are the CBA changes with development in these two species as shown in Figure 8 (right), the CBA decreases with age in Homo sapiens and increases in Pan troglodytes. During development the relative positioning of the cranium and the facial structures change in opposite directions in the two species, that is, during development in the human the cranial base flexes and in the nonhuman primate it extends.

FIGURE 7 Drawings of the skull and facial bones of the gorilla (G), Australopithecus africanus (A), H. neanderthalensis (N), and Homo sapiens (H). The values indicating the angle from the base of the skull to the anterior facial bones decrease progressively with evolution of the human craniofacial structure documenting the downward and backward rotation of the facial framework. Source: From Ref. 23.

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FIGURE 8 The measurement of the cranial base angle (CBA) in man is shown in the left panel. The right panel shows the CBA during development for the chimpanzee P. troglodytes (open symbols) and man H. sapiens (closed symbols). Note that the CBA is longer in the young chimpanzee than the human and that, during development, the two diverge. Source: From Ref. 27.

Changes in the Maxilla “Descent of larynx is attributable to upright posture in man” (10). Upright posture matched a coordinated and (relatively) rapid rotation of growing forebrain upon a retreating facial framework, while the nasal airway became diminished in size and function. Flexion of CBA required compression of the maxilla, both in volume and surface area, effectively reducing paranasal sinus size and olfaction acuity. The net effect of this change in maxilla creates the flatter and longer face that differentiates human appearance from primates. Further, this decrease in nose volume may increase in “upstream” resistance to airflow and further aggravate the potential for collapse in a newly acquired oropharynx. Later hominids were no longer obligate nose breathers and upon exertion they would transition to oral breathing. Potentially, this may have contributed to changes in mandibular posture, downward migration of tongue base and further descent of the hyoid. Increases in time spent mouth breathing implies a reduction in time spent with teeth in contact, negating the tongue’s postural influence upon the palate (Fig. 9). The upshot: a diminished potential for horizontal growth of the maxilla, narrowing of the palate and increasing facial height (Figs. 10 and 11). The most robust cephalometric predictors of OSA are related to this backward rotation of maxilla and mandible (11). Akin to this, Kushida described a morphometric formula for predicting OSA based on the constriction of horizontal growth, relating to the palatal distance between the first molars (12). Oropharyngeal Compression Evidence for a more recent continuing trend in facial height elongation is demonstrated, when we compare maxillae in prehistoric and modern skulls (Fig. 12). Note the considerable narrowing of the maxillary arch and posterior nasal

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FIGURE 9 (See color insert.) Closed mouth posture: the relationship of tongue, teeth, and buccinator muscles is shown in the coronal depiction of the mouth of modern humans; the tongue assumes a resting posture in proximity to the palate. The teeth are interposed between the tongue and buccinators, thereby allowing growth of the arch in relation to the relative dilating (genioglossus) and compressive (buccinator) forces, creating a “balance of forces.” Source: Courtesy of B. Palmer, DDS.

aperture in modern specimens. Like other mammals that display only minute differences in occlusion within species, this prehistoric specimen displays a broad palatal arch with room for 32 aligned teeth. Constriction of maxillary horizontal growth and the modern “V” shaped dental arch may account for our modern struggle with malocclusion, dental caries and a constricted oropharyngeal inlet (Fig. 13). While an astute clinician may be inclined to blame a “foreshortened” mandible for the clinical observation of an overbite and crowded dentition, an underdeveloped maxilla is likely the root cause. In recent decades, modern orthodontic theory has shifted paradigmatically to allow an orthopedic “first phase” of treatment intended to create horizontal expansion of maxilla. Maxillary expansion devices help to compensate for horizontal growth that was lost to an imbalance of muscle forces, commonly tendered by underlying negative postural influences secondary to nasal airway constriction (Fig. 14). Perhaps early recognition of abnormal tongue posture can lead to intervention with maxillary expansion devices, in order to allow the full potential for horizontal growth of maxilla.

FIGURE 10 (See color insert.) Open mouth posture: oral breathing drives the tongue downward and maxillary constriction occurs, increasing facial height. Source: B. Palmer, DDS.

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FIGURE 11 (See color insert.) Dental models of a mouth breather. Source: B. Palmer, DDS.

LANGUAGE, SPEECH, AND BRAIN GROWTH Altricial Brain Growth in Mammals Climatic changes and a warming of the Earth following the Ice Age produced a transition from preponderant rain forests to drier tundra, and may have driven the advance of orthograde posture and bipedalism. As open spaces began to allow for nomadic travel and dispersal of species, hominids’ new strategy for survival would include entry into the carnivore guild. These new early foragers found an abundant, new calorie-rich food source in animal carcasses, and a new diet supplemented with

FIGURE 12 (See color insert.) Comparison of palate width in hominid (left) and modern humans (right). Source: B. Palmer, DDS.

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FIGURE 13 (See color insert.) Comparison of a broad (top) versus a narrow upper dental arch (bottom). Source: B. Palmer, DDS.

bone marrow fat and protein, which enabled accelerated brain growth that maternal lactation alone could not support. Changes in weaning patterns of early hominids advanced a unique pattern of altricial-type of brain growth not seen in mammals (13). A further increase in the size of the human infant brain is dependent on a continuation of fetal brain growth, which can only be supported by a transition to adult foods at an earlier age. A new diet supplemented with bone marrow fat and protein enabled a “secondarily altricial” pattern of brain growth that maternal lactation alone could not support, while “buffing up” neural mass and adding weight atop the cervical spine.

FIGURE 14 (See color insert.) Maxillary expansion devices correct for lost horizontal growth, increasing size of choanae and nasal airway.

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Infant energy requirements begin to exceed milk production levels in the second half of the first year. An average woman can produce 1000 mL/day of milk that provides 487 kcal/day and is relatively low in protein when compared to bovine milk. Despite the risks to child survival, selection pressure may have favored early weaning and a shift toward adult foods in order to support the child during a critical time in neurological development, enabling full potential for brain size and intellect. For example, brain size of P. troglodytes and humans at birth are both about one-third of their adult size. However, in terms of absolute size, the chimpanzee brain would only need to grow about 300 mL more to reach adult volume, whereas the human brain would grow to 900 mL; both brains reach their final growth potential at six to seven years. This advanced pattern of human brain growth is “expensive,” in that developing children must devote as much as 80% of their basal metabolic rate to the brain, compared to 25% for adults (14). Despite this adaptive risk to early survival and an altered mortality rate for young adults, who are now competing with carnivores for prey (15), foraging hominids begin to leave their offspring with groups to hunt and procure a rich food source. Brain growth in size and weight may have driven the concomitant postural changes needed to satisfy a new cranial “balancing act.” Spinal mechanics, notably cervical spine curvature, are adapted from head posture and favor upright stance, bipedalism, and horizontal vision. Unlike other mammals, who remained quadrupeds, upright posture would drive changes in cervical spine, relocate the tongue base to the pharynx and influence the development of a new compartment, oropharynx. Cerebral Control Over Pharynx In addition to the tasks of respiration and deglutition, speaking humans would require exquisite neural control to manage the pharyngeal airway in this motor act. Control of voluntary respirations and speech are closely held within the frontal lobes of the brain—the most recent evolutionary development. The first step toward managing a common aero-digestive tract requires relay of afferent information related to pharyngeal contents. Indeed, the pharyngeal mucosa and its juxtaposed layers of interwoven and specialized muscle fibers are innervated with extremely dense terminal nerve branches (Fig. 15). Banding patterns of these fibers and their variety and types of motor endplates allude to specialization within constrictors and the upper esophageal sphincter in humans. Further, the presence within these muscle groups of unusual myosin heavy chain isomers, including low-tonic and alpha-cardiac types, has been demonstrated (16). The appearance of multiple isomers within the pharynx and some other cranial muscles is thought to be associated with unique functional requirements that require precise control. Acquisition of Speech Predisposes to Obstructive Sleep Apnea Understandably, anthropological studies focus primarily on the “vocal tract” rather than respiratory control and speech-related behaviors. Our goal is to surmise and infer functional behavior from studies of fossils. During hominid evolution, the descent of the hyoid/larynx, rotation of the facial framework, and flexion of cranial base angle lead to progressive pharyngeal constriction and susceptibility to OSA. We speculate that these changes are principally related to the development of the motor act of speaking. A variety of models have been proposed to define the characteristics of the pharynx required for acquisition of speech.

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FIGURE 15 (See color insert.) Cricopharyngeus muscle with multiple innervations and motor endplates to muscle fibers. Source: From Ref. 16.

Debate continues as to whether early hominids, and particularly Neandertals, had the pharyngeal height necessary to form essential vowel sounds. Is there a predictive value and correlation between the collapsibility of the pharynx in OSA and proposed models for the predicted acquisition of speech? By performing skull base reconstructions of H. neanderthalensis from La Chapelle-aux-Saints, Lieberman and Crelin (17) predicted a position and length for larynx that was not compatible with the accepted requirements for speech (Fig. 7). In their model comparing Neandertals, chimpanzee and the human infant, they advanced the hypothesis that an anatomic basis for speech, based on a minimum laryngeal descent, precluded all three species from producing the essential vowel sounds (i) (a) (u) due to reduced pharyngeal space. A new model for predicting total pharyngeal height and basis for speech proposed by Boe et al. (18) in 2002, contrasts with the previous findings of Lieberman and Crelin. New reconstructions of La Chapelle-aux-Saints and of a newer specimen of Neandertal, La Ferrassie 1, which had an intact hyoid show that the Neandertal skull base did not differ significantly from modern humans, leading to the supposition that there has been little or no change in the position or shape of hyoid in the last 60,000 years of evolution. Based on cephalometric landmarks, Boe et al. were able to develop a laryngeal height index from palatal distance and laryngeal height. Application of their model to Neandertal and young humans predicts that Neandertals were capable of speech. However, if we assume that a measurement of laryngeal height mirrors the modern cephalometric measures, we conclude that the speech predisposition of Neandertals is low, based on this phonetic/anatomic model (Fig. 6). The hyoid bone may provide an important clue regarding the collapsibility of the pharynx in hominids. In nonhuman mammals, a complete, “strutted” hyoid provides a mechanical anchoring of the epiglottis, and, thereby, of the ventral wall of the pharynx in the presence of substantial velo-epiglottal overlap. This provides pharyngeal stabilization in the absence of neuromuscular reflex control of pharyngeal muscles during sleep. By contrast, the “floating” hyoid of the human implies that the position of the ventral pharyngeal wall depends principally on neuromuscular forces. Although this provides for a highly compliant pharynx, a requirement for speech and tone modulation, it

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FIGURE 16 Logarithmic plot of hyoid height versus depth for G. gorilla, P. troglodytes, H. sapiens, and the fossil from A. afarensis. Note that the shape of the A. afarensis hyoid is ape-like and differs substantially from the hyoid of modern man. Source: From Ref. 28.

also allows the opportunity for easy collapse of the pharynx during sleep when muscular forces acting on the hyoid are greatly reduced. Thus, we are fairly bequeathed a new fate (sleep apnea) that squares our debt to unparalleled success as a species provided by speech and language. Two hyoid bones have been found in hominids. The more ancient one (3,300,000 years old), a spectacularly well-preserved child skeleton of Australopithecus afarensis reveals a large, cup-shaped hyoid (25). As shown in Figure 16, the dimensions and shape of this hyoid are compatible with an ape-like hyoid and differ clearly from modern man. This suggests that A. afarensis had pharyngeal air sacks and a noncompliant pharynx, consistent with an apelike pharynx and vocal system. The second hyoid specimen is from a 60,000-year old Neandertal skeleton and is an incomplete or “floating hyoid.” Life History and Emergence of Obstructive Sleep Apnea What about neural control of pharynx during nocturnal breathing? Snoring was not an evolutionary advantage for Homo sapiens, albeit, some predators may have been alarmed by the world record of 112 decibels (19). One may argue that as life history changed for our immediate ancestors, we had no need to guard against OSA. A least until life span (only recently) increased beyond 40 to 50 years. Thus, the need to develop protective reflexes would be gratuitous and conspicuously absent in all upright hominids. Despite a lack of safeguards for our nocturnal respirations, a further descent of hyoid and expansion of oropharynx, along with the acquisition of the cortical equipment needed for speech, allowed a significant intellectual leap ahead of rival species. The natural history of a disease may, or may not, impact evolutionary selection processes depending on the timing and emergence of clinical symptoms or death. Assuming that early hominids lived no more than a few decades at best, and assuming a body habitus consisting of predominately lean muscle, little evolutionary pressure might be brought to bear with regard to OSA. Indeed, selection may have favored the male with a deeper voice (lower hyoid) and robust, threatening tones. Alternatively, even with a life span beyond 50 years, if OSA was not an issue

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during childbearing years, there would be no selective pressure for the inheritance of protective guards against pharyngeal collapse. ANIMAL MODELS OF OBSTRUCTIVE SLEEP APNEA Obesity and Bony Abnormalities Animal models of human disease have often provided interesting insights into mechanisms of, or, therapy for the disorder in the human counterpart. Since obesity play a key role in the pathogenesis of human OSA, one might a priori assume that an obese animal would exhibit OSA. Surprisingly, this has not proven to be the case. As we shall see, this puzzling failure of obesity to produce OSA is a clue to one of the features of the pharynx in the human that is pivotal in the genesis of the human illness. Several strains of obese rodents exist and these have been examined for the presence of OSA. These studies have failed to find OSA. Another species known to become obese is the pig, and while one initial report of sleep apnea in obese pigs appeared to reveal OSA (20), subsequent investigations failed to confirm this finding (21). The one confirmed example of OSA in nonhuman species is the English bulldog. This animal experiences apnea, shown to be obstructive, during rapid eye movement (REM) sleep. The distinctive feature of this animal is its brachiocephalic “retropositioning,” which results in dorsal placement of the maxilla and mandible. The consequence of this bony “malformation” is a reduction in oral volume and narrowing of the retropalatal space. While awake and during non-rapid eye movement (NREM) sleep, activation of various pharyngeal dilators maintain a patent airway. This activity is eliminated during REM sleep and OSA results. In summary, the English bulldog appears to be the only naturally occurring animal model of OSA. In this model, OSA results from bony malformations that include underdevelopment of the maxilla and/or mandible. A striking finding relates to what does not exist, that is, obese nonhuman mammals do not experience OSA. While the massively obese Vietnamese potbelly pig does not have OSA, it displays features of the “Pickwickian syndrome,” that is, daytime somnolence, respiratory failure, and, probably, cardiac failure aptly dubbed by one wag, the “pig-wickian syndrome.” While these animals do not display sleep apnea, their breathing during sleep fits the pattern of high upper airway resistance (HUAR), wherein inspiratory flow limitation occurs during consecutive breaths for prolonged periods (Fig. 17). This leads to alveolar hypoventilation during both REM and NREM sleep. Why the obese pig displays HUAR and not OSA is probably explained by observations of the mechanics of the passive pharynx. To evaluate this, lean and obese pigs were studied under general anesthesia and with complete muscular paralysis (Fig. 18). The observed static pressure-area relationships of the passive pharynx revealed several dramatic findings. First, the closing pressure in lean and fat pigs was in the range of −15 to −20 cmH2O, much lower than that observed for normal humans (−2 to −4 cmH2O) and for patients with OSA (+2 to +4 cmH2O). This means that a higher transmural pressure is required in order to completely close the pharynx in the pig, even the obese pig, than in humans. Another feature that distinguishes the pig from the human pharynx is the shape of the pressure–area relationship, the pig being linear and the human being exponential. This means that in the pressure range 2–4 cmH2O above closing pressure, the passive pharynx of the pig is much less compliant than its human

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FIGURE 17 Tracings from an obese unanesthetized Vietnamese pot-bellied pig. Airflow (L/sec) and transpulmonary pressure (cmH2O) are recorded while awake, and in non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Note that no apnea is recorded but that resistance is high in all conditions and inspiratory flow limitation appears during REM sleep. Source: From Ref. 29.

counterpart. A region of high compliance allows dynamic collapse of the pharynx and the development of inspiratory flow limitation in the human. The second distinctive anatomic feature of humans is the nonarticulated or “floating” hyoid. The pig and other mammals have a U-shaped hyoid that attaches dorsally. Together, these two features could stabilize the pharynx in nonhuman mammals. Dorsal movement of the hyoid is prevented by strutting, and through the aryepiglottic ligament, this limits dorsal rotation of the epiglottis, thereby stabilizing the oropharynx and velopharynx through the velo-epiglottal overlap. Rhesus Monkey Model of Nasal Constriction “Elimination of nasal airway interferences followed by a change from oral to nasal respiration may result in improvement of certain aspect of facial and dental deviations” (22). The first attempt to investigate the relationship between nasal obstruction

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FIGURE 18 Pressure-area relationships of the passive pharynx are derived from data on humans and pigs and illustrate the differences in shape between the two species as well as the differences in effect from obesity. Abbreviation: OSA, obstructive sleep apnea.

and aberrant facial development made only casual reference to OSA. However, this early work did provide evidence for a link between the influence of environmental factors upon tongue posture and, as a corollary, the final position of jaws, dentoalveolar structures, and by the nature of their juxtaposition, the upper airway. In studies using the developing rhesus monkey as a model for obstructed nasal breathing, Harvold and others have observed and shown significant changes in normal respiratory physiology and functional form (22). Findings demonstrate compensatory recruitment of accessory muscles and a significant influence upon tongue posture that led to disruption of the natural balance between buccinators and genioglossus upon the developing alveolar arches, leading to malocclusion and downward mandibular posture. Compared to controls, changes in subjects with nasal airway occlusion consistently demonstrated increased facial height, posterior rotation of the mandible and malocclusions. It was postulated that differences in the degree of aberrant change may be explained by which muscles were recruited by each subject and how they were used for deviant respiration. Following removal of the obstruction, some monkey subjects did not reacquire normal posture and facial form. Similar changes are seen in children that display habitual mouth breathing posture and are at high risk of sleep-disordered breathing. Changes in these monkey subjects closely mimic reports from orthodontic literature dating back to the 1950s, when Brash described unfavorable changes in craniofacial growth in mouth breathing children (23). Descent of Hyoid: Chimpanzees Mirror Human Ontogeny Theories of speech physiology and the evolution of language in Homo species hold as a prerequisite the possession of adequate supraglottic space to produce sibilant and vowel sounds with enough resonance and force to form words. The “uniqueness” of the human oropharynx and our laryngeal ratio of 1:1 provide the platform necessary for these functions. But comparisons using magnetic resonance imaging (MRI) to study young chimpanzees in their early developmental stages have shown a similarity to humans with the brief appearance of an oropharynx, which may hold a clue as to why a common ancestor of extant hominids found an evolutionary path toward flatter faces, a descending hyoid and eventually, sleep-disordered breathing. However, it is unlikely that the acquisition of speech drove the secondary descent of

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hyoid in humans. Rather, postural shifts along with progressive curvature of the cervical spine compelled the dynamic descent of the pharynx and our final susceptibility to OSA. In nonhuman mammals, the distance from incisors to velum (horizontal growth) is always greater than distance from velum to glottis (vertical growth) (Fig. 6). The greater the ratio becomes, the more we depart from potential for a collapsible pharynx. In humans and chimpanzees alike, early development depicts similarities between species in the development of upper airway anatomy. During early infancy, both species display similar growth patterns in both of these dimensions, with an apparent shift in growth during late infancy that emphasizes laryngeal descent in humans, and horizontal growth of the oral cavity in chimpanzees (24). In 2006, Nishimura (24) studied three chimpanzee subjects during their early development with sagittal tomographic images across ages four months to five years in order to identify cephalometric landmarks that corresponded to human landmarks in a similar study by Lieberman et al. in 2001 (25) spanning one month to 13.9 years of age in children. Dental emergence was used to adjust chronological age between the two species. In early infancy, these chimpanzees displayed a rapid growth and laryngeal descent that mimicked human growth pattern, decreasing vocal tract ratio in both species. Thereafter, the direction of change in the ratio increases in chimpanzees, where a dominant growth in palatal length continues until adulthood. Humans continue to grow in the vertical dimension of larynx throughout later infancy, childhood, and puberty, permanently separating the larynx into its three distinctive compartments—velopharynx, oropharynx, and hypopharynx. The brief emergence of an analogous oropharynx in chimpanzees, however, may lend clues to a common evolutionary thread between species, lending credibility to postural pressure as a driving force toward pharyngeal elongation, and a case for descent of the larynx and hyoid prior to divergence of the human from the chimpanzee lineage. Or, at least, that descent of the larynx is not “unique” to human lineage. In all other mammalian species, the hyoid remains firmly strutted to the laryngeal skeleton and thus precludes the formation of a collapsible segment. At what point then, did a “floating” hyoid become incorporated within the complex functional matrix of the larynx and become a competitive advantage for extant hominids? Cephalometric studies have demonstrated that a clear predisposition for OSA is found with a greater mandibular plane to hyoid bone (MP-H) distance, a measure of hyoid descent. Separation of the velum from epiglottis carries with it profound hazards and must command new levels of cerebral and functional control over the pharynx in order to ensure species survival. Therefore, early human species must have found any associated advantages, such as orthograde posture, to be paramount long before the advent of sophisticated articulation and language. A yet undiscovered common ancestor to distant hominids may hold the key to understanding how modern species came to acquire and appreciate a vulnerable airway. CONCLUSIONS The first terrestrial air breathers developed novel techniques to process oxygen in a new environment that allowed for an explosion of successful quadruped species. The evolution and development of the respiratory tract in land animals demanded precise control over an inflating mechanism, a nasal/oral cavity, and most recently, a highly partitioned pharynx. Phylogeny allows us to draw comparisons and define

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the differences among many species that display specialized form and function, whereas natural selection favors traits that secure survival and propagation of species. The elongation and increased flexibility of the pharynx in early man ushered in speech, but the birth of syntax and language would call upon our uniquely human capacity to combine symbolism and abstract thoughts together, ultimately summoning the question... “What if?” Cleverness in scrimmage with other species engendered a foundation for life extension. However, these advances concealed a dark side that tends to “only come out at night” in the form of pharyngeal obstruction during sleep. Despite our unique susceptibility to OSA and its burgeoning expression in recent history, the utility of our collapsible pharynx has, somehow, outweighed all other selection pressures in species Homo sapiens. Will future evolution find us further uninhibited … and drowsy? REFERENCES 1. Maina JN. Functional Morphology of the Vertebrate Respiratory Systems. Engield, NH & Plymouth, U.K.: Science Publishers, Inc, 2002. 2. McMahon BR. A functional analysis of aquatic and aerial respiratory movements of an African lungfish, Protopterus aethiopicus, with reference to the evolution of the lung ventilation mechanism in vertebrate. J Exp Biol 1969; 51:407–430. 3. Vasilakos K, Wilson RJA, Kimura N, Remmers J. Ancient gill and lung oscillators may generate the respiratory rhythm of frogs and rats. J Neurobiol 2005; 62(3):369–385. 4. Tenny SM. A synopsis of breathing mechanisms. In: Wood SC, Lenfant C, eds. Evolution of Respiratory Process. A Comparative Approach. New York and Basel: Marcel Dekker, 1979:51–106. 5. Mellen NM, Janczewski WA, Bocchario CM, Feldman JL. Opioid-induced quantal slowing reveals dual networks for respiratory rhythm generation. Neuron 2005; 37:821–826. 6. Crelin ES. The human vocal tract: anatomy, function, development, and evolution. New York: Vantage Press, 1987. 7. Cantalupo C, Hopkins WD. Asymmetric Broca’s area in great apes. Nature 2001; 414:505. 8. Tattersol I. Monkey in the Mirror: Essays on the Science that Make Us Human. New York and Oxford: Oxford University Press, 2002. 9. Fogal RB, Malhotra A, White DP. Sleep 2: Pathophysiology of obstructive sleep apnoea/ hypopnoea syndrome. Thorax 2004; 59:159–163. 10. Wind J. Primate evolution and the emergence of speech. In: de Grolier E, Lock A, Peters CR, Wind J, eds. The Origin of Evolution of Language and Speech. New York: Harwood Academic, 1983. 11. Lowe AA, Fleetham JA, Adachi S, Ryan CP. Cephalometric and computed tomographic predictors of obstructive sleep apnea severity. Am J Orthod Dentofacial Orthop 1995; 106(6):589–595. 12. Kushida C, et al. A predictive morphometric model for the OSAS. Ann Intern Med 1997; 127(8):581—587. 13. Kennedy GE. From the ape’s dilemma to the weanling’s dilemma: early weaning and its evolutionary context. J Hum Evol 2005; 48:123–145. 14. Aiello L, Wheeler P. The expensive-tissue hypothesis: the brain and the digestive system in human and primate evolution. Curr Anthropol 1995; 36:199–221. 15. Berger T, Trinkaus E. Patterns of trauma among the Neandertals. J Archaeol 1995; 22:841–852. 16. Upperairwayproject.com. 17. Lieberman P, Crelin ES. On the speech of Neanderthal man. Linguist Inquiry 1971; 2:203–222. 18. Boe LJ, Heim JL, Honda K, et al. The potential Neandertal vowel space was as large as that of modern humans. Journal of Phonetics 2002; 30:465–484. 19. Guiness Book of World Records (2001).

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20. Lonergan RP III, Ware JC, Atkinson RL, Winter WC, Suratt PM. Sleep apnea in obese miniature pigs. J Appl Physiol 1998; 84(2):531–536. 21. Tuck SA, Dort JC, Olson ME, Remmers JE. Monitoring respiratory function and sleep in the obese Vietnamese pot-bellied pig. J Appl Physiol 1999; 87(1):444–451. 22. Harvold EP, Tomer BS, Vargervik K, et al. Primate experiments on oral respiration. American Journal of Orthodontics 1981; 79(4):359–372. 23. Brash JC. The etiology of irregularity and malocclusion of teeth. Dental Board of the United Kingdom, 1956. 24. Nishimura T, Mikami A, Suzuki J, et al. Descent of the hyoid in chimpanzees: evolution of face flattening and speech. J Hum Evol 2006; 51:244–254. 25. Lieberman DE, McCarthy RC, Hiiemae KM, Palmer JB. Ontogeny of postnatal hyoid and larynx descent in humans. Arch Oral Biol 2001; 46(2):117–128. 26. Wong K. Lucy’s baby: an extraordinary new human fossil comes to light. September 20, 2006, www.scientificamerican.com. 27. Lieberman D, McCarthy RC, The ontogeny of cranial base angulation in humans and chimpanzees and its implications for restructuring pharyngeal dimensions. J Hum Evol 1999; 36:487–517. 28. Alemseged Z, Spoor F, Kimbel WH, et al. A juvenile early hominin skeleton from Dikika, Ethiopia. Nature 2006; 443(7109):296–301. 29. Tuck SA, Remmers JE. Mechanical properties of the passive pharynx in Vietnamese pot-bellied pigs: 1 Statics. J Appl Physiol 2002; 92(6):2229–2244.

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Upper Airway Anatomy Avery Tung University of Chicago Hospitals, Chicago, Illinois, U.S.A.

INTRODUCTION If one were to design a living organism needing to breathe, eat, and communicate from scratch, one would be unlikely to ask a single body structure to fulfill these apparently mutually incompatible functions. It would clearly be simpler (as well as safer and more efficient) to separate the breathing and eating orifices, and relegate communication to a different structure altogether. That humans perform all of these tasks (and others) using a single structure is an impressive feat of engineering, and reflects the deceptive complexity of the upper airway and its anatomy. Serving not only the neurologic system (via speech, taste, and smell), but also the gastrointestinal and respiratory systems, the upper airway is nearly unique among body structures in merging multiple diverse functions into an efficient multipurpose whole. A list (Table 1) of functions of upper airway structures demonstrates this remarkable versatility. At any given time (and often nearly simultaneously), the upper airway must act as a humidified, heating and cooling conduit for inspired gases, a sensory organ, a speech generator, and a passageway for food. Enough of these functions are both complex and mutually incompatible (e.g., speaking and eating) that the relative lack of common diseases involving malfunction of the upper airway is surprising. Of the potential ways that the upper airway can malfunction, it then comes as somewhat of a surprise to realize that the relatively simple “gas conduit” role can be the source of significant morbidity and mortality. After all, a straightforward snorkel seems to serve this role admirably well in recreational divers. Yet, it is clear that in obstructive sleep apnea (OSA), a combination of anatomical and neurological control abnormalities that occur during sleep can combine to consistently restrict, or even completely prevent gas flow into the lungs. This chapter will review the anatomy of the upper airway, identify areas that have been implicated as playing a role in sleep apnea, and briefly discuss how abnormal function of the upper airway can generate the obstruction to airflow during sleep that plays an integral role in OSA. ANATOMY OF THE UPPER AIRWAY The upper airway is defined as the passageway for gas and food beginning at the mouth and nose and ending at the epiglottis and vocal cords. It can best be thought of as an “X” shape, with two distinct entry points from the outside (mouth and nose) which join together in the middle (pharynx) before splitting apart again to form the larynx and esophagus. As a result, describing the upper airway involves exploring the anatomy of two entry passages, a set of joined structures shared between them, areas where passages merge and bifurcate, and a 90° change in direction from posterior to caudal that occurs where the arms of the “X” join together. To best describe the anatomy and function of the separate parts of this complex 81

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TABLE 1 Functions of the Upper Airway Mastication Communication Breathing Swallowing Taste Smell

structure, this chapter will consider each relevant structure as it would be encountered by gas initially entering the mouth or nose, traversing the relevant structures, and exiting via the trachea. OVERVIEW Gas entering the upper airway must first traverse either the nasal passages or the oral cavity. Gas entering through the nostrils first moves posteriorly through the nasal passage, and is warmed, filtered, and humidified. After passing through the nasal cavity, gas exits under an arch of lymphoid tissue (adenoids) into a separate cavity called the nasopharynx, which makes a 90° turn downward and joins up with air entering the mouth, passing posteriorly through the oral cavity, and entering the oropharynx. The nasopharynx and oropharynx join together to form a single passage which then progresses caudally as the pharynx and hypopharynx before bifurcating again below the glottic aperture, creating the posteriorly located esophagus and the anteriorly located trachea. The trachea is protected both by the epiglottis, which folds down to cover the airway during swallowing, and the vocal cords, which close when abnormally stimulated from above. The esophagus has no such flap, but instead a muscular sphincter that separates the digestive system from the airway. A detailed description of each structure follows below. Nose Anterior Nasal Cavity The nose serves to warm, moisten, and filter air entering the respiratory system. The upper third of the nose receives support from paired nasal bones and the maxilla, and the lower two-thirds is supported by cartilage. The nasal valves, situated in the anterior portion of the nose, provide resistance to control nasal airflow and are divided into external and internal portions. The external nasal valve is comprised of the columella, the nasal floor, and the nasal rim [inferior border of the lower lateral cartilage (alae nasi)] (Fig. 1). The internal nasal valve, which is responsible for the majority of airflow resistance, is situated at the junction of the superior aspect of the nasal septum and the upper lateral cartilage. Although the nostrils are oriented downwards, they serve as the opening to a nasal cavity whose major dimension is oriented anteriorly and posteriorly (Fig. 2). The nasal cavity is the most superior structure in the upper airway, and is located directly above the oral cavity. The hard palate separates the nasal cavity from the oral cavity, forming the floor of the nasal cavity and the ceiling of the oral cavity. Unlike the oral cavity, there is no structure like the tongue, which can change shape and cause obstruction. As a result, the nose is rarely implicated in the pathogenesis of OSA. Nevertheless, in normal patients nasal obstruction increases the number of apneas and hypopneas (1), and can induce both subjective and objective disturbances in sleep (2). While nasal pathology can

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FIGURE 1 (See color insert.) External nasal valve. Source: Photograph courtesy of Kannan Ramar, MD.

clearly shift airflow to the oral cavity, increasing dependence on a patent oral airway, specific surgical or medical treatment of nasal obstruction does not clearly improve OSA (3). Nevertheless, nasal positive pressure devices appear to have therapeutic efficacy in OSA, and are currently considered a mainstay of nonsurgical therapy (4). Laterally, the outer walls of the nasal cavity is bounded by the inner surfaces of the maxillae, which form a sloping wall with three scroll-shaped turbinates oriented anteroposteriorly one above the other. The space below the inferior turbinate is termed the inferior meatus, with the middle meatus located between the inferior and middle turbinates, and the superior meatus situated above the middle turbinate. The opening of the Eustachian tube lies in the lateral wall of the nasopharynx below the inferior turbinate. Sensory innervation of the nasal mucosa

Nasal Cavity Nasopharynx Adenoids

Hard Palate

Soft Palate Palatine Tonsils Oral Cavity Oropharynx Tongue

Lingual Tonsils Epiglottis

Genioglossus

Hyoid Bone Hypopharynx

Thyroid Cartilage

Esophagus Larynx

Vocal Cords

FIGURE 2 (See color insert.) Sagittal view of nasal and oral cavities. Source: Figure courtesy of Clete A. Kushida, MD, PhD.

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derives from the first and second divisions of the trigeminal nerve and sympathetic innervation is from the superior cervical ganglion. Deviated nasal septa, polyps, turbinate hypertrophy, or engorged nasal mucosa due to changes in sympathetic tone or infection may all represent common causes of nasal obstruction. In addition, secretions from paranasal sinuses may further obstruct airflow. Obstructed nasal airflow, although rarely implicated in the pathogenesis of sleep apnea, may worsen existing pharyngeal obstruction by increasing the resistance to airflow and thus causing a greater negative pressure in the pharynx (5). Obstructed nasal airflow may also prevent effective use of nasal continuous positive airway pressure (CPAP), which has demonstrated benefit in reducing obstructive symptoms (4). Posterior Nasal Cavity/Nasopharynx The rear of the nasal cavity changes dramatically where it passes under a mass of lymphoid tissue (called adenoids) and joins the posteriorly located nasopharynx. At this junction, the floor of the nasal cavity (hard palate) changes to form the soft palate and the airspace makes a 90° turn downwards behind the posterior tip of the soft palate. Airflow moving into the nose thus moves horizontally through the nasal cavity, passes into the nasopharynx, impacts against the posterior wall of the nasopharynx, and is directed downward past the tip of the soft palate to join the flow of air from the oral cavity (Fig. 2). Positioned at the junction of the nasal cavity and nasopharynx is the soft palate, a muscular flap that hangs nearly vertically and represents the posterior edge of the hard palate (Fig. 2). It terminates in the uvula, and is usually visible on routine oral examination. The soft palate is mobile, and is controlled by the levator palatine (which elevates the palate) and the tensor palatine which stiffens it. Because the nasopharynx sits above the soft palate, and the oral cavity/oropharynx sits below, changes in soft palate position can cause either the mouth or nose to carry the bulk of airflow duties. By relaxing and falling anteriorly and caudally, for example, the soft palate increases the size of the nasopharynx and facilitates nasal breathing. Conversely, by elevating and pivoting posteriorly to contact the posterior pharyngeal wall, the soft palate facilitates oral breathing. Disorders of the soft palate are rarely implicated in OSA, but removal of the uvula along with the tonsillar pillars is pursued as part of a therapeutic strategy in the surgical treatment of OSA (6). The adenoids are a mass of lymphoid tissue located in the mucous membrane of the posterior wall of the nasopharynx (Fig. 2). They represent part of Waldeyer’s ring, a chain of lymphoid tissue segments that includes the palatine tonsils (at the junction of the oral cavity and the oropharynx) and lingual tonsils (at the base of the tongue). Although this ring is not necessary for survival, it circumnavigates the naso- and oropharynx with the apparent role of guarding against pathogen invasion. As discussed above, enlargement of the palatine tonsils (whether by hypertrophy or from infection) has been implicated in OSA, both in pediatric (7) and adult (8) cases, and similarly, adenoid enlargement can worsen nasal airflow. Mouth Anterior Oral Cavity and Tongue The mouth, or oral cavity, is particularly important in current models of the detection and/or pathogenesis of sleep apnea. Multiple studies suggest that the area of primary obstruction occurs where the rear of the oral cavity joins up with the

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oropharynx (9,10). A clear understanding of the anatomy of the mouth, therefore, is extremely relevant both for discerning the possible mechanisms of sleep-related obstruction, and in the identification of anatomically-fixable causes. The lips are the first structure encountered upon entering the oral cavity (Fig. 2, oral cavity viewed sagittally; Fig. 3, oral cavity viewed anteriorly). Once inside the mouth, the oral cavity is grossly bordered by the hard and soft palate superiorly (the “roof”), the lingual mucosa inferiorly (the “floor”), the buccal mucosa laterally on each side (walls), and the anterior pillars of the palatine tonsils, which mark the junction between the oral cavity and the posteriorly-located oropharynx. Although the ceiling and walls of the oral cavity are relatively simple structures, the floor of the mouth is significantly more complicated. Although the lingual mucosa forms the lowermost boundary of the oral cavity, it lies under the anterior two-thirds of the tongue, which occupies a large fractional volume of the mouth. Unless the tongue is lifted during physical examination, it almost completely overlies the lingual mucosa on physical examination. From an airway patency perspective, the tongue plays a critical role. In most states of pharmacologically-altered consciousness, particularly in the supine position, loss of tongue and neck muscle tone causes the tongue to fall back into the posterior pharynx, often resulting in partial or complete obstruction (11). As a result, understanding structural aspects of the tongue is perhaps the most important aspect of the anatomy of the oral cavity. The tongue is a large, multifunctional muscle capable of a striking diversity of movements and shapes. It can be divided into an anterior and posterior portion, marked by a line of specialized circumvallate papillae on its surface. The posterior tongue is wide-based and attached to the floor of the mouth. The anterior tongue is also attached to the floor of the mouth, but by a much thinner and flexible membranous frenulum that allows it to move with near-complete freedom. Muscularly, the tongue is composed of intrinsic muscles which help it to change shape, and a series of external muscles which connect to the styloid (styloglossus), hyoid (hyoglossus), Soft Palate

Uvula

Tongue

Palatine Tonsils

FIGURE 3 (See color insert.) Anterior view of the oral cavity. Note hypertrophied tonsils. Source: Photograph courtesy of Kannan Ramar, MD.

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and mandible (genioglossus) and allow it to move around the oral cavity. Because of its connection to mandibular and neck structures, it is easy to see that changes in the relative position of the mandible to the neck can alter the position of the tongue in the mouth and worsen or improve airway obstruction. Maneuvers to move the mandible away from the posterior neck, for example, are basic initial steps for anesthesiologists trained to maintain airway patency during anesthesia and other drug-induced states of unconsciousness. Moreover, of all craniofacial measurements, mandibular length is the craniofacial structural dimension that predicts clinical sleep apnea to the greatest degree (12). This pattern of muscular connection also explains why a retrognathic or posteriorly-located mandible predisposes to airway obstruction during both naturally-occurring sleep and anesthetized states (13). Clinically, placement and size of the tongue has been proposed as potentially affecting the risk of OSA. Tongue size in particular correlates strongly with sleep apnea, as does total soft tissue (14). As a result, examining the relaxed tongue, and determining whether or not it sits above the occlusal plane of the mandibular teeth, can inform regarding the potential diagnosis of sleep apnea (15). Motor innervation to the tongue is supplied by cranial nerves XII (hypoglossal) and IX (glossopharyngeal). Sensation is provided by cranial nerve IX for the posterior third of the tongue and VII (facial) for the anterior two-thirds. Although a decrease in laryngeal sensation does correlate with severity of OSA (16), OSA is currently not thought to involve cranial nerve pathology. Posterior Oral Cavity/Oropharynx As air moves towards the back of the mouth, the oral cavity joins with the oropharynx (Fig. 4). Because numerous static and functional imaging studies of OSA have identified this area as the site where obstruction most probably occurs during sleep (17), much attention has been focused on evaluating anatomic structures for diagnostic purposes and surgical remodeling for curative purposes. One obstacle to appreciating the anatomy of the posterior oral cavity and pharynx is the use of multiple terms to describe similar areas. Such use of multiple terminologies is particularly confusing with respect to upper airway anatomy and sleep apnea because the areas with greatest involvement in airway obstruction during sleep are typically those with multiple names. For the purposes of this chapter, the following definitions and terms will be considered equivalent: 1. “Retropalatal oropharynx” and “velopharynx.” These two terms both refer to the portion of the oropharynx lying behind the soft palate, directly below the nasopharynx and posterior to the oral cavity. This area forms the part of the “X” where the nasal and oral cavities meet. It is defined anatomically by the soft palate anteriorly (which partially separates it from the oral cavity), the pharyngeal constrictor muscles posteriorly, the nasopharynx superiorly, and the distal oropharynx inferiorly. For this chapter, the two terms will refer to the same area. 2. “Retroglossal region of the oropharynx” and “hypopharynx.” This portion of the oropharynx is generally defined as extending from the tip of the soft palate to the tip of the epiglottis. It is located just below (caudal to) the retropalatal region described above. It is bounded anteriorly by the posterior tongue and epiglottis, superiorly by the retropalatal oropharynx, inferiorly by the esophagus and larynx, and posteriorly and laterally by the pharyngeal constrictors. For this chapter, the two terms will refer to the same area.

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Sinus

Nasal Cavity Nasopharynx

Hard Palate Soft Palate Oral Cavity

Retropalatal

Tongue

Retroglossal

Oropharynx

Epiglottis Hypopharynx

Esophagus Trachea FIGURE 4 (See color insert.) Sagittal magnetic resonance imaging of airway and division of oropharynx. Source: Figure courtesy of Clete A. Kushida, MD, PhD.

At the level of the circumvallate papillae, a V-shaped line of mushroom shaped structures separating posterior from anterior tongue, the floor of the oral cavity essentially becomes the posterior tongue. The posterior-most portion of the hard palate changes to a soft palate “flap” that forms the ceiling of the oral cavity. The walls of the cavity are formed by the palatoglossus muscle, which forms an anterior arch, and the palatopharyngeus muscle, which forms a similar arch slightly more posterior, and the palatine tonsils which sit between them. These flaps can readily be seen on physical examination, and large palatine tonsils represent a clear (and fixable) anatomic predisposition to sleep apnea (18). The palatine tonsils lie between the palatoglossus and palatopharyngeus arches, and separate the oral cavity from the oropharynx. The oropharynx is defined as the airway extending from the arches described above (and soft palate superiorly) down to the superior tip of the epiglottis, which arises from the posterior base of the tongue and projects into the pharyngeal space. Immediately superior to and continuous with the oropharynx is the nasopharynx (described above). As the pharynx plays an overall pivotal role in the genesis of sleep apnea, it will be discussed in separate sections. Pharynx From an OSA perspective, the pharynx (comprised of the oropharynx and nasopharynx as described above) is the most important structure in the upper airway. Many surgical procedures used to treat sleep apnea are performed on the upper

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pharynx, where the nasal cavity joins with the nasopharynx and the oral cavity joins with the oropharynx, making identification of anatomic abnormalities in this area particularly important to understand. The pharynx is a 12 to 15 cm long muscular tube that begins at the level of the soft palate and extends vertically downward to the cricoid cartilage where it becomes continuous with the esophagus (Figs. 2 and 4). In normal patients, this tube is oval in cross section, with the long dimension oriented from medial to lateral. Immediately superior to the soft palate is the nasopharynx, and immediately below the soft palate is the posterior oral cavity. At the level of the soft palate, therefore, the pharynx communicates with, and receives airflow from, the nasopharynx above, and the oral cavity anteriorly. This upper pharyngeal region (just below the nasopharynx and above the epiglottis) is usually termed the oropharynx, and is divided into the retropalatal region (the area adjacent to the soft palate) and the retroglossal region (from the distal margin of the soft palate to the base of the epiglottis). Below the oropharynx lies the hypopharynx, which generally stretches from the base of the tongue/ epiglottis to the larynx. This chapter will deal with each of these regions in turn. Oropharynx: Retropalatal Region At this level, the anterior wall of the vertically-oriented oropharynx is formed by the soft palate and tongue, and the posterior wall formed by pharyngeal constrictor muscles (superior, middle, and inferior). These muscles arise from multiple locations, including the medial pterygoid plate (superior), hyoid (middle), and thyroid cartilage (inferior). Their primary functions are to propel food towards the esophagus and aid with phonation. The lateral pharyngeal walls vary anatomically in patients with sleep apnea, and appear to play a large role in altering the shape of the upper airway (19). In the retropalatal region, the lateral pharyngeal walls are formed by several structures, including the pharyngeal constrictors, muscles of the extrinsic tongue (styloglossus, hyoglossus, and stylopharyngeus), muscles of the soft palate (palatoglossus and palatopharyngeus), and muscles of the larynx (stylohyoid and stylopharyngeus). In addition to these structures, the palatine tonsils and parapharyngeal fat pads also contribute to the lateral pharyngeal walls. Variability in the size of the lateral pharyngeal walls at the retropalatal level clearly associates with sleep apnea (19). Imaging studies in patients with sleep apnea demonstrate enlargement of the lateral walls and consequent narrowing of the oropharyngeal airspace (20). Magnetic resonance imaging (MRI) studies indicate that this enlargement is due partly to overall tissue enlargement, and partly to larger parapharyngeal fat pads. In one 2003 study, a one SD increase in lateral pharyngeal wall thickness was associated with a 2.3-fold increased risk of sleep apnea (14). The resulting narrowing in airway size has been estimated to reduce the crosssectional area of the retropalatal region by more than 50% at its smallest point. The primarily bilateral lateral location of airway narrowing in patients with sleep apnea is consistent with other anatomical abnormalities that correlate with the clinical syndrome. In one 1995 study of 157 patients with sleep apnea and 279 matched controls, a high, arched, and narrow hard palate resulted in an odds ratio of 10 for clinical OSA (21). Although it is unclear how lateral narrowing of the retropalatal airway leads to airway obstruction at night, one hypothesis is that such anatomy shrinks the overall size of the airway, increasing the potency of negative inspiratory forces and predisposing to airway closure when negative inspiratory forces are present.

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Oropharynx: Retroglossal Region Below the retropalatal area, the pharynx continues vertically downwards, but increases in size. The retroglossal area of the oropharynx extends from the tip of the soft palate superiorly to the base of the epiglottis inferiorly (Figs. 2 and 4). The crosssectional area of the pharynx is generally larger in the retroglossal than the retropalatal area, but has also been implicated in the pathogenesis of OSA. Several lines of evidence implicate the retroglossal area in the pathogenesis of sleep apnea. First, computed tomography (CT) imaging studies demonstrate that the cross-sectional size of the retroglossal pharynx is smaller in patients with sleep apnea than in normal subjects (22). Secondly, continuous monitoring of airway pressures during overnight polysomnography demonstrates a roughly even split between obstruction at the retropalatal area and the retroglossal area, and moreover that during rapid eye movement (REM) sleep airway obstruction occurred at a more caudal level than during non-REM (NREM) sleep (23). Although a wide variety of approaches (imaging, catheter measurements, fluoroscopy/endoscopy) have been used to determine the site of airway occlusion, most agree (17) that airway occlusion occurs between the uvula and the epiglottis, with varying degrees of specificity as to the exact location. As with the retropalatal region of the oropharynx, the reduction in crosssectional area of the retroglossal area occurs mainly via thickening of the lateral pharyngeal walls. This thickening changes the cross-sectional dimension of the oropharynx from an oval with the long axis oriented laterally to a more circular shape with the long axis oriented anteroposteriorly. Hypopharynx The lower boundary of the retroglossal region of the oropharynx is often termed the hypopharynx (Figs. 2 and 4). In this area, the pharyngeal passage is bounded anteriorly by the base of the tongue and the epiglottis, and posteriorly/laterally by the inferior pharyngeal constrictor. Further caudad, below the epiglottis, the hypopharynx becomes contiguous with the esophagus, and the larynx splits off anteriorly with the vocal cords located immediately below the epiglottis (Fig. 2). Although obstruction clearly occurs in anatomically normal patients in the retroglossal region, obstruction at or below the level of the epiglottis is generally thought to occur less frequently. In this region, however, anatomical abnormalities can play a significant role in increasing the likelihood of airway obstruction. Three structures that can have a demonstrated influence on the incidence of sleep apnea are the tongue, lingual tonsil, and the epiglottis (Fig. 2). Particularly when combined with a mandible that is short, tongue size correlates with the incidence of sleep apnea and can play a role in airflow obstruction at the level of the epiglottis. By occupying space in the hypopharyngeal area, the tongue can force associated structures posteriorly, narrowing the pharynx in the anteroposterior dimension. Moreover, an abnormally large tongue can displace the epiglottis, causing it to angle backwards into the hypopharynx, potentially predisposing to obstruction with further loss of tongue muscle tone. This effect frequently increases the difficulty of airway management for anesthesiologists, and forms the basis for oral airway appliances designed to keep the tongue away from the posterior pharyngeal wall (24). In addition to tongue size, lingual tonsillar abnormalities can also predispose to airflow obstruction. The lingual tonsil is a mass of lymphoid tissue that sits at the base of the tongue just above the epiglottis. It is not usually visible on routine oral

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examination, and is part of Waldeyer’s ring, described above as the ring of lymphoid tissue serving to filter out potential pathogens. Although the lingual tonsil does not normally predispose to sleep apnea, it can confound direct laryngoscopy and intubation by interfering with placement of the laryngoscope in the vallecular area between the epiglottis and the base of the tongue. It is also easy to see anatomically how hypertrophy of the lingual tonsil can affect the size of the retroglossal area. In pediatric patients with Down syndrome, for example, lingual tonsillar hypertrophy correlates to severity of sleep apnea, and resection of lingual tissue improves obstructive symptoms (25). In adults, the incidence of lingual tonsillar hypertrophy is not known, but has been identified as a risk factor in difficult intubation (26). Other anatomical abnormalities at the level of the epiglottis are occasional causes of sleep apnea. One rare cause is prolapse of the epiglottis into the airway during inspiration with obstruction of airflow. Although usually a syndrome associated with tracheomalacia, this phenomenon has been implicated in up to 11% of patients with sleep apnea who fail to improve with uvulopalatopharyngoplasty (27). In such cases, epiglottidectomy appears to improve obstructive symptoms, supporting the prolapsing mechanism (28). Larynx Below the epiglottis the upper airway splits into the esophagus, located posteriorly, and the trachea located anteriorly. The larynx spans this area, bounded superiorly by the epiglottis, inferiorly by the vocal cords, and laterally by aryepiglottic folds that work in concert with the epiglottis to close off the lower airway to food and water. This region has three basic functions: phonation, protection of the lower airway, and gas exchange. Abnormalities of this area are rarely associated with OSA. Anatomically, redundant mucosa in the arytenoid-aryepiglottic fold area has been associated with sleep apnea although the mechanism of inspiratory airflow obstruction is unclear (29). Functionally, abnormalities of vocal cord function are strong potential causes of sleep apnea, but only rarely play a significant role. Bilateral cord paralysis, for example, is associated with snoring (30), but does not appear to associate with symptomatic sleep apnea (31), suggesting that this mechanism of airflow obstruction is relatively uncommon. Tracheal abnormalities below the vocal cords such as tracheomalacia are also extremely uncommon causes of obstructed airflow. In fact, the opposite phenomenon: sleep apnea leading to increased negative airway pressure and tracheomalacia has been reported in a pediatric patient (32). Overall, despite the complexity of laryngeal function, abnormalities of upper airway function in this area are rare causes of sleep apnea. CONCLUSIONS Although the exact pathogenesis of OSA remains incompletely understood, the final common pathway ultimately involves the upper airway. The complexity and myriad functions of the upper airway, however, prevents ready identification of the location, source, and mechanism of inspiratory airflow obstruction. As a result, predictions of disease likelihood or severity from simple anatomical measurements have been frustratingly difficult. This variability in the presentation of sleep apnea contributes to the challenge of identifying or treating such patients, and the difficulty in managing such patients across different vigilance states. Accumulated data, however, suggest some important general principles. Although possible, clinically significant airflow obstruction rarely occurs in the

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nasal cavity, nasopharynx, larynx, or oral cavity. Most cases of OSA involve the relatively small portion of the pharynx bounded superiorly by the nasopharynx and inferiorly by the tip of the epiglottis. Functionally, abnormalities of this area include loss of tongue muscle tone, loss of muscle tone in pharyngeal constrictor muscles predisposing to airway collapse during inspiration, and abnormalities in epiglottis function. Anatomically, specific abnormalities in this small segment of the pharynx clearly play a large role. Mandibular length and tongue size, for example, correlate with the incidence of sleep apnea. In children, the size of the palatine and lingual tonsils appear to play a role. Finally, abnormally large thickening of the parapharyngeal constrictor muscles and increased fat deposition in this area are significantly more common in patients with sleep apnea, and literally change the cross-sectional shape of the pharynx in this region. Although the mechanism of this thickening is not known, the tangible effects of parapharyngeal wall thickening and fat deposition have been demonstrated on MRI (22) and provide a potential basis for relating anatomy to function. It is estimated that 90% of patients with sleep apnea remain undiagnosed (33). The inability to visualize known anatomical aspects of the upper airway, and variable clinical presentation, both contribute to the difficulty in making the diagnosis. Nevertheless, a clear understanding of airway anatomy and of the specific aspects of the airway that are implicated in airflow obstruction is important in addressing a disease that is likely to increase in incidence and severity in the near future. REFERENCES 1. Taasan V, Wynne JW, Cassisi N, et al. The effect of nasal packing on sleep-disordered breathing and nocturnal oxygen desaturation. Laryngoscope 1981; 91(7):1163–1172. 2. Olsen KD, Kern EB, Westbrook PR. Sleep and breathing disturbance secondary to nasal obstruction. Otolaryngol Head Neck Surg 1981; 89(5):804–810. 3. Friedman M, Tanyeri H, Lim JW, et al. Effect of improved nasal breathing on obstructive sleep apnea. Otolaryngol Head Neck Surg 2000; 122(1):71–74. 4. Ballester E, Badia JR, Hernandez L, et al. Evidence of the effectiveness of continuous positive airway pressure in the treatment of sleep apnea/hypopnea syndrome. Am J Respir Crit Care Med 1999; 159(2):495–501. 5. Ryan CF. Sleep x 9: an approach to treatment of obstructive sleep apnoea/hypopnoea syndrome including upper airway surgery. Thorax 2005; 60(7):595–604. 6. Sher AE, Schechtman KB, Piccirillo JF. The efficacy of surgical modifications of the upper airway in adults with obstructive sleep apnea syndrome. Sleep 1996; 19(2):156–177. 7. Valera FC, Avelino MA, Pettermann MB, et al. OSAS in children: correlation between endoscopic and polysomnographic findings. Otolaryngol Head Neck Surg 2005; 132(2): 268–272. 8. Erdamar B, Suoglu Y, Cuhadaroglu C, et al. Evaluation of clinical parameters in patients with obstructive sleep apnea and possible correlation with the severity of the disease. Eur Arch Otorhinolaryngol 2001; 258(9):492–495. 9. Trudo FJ, Gefter WB, Welch KC, et al. State-related changes in upper airway caliber and surrounding soft-tissue structures in normal subjects. Am J Respir Crit Care Med 1998; 158(4):1259–1270. 10. Suto Y, Matsuo T, Kato T, et al. Evaluation of the pharyngeal airway in patients with sleep apnea: value of ultrafast MR imaging. AJR Am J Roentgenol 1993; 160(2):311–314. 11. Shorten GD, Opie NJ, Graziotti P, et al. Assessment of upper airway anatomy in awake, sedated and anaesthetised patients using magnetic resonance imaging. Anaesth Intensive Care 1994; 22(2):165–169. 12. Miles PG, Vig PS, Weyant RJ, et al. Craniofacial structure and obstructive sleep apnea syndrome--a qualitative analysis and meta-analysis of the literature. Am J Orthod Dentofacial Orthop 1996; 109(2):163–172.

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13. Lowe AA, Fleetham JA, Adachi S, et al. Cephalometric and computed tomographic predictors of obstructive sleep apnea severity. Am J Orthod Dentofacial Orthop 1995; 107(6):589–595. 14. Schwab RJ, Pasirstein M, Pierson R, et al. Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging. Am J Respir Crit Care Med 2003; 168(5):522–530. 15. Goldberg AN, Schwab RJ. Identifying the patient with sleep apnea: upper airway assessment and physical examination. Otolaryngol Clin North Am 1998; 31(6):919–930. 16. Nguyen AT, Jobin V, Payne R, et al. Laryngeal and velopharyngeal sensory impairment in obstructive sleep apnea. Sleep 2005; 28(5):585–593. 17. Rama AN, Tekwani SH, Kushida CA. Sites of obstruction in obstructive sleep apnea. Chest 2002; 122(4):1139–1147. 18. Nakata S, Noda A, Yanagi E, et al. Tonsil size and body mass index are important factors for efficacy of simple tonsillectomy in obstructive sleep apnoea syndrome. Clin Otolaryngol 2006; 31(1):41–45. 19. Schwab RJ, Gupta KB, Gefter WB, et al. Upper airway and soft tissue anatomy in normal subjects and patients with sleep-disordered breathing. Significance of the lateral pharyngeal walls. Am J Respir Crit Care Med 1995; 152(5 Pt 1):1673–1689. 20. Schellenberg JB, Maislin G, Schwab RJ. Physical findings and the risk for obstructive sleep apnea. The importance of oropharyngeal structures. Am J Respir Crit Care Med 2000; 162(2 Pt 1):740–748. 21. Guilleminault C, Partinen M, Hollman K, et al. Familial aggregates in obstructive sleep apnea syndrome. Chest 1995; 107(6):1545–1551. 22. Schwab RJ, Gefter WB, Hoffman EA, et al. Dynamic upper airway imaging during awake respiration in normal subjects and patients with sleep disordered breathing. Am Rev Respir Dis 1993; 148(5):1385–1400. 23. Shepard JW Jr, Thawley SE. Localization of upper airway collapse during sleep in patients with obstructive sleep apnea. Am Rev Respir Dis 1990; 141(5 Pt 1):1350–1355. 24. Ferguson KA, Cartwright R, Rogers R, et al. Oral appliances for snoring and obstructive sleep apnea: a review. Sleep 2006; 29(2):244–262. 25. Suzuki K, Kawakatsu K, Hattori C, et al. Application of lingual tonsillectomy to sleep apnea syndrome involving lingual tonsils. Acta Otolaryngol Suppl 2003; (550):65–71. 26. Ovassapian A, Glassenberg R, Randel GI, et al. The unexpected difficult airway and lingual tonsil hyperplasia: a case series and a review of the literature. Anesthesiology 2002; 97(1):124–132. 27. Catalfumo FJ, Golz A, Westerman ST, et al. The epiglottis and obstructive sleep apnoea syndrome. J Laryngol Otol 1998; 112(10):940–943. 28. Golz A, Goldenberg D, Westerman ST, et al. Laser partial epiglottidectomy as a treatment for obstructive sleep apnea and laryngomalacia. Ann Otol Rhinol Laryngol 2000; 109(12 Pt 1):1140–1145. 29. Naganuma H, Okamoto M, Woodson BT, et al. Cephalometric and fiberoptic evaluation as a case-selection technique for obstructive sleep apnea syndrome (OSAS). Acta Otolaryngol Suppl 2002; (547):57–63. 30. Aziz L, Ejnell H. Obstructive sleep apnea caused by bilateral vocal fold paralysis. Ear Nose Throat J 2003; 82(4):326–327. 31. Misiolek M, Namyslowski G, Karpe J, et al. Obstructive sleep apnea syndrome and snoring in patients with bilateral vocal cord paralysis. Eur Arch Otorhinolaryngol 2003; 260(4):183–185. 32. Peters CA, Altose MD, Coticchia JM. Tracheomalacia secondary to obstructive sleep apnea. Am J Otolaryngol 2005; 26(6):422–425. 33. Young T, Evans L, Finn L, et al. Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep 1997; 20(9):705–706.

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Physiology and Dynamics of the Upper Airway Jingtao Huang and Carole L. Marcus Sleep Center, The Childrens’ Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A.

INTRODUCTION Obstructive sleep apnea (OSA) is a disease limited almost exclusively to humans. The only animal model for OSA is the English bulldog (1), which serves as a model for OSA secondary to craniofacial anomalies (midfacial hypoplasia), rather than the more common human condition of OSA secondary to obesity. Despite attempts to develop animal models of OSA related to obesity, even very obese animals, such as pigs or obese knockout mice, do not develop OSA. Why then, does this disease occur so often in humans? The human pharynx has several functions which compete with each other (2), including breathing (during which the pharynx must remain patent) and swallowing (during which the pharynx must collapse). Some unique characteristics of the human have affected the ability of the upper airway to perform these different functions: the ability of humans to perform complex speech, and the upright position of humans compared to quadrupedal animals. This inherent tendency for the human upper airway to collapse predisposes it to abnormal collapse during sleep, that is, obstructive apnea. EMBRYOLOGY During embryologic development, the upper airway structures are all formed from adjacent segments of the foregut. The branchial arches give rise to most of the upper airway structures, including the jaw, sinuses, palate, and pharynx (Fig. 1A). A laryngotracheal groove forms in the ventral wall of the primitive pharynx, and eventually develops into the larynx, trachea, and esophagus (Fig. 1B). This communal origin of the different upper airway and gastrointestinal components helps explain how these structures interrelate in the coordination of the complex functions of the pharynx (3). NONRESPIRATORY FUNCTIONS OF THE UPPER AIRWAY The upper airway has a number of functions in addition to breathing. Unfortunately, adaptations that enhance the airway for certain functions may result in impairment of other functions. Speech and swallowing require that the upper airway be collapsible. Speech There are several major differences between the upper airway in humans compared to animals. It is presumed that these evolutionary changes occurred primarily to 93

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pharyngeal pouches

foramen cecum

median tongue bud

primitive pharynx

esophagus

hypobranchial eminence branchial arches

branchial arches

(A)

laryngotracheal level of section for diverticulum Figure 7.1(B) (B)

4th pharyngeal pouch laryngotracheal groove foregut

FIGURE 1 (See color Insert.) (A) Four-week embryo depicting branchial arches that develop into the majority of upper airway structures. (B) Laryngotracheal groove in the floor of the primitive pharynx of four-week embryo that eventually develops into the larynx, trachea, and esophagus (horizontal section at the level shown in A). Source: Figure courtesy of C. Kushida.

facilitate speech, but they may also be related to the bipedal stance of humans, such as allowing for binocular vision. In humans, the face is foreshortened, the tongue is located in the oropharynx rather than being located only in the oral cavity, the soft palate is short, the oral cavity-skull base is at an acute angle and the larynx has descended into the neck (4). In other animals, including primates, the larynx and epiglottis are superior to the oropharynx, at the skull base or the first cervical vertebra (4). In humans, the tip of the epiglottis is located at the midlevel of the C1 vertebra at birth in order to facilitate suckling, but then descends to the inferior border of C3 by adulthood (5). The laryngeal descent allows for better voice production, but results in a common pharyngeal area through which both air and food need to pass, thereby increasing the risk for aspiration. Swallowing The pharynx needs to be collapsible in order to facilitate swallowing. In order to prevent aspiration, swallowing is associated with a short, obligatory central apnea with laryngeal closure, followed by expiration (6). In addition to swallowing of food, non-nutritive swallowing occurs to clear saliva, airway secretions and refluxed gastric contents. Non-nutritive swallowing is also associated with brief apneas. PHYSIOLOGIC FUNCTIONS OF SELECTED REGIONS OF THE UPPER AIRWAY Physiology of the Nose Although not often considered in the etiology of OSA, the nose plays an important role in the development of upper airway resistance during sleep [see review by Rappai et al. (7)]. The nose accounts for two-thirds of the total airway resistance during sleep (8). Inspiratory work of nasal breathing is 1.6 times that of the expiratory work of breathing (9). The nasal resistance is influenced by the degree of vascular engorgement of the middle and inferior turbinates. The nasal resistance is not static. In most adults, a nasal cycle has been demonstrated in which nasal turbinate congestion and decongestion alternates between the right and left nares

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every one to seven hours (10). In contrast, young children have a regular pattern of fluctuations in nasal resistance, approximately every hour, which does not alternate between the right and the left (11). Nasal resistance changes with position. During lateral positioning, resistance increases in the dependent nostril (9,12). The importance of the nasal route of breathing during sleep is illustrated by the fact that nasal packing for epistaxis can result in obstructive sleep apnea and even death (13). Allergic rhinitis can be associated with OSA (14,15). In infants, the nasal route of breathing is especially important. It used to be thought that infants were obligate nasal breathers, but it has been shown that, although infants are preferential nasal breathers, some will breathe through their mouths when their nares are occluded (16,17). Infants with nasal obstruction are at risk for obstructive apnea, and infants with untreated choanal atresia may die, despite the fact that their oropharynx is patent. In addition to being a route for airflow, the nose and sinus cavities act as a heater and humidifier of inspired air, and as a voice resonator to enhance speech. Upper Airway Muscles The noncartilaginous portion of the upper airway, which extends from the nares to the vocal cords, is a hollow muscular tube that has the potential to collapse in order to allow swallowing and phonation. Narrowing of the cartilaginous portion of the upper airway typically results in stridor and fixed obstruction while awake and asleep, rather than snoring and OSA. There are a large number of muscles affecting the upper airway (Table 1). As noted above, these muscles perform many functions besides breathing, including speech, swallowing, coughing, and facial expressions. Weakness of these muscles (e.g., in patients with muscular dystrophy) (18), or incoordination of the muscles (e.g., in patients with spasticity) (19), can result in OSA. Physiology of the Larynx Laryngeal abnormalities may contribute to OSA, although they are more often a cause of fixed upper airway obstruction during both wakefulness and sleep. During normal breathing, vocal cord opening precedes the onset of inspiratory airflow (20). The glottis widens on inspiration (with abduction of the vocal cords) and narrows on expiration (associated with adduction of the vocal cords towards the midline). Hypoxia and hypercapnia result in widening of the glottis (21), whereas hypocapnia results in glottic narrowing (22). Vocal cord paralysis is a rare cause of obstructive apnea. Obstructive apnea may occur in subjects receiving mechanical ventilation via a modality that results in incoordination between diaphragmatic movement and vocal cord opening, such as negative pressure ventilation or diaphragm pacing. Active glottic closure occurs under a number of physiologic and pathophysiologic conditions. In neonates, laryngeal braking (partial laryngeal closure during expiration) occurs, as does active glottic closure during central apneas. This helps maintain a high lung volume in the face of a very compliant chest wall, and hence maintains alveolar oxygen stores. In all age groups, inspiratory breath holding with active glottal closure occurs during Valsalva maneuvers such as defecation, micturition, and parturition (2).. Laryngeal chemoreflexes are present to protect the airway from aspiration. These reflexes have been studied in the most detail in neonates. The laryngeal chemoreflex is stimulated by contact of acidic or hypotonic liquids (but not saline)

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TABLE 1 Upper Airway Muscles and Action Location Nose

Lips

Oropharynx

Hypopharynx

Muscle Dilator naris Alae nasi Procerus muscles Compressor naris Musculus orbicularis oris Levator labii superioris Depressor labii inferioris Levator anguli oris Depressor angulis oris Zygomaticus major Zygomaticus minor Risorius Tensor veli palatini Levator veli palatini Musculus uvulae Palatoglossus Palatopharyngeous Genioglossus Hyoglossus Styloglossus Superior constrictor Middle constrictor Digastric Stylohyoid Mylohyoid Stylopharyngeus Salpingopharyngeus Geniohyoid

Larynx

Omohyoid Sternohyoid Thyrohyoid Sternothyroid Inferior constrictor Posterior cricoarytenoid Oblique arytenoids Cricothyroid Thyroarytenoid Lateral cricoarytenoid Transverse arytenoids

Source: From Ref. 103.

Action Dilates the ala laterally Elevates the mobile alar cartilage and flares the nostrils Elevates the mobile alar cartilage Compresses mobile alar cartilage Constricts lips Elevates the upper lip Depresses the lower lip Elevates the angle of the mouth Pulls the corner of the mouth downward Elevates the corner of the mouth laterally Elevates the upper lip Draws the corner of the mouth laterally Tenses the soft palate Elevates the soft palate Shortens the uvula Elevates and retracts the posterior portion of the tongue Elevates the pharynx Protrudes and depresses the tongue Depresses and retracts the tongue Elevates and retracts the tongue Constricts pharynx Constricts pharynx Elevates the hyoid, depresses the mandible Elevates the hyoid Elevates the hyoid and tongue; depresses mandible Elevates the larynx Elevates the larynx Elevates and displaces the hyoid forward; depresses the mandible Depresses the hyoid Depresses the hyoid Depresses the hyoid Depresses the hyoid Constricts pharynx Abducts and laterally rotates arytenoid cartilage Approximates laryngeal cartilages, adducts vocal cords Displaces thyroid cartilage anteriorly and lengthens the vocal ligaments Displaces arytenoids cartilage anteriorly, adducting vocal cords Adducts vocal cords Adducts vocal cords

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on the laryngeal mucosa (2). This activates the superior laryngeal nerve (23). The resulting vagal response causes laryngospasm, central/mixed/obstructive apnea and bradycardia, as well as a sympathetic nervous system response resulting in hypertension and redistribution of blood flow (2). Although these reflexes are protective in nature, they can lead to prolonged central apnea, bradycardia, and hypoxemia in the newborn. Swallowing, coughing and arousal from sleep occur as part of the laryngeal chemoreflex. With maturation, coughing, and arousal occur more often than swallowing and apnea (23). In adult dogs, it has been shown that injecting water into the larynx results in coughing if the dog arouses, but central apnea if the dog fails to arouse; that is, arousal is necessary to produce a cough (24). DYNAMICS OF THE UPPER AIRWAY DURING SLEEP OSA results from a combination of structural upper airway narrowing and abnormal upper airway neuromotor tone. In some patients with OSA, there are major anatomic abnormalities such as craniofacial anomalies, adenotonsillar hypertrophy, or narrowing of the upper airway secondary to obesity. However, some patients with OSA do not have clear anatomic abnormalities. Furthermore, patients with OSA do not have airway collapse during wakefulness. Clearly, therefore, dynamic upper airway neuromotor factors play a role in maintaining airway patency during sleep. Sleep is associated with a decrease in upper airway neuromotor tone. As a result of this decrease in tone, upper airway resistance doubles compared to wakefulness (25). Because the upper airway resistance comprises nearly half of the total pulmonary resistance (8), even small increases in upper airway resistance can have a significant impact on breathing. The Starling Resistor Model The upper airway has been shown to behave like a Starling resistor (26,27) (Fig. 2). The Starling resistor model has been characterized for a number of biological systems, including blood vessels (28) and the lower airways (29). This model

Pcrit

PHypopharyngeal

PNasal Upstream (nasal) segment

Downstream (hypopharyngeal) segment Collapsible segment

FIGURE 2 The Starling resistor model of the upper airway. The upper airway is represented as a tube with a collapsible segment. The segments upstream (nasal) and downstream (hypopharyngeal) from the collapsible segment have fixed diameters and resistances and pressures. Collapse occurs when the critical pressure surrounding the airway (Pcrit) becomes greater than the pressure within the airway.

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describes the major determinants of airflow in terms of the mechanical properties of collapsible tubes. The upper airway can be represented as a tube with a collapsible segment, the resistance of which is equal to zero. The segments upstream (i.e., nasal) and downstream (i.e., hypopharyngeal) from the collapsible segment have fixed diameters and resistances. In this model of the upper airway, inflow pressure at the airway opening (the nares) is atmospheric, and downstream pressure is equal to tracheal pressure (generated by the diaphragm). Collapse occurs when the pressure surrounding the collapsible segment of the upper airway (critical tissue pressure, Pcrit) becomes greater than the pressure within the collapsible segment of the airway. In the normal subject with low upstream resistance or markedly subatmospheric Pcrit, downstream pressure never approaches Pcrit; thus airflow is not limited and is largely determined by negative tracheal (inspiratory) pressure. However, if downstream pressure falls below Pcrit, inspiratory flow reaches a maximum (inspiratory airflow limitation), and becomes independent of downstream pressure swings. Under these circumstances, nasal resistance and Pcrit determine maximal inspiratory flow (VImax) as described by the following equation: VImax = (PN − Pcrit)/RN; where PN = nasal pressure and RN = nasal resistance. Airflow will become zero (i.e., the airway will occlude) when PN falls below Pcrit (Fig. 3). The measurement of upper airway pressure-flow relationships provides a useful model for understanding upper airway collapsibility. Both Pcrit and the slope of the upper airway pressure-flow curve have been used to characterize upper airway dynamics. Measurements can then be used to compare upper airway collapsibility between different groups of subjects, or in the same subjects before and after interventions. Pressure-flow measurements can be readily obtained during natural sleep by having the subject sleep while wearing a nasal mask. Inspiratory airflow is measured by a pneumotachometer attached to the mask. Nasal pressure (PN) is measured at the mask, and hypopharyngeal or esophageal pressure can be measured with catheters. PN is then gradually altered through a range of positive and negative pressures. It is important to realize that, in the living human, the upper airway pressure-flow curve reflects both structural and neuromotor factors, as the upper airway is not merely a passive conduit affected by mechanical forces, but is also affected by activation of the upper airway muscles. Structural factors include the inherent stiffness of the upper airway, the effect of extrinsic or intrinsic compressive forces on the upper airway, and

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-30 -25 -20 -15 -10 -5 0 5 10

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FIGURE 3 Maximal inspiratory flow (VImax ) is plotted against nasal pressure (PN) (left panel) and esophageal pressure (Pes) (right panel) for a child with primary snoring. Pcrit is the nasal pressure at which airflow is zero, which in this case is −29 cmH2O. VImax varies in proportion to the level of PN applied, but does not correlate with esophageal pressure. This is consistent with the Starling resistor model, which states that, under conditions of flow limitation, airflow is proportional to the upstream (nasal) pressure, and is independent of the downstream (esophageal) pressure. Source: From Ref. 102.

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the three-dimensional effect of contiguous related structures (i.e., the bones, muscles, and other tissues comprising or surrounding the upper airway). Upper airway anatomy and structure is discussed in Chapter 6, and will not be discussed further here. Neuromotor factors affecting the upper airway include abnormalities of the central nervous system ventilatory drive, as well as local upper airway muscle tone, sensation and reflexes. Techniques that have been used to partition the structural versus neuromotor components of upper airway collapsibility in humans during sleep are described below [see “Subatmospheric (Negative) Pressure” section]. Measurements in the Static Upper Airway A different model has been used to characterize the static airway. This model, pioneered by Isono et al. (30), is used when the subject is under anesthesia and pharmacological muscle paralysis. Nasal pressure is applied (using CPAP or negative pressure) and the resultant upper airway area is assessed via bronchoscopy. By altering nasal pressure, pressure-area flow curves can be established. In this model of the atonic airway, the closing pressure (i.e., the nasal pressure at which the airway is collapsed) is determined by structural factors alone. Limitations of Studies on Upper Airway Collapsibility As it is difficult to evaluate baseline upper airway neuromotor tone, most studies have focused on evaluating the pharyngeal response to provocative stimuli such as hypoxemia, hypercapnia and subatmospheric (negative) pressure. Although many studies used genioglossal electromyographic (EMG) measurements, it should be kept in mind that EMG activity does not necessarily reflect the functional activity of the muscle. Virtually all the studies discussed below were conducted during non-REM (rapid eye movement) sleep, as the lower arousal threshold during REM sleep makes these studies very difficult to perform. Determinants of Upper Airway Neuromotor Tone The functions of the pharynx are governed by more than 30 pairs of muscles, which together modulate pharyngeal patency. Hence, it is not surprising that pharyngeal patency is affected by changes in muscle tone. Previous studies have shown that, when upper airway muscle function is decreased or absent, for example, in postmortem preparations (31) or under anesthesia (30), the airway is prone to collapse. Conversely, stimulation of the upper airway muscles with electrical stimulation results in decreased collapsibility (32). These studies confirm that the tendency of the upper airway to collapse is inversely related to the level of activity of the upper airway dilator muscles (Fig. 4). Factors modulating pharyngeal tone include sleep state, chemoreceptor afferents (33), upper airway pressure, and flow receptors (34), posture and neck position (35) and changes in lung volume (36). Sleep State Upper airway muscle tone decreases with sleep onset, resulting in increased upper airway collapsibility. Mezzanotte et al. demonstrated that adult patients with OSA compensated for their narrow upper airway during wakefulness by increasing their upper airway EMG activity (37). This compensatory mechanism was lost during sleep (38). It is unclear whether patients with OSA have a relatively greater decrement in upper airway tone at sleep onset compared to normals and therefore

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Closing pressure (cm H2O)

0

-10

-20

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Atonic Hypotonic Activated

-40

Infants

Children

Adults

FIGURE 4 The upper airway closing pressure is shown for infants, children, and adults. Note that the closing pressure in the atonic state was measured using static techniques (changes in airway area in response to changes in nasal pressure) whereas the closing pressure in the hypotonic and activated states was measured using dynamic techniques (changes in airflow area in response to changes in nasal pressure). Hypotonic measurements could not be obtained in infants. In the atonic state, the prepubertal child has a less collapsible upper airflow than either infants or adults, whereas in the dynamic state, both infants and children have a less collapsible upper airway than adults. Source: From Refs. 57, 83, 96, 97, and 104.

develop obstructive apnea, or whether they have a “normal” decrement in tone at sleep onset but an increased structural load, and therefore develop upper airway obstruction. In children, one study showed that children with OSA had greater genioglossal EMG activity (expressed as percentage of maximal awake activity) during wakefulness than controls, and a greater decline in EMG activity during sleep onset (39). These EMG studies have been corroborated by direct measurements of upper airway collapse. Malhotra et al. (40) used brief pulses of negative pressure to show that normal subjects had a less collapsible upper airway awake than asleep. Suratt et al. (41) measured upper airway collapsibility during wakefulness in adults with OSA compared to controls. Ten of 12 subjects with OSA had upper airway collapse when breathing at subatmospheric pressures. This response was variable, with subjects showing airway collapse at different pressures during different trials. The mean lowest mask pressure producing collapse in subjects with OSA was –22 ± 6 cmH2O, whereas in some trials collapse did not occur at pressures as low as −51 ± 24 cmH2O. In contrast, only two of the 12 controls had airway collapse at any pressure level. Thus, the airway closing pressure during wakefulness is much lower than during sleep. Similarly, children with OSA have been shown to have a much less collapsible airway awake than asleep (42). During REM sleep, there is a further decrease in upper airway tone compared to non-REM (NREM) sleep, predisposing subjects to obstructive apnea. Indeed, in

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children, more than half of obstructive apneas occur during REM sleep (43), whereas in adults the REM AHI is about the same as the NREM AHI (44). Surprisingly, however, studies have not shown a difference in either Pcrit or retroglossal compliance between stage 2, slow wave and REM sleep (45–47). Sleep fragmentation, induced in normal subjects using acoustic stimuli, results in a higher Pcrit (48). This has been attributed to the inhibitory effect of sleep deprivation/fragmentation on ventilatory drive (49), although more recent studies suggest that in fact sleep deprivation may not inhibit the ventilatory drive (50). Ventilatory Drive The drive to the upper airway muscles is affected by the overall central nervous system ventilatory response to respiratory stimuli. In one small study, the occlusion pressure in 100 milliseconds (P0.1) correlated with the slope of the pressure-flow curve during sleep, although there was no correlation between P0.1 and slope during wakefulness (46). As the upper airway muscles are accessory muscles of respiration, they respond to changes in oxygen and carbon dioxide levels. This can be most readily appreciated in the superficial alae nasi muscles. Thus, patients with hypoxemia and respiratory distress will have flaring of the nostrils. In an animal model, hypoxia and hypercapnia have been shown to increase both hypoglossal and recurrent laryngeal nerve activity. Whereas phrenic and recurrent laryngeal nerve activity increase linearly with increased chemical drive, the response of the hypoglossal nerve is curvilinear, that is, increasing more with greater levels of chemical drive (33). Similarly, another study used EMGs to show that diaphragmatic EMG increased linearly as arterial oxygen saturation fell, whereas the genioglossal EMG increased only when the saturation fell below a certain threshold (51). Exogenous CO2 results in increased phasic nasae ali and genioglossal EMG, increased airflow and a lower Pcrit in an animal model with an isolated upper airway (52). These studies show that chemosensitivity causes upper airway neuromotor activation, resulting in decreased upper airway collapsibility. In humans, the upper airway response to hypoxemia has been studied in both healthy adults and adults with OSA. During wakefulness, normal subjects demonstrate an increase in genioglossal EMG when exposed to hypoxemia (53), whereas subjects with OSA demonstrate a decrease (54). Similar studies have not been performed in children, although one case was reported where a child with OSA developed upper airway obstruction during wakefulness while undergoing hypoxic ventilatory response testing (55). In normal adults, there is a brisk upper airway response to exogenous CO2 during wakefulness (56), but little response during sleep (57,58). The response during sleep is greater when hypercapnia is combined with an inspiratory resistive load (59). In contrast to adults, normal children have a brisk upper airway response to CO2 during sleep (57). Subatmospheric (Negative) Pressure Continuous positive airway pressure (CPAP) results in decreased upper airway neuromotor tone (60), whereas subatmospheric pressure application results in increased upper airway neuromotor tone. The pharyngeal response to subatmospheric pressure is thought to be a centrally-mediated reflex, as suggested by (i) the rapid timing of the response in comparison to voluntary activation (61); (ii) functional magnetic resonance imaging studies showing activation of central nervous system centers in response to upper airway loading (62); (iii) changes in the response

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to loading in sleep compared to wakefulness (63,64); and (iv) the fact that the upper airway respose to disparate stimuli such as hypercapnia and inspiratory loading is similar (37). The upper airway contains pressure receptors in the mucosa of the nasopharynx and larynx (the presence of receptors in the oropharynx has not been clearly demonstrated) (65). Negative pressure receptors appear to be the same as those for CO2 (66), although they are separate from receptors for other stimuli such as cold, airflow, and irritants (65). Afferent stimuli are conducted along the trigeminal, glossopharyngeal and vagal nerves (67). Central pathways have not been clearly delineated, but are thought to involve the locus coeruleus, caudal raphe, mesopontine tegmentum, and medullary reticular formation (68). Stimulation of these subatmospheric pressure receptors result in both inspiratory and expiratory activation of numerous upper airway dilator muscles (67). In normal adults, the upper airway is activated in response to applied subatmospheric pressure during wakefulness but little response is observed during NREM sleep using most techniques (57,63,69). In some studies, the application of a brief pulse of significantly negative pressure did result in genioglossal EMG activation during sleep. In contrast to adult studies, normal children have a vigorous response to subatmospheric pressure during NREM sleep (57). REM sleep has not been well evaluated. One study tested the response to subatmospheric pressure during REM sleep in adults, and found that the genioglossal EMG actually decreased compared to baseline in response to the pressure challenge (70). The airway response to changes in applied nasal pressure is not instantaneous, but typically takes three breaths to develop in both adults and children (71–73). The timing of the response is dependent on the degree of pressure change, and there is considerable heterogeneity in the response among different individuals (73). In infants, the response often occurs in the first one to two breaths (74,75). The timing of the response to changes in nasal pressure has been used to develop techniques to evaluate the collapsibility of the activated versus the hypotonic airway. Activated pressure-flow curves can be developed by decreasing the nasal pressure decrementally in a stepwise fashion, thereby activating upper airway muscles (Fig. 5). Hypotonic curves are performed by applying a positive nasal holding pressure (thereby inhibiting upper airway muscle tone), and dropping the nasal pressure to lower levels for only three breaths at a time (Fig. 5). These techniques have been used to understand the effect of upper airway reflexes during sleep (Fig. 4). It should be noted that the hypotonic technique does not result in a totally atonic airway, as shown by the differences in closing pressure/Pcrit using the hypotonic technique in naturally sleeping subjects compared to measurements in paralyzed subjects. Activated PN

Hypotonic PN TIME

FIGURE 5 The techniques for obtaining pressure-flow curves for the activated and hypotonic airway are shown. See text for details. Abbreviation: PN, nasal pressure. Source: From Ref. 57.

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Lung Volume Sleep is associated with a decrease in lung volumes (76). Decreased lung volume results in decreased upper airway size, and vice versa (77). This is probably due in part to mechanical interaction: increased lung volume results in caudal displacement and hence stretching and stiffening of the trachea, which decreases upper airway collapsibility (35). Decreased lung volume has been shown to result in increased pharyngeal resistance; this may be one mechanism for the increase in upper airway resistance during sleep. Although upper airway muscle activation (as demonstrated by genioglossal EMG) increases with low lung volumes, this compensatory mechanism is not sufficient to offset the increase in upper airway collapsibility (36). Posture and Neck Position Neck hyperextension results in increased airway cross-sectional area (78), airflow and decreased Pcrit by stretching the airway (35); the converse occurs with neck flexion. An open mouth further increases upper airway collapsibility (78), whereas advancing the mandible using a jaw thrust maneuver stretches the airway, resulting in an increased airway cross-sectional area for a given upper airway pressure (79). Clinically, intraoral appliances that advance the mandible have been used to treat OSA. In the anesthetized, paralyzed adult with an atonic airway, the airway is more collapsible in the supine position than in the prone position (80). The same finding has been shown for supine versus lateral positioning during natural sleep (45,81), even though genioglossal EMG activity is greater in the supine position (81). It is thought that the increased upper airway collapsibility in the supine position is due to prolapse of the base of the tongue. The disparity between the EMG activity and upper airway collapsibility may be due to an ineffective genioglossal compensatory mechanism for the anatomical airway narrowing. In children with OSA, breathing is actually better in the supine than the prone position (82). This may be because, in children, obstruction usually occurs at the levels of the adenoids or soft palate rather than the tongue (83). In anesthetized, paralyzed infants, the airway is more collapsible in the prone position than in the supine position (84). In these infants, rotation of the neck also increased upper airway collapsibility. It has been postulated that the increased collapsibility in the prone position is due to pressure on the jaw in the supine position, pushing the mandible posteriorly. This change in airway collapsibility in the prone position may partially explain why infants are at greater risk of sudden infant death syndrome when sleeping prone. Gender OSA is more common in males than females. Possible reasons for this include differences in airway anatomy and subtle differences in ventilatory control between the sexes. During wakefulness, there are no differences in upper airway collapsibility, pharyngeal resistance or EMG responses to subatmospheric pressure between males and females (85). Rowley et al. (86) showed that females had a less compliant upper airway than males during sleep, but this difference disappeared when corrected for differences in size. A small study found no difference in upper airway collapsibility during sleep between males and females (46). Thus, the different prevalence of OSA between males and females does not appear to be due to differences in upper airway collapsibility.

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Airway Length A study used computer modeling to demonstrate that longer airways are more collapsible than shorter airways (85). This may partially explain the paradox that children have narrower airways than adults, and females have narrower airways than males, yet their airways are less collapsible, respectively. Upper Airway Sensation The afferent (sensory) loop of the upper airway negative pressure reflex also plays a role in promoting airway stability. This has been shown in experiments where topical lidocaine was applied to the upper airway. During wakefulness, topical nasopharyngeal anesthesia results in increased upper airway collapsibility in both children (87) and adults (88). Similarly, during sleep, the application of topical nasopharyngeal anesthesia in adults results in increased upper airway collapsibility, leading to obstructive apnea (89–92). The resultant worsening of apnea appears to be due at least in part to changes in muscle tone (93), but also to blunting of the arousal response (89,90). Upper airway sensation has been shown to be abnormal in patients with OSA. A number of studies have shown decreased pharyngeal sensation in response to stimuli such as temperature, touch and vibration (94–96). It is speculated that this is due to sensory nerve damage secondary to vibrational trauma from years of snoring (97), but it is also possible that these patients have a primary defect in upper airway sensation, resulting in a more collapsible upper airway. This blunted sensation could, in turn, lead to increased upper airway collapsibility. Development Upper airway collapsibility changes with age. OSA is less common in children than in adults, and is most prevalent in the elderly. This is surprising, as children have a narrow airway and therefore would be expected to have increased upper airway collapsibility. Studies of upper airway collapsibility have been obtained in subjects ranging from infancy to middle age. To the best of our knowledge, upper airway collapsibility has not been studied directly in the elderly. Studies of the atonic airway in normal, paralyzed subjects show that the upper airway closing pressure is lower in children (−7 ± 5 cmH2O) (83) than adults (−4 ± 3 cmH2O) (98); infants had a similar closing pressure to adults (−4 ± 3 cmH2O) (99) (Fig. 4). This differed from studies of upper airway collapsibility during natural sleep using the activated technique, which showed that the slope of the activated pressure-flow curve increased linearly with age from infancy to mid-adulthood, with a correlation coefficient of 0.75 (p < 0.001) (100). Thus, under conditions of normal neuromuscular activity, infants have a less collapsible airway than adults. There are several explanations for these findings. Infants and children maintain brisk upper airway reflexes to subatmospheric pressure and CO2 during sleep, in contrast to adults (Fig. 6) (57,74,75). Children also have a higher ventilatory drive than adults (101). Thus, normal children compensate for their narrower upper airway by increasing upper airway tone during sleep. In addition, it is possible that the shorter length of the airway in the child helps maintain airway patency. It is also possible that other structural factors, such as differences in upper airway wall stiffness, play a role. Upper Airway Collapsibility in OSA Pcrit is positive in patients with OSA, suggesting that their upper airway will collapse during normal, atmospheric breathing (26,102). One study found a mean

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10

FIGURE 6 An example of pressure-flow measurements obtained during sleep using the activated (filled circles) versus hypotonic (open circles) techniques in a normal child is shown. The intermittent technique results . in a more collapsible upper airway, with a steeper slope and a higher Pcrit. Abbreviations: V Imax, maximal inspiratory airflow; PN, nasal pressure. Source: From Ref. 57.

Pcrit of 3 ± 2 cm H2O in patients with obstructive apnea (102). Pcrit then progressively decreases to more and more negative (subatmospheric) levels in patients with lessening degrees of upper airway obstruction, consistent with these patients having only partial obstruction (Fig. 7). Thus, the mean Pcrit is −2 ± 1 cm H2O for patients with primarily hypopneas (102), −4 ± 2 cmH2O for patients with upper airway resistance syndrome (103), –7 ± 3 cmH2O for primary snorers (102), and −15 ± 6 cmH2O for normals (103). In children, a similar spectrum is seen, although the airway tends to be less collapsible at all levels of disease compared to adults. Thus, children with 10

Pcrit (cm H2O)

5 0 –5 –10 –15 –20 –25 Nonsnorer UARS

PS

Hypopneas OSA

FIGURE 7 Pcrit increases progressively with the degree of upper airway obstruction during sleep. Abbreviations: OSA, obstructive sleep apnea; PS, primary snoring; UARS, upper airway resistance syndrome. Source: From Refs. 100, 101.

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OSA have a Pcrit in the positive range (1 ± 3 cmH2O), and children with primary snoring have a very negative Pcrit (−20 ± 9 cmH2O) (104). In normal children, the airway is so resistant to collapse that Pcrit often cannot be measured (46). In children with OSA, upper airway reflex responses to subatmospheric pressure and CO2 are blunted compared to age-matched controls (42). It is not known whether this is a primary cause of OSA, or whether it is a secondary phenomenon resulting from habituation to chronic hypoxemia, hypercapnia, or sleep disruption. CONCLUSIONS Unique attributes of the human upper airway render it susceptible to collapse. This tendency to collapse is countered during wakefulness by neuromotor mechanisms, such as reflex muscle activation in response to stimuli such as subatmospheric pressure and hypercapnia. However, these mechanisms are diminished or absent during sleep. In some individuals, the degree of upper airway collapsibility during sleep is severe enough to result in OSA. Procedures are available that allow upper airway collapsibility to be measured in humans during natural sleep, facilitating research. Further research is needed to evaluate the effects of modifying factors on upper airway collapsibility. Eventually, this may lead to future treatment options for OSA. REFERENCES 1. Hendricks JC, Kline LR, Kovalski RJ, et al. The English bulldog: a natural model of sleep-disordered breathing. J Appl Physiol 1987; 63:1344–1350. 2. Praud JP, Reix P. Upper airways and neonatal respiration. Respir Physiol Neurobiol 2005; 149:131–141. 3. Moore KL. The developing human. Philadelphia: W.B. Saunders, 2006. 4. Davidson TM. The great leap forward: the anatomic basis for the acquisition of speech and obstructive sleep apnea. Sleep Med 2003; 4:185–194. 5. Westhorpe RN. The position of the larynx in children and its relationship to the ease of intubation. Anaesth Intensive Care 1987; 15:384–388. 6. Paydarfar D, Gilbert RJ, Poppel CS, et al. Respiratory phase resetting and airflow changes induced by swallowing in humans. J Physiol 1995; 483(pt 1):273–288. 7. Rappai M, Collop N, Kemp S, et al. The nose and sleep-disordered breathing: what we know and what we do not know. Chest 2003; 124:2309–2323. 8. Ferris BG, Mead J, Opie LH. Partitioning of respiratory flow resistance in man. J Appl Physiol 1964; 19:653–658. 9. Cole P, Niinimaa V, Mintz S, et al. Work of nasal breathing: measurement of each nostril independently using a split mask. Acta Otolaryngol 1979; 88:148–154. 10. Hasegawa M, Kern EB. The human nasal cycle. Mayo Clin Proc 1977; 52:28–34. 11. van Cauwenberge PB, Deleye L. Nasal cycle in children. Arch Otolaryngol 1984; 110:108–110. 12. Haight JS, Cole P. Unilateral nasal resistance and asymmetrical body pressure. J Otolaryngol Suppl 1986; 16:1–31. 13. Taasan V, Wynne JW, Cassisi N, et al. The effect of nasal packing on sleep-disordered breathing and nocturnal oxygen desaturation. Laryngoscope 1981; 91:1163–1172. 14. McNicholas WT, Tarlo S, Cole P, et al. Obstructive apneas during sleep in patients with seasonal allergic rhinitis. Am Rev Respir Dis 1982; 126:625–628. 15. Young T, Finn L, Palta M. Chronic nasal congestion at night is a risk factor for snoring in a population-based cohort study. Arch Intern Med 2001; 161:1514–1519. 16. Miller MJ, Martin RJ, Carlo WA, et al. Oral breathing in newborn infants. J Pediatr 1985; 107:465–469.

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Upper Airway Pathology John G. Park Mayo Clinic College of Medicine, Rochester, Minnesota, U.S.A.

Teofilo Lee-Chiong National Jewish Medical and Research Center, Denver, Colorado, U.S.A.

INTRODUCTION The upper airway encompasses the region from the nose to the vocal cords. This region is unique due to lack of supporting cartilages or bony structures and its involvement in phonation, swallowing, and respiration. Due to its lack of bony support, this area becomes collapsible during sleep, resulting in snoring and obstructive sleep apnea (OSA). Any alterations due to congenital craniofacial abnormalities, hypertrophy of upper airway structures, or impaired neuromuscular control of the upper airways can result in increased collapsibility, and therefore, higher likelihood of sleep-related breathing disorders (SRBDs). Often, such SRBDs can start early in life. For example, habitual snoring is often present during the first years of life and it is estimated that up to two-thirds are due to adenotonsillar enlargement (1). Peak prevalence of SRBDs in children occurs between the ages of two and eight years, which is coincident with peak size of lymphoid tissues (2). In many patients, there may be more than one specific site of obstruction and they may have more than one mechanism responsible for narrowing of their upper airways. Therefore, the severity of OSA might not correlate with the degree of craniofacial disfigurement. Routine screening for OSA is recommended for patients with craniofacial syndromes and other upper airway abnormalities. Evaluation for subtle forms of anomalies should be undertaken in patients whose OSA persists despite adenotonsillectomy. Periodic reassessment is important as growth-related alterations in craniofacial structures may significantly affect upper airway anatomy and function. Patients with craniofacial syndromes may require more extensive evaluation, preferably by a multidisciplinary team. After the presence and severity of OSA has been established by polysomnography, additional studies (e.g., imaging and nasendoscopy) may be required to assess the anatomic site(s) of upper airway obstruction. Imaging studies may include lateral cephalometric views, computed tomography (CT) or magnetic resonance imaging (MRI) to visualize both key skeletal structures and soft tissues, such as tonsils and adenoids, and their relationships to each other. Nasendoscopy enables visualization of the site(s) of upper airway obstruction and can guide surgical decisions for some patients with craniofacial deformities. Plastic three-dimensional models of the craniofacial skeletal structures using stereolithographic analogs are also available to aid surgical treatment of complicated bony anomalies. Interventions may involve staged orthodontic and surgical procedures. While continuous positive airway pressure (CPAP) therapy alone may be effective, in severe cases where surgery is essential, tracheostomy may be required prior to or between specific surgical procedures. 111

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Here we will review various upper airway abnormalities and craniofacial syndromes that may contribute to OSA. NASAL OBSTRUCTION Acute nasal obstruction in otherwise healthy individuals (without prior history of OSA) increases respiratory disturbance in sleep (3,4). Furthermore, those who subjectively complain of nasal congestion have increased likelihood of snoring and having SRBDs (5,6). Liistro et al. concluded that, among others, crowded oropharynx (high Mallampati score) and nasal obstruction are risk factors for OSA (7). Adenoid hypertrophy is the most common cause of chronic nasal obstruction in young infants and children (8). Correction of nasal septal deviation or adenotonsillectomy has normalized sleep studies in some children (9,10). Turbinate hypertrophy may also be a factor in nasal obstruction and can often be treated by radiofrequency ablation or turbinectomy. In spite of these findings, the impact of nasal obstruction in those with OSA remains inconclusive. There is increasing evidence that nasal obstruction, per se, may not be the primary determinant of OSA. This is in spite of nasal airway resistance accounting for approximately 50% of total respiratory resistance to airflow (11). Regardless, the outcome of treating nasal obstruction on SRBDs has been variable, especially in adults. While medical and surgical treatments resulted in significant improvements in nasal resistance, subjective sleep quality, and sleep architecture, they did not significantly affect the apnea-hypopnea index (AHI) (12,13). Miljeteig et al. measured nasal resistance during wakefulness in 683 patients and found no relationship between presence of unilateral or bilateral nasal obstruction, and severity of OSA (14). Virse and Pirsig suggest that since pharyngeal resistance increases during sleep while nasal resistance remains constant, improvement in nasal resistance alone may not improve OSA unless pharyngeal resistance is also improved (13). Nishimura et al. reviewed 149 adults and children with SDB who underwent a variety of upper airway surgeries and found that only 48% of adults had significant improvement in AHI, while 86% of children experienced similar improvement (10). The apparent discrepancy between the outcomes of upper airway surgery in children versus the adults may, in part, be explained by adaptive mechanisms. Studies on developing rhesus monkeys have shown that predominant oral breathing during development resulted in alterations in upper airway muscle use as well as morphological changes in the mandible (15,16). Similarly, Shintani et al. suggests that, in children, oral breathing due to nasal obstruction may cause craniofacial alterations during development (17). This suggests that upper airway surgery, which normalizes the airflow pattern, has a greater impact when performed early in the development years, and that this impact may wane with delay in treatment. Other factors that may affect the success of upper airway surgery include obesity and upper airway skeletal development. In adults with a body mass index (BMI) < 25, nasal cross-sectional area correlated with the respiratory disturbance index and CPAP pressure requirement (18). In contrast, Schechter et al. concluded that there was no relationship between cross-sectional area of the nasal cavity and CPAP tolerance in his study group of patients whose average BMI was 35 (19). Thus, in nonoverweight patients, nasal obstruction appears to have a greater impact on the development of OSA. Similarly, Series et al. concluded that surgical correction of nasal obstruction was more effective in treating OSA in those without skeletal abnormalities as measured by cephalometrics (20).

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There are several case reports of antrochoanal polyps resulting in OSA (21,22). An antrochoanal polyp is a benign lesion that can arise within the antral mucosa. This polyp can be unilateral or bilateral and it grows by extension into the nasopharynx. Treatment is surgical resection by a combination of endonasal and transoral procedures (21). Rare causes of nasal obstruction in children include frontonasal encephalocele, glioma, juvenile nasopharyngeal angiofibroma, hemangioma, craniopharyngioma, inverted papillloma, melanotic neuroectodermal tumor, nasopharyngeal carcinoma, non-Hodgkin’s lymphoma, rhabomyosarcoma, and esthesioneuroblastoma (8). Choanal Atresia The choana represents the posterior nasal passage. Choanal atresia is the most common cause of congenital nasal obstruction requiring surgical correction (23). Choanal atresia may be unilateral or bilateral. Bilateral atresia is usually diagnosed at birth due to feeding difficulty. Unilateral disease may be diagnosed later in childhood because of unilateral rhinorrhea or recurrent sinusitis (23). Less common causes of nasal obstruction in pediatric patients include nasal tumors, dermoid cysts, gliomas, and encephaloceles. A CT scan or an MRI is essential in establishing the correct diagnosis, determining the severity of atresia, and aiding surgical planning. The estimated occurrence of choanal atresia is 1 in 5000 to 8000 births (8,24), with a male-to-female ratio of roughly 1:2 (24). Approximately 30% to 50% are thought to be bilateral choanal atresia (24). This disorder can occur as a part of the CHARGE syndrome (a nonrandom clustering of congenital anomalies including coloboma, heart defects, choanal atresia, retarded growth and development, genital hypoplasia, ear anomalies, and deafness) or in isolation (8). Children with bilateral choanal atresia are twice as likely to have other anomalies (25). Therefore, especially in those with bilateral disease, a thorough evaluation is essential as certain associated anomalies, such as congenital heart disease, may result in increased surgical risks. Other reported associated anomalies (other than those already associated with CHARGE syndrome) include craniofacial abnormalities (e.g., Treacher-Collin syndrome and Apert’s syndrome), chromosomal anomalies, and neurological abnormalities (e.g., mental retardation and epilepsy) (24). Diagnosis is suspected when an infant is having respiratory or feeding difficulties or a catheter cannot be passed into the nasopharynx (8). Treatment of unilateral atresia may be delayed, but with bilateral atresia, prompt treatment is often necessary. Initial management may include the use of a McGovern nipple. This is a nipple with a large central opening allowing air movement through the mouth (8). Surgical approach is dependent on patient’s age but endonasal approach of stent placement appears to be the safest and best tolerated, especially in the very young (24). Due to increased incidence of restenosis, careful follow-up is necessary and if it occurs, a more extensive transpalatal approach of incision through the gingiva and resection of the vomer may be required (24,26). LARYNGOMALACIA Anatomically, the larynx is the region between the epiglottis and the cricoid cartilage. Developmental stridor that eventually resolves with age, was recognized as early as 1853 and it was not until 1960 that the term laryngomalacia (LM) was coined (27). Congenital LM is the most common genetic disorder of the larynx and is among the most common causes of stridor in young children (28). The exact pathophysiology

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remains unclear but deficits in either neuromuscular or cartilage support are suspected. Prevalence of LM is as high as 38% of pediatric bronchoscopies (29). Severe congenital LM can result in upper airway obstruction, along with other symptoms, such as cyanotic episodes, feeding difficulties, failure to thrive, developmental delays, cor pulmonale, or even death (28). Definitive diagnosis is made by endoscopy, ideally awake and during quiet breathing (28). Thorough inspection of the airway is required since 27% of those with congenital LM may have additional airway pathology (30) and vocal cord abduction, such as during crying spells, can mask LM. Interestingly, some children may exhibit normal airways during wakefulness but have supraglottic collapse during sleep or exercise, referred to as state-dependent LM (31,32). There are several case reports of congenital or acquired LM resulting in OSA (27,33). Belmont et al. reported central sleep apnea in association with LM (27). Goldberg et al. reviewed 39 cases of pediatric OSA patients who underwent upper endoscopy and found LM in 44% and inspiratory pharyngeal wall collapse in 38% (34). Interestingly, of those with LM and OSA, only 29% had stridor (34). Conversely, Li et al. suggests that acquired LM can be due to OSA and osteogenesis imperfecta (35). Acquired LM can also result from neurologic insult, such as anoxic or traumatic brain injury, but these can have spontaneous improvement concurrent with their neurologic recovery (36–39). Mild LM can be carefully observed as it may be self-limited. Most children with congenital LM are symptom-free by 18 to 24 months of age (40). With severe LM, surgery with some degree of supraglottoplasty is the treatment of choice (28,41,42). OSA in adults with LM can be successfully treated with some variation of supraglottoplasty (43–45). CPAP, while helpful, may not be well-tolerated by some patients (33). In some cases, tracheostomy may be required (33). Due to frequent occurrence of concurrent airway pathology, a thorough preoperative assessment of the airway is essential (41). Interestingly, Cunningham et al. described two children who developed acquired LM after adenotonsillectomy for OSA (40). They and others concluded that such cases of acquired LM may be due to flow limitation secondary to adenotonsillar hypertrophy resulting in increased negative pressure leading to flaccidity of the larynx (40,46). Less common causes of laryngeal obstruction include laryngocele, lymphangioma, or hemangioma, which can be detected by CT or MRI (23). While laryngoceles may not need to excised, lymphangiomas can be difficult to treat and typically require complete excision (23). A related syndrome is DiGeorge syndrome. It was first reported in 1959 as a genetic disorder that involves various organs, most commonly the thyroid and parathyroid glands. Upper airway involvement includes tracheoesophageal fistula, choanal atresia, LM, and tracheomalacia (TM) (47). The incidence is approximately 1 in 5000 births (48). TRACHEOMALACIA AND TRACHEAL STENOSIS TM refers to accentuated collapse of the trachea during various respiratory cycles due to weakening of the supporting cartilages (49,50). Recognized as early as 1930s, TM is the most common congenital disease of the trachea. While the majority of TM results in collapse of the airway during exhalation, extrathoracic TM can result in upper airway obstruction during inspiration (51).

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Primary forms are due to congenital anomalies. In most infants, the disease is self-limiting, but in those with associated connective tissue disease, problems with TM can ultimately lead to death (49). Secondary, or acquired, forms are more common in all age groups and it is usually due to trauma or compression of the airways resulting in weakening of the cartilaginous support. Patients with TM can present with wheezing, feeding difficulties, recurrent dry cough, reflux, anoxic spells, breathholding spells, reflex apnea, or acute lifethreatening events (49,51,52). While the majority of TM should not cause problems with sleep apnea, TM can be associated with several other conditions such as Down, Williams-Campbell, DiGeorge, CHARGE, Hunter’s, and Hurler’s syndromes, subglottic stenosis, LM, or vocal cord paralysis, which can contribute to SRBDs, mainly in children (49,51). The incidence of TM is estimated to be 1 per 1,445 infants (52). Up to 30% of infants undergoing bronchoscopy have TM (49). Together with LM, it is the most common postbronchoscopic diagnosis in neonates (53). There are few case reports in non-English literature that suggests OSA is secondary to tracheal stenosis and one report that suggests OSA causes TM (54–56). In infants, central apnea has also been associated with tracheal stenosis (57). Bronchoscopy is the gold standard for diagnosis (at least 50% narrowing of the tracheal diameter), but CT and MRI scans can be helpful in delineating surrounding structures that may contribute to secondary causes (49). Treatment options include observation (as many infants may improve by two years of age), CPAP, or external or internal splinting of the trachea (49). Children with tracheal stenosis may present with similar symptoms as TM, and diagnosis is also made by endoscopy. Treatment approaches are similar except that vasculopexy may be considered if thoracic vessels are focally compressing the trachea (57). CONGENITAL PHARYNGEAL STENOSIS There are case reports of congenital pharyngeal stenosis and its association with OSA (24). There are three types of congenital pharyngeal stenosis based on associated anomalies. Type 1 is associated with mandibular hypoplasia (e.g., Pierre Robin syndrome); type 2 is associated with mandibular dysostosis; and type 3 is associated with structural abnormalities (24). Kawashiro et al. suggests that uvula splitting results in excellent outcomes in these cases (24). HURLER’S AND HUNTER’S SYNDROMES These two syndromes are forms of mucopolysaccharidosis, which result in accumulation of glycosaminoglycan deposition in various tissues due to absence of essential enzymes. Orliaguet et al. suggests that the prevalence of OSA in various forms of mucopolysaccharidosis is approximately 40% (58). Hurler’s Syndrome Also known as mucopolysaccharidosis I, Hurler’s syndrome was first described by Hurler in 1919 and it is inherited as an autosomal recessive trait (59). Survival is typically less than 10 years, and death is due to cardiac or respiratory failure. While the child may initially appear to be developing normally, as the mucopolysaccharide continues to deposit in various organs, he or she progressively

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deteriorates, both mentally and physically. Clinical manifestations include mental retardation, dwarfism, hepatosplenomegaly, flexion contractures, corneal clouding, and hydrocephalus (59). Diagnosis is based on these clinical findings as well as increased levels of chondroitin sulfate B and heparin sulfate in urine (59). Due to glycosaminoglycan deposition in the structures of the upper airway, patients can develop airway obstructions and OSA. CPAP can be an effective treatment (60,61). Hunter’s Syndrome Also known as mucopolysaccharidosis II, Hunter’s syndrome is an X-linked recessive disorder. It was initially described by Charles Hunter in 1915 (62). Hunter’s syndrome is considered to be one of the rarest forms of mucopolysaccharide disorders, with a prevalence of less than 20 per million births (1 in 132,000 male births) (58,63). Systemic manifestations are due to accumulation of dermatan and heparin sulfates in various tissues resulting in macrocephaly, developmental delay, dysmorphic facies, skeletal abnormalities, joint contractures, hepatosplenomegaly, cardiac vascular disease, and hirsutism (63). Phenotypic expression usually occurs by two to four years of age (64). Children with this syndrome may have macroglossia and a high-arched palate along with tonsillar and adenoid hypertrophy, a reduced nasopharyngeal cavity, and TM, which may predispose them to upper airway obstructions and OSA (58,61–63,65). Depending on the extent of involvement of the central nervous system, cranial nerves, and cardiac system, these children may also develop central sleep apnea (58). There are mild and severe variants with life expectancies of 15 and 50 years, respectively (63). Death is usually due to cardiac or pulmonary failure (62,63). Diagnosis is based on clinical findings, urine and serum iduronate sulfatase analysis, psychomotor tests, and genetic evaluations (63). In both of these syndromes, due to the progressive nature of glycosaminoglycan deposition in tissues, tonsillectomy or uvulopalatopharyngoplasty will have temporary benefits, and patients may do better with CPAP (58,60,61,64). Depending on the extent of airway involvement, they may ultimately require tracheostomy (64). Due to potential extensive involvement of the upper and lower sections of the airway and surrounding structures, careful evaluation of their entire airway, either by endoscopy or by CT, is essential. Treatment includes bone marrow transplant (BMT) for the transfer of normal stem cells that can produce the deficient enzyme (63,66). Although this treatment will not reverse the disease, BMT may prevent further progression. Malone et al. reported on the resolution of OSA following BMT in a patient with Hurler’s syndrome (66). MAXILLARY HYPOPLASIA Maxillary (midface) hypoplasia can be associated with the syndromic craniosynostoses secondary to defects in fibroblast growth factor receptor (FGFR) (e.g., Apert’s syndrome, Crouzon’s syndrome, Pfeiffer’s syndrome, Stickler’s syndrome, AntleyBixler’s syndrome, and Down syndrome) (67). Individuals typically possess a facial profile that is either flattened or scaphoid in shape. Midfacial hypoplasia displaces the palate posteriorly in closer proximity to the posterior wall of the nasopharynx.

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Apert’s, Crouzon’s, and Pfeiffer’s syndromes involve mutations of the FGFR genes and are characterized by craniosynostosis that impairs the anterior–posterior growth of the cranium (68). Crouzon’s Syndrome In Crouzon’s syndrome, abnormally early fusion of the cranial bones leads to a small head (Figs. 1A and 1B). Other features include a small jaw, nasal septal deviation and in some cases, a cleft palate. It can either be inherited as an autosomal dominant disorder or arise as a new mutation. There is no difference in prevalence between genders. Upper airway obstruction occurring as a result from choanal stenosis, maxillary hypoplasia, posterior displacement of the tongue, and lengthened soft palate can give rise to OSA. CPAP is a useful therapy for these patients with OSA (69). Distraction osteogenesis Le Fort III midfacial advancement for patients with severe OSA has been reported (70). Apert’s Syndrome Apert’s syndrome (acrocephalosyndactyly type I) is the result of premature fusion of the cranial bones. Some cases are inherited in an autosomal dominant pattern, although most cases appear to arise as fresh mutations. It can affect both genders. Affected individuals present with a small nose, large jaw, and malformed hands (Figs. 2A and 2B). Breathing difficulties may develop early in life, but resolve by three to four months with growth of the nasal passages. Upper airway abnormalities can give rise to OSA (71). Pfeiffer’s Syndrome Patients with Pfeiffer’s syndrome (acrocephalosyndactyly type V) may develop OSA due to an abnormality of the upper airway, specifically involving the pharyngeal

FIGURE 1 (See color insert.) (A) Profile of a patient with Crouzon’s syndrome, characterized by a small head, flattened facial profile, and maxillary hypoplasia. The image of the patient has been significantly altered to protect the patient’s identity. (B) Skull radiograph of the same patient with Crouzon’s syndrome. Note the maxillary and midfacial hypoplasia that displaces the palate posteriorly.

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FIGURE 2 (See color insert.) (A) Profile of a patient with Apert’s syndrome, characterized by maxillary and midfacial hypoplasia that displaces the palate posteriorly. The image of the patient has been significantly altered to protect the patient’s identity. (B) Photograph of the mouth of the same patient with Apert’s syndrome. Note the cleft palate and the maxillary malformation that may lead to upper airway narrowing.

area (71). Sleep apnea can also be secondary to a tracheal cartilaginous sleeve, a congenital cartilage malformation (72). Antley-Bixler’s Syndrome Antley-Bixler’s syndrome is characterized by craniosynostosis, frontal bossing, midfacial hypoplasia, choanal atresia or stenosis, and depression of the nasal bridge. Although most cases are sporadic, some cases present as an autosomal recessive disorder. Periods of apnea have been described among patients with Antley-Bixler’s syndrome. Choanal stenting can reduce airway obstruction secondary to choanal atresia/stenosis (73). Stickler’s Syndrome Stickler’s syndrome is an autosomal dominant connective tissue disorder with ophthalmologic (e.g., myopia, cataracts, glaucoma, retinal detachment, and vitreoretinal degeneration) and nonophthalmologic features (e.g., midfacial hypoplasia, cleft palate, and dysplastic bony abnormalities) (74,75). Down Syndrome Down syndrome (Trisomy 21) is caused, in most cases, by an extra chromosome at the 21 position. Incidence increases with advanced maternal age. Both genders can be affected. Characteristic craniofacial features include a small head, maxillary hypoplasia, small nose, and a short neck, all of which increases the risk for developing OSA. An enlarged tongue is typical. Affected individuals tend to be hypotonic. In addition to OSA, patients may present with central apneas, hypoventilation, and hypoxemia (76). Improvements in OSA indices have been reported following CPAP

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therapy or upper airway surgery (e.g., adenotonsillectomy, uvulopalatopharyngoplasty, tongue reduction, or maxillary advancement) (77–79). CLEFT PALATE REPAIR Individuals with cleft palate have a significant narrowing of the anterior–posterior dimension of the pharynx, a lower hyoid position, and a longer uvula (80). Clinically significant OSA can develop following repair of cleft palate using flap pharyngoplasty, sphincter pharyngoplasty, or velopharyngeal ring ligation procedure (81). Sphincter pharyngoplasty, by creating a dynamic sphincter, reduces the velopharyngeal size and may cause upper airway obstruction during sleep (82,83). OSA can occur in the immediate postoperative period, and may, in some cases, resolve spontaneously within several months (84,85). MANDIBULAR HYPOPLASIA Individuals with mandibular hypoplasia develop OSA as a result of the posterior displacement of the base of the tongue in closer proximity to the posterior pharynx. Pierre Robin Syndrome Features of Pierre Robin syndrome includes a small jaw and retro-positioned tongue and soft palate. Mandibular hypoplasia in utero displaces the tongue posteriorly, which in turn, impairs the midline closure of the palate (86). A cleft palate is present in many cases. Pierre Robin syndrome affects both genders equally. Breathing difficulties that develop during the early days of life improve significantly during the first two years of life with growth of the lower jaw in relation to the cranial dimensions. Nonetheless, persistent micrognathia can increase the risk of snoring and OSA in later life, and serial polysomnography is recommended (87,88). Management of OSA includes nasal CPAP and surgical intervention including adenotonsillectomy and, in severe cases, tracheostomy (89). Careful follow-up after cleft palate repair is required as this may give rise to worsening of upper airway obstruction (90). Treacher Collins Syndrome Treacher Collins syndrome is an inherited autosomal dominant disorder characterized by an underdeveloped mandible, receding chin, and malar hypoplasia. It is caused by mutations in the Treacher Collins-Franceschetti syndrome 1 (TCOF1) gene coding for the Treacle protein (91,92). Cases of OSA in patients with Treacher Collins syndrome that resolved with mandibular advancement surgery have been described (93,94). Goldenhar’s Syndrome In Goldenhar’s syndrome, abnormalities of the upper airway can lead to upper airway obstruction (95). Affected patients may also have vertebral abnormalities, dermal cysts, and auricular malformations. The disorder can either be inherited as an autosomal dominant or recessive disorder or occur sporadically. Men are affected more commonly than women (96).

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TONGUE ENLARGEMENT Beckwith-Wiedeman Syndrome Individuals with Beckwith-Wiedeman syndrome have unusually large tongues, small noses, umbilical abnormalities (e.g., hernia or omphalocele), and renal/ adrenal tumors. Beckwith-Wiedeman syndrome affects both genders. CONCLUSIONS Various functional and anatomical pathology of the upper airway can result in SRBDs. With numerous inherited disorders resulting in upper airway pathology, when confronted with phenotypical characteristics, the physician must remain diligent in assessing for SRBDs. Routine screening for OSA in those with craniofacial syndromes is recommended and in certain syndromes, extensive evaluation with endoscopy may be necessary. In some patients, further growth of the upper airway may alleviate SRBDs. In most others, some treatment is necessary. While tonsillectomy may be efficacious, in many patients the site of obstruction may be multiple such that careful follow-up is usually required. Otherwise, CPAP therapy is effective, although tracheostomy or extensive upper airway/craniofacial surgery is required in some patients. Due to growth-related anatomical changes or further changes inherent in certain syndromes (e.g., restenosis, mucopolysaccharidosis, etc.), periodic reassessment is essential in the care of these patients. REFERENCES 1. Gryczynska D, Powajbo K, Zakrzewska A. The influence of tonsillectomy on obstructive sleep apnea children with malocclusion. Int J Pediatr Otorhinolaryngol 1995; (suppl 32): S225–S228. 2. Kennedy JD, Waters KA. Investigation and treatment of upper-airway obstruction: childhood sleep disorders I (erratum appears in Med J Aust 2005; 182(9):471). Med J Aust 2005; 182(8):419–423. 3. Zwillich CW, Pickett C, Hanson FN, Weil JV. Disturbed sleep and prolonged apnea during nasal obstruction in normal men. Am Rev Respir Dis 1981; 124(2):158–160. 4. Suratt PM, Turner BL, Wilhoit SC. Effect of intranasal obstruction on breathing during sleep. Chest 1986; 90(3):324–329. 5. Young T, Finn L, Kim H. Nasal obstruction as a risk factor for sleep-disordered breathing. The University of Wisconsin Sleep and Respiratory Research Group. J Allergy Clin Immunol 1997; 99(2):S757–S762. 6. Young T, Finn L, Palta M. Chronic nasal congestion at night is a risk factor for snoring in a population-based cohort study. Arch Intern Med 2001; 161(12):1514–1519. 7. Liistro G, Rombaux P, Belge C, Dury M, Aubert G, Rodenstein DO. High Mallampati score and nasal obstruction are associated risk factors for obstructive sleep apnoea. Eur Respir J 2003; 21(2):248–252. 8. Handley GH, Reilly JS. Nasal obstruction in children. Otolaryngol Clin North Am 1989; 22(2):383–396. 9. Mauer KW, Staats BA, Olsen KD. Upper airway obstruction and disordered nocturnal breathing in children. Mayo Clin Proc 1983; 58(6):349–353. 10. Nishimura T, Morishima N, Hasegawa S, Shibata N, Iwanaga K, Yagisawa M. Effect of surgery on obstructive sleep apnea. Acta Oto-Laryngologica Supplement 1996; 523:231–233. 11. Scherer PW, Hahn, II, Mozell MM. The biophysics of nasal airflow. Otolaryngol Clin North Am 1989; 22(2):265–278. 12. McLean HA, Urton AM, Driver HS, et al. Effect of treating severe nasal obstruction on the severity of obstructive sleep apnoea. Eur Respir J 2005; 25(3):521–527.

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66. Malone BN, Whitley CB, Duvall AJ, et al. Resolution of obstructive sleep apnea in Hurler syndrome after bone marrow transplantation. Int J Pediatr Otorhinolaryngol 1988; 15(1):23–31. 67. Perkins JA, Sie KC, Milczuk H, Richardson MA. Airway management in children with craniofacial anomalies. Cleft Palate Craniofac J 1997; 34(2):135–140. 68. Kaplan LC. Clinical assessment and multispecialty management of Apert syndrome. Clin Plast Surg 1991; 18(2):217–225. 69. Hui S, Wing YK, Kew J, Chan YL, Abdullah V, Fok TF. Obstructive sleep apnea syndrome in a family with Crouzon’s syndrome. Sleep 1998; 21(3):298–303. 70. Uemura T, Hayashi T, Satoh K, et al. A case of improved obstructive sleep apnea by distraction osteogenesis for midface hypoplasia of an infantile Crouzon’s syndrome. J Craniofac Surg 2001; 12(1):73–77. 71. Mixter RC, David DJ, Perloff WH, Green CG, Pauli RM, Popic PM. Obstructive sleep apnea in Apert’s and Pfeiffer’s syndromes: more than a craniofacial abnormality. Plast Reconstr Surg 1990; 86(3):457–463. 72. Noorily MR, Farmer DL, Belenky WM, Philippart AI. Congenital tracheal anomalies in the craniosynostosis syndromes. J Pediatr Surg 1999; 34(6):1036–1039. 73. Hassell S, Butler MG. Antley-Bixler syndrome: report of a patient and review of literature. Clin Genet 1994; 46(5):372–376. 74. Kronwith SD, Quinn G, McDonald DM, et al. Stickler’s syndrome in the Cleft Palate Clinic. J Pediatr Ophthalmol Strabismus 1990; 27(5):265–267. 75. Nielsen CE. Stickler’s syndrome. Acta Ophthalmol (Copenh) 1981; 59(2):286–295. 76. Cooney TP, Thurlbeck WM. Pulmonary hypoplasia in Down’s syndrome. N Engl J Med 1982; 307(19):1170–1173. 77. Marcus CL, Keens TG, Bautista DB, von Pechmann WS, Ward SL. Obstructive sleep apnea in children with Down syndrome. Pediatrics 1991; 88(1):132–139. 78. Strome M. Obstructive sleep apnea in Down syndrome children: a surgical approach. Laryngoscope 1986; 96(12):1340–1342. 79. Donaldson JD, Redmond WM. Surgical management of obstructive sleep apnea in children with Down syndrome. J Otolaryngol 1988; 17(7):398–403. 80. Rose E, Thissen U, Otten JE, Jonas I. Cephalometric assessment of the posterior airway space in patients with cleft palate after palatoplasty. Cleft Palate Craniofac J 2003; 40(5):498–503. 81. Abyholm F, D’Antonio L, Davidson Ward SL, et al. Pharyngeal flap and sphincterplasty for velopharyngeal insufficiency have equal outcome at 1 year postoperatively: results of a randomized trial. Cleft Palate Craniofac J 2005; 42(5):501–511. 82. Saint Raymond C, Bettega G, Deschaux C, et al. Sphincter pharyngoplasty as a treatment of velopharyngeal incompetence in young people: a prospective evaluation of effects on sleep structure and sleep respiratory disturbances. Chest 2004; 125(3):864–871. 83. Wang KT, Wei FC, Zhao HQ, Song L, Wang Y, Chen AW. A serious complicationobstructive sleep apnea hypopnea syndrome after velopharyngeal ring ligation procedure: report of 6 cases. Shanghai Kou Qiang Yi Xue 2005; 14(2):123–126. 84. Orr WC, Levine NS, Buchanan RT. Effect of cleft palate repair and pharyngeal flap surgery on upper airway obstruction during sleep. Plast Reconstr Surg 1987; 80(2): 226–232. 85. Liao YF, Yun C, Huang CS, et al. Longitudinal follow-up of obstructive sleep apnea following Furlow palatoplasty in children with cleft palate: a preliminary report. Cleft Palate Craniofac J 2003; 40(3):269–273. 86. Jones KL, Smith DW. Facial defects as major feature. Smith’s Recognizable Patterns of Human Malformation. Philadelphia: WB Saunders, 1997:234–235. 87. Bull MJ, Givan DC, Sadove AM, Bixler D, Hearn D. Improved outcome in Pierre Robin sequence: effect of multidisciplinary evaluation and management. (see comment). Pediatrics 1990; 86(2):294–301. 88. Freed G, Pearlman MA, Brown AS, Barot LR. Polysomnographic indications for surgical intervention in Pierre Robin sequence: acute airway management and follow-up studies after repair and take-down of tongue-lip adhesion. Cleft Palate J 1988; 25(2):151–155. 89. Deegan PC, McGlone B, McNicholas WT. Treatment of Robin sequence with nasal CPAP. J Laryngol Otol 1995; 109(4):328–330.

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Control of Breathing in Sleep Curtis A. Smith and Jerome A. Dempsey The John Rankin Laboratory of Pulmonary Medicine, Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A.

Steven R. Barczi and Ailiang Xie Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A.

INTRODUCTION It is well known that sleep is associated with unstable breathing in large numbers of otherwise healthy people. Approximately 2% to 3% of children, 2% to 9% of middle aged adults, and up to 15% of adults over age 65 have moderate-to-severe obstructive sleep apnea (OSA) [apnea-hypopnea index (AHI) ≥ 15 events per hour] (1). The definition of clinically-significant sleep apnea is somewhat arbitrary and there is no consensus as to how much sleep apnea should be considered pathological. It is important to note that a clinically-significant AHI in someone with a chronic disease might not be significant in an otherwise healthy individual. That is, there may be an interactive effect of certain chronic diseases with the AHI that can affect mortality (2). While sleep apnea has been associated with significant morbidity, especially cardiovascular disease, cognitive, behavioral and neural deficits, and metabolic disorders (3–5), these data are largely correlative. However, data are accumulating from direct, interventional studies that induce or relieve OSA (6–8) suggesting a link between OSA and hypertension. In addition, animal studies have used intermittent hypoxic exposures to mimic the hypoxemia experienced during OSA and have demonstrated cognitive deficits (9), persistent ventilatory changes (10), and regional changes in brain tyrosine hydroxylase activity (11). Thus, there is growing support for persistent, long-term pathological sequelae of sleep apnea. Given the large number of people that may be affected by sleep apnea, understanding the pathogenesis of sleep apnea/hypopnea is clearly of considerable importance. OSA is a term that is commonly used to describe any apnea/hypopnea encountered in sleep and it implies that obstructive events can be ascribed to anatomical deficits in the upper airway. In this chapter, we will present the view that almost all OSA events also require a deficit or deficits in neural control that are unmasked by sleep. Collectively, these deficits often result in “central” apneas that is, a cessation of respiratory motor output. Thus, in our view, most apneas/hypopneas in sleep are really “mixed” having both an obstructive and central component. We do not mean to suggest that anatomical predisposition to upper airway narrowing or closure is not important. For example, Wellman et al. (12) have shown that critical airway closing pressure in OSA patients correlates well with the AHI. Since a more positive critical airway closing pressure value indicates an increased likelihood of upper airway collapse the interpretation of these findings is that upper airway obstruction is a major, but not necessarily the only, determinant of the AHI. 125

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Further, correlative evidence from a middle-aged population suggests that about two-thirds of the AHI could be accounted for by a combination of upper airway cephalometric dimensions plus body mass index (BMI). The remaining one-third was unexplained and attributed to deficits in ventilatory control (13). This idea is consistent with data from sleep apnea patients who continued to breathe periodically following a chronic tracheostomy that would have eliminated any obstructive component (14). It is also consistent with data demonstrating a link between oscillations in ventilatory drive and airway obstruction in susceptible individuals (15,16). Finally, Younes et al, (17) have shown that patients with severe OSA had higher “loop gains” (Gl, an index of propensity for breathing instability; see detailed discussion below) than patients with moderate OSA despite the presence of continuous positive airway pressure to maintain upper airway patency. The goal of this chapter is to examine critically how sleep influences the neural control of breathing and how these influences relate to apneas and hypopneas during sleep. While we have attempted to make this chapter as comprehensive as space permits, we will emphasize certain key concepts that we think are especially important for understanding this problem, namely: (i) Sleep unmasks a highly sensitive apneic threshold for hypocapnia. (ii) This apneic threshold is not fixed relative to eupneic, sleeping PaCO2 (partial pressure of arterial carbon dioxide) but can vary depending on underlying ventilatory drive, which can be altered by environmental or pathological conditions. (iii) Hypocapnia or hyperventilation during spontaneous eupnea per se during sleep or wakefulness is not a good predictor of the propensity for apnea. (iv) Carotid body chemoreceptors are the primary chemoreceptors responsible for the genesis of apneas. (v) Loop gain as a predictor of propensity for apnea must be used with caution. (vi) Changes in cerebral blood flow reactivity may be very important in determining gain or sensitivity to changing partial pressure of arterial carbon dioxide (PaCO2). (vii) Pulmonary mechanoreflexes may be important, in combination with hypocapnia, in initiating central apneas. HALLMARKS OF SLEEP APNEA In order to provide a common frame of reference for the discussion that follows we first describe the most common features of apneic events in sleep that contains both “central” and obstructive elements. We will discuss the causes of each of these events in detail in subsequent sections; for the present we simply describe the sequence of events to orient the reader. Figure 1 is a polygraphic recording of several apneic episodes from a patient with severe sleep apnea. The precipitating event is typically a transient hyperventilation or “ventilatory overshoot.” This overshoot can result from a number of causes such as a sudden reduction in upper airway resistance, a change in sleep stage, a change in sleep stage plus a reduction in upper airway resistance, or a spontaneous sigh stimulated by a small decrease in lung volume, but the net effect is a transient hypocapnia. The transient hypocapnia is sensed by the chemoreceptors and ventilatory efforts cease, that is, central apnea. After a time, ventilatory efforts resume, generating large sub-atmospheric pressures in the airway in the absence of flow indicating that airway obstruction has occurred. The central and subsequent obstructive apnea constitute a hypoventilation or “ventilatory undershoot.” During this interval PaCO2 will rise and partial pressure of arterial oxygen (PaO2) and pHa will fall (so-called “asphyxic stimuli”). After several such efforts typically a transient arousal occurs and airflow abruptly resumes resulting in a ventilatory overshoot and perpetuation of the cycle. In susceptible individuals this frank periodic breathing can occupy most of the sleep period. While we believe the foregoing is the most typical

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FIGURE 1 A polygraph record from a subject with typical “mixed” sleep apnea. Note the ventilatory overshoot that precedes each central apnea. Airway closure may occur during the central apnea, which manifests as increasing inspiratory efforts (more negative esophageal pressure, Peso) when respiratory motor output resumes. Partway through the obstructed breaths a brief electroencephalographic arousal typically occurs, providing enough drive to the upper airway muscles to restore patency. Airflow abruptly increases at this point initiating another ventilatory overshoot, and the cycle repeats.

scenario of OSA, that is, so-called “mixed apneas,” it is important to recognize that apneas can vary from purely central in origin to purely obstructive. CHANGES IN THE CONTROL OF BREATHING IN SLEEP Reduced Respiratory Motor Output It has been known for many years that the onset of non-rapid eye movement (NREM) sleep results in hypoventilation, that is, decreased ventilation and increased PaCO2 (+2 to +8 torr) and concomitant respiratory acidosis even in healthy individuals (18– 27). It is well known that ventilation tracks metabolic rate closely (28–30), so one would expect a slight reduction in respiratory motor output due to the slight reduction in metabolic rate observed at sleep onset. The decrease in ventilation therefore, is in excess of that required to compensate for the concomitant slight (~8–12%) decrease in metabolic rate (31–35) that is typically observed at sleep onset. We now know that this decreased ventilation results from loss of “wakefulness drives” from suprapontine regions of the brain (36–38) which results in reduced activity in respiratory neurons (39,40) in the ventilatory controller regions of the medulla. The ultimate cause must be reduced drive at the level of the ventilatory rhythm generator(s) located in the pre-Bötzinger complex and/or the retrotrapezoid nucleus/parafacial respiratory group, however the location(s) of the primary pacemaker is controversial (41). While it is not known exactly how these neurons are regulated, Orem and colleagues have shown that neurons in and around the ventral respiratory column that were loosely associated with breathing (low η2 statistic value) markedly reduced their activity in sleep. In contrast, neurons with activity that was tightly coupled with breathing (high η2) only reduced their activity slightly (40,42). Although Orem’s data is correlative it suggests a modulatory role for the low η2 neurons on the activity of the more respiratory-related neurons that are

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FIGURE 2 This figure illustrates how a transient increase in upper airway resistance during sleep can cause hypopnea. Note the sudden reduction in the flow and tidal volume (VT) at the 6th breath associated with an abrupt increase in respiratory resistance (probably due to change in sleep state) and a more negative esophageal pressure. Resistance values listed are measured at the esophageal pressure nadir at peak inspiration. Source: From Ref. 123.

presumably closely linked to rhythm generation. However, regardless of the mechanism, the end result is diminished neural respiratory motor output in NREM sleep. It is intuitive that this decreased activity would result in reduced ventilation, but it is often not appreciated that motor output to the muscles of the upper airway, which control airway patency, is also reduced. Even in healthy individuals this may lead to mildly increased upper airway resistance, snoring, and hypopneas (43,44) (Fig. 2). Airway narrowing is particularly evident during late expiration in the velopharynx although complete closure is rare in healthy individuals (45). However, NREM sleep [rapid eye movement (REM) sleep discussed below] does much more than simply reduce the respiratory motor output to the ventilatory “pump” and upper airway muscles. Sleep has marked effects on neural ventilatory control which, while nonpathological in healthy persons, can be of great significance in persons who are predisposed to apneas/hypopneas by virtue of anatomical deficits and/or deficits in ventilatory control. There are at least three major mechanisms that may be involved: (i) Sleep unmasks a sensitive apneic threshold for hypocapnia (see above). (ii) Control of breathing in sleep depends exclusively on reflex inputs. (iii) Cerebral blood flow is increased and cerebrovascular reactivity is reduced. Sleep Unmasks a Highly Sensitive Apneic Threshold An apneic threshold for hypocapnia is unpredictable and often unobtainable in wakefulness. In NREM sleep however, when the wakefulness stimuli are absent, a sensitive and reproducible apneic threshold for hypocapnia is revealed. By “sensitive” we mean that the partial pressure of carbon dioxide (PCO2) at the apneic point is only a few torr below the sleeping eupneic PaCO2 and it is usually very close to

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FIGURE 3 Polygraph recording from a healthy subject during NREM sleep. At arrows, two augmented tidal volumes (VTS) were supplied via pressure support ventilation. Note that apnea ensued after the second augmented breath (~10 seconds). Abbreviations: EEG, electroencephalogram; PETCO2, end-tidal partial pressure of carbon dioxide; Pm, airway pressure at mouth; SaO2, oxyhemoglobin saturation; VI, inspiratory volume. Source: From Ref. 124.

the waking eupneic PaCO2. This is illustrated in Figure 3, which shows that during sleep the hypocapnia resulting from two artificially augmented breaths drives the PCO2 below the apneic threshold and apnea ensues. How this sensitization comes about is discussed in the next section. Importance of Reflex Inputs—Reduced Gain of Chemoreflexes Central neural NREM sleep effects must also impinge on those portions of the ventilatory controller that subserve the integration of chemoreceptor inputs because it has been well established that the chemosensitivity to hypercapnia and hypoxia is reduced during sleep (26,46,47) although this effect may be less marked in premenopausal women than in men of a similar age (48). This effect is sometimes referred to as “resetting” of the chemoreceptors. Thus, a hypercapnic or hypoxic stimulus occurring during eupnea would be less well compensated during sleep. This can be of particular importance to the production of ventilatory overshoots since a decreased gain would tend to damp overshoots and, all else being equal, would act as a stabilizing influence. Concept of Loop Gain (See Also Chapter 11) Before proceeding, we present a short summary of Gl theory. These concepts are important because they are used in most models of ventilatory control in sleep; they will also serve to facilitate the discussion that follows. The concept of loop gain has provided a number of insights into the genesis of breathing instability. Briefly, theoretical models of periodic breathing (49,50) suggest that a Gl of the ventilatory control system ≥ 1 result in unstable breathing whereas Gl < 1 are stabilizing; the smaller the loop gain the more stable the system. Most models identify three different gains that combine to determine the Gl. These are

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controller gain (Gc), plant gain (Gp), and mixing gain (Gm). Gc is generally taken to . be those factors which determine the ventilatory response to CO2, that is, ΔVi (change in inspiratory flow)/ΔPCO2 (change in partial pressure of carbon dioxide). Gp .is taken to be the change in gas tensions in mixed capillary blood per unit change . in Vi, that is, ΔPCO2/ΔVi. Gm consists of those factors that determine changes in gas tensions at the chemoreceptors for a given change in pulmonary gas tensions (e.g., blood mixing, diffusion characteristics). Hence, Gl = Gc × Gp × Gm. If chemosensitivity is reduced, Gc is reduced and Gl falls (if Gp and Gm did not change) which should stabilize the control system. Until recently, one of the limitations of the loop gain approach has been the reliance on the ventilatory response to hypercapnia to establish Gc. A method for estimating Gl in response to hypocapnia (i.e., below eupnea; probably of greater relevance for the genesis of apneas and hypopneas) is now available, the “tidal volume amplification factor” technique (12,17). Despite this advance and the theoretical usefulness of Gl, Gl alone cannot predict the onset of apnea, that is, the magnitude of the apneic threshold. While the Gl concept works well to predict a propensity for unstable breathing it is less useful in predicting apnea. Khoo (51) has pointed out that Gl is a useful predictor of instability only over the range of linear changes in the ventilatory CO2 response slope. For example, a high Gl could result in a larger postapnea ventilatory overshoot resulting in a greater decrease in PaCO2 thus increasing the likelihood of a subsequent apnea. Once apnea ensues, Gc, and therefore Gl would actually be reduced (i.e., the CO2 ventilatory response slope is zero), thereby predicting a stable breathing pattern even in the face of apnea. In our view, the concept of “CO2 reserve” as discussed in the following sections is more useful in explaining the genesis of apneas during sleep. The CO2 Reserve and Apnea Figure 4 describes the relationship between ventilation and PaCO2 at a given metabolic rate and illustrates the concepts underlying the sensitive apneic threshold for hypocapnia during NREM sleep. Point A represents eupnea during NREM sleep. If ventilation increased to point B, the apneic threshold for CO2 was reached and apnea (zero ventilation) ensues (point C). In this example, the difference in PaCO2 between these points A and C is about three torr; we refer to this difference as the “CO2 Reserve.” The amount of ventilation required to achieve this degree of hypocapnia is represented by the vertical leg of the shaded triangle and the change in PaCO2 by the horizontal leg. The magnitude of the CO2 reserve is the result of two factors: (i) The gain of the ventilatory response to CO2 below eupnea (slope of dashed line in Fig. 4). (ii) The decrease in PaCO2 for a given increase in ventilation or Gp. Figures 5 and 6 illustrate the effects on the CO2 reserve of a pure change in Gp (hyper/hypoventilation) versus a change in the ventilatory response slope below eupnea. If only Gp changes without a change in the gain of the ventilatory response below eupnea it is clear that the CO2 reserve will. widen as alveolar ventilation/min . (Va) increases (decreased Gp) and narrow as Va falls (increased Gp). If only the . gain of the ventilatory response below eupnea changes (no change in eupneic Va) a high gain (steeper slope; arrows on Fig. 6) will result in a narrowed CO2 reserve and a low gain will result in a widened CO2 reserve. Thus, any changes in gain below eupnea and Gp that reduce (“narrow”) the CO2 reserve predispose toward apnea; changes that widen CO2 reserve tend to stabilize breathing.

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FIGURE 4 Schematic of the relationship between ventilation and PaCO2 at a given metabolic rate (solid curved line). Point A represents eupnea during NREM sleep. Increasing ventilation to point B, decreases the CO2 to the apneic threshold and apnea (zero ventilation) ensues (point C). The difference (“CO2 reserve”) in PaCO2 between points A and C is about three torr. The vertical leg of the shaded triangle represents the amount of ventilation required to achieve this degree of hypocapnia; the horizontal leg represents the change in PaCO2. Two factors determine CO2 reserve magnitude: (i) The gain of the ventilatory response to CO2 below eupnea (dashed arrow). (ii) The decrease in PaCO2 for a given increase in ventilation or plant gain, determined by the position along the isomet. abolic line, that is, the degree of hyper- or hypoventilation. Abbreviations: V CO2, carbon dioxide . production/min corrected for standard conditions; VA, alveolar ventilation/min; k, constant; PaCO2, partial pressure of arterial carbon dioxide.

FIGURE 5 The effects on the CO2 reserve of a change in plant gain (hyper/hypoventilation). If only plant gain changes without a change in the gain of the ventilatory response below eupnea, CO2 . reserve will widen . as VA (alveolar ventilation/min) increases (hyperventilation; decreased plant gain) and narrow as V A falls (increased plant gain). Abbreviation: PaCO2, partial pressure of arterial carbon dioxide. Source: From Ref. 124.

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FIGURE 6 The effects of a change in the ventilatory response slope. below eupnea. If only the gain of the ventilatory response below eupnea changes [no change in VA (alveolar ventilation/min)] a narrowed CO2 reserve results from a high gain (steeper slope) and a widened CO2 reserve results from a low gain. Abbreviation: PaCO2, partial pressure of arterial carbon dioxide. Source: From Ref. 124.

The CO2 reserve is typically determined using pressure support ventilation (PSV) to lower the PaCO2 by increasing the tidal volume (Vt) of each breath while allowing the subject to determine breathing frequency. This is a transient test in which a given level of pressure support is applied abruptly during sleep; if clear periodic breathing ensues within the first minute of pressure support ventilation then the end-tidal partial pressure of carbon dioxide (PetCO2) of the breath just preceding the first apneic breath is taken to be the apneic threshold. If no apnea is observed, then the level of pressure support is raised incrementally and the test(s) repeated until apnea is detected. Once an apneic threshold is detected, the CO2 reserve can be determined. The CO2 Reserve Is Labile Figure 7 illustrates how the CO2 reserve can change depending on prevailing ventilation during NREM sleep. This figure is plotted on the same coordinates as Figure 4 and with the same isometabolic line. Relative to control, dopamine . administration decreased .Va and caused an increase in PaCO2, whereas almitrine administration increased Va and caused PaCO2 to decrease. Despite the differences . in Va and PaCO2 the gains of the ventilatory responses below eupnea (arrows) between these three conditions were not significantly different, yet the CO2 reserve was narrowed by dopamine and widened by almitrine. This means that, in these cases, the changes in CO2 reserve were caused solely by changes in Gp required from the new . eupneic position on the isometabolic line. In contrast, acute hypoxia increased Va to virtually the same degree as almitrine and therefore had a similar reduction in Gp. Yet, the CO2 reserve was narrowed relative to control. This occurred because acute hypoxia increased the gain of the ventilatory response slope below eupnea.

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FIGURE 7 Representative data from dogs during NREM sleep illustrating the labile nature of the CO2 reserve. Dopamine administration resulted in hypoventilation compared to control; almitrine administration resulted in hyperventilation. The gains of the ventilatory response below eupnea (arrows) between these three conditions were not significantly different despite the differences in . VA (alveolar ventilation/min) and PaCO2 (partial pressure of arterial carbon dioxide), indicating that the changes in the CO2 reserve were caused solely by changes in GP (plant gain). In contrast, acute hypoxia caused virtually the same degree of hyperventilation as almitrine and therefore had a similar GP, yet the CO2 reserve was narrowed relative to control indicating that the GC (controller gain) below eupnea increased out of proportion to the decrease in GP resulting in a narrowed CO2 reserve. Source: From Refs. 55, 56, 125. Same coordinates and isometabolic line as Figure 4.

Which Chemoreceptors Are Responsible for Apnea? Another important consideration is to determine which chemoreceptors are responsible for apnea. Gl determined with the Vt amplification method is performed against a steady-state background of hyperventilation and hypocapnia (as a result of the proportional assist ventilation) where the influence of the central chemoreceptors would presumably be dominant. However, naturally occurring apneas in sleep are always transient, nonsteady-state events. In our view, the carotid body chemoreceptors are dominant for the genesis of apneas during these transient events. It been shown in anesthetized cats that carotid body chemoreceptors are quite sensitive to CO2 and that this sensitivity can interact with other carotid body chemoreceptor stimuli such as hypoxia (52). Bowes et al. (53) showed in unanesthetized, carotid body-denervated and vagally-blocked dogs that transient hyperventilation per se could result in small but significant prolongations of expiratory time (Te). However, these changes were much smaller than typical apneas, at least in terms of a percentage increase in the presence of an already greatly prolonged Te. Our data from sleeping humans (54) and dogs (55,56) showed that apnea ensued within 10 to 15 seconds of the initiation of pressure support ventilation. When the same dogs were carotid body-denervated and the pressure support ventilation repeated apneas never occurred less than 30 seconds after initiation of pressure support ventilation. It is noteworthy that, for apnea durations comparable to intact dogs, the decrease in PetCO2 required to cause apnea in denervated dogs was twice that required when the dogs were intact. Finally, Bajic et al. (57) have shown clear interaction between carotid body chemoreceptors and pulmonary stretch receptors in many respiratory neurons in the dorsal and ventral respiratory groups of the medulla in anesthetized dogs.

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However, interpretation of data from carotid body denervation studies is not straightforward. The removal of tonic carotid body afferent input has been shown to reduce cytochrome oxidase activity in the pre-Bötzinger complex of developing rats (58) and to reduce the response to focal acidosis in the medullary raphe of unanesthetized goats (59). Moreover, aortic chemoreceptor sensitivity may be upregulated following carotid body denervation (60). Finally, there was a qualitative difference in the response to systemic hypoxia in unanesthetized, carotid body denervated dogs (no change) versus dogs with intact but vascularly isolated carotid bodies maintained normoxic and normocapnic in the face of systemic (and therefore central nervous system) hypoxia (ventilation increased) (61). However, we have shown that dogs with intact but vascularly isolated carotid bodies, maintained normoxic and normocapnic, behaved similarly to carotid body-denervated dogs in that they responded to square-wave changes in fractional concentration of carbon dioxide in inspired gas (FiCO2) about 11 sec slower (30.9 s vs. 19.6 s) than when the carotid bodies were exposed to systemic circulation (62). In this same study we also demonstrated that the carotid bodies contribute to about 40% of the steady-state CO2 responsiveness, again consistent with observations in the denervated animal. Taken together, we think the data show conclusively that carotid body chemoreceptors are essential in the initiation of apnea in response to a single ventilatory overshoot. Once apnea is initiated the picture becomes much more complicated. Over time the buildup of asphyxic stimuli will be sensed centrally; this central CO2 effect could be enhanced by carotid chemoreceptor inputs via a glutamatergic pathway from the commissural nucleus of the tractus solitarius (63). The interaction of these chemoreceptor outputs with a dynamically changing sleep state could have a major influence on the magnitude and timing of subsequent ventilatory overshoots. Thus, we suggest that there is little or no role for the central chemoreceptors in the genesis of central sleep apnea; rather they would contribute to ventilatory overshoots following apneas or hypopneas. While the relative contributions of central and peripheral chemoreceptors to ventilation are unclear during periodic breathing we think that the carotid body chemoreceptors must continue to play a key, even if not exclusive, role in the control of apnea duration and magnitude of ventilatory overshoots (62,64). We have shown that when the vascularly isolated carotid bodies were perfused with hypocapnic or hyperoxic blood, hypoventilation persisted for some time despite significant (+5–7 torr) systemic CO2 retention which must have been reflected at the central chemoreceptors (65). In a similar preparation, Daristotle et al. (66) showed that some awake goats exposed to steady-state carotid body hypocapnia manifested apnea and unstable breathing despite the presence of systemic hypercapnia. Reduced Gain of Mechanoreflexes Inspiratory loads imposed by increases in airway resistance are well compensated in wakefulness; that is, Vt, Vi, Vt/Ti (tidal volume/inspiratory time), and breathing frequency (fb), are maintained near nonloaded values within a breath of the imposition of the load. This is not the case in NREM sleep since it is well known that immediate load compensation is largely lost during sleep (67–71). Typically, with inspiratory resistive or elastic loads, Ti will prolong slightly but respiratory motor output [diaphragm electromyogram (EMG) and a irway occlusion pressure (P0.1)] . are do not change, Vt and Vi fall (72), and PaCO2 rises for a few breaths. Over time ventilation will increase, albeit not to preload levels, and eventually arousal usually

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occurs. This absence of immediate load compensation suggests that those components of the ventilatory controller, active in wakefulness, that integrate mechanoreflexes must somehow be down-regulated by sleep such that acute loads during sleep are “ignored” by the respiratory controller. This permits a ventilatory undershoot in response to added loads and concomitant increase in PaCO2 and decreases in PaO2 and pHa. There also appears to be an important interaction between chemostimulation and load compensation such that increased chemoreceptor drive increases compensatory mechanoreflex responses. This was clearly demonstrated by Wilson et al.,(67) who applied inspiratory loads to subjects breathing either a normoxic or hyperoxic gas mixture. Following application of the load in hyperoxia, the return of ventilation toward preload levels was delayed for several breaths but the hyperoxia minimized chemostimulation resulting from the concomitant CO2 retention. When the load was removed, ventilation fell to preload levels. In contrast, during normoxia the increased ventilatory drive resulting from the elevation in PaCO2 resulted in hyperpnea when the load was removed. Similar postocclusion hyperventilation (following central apnea) has been demonstrated in sleeping dogs (73) and tracheostomized OSA patients (74). Thus, this chemo/mechanical interaction might contribute to the genesis of ventilatory overshoots, apnea, and periodicity. Further, the abrupt decrease in upper airway resistance that occurs at the end of an apneic episode at a time when asphyxic stimuli are elevated could serve to enhance the subsequent relative hyperventilation or ventilatory overshoot. While immediate ventilatory load compensation is compromised in sleep, protective reflexes affecting the upper airway and diaphragm are still present and will attempt to respond to obstruction or resistance. This mechanism is often referred to as the “dual reflex”; it is so called because it causes increased respiratory motor output to the upper airway muscles and it inhibits respiratory motor output to inspiratory pump muscles even to the point of apnea. While the hypopnea or apnea that results is maladaptive in that asphyxia ensues, it does protect against the development of an excessive negative intrathoracic pressure that would lead to even greater upper airway collapse. The dual reflex responds to either steady (obstruction) or oscillatory (snoring) (75) upper airway negative pressure probably by detecting airway distortion by means of receptors deep to the airway mucosa (75–77). In REM sleep, the upper airway activation portion of the dual reflex is lost and only the inhibition of respiratory pump muscles remains. Alterations in Cerebral Blood Flow and Vascular Responsiveness Changes in cerebral blood flow are of relevance to ventilatory control because the relative blood flow to the central chemoreceptors can influence the PCO2 and in their environment at a given metabolic rate. That is, the PCO2 and [H+] (hydrogen ion concentration) in the environment of the chemoreceptors is determined by local metabolic rate and blood flow as well as PaCO2 and [H+]a (arterial hydrogen ion concentration). Thus, cerebral vasodilation will tend to increase blood flow and reduce PCO2 and [H+] in brain tissue and narrow the PCO2 and [H+] differences between arterial blood and brain; cerebral vasoconstriction will increase them. Sleep has significant effects on cerebral blood flow although changes in flow may depend on sleep stage and/or duration of sleep. In healthy subjects, the onset of sleep (stages 1–2) results in an increase in cerebral blood flow as measured by cerebral blood flow velocity (78) whereas studies of deeper sleep or longer sleep durations typically

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FIGURE 8 Dependence of cerebral blood flow as cerebral blood flow velocity (CBFV) on PETCO2 (end-tidal partial pressure of carbon dioxide) in healthy human subjects. Note the increased cerebral blood flow velocity with increasing PETCO2. Abbreviation: MAP, mean arterial pressure. Source: From Ref. 85.

report a slight decrease in cerebral blood flow (79–84). REM sleep, on the other hand, increases cerebral blood flow. It is well known that cerebral blood flow is sensitive to PaCO2 in that increased PaCO2 results in increased cerebral blood flow [(85); Fig. 8]. However, of greater relevance to the control of breathing is the marked reduction in cerebrovascular responsiveness to CO2 at least during stages three to four of NREM sleep (86). In other words, the normal autoregulatory vasoconstriction in response to hypocapnia or vasodilation in response to hypercapnia is markedly attenuated. The net effect of this loss of vascular reactivity is to exaggerate changes in systemic PCO2 and [H+] at the level of the central chemoreceptor (see above). For example, if the normal increase in cerebral blood flow was reduced by sleep, a given hypocapnic insult secondary to a ventilatory overshoot would result in greater drop in PCO2/[H+] in the environment of the central chemoreceptor neurons than would occur during wakefulness.In turn, this greater drop in chemoreceptor stimuli could promote hypopnea or apnea. This effect could be of particular importance in heart failure patients with sleep apnea. Figure 9 illustrates the findings of Xie et al. (87) who showed a strong correlation between congestive heart failure patients with sleep apnea and markedly reduced cerebrovascular responsiveness to CO2. We speculate that in heart failure the central chemoreceptors may have relatively greater influence in the genesis of instability than they do in health because the exaggerated swings in PCO2 and [H+] in their environment could have major influence on Gp.

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FIGURE 9 Responsiveness of cerebral blood flow velocity/partial pressure of carbon dioxide (CBFV/PCO2) in congestive heart failure patients with and without central sleep apnea (CSA). Note that, on average, congestive heart failure patients with CSA had less cerebral blood flow responsiveness than those without CSA, which would be consistent with loss of autoregulation of [H+] in the environment of the central chemoreceptors, that is, greater changes in PCO2/[H+] for a given perturbation in PaCO2 (see text for details). Source: From Ref. 103.

THE SPECIAL PROBLEM OF REM SLEEP REM sleep is a stage of sleep that is characterized by the rapid eye movements that are common in this stage. Although the existence of REM has been known for many years, it is a complex sleep stage and we are only now beginning to understand the neurophysiology of its control. For the purposes of this chapter, REM sleep adds a layer of complexity to the effects of sleep on the control of breathing that have already been described for NREM sleep. This is due to two significant features that are unique to the REM sleep state, namely widespread skeletal muscle atonia and phasic excitation of the ventilatory rhythm generator. Skeletal Muscle Atonia Skeletal muscle atonia in REM sleep, sparing only the diaphragm, has two consequences for control of breathing: (i) Atonia of chest wall muscles results in increased chest wall compliance, and (ii) upper airway muscles are also atonic which eliminates one arm of the dual reflex, that is, the muscles cannot respond to increased motor output in response to airway distortion/collapse (see above). Excitation of Central Rhythm Generator The central rhythm generator is excited in REM. Orem et al. have shown that most, but not all, respiratory medullary neurons of all types were more active in REM sleep than in NREM sleep (88,89). Despite this excitation, ventilatory pattern and timing are interfered with by ongoing phasic REM events that probably originate with activity in the ponto-geniculo-occipital regions of the brain. These the pontogeniculo-occipital waves (90) are manifested externally by the rapid eye movements that characterize this state. The result is that eupneic ventilation in this state is characterized by a highly variable breathing pattern but on average fb is increased, Vt decreased (91), and inspiration is fractionated (i.e., short periods of diaphragm EMG

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silence) (92). The ongoing interference of breathing caused by REM events is one factor that may make breathing during REM largely independent of chemical and mechanical feedback and explain the near absence of central apneas. Xi et al. (93,94) have demonstrated that much greater levels of hypocapnia following airway occlusion or brief hypoxia are required to produce apnea in REM. This reduced and/or erratic ventilatory output during and after such insults is likely due to reduced and fractionated phrenic neural motor output to the diaphragm. Smith et al. (92) have shown in dogs during REM sleep that, in response to comparable durations of induced airway occlusion, diaphragm EMG and the integrated inspiratory tracheal pressure tended to be smaller and more variable in REM than in NREM sleep, probably as a result of the fractionations of diaphragm EMG that occurred in approximately 40% of breathing efforts. In summary, phasic REM events interfere with the development of both ventilatory undershoots and overshoots and thus greatly reduce the probability of apnea or periodic breathing. Although REM events impinging on respiratory motor output give the appearance that chemosensitivity is blunted in this sleep state, Parisi et al. (95) have shown, in sleeping goats, that this may not be the case if cerebral blood flow changes are taken into account. They observed that the CO2 chemosensitivity was unchanged across all sleep states if ventilation was expressed as a function of jugular venous PCO2, a measure that is more representative of the PCO2 in the environment of the central chemoreceptors. They interpreted their findings to mean that the increased cerebral blood flow in REM sleep attenuated the increase in CO2/[H+] at the central chemoreceptor as PaCO2 was increased (see cerebral blood flow discussion above); this would have given the appearance of reduced sensitivity if PaCO2 had been used as the stimulus. SLEEP EFFECTS ON VENTILATORY CONTROL AND SLEEP APNEA IN SPECIAL CIRCUMSTANCES In healthy individuals changes in ventilatory control brought on by sleep are benign or possibly even protective. In individuals with anatomical deficits in the upper airway and/or neural deficits in the ventilatory control system however, these benign physiological effects can lead to apnea/hypopnea. In our view, a significant portion of sleep apnea can be explained by deficits in ventilatory control that are unmasked by sleep. We present four pathophysiological conditions and one normal process (aging) to illustrate and summarize our views. Obstructive Sleep Apnea Much of the pathophysiology of OSA has been touched on in the previous sections. To summarize, we think most episodes of OSA begin with a ventilatory overshoot from whatever cause. These can include a sudden reduction in upper airway resistance (i.e., an increase in ventilation for a given subatmospheric pressure in the airway), a change in sleep stage, a change in sleep stage plus a reduction in upper airway resistance, or a spontaneous sigh stimulated by a small decrease in lung volume. The resulting hypocapnia results in a carotid body-mediated central apnea and, in individuals with anatomical deficits resulting in a more positive critical airway closing pressure, this loss of respiratory motor output results in insufficient drive to upper airway muscles and airway closure occurs. At this point, the apnea becomes an “obstructive” one even in the absence of ventilatory efforts. However,

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as asphyxic stimuli accumulate, rhythmic respiratory motor output resumes but it may be insufficient to reopen the upper airway and a period of frank obstructive apnea ensues (i.e., ventilatory efforts against a closed airway). Finally, a brief electroencephalographic (EEG) arousal provides sufficient additional respiratory motor output from the wakefulness drive to the upper airway to reopen it; the accumulated asphyxic stimuli promote several hyperpneic breaths (ventilatory overshoot) which then drives the CO2 down, another central apnea ensues and the cycle can repeat. Neurodegenerative Disease Clearly, any abnormality in the ventilatory control centers of the brain could potentially impact ventilatory control. Often, such abnormalities first become apparent during sleep. For example, progressive loss of pre-Bötzinger complex neurons by means of substance P-saporin (SP-SAP) injections led to progressively severe disruption of breathing pattern in rats (96); the disruption first became apparent during sleep and subsequently manifested during wakefulness. A similar loss of (presumptive) human pre-Bötzinger complex neurons has been documented in patients with neurodegenerative diseases (97–99). These patients also commonly experience sleep-disordered breathing despite normal breathing during wakefulness. It has been suggested by McKay et al. (96) that the increased prevalence of sleep-disordered breathing in the elderly (100) might be due in part to subtle degeneration in the human equivalent of the pre-Bötzinger complex. Special problems of sleep-disordered breathing in the elderly are discussed in detail below. Congestive Heart Failure Breathing in congestive heart failure is characterized by hyperventilation. Many congestive heart failure patients also have central sleep apneas; these patients do not increase PaCO2 with sleep and have a narrowed CO2 reserve. Thus, the protective effect of hypercapnia is lost, and the gain of the ventilatory response to CO2 below eupnea increased. Despite the slight stabilizing effect of reduced plant gain resulting from the hyperventilation, these patients are highly predisposed to central sleep apneas. We interpret this to mean that there must be a stimulus (or stimuli) associated with congestive heart failure that increases the Gc below eupnea and thereby narrows the CO2 reserve. There are at least three possibilities. It has been demonstrated that carotid body chemoreceptors are sensitized by chronic heart failure (101,102) probably because of the sustained increase in sympathetic nerve activity that occurs in this condition. Such a sensitization by itself could account for an increased gain of the ventilatory response to CO2 below eupnea. Another possibility is that the decrease in cerebral blood flow resulting from the heart failure in combination with the decrease in cerebrovascular responsiveness that occurs in this condition (103) (see above) result in greater swings in CO2/[H+] in the environment of the central chemoreceptors and in effect increase the sensitivity of the central chemoreceptors. A third candidate for the source of this stimulus in congestive heart failure is the elevated pulmonary pressure and associated vascular congestion. We have shown that mimicking the elevated pulmonary pressures with very modest, acute increases in left atrial pressure in sleeping dogs narrows the CO2 reserve (104). These data suggest that receptors in the lung [c-fibers, rapidly-adapting receptors (RARs)?],

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pulmonary vessels, or atria or some combination of these may provide the required stimulus. An unresolved question is how stimuli from these putative receptors can produce an increase in the gain of the ventilatory response to CO2 below eupnea. It is known that many chemoreceptor and mechanoreceptor afferents converge in and around the dorsal and ventral respiratory columns, therefore it seems a reasonable speculation that stimulus interaction at this level manifests as an increased gain of the ventilatory response to CO2 during determination of the apneic threshold. Hypoxia Whether due to high altitude or pathology, acute hypoxia results in hyperventilation accompanied by a narrowed CO2 reserve during sleep, and periodic breathing is frequently observed. So, despite the decreased Gp resulting from the hyperventilation, the gain of the ventilatory response to CO2 below eupnea is increased resulting in a narrowed CO2 reserve relative to normoxic control (Fig. 7). Again, the question is what causes the increased gain of the ventilatory response to CO2 below eupnea? We propose that brain hypoxia itself provides the required stimulus; our reasoning is as follows: (i) Carotid body chemoreceptor stimulation per se does not result in increased Gc below eupnea. This has been demonstrated by contrasting the effects of acute hypoxia with almitrine administration (55) (Fig. 7 and detailed discussion above). Unlike hypoxia and despite an equivalent hyperventilation, almitrine did not narrow the CO2 reserve. Rather, the CO2 reserve was widened relative to air breathing control in proportion to the location of the eupneic almitrine point . on the isometabolic line relating Va to PaCO2. (ii) It has been shown in unanesthetized dogs during NREM sleep and unanesthetized awake goats that specific brain hypoxia (PaO2 35–55 torr, carotid bodies maintained normoxic and normocapnic via exogenous perfusion; systemic blood hypoxic via decreased fractional concentration of oxygen in inspired gas (FiO2) resulted in modest but clear hyperventilation that developed quickly (< 30 sec) (61,105). We interpret this to mean that the central nervous system is capable of sensing hypoxia directly (61) and increasing ventilation in response to this stimulation. It is possible, therefore, that this direct hypoxic stimulation of the central nervous system might contribute to the increased gain of the ventilatory response to CO2 below eupnea. (iii) Short-term potentiation of breathing is a property of the ventilatory control centers in the central nervous system that maintains respiratory motor output for a time after abrupt removal of a ventilatory stimulus, such as might occur following a ventilatory overshoot when PaCO2 is falling and PaO2 rising. Exposure to hypoxia during sleep for longer than about one minute, even if PaCO2 is not allowed to fall, can greatly attenuate shortterm potentiation of breathing (106,107); simultaneous hypocapnia exacerbates this attenuation. If the short-term potentiation is attenuated or absent the apneic threshold would be achieved at a higher PaCO2 and, in effect, increase the gain of the ventilatory response to CO2 and narrow the CO2 reserve. Elderly Aging significantly increases the risk for both obstructive and central sleep apnea, however the mechanisms underlying this predisposition remain unclear. Clinical and community-based epidemiological studies support sleep-disordered breathing prevalence up to 24% to 29% in those over age 65 (108,109) when an apnea-hypopnea index of 15 is used. Sleep apnea in the older population is characterized by reduced symptoms (or underreporting of symptoms) and less severe oxygen desaturations even after correcting for BMI (110). Cross-sectional studies reveal important changes

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in musculoskeletal and central nervous system physiology and anatomy that likely predispose toward age-associated alterations in ventilatory control in sleep. These changes are discussed briefly in the context of the aforementioned control mechanisms. Older adults manifest musculoskeletal changes in the upper airway that result in increased airway collapse in sleep. Certain compensatory mechanoreflex responses appear to be blunted with aging. Specifically upper airway reflexes such as the negative pressure reflex have a diminished response in association with increasing age (111,112). This reflex involves the pharyngeal dilator musculature, plays an important role in maintaining upper airway patency (113,114) and is reduced in older adults in sleep (111). Additionally oropharyngeal anatomical changes in older subjects include a reduced mass of the genioglossus muscle with concomitant decreased generation of isometric pressures (115,116) and increased fat deposition in the parapharyngeal spaces irrespective of BMI (111). All of these factors likely lead to the observed upper airway resistance seen in aging (117). There are important age-related alterations in cerebral blood flow and vascular responsiveness that impact sleep-related breathing control. An age-associated decrease in baseline cerebral blood flow has been demonstrated in a number of cross-sectional neuroimaging studies (118–120). Furthermore vascular compliance declines in the setting of decreased arteriolar wall elastin and smooth muscle and an increase in collagen with aging (121). These findings occur in parallel with a marked increased prevalence of congestive heart failure in older adults. This decrease in vascular responsiveness may exaggerate the changes in systemic PCO2 and [H+] at the level of the central chemoreceptor described previously. It is generally agreed that sleep becomes more fragmented with age with greater numbers of arousals per sleep hour after excluding primary sleep pathology (122). These increased sleep-wake transitions place the older adult at greater risk for recurring ventilatory overshoot and hypocapnia. These events, coupled with the age-associated decrease in the compensatory mechanoreflex response, may serve as the genesis for a loop of perpetuated breathing instability. This putative mechanism may explain the increased numbers of central apneas relative to obstructive events seen in the elderly (110). CONCLUSIONS Understanding the physiological mechanisms underlying sleep apnea will probably have significant clinical importance because of the high prevalence of sleepdisordered breathing in the general population. However, the exact public health burden is unclear because of the paucity of interventional and prospective studies available in the literature that examine mild-to-moderate levels of sleep-disordered breathing both in healthy persons and those with coexisting disease. Sleep predisposes to unstable breathing because motor output to both respiratory pump and upper airway muscles is reduced in sleep, because sleep unmasks a highly sensitive and labile apneic threshold for hypocapnia, and because changing sleep states present changing drives to breathe. The carotid body chemoreceptors appear to be the dominant receptor for hypocapnic inhibition in the time frame observed in naturally occurring sleep apnea and periodic breathing. Carotid body chemoreceptor hypocapnia alone is not sufficient to produce apnea; it appears that both hypocapnia and lung stretch (both of which are present during ventilatory overshoots) are required. Central chemoreceptors likely become more important in

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influencing the magnitude of ventilatory overshoots and/or cycle length once apnea is initiated. The apneic threshold and the difference in PaCO2 between eupnea and the apneic threshold, that is, “CO2 reserve” are highly dependent on both Gp (as determined primarily by eupneic PaCO2) and also by changes in controller gain, or the slope of the PaCO2: ventilation relationship below eupnea. Thus, any type of change in the background stimulus to breathe causing hyperventilation (decreased plant gain and increased CO2 reserve) or hypoventilation (increased Gp and decreased CO2 reserve) will render one less or more susceptible to apnea and instability, whereas conditions such as hypoxia, altered cerebral blood flow regulation, or elevated pulmonary vascular pressures will enhance controller gain and narrow the CO2 reserve. There appears to be a strong link between unstable central ventilatory control and upper airway obstruction, but the consequences of this link for the diagnosis and treatment of sleep-disordered breathing require further investigation. ACKNOWLEDGMENTS This material is based upon work supported in part by the office of Research and Development, Clinical Science R&D Service, Department of Veterans Affairs and by NHLBI/NIH. REFERENCES 1. Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165:1217–1239. 2. Mooe T, Franklin KA, Holmstrom K, et al. Sleep-disordered breathing and coronary artery disease: long-term prognosis. Am J Respir Crit Care Med 2001; 164:1910–1913. 3. Meoli AL, Casey KR, Clark RW, et al. Hypopnea in sleep disordered breathing in adults. Sleep 2001; 24:469–470. 4. Punjabi NM, Polotsky VY. Disorders of glucose metabolism in sleep apnea. J Appl Physiol 2005; 99:1998–2007. 5. Veasey S. Obstructive sleep apnea: wreaking havoc with homeostasis. J Appl Physiol 2005; 99:1634–1635. 6. Brooks D, Horner RL, Kimoff RJ, et al. Effect of obstructive sleep apnea versus sleep fragmentation on responses to airway occlusion. Am J Respir Crit Care Med 1997; 155:1609–1617. 7. Brooks D, Horner RL, Kozar LF, et al. Obstructive sleep apnea as a cause of systemic hypertension. Evidence from a canine model. J Clin Invest 1997; 99:106–109. 8. Becker HF, Jerrentrup A, Ploch T, et al. Effect of Nasal Continuous Positive Airway Pressure Treatment on Blood Pressure in Patients With Obstructive Sleep Apnea Circulation 2003; 107:68–73. 9. Row BW, Liu R, Xu W, et al. Intermittent hypoxia is associated with oxidative stress and spatial learning deficits in the rat. Am J Respir Crit Care Med 2003; 167:1548–1553. 10. Morris KF, Gozal D. Persistent respiratory changes following intermittent hypoxic stimulation in cats and human beings. Respir Physiol Neurobiol 2004; 140:1–8. 11. Gozal E, Shah ZA, Pequignot J.-M, et al. Tyrosine hydroxylase expression and activity in the rat brain: differential regulation after long-term intermittent or sustained hypoxia. J Appl Physiol 2005; 99:642–649. 12. Wellman A, Jordan AS, Malhotra A, et al. Ventilatory control and airway anatomy in obstructive sleep apnea. Am J Respir Crit Care Med 2004; 170:1225–1232. 13. Dempsey JA, Skatrud JB, Jacques AJ, et al. Anatomic determinants of sleep-disordered breathing across the spectrum of clinical and nonclinical male subjects. Chest 2002; 122:1–13. 14. Onal E, Lopata M. Periodic breathing and the pathogenesis of occlusive sleep apnea. Am Rev Respir Dis 1982; 126:676–680. 15. Warner G, Skatrud JB, Dempsey JA. Effect of hypoxia-induced periodic breathing on upper airway obstruction during sleep. J Appl Physiol 1987; 62:2201–2211.

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70. Dempsey JA, Henke KG, Skatrud JB. Regulation of ventilation and respiratory muscle function in NREM sleep. Prog Clin Biol Res 1990; 345:145–154; discussion 154-145. 71. Wiegand L, Zwillich CW, White DP. Sleep and the ventilatory response to resistive loading in normal men. J Appl Physiol 1988; 64:1186–1195. 72. Gora J, Kay A, Colrain IM, et al. Load compensation as a function of state during sleep onset. J Appl Physiol 1998; 84:2123–2131. 73. Chow CM, Xi L, Smith CA, et al. A volume-dependent apneic threshold during NREM sleep in the dog. J Appl Physiol 1994; 76:2315–2325. 74. Iber C, Davies SF, Chapman RC, et al. A possible mechanism for mixed apnea in obstructive sleep apnea. Chest 1986; 89:800–805. 75. Eastwood PR, Satoh M, Curran AK, et al. Inhibition of inspiratory motor output by high-frequency low-pressure oscillations in the upper airway of sleeping dogs. J Physiol 1999; 517:259–271. 76. Harms CA, Zeng YJ, Smith CA, et al. Negative pressure-induced deformation of the upper airway causes central apnea in awake and sleeping dogs. J Appl Physiol 1996; 80:1528–1539. 77. Eastwood PR, Curran AK, Smith CA, et al. Effect of upper airway negative pressure on inspiratory drive during sleep. J Appl Physiol 1998; 84:1063–1075. 78. Kotajima F, Meadows GE, Morrell MJ, et al. Cerebral blood flow changes associated with fluctuations in alpha and theta rhythm during sleep onset in humans. J Physiol 2005; 568:305–313. 79. Townsend RE, Prinz PN, Obrist WD. Human cerebral blood flow during sleep and waking. J Appl Physiol 1973; 35:620–625. 80. Sakai F, Meyer JS, Karacan I, et al. Normal human sleep: regional cerebral hemodynamics. Ann Neurol 1980; 7:471–478. 81. Madsen PL, Schmidt JF, Wildschiodtz G, et al. Cerebral O2 metabolism and cerebral blood flow in humans during deep and rapid-eye-movement sleep. J Appl Physiol 1991; 70:2597–2601. 82. Droste DW, Berger W, Schuler E, et al. Middle cerebral artery blood flow velocity in healthy persons during wakefulness and sleep: a transcranial Doppler study. Sleep 1993; 16:603–609. 83. Hajak G, Klingelhofer J, Schulz-Varszegi M, et al. Relationship between cerebral blood flow velocities and cerebral electrical activity in sleep. Sleep 1994; 17:11–19. 84. Kuboyama T, Hori A, Sato T, et al. Changes in cerebral blood flow velocity in healthy young men during overnight sleep and while awake. Electroencephalogr Clin Neurophysiol 1997; 102:125–131. 85. Przybylowski T, Bangash MF, Reichmuth K, et al. Mechanisms of the cerebrovascular response to apnoea in humans. J Physiol 2003; 548:323–332. 86. Meadows GE, Dunroy HMA, Morrell MJ, et al. Hypercapnic cerebral vascular reactivity is decreased, in humans, during sleep compared with wakefulness. J Appl Physiol 2003; 94:2197–2202. 87. Xie A, Skatrud JB, Khayat R, et al. Cerebrovascular response to carbon dioxide in patients with congestive heart failure. Am J Respir Crit Care Med 2005; 172(3):371–378. 88. Orem J. Medullary respiratory neuron activity: relationship to tonic and phasic REM sleep. J Appl Physiol 1980; 48:54–65. 89. Orem JM, Lovering AT, Vidruk EH. Excitation of medullary respiratory neurons in REM sleep. Sleep 2005; 28:801–807. 90. Dunin-Barkowski WL, Orem JM. Suppression of diaphragmatic activity during spontaneous ponto-geniculo-occipital waves in cat. Sleep 1998; 21:671–675. 91. Gould GA, Gugger M, Molloy J, et al. Breathing pattern and eye movement density during REM sleep in humans. Am Rev Respir Dis 1988; 138:874–877. 92. Smith CA, Henderson KS, Xi L, et al. Neural-mechanical coupling of breathing in REM sleep. J Appl Physiol 1997; 83:1923–1932. 93. Xi L, Chow CM, Smith CA, et al. Effects of REM sleep on the ventilatory response to airway occlusion in the dog. Sleep 1994; 17:674–687. 94. Xi L, Smith CA, Saupe KW, et al. Effects of rapid-eye-movement sleep on the apneic threshold in dogs. J Appl Physiol 1993; 75:1129–1139.

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10

Arousal from Sleep Péter Halász Department of Neurology, National Institute of Psychiatry and Neurology, Budapest, Hungary

INTRODUCTION To speak about arousal in sleep may sound controversial. There is, however, strong evidence that one of the essential features of sleep is the arousability and the presence of abundant arousals, without awakening, with electroencephalographic (EEG), autonomic and behavioral signs (1). The concept of arousal has a long history, which is closely connected with the development of concepts about the neurophysiology of sleep and wakefulness. The criteria and measure of arousal are controversial issues; hence, arousal has several definitions (2–7) and several EEG, behavioral and autonomic aspects. The EEG resultant of arousal has massive impact on the evolution of the sleep profile. Behavioral and autonomic concomitants of arousal may or may not be present at the same time. When they are present, they can be graduated in intensity, while presence or absence of the single components of the arousal constellation depends on the involvement of the specific cerebral compartments. The questions are: which constellations or single signs are sufficient to be accepted as arousal markers? How should we consider specific EEG phasic events characterized by slow waves but still endowed with activating properties? How should we classify the somato-vegetative phenomena not associated with any detectable EEG modification? The overview and reconsideration of this topic seems to be appropriate for several reasons. In the last few years, a considerable pool of data has been accumulated from different sources with heterogeneous approaches and views about the phenomenology of arousals in sleep both under physiological and pathological circumstances. In relation to disordered breathing during sleep, several new autonomic arousal parameters were used and correlated with pathological sleep events, bringing more detailed knowledge about the autonomic components of the respiratory-related arousal. There is an endeavor to categorize and standardize arousals from sleep; however, there are several contradictions and controversial views around this issue. The American Sleep Disorders Association (ASDA) [now American Academy of Sleep Medicine (AASM)] produced a consensus report (8) on the criteria of arousals in sleep in the early nineties. Arousal is defined as a rapid modification in EEG frequency, which can include theta and alpha activity, and/or frequencies higher than 16 Hz but not spindles. It can be accompanied by an increase of electromyographic activity, of cardiac frequency or by body movements. An arousal must be preceded by at least 10 seconds of continuous sleep. According to these rules, slow EEG features such as K-complexes and transient delta activities are not scored as arousals unless these patterns are associated with an EEG frequency shift towards theta, alpha, or beta rhythms. For these reasons, this proposed scoring system for arousals was strongly criticized by the other research groups who have been engaged in the study of the microstructure of sleep for the last 30 years (9). The conceptual basis of the ASDA criteria is that arousal is a marker of sleep disruption, a detrimental and harmful thing, while our research group’s conceptual basis indicates arousals as elements weaved into the texture of sleep taking part in the regulation of the 149

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sleep process (10). Beyond different conceptual approaches, the essence of the controversy is to include or exclude into the concept of arousal those evoked or spontaneous elements of the EEG which are characterized by high voltage slow rhythms and/or K-complexes and spindles instead of the traditional shift towards rapid rhythms and voltage decrement, associated with the same kind of behavioral and autonomic changes typically accepted as arousal signs. From a practical point of view, it is questionable why those elements which are not signs of cortical activation even though they are proved to be reactive EEG patterns are disregarded as arousals or as a prearousal activation although most times they precede the EEG arousal signs (11–14). In effect, the ASDA criteria fails to consider the abrupt appearance of slow sleep elements (K-complexes, delta bursts) as arousals even when they are associated with somato-vegetative modifications identical to those observed during arousals (15–17). WHAT IS A MICRO-AROUSAL AND HOW IS IT RELATED TO THE COURSE OF SLEEP? The term micro-arousal (MA) was first systematically used to designate those phasic EEG events which were not associated with awakenings regardless of their desynchronizational or synchronizational (sleep response-like) morphology and regardless of their connection with autonomic or some sort of behavioral arousal (3). Concerning the traditional desynchronization-type morphology, the phenomenon was described entirely by the early work of Scheiber et al. (4) named at that time as “phases d’activation transitoire” (PAT). The criteria for MA in NREM sleep given by Scheiber et al. (4) were the following: increase in EEG frequencies in conjunction with decrease of amplitudes, disappearance of delta waves and spindles, transitory enhancement of muscle tone or phasic appearance of groups of muscle potentials, movements of the limbs or changes in body posture, transitory rise in heart rate. In rapid eye movement (REM) sleep the criteria for MA were: temporary disappearance of eye movements and appearance of alpha activities. The duration of these changes varied from some seconds to more than 10 seconds. The temporary “activation” is followed by “deactivation” leading to a bi-phasic character of the phenomenon. The occurrence of PAT-like MAs is inversely proportional to the depth of sleep, occurring more frequently in superficial than in deep sleep with the highest incidence during REM sleep and stage 1, appearing the less frequently during stage 3 and 4. The distribution of MAs is not homogeneous across the sleep cycles. MAs are more frequent during the ascending slopes of the cycles compared to the descending slopes, and their frequency increases from evening to morning (4,18–21). THE CONCEPT OF CORTICAL AND SUBCORTICAL (AUTONOMIC) AROUSAL Those clear-cut arousals, which have enough activating strength to change the level of vigilance on a macro-scale, are characterized by a three-fold phenomenology involving simultaneously EEG, behavioral and autonomic compartments. The conventional definition of arousal includes a cluster of physiologic manifestations expressed by an activation of electrocorticographic rhythms, an increase of blood pressure and muscle tone and a variation of heart rate. Arousal has been considered as an essential element for restoration of homeostasis during respiratory and cardiovascular failure during sleep providing an excitation drive to vital processes. Arousal, by definition, means cortical activation. However, somatosensory and auditory

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stimulation during sleep may result in cardiac, respiratory, and somatic modifications without overt EEG activation (22,23). This observation implies that there is a range of partial arousal responses with EEG manifestations different from classical arousals and even without any EEG response. The different arousal responses rely on the different combinations of the central and peripheral components, on the intensity scale of their manifestation, and on the morphological variations of the cortical reactions. The spectrum of combination of the three compartments in which arousal can appear is a matter of debate. Any behavioral expression, which occurs associated with low voltage fast EEG activities, is classified as a “behavioral arousal” (24). Similar features are shown by “movement arousals” described as any increase in electromyographic activity, which is accompanied by a change in any other EEG channel (25). When the EEG compartment is involved with transient desynchronization patterns, regardless of the participation of the autonomic system or behavioral components, it was held as “cortical arousal” (8). When there is evidence of vegetative or behavioral activation associated with an EEG pattern different from conventional arousal the event was defined as “subcortical arousal” (26). When an autonomic activation appears isolated or in conjunction with a respiratory event, but without any concomitant EEG sign, it is commonly defined as an “autonomic arousal” (27,28). There is an autonomic “overarousal” compared to the periods of arousal from continuous awake state during periods of awakening from NREM sleep (29), that represents also a certain kind of quantitative decoupling between the autonomic and other components of arousal. The dichotomy of EEG/autonomic arousal versus movement/behavioral arousal does not need much explanation; hence placed on a gradual scale the latter represents obviously a stronger activation. This is supported by the fact that movement and behavioral arousals without either EEG or autonomic concomitants do not exist. In contrast, there is evidence that an arousal from sleep is associated with heart rate acceleration and blood pressure increase even in the absence of any behavioral or somato-motor activity (30,31). The hierarchical relationship between the compartments becomes clearer by taking into consideration the time relationships between the components (32,33). Since the autonomic component may precede the EEG component (34), the cortical compartment could not be considered as the univocal source of autonomic activation. As both the EEG and vegetative reactions can appear decoupled, these two kinds of arousal manifestations may have separate and independent physiological substrates activated simultaneously by the same input. The temporal overlap between cortical, somato-motor and vegetative events within the same arousal episode does not necessarily imply synchrony and the order of activation of the single compartments can vary in the different physiological or pathological circumstances (35). In arousal phenomena during sleep there is no mandatory chronological and etiological subordination. The phenomenon takes place within interactive loops in which the cerebral cortex can be the starting or the ending point but anyway a source of control. The origin of arousal should be defined by the subsystem primarily activated or perturbed. The arousal can be generated directly by the cortex under the impulse of the physiologic evolution of sleep or in response to a sensorial perturbation, such as respiratory interruption, noisy environment, alteration of blood pressure or heart rate, or a movement disorder. In any case, it is the involvement of the brain that makes arousal a unitary phenomenon in which activation is modulated through a hierarchy of responses ranging from the generalized activation of all subsystems to the controlled attenuation of arousal-inducing activation (36).

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AROUSALS PRECEDED BY SYNCHRONIZATION (SLOW WAVES, K-COMPLEXES) EEG CHANGES Overall, arousal phenomena are characterized by an extensive variability not only due to different degrees of behavioral or autonomic participation but also by the wide variability of EEG features. The recognition of this kind of variability has a long story and has required a development of views. Evidence comes from two different sources. One of them is the research on K-complexes, which are distinctive elements of NREM sleep, especially of stages 2 and 3, endowed with controversial properties. Some investigators (14,37) consider these features as partial forms of arousal, while others (38,39) indicate these elements as sleep-protective events. According to a combinatory viewpoint, these events endowed with both activating and preserving properties (40,41) were found in the “forced awakening” method. In this paradigm subjects were questioned about quantitative and qualitative aspects of stimulus recall evoked by “oddball” type stimuli in parallel with recording of the evoked cortical responses, after being aroused by the stimuli from naps. In subjects whose quality of recall was excellent, P300 waves were indistinguishable from those obtained before sleep. When P300 was found attenuated, delayed and desynchronized, recall was quantitatively degraded and P300 was concomitant to or replaced by sleep negativities (varieties of late negative components being part of the K-complex) in subjects in whom stimulus recall was severely degraded or absent (42). They concluded that K-complex analog sleep negativities have two aspects being, on the one hand, arousal driven and, on the other hand, “erasers” preventing accurate memory encoding and retrieval of the stimulus, promoting consequently sleep. The same “Janus faced” nature of K-complexes were stressed by us previously (18) and the possible functional importance of this aspect will be treated later. An increase in the amplitude of the K-complex N350-550 and P900 components after sleep deprivation has been described (43). This again shows clearly that K-complex characteristics are very close to those of delta sleep (39). The first studies of K-complexes showed that these graphoelements, which are held to be the building stones of slow wave sleep (44), are elicitable by all modalities of sensory stimuli (14,45–47) and are accompanied by autonomic discharges identical to those seen for arousals (37,48–55). Later it was shown that K-complexes rarely remain single events but are accompanied by other rhythms such as K-delta, K-alpha, and K-spindle according to the nature of the associated rhythm (12,56,57). These complex events beginning with K-complexes are frequently followed by long lasting changes in the ongoing EEG, associated with distinct autonomic modifications (Table 1). Accordingly they could be considered as a “synchronization type” of MA (58). We reported that arousals proceeded by slow waves and K-complexes have a different distribution across the sleep stages compared to the “desynchronization type” of MA. The former showed the greatest occurrence rate during slow wave sleep, being TABLE 1 Synchronization Type of Micro-Arousals Associated with K-Complex(es) Name Single K-complex(es) K-sigma K-alpha K-delta

EEG morphology

Description

Single or serial K-complexes K-complex followed by a sleep spindle K-complex followed by alpha runs K-complex followed by or mixed up with delta group

Roth et al., 1956 (14) Johnson and Karpan, 1968 (51) Raynal et al., 1974 (56) Halász and Ujszászi, 1991 (12)

Abbreviation: EEG, electroencephalogram.

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most frequent in stage 2 (40,48) scored 5820 events during the night sleep of 21 young adult volunteers. Thirty-two percent of events were scored as arousals and in 40% they were preceded by isolated K-complexes. PAT type arousals represented 23% of the total arousals whereas delta and K-complex bursts tended to occur mostly during the first two sleep cycles. Other types of MA and clear-cut PAT occurred during all sleep cycles with a greater density in light and REM sleep. In an analysis carried out on 40 healthy subjects (59), ascertained that the number of arousals during NREM sleep increase with age. In the same sample, 87% of arousals were preceded by a K-complex or delta activity and showed a positive correlation with stages 1 and 2 (60). Delta and K-bursts were concentrated in the first three sleep cycles and presented a divergent behavior compared to ASDA arousals (61). Another line of evidence for different types of arousals came from the discovery of CAP (7) (Fig. 1). It was shown that the CAP A-phase behaves like the synchronization arousals, can be elicited by sensory stimuli and is associated with clearly detectable autonomic discharges. Later, the Parma school differentiated within the CAP A-phase three subtypes. In subtype A1, EEG synchrony is the predominant activity. If present, EEG desynchrony occupies < 20% of the entire

FIGURE 1 Example of a CAP sequence (top) and non-CAP (bottom) in stage 2 NREM sleep. Notice that CAP occurs as a spontaneous phenomenon in the absence of any respiratory or muscle abnormality. Abbreviations: CAP, cyclic alternating pattern; EKG, electrocardiogram; EMG, electromyogram; EOG, electro-oculogram; NREM, non-rapid eye movement; O-N, oronasal; PNG, pneumogram; THOR PNG, thoracic pneumogram; TIB ANT L, left anterior tibialis muscle; TIB ANT R, right anterior tibialis muscle.

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(A)

F4 G2 T4 G2 P4 G2 O2 G2 F3 G2 T3 G2 P3 G2 O1 G2 Cz G2 ECG1+ ECG1EMG1+ EMG1-

(B)

F4 G2 T4 G2 P4 G2 O2 G2 F3 G2 T3 G2 P3 G2 O1 G2 Cz G2 ECG1+ ECG1EMG1+ EMG1-

240 µV

1 sec

150 µV 1 sec

FIGURE 2 (A) Synchronization type micro-arousal. The electroencephalographic (EEG) pattern is dominated by K-complexes and deltas; the polygraphy shows a phasic increase in muscle activity and transitory tachycardia. (B) Desynchronization type of micro-arousal. The EEG pattern shows decrease in amplitudes and increase of the frequencies. The polygraphy indicates tachycardia and increase in muscle activity. Abbreviations: ECG, echocardiogram; EMG, electromyogram; μV, microvolt.

phase A duration. Subtype A1 is generally associated with mild autonomic and muscle activity. Subtype A2 contains a mixture of slow and rapid rhythms with 20% to 50% of phase A occupied by EEG desynchrony. Subtype A2 is linked with a moderate increase of muscle tone and/or cardiorespiratory rate. In subtype A3, the EEG activity is predominantly fast–low voltage rhythms with > 50% of phase A occupied by EEG desynchrony. Subtype A3 is coupled with a remarkable activation of muscle tone and/or autonomic activities (62). The distribution of the different phase A subtypes has been proven to be different across the sleep cycles. Subtype A1 occurs most frequently in the first cycles of sleep and during the descending slopes of the cycles, while subtypes A2 and A3 are more frequent during the later part of sleep and in the ascending slopes of the cycles (20). Therefore, we can identify the A1 subtype with the “synchronization arousal” of Halász et al. (58), and the A3 subtype with the original PAT of the Strasbourg School (4), while the A2 subtype is a mixed one between the two (61) (Fig. 2). Now it is clear that the most important factors that determine the variable EEG morphology of arousals appearing in NREM sleep are the linkage with stages and the position of the given arousals within the course of sleep. Other important factors are the nature and the intensity of the stimulus originating the arousal. INFLUENCE OF SENSORY STIMULATION ON THE FORMATION OF BOTH TYPES OF AROUSALS Sensory stimuli can evoke EEG arousals with or without behavioral and autonomic changes and their phenomenology are exactly the same as experienced in the so-called spontaneous arousals. Ehrhart and Muzet (63) showed in their early work that PAT could be elicited by sensory stimuli. Stimulation decreased the number of

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the spontaneous PATs; however, the total (spontaneous + evoked) number of PATs was similar to the number of the spontaneous PATs without stimulation. Under the influence of a psychostimulant drug the frequency of MAs slightly increased and the difference in the distribution between the descending and ascending slope of the cycles disappeared. Sensory stimulation did not affect the average frequency of MAs, but occurrence during deep sleep increased and the difference between the ascending and descending slopes decreased in a relevant way due to the increased frequency during the descending slopes. Elicitability by sensory stimulation was best in superficial sleep stages and worst in deep sleep that means it went parallel with rates of spontaneous occurrence (3). Concentrating purely on K-complexes regardless to the associated other rhythms Halász (18) found a significant increase in the number of K-complexes under the influence of continuous random sensory stimulation during stage 2 of NREM. The elicitability of K-complexes was higher during the ascending slopes of cycles (where the spontaneous occurrence rate was also higher) compared to the descending slopes. Under stimulation the number of spontaneous K-complexes decreased, but the total number (spontaneous + evoked) of K-complexes increased. Using sound stimulation with 90-dB tones at 625 Hz, 1/1 to 1/5 minutes rate delivered by headphones, Levine et al. (64) found that the number of “natural arousals” decreased during nights with frequent (1/1 min) stimulation resulting into abundant evoked arousals. The polygraphic characteristics of these arousals were not shown, but similar findings were described in more recent studies (39). STATE-SPECIFIC REACTIVITY IN SLEEP Here we arrive at a more dynamic view in the understanding of the nature of arousal in sleep. We must introduce an otherwise well-known biological concept, namely the “state-specific reactivity.” In a certain biological state the reactivity of the organism to stimulation is determined by the given state in which the input arrives. It is well known that the sensory reactivity is different in REM and NREM sleep. However, the change in reactivity within NREM depending on whether the stimulus arrives during the descending or ascending part of the sleep cycle needs further elaboration. First of all we should know more about the phenomenological and physiological differences of the two slopes. There are not many studies on this topic. The first mention about the asymmetry of the descending slope (DS) and ascending slope (AS) of sleep cycles was made by the work of Williams et al. (65,66) and confirmed by automatic analysis of sleep signals (67,68). It was noticed that on the DS the deepening of sleep occurs more slowly and gradually while the duration of AS is shorter, the changes are more abrupt, and sometimes a stage could be skipped. Later Sinha et al. (69) claimed to forecast the times of morning awakening by studying the tendency of AS tangential across the hypnograms. Halász (18) measured and compared the duration of DS and AS slopes and the number and sequence of phase shifts in healthy volunteers. The net result of this study was that sleep cycles—at least in the first part of sleep where this phenomenon was possible to investigate— are asymmetric: the DS is longer and sleep stages shift gradually, the AS proved to be shorter, and changes less gradual. In other words, the AS is steeper and 30% to 50% shorter compared to the DS as demonstrated by Terzano et al. (20). The differences described previously about the frequency and morphological features of arousals are in harmony with the asymmetric dynamics of the two slopes of sleep cycles. Across the DS and most prominently in the first cycles arousals are

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less frequent, show slower EEG activities, are associated with only mild autonomic perturbations, while across the AS arousals are more frequent, and the EEG morphology and the concomitant autonomic changes fulfill better the conventional arousal expectations (20). These polysomnographic findings were confirmed by computerized analysis that showed an increase of very fast rhythms in the final part of the sleep cycle, when NREM sleep precedes the onset of REM sleep and a sharp reduction of these rapid rhythms at the beginning of NREM sleep in the following sleep cycle (70). On the basis of these findings, we may speculate that the differences in arousals might reflect an intimate relationship between state responsivity and the tendencies of state shifts according to the sleep profile (Table 2). State determines sensory responsivity and the sensorial stimulation—both in experimental and spontaneous situations—may contribute to the state shifts. Naturally the sleep state shifts are determined basically by chemical changes governed by brain stem influences. During the NREM–REM cycles, there are slowly moving tonic changes; cyclic alternation of brainstem aminergic and cholinergic influences underlie these changes. Besides the involvement of chemical changes, the alternation of DS and AS during the NREM component of the sleep cycle can be influenced by the appearance of arousals, which reflect also the influence on the sleep process by external factors. The dynamics experienced in arousals suggest that sensory stimuli may participate in the determination of the sleep profile and cooperate in shaping the course of sleep cycles. We can formulate this kind of double, “tonic” and “phasic” regulation, in which the effect of “phasic” arousals are tuned by the background “tonic” chemical influences, and at the same time the “phasic” stimulation contributes to the changes in “tonic” influences. It is clear that the DS and AS portions of the sleep cycles represent two different substates. During the DS slope sleep promoting influences are overwhelming and the arousal system is more inhibited compared to the AS (71). During this tonic sleep dominance the thalamocortical system works in the bursting mode and the influence of brainstem arousal systems are tonically repressed (72). Accordingly, in the DS phasic arousal events are rare and they are often mixed with sleep-like responses. Here we do not observe a complete breakdown of NREM bursting mode in the thalamocortical network but it seems as if the distinct subsystems are in conflict and influence each other reciprocally throughout the arousal response (11,73). The slow EEG pattern elicited by the arousing stimulus, which characterizes the first part of the response, seems to prevent or attenuate the depolarizing influence of TABLE 2 Sleep Features and Micro-Arousal Characteristics Corresponding to Slopes of the Sleep Cycles Descending slope Duration Transition of stages Synchronization type arousal Desynchronization type arousal Conjoined autonomic signs in arousal Conjoined behavioral signs in arousal Association of arousals and stage shifts Stimulus/answer relation Assumed function of arousals Abbreviation: REM, rapid eye movement.

Longer Stepwise Overwhelming Rare Very rare Very rare Only at the end of cycle trough Weak Promoting sleep

Ascending slope Shorter Skipped stages Rare Overwhelming Frequent Frequent Regular Close Preparing REM sleep

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cholinergic innervations of thalamic relay cells. The outcome is a balance between antiarousal and arousal responses. There are two experimental studies investigating the effect of arousal stimuli during the bursting mode. Szymusiak et al. (74) applied rostral midbrain monophasic electric stimulation with 0.2 ms duration and 100 to 800 μA and registered a state dependent effect on thalamic single unit activity. While the stimulation during wake state and REM sleep evoked a short-latency action potential, during NREM sleep the stimulation commonly evoked a high frequency burst of action potentials followed by a period of suppressed discharge. In the majority of neurons a second rebound burst of action potentials followed the period of discharge suppression. The average interval between the initial and rebound burst was similar to the interburst interval recorded in the same cells during spontaneous EEG spindles. The authors conclude that stimulation of the reticular formation evoked rhythmic discharges dependent upon the presence of thalamocortical synchronization. Mariotti et al. (75) showed that in the nucleus VPL of the thalamus, the response to peripheral physiological stimulation during NREM sleep shows three main components: a very brief and scanty excitatory response, followed by a long period of discharge suppression and by excitatory rebound. The landmark of arousal is a strong increase of the excitatory response and a marked reduction of the inhibitory phase, eventually with disappearance of the rebound. Another possibility to understand the slow wave response to sensory stimulation would be that the sensory input which arrives to the cortical level meets the slow (< 1 Hz) depolarizing oscillation, nowadays identified as the K-complex generator (76), in the phase of a fast (30–40 Hz) rhythm which allows MAs in slow wave sleep without long-lasting interruption of the inhibitory rebound sequences in the thalamocortical network. After the cycle turns to the AS the dominance of NREM sleep decreases. The neurochemical background of this weakening influence in the second part of the cycle could be the combined result of a decreased amount of NREM sleep supporting monoamines and an increasing antagonizing REM promoting cholinergic influence, according to the “reciprocal-interaction” hypothesis of Hobson and McCarley (77). This decrease in NREM sleep promoting influence results in a reinforcing increase in the phasic arousal activity which has now a better cortical arousal effect reflected in the more arousal like type EEG, behavioral and autonomic activity. This increase in arousal activity results also in more intensive arousals conjoined from time to time with not only transitory EEG reactions but also with stage shifts. The gradual weakening of NREM sleep and the increasing dominance of REM forces can explain the asymmetrical conformation of the sleep cycle with a smoother DS and a steeper AS. HIERARCHY OF AROUSALS—THE CONCEPT OF A CONTINUUM As it was previously illustrated, arousals are variable according to the different combinations of their EEG, behavioral and autonomic activities. The phenomenology of arousals is influenced not only by stages and sleep depth but also by the tendency of the sleep process. If arousals really reflect the same physiological process of activation they should behave in a stereotyped way regardless of the involvement of the different peripheral compartments. Sforza et al. (49) showed a stereotyped rising and falling in the delta, theta, and alpha power associated with periodic leg movements regardless to the presence or absence of EEG arousals and regardless to the absence or presence of slow EEG waves in arousals (Fig. 3). Similar spectral changes were described by Halász and Ujszászi (12) in different K-complexes

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FIGURE 3 Left: Electroencephalographic spectrum percentage changes in μV2 divided to three bands before and after a PLM event (onset is indicated by the arrow) with and, without micro-arousal, and with slow wave activity. (after Ref. 51.) Right: poststimulus power spectra according to different response types during NREM stages 2 to 3. Grand averages of four subjects’ means, weighted with the individual response number. The poststimulus power values are compared to the corresponding data for the prestimulus 2 seconds. Cz-A1 derivation. Prestimulus power spectra on the top. Abbreviations: KA, K-complex followed by alpha spindle; KD, K-complex followed by delta group; KS, K-complex followed by sleep spindle; PLM, Periodic Limb Movement. Source: From Ref. 12.

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preceding arousals. In this work we also showed the deactivation of sigma activity followed by a long-lasting poststimulus inhibition. Because spindles are the expression of the thalamic filter to the passage of stimuli, their transient disappearance could provide a time window for momentarily improved sensorial transmission through the thalamic relay. In spite of the differences in the intensity dimension and in the EEG, autonomic and behavioral components, the variable forms of arousals are supported by a uniform background along a hierarchic continuum (15,78) concerning the degree of activation they produce (Table 3). PATHOLOGIC AROUSALS, WITH SPECIAL EMPHASIS ON SLEEPRELATED BREATHING DISORDERS Spontaneous arousals are natural guests of the sleeping brain (59) and appear regularly embedded within the CAP process (60,61). However, arousal phenomena are also known to occur in response to sleep-disturbing factors. Increased amount of arousals is a regular finding of obstructive sleep apnea syndrome (OSAS). The respiratory effort-related arousals (RERAs) known as obstructed events are also typical manifestations of secondary cortical events. They do not meet the criteria for apnea or hypopnea, nevertheless cause an arousal. RERAs are defined as progressive negative Pes (esophageal pressure) lasting ≥ 10 second, culminating in an arousal without apnea or hypopnea. The frequency of RERAs is increased both in OSAS and in the upper airway resistance syndrome (UARS) as a reaction of the sleeping brain

TABLE 3 EEG, Autonomic and Behavioral Symptoms in Different Types of MA During NREM and REM Sleep MA during NREM sleep Synchronization type (K-delta, phase A1 subtype of CAP) Mixed synchronization and desynchronization type (K-alpha, phase A2 subtype of CAP) Desynchronization type (PAT, phase A3 subtype of CAP) Stage shift

PAT type

EEG

Autonomic

Series of K-complexes, Mild, short vegetative deltas, spindles signs (pulse rate increase) if at all Slow waves, Transitory pulse rate K-complexes increase followed by spindles and faster frequencies Acceleration and Transitory pulse rate amplitude decrease increase, PAT, of EEG blood pressure elevation Transition from a Maintaining or long deeper to a lighter standing tonic NREM sleep stage increase in pulse rate or in other autonomic functions Transitory disappear- Transitory pulse rate ance of any REM increase and/or any specific graphoeleother signs of ment, appearance elevated sympaof alpha rhythms thetic tone

Behavior No signs

Short-lived increase of muscle activity at the end

Transitory muscle activity increase and/ or movements Transitory or longer standing increase of muscle tone

Transitory reappearance of muscle tone, disappearance of eye movements

Abbreviations: CAP, cyclic alternating pattern; EEG, electroencephalogram; MA, micro-arousal; NREM, non-rapid eye movement; PAT, phases d’activation transitoire; REM, rapid eye movement.

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to a repetitive breathing disturbance. RERAs are secondary to subtle obstructions of the upper airway during sleep and may cause excessive daytime sleepiness in the absence of apneas and hypopneas. The abundance of RERAs in sleep-disordered breathing (SDB) has supported the idea that arousals are signs of disturbed sleep reflecting abnormal breathing (79). The association of esophageal pressure alterations and arousals without apneas or hypopneas confirms this belief and has been one of the main reasons for considering arousals as an epiphenomenon of SDB. However, arousals can be elicited by nonrespiratory disturbance. The so-called spontaneous arousals could in effect be evoked by some organic triggers such as stimuli from the intestinal tract, excessive bladder loading or organ dysfunction. Accordingly, the occurrence and distribution of spontaneous arousals should follow the randomness of internal phenomena across the night. However, examining the distribution of normal arousals during the night we can ascertain that they are not randomly distributed as they tend to vanish in the first part of the descending slope of sleep cycles (71) and appearing mainly concentrated in the ascending slope of the sleep cycles (3,21). In particular, arousals are common before and during REM sleep but are rare during slow wave sleep (63,80). These findings indicate that a certain amount of arousals is related to the intrinsic organization of sleep regardless of any superimposed factors. Accordingly, it is possible to discriminate between spontaneous arousals and induced ones on the basis of identifying those arousals belonging to the physiological sleep structure. This implies that when sleep is not severely disrupted, pathologic arousals tend to appear in those portions of sleep in which they have higher probabilities to occur spontaneously. In SDB, a certain amount of respiratory-induced arousals may simply replace the spontaneous ones as expected from their natural distribution across the night. It has been ascertained that a CAP sequence preceding REM sleep is a structural marker of sleep organization (81). This means that this transitional portion of sleep coexists with an underlying oscillation of vegetative functions. The occurrence of unstable sleep in this particular position within the sleep cycle can be an important point in monitoring respiratory oscillations and titrating ventilatory support (82). Moreover, Poyares et al. (83) hypothesized an inability of the CNS to manifest spontaneous arousals when they are frequently induced by a specific disturbance. It was shown that acoustic stimulation during sleep increases the amount of noise-induced arousals and reduces the amount of spontaneous ones (3). These findings indicate a mutual relationship between physiological and pathological arousals. Concerning the relationship of autonomic and EEG representations of the different degree of arousals related to pathologic breathing, several parameters have been used in recent works. The relationship between apneic events and arousal were explored in more detail. Arousals were considered necessary for upper airway opening in obstructive hypopneas-apneas. However, Younes et al. (84) showed that the temporal relation between arousals and opening was inconsistent between and within patients. They also noticed that “arousal may take the form of an increase in delta power near the end of obstructive events,” also mentioned by Poyares et al. (83), Black et al. (36) and Berry et al. (85). The elicitability of K-complexes by respiratory stimuli (86) is a similar problem. Dingli et al. (87) analyzed arousability related to respiratory events. Around 30% of apneas/hypopneas were not terminated by visible EEG arousals corresponding to the ASDA criteria of arousals. Arousal induction was not affected by oxygen desaturation, event type, duration or time of the night, but strongly determined by the

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sleep state: slow wave sleep being associated with less cortically apparent, visually detected arousals. Sleep-disordered breathing covers a spectrum from complete upper airway obstruction to more subtle breathing abnormalities (obstructive hypopnea, upper airway resistance episodes). Several of autonomic events are associated with respiratory events: heart rate, blood pressure, and peripheral vascular resistance changes. The severity of the pathologic respiratory event is proportional with the consequent arousal. Arousals associated to apneas were more pronounced than with hypopneas. Apneas and events > 20 seconds with a minSAO2 < 86% were more frequently associated with arousal > 11 seconds (90). Using peripheral arterial tonometry in a child population with obstructive sleep apnea Tauman et al. (89) found a strong correlation between the tonometric events and EEG arousals (corresponding to ASDA criteria), however only 35% of respiratory events were associated with EEG arousal, compared to 92% being associated with tonometric events. So a significant proportion of pathologic respiratory event-related tonometric changes were not accompanied by EEG arousals. Pulse transit time (PTT) is a noninvasive measure of blood pressure. The PTT is the interval between the R-wave of the electrocardiogram (ECG) and the arrival of the photoplethysmographic pulse at the finger. The travel time of the pulse wave is inversely proportional to arterial wall stiffness, which is determined by blood pressure. Therefore PTT is an index that is inversely related to blood pressure. Katz et al. (90) concluded that in children arousal indicated by the PTT are more sensitive measure of obstructive events than apparent EEG arousals. The same was described by Pepin et al. (91). Katz et al. (90) also found that the PTT index was significantly higher in upper airway resistance syndrome (UARS) than in primary snoring. The pulse wave amplitude measured by photoplethysmography is also a good peripheral marker of arousals related to respiratory events (92). It measures the relative absorption of red light and infrared light to hemoglobin and oxyhemoglobin. The system utilizes the fact that arterial blood flow pulsates and other fluids and tissues do not. The pulsation of arterial blood flow modulates light passing through it. The attenuation of light energy due to arterial blood flow can be detected and isolated, can be converted to an electronic signal for calculation of SaO2 and provides a semi-quantitative measure of pulse wave amplitude. Obstructive respiratory events provoke a relative bradycardia and vasodilation followed by heart frequency increase and vasoconstriction. Finger pulse wave amplitude changes are generally higher than the heart rate response. The magnitude of autonomic changes seems to be related to the intensity of central nervous activation (92). Visually-scored EEG arousal is associated with an increase of heart rate index, while PTT is associated with a drop in parasympathetic index, after the respiratory events. Patients with mild OSAS presented persistently shorter R–R intervals (RRI) when compared to patients with UARS. The latter also exhibited a significant decrease in parasympathetic index [(high frequency (HF)] at the termination of a respiratory event. The HF component was significantly decreased in patients with UARS, showing a predominant involvement of the parasympathetic tone in patients with UARS in comparison to those with OSAS (93). It can be concluded that the more recently introduced autonomic arousal measures are more sensitive indicators of arousal than the classical ASDA EEG arousal criteria. Taking into consideration the so called “microstructural” measures

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of sleep oscillations (1) in the future more correlation can be excepted between the complex autonomic and EEG arousal concomitants of respiratory events (94). Learning more and more about the arousal related repetitive autonomic activation the question of possible adverse cardiovascular outcomes is raised. In other words what is the price of the short term adaptive role, promoting the termination of the actual pathologic event, in the long run? This question awaits further long-term follow up studies. Secondary arousals are linked not only to breathing events but also to motor phenomena. Sleep bruxism is a typical example of oromotor events associated with sleep arousals (32,95). Periodic limb movement are other typical manifestations of motor events occurring in close temporal relation with arousals. Pain syndromes are also commonly associated with increased arousals (96,97). However, frequent arousals can occur in clinical conditions lacking any detectable internal or external factors of perturbation. Primary insomnia, a sleep disorder without any evidence of mental, substance-induced or medical disturbance, shows increased arousal prevalence and CAP frequency compared to normal sleep (98). AROUSALS GATING PATHOLOGICAL EVENTS The gating-effect among pathological arousals has the more abundant literature. Several pathological sleep events were found to be associated with different forms of MA. The most explored sleep perturbation in association with different sleep pathologies is the CAP pattern. Within CAP it is almost always the A-phase that is connected with these abrupt manifestations of pathological sleep events. Therefore, CAP A-phase was interpreted as a kind of “gate” through which the pathological events occur more easily. The gating effect had been demonstrated in the last years among several sleep disturbances for example, periodic limb movements (PLMs) (99–101), sleep bruxism (34), OSAS (82), and epilepsy (102–106). Pathologic breathing events have a strong activatory effect resulting subcortical (autonomic) and EEG arousal with or without behavioral concomitance, and there is a self strengthening vicious circle between gating and perturbations caused by the pathological event. Two contradictory trends act at the same time: arousal on one hand contributing to the termination of the pathologic event, but on the other hand sleep protecting tendencies working to preserve sleep continuity. Where sleep promotion is stronger, as in childhood, the EEG arousal is less prominent and the subcortical arousal seems to be “enough” to promote the termination of the event (94). FUNCTIONS OF AROUSAL DURING SLEEP The data available on arousal activity during NREM sleep clearly indicate that arousal is really woven into the texture of sleep. What are the functions of the ongoing arousal activity during NREM sleep, the essence of which is conventionally held just the opposite to arousal? As it was shown, arousals, and arousability in general, ensure the reversibility of sleep, without which it would be identical to coma. Arousals provide a connection of the sleeper with the surrounding world maintaining the selection of relevant incoming information and adapting the organism to the dangers and demands of the outer world. In this dynamic perspective, the ongoing phasic events carry on the one side arousal influences and on the other side elements of information processing. Therefore, arousal and information processing are the two sides of the same coin in sleep. The latter statement is elegantly supported by

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the increasing investigation of different components of K-complexes and their relationship with the presence/absence and different features of cognitive workup during NREM sleep (46,47,107–110). The other function of arousals is tailoring the more or less stereotyped endogenously determined sleep process driven by chemical influences according to the internal and external demands. This is why the sleep process is variable from night to night, lending flexibility to the process. The different forms of arousals provide the phasic regulation prevailing on the top of the slower waves of preprogrammed chemical codes, shaping in a certain limited way the sleep process. This regulation is able to modify mainly the AS of the sleep cycles and prevails in the last third of the night sleep. Speculations on how sleep and wakefulness are regulated consider homeostatic and circadian factors as essential to explain the timing of alternations of awake state and sleep (111), while reciprocal interactions of brainstem neuronal systems are indicated to be involved in the alternations of NREM and REM sleep (77,112). In this perspective, arousals shape the individual course of night sleep as a variation of the sleep program (112). Based on the data gathered from the study of MA it seems plausible instead those arousals have a more essential role in the reciprocal interactions between NREM sleep and wake and between NREM sleep and REM sleep. We can envisage control of sleep/wakefulness as a tonic regulation under endogenously driven reciprocal antagonistic chemical influences like the promoted “sleep switch” (flip-flop) model based on the hypothalamic control of sleep and wakefulness (113). However, this interpretation cannot explain the intermediate states and flexibility of the system. This is assured by the parallel “phasic” regulation provided by arousals in sleep, proposed to be incorporated in the current models of sleep regulation. Figure 4 shows schematically the parallel working mode of the “tonic” modulation (mainly intracerebral, slow, and chemical)

– S

W

S

W

–

DS

AS

FIGURE 4 A model of the parallel working mode of “tonic” and “phasic” modulation. S = sleep promoting system, W = waking arousal system exerts mutual reciprocal antagonistic influences on each other (indicated by negative signs). Arrows indicate the influence of the inner and outer sensorial surrounding (interrupted arrows) and of the endogenous chemical input (continuous arrows). The thickness of the arrows indicates the amount of the influence. The interrupted bars and positive signs along the circuit between W and S indicate arousal impulses in the form of phasic events and/or micro-arousals. For further explanation see the text. Abbreviations: AS, ascending slope; DS, descending slope.

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and “phasic” changes (mainly extracerebral, faster, neuronal-synaptic). The sleep promoting system (S) and waking arousal system (W) exert mutual reciprocal antagonistic influences on each other. The detailed description of this relationship and the role of the players (both chemical and neural) of the two interacting subsystems are brilliantly illustrated in Saper et al. (113). In the DS of sleep cycles the S system is dominating. When the S system inhibits the W system to a certain extent (during DS) sensorial input from the inner and outer surrounding (interrupted thick arrows) result into rare and mild arousals. In contrast, during the AS the tonic inhibition of the W system decreases and this facilitates a greater frequency of arousals. The continuous arrows represent the endogenous chemical and the interrupted arrows the sensorial inflow. The thickness of the arrows reflects the proportion of the relations. This model is able to explain why sleep gets deeper during the DS even in the presence of sensory stimulation, and how arousals can promote, during the AS, the avalanche of the awakening process. Here the dynamic changes across the sleep cycle are fuelled not only by chemical influences but by the parallel sensorial input which has state specific different functions during the two slopes of the cycle, being sleep promoting during DS and supporting the arousal process during AS. The sleep promoting effect of sensory stimuli during the process of falling asleep has been reported by several authors (114–116), starting from Pavlov (117) in 1928. CONCLUSIONS Every biological system tries to assure autonomy to achieve independence from the surrounding, and at the same time relies on the interrelationship between the organism and the surrounding world, which is essential for adaptation and survival of the system. Therefore, an organism should avoid external stimuli and try to regain the original prestimulus state, but paradoxically, it will use the stimulus for building up its autonomic state. The reciprocal interplay of the sleep/wakefulness system is a suitable example of how external stimuli are used in a process modeling the internal structure and serving the separation of the organism from the outer world (118). Anyway, the sleeping brain can offer manifold types of MA to internal or external inputs. Besides the classical low voltage fast rhythms EEG arousals, highamplitude EEG bursts, be they like delta-like or K-complexes, reflect a possible arousal process. The different MAs are associated with increasing magnitude of vegetative activation ranging hierarchically from the weaker slow EEG types (coupled with mild autonomic activation) to the stronger rapid EEG types (coupled with a vigorous autonomic activation). In the breathing disorders of sleep we are in the process to learn more and more about the complexity and hierarchy of the autonomic compartment of the coupled arousal, being adaptive by serving the termination of the pathological event and on the other hand maladaptive hence disrupting sleep continuity resulting in sleep fragmentation. In physiological conditions, the slow and fast MAs are not randomly scattered but appear structurally distributed within sleep where they also are endowed with a cyclic nature expressed by the periodic dimension of CAP. It is known that arousal responses differ in the various sleep disorders. Understanding the role of arousals and CAP and the relationship between physiologic and pathologic MA can shed light on the adaptive properties of the sleeping brain and provide insight into the pathologic mechanisms of sleep disturbances.

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49. Sforza E, Juony C, Ibanez V. Time-dependent variation and autonomic activity during periodic leg movements in sleep. Implications for arousals mechanisms. Clin Neurophysiology 2002; 113:883–891. 50. Ackner B, Pampiglione G. Some relationships between peripheral vasomotor and EEG changes. J Neurol Neurosurg Psychiat 1957; 20:58–64. 51. Johnson LC, Karpan WE. Autonomic correlates of the spontaneous K-complex. Phychophysiol 1968; 4:444–452. 52. Fruhstorfer H, Partanen J, Lumio J. Vertex sharp waves and heart action during the onset of sleep. EEG Clin Neurophysiol 1971; 31:614–617. 53. Takigawa M, Uchida T, Matsumoto K. Correlation between occurences of spontaneous K-complex and the two physiological rhythms of cardiac and respiratory cycles. Brain and Nerv 1980; 32:127–133. 54. Guilleminault C, Stoohs R. Arousal, increased respiratory efforts, blood pressure and obstructive sleep apnoea. J Sleep Res 1995; 4:117–124. 55. Hornyak M, Cejnar M, Elam M, et al. Sympathetic muscle nerve activity during sleep in man. Brain 1991; 114:1281–1295. 56. Raynal D Montplaisir J, Dement WC. K-alpha events in hypersomniacs and normals. Sleep Res 1974; 3:144. 57. MacFarlane JG, Shahal B, Mously C, et al. Periodic K-alpha sleep EEG activity and periodic limb movements during sleep: comparisons of clinical features and sleep parameters. Sleep 1996; 19:200–204. 58. Halasz P, Ujszaszi J, Gadoros J. Are microarousals preceded by electroencephalographic slow wave synchronization precursors of confusional awakenings? Sleep 1985; 8: 231–238. 59. Boselli M, Parrino L, Smerieri A, et al. Effect of age on EEG arousals in normal sleep. Sleep 1998; 21:351–357. 60. Terzano MG, Parrino L, Rosa A, et al. CAP and arousals in the structural development of sleep: an integrative perspective. Sleep Med 2002; 3:221–229. 61. Parrino L, Smerieri A, Rossi M, et al. Relationship of slow and rapid EEG components of CAP to ASDA arousals in normal sleep. Sleep 2001; 24:881–885. 62. Terzano MG, Parrino L, Smerieri A, et al. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med 2001; 2:537–553. 63. Ehrhart J, Muzet A. Fréquence et durée des phases d’activation transitoire au cours du sommeil normal chez L’homme. Arch Scr Physiol 1974; 28:213–260. 64. Levine B, Roehrs T, Stepanski E, et al. Fragmenting sleep diminishes its recuperative value. Sleep 1987; 10:590–599. 65. Williams HL, Hammack JT, Daly RL, et al. Response to auditory stimulation, sleep loss and the EEG stages of sleep. EEG. Clin Neurophysiol 1964; 16:269–279. 66. Williams HL, Harman W, Agnew MA, Jr, et al. Sleep patterns in the young adult female: an EEG study. EEG. Clin Neurophysiol 1966; 20:264–266. 67. Dijk DI, Brunner DP, Borbely AA. Time course of EEG power density during long sleep in humans. Am J Physiol 1990; 258:650–651. 68. Merica H, Fortune RD. A neuronal transition probability model for the evolution of power in the sigma and delta frequency bands of sleep EEG. Physiol Behav 1997; 62:585–589. 69. Sinha AK, Smythe H, Zarcone VP, et al. Human sleep-electroencephalogram: a damped oscillatory phenomenon. J Theor Biol 1972; 35:387–393. 70. Ferri R, Cosentino FI, Elia M, et al. Relationship between delta, sigma, beta, and gamma EEG bands at REM sleep onset and REM sleep end. Clin Neurophysiol 2001; 112: 2046–2052. 71. Evans BM. Cyclical activity in non-rapid eye movement sleep: a proposed arousal inhibitory mechanism. EEG. Clin Neurophysiol 1993; 86:123–131. 72. Steriade M, Llinas R. The functional states of the thalamus and the associated neuronal interplay. Physiol Rev 1988; 68:649–742. 73. Steriade M, McCarley RW. Brainstem control of wakefulness and sleep. New York: Plenum Press, 1990. 74. Szymusiak R, Shouse MN, McGinty D. Brainstem stimulation during sleep evokes abnormal rhythmic activity in thalamic neuron sin feline penicillin epilepsy. Brain Res 1996; 713:253–260.

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75. Mariotti M, Formenti A, Mancia M. Responses of VPL thalamic neurons to peripheral stimulation in wakefulness and sleep. Neurosci Lett 1989; 102:70–75. 76. Amzica F, Steriade M. The K-complex: its slow ( 75% reduction in of the cross-section area. In addition to the retropalatal narrowing, retroglossal and hypopharyngeal narrowing of > 75% was observed in 38% and 22% of patients respectively. There were also secondary sites of narrowing (25–75% reduction in UA area) present in up to a third of subjects.

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Pharyngeal pressure recordings confirm the retropalatal region as the main site of collapse. Although the retropalatal region is the most common site of collapse, studies have shown that collapse is a dynamic process involving more than one area of the UA. Based on pharyngeal pressure monitoring, the sequence of UA collapse involves a caudal extension from the retroglossal to the hypopharyngeal regions (42,43). This caudal extension of UA collapse is accentuated during REM sleep (42). More limited observations have demonstrated that UA reopening proceeds from a caudal to cranial sequence equally divided between sudden and gradual opening of the UA during inspiration (44). The significance of this latter finding remains unclear. Further studies have focused on predicting the main site of UA collapse in a particular individual. Watanabe et al. demonstrated that the sites of UA collapse differ depending on body habitus (presence of obesity) and craniofacial abnormalities. Collapse at the velopharynx was associated with obesity and collapse at the velopharynx and oropharynx was associated with craniofacial abnormalities (10). While different sites of collapse in the UA have been observed, it should be noted that MRI and other imaging modalities suggest the tongue and soft palate act as a single structure applying a collapsing force on the UA in the supine position. There is usually no air interface between the tongue and the soft palate. Clinically, it has been difficult to evaluate the site of UA collapse in an individual and to apply this information towards a targeted therapy. Advances in technology with methods such as optical coherence tomography will likely yield a much more dynamic and comprehensive understanding of the sequence of UA collapse and reopening (45). Collapsibility in Obstructive Sleep Apnea Given that the UA is a collapsible tubular structure, a Starling resistor model of the UA can be applied to model the effects of changes in pressure on flow and collapsibility. The Starling resistor model implies that downstream (i.e., tracheal) negative pressure can reduce UA size but generally will not collapse the airway (39,46). Thus, it is the Pus (the pressure upstream to the collapsing segment) and/or Pout (representing tissue pressure and pharyngeal wall compliance and the effect of UA muscles) that must be altered to affect airway patency (39) (Fig. 1) Schwartz et al. measured Pcrit, the critical pressure at which the UA collapses as a means of quantifying collapsibility. Pcrit was compared in normal subjects, snorers and those with OSA. Grouping together the data from several studies, Schwartz and associates proposed the model of a spectrum of Pcrit values that increased with the degree of sleep-disordered breathing. A Pcrit of –8 cm H2O or less was present in normal people. As the Pcrit increases progressively to greater than atmospheric pressure there is an increase in severity of sleep-disordered breathing from snorer (–8 to –4 cm H2O), to obstructive sleep hypopneas (–4 to 0 cm H2O) and finally to apneas (Pcrit > 0 cm H2O) (39,47,48). It has been argued that the Pcrit may be influenced by muscle activity as well as anatomy. Although different techniques (active vs. passive) have been used to assess Pcrit, it is generally a product of the interactions between the soft tissue pressure, the compliance of the pharyngeal wall and the UA muscle activity and thus provides a measure of how anatomical factors influence collapsibility in an individual. The principal collapsing forces are the pressure of the tissues surrounding the airway and the intraluminal pressure. The major protective

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FIGURE 1 This figure illustrates the model of a rigid tube with a collapsible segment interposed within a sealed box. In (A) the pressure within the collapsible segment (Pin) and the pressure outside the collapsible segment within the box (Pout) are identified. In (B) Pout = +10 cm H2O and the pressure upstream to the collapsible segment (Pus) equals +5 cm H2O. Because Pin (+5 cm H2O) is less than Pout, the collapsible segment (Pus) remains collapsed and occluded. Parts (C) and (D) illustrate the addition of +15 cm H2O to the upstream side of the collapsible segment (Pus). In (C), the pressure within the tube downstream to the collapsible segment (Pds) is +9 cm H2O (< Pcrit, the critical pressure of the collapsible segment) and the collapsible segment collapses or flutters to maintain the intraluminal pressure at its downstream end at +10 cm H2O (Pcrit). In (D), Pds is + 11 cm H2O (> Pcrit) and the collapsible segment is widely open. Source: From Ref. 39.

mechanisms consist of the dilating and UA stiffening forces of the UA muscles. To exclude the effect of muscle activity, Isono et al. studied the Pclose, the pressure at which the UA collapsed as noted by pharyngeal endoscopy under general anesthesia with neuromuscular paralysis. In this study, 40 patients with sleep apnea, subdivided into a mild and severe OSA group, and 17 normal controls were evaluated. Their work was consistent with the findings of Schwartz et al. in that the closing pressure is positive in sleep apnea (mild OSA 1 cm H2O, severe OSA 3 cm H2O) and negative in normal controls (–4 cm H2O) (21). By providing a means to quantify UA collapsibility, the Pcrit permitted a comparison of the UA in sleeping subjects and demonstrated that the UA is more collapsible in OSA than normal controls. Schwartz et al. proposed, based on the spectrum of Pcrit and OSA severity that Pcrit could stratify OSA patients into different categories of UA collapsibility which may then be used to predict the likelihood of response to various therapies. In the OSA group with a high Pcrit, anatomy is the dominant factor and it is likely that continuous positive airway pressure (CPAP) will be the only effective therapy. In patients with a Pcrit in the intermediate range, other factors may contribute significantly to the pathogenesis of OSA and treatment directed at other variables important in the pathogenesis of OSA may be of benefit. However, this approach remains to be validated clinically. Sforza et al. assessed whether Pcrit could be used to assess OSA severity in a large series of 106 patients. They noted that Pcrit explained less than 5% of variance of OSA severity as measured either by apnea-hypopnea index (AHI) or by effective CPAP level (35) and proposed

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that other factors such as muscle activity must play an important role. Thus, variability in Pcrit in different OSA patients implies that the anatomy alone does not account for all different forms of OSA. Mechanisms Balance of Forces In OSA, recurrent UA collapse occurs only during sleep. In other words, regardless of the severity of the collapsibility of the UA and the deficiencies in anatomy, compensatory forces can maintain airway patency during wakefulness. The opposing forces have been expressed in the balance of forces concept put forward by Remmers et al. in this model, deficient anatomy is opposed by the activation of the UA muscles (21). This model implies that UA muscles in OSA patients during wakefulness must maintain a higher degree of activity to compensate for an abnormal anatomy. This neurocompensatory reflex mechanism was demonstrated by Mezzanotte et al. (49,50). These studies demonstrated a marked increase in genioglossus (GG) muscle activity (percentage of maximal activity in phasic inspiratory tone as well the baseline tone during the expiratory phase) in awake OSA patients as compared to normal controls. Tensor palatini (TP) also had a markedly higher baseline in patients with OSA (51). Thus, a complex neuromuscular regulatory control system for the pharyngeal dilators has evolved to maintain UA patency (52). This system appears to maintain the required balance for UA patency but is altered in sleep when UA muscle activity decreases. Neuromuscular Compensation Background There are many UA muscles that serve the different functions such as respiration, swallowing and speech. In attempting to understand the mechanisms of UA collapse, workers in the field have focused mainly on the muscles which maintain UA patency although those that act to collapse the UA, such as PC, have also been evaluated. PC form the wall of the UA from the nasopharynx to the epiglottis and was hypothesized to contribute to UA collapse in OSA. However, Kuna et al. demonstrated that PC not only do not contribute to the pathogenesis of OSA, but that under certain circumstances, PC may help to protect pharyngeal airway patency by stiffening the airway (53). Awake In general, the UA muscles which maintain UA patency can be divided into two groups: the phasic and tonic muscle groups which are controlled by separate groups of neurons. The neuronal groups differ in terms of their firing patterns with respect to the respiratory cycle. The inspiratory phasic muscles, which include the GG, increase activity (from neuronal firing) during inspiration to maintain airway patency in the setting of negative intraluminal pressures generated by an inspiratory effort. During expiration, the activity of an inspiratory phasic muscle returns to a reduced but “tonic” level. On the other hand, the tonic muscles, of which the TP is sometimes studied, are not influenced by respiration and maintain a constant level of activity throughout the respiratory cycle. TP, as its name suggests, tenses the soft palate and is thought to play a stabilizing role in the UA. Studies have focused on the GG, an easily accessible muscle, to understand UA motor control. GG is an extrinsic muscle of the tongue which is likely representative of phasic muscles key to the pathogenesis of OSA. Stimulation of GG via the

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hypoglossal nerve results in depression and protrusion of the tongue (54,55). GG activation with electrical stimulation results in a increase UA patency and decrease in UA collapsibility on the order of 3–4 cm H2O of Pcrit (56). Neuromuscular control is crucial for the GG muscle as it forms the anterior wall of the oropharynx and a sufficient loss of muscle activity will cause the tongue to fall back to compromise the oropharyngeal lumen (3,55). Given its role in the pathogenesis of OSA, GG activity is modulated by multiple protective mechanisms: the negative pressure reflex (NPR), premotor inputs (which activate the GG in preparation for the onset of airflow), respiratory cycle dependent activation from the central pattern generator, and the awake state (1,46,52). An important protective mechanism is the NPR. NPR is activated by local mechanoreceptors in the larynx and nasal mucosa which respond to the collapsing threat of diminishing pharyngeal pressure and results in rapid activation of GG (50,57–61). The NPR affects the phasic UA muscles when the stimulus is delivered as a pulse or even during tidal inspiration. In contrast, the tonic muscles, such as the TP, do not respond to negative pressure during tidal inspiration or resistive loading, but do activate to pulses of negative pressure, which is a less physiological stimulus (62). The high baseline phasic waking activity of GG in OSA can be attributed to a large extent to the NPR responding to local conditions in the setting of deficient UA anatomy. This is supported by the observation that while the intraluminal pressure in the UA of OSA patients is more negative than in normal subjects, the slope of the UA pressure versus GG EMG activity is the same between normal controls and OSA patients across a range of CO2 levels, negative pressures and breathing efforts (50). The greater GG EMG activity in OSA is therefore simply related to the greater negative pressure present in the UA owing to the smaller UA lumen and the increased tonic activity present in the GG during expiration. Evidence for the importance of the NPR comes from the application of nasal CPAP to eliminate the NPR and compensate for the deficient anatomy of the UA during wakefulness in OSA patient. Presumably, CPAP can abolish the NPR by minimizing negative pressure; therefore, the resulting marked reduction in GG activity (both peak phasic with inspiration and the baseline tonic expiration component) implies that NPR is the major factor in maintaining GG activity. However, CPAP has many other effects, including increased lung volume, which may influence GG activity independent of NPR. This reduced level of GG activity on CPAP still remains above the baseline for normal controls. This implies that in addition to the NPR, other factors contribute to the neurocompensatory response in OSA. As discussed below, other mechanisms contributing to increased UA tone in awake OSA patients include increased input from pattern generating neurons (RPGN), the independent effect of wakefulness, or even a neural learning effect (neuronal plasticity) (63). Similar processes may account for a higher baseline TP in patients with OSA (51). A second protective mechanism modulating GG activity is mediated by the central RPGN in the medulla. Inspiration results in activation of the GG concomitantly with diaphragmatic activation and serves to prepare the UA for the resulting drop of pressure in the airway. The augmentation of UA muscle activity in response to rising CO2 and/or decreasing O2 may also be mediated via the RPGN (64,65). A third protective mechanism is the tonic excitatory influence of wakefulness on the UA muscles (66). This so called “wakefulness stimulus” is proposed to be mediated by the neurotransmitters such as serotonin and noradrenaline which play key roles in the arousal system (67,68).

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A fourth protective mechanism is referred to as preactivation, which describes the activation of the GG prior to the onset of airflow. This is a protective modulation of UA muscle tone that is linked to the respiratory cycle (69–71). Premotor neurons supply inputs to the hypoglossal to lead to this activation prior to phrenic activation. In theory, the presence of preactivation allows the UA to prepare for the collapsing influence of subsequent airflow. Loss of preactivation, for example, as occurs during passive mechanical ventilation, does lead to a modest increase in pharyngeal resistance. The Effect of Sleep The balance of forces maintaining UA patency during wakefulness changes substantially during sleep with the balance shifting in favor of UA collapse in patients with OSA. Much work has been done to understand the changes induced by sleep in UA muscle and the neurocompensatory reflexes by which UA patency is defended. Sleep, by decreasing the “wakefulness” neuronal stimulus results in a substantial decrease in the activity of the pharyngeal muscles. In normal subjects there is a large decrease in tonic muscle activity from the awake baseline, for example, TP decreased by over 60% from wakefulness to delta sleep (51,72). This activity does not usually recover unless arousal occurs. With respect to phasic muscles, the GG activity decreases with sleep onset and then, as sleep progresses, gradually increases to a level similar to or above the baseline waking state (50,51,63,73). The increase in GG activity during sleep is stimulated by the chemical drive from increasing CO2 levels as sleep progresses in combination with increased UA resistance (74). This represents a compensatory response of the GG to the increasing UA resistance, which is due, at least in part to, decreased UA tonic muscle activity. A sizable reduction in the NPR occurs during NREM sleep and an even further reduction occurs during REM sleep. There is both a slower and less effective response to negative pressure (62,75–77). There is also a positional component to the GG and TP response to negative pressure in NREM. For example, in the supine position, there is a significantly greater NPR response than in the lateral decubitus position (78). Finally, the central respiratory pattern generator’s protective effect on UA tone appears to be one of the neuroprotective mechanisms that is spared or at least minimally affected during sleep, although this remains to be studied. In other words, preactivation of UA dilators before the onset of inspiration seems to persist whether awake or sleep. While sleep results in consistent decrements in baseline muscle activity in normal subjects, this decrement in baseline muscle activity is much greater in OSA patients for both tonic and phasic muscle groups. The loss of the protective effect of the wakefulness stimulus and the reduction in the NPR reflex with sleep onset are thought to account for most of the reduction of both phasic and tonic muscle groups. In OSA, the baseline several-fold increase in muscle activity is substantially reduced to a greater extent than that of normal controls with decrements being largest and most consistent in the TP. There is also a large reduction of the elevated awake GG activity with sleep onset, as compared with normal controls (63). This reduction is followed by a gradual increase in GG activity as sleep advances and UA resistance increases, consistent with the continued presence of the NPR. Fogel et al. noted an increase in GG activity in response to obstructive events but determined that the

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increased activity appeared inadequate to reestablish UA patency without arousals in most cases (63). THEORY OF UPPER AIRWAY COLLAPSE To summarize, the reason for airway collapse relates to deficient anatomy, resulting in more collapsibility which requires increased neuromuscular compensation awake and asleep to maintain UA patency. Unfortunately the complex and finely tuned neurocompensatory system present during wakefulness is lost with sleep onset leaving the airway vulnerable to collapse. From the discussion of the Starling resistor physiology, it is important to understand that negative inspiratory pressure is not the major mechanism for UA collapse and does not explain the timing of UA narrowing and end expiratory collapse. Rather, the key mechanism for UA collapse is a decrease in compensatory UA muscle activity and potentially a fall in endexpiratory lung volume. The timing of UA narrowing prior to collapse is not limited only to the inspiratory phase but also present during end-expiration. This is supported by several lines of evidence. First, UA resistance studies have demonstrated that airway collapse can occur during both inspiration and expiration (79). Using endoscopic studies, Morrell et al. have demonstrated that in OSA, there is a progressive reduction in the end-expiratory cross-sectional area in the breaths preceding the UA collapse associated with an obstructive apnea (80). Furthermore, esophageal pressures were significantly less negative when UA collapse did occur than during the preceding breaths, implying that negative pressure was not the cause of the UA collapse or apnea. Similar findings of end-expiratory reduction in UA lumen size using dynamic CT studies have been reported by Schwab et al. (81,82). The authors used this dynamic imaging technique to describe 4 phases of the respiratory cycle in relation to UA cross-sectional area in awake OSA patients and normal subjects (83). In early inspiration (phase 1), there is a slight increase in UA size, this remains stable during the rest of inspiration, (phase 2). During early-expiration (phase 3) there is a larger increase in UA size, related to the positive airway pressure of expiration. Finally, during end-expiration (phase 4), there is a large drop in UA size to its smallest level. Reduction in UA size at end-expiration is thought to relate to the decline in positive airway pressure during exhalation and the loss of phasic inspiratory muscle activity to support the UA. The main explanation for the reduction in UA size is because respiratory drive is lowest at end-expiration. In addition, lung volume is minimal at end-expiration which may contribute to pharyngeal compromise at this portion of the respiratory cycle. Thus, why the airway collapses is related in large part to the decrease in muscle activity protecting the UA. But the influence of specific factors on this muscle activity remains to be clearly defined. A promising new line of research to understand the myriad of influences that control GG activity involves the assessment of single motor units of the GG (84,85). This detailed assessment of individual motor units yields much more information about the activity of particular motor neurons in the brainstem and permits differentiation of the various components that make up the phasic increase in muscle activity related to inspiration as well as the component baseline tonic activity during expiration. The reasons behind why UA collapse occurs when it does, either during the inspiratory or the expiratory portion of a single respiratory cycle or during particular stages of sleep remains unknown. What is clear is that UA anatomy alone fails to explain most of the variance in the apnea hypopnea index (AHI) (36) and also fails

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to explain why the UA collapse during certain portions of the night and not in others. Furthermore, two individuals with the same anatomical propensity to UA collapse, as determined by Pcrit, may have quite different OSA severity. Clearly other factors need to be evaluated in order to understand the pathogenesis of OSA. There are likely many components that come together to explain the severity of OSA in a given individual (1,46). A discussion of the newer phenotypes as theories of pathogenesis of OSA follows. NEWER THEORIES OF UPPER AIRWAY COLLAPSE (FIG. 2) Lung Volume Lung volume can affect UA collapsibility independent of UA muscle activity. Decreased end-expiratory lung volume results in increased UA collapsibility while increased lung volume has the opposite effect. Van de Graaff demonstrated, in a dog model, that there were several potential underlying mechanisms for lung volume

FIGURE 2 Proposed conceptual model of the pathogenesis of obstructive sleep apnea (OSA) during sleep. Deficient upper airway (UA) anatomy combined with sleep-induced decrements in protective reflexes result in UA collapse and OSA. In addition to anatomy, decreased lung volume, sensory impairment, myopathy and increased surface tension can all contribute to increase UA collapsibility either directly or by impairing reflex neuromuscular compensation (RNC) (* can affect RNC). Reflex compensation for deficient anatomy (dashed arrows and boxes on the left side) acts to prevent UA collapse while awake, and may contribute to maintaining UA patency during sleep. Although not illustrated for simplicity, a high arousal threshold leads to sleep fragmentation and leaves an insufficient amount of time for RNC to occur. An increased loop gain (represented by the ellipses, see text for further discussion) can aggravate OSA as the ventilatory response to a respiratory disturbance (apnea in this case) is excessive and leads to loss of output from the respiratory central pattern generator and further cycles of obstructive apneas. Abbreviation: CPG, central pattern generator.

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effects including UA stiffening via tracheal traction from the lung and mediastinal structures, as well as the pressure gradient that develops with inspiration (86). Subsequently, other workers have reproduced these results in humans (87–89). Investigators have also demonstrated that similar changes in lung volume result in a greater effect on UA patency among sleep apneics, consistent with a more compliant UA in OSA (90,91). A number of human studies regarding lung volume influences on the UA have been performed during wakefulness which complicates interpretation of the findings. Behavioral factors may explain the increased UA size, for example, behaviorally increased GG activity at total lung capacity. Heinzer et al. quantified the effect of lung volume changes on UA collapsibility during sleep by using the CPAP level required to prevent flow limitation as a surrogate marker of collapsibility (92). The authors demonstrated that relatively small changes in lung volume resulted in major changes in the CPAP level. CPAP requirements decreased by 7 cm H2O and increased by 5 cm H2O for lung volume changes of +421 ml and –567 ml respectively. There are several important implications stemming from this study. These data suggest that the therapeutic effect of CPAP results from not only the splinting effect of the UA but also the changes in lung volume. The magnitude of the volume effect as opposed to the UA splinting effect of CPAP however remains to be defined, since these mechanical effects are difficult to uncouple in humans. Furthermore, scenarios which result in decrements in lung volumes occur frequently and may be underappreciated in the pathogenesis of OSA and UA collapse. The supine position is associated with an important decline in functional residual capacity (FRC) in normal individuals. With sleep, FRC decreases progressively as sleep transitions from stage 2 to stage 3–4 or REM, reaching a nadir in REM where FRC is reduced by 300–500 ml versus baseline during wakefulness (93,94). The increased UA collapsibility in obese patients (90) may be partly related to a greater decrease in FRC in the supine position because of increased weight from fat deposition in the abdomen and thorax (19). Several classic experiments were not controlled for lung volume, such as the study by Isono et al. (21) evaluating UA collapsibility under general anesthesia and with neuromuscular paralysis. Both factors are known to reduce lung volumes significantly, especially in the setting of hyperoxia. In obese patients, weight loss was shown to be associated with decreased collapsibility, based on reduction in Pcrit (95), with decreased fat deposits around the neck being the presumptive mechanism. However, weight loss may have significant effect on FRC and lung volumes and this may in itself lead to reduction in UA collapsibility (36). Changes in size of the UA during and inspiratory-expiratory cycle (80–82) may be partly mediated by changes in lung volume. The UA is most reduced at end-expiration when lung volume is at its nadir. In summary, factors other than the balance of UA anatomy and UA muscles contribute to UA collapse. Lung volume may be an important factor that has previously been under appreciated. Further work is required to evaluate the magnitude of lung volumes effects. In particular, the effect of sleep on lung volume in OSA patients or on the changes in lung volume and the AHI remain to be explored. Ventilatory Control Instability OSA patients experience repeated UA collapse alternating with episodes of UA patency. It has been hypothesized that ventilatory control instability, rather than

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UA instability per se, may be driving this oscillatory breathing pattern in some OSA patients. This is supported by the observation that periodic breathing persists following tracheostomy in some OSA patients (96), suggesting that deficient anatomy is not the only cause of OSA. Ventilatory instability may predispose to UA collapse through reduction in respiratory drive at the nadir of cyclic breathing, which in turn reduces pharyngeal muscle activation and increases the risk for collapse (1,97). Respiratory Drive and Upper Airway Patency Respiratory drive is an important factor in maintaining UA patency. Central apnea, a situation defined by loss of respiratory drive, is associated with narrowing or collapse of the UA (98,99). Badr et al. demonstrated that the majority of central apneas, whether induced by hypocapnia or spontaneous, are associated with complete pharyngeal occlusion. Furthermore, this collapse is more likely to occur in patients with OSA, that is, patients who have a more collapsible airway, as opposed to normal controls (99). Intermittent hypoxia-induced periodic breathing can induce OSA by causing recurrent UA collapse during the waning phase of ventilatory cycling (97,98,99). Thus, loss of respiratory drive is another situation in which the UA may collapse without requiring negative intraluminal pressures or markedly abnormal pharyngeal anatomy. Loop Gain Younes et al. developed a method, using proportional assist ventilation, to quantify the degree of ventilatory control instability or loop gain (101). Loop gain (LG), describes the propensity for a system controlled by feedback loops, such as the respiratory chemical control system, to develop unstable periodic behavior (102). LG can be defined as the ratio of the ventilatory response to a disturbance. In the context of OSA, this represents the ventilatory response to an apnea or hypopnea (45) (Fig. 3).

FIGURE 3 Loop gain. The ventilatory response to an apnea (first disturbance in both figures) is demonstrated for (A) an individual with a loop gain (LG) of 0.5 and (B) an individual with an LG of 1. In (A) ventilation quickly returns to a regular pattern, whereas in (B) a sustained oscillation is established. In a system with a loop gain (LG) < 1, the effect of a disturbance will gradually dissipate, as the response (e.g., hyperpnea) to an initial disturbance engenders a cycle of progressively smaller series of disturbance and response. In a system with a LG greater or equal to 1, this will lead to perpetuation or even amplification of the disturbance over time and periodic breathing. Source: Modified from Ref. 46.

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Loop Gain in Obstructive Sleep Apnea LG is higher in severe OSA as compared to mild OSA (101) or normal controls (103). A higher LG means a greater tendency to develop periodic breathing with its associated fluctuation in respiratory drive and change in UA size. Thus if a high LG is important in OSA, it should correlate with OSA severity based on AHI. Wellman et al. studied the impact of LG on apnea severity (104). The authors stratified OSA patients into 3 groups based on Pcrit, a marker of anatomical collapsibility, and determined LG using the methodology developed by Younes et al. They found that LG had a significant impact on AHI severity, r = 0.88, p = 0.002, but only in the OSA group with an atmospheric Pcrit between –1 cm H2O and 1 cm H2O. The authors proposed that when anatomy is severely compromised, that is, Pcrit greater than 1 cm H2O, then LG has no opportunity to influence UA size or OSA severity because the airway is so collapsible that almost any decrease in UA muscle tone will cause UA collapse and more subtle fluctuations in UA muscle tone will be masked by this collapse. When anatomy is less vulnerable, that is, the UA is collapsible only with pressure below –1 cm H2O, then LG is also unlikely to affect OSA severity, as the decreases in UA muscle activity associated with fluctuations in respiratory drive are insufficient to collapse the UA. However, when anatomy is borderline or “intermediate,” as demonstrated by a Pcrit –1 to 1 cm H2O, then LG plays a central role in OSA severity, likely by altering respiratory drive and indirectly altering UA muscle tone. Loop gain can be divided into three major constituents: plant gain, controller gain and mixing gain. Plant gain is the ratio of the change in CO2 for a given change of ventilation or the efficiency of CO2 excretion. Plant gain is affected by CO2 levels, V/Q matching and FRC. By definition, conditions that result in a large quantity of CO2 excreted for a given level of ventilation will represent a high plant gain situation. For example, low dead space, low metabolic rate, low cardiac output, and a high PCO2 are factors that may raise plant gain. Controller gain is the ratio of change in ventilation for given level of CO2 detected by the chemoreceptors and is affected by chemosensitivity, lung mechanics, and respiratory muscle force (i.e., not just chemosensitivity but also chemoresponsiveness). Bilevel positive airway pressure, for example, can increase controller gain by increasing the change in ventilation for any given level of CO2. Controller gain is affected by hypoxia, heart failure and increased pulmonary vascular pressures (105). Mixing gain is the delay related to mixing of gases in blood and the circulatory delay from the lung capillaries to the chemoreceptors. Mixing gain is not thought to be a major factor in OSA where circulation time is similar in most OSA patients provided there is no superimposed heart failure. CO2 Reserve and Loop Gain During sleep, ventilation is primarily regulated by metabolic mechanisms, with a predominant role for CO2 in this negative feedback system (106). In normal NREM sleep the eupneic PaCO2 increases by 2–5 mmHg because of sleep-associated hypoventilation. There exists an apnea threshold, defined as the level of CO2 below which ventilatory drive is absent and apnea ensues (107). The apnea threshold is normally 1–2 mmHg below the waking PCO2 level. An important point is that once the apnea threshold is breached, respiration does not resume until the PaCO2 has increased significantly above not only the apnea threshold but the eupnea threshold (105). The physiologic delay in the system caused in part by the circulatory time (i.e., mixing gain), results in the overshoot in CO2. The ensuing ventilatory response in turns produces a hypocapneic overshoot. In the right context, this fluctuation in

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. FIGURE 4 Diagrammatic representation of .the relationship between alveolar . . ventilation (V A ) and alveolar PCO2 (PACO2) at a fixed (resting) VCO2 [250 mL/min; PACO2 = (V CO2 / V A ) × K]. See text for full explanation. Source: Modified from Ref. 108.

CO2 can reach a nadir where respiratory drive is reduced sufficiently to produce a loss of UA patency. In other words, during sleep, CO2 is the feedback signal that engenders a ventilatory response, measured as a component of loop gain. Dempsey et al. have termed the difference between PaCO2 at eupnea and the apnea threshold, the CO2 reserve (105). Loop gain can be understood in term of its effect on the CO2 reserve. In general, the CO2 reserve is affected by changes in plant gain but also by changes in controller gain. This is best illustrated graphically (108). In Figure 4, the solid line represents the relationship between CO2 and alveolar ventilation at a constant level of CO2 production. The slope of this solid line is the inverse of plant gain. The dashed line represents the relationship between the base(point 1) and the apnea threshold (point 2), which is the CO2 line CO2 at eupnea . level where Va falls to zero. For a given change in CO2 from eupnea to apnea, depending on the starting eupneic level of CO2, the change in ventilation is quite

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variable, as denoted by height of the vertical dotted line above the solid line. Notably, as plant gain increases, for example with hypercapnea, there is a much smaller change in ventilation needed to reach the apnea threshold. The CO2 reserve decreases to a moderate extent, changing inversely with plant gain. The controller gain is represented by the slope of the dashed line. As can be seen in Figure 4B, an increase in controller gain, as represented by increasing the slope of the dashed line, results in a much greater decrease in the CO2 reserve. This results in increased system instability as small fluctuation in CO2 may now lead to respiratory disturbances (apneas) with their resulting destabilizing responses. As discussed above, either a high plant gain or a high controller gain can destabilize ventilatory control by reducing the difference between baseline CO2 and the apnea threshold. Neuromuscular Compensation During Sleep During sleep, most patients with OSA have periods of stable breathing (109,110). Younes proposed that some form of neuromuscular compensation must be present to account for this (109). UA muscles have been shown to respond, albeit to a lesser extent than during wakefulness, to negative pressure, CO2 and resistive loading during sleep (74,78,111). Using a CPAP “dial down” technique to assess passive collapsibility, Younes demonstrated that non-anatomic factors accounted for most of the variability in OSA severity. He proposed that it is the ability to recruit UA muscles, or “compensatory effectiveness” that is the key factor in determining the severity of OSA. This remains speculative, and more work is required to evaluate the importance of compensatory effectiveness and OSA severity. Arousal Threshold Arousal from sleep is determined by ventilatory effort as can be demonstrated using esophageal pressure (110,112) and is not related to the degree of hypoxia or hypercapnea. There is a large interindividual variation in ventilatory effort required for arousal both in normal subjects and in OSA patients (113). This difference in arousal thresholds may have important interaction with the ability of the UA to compensate for OSA. Individuals with a low arousal threshold may wake up too soon after the UA narrows or collapses, before UA muscle recruitment ( i.e., neuromuscular compensation) to reopen the UA can occur. Younes has proposed that arousals are not always necessary to reopen an occluded airway but may be contributing to the pathogenesis of OSA by leading to hyperventilation and ventilatory overshoot with hypocapnia. An arousal, therefore, becomes a destabilizing factor (114). Motor and Sensory Deficits Some authors have proposed that recurrent UA collapse may result in nerve and muscle injury from the mechanical trauma of pressure changes, tissue vibration, and eccentric muscle contractions (115,116). The increased activation of UA muscles in OSA may also lead to muscle injury (117). The resulting myopathy or muscle injury can contribute to UA collapse by reducing the effectiveness of muscular force generation to dilate the UA. In OSA, GG muscle fibers shift to a predominance of fast twitch type II fibers which are more sensitive to fatigue (118). Carrera et al. suggested that this change was the result of OSA, as it was not present in the GG of CPAP treated patients. In addition to shift in muscle fiber type, there is also evidence of increased in vitro fatigability and a shift in glycolytic

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enzyme activity (118,119). There is evidence that, a T-lymphocyte predominant inflammation is present not only in the mucosa (CD4 and CD8) but also involves the muscle (CD4 mainly) of the UA in OSA (120). These inflammatory changes were associated with evidence of denervation of the muscles suggesting at least two mechanisms for contractile dysfunction, a direct T-lymphocyte effect on the muscle and denervation. Another mechanism by which neuromuscular compensation may be impaired is from mucosal sensory dysfunction. Several groups have demonstrated that decreased UA sensation with topical anesthesia can result in decreased UA dilator muscle activity and either development of OSA in normal controls or worsening of OSA (111,121,122). Investigators have subsequently confirmed the presence of a sensory impairment in the UA of OSA patients (116,123). Nguyen et al. demonstrated the presence of sensory impairments to pressure pulses at multiple levels in the UA and that decreased sensation at the larynx (level of the aryepiglottic eminence) correlated with OSA severity as measured by AHI (124). Although preliminary, these data have raised the hypothesis that in addition to anatomy and other factors discussed above, sensory impairment can contribute to OSA severity by impairing compensatory protective reflexes and that muscle injury from inflammation and mechanical trauma may also reduce the effectiveness of these protective reflexes. However, studies comparing the magnitude of the NPR in OSA patients versus controls have demonstrated no major impairment in reflexes in OSA despite the observed sensory losses in the other experiments. Other Mechanisms Kirkness et al. have shown that surface tension of the liquid lining the UA wall mucosa can affect UA collapsibility by applying a technique that quantifies surface tension as the force required to separate 2 surfaces bridged by a droplet of the liquid studied. These authors noted a higher surface tension in OSA patients and that a reduction in surface tension with surfactant led to a reduction in UA collapsibility as demonstrated by a decline in Pcrit and improvement in severity of OSA as measured by the respiratory disturbance index (RDI) (125,126). The magnitude of decrease in Pcrit of 2–3 cm H2O with surfactant therapy is similar to that achieved with changes in body position, thus it has a non-negligible impact for certain patients (127). The 30% improvement in RDI is consistent with the previous work from other groups that have shown improvements in RDI of approx 20% with UA surfactant therapy (125,128,129). NEUROTRANSMITTERS AND SLEEP (FIG. 5) A detailed understanding of the central nervous system (CNS) mediators of pharyngeal muscle activity during wakefulness and sleep remains elusive. However, it is likely that the series of neurotransmitter systems (including cholinergic, adrenergic, serotonergic, histaminic and orexinergic) that modulate wakefulness and sleep (130,131) can also act on the motor nuclei of UA muscles to influence UA patency. The role of inhibitory neurotransmitter such as glycine and GABA remains to be evaluated. Excitatory neurotransmitter such as serotonin (5-HT) and noradrenaline (NE) are believed to be the major modulators of this control mechanism. Evidence in support of this consists of the anatomical relationships of neuronal projections, the effect of receptor agonists and antagonists, and the timing of firing and activity of these neuronal groups across waking and sleep states. Studies have focused on

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FIGURE 5 Schema of the neuronal circuitry that is currently believed to be involved in the pontine regulation of rapid eye movement (REM) sleep and generation of motor atonia. Decreased discharge in dorsal raphé and locus coeruleus complex neurons preceding and during REM sleep progressively disinhibits pontine cholinergic neurons of the laterodorsal and pedunculopontine tegmental nuclei (LDT/PPT) via withdrawal of serotonin (5-HT)-mediated and noradrenalinemediated inhibitory inputs. Activation of these LDT/PPT neurons then leads to increased acetylcholine (ACh) release into the pontine reticular formation, resulting in activation of the neuronal systems that mediate ascending and descending signs of REM sleep (e.g., cortical desynchronization and motor atonia, respectively). Postural motor atonia in REM sleep is produced by postsynaptic inhibition of motor neurons by γ-aminobutyric acid (GABA) and glycine. Neurons of the medullary reticular formation are thought to drive this inhibition, themselves being driven by neurons in the pontine reticular formation (the reticular structures are indicated by the boxes). Whether hypoglossal (XII) motor neurons are also postsynaptically inhibited in REM sleep by similar mechanisms is uncertain. Hypoglossal motor neurons also receive excitatory inputs from the locus coeruleus complex and medullary raphé that may also contribute to reduced genioglossus muscle activity in sleep, especially REM sleep. Co-release of thyrotropin-releasing hormone and substance P from raphé neurons may contribute to this process. The influences of other neural systems that are potentially modulated by sleep states are not included for clarity. +, excitation; –, inhibition; M, muscarinic. Abbreviations: LDT, PPT, laterodorsal and pedunculopontine tegmental nuclei; TRH, thyrotropinreleasing hormone. Source: From Ref. 67.

the hypoglossal motor nucleus (HMN) and the GG as the experimental model for the controller and pharyngeal airway muscle (67,68). 5-HT is one of the most studied neurotransmitter in this context. Raphe serotonergic neurons project to the HMN and to other motor nuclei controlling pharyngeal dilator muscles. Serotonin receptor agonists are excitatory to hypoglossal motor neurons (132,133). 5-HT antagonism in bulldogs leads to decreased pharyngeal muscle activity and decrease UA size with worsening of OSA in these animals with already compromised UA anatomy (134). The firing of the medullary raphe serotonergic neurons are state dependent, decreasing progressively from wake to slow wave and REM sleep (135,136). However, Sood et al. have shown that 5-HT has a minimal effect on the HMN drive unless there are additional mechanisms to augment the effects of 5-HT (137). In animal models, respiratory stimulation by intermittent hypoxia can lead to augmentation of hypoglossal motor activity, a protective mechanism mediated by 5-HT (138,139). Over a longer time period, Veasey et al. (140) reported that long term intermittent hypoxia can impair the 5-HT

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mediated augmentation of HMN activity (140). In keeping with these animal data, clinical studies of serotonin reuptake inhibitors have generally demonstrated limited improvements in OSA severity (141-144). Noradrenergic neurons in the locus coeruleus also show a state dependence with reduction in firing activity from waking to NREM and finally REM sleep (145). Locus coeruleus (LC) neurons project throughout the brain and are thought to increase the excitability of the hypoglossal neurons (67). However, further studies of the role of noradrenaline are required. Similarly data supporting a role for thyrotropin-releasing hormone (TRH) and substance P, both of which co-localize with 5-HT and have an excitatory effect on the HMN, remain preliminary. Other neurotransmitter systems such as histamine, acetylcholine and orexin (146) may also play a regulatory role through monosynaptic connections to the hypoglossal motor nucleus. Clearly, much further work remains to be done before the pathogenesis of UA collapse is understood at the neuronal level. CONCLUSIONS OSA is the result of a combination of anatomical factors that predispose the UA to collapse superimposed on a decrease in neuromuscular compensation during sleep. The severity of OSA is likely determined by many factors that affect the UA. In this chapter we reviewed the anatomical basis of OSA, discussed the uniqueness of the human UA, as well as the neuromuscular protective factors and their controlling factors. We explored other mechanisms in the pathogenesis of OSA, such as decreased lung volume, increased ventilatory control instability, low arousal threshold, as well as, sensory and/or motor dysfunction. These theories of OSA are not mutually exclusive and likely play different roles in different individuals leading to the spectrum of OSA phenotypes. OSA is a disease where anatomy, while key in determining who develops OSA, does not account by itself for the severity of OSA. Further advances in understanding the pathogenesis of OSA will likely add to our armamentarium of diagnostic and treatment modalities in OSA. REFERENCES 1. White DP. The Pathogenesis of Obstructive Sleep Apnea: Advances in the Past 100 Years. Am J Respir Cell Mol Biol 2006; 34(1):1–6. 2. Bickelmann AG, Burwell CS, Robin ED, Whaley RD. Extreme obesity associated with alveolar hypoventilation; a Pickwickian syndrome. Am J Med 1956; 21(5):811–818. 3. Remmers JE, deGroot WJ, Sauerland EK, Anch AM. Pathogenesis of upper airway occlusion during sleep. J Appl Physiol 1978; 44(6):931–938. 4. Schwab RJ. Pro: sleep apnea is an anatomic disorder. Am J Respir Crit Care Med 2003; 168(3):270–271. discussion 273. 5. Strohl KP. Con: sleep apnea is not an anatomic disorder. Am J Respir Crit Care Med 2003; 168(3):271–272. discussion 272–273. 6. Remmers JE. Wagging the tongue and guarding the airway. Reflex control of the genioglossus. Am J Respir Crit Care Med 2001; 164(11):2013–2014. 7. Davidson TM, Sedgh J, Tran D, Stepnowsky CJ, Jr. The anatomic basis for the acquisition of speech and obstructive sleep apnea: evidence from cephalometric analysis supports The Great Leap Forward hypothesis. Sleep Med 2005; 6(6):497–505. 8. Davidson TM. The Great Leap Forward: the anatomic basis for the acquisition of speech and obstructive sleep apnea. Sleep Med 2003; 4(3):185–194. 9. Kuna S, Remmers JE. Anatomy and physiology of upper airway obstruction. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine, 3rd ed. Philadelphia: W.B. Saunders Company, 2000:840–858.

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127. Schwartz AR, Schneider H, Smith PL. Upper airway surface tension: is it a significant cause of airflow obstruction during sleep? J Appl Physiol 2003; 95(5):1759–1760. 128. Morrell MJ, Arabi Y, Zahn BR, Meyer KC, Skatrud JB, Badr MS. Effect of surfactant on pharyngeal mechanics in sleeping humans: implications for sleep apnoea. Eur Respir J 2002; 20(2):451–457. 129. Jokic R, Klimaszewski A, Mink J, Fitzpatrick MF. Surface tension forces in sleep apnea: the role of a soft tissue lubricant: a randomized double-blind, placebo-controlled trial. Am J Respir Crit Care Med 1998; 157(5 Pt 1):1522–1525. 130. Jones BE. From waking to sleeping: neuronal and chemical substrates. Trends Pharmacol Sci 2005; 26(11):578–586. 131. Espana RA, Scammell TE. Sleep neurobiology for the clinician. Sleep 2004; 27(4):811–820. 132. Kubin L, Tojima H, Davies RO, Pack AI. Serotonergic excitatory drive to hypoglossal motoneurons in the decerebrate cat. Neurosci Lett 1992; 139(2):243–248. 133. Jelev A, Sood S, Liu H, Nolan P, Horner RL. Microdialysis perfusion of 5-HT into hypoglossal motor nucleus differentially modulates genioglossus activity across natural sleep-wake states in rats. J Physiol 2001; 532(Pt 2):467–481. 134. Veasey SC, Panckeri KA, Hoffman EA, Pack AI, Hendricks JC. The effects of serotonin antagonists in an animal model of sleep-disordered breathing. Am J Respir Crit Care Med 1996; 153(2):776–786. 135. Jacobs BL, Azmitia EC. Structure and function of the brain serotonin system. Physiol Rev 1992; 72(1):165–229. 136. Kubin L, Davies RO, Pack AI. Control of Upper Airway Motoneurons During REM Sleep. News Physiol Sci 1998; 13:91–97. 137. Sood S, Morrison JL, Liu H, Horner RL. Role of endogenous serotonin in modulating genioglossus muscle activity in awake and sleeping rats. Am J Respir Crit Care Med 2005; 172(10):1338–1347. 138. Bocchiaro CM, Feldman JL. Synaptic activity-independent persistent plasticity in endogenously active mammalian motoneurons. Proc Natl Acad Sci USA 2004; 101(12):4292–4295. 139. Ling L, Fuller DD, Bach KB, Kinkead R, Olson EB, Jr., Mitchell GS. Chronic intermittent hypoxia elicits serotonin-dependent plasticity in the central neural control of breathing. J Neurosci 2001; 21(14):5381–5388. 140. Veasey SC, Zhan G, Fenik P, Pratico D. Long-term intermittent hypoxia: reduced excitatory hypoglossal nerve output. Am J Respir Crit Care Med 2004; 170(6):665–672. 141. Veasey SC. Serotonin agonists and antagonists in obstructive sleep apnea: therapeutic potential. Am J Respir Med 2003; 2(1):21–29. 142. Hanzel DA, Proia NG, Hudgel DW. Response of obstructive sleep apnea to fluoxetine and protriptyline. Chest 1991; 100(2):416–421. 143. Kraiczi H, Hedner J, Dahlof P, Ejnell H, Carlson J. Effect of serotonin uptake inhibition on breathing during sleep and daytime symptoms in obstructive sleep apnea. Sleep 1999; 22(1):61–67. 144. Berry RB, Yamaura EM, Gill K, Reist C. Acute effects of paroxetine on genioglossus activity in obstructive sleep apnea. Sleep 1999; 22(8):1087–1092. 145. Aston-Jones G, Bloom FE. Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle. J Neurosci 1981; 1(8):876–886. 146. Young JK, Wu M, Manaye KF, et al. Orexin stimulates breathing via medullary and spinal pathways. J Appl Physiol 2005; 98(4):1387–1395.

12

Risk Factors Kannan Ramar and Christian Guilleminault Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, U.S.A.

INTRODUCTION Obstructive sleep apnea (OSA) is a disorder that commonly affects middle-aged women and men in the United States (1). The prevalence of OSA reported in the literature has a wide range owing to inconsistencies in the definition and sampling biases. On the basis of pooled data from four large prevalence studies that used similar in-laboratory monitoring, diagnostic criteria, and sampling methods, it is estimated that one in five white adults with a body mass index (BMI) of 25–28 kg/m2 have an apnea-hypopnea index (AHI) ≥ 5 to < 15 (mild disease) and 1 in 15 have an AHI ≥ 15 (moderate to severe disease) (1–5). This classification of disease severity is often used, though it has little validity. It is known that long apneas or hypopneas may lead to important drops in oxygen saturation (SaO2), and at the same time owing to their duration, to a lower number of events per time unit. An alternative proposed method is to use a combined number of events per unit time and level of SaO2 drops as criteria for severity, but even this approach may not be solid, as neurocognitive function and alertness may be affected even with snoring and shorter duration of hypopneas than with long obstructive apneas; “severity” is thus also based on these variables that are normally used to define “impairment.” OSA is characterized by sleep fragmentation owing to repeated arousals and disruption of normal sleep architecture secondary to partial or complete closure of the upper airway during sleep (6,7). In nonobese subjects, OSA is typically described with the polysomnographic pattern of OSA and hypopnea and associated symptoms, particularly tiredness, fatigue and/or complaints of sleepiness. As many patients may be seen after years of evolution of undiagnosed sleep-disordered breathing, it is often difficult to identify the initial factor responsible for the OSA polysomnographic pattern. Was OSA present before the change in morphology, and did it play a role in the occurrence of the current morphology? Or was the noted abnormal breathing during sleep a consequence of change in the subject’s morphology? Often it is impossible to answer this question when patients are first seen, as they may clearly be overweight at entry. In fact it would be better not to use the term OSA in the presence of obese patients with polysomnographic patterns of sleepdisordered breathing. Also one should be careful in attributing specific complications such as cardiovascular (CV) or metabolic complications to OSA when obesity is present, as obesity per se can also lead to the same complications. Data analyses using specific statistical methods such as multiple regression analysis may help in a large cohort, but the cohort should include at least as many nonobese as obese individuals with OSA to provide valid responses. The major problem, as shown below, is that obesity per se, is a risk factor for OSA and sleep disruption. There are many notable risk factors for OSA, with obesity and craniofacial features being by far the most important. Evaluating and assessing risk factors for 197

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OSA helps the clinician to classify subjects as high risk (urgent need for polysomnography) or low risk. In this chapter, we review and discuss the various risk factors for OSA that might help to increase our awareness for a diagnosis of OSA. OBESITY (SEE ALSO CHAPTER 20) There are multiple established factors that predispose one to OSA, ranging from genetic makeup to upper airway abnormalities to various craniofacial phenotypes, but excess weight is the strongest risk factor (8). The BMI (weight in kg/height in m2) is commonly used to define and quantify obesity with a cutoff value of 25. The association between obesity and OSA has long been appreciated. In 1836, Charles Dickens in The Pickwick Papers (first published in 1835) recognized that obesity was a risk factor for sleep apnea in his detailed description of the “fat boy Joe” (independently, Joe could be a model for teen-age obesity with OSA or for Prader-Willi syndrome with OSA and hyperphagia): “and on the box sat a fat and red-faced boy, in a state of somnolency … ‘Joe!—damn that boy, he’s gone to sleep again:’ … The fat boy rolled slowly off the box … ‘Joe, Joe!’ said the stout gentleman. ‘Damn that boy, he’s gone to sleep again. Be good enough to pinch him, sir–in the leg, nothing else wakes him … Joe! Joe!’ … [He] taps on the head with a stick, and the fat boy, with some difficulty, roused from his lethargy. ‘Come hand in the eatables.’ There was something in the sound of the last word which roused the unctuous boy. He jumped up and the leaden eyes, which twinkled behind his mountainous cheeks, leered horribly upon the food...” In 1956, OSA was recognized as a disease of obesity and hypoventilation: the Pickwickian syndrome (9). Because then, observations of patients diagnosed with OSA and findings from studies have overwhelmingly supported a strong and likely causal role of excess weight in this condition. There is a graded increase in OSA prevalence with increasing BMI as shown by several crosssectional studies (2,3,10–14) and population-based studies (1,15–23). Almost all have found significant associations between OSA and measures of excess body weight. There seems to be little controversy that the associations seen in observational studies represent a causal role of excess weight in OSA. However, there are several important questions regarding the nature of the association that continue to merit examination, including the magnitude of the association and variability of response of OSA to excess weight; the role of excess weight in the natural history of OSA; the usefulness of weight control as a preventive measure or as a treatment for OSA; the importance of excess weight in subgroups defined by sex, ethnicity, and age; and the importance of specific distribution of excess fat in the body. The connection between body mass and OSA is evident not only in a higher incidence of OSA but also in its severity. Weight Gain and Obstructive Sleep Apnea Several studies have shown a good correlation between weight gain and OSA; even an increase in BMI of just 1 standard deviation is associated with a four-fold increase in risk for OSA (1). One study showed that a BMI of at least 25 kg/m2 had a sensitivity of 93% and a specificity of 74% for OSA (14). In a longitudinal analysis of a subset (n = 690) of the Wisconsin cohort with a four-year follow-up, a 10% increase in weight was associated with a six-fold greater risk of developing OSA among persons initially free of OSA (24). The five-year incidence of new sleep-disordered breathing (SDB) was investigated in the Cleveland

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family study (25). Of the 286 men and women (mean age = 36.8 year) who had no SDB (indicated by AHI < 5) at baseline, the incidence of new SDB (defined by developing AHI ≥ 15 at follow-up) was 3.3% for those whose baseline BMI was < 24, and 22% for those whose baseline BMI was ≥ 31. Longitudinal data from the Sleep Heart Health Study were used to examine five-year changes in weight and AHI based on in-home polysomnography of 2,968 men and women ages 40 to 95 year (26). Results indicated that in men, the odds ratio for a five-year increase in AHI of ≥ 15 with a gain of at least 10 kg was 5.2. All of these studies clearly show an increase in the incidence and severity of OSA with weight gain. Weight Loss and Obstructive Sleep Apnea While there is now strong evidence that excess weight is a risk factor for OSA as mentioned above, there is also data that shows a consistent trend in feasibility of weight loss as a means of reducing the incidence and severity of OSA. Several small studies of surgical or dietary weight loss interventions in clinical populations of patients with OSA who are obese have shown consistent and substantial decrease in OSA severity following weight loss (27,28); an approximately 3% reduction in AHI is associated with each 1% reduction in weight (4). These latter findings have important clinical implications for overweight patients with OSA who are poor candidates for continuous positive airway pressure (CPAP) therapy. Similarly, the Wisconsin longitudinal cohort study with a four-year follow-up period showed a decrease in AHI by 26% with a 10% weight loss. Again, looking at the longitudinal data from the Sleep Heart Health Study on 2,968 men and women ages 40 to 95 year (26), results showed that although weight loss predicted a decrease in AHI, the effect was weaker than that of weight gain on an increase in AHI. The odds ratio for a loss in AHI ≥ 15 that occurred with a loss ≥ 10 kg was 2.9 compared to the odds ratio of 5.2 for a five-year increase in AHI of ≥ 5 with a gain ≥ 10 kg. It is important to understand that though these studies showed a significant decrease in weight, it was not associated with complete elimination of OSA. Effect of Obesity on Obstructive Sleep Apnea As seen before, studies have suggested that obesity increases the incidence and severity of OSA, and it has been hypothesized that excess body weight affects breathing in numerous ways that include: (i) change in upper airway structure (e.g., altered anatomy), (ii) change in upper airway function (e.g., increased collapsibility), (iii) unstable relationship between respiratory drive and workload, and (iv) exacerbation of OSA events via obesity-related reductions in functional residual capacity and increased whole-body oxygen demand (29–31). These hypotheses are supported by studies that have shown that in obese men with OSA, weight loss increases the upper airway cross-sectional area, and thereby decreases the severity of OSA (32,33). Fat Distribution Accumulation of fatty tissue in the body can vary from person to person, with accumulation occurring in the upper part or lower part of the body. The greatest risk for SDB is associated with weight gain in the upper part of the body: the centripetal pattern of obesity, with fat preferentially distributed to the abdominal viscera, upper body, and neck (android obesity compared to gynecoid obesity,

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where the distribution of fat is predominantly in the lower thighs and buttock areas). The pattern of fat distribution has been independently associated with a higher incidence of OSA in men, but not in women, in various studies. Although the waist-to-hip ratio was not independently associated with a higher incidence of OSA in women, the risk factor pattern seen in this cohort of a greater risk of diabetes and higher triglycerides with higher respiratory disturbance index (RDI) suggests that those with higher AHIs could have a more central distribution of body fat. These relations must be explored within subgroups of similar BMI. In the Wisconsin sleep study, the gender relationship was minimal when adjusted for BMI and body fat distribution, suggesting that the more central body fat distribution of men may explain the gender difference in the prevalence of SDB (34). A study of visceral fat quantified by computed tomography (CT) scan in obese and apneic patients found that those with apneas had a higher proportion of visceral fat (35). Although cross-sectional studies have shown an association between central obesity and OSA, there is no consensus that a particular body habitus phenotype is most important as a risk factor for OSA. Also, neck circumference is an important risk factor for OSA as discussed later which would probably indicate that upper body obesity (fat deposition around the upper airway or fat deposited in the parapharyngeal fat pads), rather than a more generalized distribution of body fat, is important for the development of sleep apnea. In fact, studies have shown that parapharyngeal fat pad volume is greater in obese subjects developing apnea than in nonobese subjects developing apnea (29,36,37). Also, nonobese subjects developing apnea have larger parapharyngeal fat pads than normal subjects (29). However, it is not just the parapharyngeal fat pads that are enlarged in patients with OSA. Neck Circumference Neck circumference is the most powerful predictor of OSA among all anthropometric variables studied so far (38). Though early studies of sleep apnea emphasized the importance of obesity as a significant determinant of sleep-disordered breathing, subsequent investigations pointed out the importance of regional, rather than generalized obesity, particularly the importance of neck circumference. Neck circumference is measured at the superior border of the cricothyroid membrane with the subject in the upright position. A neck circumference greater than 40 cm should alert to the presence of OSA in that particular subject. Katz et al. (39) reported that the mean neck circumference was 43.7 cm (± 4.5 cm) in subjects with OSA and 39.6 cm (± 4.5 cm) in subjects without OSA. Also, there was a better correlation between neck circumference and the severity of OSA (40) than BMI or other indexes of obesity. Other investigators confirmed this observation as well (41,42). Kushida et al. (43) reported a sensitivity of 61% and a specificity of 93% for OSA when the neck circumference was 40 cm and above, regardless of the subject’s gender. Currently, neck size is considered to be one of the most important physical characteristics of patients with sleep apnea. Summary on Obesity and Obstructive Sleep Apnea Weight gain appears to greatly increase the chances of developing OSA in individuals without OSA, and appears to accelerate progression of OSA in persons already afflicted. Weight loss is an effective means of reducing OSA severity in overweight persons with OSA. Clinical studies usually examined weight loss due primarily to reduced caloric intake; studies of weight loss owing to increased caloric output

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(e.g., more exercise) are lacking, and it is possible that increased exercise or a combination of both may produce more favorable OSA-related outcomes than expected from diet-associated weight loss alone. There continues to be a need for both randomized studies and rigorous observational prospective studies of exercise, weight loss, or weight control in persons with a wide spectrum of body habitus and OSA severity that include more women and have extended follow-up periods. Effective diet and exercise modification programs exist that can yield long-term weight loss. Thus, it seems clear that weight control is likely to be the best nonmedical means of treating or arresting the progression of OSA in clinical settings, and reducing the prevalence of OSA and its associated sequelae in a public health context. However, there are also examples of significant weight loss, with return to a normal BMI and persistence of OSA, chronic snoring and associated symptoms. This indicates that obesity may be associated with other risk factors of OSA, and the question of the interaction with these factors and the development of obesity are currently unresolved. It is easy to recognize obesity as a risk factor for OSA, but many obese patients and their bed partners state that snoring preceded the weight increase in the patients. There are clearly unresolved issues in naming the primary factor leading to abnormal breathing during sleep, and in the complete description of the subsequent cascade of events leading to the obese snoring patient. CRANIOFACIAL FEATURES (SEE ALSO CHAPTER 8) Upper Airway Soft Tissues and Skeletal Features Craniofacial risk factors play a significant role in OSA, especially in nonobese subjects; in obese subjects, it may be an added risk factor or vice versa. Craniofacial abnormalities may favor early appearance of abnormal breathing during sleep even with a moderate weight increase (44,45). Clinical exam and cephalometric analysis help to evaluate craniofacial morphologic features (46–48). Numerous studies using cephalometrics have demonstrated craniofacial abnormalities in patients with OSA compared with age- and gender-matched control subjects. These studies, in general, have demonstrated that patients with sleep apnea have a small, hypoplastic and/or retroposed mandible and maxilla, narrow posterior airway space, and inferiorly positioned hyoid bone (49–52). As a consequence, the tongue, soft palate, and soft tissues surrounding the upper airway are displaced posteriorly, thereby narrowing the airway lumen. Extreme examples of such abnormalities occur in subjects with congenital craniofacial dysplasia, such as those with Apert’s, Pierre Robin, and Treacher Collins syndromes (53–55), which are associated with very high prevalence of OSA. Even in the absence of such distinct craniofacial abnormalities, cephalometric studies have still shown subtle retro displacement and shortening of the mandible and maxilla in OSA patients compared with normal subjects (51,52,56). Shorter and more posteriorly displaced mandibles have been confirmed in most OSA patients and correlate with a reduced posterior pharyngeal space (57,58). Medial displacement of the mandibular rami (thereby decreasing the lateral dimensions) may also occur and reduce intramandibular volume (58). The retropositioning of the maxilla also tends to be a risk factor for OSA by displacing the hard palate and the soft tissues posteriorly, closer toward the posterior pharyngeal wall and consequently reducing the size of the airway lumen. The position of the hyoid bone is important with displacement being a risk factor for OSA (59). The hyoid bone serves as a central anchorage for the tongue

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muscles and thereby partly determines the position of the tongue. Unlike other mammals, the hyoid bone in humans is not attached to the cervical spine. This detachment of the hyoid bone is needed for the development of speech (60), but makes the upper airway much more compliant and susceptible to collapse in humans compared to other mammals because of the lack of rigid bony support. The hyoid bone is displaced inferiorly in OSA patients compared with normal subjects (61). This inferior displacement of the hyoid bone may be accompanied by an inferior displacement of the tongue into the hypopharyngeal area, though this is not yet clear (50). Also, the position of the hyoid bone determines the severity of OSA. Several studies have shown that anterior displacement of the hyoid bone in all patients with OSA and inferior displacement in nonobese OSA patients were significant predictors of the severity of OSA (62,63). The more inferiorly displaced the hyoid, the greater the AHI (59). All these anatomic variations reduce the size of the upper airway in OSA patients. However, the reduction in mandibular length appears to be the most common and, probably, the most important skeletal abnormality predisposing to OSA (50). The other craniofacial features that augment risk for OSA include a high and narrow hard palate, and an abnormal overjet (i.e., overlapping distance between upper and lower central incisors, a dental pattern indicative of abnormal growth pattern of the maxilla, maxilla and mandible, or mandible) (64,65). Several studies have also demonstrated family aggregation of craniofacial morphology (reduction in posterior airway space, increase in mandibular to hyoid distance, inferior hyoid placement) in patients with sleep apnea (66,67). The data from these studies indicate that elements of craniofacial structure in patients with sleep apnea are inherited. Demonstrating heritability of upper airway structures provides further support for the importance of upper airway anatomy as a risk factor for the development of OSA. Also, various studies with nasal endoscopy (68–70), fluoroscopy (71), conventional and electron beam tomography (72,73), acoustic reflection (74), and magnetic resonance imaging (MRI) (75–77) have been used to examine the anatomy of the pharynx and have confirmed that the craniofacial features with pharyngeal anatomy play a significant role in the development of OSA. Soft Tissues of the Pharynx The soft tissues of the pharynx that are important in reducing airway size include the tonsils, soft palate, uvula, tongue, and the lateral pharyngeal walls (78). The lateral pharyngeal walls are complex structures made up of numerous pharyngeal muscle groups that include the hyoglossus, styloglossus, stylohyoid, stylopharyngeus, palatoglossus, palato-pharyngeus, and the pharyngeal constrictors. The lateral pharyngeal walls are responsible for changes in upper airway caliber in subjects with and without apnea during wakefulness and sleep (78–80). Thickening and enlargement of the lateral pharyngeal walls has been shown to be the predominant factor resulting in airway narrowing in subjects with apnea. Schellenberg et al. (81) conducted a study trying to identify the upper airway bony and soft tissue structural abnormalities determined by physical examination that were associated with an increased risk for OSA, and found narrowing of the airway by the lateral pharyngeal walls (odds ratio of 2.5) had the highest association with OSA followed by tonsillar enlargement, enlargement of the uvula, and tongue enlargement (odds ratios of 2.0, 1.9, and 1.8, respectively). In that study, low-lying palate, retrognathia, and overjet were not found to be significantly associated with OSA. Controlling for BMI and neck circumference, only lateral pharyngeal narrowing and enlargement

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of the tonsils maintained their significance (odds ratios of 2.0 and 2.6, respectively). A subgroup analysis examining differences between male and female subjects showed that no oropharyngeal risk factor achieved significance in women while lateral narrowing was the sole independent risk factor in men. These findings suggest that enlargement of the oropharyngeal soft tissue structures, particularly the lateral pharyngeal walls, is associated with an increased likelihood of OSA independent of obesity and neck circumference. This is clinically relevant as observed in subjects with OSA who have reduced lateral dimension (and a normal or greater anteroposterior dimension) owing to enlargement of the lateral walls compared to subjects without OSA who have a greater lateral dimension (78,79). In addition to this conformational change of pharyngeal narrowing, reduced electromyographic activity of these muscle groups during sleep may predispose these structures to collapse during sleep (82). Other soft tissues that are important as risk factors for OSA include the tonsils, soft palate, uvula, and tongue. As mentioned before, Schellenberg et al. (81) demonstrated that tonsillar enlargement is associated with an increased risk for OSA even when controlled for BMI and neck circumference. Enlargement of the palatine tonsils can lead to airway obstruction by decreasing airway caliber (83,84). Enlargement of the uvula is associated with an increased risk for OSA except when BMI and neck circumference were forced into the regression model (81). Pathologic studies have demonstrated thickening, fibrosis, and fat deposition in the uvula and soft palate of patients with OSA while other studies have shown an increase in the amount of muscular tissue, number of lymphocytes, and thickness of the lamina propria in the uvulas of patients with OSA as compared with normal subjects (85,86). A case-control design study examined the upper airway soft tissue structures in 48 control subjects and 48 patients with sleep apnea using three-dimensional MRI analysis (rather than the conventional two-dimensional analysis), and found that the volume of the upper airway soft tissue structures is enlarged in patients with sleep apnea and that this enlargement is a significant risk factor for sleep apnea (37). After covariate adjustments for gender, ethnicity, age, craniofacial size, and visceral neck fat, the volume of the lateral pharyngeal walls, tongue, and total soft tissue were significantly larger in patients with sleep apnea than in normal subjects. Not only were these structures enlarged in patients with sleep apnea compared with normal subjects, there was a significantly increased risk of developing sleep apnea. The study also showed that the larger the volume of the tongue, lateral pharyngeal walls, and total soft tissue, the higher the risk of developing OSA: lateral pharyngeal wall odds ratio (OR) 6.01, tongue OR 4.66, and soft tissue OR 6.95. Tongue enlargement was found to be associated with an increased risk for sleep apnea, but this association lost significance when adjusted for BMI and neck circumference. Cephalometric evaluation and computerized tomography (CT) studies have also shown that patients with OSA have enlarged tongues compared to control subjects (45,87). Apart from tongue enlargement, the length of the tongue is also a significant risk factor as it might obstruct the hypopharynx by projecting posteriorly in a supine position (88). All these data provide very strong support for the importance of upper airway anatomy in predisposing patients to SBD. One factor that may impact most of the above studies is that all reported analyses were performed on adult patients with an already fixed craniofacial skeleton. A developmental paradigm should be used to better understand the role of the craniofacial skeleton in the appearance of abnormal breathing during sleep. OSA is well demonstrated in children; Guilleminault et al. (89) and Ali et al. (90) have

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reported the only long-term longitudinal studies of children treated with tonsillectomy and adenoidectomy and investigated 10 year to 12 year later. Both authors reported that children who were considered to have normal breathing postsurgery, presented with OSA years later. Guilleminault et al. linked the reappearance of abnormal breathing during sleep to the presence of unrecognized and untreated modest skeletal changes in early childhood, and the progressive impact over time of these early life changes on the normal development of the upper airway. Nose Nasal obstruction can be a risk factor for OSA by playing a role in upper airway collapsibility, though it remains a point of conjecture. A deviated nasal septum or mucosal swelling leading to enlarged turbinates from allergic or nonallergic rhinitis can cause nasal obstruction. This causes an increase in airflow resistance upstream from the collapsible portion of the pharynx. As a result, the degree of negative collapsing pressure is increased on inspiration, rendering the pharynx more collapsible. Indeed, experimentally induced nasal obstruction can induce SDB (91,92). A prospective study of OSA in seasonal allergic rhinitis patients indicated that the AHI increased during the allergen season (93). In the Wisconsin cohort, the odds ratio for polysomnographically identified OSA with chronic versus no nighttime nasal congestion was 1.8. A nasal congestion–OSA link has been tested in depth by the Wisconsin sleep cohort epidemiology study, with nasal airflow measured by anterior rhinometry, and self-reported frequency of acute, seasonal, and chronic nasal congestion at night collected before overnight polysomnography (94). Nasal congestion was associated with OSA indicated by an AHI of five or greater, but was most strongly related to habitual snoring regardless of AHI. The odds ratio for habitual snoring and chronic severe nasal congestion at night was 3.3. Longitudinal data demonstrated that the odds of habitual snoring increased over a five-year study period in people with chronic, severe nasal congestion compared to people with no congestion (95). Increased nasal airflow resistances owing to allergic rhinitis can induce or worsen OSA and can be alleviated by intranasal steroids or spontaneous resolution of rhinitis (93,96). However, surgical correction of a deviated nasal septum does not consistently alleviate OSA, although it may reduce CPAP requirements in patients with severe OSA (97) and may improve CPAP compliance. Although nasal obstruction may contribute to upper airway collapsibility, data are insufficient and further studies are needed to clarify its role. Investigation of the rhesus monkey has provided some insight regarding the effect of abnormal nasal resistance during early life on the development of craniofacial features as a risk factor for OSA in adulthood. Harvold et al. (98) created increased inspiratory nasal resistance at birth in this animal model using small silicone tubes placed in the nares and held by a posterior ligature. This abnormal nasal resistance led to a functional change in EMG discharges with induction of rhythmic discharges in several craniofacial muscles including geniohyoid, genioglossus, massetric, and even intercostal muscles. With these EMG changes there were changes in posture of the tongue and the head, and development of mouth breathing. In developing animals, abnormal nasal resistance leads to functional changes in EMG firing, mouth breathing and secondary morphometric changes with abnormality of growth leading to a narrowing of the maxilla and mandible. Some of the observed changes include narrowing of dental arches, decrease in maxillary arch length, anterior cross bite, maxillary overjet and increase in anterior

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face height; all changes that impact the size of the upper airway and lead to specific narrowing of this malleable tube. In children similar changes have been well documented with the presence of enlarged tonsils and adenoids. However, increased nasal resistance is not only related to enlargement of lymphoid tissues, but also to chronic enlargement of nasal inferior nasal turbinates, deviated nasal septum, trauma on bridge of nose and so on, all factors that increase nasal resistance. As mentioned above, repetitive upper airway infection, chronic respiratory allergies, and body position while asleep have been implicated in the development of abnormal nasal resistance in early childhood and have been considered factors impacting the growth of the craniofacial skeleton. Several negative feedback loops have been demonstrated as examples of how increased nasal resistance leads to mouth breathing that induces irritation and enlargement of tonsillar tissue, and enhances nasal-oropharyngeal resistance. Understanding the impact of early life development on enhanced nasal resistance is critical owing to the speed of craniofacial growth: by four year of age 60% of the adult face is built and about 90% by 11 to 12 year of age. This speed of growth is an important factor as the craniofacial skeleton will have reached most of its adult size much before the full impact of the pubertal hormonal secretion will have manifested itself on muscles and soft tissues located in the upper airway. The combined geniohyoid and genioglossus muscles are the largest muscle mass for the contained bony cavity in the human body. This large muscle mass may account for lack of adequate development of facial skeletal features, leading to reduced airway lumen. The presence of early life abnormal nasal resistance, absence of treatment for the causes of this resistance, and neglect of treatment of already induced skeletal abnormalities owing to early life environmental factors will leave a narrow skeleton that will not be able to appropriately accommodate the soft tissues enlarged under the hormonal pubertal surge. The fact that these changes happen during a fast growth period means that factors involved in the normal growth of the skeleton interact continuously. Some of these growth factors are under genetic influence, and are thus difficult to act upon. Some of these genetic factors are already obvious at birth, some are associated with ethnicity (e.g., far-East Asian vs. Caucasian), and others are familial, but these genetic factors influence the continuous growth of craniofacial features particularly during the prepubertal years, and will continuously interact with environmental factors, potentially increasing nasal resistance. Another potential barrier in addressing the issue of OSA, particularly in children and adolescents is the medical education system. Under this system, teaching and treatment of craniofacial skeleton changes usually falls into the hands of dentists and orthodontists, while training and treatment of abnormal naso-oropharyngeal tissues is usually placed under the auspices of otolaryngologists and allergists. There is lack of integration between the specialties during investigation and treatment of a child’s upper airway problem. GENDER The presence of gender differences in OSA and risk factors were not well understood prior to the publication of population-based studies that included both genders. Some authors studied healthy community dwellers (1,23,99), while others studied patients referred to sleep clinics specifically for evaluation of possible sleep apnea (100). In the widely quoted Wisconsin sleep cohort study (1) the prevalence of SDB in men (24%) was almost three times higher than in women

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(9%). Also the severity of OSA was gender specific. SDB was believed to be rare in women, particularly in the premenopausal state compared to men, although this difference becomes less significant for postmenopausal women. Although the gender differences are less striking than once thought, there still appears to be a higher prevalence of OSA in men than in premenopausal women. The reasons for gender differences in the prevalence and severity of sleep apnea are multifactorial. Some of the most commonly proposed hypotheses include differences in the effect of weight, differences in body fat distribution, abnormalities in upper airway mechanics, control of breathing, and structural differences in upper airway dimensions. Although some of these hypotheses are still debatable, we will briefly go through them. Weight and Gender Premenopausal women who had a diagnosis of OSA were reported to be strikingly more obese than men or postmenopausal women with OSA (101,102); suggesting that women were less vulnerable to the effects of weight on SDB, particularly before menopause. Body habitus or regional distribution of fat may also explain gender and menopausal differences. As mentioned before, obese men have increased centripetal fat distribution, which increases the risk of developing OSA, most likely through fat deposition in the neck (increased neck circumference), causing narrowing of the pharyngeal lumen (29,32,33). Obese women are less susceptible than obese men to the development of OSA, most likely because of less fat deposition in the neck (75). Nevertheless, as women go through menopause, fat distribution takes on a more centripetal pattern; this is associated with a greater tendency to develop OSA (1). A greater male vulnerability to SDB from obesity has been reported in some but not all studies. In a study of progression of SDB in the Cleveland family study, the interaction of age, sex, and BMI was significant in predicting changes in AHI (103). In women, the increase in AHI with increased weight was less than that observed in men, regardless of age and baseline BMI. Similar findings were reported by Newman et al. (26) in the analysis of five-year prospective data of the sleep heart health study. AHI was more likely to increase in men compared to women for a given weight increase. For a weight gain ≥ 10 kg, the OR for a progression in AHI ≥ 15 was 5.2 for men and 2.6 for women. Although suggestive, a statistically significant interaction of gender and BMI or other obesity marker on SDB (e.g., a higher OR for SDB and BMI in men vs. women) was not found in two other large population studies (2,3). Upper Airway Mechanics and Gender Examination of pharyngeal resistance in men and women reveals conflicting results. Early investigations (104) found that in awake-normal men, pharyngeal resistance is double that in normal women; however, this difference was not reproduced in a younger group of men and women studied using different techniques (105). In a more recent study, Trinder et al. (106) found similar pharyngeal resistance during sleep in healthy men and women, but men exhibited greater increments in upper airway resistance than women during established slow wave sleep. Other investigators (107,108) failed to find consistent differences in pharyngeal structure and/or function during sleep. For example, Rowley et al. (108) measured upper airway

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resistance and critical closing pressure in normal men and women during sleep and found no gender-related differences. Control of Breathing and Gender A lack of consistent results is found in studies of ventilatory responses in men and women (109). Early investigations during wakefulness revealed (110) that obese men have depressed responses to hypoxia and hypercapnea, which may predispose them to SDB. Later studies performed during sleep (111), also indicated that men are less able to preserve ventilatory motor output during hypocapnia than women, thus predisposing them to development of OSA. The complexity of the problem is illustrated by the work of Pillar et al. (112), who showed that despite having similar central drive and ventilatory response to resistive loading, men had increased susceptibility to upper airway collapse during loaded breathing; the authors concluded that perhaps anatomic factors or intrinsic tissue properties are responsible for the observed gender differences in sleep apnea. Based on the available data, it is still too premature to conclude that women have higher upper airway dilator muscle activity than men, thus accounting for reduced severity and lower prevalence of sleep apnea as compared to men. Differences in the upper airway anatomy between men and women are also a subject of some disagreement, although very few studies have specifically addressed this issue. The major reason for the divergent findings is probably the fact that most measurements were carried out during wakefulness. Other reasons include differences in techniques, sample size, type of population studied, and lack of polysomnography in some studies. For example, some acoustic reflection measurements of pharyngeal area (all performed during wakefulness) found that normal women have a smaller pharynx than men (111,113), while others did not (114,115). Mohsenin (111) did find that men with sleep apnea had a larger pharyngeal cross-sectional area than women, but the correlation between pharyngeal area and apnea severity was inconsistent, present in men but not in women. Cephalometric measurements do not demonstrate consistent differences in pharyngeal area between men and women; Computed tomography (CT) and MRI studies noted more fat deposited along lateral pharyngeal walls in patients with sleep apnea than in control subjects (29,30,36,78). However, Whittle et al. (73) found similar total parapharyngeal fat volume in normal men and women, but greater total neck soft-tissue volume in men compared to women. Schwab (116) pointed out current uncertainties regarding differences in upper airway structure and function between men and women, and concluded that there must be other important factors, in addition to gender, that affect upper airway caliber and increase the risk for sleep apnea. Kapsimalis and Kryger (117,118) reviewed the entire subject of gender and sleep apnea, and concluded that several other factors in addition to obesity and upper airway fat must contribute to increased risk of sleep apnea observed in men. For example, the length of the pharynx is greater in men with OSA, and this may place them at greater risk for upper airway collapse compared to women (119). Menopause Postmenopausal women are at greater risk for OSA than premenopausal women. In the population-based Wisconsin sleep cohort study (120), postmenopausal women had three times the odds of having moderate to severe OSA compared with

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premenopausal women, independent of age, BMI, and other confounding factors. The duration of menopause up to five-year postmenopause also was a risk factor for OSA (120). Similarly, in the Pennsylvania population-based cohort of 1000 women, a four-fold greater risk of OSA was found in postmenopausal women not using hormone replacement therapy (HRT) compared to premenopausal women (2). In the same study, postmenopausal women who were using HRT, were not at increased risk for OSA (OR 0.9) (2). These study results thereby, suggest a role for low progesterone/estrogen or high testosterone in the pathogenesis of OSA, but data are still sparse. Though menopause is associated with increased central body fat (as mentioned in the Weight and Gender Section), it is unlikely that this mechanism alone can account for the increased risk for OSA. If low levels of estrogen and progesterone do indeed increase the risk for OSA, then HRT should reduce the risk for OSA. Studies looking at HRT in postmenopausal women have been conflicting, in the least. A couple of observational studies did show an inverse association between HRT and OSA (2,4), but several other studies have had conflicting results (121–125). In a blinded randomized crossover trial involving postmenopausal women, Polo–Kantola et al. (126) found that HRT had a weak effect in reducing apnea and hypopnea. Findings from the sleep heart health study (127) of 2994 women aged 50 year or older showed HRT users had a 40% to 50% reduction in OSA prevalence (defined as apnea–hypopnea index ≥ 15) compared to nonusers. Though HRT may be a viable therapeutic option in reducing the risk for OSA, given the increased risk of CV disease and uterine and breast cancer with HRT (128,129), further randomized clinical trials are needed before a rational approach can be defined, and HRT can be used as a therapeutic drug to reduce the prevalence of OSA in postmenopausal women. Postmenopausal women are at greater risk for developing OSA compared to premenopausal women at any given BMI. Bixler et al. (2) noted that, in the Pennsylvania cohort sample (3), all of the premenopausal women with OSA (AHI > 15) were obese (BMI ≥ 32), compared with only 42% of postmenopausal women who were not using HRT. In a report from the Wisconsin cohort, at any given BMI, the prevalence of OSA (indicated by AHI ≥ 5) was higher in postmenopausal compared to premenopausal women, but OSA did occur in both post- and premenopausal women who were not obese (120). Clinical reports of greater obesity in premenopausal women, compared with postmenopausal women, suggest that association between excess weight and OSA is stronger for postmenopausal women. Thus, both population and clinical studies indicate that BMI has a weaker effect on OSA in premenopausal women compared to postmenopausal women. Progesterone is a respiratory stimulant and may stabilize the respiratory control system, thereby protecting against OSA, but experimental trials have failed to show a protective effect regarding upper airway collapse. Testosterone, on the other hand, contributes to fat deposition in the neck and upper body that may contribute to the development of OSA through reduced upper airway size. In support of this hypothesis is the increased prevalence of OSA in women with high endogenous levels of testosterone (130,131) and the induction of OSA through administration of testosterone to women and hypogonadal men (132). However, androgen blockade did not affect OSA in men (133). In summary, though menopause is recognized as an important risk factor for OSA, the role of mechanisms of sex hormones in OSA is unclear and requires further investigation. Also, understanding why premenopausal women are protected from OSA is likely to be a fruitful area of inquiry.

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ETHNICITY Data are limited on the occurrence of OSA in non-white populations and therefore, important racial or ethnic prevalence patterns and risk factors are poorly understood. Some studies have shown that the prevalence of OSA and disease severity is higher in African-Americans compared to Caucasians (134,135). Ancoli-Israel et al. (134) studied community dwelling adults, aged 65 year or greater, by in-home monitoring, and found that the odds of having an AHI ≥ 30 was 2.5 times greater in African-Americans compared to Caucasians, when controlled for BMI and other confounding factors. In the Cleveland family study, Redline (135) found a higher prevalence of OSA in African-Americans compared to Caucasians when studying a racially heterogeneous group of participants less than 25 year of age. In this study, the increased prevalence was not accounted for by differences in exposure to alcohol or tobacco or differences in BMI. Variable age of puberty, speed of development of secondary sexual characteristics, and mucosal enlargement associated with hormonal surge may have biased these findings. In contrast, the multicenter Sleep Heart Health Study (13) of more than 6,000 participants, did not show a difference in prevalence of OSA in African-Americans compared to Caucasians after adjustment for age, sex, and BMI. A study by Schmidt-Nowara (136) found a prevalence of 27.8% and 15.3% for regular loud snoring in Hispanic-American men and women, respectively. A population-based survey conducted by Kripke et al. (137) estimated a higher OSA prevalence of 16.3% for U.S. Hispanics and racial minorities as compared to about 5% of non-Hispanic Whites aged 40 to 64 years, though it was not controlled for comorbid conditions. Further analysis of the results revealed that Asians might have an even higher prevalence of OSA as compared to Hispanics and Blacks. A study has also suggested that Asian subjects have higher disease severity compared with White subjects (138). This is probably owing to differences in craniofacial anatomy as shown by various studies that for a given degree of severity of OSA, Asians have shorter maxillae and mandibles, smaller anterior–posterior facial dimensions, and lower BMI than Whites (139–141). This suggests that craniofacial anatomy of the bony cage is of greater risk than obesity and soft tissue factors in the development of OSA in Asians than in Whites. On the other hand, soft tissue factors, including increased tongue area and increased soft palate length, play a greater role in susceptibility to OSA in African-Americans than in Whites (142,143). In contrast, a study in New Zealand comparing sleep apnea severity among Maori, Pacific Islanders, and Europeans reported that race was not as an important predictor of severity when adjusted for factors such as neck size, BMI, and age (144). Studies of men and women in Hong Kong (11,12) and Korea (145) and men in India (146) have reported prevalences of OSA similar to those of Western nations. AGE (SEE ALSO CHAPTER 4) The association between age and OSA is complex, controversial, and not well understood (because of confounding factors that are not totally eliminated during analysis) (147). Several studies have shown a higher prevalence of OSA in elderly persons compared to middle-aged persons (148–150). Though OSA prevalence appears to increase steadily with age in midlife, age trends in older age groups (above 65 year) do not indicate a simple positive correlation of OSA with age, and rather appears to plateau after age 65 for undetermined reasons (13). Ancoli-Israel et al. (151) reported on an 18-year follow-up of community-dwelling adults aged

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> 65 year at baseline and found that aging was not predictive of changes in AHI. More studies on outcomes of sleep disorders in the elderly are needed. SMOKING Smoking is a possible risk factor for OSA but few studies on this topic have been reported. There are various proposed mechanisms for the role of smoking in OSA that include airway inflammation and smoking-related diseases, as well as effects of declining blood nicotine levels on sleep stability. There are several cross-sectional epidemiologic studies (16,18–20) of OSA that have found positive associations with cigarette smoking. Wetter et al. (152) were specifically looking for an association between smoking and OSA, and found that current smokers were three times more likely to have OSA than were former or never smokers. As the prevalence of OSA was the same between nonsmokers and former smokers, in that study, it is possible that if current smokers quit smoking, the OSA prevalence may decrease. Surprisingly, the sleep heart health study (22) found an inverse association between current smoking and OSA. Although, there is a biological plausibility for smoking as a risk factor for OSA, it is not yet firmly established. ALCOHOL We know from experimental studies that acute alcohol ingestion is a risk factor for increased AHI. Most studies (153–158) in which defined quantities of alcohol were administered to healthy subjects or patients with OSA before bedtime have demonstrated harmful effects on nocturnal respiration, including increased number and duration of hypopnea and apnea events. Alcohol ingestion has been demonstrated to acutely increase nasal and pharyngeal resistance in awake-subjects (159), and it is reasonable to hypothesize that this effect may compromise breathing during sleep. There have been several population-based cross-sectional epidemiologic studies looking at long term alcohol association with OSA (16,20,23,160), but they have not consistently demonstrated significant associations between self-reported typical alcohol consumption and OSA. In summary, studies that involved administration of alcohol near bedtime imply an adverse acute impact on breathing during sleep, but the effect of long-term alcohol use patterns on the occurrence of OSA is unknown. SPECIFIC DISEASES There are specific diseases and conditions that act as risk factors for the development of OSA. Macroglossia is a condition which can narrow the airway lumen, and any disease state that causes macroglossia could therefore be a risk factor for development of OSA. Studies have also shown a large prevalence of OSA in patients with macroglossia as a primary or secondary condition (e.g., in hypothyroidism, acromegaly, amyloidosis, Down syndrome, and so on) (161). Polycystic Ovarian Syndrome Polycystic ovarian syndrome (PCOS) is a common disorder affecting 5% to 10% of women of reproductive age and is characterized clinically by oligomenorrhea, clinical signs of excess androgen and central obesity (increased waist to hip ratio). Women with PCOS have a higher prevalence of OSA compared to age- and weight-matched

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groups of reproductively normal control women, suggesting it is a risk factor. The increased prevalence is hypothesized to be secondary to androgen excess and central obesity (162). Stroke Small retrospective studies have shown a higher prevalence of OSA in stroke victims (163–165) at around 60% to 70%. Even at three months after a stroke, though SDB improves, the prevalence is still around 50% (166). The presence of motor impairment of the upper airway secondary to stroke easily explains the increase in SDB poststroke. Presence of OSA poststroke is important to recognize as OSA can significantly increase the risk of a second stroke. Other Neurologic Conditions Neurological disorders associated with OSA include the Shy–Drager syndrome of multisystem degeneration (central and obstructive apnea) and neuromuscular diseases involving facial and thoracoabdominal musculature such as polio, myotonic, and muscular dystrophies. Patients with acquired or hereditary neuropathies (such as amyotrophic lateral sclerosis and Charcot–Marie–Tooth disease) are also at a higher risk for developing OSA. A variety of other medical conditions such as craniofacial or skeletal malformations are commonly associated with OSA, and act as risk factors by narrowing the airway lumen. Patients with congenital syndromes of micrognathia such as the Pierre Robin, Crouzon’s, Hunter’s, and Treacher Collins syndrome (as mentioned in the section of craniofacial features), and subjects with cleft palates repaired by a pharyngeal flap have developed iatrogenic obstruction. Cranial base abnormalities associated with OSA include achondroplasia, and Klippel–Feil malformations. Children with Prader–Willi syndrome may suffer OSA owing to morbid obesity or possibly other factors as OSA has also been noted before occurrence of obesity. Treatment of Prader–Willi syndrome with growth hormone may worsen OSA and may even lead to death (167). Secondary kyphoscoliosis can worsen nocturnal respiratory function. This is demonstrated in postpoliomyelitis syndrome, where the combination of brainstem neuronal degeneration and kyphoscoliosis related to initial muscle impairment would lead to severe OSA. Lesions of the temporomandibular condyle leading to retrognathia may be developmental or acquired owing to rheumatoid arthritis, osteomyelitis, or trauma and can lead to the development of OSA. SNORING Snoring and repeated upper airway occlusion have been shown to lead to edema and swelling of upper airway soft tissue structures, which may contribute to further narrowing of the upper airway, making it more susceptible to collapse and thereby, the development or worsening of OSA. There may also be other mechanisms where snoring could be a risk factor for the development or worsening of OSA. The upper airway lumen patency during inspiration is maintained by the pharyngeal dilator muscles. These muscles are activated by a reflex mechanism as a reaction to the negative intrapharyngeal pressure, which is probably mediated by intra- or submucosal mechanosensory receptors. Therefore, having an injury that involves both motor and sensory fibers in this area (leading to upper airway muscles

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less able to respond to negative airway pressure) could potentially lead to the development or worsening of OSA. There is evidence today that both of these types of lesions exist in patients with OSA. A study by Horner et al. supports this hypothesis. They found that topical anesthesia of the upper airway induced apneas in both snorers and normal subjects (168). Vibrating tools used at work have been shown to cause local nerve lesions in the hands (169). Similarly, snoring produces low-frequency vibration; repeated vibratory trauma owing to snoring causes injury or remodeling of the upper airway muscles or nerves. This concept is supported by the fact that patients with OSA have an impaired ability to detect sensory stimuli in the upper airway and therefore the inability to detect negative pressure changes. To investigate sensory nervous function, Larsson et al. (170) tested temperature thresholds for heat and cold on the tonsillar pillars of control subjects (who did not snore) and patients with OSA. They found significant differences in patients with OSA (6 out of the 15 patients) where they were completely unable to differentiate between heat and cold, though no differences were found at the tip of the tongue, indicating a very local sensory dysfunction. Friberg et al. (171) also found differences in vascular reactivity in the soft palatal mucosa using electrical stimulation in subjects with habitual snoring and OSA patients, compared to normal controls. The normal response of vasodilatation was exaggerated in habitual snorers and patients with mild OSA compared to normal control subjects, whereas patients with severe OSA exhibited a marked reduction in reactivity. The latter finding could be explained by an almost complete loss of afferent C fibers while the exaggerated response in habitual snorers and mild OSA patients may be the result of minor lesions with consequent reinnervation leading to increased sensitivity to mechanical stimuli. Kimoff et al. (172) further substantiated sensory dysfunction in OSA patients and in nonapneic snorers compared to nonsnoring control subjects when they studied two-point discrimination and vibratory sensation in the upper airways. They found no significant difference between snorers and patients with OSA. When 16 patients with OSA were retested after CPAP treatment, vibration thresholds had significantly improved though the two-point discrimination did not change. This is consistent with the fact that relapse of OSA usually takes place when CPAP treatment is interrupted. There is also evidence of motor neuron lesions and actual damage to the muscles themselves that could lead to partial paresis of the pharyngeal dilator muscles, rendering them less able to generate force to maintain the airway lumen. Woodson et al. (173) found disruptive changes with atrophy in the muscle fibers of the soft palate in patients with OSA and heavy snorers compared with nonsnorers under light microscopy. Under electron microscopy, they found degenerative changes in the neurons from the soft palate and uvula of patients with OSA. Friberg et al. (174) compared biopsies of palatopharyngeus muscle from nonsnoring controls, habitual snorers, and patients with OSA, and found the degree of muscle pathology increased in parallel with the proportion of obstructive breathing during sleep. All the patients with OSA exhibited histological abnormalities, including signs of motor neuron lesions. More recent studies have confirmed the absence of perception of sensory input with specific stimuli during sleep. Affifi et al. (175) have shown that occlusion stimuli are impaired during NREM sleep, and Kimoff et al. (176) have shown that sensory impairment also involves the upper larynx with delayed response in superior arytenoids reflex. Though these studies support the hypothesis that upper airway afferent and efferent nerve lesions are present in some patients with snoring, and in most patients

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with OSA, there is currently no clear-cut proof that these lesions increase in parallel with clinical progression from habitual snoring to OSA. BODY POSITION It has been suggested that sleeping in the lateral position as opposed to a supine position dramatically reduces the appearance of OSA episodes, and in some cases may be the only type of treatment needed to prevent disordered breathing during sleep. Cartwright and Lloyd et al. (177–181) defined “positional patients” (PP) as those OSA patients in whom the RDI was at least twice as high in the supine position as in the lateral position. The proportion of OSA PP described in different reports varies from 9% to 60%. Other OSA patients who had an RDI in the supine position that was less than twice that in the lateral position were called “nonpositional patients” (NPP). In fact, the degree of severity of OSA in these patients is mostly related to the sleep time spent or not spent in the supine position. Fouke and Strohl (182) found an average 23% decrease in pharyngeal cross-section in the supine position. Similarly, Pae et al. (183) found that in the supine position there was an average 29% decrease in the oropharyngeal area in normal patients and an average 37% decrease in OSA patients. Sleeping in a supine position causes a further narrowing of all segments of the pharynx during the awake-state in normal (182–184) as well as in OSA patients. Cephalometric data have shown that many OSA patients have a narrower pharynx than non-OSA control subjects (185–187). Anch et al. (188) found that the supine position continued to have an effect even after constriction of nasal mucosal and submucosal vasculature, suggesting that the effect of position is associated more with pharyngeal resistance than any nasal component. Gravity may affect pharyngeal aperture, as mobile soft tissues such as the tongue and soft palate move posteriorly to the pharyngeal wall as the body shifts from an upright to a supine position. Gravity also causes an increase in the tongue cross-sectional area (183), uvular width (189), and soft palate thickness, which would also contribute to the reduced pharyngeal cross-sectional area. These anatomic changes, which occur in the upper airway can have an increased effect in OSA patients who may already have a lower than normal intrapharyngeal pressure during inspiration. The level of respiratory distress in subjects with OSA as measured by AHI or RDI, is about 40% to 50% lower when patients lie on their side than when they lie on their back. In a retrospective analysis of anthropomorphic, polysomnographic, and multiple sleep latency test (MSLT) data of a large group of OSA patients, Oksenberg et al. (180) found that the PP patients on average were thinner and younger, had less severity of breathing abnormalities, their sleep quality was better preserved, and they were therefore less sleepy based on their MSLT data compared to their counterparts. This and a study by Swieca et al. (190) showed that most of the breathing abnormalities, especially in patients with mild to moderate OSA may occur when they sleep in the supine position. Consequently, these OSA patients are the ones in whom positional therapy (avoiding the supine posture) should play an important role in their treatment. Some of them will eliminate all breathing abnormalities merely by avoiding the supine posture. On the contrary, patients with severe OSA are less likely to be positional as their condition is so severe that they have breathing abnormalities in all different body postures; though Oksenberg et al. (181) showed that even in patients with severe OSA the apneic events occurring in the supine

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TABLE 1 Risk Factors and Possible Risk Factors for OSA Risk factors for OSA Obesity Fat distribution Neck circumference Craniofacial features Nasal resistance Gender Menopause Ethnicity

Possible risk factors for OSA Age Smoking Alcohol Snoring Body position

Specific diseases that are risk factors for OSA Conditions causing macroglossia Polycystic ovarian syndrome Neurological conditions (stroke, neuromuscular diseases, etc.) Congenital abnormalities that cause retrognathia

Abbreviation: OSA, obstructive sleep apnea.

position are more severe than those occurring while sleeping in the lateral position in spite of having a high number of apneic events in both positions. If mild–moderate OSA patients are mainly PP, it could also mean that “positionality” is perhaps a characteristic of the natural development of the OSA entity, and as the severity increases (as occurs with increase in weight) the positional OSA patient may convert into a nonpositional one. The reverse also appears to be true and this was demonstrated in obese OSA patients who after losing weight were converted from NPP into PP. Body position may work synergistically with airway resistance and other factors as key elements in the etiology of OSA. More study is needed to compare the pharyngeal cross-sectional areas in the lateral position versus supine position in both normal subjects and OSA patients during sleep. CONCLUSIONS In review, OSA is a common and serious disorder that requires appropriate recognition, diagnosis and treatment. Occurrence of OSA is most commonly identified with a polysomnographic pattern leading to sleep disturbance, variable SaO2 changes and autonomic nervous system stimulation. OSA can be induced by many factors. This chapter discussed the common risk factors for the development of OSA as summarized in Table 1. As we become more knowledgeable of these risk factors it helps us to employ them to diagnose subjects with OSA. Also, acting upon these risks may help us to prevent the occurrence of OSA. Further studies are required to clarify the role of these risk factors; as the public becomes increasingly aware of this disorder, the future of OSA management continues to look very promising. REFERENCES 1. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328:1230–1235. 2. Bixler EO, Vgontzas AN, Lin HM, et al. Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med 2001; 163:608–613. 3. Bixler EO, Vgontzas AN, Ten Have T, et al. Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med 1998; 157:144–148. 4. Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165:1217–1239.

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13

Familial and Genetic Factors Sanjay R. Patel Sleep and Epidemiology Research Center, Department of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A.

Peter V. Tishler Partners Center for Genetics and Genomics, Channing Laboratory, Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts, U.S.A.

INTRODUCTION A familial basis for obstructive sleep apnea (OSA) was first postulated in 1978 by Strohl et al. who reported on three brothers with this disorder (1). Since that time, it has become increasingly clear that OSA commonly clusters within families (2–4). Part of this clustering is due to the familial aggregation of features that predispose to OSA, such as obesity. The correlations in sleep-disordered breathing among family members persist even after accounting for these risk factors, however, suggesting that genetic susceptibility plays an important direct role in the pathogenesis of OSA (2,3). Knowledge of sleep apnea genetics may provide not only an opportunity to better understand an individual’s predisposition to develop OSA and its neuropsychiatric and cardiovascular consequences, but also insight into the molecular pathways that, when dysregulated, produce OSA. The ability to predict individual risk will allow for more efficient prevention and screening programs, while knowledge of pathophysiology may lead to novel treatment strategies that specifically target the molecular defects. Although a genetic basis for OSA is clear, it is not a simple monogenic disorder following a clear inheritance pattern (5). Rather there appears to be a complex interaction of both genetic and environmental risk factors that define individual predisposition to apnea. Given this complexity, much work has centered on identifying phenotypes with a simpler etiologic basis from which to inform studies of OSA. We shall review studies that have examined the genetics of OSA and its consequences. We shall focus initially on two phenotypic approaches. The first approach is the study of simpler phenotypes that are on the pathway to OSA but presumably have fewer contributors to variability—both genetic and nongenetic. These characteristics are termed “intermediate phenotypes.” If the genetic loci explaining variability in these intermediate phenotypes can be identified, some of the variability in OSA status may also be explained via these causal pathways. The second approach centers on understanding the basis of severe but rare forms of sleep-disordered breathing that do follow simple inheritance patterns. The lesson that may be extrapolated from these hyperboles is that other less deleterious polymorphisms in the same genes or in other genes involved in the same biochemical pathways may have a milder effect on phenotype. At the same time, these less severe polymorphisms may be more common and so contribute importantly to defining OSA susceptibility from a population perspective. Although no polymorphisms have yet been proven to influence OSA risk, a number of potential susceptibility genes have been identified through these approaches (Table 1). Finally, we shall review the growing literature on the genetics of bona fide OSA. 223

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TABLE 1 Candidate Genes for Apnea-Related Phenotypes Gene ACE ADRB2 ADRB3 APOE ASCL1 BDNF ECE1 EDN1 EDN3 EDNRA FGFR1 FGFR2 FGFR3 GCCR GDNF GGT1 GNB3 HCRTR2 HIF1A HP HTR2A HTR2C LEP LEPR MC3R MC4R MECP2 MSX1 MSX2 NDN NOS3 PHOX2B PMP22 POMC PPARG PTCH RET SLC6A4 SHH SNRPN TGFBR1 TGFBR2 TCOF1 UCP2 UCP3

Chromosomal location 17q23 5q32–q34 8p12–p11.2 19q13.2 12q22–q23 11p13 1p36.1 6p24–p23 20q13.2–q13.3 4q31.2 8p11.2–p11.1 10q26 4p16.3 5q31 5p13.1–p12 22q11.1–q11.2 12p13 6p11–q11 14q21–q24 16q22.1 13q14–q21 Xq24 7q31.3 1p31 20q13.2 18q22 Xp28 4p16.1 5q34–q35 15q11–q13 7q36 4p12 17p11.2 2p23.3 3p25 9q22.3 10q11.2 17q11.1–q12 7q36 15q12 9q33–q34 3p22 5q32–q33.1 11q13 11q13

Gene product angiotensin converting enzyme β2-adrenergic receptor β3-adrenergic receptor apolipoprotein E human achaete-scute homolog 1 brain-derived neurotrophic factor endothelin-converting enzyme 1 endothelin 1 endothelin 3 endothelin receptor, type A fibroblast growth factor receptor 1 fibroblast growth factor receptor 2 fibroblast growth factor receptor 3 glucocorticoid receptor glial cell line-derived neurotrophic factor γ-glutamyltransferase 1 guanine nucleotide-binding protein β3 hypocretin 2 receptor hypoxia-inducible factor 1, α subunit haptoglobin serotonin 2A receptor serotonin 2C receptor leptin leptin receptor melanocortin 3 receptor melanocortin 4 receptor methyl-CpG-binding protein 2 muscle segment homeobox 1 muscle segment homeobox 2 necdin nitric oxide synthase 3 paired-like homeobox 2B peripheral myelin protein 22 proopiomelanocortin peroxisome proliferator-activated receptor γ patched ret protooncogene serotonin transporter sonic hedgehog ribonucleoproteins including HBII-52 transforming growth factor-β receptor, type 1 transforming growth factor-β receptor, type 2 treacle uncoupling protein 2 uncoupling protein 3

Associated phenotype VC, OSA, HTN Obesity Obesity OSA, CD CCHS VC, CCHS CFD, VC CFD, VC CCHS CFD, VC CFD CFD CFD Obesity VC, CCHS VC Obesity IHS VC CVD OSA PWS Obesity, VC Obesity Obesity Obesity Rett Syndrome CFD CFD PWS VC CCHS CMT1A Obesity Obesity CFD VC, CCHS OSA, SIDS CFD PWS CFD CFD TCS Obesity Obesity

Abbreviations: CCHS, congenital central hypoventilation syndrome; CD, cognitive dysfunction; CFD, craniofacial development; CMT1A, Charcot-Marie-Tooth syndrome type 1A; CVD, cardiovascular disease; HTN, hypertension; IHS, idiopathic hypersomnolence; OSA, obstructive sleep apnea; PWS, Prader-Willi syndrome; SIDS, sudden infant death syndrome; TCS, Treacher Collins syndrome; VC, ventilatory control.

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INTERMEDIATE PHENOTYPES Intermediate phenotypes can be defined at many levels, from traits describing the functioning of the entire organism or a particular organ system to traits related to cellular or even molecular processes. Because OSA research has classically defined risk factors in terms of physiologic traits at the level of the whole organism, however, we have concentrated our discussion on traits defined on a macroscopic scale as well. The physiologic traits established or hypothesized to be risk factors for the development of OSA have been reviewed recently (6), as has the evidence for a genetic basis for these phenotypes (7). Anatomic factors appear to be very important in this regard. They include body habitus (obesity) and variation in upper airway shape and volume, the latter reflecting both bony and soft tissue geometry. Neurologic phenotypes are also important. Of these, the control of ventilatory drive has been the most completely studied. Other traits, such as the arousal threshold to a respiratory load and responsiveness of the genioglossus muscle and/or hypoglossal nerve, may also importantly affect OSA pathogenesis. Genetic variability in any of these traits may explain individual susceptibility to developing OSA. A genetic basis for many of these traits has been suggested, and polymorphisms that predispose an individual to develop abnormalities in one or more of these intermediate phenotypes would therefore also be expected to increase OSA susceptibility. Obesity (See Also Chapter 20) It has long been recognized that obesity is one of the most important risk factors for OSA. Relative to those with a stable weight, a 10% increase in weight over 4 yr is associated with a six-fold increase in the incidence of moderate to severe OSA (8). Weight loss trials have found substantial reduction in apnea severity with moderate weight reduction (9,10). There are multiple mechanisms by which obesity predisposes to OSA (11). Patients with OSA have increased volume of parapharyngeal fat pads that may impinge on the airway lumen (12). Fat deposition in the neck predisposes the upper airway to collapse. Fat deposition in the torso increases work of breathing, predisposing to hypoventilation. This mechanical effect also reduces resting lung volumes, which is in turn reduces parenchymal traction on the trachea making the airway more collapsible. Finally, the metabolic effects of obesity on hormones such as leptin may contribute to OSA pathogenesis. Leptin, an adipocyte-derived hormone that stimulates the perception of satiety in the hypothalamus and regulates appetite, may also affect ventilatory drive (vide infra) (13,14). Weight has been clearly established to have a strong genetic component. Twin studies have consistently estimated that 60% to 80% of the variance in body mass index is explained by familial factors (15–18). Rare mutations in at least ten genes have been identified as monogenic causes of severe childhood obesity (19). These include LEP, LEPR, MC3R, MC4R, and POMC (Table 1), all of which encode key proteins in the leptin signaling pathway. In most individuals however, obesity is a polygenic disorder, with multiple genes, each with small to modest effects, together accounting for familial similarities. Over 50 whole genome linkage scans for obesity phenotypes and hundreds of association studies have been conducted, and dozens of candidate genes have been identified as potential risk factors (19,20). Among the strongest candidates with

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at least 10 studies reporting positive associations each are polymorphisms in ADRB2, ADRB3, GCCR, GNB3, LEPR, PPARG, UCP2, and UCP3 (19). In addition, strong linkage evidence exists for susceptibility loci at multiple regions in the genome, including chromosomal regions 1p (near the LEPR locus), 2p (near the POMC locus), and 11q (19). Because central obesity, upper body obesity, and fat in the neck and upper airway may be more important risk factors for OSA than generalized obesity (21,22), genetic polymorphisms that influence fat deposition patterns may also play an important role in defining OSA susceptibility. Independent of overall obesity, measures of an android fat pattern and central obesity have heritabilities as high as 40% to 50% (23,24). Craniofacial Structure (See Also Chapter 12) Anatomic features of the face or pharynx that narrow the upper airway will also tend to predispose individuals to airway collapse during sleep. This applies to both bony and soft tissue structures surrounding the upper airway. Among the bony phenotypes associated with OSA are brachycephalic head form, reduced anterior-posterior dimension of the cranial base, reduced nasion-sella-basion angle, inferiorly displaced hyoid, retropositioned maxilla, mandibular retrognathia and micrognathia (25–31). Soft tissue predictors include an elongated soft palate, macroglossia, adenotonsillar hypertrophy, and increased volume of the lateral pharyngeal walls (30–34). Both twin and family studies support a genetic basis for determining craniofacial phenotypes. The shape of the head, as defined by the cephalic index (ratio of head width to head length), appears to be almost completely genetically determined (35). Other bony features predictive of OSA such as the length of the cranial base and the nasion-sella-basion angle are also significantly heritable (36). Several studies have assessed upper airway dimensions in relatives of OSA probands. Compared to relatives of controls, relatives of apneics have both bony and soft tissue craniofacial features that predispose to OSA, such as retropositioned maxillae and mandibles, longer soft palates, and larger tongues (3,4,37). Over one third of the variability in the volume of soft tissue airway structures including the tongue and lateral pharyngeal walls can be explained by familial factors (38). Many candidate genes for craniofacial abnormalities have been identified. Mutations in genes belonging to the fibroblast growth factor (e.g., FGFR1, FGFR2, FGFR3), transforming growth factor beta (e.g., TGFBR1, TGFBR2), homeobox (e.g., MSX1, MSX2), and sonic hedgehog (e.g., PTCH, SHH) pathways have been identified as causes of cleft lip/palate, craniosynostosis, and other facial abnormalities (39). Other candidate genes known to play a role in craniofacial development include the retinoic acid receptors (40), genes on the endothelin pathway (e.g., ECE1, EDN1 and EDNRA) (41–43), and TCOF1, the cause of Treacher Collins syndrome (vide infra) (44). Ventilatory Control (See Also Chapters 7 and 9) Abnormalities in ventilatory control may predispose to OSA by several mechanisms. In the presence of a blunted ventilatory drive, neural stimulation of the upper airway dilator muscles may become insufficient to maintain airway patency. Conversely, an overly robust ventilatory drive may lead to instability of breathing with alternating cycles of hyperventilation and hypoventilation (45). During the

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nadir of this cycling, neural output to the upper airway dilators may again fall below the threshold required to keep the airway open (46). Variation in human ventilatory responsiveness appears to be subject to major genetic control (47). A number of studies have established a strong correlation in the ventilatory response to hypoxia in twins, with stronger correlations between monozygotic than dizygotic twins (48–51). A reduced hypoxic ventilatory response has been found in first-degree relatives of patients with various pulmonary disorders (52–55). Nearly one third of the variance in hypoxic ventilatory response in a Tibetan population could be explained by genetic factors (56). The response to hypercapnia, on the other hand, has not been found to consistently display familial aggregation (48,50,51,57). Studies of families with multiple cases of OSA have demonstrated that both OSA-affected and OSA-unaffected individuals have a reduced respiratory response to hypoxia (58,59). Under the stressor of inspiratory resistive loading, differences between nonapneic individuals from OSA families and control families also have also been identified (60). Reductions in both respiratory impedance and tidal volume have been reported in relatives of OSA probands in response to loading (59,61). An inherited predisposition to a blunted hypoxic ventilatory response, which may require the appropriate stress to become manifest, may contribute to the genesis of OSA. Specific genes affecting OSA susceptibility through effects on ventilatory drive remain to be identified. However, some biochemical phenomena or mutations that may affect OSA susceptibility through effects on ventilatory drive have been identified in experimental systems. Nitric oxide appears to be a major molecular factor controlling minute ventilation and the normal ventilatory response to hypoxia (62,63). Mice deficient in genes governing the synthesis or activation of nitric oxide (e.g., the homologs of NOS3 and GGT1) have absent hypoxic ventilatory responses (62,64). Hypoxia-inducible factor 1 plays an important role in adaptation to chronic hypoxia, and a loss of function allele at the HIF1A homolog severely blunted the normal ventilatory response of mice to prolonged hypoxia (65,66). Mice lacking one or two copies of the gene for brain-derived neurotrophic factor (BDNF) hypoventilate and demonstrate reduced responsiveness to hyperoxia (67). Mouse models suggest that genes important in the endothelin pathway (e.g., ECE1, EDN1, EDNRA), which is also important in craniofacial development, as well as the RET pathway (e.g., GDNF, RET) have effects on ventilatory control (43,68–71). All of these pathways are important in neural crest migration, and mutations in many of these genes have been identified as causes of congenital central hypoventilation in humans (vide infra). Because of evidence that angiotensin II can modulate afferent activity from the carotid body chemoreceptor, a functional insertion/deletion polymorphism in the angiotensin converting enzyme (ACE) gene has been investigated as a determinant of ventilatory drive (72). The insertion allele associated with lower tissue ACE levels has been associated with a greater ventilatory response to exertional hypoxia as well as greater oxyhemoglobin saturation on rapid ascent to altitude (73,74). As noted previously, leptin appears to have important effects on ventilatory drive. Mice homozygous for a knockout mutation in leptin hypoventilate and have a blunted ventilatory response to hypercapnia (13). Exogenous leptin administration corrects these abnormalities independent of weight changes (14). In humans, obesity is typically associated with large elevations in leptin levels (75). Individuals with OSA have further elevations in leptin compared to obesity-matched controls (76). Whether leptin influences ventilatory drive in humans is an active area of investigation.

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DISORDERS THAT AFFECT SLEEP AND BREATHING Sudden Infant Death Syndrome Sudden Infant Death Syndrome (SIDS) is a disorder in which cardiopulmonary collapse during sleep occurs within the first year of life. The primary cause remains unknown, but a leading hypothesis suggests that SIDS is caused by a defect in statemodulated central regulation of cardiopulmonary function (77). Abnormalities in ventilatory control and craniofacial morphology are postulated to be important in the pathogenesis of SIDS; and, as previously described, there is strong evidence for an important genetic component in both of these pathways. SIDS has been found to aggregate within families (78). The risk of SIDS in a sibling of a SIDS patient may be as much as 10-fold greater than controls, and five times greater in second degree relatives (79,80). Defects in serotonergic neurotransmission controlling ventilation, arousal, and autonomic tone have been postulated to be important in SIDS pathophysiology. Reduced serotonin transmission has been reported in several medullary nuclei (81–84). A Japanese study reported that the insertion variant of an insertion/deletion polymorphism in the promoter region of the SLC6A4 gene was associated with SIDS (85). This gene encodes a serotonin transporter protein which clears serotonin from the synaptic space. The insertion allele increases transcriptional efficiency of the gene, leading presumably to reductions in serotonin neurotransmission (86). The association with the insertion variant has been confirmed in Caucasian and African–American populations (87). In addition, because the frequency of this polymorphism is lowest among Japanese and highest among African–Americans, this allele may explain the ethnic variation in SIDS incidence (which rises from Japanese to Caucasian to African–American populations). In addition, an intronic polymorphism in SLC6A4 that also increases gene transcription was associated with SIDS in an African–American cohort, although no association was found in a population of Caucasians (88,89). Interestingly, SIDS and OSA have been found to cosegregate within families. In other words, SIDS is more common in families where a member has OSA; and OSA is more common in families with a SIDS child (90–94). Families with both OSA and SIDS cases have been reported to have particular craniofacial structures, including brachycephaly, leading to upper airway narrowing as well as reduced hypoxic ventilatory responsiveness (90,91). In fact, children who survive a SIDS event may later develop OSA. All seven near-miss SIDS cases in one study had OSA when studied at one year of age (93). Although this relationship has not been proven (95), these data suggest the two disorders may have shared genetic predisposition acting via ventilatory control and/or craniofacial structure pathways. Congenital Central Alveolar Hypoventilation Syndrome Congenital central alveolar hypoventilation syndrome (CCHS) is a primary disorder of ventilatory drive, due specifically to a lack of sensitivity to hypoxia and hypercapnia (96). This syndrome is classically characterized by hypoventilation, hypoxemia and hypercapnia during sleep, with normal ventilation and blood gas economy during wakefulness. More severely affected individuals may hypoventilate in both states (97). Affected individuals additionally lack a perception of dyspnea. The association of CCHS with Hirschprung disease and other manifestations of autonomic dysfunction suggests that altered neural crest differentiation is central to the pathogenesis of this disease (98,99).

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Studies of the genetics of CCHS were reviewed in 2005 (100). Descriptions of CCHS in twins and first-degree relatives demonstrated a familial basis for this syndrome in some cases, and segregation analysis suggested the involvement of a codominant gene of large effect (101–103). Subsequently, genes important in neural crest migration and function were causally linked to CCHS. Alterations in PHOX2B appear to be the most prevalent causal mutations (104). PHOX2B encodes a highly conserved homeobox domain transcription factor important in promoting neural differentiation during early embryogenesis. Mice missing one copy of this gene display blunted ventilatory responses to hypercapnia and hypoxia (105). In studies of children with CCHS, the most common mutations of PHOX2B are expansions of the polyalanine tract in exon 3, accounting for 40% to 97% of affected individuals in various studies (106–108). Furthermore, the number of additional alanine repeats has been correlated with disease severity, as is generally observed in repeat expansion genetic diseases (108,109). Frameshift and nonsense mutations leading to loss of function of the gene product have also been described (106–108). Isolated cases of CCHS have also been associated with mutations in other genes involved in neural crest maturation, such as EDN3, RET, GDNF, ASCL1 and BDNF (107,110,111). As described previously, murine knockout models of many of these genes have been demonstrated to have defects in ventilatory control. Prader-Willi Syndrome Prader-Willi syndrome (PWS) is a genetic syndrome characterized by hyperphagia, hypogonadism, mental retardation, and hypotonia (112). Although prenatal growth retardation is common, with short stature secondary to growth hormone abnormalities, the hyperphagia results in early onset obesity. OSA is common in these patients and excessive daytime sleepiness often begins in childhood (113). Besides morbid obesity, PWS patients have other risk factors predisposing to OSA. These include blunted ventilatory responsiveness to both hypoxia and hypercapnia, diminished arousal thresholds to respiratory stimuli, and muscular hypotonia (114–116). PWS displays parent-of-origin effects in that the disorder is primarily caused by a loss of the paternally inherited copy of a region on chromosome 15q, either through deletions or maternal uniparental disomy (117). The causal gene appears to be maternally imprinted (i.e., expression of the maternally inherited allele is suppressed through methylation of the locus). Several candidate genes in this region including NDN and SNRPN have been shown to express maternal imprinting in experimental models (118,119). The gene product of NDN, necdin, is a DNA-binding protein that may regulate the permanent arrest of cell growth of postmitotic neurons during development (118,120). It appears to be both physically and functionally closely associated with the MSX2 protein, important in craniofacial development (121). Necdin-deficient mice have been shown to have abnormal neuronal activity in the pre-Botzinger complex (the medullary respiratory pattern generator) associated with a blunted respiratory drive (122). SNRPN encodes a small nucleolar RNA, HBII-52, that appears to bind to and regulate the processing of mRNA from the serotonin 2C receptor gene (HTR2C) (119). The binding of HBII-52 changes the splicing of the gene, creating an isoform with increased binding affinity to serotonin. PWS patients have been shown to have a relative reduction in the high affinity form of this receptor (119). Given the importance of the serotonergic system in regulating appetite, wake/sleep, and upper

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airway muscle tone, this abnormality may be the cause of the sleep apnea phenotype in this disorder. Other Congenital Syndromes Many other congenital disorders are associated with OSA, and several may be especially instructive. The Treacher Collins syndrome is an autosomal dominant disorder characterized by micrognathia, cleft palate, hypoplastic zygomatic arches, and upper airway obstruction. The causal gene, TCOF1, which encodes a protein that regulates ribosomal DNA transcription, appears to be important in branchial arch development (44,123,124). Individuals with the disorder have loss of function mutations in the TCOF1 gene (44,125). A functional polymorphism in the canine homolog of TCOF1 has been found to predict skull and facial shape among dog breeds (126). Charcot-Marie-Tooth (CMT) disease is a collection of hereditary polyneuropathies. The most frequent subtype, CMT1A, was initially associated with duplication of an 1100 kb region on chromosome 17p (127). Subsequently, point mutations in the PMP22 gene, which lies in the duplicated region, have also been found to cause this disorder (128). Transgenic models of CMT1A suggest that overexpression of the gene product of PMP22, a major component of myelin, causes demyelination, resulting in the progressive sensory and peripheral neuropathy characteristic of this disorder (129,130). In a study of 14 members of a CMT1A pedigree who underwent polysomnography, all 11 individuals with PMP22 duplication were found to have OSA although none of the unaffected members did (131). Because OSA is associated with both sensory and motor neuronal abnormalities in the upper airway, including degeneration of myelinated nerve fibers (132–134), these overexpression mutations in PMP22 may facilitate the development of OSA by affecting pharyngeal innervation. Rett syndrome is an X-linked dominant disorder characterized by normal development for the first year of life followed by neurodevelopmental deterioration resulting in mental retardation, autism, hypotonia, and seizures. In addition, affected subjects display unusual respiratory patterns during wakefulness, including apneustic breathing and hyperventilation followed by apnea. Breathing during sleep is typically normal (135,136). Affected patients have reduced levels of monoaminergic metabolites in cerebrospinal fluid (137). The majority of Rett cases is associated with a loss of function mutation in MECP (138). The protein encoded by MECP binds to methylated DNA, where it serves as a repressor of gene transcription (139). Knockout of the murine homolog of MECP results in an apneustic respiratory phenotype that correlates with a loss of tyrosine hydroxylase-expressing neurons in the medulla and reduced medullary levels of norepinephrine (140). The firing pattern from medullary respiratory neurons from these mice normalizes after application of exogenous norepinephrine, further supporting a role for adrenergic pathways in the pathogenesis of respiratory dysrhythmia in Rett syndrome. OBSTRUCTIVE SLEEP APNEA GENETICS Phenotype Selection An important consideration in understanding the role of genetics in sleepdisordered breathing is the identification of the most relevant phenotype. A feature of OSA shared by many other complex disorders is the lack of a standardized phenotypic definition of disease. Many physiologists define OSA based solely on

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polysomnographic criteria—an apnea-hypopnea index (AHI) above a certain threshold, with evidence that these events are obstructive in nature. However, the recently revised International Classification of Sleep Disorders (ICSD-2) provides a clinical definition of OSA which combines polysomnographic criteria with referable symptoms: an AHI ≥ 5 events/hr of sleep with evidence of respiratory effort during all or part of each respiratory event, and at least one characteristic OSA symptom (unintentional sleep episodes during wakefulness, daytime sleepiness, unrefreshing sleep, fatigue, or insomnia; awakenings with breath holding, gasping, or choking; or bedpartner reports of loud snoring, breathing interruptions, or both during the patient’s sleep) (141). If a patient does not have the latter symptoms, the patient may still meet ICSD-2 criteria for the diagnosis of OSA with an AHI ≥ 15 and with evidence of respiratory effort during all or part of each respiratory event, providing that the disorder is not better explained by any other condition. The term, obstructive sleep apnea syndrome (OSAS) is frequently used synonymously with the first ICSD-2 definition, that is, an elevated AHI with characteristic symptoms. An important issue is whether heritability is greater for a phenotype based strictly on AHI level or for one that uses associated symptoms. The choice of a dichotomous phenotype, such as OSA or OSAS, versus a continuous phenotype, such as AHI alone, needs also be considered. Although continuous phenotypes typically provide more power for identifying genetic susceptibility loci and do not require arbitrary threshold cutoffs that may vary across subgroups (e.g., children vs. adults), they also make more assumptions regarding scaling (e.g., is a doubling of AHI from 2 to 4 of equivalent importance as one from 10 to 20?) that may or may not be appropriate. Familial Aggregation The convincing evidence that snoring, symptoms of sleep-disturbed breathing and OSA cluster within families has been reviewed recently in some detail (7). The prevalence of OSA in first-degree relatives of OSA probands is 21% to 84% (2,4). The odds ratio for OSA in relatives of apneics compared to relatives of controls is elevated, with estimates ranging from 2 to 46 (2–4,92). In addition, OSA risk increases with the number of relatives who also have OSA (2). The heritability (the percentage of the total variance explained by familial factors) of the AHI is 30% to 40% in both Caucasian and African–American populations (142–144). Linkage Analyses Segregation analysis, statistical modeling of pedigree data to determine the most likely mode of inheritance of a trait, has been applied to sleep-disordered breathing. In 177 Caucasian and 125 African–American families participating in the Cleveland Family Study, segregation analysis yielded evidence for an oligogenic inheritance pattern for AHI in both ethnic groups (5). Among Caucasians, the best-fitting model for age-adjusted AHI was one in which an autosomal recessive gene accounted for 27% and 32% of the variance in males and females, respectively. Other familial factors, either environmental or polygenic, accounted for an additional 11% of the variance. Adjusting the AHI for both age and body mass index (BMI) nearly abrogated the major gene effect in males, but maintained distributions consistent with a major gene in females. In African–Americans, a codominant gene appeared to account for 35% of the total variance in AHI even after adjusting for both age and BMI. An additional 8% of the variance seemed to result from multifactorial familial

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effects. Thus, genes of large effect appear to be important in OSA pathogenesis, particularly among African–Americans. Genome-wide linkage analyses, in which one searches for gene loci that cosegregate with the phenotype of interest, are a means for identifying chromosomal regions in which susceptibility genes may lie. Transmission of alleles at a particular genetic marker through a pedigree in parallel with a trait of interest (i.e., linkage) suggests that a susceptibility locus for that trait lies in physical proximity to the genotyped marker. Evidence for linkage is typically given by the lod score, which is the logarithmic odds ratio of linkage to no linkage. Linkage studies for OSA have only just begun. The Cleveland Family Study has reported whole genome linkage findings in a cohort of Caucasians and a cohort of African–Americans (142,143), Although none of the linkage findings achieved genomewide significance (lod > 3), several regions had intermediate lod scores, indicative of possible linkage in the setting of complex, multifactorial diseases (145). Among Caucasians, the highest lod scores for AHI were reported at chromosomal regions 2p, 12p, and 19q with scores of 1.6, 1.4, and 1.4 respectively (142). Further fine mapping of the Caucasian cohort at chromosome 19q has resulted in increased evidence of linkage to OSA in this region, with a peak lod score of 2.5 (146). Among African-Americans, a region with suggestive evidence of linkage has been reported on chromosome 8q, with a lod score of 1.3 (143). Candidate Genes The number of gene loci postulated to affect OSA risk continues to grow. APOE is a leading candidate as an OSA susceptibility locus. This gene codes for apolipoprotein E which is important in the clearance of chylomicra and very low density lipoprotein (VLDL) remnants from the circulation. There are three common polymorphisms of this gene known as ε2, ε3, and ε4. The Wisconsin Sleep Cohort Study, a cohort of middle-aged Americans primarily of Caucasian descent, reported that the ε4 allele was associated with OSA (147). They noted a dose response effect such that heterozygotes for ε4 had an odds ratio (OR) for sleep apnea of 2.1 while homozygotes had an OR of 3.9. These findings could not be replicated in a cohort of elderly JapaneseAmerican men, however (148). An explanation for this discrepancy was suggested by work from the Sleep Heart Health Study (149). These investigators found that age had a modifying effect on the OSA—APOE genotype association: among those less than 65 the OR for OSA was 3.1 in ε4 carriers, but no association with OSA was found in those over 65. These data suggest that APOE ε4—related OSA may be a more severe form of disease with earlier age of onset. The mechanism by which the APOE protein might affect OSA pathogenesis is unknown. Furthermore, data from the Cleveland Family Study cohort, although also providing evidence for strong linkage to AHI of the region near the APOE locus on chromosome 19, could not associate this linkage with the actual APOE genotype (146). In addition, APOE genotype was not associated with OSA status. These results indicate that the susceptibility locus for OSA-related phenotypes is not APOE but another locus close to it, and suggest that the previous associations may be due to linkage disequilibrium between the causal polymorphism and the APOE ε4 allele. The POMC locus lies in the region on chromosome 2p that is linked to AHI. Mutations in POMC are known to be a monogenic cause of obesity (142,150), and polymorphisms of the gene appear to be associated with measures of obesity (151). The gene product, pro-opiomelanocortin, is synthesized in the arcuate nucleus of

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the hypothalamus and cleaved to yield melanocortin peptides, which act as suppressors of appetite through the melanocortin 4 receptor (MC4R) (152). Leptin stimulates expression of POMC (153), and in fact, common polymorphisms of POMC are associated with serum leptin levels (154). The actions of leptin on appetite and satiety thus appear to be mediated by POMC. Similarly, the actions of leptin on the hypercapnic ventilatory response in mice are mediated by melanocortin, and thus also by POMC (155). Despite this provocative association of POMC and ventilatory control in experimental animals, no association studies of POMC genotype and OSA have yet been reported. The SLC6A4 gene on chromosome 17q, discussed previously in relation to SIDS (vide infra), has also been investigated in sleep apnea. The insertion polymorphism in the promoter region that results in increased expression of the serotonin transporter was associated with OSAS in men but not women in a small Turkish study, but this association was not found in a Chinese sample (156,157). The intronic polymorphism in SLC6A4, on the other hand, was associated with OSAS in both studies. However, the associated allele was one that reduces protein expression and is the opposite of the one associated with SIDS (89). Thus, the significance of these findings is unclear at this time. Another gene important in serotonin neurotransmission is HTR2A on chromosome 13q, which encodes the serotonin 2A receptor. A polymorphism in an upstream region of this gene has been recently reported to be associated with OSAS in a Turkish cohort (158). This polymorphism has been shown in vitro to increase expression of the receptor protein (159). In light of the previously discussed association between the ACE insertion/ deletion polymorphism and hypoxic ventilatory response as well as reports of an association with muscle fiber composition, this gene has at least two potential mechanisms by which it may influence OSA susceptibility (73,160). Several studies of Chinese cohorts have reported an association between this polymorphism and OSAS (161–163), but more recent studies in both Chinese and Caucasian populations have been unable to confirm this finding (164–167). OBSTRUCTIVE SLEEP APNEA AND RELATED DISORDERS Pleiotropy OSA is strongly associated with a number of other disorders, including obesity, hypertension, insulin resistance, and the metabolic syndrome. Although these associations may be causal in nature (obesity causes OSA or OSA causes hypertension), the possibility of shared etiologies also exists. Because each of these disorders has a strong genetic underpinning, many have suggested that shared genetic risk factors may exist for these traits. In one study, 50% and 55% of the genetic variance in AHI was shared with BMI and leptin respectively (168). Clearly because obesity predisposes to OSA, any gene that influences obesity risk will also be a susceptibility gene for OSA. However, evidence exists that some polymorphisms may increase the risk of both obesity and OSA through independent mechanisms. This genetic pleiotropy has been described in other settings. The ε4 allele of APOE is an established risk factor for both coronary disease and Alzheimer’s dementia (169,170). CLOCKknockout mice exhibit both disrupted circadian rhythms and increased appetite, resulting in an obesity and metabolic syndrome phenotype (171,172). Pleiotropic effects on sleep and other functions may be exhibited by several pathways under proper circumstances. Cogent possibilities are the orexin system, which regulates

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both the sleep/wake state and appetite (173) and serotonergic systems, that influence hypothalamic satiety centers, ventilatory control and upper airway dilator muscle tone (174,175). In the setting of OSA, genes that affect multiple intermediate phenotypes (such as obesity and ventilatory control) may play a much more important role in apnea pathogenesis than loci affecting only one pathway. Evidence from the genome-wide linkage scans in the Cleveland Family Study suggests that such pleiotropy does exist in OSA. Among Caucasians, evidence for linkage to the same region of chromosome 2p was reported for both AHI and BMI, with lod scores of 1.6 and 3.1, respectively, suggesting that a susceptibility locus exists in this region for both traits (142). When AHI was adjusted for BMI, the peak lod score was not substantially affected (dropping to only 1.3), suggesting that the effects on AHI of any potential locus in this area are independent of obesity. A potential pleiotropic candidate gene in this region is POMC. As described previously, leptin influences both appetite and ventilatory control through POMC and melanocortin. POMC might thus have a similar effect on ventilatory control, and through this a role in modulating the liability for OSA. Among African–Americans in the Cleveland Family Study, a region on chromosome 8q exhibited peak lod scores of 1.3 and 1.6 for AHI and BMI, respectively (143). The lod score for AHI did not substantially change after adjusting for BMI, suggesting that this could also represent a pleiotropic genetic locus. Obstructive Sleep Apnea Consequences OSA is a risk factor for the development of a range of neurocognitive, cardiovascular, and metabolic abnormalities including daytime sleepiness, memory and learning impairments, depressed mood, hypertension, heart failure, stroke, and insulin resistance. Most, if not all, of these disorders have themselves been established to have a genetic basis. For example, the heritability of sleepiness as measured by the Epworth Sleepiness Scale is nearly 40% (176). A functional polymorphism in HCRTR2, which encodes the hypocretin-2 receptor, has been reported to be associated with idiopathic hypersomnolence (177). It would not be surprising, therefore, if susceptibility genes such as HCRTR2 contribute to an individual’s risk for developing this consequence in the setting of OSA. Indeed, in families selected for OSA, the risk of daytime sleepiness was found to rise with the number of sleepy relatives: the odds of excessive daytime sleepiness were increased 1.5, 2.4, and 3.7-fold among those having one, two, or three relatives with the same manifestation (178). Whether this pattern is different from that in a nonapneic population is unclear. As another example, a study of cognition and the APOE locus found that increasing AHI was correlated with memory impairment among individuals with an ε4 allele, although no relationship was found between apnea and cognition among those without this allele (179). These results suggest that the ε4 isoform of APOE may increase susceptibility to memory loss from OSA. Work is only now beginning to understand how the stressors of hypoxemia, hypercapnia, and recurrent arousal from OSA may interact with individual genetic makeup to affect the risk of developing complications. The effect of relatively minor functional differences may be clinically silent unless the biologic system is stressed by exposure to repetitive hypoxia, for example. Because OSA has a genetic basis, the genetics of the complications of OSA can be thought of as a study of gene-by-gene

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interactions. Examples of a potential gene by OSA interaction have been reported in the setting of cardiovascular disease. The insertion/deletion polymorphism in the ACE gene has been postulated to influence hypertension susceptibility in OSA. The higher levels of plasma ACE activity associated with the deletion allele provide a physiologic mechanism by which it might increase blood pressure (180). A study of participants in the Wisconsin Sleep Cohort Study found that the hypertensive effect of this allele depended on OSA status (167). The deletion polymorphism was not associated with hypertension in those without sleep apnea (AHI < 5) or severe apnea (AHI > 30). However, in those with mild to moderate apnea (AHI 5–30), one copy of the deletion polymorphism doubled the odds of hypertension while two copies increased the odds 4.7-fold. The HP gene, which encodes haptoglobin, has also been proposed as a modifier of cardiovascular risk. A key role of haptoglobin is binding free hemoglobin, thereby preventing hemoglobin-induced oxidative damage. It also appears to reduce inflammation by inhibiting prostaglandin synthesis. An intragenic duplication polymorphism in HP has been shown to lead to a protein with reduced antioxidant and anti-inflammatory capabilities (181). The importance of this polymorphism may be magnified in the setting of increased oxidative stress and inflammation owing to OSA (182,183). In support of this theory, an Israeli study reported that homozygosity for the duplication allele is associated with an increased prevalence of cardiovascular disease among those with OSA, whereas no association was found in nonapneics (184). However, these findings could not be replicated in the Sleep Heart Health Study (185). Clearly, more research is needed to confirm these preliminary findings. Nevertheless, the possibility that genetic makeup determines the effects of sleep apnea on an individual opens up new prospects both for understanding the pathophysiology of OSA-induced morbidity as well as for tailoring therapeutic decisions regarding apnea treatment based upon individual genetic susceptibility. CONCLUSIONS There is now firm evidence that genetics plays an important role in defining OSA susceptibility. Research on both intermediate phenotypes as well as monogenic disorders of sleep disordered breathing has provided candidate genes and biochemical pathways that may be important in expression of the OSA phenotype. Examples include regulators of neural crest cell differentiation for traits related to ventilatory control and the leptin signaling pathway for both obesity and ventilatory phenotypes. Polymorphisms affecting serotonergic pathways have the potential to affect OSA susceptibility through multiple mechanisms including obesity, ventilatory drive, and upper airway muscle function. Linkage studies of apnea-related phenotypes provide another path for identifying genes related to the OSA phenotype. Often, the function of genes ascertained in this manner is unknown. Efforts to identify the action of these genes are thus essential. Research on the consequences of OSA is only just beginning, but preliminary work suggests that these traits are also genetically determined in part. Further association studies in larger populations, transgenic animal model work, and research into molecular mechanisms will be required to confirm associations between specific polymorphisms and OSA and related traits, and to establish that the relationships are causal in nature. We hope that we have provided a road map that will assist in the direction of this research effort.

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The Spectrum of Sleep-Disordered Breathing Adnan Habib and Barbara Phillips The Division of Pulmonary, Critical Care, and Sleep Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, U.S.A.

INTRODUCTION The term “sleep-disordered breathing” (SDB) encompasses a number of different clinical disorders. These conditions result from several different pathophysiologic mechanisms and represent different points along a continuous spectrum of severity. Further, their frequency, presentation, and consequences vary across the life-span. SDB is a spectrum, just as blood pressure is a spectrum. And, just as with blood pressure, the boundary between safe and unsafe levels of SDB is uncertain and changeable. An American Academy of Sleep Medicine Task Force Report published in 1999 defined four separate syndromes associated with abnormal respiratory events during sleep among adults, including obstructive sleep apnea (OSA)-hypopnea syndrome, central sleep apnea (CSA)-hypopnea syndrome, Cheyne–Stokes breathing syndrome, and sleep hypoventilation syndrome (1). Since the 1999 consensus report, sleep-related breathing disorder (SRBD) definitions and measurements have evolved. The newly revised (second edition) international classification of sleep disorders, 2nd edition (ICSD-2) includes broad categories of SRBDs, including the CSA syndromes, the OSA syndromes, sleeprelated hypoventilation/hypoxemic syndromes, sleep-related hypoventilation/ hypoxemia owing to medical conditions, and “other” SRBDs (2). Table 1 lists this classification scheme in its entirety. Note that some terms that are or have been commonly used, such as the upper airway resistance syndrome (UARS), Pickwickian syndrome, and obesity-hypoventilation syndrome, do not exist in this nosology. SRBDs are indeed a heterogeneous group of conditions that may be associated with alterations in the structure of sleep, in sleep quality, and in gas exchange during sleep. This chapter will focus primarily on the obstructive and CSA syndromes. THE OBSTRUCTIVE SLEEP APNEA SYNDROMES Overview OSA is by far the most prevalent and deadly of the SRBDs. Obstructed breathing encompasses a continuous spectrum of severity. Snoring without electroencephalographic arousal is on one end of the spectrum. As obstructed breathing increases in severity, disruptive snoring causing respiratory effort-related arousals (RERAs) occurs. At this point, depending on the sensitivity of the measurement tools, SDB may be detectable. However, if insensitive measures are used, this form of SDB can be overlooked. Multiple RERAs have been called the UARS, and can cause many of the sequellae of frank sleep apnea (3). Beyond UARS, mild, moderate, and severe sleep apnea can be identified. At the extreme end of the spectrum is the condition 245

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TABLE 1 The Sleep-Related Breathing Disorders CSA syndromes Primary CSA CSA due to Cheyne-Stokes breathing pattern CSA due to high-altitude periodic breathing CSA due to medical condition not Cheyne-Stokes CSA due to drug or substance abuse CSA of infancy Obstructive sleep apnea syndromes Obstructive sleep apnea, adult Obstructive sleep apnea, pediatric Sleep-related hypoventilation/hypoxemic syndromes Sleep-related nonobstructive alveolar hypoventilation, idiopathic Congenital central alveolar hypoventilation syndrome Sleep-related hypoventilation/hypoxemia due to medical conditions Sleep-related hypoventilation/hypoxemia due to pulmonary parenchymal or vascular pathology Sleep-related hypoventilation/hypoxemia due to lower airways obstruction Sleep-related hypoventilation/hypoxemia due to neuromuscular and chest wall disorders Other sleep-related breathing disorder Sleep apnea/sleep-related breathing disorder, unspecified Abbreviation: CSA, central sleep apnea. Source: From Ref. 2.

formerly known as the “Pickwickian Syndrome,” with right heart failure, obesity, and hypoventilation during sleep. Pathophysiology of Airway Obstruction (See also Chapters 6–11) Airflow obstruction during sleep encompasses a spectrum from transient upper airway resistance to repetitive reduction or total cessation of airflow, despite the presence of increasing respiratory effort. This airflow obstruction can be associated arousals, arrhythmias and oxygen desaturation of varying severity. The recurrent upper airway collapse can occur at any level of the nasopharynx or oropharynx, but most commonly occurs at the area of the velopharynx, behind the uvula and soft palate. The mechanisms responsible for recurrent upper airway closure still are not completely understood. The patency of the normal upper airway is determined by pharyngeal transmural pressure, defined as the difference between the pressure within the airway lumen and the pressure exerted by tissues surrounding the site of collapse. Upper airway dysfunction and the specific sites of narrowing or closure are influenced by the underlying neuromuscular tone, anatomy, upper-airway muscle synchrony, and the stage of sleep. Partial collapse results in snoring, hypopneas, and sometimes, prolonged obstructive hypoventilation. Complete closure results in an apnea. Obstructive events during sleep are typically terminated by increased effort, arousal, and resumption of airflow. Upper-airway size is determined by soft tissue and skeletal factors, which also play a role in maintaining upper airway patency during sleep. Factors such as obesity, congenital or acquired narrowing of the upper airway, nasal obstruction, or abnormal arousal thresholds may also influence the incidence and severity of SDB. Snoring At the “minimal” end of the spectrum of airway obstruction during sleep is snoring. Snoring is caused by vibration of upper airway structures during sleep.

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Primary snoring is snoring that occurs without associated apnea, gas exchange abnormalities, or arousals. Snoring can be a life-long behavior; approximately 10% of children snore on all or most nights (4–6). Primary snoring does not appear to progress to OSA in young children and may, in fact, resolve over time (5–7). In adults, the prevalence of snoring is much higher than in children, although its true prevalence is difficult to ascertain because of problems with defining and measuring snoring. However, about 50% of men and 25% of women snore, and the prevalence increases with age (8). While snoring is generally regarded as a nuisance, it is important to note that snoring has been associated with many of the complications of sleep apnea, including hypertension (9,10), cardiovascular disease (11,12), pre-eclampsia (13), cognitive dysfunction (14,15), and poor diabetic control (16). Snorers have higher PaCO2 levels than do nonsnorers (2). Even Murray Johns’ original application of the Epworth Sleepiness Scale resulted in slightly higher (although still normal) scores for snorers compared with nonsnorers (17). At present, treatment of snoring is largely considered “cosmetic,” and most insurance does not cover treatment for snoring. Despite this, physicians have addressed the treatment of snoring in a variety of ways, including use of nasal steroids (18), oral appliances (19) and surgery (20); these approaches have been reported to result in improvement of symptoms in addition to the acoustic problem, which further supports the notion that “primary” snoring may have a negative impact on the snorer. In short, intermittent snoring is the mildest form of SDB and some evidence suggests that even “simple” or primary snoring may have significant consequences. Upper Airway Resistance Syndrome The term “UARS” was first described (like so many things) by Guilleminault (3). The first report of this condition centered on patients who had been diagnosed with idiopathic hypersomnia, but who were found to demonstrate increased inspiratory work of breathing during sleep. In the original investigation, Guilleminault et al. measured respiratory effort with an esophageal balloon, demonstrating repetitive episodes of increasingly negative intrathoracic pressures, leading up to and terminating in arousals. These events were termed RERAs; more than five of these events per hour of sleep were deemed to cause excessive sleepiness, and the existence of this finding in a symptomatic individual was named the UARS (3,21). RERAs cause sleep fragmentation by recurrent microarousals without evidence of apneas, hyperpnoea, or even hypoxia. In the ICSD-2, UARS is subsumed under the diagnosis of adult OSA (2), “because the pathophysiology does not significantly differ from that of OSA” However, two caveats are in order: 1. RERAs and increased inspiratory work of breathing may exist even in the absence of snoring, and may not be identified without the use of an esophageal or nasal pressure transducer (3,21–25). 2. Several investigators, including the original describer of UARS, believe that it is a separate entity (3,21,24). Certainly, it tends to present differently from OSA, with women more likely to be affected and depression a more prominent feature. In fact, Guilleminault has demonstrated differences in sleep structure between those with OSA and those with UARS (21) and postulates that increased sensitivity to inspiratory flow resistance predisposes to the development of UARS rather than to the development of frank obstruction; the afflicted individual arouses prior to total airway occlusion, resulting in a disorder more of sleep disturbance than of hypoxemia.

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Terminology aside, UARS, in the absence of frank apnea, has been associated with significant sequellae. Gold has demonstrated that both men and women with fibromyalgia are likely to have UARS, and that treatment of the inspiratory airflow resistance may improve symptoms of fibromyalgia (25,26). Exar and Collop (27) have demonstrated that UARS, again detectable only by means of an esophageal manometer, is associated with periodic limb movements. Children with UARS have no evidence of apnea, hypopnea, or gas exchange abnormalities on standard polysomnography (PSG). The diagnosis of UARS is made with the use of esophageal pressure monitoring to identify increased respiratory effort and related arousals (28–30). When PSG is performed without esophageal pressure monitoring, snoring with marked paradoxical breathing movements or repetitive arousals may be suggestive of UARS. The incidence of UARS in children is unknown. It is also unknown if tonsillectomy (which may be an effective treatment for sleep apnea in children) is effective in treating UARS. Obstructive Sleep Apnea The terms obstructive sleep apnea syndrome (OSAS) and obstructive sleep apnea hypopnea syndrome (OSAHS) will be used interchangeably here. The presence of OSA (or OSAHS) is based on the apnea plus hypopnea index (AHI). The AHI is the most commonly used criterion to establish the diagnosis of OSA and to quantify its severity. The AHI is defined as the sum of apneas and hypopneas divided by the hours of sleep. Diagnostic criteria for apneas and hypopneas are based on the sleep heart health study (SHHS) of more than 6000 middle-aged adults. In the SHHS, both apneas and hypopneas required at least a 10 sec reduction of airflow (to 30% of baseline for apneas and to 70% of baseline for hypopneas) (31,32). In order to achieve acceptable inter-rater reliability in the SHHS, both apneas and hypopneas required an oxygen desaturation of 4% or more; of note, the definition of apnea used by center for medicare and medicaid services (CMS, formerly HCFA) does not require oxygen desaturation (though the definition of hypopnea does) (33). In obstructive apneas and hypopneas, reduction of airflow occurs despite continued ventilatory efforts. In central apneas, respiratory effort is not detectable during the reduction in airflow. The term “respiratory disturbance index” (RDI) is also used to describe SDB, and may include apneas, hypopneas, snore-arousals or RERAs. The most severe form of SDB is the obesity-hypoventilation syndrome (formerly called the Pickwickian syndrome). The ICSD-2 notes that, “Use of the terms Pickwickian syndrome and obesity hypoventilation syndrome …” is discouraged because these terms apply primarily to central hypoventilation disorders rather than to upper airway obstruction. These terms now appear in the ICSD-2 under sleep-related hypoventilation/hypoxemia owing to neuromuscular and chest wall disorders. CMS operationally defines OSA by limiting coverage of treatment costs to certain levels of AHI (Table 2) (33). In brief, sleep apnea is an AHI of five events or more per hour of sleep with sequellae, or 15 or more events per hour of sleep without sequellae (2). The newly revised ICSD-2 defines the obstructive sleep apnea hypopnea syndrome (OSAHS) when a patient has five or more obstructed breathing events per hour of sleep with the appropriate clinical presentation (2). Obstructed breathing events may include apneas, hypopneas, or RERAs, and the patient is diagnosed with OSA provided that the disorder is not better explained by other conditions. (Table 3). The ascertainment of the AHI or RDI currently rests with PSG. A nocturnal PSG includes recordings of airflow, ventilatory effort, oxygen saturation, electrocardiogram, body position, electromyography (EMG), and electroencephalography

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TABLE 2 Centers for Medicare and Medicaid Services Criteria for CPAP Treatment of Sleep Apnea CPAP will be covered for adults with sleep-disordered breathing if: AHI > 15 or AHI > 5 with (“mild, symptomatic”) Hypertension Stroke Sleepiness Ischemic heart disease Insomnia Mood disorders Abbreviations: AHI, apnea plus hypopnea index; CPAP, continuous positive airway pressure. Source: From Ref. 33.

(EEG). Placement of EEG leads is based on the international 10/20 system. Body position, recorded by sensors or technician observation, and video recording are also recommended. Standards for performance of nocturnal PSG were revised and published in 2005 (34). Use of nasal pressure transducers (rather than thermistry) has long been recommended, in order to ascertain the presence of subtle inspiratory airflow limitation, but current practice continues to accept the rather insensitive tool of thermistry. Quantifying the Severity of Obstructive Sleep Apnea Several different schemes have been advanced to classify sleep apnea as “mild, moderate, or severe.” As described in Table 2, CMS covers continuous positive airway pressure (CPAP) treatment for those patients with an AHI between 5 and 15, and describes them as “mild, symptomatic.” An expert consensus statement has suggested that OSA can be classified based on the RDI into mild (RDI 5–15), moderate (RDI 15–30) or severe (RDI > 30) (1). Unfortunately, most attempts to quantitate the severity of OSA rely on the AHI, which is a flawed metric in that it does not take into account symptoms, degree TABLE 3 ICSD-2: Diagnostic Criteria for Obstructive Sleep Apnea, Adult At least one of the following applies: The patient complains of unintentional sleep episodes during wakefulness, daytime sleepiness, unrefreshing sleep, fatigue or insomnia The patient wakes with breath holding, gasping or choking The bed partner reports loud snoring, breathing interruptions, or both during the patient’s sleep Polysomnographic recording shows the following: Five or more scoreable respiratory events (i.e., apneas, hypopneas, or RERAs) per hour of sleep Evidence of respiratory effort during all or a portion of each respiratory event (In the case of a RERA, this is best seen with use of esophageal manometry) or Polysomnographic recording shows the following: Fifteen or more scoreable respiratory events (i.e., apneas, hypopneas, or RERAs) per hour of sleep Evidence of respiratory effort during all or a portion of each respiratory event (In the case of a RERA, this is best seen with use of esophageal manometry) Abbreviations: ICSD-2, The International Classification of Sleep Disorders, 2nd ed.; RERA, respiratory effortrelated arousal. Source: Modified from Ref. 2.

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TABLE 4 Limitations of the AHI as a “Gold Standard.” Each of These Patients Has the Same AHI Patient 1 AHI (events/hr) Apnea duration (sec) Lowest SaO2 (%) REM on study (%) SWS on study (%) Arousals/hr Cardiac arrhythmias

10 10–18 90 18 12 8 none

Patient 2 10 10–180 61 0 0 180 +with sinus arrest

Abbreviations: AHI, apnea plus hypopnea index; REM, rapid eye movement; SWS, slow wave sleep.

or duration of hypoxemia, disturbance in sleep structure, or cardiac arrhythmias. This problem is illustrated in Table 4. Neither the AHI nor the RDI is an excellent ways to quantitate the severity of sleep disordered breathing, because they do not account for duration of respiratory events, degree of oxygen desaturation, associated cardiac arrhythmias, or amount of sleep disturbance. Despite the consensus statement, there is lot of ambiguity about the type and level of respiratory abnormalities that must be used as a cutoff for significant disease. Further, the criteria used for scoring hypopneas influence the diagnosis of OSA and the rating of its severity. Different scoring criteria for hypopneas may result in varying apnea-hypopnea indices (35,36).This is at least in part owing to the lack of knowledge of normal sleep variation in the number of respiratory events that exists in normal asymptomatic individual who have no evidence of long-term morbidity. Though full PSG is considered the “gold standard” for the diagnosis of sleep disorders, there are no studies assessing the validity of PSG for making a diagnosis of OSA in adults. The best data about prediction of outcomes of SDB is, in fact, based on portable monitoring (31,37). Yet another problem with PSG as the definitive test for sleep apnea is the fact that overnight PSG may be falsely negative owing to one or more of the following conditions: (i) poor quality sleep during the study, with reduced or absent REM sleep; (ii) sleeping on one’s side during the study instead of one’s usual supine sleeping position; (iii) omitting one’s usual alcohol or sedative agent on the night of the study, which may precipitate SDB in many patients; and (iv) insensitive monitors of airflow or respiratory effort, which allow subtle decrements in airflow or marked increases in respiratory effort (such as RERAs) to go undetected. Despite problems with the AHI and RDI, these measures do predict outcomes in some work, especially the ongoing studies of the SHHS (31,33,37), Wisconsin Sleep Cohort Study (38–40), and the Cleveland family study (41). The risk of cardiovascular outcomes, notably hypertension, has been linearly related to the AHI (37,42–45), as have the risk of cardiovascular disease and strokes (46–48). A dichotomy emerges with regard to severity criteria for OSA and its sequellae. SDB appears to be a significant, independent risk factor for many adverse outcomes, particularly cardiovascular sequellae at very low AHIs (37,42,49). There is a statistically significant increase in the risk of hypertension and cardiovascular disease for AHIs (using SHHS criteria, which are essentially based on oximetry) earlier even one event per hour. The risk of car crashes is increased in those with AHIs of 5 or 10 events/hr of sleep (49,50). But the risk of death appears to be increased primarily in “severe” (high levels of AHI) sleep apnea (46,47,51).

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The Phenotype of OSA: Effects of Age and Gender The clinical presentation of OSA may be different in children, in women, and in older individuals than as it is classically described. In children, snoring, mouth breathing, and labored breathing at night may prompt parents to seek medical attention for their children. The presenting problem in children with SDB depends on the child’s age. OSA can cause a variety of daytime and nighttime symptoms in children .The clinical features of OSA in children include nocturnal symptoms such as snoring, mouth breathing, diaphoresis, labored breathing, paradoxical respiratory effort, observed apnea, restlessness, unusual sleep positions, and enuresis (2,53–57). Daytime symptoms may include mouth breathing, nasal obstruction, nasal speech, poor school performance, morning headaches, fatigue, behavior problems, deficient attention span, and failure to thrive. Poor academic performance in the teenaged years may be associated with SDB, and may resolve after successful treatment (54). Although the hallmark of adult OSA is excessive daytime sleepiness, this symptom is less common in children with OSA (2,53–57). Children with often have larger tonsils and adenoids or other craniofacial abnormalities, but obesity may also play a role. The diagnostic criteria from the ICSD-2 for pediatric OSA are included in Table 5; note that only one scoreable respiratory event per hour (as opposed to 5 per hour for adults) is required. Similarly, the presentation of obstructive apnea in older individuals may further expand the spectrum beyond what is considered classic. Several studies have investigated the prevalence and natural history of SDB in the elderly. Breathing disturbances during sleep increase in number with increasing age. Early studies of the frequency of sleep apnea in the elderly found prevalence rates of 24% to 73% TABLE 5 ICSD-2: Diagnostic Criteria for Obstructive Sleep Apnea, Pediatric The caregiver reports snoring, labored or obstructed breathing, or both snoring and labored or obstructed breathing during the child’s sleep The caregiver of the child reports observing at least one of the following: Paradoxical inward rib-cage motion during inspiration Movement arousals Diaphoresis Neck hyperextension during sleep Excessive daytime sleepiness, hyperactivity, or aggressive behavior A slow rate of growth Morning headaches Secondary enuresis Polysomnographic recording demonstrates one or more scoreable respiratory events per hour Polysomnographic recording demonstrates either one of the following: At least one of the following is observed: Frequent arousals from sleep associated with increased respiratory effort Arterial oxygen desaturation in association with the apneic episodes Hypercapnia during sleep Markedly negative esophageal pressure swings Periods of hypercapnia, desaturation, or hypercapnia and desaturation during sleep associated with snoring, paradoxical inward rib cage motion during inspiration, and at least one of the following: Frequent arousals from sleep Markedly negative esophageal pressure swings The disorder is not better explained by another current sleep disorder, medical or neurological disorder, medication, or substance use disorder Abbreviation: ICSD-2, The International Classification of Sleep Disorders, 2nd ed. Source: Modified from Ref. 2.

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(58–62), and both longitudinal and cross-sectional studies have shown that sleep apnea prevalence increases or is stable with increasing age (61). Thus, that clinical populations have tended to find a peak prevalence of clinically-significant SDB in middle age, population-based studies have found increasing levels of SDB with aging, despite the effect that obesity is less prevalent with aging. There appear to differences in the effect of obesity as a risk factor for SDB by age and gender; obesity is a more important risk factor for SDB in middle-aged women than it is in men, whereas age is a more important risk factor for men than it is for women (61,63–64). In fact, work by Tishler indicates that after the age of menopause, the male-female ratio for sleep apnea is about 1:1, and neither gender not obesity is a significant risk factor (41). There are complex and multiple reasons for the increase in SDB with age, including changes in airway structure, sleep structure, and respiratory drive (61). It might also be that the varying and imprecise diagnostic criteria for SDB do not adequately distinguish between true pathology and normal changes with aging, and that we have branded normal changes of aging as pathologic based on inappropriate criteria (63,65). Studies of the natural history of SDB lend some insight into this last issue. Early studies from clinical populations suggested that SDB in the elderly does not confer the increased mortality and morbidity in otherwise healthy seniors that are seen in younger individuals with OSA (66,67), and this has been confirmed (46). The reasons for this are unclear, but may include a survivor effect or the fact that many sequellae, particularly cardiovascular sequellae, of OSA are so prevalent with aging that the additional causal contribution of SDB is minimal. Whatever the reason, difference in outcome of OSA between younger and older populations suggests that two types of sleep apnea may exist. The first type is the type primarily seen in clinical sleep centers, in which individuals present because of symptoms. It peaks in prevalence at about age 50, and is associated with increased morbidity and mortality. The second type is an age-dependent type and it is unclear if it is associated with the same increase in morbidity and mortality. In addition to affecting the age-related epidemiology of sleep apnea, gender also affects the clinical presentation of SDB. Women with sleep apnea are more likely to present with insomnia and to have depression and thyroid disease than are their male counterparts (68). Further, women with sleep apnea may have a more severe negative impact on their driving ability than men with sleep apnea of the same severity (69). THE DIAGNOSTIC DILEMMA It is clear that the phenotype of obstructive SDB varies widely, as pointed out by Redline in her discussion of the “OSA phenotype” (70). There are differences in the clinical presentations, severity, and in prevalence of sleep apnea that are moderated by age, gender, and methodology. Prevalence estimates of OSA depend on how it is defined and measured. In truth, the demarcation between snoring and troublesome SDB is not at all clear, as is hopefully demonstrated by the foregoing. Stradling wrote, “Arguments over the definition of obstructive sleep apnoea/ hypopnoea syndrome (OSAHS) have still not been satisfactorily resolved. As a result, robust estimates of the prevalence of OSAHS are not possible. New approaches are needed to identify those who have ‘CPAP responsive’ disease, enabling more accurate estimates to be made of the prevalence of the sleep apnoea

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syndrome in the community (71)” The lack of precise, consistent and uniform diagnostic criteria and procedures for OSA case-finding limits our ability to discern the true prevalence, risk factors, and outcomes. In short, there is a very broad spectrum of obstructed SDB disorders. THE CENTRAL SLEEP APNEA SYNDROMES CSA is characterized by repetitive episodes of apnea unaccompanied by upper airway obstruction or discernable respiratory effort. By definition, each respiratory event consists of reduced airflow, 10 seconds or longer in duration, associated with a reduction in esophageal pressure excursions from baseline levels and often with oxygen desaturation and arousals (72). Again, a spectrum of severity exists, with no clear boundary between normal and pathologic. For example, central apneas can be a normal physiologic phenomenon in healthy people during sleep onset or at REMsleep onset. Conversely, patients who have obstructive apnea may also develop episodes of apparent central apnea, and apneas that begin as central may become obstructive as respiratory effort is restored. This phenomenon has been termed “mixed” apneas, and exemplifies the overlap between obstructive and central apnea. Central apnea can occur either as an isolated event or in a periodic pattern. Arousals causing deep breaths often produce brief central apneas that may be related to transient hypocapnia. Sustained patterns of central apneas and hypopneas are typically seen in NREM rather than REM sleep and may be amplified by arousals. In the ICSD-2 CSA is broken down into primary, owing to Cheyne–Stokes, owing to high altitude, owing to a medical condition (not Cheyne–Stokes), owing to substance abuse, and of infancy (2). The diagnostic criteria for primary CSA-hypopnea syndrome are included in Table 6. Note that at least five central apnea-hypopneas per hour of sleep are required to make this diagnosis in adults. Esophageal pressure monitoring is the reference standard measurement of central apnea-hypopneas; other methods, such as respiratory inductance plethysmography (RIP), surface diaphragmatic EMG, thermal sensors, expired CO2, piezosensors and strain gages, are relatively insensitive in identifying these events, though strain gages are most commonly used (1). It is these authors’ experience that primary CSA in adults is relatively uncommon; most central apnea in adults has a discernable cause, typically including heart failure, central nervous system disease, or respiratory depressant use. Cheyne–Stokes respiration (CSR) is the most common form of the CSAs. CSR is characterized by cycles of crescendo–decrescendo breathing pattern with central apnea or hypopnea occurring at the nadir of ventilatory drive. Transient arousals TABLE 6 ICSD-2: Diagnostic Criteria for Primary Central Sleep Apnea The patient reports at least one of the following: Excessive daytime sleepiness Frequent arousals and awakenings during sleep or insomnia complaints Awakening short of breath Polysomnography shows five or more central apneas per hour of sleep The disorder is not better explained by another current sleep disorder, medical or neurological disorder, medication use, or substance use disorder Abbreviation: ICSD-2, The International Classification of Sleep Disorders, 2nd ed. Source: Modified from Ref. 2.

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that occur at the crest of hyperpnea may lead to sleep fragmentation and excessive somnolence. According to the ICSD-2, Cheyne–Stokes breathing syndrome is diagnosed based on the following criteria: PSG shows at least 10 central apneas and hypopneas per hour of sleep in which the hypopnea has a crescendo–decrescendo pattern of tidal volume accompanied by frequent arousals from sleep and derangement of sleep structure (2). According to the ICSD-2, the criteria for this condition include association with a serious medical illness such as heart failure, stroke, or renal failure. CSR tends to occur in heart failure patients with elevated pulmonary venous pressures and carries a particularly poor prognosis (73). The prevalence of sleep apnea in patients who have CHF is about 50% (73–77). SLEEP-RELATED HYPOVENTILATION/HYPOXEMIC SYNDROMES By and large, the sleep-related hypoventilation/hypoxemic syndromes are not primary sleep disorders, but rather medical disorders made worse by sleep. Nevertheless, these conditions exist in the broad spectrum of SRBD. Their presentation can be extremely variable: adults with idiopathic hypoventilation may present with severe hypoventilation with absent or modest coexisting pulmonary disease and in the absence of known causes of hypoventilation, such as myxedema, structural brainstem abnormalities, or sleep apnea, but this is exceedingly rare. This category of SRBD includes several effete terms, including obesity hypoventilation syndrome and Pickwickian syndrome (2,72). CONCLUSIONS The presentations, consequences, severity and mechanisms of the SRBDs include a broad spectrum of disease. The presentation of SDB is markedly influenced by characteristics of the affected individual, especially age and gender. Further, there is considerable overlap between breathing problems during sleep. These factors result in the need for a high index of suspicion and for an individualized approach to the management of patients who have breathing disorders of sleep. GLOSSARY OF TERMS AND ABBREVIATIONS AASM AHI BMI CMS CPAP CSA CSR EDS EEG ESS ICD-10-CM ICSD-2 OSA

American Academy of Sleep Medicine (formerly the American Sleep Disorders Association or ASDA) apnea plus hypopnea index body mass index Centers for Medicare and Medicaid Service, formerly the Health Care Financing Administration, or HCFA continuous positive airway pressure central sleep apnea Cheyne-Stokes respiration excessive daytime sleepiness electroencephalography Epworth sleepiness scale International Classification of Diseases, 10th Edition, Clinical Modification International Classification of Sleep Disorders, 2nd Edition obstructive sleep apnea

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obstructive sleep apnea syndrome obstructive sleep apnea-hypopnea syndrome polysomnography, AKA sleep study respiratory disturbance index rapid eye movement respiratory effort-related arousal respiratory inductance plethysmography sleep-disordered breathing, a vague term meant to encompass most sleep and breathing problems. sleep-related breathing disorder Sleep Heart Health Study upper airway resistance syndrome, a variant of sleep apnea

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42. Lavie P, Herer P, Hoffstein V. Obstructive sleep apnoea syndrome as a risk factor for hypertension: population study. BMJ 2000; 320:479–482. 43. Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. JAMA 2000; 283:1829–1836. 44. Grote L, Ploch T, Heitmann J, Knacck L, Penzel T, Peter JH. Sleep-related breathing disorder is an independent risk factor for systemic hypertension. Am J Respir Crit Care Med 1999; 160:1875–1882. 45. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000; 342:1378–1384. 46. Lavie P, Lavie L, Herer P. All-cause mortality in males with sleep apnoea syndrome: declining mortality rates with age. Eur Respir J 2005; 25:514–520. 47. Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005; 353:2034–2041. 48. Peker Y, Hedner J, Norum J, et al. Increased incidence of cardiovascular disease in middle-aged men with obstructive sleep apnea. A 7-year follow-up. Am J Respir Crit Care Med 2002; 166:159–165. 49. Young T, Blustein J, Finn L, Palta M. Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults. Sleep 1997; 20:608–613. 50. Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. Cooperative Group Burgos-Santander. N Engl J Med 1999; 340:847–851. 51. Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005; 365:1046–1053. 52. Brooks LJ, Topol HI. Enuresis in children with sleep apnea. J Pediatr 2003; 142:515–518. 53. Lind MG, Lundell BP. Tonsillar hyperplasia in children. A cause of obstructive sleep apneas, CO2 retention, and retarded growth. Arch Otolaryngol 1982; 108:650–654. 54. Gozal D, Pope DW Jr. Snoring during early childhood and academic performance at ages thirteen to fourteen years. Pediatrics 2001; 107:1394–1399. 55. Guilleminault C, Korobkin R, Winkle R. A review of 50 children with obstructive sleep apnea syndrome. Lung 1981; 159:275–287. 56. Rosen CL. Clinical features of obstructive sleep apnea hypoventilation syndrome in otherwise healthy children. Pediatr Pulmonol 1999; 27:403–409. 57. Marcus CL, Curtis S, Koerner CB. Evaluation of pulmonary function and polysomnography in obese children and adolescents. Pediatr Pulmonol 1996; 21:176–183. 58. Ancoli-Israel S, Kripke DF, Klauber MR, et al. Sleep-disordered breathing in the community-dwelling elderly. Sleep 1991; 14:486–495. 59. Ancoli-Israel S, Kripke DF, Klauber MR, et al. Natural history of sleep disordered breathing in community-dwelling elderly. Sleep 1993; 16:S25–S29. 60. Hoch CC, Dew MA, Reynolds CF, et al. Longitudinal changes in diary-and laboratorybased sleep measures in “old old” and “young old” subjects: a three-year follow-up. Sleep 1997; 20:192–202. 61. Bliwise DL. Normal Aging. In: Kryger MH, Roth T, and Dement WC, eds. Principals and Practice of Sleep Medicine, 4th edn. Philadelphia: WB Saunders, 2005:24–37. 62. Phoha RL, Dickel MJ, Mosko, SS. Preliminary longitudinal assessment of sleep in the elderly. Sleep 1990; 13:425–429. 63. Redline S, Kimp K, Thishler PV, et al. Gender differences in sleep disordered breathing in a community-based sample. Am J Respir Crit Care Med 1994; 149:722–726. 64. Redline S, Kapur V, Sanders, et al. Effects of varying approaches for identifying respiratory disturbances on sleep apnea assessment. Am J Respir Crit Care Med 2000; 161:369–374. 65. Phillips BA, Berry DTR, Lipke-Molbe T. Sleep-disordered breathing in healthy aged persons: Fifth and final follow-up. Chest 1996; 110:654–658. 66. He J, Kryger MH, Zorick FJ, Conway W, Roth T. Mortality and apnea index in obstructive sleep apnea; experience in 385 male patients. Chest 1988; 94:9–14. 67. Shepertycky MR, Banno K, Kryger MH. Differences between men and women in the clinical presentation of patients diagnosed with obstructive sleep apnea syndrome. Sleep 2005; 28:309–314.

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68. Pichel F, Zamarron C, Magan F, Rodriguez JR. Sustained attention measurements in obstructive sleep apnea and risk of traffic accidents. Respir Med 2006; 100(6): 1020–1027. 69. Redline S. Genetics of obstructive sleep apnea. In: Kryger MH, Roth T, and Dement WC, eds. Principals and Practice of Sleep Medicine, 4th edn. Philadelphia: WB Saunders, 2005:1013–1022. 70. Stradling JR, Davies RJ. Obstructive sleep apnoea/hypopnoea syndrome definitions, epidemiology, and natural history. Thorax 2004; 59:73–78. 71. White DP. Central Sleep Apnea. In: Kryger MH, Roth T, and Dement WC, eds. Principals and Practice of Sleep Medicine, 4th edn. Philadelphia: WB Saunders, 2005:969–982. 72. Lanfranchi PA, Braghiroli A, Bosimini E, et al. Prognostic value of nocturnal CheyneStokes respiration in chronic heart failure. Circulation 1999; 99:1435—1440. 73. Javaheri S, Parker TJ, Wexler L. Occult sleep-disordered breathing in stable congestive heart failure. Ann Intern Med 1995; 122:487–492. 74. Javaheri S, Parker TJ, Liming JD. Sleep apnea in 81 ambulatory male patients with stable heart failure. Types and their prevalences, consequences, and presentations. Circulation 1998; 97:2154–2159. 75. Javaheri S. Central sleep apnea-hypopnea syndrome in heart failure: prevalence, impact, and treatment. Sleep 1996; 19:S229–S231. 76. Bradley TD. Floras JS. Sleep apnea and heart failure: Part II: central sleep apnea. Circulation 2003; 107:1822–1826.

Section II: Associations and Consequences

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Morbidity and Mortality Christine Won Stanford University Center of Excellence for Sleep Disorders, Stanford, California, U.S.A.

Dominique Robert Emergency and Intensive Care Department, Edouard Herriot Hospital, Lyon, France

INTRODUCTION Large observational studies have shown obstructive sleep apnea (OSA) does in fact confer greater mortality compared to the general population. In one study of 14,589 adult males aged 20 to 93 years, a crude all-cause mortality rate of 5.6 per 1000 person-years was observed (1). Moreover, the mortality rate showed a dose-response relationship to apnea severity, with the mortality risk in men with moderate and severe sleep apnea being significantly increased compared to that of the general population. The difference was most dramatic amongst men less than 50 years of age. In a similar study of 475 men with OSA, mortality was significantly reduced in those treated with surgery, weight loss, or continuous positive airway pressure (CPAP), compared to the untreated group. The mortality of the untreated group was higher than that of the general population even after adjustment for age and sex, and again, greater differences were observed in the group less than 50 years of age (2). Sleep apnea has been implicated in many cardiovascular and noncardiovascular diseases, and therefore these observations are not surprising. In the following sections, we will discuss the relationship of sleep apnea to several common diseases, and their specific morbidities and mortalities. OBESITY (SEE ALSO CHAPTER 20) Obesity has long been recognized as a risk factor for sleep apnea. Other than male gender, it is the strongest identified risk factor for OSA (3). Approximately 70% of patients with OSA are obese, and the risk of OSA is 10-fold more in obese individuals compared with the general population (4). Prospective population-based cohort studies have shown obesity, and particularly central obesity, predicts greater OSA severity (5). A 10% weight gain is correlated with up to a six-fold increase in odds of developing moderate-to-severe OSA, and a 32% increase in the apnea-hypopnea index (AHI). Meanwhile, a 10% weight loss is associated with a 26% reduction in AHI (6). Many factors modulate the risk of OSA in obese individuals, including age, gender, and ethnicity. In young adults, a body mass index (BMI) greater than 28 kg/ m2 has been correlated with an increased risk for OSA (7). In middle-aged adults, the prevalence of OSA is greater than 50% when BMI is greater than 50 kg/m2 (8). Although the risk of OSA increases with age, the association with obesity appears to be less for persons greater than 50 years of age (5,7,9). Men seem to be more susceptible that women to OSA with increasing BMI, presumably because of the preferential 259

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distribution of adiposity in men around their neck and upper airways (10). Ethnicity may also modulate the risk of OSA in obese individuals. For example, Asian cohorts have shown greater susceptibility to OSA at lower BMIs, perhaps because of their preferential tendency for centrally distributed adiposity. In contrast, Pacific Islanders have more lean mass, and less prevalence of OSA for any given BMI (11,12). The mechanisms by which obesity causes OSA are not completely understood, and many potential pathways have been hypothesized. The most common theory is that fat deposition in the neck and airway lumen leads to increased collapsibility of the upper airway. In addition, fat accumulated around the chest and abdomen may result in restricted lung volumes, which has shown to independently predispose to upper airway collapse (13). Another potential mechanism for which obesity causes OSA is through the hormone, leptin, which is produced by adipose tissue and regulates appetite and body weight (14,15). Leptin levels have shown to be elevated in both obesity and OSA independently, suggesting these are both leptin-resistant states (16). In addition to promoting weight, leptin resistance has been shown to cause hypoventilation by blunting the response to hypercapnia, and thereby contributing to the pathogenesis of sleep-disordered breathing (SDB) (17–19). The pathophysiology of OSA and obesity are intimately linked, and while obesity is a well-recognized risk factor for sleep apnea, growing evidence support that OSA may independently contribute to the development of obesity as well. Intermittent episodes of hypoxemia, hypercapnea, autonomic instability, and arousals that accompany apneas and hypopneas during sleep may cause dysfunction of central mechanisms that regulate metabolism and appetite, which may eventually lead to obesity (20). Compared to weight-matched controls, sleep apneic patients have elevated leptin levels, which reduce with effective OSA treatment (18,19,21). Again, these findings suggest OSA may contribute to leptin resistance and thus predispose to weight gain. Sleep disruption has also been shown to increase the levels of the appetite-stimulating hormone, ghrelin, and increase hunger and appetite scores (22,23). In addition, OSA causes sleep deprivation with concomitant daytime fatigue and excessive somnolence, which lead to decreased physical activity and reduced daily energy expenditure. Epidemiological studies consistently show that sleep deprivation is a risk factor for the development of obesity (24,25). Obesity represents a major public health crisis in the United States as well as in other developed countries. Over the past 40 years, the average BMI in men and women in the United Stages aged 20 to 74 years has increased from 25 kg/m2 to 28 kg/m2, with a projected increase over the next few years (26,27). Obesity may be responsible for up to 325,000 deaths per year in the United States (28). Sleep apnea is similarly rampant, affecting more than 15 million Americans, with a prevalence that is also rising partly as a consequence of increasing obesity. Obesity and OSA share similar comorbidities including hypertension, hyperlipidemia, and hyperinsulinemia, all of which are associated with increased mortality (29,30). Even in the absence of these comorbidities, however, the combination of sleep apnea and obesity appears to be associated with increased sudden cardiac death. CPAP treatment for OSA in obese patients has shown to result in weight loss, and conversely, weight loss has shown to improve AHI (6,31–34). In a randomized study of 23 obese patients with sleep apnea subject to dietary weight loss, the treatment group was found to have a mean weight loss of 9% and a reduction in AHI by 47% while the control group remained stable in their weight and AHI (35). Other

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controlled and uncontrolled studies for both dietary and surgical weight loss have shown similar findings (36–39). In summary, obesity and sleep apnea are highly morbid diseases, which often coexist, and likely worsen the other. Current evidence indicates a positive impact of weight loss on the severity of sleep apnea. Further studies are needed to see if treating OSA can significantly improve the incidence of obesity. INSULIN RESISTANCE AND GLUCOSE TOLERANCE (SEE ALSO CHAPTER 19) Results from earlier studies looking at the association between OSA and insulin resistance or glucose intolerance were mixed, probably because they were small studies that often did not control for confounders such as obesity and family history. More recently, however, several large epidemiologic studies have explored this relationship between OSA and metabolic abnormalities, and now support a correlation and possible causal relationship between sleep apnea and glucose intolerance, insulin resistance, and type 2 diabetes mellitus (40). In a 10-year cohort study of 2668 Swedish men aged 30 to 69 years, habitual snoring was associated with self-reported diabetes independent of other risk factors (41). Similarly, the Nurses’ Health Study, which looked at 69,852 women aged 40 to 65 years prospectively over a 10-year period, found a greater than two-fold increase in the risk of diabetes mellitus in snorers, again independent of BMI and other confounders (42). The Sleep Heart Health Study found in their 2656 community-based subjects who underwent an overnight home polysomnography and fasting glucose or two-hour glucose tolerance test, that SDB was associated with glucose intolerance and insulin resistance independent of age, gender, BMI, waist circumference, and self-reported sleep duration. Moreover, there was a dosedependent relationship between the respiratory disturbance index (RDI) and the degree of insulin resistance, as well as between the severity of sleep-related hypoxemia and the degree of both insulin resistance and glucose intolerance. In the Wisconsin Sleep Cohort, however, while diabetes mellitus was more likely to be comorbid in those with moderate to severe OSA compared to those with AHI 11) was 1.6 compared to the group in the lowest quartile (AHI < 1.3) (72). In one observational study, multivariate analysis adjusted for potential confounders showed untreated severe OSA significantly increased the risk of fatal cardiovascular events, including fatal strokes (OR 2.9) and nonfatal cardiovascular events, including nonfatal strokes (OR 3.2) in men compared to healthy controls over a 10-year follow-up period (73). In another observational cohort study of 1022 subjects, OSA was found to be an independent predictor of stroke or death from any cause (hazard ratio 2.0). The severity of OSA at baseline was associated with an increasing trend in the risk of stroke or death (74). Interestingly, however, a meta-analysis of 31 publications reporting the circadian timing of 11,816 strokes demonstrated that sleep does not represent a vulnerable phase especially for intracerebral and subarachnoid hemorrhages. In general, stroke onset is correlated with physical activity and occurs principally between 6 a.m. and noon (75). Other areas of uncertainty are whether SDB in stroke patients represents predominantly obstructive or central apneas, and whether CPAP therapy will modify long-term outcomes. Although more studies are needed, at least some data suggest CPAP therapy in stroke or TIA patients presenting with sleep apnea two months after the event, have less risk of recurrent cerebrovascular events compared to untreated apneics during an 18-month follow-up (76). In summary, the relationship between OSA and stroke or TIA is complex. Although sleep apnea and stroke have shown to be comorbid, their temporal relationship needs to be clarified, as does the effect of CPAP on cerebrovascular morbidity and mortality. Heart Failure In addition to raising systemic blood pressure, OSA has other pathologic mechanisms by which it may affect heart function. For example, large negative intrathoracic pressures generated during inspiratory efforts may increase transmural cardiac pressures and increase afterload. Large negative intrathoracic pressures may also result in increased venous return and increased preload with pulmonary congestion. Also, hypoxemia that accompanies apneas may lead to decreased oxygen delivery to the myocardium causing ischemic changes and also increases arterial pulmonary pressure. Finally, frequent arousals increase sympathetic nervous activity, which may lead to further myocardial damage. Given these potential mechanisms, it might be expected that OSA contributes to ventricular hypertrophy and heart failure (HF). However, current evidence for such a relationship between OSA and HF remains unclear (77–80). Prevalence studies show SDB occurs frequently in patients with HF. Central apneas have long been recognized as a frequent phenomenon in HF, but more

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recently, obstructive apneas are being commonly reported as well. The prevalence rates of OSA in HF ranges from 12% to 53%, with men having slightly higher rates than women (81–84). The Sleep Heart Health Study found a dose-dependent relationship in the risk of HF according to severity of OSA. In this cross-sectional analysis, the adjusted OR for HF in subjects with OSA in the highest quartile (AHI > 11) compared to those in the lowest quartile (AHI < 1.3) was 2.4 (72). While large prospective longitudinal studies are lacking, there are smaller trials looking at the impact of CPAP on HF to suggest a possible causal relationship between OSA and HF. An uncontrolled study of 25 patients with severe OSA (mean AHI 81) associated with significant oxygen desaturation (mean time spent with SaO2 < 90% equaled 64% of recording time) found a high prevalence of left ventricular hypertrophy at baseline (88%) and a significant reduction in interventricular septal distance after six months of CPAP therapy (85). In another CPAP trial, 24 subjects with OSA and depressed left ventricular function [ejection fraction (EF) < 45%] were randomized to medical therapy alone or addition of CPAP to their existing regimen. The CPAP group had significant improvement in blood pressure, heart rate, and left ventricular EF (mean 25% to 34%) (68). In the largest randomized trial to date, 40 subjects with HF and OSA were randomized to either CPAP or no CPAP over three months. The CPAP group had increased left ventricular function (ejection fraction from 38% to 43%), and concomitant improvement in quality of life as assessed by the Medical Outcomes Study Short Form-36 (SF-36), as well as a significant reduction in self-reported sleepiness (86). Although these short-term randomized trials demonstrate CPAP therapy improves cardiac function, there is yet any data to suggest treating OSA in patients with HF improves morbidity and mortality. Arrhythmias Several nonfatal arrhythmias such as bradyarrhythmias, premature beats, and atrial fibrillation, have been described during both sleep and wake in OSA. The Sleep Heart Health Study showed persons with severe OSA [respiratory disturbance index (RDI) > 30] had two- to four-fold higher odds of having complex arrhythmias compared to those without OSA even after adjusting for potential confounders (87). Atrial fibrillation is the most common sustained arrhythmia, affecting more than two million people in the United States (88). The Sleep Heart Health Study described atrial fibrillation in 4.8% of persons with OSA compared to only 0.9% in those without OSA (87). Likewise, OSA seems to be substantially more prevalent in patients with atrial fibrillation (48%) compared to patients with established cardiovascular disease but without a history of atrial fibrillation (32%) (89). Patients with untreated sleep apnea also have a higher risk of recurrent atrial fibrillation after successful cardioversion compared to patients without known sleep apnea, independent of age, sex, antiarrhythmic therapy, BMI, functional status, echocardiographic measures, or coexisting diabetes or hypertension. Furthermore, treatment of sleep apnea with CPAP significantly reduces the risk of recurrent atrial fibrillation. In those with untreated OSA, the risk of recurrence is associated with lower nocturnal oxygen saturation. The risk of sudden death from cardiac causes in the general population is greatest during the morning hours after awaking than during the other six-hour intervals of the day. Also, there is a marked nadir in the risk of sudden death from cardiac causes during conventional sleep hours (90). The reason for this increased risk of cardiovascular death in the mornings is thought to relate to increased sympathetic

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activity during this time which may predispose susceptible persons to cardiac ischemia and fatal arrhythmias. In OSA, however heightened sympathetic activity more often occurs during the nighttime or sleeping hours when apneas are frequently occurring. In addition, apneas can cause prolonged or repetitive hypoxemia, hypercapnia, blood pressure instability, and increased cardiac demand, all of which may contribute to greater cardiovascular detriment during sleeping hours. This may explain why unlike in the general population, sudden cardiac death in patients with OSA has been observed to occur more commonly during the typical sleeping hours of 10 p.m. and 6 a.m. (91). The severity of OSA was shown to correlate directly with the risk of nocturnal death from cardiac causes, with the relative risk of sudden death from cardiac causes being 40% higher in those with AHI ≥ 40 compared to those with AHI 5 to 39. Interestingly, however, while there was a difference in the observed time of death between persons with OSA and without OSA, there was no difference in the average age of death amongst the two groups indicating that OSA may not actually hasten cardiac death. Coronary Artery Disease The reported prevalence of OSA in patients with coronary artery disease (CAD) ranges from 14% to 65% (92–96). Cross-sectional, case-control, and longitudinal studies have reported a two- to fourfold increases in risk of myocardial infarction in snorers compared to nonsnorers even after considering the role of potential confounding factors, such as obesity, hypertension, smoking, and alcohol (97–99). The apnea index has also shown to be an independent risk factor for ischemic heart disease (92). In addition, OSA imparts greater cardiovascular morbidity and mortality in patients with OSA and coexisting CAD. A threefold increase in the risk of cardiovascular death after adjusting for other risk factors was seen in patients with CAD and OSA (100,101). Nonfatal cardiovascular events, such as myocardial infarction and acute coronary syndrome, were also more likely in patients with CAD and OSA (102). Finally, it was reported that in women who had a history of unstable angina or myocardial infarction and who underwent two coronary angiographies separated by a mean interval of 3.25 years, those with snoring showed a more significant progression of coronary artery luminal narrowing than nonsnoring women (0.18 mm compared with 0.07 mm) after adjustment for possible confounders (103). Again, whether CPAP therapy for OSA modulates one’s risk for the development of CAD is unclear. However, studies do suggest CPAP therapy in patients with documented CAD does decrease both nonfatal and fatal cardiovascular events. In one study, the treatment of OSA in patients with known CAD reduced occurrences of cardiovascular death, acute coronary syndrome, hospitalization for heart failure, or need for coronary revascularization (101). In addition, the time for these events to occur was longer in the patients who used CPAP. In summary, current evidence suggests sleep apnea is a risk factor for the development of coronary artery disease. Moreover, OSA worsens clinical outcome in patients with existing CAD, and CPAP therapy may help to ameliorate these detrimental effects. Pulmonary Hypertension Pulmonary hypertension is common in patients with OSA, with reported estimates between 17% and 53% (104,105). Pulmonary artery pressure is generally only mildly

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elevated (20 to 52 mmHg), unless there is underlying lung or heart disease, obesityhypoventilation syndrome, or chronic daytime hypoxemia, in which case pulmonary hypertension may be severe (106). Several mechanisms have been proposed to explain the relationship between OSA and pulmonary hypertension. Hypoxemia from apneic episodes may induce pulmonary vasoconstriction, and subsequent smooth muscle hypertrophy and vascular remodeling (107). Large negative intrathoracic pressures generated during obstructive apneas may increase left ventricular transmural pressure causing greater myocardial oxygen demand and reduced cardiac output (108). Finally, frequent arousals during sleep may cause excessive sympathetic nervous system activity, which promotes pulmonary vascular constriction (109). Several studies suggest CPAP treats pulmonary hypertension, with treatment effect being greatest in those with higher baseline pulmonary artery pressures (110–112). Since the effect appears to be greater in more severe disease, the true benefit of identifying and treating OSA may apply to those with significant pulmonary hypertension. However, most of these CPAP trials have been small, and include subjects with mostly normal or only slightly elevated pulmonary artery pressures. Moreover, there are currently no studies investigating the impact of CPAP on clinical outcomes such as exercise tolerance and mortality in pulmonary hypertension. DEPRESSION Sleep and mood are intricately related, so it comes as no surprise that depression, along with other mood disorders, is quite common in OSA (113). Studies suggest a prevalence rate of 20% to 40% of depressive symptoms in patients with OSA (4,114,115). No association between the severity of OSA and severity of depression has been observed in men, however, such a relationship has been described in women (116). Furthermore, women more often report clinical depression as their presenting symptom of sleep apnea (117). OSA may affect mood by causing sleep deprivation with its attendant excessive daytime sleepiness and cognitive dysfunction. Social and job performance may be severely impaired, and the patient with untreated OSA may be stigmatized or reprimanded for what incorrectly is deemed as laziness. All of these consequences of OSA may contribute to depression in the sleep-deprived individual. In addition, OSA has also been linked to other conditions related to depression, such as fibromyalgia and chronic fatigue syndrome (118,119). How sleep apnea plays a role in this constellation of diseases remains unclear. Furthermore, how CPAP therapy in patients with OSA suffering from depression and these comorbid states may influence outcomes has yet to be explored. Suicides are a major cause of mortality in depressed patients. Sleep deprivation, insomnia, and poor sleep quality have all been associated with suicidality (120–122). Depression and suicidality in SDB has been less studied, but at least one study suggests in a specific population of female sexual assault survivors, symptoms of sleep-related breathing disorders relate to greater levels of depression, anxiety, posttraumatic stress, impaired quality of life, and suicidality (123,124). MOTOR VEHICLE ACCIDENTS (SEE ALSO CHAPTER 24) Sleep apnea confers a two- to sevenfold increase in the risk of motor vehicle accidents compared to the general population, and is responsible for 15% to 20% of all

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motor vehicle crashes and thousands of injuries and deaths a year (125). A metaanalysis of studies from 1980 to 2003 in conjunction with data from the National Safety Council showed more than 800,000 drivers were involved in OSA-related motor-vehicle accidents in the year 2000. These collisions cost $15.9 billion and 1400 lives that year (126). Studies have shown 12% to 31% of drivers with OSA report having had at least one motor vehicle accident, compared to 3% to 15% of drivers without OSA (127,128). The motor vehicle accident rate has been reported as 13 per million kilometers in patients with more severe OSA (AHI > 34), one per million kilometers in patients with milder OSA (AHI 10 to 34), and 0.8 per million kilometers in healthy drivers (127). Increased risk of motor vehicle accidents seems to be independent of other risk factors including age, sex, work shift, daytime naps, alcohol and coffee intake, and history of neurologic diseases (128). Reported sleepiness does seem to impact the accident rate in individuals with OSA as well as with other sleep disorders. The incidence of sleep-related accidents was 3% to 7% per year of reported excessive sleepiness in sleep-disordered patients, however, the mean sleep latency in the Multiple Sleep Latency Test (MSLT) did not differ significantly in patients with and without accidents (129). There is conflicting evidence as to whether the severity of OSA affects the risk of motor vehicle accidents. One study reported twice the risk of sleep-related accidents in severe apneics compared to those with mild or moderate OSA (129). However, in a separate large communitybased study, the OR for having a motor vehicle accident was 3.4 for habitual snores, 4.2 for men with mild OSA, and 3.4 for men with moderate and severe OSA. But for men and women together with moderate and severe OSA, the OR for having a motor vehicle accident within five years was 7.3 (130). The disparate results are likely due to differences in study population and methodology. Several studies indicate that treating sleep apnea significantly reduces motor vehicle accidents (127,131–133). In a small prospective study, patients with OSA had a higher automobile crash rate compared to all drivers in the state of Colorado (0.07 vs. 0.01 crash per driver per year). Patients treated with CPAP over a two-year period decreased their crash rate to levels comparable to the rest of the state of Colorado. Untreated patients, on the other hand, continued to have high crash rates comparable to their baseline rates (131). Another study similarly showed CPAP reduced motor vehicle crash rates from 0.18 per driver per year to rates comparable to persons without OSA (0.06 crash per driver per year). Again, the untreated group maintained high accident rates (132). When vigilance testing and MSLTs were also assessed in sleep apneics before and after one year of CPAP, not only did accident rates decrease from 0.8 to 0.15 per 100,000 kilometers, but sleeping spells, fatigue, vigilance test reaction time, and daytime sleep latency also improved with treatment (133). Patients undergoing other treatment modalities for OSA, such as surgery or oral appliances, have not been studied, but presumably if their OSA is effectively treated with attendant improvement in daytime sleepiness and concentration, they too would benefit from reduced accident-related morbidity and mortality. Unfortunately, sleep apnea is a common problem in commercial drivers, with prevalence estimates up to 5%, compared to the 2% to 4% prevalence rate of the general population (130,134,135). About 40% of long-haul truck drivers and 21% of short-haul truck drivers reported having problems with staying alert on at least 20% of their drives. Over 20% of the long-haul drivers also reported having dozed off at least twice while driving, and 17% reported near misses due to dozing off at the wheel (130). To address some of these problems, the American College of Chest

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Physicians, the American College of Occupational and Environmental Medicine, and the National Sleep Foundation convened a task force to review current literature and construct recommendations for the evaluation and treatment of sleep disorders in commercial drivers (136). The recommended first-line therapy for commercial drivers with OSA is CPAP. The minimum average use must be four hours per night, and adherence data must be readily available. The time to improvement after initiating CPAP is not well defined. Driving performance on a simulator task has been reported to improve within three days of starting CPAP (137,138), while a randomized, placebocontrolled trial showed driving performance improved after one month (139). The 2006 task force suggests drivers be re-evaluated between two and four weeks after starting CPAP therapy, and those drivers may return to work after this evaluation if adherent to CPAP. Surgery and oral appliances are alternative treatments, and drivers may return to work if these treatments reduce their AHI to less than 10 (136). Findley et al. (140) calculated that treating 500 patients for three years would prevent 180 serious crashes and 36 personal injuries (from 20% of crashes). The prevention of 180 serious crashes and 36 serious injuries would amount to $369,000 in direct property damage and medical expenses, and $648,000 in lost wages, legal expenses, and administrative costs. The total savings for treating 500 patients over three years would surpass $1,000,000. Treating all drivers in the United States suffering from OSA with CPAP would cost $3.2 billion, but save $11.1 billion in collision costs, and save 980 lives yearly (126). CONCLUSIONS In summary, OSA is a serious and prevalent disease. Morbidity and mortality arises by various disease mechanisms. Obesity, insulin resistance or glucose intolerance, hypertension, stroke, heart failure, cardiac arrhythmias, coronary artery disease, pulmonary hypertension, and depression are associated with OSA. Sleep apnea also confers an increased risk for motor vehicle accidents compared to the general population; treatment of sleep apnea with CPAP has been demonstrated to improve accident rates in affected individuals. We can expect to have great impact on lives by further studying the health consequences of sleep apnea, and pursuing treatment for this common but deadly disorder. REFERENCES 1. Lavie P, Lavie L, Herer P. All-cause mortality in males with sleep apnoea syndrome: declining mortality rates with age. Eur Respir J 2005; 25(3):514–520. 2. Marti S, Sampol G, Munoz X, et al. Mortality in severe sleep apnoea/hypopnoea syndrome patients: impact of treatment. Eur Respir J 2002; 20(6):1511–1518. 3. Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328(17):1230–1235. 4. Vgontzas AN, Tan TL, Bixler EO, Martin LF, Shubert D, Kales A. Sleep apnea and sleep disruption in obese patients. Arch Intern Med 1994; 154(15):1705–1711. 5. Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165(9):1217–1239. 6. Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal study of moderate weight change and sleep-disordered breathing. JAMA 2000; 284(23):3015–3021. 7. Ancoli-Israel S, Gehrman P, Kripke DF, et al. Long-term follow-up of sleep disordered breathing in older adults. Sleep Med 2001; 2(6):511–516.

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Central and Autonomic Nervous Systems Ian M. Colrain Human Sleep Research Program, SRI International, Menlo Park, California, U.S.A.

John Trinder Department of Psychology, University of Melbourne, Parkville, Victoria, Australia

INTRODUCTION The major symptom and consequence of obstructive sleep apnea (OSA) is daytime sleepiness, owing to the impact of OSA on the central nervous system (CNS) via sleep fragmentation. Further, the observed elevation in cardiovascular morbidity in OSA is due, at least in part, to its impact on the autonomic nervous system (ANS). These behavioral and medical pathological symptoms are indirect manifestations of OSA’s impact on the nervous system and are reviewed elsewhere in this book (see Chapter 1, “neurocognitive effects”; see Chapters 17 and 18, “cardiac and cardiovascular effects”; see Chapter 22, “sleepiness”). This chapter reviews the evidence for more direct measurable impacts of OSA on CNS and ANS anatomy and function, by looking at studies of brain morphology and brain function using various imaging methodologies, and ANS function using a variety of different measures. THE IMPACT OF OBSTRUCTIVE SLEEP APNEA ON THE CENTRAL NERVOUS SYSTEM Evoked Potentials During Wakefulness Unfortunately, there have been relatively few studies utilizing evoked potentials as measures of CNS function in OSA and interpretation of the results is made difficult by variations in methodology, and variable disease status in the typically small sample sizes. Evoked potentials utilize a simple mathematical process to enable the resolution of a specific electroencephalographic (EEG) response (signal) from a background of ongoing irrelevant EEG activity (noise) unrelated to the stimulus or task characteristics. This is achieved by time-locked averaging of responses to stimuli under two assumptions. First, the unrelated EEG “noise” will be distributed randomly relative to the stimulus and thus will tend to average to zero. Second, that the response, or EEG “signal,” has an invariant temporal relationship with the stimulus. Under these assumptions averaging increases the signal-to-noise ratio (SNR); however, the number of responses needed to resolve specific signals will be largely determined by the intrinsic SNR. For example, averaged auditory brainstem responses of 1 or 2 μv occur in the presence of 20–30 μv background EEG and require many more responses to be reliably seen than averaged K-complex components that are typically more than double the background amplitude. The waveform resulting from the averaging process contains a series of positive and negative peaks (components) that are thought to reflect activity in underlying generators within the brain. In evoked potentials collected during wakefulness, early components (< 80 msec 275

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approx) typically reflect sensory processing, those around 100 msec are sensitive to arousal and attention and later components are thought to reflect higher-order CNS processing related to cognitive function. The respiratory-related evoked potential (RREP) reflects CNS responses to stimulation of the respiratory system. The early waveform components are P1 and Nf, occurring between 40 msec and 80 msec after the start of a pressure change induced by an occlusion or load stimulus. P1 is best recorded over parietal scalp regions (1–5) and has been source-localized to be bilaterally produced in primary somatosensory cortex (2). Nf is best recorded over frontal scalp regions (1–5) and has been source-localized to be produced bilaterally in the supplementary motor area of the frontal lobes (2). Three studies have investigated early sensory components of RREPs in OSA. Gora, Trinder, Pierce, and Colrain (6) compared responses to inspiratory occlusions during wakefulness in six OSA patients and six matched controls, and found no differences in either P1 or Nf. However, the patients were young (mean age of 38.83 ± 4.75 yr) and their OSA was mild [mean respiratory disturbance index (RDI) of 9.62 ± 3.65]. Afifi, Guilleminault, and Colrain (7) compared 10 OSA patients with more moderate-to-severe pathology (mean RDI of 21 ± 11) to 10 controls, but nonetheless also reported no differences in P1 or Nf amplitudes. However, Nf latency was shorter in the OSA group. A third study by Akay, Leiter, and Daubenspeck (8) evaluated early RREP responses in 14 OSA patients compared to 18 controls. The mean apnea-hypopnea index (AHI) of the OSA group was high (57.5 ± 11.2) and ranged from 16 to 154.3. Rather than reporting peak amplitudes, the authors reported the global field power (GFP) from 60 electrodes integrated over a latency window designed to cover the early sensory component processes (55–70 msec). They found the OSA patients to have significantly smaller GFP in this window than controls at both pulse magnitudes. However, the OSA patients were substantially older and heavier than the controls [57.2 ± 11.5 vs. 44.6 ± 3.6 yr; body mass index (BMI) of 34.6 ± 2.1 vs. 27.9 ± 1.0 kg/m2], so that the level of significance was at p = .044, when age, gender and BMI were used as covariates. Further as pointed out by Sana and Grippo (9), all of the OSA patients had been undergoing continuous positive airway pressure (CPAP) treatment, and the subjects were studied on the mornings following what appeared to be CPAP titration nights in a sleep clinic, whereas the controls were studied after a normal night’s sleep in their homes. Another major difference to the Gora and Afifi studies was the stimulus. Rather than a resistive load, Akay et al. used negative pressure pulses at –5 and –10 cm H2O. Thus, the possibility remains that the group differences observed were due either to some adaptation to negative pressure following CPAP stimulation or owing to a poor night’s sleep prior to the study in the OSA subjects. However, in the context of the Gora and Afifi studies it could also be argued that effects are only seen when pathology is more severe. The literature to date, however, has evaluated only 30 OSA patients with highly variable pathology, age and BMI, using three different stimulus protocols and two very different analysis strategies. The most reasonable conclusion is that we still do not know whether CNS processing of airway loading is altered during wakefulness in OSA. Later evoked potential components are thought reflect subsequent processing of sensory or cognitive content of the stimulus. Of these, two have been studied in OSA, the N1 and the P300. The N1 occurs at around 100 msec and is influenced by both sensory input (10), and cognitive factors such as attention and arousal (11). Gora et al. (6) reported the RREP N1 to be decreased in OSA patients, and they interpreted this as probably being owing to sleepiness. However, Afifi et al. (7) failed to

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replicate this finding in OSA patients with more severe sleep-disordered breathing (SDB) and thus probably greater levels of sleepiness (although sleepiness was not measured in either study). Zhang, Wang, Li, Huang, and Cui (12) conducted an interesting study evaluating a cognitive event-related potential component (N270) in a modified match-to-sample Sternberg memory task. They compared 12 OSA patients with mild desaturation (> 1% of total sleep time [TST] with SaO2 of 89–90%), to 12 patients with more severe desaturation (> 1% of TST with SaO2 < 80%) and to 20 controls. Decreased amplitudes to a memory conflict-related stimulus were decreased in both patient groups relative to controls, with a larger effect in the more severe desaturation group. P300 is the most commonly studied cognitive evoked response potential (ERP) component and it has been studied in OSA patients in the context of auditory, visual and RREPs. A complicating factor in the interpretation of P300 is its amplitude has been shown to be under some genetic control (13,14). Thus given that OSA itself has a genetic component (15–17), P300 differences between OSA patients and controls might represent the impact of pathology on the CNS, or a genetic predisposition. Studies have generally found that P300 latency increases when there is difficulty in discriminating between stimuli; however, it has also been shown to increase in the elderly (18) and with drowsiness preceding sleep onset (19,20). P300 has been assessed in a variety of clinical populations [see Polich (21) for review] and has unfortunately shown a marked lack of clinical specificity, with reduced amplitude and/or increased latency being a common finding in many disorders or syndromes. Studies conducted on symptomatic OSA patients during wakefulness have reported mixed results, with some evidence for an increased P300 latency to visual (22–25) and auditory stimuli (23,26,27), although no effect on auditory P300 latency has also been reported (7,24,25). Some studies have found OSA patients to have reduced auditory P300 amplitudes (26,27) while others have not (7,23–25,28). Likewise, improved nocturnal sleep following effective CPAP treatment has (26,27) and has not (22,24) been associated with decreased P300 latency relative to that seen prior to treatment. Neither of the studies that have evaluated respiratory-related P300 in OSA patients found a significant effect on amplitude or latency of the response relative to controls (6,7). To the extent that any conclusions can be drawn from this literature, it appears that the severity of OSA might be the determining factor in the outcome. Severe OSA patients appear to have delayed P300 latency and reduced amplitude. Inoue et al. (28) found a direct correlation between the percentage of time during sleep spent under 90% oxygen saturation and P300 latency; Sangal and Sangal (29) reported significant correlations between P300 amplitude and respiratory disturbance index, % stage 1 sleep and the maintenance of wakefulness test. Thus severity of OSA as mediated through hypoxia could be the main variable affecting P300 abnormalities and could explain why such abnormalities are not uniformly present in studies. Evoked Potentials During Sleep OSA is a sleep-specific condition, and an alternative approach to the study of the impact of OSA on CNS function is to look for differences in CNS processing of stimuli presented during sleep. During non-rapid eye movement (NREM) sleep, evoked responses are dominated by evoked K-complexes and vertex sharp waves, and the averaged evoked response has a series of peaks including P200, N350, N550, and P900. The N550 is almost entirely owing to inclusion of K-complexes in the average. The N350 and P900 are contributed to by K-complexes but can occur in their absence

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(and N350 can also be produced by vertex sharp wave inclusion). All components are modality independent and can be elicited readily by auditory and respiratory stimuli, and appear to represent sleep-specific CNS processing. The K-complex in particular acts as a novel probe of CNS function in that its production requires the nervous system to be in an appropriate state such that sufficient numbers of frontal neurons (and possibly glial cells) must be present and functionally interconnected, to permit the production of a synchronized delta frequency response. Gora et al. (6) compared respiratory-related evoked responses during sleep between OSA patients and controls. They reported a lower incidence of K-complex production and reduced amplitude of N550 in the averaged K-complex responses, despite occlusions producing a greater change in intrathoracic pressure (and thus a more intense stimulus) in the OSA patients. Their interpretation of the result was that OSA patients have a raised threshold to respiratory events during sleep and a damped cortical response to them when the threshold is exceeded. Afifi et al. (7) replicated Gora’s result, with OSA patients having both a significantly decreased number of evoked K-complexes and a significantly smaller N550 to inspiratory occlusion stimuli. They also reported that N550 latency as significantly delayed in OSA patients. Importantly, Afifi et al. also studied auditory evoked responses during sleep, finding no significant difference in N500 latency or amplitude or K-complex elicitation rate between the OSA patients and the control subjects. Afifi et al. interpreted the presence of normal auditory evoked potentials during sleep in the context of markedly altered respiratory evoked potentials as pointing to a specific dysfunction in responding to respiratory stimuli during sleep in OSA patients. Brain Morphology Using Magnetic Resonance Imaging Three studies have reported voxel based morphometry data to evaluate gray matter, white matter and cerebrospinal fluid (CSF) volumes in the brains of OSA patients and controls. In the first published, Macey et al. (30) studied 21 male OSA patients and 21 male controls. The OSA patients had a mean AHI of 34 ± 20 events/hr. T1 weighted scans were collected using a 1.5 Tesla magnet, and analyzed with automated voxel based morphometry (VBM). They reported that increasing age was correlated significantly with decreasing total brain gray matter volume (GMV) in the control subjects, but not in OSA patients, despite displaying a very similar pattern of decreasing GMV with age. When regional differences were examined, the impact of OSA was less than that of age, with OSA patients having significantly reduced GMV in a number of discrete regions (2% to 18% less). Regions showing bilateral differences included the posterior lateral parietal cortex the anterior superior frontal gyrus multiple sites in the lateral prefrontal cortex and the parahippocampal gyrus (although with more widespread loss in the left hemisphere). Differences in the right hemisphere included the hippocampus, the post central gyrus and the quadrangular lobe of the cerebellum, while differences in the left hemisphere included the anterior cingulate gyrus, and the ventral lateral frontal cortex. In a smaller study, Morrell et al. (31) studied seven male OSA patients with a median AHI of 28 events/hr, mean nocturnal oxygen desaturation of 94% (mean nadir of 71%) and mean Epworth sleepiness scale score of 14. They conducted very similar imaging and the same VBM analysis strategy as Macey et al. (30), but reported GMV differences between OSA and control subjects only in the left

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hippocampus, with OSA patients showing a 6% reduction relative to controls. A major difference between the two studies was how they dealt with statistical comparisons between the two groups of subjects. In the Macey et al. study, differences between OSA and controls were considered significant if p < 0.001, with a minimum cluster size of 350 voxels, but no correction was made for multiple comparisons. Morrell et al. used a more severe criterion and considered differences significant if p < 0.01, after correcting for multiple comparisons. The most recently published study to investigate brain structure in OSA was conducted by O’Donoghue et al. (32). They studied 27 male OSA patients with severe OSA (AHI ≥ 30 events/hr and ≥ 15% of the night spent with SaO2 of less than 90%), and 24 age-matched male control subjects. All subjects had a screening polysomnogram, and all potential subjects with any comorbid respiratory or other medical problems were excluded. Twenty-three of the OSA patients were rescanned following 6 mo of nasal CPAP treatment. T1-weighted images were collected in a 3 Tesla scanner with coronally acquired spoiled gradient-recalled echo (SPGR). Data were analyzed using both VBM [as per the Macey et al. (30) and Morrell et al. (31) studies], and manually traced region of interest (ROI) analysis for whole brain, bilateral temporal lobes and bilateral hippocampi. The statistical analysis used age as a covariate, and differences were considered significant between groups if p < 0.05 following corrections for multiple comparisons. None of the analyses displayed significant differences between OSA patients and controls. Further there were no differences in temporal lobe or hippocampal volumes in patients pre- and post-treatment, with a small (4%) but significant increase in total brain volume after CPAP. When a less stringent p value (p < 0.001, uncorrected) was applied, some regions did display smaller GMV values in the OSA group. However, the observed loss was less extensive than that reported by Macey et al. (30), and some regions were seen to be elevated in the OSA group. The conflict between the findings of Macey et al. (30) and those of O’Donoghue et al. (32) and Morrell et al. (31) highlight the difficulty in interpreting VBM data. In particular, the major impact of decisions that are made when setting probability thresholds for consideration of significant group differences, and whether or not to correct for multiple comparisons. These issues are also inherent problems for the interpretation of functional MRI (fMRI) data as will be seen in the section on Functional Magnetic Resonance Imaging. Another issue not apparent in the article by Macey et al. article but made obvious in the related fMRI studies on the same subjects (33–36) is that at least half of the OSA patients had either significant medication use and/or comorbidities, whereas the patients in the studies by Morrell et al. and O’Donoghue et al. were carefully screened so that any differences between groups could be attributed to OSA per se. The remaining studies have investigated the relationship of SDB to MRI indices of brain structure and have concentrated on white matter. A small study of four severe apneics (mean RDI = 61.5) and four mild apneics (mean RDI = 8.5) in their late sixties, demonstrated that the severe group had significantly greater incidence of white matter hyperintensities than the mild group (37). Davies et al. (38) studied 45 moderate-to-severe OSA patients and 45 controls matched for age, BMI, and heart disease. No differences were found in the subjectively rated number of white matter abnormalities between the two groups, with 15 of the apneics having some form of abnormality versus 16 of the controls. It should be noted, however, that the mean daytime blood pressure (BP) values were 132.9/87.4 for the patients and 131.2/82.8 for the controls. Thus the careful case matching in this study may have attenuated any real effect of OSA on the brain, if such an effect is related to elevations in BP.

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Kamba et al. have conducted a series of studies evaluating magnetic resonance spectroscopy (MRS) in OSA. MRS provides a measure of the chemical composition of measured tissue and can quantify a number of chemical compounds in gray and white matter. The most commonly reported are N-acetylaspartate (NAA), thought to be a measure of neuronal integrity; choline (Cho) associated with cell membrane synthesis and turnover; and creatine (Cre), which is influenced by the state of high-energy phosphate metabolism (39). In the first study, Kamba, Suto, Ohta, Inoue, and Matsuda (40) studied 25 OSA patients and 15 controls while awake. Eleven patients were classified as mild [apnea index (AI) < 20] and 12 and moderate-severe (AI ≥ 20). NAA Cre and Cho peaks were measured with NAA/Cre and NAA/Cho ratios calculated. Lactate was also assessed. Group differences were significant for the NAA/Cho ratio in white matter, with the moderate-severe OSA patients having lower values than both the mild OSA and control subjects. No differences were found in NAA/Cre or lactate in white matter, and no cerebral cortex measures differentiated the groups. In a second study, Kamba, Inoue, Higami, Suto, Ogawa, and Chen (41) investigated MRS in 55 patients with habitual snoring and excessive daytime sleepiness (mean AHI of 43.8 ± 30 events/hr); again, data were collected while subjects were awake. Age, hypertension and AHI were all significant predictors of smaller NAA/ Cho ratios in periventricular white matter, but hyperlipidemia and minimum SaO2 levels were not. Again there appeared to be no effect of OSA on NAA/Cho ratios in cerebral cortex. As pointed out by the authors, the meaning of decreased NAA/Cho is unclear, but it is believed to be an indicator of cerebral metabolic impairment. The third study evaluated 31 patients with habitual snoring or OSA using proton MRS to assess lactate levels in white matter in the right centrum semiovale during sleep and wakefulness (42). Again a range of SDB was observed (mean AHI 44 ± 31 events/hr). Elevated lactate levels were found only in those subjects over the age of 50 with an AI ≥ 26 events/hr. No lactate buildup was observed in the remaining patients during sleep or in any patients during wakefulness. The data indicate that at least in the older more severe OSA patients, the presence of apneas led to acute decreases in oxidative metabolism in white matter. In summary, the brains of OSA patients display acute decreases in cerebral metabolism of white matter during sleep that does not persist to periods of wakefulness. There is also some evidence of decreased functional integrity of white matter as measured by NAA/Cho ratios, with some possibility of increased incidence of white matter hyperintensities. However, there is very little difference if any, in measures of gray matter volume. Functional Magnetic Resonance Imaging Two studies have been conducted using fMRI to assess brain activation during cognitive tasks in OSA patients. In the first, Thomas, Rosen, Stern, Weiss, and Kwong (43) studied 16 OSA patients and 16 controls. OSA patients had an RDI > 40 events/hr, with disease for at least 5 yr. Patients were divided into those who showed SaO2 levels < 90% (hypoxic group) and those who maintained SaO2 levels ≥ 90% (nonhypoxic). These groups did not differ in RDI but the hypoxic group had a significantly higher AHI (43.9 ± 25.9 vs. 4.5 ± 7.5). Six of the OSA patients were rescanned following eight weeks of compliant CPAP treatment. The fMRI protocol consisted of 3 T echo planar imaging (EPI). Analysis involved voxel based cluster analysis between OSA and controls, between hypoxic

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and nonhypoxic OSA and in six patients between pre- and post-treatment scans. Threshold for significance was set at p < 0.05, (Bonferroni corrected for multiple comparisons) with a minimum cluster size of 200 voxels (200 mm3). Three ROI analyses were conducted on regions previously shown to be activated during working memory tasks, the dorsolateral prefrontal cortex, the anterior cingulate and the inferior parietal lobule in posterior parietal cortex. Subjects were scanned during an n-back working memory task. The OSA group had significantly lower performance accuracy (94 ± 1.3% vs. 85.8 ± 4.1) and significant slower performance speed (908 ± 377 vs. 596 ± 117 msec) on the memory task, but did not differ on a control reaction time task. The hypoxic and nonhypoxic subgroups did not differ in performance. All healthy subjects showed activation of lateral and medial prefrontal and posterior parietal cortex. There was no evidence of prefrontal activation in OSA pretreatment, and a reduced spatial extent of posterior parietal activation. However, the nonhypoxic patients showed greater posterior activation than hypoxic patients. Despite improvements in sleep and daytime sleepiness, there was no increase in lateral prefrontal activation following CPAP treatment. In the second study, Ayalon, Ancoli-Israel, Klemfuss, Shalauta, and Drummond (44) studied 12 OSA patients and 12 matched controls. The OSA group had a mean AHI of 35.1 ± 21.1 and a mean oxygen desaturation index of 37.32 ± 24.2. fMRI acquisition was conducted at 3 T using EPI sequences. The task was different to that of Thomas et al. and consisted of a verbal learning protocol, with immediate recall following each session. Analysis was based on cluster comparisons, with a threshold of p < 0.05 uncorrected and a minimum cluster size of 12 voxels (768 mm3). The verbal learning task activated mainly a left hemisphere network, previously described in similar experiments. These included inferior, middle and superior frontal gyri; left middle temporal gyrus; and bilateral parahippocampal gyri. There was decreased activation in association with the task in bilateral medial dorsal thalamic nuclei, right declive, right precuneus and right inferior parietal lobule. Better recall performance was significantly correlated with increased activation in the left inferior frontal gyrus and left supramarginal area, and significantly negatively correlated with activity in the left inferior parietal lobule. OSA patients had the same level of recall performance as the controls, but this was associated with significantly greater levels of activation in several brain regions in both hemispheres. Thus as this group has previously reported in sleep deprived subjects, verbal learning performance was maintained at normal levels in those experiencing fragmented sleep but this requires the use of brain regions not typically involved in the task, in a form of compensatory recruitment of brain tissue. The two studies, while appearing to be producing opposite results are not strictly comparable. Different scanners and thus scanning protocols were used. Different analysis packages were employed with different significance threshold criteria. Most importantly, different tasks were employed and both studies used relatively small number of subjects. Hopefully both groups and other researchers will continue to add to the data, and clarification of the role of OSA in influencing underlying brain processes subserving cognitive function will eventually emerge. The only other fMRI studies comparing fMRI data in OSA and control subjects are a series four articles (33–36) published by Harper’s laboratory investigating responses to respiratory or autonomic stimuli in OSA. Most of the studies were conducted at the same time as the structural data collection reported by Macey et al. (30) with stimuli consisting of three Valsalva maneuvers (producing pressure

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changes ≥ 10 mmHg) (34), a single expiratory load for 90-sec (≥ 10 mmHg) (35), a single 90-sec cold pressor challenge [a bag of deuterium oxide (D2O) at 4ºC], (33) or a single 60-sec inspiratory load [–6 to –15 mmHg) (36). Across the four studies subject numbers were between 7 and 10 OSA patients and between 12 and 16 controls. None of the controls had polysomnographic (PSG) evaluation and the AHI of the OSA patients varied between 8 and 95 events/hr. For at least two of the measures (Valsalva and inspiratory loading) repeat sessions were conducted with those sessions added into the analysis to “reduce within subject variability” [(36), p. 47]. EPI was used at 1.5 T. For some stimuli, ROI analyses were conducted, but the main analysis strategy was cluster analysis where voxels were compared between groups to see where significant differences in the blood oxygen level dependent response occurred. Typically the threshold for significance was set at p < 0.05 without correction for multiple comparisons. Given the above, it is clear that the data are difficult to interpret. First, the OSA patients have very variable pathology and the subject numbers are very low. Indeed as the authors acknowledge, in some cases the numbers are lower than the published requirements for the statistical approach used [(36), p. 58]. Second, the number of stimuli presented was very low with only 10 or so volumes available for analysis during a challenge. fMRI experiments typically utilize multiple stimulus repetitions in an effort to increase the very low signal to noise ratio inherent to EPI imaging. Third, the statistical approach used, with a low threshold alpha value and the failure to correct for multiple comparisons, dramatically increases the chance of type 1 error. Finally, the results reported are typically interpreted in the context of their own finding of gray matter deficits in OSA (30) that has not been replicated in other studies (31,32). What is clear from these studies however is that the magnitude and timing of the autonomic nervous system response to the stimuli was altered in OSA patients. This phenomenon will be discussed in greater detail in the remainder of this Chapter. Summary of Central Nervous System Effects There is some evidence from evoked potential studies and brain morphometry and MRS studies of white matter that OSA can have a significant impact on the CNS, probably in a dose-dependent manner, with effects diminished or absent in less severe patients While fMRI as a technique shows great promise in the assessment of OSA effects, insufficient studies have been conducted to date to draw any clear conclusions. Further, questions relating to the impact of treatment, and issues of the cause of any observed changes or the role of genetics versus pathology per se in producing them, remain unanswered at this point. THE IMPACT OF OBSTRUCTIVE SLEEP APNEA ON THE AUTONOMIC NERVOUS SYSTEM The Measurement of Autonomic Activity During Sleep: Methodological Comments The methods of assessment of cardiovascular function and control in humans during sleep are limited by the requirement that the measurement technique does not excessively disturb sleep. It is for this reason that the vast majority of studies have either assessed measures of cardiac output (e.g., heart rate, HR; blood pressure, BP; baroreflex activity, BR), or measures of BP (BPV) or HR variability (HRV). While other methods have been applied, most notably, muscle sympathetic nerve activity

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(MSNA) recordings and measures of catecholamine levels, fewer studies have employed these methods, in large part because they are intrusive, technically difficult and, in the case of catecholamine levels, lack sensitivity. Because the use of HRV/BPV analyses is so widespread and because these methods have been widely misunderstood, leading to some confusion in the literature, we will briefly comment on them. Cardiac variability can be assessed in either the time domain or spectral domain. An example of a time domain measure would be the standard deviation of all HR inter-beat-intervals (IBI) within a specified period, while an example of a spectral measure would be the power within a specified frequency band (e.g., 0.04 to 0.15 Hz) derived from a fast Fourier transform (FFT) or autoregressive analysis of BP IBIs. Two broad concepts have been derived from variability analysis. The first, derived primarily from time domain methods, is that high HRV is a sign of cardiovascular health, as it indicates that HR is effectively varying in order to maintain BP within a narrow range. The second is the concept that variability within particular frequency ranges indicates particular physiological processes, such as for example, the identification of variation in HR IBI at the respiratory frequency (respiratory sinus arrhythmia) with parasympathetic nervous system (PNS) activity. It is the latter concept that has been widely used and widely misunderstood. The power by frequency distribution derived from spectral analysis of HR IBI shows a number of peaks, two of which have been intensively studied. One occurs at the respiratory frequency of approximately 0.25 Hz, with a range from 0.15 to 0.4 Hz. This “high frequency” (HF) component is widely accepted as a measure of PNS activity, although there are well known measurement artifacts, such as variations in respiratory rate and intensity of respiratory drive that distort the measure. The second peak occurs at approx 0.1 Hz, with a range from 0.04 to 0.15 Hz. and is typically referred to as the “low frequency” (LF) component. There has been considerable debate regarding what is measured by the LF component (see the following references for a range of views: 45–47). The most widely held position is that it measures some combination of PNS and sympathetic nervous system (SNS) activity. In order that the technique can be used as an index of SNS activity, it has been common practice to “neutralize” the PNS contribution to the LF component by computing a LF/HF ratio. However, the underlying assumption of this strategy, that the PNS components in the numerator and denominator will cancel each other out, is unlikely to be true as they are derived from different physiological processes (45), and because there are significant nonlinear relationships between the two components (48). As a consequence, at best, the LF component reflects a balance between sympathetic and vagal influences [although see (46) for a critique of this conclusion]. Like any ratio, a change in its value, such as might occur in going from wakefulness to sleep, cannot be specifically attributed to changes in either component and thus a decrease in the ratio is as likely to be due entirely to an increase in the HF component, regarding a change in the sympathetic contribution to the LF component. A further difficulty with the technique is that not all power in the distribution is attributable to the PNS and SNS. Thus, physical activity, body movements during sleep, the apnea-hyperpnea cycle in OSA patients and so forth all add power over a range of frequencies, resulting in variations in the total power (TP). The generally adopted solution to this problem has been to express power within the frequency ranges of interest as a proportion of TP. For example LF power is expressed as LF/ TP. However, as TP is primarily made up of LF + HF, the expressions LF/TP and HF/TP are highly correlated and both are highly correlated with the LF/HF ratio. Indeed, in many articles TP is defined as LF + HF power, in which case the three

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quantities are perfectly correlated and all reflect the same ratio of PNS to SNS activity. So, what can be learned from HRV/BPV analyses? First, the literature indicates that with appropriate care in the collection and analysis of the data, the HF component of HRV reflects vagal influences on the heart moderately well, while the 0.04 to 0.15 Hz component of BPV reflects sympathetically-mediated vasomotor tone. Further, with more sophisticated computational models, such as multivariate autoregressive analyses that allow the dynamic relationship between respiration, HR and BP to be decomposed (49), or autoregressive models with exogenous input that allow variations in the respiratory component to be partialled-out (50), it is possible to identify more complex relationships from data collected during undisturbed sleep. Nevertheless, it remains the case that the LF component of HRV at best, and as we have noted even this is disputed, only provides information on sympathovagal balance. Thus, there is no direct noninvasive measure that may be applied during sleep of sympathetic influences on the heart. This point will be taken up again toward the end of the following section. Autonomic Cardiovascular Activity Druing Sleep: Healthy Individuals Both HR and BP show clear diurnal variation, with higher levels of activity during daytime wakefulness [e.g., (51–54)]. The diurnal pattern is a consequence of a range of physiological processes, with the most obvious being 24-hr variations in physical activity and posture. While changes in these factors are in part secondary to sleep and circadian influences, it is also the case that both sleep and the circadian system have direct effects on cardiovascular activity. Thus, studies that have specifically investigated sleep have shown that there is a sleep-specific effect on both HR and BP (55–57) and a circadian influence on HR (58–60) that are independent of changes in posture and physical activity (55,57). In the case of BP, the effect of sleep occurs abruptly at sleep onset, such that the tonic NREM sleep level is reached soon after the attainment of stable NREM sleep, with systolic BP falling 15 mmHg or more (61). Further, the rise in BP in the morning is similarly dependent on the transition to wakefulness and consistent with this pattern, the 24-hr variation has been most successfully modeled by a square wave function (62), giving rise to the expression, “dipping BP profile.” Within sleep BP levels during rapid eye movement (REM) sleep approximate relaxed wakefulness (61), with transient increases in association with phasic REM events (57,63). Within a particular sleep stage BP is constant over the sleep period, with the average level increasing during sleep owing to the increasing proportion of REM sleep as the night progresses (61). Several studies have investigated the influence of the circadian system on BP using the constant routine procedure and have not shown circadian effects (59,60). Further, Carrington et al. (55) observed that if sleep onset was delayed by three hours the diurnal fall in BP was similarly delayed, suggesting a strong influence of sleep onset, but little effect of the circadian system. In contrast to BP, HR shows a strong circadian influence with the nadir of the oscillation occurring during the normal sleep period (58–60). Under normal circumstances sleep onset occurs during the falling phase of HR and thus sleepspecific effects can be difficult to isolate. Nevertheless, a sleep onset specific fall in HR has been reported [e.g., (55,58,61)]. As with BP, HR is higher during REM than NREM at any particular time within the sleep period (61), with transient tachycardia in association with phasic events (57,63). However, because HR falls under circadian influence in both sleep states and because REM sleep is concentrated in

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the second half of the night, whole night averages do not always show REM-NREM differences. In summary, in normal healthy individuals cardiac activity is markedly reduced in NREM sleep through a combination of physiological processes such as lowered activity and postural changes, but also because of direct influences from sleep and circadian mechanisms. Because NREM sleep constitutes 80% to 85% of the sleep period, sleep is a period of cardiovascular quiescence, a state that has been referred to as a “cardiovascular holiday.” Changes in cardiac activity during sleep are typically attributed to changes in autonomic control. Thus, sleep is associated with an increase in baroreflex sensitivity (BRS) (54,64,65) and a downward resetting of the reflex. BR resetting is indicated by pharmacologic studies (66), and is also strongly suggested by the simultaneous falls in BP and HR in association with a maintenance, or slight increase, in BRS during sleep onset (55). The time course of the changes suggests peripheral resetting owing to the sleep-related fall in BP unloading the system, although central resetting has not been ruled out. Time domain analyses of heart rate variability indicate overall higher variability during sleep, although this is owing to large increases in the higher frequency components in association with a smaller decrease in low frequency components (67). Both 24-hr variability and the sleep-related increase in higher frequency activity decrease with increasing age (67), effects attributed to an age-related fall in parasympathetic activity. Similarly, a range of disorders that are associated with cardiovascular dysfunction have also been shown to be associated with reduced heart rate variability (47). PNS activity, as reflected in both time and frequency domain measures, has been shown to increase during NREM sleep. In the case of frequency domain measures, several studies have identified increases in absolute power in the respiratory frequency range (respiratory sinus arrhythmia) that could not be attributed to changes in total spectral power (54,61,63,68). Further, there is evidence that in humans HRV in the HF range can be eliminated by pharmacologic blockade of the PNS by atropine (69) and that atropine eliminates the sleeprelated increase in HRV (63). There is also strong evidence of a fall in sympathetic vascular tone during NREM sleep. Thus, the following conclusions are supported by these data: sleep is associated with peripheral vasodilation (70); the 0.4 to 0.15 Hz component of blood pressure variability, which is thought to reflect sympathetic vascular tone, is reduced during NREM sleep (71); and MSNA is reduced (72,73). Both increased PNS activity and decreased vascular tone are reversed in REM sleep (54,63,68,73). Thus, in young healthy individuals cardiovascular activity is reduced during NREM sleep through baroreflex resetting, reduced sympathetic vascular tone and elevated PNS activity. It is important to note that there is no direct evidence of a change in SNS input to the heart during NREM sleep. This is because neither absolute measures of LF activity, nor the various ratios of low to high frequency activity, uniquely reflect SNS activity. The widely held assumption that sympathetic cardiac influences on the heart are reduced during NREM sleep depends on the application of two basic principles of autonomic control, the generalized activation of the SNS, which would allow one to generalize from peripheral to central sympathetic withdrawal, and the reciprocal relationship between the PNS and SNS branches, which would allow one to infer SNS withdrawal from an increase in PNS activity. However, whether these principles hold during the relatively subtle changes that occur during

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sleep is highly uncertain. Thus the contribution of the withdrawal of sympathetic influences on the heart during NREM sleep remains unproven. Autonomic Cardiovascular Activity in Obstructive Sleep Apnea Cardiovascular activity during sleep in OSA patients is characterized by rhythmic variability in synchrony with the apneic and ventilatory phases of obstructive events, with increases in HR and BP occurring several seconds after apnea termination (74). The consequence is that average HR and BP levels are often higher in OSA patients over the sleep period as compared to control subjects. Indeed, in some patients the normal sleep-related fall in BP is eliminated producing what has been referred to as a “nondipping profile” (75). In addition, an elevation of waking BP is common in OSA patients. Both the elimination of the sleep-related fall in BP and waking hypertension appear to be owing to the accumulated effect of three factors: an exaggeration of negative intrathoracic pressure in response to inspiratory effort during the obstruction; hypoxia; and arousal from sleep. The mechanisms by which these pathophysiological processes result in abnormal cardiovascular patterns are complex and beyond the scope of this chapter. Nevertheless, there does appear to be common final pathways. In particular, all three processes lead to alterations in baroreflex and sympathetic vasomotor control (74). Measures of HR and BP variability during sleep in OSA patients are complicated by the direct effects of repetitive obstructions on HR and BP. As a consequence, time domain measures of variability indicate increased HRV; indeed, elevated HRV has been suggested as a screening tool for OSA (76). In contrast, time domain measures of HR and BP variability in OSA patients during relaxed wakefulness in the supine position (without apneas), indicate reduced HR and increased BP variability (77). The latter result suggests impaired BR function in OSA patients. Indeed, there is evidence that BRS is reduced in OSA patients (78,79), although this effect has not been shown by all studies (80). However, several studies have shown increases in BRS with acute application of CPAP. Thus, Tkacova et al. (81) identified an increase in BRS, with indirect evidence of resetting of the baroreflex to a lower BP level, after acute treatment with CPAP, in OSA patients with congestive heart failure. They inferred that the OSA was associated with an elevated set point and reduced BRS. Further, a small increase in BRS has been observed in severe OSA patients during acute CPAP treatment (82). In addition to impaired control of BP, these effects would contribute to sympathetic activation of vasomotor tone. However, it should also be noted that a reciprocal effect is also likely, in which increased vasomotor tone, if maintained, would upwardly reset the BR. A number of studies have demonstrated that OSA is associated with an increase in sympathetic vasomotor tone. Thus, MSNA is higher in OSA patients (83–85), an effect that is reduced with CPAP treatment (86–88). The higher MSNA appears to be largely mediated by hypoxia (89,90). Further, Imadojemu, Gleeson, Gray, Sinoway, and Leuenberger (91) have shown that the increase in MSNA results in peripheral vasoconstriction, causing the surge in BP following an apnea, rather than it being owing to increased cardiac output. Studies have consistently demonstrated higher norepinephrine levels in OSA patients during both sleep and wakefulness indicating elevated sympathetic arousal (92), an effect that may relate to the occurrence of movement arousals during sleep (93), although of course the particular organs involved in the elevated sympathetic activity remain unknown from the catecholamine measures themselves. Further, norepinephrine levels are reduced by acute CPAP treatment (94,95).

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Traditional spectral measures of HRV have been of limited value in evaluating autonomic control in OSA patients during sleep because, in addition to the limitations discussed above, the repetitive apnea-hypopnea cycles while primarily adding variance to the very low frequency range, also contribute to the LF and HF components (96). This has the effect of masking the direct respiratory-cardiac neural coupling (50). A number of groups have now introduced more sophisticated mathematical models to analyze HRV and have applied these techniques to OSA patients. Khoo et al. have introduced a technique in which perturbations are introduced to the respiratory system via random presentations of positive airway pressure. Respiratory, cardiac and hemodynamic responses are then analyzed by computational models that decompose the components of HRV in different ways. As described earlier, these models include the ability to characterize the dynamic relationships between respiration, HR and BP, including feed forward and feedback influences [closed loop analyses (97)] and the separation of respiratory contributions into direct respiratory-cardiac neural coupling from the effect of feedback from pulmonary stretch receptors (98). In general these studies have demonstrated impaired parasympathetic control in OSA, as indicated by reduced baroreflex gain and reduced respiratory-cardiac neural coupling in patients (98), with the effect of OSA on baroreflex gain being greater during sleep than wakefulness (99). Further, these effects have been shown to be ameliorated by CPAP treatment (97,100). Summary of Autonomic Nervous System Effects A variety of techniques have demonstrated that NREM sleep is associated with reduced sympathetic vasomotor tone, downward resetting of the baroreflex, marginally increased BRS and elevated parasympathetic activity. These effects are reduced or lost during sleep in OSA patients, but are reversible with CPAP treatment. However, owing to technical limitations the effect of sleep or OSA status on sympathetic neural innervation of the heart remains unknown. CONCLUSIONS Recent technological advances open up the possibility of careful study of the impact of OSA on the CNS and ANS during wakefulness and sleep. Clearly, the current state of knowledge is greater for the ANS than the CNS, with OSA’s impact on CNS structure and function being extremely understudied. Future studies will require larger subject numbers, more careful attention to the impact of disease severity and should involve when possible studies conducted during sleep, where at least in terms of EEG evoked potentials and measures of ANS function, clear differences can be observed. Issues currently unresolved such as the relative impact of sleep deprivation versus intermittent hypoxia, the relative impact of genetics and the role of treatment in reversing any observed effects, are in principle resolvable. REFERENCES 1. Davenport PW, Colrain IM, Hill PM. Scalp topography of the short-latency components of the respiratory-related evoked potential in children. J Appl Physiol 1996; 80(5):1785–1791. 2. Logie ST, Colrain IM, Webster KE. Source dipole analysis of the early components of the RREP. Brain Topogr 1998; 11(2):153–164. 3. Webster K, Colrain I. The relationship between respiratory-related evoked potentials and the perception of inspiratory resistive loads. Psychophysiology 2000; 37(6):831–841.

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98. Jo JA, Blasi A, Valladares E, Juarez R, Baydur A, Khoo MC. Determinants of heart rate variability in obstructive sleep apnea syndrome during wakefulness and sleep. Am J Physiol Heart Circ Physiol 2005; 288(3):H1103–H1112. 99. Jo JA, Blasi A, Valladares E, Juarez R, Baydur A, Khoo MC. Model-based assessment of autonomic control in obstructive sleep apnea syndrome during sleep. Am J Respir Crit Care Med 2003; 167(2):128–136. 100. Khoo MC, Belozeroff V, Berry RB, Sassoon CS. Cardiac autonomic control in obstructive sleep apnea: effects of long-term CPAP therapy. Am J Respir Crit Care Med 2001; 164(5):807–812.

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Cardiac Arrhythmias and Congestive Heart Failure Sheree Chen Kaiser Permanente, Vallejo, California, U.S.A.

T. Douglas Bradley Department of Medicine, Toronto General Hospital of the University Health Network, Toronto, Ontario, Canada

INTRODUCTION Obstructive sleep apnea (OSA) is a common condition in adults that has adverse effects on the cardiovascular system. In this chapter, we will discuss the potential relationship of OSA to cardiac arrhythmias and congestive heart failure (CHF). This chapter will also touch upon the possible relationship of central sleep apnea (CSA) to cardiac arrhythmias and CHF. To understand the various proposed mechanisms by which OSA may contribute to the development of cardiac arrhythmias and CHF, it will be necessary to provide an overview of the influence of OSA on the cardiovascular autonomic nervous and conduction systems, and on cardiac mechanics. CARDIOVASCULAR AUTONOMIC SYSTEM The sinus node of the heart has intrinsic pacemaker activity. The intrinsic heart rate determined by this pacemaker activity is subject to modulation mainly by autonomic regulation of the sinus node. The sinus node is densely innervated with postganglionic adrenergic (sympathetic) and cholinergic (parasympathetic) nerve terminals (1). Neurotransmitters modulate the sinus node discharge rate by stimulation of the beta adrenergic (sympathetic) and muscarinic (parasympathetic) receptors. Sympathetic stimulation via binding of receptor agonists on beta 1 and beta 2 receptors results in sinus node firing, which in turn prompts an increase in heart rate (positive chronotropic response). Conversely, parasympathetic (or vagal) stimulation via binding of acetylcholine to the muscarinic type 2 receptors result in a decrease in sinus node firing, which produces a decrease in heart rate (negative chronotropic response) (2). Under normal resting conditions, modulation of heart rate via the sinus node is influenced predominantly by parasympathetic activity. In general, tonic vagal input into the sinus node slows heart rate below the intrinsic sinus rate. The vagus also has phasic activity that modulates heart rate. Vagal effects on the sinus node depend on which phase of the cardiac cycle the vagal discharge occurs and the background sympathetic tone. Periodic vagal bursting (as may occur each time a systolic pressure wave arrives at the baroreceptor regions in the aortic arch and carotid sinuses) induce phasic changes in sinus cycle length (i.e., the inverse of heart rate) and can entrain the sinus node to discharge faster or slower at a rate identical to the rate of vagal bursts. The most obvious manifestation of this vagal modulation 293

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is the breath-to-breath alterations in heart rate that accompany normal breathing known as respiratory sinus arrhythmia (RSA). During inspiration, the fall in stroke volume and blood pressure unloads carotid baroreceptors and reflexively causes vagal withdrawal at the sinus node and an accompanying rise in heart rate. Conversely, during expiration, the rise in stroke volume and blood pressure reflexively increase vagal input into the sinus node causing a fall in heart rate. Sinus node automaticity may transiently accelerate after cessation of vagal stimulation causing postvagal tachycardia (10). Similarly, surgical vagotomy or pharmacological vagal blockade (e.g., by atropine) increase heart rate and abolish RSA. During exercise, sympathetic input into the sinus node increases causing heart rate to increase. The atrial ventricular node (AVN), like the sinus node, has both adrenergic (sympathetic) and cholinergic (parasympathetic) innervation. For unknown reasons, autonomic nerves on the left side affect the AVN more than the sinus node, whereas autonomic nerves on the right side affect the sinus node more than the AVN. Neither sympathetic nor parasympathetic stimulation influences conduction through the His bundle, but they both can influence AVN conduction. Sympathetic stimulation increases conduction velocity through the AVN and reduces its refractory period. Parasympathetic stimulation slows conduction through the AVN and increases its refractory period. Cardiac responses to brief vagal bursts begin after a short latency and dissipate quickly. This is in contrast to sympathetic stimulation, where cardiac responses commence and dissipate slowly (i.e., a low-pass filter effect). The balance between the effects of the sympathetic and parasympathetic systems is thought to be reflected in the beat-to-beat changes of the cardiac cycle. Specifically, changes in the sympatheticparasympathetic balance can be reflected in variations of the sinus rhythm that oscillate around the mean heart rate. Small adjustments in heart rate are engendered by cardiovascular control mechanisms that may involve alterations in respiratory movement, gas exchange, chemoreflexes, intrathoracic pressure, thermoregulation, and peripheral vascular resistance, that can in turn affect respiratory centers, and consequently, cardiovascular centers. The actual analysis of variation in the instantaneous heart rate time series using the beat-to-beat R-wave to R-wave (R-R) interval is known as heart rate variability (HRV) analysis (3). Analysis of HRV in the frequency domain is known as frequency spectral analysis and has been used to estimate the degree of sympathetic and parasympathetic modulation of RR-intervals. It quantifies the density (i.e., power) of R-R intervals at any given R-R interval frequency. There are three main frequency bands of interest, which are very low frequency (VLF, < 0.05 Hz), low frequency (LF, 0.05–0.15 Hz), and high frequency (HF, 0.15–0.5 Hz). The sympathetic nervous system is more slowly adapting, and consequently affects heart rate and blood pressure oscillations at VLF and LF zones. The parasympathetic nervous system has a rapid onset and dissipation of cardiovascular effects, and consequently affects heart rate and blood pressure oscillations in the HF zone, which is predominantly respiratory related (i.e., RSA) (4). The ratio of LF to HF components has been used as a measure of the sympathovagal balance. RSA manifests itself as a peak in HRV in the HF band. Autonomic control of the cardiovascular system plays a crucial role in providing the second-to-second adjustments of blood pressure and heart rate that are critical to maintaining cardiac output and tissue perfusion in humans under conditions of postural changes and physical activity. Autonomic integration between cardiovascular, renal, gastrointestinal, and temperature control allows each organ to

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maintain homeostasis within the entire organism and to compensate for stressful circumstances or disease. Therefore, when autonomic control of the cardiovascular system is disrupted, significant impairment in function can result. Baroreceptors and Chemoreceptors The heartbeat-to-heartbeat alterations in blood pressure, heart rate, and oxygen delivery that are required to maintain homeostasis rely on integration of stimuli sensed by arterial and cardiopulmonary baroreceptors, and chemoreceptors on the afferent side, in conjunction with reflex responses to these stimuli on the efferent side (i.e., baroreflex and chemoreflex, respectively). Arterial baroreceptors, located at the aortic arch, the origin of the left subclavian artery, and the carotid artery sinus, respond to the amount of stretch (or vascular pressure) applied to the arterial wall. Information collected is sent via projections from the baroreceptors to the nucleus tractus solitarius, which then projects to the hindbrain nuclei that govern efferent sympathetic and parasympathetic activity. Afferent information from the baroreceptors is integrated on a beat-to-beat basis to alter the level of efferent sympathetic and parasympathetic outflow in order to minimize fluctuations in arterial blood pressure. Cardiopulmonary receptors are located in the walls of the atria and pulmonary artery and in the lung. They respond primarily to changes in volume (i.e., stretch), but are also sensitive to pressure (i.e., tension). They project via vagal afferents to the nucleus tractus solitarius and via spinal sympathetic afferents to the spinal cord. Stimulation of these receptors produces bradycardia, vasodilation, and inhibition of vasopressin. Under normal conditions, activation of the carotid sinus and aortic arch baroreceptors by an increase in blood pressure reflexively inhibits sympathetic outflow and increases cardiac vagal outflow, with subsequent decreases in heart rate, cardiac contractility, vascular resistance, and venous return. A drop in blood pressure has the opposite effect. However, the arterial baroreflex may not be the only feedback mechanism involved in acute blood pressure regulation. Endogenous nitric oxide constitutes a second system, which, by acting locally at the vascular endothelium level through a feedback mechanism, is involved in the short-term regulation of blood pressure (5,6). Furthermore, inspiration decreases, and expiration enhances the cardiac vagal response to baroreflex activation. These responses are dependent on respiratory rate such that tachypnea impairs baroreflex modulation of heart rate and sympathetic nerve traffic. This is manifest as a reduction in the degree of RSA (i.e., HRV at HF). Peripheral chemoreceptors, located in the carotid bodies, respond primarily to hypoxia and, to a lesser extent, hypercapnia, whereas central chemoreceptors, located on the ventral surface of the medulla, respond exclusively to hypercapnia (7–9). Both peripheral and central chemoreceptor activation elicit increases in sympathetic nerve traffic (10–13). Activation of peripheral chemoreceptors, in the absence of breathing (which removes input from pulmonary stretch receptors), increases sympathetic vasoconstrictor activity to peripheral blood vessels with simultaneous increase in cardiac vagal activity resulting in an overall bradycardic response (14,15). However, in the presence of breathing (with input from pulmonary stretch receptors), peripheral chemoreceptor stimulation inhibits cardiac vagal outflow, which results in an overall tachycardic response. Slutsky et al. found slow-acting receptors (pulmonary stretch receptors) were responsible for the tachycardic response seen during exposure of rabbits to isocapnic, progressive hypoxia (16). Central chemoreceptor activation increases sympathetic nerve traffic and blood pressure (10). The increase in blood pressure and minute ventilation resulting

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from the chemoreceptor activation also participates in a negative feedback loop that inhibits the sympathetic response to chemoreflex activation. The chemoreflexes are an important mechanism for regulation of both breathing and autonomic cardiovascular function. Under normal conditions, chemoreflexes are blunted during sleep compared with wakefulness (17). In healthy individuals, the activation of baroreflexes attenuates ventilatory, sympathetic, and bradycardic responses to peripheral chemoreflex excitation. CARDIOVASCULAR CHARACTERISTICS OF NORMAL SLEEP The transition from wakefulness to non-rapid eye movement (NREM) sleep produces an abrupt, but slight decrease in minute ventilation and PaO2, and a slight increase in PaCO2 (18,19). During deeper stages of NREM sleep, respiration is predominantly under metabolic control with a very regular pattern of breathing (20). Cardiovascular autonomic regulation also undergoes alterations at the transition from wakefulness to NREM sleep. These are characterized by an increase in parasympathetic nervous system tone and a decrease in sympathetic nervous system activity (SNA), which results in a decrease in heart rate, blood pressure, stroke volume, cardiac output, and systemic vascular resistance (21–26). Baroreflex sensitivity is increased during NREM sleep compared with wakefulness (27,28). During rapid eye movement (REM) sleep, respiration becomes less dependent on metabolic drive and more dependent on behavioral factors (29). Breathing becomes more irregular, and there is a further decrease in overall alveolar ventilation with a resultant increase in PaCO2. In contrast, SNA, heart rate, and blood pressure increase to levels similar to those of relaxed wakefulness (24,26). Spontaneous arousal will occasionally occur in NREM sleep. Arousals cause an abrupt increase in chemosensitivity and augmented ventilation that exceeds what is expected for the ambient PaCO2. They also provoke a sudden increase in SNA, and the withdrawal of cardiac vagal activity (30) that is accompanied by abrupt increases in heart rate and blood pressure, which exceed normal waking levels. Thus, arousals represent a distinct transient state of heightened respiratory and cardiovascular activity (31). PATHOPHYSIOLOGICAL MECHANISMS IN OBSTRUCTIVE SLEEP APNEA OSA is characterized by repetitive episodes of upper airway collapse during sleep. Complete or partial obstruction of airflow precipitates apneas and hypopneas, respectively. Reflexes triggered by upper airway collapse and chemical stimuli during airway occlusion cause vigorous, but ineffectual inspiratory efforts and consequent generation of exaggerated negative intrathoracic pressure. The combination of apnea-induce hypoxia, hypercapnia, and inspiratory efforts eventually trigger transient arousals that activate pharyngeal dilator muscles, which abruptly restores upper airway patency and airflow. Repetitive cycles of obstructive apneas alternating with ventilatory phases counteract the normal sleep-related relaxation of the cardiovascular system with potential adverse consequences (32). Autonomic and hemodynamic responses to OSA are complex and include the effects of apnea, generation of exaggerated negative intrathoracic pressure, hypoxia, hypercapnia, and arousal from sleep (33).

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Immediate Effects of Variations in Intrathoracic Pressure Ineffective inspiratory efforts that accompany obstructive apneas and hypopneas produce exaggerated negative intrathoracic pressure swings (33,34). The increased negative intrathoracic pressure increases the pressure difference between extracardiac and intracardiac pressure resulting in an increase in left ventricular (LV) transmural pressure, and hence afterload, without increasing blood pressure (35,36). Increased negative intrathoracic pressure also causes an increase in venous return to the right ventricle, resulting in its distension and leftward shift of the interventricular septum, which can impede LV diastolic filling (37). Stroke volume will decrease with the combination of increased LV afterload and decreased LV preload during obstructive apneas that is proportional to the negative intrathoracic pressure generated (38–41). The fall in stroke volume, and therefore, cardiac output, causes a fall in blood pressure that suppresses the carotid sinus baroreceptor and reflexively augments sympathetic outflow. Counteracting this effect is the profound negative intrathoracic pressure, which increases the transmural intrathoracic aortic pressure, which activates the aortic baroreceptors and inhibits sympathetic outflow (42,43). Because aortic baroreceptor reflexes predominate, the net effect is suppression of sympathetic vasoconstrictor outflow to muscle [muscle sympathetic nerve activity (MSNA)] during the initial part of apnea. However, towards the end of the apnea, MSNA rises in response to the hypoxia. Blood pressure tends to rise towards the end of the apnea depending on the amount of hypoxic stimulus and the magnitude of the sympathetic response (44). Immediate Effects of Hypoxia and Hypercapnia The combination of apnea, hypoxia, and hypercapnia, which occurs during obstructive apneas, increases SNA (45). Hypoxia has varying influences on heart rate depending on the presence or absence of airflow, and the balance of its parasympathetic and sympathetic stimulatory effects. In the absence of airflow, hypoxic stimulation of the carotid body is vagotonic, and causes bradycardia (46,47). This is part of the diving reflex, that is so called because of its prominence in diving marine mammals. Features of this diving reflex are peripheral vasoconstriction, thereby preserving blood flow to the brain and heart vessels, and profound bradycardia as a means of limiting cardiac oxygen demand. This protective mechanism allows homeostasis during prolonged periods of apnea and may be activated in some patients with OSA (48). In the presence of airflow (i.e., hypopnea), hypoxia will cause a tachycardic response. This is the result of lung expansion that stimulates pulmonary stretch receptors that, in turn, inhibits vagal outflow to the heart, leading to unopposed cardiac sympathetic discharge. Heart rate responses to airway obstruction can differ among people due to differences in severity of hypoxia, intrinsic hypoxic chemosensitivity, and the relative influence of hypoxia on vagal and sympathetic input to the sinoatrial node (49,50). Bradycardia was originally thought to be the common response to obstructive apneas (51), but subsequent studies have shown that bradycardia is uncommon, and heart rate can either rise, fall, or remain unchanged during obstructive apneas (52). Immediate Effects of Arousals On the one hand, arousal from sleep at the end of obstructive apneas is a critical defense mechanism that activates upper airway dilator muscles, and facilitating

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resumption of airflow, thereby preventing asphyxiation (19). On the other hand, it disrupts sleep, and causes sympathetic activation, vagal withdrawal and acute surges in heart rate and blood pressure following termination of apnea (30). The degree to which arousals influence heart rate and blood pressure is, however, somewhat controversial. O’Donnell et al. found that, in dogs, apneas terminated prior to arousal caused increases in blood pressure, but apneas terminated by an arousal caused a further increase (53). It has also been reported that postapneic surges in heart rate and blood pressure occur during the ventilatory period of spontaneous and voluntary periodic breathing during wakefulness even in the absence of hypoxia or arousals from sleep (31,54). These observations suggest that increases in ventilation itself can increase heart rate and blood pressure. Trinder et al. found that cardiovascular activation at arousal from sleep is a transient, reflex-like response that is more pronounced than that during normal wakefulness (55). The blood pressure surge is attenuated by beta-blockade, while the heart rate surge is prevented by parasympathetic blockade (30). There is mounting evidence that arousals from sleep at apnea termination make an immediate contribution to increased heart rate and blood pressure, but its effects on sympathetic activity, heart rate, and blood pressure do not appear to carry over into wakefulness (56). Chronic Effects of Obstructive Sleep Apnea OSA is associated with chronic abnormalities of cardiovascular autonomic regulation, both during sleep and wakefulness, characterized by increased SNA, reduced baroreflex sensitivity and HRV, and increased blood pressure variability (32,57–63). It is not completely understood how OSA leads to persistent sympathetic activation, but stimulation of the carotid bodies by intermittent hypoxia has been implicated. In healthy humans, short-term sustained and intermittent hypoxic challenges, with or without hypercapnia, cause elevations in MSNA that persist for 20 minutes or more following withdrawal of the exposure (64–66). Hypoxia may also contribute to persistent blood pressure elevations, possibly via sympathetic activation, since overnight sustained hypobaric hypoxia causes increases in blood pressure that carry over into wakefulness in healthy humans (67). In addition, exposure of spontaneously hypertensive rats to intermittent hypoxia causes sustained increases in blood pressure that are prevented by carotid body denervation (68). Increased sensitivity of the peripheral chemoreceptors has also been implicated in the development of elevated sympathetic tone both during sleep and wakefulness in OSA patients. For example, it has been reported that patients with OSA have increased peripheral chemoreflex sensitivity (69) and pressor responses to hypoxia (70). In normoxic patients with OSA, 100% oxygen desensitizes peripheral chemoreceptors thereby reducing MSNA, heart rate, and blood pressure (71). However, others have reported that peripheral chemosensitivity is either not affected, or is depressed by OSA (72,73). Thus, although it appears that peripheral chemoreflexes play a role in sympathetic activation and sustained blood pressure elevations in patients with OSA, it remains unclear whether peripheral chemoreflex sensitivity is altered in patients with OSA. As stated previously, activation of carotid sinus and aortic arch baroreceptors from an increase in blood pressure reflexively inhibits sympathetic outflow, increases cardiac vagal outflow, and reduces heart rate. In patients with OSA, repetitive nocturnal surges in blood pressure may blunt baroreflex sensitivity and disinhibit sympathetic nerve traffic, while inhibiting parasympathetic outflow (74). In dogs,

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experimentally-induced OSA caused an increase in the baroreflex set point to a higher blood pressure without changing baroreflex sensitivity (75). The implications of these findings in quadruped dogs for bipedal humans who undergo more extreme postural changes and alterations in baroreceptor stimulation are not clear. For example, it has been shown that treatment of OSA by continuous positive airway pressure (CPAP) in patients with and without CHF causes an immediate and sustained increase in nocturnal baroreflex sensitivity, and a decrease in the baroreflex set point in association with reductions in blood pressure (76–78). These findings suggest that baroreflex sensitivity is depressed and its set point increased in patients with OSA, and that these abnormalities are at least partly reversible by treatment of OSA. Heart rate varies over time within different frequency bands, and is influenced by autonomic activity. HRV can therefore be used to evaluate vagal and sympathetic influences on the sinus node, and is also a marker of cardiovascular health (79). Under normal conditions, the main influence on HRV is respiration such that heart rate rises during inspiration and falls during expiration (i.e., RSA). Variability of R-wave to R-wave (i.e., R-R) intervals at the respiratory frequency (0.15–0.5 Hz) is also referred to as high-frequency HRV and is predominantly under the influence of vagal modulation. Heart rate also varies at low (0.05–0.15 Hz) and very low ( 30 and 338 patients with a RDI < 5 per hour of sleep]. They found individuals with severe OSA had two- to fourfold higher odds of complex arrhythmias than those without OSA even after adjustment for potential confounders. This would suggest there is a relationship between OSA and cardiac arrhythmias, but the exact relationship still remains to be elucidated. Various bradyarrhythmias and tachyarrhythmias have been described in patients with OSA. Bradyarrhythmias The prevalence of nocturnal bradyarrhythmias in OSA patients have been reported to be anywhere from 5% to over 50% (106,107,110). Early observational studies reported a high prevalence of bradyarrhythmias in patients with OSA, and concluded that there was evidence of a causal relationship between OSA and bradyarrhythmias (106,107,110). More recent prospective and epidemiological studies that have included control groups have found the prevalence to be lower, although there are discrepancies in the findings (108,109,111,112). Earlier studies included subjects with the most severe OSA without mention of their cardiac status (i.e., coronary artery disease, depressed left ventricular systolic function, or CHF). Flemons et al. (108) conducted the first prospective study that included a control group. They studied the prevalence of arrhythmias in a consecutive series of patients referred for assessment of clinically suspected OSA, but without evidence of serious cardiac or respiratory comorbidity. OSA was present in 76 of 173 patients studied, with a median of 33 apneas and hypopneas per hour of sleep [apnea-hypopnea index (AHI)]. They found no statistically significant difference in the prevalence of bradyarrhythmias on Holter monitoring [specifically, second degree atrioventricular (AV) block and sinus arrest] between patients with and without OSA. Mehra et al. (109) reported similar findings. Roche et al. (111), however, found nocturnal sinus pauses and bradycardia to be more prevalent in patients with than in those without OSA (p < 0.02 and p < 0.01, respectively). Furthermore, these bradyarrhythmias were more frequent in patients with an AHI > 30. Bradyarrhythmias observed in patients with OSA include sinus bradycardia, sinus arrest, and first, second, or third degree AV block (106,107,110,111). An example of second degree heart block occurring during an obstructive apnea is shown in

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FIGURE 1 Polysomnographic recording demonstrating development of second-degree atrioventricular heart block in the electrocardiogram (EKG) channel during an obstructive apnea. Abbreviations: EEG, electroencephalogram; EMGsm, submental electromyogram; EOG, electro-oculogram; REM, rapid eye movement; SaO2, arterial oxyhemoglobin saturation; VT, tidal volume.

Figure 1. The main mechanism appears to be apnea-related hypoxemia that stimulates parasympathetic input to the heart, rather than any intrinsic abnormality of the cardiac conduction system (51,113). For instance, Zwillich et al. (114) found the degree of apnea-associated bradycardia was related to the degree of apnea-related hypoxemia, and that oxygen administration attenuated the fall in heart rate. Koehler et al. (115) also found that bradyarrhythmias were related to apnea-induced hypoxemia, but they did not find a strong correlation between bradyarrhythmias and the degree of hypoxemia. They reported that in 60% of apnea-related bradyarrhythmic episodes oxygen saturation was severe, falling below 72%. However, in the remaining OSA patients, bradyarrhythmias occurred with only mild hypoxemia (i.e., oxygen saturation fell 5% or less). It is possible that in some OSA patients with increased peripheral chemoreceptor sensitivity, even mild oxygen desaturation can provoke bradycardia (116), but this possibility remains to be examined. Tilkian et al. (106) also found that bradyarrhythmias in OSA patients were corrected with administration of atropine, and resolved following abolition of OSA by tracheostomy. Furthermore, others have shown that treatment of OSA by nasal CPAP/bilevel airway pressure (114,117,118), either markedly attenuates or completely abolishes the bradyarrhythmias. These observations established a cause-effect relationship, and implicated hypoxemia-induced parasympathetic stimulation of the heart in the pathogenesis of such bradyarrhythmias (51,106,113). Thus, in patients found to have nocturnal bradycardia in association with OSA, it may be prudent to treat the OSA before proceeding to pacemaker therapy. Ventricular Arrhythmias Studies on prevalence of ventricular arrhythmias in OSA patients are difficult to interpret. Early studies in patients with OSA did not distinguish between those who have and those who did not have decreased LV systolic function (LVSF). Ventricular arrhythmias are more common in patients with depressed than in those with normal LVSF in the absence of other risk factors. Furthermore, premature ventricular beats in the context of a structurally normal heart with no clinical symptoms are not uncommon, and are generally benign (119).

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VENTRICULAR ARRHYTHMIAS IN OBSTRUCTIVE SLEEP APNEA PATIENTS WITH NORMAL LEFT VENTRICULAR SYSTOLIC FUNCTION Three studies have examined the prevalence of ventricular arrhythmias in OSA patients with normal LVSF (108,109,120). The ventricular arrhythmias evaluated in these studies included isolated ventricular premature beats occurring at a rate of > 30/hour, grouped premature ventricular beats, and nonsustained ventricular tachycardia defined as six or more consecutive ventricular ectopic complexes at a rate greater than 120 beats/minute and lasting less than 30 seconds. Flemons et al. (108) found that the prevalence of couplets and ventricular tachycardia in patients with OSA was no higher than in patients without OSA (1.3% vs. 4.1%, respectively). The prevalence of ventricular premature beats (> 30/hr) was similarly low (2.6% vs. 6.2%). Aydin et al. (120) also found no significant difference in the prevalence of ventricular premature beats, couplets, or nonsustained ventricular tachycardia between 36 OSA patients with normal LVSF and age- and BMI-matched controls. However, a limitation of these studies is that their small samples sizes may not have provided sufficient statistical power to detect significant differences between the OSA and control groups. In a much larger sub-study from the Sleep Heart Health Study, Mehra et al. (109) compared the prevalence of ventricular arrhythmias in 228 subjects with OSA (AHI > 30) and 338 subjects without OSA (AHI < 5) matched for age, sex, race/ethnicity, and BMI. They found that the prevalence of ventricular premature beats at a rate of > 5/hr in OSA patients was 35.1% compared to 21.3% in patients without OSA (p = 0.0003). For complex ventricular ectopy (includes bigeminy, trigeminy, quadrigeminy, or nonsustained ventricular tachycardia), the prevalence was 25% in patients with OSA compared to 14.5% in patients without OSA (p = 0.002). When corrected for age, race, triglycerides, cholesterol, and heart failure, this difference still held. In addition, the odds ratio (OR) for nonsustained ventricular tachycardia was 3.4 and for complex ventricular ectopy was 1.74 times that of the control group. In view of inconsistencies between this study and the other two described above, controversy remains as to whether OSA is an independent risk factor for ventricular arrhythmias in patients with normal LVSF. Gami et al. (121) made the interesting observation that in patients with OSA the relative risk of sudden death between midnight to 6 a.m. was 2.57 compared to the general population, whose peak risk for sudden cardiac death was from 6 a.m. to noon. This suggested that obstructive apneas during sleep contributed to these sudden deaths. However, the study made the assumption that sudden deaths were due to ventricular arrhythmias in the absence of documentation by electrocardiographic monitoring. Therefore, these investigators did not demonstrate the cause of patients’ sudden death or whether these patients had cardiac arrhythmias prior to their deaths. Furthermore, although it demonstrated a higher risk of sudden death at night in those with OSA than in those without OSA, the study did not address whether OSA was associated with an overall increased risk of sudden cardiac death. VENTRICULAR ARRHYTHMIAS IN OBSTRUCTIVE SLEEP APNEA PATIENTS WITH DEPRESSED LEFT VENTRICULAR SYSTOLIC FUNCTION Ventricular arrhythmias are more common in patients with depressed than with normal LVSF, and their presence in such patients predicts an increased risk of

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FIGURE 2 Polysomnographic recordings of a patient with congestive heart failure (CHF) and obstructive sleep apnea (OSA). The upper panel shows frequent ventricular premature beats occurring during obstructive apneas prior to institution of treatment with continuous positive airway pressure (CPAP). The lower panel shows a polysomnographic recording of the same patient, one month later while on CPAP. Note that elimination of OSA by CPAP is accompanied by suppression of ventricular premature beats. Abbreviations: ECG, electrocardiogram; EEG, electroencephalogram; EOG, electro-oculogram; SaO2, arterial oxyhemoglobin saturation; VT, tidal volume. Reproduced by permission from Thorax (125).

sudden cardiac death (122,123). Two studies suggest that OSA may promote ventricular arrhythmias in patients with impaired LVSF. Fichter et al. (124) studied 38 patients with depressed LVSF and life-threatening ventricular arrhythmias treated with an implantable cardioverter-defibrillator. They found a higher prevalence of ventricular arrhythmias in those patients with than in those without sleep-disordered breathing. Ryan et al. (125) examined the effects of CPAP on the frequency of ventricular premature beats in 18 patients with CHF and OSA in a randomized controlled clinical trial lasting one month. Compared to the 10 subjects in the control group not receiving CPAP, the frequency of ventricular premature beats decreased by 58% ( p = 0.011) in the eight subjects randomized to CPAP. An example of suppression of ventricular premature beats by CPAP in a patient with CHF and OSA is shown in Figure 2. This improvement occurred in association with a reduction in sympathetic nervous system activity and an improvement in LV ejection fraction (LVEF). These studies suggest that OSA may provoke ventricular ectopy that is at least partially reversible by treatment of OSA. Ventricular arrhythmias can arise from re-entry, triggered automaticity, and enhanced automaticity. OSA may therefore increase the risk for ventricular arrhythmias by initiating one of these mechanisms. Triggered automaticity refers to pacemaker activity due to a stimulated action potential that may arise in OSA

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patients due to enhanced sympathetic nervous system activity with apnea-related hypoxemia and arousal (126). Increased sympathetic outflow to the ventricles can trigger ventricular premature beats (127). Abnormal automaticity involves spontaneous cardiac impulse formation and may occur in OSA due to hypoxemia and respiratory acidosis that accompany respiratory events. For example, as discussed earlier in this chapter, the frequency of ventricular premature beats decreases during NREM sleep paralleling the decline in sympathetic outflow and the increase in vagal outflow to the heart. In patients with OSA, the opposite occurs: sympathetic tone increases and a vagal tone decreases. The overall effect on the heart is an increase in impulse conduction velocity, a shortened myocardial refractory period, an increase in amplitude of after potentials, and an increase in spontaneous depolarization of action potentials, all predisposing the myocardium to ventricular arrhythmias. Zhou et al. (128) demonstrated in dogs that during acute myocardial ischemia hypothalamic stimulation (which increases sympathetic outflow to the heart) increases the incidence of ventricular arrhythmias that can be inhibited by peroneal nerve stimulation, which increases parasympathetic outflow to the heart. Taken together, these studies suggest that OSA can provoke ventricular arrhythmias and implicate augmented central sympathetic outflow in the causative pathway. Atrial Fibrillation A number of studies have examined the potential relationship between sleepdisordered breathing and atrial fibrillation (AF). However, because of different patient populations, study designs, and classifications of sleep-disordered breathing, it is difficult to determine the extent to which sleep-disordered breathing is associated with AF. For example, cross-sectional epidemiological data from the Sleep Heart Health Study population found 4.8% (n = 228) of patients with sleepdisordered breathing compared to 0.9% (n = 338) of patients without sleep-disordered breathing had atrial fibrillation (p = 0.003). The unadjusted odds ratio for atrial fibrillation was 5.66. When adjusted for age, sex, BMI, and coronary artery disease, the odds ratio was still significant at 4.02 (1.03–15.74). However, a clear distinction between CSA and OSA was not made, so it is difficult to know whether the increased odds for AF was a function of central, obstructive or both types of sleep apnea. In a nonrandomized observational study, Kanagala et al. (129) found that among patients with AF who underwent electrical cardioversion, those whose sleep-disordered breathing was not adequately treated had double the risk for recurrence of AF within one year compared to those whose sleep-disordered breathing was adequately treated with CPAP (82% vs. 42%). These data further suggest an association between sleep-disordered breathing and AF. Again, however, it is not clear that a careful differentiation between central and OSA was made. Figure 3 shows a clear conversion from sinus rhythm to AF after a long obstructive apnea accompanied by severe oxygen desaturation. In another study, Gami et al. (130) examined the prevalence of undiagnosed sleep-disordered breathing in 151 consecutive patients undergoing electrocardioversion for AF and 312 consecutive patients without past or current AF referred to a general cardiology practice. Patients in each group had similar ages, gender distributions, BMIs, and rates of diabetes, hypertension, and CHF. The presence of sleep-disordered breathing was indirectly assessed on the basis of scores from the Berlin questionnaire that was administered to these subjects (the accuracy of

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FIGURE 3 Thirty-second polysomnographic recording of a patient in sinus rhythm who converts to atrial fibrillation after a prolonged obstructive apnea accompanied by severe oxygen desaturation. Note: C3, left central electrode; O2, right occipital electrode; A1 and A2, left and right auricular (reference) electrodes. Abbreviations: EKG, electrocardiogram; SaO2, arterial oxyhemoglobin saturation.

the questionnaire in this population was validated by performing complete overnight polysomnography in a small subset of 44 patients). They found that 49% of patients with AF had presumed sleep-disordered breathing compared to 32% of patients in the general cardiology clinic (p = 0.0004). However, since the diagnosis of sleep-disordered breathing in most subjects was made on the basis of a questionnaire rather than polysomnography, these data can only be considered as suggestive of a relationship between AF and sleep-disordered breathing. Moreover, it is not clear whether a distinction was made between CSA and OSA. No cases of CSA were reported, yet some of these patients had CHF in which there is a high prevalence of CSA. This lack of mention of the presence of CSA in this population therefore suggests that CSA and OSA were not clearly distinguished, such that it is difficult to determine whether the relationship between sleep-disordered breathing and AF was related mainly to OSA or to CSA. In contrast to Gami et al., Porthan et al. (131) found that the prevalence of sleep-disordered breathing in patients with lone AF was no higher than in control subjects without AF. Leung et al. (134) examined the prevalence of lone AF in 60 patients with CSA and normal LVSF (i.e., idiopathic CSA) to that in 60 patients with OSA and 60 subjects without sleep apnea matched for age, sex, and BMI. The prevalence of AF among patients with idiopathic CSA was significantly higher than in patients with OSA or

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those with no sleep apnea (27%, 1.7%, and 3.3%, respectively, p < 0.001). However, the prevalence of AF in patients with OSA did not differ significantly from those with no sleep apnea. Thus, in this study, where care was taken to distinguish between CSA and OSA, the presence of AF was strongly linked to CSA, but not to OSA. Thus, data on the potential relationship between sleep-disordered breathing and AF are inconsistent. In the only study where a distinction between OSA and CSA was made, an association between AF and idiopathic CSA, but not OSA was identified. In other studies, where such a distinction was not made, it is not clear whether AF was associated with OSA or with CSA. Thus further work will be required to determine whether AF is associated with sleep-disordered breathing, and if so, whether this relationship is mainly with CSA or with OSA or both. AF has been reported in association with sleep apnea in patients with CHF. In one study, Javaheri et al. (132) reported a higher prevalence of AF in CHF patients with sleep-disordered breathing, but did not report whether this association was with OSA or with CSA. Sin et al. (133) found a significantly higher prevalence of AF in CHF patients with CSA than in those with OSA or without sleep apnea. Furthermore, multivariate analysis demonstrated a fourfold increase in the odds of having CSA among patients with AF, but no relationship between AF and OSA, consistent with the findings of Leung et al. (134) in patients without CHF. In another study, Mooe et al. (135) observed that the presence of sleep apnea preoperatively predicted occurrence of AF following coronary bypass surgery, but did not distinguish between central or obstructive sleep apnea. Although CSA might play a role in the pathogenesis of AF, it seems more likely that AF plays a role in the pathogenesis of CSA. Because AF leads to decreased pumping efficiency of the heart, cardiac output is lowered, and pulmonary vascular pressures are raised. This can trigger hyperventilation and hypocapnia through stimulation of pulmonary vagal irritant receptors (136–138). Hypocapnia, especially in concert with a lowered cardiac output, predisposes to respiratory control system instability and CSA. On the other hand, OSA may trigger AF through various mechanisms. Increases in sympathetic tone in patients with OSA can contribute to the generation of AF (100). There is also some evidence to suggest that marked increases in cardiac transmural pressures and distension of the atria, similar to those which occur during negative pressure generation during obstructive apneas (139,140), may promote AF via stimulation of stretch-activated atrial ion channels (141). Additionally, OSA is associated with elevated levels of inflammatory mediators, including C-reactive protein (89), which in turn, is directly associated with an increased prevalence of AF (142). However, in the absence of evidence from randomized trials, it remains uncertain whether treating OSA can either prevent AF or in those patients with coexisting AF, reverse it or prevent its recurrence following cardioversion. In summary, there is evidence for a relationship between AF and both OSA and CSA. There appears to be more evidence for a link between CSA and AF, but studies up to this point are difficult to interpret as a whole because of the inconsistencies or lack of classification of the type of sleep-disordered breathing. In the case of CSA, it appears that AF is more likely to predispose to CSA, whereas, in the case of OSA, despite conflicting reports on the relationship between OSA and AF, it appears that OSA might contribute to the development of AF (128,129). Further studies will be required to examine the pathophysiology underlying any potential relationship between AF and OSA, since it may have therapeutic implications.

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CONGESTIVE HEART FAILURE AND SLEEP-DISORDERED BREATHING OSA and CSA occur frequently in patients with CHF. Whereas there is evidence to suggest that OSA can play a role in the development and progression of CHF through mechanisms described above, this remains to be firmly established. On the other hand, it is widely accepted that CHF is a cause of Cheyne-Stokes respiration (CSR) with CSA (CSR-CSA). OBSRUCTIVE SLEEP APNEA AND CONGESTIVE HEART FAILURE Cross-sectional data from 6424 men and women participating in the Sleep Heart Health Study, showed that the presence of OSA (defined as an AHI ≥ 11/hr) was associated with a 2.38-fold increased likelihood of having CHF, independent of several other risk factors (143). The prevalence of OSA among patients with CHF due to LV systolic dysfunction has been reported to be anywhere from 12% to 53% (144–147), and in one report, 35% of patients with diastolic heart failure (175). These differences are probably related to differences in patient populations and background medical therapy for CHF. In patients with CHF and LV systolic dysfunction, risk factors for OSA differ between men and women: the main risk factor in men is obesity, whereas in women it is age greater than 60 years (151). Hypertension is the most common risk factor for LV hypertrophy and CHF (148). Since there is good evidence that OSA is a cause of systemic hypertension (149), it is reasonable to postulate that OSA is also associated with LV hypertrophy and CHF. Among studies that have examined potential relationships between OSA and LV hypertrophy in adults, LV thickness or mass was increased in association with OSA (150–152). However, when differences in BMI and blood pressure were taken into account, the relationship between OSA and increased LV thickness or mass were no longer significant. Thus a clear-cut association between OSA and LV enlargement, independent of other confounding factors, has not been established in adults with normal LV systolic function. In children, however, Amin et al. (153) reported children with normal LV systolic function and OSA had a significantly greater LV mass than those without OSA after controlling for confounding factors. In patients with CHF due to nonischemic dilated cardiomyopathy, Usui et al. (154) found among those who had OSA, the LV was thicker than among those without OSA, and that this thickening affected predominantly the interventricular septum independently of confounding factors. Septal thickness was directly proportional to the AHI. This selective effect on the septum may have been due to its unique exposure to right and left sided stresses during obstructive apneas. Exaggerated negative intrathoracic pressure increases venous return causing distension of the right ventricle with a paradoxical leftward shift and stretching of the septum (155). Apnea-induced hypoxic pulmonary vasoconstriction increases right ventricular afterload. Furthermore, increases in systemic blood pressure induced by OSA exposes the septum to increased LV afterload. Hence, during sleep, the septum is exposed uniquely to the combined effects of increased right ventricular preload and afterload, and increased LV afterload, while the LV posterior wall is only exposed to increased LV afterload. These factors likely combine to make the septum particularly susceptible to any remodeling forces imposed by OSA. This greater degree of LV thickening may put CHF patients with OSA at greater risk for adverse cardiovascular events than those without OSA (156).

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As discussed earlier in this chapter, OSA causes adverse hemodynamic, autonomic, sympathetic, and vascular endothelial effects that may contribute to the development or the progression of LV systolic heart failure. Long-term exposure to elevated sympathetic neural activity can result in hypertension and induce LV hypertrophy and apoptosis of myocytes (157). This LV remodeling can lead to impaired LV systolic function. Indeed, dogs exposed to experimentally-induced OSA for several months develop systemic hypertension, LV hypertrophy and systolic dysfunction (158,159). A number of clinical trials have tested the cardiovascular effects of treating OSA with CPAP in patients with CHF. In a nonrandomized trial involving application of CPAP followed by secondary withdrawal, Malone et al. (160) first reported that treatment of OSA in patients with nonischemic dilated cardiomyopathy over one month, led to an increase in LVEF that was accompanied by an improvement in New York Heart Association functional class. These improvements reversed upon withdrawal of CPAP within one week. Subsequently, Kaneko et al. (161) randomized 24 patients with CHF and OSA (LVEF ≤ 45%, AHI ≥ 20/hr of sleep) to either a control group, who received optimal medical therapy for CHF, or a treatment group, who in addition received CPAP at night. Over one month, CPAP alleviated OSA in association with a 9% increase in LVEF (from 25 ± 3 to 34 ± 2%, p < 0.001) and reductions in daytime systolic blood pressure (from 126 ± 6 to 116 ± 5 mmHg, p = 0.02) that were more pronounced than in the control group. These effects did not differ between patients with ischemic and nonischemic causes of CHF. The same group also demonstrated that CPAP reduced daytime blood pressure, in association with a reduction in muscle sympathetic vasoconstrictor nerve traffic (162), and reduced the frequency of nocturnal ventricular premature beats (163). In the largest randomized trial to date, Mansfield et al (164) tested the effects of CPAP in 40 patients with CHF and OSA over three months. Subjects had milder LV dysfunction and OSA (LVEF ≤ 55%, AHI ≥ 5/hr of sleep) compared to subjects in the study by Kaneko et al. (161), but had to have a complaint of daytime sleepiness to be enrolled. In CPAP-treated patients LVEF increased (from 38 ± 3 to 43 ± 0%, p = 0.04), and nocturnal urinary norepinephrine levels decreased (p = 0.036). This was accompanied by significant improvement in quality of life assessed by the Medical Outcomes Study Short Form-36 (SF-36), and a significant reduction in self-reported sleepiness. However, no change in blood pressure was observed. The smaller improvement in LVEF in this study than in the study by Kaneko et al. (161), without a reduction in blood pressure, was probably due to milder LV dysfunction and OSA. Further trials will be required to determine whether these generally beneficial effects of CPAP will translate into long-term improvements in cardiovascular outcomes, such as morbidity and mortality. At present, the only clear-cut indication for treating OSA in patients with CHF would be a complaint of daytime sleepiness. CENTRAL SLEEP APNEA AND CONGESTIVE HEART FAILURE The prevalence of CSA in patients with CHF with LV systolic dysfunction has been reported to vary widely from 15% to 50% (165–168), and in one report of patients with diastolic heart failure, 20% (169). In patients with CHF, risk factors for CSA include male sex, hypocapnia (PCO2 < 38 mmHg), AF, age greater than 60 years and elevated pulmonary capillary wedge pressure (170–172). However, cardiac output and lung to chemoreceptor circulation time have been reported not to differ between CHF patients with and without CSR-CSA (172–174).

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In patients with CHF, CSA is characterized by cyclic alterations between central apneas and hypopneas, and hyperventilation with a waxing and waning pattern of tidal volume (i.e., CSR-CSA). Central apneas are triggered by hyperventilation (often precipitated by an arousal from sleep) and a fall in PCO2 below the apnea threshold (174) causing a temporary inhibition of motor output to the respiratory muscles, and hence, cessation of airflow. Ventilation resumes once PCO2 rises above the apnea threshold. Raising PCO2 through inhalation of CO2 abolishes CSR-CSA (175). In CHF, CSA arises from respiratory control system instability. This is a consequence of chronic hypocapnia in which PCO2 is closer to the apnea threshold than normal (176). Under these conditions, only a slight augmentation in ventilation, such as occurs during arousals from sleep, is sufficient to drive PCO2 below the apnea threshold. Chronic hypocapnia is related to elevated LV end-diastolic volumes, to pulmonary congestion that may provoke hyperventilation through stimulation of pulmonary vagal irritant receptors, and to increases in central and peripheral chemosensitivity (171,172,177–179). There is also evidence that CHF itself can cause augmentation of peripheral chemoresponsiveness (180) and leptin may heighten CO2 sensitivity in patients with CHF (181). This is an intriguing observation that will need to be studied further. In the presence of augmented chemosensitivity, the ventilatory response to the fall in SaO2 and increase in PaCO2 at apnea termination is exaggerated, especially if an arousal occurs, causing ventilatory overshoot. This perpetuates CSR-CSA by causing a fall in PCO2 below threshold, followed by cyclic alternation of hyperventilation and apneas. Although low cardiac output and prolonged circulation time appear not to be involved in the pathogenesis of central apneas, they do influence the characteristics of the CSR-CSA cycle: the lower the cardiac output and the longer the lung to chemoreceptor circulation time, the longer the duration of the hyperventilatory phase of the CSR-CSA cycle (182). Compared to patients with CSA but without CHF, those with CHF have a longer hyperpnea and periodic breathing cycle related to lower cardiac output (182). However, apnea duration does not differ between patients with and without CHF, and is not related to cardiac output. Thus, CSR-CSA is a form of periodic breathing with a prolonged cycle duration due to reduced cardiac output. Like OSA, CSA causes intermittent nocturnal hypoxia, surges in sympathetic nervous system activity and blood pressure (183). In contrast to OSA, however, CSA does not cause generation of exaggerated negative intrathoracic pressure (174). Consequently, the impact of CSA on LV preload and afterload is less than in OSA. Sympathetic activity is higher during sleep and wakefulness in CHF patients with CSA than in those without CSA (184–186). CSR-CSA can also facilitate ventricular ectopy, probably through surges in sympathetic activity, that is attenuated by alleviation of CSR-CSA (187). Some studies have reported that the presence of CSR-CSA in patients with CHF confers an increased risk of death and cardiac transplantation independently of known risk factors (188–190), while others have not (144,191). Thus, controversy remains about the prognostic significance of CSR-CSA in CHF. Advances in medical therapy for CHF might also alter the prognostic significance of CSR-CSA over time (213). If CSR-CSA has adverse effects on outcomes in patients with CHF, then theoretically its treatment might improve cardiovascular function and prognosis. Unfortunately, there are no published guidelines on treatment for CSR-CSA in

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patients with CHF. Nevertheless, since CSR-CSA is largely a consequence of CHF, first-line therapy should be optimization of CHF treatment. Case series suggest intensification of pharmacological therapy for CHF can attenuate CSA (192,193). Similarly, in nonrandomized trials, cardiac resynchronization therapy with a biventricular pacemaker, which improved cardiac function, was accompanied by alleviation of CSA (194,195). Furthermore, heart transplantation can also alleviate CSA in CHF patients (196). However, resynchronization therapy or heart transplantation was not instituted specifically for CSR-CSA, but rather for worsening CHF. Consequently, CSR-CSA cannot be considered an indication for these interventions. Nevertheless, these findings suggest that optimizing CHF therapy can stabilize ventilatory control and attenuate CSR-CSA. Oxygen has been used to treat CSR-CSA. The rationale is that by attenuating oxygen desaturation during central apneas, it should desensitize the peripheral chemoreceptors thereby preventing ventilatory overshoot, thus dampening cyclic ventilation. Small randomized trials of two to four weeks duration have demonstrated that nocturnal supplemental oxygen reduces the AHI by approximately 50% in CHF patients with CSR-CSA (197–199). These studies also showed that supplemental oxygen reduced overnight urinary norepinephrine excretion and increased peak oxygen consumption and ventilatory efficiency, but had no effect on daytime plasma norepinephrine, brain natriuretic peptide, neurocognitive function, sleepiness, or quality of life. None of these studies examined the effects of supplemental oxygen on cardiac function, morbidity, or mortality. Consequently, the evidence does not support the use oxygen for therapy of CSR-CSA in patients with CHF. CPAP has been used to treat CSR-CSA in patients with CHF. The rationale for its use in this setting is twofold. First, by increasing intrathoracic pressure, positive airway pressure inflates the lungs thereby increasing their oxygen reservoir, so that there is less desaturation during apneas. This should dampen ventilatory overshoot. Second, the increase in intrathoracic pressure also reduces LV preload and afterload, thereby reducing LV end-diastolic pressure and augmenting cardiac output in CHF patients with elevated LV filling pressures (171,200,201). Improvements in hemodynamic function should then alleviate CSR-CSA. Single-center randomized trials lasting one to three months have demonstrated that CPAP of 7.5 cmH2O to 12.5 cmH2O alleviates CSR-CSA, increases inspiratory muscle strength and LV ejection fraction, reduces nocturnal and daytime sympathetic nervous system activity, and reduces mitral regurgitation (185, 202–204). In one study, Sin et al. (190) reported that treatment of CSR-CSA by CPAP in patients with CHF was associated with a nonsignificant trend for a reduction in the combined rate of death and cardiac transplantation. However, since these studies were performed prior to the widespread use of beta-blockers, which have had a beneficial impact on mortality in patients with CHF (205), their applicability for patients on these agents is uncertain. The largest trial of CPAP for CSR-CSA, the Canadian Continuous Positive Airway Pressure for Patients with Central Sleep Apnea and Heart Failure Trial (CANPAP) involved 258 patients in 11 centers (206). The primary outcome was the combined rate of death and heart transplantation over a 64-month period. More than 75% of patients in this study were on beta-blockers. CANPAP confirmed that CPAP attenuates CSR-CSA, increases nocturnal oxygen saturation, increases LVEF and reduces plasma norepinephrine levels. It also increased six-minute walking distance. Despite these physiological improvements, however, CPAP had no effect on mortality or heart transplant rate, frequency of hospitalizations, or quality-of-life.

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Therefore, the data do not support its routine use in patients with CSR-CSA and CHF to prolong life. Only one study has compared oxygen and CPAP for therapy of CSR-CSA in CHF patients. Arzt et al. (207) allocated 10 consecutive patients to nocturnal oxygen and the next 16 consecutive patients to CPAP at 8 cmH2O to 10 cmH2O for three months. Both CPAP and oxygen reduced the AHI by two-thirds, but only CPAP improved ventilatory efficiency and LVEF. Neither intervention had any effect on peak oxygen consumption during exercise. These findings indicate that while both oxygen and CPAP attenuate CSR-CSA to a similar degree, positive airway pressure has an additional affect that improves cardiac function more than oxygen. Adaptive servo pressure support ventilation has also been tested for CSR-CSA in patients with CHF. Pepperell et al. (208) demonstrated that it alleviated CSR-CSA over one month, and reduced nocturnal urinary metanephrine and daytime brain natriuretic peptide concentrations. However, it had no effect on LVEF (see online data supplement of reference 208). Clinical outcomes were not assessed. In summary, while oxygen, CPAP, and adaptive servo pressure support ventilation have all been shown to attenuate CSR-CSA, and to cause beneficial effects on various aspects of cardiovascular physiological function, none has been shown to reduce morbidity or mortality. Moreover, since most patients with CHF who have CSR-CSA do not complain of daytime sleepiness, one should not anticipate that interventions for CSR-CSA would reduce sleepiness. Thus, at present, the evidence does not support the use of interventions specifically targeted at CSR-CSA for routine use in patients with CHF. Further trials will be required to determine what, if any, interventions are effective in alleviating CSR-CSA and in improving clinically important outcomes such as morbidity and mortality. CONCLUSIONS In this chapter, we have reviewed the various effects that sleep-related breathing disorders (including both OSA and CSA) may have on the cardiovascular system. Evidence so far suggests that OSA has adverse effects on the cardiovascular system mainly through pathological mechanical stresses and disruption of the normal balance of sympathetic and parasympathetic cardiovascular tone. OSA does this through various mechanisms including generation of negative intrathoracic pressure, intermittent hypoxia, and arousals from sleep. There is also accumulating evidence that OSA facilitates oxidative stress and elaboration of various inflammatory mediators, which have been linked to cardiovascular diseases. Other as yet unidentified mechanisms may also be at play. More definitive studies are needed to further elucidate mechanisms, whereby OSA may contribute to cardiovascular risk. In addition, more definitive randomized trials are required to determine whether treating OSA improves cardiovascular morbidity and mortality in patients with cardiac arrhythmias, or CHF to guide cardiologists in deciding when to suspect OSA and, where present, what the indications are for therapy. Regarding CSA in the setting of CHF, it appears that it augments sympathetic nervous system activity and ventricular ectopy. However, it is not clear whether this breathing disorder continues to increase the risk of mortality in the face of improvements in therapy for CHF. Thus, further studies are required to answer this question. Moreover, much work remains to be done to determine whether interventions that specifically target CSA in patients with CHF will improve morbidity and mortality.

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Hypertension and the Cardiovascular System Rohit Budhiraja Division of Pulmonary and Critical Care Medicine, Southern Arizona VA Healthcare System, Tucson, Arizona, U.S.A.

Stuart F. Quan Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, Arizona, U.S.A.

INTRODUCTION Sleep-disordered breathing (SDB) is a widely prevalent spectrum of disorders and encompasses conditions such as simple snoring, central and obstructive sleep apnea (OSA) and upper airway resistance syndrome (UARS) (see also Chapter 14). OSA is characterized by episodic partial or complete obstruction of the upper airway (UA) resulting in a decrease in volume of (hypopneas) or complete cessation of (apneas) ventilation. These episodes may be associated with autonomic oscillations with alterations in heart rate and systemic and pulmonary arterial blood pressure. These, in turn, may play an integral role in mediating the putative cardiovascular consequences of OSA. OSA has been associated with diverse cardiovascular sequelae, including systemic and pulmonary hypertension (PH), arrhythmias, and stroke (1). Several trials have consistently shown an association between SDB and hypertension, and to a lesser extent, coronary artery disease or stroke. The association between OSA and cardiovascular disorders is independent of the obesity, a frequent characteristic of OSA patients (2). OSA AND HYPERTENSION One of the earliest reports suggesting a link between OSA and hypertension described a rapid decrease in systemic blood pressure in a subject undergoing tracheostomy (3). In recent years, compelling data have established a strong association between OSA and hypertension. While this association can be confounded by numerous variables including obesity, age and physical activity, large epidemiological investigations have controlled for these variables and found OSA to be an independent determinant of elevated blood pressure. The Wisconsin Sleep Cohort Study, a prospective cohort of 709 state employees in Wisconsin, employed a longitudinal design and reported a dose-response relationship between presence of OSA at baseline and the subsequent development of hypertension (4). The odds of developing hypertension over a four to eight-year follow-up period was higher with increasing apnea-hypopnea index (AHI): odds ratios (OR) were 2.03 for AHI = 5– 15/hr and 2.89 for AHI ≥ 15/hr. The association was independent of confounding factors such as age, sex, body mass index (BMI), waist and neck circumference and presence of baseline hypertension. Cross-sectional analyses from another large, prospective community based cohort, the Sleep Heart Health Study, revealed an OR of 1.37 for presence of hypertension in subjects with AHI ≥ 30/hr compared to those with AHI < 1.5/hr (5). Yet another large prospective study of 73,231 U.S. female 323

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nurses aged 40 to 65 year (Nurses’ Health Study) found a higher incidence of hypertension in women with snoring, a frequent symptom of OSA, than in those without, over an eight-year follow-up period (6). The relative risks of hypertension were 1.29 [95% confidence interval (CI): 1.22–1.37] for occasional snorers and 1.55 (95% CI: 1.42–1.70) for frequent snorers. The results from large epidemiologic studies have been duplicated in clinic settings. A cross-sectional analysis of data from 591 patients who were referred for a sleep study and had no history of systemic hypertension, revealed an increased prevalence of hypertension in patients with OSA, and conversely, more severe OSA in patients with hypertension (7). Furthermore, the severity of OSA increased with worsening hypertension: mean AHI were 15.7, 18.9, 27.2, and 30.3 in patients with normal blood pressure and grade 1, grade 2, and grade 3 hypertension, respectively. Similarly, a high prevalence of OSA has been described in patients with drugresistant hypertension (83% of 41 patients studied, mean age 57 years and taking an average of 3.6 different antihypertensive medications daily) (8). Yet another study of patients using regular antihypertensive medications for more than six months found a higher mean AHI among the subjects with poorly controlled hypertension than among those whose hypertension had been optimally controlled (9). An association between OSA and hypertension has been found not only in adults, but also in children. Daytime hypertension was reported as early as 1972 in two nonobese children with OSA and was corrected with tracheostomy (3). The Tucson Children’s Assessment of Sleep Apnea Study, a prospective community study wherein 500 Hispanic and Caucasian children aged 6 to 11 years underwent unattended home polysomnography, found OSA to be an independent predictor of elevated blood pressure in children (10). Similarly, another study found significantly higher diastolic blood pressure during both wakefulness and sleep in children with OSA compared to those with primary snoring (11). The variability in blood pressure during wakefulness and sleep is also increased in children with OSA (12). One study suggested that OSA is not only associated with hypertension in children, but also remodeling and hypertrophy of the left and the right ventricles (LV, RV) (13). The blood pressure usually drops by 10% to 20% compared to the daytime values during sleep in normal people. Absence or blunting of this expected decrease in blood pressure, or “nondipping,” is associated with a higher risk of hypertensive complications (14,15). OSA patients have a higher prevalence of nondipping compared to controls without OSA (16). One study found that dipping correlated positively with the amount of stage 4 sleep and negatively with the wakefulness after sleep onset, and the authors concluded that good sleep quality was associated with dipping (17). Recurrent apneas and arousals in patients with OSA may lead to poor quality sleep and may possibly contribute to nondipping. Trials demonstrating alleviation of hypertension with treatment of OSA provide further evidence for a role for OSA in development of hypertension. A prospective study utilizing automated ambulatory 24-hour blood pressure monitoring reported a lower blood pressure at a nine months follow-up period than at baseline in 40 of 52 patients with OSA started on continuous positive airway pressure (CPAP) therapy (18). Another retrospective study observed an 11.2 mmHg drop in systolic blood pressure and a 5.9 mmHg drop in diastolic blood pressure with an average 12.1 months of CPAP use in hypertensive patients with OSA (19). Regular CPAP use has also been demonstrated to attenuate hypertension in patients with refractory hypertension (poorly controlled hypertension despite daily use of three or more types of antihypertensive medications) (8).

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The effect of CPAP therapy on hypertension has been studied not only in comparison to placebo, but also with subtherapeutic or “sham” CPAP. One such study in 118 patients found a decline in blood pressure with therapeutic, but not subtherapeutic, CPAP; especially in patients with severe OSA or those who were already being treated with antihypertensive medications (20). Another study reported a decline in blood pressure by 10 mmHg with therapeutic, but not subtherapeutic, CPAP in 16 patients with SDB after nine weeks of therapy (21). Notably, the decrement in blood pressure has been shown with diverse modalities for treating OSA, thus precluding any “modality-specific” effects on blood pressure. One study demonstrated a reduction in daytime systolic and diastolic blood pressure and 24-hour diastolic blood pressure after treatment of OSA with mandibular advancement splint for four weeks (22). Another study found an improvement in blood pressure in proportion to the oxygen desaturation time in 65 patients with OSA after surgery (23). Collectively, these findings suggest a direct role for OSA in causation of hypertension, rather than this association being incidental or resulting from a common etiology such as obesity for these two disorders. However, despite a large proportion of data demonstrating an improvement in blood pressure with therapy for OSA, a few studies have reported contrasting results. One such study did not find a reduction in blood pressure or prevalence of hypertension in OSA patients after treatment with CPAP (24). Another study, albeit on normotensive OSA patients, found no decrease in systolic blood pressure and statistically significant but minimal reduction in diastolic blood pressure with CPAP therapy (25). The blood pressure drop was more pronounced in subjects with 4% desaturation frequencies above 20/hr. A multicenter randomized, placebo (sham CPAP)-controlled, parallel-group study of SDB patients with normal average blood pressure did not show a significant effect of CPAP therapy on blood pressure, when used for six weeks (26). Several factors may contribute to the negative results seen in some studies evaluating the effect of OSA treatment on blood pressure (27). These include a lack of a consistent definition for apneas and hypopneas, lack of proper randomization, inadequate blinding, short follow-up duration, and inclusion of both hypertensive and normotensive subjects. The final line of evidence substantiating the causal role of OSA in the development of hypertension comes from animal models. One study showed an acute nighttime blood pressure elevation, and eventually, sustained daytime hypertension, in canines in which OSA was experimentally induced using an occlusion valve attached to the endotracheal tube (28). Another study demonstrated increased systemic blood pressure, LV and RV mass after five weeks of intermittent hypoxia in a mouse model of OSA (29). In summary, the current data strongly support a causal relationship between OSA and hypertension. The studies have spanned diverse geographic areas and ethnic and age groups and have reached a similar conclusion. Accordingly, the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure describes OSA as a cause of hypertension (30). Furthermore, the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents recommends that a sleep history be obtained in children with hypertension to exclude sleep apnea (31). Thus, it is imperative that OSA be considered in patients with hypertension, especially those who are resistant to standard antihypertensive treatment.

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OSA AND ISCHEMIC HEART DISEASE While the data supporting an association between OSA and hypertension are most robust, many investigations suggest a similar association between OSA and ischemic heart disease (IHD). An early study reported an increased relative risk (RR = 2) for angina in men with regular self-reported snoring adjusting for hypertension and BMI (32). Several studies have since described nocturnal ST-segment depressions in patients with OSA (33–35). Recent studies provide further proof of a relationship between OSA and IHD. A cross-sectional analysis of data from the Sleep Heart Health Study demonstrated an increasing prevalence of coronary heart disease as OSA severity worsened (36). In one study, overnight polysomnography was performed in survivors of acute myocardial infarction (MI) and in 53 age-matched controls without evidence of IHD. The MI patients had a higher apnea index (mean = 6.9) compared with the controls (mean = 1.4) (37). Other studies have suggested increased cardiovascular mortality in OSA patients (38,39). Several large longitudinal studies have served to provide evidence supporting a causal relationship between OSA and IHD. One large prospective observational study found a threefold increase in fatal and nonfatal cardiovascular events (MI, stroke or acute coronary insufficiency requiring invasive management) in untreated severe apneics compared to healthy controls during a mean follow-up period of 10.1 years (40). Another prospective study followed 54 patients with both angiographically-determined coronary artery disease and polysomnographicallydiagnosed OSA (AHI ≥ 15) for a median of approximately seven years (41). Only 24% of the subjects treated with CPAP or UA surgery (n = 25) reached one of the predetermined end points—cardiovascular death, acute coronary syndrome, hospitalization for heart failure, or need for coronary revascularization—compared to a majority (58%) of those who declined OSA treatment (n = 29). A prospective study of 182 men with (n = 60) or without (n = 122) OSA, derived from a sleep clinic cohort, found a higher incidence of cardiovascular disease in those with OSA than those without (37% vs. 7%) (42). Furthermore, inadequately treated cases for OSA had more adverse cardiovascular events than the efficiently treated subjects (57% vs. 7%). Two large studies have used snoring as a surrogate for OSA. Analyses from Stockholm Female Coronary Angiographic Study revealed that among subjects with a history of unstable angina or MI, women with snoring had a more pronounced progression of coronary artery luminal narrowing on comparing two coronary angiographies separated by a mean interval of 3.25 years than those without snoring (43). The Nurses Health Study found the age-adjusted relative risks for incidence of cardiovascular disease to be 1.46 (95% CI: 1.23–1.74) for occasional snorers and 2.02 (95% CI: 1.62–2.53) for frequent snorers, compared with nonsnorers (44). In contrast, one small study did not find a difference in death by cardiovascular complications between patients with OSA (4 of 25) and those without OSA (5 of 25) (45). However, it is likely that the study was not powered to detect such a difference. In contrast to the evidence demonstrating improvement of hypertension with OSA treatment, there is less data evaluating the role of OSA therapy in attenuating cardiovascular morbidity. In a retrospective five years follow-up of 71 patients who underwent a tracheostomy for OSA and 127 for whom weight loss had been recommended, the crude cardiovascular mortality rate was 6.3/100 patients/5 yrs in the conservatively treated group versus zero in tracheostomy group (46). More recently, one study followed 168 patients with OSA for a mean of 7.5 years (47).

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Sixty-one patients did not tolerate the CPAP and constituted the untreated group, the rest were treated with CPAP. Deaths from cardiovascular disease were more common in the untreated group than in the CPAP-treated group (14.8% versus 1.9%) during the follow-up period. Additionally, in a previously cited prospective observational cohort study of 1651 men, use of CPAP over an average 10.1 years follow-up reduced the increased incidence of fatal and nonfatal adverse cardiovascular events in apneics to the same rate as nonapneic participants (40). Multiple echocardiographic abnormalities, including left ventricular hypertrophy, left atrial enlargement, right atrial enlargement, and right ventricular hypertrophy have also been described in patients with OSA (48,49). OSA is associated with left ventricular systolic and diastolic dysfunction (50). Furthermore, left ventricular diastolic dysfunction improves with treatment of OSA (48,49). The details of the effect of OSA on left ventricular function are provided elsewhere in this book. In summary, several recent studies support the notion that OSA is a causal factor in the development of IHD. There is less, but nonetheless suggestive, data regarding the beneficial impact of OSA therapy on improvement of cardiovascular morbidity. OSA AND STROKE A high prevalence of SDB including OSA is observed in stroke victims (51–53). However, it is not clear whether SDB is a cause or consequence of cerebrovascular disease. Several studies have demonstrated an increase in the intima-media thickness of the carotid arteries, a recognized marker of atherosclerosis, in patients with OSA (54–57). While one study found increased intima-media thickness in subjects with severe OSA compared to those with mild, or those without, OSA (54), another found the mean and the minimum nocturnal oxygen saturations to be associated with carotid plaque formation (55). Yet another found increased carotid plaques, and serum levels of inflammatory markers including C-reactive protein (CRP), interleukin (IL)-6, and IL-18 in patients with OSA (56). A fourth study found AHI to be an independent predictor of both intima-media thickness of the carotid arteries and the carotid-femoral pulse wave velocity, another indicator of atherosclerosis (57). These associations were independent of other risk factors for atherosclerosis and suggest the role of OSA in causation of cerebrovascular disease. A prospective study found a high incidence of SDB, primarily OSA, in 120 patients with acute stroke by respiratory monitoring commenced within 24 hours of the onset of neurological symptoms (58). This suggests that UA obstruction is common, and is detectable early, in patients with stroke. However, no independent correlation was found between overall stroke severity and the development of UA obstruction. OSA is associated with higher mortality in patients with stroke, and poor functional outcomes in survivors (59,60). Diverse potential mechanisms including reduced cerebral blood flow during respiratory events and associated hypoxemia have been proposed whereby OSA may worsen the outcomes in stroke patients (61). However, whether OSA or central sleep apnea is the predominant SDB type in patients with stroke, and the course of the sleep disorder after the neurological event are still not clear (58,60). Furthermore, there is no evidence currently to suggest whether CPAP therapy can modify outcomes in these patients. The clinical data continue to accrue suggesting an increased risk of stroke in patients with OSA. In the Sleep Heart Health Study, stroke prevalence was higher amongst those with OSA in comparison to participants without OSA (36).

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One prospective study, referred to above, demonstrated a higher incidence of stroke and other cardiovascular disorders, in patients with OSA (40). Another observational cohort study which excluded preexisting cerebrovascular disease, found an increased risk of first-time stroke or death from any cause in OSA patients during a median 3.4-year follow-up period (62). While this study was not designed or powered to investigate the effect of OSA treatment, the increased risk of either outcome persisted despite the administration of various therapies. The authors hypothesized the relatively short follow-up, the incomplete adherence to CPAP therapy, and presence of comorbidities in the population as a possible explanation of this observation. However, the finding does reinforce the need for further evidence before the treatment for OSA can reasonably be recommended to attenuate cerebrovascular morbidity or mortality in those without other reasons to initiate this therapy. Investigators have also assessed the magnetic resonance imaging (MRI) findings in patients with SDB, with variable conclusions. One such analysis from the sleep heart health study demonstrated that arousals, but not respiratory events, were associated with MRI brainstem white matter disease (63). However, another study based on the data again from the Sleep heart health study demonstrated a progression of MRI cerebral white matter disease among those with a Cheyne–Stokes breathing pattern or central sleep apnea, but not obstructive apnea (64). A recent study found SDB diagnosed by overnight pulse oximetry and defined by 3% oxygen desaturation index >5.6 times per hour (highest quartile) during sleep to be associated with presence of silent cerebral infarcts on MRI in 170 individuals with multiple risk factors for cardiovascular disorders, including hypertension, hypercholesterolemia, diabetes, and obesity (65). However, because these subjects did not have polysomnography, the nature of the respiratory events (central vs. obstructive) is not clear. These results are also in contrast to prior, albeit smaller, studies (66,67). In summary, while some data suggest an association between stroke and OSA, whether the association is causal is not clear. Also, the direction of causality, if any, is yet to be determined. Consistent data from large epidemiological and prospective studies will be needed to answer these questions, and to reliably predict the effect of OSA on stroke outcomes. OSA AND PH While several studies have suggested an association between OSA and PH, many of these were either small, methodologically flawed, lacked adjustment for confounders or used a lower pulmonary artery pressure cutoff for defining PH than the currently recommended criteria (68). It has been suggested that PH in OSA may frequently be explained by other concomitant risk factors, such as parenchymal disorders, left heart disease, or obesity (68). Currently, data supporting OSA as a cause of PH are weak and argue against routine evaluation for PH in OSA patients (68). Conversely, routine polysomnographic evaluation for OSA in patients with PH cannot be recommended. However, a patient with PH who has symptoms suggestive of OSA should undergo polysomnography and the sleep disorder and hypoxemia, if present, should be adequately treated. PATHOGENESIS The mechanistic paradigm, whereby OSA may lead to cardiovascular pathology, is yet to be clearly elucidated. However, the current evidence suggests that the presence

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of hypoxemia may be one important factor. In support of this hypothesis is the observation that the severity of nocturnal oxygen desaturation is associated with increased odds of development of hypertension (5,69). Hypoxemia may plausibly lead to increased sympathetic activity, oxidative stress (70), and inflammation (71), release of endothelin (72), endothelial dysfunction and impaired baroreflex sensitivity (73). By virtue of these effects, hypoxemia plays a pivotal role in mediating the changes that lead to hypertension and potentially other cardiovascular disorders. In addition to the potential consequences of hypoxemia, sympathetic activation associated with recurrent arousals, oxidative stress and metabolic dysregulation that are frequently seen in patients with OSA, may further promote pathological changes (Fig. 1). The increase in blood pressure may also be associated with increase in PaCO2 or increased respiratory effort (74). Furthermore, occurrence of cardiac ischemic episodes may be related to oxygen desaturation and to the sympathetic nervous system-related postapneic surges in heart rate (33). Persons with hypertension and OSA have more severe autonomic nervous dysfunction than hypertensives without OSA (75). Sympathetic activation and microarousals at the end of the apnea possibly contribute to the observed increase in blood pressure (76). Sympathetic activity is increased in OSA patients during wakefulness and further increases during sleep (76,77). Other investigations report

Obstructive sleep apnea

Apneas/Hypopneas Increased respiratory effort

Hypoxemia Inflammation

Oxidative stress

Increased sympathetic activity

Recurrent arousals

Endothelial dysfunction

Hypercoagulable state

Vasoconstriction

Cardiovascular consequences FIGURE 1 Possible pathogenetic mechanisms in obstructive sleep apnea that may lead to cardiovascular consequences.

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an increase in catecholamine levels in these patients (78,79). The increased sympathetic activity may lead to vasoconstriction, and thence, to an increase in blood pressure. Studies performed before and after treatment for OSA provide additional data supporting a role for increased sympathetic activity in the pathogenesis of hypertension related to OSA. One study found a decrement in the blood pressure as well as plasma norepinephrine levels in hypertensive OSA patients treated with CPAP (80). Another study showed reduction in muscle sympathetic nerve activity with six months of CPAP therapy in patients with OSA (77). An improvement in autonomic nervous system dysfunction has also been demonstrated with medical or surgical therapy in patients with OSA (81,82). OSA is characterized by oxidative stress, which may be a result of both increased production of free radicals and decreased antioxidant capacity (83). While hypoxia-reoxygenation may be the primary etiology of oxidative stress, sympathetic activation may also plausibly contribute to free radical production (84). There is enhanced release of free oxygen radicals from neutrophils and monocytes of OSA patients which declines with effective CPAP therapy (85,86). Lipid peroxidation is also increased in patients with OSA (87) and reduces with CPAP therapy (88). Several studies suggest oxidative stress to be a risk factor for IHD (89,90). Chronic intermittent hypoxia, as may be expected in OSA, leads to oxidative stress and LV dysfunction in an animal model (91). Oxidative stress in OSA has also been linked to dysfunction of HDL, an antiatherogenic molecule (92). A recent study found an improvement in the endothelial function in OSA patients after injection of vitamin C, an antioxidant, suggesting a direct role of oxidative stress in propagating endothelial dysfunction (93). Systemic vascular inflammation appears to be common in OSA. The levels of CRP, a hepatic acute phase reactant, are increased in OSA (94 –96). Tumor necrosis factor (TNF)-α and IL-6 levels are also elevated (97). The sources of inflammation in OSA are yet to be defined, but may include repetitive hypoxemia and cytokine induction from localized UA inflammation (98). Notably, increased circulating levels of inflammatory markers are significantly associated with IHD (99,100). Endothelial dysfunction has been described in patients with OSA and may constitute an important etiological mechanism linking OSA to cardiovascular disorders (101,102). The sources of the endothelial injury remain elusive but could include hypoxemia and inflammation. The normal vascular endothelium plays a prominent role in maintenance of vascular tone, hemostasis, leukocyte trafficking, transduction of luminal signals to abluminal vascular tissues, production of growth factors, and barrier function (103). Adverse consequences from endothelial dysfunction may accrue from alteration in the production of anticoagulant factors, vasoconstriction, and vascular smooth muscle proliferation. Indeed, patients with OSA have increased procoagulant and decreased anticoagulant activity (104). Endothelium-dependent flow-mediated dilation is lower in subjects with OSA than those without OSA (101,102), and increases with nasal CPAP therapy (101). These patients also have reduced circulating levels of nitric oxide or its derivatives (105,106), and the levels increase with CPAP therapy (106). Conversely, plasma concentrations of asymmetric NG, NG-dimethylarginine, an endogenous inhibitor of endothelial nitric oxide synthase, are increased in OSA and decrease with CPAP therapy (107). Activated factor VIIa, XIIa, soluble P-selectin, and thrombinantithrombin complex are elevated in patients with OSA, indicating a hypercoagulable state (108). Thus, vascular endothelial dysfunction resulting from OSA may be an mediator of cardiovascular pathology, leading to vasoconstriction,

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vascular proliferation, hypercoagulability, thrombosis and eventually, adverse cardiovascular events. A genetic predisposition may also confer an increased risk for development of hypertension and cardiovascular disease in some patients with OSA. A deletion polymorphism of the angiotensin-converting enzyme gene may interact with OSA and significantly increase the risk of hypertension in patients with mild to moderate OSA (109). OSA is also associated with a polymorphism in TNF-α gene. The prevalence of TNF-α (–308A) allele, responsible for overproduction of TNF-α, is higher in patients with OSA compared to normal controls (110). With respect to cerebrovascular disease, cyclical variation in cerebral blood flow occurs with apneas (111,112). In addition to repetitive hypoxemia, oxidative stress and inflammation associated with OSA, this also may be responsible for initiating or propagating cerebrovascular pathology and the occurrence of stroke. OSA may also result in metabolic derangements, which are described in Chapter 19. Metabolic derangements may further contribute to adverse cardiovascular outcomes in OSA patients. CONCLUSIONS The current evidence strongly suggests a role for OSA in development of cardiovascular and cerebral vascular disorders. The strongest association appears to be between OSA and hypertension. Nevertheless, data favoring the role of OSA in causation of IHD and stroke are compelling. However, additional longitudinal trials will be needed to confirm these associations and to establish causality. Furthermore, larger, rigorously designed studies are still needed to understand the effects of OSA therapy on cardiovascular outcomes. REFERENCES 1. Budhiraja R, Quan SF. Sleep-disordered breathing and cardiovascular health. Curr Opin Pulm Med 2005; 11:501–506. 2. Quan SF, Gersh BJ. Cardiovascular consequences of sleep-disordered breathing: past, present and future: report of a workshop from the National Center on Sleep Disorders Research and the National Heart, Lung, and Blood Institute. Circulation 2004; 109:951–957. 3. Coccagna G, Mantovani M, Brignani F, et al. Tracheostomy in hypersomnia with periodic breathing. Bull Physiopathol Respir (Nancy) 1972; 8:1217–1227. 4. Peppard PE, Young T, Palta M, et al. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000; 342:1378–1384. 5. Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. Jama 2000; 283:1829–1836. 6. Hu FB, Willett WC, Colditz GA, et al. Prospective study of snoring and risk of hypertension in women. Am J Epidemiol 1999; 150:806–816. 7. Grote L, Hedner J, Peter JH. Mean blood pressure, pulse pressure and grade of hypertension in untreated hypertensive patients with sleep-related breathing disorder. J Hypertens 2001; 19:683–690. 8. Logan AG, Perlikowski SM, Mente A, et al. High prevalence of unrecognized sleep apnoea in drug-resistant hypertension. J Hypertens 2001; 19:2271–2277. 9. Lavie P, Hoffstein V. Sleep apnea syndrome: a possible contributing factor to resistant. Sleep 2001; 24:721–725. 10. Enright PL, Goodwin JL, Sherrill DL, et al. Blood pressure elevation associated with sleep-related breathing disorder in a community sample of white and Hispanic children: the Tucson Children’s Assessment of Sleep Apnea study. Arch Pediatr Adolesc Med 2003; 157:901–904.

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83. Lavie L. Obstructive sleep apnoea syndrome—an oxidative stress disorder. Sleep Med Rev 2003; 7:35–51. 84. Zhang GX, Kimura S, Nishiyama A, et al. Cardiac oxidative stress in acute and chronic isoproterenol-infused rats. Cardiovasc Res 2005; 65:230–238. 85. Schulz R, Mahmoudi S, Hattar K, et al. Enhanced release of superoxide from polymorphonuclear neutrophils in obstructive sleep apnea. Impact of continuous positive airway pressure therapy. Am J Respir Crit Care Med 2000; 162:566–570. 86. Dyugovskaya L, Lavie P, Lavie L. Increased adhesion molecules expression and production of reactive oxygen species in leukocytes of sleep apnea patients. Am J Respir Crit Care Med 2002; 165:934–939. 87. Lavie L, Vishnevsky A, Lavie P. Evidence for lipid peroxidation in obstructive sleep apnea. Sleep 2004; 27:123–128. 88. Barcelo A, Miralles C, Barbe F, et al. Abnormal lipid peroxidation in patients with sleep apnoea. Eur Respir J 2000; 16:644–647. 89. Schwenke DC. Antioxidants, dietary fat saturation, lipoprotein oxidation and atherogenesis. Nutrition 1996; 12:377–379. 90. Griendling KK, FitzGerald GA. Oxidative stress and cardiovascular injury: Part I: basic mechanisms and in vivo monitoring of ROS. Circulation 2003; 108:1912–1916. 91. Chen L, Einbinder E, Zhang Q, et al. Oxidative stress and left ventricular function with chronic intermittent hypoxia in rats. Am J Respir Crit Care Med 2005; 172:915–920. 92. Tan KC, Chow WS, Lam JC, et al. HDL dysfunction in obstructive sleep apnea. Atherosclerosis 2006; 184:377–382. 93. Grebe M, Eisele HJ, Weissmann N, et al. Antioxidant vitamin C improves endothelial function in obstructive sleep apnea. Am J Respir Crit Care Med 2006; 173(8):897–901. 94. Shamsuzzaman AS, Winnicki M, Lanfranchi P, et al. Elevated C-reactive protein in patients with obstructive sleep apnea. Circulation 2002; 105:2462–2464. 95. Yokoe T, Minoguchi K, Matsuo H, et al. Elevated levels of C-reactive protein and interleukin-6 in patients with obstructive sleep apnea syndrome are decreased by nasal continuous positive airway pressure. Circulation 2003; 107:1129–1134. 96. Guilleminault C, Kirisoglu C, Ohayon MM. C-reactive protein and sleep-disordered breathing. Sleep 2004; 27:1507–1511. 97. Entzian P, Linnemann K, Schlaak M, et al. Obstructive sleep apnea syndrome and circadian rhythms of hormones and cytokines. Am J Respir Crit Care Med 1996; 153:1080–1086. 98. Mills PJ, Dimsdale JE. Sleep apnea: a model for studying cytokines, sleep, and sleep disruption. Brain Behav Immun 2004; 18:298–303. 99. Ridker PM, Hennekens CH, Buring JE, et al. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 2000; 342:836–843. 100. Ferroni P, Basili S, Martini F, et al. Serum metalloproteinase 9 levels in patients with coronary artery disease: a novel marker of inflammation. J Investig Med 2003; 51:295–300. 101. Ip MS, Tse HF, Lam B, et al. Endothelial function in obstructive sleep apnea and response to treatment. Am J Respir Crit Care Med 2004; 169:348–353. 102. Nieto FJ, Herrington DM, Redline S, et al. Sleep apnea and markers of vascular endothelial function in a large community sample of older adults. Am J Respir Crit Care Med 2004; 169:354–360. 103. Budhiraja R, Tuder RM, Hassoun PM. Endothelial dysfunction in pulmonary hypertension. Circulation 2004; 109:159–165. 104. von Kanel R, Dimsdale JE. Hemostatic alterations in patients with obstructive sleep apnea and the implications for cardiovascular disease. Chest 2003; 124:1956–1967. 105. Schulz R, Schmidt D, Blum A, et al. Decreased plasma levels of nitric oxide derivatives in obstructive sleep apnoea: response to CPAP therapy. Thorax 2000; 55:1046–1051. 106. Ip MS, Lam B, Chan LY, et al. Circulating nitric oxide is suppressed in obstructive sleep apnea and is reversed by nasal continuous positive airway pressure. Am J Respir Crit Care Med 2000; 162:2166–2171. 107. Ohike Y, Kozaki K, Iijima K, et al. Amelioration of vascular endothelial dysfunction in obstructive sleep apnea syndrome by nasal continuous positive airway pressure— possible involvement of nitric oxide and asymmetric NG, NG-dimethylarginine. Circ J 2005; 69:221–226.

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Endocrine Function and Glucose Metabolism Katherine Stamatakis Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, U.S.A.

Naresh M. Punjabi Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland, U.S.A.

INTRODUCTION It is well documented that normal endocrine function varies predictably over the 24-hour day and is partly regulated by a genetically determined circadian clock. Extensive research in animals and humans has demonstrated reciprocal interactions between sleep and the endocrine system. Circadian and ultradian oscillations in the sleep–wake cycle can influence the regulation of several endocrine and metabolic axes. Conversely, alterations in endocrine function can influence sleep–wake regulation and/or predispose to specific sleep disorders. The primary objective of this chapter is to review the reciprocal interactions between obstructive sleep apnea (OSA) and the neuroendocrine system with a particular emphasis on the adverse effects of OSA on endocrine function and glucose metabolism. The neuroendocrine system is delicately balanced to optimize response to internal and external cues through a cascade of neuronal and chemical inputs. Chemical communications, from hypothalamic releasing factors to pituitary and other glandular hormones, are autoregulated through interacting feedback loops. The characteristic episodic release of most hormones serves as a basis for their controlled release and serves to prime and reset the cellular response for optimal sensitization. Chronic perturbations that disrupt or distort any of the elements of this balanced system may lead to hormone deficiency, excess or resistance, with ultimate pathologic consequences to affected physiologic systems. Empirical evidence from epidemiological and clinical studies indicates that OSA is associated with a plethora of health-related outcomes (1). Excessive daytime sleepiness, decrements in cognitive function, impaired quality of life, and an increase in motor vehicle accidents are some of the many adverse outcomes attributed to OSA. Observational and experimental data now also associate OSA with vascular consequences including hypertension, cardiovascular disease, and metabolic dysfunction. Although the precise pathogenesis of medical consequences in OSA remains to be fully elucidated, recurrent arousals from sleep and intermittent hypoxemia are two characteristic features of OSA that are consistently implicated in the putative causal pathway. Obstructive apneas and hypopneas often terminate with microarousals that perturb the normal evolution of the sleep cycle and may alter endocrine function. Moreover, episodic hypoxia in OSA provides an additional stimulus that could further augment the endocrine response to sleep fragmentation. Thus, there is good biologic basis to consider aberrancies in endocrine function as yet another set of adverse outcomes that occur during the chronic and insidious 337

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evolution of OSA. In the sections that follow, the scope of endocrine dysfunction in OSA is briefly examined along with possible etiologic mechanisms and the response to treatment. THE SOMATOTROPIC AXIS Under input from higher brain centers, episodic release of growth hormone (GH) from the somatotropic cells of the anterior pituitary is orchestrated by the hypothalamus through two hypophysiotropic hormones—growth hormone-releasing hormone (GHRH) and somatostatin. More recently, ghrelin produced by the hypothalamic arcuate nucleus has also emerged as a key factor alongside GHRH and somatostatin in the complex control of growth hormone secretion (2). The bidirectional relationship between somatotropic function and sleep is evident from the findings that exogenous administration of GHRH and somatostatin analogs (e.g., octreotide) modulate non-rapid eye movement (NREM) sleep and that secretion of growth hormone is, in part, elicited by sleep onset (3–5). In humans, the 24-hour profiles of growth hormone are characterized by pulsatile release which, in men, tends to predominantly occur after sleep onset and in conjunction with slow wave sleep (3). Although the amount of growth hormone released in women in association with NREM sleep is a small fraction of the total growth hormone output over a 24-hour period, the sleep–related increase in growth hormone is still present. Experiments conducted over three decades ago verified the interdependence between sleep and somatotropic function by demonstrating a suppression of growth hormone in sleep deprived subjects (6,7). Given the robust bidirectional relationship between growth hormone secretion and NREM sleep it is not surprising to find diminished somatotropic output in OSA. Studies using frequent blood sampling techniques have shown reduced growth hormone concentrations in OSA in proportion to the amount of reduction in slow wave sleep (8,9). Restoration of normal sleep architecture in OSA patients with continuous positive airway pressure (CPAP) even for a single night is followed by a characteristic increase in growth hormone concentrations particularly during slow wave sleep (8,9). Because of the pulsatility of growth hormone secretion, several investigators have also used single measurements of insulin-like growth factor-1 (IGF-1) to identify somatotropic abnormalities in OSA (10–12). The biologic functions of growth hormone, in particular growth and development, are primarily mediated by IGF-1, a peptide, which is similar to insulin in structure. Circulating levels of IGF-1, which is produced predominantly (~80%) by the liver (13), are determined by ambient concentrations of growth hormone and provide a global measure of overall somatotropic function (14). Indeed, several studies have shown that serum levels of IGF-1 are lower in patients with OSA compared to control subjects and that there is an appropriate increase in IGF-1 levels with CPAP use in adults (8,9,15) or with adenotonsillectomy in children (16). Somatotropic hypoactivity in OSA may also be accompanied by a decrease in peripheral sensitivity to growth hormone as evident by an attenuated IGF-1 response to administration of exogenous growth hormone (11). Somatotropic dysfunction in OSA is likely to be determined by the degree of sleep fragmentation given the close connection between sleep and growth hormone regulation. In fact, awakenings from sleep abolish the GHRH-related secretion of growth hormone in normal subjects whereas reinitiating sleep re-establishes the normal response (17,18). In addition, there is evidence to suggest that hypoxia may also independently modify somatotropic function. Exposure to intermittent hypoxia

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in animal models has been shown to suppress of growth hormone secretion (19–22). Furthermore, indirect evidence from experiments of altitude hypoxia (23,24) and in patients with obstructive lung disease (25) indicates that hypoxia is associated with a decrease in somatotropic activity. However, the effects of altitude hypoxia on somatotropic function (26,27) have not be consistent possibly related to use of single time-point measurements of growth hormone levels in study samples of limited size. Despite the need for further work clarifying intermediate mechanisms, there is sufficient evidence to link OSA-related sleep disruption and intermittent hypoxemia to derangements in somatotropic function. THE CORTICOTROPIC AXIS The hypothalamic-pituitary-adrenal axis is a stress-responsive neuroendocrine system, which is vital in maintaining normal physiologic function and in reestablishing homeostasis after a stressful stimulus. Stress-related afferent input is relayed to the hypothalamus which, in turn, leads to an increase in the production and release of corticotropin-releasing hormone (CRH), vasopressin, and oxytocin—factors that dictate the physiologic response to stress. CRH, vasopressin, and oxytocin secreted from neurons located within the paraventricular nucleus of the hypothalamus and the supraoptic nucleus (28–30), act directly on the anterior pituitary corticotroph cells to increase production and secretion of adrenocorticotropic hormone (ACTH). ACTH released from the anterior pituitary acts on the zona fasciculata cells of the adrenal cortex to stimulate the synthesis and release of cortisol. Regulation of blood pressure, cardiovascular function, carbohydrate metabolism, and immune function are some of the many actions of cortisol, which is regulated by negative feedback inhibition at the level of the paraventricular nucleus and the anterior pituitary. Cortisol levels in the unstressed state display a typical pattern over a 24-hour period with a gradual increase two to three hours after sleep onset. The levels eventually peak within one to two hours after awakening (~9 a.m.) and decrease over the course of the day to a nadir just before sleep onset. As true for other neuroendocrine axes, a bidirectional association also exists between corticotropic function and sleep (31). For example, administration of CRH decreases slow wave and REM sleep and increases the frequency of awakenings (32,33). Conversely, initiation of sleep, and in particular the occurrence of slow wave sleep, coincides with inhibition of corticotropic function (34,35). Additionally, abrupt awakenings are associated with increases in cortisol secretion (36,37) and partial sleep deprivation can elevate the circadian trough of the cortisol cycle (38). Repeated episodes of hypoxemia along with sleep fragmentation in OSA provide compelling reasons to expect a “stress-related” increase in corticotropic activity. Contrary to this expectation, abnormalities in corticotropic function (39) have not been consistently demonstrated. Several clinic-based studies on OSA have failed to identify alterations in cortisol levels (10,40) or in its circadian rhythm (41,42). Furthermore, investigations that have assessed the effects of CPAP have found no treatment associated change in corticotropic function (9,39,43–45). The lack of a measurable effect of OSA could imply that either the acute nocturnal changes associated with apneas or hypopneas are insufficient to trigger a sustained stress response or perhaps that the techniques used to assess corticotropic function (i.e., isolated serum levels) are insensitive in detecting altered function. In fact, Lanfranco et al. have shown that, in response to exogenous CRH administration, obese patients with OSA manifest an exaggerated ACTH response but normal

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cortisol secretion compared to obese control subjects (46). Thus, provocative challenge testing and/or mapping the 24-hour cortisol rhythm may be necessary to identify aberrant function. Given the conflicting and limited amount of empirical evidence, a definitive conclusion regarding the adverse effects of OSA on corticotropic function in patient samples awaits further study. In contrast to the clinical studies of OSA, experimental research has shown that sleep disruption or exposure of hypoxia can exert negative effects on corticotropic function. As noted above, sleep curtailment can increase the circadian nadir and nocturnal awakenings elicit abrupt increases in serum cortisol (36–38). Hypoxia may also modify cortisol section, although the effects may vary as a function of exposure type (acute or chronic) and duration. Studies on the effects of altitude or hypobaric conditions indicate that the physiologic stress of acute or short-term hypoxia can increase cortisol secretion (27,47–52) perhaps in a biphasic pattern that is characterized by an initial fall (53). With acclimatization to high altitude, adaptive mechanisms may counteract the acute effects and normalize corticotropic function. Thus, intermittent hypoxia in OSA is likely to have complex actions on corticotropic activity that will require careful consideration of the circadian pattern as well as any habituation that may result. Characterizing even minor deviations could stand to incur pathophysiological consequences as stress-related hyperactivity in cortisol secretion tends to correlate with central obesity, hypertension, and metabolic dysregulation. THE THYROTROPIC AXIS Synthesis and secretion of thyroxine (T4) and triiodothyronine (T3) by the thyroid gland are regulated by the anterior pituitary hormone. Thyroid-stimulating hormone (TSH) secretion by the anterior pituitary thyrotrophs is determined by a classic negative feedback loop that is characterized by an increase in TSH levels when serum thyroid hormone concentrations fall and a decrease in serum TSH levels when they rise. Both T4 and T3 circulate in plasma bound to proteins and have a wide array of biological effects which include gene transcription, protein biosynthesis, overall energy production and regulation, cardiovascular and respiratory function, and carbohydrate metabolism. Secretion of TSH is under the influence of hypothalamic thyrotropin-releasing hormone (TRH). Although pituitary modulation of thyroid function is, in part, mediated by other hormones and neuropeptides, TSH release is also intimately associated with the sleep–wake cycle. TSH secretion exhibits a pulsatile and circadian pattern (54–56). In normal subjects, the pulsatile pattern in TSH is characterized by six to nine low amplitude pulses during the day (56–58). The circadian rhythm of TSH secretion is characterized by a nocturnal surge that occurs before sleep onset followed by a gradual decline during the sleep period (54). With delayed sleep onset or sleep restriction, the surge in nocturnal TSH secretion is enhanced and protracted suggesting that sleep has an inhibitory effect on TSH secretion (59,60). Furthermore, nocturnal awakenings relieve this inhibition and can increase TSH secretion (61). The effects of abnormal thyroid function and in particular hypothyroidism on risk for OSA are well established. Although limited and conflicting, cross-sectional data suggest that OSA may be more prevalent in patients with hypothyroidism (62,63). Whether the occurrence of OSA is directly caused by decrease in thyroidal hormones or whether it is due to confounding factors (e.g., obesity) that are common in hypothyroidism remains controversial (63). Hypothyroidism leads to widespread

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accumulation of hyaluronic acid in the skin and subcutaneous tissues which gives rise to myxedematous appearance in these patients. Such deposition of mucoproteins in the upper airway causes enlargement of the tongue and the pharyngeal and laryngeal mucous membranes thereby increasing the propensity for upper airway collapse during sleep (64). In addition to these mechanical alterations, there is evidence to suggest that hypothyroidism leads to a decrease in central ventilatory drive (65,66). Thus, patients with hypothyroidism may have increase susceptibility for OSA due to the combined effects of mechanical abnormalities and/or suppressed central respiratory control output. Studies investigating the effects of OSA on thyroid function have used different techniques to assess the state of the hypothalamic-pituitary-thyroidal axis. Because TSH secretion is primarily determined by the levels of circulating thyroid hormone, measuring serum TSH provides a sensitive method for evaluating the functional status of this axis. Cross-sectional surveys of patients from several referral centers have been conducted to determine whether routine thyroid function testing is necessary to identify those with undiagnosed or subclinical hypothyroidism (46,67–73). Consistently, these studies have failed to demonstrate even a modest prevalence of subclinical hypothyroidism in OSA. The lack of a robust association between OSA and serum TSH levels or the TSH response to a TRH stimulation (39,46) has cast doubt on possibility that repetitive arousals and/or hypoxemia in OSA can affect thyroid function. However, studies on the effects of CPAP therapy suggest otherwise. Uncontrolled (39) and controlled (45) data from clinic-based samples show a lowering of TSH values after initiating CPAP therapy and thus argue for the notion that OSA could alter thyroid function. The decrease in TSH with CPAP could be related to normalization of the TSH secretory pattern with restoration of normal sleep architecture and resolution of physiologic stress induced by nocturnal hypoxia. In fact, animal models on the effects of hypoxia have consistently shown a decrease in thyroidal (T4 and T3) hormones (74–81). Although changes in circulating TSH levels have varied across studies possibly due to the degree and duration of hypoxia, experimental data collectively suggest that hypoxia can produce abnormalities of the hypothalamic-pituitary-thyroidal axis. Because many of the clinical studies are fraught with methodological shortcomings including limited sample size, inadequate comparison groups, and differences in the assessment of thyroidal function, routine thyroid function tests for screening do not appear to be warranted until more supporting data for such practice becomes available. Meanwhile, clinicians involved in the care of patients with OSA or those with hypothyroidism should be aware of the potential bidirectional relation between these conditions and obtain thyroid function tests in OSA or refer patients with a thyroidal endocrinopathy for overnight polysomnography based on the suggestive evidence obtained from the clinical history and physical examination. THE PROLACTIN SYSTEM Prolactin is a polypeptide hormone that is synthesized and secreted by lactotrophs of the anterior pituitary. Formerly considered a hormone involved primarily in reproduction and lactogenesis, it is now known that prolactin has pleiotropic effects which also include modulation of the immune response, angiogenesis and regulation of osmotic balance. Release of prolactin from the pituitary is regulated by the hypothalamus which integrates the internal circadian rhythm with environmental stimuli (e.g., stress) and other physiologic signals (e.g., estradiol, glucocorticoids).

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The integrated response determines whether prolactin stimulating or inhibiting factors are released by the hypothalamic regulatory circuit. Prolactin inhibition is mediated by release of factors such as dopamine, somatostatin, and gamma-amino butyric acid, whereas prolactin stimulation is mediated by release of oxytocin, vasoactive intestinal peptide, GHRH, neurotensin, and TRH (82). As with the other neuroendocrine axes reviewed thus far, prolactin secretion is also closely associated with the sleep–wake cycle (83–85). Circulating prolactin levels display a characteristic diurnal pattern with the highest values occurring during sleep and lowest values occurring during wakefulness. Moreover, there is ample evidence indicating that prolactin release increases during the sleep period whether sleep occurs at night or during the day after a night of sleep loss. Superimposed on the sleep-related changes, prolactin secretion also manifests a sleep-independent endogenous circadian pattern that is more prominent in women than men (84). Furthermore, nocturnal awakenings and difficulty with sleep maintenance has been correlated with lower prolactin levels (85,86). Finally, prolactin itself may be important in the regulation of slow wave and REM sleep (87,88). Because normal sleep plays an important role in the regulation of prolactin secretion, it would be expected that patients with OSA manifest functional abnormalities in this axis. Specifically, the sleep-related increase in prolactin secretion should be attenuated due to the repetitive arousals that occur with apneas and hypopneas. However, data relating OSA to serum prolactin levels or its 24-hour secretory pattern are sparse and do not support an independent association between lactotroph function and OSA severity (45,46,89–92). Moreover, studies on the effects of CPAP treatment have provided conflicting results with one study showing no change (45) but another (39) showing a decrease in serum levels after treatment. It was speculated that the decrease in daytime prolactin levels in the latter study are possibly due to restitution of normal sleep leading to the expected increase in nocturnal prolactin and subsequent decrease in daytime levels. However, it is possible that, in addition to the favorable effects on sleep quality, reversal of intermittent hypoxemia with CPAP may also mitigate the untoward effects of OSA on prolactin secretion. Experimental research using animal models shows a complex response in circulating prolactin levels with hypoxic exposure. Acute hypoxic exposure (approximately two hours) decreases (20,93), whereas sustained exposure (24 hours–25 days) increases serum prolactin levels (20). Thus, improvement in sleep quality and nocturnal oxygenation are likely to be responsible for changes in nighttime and daytime profiles of serum prolactin. Clearly, empirical data on the overall impact of OSA, intermediate mechanisms, and treatment effects remain insufficient and thus emphasize the need for continued research to elucidate potential effects of OSA on prolactin secretion. THE GONADOTROPIC AXIS Regulation of gonadotropic function involves coordination of hormones at the level of the hypothalamus and the anterior pituitary. The two gonadotropic hormones released by the pituitary gland, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), are essential for normal sexual function in males and females. In males, LH and FSH regulate testicular hormone secretion (testosterone and estradiol) and spermatogenesis. In females, the pituitary gonadotrophs stimulate production and secretion of ovarian hormones (estradiol and progesterone) and regulate the menstrual cycle. Gonadotropic cells in the anterior pituitary are

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stimulated to secrete LH and FSH by LH-releasing hormone (LHRH), a peptide hormone produced in the hypothalamus. Neural control of LHRH release is centered in the arcuate nucleus region of the hypothalamus, whose activation triggers an immediate release of the hypothalamic releasing factor. Less immediate regulation of the gonadotropic axis occurs through both negative and positive feedback effects of circulating levels of gonadal steroid hormones, which are exerted at both hypothalamic and pituitary levels of control. Episodic release of LHRH at approximately 90-minutes intervals maximizes the release and biopotency of gonadotropins throughout the 24-hour day. Circadian rhythmicity of gonadotropins (i.e., LH and FSH) is exhibited particularly during adolescence, a period which is characterized by large amounts of high frequency pulsatile release of gonadotropins at night. With increasing age into the adolescent years, the pulsatile nature of gonadotropin secretion during the day increases thus reducing the diurnal variability in gonadotropin activity. In young adult men, testosterone is secreted episodically in response to LH (94). Testosterone levels are at a maximum in the early morning and reach a nadir during the evening hours (95). The nocturnal increase in testosterone in men is related to the cycling of NREM and REM sleep and is positively correlated with the latency to first REM episode and the number REM cycles (96). Fragmentation of sleep can disrupt this association and delay the sleep-related rise in testosterone levels (97). Aging further blunts the testosterone rhythm in men as evident by the finding that the 24-hour average testosterone levels are lower in older compared to younger men (98). In women, gonadal function is also affected by sleep and is further modified by the phase of the menstrual cycle. In early parts of the follicular and luteal phases, the frequency of gonadotropin pulses decrease during sleep, whereas the amplitude increases (99). In the latter parts of follicular and luteal phases, gonadotropin dynamics become less dependent on sleep cycle (100). As is the case for most neuroendocrine axes, the association between OSA and gonadotropic axis appears to be bidirectional. Given the well-established male predisposition and the increase in OSA risk after menopause (101), the fundamental question of whether gonadal hormones play an important role in the pathogenesis of OSA has been a topic of extensive research. Although not all of the available studies agree on the mechanistic significance of gonadal hormones in OSA, administration of androgens (i.e., testosterone) has been shown to lead to the development of OSA (102–109). In contrast, estrogen and/or progesterone are believed to be protective from OSA. Epidemiologic data from several large population and community-based studies have shown that the prevalence of OSA increases after menopause and is favorably affected by hormone replacement therapy (110–112). Whether such evidence speaks to the mechanistic role of estrogen, progesterone, or both remains to be delineated further. Exogenous administration of medroxyprogesterone diminishes the frequency of disordered breathing in patients with OSA and obesity hypoventilation syndrome (113–127). Observationally, derangements in gonadotropic function have been demonstrated in OSA. Lower levels of gonadotropic (39) and gonadal hormones (10,128) have been identified in male patients with OSA. However, inconsistencies across studies and, in particular, the specific components of the gonadotropic axis that are altered by OSA exist possibly due to the use of isolated single time-point measurements of gonadotropic function. Fortunately, serial overnight measurements of LH and testosterone have been conducted in several studies that report diminished gonadotropic hormone secretion during sleep in male patients with OSA compared

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to control subjects (129–131). Although limited, there is some data to suggest that women with OSA have lower levels of estradiol and progesterone compared to women without OSA independent of menstrual cycle phase or menopausal status (132). Dysmenorrhea and amenorrhea are also more prevalent in women with OSA compared to women with other sleep disturbances such as insomnia (133). While cross-sectional studies have demonstrated OSA-related derangements in gonadotropic function, disentangling the directionality of association is not readily possible and requires either treatment-based or experimental approaches. Several treatment studies with CPAP have provided corroborating evidence for the causal role of OSA in altering gonadotropic function. Frequently-sampled measurements of LH and testosterone indicate that overnight profiles improve following CPAP therapy (134). Other studies comparing gonadotropic hormones before and after CPAP (10,39,45) or surgical therapy (135) also indicate that gonadotropic function improves with treatment. OSA-related dysfunction of the gonadotropic axis may result from accumulated sleep loss, which has been acutely shown to dampen the amplitude of pulsatile LH release (136), reduce gonadal steroids (137) and delay in nocturnal testosterone rise in men (97). In addition to the adverse effects of sleep disruption and accumulated sleep debt in OSA, three distinct lines of convergent evidence suggest that hypoxia is likely to be an important determinant of altered gonadotropic function in OSA. First, patients with chronic obstructive lung disease show diminished levels of LH and testosterone. The depression in gonadal hormones is related to the severity of hypoxia and improves with supplemental oxygen therapy (25,138,139). Second, decrease in gonadal hormones in humans has also been reported with exposure to high altitude or hypobaric hypoxia. Studies focused on mountaineering (140–144) or hypobaric simulations show a decrease in testosterone levels in men with increasing hypoxia (145). Alterations in progestins and estrogens have also been documented in women at altitude (146,147) and there is some evidence suggesting that low inspired oxygen may have a negative effect on fertility in high altitude populations (148). Finally, the significance of hypoxia in modulating the gonadotropic axis is also seen in a number of animal studies that show suppression of gonadotropic (149) and gonadal hormones (150–152) with hypoxia. Further work is undoubtedly needed to assess the relative impact of hypoxia versus sleep disturbance and further elucidate the mechanisms by which OSA may impact the hypothalamic-pituitary-gonadal axis. GLUCOSE METABOLISM IN OSA Although not considered as a classical neuroendocrine axis, glucose regulation has also been shown to be abnormal in OSA. A growing body of literature implicating OSA as a precursor for vascular morbidity has prompted intense research on the ways in which it may contribute to the development of hypertension and cardiovascular disease. Much attention has been focused recently on the role of OSA in mediating glucose intolerance and insulin resistance. Several comprehensive reviews summarizing the available studies have been published (153,154). Thus, the focus herein will be to provide a brief summary of the available data and highlight the potential mechanisms through which OSA could alter glucose homeostasis. Results from earlier studies on OSA and metabolic dysfunction provided mixed evidence for an independent association between the two conditions. Part of the controversy in identifying an independent association was likely related to the methodological limitations such as the use of small study samples with limited

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statistical power and inadequate consideration for the confounding effects of obesity. Since then, findings from several observational and experimental studies have shown that OSA and its physiologic concomitants—sleep fragmentation and intermittent hypoxia—may have an important role in the pathogenesis of glucose intolerance, insulin resistance, and type 2 diabetes mellitus (153,154). Data from cross-sectional studies that have used self-reports or overnight polysomnography to ascertain breathing abnormalities during sleep show robust associations between OSA and metabolic dysfunction even after controlling for obesity, thereby addressing a major pitfall in earlier reports. While concerns for residual confounding due to obesity remain in most studies that relied on indirect measures such as body mass index and waist circumference, recent work has improved assessment of obesity by also including assessments of visceral adiposity by computerized tomography (155,156). While the exact magnitude of the association between OSA and metabolic dysfunction remains to be fully defined, OSA severity, as assessed by the apneahypopnea index, is independently correlated with the degree of fasting hyperglycemia, glucose intolerance, insulin resistance, and prevalent type 2 diabetes mellitus. Furthermore, indices of nocturnal hypoxemia and sleep fragmentation also independently associate with the degree of metabolic dysfunction. Even as evidence has been building in support of an association, the causal role of OSA in metabolic dysfunction is far from conclusive. Recently, attention has been drawn to the possibility that OSA may not be associated with metabolic dysfunction by two distinct lines of research. First, studies in pediatric samples have been unable to verify an independent association between OSA and abnormalities in glucose homeostasis (157,158). Second, data from the Wisconsin sleep cohort study have shown that while sleep apnea is associated with prevalent diabetes, the association with fouryear incident diabetes was attenuated after adjusting for waist circumference (159). In light of inconclusive evidence, availability of empirical data that suggests an association between OSA and metabolic dysfunction has revived interest in defining whether CPAP has a favorable effect on glucose metabolism (160,161). While well-controlled studies are still lacking, CPAP has been shown to improve insulin sensitivity in patients with OSA (160). The improvement in insulin sensitivity was observed without a concurrent change in body weight and was greater in nonobese than obese patients. In addition, identification and treatment of OSA in patients with type 2 diabetes improves glycemic control (161). Information derived from such studies has implications not only for clinical practice but also for whether, along with obesity, OSA is in the causal pathway to metabolic dysfunction. Without a doubt, well-controlled studies are needed to define whether other factors (e.g., duration of disease, obesity) determine treatment outcomes in OSA and whether CPAP can curtail the eventual onset of metabolic syndrome and type 2 diabetes mellitus. For now, OSA case-identification should be considered in patients with type 2 diabetes mellitus and health care professionals should have a lower threshold for assessing metabolic abnormalities in patients with OSA. If continued research supports the alleged causal link between OSA and metabolic dysfunction, what then are the potential intermediates that contribute to this association? Pioneering work on the effects of sleep loss has shown that normal sleep duration and quality are important for glucose homeostasis (162). Increase in sympathetic activity, derangements in corticotropic function, and aberrancies in systemic inflammation have been documented with sleep loss (163). Hypoxia has also been documented to increase sympathetic activation and promote the release of proinflammatory cytokines including interleukin-6 and tumor necrosis factor-α that

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Thyrotropic (TSH, T4, T3) ? Lactotropic (Prolactin)

?

Corticotropic (ACTH, Cortisol) ?

Obstructive Sleep Apnea –

Gonadotropic (LH, FSH)

–

Somatotropic (GH / IGF-1)

– Glucose metabolism (glucose intolerance insulin resistance)

FIGURE 1 Effects of obstructive sleep apnea on endocrine function and glucose metabolism. (–) indicates an established negative effect whereas (?) indicates lack of conclusive evidence on the relationship between obstructive sleep apnea and a specific endocrine axis. Abbreviations: ACTH, adrenocorticotropic hormone; FSH, follicle-stimulating hormone; GH, growth hormone; IGF-1, insulin like growth factor-1; LH, luteinizing hormone; T3, triiodothyronine; T4, thyroxine; TSH, thyroidstimulating hormone.

can unfavorably influence glucose and insulin homeostasis (154). Although the biologic basis for altered glucose metabolism in OSA remains to be better defined, the potential implications for a causal association are far reaching given the epidemic of type 2 diabetes mellitus in the United States and worldwide. CONCLUSIONS It is now evident that the classical view of OSA as a disorder of excessive daytime sleepiness and neurobehavioral impairment is being challenged by the burgeoning collection of clinical, epidemiological, and experimental data that show much wider health-related implications. OSA is being increasingly highlighted as a precursor for diurnal hypertension and cardiovascular disease. The physiologic stress of nocturnal hypoxia and recurrent arousals associated with OSA is unquestionably a significant trigger for the cascade of pathophysiologic events that are responsible for several neuroendocrine aberrations (Fig. 1) which, in turn, may contribute to the increased medical morbidity and mortality in patients with OSA. REFERENCES 1. Pack AI. Advances in sleep-disordered breathing. Am J Respir Crit Care Med 2006; 173(1):7–15. 2. Kojima M, Hosoda H, Date Y, Nakazato M, Matsuo H, Kangawa K. Ghrelin is a growthhormone-releasing acylated peptide from stomach. Nature 1999; 402(6762):656–660. 3. Van Cauter E, Plat L, Copinschi G. Interrelations between sleep and the somatotropic axis. Sleep 1998; 21(6):553–566. 4. Steiger A, Antonijevic IA, Bohlhalter S, Frieboes RM, Friess E, Murck H. Effects of hormones on sleep. Horm Res 1998; 49(3–4):125–130. 5. Obal F Jr, Krueger JM. GHRH and sleep. Sleep Med Rev 2004; 8(5):367–377. 6. Sassin JF, Parker DC, Mace JW, Gotlin RW, Johnson LC, Rossman LG. Human growth hormone release: relation to slow-wave sleep and sleep-walking cycles. Science 1969; 165(892):513–515. 7. Beck U, Brezinova V, Hunter WM, Oswald I. Plasma growth hormone and slow wave sleep increase after interruption of sleep. J Clin Endocrinol Metab 1975; 40(5):812–815.

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Obesity Mark Eric Dyken Sleep Disorders Center, Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, U.S.A.

Mohsin Ali State University of New York, Upstate Medical Center, Syracuse, New York, U.S.A.

Shekar Raman and Kim E. Eppen University of Iowa Hospitals and Clinics, Iowa City, Iowa, U.S.A.

INTRODUCTION The association between obesity and obstructive sleep apnea (OSA) has long been recognized. As early as 1956, OSA was included as part of the Pickwickian syndrome in Burwell et al. reference to “Joe the Fat Boy,” a rotund and sleepy, heavy snorer depicted in Charles Dickens’ “The Posthumous Papers of the Pickwick Club” (1,2). Because that time, obesity has been found to be one of the strongest risk factors for OSA, as it is reported in up to 70% of apneics (whereas OSA has been documented in 40% of obese subjects) (3–8). Cross-sectional studies have shown that an increase in body weight parallels the risk of developing OSA (9). A prospective, population-based study found that a 10% weight gain resulted in a sixfold increase in the odds of developing OSA, whereas a 10% weight loss led to a 26% decrease in the apnea-hypopnea index (AHI) (10). An 8-year study of 282 subjects in the Wisconsin Sleep Cohort Study showed that increases in AHI were significantly greater in obese compared with nonobese subjects (with an overall mean increase from 2.5 to 5.1 events/hr) (9). In the Cleveland Family Study of 232 subjects, the mean AHI increased from 2.0 ± 1.4 to 6.2 ± 7.9 over a 5-year period. Significant predictors of a higher AHI at follow-up included excessive body weight and central obesity (9). DEFINITION OF OBESITY Obesity can be considered an excessive accumulation of adipose tissue that results in a generalized, and relatively large, increase in total body mass. Nevertheless, the true utility of an obesity definition relates to the correlations that it provides between specific anthropomorphic measures (in association with total body weight, its distribution and composition) and comorbidities such as OSA, hypertension, and vascular disease. The most widely used clinical measure of obesity is the body mass index (BMI; weight in kilograms per square meter of height), as many comorbidities tend to increase with greater BMIs (3–5). In the United States, BMI is used to classify levels of obesity: class I, 30.0–34.9 kg/m2; class II, 35.0–39.9 kg/m2; and class III, ≥ 40 kg/m2 (definitions in children use variable cutoffs points ) (11). Possibly 30% of adults with a BMI > 30 kg/m2, and 50% with a BMI > 40 kg/m2 have OSA (12). 355

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ANATOMY AND PATHOPHYSIOLOGY Evidence suggests that obesity can lead to OSA through fat deposition in and around the upper airways, as weight loss has been shown to decrease upper airway collapsibility in apneics (13,14). This rationalization has been used to explain the relationship between OSA and hypothyroidism (15,16). In one study 7.7% of patients with hypothyroidism had severe OSA compared to only 1.5% of the controls. The association was largely related to obesity and suspected to be the result of reduced upper airway patency and function, secondary to fatty and myxedematous infiltration (15,16). Strategically localized depositions of adipose tissue might promote sleeprelated breathing problems by altering normal upper airway anatomy and function, disrupting the normal relationship between respiratory drive and load compensation, reducing functional residual capacity, and as a result, increasing the body’s demand for oxygen. This predisposition to OSA is suggested by the frequent reports of apnea in patients with general and central (truncal, abdominal, intra-abdominal, or visceral) obesity, and in those with a relatively large neck circumference (7,14,17–24). Upper Airway Anatomy Characteristically, OSA results from anteroposterior airway restriction at the retropalatal (immediately behind the soft palate) and/or retroglossal (base of the tongue) level (25,26) During an apnea, the transmural pressure (pharyngeal luminal pressure minus the pressure of surrounding tissue) causing pharyngeal collapse is referred to as the closing pressure. The closing pressures measured by Schwartz et al. [< −8 cwp (centimeters of water pressure) and > 0 cwp, for nonapneics and severe apneics respectively], suggest that apneics have an anatomy that is intrinsically predisposed to collapse (27,28) This may in part be owing to a relatively small pharyngeal airway (as has been documented in apneics using magnetic resonance imaging (MRI) and computed tomography (CT) scans, acoustic reflection, and endoscopy) (26,28–30). A small pharyngeal lumen is at increased risk for potential obstruction from the greater volumes of fatty tissue that have been documented in the uvula, tongue, soft palate, and lateral parapharyngeal walls of obese patients with OSA(19,30,31–36). In fact, one area that is often overlooked is side-to-side narrowing, caused by the lateral pharyngeal walls, that can lead to obstructions (Figs. 1,2) (30). This type of narrowing, which may be a major cause of apneic events, is poorly responsive to surgical intervention. In addition, compensatory enlargement of muscular structures such as the tongue, soft palate, and lateral pharyngeal walls, may also predispose obese individuals to nocturnal respiratory obstructions (35,30,31–36). Weight loss has been documented to decrease not only the size of parapharyngeal fat pads, but also the musculature of the lateral pharyngeal walls as well (37). Neck Circumference In obese patients, much of the apnea effect may be through deposition of fat in the neck, leading to subsequent narrowing of the pharyngeal airway (14). The total amount of fatty tissue surrounding the upper airways may predispose to OSA when the force of extraluminal adipose becomes greater than the contracting forces of the

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FIGURE 1 (See color insert.) Endoscopic view of upper airway prior to Mueller maneuver. The base of tongue is minimally obstructing the airway and there is some fullness of the lateral pharyngeal walls.The view of the glottis is partially obstructed by the base of tongue and the epiglottis.

dilator muscles responsible for maintaining pharyngeal patency (18,35). Sheldon et al. suggested that fat in the space surrounded by the mandibular rami may increase tissue pressure that results in a narrow, increasingly collapsible, upper airway at greater risk for OSA (19,35). Some studies suggest that a large neck circumference, as a simple estimate of the amount of fatty tissue in and around the upper airway, is a better predictor for OSA than BMI (6,14,20–24). Even after controlling for BMI, MRI techniques show that apneics tend to have relatively greater upper airway adiposity compared to nonapneics (17).

FIGURE 2 (See color insert.) Endoscopic view of the upper airway during Mueller maneuver (attempted inspiration with the mouth and nasal passages closed, causing negative pressure in the airway). There is significant airway constriction in all dimensions. Lateral pharyngeal wall collapse is causing side-to-side narrowing and the base of tongue and epiglottis are reducing the airway in the anteroposterior dimension. This multidimensional collapse is thought to mirror obstruction that occurs during sleep.

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Katz et al.’s (22) measurements (taken at the superior border of the cricothyroid membrane in upright subjects), showed mean neck circumferences in apneics and nonapneics of 43.7 cm and 39.6 cm, respectively. It has also been suggested that in men and women, respective neck circumference measurements greater than 43.0 cm and 41.0 cm, are predictive for polysomnographically diagnosable OSA (14,38). Nevertheless, as Kushida et al. found that a neck circumference greater than 40 cm is predictive for OSA in both men and women (with a sensitivity of 61% and specificity of 93%), many experts consider this measure the marker for concern during routine sleep examinations (23). Central Obesity Investigators have used neck circumference as an estimate of central and upper body obesity (6,39–41). Some researchers qualify central obesity using waist-to-hip circumference and waist circumference to height ratios, as they may be better predictors of comorbidities than BMI (41–44). A strong correlation has been reported between waist and neck circumference in regard to predicting OSA, type 2 diabetes, hypertension, coronary artery disease, and stroke (11,14,45,46). As such, multiple measures of central obesity approximate fat deposits in the neck that narrow the pharyngeal airway (21). AGE The tendency toward developing central obesity in middle age has been reported to increase the risk of OSA up to 14 times (47,48). Kripke et al. showed a 50% prevalence rate of OSA in subjects from 50 to 64 year of age when the BMI was greater than 50 kg/m2 (49). Although the prevalence of OSA steadily increases after middle age (reported in up to 56% of women, and 70% of men, ages 65 to 95 year) an 18-year follow-up study of older adults showed that changes in BMI only weakly associate with changes in AHI. Evidence suggests that the association between obesity and OSA severity is strongest in middle age, and becomes less of a risk factor in individuals over 50 years of age (9,50,51). GENDER Obesity has been associated with OSA in men and women. A BMI greater than 28 kg/m2 has been shown to place both men and women at greater risk for OSA (50). Nevertheless, population-based studies have also shown that men have up to a threefold greater risk for OSA compared to women (52) It has been hypothesized that gender-related differences in the pattern of adipose deposition in and/or around the upper airway may, in part, account for this higher male risk (53,54). Male Predominance Although MRI studies have shown relatively greater volumes of tongue, soft palate, and upper airway soft tissue in men, no significant gender-related differences have been appreciated in the lateral pharyngeal fat pads (a relatively common characteristic of obese apneics) (54). Nevertheless, men generally have a greater amount of abdominal fat compared to women, and the anatomic concomitants of this central, “android,” or male obesity pattern include a large neck circumference (39,43,44,55,56).

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The association of excessive neck adiposity is not as striking in the lower body “gynecoid” type obesity (typically affecting predominately the hips and lower extremities) characteristically found in overweight women (57–59). As such, some experts hypothesize that extraluminal adipose tissue, defined by neck circumference, explains the major gender-related differences regarding obesity and OSA (60). Menopause Nevertheless, the overall female-to-male ratio for OSA of 1:3.3, reduces to 1:1.44, in the post-menopausal population (61). Obesity owing to weight gain from normal aging processes and reduced physical activity has been reported in at least 35% of women aged 40 to 74 year (62–65). It has been suggested that postmenopausal estrogen deficiency predisposes to central adiposity, whereas hormonal replacement therapy can lead to a general increase in weight and total body fat (62,66,67).Bixler et al. found the prevalence of an AHI from 0 to 15 events/hr, for pre- and postmenopausal women to be 3.2% and 9.7% respectively (an AHI > 15 was found in 0.6% and 2.7% respectively) (61). Young et al., using an AHI > 5 to define significance, showed that postmenopausal women were 2.6 times more likely to have OSA compared to premenopausal subjects, and 3.5 times more likely to have an AHI > 15 (62). Many of these polysomnographic findings are independent of BMI. Nevertheless, greater adiposity in the region of the upper airway has been shown with MRI in relatively nonobese subjects with OSA compared with BMI-matched counterparts without OSA (19). As such, it is suspected that the development of a more android (central) obesity pattern, and its relation to a larger neck and/or greater volumes of adiposity in and around the upper airways explains, in large part, the relative increased prevalence for OSA in postmenopausal women (61,62). Similar rationale has been considered when explaining the strong association of OSA with polycystic ovary disease, as associated androgen excess also predisposes to a more central type of obesity (68). Pregnancy Finally, the high prevalence of snoring and choking-related awakenings reported during pregnancy (a state associated with a physiologic central obesity body habitus), is suggestive for OSA (69,70). This is supported by polysomnographic evidence that snoring women during the third trimester of pregnancy have greater upper airway resistance, compared to nonsnoring controls (70). GENETICS Given the strong relationship between OSA and central obesity, it is not surprising that the possible genetic concomitants of OSA (including inheritable and genderrelated patterns of fat deposition), were considered as early as 1978 (71). In this regard, compared to controls, an excessive volume of upper airway soft tissue (in the lateral pharyngeal walls and tongue) has been reported in the siblings of apneics (72). Ethnicity Ethnicity issues have allowed a genetic explanation for why some populations suffer obesity-related comorbidities at relatively low BMIs (19,73–76). The BMI

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standards for obesity in the Japanese (25 kg/m2) and Chinese (28 kg/m2), are based on their respective relative risks for hypertension and diabetes (74,75) These findings might be explained by the greater tendency toward central adiposity in some Asian populations (74,75) In contrast, some Pacific Island populations may be relatively protected with greater lean body mass at any given BMI (76,77). Genome Studies Palmer et al. provided the first genome-wide linkage analyses of OSA phenotypes (78,79). Linkage between the AHI and BMI was tested across the autosomal chromosomes in Caucasians and African-Americans. The heritability of AHI in both groups was approx 33%, whereas the heritability of BMI was over 50% (80). For Caucasians, it was suggested that if a susceptibility gene for AHI exists on chromosome 12p, it may mediate its effect through obesity (78). Cause and Effect Considerations Although it is heuristically pleasing when considering obesity as a cause for OSA, recent research also suggests the possibility that a genetic predisposition to OSA might lead to obesity in some cases (81,82). In genetically-prone individuals with OSA, intermittent episodes of hypoxemia, hypercarbia, autonomic instability, and microarousals could lead to a dysfunction of central mechanisms controlling metabolism and appetite, resulting eventually in obesity (83). The synergistic effects of excessive sleepiness and lessened physical activity that is intrinsic to OSA would lower energy expenditure and produce an even greater tendency for weight gain in such circumstances. Genetic studies suggest that apnea can occur from dysfunction of a serotoninrelated gene at Xq24 (84). Serotonin normally stimulates satiety centers in the arcuate nucleus of the hypothalamus, and increases muscular activity in the tongue and upper airway dilators (85,86) A gene aberration resulting in reduced serotonergic activity could promote OSA as a result of reduced upper airway muscular tone, and/or an increase in appetite and obesity. It has also been suggested that apnea-related sleep disruptions can increase hypothalamic serotonin production to the point of depletion, leading to cortisol elevation, low growth hormone levels, and a subsequent craving for carbohydrates (87–89). This response can be blunted with continuous positive airway pressure (CPAP) therapy, but if untreated, could lead to obesity (88,89). In fact, a reduction in central body fat has been documented with growth hormone therapy, and also in apneics using CPAP, even without concomitant changes in BMI (90–92) These findings have allowed Grunstein to consider growth hormone as a potential treatment for some forms of OSA (81,90,92). In addition, it has been shown that the peroxisome proliferator-activated receptor-γ (PPARG) gene on chromosome 3p25 encodes for a protein important in adipocyte differentiation (93). Variants of this gene have been implicated in obesity (93,94). As hypoxia can suppress PPARG gene transcription, it has been suggested that OSA may promote obesity in those with a mutation in PPARG (95). Finally, uncoupling protein-1 and uncoupling protein-2 have been located in mitochondrial proton channels which divert energy from adenosine triphosphate (ATP) synthesis to thermogenesis, to increase energy consumption (96). Polymorphisms in genes encoding for these proteins have been associated with obesity (97) In such polymorphisms, it has been hypothesized that apneic events, possibly

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through relative sleep deprivation, might increase expression of dysfunctional proteins, and lead to obesity (98,99). COMORBIDITIES Central obesity and OSA have both been independently associated with a variety of risk factors for vascular disease that are neatly (albeit oversimplistically and incompletely), summarized in the metabolic syndrome (dysmetabolic syndrome, insulin resistance syndrome, syndrome X) (9,43,100–111). The Metabolic Syndrome In 2002, the National Cholesterol Education Program Adult Treatment Panel III set standards that allowed the diagnosis of the metabolic syndrome to be made when three or more of the following were found to coexist: central obesity, as defined by waist circumference > 102 cm in men, > 88 cm in women; hypertriglyceridemia, 1.69 mmol/L (> 150 mg/dl); low high-density lipoprotein cholesterol < 1.04 mmol/ L (< 40 mg/dl) in men, and < 1.29 mmol/L (< 50 mg/dl) in women; high blood pressure, ≥ 130/85 mmHg; and high fasting glucose, ≥ 6.1 mmol/L (> 100 mg/dl) (112). Nevertheless, the standards recommended for waist circumference were controversial as one size does not necessarily fit all. In 2005, a global consensus statement by the International Diabetes Federation (IDF) indicated that differing genetic/ ethnic correlations between central obesity measurements and a variety of comorbidities demanded a lower cutoff point for waist circumference for certain populations (74,113,114). The IDF considered the measurements from 2002 as appropriate for use in the United States, but recommended new standards for Europeans (> 94 cm for men, > 80 cm for women), South Asians and Chinese (> 90 cm for men, > 80 cm for women), and Japanese (> 85 cm for men, > 90 cm for women) (113). Reaven suggested that a genetic predisposition for central obesity, and a sedentary lifestyle, can lead to insulin resistance, which causes hyperinsulinemia (with impaired glucose tolerance, increased very-low-density lipoprotein secretion, catecholamine excess and renal retention of salt and water), resulting in blood pressure elevation (100). These effects in combination promote atherosclerosis and cardiovascular disease (39,40). Leptin A key element in the metabolic syndrome may be apnea-induced dysfunction of leptin; an adipocyte-derived protein hormone (a product of the ob gene), that normally signals the ventromedial, arcuate, and paraventricular hypothalamic nuclei to regulate appetite and energy expenditure (115). Central obesity, associated with the more metabolically-active brown adipocytes, results in elevated leptin levels (99,101). The effects of OSA could lead to persistently elevated plasma levels of dysfunctional leptin or leptin resistance (101,109,116) Central brown adipose tissue appears critical for the metabolic syndrome as a simple elevation of BMI may not necessarily lead to insulin resistance, increased in sympathetic nervous system activity, elevated blood pressures, or chronic hypertension (99,105,117–120). Normally leptin reduces pancreatic secretion of insulin (121). Leptin resistance is associated with insulin resistance. Insulin resistance is evidenced as a decreased sensitivity of peripheral tissues to the metabolic effects of insulin, with compensatory hyperinsulinemia, and a tendency toward hyperglycemia and type 2 diabetes mellitus,

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a reduction in the satiety-promoting effects of insulin (with an inability to activate arcuate nuclear pathways for satiety) (105,121). In the metabolic syndrome, chronic hyperleptinemia may involve a preservation of leptin’s normal pressor effects on the ventromedial and dorsomedial hypothalamic nuclei (122–125). Subsequent elevation of sympathetic adrenergic activity to the renal system may stimulate the renin-angiotensin system, leading to sodium retention through increased tubular resorption, volume expansion, and hypertension (116,126,127). The renin-angiotensin system has been shown to be activated in obesity, and OSA is associated with high levels of angiotensin II and aldosterone that correlate with blood pressure (128,129). Elevated leptin levels may also negatively affect respiratory control through chemoreflex functions (130). Patients with OSA have been reported to have plasma leptin levels 50% higher than controls, and often report significant weight gain immediately prior to their diagnoses (125,131). Treatment of OSA with CPAP can reduce leptin levels, central obesity, insulin resistance, hyperleptinemia, and hypertension (90,102,132). This supports the hypothesis that a primary leptin problem, resulting in central obesity and OSA, could lead to the metabolic syndrome (a problem nine times more likely in apneics) (126,102,131,133). In addition to leptin, OSA might predispose to elements of the metabolic syndrome through increases in other adipocyte inflammatory mediators (possibly as a result of intermittent hypoxia and/or sleep disruption), such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and adiponectin (104,107,134–137). Elevated serum levels of IL-6 associated with OSA, insulin resistance and type 2 diabetes mellitus have been shown to decrease in apneics after CPAP therapy (135,136). In OSA, there is an elevation of TNF-α (a cytokine mediator) possibly leading to an interference with the normal effects of insulin, and dysregulation of body fat and triglycerides (104,135,138). Finally, high levels of dysfunctional adiponectin (a protein hormone that regulates glucose and lipids, with anti-inflammatory effects on the cellular lining of blood vessel walls) may occur in OSA (137). Hypertension Hypertension can be found in up to 40% of apneics (118-120). Epidemiological studies on the other hand, have indicated that obesity may account for up to 65% and 78% of the respective risks for hypertension in women and men (139,140). The metabolic syndrome suggests that in many cases, central obesity may lead to OSA, hypertension, and a subsequent predisposition for a variety of vascular diseases (48,141). During obstructive apneic events, microneurography, using a tungsten needle placed into the peroneal nerve, has allowed the direct measurement of efferent sympathetic activity from postganglionic unmyelinated C fibers (142,143). In patients with OSA, during polysomnography with simultaneous monitoring of sympathetic nerve activity and blood pressure, peak sympathetic activity increased by 246% during rapid eye movement (REM) sleep, whereas the mean blood pressure increased from 92 mmHg in wakefulness to 127 mmHg in REM (144). CPAP therapy has been shown to result in significant decreases in sympathetic activity and blood pressure in apneics (145,146). The synergism between OSA and central obesity may promote chronic hypertension, as waking sympathetic tone remains elevated in apneics (126,147,148). Additional evidence of autonomic dysfunction suggests that the gain of the baroreflex (key in the beat-to-beat regulation of arterial blood

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pressure) may be reduced in central obesity and OSA (as evidenced by an increase in blood pressure variability), and may also predispose patients to persistent hypertension (149,150). Vascular Disease Central obesity and OSA are both independently associated with cardiovascular and cerebrovascular risk factors exemplified in the metabolic syndrome (43,100,102, 103,105,107,108). In addition, obesity-induced apneas provide for a chronic oxidative stress that could lead to atherosclerosis, heart disease and stroke (39,40,151–155). The multiple hypotheses concerning the mechanism of this dysfunction include hypoxemic-induced blockade of nitric oxide synthase, inactivation of nitric oxide, activation of angiotensin II and thromboxane receptors, increased generation of endothelin-1, and the effects of superoxide anion and hydrogen peroxide on vascular smooth muscle cells (156,157). In addition, the elevation of C-reactive protein in obesity and OSA suggests that activation of systemic inflammation may also predispose to endothelial damage (158,159). Congestive Heart Failure Longitudinal studies have shown that systemic hypertension, through ischemic heart disease, is the most common risk factor for congestive heart failure (CHF) (160). Although central sleep apnea is a classic finding (reported in 33% to 60% of patients with CHF), OSA is also common (161–163). In subjects with CHF, OSA has been reported with an overall prevalence from 11% to 37%, and a greater prevalence in men (38%) than women (31%) (162,163). The main risk factor for OSA in men with CHF has been reported to be obesity, whereas for women it is older age. Stroke In some cases, obesity appears to be a risk factor for ischemic stroke, while an inverse relationship between BMI and hemorrhagic stroke has been suggested (164–168). The Nurses’ Health Study reported a greater than twofold increase in the risk for ischemic stroke in women with a BMI > 32 kg/m2 (compared with a BMI < 21 kg/m2) (166). A weight gain greater than 11 kg also increased the overall risk for stroke. A prospective study of more than 39,000 women also showed that a BMI > 30 kg/m2 significantly increased the risk for ischemic stroke when compared to subjects with a BMI less than 25 kg/m2 (169). In men, a number of studies support an association between BMI and increased risk of stroke (164,165,170). A population-based study found a twofold increase in the risk for total, ischemic, and undetermined stroke for men with a BMI > 30 kg/m2 (compared with a BMI from 20.0 to 22.5 kg/m2) (164). Data from the Physicians’ Health Study also showed that men with a BMI > 30 kg/m2 had a greater than twofold risk increase for all stroke types (166). Our group performed a prospective, controlled polysomnographic study of nonselected, consecutively encountered inpatients with recent stroke, of which 82% of the women and 69% of the men were previously diagnosed with systemic hypertension (155). In the stroke group, OSA was diagnosed in 64% of the women and 77% of the men (no subject had significant central sleep apnea). Although obesity was not a factor in men, the female subjects with stroke had a mean BMI of 32.9 ± 1.7 kg/m2, whereas women controls had a mean BMI of 25.1 ± 1.2 kg/m2. In addition, 54% of our patients suffered their strokes

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while asleep, and in a 4-year follow-up, 80% of the individuals who subsequently died had their original strokes during sleep. As such, we speculate that the chronic comorbidities associated with OSA (including obesity and hypertension), may have predisposed many of these patients to stroke (142). EPIDEMIOLOGY The “global epidemic” of obesity is partially related to the overeating and inactivity promoted by industrialized society (171,172). In highly developed countries, like the USA, obesity is increasingly prevalent, with up to 24.9% of women, and 19.9% of men having a BMI greater than 30 kg/m2 (173,174). Mortality and Morbidity Obesity may be responsible for up to 325,000 deaths per year in the United States (174). This may, in part, be related to the fact that up to 30% of adults with a BMI > 30 kg/m2, and 50% with a BMI > 40 kg/m2, has OSA (12). Comorbidities associated with obesity and OSA, as described in the metabolic syndrome (which affects about 25% of adults), increase the risk of death from coronary artery disease alone by approximately three times (12,175–178). In the pediatric population, obesity also has also been shown to increase cardiovascular risk factors, such as hyperlipidemia, hyperinsulinemia, and early atherosclerosis (179). In a 10-year study of men, mortality from cardiovascular disease was found to increase when the BMI was greater than 30 kg/m2 (180). A BMI of greater than 40 kg/m2 was reported to increase the relative risk of death 2.6 times for men and 2.0 times for women (when compared to a BMI between 23.5 and 24.9 kg/m2). Health Care Costs In the USA the direct and indirect costs of obesity have been estimated at $117 billion a year (181,182). An elevated BMI has been associated with significant lifetime medical care costs for hypertension, hypercholesterolemia, type 2 diabetes mellitus, heart disease, and stroke (180). Cumulative lifetime total healthcare, pharmacy, outpatient and inpatient service costs have been found to be significantly increased in individuals with a BMI greater than 30 kg/m2 (average pharmacy costs of $5000) (183). THERAPY In obese apneics, long-term CPAP use has resulted in a decrease of total body and central adiposity (90,184). The effects of weight loss on the AHI, after diet and surgical interventions for weight reduction, have been reported in many small, uncontrolled, selected populations, (followed over short periods of time), and in prospective, population-based studies (10,185–187). Weight Loss A randomized, controlled study by Smith et al. emphasized the importance of dietinduced weight loss in the treatment of obese apneics (188). Their treatment group experienced a mean weight loss of 9%, with a significant mean reduction in AHI from 55 events/hr to 29 events/hr (47%). The nondiet control group showed an increase in mean weight and AHI. This work was complemented by Schwartz et al.,

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who showed that a 1.5-year period of diet in obese male apneics led to a reduction in the mean BMI, from 42 to 35 kg/m2 (17%), with a significant (60%) decrease in the mean AHI (from 83 to 33 events/hr)(13). Their nondieting control group had a stable mean BMI of 38 kg/m2, with no significant reduction in AHI. Findings from the Wisconsin Sleep Cohort Study of 690 men and women, suggested that neglecting therapies directed toward obesity could lead to further increases in weight and worsened OSA (10). After a four-year period, an increase in the mean weight from 85 to 88 kg resulted in a mean increase in the AHI from 4.1 to 5.5 events/hr. In addition, in subjects with OSA, a 1% increase/decrease in body weight was expected to result in a respective 3% increase/decrease in the AHI. Our Experience We have studied the treatment of OSA and obesity in a group of young people with Down syndrome (189). Although the prevalence of OSA in the general pediatric population is only 0.7%, the tendency for obesity (with an enlarged neck and tongue), in children with Down syndrome may predispose them to OSA and comorbidities such as pulmonary hypertension and cardiac disease (190–192). We polysomnographically evaluated a population of consecutively encountered, nonselected, young patients with Down syndrome and found OSA in 79% of the subjects (189). The Pearson correlation coefficient was computed to examine the association of apnea index (AI) and lowest oxygen saturation (SaO2) value with BMI. In the total population studied, a higher BMI was significantly associated with higher AI (r = 0.62; 95% CI, 0.23-0.84; p = .005) and lower SaO2 level (r = −0.55; 95% CI, −0.13 to −0.80; p = .02). Although treatment of childhood OSA usually involves tonsillectomy and adenoidectomy, in Down syndrome, isolated tonsillectomy is preferred (owing to risks for velopharyngeal insufficiency) (193). CPAP has also been effective and well tolerated in this population, and may lead to a reduction in pulmonary artery pressures (when pulmonary hypertension is a concern) (192,194). And although concomitant focused treatment of obesity (with diet and exercise) is recommended, it has generally been reported as being difficult to successfully implement (195). We will address our multidisciplinary therapeutic approach to weight loss in obese apneics in the following case report. Case Report A 29 year-old young man with Down syndrome, with a history of snoring, excessive daytime sleepiness, and central obesity (weight, 98.2 kg; BMI, 43.6 kg/m2), had an overnight polysomnogram that was diagnostic for OSA, with an AHI of 18.8 events/hr, and an SaO2 low of 79% (with a waking baseline of 95%). The use of CPAP (at 10 cm of water pressure) led to the resolution of all major obstructions, and the SaO2 remained greater than 95%, including during REM sleep, while the patient was lying in the supine position, with the head of bed flat. The University of Iowa Sleep Disorders Program is associated with a specialized Pulmonary Rehabilitation Weight Loss Program (PRP) (196). The patient was enrolled in this program, and in combination with CPAP therapy, he has been able to maintain significant weight loss, to a low of 63.3 kg (Fig. 3). In the PRP, a comprehensive, multidisciplinary assessment was performed by a team of experts in weight reduction, which included physicians, registered nurses, physical therapists, social workers, and dieticians. Our patient’s exercise tolerance

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FIGURE 3 (See color insert.) (A and B) Visual comparisons between photographs taken at the time of the diagnosis of obstructive sleep apnea (left), and at five years postdiagnosis (right), demonstrate the effects of aggressive therapeutic interventions, which included CPAP therapy and a well-coordinated diet and exercise program, easily recognized as a significant reduction in central adiposity.

was first assessed using a physician-supervised treadmill, symptom-limited graded (incremental) exercise test (GXT), with continuous 12-lead electrocardiogram (ECG) and pulse-oximetry monitoring. Manual blood pressure recordings, Rating of Perceived Exertion scores (original Borg Scale, range 6–20: easiest to hardest) and Rating of Symptoms scores (revised Borg Scale, dyspnea and/or other symptoms, range 0-10: . least to most) were obtained after each stage of exercise. His peak oxygen uptake (V o2) was estimated from standardized metabolic prediction equations at

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Obesity TABLE 1 Anthropometric/Physical Measurements of a Patient with Down Syndrome and Obstructive Sleep Apnea During a Successful Weight Loss Program That Required Diet, Exercise, and the Use of CPAP Anthropometric measurements Height (m) Weight (kg) BMI (kg/m2) Neck circumference (cm) Waist circumference: At iliac-crest (cm)

At time of OSA diagnosis

1st session PRP (2 years postOSA diagnosis)

Last session PRP (3 months following 1st session)

5 years postOSA diagnosis follow-up

1.56 98.2 40.4 44.0 110.0

1.56 87.4 35.9 42.5 110.0

1.56 86.8 35.7 40.0 102.5

1.56 64.0 26.0 36.8 93.3

Abbreviations: BMI, body mass index; CPAP, continuous positive airway pressure; OSA, obstructive sleep apnea; PRP, pulmonary rehabilitation weight loss program.

the end of the test (197,198). He then underwent progressive aerobic and upperextremity resistive exercise training (three times per week for eight weeks), and attended 12 educational classes on disease/health self-management, nutrition, and exercise. Short-term (two weeks) program goals included: 40 min of multi-mode (treadmill, Air-Dyne bike, stationary step, and standing arm ergometry) aerobic exercise, three times a week, at an average intensity of 50% of his peak estimated . VO2. Long-term (eight weeks) goals included 40 min of multi-mode aerobic exercise, . three times per week, at an average intensity of 85% of his peak estimated VO2. During exercise, his heart rate was to be maintained at < the peak rate achieved on the initial GXT. His specific exercise regimen included walking the treadmill for 20 minutes, using the Air-Dyne or Nu-Step (recumbent stepping) machines for 10 minutes, stationary stepping (4, 6, or 8 inch-steps, at a metronome-paced cadence) for five minutes, standing arm ergometry for five minutes, and upper extremity resistance exercises (using weights or resistance bands). By the fourth week of the program he began exercising at home for an additional three days per week, following the physical therapist’s written guidelines. One of the primary goals of the program was behavior modification over an eight-week period to promote long-term behavior changes in regard to selfmanagement of the exercise program, healthy eating patterns, and CPAP adherence. In the maintenance program, our patient has been given a level of responsibility where he must continue to weigh himself three times a week and follow written exercise plans (which includes at least 60 minutes of aerobic Exercise, swimming laps and walking on a treadmill, three–five times per week, and a resistance training Exercise three times per week). Five years following the diagnosis of OSA our patient has achieved a sustained reduction in weight, BMI, and neck and waist circumference (Table 1). Subjectively, he reports improvements in independent living and general level of activity compared to when he was heavier. Prior to weight loss, our patient worked intermittently as a bus boy in a pizzeria. He now lives independently and has fulltime employment. In this case, the sound support network provided by the PRP motivated the patient to successfully lose weight and maintain long-term adherence to healthy eating and exercise (196).

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CONCLUSIONS It has been hypothesized that in genetically-prone individuals, an underlying metabolic dysfunction, as exemplified by “leptin resistance,” can be exacerbated by factors like the sedentary lifestyle associated with modern, industrialized society, to subsequently promote a central pattern of obesity (evidenced by a relatively large neck and waist circumference), that is associated with a deposition of upper airway adipose tissue which can predispose to sleep-related apneic events. Central obesity and OSA are both associated with a variety of comorbidities (characterized in the metabolic syndrome), which promote a variety of vascular diseases that are associated with high levels of morbidity and mortality (199–201). Given the present epidemic of obesity and its strong relationship with OSA, the potential health costs are staggering. As such, when addressing the obese apneic, it is paramount that therapeutic interventions include a strong support system that promotes a longterm, well-organized weight loss program that is based on diet and regular exercise, in addition to the other mainstay treatments for OSA.

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Mood and Behavior Mark S. Aloia and Amanda Schurle Bruce Department of Medicine, National Jewish Medical and Research Center, Denver, Colorado, U.S.A.

INTRODUCTION There is a compelling and complex connection between sleep, mood, and behavior. The literature demonstrating the negative effects of sleep abnormalities on functioning has developed rapidly over the past few decades. Sleep disorders such as obstructive sleep apnea (OSA) have the potential to significantly disrupt sleep, which can lead to deficits in a variety of aspects of functioning. The daytime consequences of OSA can include excessive sleepiness, mood problems, cognitive problems, and functional impairments. OSA can also contribute to certain serious physical health problems, like hypertension, increased risk of heart disease, and increased risk of stroke. Not surprisingly, a patient’s quality of life is usually negatively affected. The following chapter will focus on the effects that OSA can have on mood and behavior. Because behavior is a broad and complex term, we will concentrate on the “neurobehavioral” sequelae of OSA, including cognitive impairments in a variety of different domains. We will also briefly mention the effects of OSA on sexual dysfunction and work-related behavior. We will address questions of specificity including which aspects of mood and neurobehavioral function are most affected by OSA. The chapter will also address questions designed to both summarize the existing literature and to theorize about the mechanisms behind the findings and their implications for future research. OSA AND MOOD OSA has the potential to affect a variety of different aspects of a person’s life. Psychological distress is frequently a consequence of OSA. Large scale studies have reported a higher prevalence of psychiatric disorders among sleep apnea patients than in the general population (1). Mood disturbances, including depressive symptoms, are particularly common in sleep apnea patients. Depression and OSA Depressive symptoms are widely recognized as characteristic clinical sequelae of OSA (2–4). In fact, almost three decades of research links depression and OSA (5,6). One of the first studies to examine the relationship between depression and OSA was conducted by Guilleminault et al. (7). These investigators determined that almost one-fourth of their sample of male OSA patients had previously seen a psychiatrist for anxiety or depression-related concerns. Reynolds et al. (8). found that about 40% of their male OSA patients met the research diagnostic criteria for an affective disorder such as depression. Further, they noted that overall, OSA patients rated themselves as being mildly to moderately depressed, with increased 377

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depressive symptoms associated with higher levels of reported daytime sleepiness (8). The connection between depressive symptoms and daytime sleepiness is a finding that has been replicated in several more recent studies. Cormorbidity between depression and OSA was highlighted by a study published in 2005 demonstrating a 20% overlap (6). The association between OSA and mood disturbance is not limited, however, to clinical depression. There is also a relationship between OSA and subclinical depressive symptoms. That is, even if an OSA patient does not meet the criteria for a diagnosis of major depressive disorder, she or he may exhibit meaningful depressive symptomatology (5). Studies examining the relationship in this manner often use continuous measures of depressive symptomatology rather than dichotomized depression groups. Aloia et al. (9) determined that one-third of their sample of OSA patients scored in the mild to severe range of depression as measured by the Beck Depression Inventory—2nd edition (BDI-II). These results were comparable to those reported in at least one other study that used the BDI and determined 36% of their OSA patients reported depression (10). A study using a sample of older adults reported similar findings, whereby mild to severe symptoms of depression were considered common among older adults with OSA (11). Finally, Bardwell et al. (12) concluded that the relatively high levels of depressive symptoms seen in OSA patients need to be taken into consideration when conducting polysomnographic studies. The depression scale on the Minnesota Multiphasic Personality Inventory (MMPI) has frequently been used to assess mood disturbance in OSA patients. Elevations on the depression scale of this self-report measure may or may not be indicative of a major depressive disorder diagnosis. The MMPI can also assess subclinical levels of psychopathology. One early study found that 28% of male OSA patients had elevated scores on the depression scale of the MMPI (7). Studies have because corroborated this finding (13). Platon et al. (14) even demonstrated significant elevations for OSA patients on several additional MMPI scales when compared with normal controls. Aikens and Mendelson compared the MMPI profiles of OSA patients to age- and gender-matched nonapneic snorers and found that although there were similarities in their reports, OSA patients reported more intense depressive symptoms (15). These investigators also reported in another study that the most severe OSA patients demonstrated the greatest psychological distress (16). Not all research has reported an association between depression and OSA, however. Several studies reported no increased prevalence of depression among sleep apnea patients (17–19). Pillar and Lavie (1998) conducted a large-scale investigation assessing over two thousand sleep apnea patients on the Symptom Checklist-90 self-report inventory to examine psychological distress (20). Male patients demonstrated no relation between the severity of apnea and depression or anxiety. Female patients, however, reported more depression and anxiety than male patients overall, and the levels were positively correlated with apnea severity. Another study by Phillips et al. (21) also failed to observe a relationship between depression and OSA. It was a five years observational study using older adults that did not find any significant depressive symptoms in OSA patients when compared with nonapneic controls. Methodological differences among studies, depression measures, and cohorts are likely to play a role in these differential findings. While findings are mixed, the majority of the studies conducted do report a relationship between depression and OSA. Further studies should include longitudinal designs to help better understand the relationship between depression and OSA and the effects of treatment and/or time (6). In addition, it will be

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important for future research in this field to use accurately matched controls; consistent, well-validated measures of depressive symptoms; and large sample sizes including both men and women OSA patients representing a broad age range. This can allow for an examination of the complex relation between depression, OSA, gender, age, and other potentially contributory factors. Consequences of Depression in OSA Depression has the ability to impact OSA patients in a variety of ways. In general, depression can cause problems in a broad range of functional areas including cognition, work, interpersonal relationships, and overall quality of life. OSA is also known to negatively affect quality of life (22–24). A recent study demonstrated a significant positive relationship between quality of life and depressive symptomatology in patients with severe OSA (25). Although few other studies have addressed this directly, it is not difficult to understand how these constructs likely overlap in OSA patients. Functional abilities can also be affected by depression if depression interferes with the treatment of OSA. A few studies have attempted to uncover such a relationship. One study found that there was no association between baseline anxiety or depression scores as measured by the Hospital Anxiety and Depression Scale (HADS) and subsequent treatment adherence (26). More recently, however, another study using the HADS determined that there was a significant relationship between both elevated anxiety and depression scores and poor treatment adherence (27). These discrepant findings highlight the need for future research to examine the effect that depressive symptoms can have on treatment adherence in OSA patients. Increased understanding and awareness of this complicated relationship has the potential to improve the effectiveness of treatment of both depression and OSA (6). Ultimately, it will be of vital importance to continue to investigate the impact of depression among OSA patients. Moderators of Depression in OSA Depressive symptoms, although arguably common in OSA, are not universal. Investigators have recently become interested in identifying moderators of the relationship between depression and OSA. Gender has been one promising potential moderator. An early study reported that depressive symptoms were more common among male sleep apnea patients than female patients (11). In contrast, the largely negative findings from the Pillar and Lavie study reported that women with OSA scored higher on depression and anxiety scales than did men (20). Aloia et al. (9) examined this potential relationship in light of another potential moderator, obesity. The premise was that obesity could contribute to the cognitive aspect of depression, while apnea severity could contribute to the somatic aspect. In this study, only men demonstrated a relationship between apnea severity and somatic symptoms of depression, even after controlling for obesity. Women, on the other hand, only showed a relationship between obesity and the cognitive aspects of depression, independent of apnea severity (9). Based on these findings, it is likely that men and women with OSA manifest depressive symptoms differently, and these manifestations may or may not be mitigated by the presence of obesity. One explanation for this is that there may be fundamental gender differences, encompassing psychological differences, emotional differences, and dissimilar social pressures (20). For example, the somatic symptoms of depression may be easier for men to identify

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than the cognitive aspects. Men, on the other hand, may be more hesitant than women to admit feelings of guilt or worthlessness. Another potential explanation is the possibility that women could ruminate more on their symptoms than men. Finally, it is possible that the different societal norms regarding desirable body shape for men and women influence gender as a moderating factor. Specifically, men may focus less on their weight, resulting in fewer cognitive aspects of depression (e.g., self-loathing) and less overall concern about obesity (9). Alternatively, women may focus on their weight more than men, making cognitive aspects of depression more relevant than somatic complaints. Clearly, the gender-specific manifestation of depression and the mechanisms underlying these complex relationships deserve closer attention. Another factor that has been considered as a moderator in the relationship between depression and OSA is one’s coping strategy. Research has shown that the use of passive coping strategies has been associated with increased depression in individuals diagnosed with various chronic illnesses. Bardwell et al. examined the relationship between coping and depressive symptoms in OSA patients and results were consistent with past research (28). They concluded that given the variability of coping skills, some patients may experience more severe depression as a consequence of OSA (28). Sleepiness may also be a potential moderator of depression in OSA, as several studies have determined that OSA patients who reported higher daytime sleepiness were also more likely to report more depressive symptoms (8,9,29). It is possible that there may be additive consequences of several of the aforementioned moderators. For example, if a female OSA patient is obese and suffers from excessive daytime sleepiness, she may be more likely to experience depressive symptoms than a normal weight female OSA patient who is not excessively sleepy. Further, if a male OSA patient is obese, yet uses passive coping strategies in his daily life, he may be more prone to depressive symptoms than an obese male OSA patient who uses more active coping strategies. Future studies should consider these potential moderators in their designs to better address this intricate relationship experimentally. Consideration should be given to a host of different potential moderating factors, including those already tested, in addition to some novel factors including social support, religion/spirituality, or exercise. Mediators of Depression in OSA Moderators address the question of the condition under which the relationship most likely exists, whereas mediators address the mechanisms by which the existing relationships act. Several potential mechanisms have been proposed. OSA can frequently lead to feelings of fatigue, sleepiness, and lethargy. These somatic symptoms are also common characteristics of depression (20). Thus, one possibility for elevated depression scores in OSA patients is that they may frequently endorse such overlapping somatic-related items on self-report measures of depression. Therefore, rather than being related to a distinct psychiatric condition, affirmative responses may more accurately reflect the consequences of apnea severity. Indeed, this view that depression is largely an epiphenomenon of OSA has led some researchers to conclude that the relationship should be conceptualized as a mood disorder secondary to a medical sleep disorder (8). Support for this hypothesis comes largely from studies showing reduced depressive symptoms following treatment (30–33). Depression seen in OSA patients has also been conceptualized as a milder form of clinical depression, namely an adjustment disorder with depressed mood (5).

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An alternative explanation is that the relationship between OSA and depression is indirect, mediated by a common correlate of OSA. Several studies have attempted to identify such mediators. One likely contributor is obesity (see also Chapter 20). Obesity is the strongest risk factor for the development of OSA (34,35). In morbidly obese patients, those with a body mass index (BMI) ≥ 40 kg/m2, the rates of OSA range from 69% to 98% (36,37). In fact, one study found that a 10% increase in body weight increased the relative risk of developing moderate to severe OSA sixfold (38). Moreover, several studies have shown an increased prevalence of depression among obese patients even without consideration of the presence of OSA (39–41). One theory to explain this relationship suggests that obese individuals suffer body image dissatisfaction, discrimination, guilt from past unsuccessful attempts to lose weight, and psychosocial distress (42). In fact, body image dissatisfaction has been shown to partially mediate the relationship between obesity and depression (43,44). Experimental studies also support this notion, showing that changes in body image associated with significant weight loss are associated with significant reductions in depressed mood (45). These studies suggest that obese patients may endorse a different, more cognitive aspect of depression than the more fatigue-related, somatic aspect that may be associated with sleep problems. The Aloia et al. study mentioned above also supports the role of obesity as a potential mediator for the relationship between OSA and depression, especially in women (9). One study even suggested that the relationship between depression and OSA can be completely explained by obesity. Bardwell et al. (46) found that when controlling for age, BMI, and hypertension, the relationship between psychological distress and sleep variables disappeared. They concluded that most of the findings reporting a relationship between psychological measures and apnea can be explained by those factors. Although, it appears probable that obesity is involved in the relationship between OSA and depression, not all studies have noted such. In a large epidemiological study of almost 19,000 Europeans assessed by a telephone survey, Ohayon determined that over 17% of subjects with a diagnosed DSM (Diagnostic and Statistical Manual of Mental Disorders) breathing-related sleep disorder also presented with a major depression disorder diagnosis (47). Of note is the fact that this correlation persisted even after controlling for obesity and hypertension. Finally, it is worth mentioning that a common neurobiological risk factor may be another potential mediator for the relationship between depression and OSA (6). At the neurotransmitter level, the serotonergic system plays a primary role in regulating both mood and the sleep-wake cycle. Depression has been shown to be associated with a decrease in serotonergic neurotransmission, which leads some to assume that this is responsible for the alterations in sleep as well (48). There is a clear need for additional clinical and experimental research to clarify the complex nature of the association between depression and OSA, and the possible mediators of the relationship. OTHER AFFECTIVE DISORDERS AND OSA While the relationship between depression and OSA has been studied extensively, OSAs connection to other affective disorders such as bipolar disorder and cyclothymia is less certain. Bipolar disorder involves episodes of both depression and mania. Cyclothymia includes fluctuating mood disturbance as well; however, the fluctuations are not as dramatic (49). There have been a few case studies of bipolar disorder and/or manic episodes in sleep apnea patients (50–52). These studies focused primarily, however, on the complicating effect that OSA had on the bipolar

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disorder and did not pull from a clinic base of OSA patients. Cyclothymia was found to be relatively common in an early study. Reynolds and colleagues reported that 49% of their male sample met diagnostic criteria for cyclothymia (8). This area of research is largely unexplored and future studies are necessary to learn more about the incidence, presentation, and consequences of bipolar disorder and cyclothymia among OSA patients. Irritability, Anger, Anxiety, and OSA In addition to mood problems such as depression, other psychiatric disorders and/ or symptoms have been shown to be more common among OSA patients. Anxiety, somatization, obsessive-compulsive symptoms, and hostility have been reported among OSA cohorts (20,53,54). Irritability has been shown to be commonly associated with OSA. Further, anger has also been shown to be related to OSA, even when controlling for other variables such as age, body mass, and hypertension (46). In general, anxiety and depression are highly comorbid psychiatric symptoms. Considering the numerous studies reporting a strong correlation between depression and sleep apnea, one might suspect that the prevalence of anxiety symptoms among OSA patients would also be relatively high. A recent article supported this notion, stating that it is possible that anxiety is one of the most common functional abnormalities among OSA (55). Indeed, one study reported that for patients with severe OSA, their anxiety level was positive correlated with apnea severity (53). In addition, Sharafkhaneh et al. (1) reported that 16.7% of sleep apnea patients reported anxiety disorders and 11.9% of OSA patients reported post-traumatic stress disorder (PTSD). However, it should be noted that these data were obtained from a sample of male veterans that are at increased risk for anxiety, particularly PTSD. A potential relationship between OSA and nocturnal panic attack symptoms has also been cited, and investigators suggest that a differential diagnosis of nocturnal panic disorder should be considered (56). Like bipolar disorder and cyclothymia, there are a paucity of research studies examining the prevalence and consequences of both anxiety disorders and subclinical anxiety symptoms among sleep apnea patients. Anxiety remains one of the least explored, yet likely fruitful, mood associations with OSA. SEXUAL DYSFUNCTION AND OSA A behavioral dysfunction that may be potentially related to anxiety is sexual dysfunction. Sexual dysfunction is a known behavioral consequence of sleep apnea. Erectile dysfunction (ED) is a frequent complaint of male OSA patients. One study reported, however, that only severe OSA (not mild or moderate) was associated with ED (57). Predictive risk factors for male OSA patients to have ED were older age, morning tiredness, and greater apnea severity (57). Decreased libido has also been reported. Hypoxemia and sleep fragmentation have been shown to suppress testosterone levels in male OSA patients (58). Encouraging findings from several recent studies suggest that sexual dysfunction related to OSA, specifically ED, can be improved with continuous positive airway pressure (CPAP) treatment (59,60). OSA AND NEUROBEHAVIORAL FUNCTION OSA can cause significant daytime behavioral and adaptive deficits. Functional impairments like sleepiness, impaired driving, increased risk of accidents, and

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decreased quality of life are frequent consequences of sleep apnea (61,62). Behavioral effects of OSA are often referred to as “neurobehavioral” consequences because they are presumed to be directly related to brain function (63). Neurobehavioral functioning is a broad term that includes several specific cognitive functions. Numerous studies have examined these specific cognitive functions and some have attempted to identify a “pattern” of cognitive dysfunction in OSA. Such patterns will be summarized below. Following that summary, theoretical models describing potential mechanisms involved in this relationship are discussed. Cognitive Functioning and OSA Neurocognitive testing is common in studies involving OSA. The cognitive sequelae of the disorder are generally well-documented (64–66). Cognition can be examined as a unitary function or it can be divided into several specific domains (e.g., memory, attention, executive functioning, etc.). The utility of each type of examination depends upon the question being asked and the degree to which each approach would adequately address a given hypothesis. Studies of global impairment may be better suited for addressing the overall effects of a particular variable on cognition. Impaired cognition among OSA patients is not global, however. In fact, apnea patients may exhibit relatively few deficits in the global cognitive domain when compared to normal controls (65). Language abilities are also frequently spared in OSA (65). Studies which limit themselves to global functioning cannot have a true appreciation for the various components of cognition that contribute to this global score. Domain-specific questions, on the other hand, can help uncover specific deficits that are otherwise masked by studies of global cognitive functioning. Domains can be divided in several ways but common domain names include: executive functioning, memory, attention, vigilance, visuospatial ability, constructional ability, psychomotor functioning, and language. One should remember, however, that each of these domains may also have sub-domains that further break apart their complex nature (e.g., executive functioning), and that domains are not mutually exclusive in their functions. For OSA patients, the domains of cognitive functioning are affected differentially. Vigilance, including sustained attention, controlled attention, efficiency of information processing, and response time, is the most commonly assessed cognitive construct in OSA and has been found to be the most consistently affected cognitive domain in apnea patients (65). Executive functioning, which includes processes involved in planning, initiation, and execution of goal-oriented behavior and mental flexibility, is another affected domain. Some argue that it is the most prominent area of cognitive impairment in untreated sleep-disordered breathing and that the dysfunction extends to children with sleep apnea as well as adults (67). It should be noted that the broad construct of executive functioning makes it somewhat difficult to accurately describe the deficits and to construct a model explaining causes of the impairment. Examples of executive functioning range from working memory, set-shifting, perseveration, planning, abstract reasoning, and verbal fluency. Even more, executive functions are in part supported by adequate attention. Therefore, attentional problems could represent the root cause of executive dysfunction. Despite it being a broad construct, OSA patients clearly perform consistently more poorly on tests of executive functioning than matched controls (65). Similar to executive function, but to perhaps a lesser degree, learning and memory constitute a broad, complex domain that includes verbal memory, visual

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memory, short-term memory, and long-term memory. Memory performance deficits can be attributed to several areas: initial learning, free recall, or forgetfulness, each of which has different implications (65). Again, despite being a broad domain, OSA patients perform more poorly on tests of memory and learning than matched controls (65,68,69). Psychomotor performance is a domain that has been assessed less frequently that the aforementioned types of cognition. Most studies, however, show OSA patients to be impaired relative to controls (65). Specifically, OSA patients perform relatively poorer on tests of fine-motor coordination (70–73). Not all studies have reported impairment on tests of motor speed (73,74). Overall, there has been relatively little discussion of this domain as a primary source of impairment (65). The mechanism for psychomotor dysfunction is not clear. One explanation for psychomotor difficulties is excessive sleepiness, but this does not account for the discrepancy between tests of fine-motor skills and motor speed. Few studies have been conducted examining cognitive dysfunction associated with OSA in older adults. A large-scale study in France reported that participant reports of snoring and/or breathing cessation during sleep were associated with greater impairment on tests of attention and information processing, even after controlling for several extraneous variables (75). These findings were significantly associated with cognition only when daytime sleepiness was also reported. A longitudinal study employed more stringent criteria for diagnosing OSA. Cohen-Zion et al. (76) examined the sleep and global cognitive functioning of 46 communitydwelling older adults over the course of four years. These investigators found that increases in apnea severity and daytime sleepiness were associated with respective decreases in global cognitive functioning over time. Moreover, the findings seemed to be driven by daytime sleepiness when regression models were employed. An intriguing study by Antonelli-Incalzi et al. (77) compared older apneics to patients with either Alzheimer’s disease or multi-infarct dementia (MID) on a battery of neuropsychological tests. This study suggested that the cognitive profile of apnea is most like that seen in MID. They relate this finding to the probable involvement of subcortical brain regions in apnea, a relationship that has also been posited by other investigators (65). Potential Mechanisms for Neurobehavioral Dysfunction The theoretical models discussed below propose certain mechanisms that may be involved in the relationship between OSA and cognition. Beebe and Gozal put forth a model of the mechanisms of cognitive impairment in OSA in 2002 (67). The model suggested that OSA has a predilection for affecting the frontal lobes of the brain compared to other brain regions. It outlines the two primary mechanisms (i.e., sleep fragmentation and hypoxemia) as the causes of frontal lobe dysfunction. Hypoxemia is thought to result in cellular changes to the prefrontal cortex that directly affects function, while sleep fragmentation is posited to preferentially affect the frontal lobes of the brain by disrupting the normal restorative process of sleep. Together, the hypoxemia and sleep fragmentation adversely affect the executive functioning of the frontal lobes. Evidence for this is presented in the model mainly by way of sleep deprivation studies, which show a strong relationship to executive functions. The executive model has several strengths. First, it was one of the first models to thoughtfully take a neuro-functional approach to explain the cognitive dysfunction seen in OSA. The model also employed both basic and clinical studies as evidence. There were, however, some weaknesses to the model. Data from carbon monoxide

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poisoning studies and sleep deprivation studies were extrapolated to the conditions of hypoxemia and sleep fragmentation in general. These analogies may or may not be appropriate. In addition, the effects of sleep fragmentation and hypoxemia on brain regions other than frontal lobes were not incorporated into this early model. Finally, as mentioned earlier, executive dysfunction is complex and multifactorial, something acknowledged by the authors. Regardless of this criticism, the authors undertook a very complex task: to develop a comprehensive, neurofunctional model of OSA. Another proposed model is the attentional model. Certainly attentional problems have been implicated in OSA. Verstraeten and Cluydts have recently published two papers making the case that higher-order cognitive dysfunction in OSA can be explained by the impairment of basic attentional processes and slowed mental processing (78). The first paper proposed a theoretical model of neurocognitive functioning marked by the hierarchical ordering of cognitive processes that can lead to the appearance of higher-order cognitive dysfunction. This theoretical paper is quite interesting as it is the first to recognize that higher-order cognitive processes are complex enough to often rely on more basic attentional and lower-level cognitive processes. The authors made the case that executive dysfunction per se should be interpreted cautiously in the case of sleep apnea, given the potentially profound effects of sleep disruption on arousal, basic processing speed and attentional ability. The conclusion of this paper is that investigators should consider developing studies that allow them to systematically control for lower level functions in the assessment of high-order cognitive ability. The second study attempted to demonstrate this theory by fractionating these functions to determine the degree to which the reliance of higher order functions on attention can lead to the misinterpretation when considering the functional deficits in OSA. Deficits in OSA patients were seen in processing speed, attentional capacity, and short-term memory span, with no differences seen in executive functions per se. The investigators provided these data as evidence for this hierarchical model of dysfunction in OSA, making the case that executive dysfunction may be misinterpreted without knowledge of lower-order skills. This series of studies is quite compelling and encourages investigators to consider cognitive functions in a hierarchical manner (79). Indeed, identifying the basic functional deficits that underlie these more complex deficits can lead to a better understanding of the neurofunctional mechanisms impaired in OSA. The one lacking component of this work is the provision of data to support any specific mechanisms related to sleep fragmentation or hypoxemia. Future research will undoubtedly address this gap in the model and may augment the executive model described in the first paragraph of this section. The microvascular theory as a model for cognitive dysfunction in OSA was first put forth by Aloia et al. in 2004, owing in large part to the work of Somers et al. (80). Aloia et al. (81) culled mechanisms of dysfunction from the cardiovascular literature and determined that because cardiovascular dysfunction was a wellsupported consequence of OSA it was reasonable that vascular compromise might also exist in the brain. It was determined that, based on hypoxia literature. hypoxemia would preferentially affect regions of the brain that were metabolically active during the event and fed by small vessels. Damage to the small vessels might in fact precede large vessel stroke and may result in a predictable pattern of cognitive dysfunction associated with small vessel brain disease. The pattern would involve deficits in motor speed and coordination, executive dysfunction, memory impairment, and some problems with attention and mental processing speed. After a review of the

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literature, Aloia et al. (65) argued that this pattern of cognitive dysfunction was indeed present in OSA and may represent microvascular disease. Several supporting studies for this model were presented, highlighting the involvement of the white matter in OSA, an area fed primarily by small vessels and susceptible to ischemic disease. Functional and structural studies were presented, though few had been completed at the time of the original publication. In closing the paper, it was demonstrated in a small sample that evidence of microvascular disease could be seen on brain MRI in OSA. Since the publication of this review, several studies have been published to support and refute this model. One supportive study identified a subgroup of OSA patients with cognitive dysfunction that likened a pattern seen in multi-infarct dementia. However, other studies have failed to find an association between white matter ischemic disease and OSA severity using large-scale epidemiological data in older adults. One primary limitation of the model was that it did not attend strongly to the differential effects of sleep fragmentation and hypoxemia. The model is promising in that it is parsimonious and incorporates a known mechanism of dysfunction in OSA, vascular compromise, into the cognitive realm. Further research, however, is needed to defend, refute, or expand the model and to relate its effects to complaints of fatigue and sleepiness. The most recent model was posited by Beebe in 2005 (63). This model is the most comprehensive to date and pulls upon the strengths of the aforementioned models to develop a heuristic model of the mechanisms underlying cognitive dysfunction in OSA. Beebe first acknowledged the weaknesses of his initial model and the strengths of the other models. Beebe went on to propose a modified model that states that the effects of sleep fragmentation and hypoxemia are not likely to be effectively isolated from one another. He stated that their interaction may in fact be synergistic. Moreover, he presented the likelihood that these mechanisms interact with certain vulnerable brain regions, highlighting specifically the hippocampus, prefrontal cortex, subcortical gray matter, and white matter. The inclusion of the subcortical gray and white matter reflects an appreciation for the potential involvement of the small vessels of the brain. Beebe also attended to the possibility that findings in studies of the potential mechanisms of cognitive dysfunction are dependent in part on task demands and the environment under which testing is conducted. This addition shows an appreciation for the complexity of executive dysfunction as multifactorial and broadens the executive and attentional models by including several other cognitive tasks that may be impaired in OSA simply owing to the demands that they present for the implicated brain regions. Finally, Beebe went beyond the other models by incorporating two additional areas to consider: (i) risk and resilience factors, and (ii) direct effects on cognition outside of those involved in OSA. When discussing risk and resilience, Beebe acknowledged recent work by Alchanatis et al. (82) showing that there may be moderators of dysfunction in OSA; for example, intelligence has been proposed as one moderator for vigilance problems in OSA. This study identified cognitive reserve (high premorbid cognitive ability that results in resistance to cognitive decline with insult) as a resilience factor, but the heuristic model also includes age, sex, sociodemographic factors, and duration of illness. Others have also proposed the inclusion of moderators of dysfunction noting that several patients with severe OSA do not suffer dysfunction at all, while others with mild OSA show significant impairment. Finally, the model incorporates genetic endowment, prior experience with testing, and sociodemographic factors as possible extraneous variables when considering the mechanisms of cognitive dysfunction in OSA. The model needs to be tested, but there are more strengths to

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this model than there are weaknesses. The model is testable with large datasets and is more inclusive than previous models. It is not, however, overly inclusive and specifies brain regions likely to be involved without implying that all regions are equally vulnerable. Perhaps most importantly, the model highlight the likely effect of moderating factors for cognitive impairment in OSA, something that has only recently been addressed in the literature. MOOD, BEHAVIOR, AND TREATMENT OF OSA The most common and effective treatment for OSA is positive airway pressure (PAP). When properly used, PAP has been shown to dramatically reduce morbidity and mortality (83–85). Due in part to these encouraging findings, the effect that PAP treatment has on mood, cognition, and behavior has recently become an area of interest. Despite its effectiveness, however, long-term adherence to PAP treatment is less than optimal, with approx 25% of patients discontinuing use within a year (86). Commonly cited reasons for poor adherence include physical discomfort as well as psychological factors (87–91). Investigators have become interested in learning how treatment of OSA affects mood. There are several studies that found that mood disturbance and depressive symptoms were reduced following treatment with CPAP (30–33,92,93). Strangely, the improvement in mood sometimes happens even when treatment adherence is poor (10). Not all studies have reported such encouraging findings. Improvement in depression following CPAP treatment is not a universal finding (53,94). In fact, treatment studies with short-term follow-ups have found that symptomatic improvement may instead reflect a placebo response (95,96). There are several possible explanations for the discrepant findings. One is that it is difficult to design a good placebo condition for CPAP. Recently, studies have begun to use insufficient PAP as a treatment control (6). In addition, the specific measures employed and poor adherence may complicate the picture. Findings related to anxiety and treatment of OSA are also controversial. One study did not find any changes in the anxiety levels of OSA patients after treatment with CPAP (53). A more recent study, however, did find that trait anxiety levels decreased at one month and three months post-treatment with CPAP (32). Again, more research is needed in this domain to clarify the effect of PAP treatment on mood in OSA patients. Finally, treating sleep apnea with PAP also has the potential to affect cognitive functioning. Aloia et al. (65) published a critical review of the literature on the neuropsychological sequelae of OSA. They concluded that the majority of studies examining the connection between PAP and OSA have indeed cited a positive relationship between treatment adherence and improved performance on various cognitive tests (97,98). As previously mentioned, findings from several recent studies suggest that sexual dysfunction related to OSA, can also be improved with CPAP treatment (59,60). Based on the findings that indicate that treatment adherence is associated with improved mood, increased cognitive functioning, and decreased sexual dysfunction, if adherence to prescribed PAP use can be increased, more OSA patients will experience such benefits. Studies are currently underway to determine what is most effective in improving treatment adherence. CONCLUSIONS This chapter has covered the effects that OSA has on various aspects of mood and neurobehavioral functioning. In closing, it appears clear that OSA affects both mood

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and behavior. Such mood problems and cognitive dysfunction have the ability to functionally compromise the quality of life in sleep apnea patients. These problems may be moderated by extraneous factors such as gender or obesity, but these factors, as well as the mediators of these relationships are still being examined. It will be vital for future programs of research to create a concise model of the specific pathways of interaction between OSA, mood, and behavior. One goal of this line of research is eventually to identify modifiable sleep-related factors for dysfunction, including treatment adherence, which can maximize functioning despite having a diagnosed sleep disorder. REFERENCES 1. Sharafkhaneh A, Giray N, Richardson P, Young T, Hirshkowitz M. Association of psychiatric disorders and sleep apnea in a large cohort. Sleep 2005; 28(11):1405–1411. 2. Day R, Gerhardstein R, Lumley A, Roth T, Rosenthal L. The behavioral morbidity of obstructive sleep apnea. Prog Cardiovasc Dis 1999; 41(5):341–354. 3. Bassiri AG, Guilleminault C. Clinical features and evaluation of obstructive sleep apneahypopnea syndrome. In: Kryger MH, Roth T, Dement WC, eds. Principles and Practice of Sleep Medicine, 3rd ed. Philadelphia: W.B. Saunders Company, 2000:869–878. 4. Quereshi A, Ballard RD. Obstructive sleep apnea. J Allergy Clin Immunol 2003; 112(4):643–651. 5. Baran AS, Richert A. Obstructive sleep apnea and depression. CNS Spectrums 2003; 8(2):128–134. 6. Schroder CM, O’Hara R. Depression and obstructive sleep apnea (OSA). Ann Gen Psychiatry 2005; 27:4–13. 7. Guilleminault C, Eldridge FL, Tilkian A, Simmons FB, Dement W. Sleep apnea syndrome due to upper airway obstruction: a review of 25 cases. Arch Intern Med 1977; 137:296–300. 8. Reynolds CF, Kupfer DJ, McEachran AB, Taska LS, Sewitch DE, Coble PA. Depressive psychopathology in male sleep apneics. J Clin Psychiatry 1984; 45:287–290. 9. Aloia MS, Arnedt JT, Smith L, Skrekas J, Stanchina M, Millman RP. Examining the construct of depression in obstructive sleep apnea syndrome. Sleep Med 2005/3 2005; 6(2):115–121. 10. Means MK, Lichstein KL, Edinger JD, et al. Changes in depressive symptoms after continuous positive airway pressure treatment for obstructive sleep apnea. Sleep Breath 2003; 7(1):31–42. 11. Bliwise DL, Yesavage JA, Sink J, Widrow L, Dement W. Depressive symptoms and impaired respiration in sleep. J Consult Clin Psychology 1986; 54:734–735. 12. Bardwell WA, Moore P, Ancoli-Israel S, Dimsdale JE. Does obstructive sleep apnea confound sleep architecture findings in subjects with depressive symptoms? Biol Psychiatry 2000; 48:1001–1009. 13. Beutler LE, Ware JC, Karacan I, Thornby JI. Differentiating psychological characteristics of patients with sleep apnea and narcolepsy. Sleep 1981; 4:39–47. 14. Ramos Platon MJ, Espinar Sierra J. Changes in psychopathological symptoms in sleep apnea patients after treatment with nasal continuous positive airway pressure. Int J Neurosci 1992; 62:173–195. 15. Aikens JE, Mendelson WB. A matched comparison of MMPI responses in patients with primary snoring or obstructive sleep apnea. Sleep 1999; 22(3):355–359. 16. Aikens JE, Caruana-Montaldo B, Vanable PA, Tadimeti L, Mendelson WB. MMPI correlates of sleep and respiratory disturbance in obstructive sleep apnea. Sleep 1999; 22(3):362–369. 17. Lee S. Depression in sleep apnea: a different view. J Clin Psychiatry 1990; 51:309–310. 18. Flemons WW, Whitelaw WA, Brant R, Remmers JE. Likelihood ratios for a sleep apnea clinic prediction rule. Am J Respir Crit Care Med 1994; 150:1279–1285. 19. Klonoff H, Fleetham J, Taylor R, Clark C. Treatment outcome of obstructive sleep apnea: Physiological and neuropsychological concomitants. J Nerv Men Dis 1987; 175(4):208–212.

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20. Pillar G, Lavie P. Psychiatric symptoms in sleep apnea syndrome: effects of gender and respiratory disturbance index. Chest 1998; 114:697–703. 21. Phillips B, Berry D, Lipke-Molby T. Sleep-disordered breathing in healthy, aged persons. Fifth and final year follow-up. Chest 1996; 110(3):654–658. 22. Gall R, Isaac L, Kryger MH. Quality of life in mild obstructive sleep apnea. Sleep 1993; 16:S59–S61. 23. Fornas C, Ballester E, Arteta E, et al. Measurement of general health status in obstructive sleep apnea hypopnea patients. Sleep 1995; 18:876–879. 24. D’Ambrosio C, Bowman T, Mohsenin V. Quality of life in patients with obstructive sleep apnea: effects of nasal continuous positive airway pressure; a prospective study. Chest 1999; 115:123–129. 25. Akashiba T, Kawahara S, Akahoshi T, et al. Relationship between quality of life and mood or depression in patients with severe obstructive sleep apnea syndrome. Chest 2002; 122:861–865. 26. Lewis KE, Seale L, Bartle IE, Watkins AJ, Ebden P. Early predictors of CPAP use for the treatment of obstructive sleep apnea. Sleep 2004; 27(1):134–138. 27. Kjelsberg FN, Ruud EA, Stavem K. Predictors of symptoms of anxiety and depression in obstructive sleep apnea. Sleep Med 2005; 6(4):341–346. 28. Bardwell WA, Ancoli Israel S, Dimsdale JE. Types of coping strategies are associated with increased depressive symptoms in patients with obstructive sleep apnea. Sleep 2001; 24:905–909. 29. Sforza E, de Saint Hilaire Z, Pelissolo A, Rochat T, Ibanez V. Personality, anxiety and mood traits in patients with sleep-related breathing disorders: effect of reduced daytime alertness. Sleep Med 2002; 3(2):139–145. 30. Millman RP, Fogel BS, McNamara ME, Carlisle CC. Depression as a manifestation of obstructive sleep apnea: Reversal with nasal continuous positive airway pressure. J Clin Psychiatry 1989; 50(9):348–351. 31. Borak J, Cieslicki J, Szelenberger W, et al. Psychopathological characteristics of the consequences of obstructive sleep apnea prior to and three months after CPAP. Psychiatria Polska 1994; 28(suppl 3):33–44. 32. Sánchez AI, Buela-Casal G, Bermúdez MP, Casas-Maldonado F. The effects of continuous positive air pressure treatment on anxiety and depression levels in apnea patients. Psychiatry Clin Neurosci 2001; 55:641–646. 33. McMahon JP, Foresman BH, Chisholm RC. The influence of CPAP on the neurobehavioral performance of patients with obstructive sleep apnea hypopnea syndrome: a systematic review. WMJ 2003; 102(1):36–43. 34. Kripke DF, Ancoli-Israel S, Klauber MR, Wingard DL, Mason WJ, Mullaney DJ. Prevalence of sleep-disordered breathing in ages 40–64 years: a population-based survey. Sleep 1997; 20:65–76. 35. Wilhoit SC, Suratt PM. Obstructive sleep apnea in premenopausal women. A comparison with men and with postmenopausal women. Chest 1987; 91:654–658. 36. Valencia-Flores M, Orea A, Castano VA, et al. Prevalence of sleep apnea and electrocardiographic disturbances in morbidly obese patients. Obes Res 2000; 8(3):262–269. 37. Stanchina M, Johnson L, Roye GD, et al. Left ventricular dysfunction in morbidly obese individuals and the moderating effect of OSA. Am J Respir Crit Care Med 2004; 169:A745. 38. Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal study of moderate weight change and sleep-disordered breathing. JAMA 2000; 284(23):3015–3021. 39. Black DW, Goldstein RB, Mason EE. Prevalence of mental disorder in 88 morbidly obese bariatric clinic patients. Am J Psychiatry 1992; 149:227–234. 40. Goldstein LT, Goldsmith SJ, Aner K, Leon AC. Psychiatric symptoms in clients presenting for commercial weight reduction treatment. Int J Eating Disord 1996; 20:191–197. 41. Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health 2000; 90:251–257. 42. Wooley S, Garner D. Obesity treatment: The high cost of false hope. J Am Diet Assoc 1991; 91:1248–1251. 43. Friedman K, Reichmann S, Costanzo P, Musante G. Body image partially mediates the relationship between obesity and psychological distress. Obes Res 2002; 10:33–41.

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44. Sarwer D, Wadden T, Foster G. Assessment of body image dissatisfaction in obese women: Specificity, severity, and clinical significance. J Consult Clin Psychology 1998; 66:651–654. 45. Dixon JB, Dixon ME, O’Brien PE. Depression in association with severe obesity. Arch Intern Med 2003; 163:2058–2065. 46. Bardwell WA, Berry CC, Ancoli Israel S, Dimsdale JE. Psychological correlates of sleep apnea. J Psychosom Res 1999; 47:583–596. 47. Ohayon MM. The effects of breathing-related sleep disorders on mood disturbances in the general population. J Clin Psychiatry 2003; 64:1195–1200. 48. Adrien J. Neurobiological bases for the relation between sleep and depression. Sleep Med Rev 2002; 6:341–351. 49. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (4th edition-Text Revision). Washington, D.C: American Psychiatric Association, 363, 2000. 50. Strakowski SM, Hudson JI, Keck PE, et al. Four cases of obstructive sleep apnea associated with treatment-resistant mania. J Clin Psychiatry 1991; 52(4):156–158. 51. Hilleret H, Jeunet E, Osiek C, Mohr S, Blois R, Bertschy G. Mania resulting from continuous positive airway pressure in a depressed man with sleep apnea syndrome. Neuropsychobiology 2001; 43(3):221–224. 52. Fleming JA, Fleetham JA, Taylor DR, Remick RA. A case report of obstructive sleep apnea in a patient with bipolar affective disorder. Can J Psychiatry 1985; 30(6):437–439. 53. Borak J, Cieslicki JK, Koziej M, Matuszewski A, Zielinski J. Effects of CPAP treatment on psychological status in patients with severe obstructive sleep apnoea. J Sleep Res 1996; 5(2):123–127. 54. Yue W, Hao W, Liu P, Liu T, Ni M, Guo Q. A case-control study on psychological symptoms in sleep apnea-hypopnea syndrome. Can J Psychiatry 2003; 48(5):318–323. 55. El-Ad B, Lavie P. Effect of sleep apnea on cognition and mood. International Review of Psychiatry 2005; 17(4):277–282. 56. Edlund MJ, McNamara ME, Millman RP. Sleep apnea and panic attacks. Comprehensive Psychiatry 1991; 32(2):130–132. 57. Margel D, Cohen M, Livne PM, Pillar G. Severe, but not mild obstructive sleep apnea syndrome is associated with erectile dysfunction. Urology 2004; 63(3):545–549. 58. Luboshitzky R, Aviv A, Hefetz A, et al. Decreased pituitary-gonadal secretion in men with obstructive sleep apnea. J Clin Endocrinol Metab 2002; 87(7):3394–3398. 59. Goncalves MA, Guilleminault C, Ramos E, Palha A, Paiva T. Erectile dysfunction, obstructive sleep apnea syndrome and nasal CPAP treatment. Sleep Med 2005; 6(4):333–339. 60. Margel D, Tal R, Livne PM, Pillar G. Predictors of erectile function improvement in obstructive sleep apnea patients with long-term CPAP treatment. Int J Impot Res 2005; 17(2):186–190. 61. Engleman HM, Douglas NJ. Sleepiness, cognitive function, and quality of life in obstructive sleep apnoea/hypopnoea syndrome. Thorax 2004; 59:618–622. 62. George CF, Smiley A. Sleep apnea and automobile crashes. Sleep 1999; 22(6):790–795. 63. Beebe D. Neurobehavioral effects of obstructive sleep apnea: An overview and heuristic model. Curr Opin Pulm Med 2005; 11:494–500. 64. Sateia MJ. Neuropsychological impairment and quality of life in obstructive sleep apnea. Clin Chest Med 2003; 24(2):249–259. 65. Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D. Neuropsychological consequences of sleep apnea: A critical review. J Int Neuropsychol Soc 2004; 10:772–785. 66. Engleman H, Martin SE, NJ D. Cognitive function in the sleep apnea/hypopnea syndrome. Sleep 2000; 23:S102–S107. 67. Beebe D, Gozal D. Obstructive sleep apnea and the prefrontal cortex: towards a comprehensive model linking noctural upper airway obstruction to daytime cognitive and behavioral deficits. J Sleep Res 2002; 11:1–16. 68. Feuerstien C, Naegele B, Pepin J, Levy P. Frontal lobe-related cognitive funtions in patients with Sleep Apnea Syndrome before and after treatment. Acta Neurologica Belgica 1997; 97:96–107. 69. Naegele B, Thouvard V, Pepin JL, et al. Deficits of cognitive executive functions in patients with sleep apnea syndrome. Sleep 1995; 18(1):43–52.

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70. Bédard M-A, Montplaisir J, Richer F, Rouleau I, Malo J. Obstructive sleep apnea syndrome: Pathogenesis of neuropsychological deficits. J Clin Exp Neuropsychol 1991; 13(6):950–964. 71. Bédard M-A, Montplaisir J, Malo J, Richer F, Rouleau I. Persistent neuropsychological deficits and vigilance impairment in sleep apnea syndrome after treatment with continuous positive airways pressure (CPAP). J Clin Exp Neuropsychol 1993; 15(2):330–341. 72. Greenberg GD, Watson RK, Deptula D. Neuropsychological dysfunction in sleep apnea. Sleep 1987; 10(3):254–262. 73. Verstraeten E, Cluydts R, Verbraecken J, De Roeck J. Psychomotor and cognitive performance in nonapneic snorers: Preliminary findings. Perceptual and Motor Skills 1997; 84:1211–1222. 74. Knight H, Millman RP, Gur RC, Saykin AJ, Doherty JU, Pack AI. Clinical significance of sleep apnea in the elderly. Am Rev Respir Dis 1987; 136(4):845–850. 75. Ohayon MM, Vecchierini MF. Daytime sleepiness and cognitive impairment in the elderly population. Arch Intern Med 2002; 162:201–208. 76. Cohen-Zion M, Stepnowsky Jr. CJ, Marler MR, Shochat T, Kripke D, Ancoli Israel S. Changes in cognitive function associated with sleep disordered breathing in older people. J Am Geriatric Soc 2001; 49(12):1622–1627. 77. Antonelli Incalzi R, Marra C, Salvigni BL, et al. Does cognitive dysfunction conform to a distinctive pattern in obstructive sleep apnea? J Sleep Res 2004; 13:79–86. 78. Verstraeten E, Cluydts R. Executive control of attention in sleep apnea patients: theoretical concepts and methodological considerations. Sleep Med Rev 2004; 8:257–267. 79. Verstraeten E, Cluydts R, Pevernagie D, Hoffmann G. Executive function in sleep apnea: controlling for attentional capacity in assessing executive attention. Sleep 2004; 27(4):685–693. 80. Lanfranchi P, Somers VK. Obstructive sleep apnea and vascular disease. Respir Res 2001; 2:315–319. 81. Caine D, Watson JDG. Neuropsychological and neuropathological sequelae of cerebral anoxia: A critical review. J Int Neuropsychol Soc 2000; 6:86–99. 82. Alchanatis M, Zias N, Deligiorgis N, Amfilochiou A, Dionellis G, Orphanidou D. Sleep apnea-related cognitive deficits and intelligence: an implication of cognitive reserve theory. J Sleep Res 2005; 14:69–75. 83. Keenan SP, Burt H, Ryan F, Fleetham JA. Long-term survival of patients with obstructive sleep apnea treated by uvulopalatopharyngoplasty or nasal CPAP. Chest 1994; 105:155–159. 84. He J, Kryger MH, Zorick F, Conway W, Roth T. Mortality and apnea index in obstructive sleep apnea: Experience in 385 male patients. Chest 1988; 94:9–14. 85. Campos-Rodriguez F, Pena-Grinan N, Reyes-Nunez N, et al. Mortality in obstructive sleep apnea-hypopnea patients treated with positive airway pressure. Chest 2005; 128(2):624–633. 86. McArdle N, Devereux G, Heidarnejad H, Engleman HM, Mackay TW, Douglas NJ. Long-term Use of CPAP Therapy for Sleep Apnea/Hypopnea Syndrome. Am J Respir Crit Care Med 1999; 159(4):1108–1114. 87. Aloia MS, DiDio P, Ilniczky N, Perlis ML, Greenblatt DW, Giles DE. Improving compliance with nasal CPAP and vigilance in older adults with OSAHS. Sleep Breath 2001; 5(1):13–21. 88. Waldhorn RE, Herrick TW, Nguyen MCea. Long-term compliance with nasal continuous positive airway pressure therapy of obstructive sleep apnea. Chest 1990; 97(33–38). 89. Hoffstein V, Viner S, Mateika Sea. Treatment of obstructive sleep apnea with nasal continuous positive airway pressure. Patient compliance, perception of benefits, and side effects. Am Rev Respir Dis 1992; 145:841–845. 90. Kribbs NB, Pack AI, Kline LR, et al. Objective measurement of patterns of nasal CPAP use by patients with obstructive sleep apnea. Am Rev Respir Dis 1993; 147:887–895. 91. Aloia MS, Arnedt JT, Stepnowsky CJ Jr, Hecht J, Borrelli B. Predicting treatment adherence in obstructive sleep apnea using principles of behavior change. J Clin Sleep Med 2005; 1(4):346–353. 92. Engleman HM, Cheshire KE, Deary IJ, Douglas NJ. Daytime sleepiness, cognitive performance and mood after continuous positive airway pressure for the sleep apnoea/ hypopnoea syndrome. Thorax 1993; 48(9):911–914.

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93. Derderian SS, Bridenbaugh RH, Rajagopol KR. Neuropsychologic symptoms in obstructive sleep apnea improve after treatment with nasal continuous positive airway pressure. Chest 1988; 94:1023–1027. 94. Munoz A, Mayoralas LR, Barbe F, Pericas J, Agusti AG. Long-term effects of CPAP on daytime functioning in patients with sleep apnoea syndrome. Eur Respir J 2000; 15(4):676–681. 95. Yu B.-H, Ancoli-Israel S, Dimsdale JE. Effect of CPAP treatment on mood states in patients with sleep apnea. J Psychiatr Res 1999; 33:427–432. 96. Profant J, Ancoli Israel S, Dimsdale JE. A randomized, controlled trial of 1 week of continuous positive airway pressure treatment on quality of life. Heart & Lung: J Acute & Critical Care 2003; 32(1):52–58. 97. Aloia MS, Stanchina ML, Arnedt JT, Malhotra A, Millman RP. Treatment adherence and outcomes in flexible versus continuous positive airway pressure therapy. Chest 2005; 127(6):2085–2093. 98. Aloia MS, Ilniczky N, Di Dio P, Perlis ML, Greenblatt DW, Giles DE. Neuropsychological changes and treatment compliance in older adults with sleep apnea. J Psychosom Res 2003; 54(1):71–76.

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Sleepiness Douglas B. Kirsch Division of Sleep Medicine, Department of Internal Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts, U.S.A.

Ronald D. Chervin Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan, U.S.A.

INTRODUCTION Obstructive sleep apnea (OSA) has become an increasingly recognized reason for daytime sleepiness. Snoring, a common human experience, and associated apneic spells have only become associated with pathology in the last half century of medicine. One of the earliest described characters in English literature, with typical symptoms of obesity, snoring and sleepiness, is “Fat Joe” in Charles Dickens’ The Pickwick Papers. Likely intended as a caricature, he is a prime, though extreme, example of a patient with possible OSA or obesity-hypoventilation. “Damn that boy,” said the old gentleman, “he’s gone to sleep again.” “Very extraordinary boy, that,” said Mr. Pickwick; “does he always sleep in this way?” “Sleep!” said the old gentleman, “he’s always asleep. Goes on errands fast asleep, and snores as he waits at table.” “How very odd!” said Mr. Pickwick.

This chapter will discuss prevalence of sleepiness in OSA, methods of detecting sleepiness in patients with OSA, possible causes of excessive daytime sleepiness in sleep apneics, and treatment of OSA-related sleepiness. In some patients, treatment of OSA does not resolve daytime sleepiness; these instances will also be considered. PREVALENCE OF SLEEPINESS IN OBSTRUCTIVE SLEEP APNEA Within a sleep clinic referral population, excessive daytime sleepiness is the most common presenting complaint and OSA is the most common cause (2). Epidemiological studies suggest that 1% to 5% of adult males have OSA with symptoms (3). Data from the Wisconsin Sleep Cohort Study indicated that approx 24% of middle-aged men and 9% of middle-aged women suffer from OSA [defined by an apnea-hypopnea index (AHI) of at least 5 events/hr of sleep] (4). Questionnaire evaluation of these patients with an AHI > 5 demonstrated that 16% of the men and 23% of the women met the study’s criteria for hypersomnolence (daytime sleepiness, unrefreshed awakenings, and daytime sleepiness that interfered with daily living; each symptom greater than two times per week). These values lead to the often-quoted statistics that at least 4% of middle-aged men and 2% of middle-aged women meet criteria for OSA (sleep-disordered breathing and symptoms of hypersomnia). However, these data do not reflect that daytime sleepiness and unrefreshing sleep may not be the only symptoms of OSA. 393

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Other indications of daytime sleepiness from sleep apnea may include alterations in mood, neurocognitive skills, and quality of life. Data from both the Sleep Heart Health Study and the Wisconsin Sleep Cohort Study indicate that the quality of life of patients with undiagnosed OSA is affected similarly to other chronic diseases of moderate severity (5,6). Motor vehicle crashes have been associated with sleepiness, and at times appear directly correlated with OSA; 800,000 motor vehicle collisions in the year 2000 alone were related to effects from OSA (7). In another population, OSA (AHI > 10 events/hr) was shown to increase the risk of motor vehicle crashes sixfold when compared to normal control (8). Though many patients with OSA are sleepy, clearly some patient-to-patient variability exists. In fact, two patients with identical AHIs can have considerably different levels of daytime sleepiness. Why some patients do not experience daytime sleepiness is unclear; even normal patients with induced apneic spells will manifest some daytime sleepiness (9). Several reasons may be postulated regarding how OSA and continued wakefulness (or lack of sleepiness) may coexist: (i) non-sleepy patients may genetically have a central nervous system that can accommodate to the repetitive apneas, (ii) sleepy patients may be genetically predisposed to having sleepiness, (iii) patients resistant to sleepiness may have a higher sleep threshold or may have a higher level of brain activation, and (iv) available testing is not appropriate or sensitive enough to discover more subtle cognitive deficits. In some cases where subjective testing of sleepiness occurs, it is not uncommon for patients to be unaware of their deficits secondary to sleepiness, particularly with a subtle onset of the symptom (10). EVALUATION OF EXCESSIVE DAYTIME SLEEPINESS IN OBSTRUCTIVE SLEEP APNEA Both subjective and objective measures have been used to evaluate and compare levels of daytime sleepiness. The standard objective assessment of daytime sleepiness has been the multiple sleep latency test (MSLT) (11), but alternate measures may be more useful depending on the patient and the situation. Assessment of Symptoms With an increasingly frequent recognition of the symptoms of OSA, such as pauses in breathing, excessively loud snoring, and daytime sleepiness, patients more frequently seek evaluation for sleep apnea and related sleepiness on their own. However, many other patients may visit their physician for assessment of sleepiness only on the advice of family, owing to job-related difficulties, or after a near miss motor vehicle crash. Basic questions regarding the timing of excessive sleepiness, particularly related to medications or life changes, may prove useful. More in depth questions regarding the severity of sleepiness can also be helpful, particularly regarding situations in which sleepiness becomes prominent. Falling asleep during driving, interpersonal communication, or presentations may be better indications of pathological sleepiness than are naps during long train rides, tedious office work, or after a large lunchtime meal (12). Sleepiness-related symptoms can also include poor concentration, impaired memory, irritability, and emotional lability (13). In fact, when 190 patients with confirmed OSA were asked to categorize their symptoms among sleepiness, fatigue, tiredness, and lack of energy, “lack of energy” was the most commonly chosen problem, “sleepiness” the least (14). Bed partners or

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close family members can prove invaluable, as they sometimes recognize clear signs of sleepiness that are denied by the patient. Subjective Measurements Assessment of sleepiness via self-descriptive tools is often a cost-effective way of measuring excessive daytime sleepiness, particularly in larger research studies where formal objective testing may be difficult to arrange. Perhaps the most commonly used questionnaire for research purposes is the Epworth sleepiness scale (ESS). The ESS uses eight situational questions to evaluate sleepiness in recent weeks. Depending on the study, an ESS score > or ≥10 has been considered an abnormal level of sleepiness (15). When compared to 30 control patients (mean ESS score 5.9 ± 2), 55 patients with OSA had significantly higher scores (mean ESS score 11.7 ± 4). Patients with symptomatic OSA were divided by severity of respiratory disturbance index (RDI): mild OSA (RDI > 5 to 15) patients demonstrated a mean ESS score of 9.5 ± 3.3, moderate OSA (RDI > 15 to 30) patients had a mean ESS score of 11.5 ± 4.2, and severe OSA (RDI > 30) patients revealed a mean ESS score of 16.0 ± 4.4. Notably, in this study, individual ESS scores above 16 were seen only in patients with narcolepsy, idiopathic hypersomnia, and sleep apnea of at least moderate severity (16). A link of much smaller magnitude between the AHI and the ESS score was demonstrated in a much larger population. Twenty-one percent of patients with an AHI less than five events per hour and 35% of patients with an AHI > 30 had ESS scores greater than 10. However, the mean ESS scores, even at the higher AHIs, remained below 10 and the majority of patients, even with severe OSA, did not selfreport sleepiness (17). Other studies in clinical settings have been unable to establish any significant association between apnea severity and ESS scores. One evaluation of 237 patients discovered that the AHI did not correlate with the ESS score (18). Another investigation of 100 Chinese sleep apneics found no association between ESS score and either AHI or minimum oxygen saturation (19). As the AHI also fails to predict other measures of sleepiness in OSA as effectively as might be expected, similar results for the ESS do not invalidate its use to gauge subjective sleepiness in a standardized manner for patients with OSA. The ESS may be especially useful as a sequential reassessment tool for individual patients who are treated for OSA. However, the ESS score does not necessarily screen for OSA, and the ESS provides no substitute for objective measures of excessive daytime sleepiness. Other ways to assess subjective sleepiness in OSA include the Stanford sleepiness scale (SSS). This one-item question asks patients to rate their immediate level of sleepiness or alertness on a descriptive scale that is provided. Simpler single-item tests also have some published validity (18,20). Quality-of-life instruments specific to sleep disorders include the functional outcomes of sleep questionnaire (FOSQ), which assesses domains sensitive to sleepiness. Generic quality of life instruments and the Medical Outcome Study’s Short Form-36 (SF-36) in particular are often sensitive to sleepiness. Objective Measurements The most commonly used objective assessment of daytime sleepiness is the multiple sleep latency test (MSLT), along with its variants such as the maintenance of

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wakefulness test (MWT). The MSLT is included of four or five daytime 20-minute nap attempts during which sleep onset is monitored by electroencephalography (EEG), surface electromyography, and electro-oculography. The patient is asked to try to fall asleep in an environment conducive to sleep (11). Clinical guidelines have indicated that the MSLT is not routinely indicated in the assessment of patients with OSA or their response to treatment, though it may be helpful in specific cases (21). The MWT has a similar format to the MSLT, but the primary difference is that subjects are requested to attempt to stay awake during the testing periods (22). Therefore, the MWT may best demonstrate the ability to resist sleepiness in a noninteractive environment. Many practitioners use a 40- rather than 20-minute nap opportunity. The MWT and MSLT results correlate only to a limited extent (23). The MSLT has been used frequently to quantify sleepiness in patients with OSA. When 100 patients with untreated sleep apnea were evaluated, findings included a mean sleep latency of 5.9 ± 3.5 minutes (24). Similarly, 225 patients who had referrals for excessive sleepiness were examined; 40% (70 patients) were diagnosed with OSA of heterogeneous severity. Findings from the sleep apnea subgroup of patients indicated a mean ESS of 10.3 and a mean sleep latency (MSL) of 10.2 minutes on the MSL; neither ESS nor MSLT correlated with the AHI (25). Sleep apneics, narcoleptics and controls were evaluated with MSLTs and driving simulators in another study. Compared to controls (mean MSL 13 ± 2 minutes), patients with OSA had significantly shorter mean sleep latencies (mean MSL 7 ± 6 minutes). For the purposes of comparison, the narcoleptic patients in this study had a mean MSL of 4.9 ± 5 minutes. Similar results were seen with a driving simulation; controls had little difficulty with the task and sleep apneics were progressively worse over the 20-minute test, regardless of the time of day (26). Considerable research has been performed in predominately Caucasian countries; more recently, however, increasing data have arisen from non-Caucasian sources. In Singapore, 195 patients with OSA were assessed (89% men), with excessive daytime sleepiness (defined as MSL on MSLT < 10 minutes) demonstrated in 87% (27). Another study involved 296 Chinese patients (250 males, mean age 45 ± 9 year) with OSA, divided into mild (5–15 events/hr), moderate (15–30), and severe (> 30) apneics. The mean sleep latencies in these groups were 8.1 minutes, 8.3 minutes, and 6.3 minutes respectively, only demonstrating a significant difference when comparing the severe patients to the other two groups. Interestingly, ESS scores in this patient population did have a significant, albeit weak, correlation with the mean sleep latency, but did not correlate with the OSA severity (28). The correlation between ESS score and mean sleep latency is inconsistent among studies, however. An alternate population of 237 subjects did not demonstrate a relationship between ESS score and mean sleep latency on the MSLT (in minutes), as seen in Figure 1. Table 1 indicates the variability of relationships observed between ESS scores, mean sleep latency (MSL), and OSA among several studies. Sleep-onset rapid eye movement periods (SOREMPs) on MSLTs have often been associated primarily with narcolepsy (36). However, in an analysis of over 1000 patients with suspected or confirmed OSA, nearly 5% had at least 2 SOREMPs, and 11% had at least one (37). A sample of subjects in Singapore revealed one SOREMP in 13.8% of patients, two in 9.7%, three SOREMPs in 4.1%, and four in 0.5% (27). These findings demonstrate that not only the sleepiness but also that the SOREMPs associated with OSA may lead to a mistaken diagnosis of narcolepsy

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FIGURE 1 Relationship between Epworth score and mean sleep latency. The Epworth sleepiness scale score is plotted against the mean sleep latency (in minutes) on the multiple sleep latency test for 237 patients. Source: From Ref. 18.

unless a clinical assessment and nocturnal polysomnography are carefully integrated into the diagnostic process. As mentioned earlier, the MWT has been used to assess the ability to resist sleepiness. One of the largest published studies on sleep apneics and the MWT evaluated 322 patients using a modified MWT with four 40-minute naps. All of the patients had a RDI >5 events/hr, with a mean of 39.4 ± 26.8 respiratory events/hr; the mean oxygen saturation nadir was 75.8 ± 12.4%. The average MWT four-nap sleep latency was 26.0 ± 11.8 minutes (normal based on prior controls was > 33 minutes). A small subgroup of patients with sleep apnea (mean MWT sleep latency 18 ± 12.3 minutes) was treated with nasal continuous positive airway pressure (CPAP); results demonstrated a significant improvement (mean MWT sleep latency 31.9 ± 10.4 minutes) even though not all patients were perfectly compliant with CPAP treatment. The factor that most clearly correlated with the sleep latency in the MWT in this study was nocturnal respiratory-related arousals; oxygen desaturations did not correlate with the sleep latency (38). Consistent results were obtained among 10 Finnish subjects with mild sleep apnea and 10 controls: the sleep apneics demonstrated increased daytime sleepiness with a mean ESS score of 11 (3 in controls) and a 40-minute MWT mean sleep latency of 24.3 minutes (36.3 minutes in normal controls) (39). More recently, 110 patients with mild–moderate OSA (AHI 10–30 events/hr) and 41 normal controls were tested with the ESS, SSS, and MWT. Findings in the patients with OSA, and those for normal controls (in parentheses) included: a mean ESS of 10.7 ± 4.5 (5.4 ± 3.0), mean SSS of 3.1 ± 1 (2.7 ± 1.0), and a mean sleep latency on MWT of 30.7 ± 10.2 minutes (36.7 ± 6.2 minutes) (40). These differences suggest that the MWT, though perhaps testing different aspects of sleepiness than the MSLT, identifies patients with sleep apnea as being sleepier than controls. The comparison of patients with moderate (AHI < 40) and severe (AHI > 40) OSA demonstrated no significant difference on MWT testing, though there was a tendency for shorter sleep latencies. Interestingly,

Reference # 16

17

29

30

31

20

Johns

Gottlieb et al.

Gottlieb et al.

Johns

Zimmerman et al.

Chervin et al.

60 sleep laboratory-referred patients with suspected sleepiness; most had sleepdisordered breathing

46 OSA patients

108 primary snorers, 165 OSA patients

Community sample in Sleep Heart Health Study (n = 5777)

Community sample in Sleep Heart Health Study (n = 1824)

30 control and 150 patients with different sleep diagnoses

Studied population ESS scores of patients significantly different from those of controls Statistically significant but small magnitude increase in ESS score with increase in AHI As above, and snoring continued to explain some sleepiness after AHI was taken into account Primary snorers with lower ESS scores than OSA patients; ESS scores increased with severity of OSA Reduced ESS scores after CPAP treatment (compared with retrospective estimation of pretreatment ESS scores) ESS score did not correlate with AHI

Relationship between ESS and OSA

Relationships Between ESS, Mean Sleep Latency, and OSA

Study

TABLE 1 Relationship between MSL and OSA

ESS score correlated negatively with MSL (rho = −0.37, p = 0.0042)

Relationship between ESS and MSL

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25

32

33 24 19

34

35

Olson et al.

Furuta et al.

Fietze et al.

Guilleminault et al.

Chung

Benbadis et al.

Johns

Four groups of subjects: (1) 44 patients with MSLTs (2) 150 patients with various sleep diagnoses (3) 87 medical students (4) 50 patients whose spouses also filled out the ESS

102 patients with complaints of excessive daytime sleepiness

100 patients with OSA and 61 controls

100 unselected OSA patients

22 patients

10 patients with OSA

237 patients suspected or confirmed to have sleepdisordered breathing 225 subjects with various diagnoses (40% with OSA)

ESS score did not correlate with AHI ESS did not correlate with AHI and minimum O2 saturation

ESS score did not correlate with AHI

ESS score did not correlate with AHI

ESS score did not correlate with AHI

Increase in MSL after CPAP MSL did not correlate with AHI

MSL correlated negatively with AHI

MSL did not correlate with AHI

MSL correlated with severity of OSA

ESS score correlated negatively with MSL (rho = –0.42, p = 0.0001) No significant association between ESS score and MSL For group 1, ESS was correlated with the MSL (rho = 0.42, p < 0.01); three of eight items on ESS correlated significantly with MSL

No significant relationship between ESS score and MSL

No significant relationship between ESS score and MSL ESS score correlated negatively with MSL (rho = –0.30, p < 0.0001)

Abbreviations: AHI, apnea-hypopnea index; CPAP, continuous positive airway pressure; ESS, Epworth sleepiness scale; MSL, multiple sleep latency; MSLT, multiple latency test; OSA, obstructive sleep apnea. Source: From Ref. 28.

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the moderate sleep apneics tended to score higher than the more severe apneics on tests of subjective sleepiness (41). The Oxford Sleep Resistance (OSLER) test has also been used, at least in research settings, to assess daytime sleepiness. The test records responses to a lightemitting diode (LED) light; when seven iterations of LED-lighting occur without a response, sleep is scored. The OSLER and MWT were compared in 10 controls and 10 patients with OSA (mean oxygen dip rate of 32/hr, mean ESS score of 17). Mean sleep latencies in the apneic patients were shown to be 7.3 minutes with the 40-minute MWT and 10.5 minutes with the OSLER test. Overall, results of the two tests were similar (42). The OSLER has the advantage of requiring much less equipment and expense, because EEG recording of sleep is not obtained. CAUSES OF SLEEPINESS IN OBSTRUCTIVE SLEEP APNEA Causes for daytime sleepiness in patients with OSA include those that stem from the disorder itself, as well as those that are prevalent even in the absence of OSA. Table 2 below lists issues to consider as part of a complete clinical assessment for sleepiness in patients with underlying OSA. From a physiological perspective, two potential causes of sleepiness in OSA have received the most attention. Nocturnal arousals associated with apneic events may disrupt sleep more than one hundred times per hour in severe cases. This lack of sleep continuity is likely to contribute to daytime sleepiness (44). Hypoxemia associated with apneic events can be severe and also has been theorized to contribute to sleepiness (45). In a group of 37 elderly patients with and without daytime sleepiness, the sleepier patients had more severe oxygen desaturations, but did not differ in the TABLE 2 Factors Affecting Wakefulness in Patients with Sleep Apnea Sleep architecture Degrees of sleep disturbance (arousal index) Number of sleep arousals (sleep fragmentation) Duration of arousal-free sleep increases restorative effect of sleep Nature of stage-related sleep deprivation The period of disturbance (acute vs. chronic) Recovery/compensatory sleep (length of sleep/naps) Associated sleep disorders (e.g., various causes of insomnia) Patient factors Personality/psychosocial makeup Age Sex Associated disease (anxiety, Parkinson’s disease, hypothyroidism, stroke) Daytime environment Working environment/occupation Temperature Light Noise Other factors Food/drugs (caffeine, pseudoephedrine, alcohol) Exercise Posture Source: From Ref. 43.

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frequency of apneas and hypopneas (46). This study’s results were based on only subjective reports of sleepiness. In contrast, an evaluation of 466 patients undergoing polysomnograms and MSLTs demonstrated that the best predictors of mean sleep latency were variables associated with arousals; measures of hypoxemia were not found to be independently predictive (47). A third study included more than 1,000 patients with suspected or confirmed OSA who were referred for evaluation by polysomnography and MSLTs (48). The frequency of apneas and hypopneas and the minimum oxygen saturation each showed independent associations with daytime sleepiness, though neither of these associations was strong. In short, these and other clinical studies combined seem to support a contribution of sleep disruption to the sleepiness seen in OSA. Furthermore, in upper airway resistance syndrome (UARS), sleepiness cannot be attributed to hypoxemia, as by definition intermittent hypoxemia is not present in this form of sleep-disordered breathing. However, hypoxemia may well make an additional independent contribution to sleepiness seen in OSA. Experimental paradigms have also been used to determine what may cause sleepiness in OSA. Repetitive tones that created EEG arousals led to daytime sleepiness and impaired attention (49). Repetitive tones each minute during a nocturnal polysomnogram caused autonomic changes (rises in arterial blood pressure or increases in heart rate) without causing a three-second EEG arousal (50). Still, the multiple sleep latency test and maintenance of wakefulness test performed after the nocturnal polysomnograms both revealed short sleep latencies after this “nonvisible sleep fragmentation.” These findings suggest that subtle subcortical arousals may contribute to daytime sleepiness, even when arousals scored according to visible EEG changes are not apparent. The AHI is often used by clinicians to characterize the severity of sleep apnea and to predict the likelihood of consequences, including excessive daytime sleepiness. However, available data are somewhat contradictory and do not suggest that the AHI predicts sleepiness strongly. Using survival analysis, both the AHI and oxyhemoglobin desaturation were associated with MSLT-defined hypersomnolence in a sample with moderate OSA (51). The AHI was associated with MSLT-defined sleepiness severity when results from 1146 patients were analyzed, though this correlation was not strong (52). A similar outcome was observed in another 123 patients, where AHI only explained a small portion of the variance when assessing mean sleep latency (53). Both respiratory disturbance index (RDI) and oxygen desaturations failed to show associations with the mean sleep latency on MSLTs in a study of 100 subjects (24). Other features of OSA pathophysiology and other measurable variables also may contribute to daytime sleepiness. In a clinical setting, the sleeping position (supine vs. nonsupine) in which the AHI is recorded may influence sleepiness as measured by the MSLT: the supine AHI appears to predict mean sleep latency better than does the nonsupine AHI (48). The type of respiratory event may also be relevant. Although apneas and hypopneas each seem to contribute to sleepiness with equal magnitude on average, the apneas seem to make this contribution more reliably (48). Within the classification of apneas, obstructive apneas may explain a larger portion of objectively-measured sleepiness than do mixed or central apneas (48). The sleep stage in which the respiratory events occur [non-rapid eye movement (NREM) sleep vs. rapid eye movement (REM) sleep] may also alter the severity of daytime sleepiness. Typically, REM-related apneas are longer than those that occur during NREM sleep, and are associated with a greater degree of hypoxemia.

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The REM-specific RDI correlated best with mean sleep latency when 34 patients with mild OSA were studied (54). However, this finding could not be replicated in a larger sample of patients, in which the REM-specific AHI did not correlate with excessive daytime sleepiness, and the NREM-specific AHI showed a stronger association with mean sleep latency even in patients with mild sleep apnea (52). These findings were supported by another study which used survival analysis to demonstrate a lack of association between REM-specific AHI and mean sleep latency, but established that NREM-specific AHI increased the relative risk for hypersomnolence. One explanation for this finding is that the majority of sleep time (75–85%) is spent in NREM sleep, so NREM-specific AHI may be a better marker of sleep discontinuity. Also, the response to apneas may be different during REM sleep, such that the REM-AHI may not correlate with an increased numbers of arousals (55). In an interventional experiment, seven patients were enrolled in a crossover CPAP treatment trial for OSA. Patients underwent use of standard CPAP versus a positive pressure machine which corrected apneas and sleep fragmentation, but induced intermittent hypoxemia. No difference in daytime sleepiness as measured by MSLT was seen after two nights of either treatment, leading the authors to suggest that sleepiness was likely more related to sleep fragmentation than intermittent hypoxemia (56). More recently, traditional EEG-defined arousals and non-EEG markers of arousals were compared as predictors of subjective (ESS) and objective (OSLER) measures of daytime sleepiness in patients with OSA. The non-EEG markers were an autonomic arousal index, derived from pulse transit time, and a movement event index, as determined from digital subtraction of video images. The findings demonstrated that non-EEG markers in comparison to traditionally-scored arousals were as good, if not better predictors of daytime sleepiness (57). Approx 28% of apneas and hypopneas are not associated with visible cortical EEG arousals during NREM sleep (58). EEG power spectral analysis performed around apneas and hypopneas can reveal decreases in theta rhythm power even after those respiratory events not associated with visible EEG arousals (59). Many studies have focused on effects of discrete apneic events on daytime sleepiness. Such events have included apneas, hypopneas, and more subtle respiratory event-related arousals. However, elevated negative esophageal pressures are well known to occur outside these discrete apneic events, sometimes in a continuous manner without the crescendo-arousal patterns that characterize the upper airway resistance syndrome. This continuously increased work of breathing may also contribute to excessive daytime sleepiness (60,61). In support of this possibility, a computer algorithm can be used to identify changes in EEG power that occur on average with each nonapneic respiratory cycle (62). These so-called respiratory cycle-related EEG changes occur more prominently in children with OSA than those without OSA, and decrease after OSA is treated by adenotonsillectomy (63). Furthermore, the respiratory cycle-related EEG changes predict MSLT-defined sleepiness in children and adults (63,64), and subjective sleepiness in children (65). Data from adults suggest that this synchrony between EEG and respiratory cycles may reflect inspiratory microarousals that are subtle but so numerous that they predict sleepiness as well or better than does the standard AHI (64). Alteration in sleep stages, primarily reductions in slow wave (stages 3 and 4) and REM sleep, has been observed with OSA patients and may also diminish the restorative power of sleep (10). This decrease in slow wave sleep and REM sleep was observed particularly in men with higher apnea/hypopnea indices; interestingly,

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this same effect was not clearly observed in women with higher apnea/hypopnea indices (66). Mice exposed to intermittent hypoxemia demonstrate similar changes in sleep stages, though regardless of sex (67). This may suggest that hypoxemia has a larger role in sleep stage alteration and therefore daytime sleepiness than previously suspected (68). Gender-specific changes in sleep potentially could account for gender-specific differences in OSA symptoms and deserves further research. Slow wave sleep activity, which is not synonymous with NREM sleep stages 3 and 4, plays a critical role in the homeostatic process of sleep (69,70). In 10 men with OSA, total slow wave activity and the slow wave activity in the first two NREM periods clearly increased after CPAP treatment (71). In contrast, amounts of stage 3 and 4 sleep remained unchanged. A significant correlation between slow wave activity in the first sleep cycle and the MSLT results was observed in sleep apneics before treatment, but not after. The rate at which slow wave sleep declines, across successive sleep cycles during the night, could reflect dissipation of slow wave sleep need and perhaps predict daytime sleepiness in patients with OSA (71). Finally, sleepiness in OSA could arise in part from changes in metabolism, hormonal status, or levels of endogenous sleep-inducing substances (72–74). Cytokines, such as interleukin (IL)-6 and tumor necrosis factor (TNF)-α, are elevated in sleep apneics. Increased Il-6 is associated with daytime fatigue and sleepiness suffered by obese patients without sleep apnea (75). Marked improvement of sleepiness in eight OSA patients after treatment with etanercept, a TNF-α antagonist, suggests that TNF-α may contribute to sleepiness in OSA (76). MANAGEMENT OF SLEEPINESS IN OBSTRUCTIVE SLEEP APNEA The most effective treatment option for OSA and its resultant sleepiness is dependent on the severity of the OSA, patient and physician preference, and other associated medical conditions. This section will assess treatment options as they relate to daytime sleepiness in OSA. Regardless of the primary contributor to daytime sleepiness, improvement in OSA via nonsurgical or surgical treatment may improve daytime sleepiness. Perhaps one of the most dangerous side effects of OSA is the effect of daytime sleepiness on driving. Several studies have demonstrated that patients with OSA are at higher risk for motor vehicle accidents (see also Chapter 24) (8,77). There has also been an indication that motor vehicle collisions are reduced with CPAP use (78). However, MSLT results do not clearly distinguish those untreated sleepy patients who have motor vehicle crashes from those who do not (79). These concerning results suggest that physicians should appropriately evaluate and treat patients with daytime sleepiness and other signs of sleep apnea, as it may prevent drowsy driving or industrial accidents. In some cases of severe sleepiness, patients may be unable to perform adequately or safely at work, and may need short-term disability until treatment has taken effect. Continuous Positive Airway Pressure CPAP is perhaps the well-studied treatment of excessive daytime sleepiness associated with OSA. A 1997 meta-analysis of literature evaluating OSA and CPAP treatment suggested that not enough controlled trials existed to support an improvement in daytime sleepiness with use of CPAP in all patients with sleep apnea, though anecdotal reports of significant improvement of sleepiness occurred (80). The only

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randomized crossover controlled study found in this meta-analysis was of 32 patients which demonstrated a decrease in mean sleep latency on MSLT and an improvement in driving simulation during a treatment phase when compared to placebo (81). In 1999, a randomized prospective evaluation of 54 patients with therapeutic CPAP and 53 patients with subtherapeutic CPAP demonstrated a significant improvement in mean ESS score (15.5 to 7) and mean sleep latency on MWT testing (22.5 to 32.9 minutes) in the therapeutic CPAP group. These findings did not occur to the same degree in the subtherapeutic treatment group, with statistically significant improvements in the mean ESS score only (15 to 13). However, this study was limited by a study sample consisting of exclusively men with severe symptoms (82). The subjective and objective effectiveness of CPAP in diverse populations was calculated via meta-analysis of several randomized controlled studies; controls in these studies included sham CPAP, pills and conservative therapy. Overall, ESS scores improved 2.94 points and mean sleep latency increased in 0.93 minutes with use of CPAP when compared to controls. These findings were independent of sex, age, body mass index (BMI) or nationality, and were similar to those of accepted treatments of other disorders of excessive daytime somnolence (e.g., modafinil in narcolepsy). AHI and ESS did not predict effectiveness of CPAP, but limiting the analysis to studies with high AHIs (> 30 events/hr) and significant subjective sleepiness (ESS score > 10) demonstrated significant improvement in subjective sleepiness with use of CPAP (mean ESS reduction of 4.75 points). This meta-analysis used studies from relatively homogenous study populations; however, by combining the studies, a relatively heterogeneous population was formed. Many of the studies used to form this analysis were short (four to six weeks), which limits the understanding of the long-term improvements in sleepiness (83). Consistent with the weak association between apnea severity and sleepiness, patients with mild–moderate apnea (5–30 events/hr), the subject of another metaanalysis, also show prominent deficits in alertness. With CPAP treatment, ESS scores improved by 1.2 points and mean sleep latency on MWT improved by 2.1 minutes (in evaluation of two studies); both findings were statistically significant. Mean sleep latencies worsened by 0.2 minutes in MSLT testing (in evaluation of four studies), which was statistically nonsignificant. Effect sizes were small overall, raising questions regarding their clinical significance. These results only assessed daytime sleepiness in this population; the other effects of CPAP use (e.g., cardiovascular effects) were not evaluated (84). Oral Appliances Few studies have examined effects of oral appliances on excessive sleepiness, and in particular few have used objective measures to do so. One randomized controlled crossover trial included 80 patients with AHIs of 5 to 30 events/hr. Each subject had one month of nasal CPAP, mandibular advancing splint (MAS), and placebo tablet. The AHI improved from a mean of 21.3 events/hr to 4.8 events/hr with CPAP and 14.3 with MAS. ESS score improved from 10.7 (baseline) and 10.2 (placebo) to 9.2 in both CPAP and MAS. Mean sleep latency on MWT testing was 30.7 minutes at baseline and 28.0 minutes with placebo; minimal change to MWT mean sleep latencies of 30.0 minutes (CPAP) and 29.6 (MAS) were observed with the interventions (85). Other studies have also demonstrated significant improvements on Epworth scores in patients with mild-to-moderate OSA with use of oral appliances (86).

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Surgical Intervention Though several studies have demonstrated that upper airway surgery may improve the AHI, few have studied daytime sleepiness with objective measures. Uvulopalatopharyngoplasty (UPPP) is a common surgical intervention for OSA. Some investigators have had increased success with the addition of genioglossus advancement (GA). Forty-four patients evaluated pre- and post-intervention with UPPP and GA demonstrated significant improvement in ESS scores, from a mean pretest value of 14.3 ± 5 to a mean post-test value of 6.3 ± 3.2 (87). Another study examined 57 OSA patients who had failed CPAP and either had an RDI > 15 or an ESS >10. These patients underwent two possible surgeries, depending on a number of factors including age and disease severity: 42 patients underwent soft palate surgery, hyoid suspension and genioglossus advancement and 15 patients had advancement of the maxilla and mandible by 9 mm. The patients with soft palate surgery had a preoperative mean ESS score of 14.5, with postoperative improvement to a mean score of 7.5 (38 patients demonstrated improvement). The maxillofacial surgery patients had a mean ESS score of 17.8 which improved to 4.7 after surgical intervention (88). Maxillo-mandibular expansion by distraction osteogenesis in six patients was reported to improve ESS scores from 10.2 ± 1.9 to 5 ± 2.9 (89). TREATING NONSLEEPY PATIENTS Among 55 subjects who did not complain of daytime sleepiness, treatment of underlying sleep-disordered breathing failed to alter ESS scores or mean sleep latency on MSLT (90). The authors suggested that this lack of improvement may indicate that treatment of sleep-disordered breathing may not be indicated in these cases. However, increased risk of cardiovascular disease and motor vehicle crashes may be independent of daytime sleepiness, and could be potential reasons for treatment (8,91). In addition, as noted earlier, patients are often unaware of the severity of their sleepiness; treatment of sleep-disordered breathing may improve alertness or cognitive status that had been unwittingly impaired (10). PERSISTENT SLEEPINESS AFTER TREATMENT OF OBSTRUCTIVE SLEEP APNEA Although many patients are pleased with their perception of reduced daytime sleepiness after treatment for OSA, measured improvements are limited on average, as reviewed above, and a subset of patients remain persistently sleepy. For example, in one study 10 healthy controls and 14 patients with OSA (mean AHI 62.8 ± 25.8 events/hr) were treated with CPAP for six months (residual AHI 3.9 ± 6.5 events/hr) (92). The mean sleep latency on MSLT in normal patients (13.1 ± 2.5 minutes) was significantly longer than the sleep apneics pretreatment (4.1 ± 1.9 minutes) which would be expected; however, surprisingly, there was still a significant difference in sleep latency even after six months of continued CPAP treatment (8.6 ± 4.5 minutes). No assessment of subjective sleepiness was used during this study. The subjects with sleep apnea resembled the normal controls in levels of sleep disruption and had comparable nocturnal oxygenation. Two limitations of this study were the lack of CPAP adherence measurement and the presence of elevated periodic limb movements (PLMs) in the OSA group. However, other studies also have demonstrated

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continued sleepiness even with CPAP adherence (93,94) and PLMs did not correlate with the mean sleep latency in this study (92). One possibility that might explain persistent sleepiness after treatment for OSA is that patients remain undertreated by their CPAP. Though the more obvious apneas and hypopneas may be eliminated, more subtle respiratory event-related arousals or continuously excessive esophageal pressures may remain. To assess this possibility, a multicenter trial evaluated patients who had residual sleepiness after three months of regular CPAP use. These 46 subjects underwent a repeat CPAP titration with esophageal pressure manometry along with other interventions (CPAP mask refitting, addition of chinstrap, and adherence improvement). Symptoms resolved in two-thirds of these patients, but daytime sleepiness persisted in the other patients (10). Another possibility, as yet inadequately explored, is that the long-term irreversible effects of OSA on the central nervous system, rather than undertreated OSA, causes treatment-resistant sleepiness (95). Alternatively, comorbid untreated sleep disorders may sometimes explain persistent sleepiness in the setting of adequate OSA treatment. Insufficient sleep may be the most common reason for daytime sleepiness, and OSA patients are unlikely to be immune. Primary sleep disorders causing daytime sleepiness, such as narcolepsy and idiopathic hypersomnia, may also be present in some OSA patients. Periodic limb movements have been postulated to play a role in daytime sleepiness of apneics, though evidence does not support this possibility (96,97). Medical and psychiatric disorders should also be taken into account. Major depression may make important contributions to symptoms of daytime fatigue in sleep apnea patients (98). Pharmacological treatments of many medical conditions also can increase daytime somnolence. The clinician confronted with persistent sleepiness despite use of CPAP should first confirm that the CPAP is being used appropriately every night. Objective evidence can usually be downloaded from most modern CPAP units, and studies show that such data often differ from patient reports. The clinician should inquire about mask comfort and mouth breathing, and consider whether a chinstrap, heated humidity or a different mask may be helpful. Further evaluation may include a CPAP retitration (with esophageal pressure monitoring if available) and an MSLT on the following day, to provide an objective assessment of sleepiness, if the CPAP setting is not altered during the nocturnal polysomnogram. The CPAP setting should be titrated to achieve normal esophageal pressures, usually less negative than −10 cm of water (10). Alternate treatments of sleep apnea (oral appliances or surgical intervention) may also need to be considered. Pharmacological treatment of residual sleepiness in OSA should generally be used only after a verifying that sleep-disordered breathing is optimally treated. Modafinil, a wake-promoting agent, has been tested in untreated sleep apneics, with findings that subjective and objective measures of sleepiness improved (99). Two multicenter trials examined the utility of this medication in CPAP-treated patients with OSA and residual sleepiness. Pack et al. evaluated 157 patients randomized to placebo or 400 mg of modafinil, noting improvement with the study drug in both subjective (ESS) and objective (MSLT, psychomotor vigilance test) measures (100). A similar study in 305 patients by Black et al. also found a beneficial result when measured by ESS and MWT over a 12-week period of modafinil use (101). As noted earlier, certain medications have been found helpful in the treatment of sleepiness in both CPAP-treated and untreated patients. While the pharmacological treatment of sleepiness in otherwise untreated sleep apneics may theoretically

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reduce their risk for motor vehicle crashes or work-related accidents and improve their quality of life, it may not reduce their long-term health consequences of hypertension, stroke and heart disease. Therefore, treatment of the underlying sleep-disordered breathing has remained the primary focus, with use of modafinil only as an adjunct form of treatment (10). SLEEPINESS IN CHILDREN WITH OBSTRUCTIVE SLEEP APNEA Early literature suggested that children in comparison to adults have OSA much less frequently, and that affected children less often have excessive daytime sleepiness. Both assumptions have been called into question by more recent data. OSA affects 2% to 3% of children (102), and habitual snoring affects 10% or more. Although habitual snorers often have “primary snoring” without significant numbers of apneas, hypopneas, or oxygen desaturations on standard polysomnography, increasing evidence suggests that primary snoring has neurobehavioral consequences in children (103–105). Inattention and hyperactivity are often highlighted as presenting features of childhood OSA, by parents and wary sleep specialists, but these behaviors could represent efforts on the part of a sleepy child to remain awake despite chronically inadequate sleep (106). In fact, children with attention-deficit/ hyperactivity disorder, unselected for sleep complaints, prove paradoxically to be more sleepy, not less, when compared to other children of similar ages (107). The MSLT can demonstrate shortened mean sleep latencies in 3- to 12-year-old children with sleep apnea, in comparison to controls (108,109). The severity of sleep apnea—as reflected for example by the frequency of apneas and hypopneas, minimum oxygen saturation, or end-tidal carbon dioxide—predicts the mean sleep latency. Obesity also may have an independent predictive value in these subjects, after controlling for polysomnographic findings (109). However, in clinical practice children less than eight years old rarely undergo MSLTs, and older children affected by sleep apnea (or other sleep disorders) rarely demonstrate mean sleep latencies as short as those found in adults (110). The maintenance of wakefulness test has not been studied in children with OSA, to the authors’ knowledge. Despite the fact that parents and children with OSA may not describe sleepiness as the presenting complaint, they do often describe it when asked. Surveys in general pediatric waiting rooms of parents about their children have shown clear associations between reported symptoms of OSA and excessive daytime sleepiness (13). Forty-three percent of children awaiting adenotonsillectomy, in one study, as compared to only 12% of controls, had high scores on the sleepiness subscale of the Pediatric Sleep Questionnaire. Objective sleepiness, like that measured by the MSLT, is also predicted by the frequency of apneic events, though perhaps not by minimum oxygen saturation or arousal frequency. Less commonly used measures that may also help to predict subjective sleepiness in children with sleep-disordered breathing include esophageal pressures and respiratory cycle-related EEG changes (RCREC) (65). Instruments to assess subjective sleepiness in children are available, but generally less well studied than those for adults. The sleepiness subscale of the Pediatric Sleep Questionnaire has been validated in children studied by polysomnography for suspected sleep-disordered breathing (111), and remains the only subjective assessment to have been validated against MSLT results (65). The ESS, modified slightly and validated for children with suspected sleep apnea, offers the advantages of an instrument already familiar to clinicians for use in adults (112). Children with OSA score higher than controls on the modified Epworth, but no higher than

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children with primary snoring (112). Finally, the Pediatric Daytime Sleepiness Scale differentiates among several functional outcomes in children, though its performance in pediatric OSA more specifically is not known (113). Children with OSA show less sleepiness after treatment by adenotonsillectomy, though only one nonrandomized study to the authors’ knowledge has tested for this outcome (108). In this study, mean sleep latency on MSLTs increased by only about one minute one year after surgery, while it decreased by nearly the same amount in a group of control subjects who had received unrelated surgical care. The extent of improvement after OSA treatment, while not large, resembles that seen among adults treated by CPAP (83). The effect of continuous positive airway pressure on sleepiness associated with childhood OSA is not known. Likewise, the extent to which sleepiness persists or can be treated, after effective treatment for childhood OSA, remains unstudied. Modafinil, used for adult apneics with residual sleepiness, failed to receive Federal Drug Administration approval for childhood attention-deficit/hyperactivity disorder, mainly because of safety concerns. CONCLUSIONS OSA often causes significant daytime sleepiness, but many patients may not be aware of this effect. Hypersomnolence is likely to impair quality of life and increase risk for motor vehicle crashes. In pediatric OSA, inattention and hyperactivity may be more prominent, but sleepiness can also be a problem. Full evaluation of patients and appropriate treatment of sleep-disordered breathing with CPAP or other approaches are recommended as a starting point to minimize daytime sleepiness. If residual daytime sleepiness remains, further titration or correction of more subtle sleep-disordered breathing may be indicated, and in some instances medication may prove useful. The causes of the daytime sleepiness in OSA are not completely understood, though sleep disruption and to a lesser extent hypoxemia appear to play important roles. Respiratory effort and inflammatory pathways may also contribute. The importance of excessive daytime sleepiness as a marker for and consequence of OSA suggests that refinement in assessment techniques and pathophysiological understanding could make critical contributions to sleep medicine in coming years. REFERENCES 1. Dickens C. The Pickwick Papers: http://www.online-literature.com/dickens/ pickwick/4; 1837. Accessed October 18, 2006. 2. National Commission on Sleep Disorders Research. Wake up America: a national sleep alert—Report of the National Commission on Sleep Disorders Research. Washington, D.C.: Government Printing Office, 1993. 3. Davies RJ, Stradling JR. The epidemiology of sleep apnoea. Thorax 1996; 51(suppl 2): S65–70. 4. Young T PM, Dempsey J, Skatrud J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 328(17):1230–1235. 5. Finn L, Young T, Palta M, et al. Sleep-disordered breathing and self-reported general health status in the Wisconsin Sleep Cohort Study. Sleep 1998; 21(7):701–706. 6. Baldwin CM, Griffith KA, Nieto FJ, et al. The association of sleep-disordered breathing and sleep symptoms with quality of life in the Sleep Heart Health Study. Sleep 2001; 24(1):96–105. 7. Sassani A, Findley LJ, Kryger M, et al. Reducing motor-vehicle collisions, costs, and fatalities by treating obstructive sleep apnea syndrome. Sleep 2004; 27(3):453–458.

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Health-Related Quality-of-Life Cheryl A. Moyer Global REACH, Department of Medical Education, University of Michigan Medical School, Ann Arbor, Michigan, U.S.A.

Jeffrey S. Moyer Division of Head and Neck Surgery, Department of Otolaryngology, University of Michigan Hospital, Ann Arbor, Michigan, U.S.A.

Ronald D. Chervin Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan, U.S.A.

INTRODUCTION As discussed in previous chapters, obstructive sleep apnea (OSA) has significant effects on a wide range of organ systems. It is also associated with obesity, mood and behavior changes, excessive sleepiness, and motor vehicle collisions. Yet the summed effects of OSA on patients’ self-reported quality-of-life (QOL) may best reflect the personal impact of the disorder. According to Brown (1): “Quality-of-life issues may ultimately be the most interesting consequence of OSA. Loud snoring, erectile dysfunction and daytime fatigue can place a significant strain on marriages, resulting in marital problems and divorce. Daytime fatigue can cause increased accidents and diminished work performance, resulting in fewer promotions or loss of work. The stress of living with a chronic illness can result in increased anxiety and diminished quality-of-life. These factors may be the initial concerns that lead individuals to seek treatment, and improvement in quality-of-life may ultimately determine compliance with treatment” (1).

In this chapter, we describe the concept of health-related quality-of-life (HRQOL) and how it differs from traditional clinical outcome measures. We discuss the issues associated with HRQOL measurement in patients with OSA, including how quality-of-life is affected by OSA, both before and after treatment. We provide information to assist clinicians in incorporating HRQOL assessment into their own research and clinical practice. Finally, we discuss some special circumstances—such as pediatric assessment of OSA—and logistical issues to consider when choosing an instrument. QUALITY-OF-LIFE ASSESSMENT: WHAT IS IT, AND WHY MEASURE IT? In a clinical setting, measures of disease burden and treatment efficacy typically center on biomedical markers, such as laboratory values or imaging results. In addition, mortality has traditionally been viewed as a primary outcome measure of interest in evaluating a new treatment regimen. However, more patients are learning to live with chronic conditions in which mortality may not be the most appropriate outcome measure. Treatment modalities are differentiating themselves 415

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less by changes in mortality and more by the impact on patients’ lives. Thus patients’ experiences of their illness are becoming more salient to both clinicians and researchers. This has generated a growing demand for non-clinical outcome measures, such as patients’ self-reported health-related quality-of-life (HRQOL). What Is HRQOL? Health-related quality-of-life has been defined as the functional effect of an illness and its therapy upon a patient, as perceived by the patient (2). It moves beyond direct manifestations of illness to study the patient’s personal morbidity—or how the patient perceives the illness and its treatment to be affecting various aspects of their lives (3). Health-related quality-of-life is often characterized by several different domains, including such domains as physical, emotional, and social well-being. Researchers have included additional dimensions as well, but as yet, no consensus exists on exactly which dimensions need to be examined in order to accurately and comprehensively measure HRQOL. Not surprisingly, the dimensions of interest may vary by research question, clinical area of inquiry, or the goal of HRQOL assessment. Most researchers agree that regardless of the dimensions assessed, each dimension contributes only partly to a person’s overall quality-of-life. Spilker used a pyramid divided horizontally into three sections to illustrate this point (4). The overall assessment of well-being is the top section of the pyramid. The middle portion of the pyramid includes the broad domains of HRQOL that contribute to overall well-being, such as physical functioning. The bottom section of the pyramid includes the smaller components that make up each of the specific domains. For example, daytime sleepiness may be a symptom within the broader domain of physical functioning. Using this model, one may approach HRQOL assessment from a top-down (overall assessment) or a bottom-up (e.g., impact of the reduction in specific symptoms) direction. How is HRQOL Measured? Health-related quality-of-life is most often assessed through patient surveys—either self-administered surveys or surveys that are administered by a clinician or researcher. These surveys can be categorized as either generic or specific instruments. Generic instruments were developed to be used among a broad spectrum of patients with different types of illnesses. Specific instruments have been designed to measure a narrow topic of interest, such as the effects of treatment on OSA. Due to the strengths and weaknesses of both generic and specific measures (Table 1), it is often best to use one of each to obtain the most accurate clinical assessment. Generic Instruments Generic instruments are ideal for use in diverse groups of individuals (5). They range in complexity from a single indicator (such as, “Overall, how would you rate your quality-of-life?,” scored on a 5- to 10-point scale) to a detailed health profile assessment that reflects several domains of influence. One advantage of using a single indicator is that it can provide a clear data point that can be easily compared among patients. However, this relatively crude measure does not explore the myriad factors influencing the patient’s response. For this reason, many researchers and clinicians prefer to use more detailed generic instruments that allow measurement of several different aspects of HRQOL among a wide variety of patients. An example is a profile instrument, which generates a cross-sectional description of a person’s

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Health-Related Quality-of-Life TABLE 1 Types of HRQOL Instruments: Strengths and Weaknesses Strengths

Weaknesses

Generic Allow comparison between studies, instruments populations, or disorders More comprehensive than specific instruments Some have been widely validated

May lack sensitivity to detect differences among patients with a specific disease or condition May lack sensitivity to conditions that affect narrow dimensions of HRQOL May not follow framework familiar to clinicians Single-item Easy to administer and interpret Doesn’t yield information on what goes global into global score May oversimplify concept of HRQOOL Profile Generate cross-sectional view of May be confusing to interpret longitudinal patient’s overall well being changes over multiple dimensions Allow for determination of which Summary scores may lack sensitivity to dimensions of QOL are most detect changes, especially if most affected change occurs in one dimension PreferenceAllow generation of a single utility Preferences calculated from other based score for a health state instruments and not assessed directly Allow cost-utility analysis from patients may not accurately reflect patient preferences Single utility score may oversimplify differences in HRQOL Specific Well suited to detect changes among May not be available or validated for a instruments patients with specific disorders particular disease or treatment More focused on the area of interest May not discriminate among similar than generic instruments diseases Clinically sensible Do not allow cross-condition comparisons Abbreviation: HRQOL, health-related quality-of-life. Source: From Ref. 102.

quality-of-life across several dimensions. One commonly used generic health profile instrument is the Medical Outcome Study Short Form-36 (MOS SF-36), a 36-item questionnaire that summarizes HRQOL using eight subscales and two summary scores (6,7). The SF-36 has also been shortened to the SF-12 (12 items) and the SF-8 (8 items). This type of instrument allows comparison of the effects of different diseases on individual subscale domains, as well as on overall mental and physical health. Utility measures, such as Torrance and Feeny’s Health Utilities Index (8), Kaplan’s Quality of Well Being Scale (QWB) (9), or the shorter, self-administered version of the QWB, the QWB-SA (10), are based on economic theory. Utility measures ask a range of questions that help to classify respondents into various health states. These health states have typically been previously assessed by a sample of the general public, patients, or a panel of experts to determine their preference-based weights—or utilities. These utilities, expressed on a scale of 0 (death or worst possible outcome) to 1 (complete health or best possible outcome), refer to the subjective value attached to specific levels of health (5). When preference weights are derived from patients or the general public, some variant of the “standard gamble” approach is generally used, including such things as “willingness to pay” or “time tradeoff.”(11). These approaches, described in detail elsewhere (11,12), ask respondents to weigh one hypothetical outcome against another, forcing them to identify at which point they would prefer one over the other. Utility scores summarize complex HRQOL influences in a single variable and, when combined with survival data, allow calculation of quality adjusted life years (QALYs) (11–13).

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Specific Instruments While generic instruments make cross-study and cross-population comparisons possible, they may lack the sensitivity to detect differences among patients with the same disease or treatment. Specific HRQOL instruments focus on aspects of health status relevant to more narrowly defined populations. Specific HRQOL instruments may be tailored for a particular diagnosis (e.g., OSA), treatment [such as surgery or continuous positive airway pressure (CPAP)], population of patients (e.g., patient caregivers or young children), function (e.g., sexual function), or symptom (e.g., sleepiness) (14). Specific instruments are more likely than generic instruments to capture subtle differences in the impact of a given disease on a patient population, as well as the outcomes of various treatments for the same disease. Why Measure HRQOL? For clinicians working with OSA patients, an assessment of HRQOL is valuable on many levels. First, it allows clinicians to determine how problematic OSA is for the patient. Research suggests that clinical measures of disease severity are often very poorly correlated with patients’ perceptions of illness (15–17). Thus a patient may appear to have a relatively mild case of OSA, yet nonetheless be profoundly affected by it. On the other hand, a clinical measure may indicate that a treatment has worked well, yet a patient may not feel significantly better. Assessing HRQOL may also give the clinician insight into which dimensions of a patient’s life are most affected by OSA. For example, one patient with an occupation requiring intense concentration may report decrements in their occupational functioning as a result of OSA, whereas another patient with hobbies requiring physical stamina may report diminished physical or social functioning as a result of OSA. Understanding these differences between individuals may allow clinicians to address patients’ concerns more effectively. Assessing pretreatment HRQOL also allows clinicians to determine which (if any) interventions improve a patient’s quality-of-life. Clearly one of the clinician’s goals is to reduce their patient’s perception of symptoms and minimize the burden of illness. In addition to clinical markers or polysomnography, a patient’s self-reported HRQOL may prove to be a simple, inexpensive, yet valuable measure of “real world” treatment impact. Finally, HRQOL assessment can provide clinicians a window into potentially problematic issues in their patients’ lives beyond OSA. For example, consistently low mental health scores may indicate that a patient may benefit from psychotherapeutic intervention, alongside any OSA-specific intervention. This is especially salient given evidence that depressive symptoms may account for 10 times more variance in fatigue scores than clinical measures of OSA severity (18). HOW DOES OSA AFFECT QUALITY-OF-LIFE? OSA is associated with loud snoring, disrupted sleep, excessive daytime somnolence, other neurobehavioral problems, depression, hypertension, myocardial infarction, stroke, arrhythmias, sexual dysfunction, and relationship problems (1). Each of these factors can contribute to decrements in quality-of-life, both among the patients with OSA and their family members (19). Compared with matched and unmatched controls (20–23) and published normative data (24,25), patients with OSA exhibit significant impairments across a

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variety of HRQOL domains, including social functioning, physical functioning, vitality, mental health, and health perceptions. In one study, multiple regression models that adjusted for several factors predictive of HRQOL found that OSA independently predicted lower HRQOL (22). The Wisconsin Sleep Cohort Study found that diminished general health correlated with apnea in a dose-response fashion, even after controlling for age, body mass index (BMI), and other health factors (26). Similarly, increasing impairments in physical functioning, mental health, social well-being, and energy were associated with increasing severity of OSA. These results are not consistent across studies, however. The HRQOL data do not always track with severity of sleep apnea, as measured by the apnea/hypopnea index (AHI) on polysomnography or by the degree of sleep fragmentation (27). Compared to normal subjects, even patients with relatively mild OSA (respiratory disturbance indices < 20 hours) report impairment in physical, social, and emotional functioning, as well as reduced energy and mental health (28). Results from both the Wisconsin Sleep Cohort Study (n = 738) (26) and the Sleep Heart Health Study (n = 5816) (20) indicate that the degree of quality-of-life impairment noted among patients with OSA is on the same order as that seen among patients with other significant medical illness, including diabetes, heart disease, arthritis, or clinical depression (27). In addition, OSA most likely contributes to several comorbid states that also independently compromise quality-of-life, including obesity, hypertension, congestive heart failure, neurocognitive deficits, and depression (29–31). In short, patients with OSA have impairment of HRQOL to an extent similar to other disease states more traditionally associated with severe personal consequences. Impaired HRQOL has been demonstrated in referred as well as community samples. Some data suggest that OSA is associated with lower HRQOL independently from some of the many possible confounders of the relationship. Establishing a Causal Relationship The majority of the reports to date have suggested that decreases in HRQOL are caused by—not merely associated with—OSA. However, results have not been entirely consistent in this regard. According to Greenberg et al. (32), five general criteria may assist researchers in evaluating a suspected causal relationship: 1. the strength of the observed association; 2. the presence of a dose-response relationship; 3. the presence of an appropriate temporal sequence (the cause precedes the effect); 4. consistency of results across studies; 5. biologic plausibility. While a strong and biologically plausible relationship between OSA and HRQOL has been demonstrated consistently across studies, the presence of a doseresponse relationship has not been as well established. For example, more severe OSA, in comparison to mild OSA, does not necessarily translate to worse HRQOL. This may be owing to a threshold effect or perhaps an inability to measure the most salient physiological variables on polysomnography. In addition, no studies to date have documented HRQOL scores before and after the development of OSA to show

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that OSA precedes reduction in HRQOL. Nonetheless, existing literature does suggest that OSA causes significant reduction in quality-of-life. Some important evidence derives from OSA treatment studies, including randomized controlled trials that illustrate treatment effectiveness and reversibility of reduced HRQOL scores (33–37). WHAT EFFECT DOES TREATMENT HAVE ON QOL? Many treatment options exist for OSA. These include CPAP or bilevel positive airway pressure (BPAP), and a variety of surgical procedures that remove or circumvent the site of the obstruction (38). Additional treatments include behavioral intervention (such as changing sleep position, avoidance of alcohol, smoking cessation, weight loss through diet and exercise), medications (e.g., wakefulnesspromoting agents or nasal steroid sprays), and oral appliances (mandibular advancing and tongue retaining). The most common treatment for adult OSA is nasal CPAP, administered through a nasal mask to effectively splint the upper airway open. Studies consistently show that when patients with OSA adhere to a CPAP treatment regimen, their quality-of-life improves (Table 2). In many cases, HRQOL returns to levels that match healthy population controls. Research suggests that the full effects of CPAP treatment on HRQOL may not be realized until several months of consistent therapy have elapsed, and studies with short follow-ups do not show the same benefit as those with longer duration of follow-up. Not surprisingly, the quality-of-life of bed partners of OSA patients has also been shown to improve (39,40). Patient adherence with CPAP is a significant issue, however. Among other complaints, patients say that the mask can be uncomfortable, and the noise from the CPAP machine disturbs them or their bed partners. Only one study to date has used a preference-based instrument to examine the impact of OSA treatment (41). Preference-based measures are necessary for calculation of utility scores and estimation of cost-utility. In this 1994 study, the authors found that treatment with CPAP could yield an additional 5.4 Quality Adjusted Life Years (QALYs) for each OSA patient (41). It also illustrated that pretreatment utility scores (a quantitative measure of a patient’s preference for a certain outcome) were significantly lower (0.63 on a scale of 0 to 1) than post-CPAP (0.87). This study was conducted among 19 patients with OSA. Replication in a larger study population and across treatment modalities would strengthen confidence in these important findings. The literature is less well developed when it comes to the assessment of nonCPAP interventions on patients’ HRQOL. Exercise training was found to improve QOL among OSA patients, but the authors admit that exercise alone would likely not be adequate treatment for most patients with OSA (42). Weight loss as a result of bariatric surgery led to increases in HRQOL among a small cohort of severely obese patients with significant OSA (43), but it is unclear whether weight loss alone would be enough to entirely resolve OSA. Modafinil treatment among CPAP users was found to reduce functional impairments using the Functional Outcomes of Sleep Questionnaire (44). Oral appliances, such as a mandibular advancement device, appear to improve quality-of-life among OSA patients (45,46). Barnes et al. (2004) found that compared with placebo, mandibular advancement devices improved quality-of-life on both the Functional Outcomes of Sleep Questionnaire and the SF-36 (Table 3 for a (Text continues on page 424)

Boltischek et al., 1998 (104)

SF-36, NHP Part 2

N = 34

N = 51

Engleman et al., 1999 (33)

SF-36

Bennett et al., 1999 (24) N = 16 OSA dx patients, N = 67 CPAP tx patients, N = 187 control N = 29

FOSQ, SF-36

Barnes et al., 2004 (46)

Setting/methods No difference was found between CPAP and sham CPAP groups at 6-wk follow-up in terms of HRQOL.

Changes in HRQOL

(Continued)

Both active treatments improved sleep outcomes and HRQOL over placebo. MAS treatment improved QOL over placebo on the FOSQ mean score and social outcome domain, as well as the SF-36 overall physical health score. CPAP treatment improved FOSQ overall scores and activity level scores, as well as the SF-36 well-being scores. OSA patients referred to sleep clinic SF-36 role-physical and vitality scores were lower than were given SF-36 pre- and onegeneral population norms before CPAP treatment, but month-post CPAP. rose to normal levels after CPAP. MLDL was given to OSA patients MLDL showed no significant differences between CPAP treated for 3+ mo with CPAP, patients and control group. The OSA diagnosed group patients just diagnosed with OSA, showed significantly lower scores than both the control and randomly chosen controls and CPAP group on all domains (physical condition, from the same hospital. psyche, social life, and everyday life categories). OSA patients were given SF-36 Before CPAP, all SF-36 dimensions were significantly before and 8 wk after CPAP. impaired. Rose to similar levels of age- and gendermatched population post-CPAP. QOL scores did not correlate with the severity of OSA. The greatest improvements were in vitality, social functioning, and mental health. OSA patients spent 4 weeks on Baseline SF-36 scores were impaired on 7 of 8 CPAP and 4 weeks on an oral subscales. After CPAP treatment, significant improveplacebo without washout period. ments were seen in general health, role-physical, The SF-36 and NHP were given bodily pain, social functioning, and vitality. Social before and after. functioning and vitality changes were significantly greater on CPAP than on placebo. The NHP showed no change after CPAP for health and functional status.

N = 29 CPAP, Multicenter randomized, placebocontrolled, parallel-group study to N = 25 Sham evaluate the short-term (6-wk) CPAP effect of CPAP on HRQOL of nonsleepy patients with a pathologic apnea-hypopnea index. Randomized crossover trial of 3 mo N = 114 of treatment for mild to moderate OSA with: CPAP, mandibular advancement splint (MAS), and placebo tablet.

N

Munich Life Dimensions List (MLDL, Germanlanguage instrument) D’Ambrosio et al., SF-36 1999 (21)

SF-36, FOSQ

Barbé et al., 2001 (103)

Instrument used

The Impact of CPAP on HRQOL

Study authors

TABLE 2

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SF-36, FOSQ

SF-36

SF-36

SF-36, EuroQOL, FLP

SF-36, Patient Generated Index (PGI), EuroQOL

GHQ, NHP

SAQLI

Engleman et al., 2002 (34)

Hida et al., 2003 (105)

Hukins, 2004 (106)

Jenkinson et al., 1997 (107)

Jenkinson et al., 1998 (68)

Jokic et al., 1999 (108)

Mador et al., 2005 (109)

Instrument used Setting/methods

N = 98

N = 89 (PGI and EuroQOL), N = 86 (SF-36) N = 13

After 3- to 6-mo treatment with CPAP, SF-36 subscales in all three patients groups improved to the normal level.

Significant differences between MRS and CPAP were observed for both the FOSQ (effect size = 0.86) and SF-36 mental health summary score (effect size = 0.69), favoring CPAP.

Changes in HRQOL

Improvements were seen in Role Physical and Vitality domains in both groups relative to baseline (p < .001), but there were no significant differences between APAP and CPAP in terms of HRQOL. OSA patients on CPAP treatment returned to QOL levels similar to normal population. Significant improvements (moderate to large effect sizes) in the majority of dimensions on both SF-36 and FLP found post-CPAP. SF-36 Energy/Vitality dimension and FLP rest and sleep dimensions showed the greatest improvements. The EuroQOL showed increases in scores, but they were not significant. Male OSA patients were interviewed SF-36 scores were low prior to CPAP, but rose to levels prior to and 3 mos after being similar to general population at follow-up. Little change placed on CPAP. seen in EuroQOL scores. PGI scores indicated substantial improvement after CPAP. Positional OSA patients spent 2 wks Energy level scores on the NHP were slightly better with in CPAP and 2 wks in positional CPAP than with positional treatment therapy. No treatment. GHQ and NHP were difference in GHQ scores between the two treatments. given before and after each treatment arm. Randomized controlled trial to Quality-of-life improved in both groups (heated humidifidetermine whether the addition of cation + CPAP; CPAP alone), but there was no heated humidification at treatment significant difference in the extent of improvement initiation of CPAP would lead to between groups. better adherence and HRQOL.

Randomized crossover trial of 8 wks of CPAP and 8 wks of mandibular repositioning splint (MRS) treatment in patients with sleep apnea/hypopnea syndrome. Japanese patients with obesityN = 118 hypoventilation syndrome (OHS) were compared to age and BMImatched patients without OHS and age-matched patients with OSA. Randomized, single-blinded, parallel N = 46 crossover study comparing 2 mos each of treatment with CPAP and APAP in random order. N = 95 (SF-36), OSA patients were assessed before and five wk after CPAP therapy. N = 98 (EuroQOL, FLP)

N = 48

N

The Impact of CPAP on HRQOL (Continued)

Study authors

TABLE 2

422 Moyer et al.

SF-36, FOSQ

SF-36, OSAPOSI (now called the SNORE25)

SF-36

SF-36

FOSQ SNORE25

McFadyen et al., 2001 (36)

Piccirillo et al., 1998 (73)

Pichel et al., 2004 (110)

Profant et al., 2003 (111)

Woodson et al., 2003 (49)

Compared with the control group, CPAP was associated with improvements on 8 domains of the SF-36 questionnaire, with significant findings on 4 domains (effect sizes ranged from 0.37–0.77). The CPAP group showed significant improvement in HRQOL over conservatively-managed patients on all subscales of the SF-36 and the FOSQ, with effect sizes ranging from 0.5 to 2.2 SD units.

Scores on the role-physical, vitality, and emotional wellbeing subscales of the SF-36 increased significantly after surgery or CPAP. OSAPOSI scores on the sleep and awake subscales reflect improvements in response to treatment, with the largest change in the total instrument score. Patients treated with CPAP for 6 mos improved N = 42 (18-mo significantly on the vitality dimension. Patients treated cohort), for 18 mos improved significantly on 5 of the 8 SF-36 N = 43 (6-mo subscales. (Authors also distinguish between cohort), statistically significant changes and clinically N = 84 (control significant changes—all but one of the dimensions group) reflected clinically significant changes) Randomized, controlled trial of one Quality-of-life was no different between CPAP and N = 39 wk of CPAP among OSA patients. CPAP placebo users at the end of the one-wk study period. However, both groups exhibited improvements in HRQOL. N = 30 CPAP, Randomized, placebo-controlled, 2- CPAP significantly improved QOL on both the FOSQ site trial comparing Somnoplasty (effect size = 0.61) and the SNORE25 (effect size = N = 30 Sham and CPAP with sham placebo. 0.46) when compared to baseline, as well as over placebo, sham placebo on the FOSQ (effect size = 0.60). N = 30 Authors found no difference between Somnoplasty Somnoplasty treatment group and CPAP treatment group in terms of post-intervention HRQOL.

Prospective, parallel-group study to investigate psychosocial functioning before and after CPAP treatment.

N = 44 CPAP, N = 25 “conservatively managed” N = 119; 71 underwent CPAP, 48 had surgery OSA patients from 10 study centers underwent CPAP or surgery and were given the SF-36 and OSAPOSI pre-treatment, at time of treatment, and 4 mo post-treatment. OSA patients in Spain undergoing CPAP treatment (6-mo duration vs. 18-mo duration) were compared to evaluate the longterm effects of CPAP therapy.

Controlled trial of CPAP among patients with congestive heart failure and OSA.

N = 19 CPAP, N = 21 control

Abbreviations: APAP, auto-positive airway pressure; BMI, body mass index; CPAP, continuous positive airway pressure; dx, diagnosis; FLP, The Functional Limitations Profile; FOSQ, Functional Outcomes of Sleep Questionnaire; GHQ, General Health Questionnaire; HRQOL, health-related quality-of-life; NHP, The Nottingham Health Profile; OSA, obstructive sleep apnea; OSAPOSI, Obstructive Sleep Apnea Patient-Oriented Severity Index; QOL, quality-of-life; SAQLI, Calgary Sleep Apnea Quality-of-Life Instrument; SF36, Medical Outcome Study Short Form-36; SNORE25, Symptoms of Nocturnal Obstruction and Related Events Instrument; tx, treatment.

SF-36

Mansfield et al., 2004 (37)

Health-Related Quality-of-Life

423

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description of instruments). However, when compared with CPAP, mandibular repositioning splints did not appear to be as effective in improving HRQOL among OSA patients (34). Few studies have assessed the HRQOL implications of surgical interventions for OSA among adults. Such procedures include, but are not limited to: 1. uvulopalatopharyngoplasty (UPPP), which involves the removal of the uvula, inferior rim of the soft palate, and sometimes tonsils (38), 2. maxillomandibular osteotomy and advancement to increase the retrolingual space, and 3. in extreme cases, tracheostomy to bypass the upper airway obstruction (although this procedure is rarely used). In one study of laser-assisted uvulopalatoplasty, a modified alternative to UPPP, patients experienced small improvements in HRQOL (as assessed by the SAQLI, Table 3), but no improvements in daytime sleepiness (47). Another study compared UPPP to an oral appliance and found improvement in QOL among both groups at one-year follow-up, but these gains did not parallel improvements in sleep quality (45). Interestingly, UPPP patients reported being more content than oral appliance patients, but their sleep remained more disturbed. Three separate studies looked at the effects of temperature-controlled radiofrequency tongue base reduction/tissue ablation (TCRFTA) on the quality-of-life of OSA patients, although TCRFTA is not clearly indicated for OSA as opposed to snoring alone. In one study among 16 OSA patients, HRQOL improved immediately following treatment, with no significant deterioration roughly two years later (48). Woodson et al. found that TCRFTA improved quality-of-life over baseline measures and over a sham placebo treatment. In addition, the authors found no difference between CPAP treatment groups and TCRFTA treatment groups at follow-up (49). A subsequent study showed that a series of 3 TCRFTA tongue treatments improved HRQOL (using the FOSQ and the SNORE25, Table 3), and an additional two treatments further improved HRQOL among OSA patients (50). In general, despite an increasing amount of research on the effects of treatment on HRQOL in OSA, fewer data exist on non-CPAP interventions. Without this information, comparisons of CPAP, surgical, oral appliance, and behavioral treatment effects on OSA-related QOL are more difficult, as are well-informed treatment decisions for individual patients. HOW DO I INCORPORATE HRQOL ASSESSMENTS INTO MY PRACTICE? Health-related quality-of-life assessments can be useful not only for research, but also for routine patient management. These instruments can provide valuable insight into how patients perceive their day-to-day lives or benefit from treatments. The following section describes not only the process of HRQOL assessment, but also considerations that may prove important to instrument choices for both research and clinical practice. The Process of Measuring HRQOL There are three main questions that need to be answered in advance when deciding to use an HRQOL assessment. First, what is the purpose of HRQOL assessment in this situation? Second, what level of HRQOL is of greatest interest? And finally, which instrument is most appropriate for this context? Sufficient attention to these questions will

Generic, profile

Nottingham Health Profile (NHP) (60)

Sickness Impact Profile (SIP) (63) Generic, profile

Generic, profile

Medical Outcome Study Short Form-36 (SF-36) (6,7)

Type of instrument

Physical: Ambulation, mobility, body care and movement; Psychosocial: Communication, alertness behavior, emotional behavior, social interaction; Independent Categories: Sleep and rest, eating, work, home management, recreation and pastimes

Energy level, pain, emotional reactions, sleep, social isolation, physical abilities

Physical functioning, rolephysical, bodily pain, general health, vitality, social functioning, roleemotional, mental health

Domains addressed

Instruments Used in OSA HRQOL Assessments

Instrument name, citation

TABLE 3 Psychometric data

136 items in a yes/no format

Cronbach’s alphas range from 0.63 (eating) to 0.96 (overall), with most dimensions having an alpha near 0.85; moderate to strong correlations with some SF-36 subscales (-0.42 to -0.78)

36 items, Good internal consistency most and reliability coefficients scored on (Cronbach’s alpha R: a 3- to 60.68–0.93) point Likert scale 38 items in a Good reliability and validity: yes/no moderate correlations with format some SF-36 subscales (-0.18 to -0.68)

Number and type of items

(Continued)

Well validated in a variety of research settings, including general practice, industry, and several different clinical settings and population groups Well validated in a variety of research settings, as well as among a variety of patient populations: cancer, head injury, stroke, arthritis, Crohn’s disease, insurance enrollees, outpatients, etc.

Well validated in a variety of research settings and diseases: U.S. normative data derived from 1990 National Survey of Functional Health Status

Settings validated

Health-Related Quality-of-Life

425

Specific

Functional Outcomes of Sleep Questionnaire (FOSQ) (71)

Sleep, awake, medical, emotional and personal, occupational

Abbreviations: HRQOL, health-related quality-of-life; OSA, obstructive sleep apnea. Source: From Ref. 102.

Symptoms of Nocturnal Specific Obstruction and Related Events Instrument [SNORE25, formerly the OSA Patient-Oriented Severity Index (OSAPOSI)] (73)

Activity level, vigilance, intimacy and sexual relationships, general productivity, social outcome

Daily functioning, social interactions, emotional functioning, symptoms, (treatment-related symptoms)

Specific

Calgary Sleep Apnea Quality-ofLife Instrument (SAQLI) (70)

EuroQOL EQ-5D (65,39)

Ambulation, body care and movement, mobility, household management, recreation and pastimes, social interaction, emotion, alertness, sleep and rest, eating, communication, work Generic, Mobility, self-care, usual profile, activity, pain/discomfort, preferenceanxiety/depression based

Generic, profile

Domains addressed

Functional Limitations Profile (FLP) (112)

Type of instrument

Instruments Used in OSA HRQOL Assessments (Continued)

Instrument name, citation

TABLE 3 Psychometric data

Settings validated

136 items in a yes/no format

Physical dimension correlate Validated in a variety of settings, including with expanded disability multiple sclerosis status scale (EDSS) (r = patients and disabled 0.77) and with Illness outpatients Severity Score (ISS) (r = 0.76). Other subscales correlate with EDSS and ISS (0.59–0.65) (49) Five items, Test-retest reliability = 0.86– Validated in a variety of 3-point settings, including the 0.90; correlated with Health Likert general U.S., U.K., and Assessment Questionnaire response other national popula(0.46–0.76); ceiling effect scale tions, patients with found when compared to arthritis, surgery patients, SF-36 and outpatients 35 questions Validated in two separate Newly diagnosed OSA on a 7-pt studies: Overall alpha = patients before and after Likert 0.92; subscales 0.88- 0.92; starting CPAP; snorers scale positively correlated with 5 referred for domains of SF-36 (p < polysomnography 0.05) 30 items Validated in one study. Patients visiting sleep with 4- to Global score correlates disorders clinic in an 6-pt Likert with overall SIP score, and academic medical center; scales activity level dimension patients with documented correlates with PF on OSA participating in SF-36. multi-site research study 32 items on Validated in one study: Adults with apnea indices a 5-pt Overall alpha = 0.93; >5 who had not Likert correlation with global QOL previously undergone scale measure (p < 0.0001) uvulopalatoplasty

Number and type of items

426 Moyer et al.

Health-Related Quality-of-Life

427

help to ensure that data collected most accurately reflect the desired aspects of patients’ HRQOL. The Purpose of HRQOL Assessment The purpose of HRQOL assessment varies widely, including discriminating among patients, predicting possible outcomes, or evaluating outcomes (51). For HRQOL assessment, discrimination divides a large group into smaller subsets based on quality-of-life. For example, clinicians may want to identify patients with the lowest quality-of-life and greatest need of intervention. Prediction allows a clinician to classify patients and compare them to some type of standard. For example, a selfadministered survey with some ability to predict polysomnography outcomes would be considered a predictive index. An evaluative index is more appropriate if change over time is of interest, for example, in a clinical trial to determine the impact of CPAP treatment. Generic vs. Specific Assessment Once the purpose of the quality-of-life assessment is clear, the next step is to identify the desired level of assessment. If overall well-being is the primary concern, an instrument aimed at global assessment should be selected over an instrument that targets a specific domain, such as physical functioning or emotional well-being. In some cases, an even more targeted instrument, capable of identifying specified outcomes within an HRQOL domain, may be judged to be of greatest interest (52). One example might be an instrument that focuses on specific physical symptoms within the physical functioning realm of HRQOL. Note, however, that symptom assessment in isolation is not considered a sufficient HRQOL assessment (53). Nonetheless, symptom assessment with an eye toward HRQOL implications can be a critical component of outcome assessment. In many research studies, inclusion of both generic and specific instruments proves optimal. Choosing an Instrument While it may be tempting to pull together a list of questions that appear to assess the main issues associated with OSA, it is preferable to build on the expertise of the many researchers and clinicians who have already developed valid, reliable instruments to assess HRQOL. Yet the number and diversity of available HRQOL instruments available can make it challenging to identify the best instrument to use. Maunder et al. published a list of seven criteria they used to evaluate instruments that measure HRQOL: reproducibility, reliability, validity, ease of use, responsiveness to change, meaningfulness of results, and sampling of patient’s perspective (54). Reproducibility refers to the ability to use an instrument in a variety of settings, beyond the one in which it was developed. The instrument must be published, readily available for use by other researchers and clinicians, and functional in diverse environments. Instruments that require unique circumstances—such as speciallytrained staff for administering the survey—are not likely to be easily reproducible. Reliability refers to the stability of the data gathered by an instrument. Test-retest reliability, one of the most commonly reported aspects of reliability, suggests that repeated assessments should not vary significantly with time in the absence of real change in HRQOL. Another form of reliability, internal consistency, suggests that item responses within a subscale should correlate with each other well enough to show that each helps to assess the same construct, but not so strongly that

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the items are redundant (55). Cronbach’s alpha scores of about 0.70 or greater generally indicate good internal consistency (56). The validity of an instrument refers to the extent to which the desired construct is effectively assessed. In a common analogy, validity means hitting a dart board’s bull’s-eye, assuming the bull’s-eye is a true measure of the concept in question. Reliability means hitting the same spot on the dart board over and over again. A valid and reliable measure consistently hits the bull’s-eye. Evaluation of validity can be challenging in HRQOL research because there is no “gold standard” for comparison. Thus researchers need to use indirect assessments of their instruments’ validity. Three main types of validity are content, criterion, and construct validity. Content validity is a subjective determination of whether an instrument adequately represents all facets of the concept to be measured (55). It is often assessed by comparing the instrument’s items and dimensions to what is seen in clinical settings and in the research literature. Criterion validity is how an instrument corresponds to other observations that accurately measure the phenomenon of interest (57). For example, one way to test for criterion validity is to assess HRQOL before and after treatment known to be effective. Construct validity suggests that the instrument scores: 1. 2. 3. 4.

relate to other variables in a theoretically expected manner; correlate highly with other measures of the same concept (convergent validity); correlate less well with measures of different concepts (discriminant validity); or vary among groups known to differ on relevant characteristics (55).

In HRQOL trials, the assessment of construct validity might involve demonstrating that a new measure shows results similar to those of another HRQOL instrument that is already commonly used and well validated. Yet a measure that correlates too highly with a well-established instrument may not contribute much new information. Maunder et al.’s (54) ease of use criterion requires instruments to be straightforward, understandable, and fairly simple to fill out. In a clinical setting, it may also be important to choose an instrument that is not overly time-consuming to complete. Responsiveness to change refers to an instrument’s sensitivity to changes in a patient’s physical or emotional state. Clearly an instrument needs to be sufficiently sensitive to subtle changes; otherwise it is not only meaningless on an individual level but may require large sample sizes to detect overall treatment effects. Meaningfulness of results refers to the ability to easily interpret the results of an instrument. For example, response categories that are too broad (e.g., good, fair, poor) provide little information, whereas an excessive number of response options may dilute findings. Finally, Maunder et al. recommend instruments that sample the patient’s perspective, as HRQOL assessed by a health care provider may differ substantially from a patient’s assessment (54). Another issue that may affect the choice of an instrument is whether or not it was validated for the specific population of interest. This applies not only to the type of clinical population (e.g., patients with OSA), but also for patients from specific cultural backgrounds. For example, the Functional Outcomes of Sleep Questionnaire (FOSQ) was translated into Norwegian and its reliability and validity were tested within a native Norwegian population (58). Similarly, the Sleep Apnea Quality-of-Life Index (SAQLI) was validated in Chinese (59). Whenever feasible, researchers and clinicians should choose instruments that have been developed and tested in a population similar to theirs.

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INSTRUMENTS USED TO MEASURE HRQOL IN OSA PATIENTS The generic instruments that have been used to measure HRQOL in OSA patients include: the Medical Outcome Study Short Form-36 (SF-36), the Nottingham Health Profile (NHP), the Sickness Impact Profile (SIP), the Functional Limitations Profile (FLP), and the EuroQOL (EQ-5D). Specific instruments include: the Calgary Sleep Apnea Quality-of-Life Instrument (SAQLI), The Functional Outcomes of Sleep Questionnaire (FOSQ), and the Symptoms of Nocturnal Obstruction and Related Events Instrument (SNORE25), which was formerly known as the OSA PatientOriented Severity Index (OSAPOSI). Generic Instruments Medical Outcome Study Short Form-36 (SF-36) The Medical Outcome Study Short Form-36 (SF-36, Table 3) is one of the most commonly used HRQOL instruments in published literature. The 36-item responses, once recalibrated, weighted, and summed, generate eight subscales and two summary scores (mental health summary, physical health summary). The SF-36 developer, John Ware, recommends its use as a “generic core” of HRQOL assessment, to which specific HRQOL measures can be added. Researchers can then compare results across studies and measure HRQOL issues specific to the disease or population of interest. The SF-36 also has been shortened to the SF-12 and the SF-8, each of which covers the same dimensions as the SF-36, with fewer questions asked per dimension. The Nottingham Health Profile (NHP) The NHP is a generic instrument used to determine the physical, social, and psychological distress associated with medical, social, and emotional problems. The NHP was developed in England, and its reliability and validity have been well established (Table 3) (60). The responses to NHP items are summarized to produce a maximum dimension score of 100 (when all possible problems within the dimension are present) and a minimum score of 0 (when none of the problems are present). Dimensions also can be summarized to produce a total quality-of-life value, in which 100 indicates the worst quality-of-life and 0 indicates the best (61). Of the 38 questions on the NHP, fewer than half might be expected to be influenced by sleep apnea (62). The Sickness Impact Profile (SIP) The SIP has been used for more than 25 years as a measure of health status (63). It assesses the degree of impact that sickness has had on a subject’s life. The SIP includes 136 items in 12 categories. Each item has been rated by a group of professional and lay judges for perceived severity, and ratings were translated into weights for each item in each category. These weights reflect the relative impact of specified subjective ill health upon well-being. Nine of the 12 categories include items that could be affected by sleep disordered breathing (62). The Functional Limitations Profile (FLP) The FLP is a modified form of the SIP that reflects British rather than American valuations of the impact of certain subjective health states on well-being (64). Patients are asked to respond to items with reference to their perceived health state on the day of completion, and scores then range from 0 (best possible health) to 100 (worst possible health) (Table 3).

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The EuroQOL (EQ-5D) The EQ-5D includes five questions (Table 3), each with three response categories: level 1 = “no problems,” level 2 = “some problems,” and level 3 = “inability or extreme problems” (65). The responses combine to give a descriptive health state with five dimensions, such as 1,1,1,1,1 (no problems on any dimension) or 1,2,2,1,1 (some problems with self-care and usual activities, but no problems otherwise). Each of the 243 possible health states can be assigned a utility score using a preference-based assessment (time trade-off, standard gamble, direct assessment), or using UK population-based scores (66,67). In addition, the EuroQOL thermometer can help generate a single overall score, representing overall perceived health from worst to best imaginable health state. This is not a utility score; it is simply a supplemental self-assessment of overall health state (68). Note that no questions on the EQ-5D specifically address the issues common to OSA patients, like insomnia, sleepiness, tiredness, and social problems. Thus it is not surprising that research employing the EQ-5D found no change in QOL after treatment for OSA (69). Specific Instruments Calgary Sleep Apnea Quality-of-Life Instrument (SAQLI) The SAQLI (Table 3) (62,70) is an HRQOL instrument developed especially for patients with OSA. It has been demonstrated to be both reliable and valid, and the authors suggest it is unique among OSA assessment instruments because it includes potential negative consequences of treatment, thus providing a more realistic portrait of patient reported outcomes (70). Functional Outcomes of Sleep Questionnaire (FOSQ) The FOSQ (71) assesses the impact of sleep disorders on activities of daily living (Table 3). The FOSQ has been determined to be both reliable and valid, (71) and it includes sleep-specific dimensions likely to be missed by generic instruments, including vigilance (ability to stay awake) and intimacy and sexual relationships. Symptoms of Nocturnal Obstruction and Related Events Instrument (SNORE25) The SNORE25, formerly the OSA Patient-Oriented Severity Index (OSAPOSI), is a validated instrument that assesses five problem areas for patients using 32 items (Table 3) (72,73). Patients are asked to rate the magnitude of the problem for each item, and to indicate the importance of the problem to the patient. A symptomimpact score is calculated as the product of the magnitude score and the importance score. The higher the impact score, the worse the HRQOL (73). The range of scores on any one item is 0 to 20, and the entire instrument ranges from 0 to 640. Patients are also asked to rate the overall amount of bother or disturbance they experience as a result of OSA. SPECIAL CIRCUMSTANCES: PEDIATRIC HRQOL ASSESSMENT Children also suffer from OSA, with estimates ranging from a prevalence of 1% to 3% for frank OSA and possibly much higher for more subtle but still consequential forms of sleep-disordered breathing (74,75). Presentation of OSA among children and adults can differ. Whereas adults with OSA typically report sleepiness, fatigue, or lack of energy, children with OSA can exhibit hyperactivity and attention deficits

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that may supersede their sleepiness (74,76,77). Pediatric OSA can be associated with lower academic performance, aggressive behavior, elevated blood pressure, growth retardation, and other consequences that are likely to affect HRQOL (1). One study found that children with sleep-disordered breathing were 2.7 to 3.8 times more likely to have overall reduced physical health status and 2.2 times more likely to report bodily pain than controls (78). Three separate studies of children with OSA found that the level of impairment in overall functioning is widely and relatively evenly distributed, with one-third of subjects’ caregivers reporting that OSA has a relatively small impact on their children’s HRQOL, one-third reporting a moderate impact, and one-third reporting a large impact (79–81). Treatment for OSA among children can be divided into four categories: 1. surgical treatment with adenotonsillectomy (most commonly), UPPP, tracheostomy, or other maxillofacial surgery; 2. mechanical treatment with positive airway pressure; 3. conservative approach with weight loss (in the obese child), observation, and positional therapy; and 4. a medical approach using oxygen or pharmacotherapy (82). The only treatment modality that has been studied for its HRQOL implications has been surgical treatment with adenotonsillectomy (80,81,83–85). Adenotonsillectomy has been shown to increase HRQOL in the domains of sleep disturbance, physical symptoms, emotional symptoms, and daytime functioning, (80,81,83–85) although improvements are more pronounced in the short-term than the long-term (85), and some patients require further treatment for OSA (80,84). Pediatric HRQOL Instruments Generic pediatric HRQOL measures (such as the Child Health Questionnaire) and specific pediatric HRQOL measures (such as those developed to assess the impact of OSA) are available. Two common OSA-specific HRQOL instruments for use among children include the OSA-18 and the OSD-6. Two additional instruments are Cohen’s pediatric OSA Surgery QOL questionnaire and the Tonsil and Adenoid Health Status Instrument (Table 4). The Child Health Questionnaire (CHQ) The CHQ is a valid, reliable, and responsive instrument that was designed for use among children of varying levels of physical and psychosocial well-being (86). There are four different versions of the CHQ—one with 87 items for children to fill out, and three progressively shorter versions for parents to complete. The shortest of these is the CHQ-PF28, a 28-item instrument that covers 12 subscales (Table 4). This instrument is often used as the “generic core” of a pediatric HRQOL assessment that includes additional, disease-specific instruments [see (87) for more information on the CHQ]. In one assessment of children with OSA, Stewart et al. found that pediatric OSA patients scored significantly worse than healthy controls on 8 of the 12 subscales of the CHQ (88). In a separate study, the same authors showed that children with tonsil and adenoid disease scored significantly worse on 10 of the 12 subscales (89). Scores from this latter group of children were compared with other disease states, and the authors report that general health perceptions for children with tonsil and adenoid disease were similar to general health perceptions for children with asthma and arthritis (89).

Franco’s Pediatric OSA Instrument (OSA-18) (79)

Specific (pediatric patients with OSA)

Child Health Generic (pediatric Questionnairepatients) PF28 (86)

Type of instrument

Number and type of items

Psychometric data

Settings validated

Role limitations owing to Youth version has 87 Well validated: Moderate to Normative benchmarks emotional problems, items; parent excellent test-retest reliability available in the U.S.; role limitations owing version comes in across scales and summary validated in large to physical problems, 50 items or 28 measure (intraclass random school-based bodily pain, behavior, items; 4- to 6-point correlation coefficient [ICC] > and general populamental health, selfscales for each 0.5). Internal consistency tion samples as well esteem, general health item, summary and demonstrated for summary as a variety of illness perceptions, parent subscales measures and one multi-item states. Used in impact (emotional), converted to 100 scale (Cronbach’s α >.7) several pediatric OSA parent impact (time), point scale Good ability to discriminate studies, demonstrated family activities, family among healthy and ill good face validity. cohesion subjects. Sleep disturbance, 18 items rated on a Validated in one study: Good Caregivers of pediatric physical symptoms, 7-point frequency test-retest reliability (0.74– patients 6 mos to emotional distress, scale by caregiver 0.93). Correlation with RDI 12 yrs old referred for daytime functioning, (0.11–0.45), tonsil and polysomnography with caregiver concerns adenoid size (0.03–0.45): disrupted sleep and stronger correlations among hyperplasia of tonsils least subjective questions and adenoids on physical exam.

Domains addressed

Instruments Used in Pediatric OSA HRQOL Assessment

Instrument name, citation

TABLE 4

432 Moyer et al.

Health and sleep, medical visits and costs, psychosocial

76 items, most on Validated in one study: Inter5-point Likert scale rater reliability = 0.86; otherwise minimal validity/ reliability information

Caregivers of children (2–7 yrs old) with airway obstruction who underwent either tracheostomy or other surgery Specific (pediatric Physical suffering, sleep 6 items, each on a 7- Validated in one study: testCaregivers of patients patients with OSA) disturbance, speech or point Likert scale retest reliability = 0.74; with obstructive sleep swallowing problems, construct validity established disorders secondary emotional distress, (R = 0.4–0.63); responsiveto adenotonsillar activity limitations, ness demonstrated hypertrophy (2–12 yrs caregiver concern old). Administered at presentation and 4–5 wk post adenotonsillectomy. Specific (pediatric Infections, airway and 15 items Validated in 3 progressive Validated among patients with tonsil breathing, behavior, phases: Internal reliability parents of children and adenoid disease, swallowing, health care coefficients all >0.73; testundergoing also broad enough for utilization, cost of care retest reliability >0.70 on all adenotonsillectomies those with sleepsubscales; additional disordered breathing) psychometric data positive

Specific (pediatric patients with OSA undergoing surgery)

Abbreviations: HRQOL, health-related quality-of-life; OSA, obstructive sleep apnea.

Tonsil and Adenoid Health Status Instrument (89)

Obstructive Sleep Disorders-6 (OSD-6) (90)

Cohen’s Pediatric OSA Surgery QOL Questionnaire (92)

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Franco’s Pediatric OSA Instrument-18 (OSA-18) The OSA-18 is a disease specific instrument designed to assess the impact of OSA on children. It uses 18 items to assess five domains (Table 4), it has been well validated, and it has been used in at least four separate published research studies. It has been used primarily to assess the outcomes of adenotonsillectomies among children, in which it has consistently shown that children’s QOL improves after surgery (80,81,83-85). Notably, QOL improves significantly, even when OSA doesn’t entirely resolve (84). This suggests that even a partial “cure” for childhood OSA can reap enormous rewards for patients and their caregivers. Obstructive Sleep Disorders-6 (OSD-6) The OSD-6 was developed among pediatric OSA patients and surveys caregivers on their child’s sleep disturbance, speech or swallowing problems, physical symptoms, emotional symptoms, daytime activity, and caregiver concerns (Table 4) (90). This instrument is short and easy to fill out, and it appears to be responsive to clinical changes. In one study of 101 children with OSA undergoing adenotonsillectomy, the OSD-6 indicated that large, moderate, and small post-surgical improvements in QOL were seen in 74.5%, 6.1%, and 7.1% of children, respectively (91). Cohen’s OSA Surgery QOL Questionnaire Cohen et al. (92) developed a parental questionnaire to assess quality-of-life in children with OSA being treated with either tracheostomy or sleep apnea surgery (Table 4). It includes 42 health- and sleep-related questions, four medical visit- and cost-related questions, and 30 psychosocial questions. Results from the validation study indicated that 95% of all questionnaire items were ranked worse for tracheostomy patients than for the sleep apnea surgery patients, and sleep apnea surgery patients experienced far more improvements in HRQOL post-surgically than tracheostomy patients (92). This instrument does not appear to have been used in the published literature because its validation studies. Tonsil and Adenoid Health Status Instrument Stewart developed an instrument to assess HRQOL among pediatric patients with tonsil and adenoid disease (Table 4) (89). Although tonsil and adenoid disease is not the same as OSA, Stewart maintains that his instrument is comprehensive enough to be useful in the assessment of children with OSA (89). A subsequent study— among both OSA patients and patients without OSA—showed that the tonsil and adenoid health status instrument (TAHSI) indicated greater HRQOL changes among children undergoing adenotonsillectomies than among those who did not undergo surgery (88). The sample size was insufficient to examine surgical versus nonsurgical OSA patients, however. Issues to Consider in Pediatric Assessment There are myriad issues to consider when implementing an assessment among children, including use of proxy respondents. When should researchers rely upon the children themselves or upon a caregiver to provide data? Whose perspective is of greater interest? The appropriateness of the instrument also must be considered. Will a child be able to read, understand, and focus on the instrument? Will a parent know the answers to the questions being asked about their child? The best way to administer the instrument should be considered as well (e.g., self-administered, orally, in clinic, via mail, over

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the telephone, etc.). Potential maturation effects as subjects get older while participating in a longitudinal study may be important. Finally, inherent difficulties may arise with the broad range of ages included in many pediatric studies. These issues are discussed in greater detail elsewhere (93,94), but are worth considering for clinicians interested in pediatric HRQOL assessment. LOGISTICAL CONSIDERATIONS All of the instruments described in this chapter are appropriate for use with patients who have OSA. However, there are logistical issues to consider before choosing an instrument and implementing HRQOL assessment in a clinical or research practice. Some instruments, such as the SF-36 (or SF-12, or SF-8) have very wellestablished bodies of literature surrounding their use, including websites, resource manuals, score calculation software, and comparative population and diseasespecific norms. This level of development is most helpful to those unfamiliar with HRQOL instruments, and it may prove invaluable to those who wish to place their results in a broader context. However, these resources can also be a bit overwhelming to clinicians simply trying to determine how their patients are doing compared to the last time they were seen. Due to the complexity of scoring the SF-36, for example, it is unlikely that a physician would be able to take the hard copy of a patient survey, enter the results, and instantly have meaningful data. There are computerbased modules available—including online access—but obviously they require patient access to a computer terminal. There are also registration and licensing fees associated with using the SF-36 (95). Other instruments (such as the SAQLI) are scored using simple means: items for each subscale are summed and then divided by the number of questions. This can be done relatively rapidly, and it yields a number that can be quickly compared to a patient’s previous scores. While this may not provide the level of cross-study and cross-population comparability as the SF-36, the ability to obtain quick results— even while the patient is still in clinic, for example—may outweigh the benefit of using a larger instrument like the SF-36. It is also worth mentioning that many quality-of-life instruments have been validated for use among large groups of patients, with aggregate results being the desired outcome. Thus these instruments may not be particularly wellsuited to identifying subtle changes in an individual over time. Longer, more detailed instruments may be needed to assess individual outcomes, whereas shorter and less specific instruments may be sufficient to determine group-level changes over time. Researchers and clinicians who want to assess HRQOL need to investigate instruments of interest to determine if they have been used successfully in a similar patient population, in a similar setting, and with similar analysis methods. CLINICAL VS. STATISTICAL SIGNIFICANCE The minimal clinically significant difference in HRQOL is the smallest difference that clinicians and patients would care about. This concept is important because published literature often focuses on statistical significance instead. Statistical significance indicates that an instrument detects a difference not attributable to chance alone, but says little about clinical or biological importance (32). For example,

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a change of a half a point on an instrument subscale may be statistically significant, but meaningless in terms of its impact on patients. Clinicians and researchers often know what constitutes a clinically important difference when using biologic markers. A similar perspective is required for HRQOL scores. Unfortunately, determination of this value is specific to each instrument and must draw upon logical analysis of previous data and the properties of the instrument. Two primary approaches are used to determine clinical importance: the distribution-based approach and the anchor-based approach (96). The distribution-based approach depends upon statistical distribution, typically calculating an effect size by dividing the mean change in a variable by the standard deviation of the variable at baseline. A small effect size might be 0.20, a moderate effect size might be 0.50, and 0.80 or higher might be a large effect. However, there are several limitations to this approach. First, there is no evidence that these magnitudes of change are important to the patient (97). It has also been suggested that the effect size is more an estimate of the responsiveness of the questionnaire than of clinically important differences (98). Finally, reliance upon means often obfuscates heterogeneity in a population (97). The anchor-based approach has been adopted by several prominent qualityof-life research groups, defining “Minimal Important Difference” (MID) as “the smallest difference in score in the domain of interest that patients perceive as beneficial and would mandate, in the absence of troublesome side effects and excessive cost, a change in patient’s management.” (99) The MID can be used to calculate something called the “number needed to treat” (NNT)—that is, the number of patients who need to be treated with the new intervention for one patient to have a clinically important improvement over and above that which he or she would have experienced with the control intervention (100). It is determined from the proportion of patients who show a change greater than the MID on each of the treatments— allowing a more meaningful presentation of clinical trial data than standard deviations and confidence intervals (94). Schwartz et al. describe clinically relevant differences in greater detail (101). CONCLUSIONS This chapter describes HRQOL assessment and the various issues to consider when implementing an assessment in research or clinical practice. The choice of instruments should be based on three factors: (i) the purpose of the evaluation, (ii) the level of assessment to be performed, and (iii) the instrument attributes and psychometric properties. For clinical purposes, instruments should be chosen that were developed and tested in settings as similar as possible to setting of interest. For research purposes, the most comprehensive and comparable datasets will be generated by using at least one OSA-specific instrument and one generic instrument. This chapter also describes the instruments that have been used to study HRQOL in patients with OSA. The SF-36 has been the most commonly used generic measure among adults with OSA. The SF-36 is not only sensitive to treatment effects, but it has repeatedly demonstrated that OSA patients have lower quality-of-life than age- and gender-matched controls across several dimensions. Yet it does not include questions specific to OSA, aside from the loosely related “vitality” dimension. Other generic instruments, including the NHP, SIP, and the FLP, have been used to study the impact of OSA as well. Data suggest that these tools may be useful, but again, most do not include questions specific to the symptoms of an OSA patient.

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The FLP provides a potential advantage in that it includes sleep and rest dimensions. With regard to specific HRQOL instruments, the SAQLI, the FOSQ, and the SNORE25 have all been shown to be useful in the assessment of OSAs impact on adult patients’ HRQOL. Among children, the CHQ is the most commonly used generic assessment tool, and the OSA-18 and OSD-6 are the most commonly used OSA-specific instruments. The research literature is heavily skewed toward the assessment of the effects of CPAP on HRQOL among adults and the effects of adenotonsillectomy on HRQOL among children. Without a more thorough assessment of the HRQOL implications of all the possible treatment modalities for OSA, patients and clinicians are left with difficult decisions about treatment options. Logistical challenges aside, direct comparisons would be useful between the HRQOL implications of CPAP, BPAP, various surgical options, behavioral intervention, and medications among both adults and children. Since many patients eventually use more than one treatment option, studies that assess HRQOL changes over time, as patients move through a treatment trajectory, might also be useful. For example, a CPAP-naïve patient undergoing surgery may experience very different post-surgical HRQOL changes than a patient who has been on CPAP for years and only then resorts to surgery. Future research on HRQOL in OSA needs to include larger sample sizes, longer follow-up, and a broader spectrum of treatments under study. Study designs should allow for head-to-head comparisons of the HRQOL implications of different treatment options for patients with similarly presenting OSA. In addition, researchers and clinicians should consider utility-based assessment that would allow for the calculation of QALYs and cost-utility for each potential treatment option. Existing data strongly suggest a causative relationship between OSA and reduced quality-of-life, but this has not been proven. Future longitudinal, population-based research could attempt to tease out the independent effects of OSA on HRQOL when compared with the many other factors that influence a person’s HRQOL. Randomized, controlled, and blinded trials may eventually help to prove cause and effect most conclusively.

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103. Barbé F, Mayoralas LR, Duran J, et al. Treatment with continuous positive airway pressure is not effective in patients with sleep apnea but no daytime sleepiness. a randomized, controlled trial. Annals of Internal Medicine 2001; 134(11):1015–1023. 104. Bolitschek J, Schmeiser-Rieder A, Schobersberger R, Rosenberger A, Kunze M, Aigner K. Impact of nasal continuous positive airway pressure treatment on quality of life in patients with obstructive sleep apnea. Eur Respir J 1998; 11:890–894. 105. Hida W, Okabe S, Tatsumi K, et al. Nasal continuous positive airway pressure improves quality of life in obesity hypoventilation syndrome. Sleep and Breathing 2003; 7(1): 3–12. 106. Hukins C. Comparative study of autotitrating and fixed-pressure CPAP in the home: a randomized, single-blind crossover trial. Sleep 2004; 27(8):1512–1517. 107. Jenkinson C, Stradling J, Peterson S. Comparison of three measures of quality of life outcome in the evaluation of continuous positive airways pressure therapy for sleep apnoea. Sleep 1997; 6:199–204. 108. Jokic R, Klimaszewski A, Crossley M, Sridhar G, Fitzpatrick MF. Positional treatment vs continuous positive airway pressure in patients with positional obstructive sleep apnea syndrome. Chest 1999; 115(3):771–781. 109. Mador MJ, Krauza M, Pervez A, Pierce D, Braun M. Effect of heated humidification on compliance and quality of life in patients with sleep apnea using nasal continuous positive airway pressure. Chest 2005; 128:2151–2158. 110. Pichel F, Zamarrón C, Magán F, del Campo F, Alvarez-Sala R, Rodriguez Suarez JR. Health-related quality of life in patients with obstructive sleep apnea: effects of longterm positive airway pressure treatment. Respiratory Medicine 2004; 98:968–976. 111. Profant J, Ancoli-Israel S, Dimsdale JE. A randomized, controlled trial of 1 wk of continuous positive airway pressure treatment on quality of life. Heart & Lung 2003; 32(1): 52–58. 112. Patrick DL, Peach H. Disablement in the Community. Oxford, England: Oxford University Press, 1989.

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Driving Risk and Accidents Patricia Sagaspe Clinique du Sommeil CHU Pellegrin, INRETS, Bordeaux, France

Pierre Philip Clinique du Sommeil CHU Pellegrin, Université Bordeaux 2, CNRS UMR-5227, Bordeaux, France

OVERVIEW OF THE PROBLEM For many years fatigue has been associated with an increased risk of accidents, but the causes were unclear. Work or driving that is extensive or conducted during the night-time hours is associated with accidents but few reports have differentiated fatigue, which is usually seen as owing to driving time, from sleepiness, which is owing to reduced sleep (1), extended time awake or being awake at the circadian trough (2), or drugs. Epidemiological studies from the 1990s showed that sleep-related accidents represent up to 20% of all traffic accidents in industrial societies (3–5). Though drowsiness (6–8) has been identified as the reason behind fatal road crashes and many industrial accidents (9), many people drive when their alertness is at its lowest level. Connor et al. (5) have shown that driving between 2 and 5 a.m. multiplies by 5.6 the risk of traffic accidents, and that being sleepy at the wheel multiplies by eight the risk of accidents, which gives a clear measure of the associated risk of sleepiness at the wheel. The European Union aims to halve the number of road deaths by 2010, and a vast program of road safety has been initiated. To gain 20,000 life-years in Europe, we need to achieve a better understanding of traffic accidents. Because professional traffic is going to increase by 50% in the next ten years in Europe and many other continents, it is a major issue to identify new causes of accidents and new strategies to prevent them. In a similar manner, the United States has tried to improve regulations regarding work and sleepiness, several public campaigns have sensitized U.S. drivers to the risk of drowsy driving “drive alert-stay alive” and many investigations have shown the responsibility of behavior or sleep disorders in traffic. Still in both continents, major problems remain in the identification of patients at risk for traffic accidents and the best way to reduce this risk by appropriate treatments. Some sleep disorders have been extensively studied [e.g., obstructive sleep apnea (OSA)] and treatments have been evaluated that are possible countermeasures against traffic accidents, but other sleep disorders have not been studied (e.g., upper airway resistance syndrome) or the impact of these treatments (e.g., alerting drugs) do not have clear results on accident risk. In this review we propose to the reader an update on the relationship between OSA and traffic accidents by presenting the actual knowledge base and what major studies are needed to improve our patients’ safety. 443

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PREVALENCE AND ASSOCIATED RISKS Of all the sleep disorders, OSA is probably the most studied pathology with respect to traffic accidents. Several reasons could explain this fact: in the general population the prevalence of OSA is between 2% and 4% (10); in selected populations (e.g., professional drivers) the prevalence of OSA has been reported to range from 26% (11) to 50% (12); many of the OSA patients complain of severe daytime sleepiness (10); and most importantly many OSA patients report to their clinicians sleep-related accidents or near-miss accidents. Indeed, several studies performed in the last twenty years show a clear relationship between sleep disorders and traffic accidents (13–19). In the late 1980s, Findley et al. (13) published a study on a very small population of apneic non professional drivers compared to controls (29 apneics vs. 35 controls). A higher risk of traffic accidents was found among patients suffering from sleeprelated breathing disorders compared to the controls. In the early 1990s, Haraldson et al. (15) published a more sophisticated study showing that untreated apneics had a higher single-car accident rate than controls. A questionnaire was given to 140 patients with symptoms and 142 controls without symptoms associated with OSA. Seventy-three of the patients had a complete triad of OSA-associated symptoms. The ratio of drivers being involved in one or more combined-car accidents was similar for patients and control drivers, but for single-car accidents the ratio was about seven times higher for patients with a complete triad of symptoms of OSA compared to controls ( p < 0.001). When corrected for mileage driven, the total number of single-car accidents was almost 12 times higher among patients with sleep spells whilst driving, compared to controls ( p < 0.001). The reference study on OSA and accidents was a case-control study on the risk of car accidents among apneic subjects (20). Even if this study was not the first one to investigate the problem, for the first time a well-designed protocol compared apneics to controls in order to evaluate the additional accident risk related to nocturnal breathing disorders. The case patients were 102 drivers who received emergency treatment at hospitals following highway traffic accidents. The controls were 152 patients randomly selected from primary care centers and matched with the case patients for age and sex. As compared to those without sleep apnea, patients with an apneahypopnea index (AHI) of 10 or higher had an odds ratio (OR) of 6.3 [95% confidence interval (CI) 2.4 –16.2] for having a traffic accident. This relation remained significant after adjustment for potential confounds, such as alcohol consumption, visualrefraction disorders, body mass index, year of driving, age, history with respect to traffic accidents, use of medications causing drowsiness, and sleep schedule. George et al. (21) published complementary data on the relationship between the AHI and the risk of accidents. In this study on 460 apneic patients, only the most severe patients (AHI > 30) presented an accident risk factor that was higher than that of controls. Stoohs et al. (22) performed an integrated analysis of recordings of sleeprelated breathing disorders, and self-reported automotive and company-recorded automotive accidents in 90 commercial long-haul truck drivers. Seventy-eight percent of the drivers had an oxygen desaturation index (ODI) ≥ 5 per hour of sleep and 10% had an ODI ≥ 30 per hour of sleep. About 20% of drivers presented symptoms indicating very regular sleep disturbances. Truck drivers with sleep-disordered breathing had a twofold higher accident rate per mile than drivers without sleepdisordered breathing. Accident frequency was not dependent on the severity of the sleep-related breathing disorder.

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Hakkanen et al. (6) carried out another study of professional drivers. Two separate groups consisting of both long-haul (n = 184) and short-haul (n = 133) truck drivers were surveyed to examine the frequency of driver sleepiness-related problems at work during the previous three months and to assess the incidence of sleep apnea symptoms. Over 20% of the long-haul drivers reported having dozed off at least twice while driving. Near misses owing to dozing off had occurred in 17% of these drivers. Factors indicating sleep apnea occurred in 4% of the long-haul drivers and in only two short-haul drivers. Because professional drivers can suffer from inadequate sleep and sleep apnea, Pack et al. (23) evaluated the role of short sleep durations over one week at home and sleep apnea by assessing subjective sleepiness (Epworth sleepiness scale), objective sleepiness [reduced sleep latency as determined by the multiple sleep latency test (MSLT)], and neurobehavioral functioning (lapses in performance, tracking error in the divided attention driving task) in commercial drivers. Studies were conducted in 247 of 551 drivers at higher risk for apnea and in 159 of 778 drivers at lower risk. Increases in subjective sleepiness were associated with shorter sleep durations but not with increases in severity of apnea. Increases in objective sleepiness and performance lapses, as well as poorer lane tracking, were associated with shorter sleep durations. Associations with sleep apnea severity were not as robust and not strictly monotonic. The effects of severe sleep apnea (AHI at least 30 episodes/hr), which occurred in 4.7%, and of sleep duration less than 5 hr/night, which occurred in 13.5%, were similar in terms of their impact on objective sleepiness. Surprisingly, excessive daytime sleepiness as measured by the Epworth sleepiness scale has not been associated with accident risk in apneic patients (20). Lloberes et al. (24) decided to study the traffic accident rate in the last five years in patients referred to a sleep clinic because of clinical suspicion of OSA and to analyze variables related to an increased risk for traffic accidents. A series of 189 consecutive patients and a control group (CG) of 40 hospital staff workers who denied snoring, matched for age and sex with the study population, were studied. The self-reported number of accidents was significantly higher in OSA patients compared with that of CG. The self-reported number of times off the road was significantly higher in OSA patients compared with that of CG. Variables associated with an increased risk for traffic accidents were self-reported sleepiness while driving (OR 5, 95% CI 2.3–10.9), having quit driving because of sleepiness (OR 3, 95% CI 1.1–8.6) and currently working (OR 2.8, 95% CI 1.1–7.7). The study concluded that self-reported sleepiness while driving is associated with an increased risk for traffic accidents in OSA patients. In a similar research line, Masa et al. (25) interviewed 4002 randomly selected drivers to define the prevalence of drivers who are habitually sleepy while driving. The authors studied habitually sleepy drivers and an age- and sex-matched control group of drivers. Of the 4002 drivers interviewed, 145 (3.6%, CI 3.1–4.3) were habitually sleepy while driving. The habitually sleepy drivers reported a significantly higher frequency of auto crashes than control subjects (adjusted OR 13.3, CI 4.1–43). The habitually sleepy drivers had a significantly higher prevalence of respiratory sleep disorders than control subjects. For a total respiratory event index (apneas, hypopneas, and other respiratory effort-related arousals) ≥ 15 the adjusted OR was 6.0, CI 1.1–32. The authors concluded that habitually sleepy drivers are a large group of drivers (1 of 30 drivers) who are involved in several-fold more automobile crashes than control subjects. Interestingly, a combination of nocturnal breathing disorders and drug treatment can be identified as causes of accidents in professional drivers. Howard et al. (26)

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measured the relationship between excessive sleepiness, sleep-disordered breathing, drug consumption and accident risk factors in 2342 respondents to a questionnaire distributed to a random sample of 3268 Australian commercial vehicle drivers. Another 161 drivers among 244 were invited to undergo polysomnography. More than half (59.6%) of the drivers had sleep-disordered breathing and 15.8% had OSA. Sleepiness measured by the Epworth sleepiness scale was associated with an increased risk of accidents. Among the drivers, use of narcotic analgesic drugs and antihistamines was also associated with an increasing risk of accidents. All of these studies, even if they reported a great variability in the prevalence of OSA among occupational drivers (possibly explained by different diagnostic methods), they nonetheless confirm the risk of traffic accidents for apneic patients. Sleepiness at the wheel is obviously a main symptom to investigate in conjunction with the severity of the disease (AHI > 30). HOW TO EVALUATE THE DRIVING RISK IN APNEIC PATIENTS? Lloberes et al. and Masa et al. (24,25) both concluded that asking about excessive sleepiness while driving may better predict which subjects with breathing disorders during sleep have crashes rather than asking about overall sleepiness; therefore, a good clinical interview can evaluate patients’ driving risks in a vast majority of cases. Nevertheless, this strategy relies on a truthful, subjective assessment by the driver talking to a physician and deceit cannot be excluded, especially from drivers dependent on their driving licence for their job. Very few studies have investigated the relationship between objective measurement of sleepiness [MSLT or maintenance of wakefulness test (MWT) scores] and driving performance. Young et al. (27) found among the Wisconsin Sleep Cohort a correlation between MSLT scores and driving accidents in male apneic drivers. Experimentally, impaired daytime alertness causes an increase in lateral deviations during simulated (28–33) and real driving (34–37). Banks et al. (38) have compared MWT with performance on a driving simulator in healthy sleep-deprived volunteers. This was the first evidence of the predictive value of MWT on driving performance. Unfortunately, the simplified MWT (2 trials) used in the study did not correspond to the validated gold standard test (4 trials of 40 min), and only healthy individuals were tested. As pointed out by Pack et al. (23), addressing impairment in commercial drivers requires addressing both insufficient sleep and sleep apnea, the former being more common, at least in the United States. The subjective and objective evaluation of OSA remains an open question, but it is probably worth considering an objective test when medico legal issues exist in order to protect the patient and the physician against potential prosecution in the case of accidents. IMPACT OF TREATMENT ON THE ACCIDENTAL RISK Knowing the sleep-related risk, a major issue is “how accidents involving these patients can be reduced.” Haraldsson et al. (39) studied 15 male drivers with OSA, suffering from sleep spells whilst driving, and 10 matched controls in an advanced driving simulator. The clinical evaluation was conducted by a questionnaire scoring symptoms of snoring, sleep disturbances and diurnal sleepiness before and after surgery. Before uvulopalatopharyngoplasty (UPPP) the patient group showed impaired performance compared to controls. UPPP resulted in improved reaction time performance. Furthermore, 12 of the 15 patients reported a marked

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improvement regarding sleepiness whilst driving. For these clinically successful cases, the number of off-road episodes decreased substantially. In another study, Haraldsson et al. (40) tried to test the long term effects of UPPP on driving performance. In a cohort study, the long-term effect of surgical treatment on driving vigilance was evaluated on 13 middle-aged (median, 52 yr) male patients and five matched controls. Three to four years postoperatively, they were subjected to a boring 90-min-long test in an advanced driving simulator and daytime polysomnography, identical to those performed preoperatively. The patients were also asked to assess their driving skills on a self-report and their vigilance on a visual analogue scale. All but one patient reported themselves as being more vigilant and safe drivers following surgery. Objective results showed that the initial improvement in brake reaction time, lateral position deviation, and number of off-road incidents was sustained, but not always in concordance with the apnea index. The authors concluded that the positive effect of uvulopalatopharyngoplasty on vigilance and driving performance remains after four years. To confirm these experimental results, Haraldson et al. (41) compared the car accident rate of apneic patients for the first five years after surgery to the rate of the five years immediately before the operation. Data were collected by means of a selfreport questionnaire. Fifty-six patients with rhonchopathy were compared to 142 controls without rhonchopathy who had been subjected to nasal surgery. The reported habitual sleepiness while driving had disappeared in 87% (p < 0.001) of drivers who had the problem preoperatively, and the accident risk reduction (corrected for mileage) in patients was almost four times greater than the reduction in controls ( p < 0.001) after surgery. The relative rate of patients involved in any single-car accident fell by 77% ( p < 0.05), and the relative rate of single-car accidents fell by 83% ( p < 0.001). He concluded that drivers with rhonchopathy have an increased risk for car accidents, especially single-car accidents, but that this risk returns to normal after UPPP. Surgery is a traditional approach to treat nocturnal breathing disorders but nocturnal ventilation has now by far supplanted uvulopalatopharyngoplasty. Several studies have investigated the impact of continuous positive airway pressure (CPAP) on traffic accidents. Krieger et al. (42) used a prospective study to quantify the patient benefit. A total of 547 patients completed the study (153 left the study, and only partial data were available for 193). The baseline questionnaire included questions concerning accidents in the previous 12 months, asking whether patients had had an accident and, if so, whether they felt that the accident(s) were related to sleepiness, and whether the patients felt that they had had near-miss accidents owing to sleepiness. The questionnaires at 6 and 12 months included the same questions referring to the previous six months; the accidents reported on each follow-up questionnaire were cumulated and compared with the accidents during the oneyear period before treatment. The number of patients having an accident decreased with treatment for real accidents (from 60 to 36, p < 0.01), as well as for near-miss accidents (from 151 to 32, p < 0.01). The average number of accidents per patient also decreased, for real accidents (from 1.6 ± .3 to 1.1 ± 0.3, p < 0.01) and for nearmiss accidents (from 4.5 ± 6.5 to 1.8 ± 1.4, p < 0.01). The cost, in terms of days in hospital related to accidents, decreased from 885 to 84 days. George (43) also studied the impact of CPAP treatment on risk of motor vehicle accidents on 210 non professional drivers who were suffering from OSA. The results confirmed that CPAP therapy was definitely associated with a reduction in the risk of motor vehicle accidents owing to OSA. These results strongly support the recommendation that drivers who have experienced sleepiness while on the road should take steps to find out the potential cause of the problem.

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ECONOMIC IMPACT OF ACCIDENT REDUCTION One of the main questions remains whether the cost-benefit of public health programs and prevention of sleep-related accidents would yield a significant economic impact. Sassani et al. (44) estimated for the year 2000, the annual OSA-related collisions, costs, and fatalities in the United States and performed a cost-benefit analysis of treating drivers suffering from OSA with continuous positive airway pressure. A meta-analysis was performed on studies investigating the relationship between collisions and OSA. Data from the National Safety Council were used to estimate OSArelated collisions, costs, and fatalities and their reduction with treatment. Next, the annual cost of treating OSA with CPAP was calculated. More than 800,000 drivers were involved in OSA-related motor-vehicle collisions in the year 2000. These collisions cost 15.9 billion dollars and 1400 lives in the year 2000. In the United States, treating all drivers suffering from OSA with CPAP would cost 3.18 billion dollars, save 11.1 billion dollars in collision costs, and save 980 lives annually. Annually, a small but significant portion of motor-vehicle collisions, costs, and deaths are related to OSA. With CPAP treatment, most of these collisions, costs, and deaths could be prevented; therefore treatment of OSA could benefit both the patient and the public. CONCLUSIONS What can be done in the future? Already a lot has been done in this field but many questions remain open. ■

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At the diagnostic level, we do not have yet a simple objective measure to quantify the risk in our OSA patients as compared to other factors (e.g., breathalyzer for alcohol testing). Ideally, we should develop and use a “somnotest” to quantify the driving risk but up to now driving simulators or electroencephalographic (EEG) measures provide indirect and variable estimation of the driving risk. Regarding patients’ risk, we have no information on the association between upper airway resistance syndrome and traffic accidents and here again we can only assume that these risks are identical and can be evaluated using the same criteria as for OSA. Other treatments could provide an interesting alternative to prevent accidents but we have, for instance, no data on the impact of alerting substances on the driving risk of apneics, and oral appliances have not been studied in the context of driving risk. Studying the impact of extensive driving in treated and non treated apneics is also a key line in the research agenda because of the high prevalence of nocturnal breathing disorders in professional drivers. Finally, more studies are needed to better define the phenotype of apneics involved in traffic accidents. If only one patient out of thirty is the victim of a sleep-related accident it is urgent to track these subjects and develop special evaluations plus driving recommendations (e.g., no nocturnal driving) for these drivers.

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2. Dijk DJ, Czeisler CA. Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. J Neurosci 1995; 15(5 Pt 1):3526–3538. 3. Horne JA, Reyner LA. Sleep related vehicle accidents. BMJ 1995; 310(6979):565–567. 4. Philip P, Vervialle F, Le Breton P, et al. Fatigue, alcohol, and serious road crashes in France: factorial study of national data. BMJ 2001; 322(7290):829–830. 5. Connor J, Norton R, Ameratunga S, et al. Driver sleepiness and risk of serious injury to car occupants: population based case control study. BMJ 2002; 324(7346):1125. 6. Hakkanen H, Summala H. Sleepiness at work among commercial truck drivers. Sleep 2000; 23(1):49–57. 7. Connor J, Whitlock G, Norton R, et al. The role of driver sleepiness in car crashes: a systematic review of epidemiological studies. Accid Anal Prev 2001; 33(1):31–41. 8. Hakkanen H, Summala H. Fatal traffic accidents among trailer truck drivers and accident causes as viewed by other truck drivers. Accid Anal Prev 2001; 33(2):187–196. 9. Mitler MM, Carskadon MA, Czeisler CA, et al. Catastrophes, sleep, and public policy: consensus report. Sleep 1988; 11(1):100–109. 10. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328(17):1230–1235. 11. Engleman HM, Hirst WS, Douglas NJ. Under reporting of sleepiness and driving impairment in patients with sleep apnoea/hypopnoea syndrome. J Sleep Res 1997; 6(4):272–275. 12. Stoohs RA, Guilleminault C, Itoi A, et al. Traffic accidents in commercial long-haul truck drivers: the influence of sleep-disordered breathing and obesity. Sleep 1994; 17(7):619–623. 13. Findley LJ, Unverzagt ME, Suratt PM. Automobile accidents involving patients with obstructive sleep apnea. Am Rev Respir Dis 1988; 138(2):337–340. 14. Aldrich MS. Automobile accidents in patients with sleep disorders. Sleep 1989; 12(6):487–494. 15. Haraldsson PO, Carenfelt C, Diderichsen F, et al. Clinical symptoms of sleep apnea syndrome and automobile accidents. ORL J Otorhinolaryngol Relat Spec 1990; 52(1):57–62. 16. Cassel W, Ploch T, Peter JH, et al. Risk of accidents in patients with nocturnal respiration disorders. Pneumologie 1991; 45(suppl 1):271–275. 17. American Thoracic Society. Sleep apnea, sleepiness and driving risk. Am J Respir Crit Care Med 1994; 150(5 Pt 1):1463–1473. 18. Powell NB, Schechtman KB, Riley RW, et al. Sleepy driving: accidents and injury. Otolaryngol Head Neck Surg 2002; 126(3):217–227. 19. Philip P. Sleepiness of occupational drivers. Ind Health 2005; 43(1):30–33. 20. Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. Cooperative Group Burgos-Santander. N Engl J Med 1999; 340(11):847–851. 21. George CF, Smiley A. Sleep apnea & automobile crashes. Sleep 1999; 22(6):790–795. 22. Stoohs RA, Bingham LA, Itoi A, et al. Sleep and sleep-disordered breathing in commercial long-haul truck drivers. Chest 1995; 107(5):1275–1282. 23. Pack AI, Maislin G, Stally B, et al. Impaired performance in commercial drivers: role of sleep apnea and short sleep duration. Am J Respir Crit Care Med 2006; 174(4):446–454. 24. Lloberes P, Levy G, Descals C, et al. Self-reported sleepiness while driving as a risk factor for traffic accidents in patients with obstructive sleep apnoea syndrome and in nonapnoeic snorers. Respir Med 2000; 94(10):971–976. 25. Masa JF, Rubio M, Findley LJ. Habitually sleepy drivers have a high frequency of automobile crashes associated with respiratory disorders during sleep. Am J Respir Crit Care Med 2000; 162(4 Pt 1):1407–1412. 26. Howard ME, Desai AV, Grunstein RR, et al. Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers. Am J Respir Crit Care Med 2004; 170(9):1014–1021. 27. Young T, Blustein J, Finn L, et al. Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults. Sleep 1997; 20(8):608–613. 28. Haraldsson PO, Carenfelt C, Laurell H, et al. Driving vigilance simulator test. Acta Otolaryngol 1990; 110(1–2):136–140. 29. George CF, Boudreau AC, Smiley A. Simulated driving performance in patients with obstructive sleep apnea. Am J Respir Crit Care Med 1996; 154(1):175–181.

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Economic and Societal Impact Valérie Wittmann and Daniel O. Rodenstein Service de Pneumologie, Cliniques universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium

INTRODUCTION The meaning of the word “health” depends on the general context of the discourse where it is used. It is certainly not the same for a physician, for an economist or for a person with a cold, an open fracture of the tibia or with a severe chronic invalidating disease. It has become fashionable to consider health, in economic terms, as a commodity among others, with neither less nor more hierarchy or importance than energy (electricity, gas, and so on), sports or sugar. It is therefore reasonable, from that point of view, to analyze the economic flows related to a given commodity, like health, using the ultimate comparison criteria, which is money. If we accept these premises, then we can consider health as comprising a series of activities, equipment, salaries, consumables, that represent a given use of economic resources leading to certain results, and analyze whether the economic resources have been used in a valid or efficient way to attain the results, also called outcomes. It is essential for a physician to recall that nothing of the above has anything to do with truth, neither with biology or medicine. The economic discourse we are referring to reflects the dominant philosophy of our society for the time being, which is essentially materialistic with money as the main endpoint and reference. If, as frequently stated, “health is priceless,” health has certainly a cost. We benefit today from countless positive effects thanks to healthcare advances— antibiotics, anesthesia, modern surgery techniques, and so on. But economists state that the yield of any additional healthcare investment is decreasing (i.e., we add a great difference in cost, and we receive a little improvement in health). Economists also sustain that economic resources are inherently scarce, and that the attribution of resources to one end implies by necessity that those resources can’t be allocated to other ends, so that one needs some external reference scale (i.e., money) to compare the results or outcomes of the attribution of resources to several different realms in order to make meaningful comparisons and reach “best” decisions. Economic analysis is then put forward as a tool to aid in decision making. In fact, the final decision is a political one, given that in most industrialized countries a great part, if not all, of the cost of healthcare is covered by public funding. In short, this means that a series of political influences are weighted to obtain a pragmatic balance of what can be done here and now. One should not forget that the main part of health care costs represents salaries of people working in what is called the health care industry. Thus, cuts in healthcare costs may imply unemployment for some of those, who at the same time are potential voters. Similarly, the public in general may react to healthcare spending cuts as good news for people in good shape, meaning that fewer taxes will have to be paid to the state, or as bad news for people with disease states, because less money will be available to take care of their diseases and resulting 451

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disabilities. As a consequence, it is a real political duty to define the right balance between competing demands for the use of scarce resources. However, even if we accept that resources are scarce, and that there should be some limit to the resources invested in healthcare (so that some money is left for, let’s say, computer development), no one dares to define how much is enough, or what is the right amount to be earmarked for healthcare. In other words, saying that resources are scarce does not necessarily imply that they are limited to the point where an intervention is desirable or necessary. If we forget these subtle but important points, then we may misinterpret the growing and interesting literature on health economic studies. To make this point as clear as possible: if resources are scarce by definition, then they are scarce everywhere: in the United States as in Belgium. The United States spends 14% of its gross national product (GNP) in “health” to get a certain result. Belgium spends 9% of its own GNP in “health” to obtain another result, which some consider better. Is the United States spending too much or Belgium not enough? If the United States cuts down its spending in health to 9%, will that figure be reasonable, or will it still be excessive? And if the United States cuts it’s spending to 9%, should then Belgium cut its own spending to 5%? If resources are really scarce, what is the target we should set? 3%? 0%? On the other hand, how much of the outcome are we ready to lose owing to the decrease in expenditures? This shows the inherent complexity of the issue, as well as the need to use economic studies as only one of the elements in the debate, and not as the only one or even as the main one. Before coming back to health economics, but with the above caveats present in our minds, to look at what is known on the economic aspects of OSA, some basic definitions will be reviewed for those unfamiliar with this field. BASIC NOTIONS IN HEALTH ECONOMICS Clinical studies tend to determine the efficacy or the effectiveness of a treatment while the economic evaluation tries to assess efficiency. The “efficacy” tries to measure the effect of a treatment (or of any intervention), under strictly controlled circumstances. The “effectiveness” is the effect of this same treatment when transferred to daily, real-life, clinical practice. In general, an intervention loses part of its effect when transposed from controlled conditions to real-life ones. In addition, untoward effects may appear under real-life conditions that were not observed or expected under controlled conditions. Recent examples abound. In economic analysis, the “efficiency” is the relationship between the allocated means (“inputs”) and the results reached (“outputs”), in this case the effectiveness. A basic definition of efficient health care could be “effective care reached with low enough costs”. We may consider efficiency as an economic way to reach a specific goal or as a way of obtaining the highest benefit starting from limited means. If the first definition was the most relevant during the 60s and 70s, the current situation of economic crisis considers the second one as prevalent today. As explained earlier, the economic evaluation is a tool that will help us to make decisions based on the efficiency of interventions. By definition, an economic evaluation is a comparison of two possible acts, and it considers as well the means implemented as the results obtained. The clinical studies and the economic evaluations are complementary. Economic evaluations can only be performed if enough good quality clinical studies provide detailed data about health interventions.

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TYPOLOGY OF ECONOMIC EVALUATIONS Three main types of economic evaluations are generally considered: cost-effectiveness analysis, cost-utility analysis and cost-benefit analysis. The difference between the three lies primarily in the manner of expressing the effects: in natural units (such as life-years gained), in units of utility (such as life years weighted according to the quality-of-life) or in monetary units. The cost-effectiveness analysis is the most used in economic evaluations published in medical related papers. The cost-effectiveness analysis is designed to compare the costs and benefits of a healthcare intervention to assess whether it is worth implementing. In cost-effectiveness analysis the benefits are expressed in nonmonetary terms related to health effects, such as life-years gained or symptomfree days. In addition of being expressed in nonmonetary terms, the benefits can be measured through objective means. For instance, a new antihypertensive treatment may offer a 50% increase in the number of days per month with a normalized mean blood pressure with respect to the older treatment. Blood pressure is an objective measurement that can be obtained with objective methods. Similarly, life-years gained are objective numbers, that are independent of who is measuring. As with all economic evaluation techniques, the aim of cost-effectiveness analysis is to assess whether a given intervention maximizes the level of benefits (health effects) relative to the resources available. The cost-utility analysis can be considered as an extension of the costeffectiveness analysis that gathers all the effects of a health intervention under the same denominator, called “QALY” for Quality-Adjusted Life Year. The term “utility” (as currently used by health economists) refers to a cardinal value that represents the strength of an individual’s preferences for specific outcomes under conditions of uncertainty. Thus, contrary to the cost-effectiveness analysis, the cost-utility analysis incorporates subjectivity by adding to the objective data the values of the patient, his or her opinions and preferences. The cost-benefit analysis measures the effects of an intervention in monetary units. Effects, or benefits, of an intervention can be measured objectively in net monetary savings or in extra monetary costs, or subjectively, based on the “willingness to pay” of the subjects. In the medical world, this technique may be considered as less ethical. It may involve solvency, favouring the rich and disadvantaging the least fortunate. Nevertheless, the strong theoretical and conceptual bases of the cost-benefit analysis and the pure monetary denominator allow direct comparisons between investments in health care or investments in other areas whereas cost-effectiveness or cost-utility analysis are limited to comparisons of interventions of same nature.

Cost-Effectiveness Analysis A cost-effectiveness analysis associates the difference of the costs and the difference in effectiveness of two treatments. In most cases incremental cost-effectiveness ratios are used. Three main steps are necessary to proceed with a cost-effectiveness analysis: 1. Calculation of the difference of the costs (Cx – Cy) 2. Calculation of the difference in effectiveness (Ex – Ey) 3. Calculation of the incremental cost-effectiveness ratio (Cx – Cy)/(Ex – Ey)

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These steps are described in detail below: 1. Costs are seen differently from different points of view. In economics the notion of cost may be based on the value that would be gained from using resources elsewhere referred to as the opportunity cost. In other words, resources used in one intervention are not available for use in other interventions and, as a result, the benefits that would have been derived have been sacrificed. It is usual, in practice, to assume that the price paid reflects the opportunity cost and to adopt a pragmatic approach to costing and use market prices wherever possible. In cost-effectiveness analysis it is useful to distinguish between the direct and the indirect costs associated with the intervention, together with what are termed intangibles, which, although difficult to quantify, are often consequences of the intervention and should be included in the cost profile. Direct costs are for instance those related to a hospital admission, or to drugs required for treatment. Indirect costs can include absences from work, costs of illnessrelated accidents and so on. Intangibles can include pain, suffering, and adverse effects. It is essential to specify which costs are included in a costeffectiveness analysis and which are not, to ensure that the findings are not subject to misinterpretation. The calculation of the difference of costs between a new treatment x and the existing treatment y is called the incremental cost. It is equal to the total cost of x minus the total cost of y. 2. In the second step, the incremental effectiveness between x and y is calculated. It is equal to the effectiveness of x minus the effectiveness of y. The main problem is the choice of the effectiveness parameter. The nature of the cost-effectiveness analysis often forces the choice of only one parameter thus losing information about other collateral effects that could be relevant for the economic evaluation. Common effectiveness parameters are classified in two categories: primary and secondary parameters. Primary effectiveness parameters, essentially life expectancy or quality-of-life, are relevant from a societal perspective. They allow comparison between cost-effectiveness ratios of treatments for different diseases. For instance, a given amount of money invested in disease a may “buy” three extra years of life per patient, whereas the same amount invested in disease b may buy only one extra year of life per patient. In some cases, when different treatments are compared for a given disease, we can be satisfied with secondary effectiveness parameters: parameters that measure one specific outcome linked to the disease—number of cancers detected with two different diagnostic strategies, decrease in the blood pressure with two different anti-hypertensive regimens, and so on. 3. Finally, in the third step, we calculate the incremental cost-effectiveness ratio by dividing the incremental cost by the incremental effectiveness as defined above. It is critical to really use “incremental” costs and effectiveness versus average measures in order to estimate the real cost of a health intervention and avoid financially irrational decisions.

Quality-of-life Quality-of-life is a descriptive term that refers to people’s emotional, social and physical well-being, and to their ability to function in the ordinary tasks of living.

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In 1947, the World Health Organization changed the definition of health, from “absence of disease,” to a “state of physical, mental and social well-being.” This new definition is difficult to use but underlines the need to consider health from a broader point of view. Traditional health parameters such as mortality or morbidity are no longer enough to measure the real consequences of many chronic diseases, neither to evaluate the consequences of their treatments. The concept of health-related quality-of-life has been introduced to measure the effects of treatments on patient’s emotional, social and physical well-being. It has been implemented as a “sum” of fields (it is therefore a construction, such as IQ) which can be evaluated by scores calculated by representative items. It is important to realize that quality-of-life is assessed by the subject himself or herself, not by any external observer. Cost-Utility Analysis In the context of economic evaluations, we need now a technique able to resume the effects of treatments on both life expectancy and quality-of-life in one global effectiveness parameter. A theoretical answer to this question is the concept of “utility”: the value, for the patient, of a given health status versus a determined standard (perfect health). Specifically, health utilities are preferences for specific health states or treatments. They provide an approach to the comprehensive measurement of the value, from the patient’s perspective, of his health-related quality-of-life considered on a defined time length. To better understand this concept, let us consider an intervention that cures a disease, restoring (as valued by the patients suffering from this disease) perfect health for five extra years of life. Now, consider an intervention that improves another disease, but does not cure it. From the personal perspective of the patients, this may offer them a state of health that they value as “half perfect health.” If the improvement lasts for 10 extra years, then the two interventions may be viewed as offering the same value, or utility. To further clarify this matter, consider the following example. Let us examine the case of two blind people. A medical intervention gives them back the use of one eye. One of them may consider this a true miracle, valuing it enormously (for instance 0.8 on a scale from 0 to 1). The other may go on the rest of his life complaining because only one eye, and not both, was recovered, and consider the medical intervention as of little value (for instance 0.2 on the same scale). If both individuals have a life expectancy of 10 years, the medical intervention will be worth eight QALYs for the first individual, but only two QALYs for the second one. This allows therefore estimation of the time-adjusted utility of a given treatment. The general approach to measuring health utilities includes three steps: ■ ■

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Defining a set of health states of interest. Identifying individuals to provide judgments of the desirability of each health state. Aggregating across the individuals to determine scale values for each health state.

Within this general framework, however, there are a number of issues that need to be addressed. These issues include, but are not limited to: what are the relevant health dimensions; what preference-scaling method should be used (the main methods used to assess these preferences are the standard gamble and the time trade-off methods); whose preferences should be measured?

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It is important not to mix up quality-of-life measures with utility measures, which are used to calculate QALYs. Unlike utility measures, quality-of-life measures attempt to evaluate directly the impact of a disease or treatment on people’s ability to function in life, not the value that they place on a particular health state. A QALY takes into account both quantity and the value given to quality-of-life generated by healthcare interventions. It is the arithmetic product of life expectancy and a measure of the quality of the remaining life years. A QALY places a weight on time in different health states. A year of perfect health is worth one; however, a year of less than perfect health life expectancy is worth less than one. Death is considered to be equivalent to zero, however, some health states may be considered worse than death and have negative scores. QALYs provide a common ground to assess the extent of the benefits gained from a variety of interventions in terms of their impact on the patient’s perception of his health-related quality-of-life as well as survival. When combined with the costs of providing the interventions, cost–utility ratios result; these indicate the additional costs required to generate a year of perfect health (one QALY). Comparisons can be made between interventions, and priorities can be established based on those interventions that are relatively inexpensive (low cost per QALY) and those that are relatively expensive (high cost per QALY). QALYs are far from perfect as a measure of outcome, with a number of technical and methodological shortcomings. Nevertheless, the use of QALYs in resource allocation decisions does mean that choices between patient groups competing for medical care are made explicit and commissioners are given an insight into the likely benefits from investing in new technologies and therapies. Cost-Benefit Analysis The cost-benefit analysis is the most complete and concrete economic evaluation. It has been used for year in various economic areas. The main difference from cost-effectiveness and cost-utility analysis is that cost-benefit analysis expresses the result of the interventions in monetary units. This implies that not only healthrelated interventions can be compared, but also other interventions such as traffic security measures, for example. Another advantage is that costs and effects are expressed in the same unit: money. We can therefore calculate differences, not only ratios, and have an idea of the true monetary impact of one health intervention for the society: benefit or loss. Considering these advantages, the only reason why the cost-benefit analysis is not used more than other economic evaluations is because of the major difficulty of expressing in monetary units all the effects of a health intervention. Some methodologies provide shortcuts to approach this question, but none at this stage has proven to be a perfect solution. ■

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The human capital methodology is based on the idea that active people can be compared to equipment assets (such as machines or buildings) because they produce activities. The benefit of a health intervention that saves lives is the sum of all future production flows that would have been lost without this intervention. The main critic of this methodology is that it denies the value of life and qualityof-life (nonprofessionally active people generate no economic production flow). The observed preference methodology is based on an estimation of the value people recognize for their life. By comparing how much people are ready to pay or to receive to minimize or to take risks with the real risks, it is possible to

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measure the implicit monetary value people attribute to their own life. Practically this methodology is difficult to implement because there are very few situations in which we can really observe and measure the differences between the real risks and the consequences of behaviors trying to reduce them or to take the risk of not suffering from the actual risks. This is why a third methodology allows people to quantify, by themselves, in monetary units, their preferences for health interventions: the contingent valuation. Patients are asked in a questionnaire, whether they would be ready to pay for a treatment or another health intervention, and how much. This can then be compared to the actual market prices. If people are ready to pay prices in excess of market real prices, one can consider that the intervention is justified. Thus, although the method uses money as a guide, preferences and values are taken into account. Obviously, the higher the price, the less people are willing to pay, and the higher their revenue, the more they are ready to pay. Nevertheless, the contingent valuation is at this stage the best way to estimate the benefits of a treatment. But many questions still need to be answered: How to formulate the question on the readiness to pay? Whose readiness should be measured? How to describe the intervention?

Before leaving these theoretical considerations and definitions, a word of caution is worthwhile. The readers not familiar with the field of economic analysis should keep in mind that this is a relatively young field, that concepts evolve and change, that new methods and definitions are continually introduced, that statistical analysis of available data also change and become more complex, so that the present state of knowledge is no more stable and solid than in other fields of knowledge (1). OBSTRUCTIVE SLEEP APNEA Let us now try to apply economic analysis to the field of obstructive sleep apnea (OSA). This means, in simple and schematic terms, to consider the costs of the undiagnosed disease both for patients suffering from the disease, their families and friends, and for society as a whole. The costs of diagnosing the disease and treating it should then be taken into account. For this, one should compare the costs of different diagnostic strategies (for instance using cost/utility ratios), and the costs of different treatment modalities (for instance using cost/effectiveness ratios). One should also take a look at whether diagnosing and treating the disease has any measurable consequence on the costs owing to the disease (i.e., economic benefit). A comparison of the economic aspects of sleep apnea and of other diseases should be made. Only thereafter the conclusion will emerge: Is sleep apnea a disease with enough economic impact to merit the interest of society and the allocation of resources for its treatment? Or should we forget about it and turn to more appealing issues like diabetes, halitosis, cancer or athlete’s food? We will try to examine these issues in the rest of this paper. The available literature is relatively scarce so that the conclusions that could be reached will necessarily consist of approximations rather than certitudes. COSTS OF OBSTRUCTIVE SLEEP APNEA The costs of a disease can be classified in direct and indirect costs. Direct costs are for instance those related to payment of the physician, of a hospital admission, or of drugs required for treatment. Indirect costs can include absences from work, reductions in earning capacity, costs of illness-related accidents, and so on

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(nonmedical indirect costs). They can also include direct medical costs related to complications of the undiagnosed disease. These can be called indirect medical direct costs. Once the situation is serious enough that the patient seeks medical help for the specific disease we are interested in, direct costs will begin to accumulate during the process leading to a diagnosis. This process can be more or less simple and lengthy, depending on the characteristics of the disease and the awareness of it within the public and the medical profession at a given place and time. The cost will also depend on the direct costs of the technical procedures needed to establish a firm diagnosis, whether they require an admission to hospital for specific tests, whether they require very specialized personnel, and so on. In addition, these diagnostic procedures may also imply indirect costs (days off work for diagnostic procedures). If there are iatrogenic complications of the diagnostic tests, these might also imply direct and indirect costs. Medical Costs of Sleep Apnea Before the Diagnosis In the case of OSA, most of the above information is simply not available. Some studies have analyzed the global health care costs in the year before diagnosis in a cohort of patients suffering from sleep apnea and compared them to those of a control group reasonably matched for confounding factors. As far as this implies the assumption that the eventual cost difference between patients and controls depends solely on the presence of a disease process called OSA, but are not strictly related to its diagnosis or treatment, these health-related costs can be included in the indirect medical direct costs of the disease. Kapur et al. (2) explored the health care costs of 238 adult patients with sleep apnea living in Washington, D.C., U.S.A. during the year preceding the formal diagnosis of the disease. They compared these costs to those of a group of 476 age- and sex-matched subjects without sleep apnea but enrolled in the same health care program as the patients. The results showed that patients incurred significantly higher mean and median health care costs than controls during the year preceding the diagnosis of sleep apnea. Costs for patients were about twofold the costs for controls. This remained true after adjustment for the “chronic disease score,” a global measure of chronic disease status. Interestingly, patients had a more severe score than controls (essentially because of hypertension and coronary artery disease, depression, bronchial asthma and diabetes) but, as stated, this did not explain the costs differences. Moreover, there was a significant direct relationship between sleep apnea severity [assessed through the apnea-hypopnea index, (AHI)] and health care costs, so that the latter increase rapidly as the AHI increases up to a value of 30. Further increases in the AHI result in much lesser increases in health care costs. The authors calculated that the health-related cost burden for undiagnosed sleep apnea in the United States is USD$3.4 billion per year. The main problem with this study is that there was no matching between patients and controls in terms of body weight. Since obesity is common in patients with sleep apnea, the health care cost difference between patients and controls could be owing to obesity, and not to sleep apnea, if controls were leaner, which is unknown. Kryger et al. have explored in two papers the health care utilization in Canadian patients with sleep apnea during the 10 year preceding, and the two years following, the diagnosis of sleep apnea (3,4). In 181 patients and 690 age-, gender- and postal code-matched controls, mean individual costs for patients were significantly higher than those for controls (about a twofold difference) during the 10 years preceding the

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diagnosis, and these costs differences increased along the year approaching the time of diagnosis, especially for the last three years prior to diagnosis (3). The excess costs include both physician claims and hospitalizations. This study, as the preceding one, did not perform matching for body weight, so that the differences might be due not to sleep apnea, but for instance to obesity. It is not clear from the presented data whether the costs of the diagnosis are included in the calculations, which would of course artificially increase the indirect medical direct costs for patients in the last year prior to diagnosis, by adding to them the direct medical costs related to the disease. In their second study, they followed the health-related costs in 344 male Canadian patients diagnosed with, and treated for, sleep apnea (4). A total of 1324 controls without a diagnosis of sleep apnea were matched for age, gender, and postal code to the patients (three to four controls per patient). They studied the costs during the five years preceding, and during the two year following, the diagnosis of sleep apnea. The costs related to the diagnosis itself (sleep laboratory evaluation) were attributed to the first year after the diagnosis, to avoid artificially increasing the costs of the last year before diagnosis. Costs related to continuous positive airway pressure (CPAP) equipment were not considered. For follow-up purposes, patients were divided among those adhering with treatment (mainly CPAP therapy, n = 282) and those not adhering (n = 62). The data show that patients incur higher absolute costs than controls before diagnosis (somewhat less than twofold), and that these absolute costs decrease after diagnosis, but there is no statistical evaluation of these figures. The authors analyzed the significance of the cost difference between patients and controls. This shows a significant reduction in the difference between patients and controls from the last year before diagnosis to the second year after diagnosis and treatment of sleep apnea. Indeed, the differences were significant only for patients adhering with treatment, but not for nonadherent patients. However, costs remained higher for patients than for controls (no statistical analysis provided). Again, as in the previous study, differences include both physician visits and hospital stays. In the last year before diagnosis, physician-related costs were CAD$260 (all costs are in Canadian $) higher per patient, and this was reduced to $174 per patient in the second year after diagnosis for the entire patient group. The analysis according to adherence with treatment shows the following figures: adherent patients spent in physician-related claims CAD$267 more per patient than their own controls during the year before diagnosis, and CAD$181 during the second year of treatment. Nonadherent patients spent CAD$236 more per patient than their own controls during the year before diagnosis, and CAD$141 during the second year of follow-up. It is worth mentioning that nonadherent patients were significantly older and had less severe sleep apnea, and showed a trend toward less obesity. Moreover, during the five years preceding diagnosis, nonadherent patients had significantly higher costs for circulatory and genitourinary disorders. Hospital stays significantly decreased after diagnosis for the entire group of patients. The decrease was significant for patient’s adherent with treatment, but not for patients not adhering with treatment. This study is difficult to interpret, because many variables are not available. It is impossible to relate this study and the previous one, because absolute values were not compared in this study, and differences were not compared in the previous one. Obesity was taken into account in neither study. We are told that nonadherent patients do not decrease the difference in costs with respect to their controls after diagnosis, but this difference is less (though we are not told if significantly so) in absolute terms in nonadherent than in adherent patients.

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We know that nonadherent patients had more circulatory and genitourinary problems than adherent patients before diagnosis, but we don't know whether this was also the case for their respective control groups. Finally, the cost of treatment of sleep apnea in patients was not included in the calculations. If this cost is added to the spending of the patients, the results could well become nonsignificant, or even offer a reverse picture. More recently, the same group of investigators have assessed in another group of 773 patients with OSA, and in a control group matched for age, gender, residence, and family physician, the reasons for health care costs in the five years prior to diagnosis (5). Health care costs were 23% to 50% higher in patients, which is less a difference than in the previous studies from the same authors. However, in this study, the costs of medications were not included in the analysis. The excess costs were owing to a higher number of physicians visits, and to higher physician fees and hospitalizations. Patients were significantly more likely to have comorbid conditions (hypertension, chronic heart failure, and chronic obstructive airways disease). However, it is impossible from this study to assess the role of obesity, because on the one hand the body mass index (BMI) of the control group is unknown, and on the other hand, the authors found no correlation between five groups of increasing BMI and health care costs. The costs related to medications were analyzed in another paper by the same group of investigators (6). They selected 549 patients (401 men) with polysomnographically-proven sleep apnea and matched them one-to-one with controls from the general population according to age, gender, postal code, and physician. The main outcome was the cost of prescribed medication in the year before the diagnosis of sleep apnea was made. With respect to controls, more male (but not female) patients had received prescriptions in the year before diagnosis. Cases were on more medications, had more prescriptions per year, and had higher values for both defined daily doses and days of drugs supplied. Cases had also higher total costs of medications than controls. Individual costs were higher for female patients, who were also significantly older and more obese than male patients, but had a lower apnea-hypopnea index. The authors analyzed in detail cardiovascular drugs, and found a higher use in patients than controls. There was a relationship between drug use and age, gender and daytime sleepiness assessed by the Epworth sleepiness scale. Cardiovascular drugs use could be predicted by age, BMI and time spent below 90% of oxygen saturation during polysomnography. Concerning costs, female sex, increasing age, obesity, and time spent below 90% SaO2 predicted higher drug costs. Again, the main weakness of this study is that obesity was not analyzed in the control group. Thus, once more it is not possible to separate the effects of sleep apnea from the potential ones owing to obesity. A more recent study from the same group (7) analyzed physician fees and visits in 342 patients with OSA for five years before the diagnosis and five years of follow-up under therapy with CPAP. This represented 75% of all patients diagnosed with OSA and prescribed a CPAP treatment. A group of four controls per patient, matched for age, gender, postal code, and family physician, was also included in the study. An important point is that visits and fees related to the diagnosis process of OSA and institution of CPAP treatment were attributed to the first year after the diagnosis rather than to the last year before the diagnosis. The main results show that physician visits increase from the fifth to the last year before the diagnosis, whereas they decrease after the start of therapy. Visits also increase in controls in the first five years, but do not decrease in the last five years of follow-up. Total fees

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followed a similar pattern in patients. In controls, fees increased in both periods. It is worth noting that after five years of CPAP treatment, patient costs still exceeded the costs of controls, but that the difference was reduced with respect to the one existing before the diagnosis. In fact, after a great decrease in the second year after the diagnosis, costs increased again afterwards. In the year before the diagnosis, the average cost per patient was CAD$372 (CAD$149 greater than the cost five years before the diagnosis), whereas the cost per control was CAD$153. Five years later, the cost per patient had decreased to CAD$358, whereas the cost per control had increased to CAD$200. Tarasiuk et al. (8) have used a similar approach to assess health care utilization in the two years before a polysomnography based diagnosis of sleep apnea in Israel. As in Canada, physicians in Israel have no economic incentive to prevent or deter patients from medical services, because everybody has compulsory State-based complete health insurance. The authors studied 218 adult patients matched one-to-one with controls from the general population according to age, gender, area of residency, and family physician. Three outliers with extreme high costs were excluded from the patient group. Costs corresponded to direct medical costs in the two years before polysomnography: day hospital visits, hospitalization days, emergency department visits, family physician visits, specialist visits, and medications prescribed. The costs of diagnosing sleep apnea were excluded. Interestingly, these patients had moderate to severe sleep apnea (average respiratory disturbance index, RDI, 35 events per hour slept; arousal index 29 per hour slept), were classically obese (mean BMI of 33 kg/m2) but were not very sleepy according to an Epworth Sleepiness Scale mean score of 7.6 + 4.4 units. Comorbidities included hypertension in 40% of patients, diabetes mellitus in 14.5% and pulmonary diseases in 12.4%. The total costs for health care in the two years preceding the diagnosis was 70% higher in patients owing to more hospitalization days, more physician visits, and more medications prescribed, particularly those for the cardiovascular system and for the alimentary tract and metabolism. The excess costs were especially notable in patients younger than 65 years old (excess cost ratio 2.2), whereas above this age controls spent a little more than patients. In both groups, women had higher costs than men. In this study, the severity of sleep apnea, assessed through the RDI, or of obesity, assessed through the BMI, did not influence the degree of excess health care costs. As in other studies, the degree of obesity in the control population remained unknown, leaving thus some uncertainty on the respective effects of sleep apnea or obesity on the findings. The same group (9) had previously examined the question of health care utilization in children. Using a similar approach, 287 children with polysomnographically-proven sleep apnea were matched one-to-four with children from the general population according to age, gender, area of residency. None of the control children was on chronic medication. Costs were collected for the 12 months period before diagnosis. The main results showed that patients with OSA had a higher health care cost (cost ratio 2.26). This was owing to more admissions, more hospitalization days, more day hospital visits, more Emergency Department visits, and more consultations. The most frequent health problems concerned ear, nose, and throat (ENT), pulmonary and ocular disorders. The RDI had a predictive value only for children younger than five years of age. In another related study, the same authors (10) showed that treating pediatric sleep apnea with adenotonsillectomy, a treatment with proven curative value in this age group, resulted in a decrease in health care costs in the year following the intervention with respect to the year before it. Costs decreased by one-third in children with OSA submitted

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to surgery (n = 130), whereas costs did not change in a group of children with OSA but not operated on (n = 90) or in a group of 520 control children from the general population. In spite of the uncertainties of these studies, especially on the role of obesity, it appears that undiagnosed sleep apnea results, both in children and adults, roughly in a doubling in health care costs (the excess cost is directly related to disease severity at the time of diagnosis in some but not all studies); that the excess cost increases with time, and that the excess cost might decrease with an adequate treatment if well adhered to. We have voluntarily excluded from this analysis the possible consequences of sleep apnea on hypertension and stroke. We did so because there are no hard and validated data on the effects of treating sleep apnea on the evolution, treatment and costs of hypertension and stroke (although epidemiological data seem to confirm the etiologic role of sleep apnea as one of the factors causing hypertension or stroke). Indirect Costs Related to Sleep Apnea Indirect nonmedical costs have not been assessed in patients with sleep apnea. Some considerations can nevertheless be made from the studies of traffic, work, and domestic accidents in patients with this disease. Indeed, one of the main symptoms of sleep apnea is an excessive level of daytime somnolence. It is plausible that excessive somnolence, by a decrease in attention, could result in a reduction in the ability to react promptly to unexpected events. If excessive somnolence leads to overt sleep under inappropriate conditions (while driving a motor vehicle, for instance), the ability to execute motor tasks disappears, and a motor vehicle crash can ensue. Accidents owing to sleep while driving are generally described as involving a single vehicle, with no avoidance reaction, and leading to serious health consequences (very serious injuries or death). Several studies have assessed the risk of traffic accidents in patients with sleep apnea, by comparison to a control group or to a country’s general population risk. Some studies have also assessed the evolution of the traffic accident risk after treatment for sleep apnea is instituted. Similarly, some authors have studied the number of all accidents (both traffic and home accidents) before and after therapy, as well as the number of days in hospital owing to accidents and their evolution with treatment (11–17). Table 1 summarizes the most recent studies and their main results. It is clear from these data that untreated sleep apnea results in a certain amount of indirect costs. These include the costs related to the excess accidents themselves (vehicle repairs or replacement, hospital stays, days lost for work), but do not include the unknown costs related to severe injuries or lives lost. Indeed, most studies have assessed accidents not leading to serious injuries or death. It also appears that an adequate treatment reduces these indirect costs. However, the real amount of indirect costs remains unknown. Estimations of the overall costs of drowsiness related motor vehicle accidents in the United States for 1988 are in the range of USD$50 billion. However, there is no data on the part of this cost that could be attributed to sleep apnea. In 2001, C. F. P. George published a study on accidents reduction following the start of a CPAP treatment (18). Patients (n = 210) had a higher number of motor vehicles collisions than a group of control subjects (matched for age, sex, and type of driver’s license) during the three years before starting treatment. The number of collisions normalized during the three years after nasal CPAP, whereas it remained

Cohort study, general population. (913)

Case-control: (102–152)

Questionnaire cohort study Pr – 1 yr Po. (59) Case-control: (156–160); follow-up: Po. (85)

Young (13)

Teran-Santos (11)

Cassel (14)

OR for crashes in habitually sleepy drivers vs. controls

Mean accidents/driver/ 1,000,000 km during 3 yr

Traffic accidents rate per 100,000 km

OR for multiple accidents during 5 yr for subjects with AHI > 15 vs. subjects with no sleepdisordered breathing OR for one traffic accident

OR for traffic accidents for the 3 yr preceding inclusion All accidents (n/100 persons)

Main outcome

GFP 0.4 3.7 2.1 n/a

Pr 8.4 4.6 2.2 885

Po 2.5 2.4 1.3 84

OR of 13.3 for auto crashes OR of 8.5 for total respiratory event index > 15 in sleepy vs. nonsleepy drivers

All patients: 6.8, severe patients 13 (AHI > 34), moderate patients 1.1 (AHI ≤ 34), controls 0.78. 85 patients before CPAP: 10.6, after CPAP: 2.7

0.8 before CPAP to 0.15 on CPAP

6.3 (2.4–16.2) for AHI > 10

Car accidents : Domestic Work Hospital days Related to Accidents 7.3 (1.8–25)

2.3 (0.97–5.38) for single accidents 5.2 (1.07–25.29) for >1 accident

Main results

No effect of BMI, alcohol, sleeping pills. Rate of accidents in patients and controls significantly higher than official Swiss estimates of sleepiness-related accidents (0.02) Age, sex, BMI, alcohol and medications, hours driven per month, years of driving

Age, gender, alcohol, driving experience, BMI, visual refraction defects, sleep schedule, history of traffic accidents, medications

Age, gender, miles/yr

Age, gender, km/yr, alcohol

Main adjustment variables

Abbreviations: AHI, apnea-hypopnea index; CPAP, continuous positive airway pressure; GFP, general French population; n/a, nonavailable; OR, odds ratio; Pr, Pre-CPAP; Po, Post-CPAP.

Masa (16)

Interview: (4002), followed by case-control: (107–109)

Questionnaire Cohort study, 1 yr Pr, 1 yr Po. (547)

Krieger (15)

Horstmann (17)

Case-control (60–60)

Barbe (12)

Type of study (n)

Main Studies on Accidents In Sleep Apnea

1st author (ref.)

TABLE 1

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high in 27 patients not using nasal CPAP. There was no change in the number of collisions in the control group. It should be stressed that in this group of patients, driving exposure (km/yr) was roughly the double of the usual exposure in Ontario. Similar results, that treatment with CPAP normalizes the significantly increased rate of traffic accidents in patients with sleep apnea, were reported by Findley et al. In their 50 patients, they found an accident rate of 0.07 accidents per driver per year, significantly higher when compared with the rate in the general population of drivers of Colorado, of 0.01. Treatment with CPAP in 36 patients reduced the accident rate to zero in the two years of follow-up, whereas in the 14 patients not using CPAP the rate remained at 0.07 (19). Whereas most studies show that the risk for traffic accidents is increased several-folds in sleep apnea patients, one recent study in Australian commercial drivers has found that sleep apnea only doubles the odds ratio (20). More recently, Sassani et al. estimated, based on the available evidence from the literature, the annual number of traffic accidents and fatalities owing to sleep apnea and their cost in the United States (21). After performing a meta-analysis of six published papers, they used an odds ratio of 2.52 for patients with sleep apnea compared to control subjects. They excluded drivers younger than 25 years old. The total annual cost of sleep apnea-related traffic accidents was estimated at USD$15.9 billion resulting from 810,000 collisions and 1,400 fatalities. With a 70% CPAP effectiveness, treatment of all OSA patients would prevent 570,000 collisions and 980 fatalities. They also calculated that treating all patients with OSA with CPAP would cost USD$3.18 billion per year (this cost includes the cost of screening five subjects to diagnose one patient). This expenditure would result in net savings of USD$7.9 billion per year. In other words, each USD$ spent in screening, diagnosing and treating patients with OSA would save USD$3.49 in collisions and fatalities costs. Data on work and domestic accidents are much rarer. Krieger et al. (15) found a near halving of domestic and work accidents after CPAP therapy (Table 1). Lindberg et al. used a questionnaire based approach in 2,800 Swedish men (22). After 10 years, 2,000 subjects answered a second questionnaire. Subjects with a history of snoring and excessive daytime sleepiness at baseline (but not either symptom alone) had an adjusted odds ratio of 2.2 for work-related accidents in the next 10 years (accident data were retrieved from a national register). Although much less compelling, these data suggest that OSA may not only lead to traffic accidents, but also to occupational and home accidents, increasing even more the economic burden of the disease in terms of indirect costs. Sleep Apnea and its Treatment: The Patient’s Point of View Toussignant et al. studied the QALY value of CPAP therapy in 19 patients with sleep apnea, by comparing the utilities given by the patients to their health status under therapy and before diagnosis (23). They found that CPAP added an average of 5.39 QALYs. The range was large, from 0 to 28, reflecting the wide variation in the subjective value given by the patients to the change in their health status owing to the treatment of their disease. One problem with this study is that it was a retrospective study: the utilities were in fact measured after CPAP treatment had been instituted for a number of year. Therefore, the utilities reflected a comparison between “actual conditions” (relatively easy to assess) and “previous conditions” (requiring both a recall process and an imagination effort). Chakravorty et al. have confirmed that nasal CPAP resulted in a 23% improvement in the baseline severely impaired health status, adding 8.2

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QALYs to a group of 37 patients treated with CPAP, whereas the improvement in a control group treated with “lifestyle counseling” but not with nasal CPAP was of 4% with an addition of 4.7 QUALYs (significantly less than in the nasal CPAP group). These data were obtained using the standard gamble approach (24). Interestingly, when the European quality-of-life (EuroQOL) method of assessment was used, no significant differences were noticed between the nasal CPAP and the “lifestyle” groups. The authors did not conclude that there were no differences, but that the EuroQOL questionnaire was not appropriate to assess a sleep apnea population. This exemplifies one problem with questionnaires: if they don’t detect the expected differences, it is rapidly concluded that they should be replaced with other, more sensitive, instruments, which are able to show what one wants to show. Mar et al. have recently performed a similar study (25). Utilities were assessed prospectively in 46 newly diagnosed patients, both before and after three months CPAP therapy, using the EuroQOL 5D instrument. The mean age of the patients was 53 years, whereas the AHI was 41.3 per hour. The BMI was 39.7 ± 13.6 kg/m2. This is probably a very obese sample, more than what is generally seen in recently published studies. The mean gain in EuroQOL score was 0.073 units (somewhat less than found by Toussignant et al.), whereas the baseline score was 0.738. Toussignant et al. have also calculated the cost-utility ratio of CPAP therapy (23). To do this, they tried to take into account all costs related to life-long treatment, including CPAP as well as a single therapeutic polysomnography per patient. They related then this total cost to the total number of QALYs gained with the treatment, and obtained thus the cost per year of QALY added. The figure varied between (ANG$9.800 and CAD$3.500 per QALY, depending on the assumptions made to calculate costs. To get an idea of what this means, the reader might like to know that a coronary artery bypass surgery for a left main coronary artery occlusion has a cost-utility ratio of USD$6.200 per QALY. Renal dialysis costs USD$47,000 per QALY, and screening asymptomatic patients for carotid stenosis USD$120,000 per QALY. Other examples can be found at www.tufts-nemc.org/cearegistry. Mar et al., in their recent paper, using their own calculated utilities, confirm the cost-utility ratio of 7,800 € per QALY if calculations are made with a time span limited to five years, and 4,938 € per QALY if calculations are made on the basis of the lifespan (25). They also performed sensitivity analysis, and only in the worst possible case did the costutility ratio exceed 20,000 € per QALY. Direct Medical Costs: The Diagnostic Tests for Sleep Apnea This is perhaps the right point to introduce the reader not familiar with health economics in the complexities of this realm. Classically, a patient with a clinical suspicion of sleep apnea should undergo a diagnostic procedure to confirm the suspicion, and only then receive treatment. A diagnostic procedure could consist of the reference test (the gold standard, full night polysomnography), or a “validated” (not reference) test, like ambulatory respiratory polygraphy. Some people contend that giving a CPAP trial is all that is needed to identify patients with sleep apnea responsive to CPAP, and that no diagnostic test is necessary in these patients. Only those patients with a clinical suspicion of OSA and not happy with the CPAP trial would need a diagnostic work-up. If one thinks of these three strategies in economic terms, it rapidly becomes apparent that the reference test strategy will be the most expensive. The CPAP trial would probably be the cheapest (after all CPAP machines are not disposable, and if a patient brings back one machine you could still use it for

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the next patient), whereas the ambulatory polygraphy strategy would appear as intermediate in cost. However, when one introduces the utility concept into this seemingly easy economic situation, things may look otherwise. Chervin et al. have evaluated the cost-utility characteristics of three diagnostic strategies for assessing patients with a clinical suspicion of sleep apnea. The three chosen strategies were full night polysomnography followed by CPAP therapy in patients with a confirmatory test; an unattended home cardiorespiratory sleep study followed by CPAP therapy in patients with a positive result, and a treatment CPAP trial with no diagnostic test in all patients clinically suspected of sleep apnea (26). According to the sensitivity and specificity characteristics of the three strategies, a number of patients without sleep apnea (false positive patients) will receive CPAP therapy, and a number of patients with sleep apnea (false negative patients) will be denied treatment. All data used in this model study were obtained from the literature. The utility data were those of Toussignant et al. (23). Sensitivity and specificity characteristics of the three strategies were obtained from different published sources. Costs were computed from the charges for the different visits, tests, and treatment as practiced in the University of Michigan Sleep Center, which are quite higher than costs in Europe. To avoid criticisms concerning the paucity and the uncertainty of the published data and assumptions, the authors submitted their model to a wide sensitivity analysis. Using a Monte Carlo simulation, all baseline variables were simultaneously allowed to vary between reasonable limits. For instance, the average utility for CPAP in patients with sleep apnea used by the authors was the one found by Toussignant et al. in their study (23) that is, 0.87 and this was allowed to vary from 0.4 to 1. The resulting cost-utility ratios using the three strategies were then compared . Cost-utility ratios were expressed in QALYs for the first five years after the initial testing (QALY5), to take into account that most published data on adherence, survival, and so on, concern a time span of around five years. The results showed somewhat surprisingly that the more expensive strategy (i.e., full night polysomnography) resulted in the best utility: 4.019 QALY5 for full night polysomnography, 3.955 for unattended home study, and 3.934 for clinical-based decisions. The incremental cost-utility ratio for polysomnography compared to home studies was USD$13,431 per QALY, whereas this value was USD$9165 for the comparison between polysomnography and clinical-based decisions. The results were not sensitive to wide variations in the baseline characteristics, except for extreme and highly unlikely ones. The reasons why the more expensive strategy yields the best costutility ratios seem to depend not only on the sensitivity and specificity characteristics of the three strategies (favoring full-night polysomnography, which was considered as the gold standard), but also on the frequency of positive findings in the population to be tested, and on the fact that diagnostic costs are “relatively” low compared with treatment (or no treatment in false negative patients) costs. Diagnostic errors have thus a high cost consequence. Mar et al. have recently confirmed these conclusions (25). In their own study, using a cardiorespiratory polygraphy study for diagnosis, and a split night polysomnography to confirm diagnosis and to titrate nasal CPAP was compared to the use of full night polysomnography both for the diagnostic and for the separate titration nights. The increase in cost related to the second, more expensive, approach, changed little the incremental cost-effectiveness ratio, that remained below 13,000 € per QALY for a prevalence of the disease in the tested sample that was allowed to vary from 0.32 to 0.16. Reuveni et al. published a cost-effectiveness analysis comparing a home unattended partial sleep study, a hospital-attended partial sleep study, and

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hospital-attended polysomnography for the diagnosis of OSA (27). Calculations were based on literature published data, and took into account all costs, including human resources, accessories and capital expenditures. A sensitivity analysis was included, to take into account a wide range of variation in costs, data loss, technical failures, and diagnostic agreement. Results showed that unattended home partial sleep studies was the most expensive diagnostic option, with two polysomnographic studies (diagnostic and CPAP trial in OSA patients) being 10% more expensive than a combination of polysomnography and hospitalattended partial sleep studies. The authors point to the fact that pricing alone does not allow one to assess the economic impact of a medical strategy, that one should consider the whole process of a disease. In a complementary approach, Tarasiuk et al. used the “willingness to pay” approach to assess the cost-benefit of polysomnography in pediatric OSA (28). In this approach, parents of 252 children with OSA (either scheduled for polysomnography, having performed a polysomnography, or having performed a polysomnography and having been submitted to an adenotonsilectomy) were asked how much would they be willing to pay for polysomnography, considering what they expected from it or what they believed they had gained from it. In Israel, polysomnography is free of charge for patients. By comparing the amount parents were willing to pay to the actual cost of polysomnography, the utility (or theoretical cost-benefit) of polysomnography can be computed. Results sowed that, in average, parents were willing to pay for polysomnography a price that triples the actual market price. In other words, parents assign a very high value to diagnostic polysomnography. Although not considering economic aspects, a very recent paper by Senn et al. merits some consideration (29). These authors used a two-week automatically adjustable CPAP trial as a diagnostic tool for OSA in 76 patients with habitual snoring, complaints of daytime sleepiness and an Epworth sleepiness score >8. Patients willing to continue CPAP therapy and having used their CPAP for more than two hour per night were considered as having OSA. This was compared to polysomnography performed at baseline. There were 35 true positive patients (OSA with an AHI greater than 10 per hour and a positive CPAP trial), 31 true negative patients, one false positive patient and nine false negative patients. The success of the CPAP trial was highly predictive of continuing CPAP use at four months follow-up. Intuitively, this approach could save money, by decreasing diagnostic costs and reducing diagnostic waiting lists. However, the study has some serious limitations. First, exclusion criteria selected a very “pure” population, with no confounding diseases (like unstable heart failure; significant lung disease; medical, neurologic, or psychiatric disorders possibly explaining some of the symptoms). Indeed, the 76 included patients represented only 39% of the patients considered for inclusion. Second, the prevalence of OSA was quite high in this sample: 58% of the patients had OSA. With this kind of prevalence, almost any strategy has a high probability of detecting OSA. The real world situation looks much more like that of the study by Sassani et al. with a pretest probability of 20%. Therefore, the results of Senn et al. are not easy to interpret, neither in clinical nor in economic terms. Direct Medical Costs: Treatment Costs in Sleep Apnea There are no published studies assessing the costs of sleep apnea therapy. Several reports have made cost considerations of auto-CPAP therapy used as a diagnostic

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and therapeutic tool as compared to standard polysomnography and CPAP treatment, but they are flawed by simplistic and not validated assumptions. Nowadays a simple constant CPAP device with a built-in clock memory to assess adherence, which is all that is needed in the vast majority of patients (30), costs about USD$700 (or less) both in Europe and the USA. The life expectancy of one of these devices is about 5 to 10 years. Assuming a discount rate of 4%, and assuming that all treatments are effective (a reasonable assumption for polysomnographic titrated CPAP in sleep apnea) the total cost is USD$852 for a machine with a life expectancy of five years (USD$170 per year), or USD$1036 for a life expectancy twice as long (USD$104 per year). Consumables (one standard mask per year, one tubing every two year, one standard head-gear per year, filters) amount to approx USD$100 to USD$200 per year. Thus the total “pharmacological” cost for the treatment of a patient with sleep apnea with the best available therapy is something around USD$300 per year, to which the cost of electricity, neglected here, is to be added. For most patients, it is unusual to need more than a single annual adherence visit, and new polysomnographic studies are certainly not required as standard practice. Thus, the total cost including physician fees is in the range of USD$350. Mar et al. have calculated an annual cost of 358 € in Spain, a very similar value (25). For comparison purposes, the pharmacological cost for the treatment in Belgium of one patient with simple systemic hypertension, with for instance an angiotensin-converting enzyme (ACE) inhibitor and a diuretic, is about USD$360 per year. For a patient with angina pectoris, treated with a beta-blocker, aspirin, a nitrate derivate and an ACE inhibitor, the cost is in the range of USD$570 per year. A patient with mild to severe chronic obstructive pulmonary disease with frequent exacerbations treated with inhaled corticosteroids and long-acting β2 agonist, inhaled anticholinergics, two courses of antibiotics and oral corticosteroids per year will incur pharmacological costs of USD$954 per year. An important point to bear in mind is that the cost of treatment for a patient with sleep apnea is independent of the severity of the disease, which is not the case for other diseases. The cost of other treatment modalities can’t be estimated so simply, both because they vary too much among countries and practitioners, and because their effectiveness (success or failure rate) has to be taken into account to distribute the cost of the procedures failing to relieve sleep apnea among the ones succeeding in relieving the disease. Calculations for uvulopalatopharyngoplasty (UPPP) should for instance take due notice that the success rate is well below 50%, and that even when successful, the disease can recur after some year. CONCLUSIONS We have tried to review all available literature on the health economics of OSA. The first impression that comes to mind is that this source of information is not vast, is not complete, leaves too many aspects unexplored, and is frequently written in a language that is not accessible to most physicians. Economic analysis does not follow simplistic assumptions, and uses specific mathematical methodologies that are in general well beyond the skills and background of most medical practitioners. Once this has been said, some comments can nevertheless be made. If one considers only the very convincing data on nonmedical indirect, traffic accidentsrelated, costs owing to sleep apnea, it is certain that this disease is worth considering. However, the magnitude of this effect is difficult to establish. Indeed, treatment results in a measurable reduction in traffic accidents. Moreover, it appears that

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diagnosing and treating the disease may well be directly profitable in economic terms, in the sense that it could lead to a reduction in indirect costs owing to accidents in some patients with sleep apnea (those who had accidents!) that exceeds the costs of diagnosis and treatment in all patients with sleep apnea. As far as indirect medical direct costs are concerned, the data are less convincing, but suggest that the undiagnosed disease leads to a doubling in medical expenses with respect to a control population. This increase in medical expenditures is related to the severity of the disease, and is independent from the (nonsleep apnea related) chronic disease status of the patients. Treatment of the disease results in a measurable reduction of the difference in medical expenditures between patients and controls. The magnitude of this reduction (and even its mere existence) is however not well established. Again, we don’t know whether this reduction in indirect medical costs with treatment offers an economic “benefit” (the investment is recovered in excess), or merely reduces by a certain amount the excess in indirect medical costs. If we consider the patient’s point of view, it appears that the disease results in a certain decrease in the possibility to enjoy life, and that an adequate treatment is worth considering. This is an everyday clinical experience, but has been rigorously assessed in only a few studies, and in mostly severe or very obese patients. One may wonder whether extending the analysis to less severe or obese patients could alter the picture, and if the results would still show a beneficial value for CPAP. The diagnostic strategy analysis suggests that, at least in this specific disease, not making diagnostic errors is of prime importance in economic efficiency terms, so that the more expensive strategy is the one with the higher cost-utility ratio. The treatment costs analysis, though based on the author’s personal impressions in Belgium and not on serious validated data, makes sleep apnea a rather inexpensive disease to treat. A last comment is worthwhile concerning costs. Costs are very variable from country to country. A polysomnography in most European countries costs a third or less than in the United States. Thus, when conclusions are derived from the literature, it should be remembered that they could not be “translated” in economic terms to other countries or settings. Caution is essential if huge mistakes are to be avoided. We have not considered in this review the cost of year of life lost (except in the traffic accidents section). Very recently, a number of studies have strongly suggested that OSA results in a measurable decrease in survival, mainly owing to cardiovascularrelated deaths (31,32). Depending on the age of death, occupation, economic gains and so on, an average value can be computed for a life lost. In general, the net value represents a loss, although in some cases death may represent a saving. There has been as yet no published study assessing this issue. Nevertheless, it can be reasonably estimated a priori that consideration of early mortality will make OSA a more, not less, expensive disease. How can we relate all this to the clinical world? Sleep apnea is a fascinating disease, because it can seriously hamper the joy of life for the patient, his or her family, his or her friends and employers, and because a treatment exists that allows almost instantaneously, bypressing a button, to recover normal sleep and, mostly important, normal wakefulness. From the medical point of view, there is absolutely no doubt that this disease is worth diagnosing and treating. Where, then, lies the problem? The problem is to desperately try to justify this in economic terms. Because if one doesn’t, then the managers might decide that the disease, respectable as it

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may be in medical terms, is not worth considering from the financial point of view. Indeed, there are many medical conditions that may make life miserable, but that don’t carry serious economic consequences (like having a disgraceful nose, halitosis and so on). The manager may be sympathetic to the individuals suffering from these conditions, but he or she will not allow the use of scarce economic resources to take due care of them. He or she will rather devote those scarce resources to treat coronary heart disease, which has been rigorously proven to cost a lot of money, and which treatment has been shown to be profitable in economic terms. Or may be not. And one comes back to the real problem: are resources really scarce? Or are we just told that they are? The answer is neither medical nor economical nor political. The answer is philosophical, and reflects the present state of the values of our societies. REFERENCES 1. Neumann PJ, PW Stone, RH Chapman, EA Sandberg, CM Bell. The quality of reporting in cost-utility analyses, 1976-1997. Ann Intern Med 2000; 132:964–972. 2. Kapur V, Blough DK, Sandblom RE, et al. The medical cost of undiagnosed sleep apnea. Sleep 1999; 22:749–755. 3. Ronald J, Delaive K, Roos L, Manfreda J, Bahammam A, Kryger MH. Health care utilization in the 10 years prior to diagnosis in obstructive sleep apnea syndrome patients. Sleep 1999; 22:225–229. 4. Bahammam A, Delaive K, Ronald J, Manfreda J, Roos L, Kryger MH. Health care utilization in males with obstructive sleep apnea syndrome two years after diagnosis and treatment. Sleep 1999; 22:740–747. 5. Smith R, Ronald J, Delaive K, Walld R, Manfreda J, Kryger MH. What are obstructive sleep apnea patients being treated for prior to this diognosis? Chest 2002; 121:164–172. 6. Otake K, Delaive K, Walld R, Manfreda J, Kryger MH. Cardiovascular medication use in patients with undiagnosed sleep apnea. Thorax 2002; 57:417–422. 7. Albarrak M, Banno K, Sabbagh AL, et al. Utilization of healthcare resources in obstructive sleep apnea syndrome: a 5-year follow-up study in men using CPAP. Sleep 2005; 28:1306–1311. 8. Tarasiuk A, Greenberg-Dotan S, Brin YS, Simon T, Tal A, Reuveni H. Determinants affecting health-care utilization in obstructive sleep apnea syndrome patients. Chest 2005; 128:1310–1314. 9. Reuveni H, Simon T, Tal A, Elhayany A, Tarasiuk A. Health care services utilization in children with obstructive sleep apnea syndrome. Pediatrics 2002; 111:68–72. 10. Tarasiuk A, Simon T, Tal A, Reuveni H. Adenotonsillectomy in children with obstructive sleep apnea syndrome reduces health care utilization. Pediatrics 2004; 113:351–356. 11. Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. N Engl J Med 1999; 340:847–851. 12. Barbe F, Pericas J, Munoz A, Findley L, Anto JM, Agusti AG. Automobile accidents in patients with sleep apnea syndrome. An epidemiological and mechanistic study. Am J Respir Crit Care Med 1998; 158:18–22. 13. Young T, Blustein J, Finn L, Palta M. Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults. Sleep 1997; 20:608–613. 14. Cassel W, Ploch T, Becker C, Dugnus D, Peter JH, von Wirchet P. Risk of traffic accidents in patients with sleep-disordered breathing: reduction with nasal CPAP. Eur Respir J 1996; 9:2606–2611. 15. Krieger J, Meslier N, Lebrun T, et al. Accidents in obstructive sleep apnea patients treated with nasal continuous positive airway pressure: a prospective study. Chest 1997; 112: 1561–1566. 16. Masa JF, Rubio M, Findley LJ. Habitually sleepy drivers have a high frequency of automobile crashes associated with respiratory disorders during sleep. Am J Respir Crit Care Med 2000; 162:1407–1412.

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17. Horstmann S, Hess CW, Bassetti C, Gugger M, Mathis J. Sleepiness-related accidents in sleep apnea patients. Sleep 2000; 23:383–389. 18. George CFP. Reduction in motor vehicle collisions following treatment of sleep apnoea with nasal CPAP. Thorax 2001; 56:508–512. 19. Findley L, Smith C, Hooper J, Dineen M, Suratt PM. Treatment with nasal CPAP decreases automobile accidents in patients with sleep apnea. Am J Respir Crit Care Med 2000; 161:857–859. 20. Howard ME, Desai AV, Grunstein RR, et al. Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers. Am J Respir Crit Care Med 2004; 170:1014–1021. 21. Sassani A, Findley LJ, Kryger M, Goldlust E, Georges C, Davidson TM. Reducing motorvehicle collisions, costs and fatalities by treating obstructive sleep apnea syndrome. Sleep 2004; 27:453–458. 22. Lindberg E, Carter N, Gislason T, Janson C. Role of snoring and daytime sleepiness in occupational accidents. Am J Respir Crit Care Med 2001; 164:2031–2035. 23. Tousignant P, Cosio MG, Levy RD, Groome PA. Quality adjusted life years added by treatment of obstructive sleep apnea. Sleep 1994; 17:52–60. 24. Chakravorty I, Cayton RM, Szczepura A. Health utilities in evaluating intervention in the sleep apnoea/hypopnoea syndrome. Eur Respir J 2002; 20:1233–1238. 25. Mar J, Rueda JR, Duran-Centolla J, Schechter C, Chilcott J. The cost-effectiveness of nCPAP treatment in patients with moderate-to-severe obstructive sleep apnea. Eur Respir J 2003; 21:515–522. 26. Chervin RD, Murman DL, Malow BA, Totten V. Cost-utility of three approaches to the diagnosis of sleep apnea: polysomnography, home testing, and empirical therapy. Ann Intern Med 1999; 130:496–505. 27. Reuveni H, Schweitzer E, Tarasiuk A. A cost-effectiveness analysis of alternative at-home or in-laboratory technologies for the diagnosis of obstructive sleep apnea syndrome. Med Decis Making 2001; 21:451–458. 28. Tarasiuk A, Simon T, Regev U, Reuveni H. Willingness to pay for polysomnography in children with obstructive sleep apnea syndrome: a cost-benefit analysis. Sleep 2003; 26:1016–1021. 29. Senn O, T Brack, EW Russi, KE Bloch. A continuous positive airway pressure trial as a novel approach to the diagnosis of the obstructive sleep apnea syndrome. Chest 2006; 129:67–75. 30. Pepin JL, Krieger J, Rodenstein D, et al. Effective compliance during the first 3 months of continuous positive airway pressure. A European prospective study of 121 patients. Am J Respir Crit Care Med 1999; 160:1124–1129. 31. Marin JM, Carrizo SJ, Vicente E, Agusti AGN. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 2005; 365:1046–1053. 32. Campos-Rodriguez F, Peña-Griñon N, Reyes-Nuñez N, et al. Mortality in obstructive sleep apnea-hypopnea patients treated with positive airway pressure. Chest 2005; 128:624–633.

Index

AASM. See American Academy of Sleep Medicine (AASM) Accidents motor vehicle, 266–268 reduction, 448 sleep-related cost benefit, 448 ACE. See Angiotensin converting enzyme (ACE) Acute dyspnoeic attack, 15 Adenoids, 84 facies, 44 obstruction, 112–113 Adenotonsillar hypertrophy, 173 Adenotonsillectomy, 114 Adolescents, 22, 41 Adrenergic innervation, 294 Adult obstructive sleep apnea, 249 age-dependence, 45–52 AHI, 45 daytime sleepiness, 45 diagnosis, 247–248 hypertension, 324 obesity, 45 outcomes and comorbidities, 50–52 risk factors, 46–47 AF. See Atrial fibrillation (AF) Age, 45–52, 209, 251, 252 obesity, 358 AHI. See Apnea-hypopnea index (AHI) Air breathing cladogram, 62 respiratory rhythmogenic process, 62 Airway collapse etiology, 21 obstruction pathophysiology, 44, 246 Alcohol, 47, 210 Altered wakefulness, 276 Alveolar hypoventilation Pickwickian syndrome, 15 ventilation diagrammatic representation, 185

Alzheimer’s dementia, 233 American Academy of Sleep Medicine (AASM), iv, 149 American Sleep Disorders Association (ASDA), 149 Anesthesia UA collapse, 182 Anger, 382, 392 Angiotensin converting enzyme (ACE), 227 inhibitors hypertension, 468 ANS. See Autonomic nervous system (ANS) Anterior nasal cavity, 82–83 Antley-Bixler’s syndrome, 118 Anxiety, 382 Apert’s syndrome, 113, 117, 118, 201 Apnea. See also Obstructive sleep apnea (OSA) carbon dioxide reserve, 130 chemoreceptors, 133 driving risks, 446 hypoxemia, 301 obstructive, 337 frequency, 41 phenotypes candidate genes, 224 Apnea-hypopnea index (AHI), 19, 28, 393 adult OSA, 45 limitations, 250 medical treatments, 112 problems, 250 PSG, 28 sleep apnea, 445 surgical treatments, 112 Apnea Positive Pressure Long-term Efficacy Study (APPLES), 5, 6 Apneic threshold sleep, 128–129 APPLES. See Apnea Positive Pressure Longterm Efficacy Study (APPLES) Arousals, 297–298. See also Sleep arousals EEG-defined, 402 phasic, 156 threshold, 186 Arrhythmia ventricular, 302, 303

473

474 Arrhythmias, 264–265 AS. See Ascending slope (AS) Ascending slope (AS) sleep cycles, 155 ASDA. See American Sleep Disorders Association (ASDA) Atrial fibrillation (AF), 264, 305–307 CSA, 306 Atrial ventricular node (AVN), 294 Attentional model, 385 Australopithecus, 66 Australopithecus afarensis, 74 Autonomic nervous system (ANS) cardiovascular activity, 286–287 control evaluation, 287 OSA, 275–287 AVN. See Atrial ventricular node (AVN) Awakening forced, 152 NREM sleep, 151 Bariatric surgery, 24 Baroreceptors, 295–296 Baroreflex sensitivity (BRS), 285 Beckwith-Wiedeman syndrome, 120 Beebe model, 386 Benign snoring, 21 Bilevel positive airway pressure (BPAP), 420 Blood pressure, 286, 325 pulse transit time, 161 BMI. See Body mass index (BMI) Body mass index (BMI), 260, 460 CPAP, 404 Body position, 213 Borg scale, 366 BPAP. See Bilevel positive airway pressure (BPAP) Bradyarrhythmias, 264, 301 Bradycardia nocturnal, 302 Brain growth, 70–75 language, 70–75 respiratory center, 16 speech, 70–75 structure, 279 Branchial arches upper airway, 94 Breathing arousals, 160 central chemoreceptors, 134 disorders affecting, 228–230

Index [Breathing] gender, 207 sleep, 125–142, 126 upper airway obstruction, 161 Broca’s area, 65 Bronchoscopy, 115 BRS. See Baroreflex sensitivity (BRS) Bruxism sleep, 162 CAD. See Coronary artery disease (CAD) Calgary Sleep Apnea Quality-of-Life Instrument, 426, 430 Canadians sleep diagnosis, 459 Candidate genes, 232 apnea-related phenotypes, 224 SDB, 235 CAP. See Cyclic alternating pattern (CAP) Car accidents, 444 Carbon dioxide arterial, 126 chemosensitivity, 138 fractional concentration, 133 NREM sleep, 136 reserve, 142 apnea, 130 NREM sleep, 133 plant gain, 131 Cardiac arrhythmias, 293–322 nocturnal, 301 Cardiac disease, 262–266 Cardiovascular autonomic system, 293–295 Cardiovascular disease, 21, 235, 262–266, 331 Catlin, George, 11, 13 CBA. See Cranial base angle (CBA) CBVF. See Cerebral blood flow velocity (CBVF) CCHS. See Congenital central alveolar hypoventilation syndrome (CCHS) Central chemoreceptors breathing, 134 Central nervous system (CNS), 275–282 spontaneous arousals, 160 Central obesity, 358 Central rhythm generator excitation of, 137 Central sleep apnea (CSA), 245 intermittent nocturnal hypoxia, 310 oxygen, 311 primary, 253 sympathetic nervous system activity, 312 syndromes, 253–254

Index Cerebral blood flow age-related relations, 141 alterations, 135–136 REM sleep, 138 sleep, 135 Cerebral blood flow velocity (CBVF), 136 Cerebral metabolic impairment, 280 Cerebral vascular disorders OSA, 331 Charcot-Marie-Tooth (CMT), 211, 230 Chemoreceptors, 295–296 apnea, 133 breathing, 134 central, 134 peripheral, 134 Chemoreflexes gain reduction, 129 Cheyne, John, 11, 14 Cheyne-Stokes respiration (CSR), 253 CHF. See Congestive heart failure (CHF) Child Health Questionnaire (CHQ), 431–433 Children. See Pediatric obstructive sleep apnea Choanae airway, 71 atresia, 41, 113 stenting, 118 Choline cell membrane synthesis, 280 Cholinergic innervation, 294 CHQ. See Child Health Questionnaire (CHQ) Chronic pulmonary disease (COPD), 3 Circadian system sleep, 284 Cladogram air breathing, 61 Cleft palate repair, 119 Closed mouth posture, 69 CMT. See Charcot-Marie-Tooth (CMT) CNS. See Central nervous system (CNS) Cognitive dysfunction in OSA, 385–386 Cognitive functioning, 383–384 Cohen’s OSA Surgery QOL Questionnaire, 434 Commercial drivers, 267, 268 Congenital central alveolar hypoventilation syndrome (CCHS), 228–229 Congenital pharyngeal stenosis, 115 Congenital syndromes, 230 Congenital tracheal disease, 114 Congestive heart failure (CHF), 293–322, 308–309, 363 CPAP, 299 CSA, 309–312

475 [Congestive heart failure (CHF)] CSR-CSA, 310 HRV, 299 hypertension, 308 nonischemic dilated cardiomyopathy, 308–309 polysomnographic recordings, 304 SDB, 308 Constant snoring children, 34 Continuous positive airway pressure (CPAP), 212 adherence, 2 beneficial value, 469 BMI, 404 brain volume, 279 cardiac functions, 264 cost-utility ratio, 465 CSA, 311 effect, 325 endothelial nitric oxide synthase, 330 HRQOL, 421–423 lateral prefrontal activation, 281 nasal quality-of-life, 420–423 nasal cavity, 112 neurocognitive function improvements, 3 nocturnal breathing disorders, 447 obstructive symptom reduction, 84 OSA, 1, 5, 176, 199, 260–261, 276, 299, 300, 403–404 quality of life, 420–423 RDI, 249 respiratory events, 1 sleep apnea, 249 sleep apnea syndromes, 27 therapeutic effectiveness, 5 treatment studies, 344 Controller gain (Gc) hypercapnia, 130 COPD. See Chronic pulmonary disease (COPD) Coronary artery disease (CAD), 265 risk factors, 265 Cortical arousal concept, 150–151 Corticotropic axis, 339–340 Corticotropin-releasing hormone (CRH), 339 CPAP. See Continuous positive airway pressure (CPAP) Cranial base angle (CBA) flexion, 68 measurement, 68

476 Craniofacial abnormalities, 111 candidate genes, 226 Craniofacial features, 201–209 disfigurement, 111 structure, 226 CRH. See Corticotropin-releasing hormone (CRH) Cricopharyngeus muscle, 73 Crouzon’s syndrome, 117, 211 CSA. See Central sleep apnea (CSA) CSR. See Cheyne-Stokes respiration (CSR) Cyanotic episodes sleepiness, 15 Cyclic alternating pattern (CAP) NREM sleep, 153 periodic dimension, 164 Daytime sleepiness, 45, 275, 346, 408 Dement, William C, 17, 18 Dental arch, 71 Dental models mouth breathers, 70 Depression, 379–380 OSA, 266, 377–379, 380–381 suicides, 266 Descending slope sleep cycles, 155 Diabetes mellitus sleep apnea, 261 Dial down technique, 186 DiGeorge syndrome, 114, 115 Discrimination, 427 Down syndrome, 118–119 anthropometric measurements, 367 Driving risks, 443–449 Drowsy driving, 443 Dual reflex mechanisms, 135 Dysmetabolic syndrome, 361 Dyspnoeic attack acute, 15 EAD. See Extracranial artery disease (EAD) Economic evaluations typology, 453–457 Elderly, 45–52, 140–141 pathophysiology, 48–50 SHHS, 51 Endocrine function glucose metabolism, 337–346 Endothelial nitric oxide synthase CPAP, 330 Enuresis, 45

Index Epiglottis, 91 Epileptic attack sleep disorders, 18 Epworth Sleepiness Scale (ESS), 247, 397, 460 predictors, 402 EQ-5D. See EuroQOL (EQ-5D) ESS. See Epworth Sleepiness Scale (ESS) Ethnicity, 33–34, 209 obesity, 359–360 Eupnea, 132 ventilatory response slope, 132 European Union road deaths, 443 EuroQOL (EQ-5D), 426, 430 Excessive daytime sleepiness evaluation, 394–400 External nasal valve, 83 Extracranial artery disease (EAD), 36 Falstaff, John, 12 FGFR. See Fibroblast growth factor receptor (FGFR) Fibroblast growth factor receptor (FGFR), 117 Fine-motor coordination, 384 FLP. See Functional Limitations Profile (FLP) Forced awakening, 152 FOSQ. See Functional Outcomes of Sleep Questionnaire (FOSQ) Franco’s Pediatric OSA Instrument-18 (OSA-18), 434 Functional deficits, 385 Functional Limitations Profile (FLP), 426, 429 Functional Outcomes of Sleep Questionnaire (FOSQ), 420–421, 426, 428, 430 Gc, controller gain (Gc) Gender, 29–30, 103, 205–208, 251 breathing, 207 obesity, 358–359 sleep, 403 upper airway mechanics, 206–207 Genetics, 223–235 cognitive dysfunction, 386 obesity, 359–361 OSA, 223–235 Genioglossus (GG) muscles, 49, 205 Geniohyoid muscles, 205 GG. See Genioglossus (GG) muscles GHRH. See Growth hormone-releasing hormone (GHRH) Gl. See Loop gain (Gl)

477

Index Glucose metabolism, 344–346 tolerance, 261 Glycosaminoglycans, 116 Goldenhar’s syndrome, 119–120 Gonadotropic axis, 342–343 Gravity pharyngeal aperture, 213 Growth hormone-releasing hormone (GHRH), 338 Guilleminault, Christian, 17, 18 Habitual snoring, 29 Half perfect health, 455 Health care costs obesity, 364 sleep disorders, 452 defined, 452 Health insurance state-based complete, 461 Health-related quality-of-life (HRQOL), 415–436. See also Pediatric health-related quality-of-life causal relationship, 419 defined, 416 generic vs. specific assessment, 427 instruments, 417, 425 ease of use, 428 generic, 416–418 meaningfulness, 428 patient’s perspective, 428 selection, 427–428 measurement, 416–418, 424–426 reasons, 418 practice, 424–426 purpose, 427 types, 417 Heart failure, 263–264 Heart rate time domain analyses, 285 Heavy snorers, 212 High upper airway resistance (HUAR), 75 Hirschsprung disease, 228 HMN. See Hypoglossal motor nucleus (HMN) Hominids phylogenetic tree for, 66 Homo heidelbergsis, 65 Homo sapiens, 66 selection pressures, 79 Hormone replacement therapy (HRT), 31, 47, 208

HRQOL. See Health-related quality-of-life (HRQOL) HRT. See Hormone replacement therapy (HRT) HUAR. See High upper airway resistance (HUAR) Human capital methodology, 456 Hunter, Charles, 116 Hunter’s syndrome, 115 Hurler’s syndrome, 115 Hyoid bone, 201 logarithmic plot of height versus depth, 74 Hypercapnia, 95 controller gain (Gc), 130 effects, 297–298 subatmospheric pressure, 106 Hypersomnia, 408 idiopathic, 406 Hypertension, 50, 125, 263, 323–325, 329 ACE inhibitors, 468 cardiovascular system, 323–331 obesity, 363 pediatric OSA, 324 pregnancy-induced, 32 pulmonary OSA, 255–266 Hyperventilation, 133 Hypocapnia NREM sleep, 130 Hypoglossal motor nucleus (HMN), 188 Hypogonadal men, 208 Hypopharynx, 89–90, 97 Hypopnea, 125, 245 obstructive, 337 upper airway resistance, 128 Hypothalamic control sleep, 163 Hypothalamic-pituitary-adrenal axis, 339 Hypothalamic-pituitary-gonadal axis, 344 Hypothyroidism, 340 Hypotonic airway pressure-flow curves, 102 Hypotonic techniques pressure-flow measurements, 105 Hypoventilation sleep, 254 Hypoxemia, 101, 254 apnea-related, 301 upper airway response, 101

478 Hypoxia, 95 cortisol secretion, 340 effects, 297–298 nocturnal VEGF, 300 proinflammatory cytokines, 345 sleep apnea, 140 ICSD. See International Classification of Sleep Disorders (ICSD) Idiopathic hypersomnia, 406 IHD. See Ischemic heart disease (IHD) Infants, 39–42, 41, 42 Insulin resistance, 261, 361 RDI, 261 Intermediate phenotypes, 223, 224 Intermittent apnea-related hypoxia, 300 International Classification of Sleep Disorders (ICSD), 28 Intrathoracic pressure variations, 297 Intrauterine growth retardation (IUGR), 32 Irritability, 382 Ischemic heart disease (IHD), 326 development, 327 risk factor, 330 IUGR. See Intrauterine growth retardation (IUGR) Kaplan’s Quality of Well Being Scale, 417 K-complexes, 152 elicitation rate, 278 MA, 152 Klippel-Feil malformations, 211 Kyphoscoliosis, 211 Language brain growth, 70–75 Laryngomalacia, 113–114 Larynx, 90 physiology, 95–96 Laterodorsal tegmental nuclei (LDT), 188 LDT. See Laterodorsal tegmental nuclei (LDT) Left ventricle injection fraction (LVEF), 304 Leptin metabolic syndrome, 361–362 resistance, 368 Limb movements (LM), 114 Literature medical sleep-disordered breathing, 11

Index LM. See Limb movements (LM) Loop gain (Gl), 184 carbon dioxide reserve, 184–186 concept or theory, 129–130 UA, 183 ventilatory response, 183 Loud snoring, 209 Lower nocturnal oxygen saturation untreated OSA, 264 Lugaresi, Elio, 17 Lung inflation, 62 volume sleep, 103 UA collapse, 181–182, 182 LVEF. See Left ventricle injection fraction (LVEF) MA. See Micro-arousal (MA) Maintenance of wakefulness test (MWT), 396 Male. See Men Mandible advancement appliances quality-of-life, 420–421 dysostosis, 115 hypoplasia, 119–120 length sleep apnea, 91 osteotomy, 24 Maxilla hypoplasia, 117 Maxillomandibular osteotomy HRQOL, 424 Maximal inspiratory flow, 98 McGovern nipple, 114 Mean sleep latency, 398–399 Mechanoreflexes reduction gain, 134–135 Medical literature sleep-disordered breathing, 11 Medical Outcome Study Short Form-36 (SF-36), 264, 309, 395, 417, 429 Melanocortin, 233 Men hypogonadal, 208 OSA, 30 pharyngeal resistance, 206 predominance obesity, 358–359 vulnerability SDB, 206 Menopause, 31, 207 obesity, 359

Index Metabolic dysfunction, 345 Metabolic resistance, 368 Metabolic syndrome, 35–36, 361 leptin, 361–362 Micro-arousal (MA), 150 K-complexes, 152 NREM sleep, 159 sleep features, 156 synchronization type, 152, 154 Microvascular theory cognitive dysfunction in OSA, 385 MID. See Minimal important difference (MID); Multi-infarct dementia (MID) Middle-aged adults, 51 Minimal important difference (MID), 436 Minnesota Multiphasic Personality Inventory (MMPI), 378 Mixed sleep apnea, 127 Mixing gain, 130 MMPI. See Minnesota Multiphasic Personality Inventory (MMPI) Mood OSA, 266, 377–381, 387 Mood disorders, 266 Morbidity obesity, 364 OSA, 50, 259–268 Morrison, Alexander, 15 Mortality obesity, 364 OSA, 259–268 Motor atonia REM sleep, 188 Motor vehicle accidents, 266–268 Mouth OSA, 84–87 Mouth breathers dental models, 70 MSLT. See Multiple Sleep Latency Test (MSLT) Mucosal sensory dysfunction, 187 Mueller maneuver endoscopic view of the upper airway, 357 Multi-infarct dementia (MID), 384 Multiple Sleep Latency Test (MSLT), 213, 267, 394, 445 Muscle sympathetic nerve activity, 283 Muscular dystrophy upper airway muscles, 95 Nasal airway, 71 Nasal cavities anterior, 82–83

479 [Nasal cavities] CPAP tolerance, 112 posterior, 84 sagittal view, 83 Nasal obstruction, 112–113 causes, 113 increased resistance, 174–175 OSA, 204–205 Nasal pressure airway response, 102 Nasal valve external, 83 National Institutes of Health (NIH), 3 National Safety Council OSA-related motor-vehicle accidents, 267 Natural sleep pressure-flow measurements, 98 NE. See Noradrenaline (NE) Neck circumference, 200 Negative pressure, 101–102 Negative pressure reflex (NPR), 178 NREM sleep, 179 protective mechanisms, 178 Negative tracheal pressure, 98 Neural respiratory motor output NREM sleep, 128 Neurobehavioral dysfunction mechanisms, 384–385 Neurocognitive function, 4 CPAP, 3 Neurofunctional model of OSA, 385 NHP. See Nottingham Health Profile (NHP) NIH. See National Institutes of Health (NIH) NNT. See Number needed to treat (NNT) Nocturnal bradycardia, 302 Nocturnal cardiac arrhythmias, 301 Nocturnal hypoxia VEGF, 300 Nocturnal oxygen saturation lower untreated OSA, 264 Noisy respiration, 43 Non-rapid eye movement (NREM) sleep, 20 AS, 157 awakening, 151 CAP, 153 carbon dioxide, 133, 136 cognitive workup features, 163 cortical arousal effect, 157

480 [Non-rapid eye movement (NREM) sleep] hypocapnia, 130 K-complexes, 278 MA, 150 neural respiratory motor output, 128 noradrenergic neurons, 189 PNS activity, 285 polygraph records, 129 sleep rhythms, 156 sleep stages, 401 subatmospheric pressure, 102 upper airway, 100 Noradrenaline (NE), 187 Nostrils OSA, 24 Nottingham Health Profile (NHP), 425 NPR. See Negative pressure reflex (NPR) NREM. See Non-rapid eye movement (NREM) sleep Number needed to treat (NNT), 436 Obesity, 260, 355–368 adult OSA, 45 age, 358 anatomy, 356–359 cause and effect studies, 360–361 central, 358 children OSA, 261 comorbidities, 361–364 definition, 355 epidemiology, 364 ethnicity, 359–360 gender, 358–359 genetics, 359–361 genome studies, 360 hypoventilation SBRD, 254 male predominance, 358–359 menopause, 359 neck circumference, 356–357 neurobehavioral dysfunction, 384–387 OSA, 35, 75, 199, 224–225, 259–261, 365 pediatric, 261 women, 30, 381 pathophysiology, 356–359 polygenic disorder, 224 pregnancy, 359 pubic health crisis, 260 therapy, 364–365 upper airway anatomy, 356 vascular disease, 363

Index Obstructive hypopnea, 337 Obstructive respiratory events, 161 Obstructive sleep apnea (OSA), 1, 337, 457. See also Adult obstructive sleep apnea; Pediatric obstructive sleep apnea abnormalities, 90 adolescents, 22, 41 adults, 45–52 pathophysiology, 48–50 affecting quality-of-life, 418–420 affective disorders, 381–382 airway closing pressure, 125 behavior, 387 children, 23, 34, 41 daytime symptoms, 43 educational achievement, 44 sleepiness, 407–408 chronic effects, 298–299 collapsibility, 175–180 costs, 457–468 CPAP, 1, 5, 199, 260–261, 300, 324, 460 daytime symptoms, 43 depressed left ventricular systolic function, 303–307 depression, 379–380 elderly, 45–52 pathophysiology, 48–50 embryology, 93 endocrine function effects, 346 endothelial dysfunction, 300 epidemiology, 27–38, 39 familial and genetic factors, 223–235 fat distribution, 199–200 female risk, 47 fMRI, 280, 281 frequency, 41 gender, 29–30, 103, 205–208 genetics, 223–235 goal-oriented behavior, 383 gonadotropic function, 343 health consequences, 21, 44 health economics, 468 health-related quality-of-life, 415–437 history, 11–24 HR measurement, 286 generic instruments, 429–430 instruments, 429–430 hypertrophy, 173 infants, 41, 42 inflammatory effects, 299–300 life history, 74–75 linkage analyses, 231–232

481

Index [Obstructive sleep apnea (OSA)] men, 30 mood and behavior, 377–388 morbidity, 50, 259–268 mortality, 259–268 motor functions, 189 mouth, 84–87 neck circumference, 200 neurobehavioral function, 382–387 new directions, 35–36 obesity, 75, 197, 198–201, 224–225, 227, 259–261 ontogeny, 39–53 oxidative effects, 299–300 pathogenesis, 82, 171–189, 328–331 pathophysiology, 39 persistent sleepiness after treatment, 405–407 phylogeny uninhibited survey, 61–79 prevalence, 28–24 public safety, 21 quality-of-life measurements, 418 causal relationship, 419–420 treatment effect, 420–421 related disorders, 233–235 respiratory monitoring, 327 risk factors, 197–214 routine screening, 120 severity, 249 sleep, 138–139, 298 sleepiness, 395 soft tissue structures, 173 somatotropic dysfunction, 338 surgical treatment strategies, 84 therapeutic approaches, 6 treatment, 23–24, 328 vascular endothelial effects, 299–330 ventilatory control, 227 visual comparisons, 366 wakefulness, 298 weight gain, 198–199 weight loss, 199 Obstructive sleep apnea hypopnea syndrome (OSAHS), 248, 252 treatment, iii Obstructive sleep apnea syndrome (OSAS), 159, 245–252 ICSD-2 definition, 231 Obstructive sleep-disordered breathing diagnostic dilemma, 252–253 Obstructive Sleep Disorders-6 (OSD-6), 434 Old age. See Elderly

Ontogeny OSA, 39–53, 40 Oral appliances quality-of-life, 420–421 sleep, 404 Oral cavities anterior view, 85 posterior, 86–87 sagittal view, 83 Ordinary tasks of living quality-of-life, 454–455 Oropharynx, 86–87 compression, 68–70 retroglossal region, 89 retropalatal region, 89 sagittal MRI, 87 OSA. See Obstructive sleep apnea (OSA) OSA-18. See Franco’s Pediatric OSA Instrument-18 (OSA-18) OSAHS. See Obstructive sleep apnea hypopnea syndrome (OSAHS) OSAS. See Obstructive sleep apnea syndrome (OSAS) OSD-6. See Obstructive Sleep Disorders-6 (OSD-6) Osler, William, 13 Osteotomy mandible, 24 maxillomandibular HRQOL, 424 Oxidative stress, 330 Oxygen saturation lower nocturnal untreated OSA, 264 P300, 152 PAD. See Peripheral artery disease (PAD) Palate width, 70 Parasympathetic nervous system (PNS), 283 NREM sleep, 285 PC. See Pharyngeal constrictors (PC) PCOS. See Polycystic ovarian syndrome (PCOS) Pediatric constant snoring, 34 Pediatric health-related quality-of-life assessment, 430–434 anchor-based approach, 436 clinical vs. statistical significance, 435–436 distribution-based approach, 436 instruments, 431–432 logistical considerations, 435 Pediatric obesity, 261

482 Pediatric obstructive sleep apnea, 22–23, 23, 34, 41, 251 daytime symptoms, 43 definition, 28 educational achievement, 44 sleepiness, 407–408 willingness to pay approach, 467 Pedunculopontine tegmental nuclei (PPT), 188 Periodic limb movements (PLM), 162, 406 Peripheral artery disease (PAD), 36 Peripheral chemoreceptors breathing, 134 Peroxisomes proliferator-activated receptor-gamma gene (PPARG), 360 Pfeiffer’s syndrome, 117 Pharyngeal constrictors (PC), 173 Pharynx, 88–90, 91 anatomy OSA, 202 cerebral control, 72 dimensions sagittal drawings, 67 evolutionary pressures influence, 65 pressure-area relationships, 77 resistance men, 206 soft tissues, 202 Phasic arousals, 156 Phenotypes intermediate, 223 Phylogeny hominids, 66 Pickwickian syndrome, 11, 27, 75, 248 alveolar hypoventilation, 15 first sleep recordings, 15–17 pulmonary medicine domain, 16 sleep-disordered breathing, 18 SRBD, 254 Pickwick Papers, 12, 198 Pierre Robin syndrome, 115, 172, 201 PIH. See Pregnancy-induced hypertension (PIH) Plant gain, 130 Pleiotropy, 233, 234–235 PLM. See Periodic limb movements (PLM) PNS. See Parasympathetic nervous system (PNS) Polycystic ovarian syndrome (PCOS), 36, 219–211 Polygraph records NREM sleep, 129 Polysomnography (PSG), 39, 214, 304 AHI, 28 Positional patients, 213

Index Postapneic reoxygenation, 300 Posterior nasal cavity, 84 Posterior oral cavity, 86–87 Postmenopausal women, 207 PPARG. See Peroxisomes proliferator-activated receptor-gamma gene (PPARG) PPT. See Pedunculopontine tegmental nuclei (PPT) Prader-Willi syndrome, 211, 229–230 Prediction, 427 Preeclampsia, 31, 32, 33 Pregnancy obesity, 359 Pregnancy-associated weight gain, 47 Pregnancy-induced hypertension (PIH), 32 Premenopausal women, 206 Pressure-flow measurements natural sleep, 98 Pressure support ventilation (PSV), 132 Primary central sleep apnea, 253 Progesterone, 208 Prolactin system, 341–342 PRP. See Pulmonary rehabilitation weight loss program (PRP) PSG. See Polysomnography (PSG) PSV. See Pressure support ventilation (PSV) Public health crisis obesity, 260 Public safety, 21 Pulmonary hypertension, 265–266 Pulmonary medicine Pickwickian syndrome, 16 Pulmonary rehabilitation weight loss program (PRP), 365 Quality adjusted life years (QALYS), 417, 420 Quality-of-life (QOL) assessment, 415–418 defined, 415–416 treatment effects, 420 Quality of Well Being Scale (QWB), 417 Rapid eye movement (REM) sleep, 19, 30 AHI, 402 behavioral factors, 296 cerebral blood flow, 138 discovery, 1 motor atonia, 188 noradrenergic neurons, 189 sleep stages, 401 special problem, 137–138 upper airway, 100

Index RDI. See Respiratory disturbance index (RDI) Reciprocal-interaction hypothesis, 157 Reliability, 427–428 REM. See Rapid eye movement (REM) sleep Reproducibility, 427 RERA. See Respiratory effort-related arousal (RERA) Respiration, noisy OSA, 43 Respiratory center brain’s dysfunction, 16 Respiratory disturbance index (RDI), 187, 248 CPAP, 249 insulin resistance, 261 problems, 250 Respiratory effort-related arousal (RERA), 159, 245 SDB, 160 Respiratory events obstructive, 161 Respiratory inductance plethysmography (RIP), 253 Respiratory motor output reduction, 127–128 Respiratory oscillators, 61 Respiratory-related evoked potential (RREP), 276, 278 Respiratory sinus arrhythmia (RSA), 294 Respiratory sleep disorders international conference of, 19–20 Retropalatal oropharynx, 86 Rett syndrome, 230 RIP. See Respiratory inductance plethysmography (RIP) Risk accidental treatment, 446–447 adult OSA, 46–47 OSA, 197–214 sleep-disordered breathing, 23 RREP. See Respiratory-related evoked potential (RREP) RSA. See Respiratory sinus arrhythmia (RSA) SAQLI. See Sleep Apnea Quality-of-Life Instrument (SAQLI) SDB. See Sleep-disordered breathing (SDB) Sex hormones, 208 Sexual dysfunction, 382 SF-36. See Medical Outcome Study Short Form-36 (SF-36) SHHS. See Sleep Heart Health Study (SHHS)

483 Sickness Impact Profile (SIP), 425, 429 SIDS. See Sudden infant death syndrome (SIDS) Sinus rhythm polysomnographic recording, 306 SIP. See Sickness Impact Profile (SIP) Skeletal muscle atonia, 137 Skinny people sleep apnea, 18–19 Sleep, 287. See also Non-rapid eye movement (NREM) sleep; Rapid eye movement (REM) sleep apneic threshold, 128–129 autonomic cardiovascular activity, 284–286 breathing, 125–142, 126 bruxism, 162 cardiovascular activity in, 286 cardiovascular characteristics, 296 cerebral blood flow, 135 circadian system, 284 control, 127–136 cycles, 155 disorders affecting, 228–230 epidemiology, 27–36 evoked potentials, 277–278 hypothalamic control, 163 lung volume, 103 natural, 98 neuromuscular compensation, 186 neurotransmitters, 187–189 pressure-flow measurements, 98 state-specific reactivity, 155–157 surgical intervention, 405 unstable breathing, 141 upper airway, 97–106 upper airway obstruction, 105 Sleep apnea. See also Obstructive sleep apnea (OSA) accident studies, 463 age-related epidemiology, 252 airflow obstruction, 89 children, 22–23 closing pressure, 175 congestive heart failure, 139–140 CPAP, 249, 268 craniofacial morphology, 202 diabetes mellitus, 261 diagnosis, 465–466, 466 direct medical treatment costs, 467–468 elderly, 140–141 factors affecting wakefulness, 400 hallmarks, 126–127

484 [Sleep apnea] heuristic model, 46 indirect costs related, 462–464 Israel diagnosis, 461 mandibular length, 91 medical costs before diagnosis, 458–462 mixed, 127 neurodegenerative disease, 139 past twenty-five years, 20–22 pediatric, 22–23 physiological mechanisms, 141 sleep effects, 138–141 thin people, 18–19 treatment patient’s point of view, 464–465 Sleep Apnea Quality-of-Life Instrument (SAQLI), 426, 428, 430 Sleep arousals, 149–165 EEG changes, 152–154 functions, 162–164 gating pathological events, 162 hierarchy, 157–162 history, 149 sensory stimulation influence, 154–155 types, 153 Sleep-disordered breathing (SDB), 3, 34, 111, 246 biochemical pathways, 235 candidate genes, 235 heart failure, 263–264 male vulnerability, 206 medical literature, 11 MRI findings, 328 natural history, 252 obstructive diagnostic dilemma, 252–253 pathologic arousals, 159 Pickwickian, 18 respiratory-induced arousals, 160 respiratory monitoring, 327 risk factors, 23, 252 sleepiness levels, 277 spectrum, 245–255 Sleep disorders craniofacial relationships, 66–67 economic impact, 451–470 epileptic attack, 18 first treatment attempts, 17–18 perspectives, 1–6 social impact, 451–470 treatments

Index [Sleep disorders] cost-benefit analysis, 456–457 cost-effectiveness analysis, 453–454 cost-utility analysis, 455–456 Sleep Heart Health Study (SHHS), 29, 51, 248 elderly, 51 elderly subjects, 51 Sleep hypoventilation syndrome, 245 Sleepiness, 393–408 cyanotic episodes, 15 daytime, 275 adult OSA, 45 OSA, 346, 408 objective measurements, 396–400, 446 symptoms, OSA, 394 Sleep-onset rapid eye movement periods (SOREMPs), 396 Sleep recordings Pickwickian, 15–17 Sleep-related breathing disorders (SRBD), 121, 255 cardiovascular system effects, 312 Sleep-related hypoventilation, 254 Smoking, 210 SNA. See Sympathetic nervous system activity (SNA) SNORE25. See Symptoms of Nocturnal Obstruction and Related Events Instrument (SNORE25) Snoring, 27–28, 211–212, 246–247 benign, 21 children, 34 constant, 34 OSA habitual, 29 loud, 209 Somatotropic axis, 338–339 SOREMP. See Sleep-onset rapid eye movement periods (SOREMPs) Speech, 72–74 brain growth, 70–75 predisposition, OSA, 72–74 upper airway, 93 SRBD. See Sleep-related breathing disorders (SRBD) SSS. See Stanford Sleepiness Scale (SSS) Stanford Sleepiness Scale (SSS), 395 Starling resistor method, 97–99 State-specific reactivity NREM sleep, 155 REM sleep, 155

485

Index Static upper airway measurements, 99 Stickler’s syndrome, 118 Stokes, William, 14 Stroke, 211, 262–263, 327–328 obesity, 363–364 Subatmospheric pressure hypercapnia, 106 NREM sleep, 102 Subcortical arousal, 151 concept, 150–151 Sudden infant death syndrome (SIDS), 228 Suicides depression, 266 Swallowing upper airway, 94 Sympathetic nervous system activity (SNA), 296 Symptoms of Nocturnal Obstruction and Related Events Instrument (SNORE25), 426, 430 Synchronization arousal, 154 TAHSI. See Tonsil and Adenoid Health Status Instrument (TAHSI) Tasks quality-of-life, 454–455 TCRFTA. See Temperature-controlled radiofrequency tongue reduction/ tissue ablation (TCRFTA) Temperature-controlled radiofrequency tongue reduction/tissue ablation (TCRFTA) HRQOL, 424 Tensor palatini (TP), 49, 177 Terrestrial air breathers techniques, 78 Testosterone, 208 LH, 343 Thin people sleep apnea, 18–19 Thyroid, 341 Thyroid-stimulating hormone (TSH), 340, 341 Thyrotropic axis, 340–341 Thyrotropin-releasing hormone (TRH), 340, 341 Tidal volume amplification factor technique, 130 TNF. See Tumor necrosis factor-alpha (TNF-alpha) Tongue, 85, 86 enlargement, 120 Tonsil and Adenoid Health Status Instrument (TAHSI), 434

Torrance and Feeny’s Health Utilities Index, 417 TP. See Tensor palatini (TP) Trachea congenital disease, 114 negative pressure, 98 stenosis, 114–115 Tracheomalacia, 114–115 Tracheostomy HRQOL, 424 Traffic accidents sleep apnea, 464 Transient ischemic attack SDB, 262 Transpulmonary pressure airflow, 76 Treacher Collins syndrome, 41, 113, 119, 172, 201, 211 TRH. See Thyrotropin-releasing hormone (TRH) TSH. See Thyroid-stimulating hormone (TSH) Tumor necrosis factor-alpha (TNF-alpha), 36 UA. See Upper airway (UA) UARS. See Upper airway resistance syndrome (UARS) Upper airway (UA) airway length, 104 anatomy, 81–91, 172–174, 203 AHI, 180 branchial arches, 94 closing pressure, 100 collapse, 104–106, 171 lung volume, 181–182 lung volume changes, 182 motor deficits, 186–187 sensory deficits, 186–187 surface tension force, 187 theories, 180–187 configuration, 64 dynamics of, 93–106 endoscopic view Mueller maneuver, 357 functions, 82 gas entering, 82 gender, 206–207 high resistance, 75 hypopnea, 128 hypoxemia, 101 length, 174 limitations of studies, 99 lumen, 172 measurements, 99

486 [Upper airway (UA)] mechanics, 206–207 muscles, 94 actions, 96 muscular dystrophy, 95 ventilatory drive, 101 neuromotor tone determinants, 99–104 nonrespiratory functions, 93–94 NREM sleep, 100 obstruction, sleep, 105 patency, 183 pathology, 111–120 physiologic functions, 94–106 REM sleep, 100 resistance, 128 respiratory drive, 183 response, 101 sleep, 97–106, 179 speech, 93 static measurements, 99 structure, 172–173 swallowing, 94 Upper airway resistance syndrome (UARS), 159, 161, 247–248, 248, 323, 401 sensation, 104 soft tissue, 173 skeletal features, 201–205 UPPP. See Uvulopalatopharyngoplasty (UPPP) Uvula, 203 Uvulopalatopharyngoplasty (UPPP), 24, 468 HRQOL, 424 Validity, 428 Vascular endothelial growth factor (VEGF) hypoxia, 300 Vasoconstriction, 330

Index VEGF. See Vascular endothelial growth factor (VEGF) Velopharynx, 86, 175 Ventilatory control sleep effects, 138–141 UA collapse, 182–186 Ventilatory drive upper airway muscles, 101 Ventilatory response slope eupnea, 132 Ventricular arrhythmias, 302, 303 Ventricular hypertrophy, 263–264 echocardiographic abnormalities, 327 Very low density lipoprotein (VLDL), 232 VLDL. See Very low density lipoprotein (VLDL) Vocal cord paralysis, 95 Voxel based morphometry, 279–280 Wakefulness, 287 altered, 276 decreasing, 179 stimuli, 178 systems, 164 Weight gain, 198–199 pregnancy-associated OSA, 47 Weight loss, 199 obesity, 364–365 Weitzman, Elliot, 21 Williams-Campbell syndrome, 115 Wisconsin Sleep Cohort Study, 419 Women OSA menopausal, 207 obesity, 30 postmenopausal, 207 premenopausal, 206

FIGURE 5.5 A proposed phylogenetic tree for hominids. A. Afarensis (reconstruction at right) is but one of many Australopithecine species known to science. Researchers disagree about exactly how these species are related to one another, but most presume that A. afarensis was a precursor to our own genus. Source: From Ref. 26. FIGURE 5.9 Closed mouth posture: the relationship of tongue, teeth, and buccinator muscles is shown in the coronal depiction of the mouth of modern humans; the tongue assumes a resting posture in proximity to the palate. The teeth are interposed between the tongue and buccinators, thereby allowing growth of the arch in relation to the relative dilating (genioglossus) and compressive (buccinator) forces, creating a “balance of forces.” Source: Courtesy of B. Palmer, DDS.

FIGURE 5.10 Open mouth posture: oral breathing drives the tongue downward and maxillary constriction occurs, increasing facial height. Source: B. Palmer, DDS.

FIGURE 5.11 Dental models of a mouth breather. Source: B. Palmer, DDS.

FIGURE 5.12 Comparison of palate width in hominid (left) and modern humans (right). Source: B. Palmer, DDS.

FIGURE 5.13 Comparison of a broad (top) versus a narrow upper dental arch (bottom). Source: B. Palmer, DDS.

FIGURE 5.14 Maxillary expansion devices correct for lost horizontal growth, increasing size of choanae and nasal airway.

FIGURE 5.15 Cricopharyngeus muscle with multiple innervations and motor endplates to muscle fibers. Source: From Ref. 16.

FIGURE 6.1 External nasal valve. Source: Photograph courtesy of Kannan Ramar, MD.

Nasal Cavity Nasopharynx Adenoids

Hard Palate

Soft Palate Palatine Tonsils Oral Cavity Oropharynx Tongue

Lingual Tonsils Epiglottis

Genioglossus

Hyoid Bone Hypopharynx

Thyroid Cartilage

Esophagus Larynx

Vocal Cords

FIGURE 6.2 Sagittal view of nasal and oral cavities. Source: Figure courtesy of Clete A. Kushida, MD, PhD.

Soft Palate

Uvula

Tongue

Palatine Tonsils

FIGURE 6.3 Anterior view of the oral cavity. Note hypertrophied tonsils. Source: Photograph courtesy of Kannan Ramar, MD.

Sinus

Nasal Cavity Nasopharynx

Hard Palate Soft Palate Oral Cavity

Retropalatal

Tongue

Retroglossal

Oropharynx

Epiglottis Hypopharynx

Esophagus Trachea FIGURE 6.4 Sagittal magnetic resonance imaging of airway and division of oropharynx. Source: Figure courtesy of Clete A. Kushida, MD, PhD.

pharyngeal pouches

foramen cecum

median tongue bud

primitive pharynx

esophagus hypobranchial eminence th 4 pharyngeal pouch

branchial arches

laryngotracheal groove branchial arches

(A)

laryngotracheal level of section for diverticulum Figure 7.1(B)

foregut

(B)

FIGURE 7.1 (A) Four-week embryo depicting branchial arches that develop into the majority of upper airway structures. (B) Laryngotracheal groove in the floor of the primitive pharynx of fourweek embryo that eventually develops into the larynx, trachea, and esophagus (horizontal section at the level shown in A). Source: Figure courtesy of Clete A. Kushida, MD, PhD.

FIGURE 8.1 (A) Profile of a patient with Crouzon’s syndrome, characterized by a small head, flattened facial profile, and maxillary hypoplasia. The image of the patient has been significantly altered to protect the patient’s identity. (B) Skull radiograph of the same patient with Crouzon’s syndrome. Note the maxillary and midfacial hypoplasia that displaces the palate posteriorly.

FIGURE 8.2 (A) Profile of a patient with Apert’s syndrome, characterized by maxillary and midfacial hypoplasia that displaces the palate posteriorly. The image of the patient has been significantly altered to protect the patient’s identity. (B) Photograph of the mouth of the same patient with Apert’s syndrome. Note the cleft palate and the maxillary malformation that may lead to upper airway narrowing.

FIGURE 20.1 Endoscopic view of upper airway prior to Mueller maneuver. The base of tongue is minimally obstructing the airway and there is some fullness of the lateral pharyngeal walls.The view of the glottis is partially obstructed by the base of tongue and the epiglottis.

FIGURE 20.2 Endoscopic view of the upper airway during Mueller maneuver (attempted inspiration with the mouth and nasal passages closed, causing negative pressure in the airway). There is significant airway constriction in all dimensions. Lateral pharyngeal wall collapse is causing sideto-side narrowing and the base of tongue and epiglottis are reducing the airway in the anteroposterior dimension. This multidimensional collapse is thought to mirror obstruction that occurs during sleep.

FIGURE 20.3 (A and B) Visual comparisons between photographs taken at the time of the diagnosis of obstructive sleep apnea (left), and at five years postdiagnosis (right), demonstrate the effects of aggressive therapeutic interventions, which included CPAP therapy and a well-coordinated diet and exercise program, easily recognized as a significant reduction in central adiposity.