Obesity Prevention: The Role of Brain and Society on Individual Behavior

Obesity Prevention The Role of Brain and Society on Individual Behavior

Obesity Prevention The Role of Brain and Society on Individual Behavior Editorial Team Laurette Dubé (Lead Editor)

Professor, James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management; McGill University, Montreal, Canada

Antoine Bechara

Department of Psychology, University of Southern California, Los Angeles, CA, USA

Alain Dagher

Montreal Neurological Institute, McGill University, Montreal, Canada

Adam Drewnowski

Epidemiology, School of Public Health and Community Medicine; Director, Center for Public Health Nutrition, University of Washington, Washington, DC, USA

Jordan Lebel

John Molson School of Business, Concordia University, Montreal, Canada

Philip James

London School of Hygiene and Tropical Medicine, President International Association for the Study of Obesity (IASO)

Rickey Y. Yada

Advanced Foods and Materials Network, Networks of Centers of Excellence, University of Guelph, Ontario, Canada

Marie-Claire Laflamme-Sanders (Editorial Coordinator) McGill World Platform for Health and Economic Convergence, McGill University, Montreal, Canada

Amsterdam • Boston • Heidelberg • London • New York • Oxford • Paris San Diego • San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 32 Jamestown Road, London NW1 7BY, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2010 Copyright © 2010 Elsevier Inc. All rights reserved Except chapter 5 which is in the public domain No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (44) (0) 1865 843830; fax (44) (0) 1865 853333; email: [email protected]. Alternatively, visit the Science and Technology Books website at www.elsevierdirect.com/rights for further information Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-374387-9 For information on all Academic Press publications visit our web site at www.elsevierdirect.com Typeset by MPS Limited, a Macmillan Company, Chennai, India. www.macmillansolutions.com Printed and bound in United States of America 10  11  12  13  14  15  10  9  8  7  6  5  4  3  2  1

Preface Over the years, the focus of obesity prevention and treatment has shifted from genetic and biological factors to various behavioral change interventions, to, more recently, re-designing physical, social and economic environments. As the increasing prevalence of obesity and its related chronic diseases indicate, these methods have clearly not sufficed. This two-part handbook provides the scientific foundations of a bolder “Brain-toSociety” approach to obesity prevention. In this approach, biology, the individual and the environment cannot as independent factors account for individual and collective lifestyle choices. Rather, to stop the progression of the obesity pandemic, we need an integrative approach rooted in an in-depth understanding of the pathways of the motives, antecedents, actions and consequences within each level of influence on obesity, and at their interface. We must develop a scientific basis that can guide the changes in policy and action that are needed to realign biology and the environment with what the individual and society can sustain.

The approach taken in the handbook is crossdisciplinary, multi-level and multi-sector. It aims at catalyzing the development of a body of scientific knowledge that can better conceive, articulate, measure and model the interfaces of health, biological, behavioral, physical, social and economic factors that drive individual, behavioral and societal behaviors. This will help public health scientists, professionals and organizations to act more effectively as leaders in galvanizing action and policy change to shift the dynamics underlying obesity and chronic disease prevention in a sustainable manner. It will also inspire scientists, professionals and organizations in food, agriculture, business, economics, politics, media, education, engineering and other non-health domains to develop novel ways to achieve their respective objectives while simultaneously contributing to individual and societal health. Ultimately, this new frontier of science will transcend boundaries across disciplines, bridge theories and data on gene, brain, behavior and environment, and provide the basis of a bolder approach to obesity prevention and treatment.

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List of Contributors Jennie Brand-Miller, School of Molecular and Microbial Biosciences, University of Sydney, Sydney, NSW, Australia

Alfonso Abizaid, Institute of Neuroscience, Carleton University, Ottawa, Canada Johan Alsiö, Department of Neuroscience, Uppsala University, Uppsala, Sweden

Eleanor Bryant, Centre for Psychology Studies, University of Bradford, Bradford, UK

Ross Andersen, Department of Kinesiology and Physical Education, McGill University, Montreal, Canada

Benjamin Caballero, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Hymie Anisman, Institute of Neuroscience, Carleton University, Ottawa, Canada

Katherine G. Carman, Department of Economics, Tilburg University, Tilburg, The Netherlands

Narendra K. Arora, International Clinical Epidemiology Network, New Delhi, India

Kenneth D. Carr, Departments of Psychiatry and Pharmacology, New York University School of Medicine, New York, NY, USA

Livia S. A. Augustin, Unilever Health Institute, Unilever Research and Development, Vlaardingen, The Netherlands

Jean-Philippe Chaput, Department of Social and Preventive Medicine, Laval University, Quebec City, Canada

Marica Bakovic, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Xiaoye Chen, Marketing Department, Desautels Faculty of Management, McGill University, Montreal, Canada

Ruth Bell, Department of Epidemiology and Public Health, University College London, London, UK

Laura Chiavaroli, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada

Gary K. Beauchamp, Monell Chemical Senses Center, Philadelphia, PA, USA Janet Beauvais, McGill World Platform for Health and Economic Convergence, Desautels Faculty of Management, McGill University, Montreal, Canada

Stephen Colagiuri, Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, University of Sydney, Sydney, New South Wales, Australia

Antoine Bechara, Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA

Alain Dagher, Montreal Neurological Institute, McGill University, Montreal, Canada

William Bernstein, efficientfrontier.com, North Bend, OR, USA

Manoja Kumar Das, International Clinical Epidemiology Network, New Delhi, India

Lalita Bhattacharjee, National Food Policy Capacity Strengthening Programme, Food and Agriculture Organization of the United Nations, Bangladesh

John M. de Castro, College of Humanities and Social Sciences, Sam Houston State University, Huntsville, TX, USA

John Blundell, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK

Angelo Del Parigi, Senior Medical Director, External Medical Affairs, Pfizer Inc., New York, NY, USA

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List of contributors

Branden R. Deschambault, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Ross A. Hammond, Center on Social and Economic Dynamics, Economic Studies Program, The Brookings Institution, Washington, DC, USA

Scott Dickinson, Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, University of Sydney, Sydney, New South Wales, Australia

Corinna Hawkes, School of Public Health, University of São Paulo, São Paulo, Brazil

Tanya L. Ditschun, Food Science and Technology Group, Senomyx, Inc., San Diego, CA, USA Adam Drewnowski, Center for Public Health Nutrition, School of Public Health, University of Washington, Seattle, WA, USA Laurette Dubé, The McGill World Platform for Health and Economic Convergence, Desautels Faculty of Management, McGill University, Montreal, Canada Petra Eichelsdoerfer, Bastyr University Research Institute, Bastyr University, Kenmore, WA, USA Ahmed El-Sohemy, Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada Karen M. Eny, Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada Brian G. Essex, Department of Psychology, Vanderbilt University, Nashville, TN, USA Gary W. Evans, College of Human Ecology, Cornell University, New York, NY, USA Graham Finlayson, Institute of Sciences, Faculty of Medicine University of Leeds, Leeds, UK

Psychological and Health,

Ayelet Fishbach, Booth School of Business, University of Chicago, Chicago, IL, USA Robert J. Fisher, Department of Marketing, Business Economics & Law, University of Alberta, Edmonton, Canada

C. Peter Herman, Department of Psychology, University of Toronto, Toronto, Canada William B. Irvine, Department of Philosophy, Wright State University, Dayton, OH, USA Philip James, International Association for the Study of Obesity, and International Obesity Task Force, London, UK David J. A. Jenkins, Clinical Nutrition & Risk Factor Modification Center, and Division of Endocrinology and Metabolism, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Philip J. Johnson, Department of Psychology, McGill University, Montreal, Canada Peter J. H. Jones, Richardson Center for Functional Foods and Nutraceuticals, Department of Food Science, University of Manitoba, Manitoba, Canada Andrea R. Josse, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Daniel Kahneman, Center for Health and WellBeing, Princeton University, Princeton, NJ, USA Cyril W. C. Kendall, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada William D. S. Killgore, Cognitive Neuroimaging Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA

Amsterdam,

Neil King, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia

Amy A. Gorin, Department of Psychology, University of Connecticut, Storrs, CT, USA

Bärbel Knäuper, Department of Psychology, McGill University, Montreal, Canada

Jason Halford, Psychology Department, University of Liverpool, Liverpool, UK

Peter Kooreman, Department of Economics, Tilburg University, Tilburg, The Netherlands

Louise Fresco, Universiteit Amsterdam, The Netherlands

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List of contributors

Shiriki Kumanyika, Department of Biostatistics and Epidemiology and Department of Pediatrics (Gastroenterology; Section on Nutrition), University of Pennsylvania School of Medicine, Philadelphia, PA, USA Nicole Larson, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

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Carlos A. Monteiro, Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil Karl J. Moore, Strategy and Organization Department, Desautels Faculty of Management, and Dept. of Neurology & Neurosurgery, McGill University, Montreal, Canada

Clare Lawton, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK

Spencer Moore, School of Kinesiology and Health Studies, Queen’s University; Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Canada

Kathleen E. Leahy, Department of Nutritional Sciences, Pennsylvania State University, PA, USA

Howard R. Moskowitz, Moskowitz Jacobs Inc., White Plains, NY, USA

Jordan LeBel, Marketing Department, John Molson School of Business, Concordia University, Montreal, Canada

David M. Mutch, Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Canada

Catherine Le Galès, CERMES3, National Institute for Health and Medical Research, U988, Paris, France

Kristian Ove R. Myrseth, ESMT European School of Management and Technology, Berlin, Germany

Allen S. Levine, Minnesota Obesity Center; Department of Food Science and Nutrition, University of Minnesota, Saint Paul, MN, USA

Erik Naslund, Clinical Sciences, Danderyd Hospital, Karolinska Istitutet, Stockholm, Sweden

Shanling Li, Desautels Faculty of Management, McGill University, Montreal, Canada Alexandra. W. Logue, City University of New York, New York, NY, USA Dylan MacKay, Richardson Center for Functional Foods and Nutraceuticals, University of Manitoba, Manitoba, Canada Michael Marmot, International Institute for Health and Society, University College London, London, UK John C. Mathers, Human Nutrition Research Centre, Institute for Ageing and Health, Newcastle University, Newcastle on Tyne, UK

Pawel K. Olszewski, Minnesota Obesity Center; Department of Neuroscience, Uppsala University, Uppsala, Sweden Jaak Panksepp, Department of VCAPP, College of Veterinary Medicine, Washington State University, Pullman, WA, USA Heather Patrick, Department of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA Prabhu Pingali, Deputy Director, Agricultural Development, The Bill and Melinda Gates Foundation, USA Patricia P. Pliner, Department of Psychology, University of Toronto at Mississauga, Canada

John J. Medina, Department of Bioengineering, University of Washington, Seattle, WA, USA

Janet Polivy, Department of Psychology, University of Toronto at Mississauga, Mississauga, Canada

Julie A. Mennella, Monell Chemical Senses Center, Philadelphia, PA, USA

Michele Reisner, Moskowitz Jacobs Inc., White Plains, NY, USA

Lyne Mongeau, Department of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada

Lise Renaud, Social and Health Communication, Université du Québec à Montréal (UQAM), Montreal, Canada

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Denis Richard, Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada Thomas N. Robinson, Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine; Center for Healthy Weight, Stanford University School of Medicine and Lucile Packard Children’s Hospital at Stanford, Stanford, CA, USA Barbara J. Rolls, Department of Nutritional Sciences, Pennsylvania State University, PA, USA Edmund T. Rolls, Oxford Centre for Computational Neuroscience, Oxford, UK Catherine Sabiston, Department of Kinesiology and Physical Education, McGill University, Montreal, Canada Sarah-Jeanne Salvy, Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, State University of New York, USA Nishta Saxena, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Michelle A. Schamberg, Cornell University, New York, NY, USA Helgi B. Schiöth, Department of Neuroscience, Uppsala University, Uppsala, Sweden T. N. Srinivasan, Samuel C. Park Jr Professor of Economics, Yale University, New Haven, CT, USA; Stanford Center for International Development, Stanford University, Stanford, CA, USA Christine Stich, Population Health, Prevention and Screening Unit, Cancer Care Ontario, Toronto, Canada Mary Story, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA Beth M. Tannenbaum, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

Louise Thibault, School of Dietetics and Human Nutrition, McGill University, Montreal, Canada Elena Timofeeva, Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada Kraisid Tontisirin, Institute of Nutrition, Mahidol University, Thailand and Former Director, Food and Nutrition Division, Food and Agriculture Organization of the United Nations, Italy Angelo Tremblay, Department of Social and Preventive Medicine, Laval University, Quebec City, Canada Josh van Loon, School of Community and Regional Planning, University of British Columbia, Vancouver, Canada Patrick Webb, Friedman School of Nutrition Science and Policy, Tufts University, Medford, MA, USA Nancy M. Wells, Design and Environmental Analysis, College of Human Ecology, Cornell University, New York, NY, USA Geoffrey C. Williams, Departments of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA Julia M. W. Wong, Clinical Nutrition & Risk Factor Modification Center, St Michael’s Hospital, Toronto, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada Lin Xiao, Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA, USA Martin R Yeomans, School of Psychology, University of Sussex, Brighton, UK David H. Zald, Departments of Psychology, Psychiatry and Integrative Neuroscience Program, Vanderbilt University, Nashville, TN, USA Dan Zhang, Desautels Faculty of Management, McGill University, Montreal, Canada Wenqing Zhang, Desautels Faculty of Management, McGill University, Montreal, Canada.

Acknowledgments The editors would first like to thank all the authors for their dedication and hard work in producing and refining these chapters. By pushing the boundaries of their thinking and knowledge, they lay the foundation of a bolder approach to obesity prevention. The editors give their warmest thanks to Marie-Claire Laflamme-Sanders, editorial coordinator, for her brilliance and her perseverance in bringing this book to completion. The chapters assembled build upon a cycle of events on obesity, hosted by the McGill World Platform for Health and Economic Convergence from 2005 until 2008. This cycle progressively tapped into the “brain” and the “society” side of obesity prevention to develop a new way of thinking of and acting upon this problem. In this effort, we are grateful for the continued financial and substantive support of our partner organizations, which are committed to ensuring the health of all individuals around the world. These are: McGill University, the Global Alliance for the Prevention of Obesity and Related Chronic Diseases, the Fondation Lucie et André Chagnon, the Public Health Agency of Canada, the Ministère de la santé et des services sociaux, the Bill and Melinda Gates Foundation, the Dr. Robert C. and Veronica Atkins Foundation, the Robert Wood Johnson Foundation, the Institut national de la santé publique du Québec – National Collaborating Center on Public Health, the Direction de la santé publique de Montréal-Centre, Health Canada, the Agence de la santé et des services sociaux de Montréal, the Canadian Institutes of Health Research – Institute of Nutrition, Metabolism and Diabetes, the Canadian Institute for Health Information, the Canadian Institutes of Health

Research – Institute of Human Development, Child and Youth Health, the Canadian Institutes of Health Research – Institute of Neurosciences and Mental Health Addiction, the Heart and Stroke Foundation of Canada, the Centre hospitalier de l’Université de Montreal, the Fond de recherche en santé du Québec, the Réseau de recherche en santé des populations du Québec, the National Institute of Child Health and Human Development, the National Cancer Institute, the National Heart, Lung, and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Office of Behavioral and Social Sciences Research, the Health and Learning Knowledge Center, the Ministère de l’agriculture, des pêcheries, et de l’alimentation du Québec, the Montreal Neurological Institute, the Advanced Foods and Materials Network, the Canadian AgriFood Policy Institute, the International Clinical Epidemiology Network, Advertising Standards Canada, Agriculture and Agri-Food Canada, the Alliance for Innovation in Agri-Food, the American Heart Association, the Canadian Association of Principles, the Canadian Council of Food and Nutrition, the Canadian Obesity Network, the Canadian Produce Marketing Association, the Centers for Disease Control and Prevention, the Chronic Disease Prevention Alliance of Canada, Concerned Children’s Advertisers, the Culinary Institute of America, Food and Consumer Products of Canada, the International Economic Forum of the Americas/Conférence of Montréal, the Joint Consortium for School Health, MobilizeYouth, the National Obesity Observatory, ParticipACTION, the Pennington Biomedical Research Center, and the University of Washington – Center for Public Health Nutrition.

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Introduction: On the Brain-to-Society Model of Motivated Choice and the Whole-of-Society Approach to Obesity Prevention Laurette Dubé, on behalf of the Editorial Team James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management, McGill University, Montreal, Canada

O U T L I N E Introduction The Brain-to-Society Model of Motivated Choice

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A Whole-of-Society Approach to Obesity Prevention

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Handbook Overview

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The Choice Architecture and what it Means for Obesity Prevention xxvi

Introduction The spread of childhood and adult obesity and other lifestyle-related diseases continues unabated in Canada, the USA, Europe, and other industrialized countries around the world.

Obesity Prevention: The Role of Brain and Society on Individual Behavior

The WHO estimates that over 1 billion people globally are overweight, and more than 400 million are obese. The number of obese is expected to grow by 75 percent by 2015 (James, 2006). In developing countries, such as India and China, the increased prevalence of overnutrition occurs

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2010 Elsevier Inc. © 2010,

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while a large proportion of the population still suffers from undernutrition (see Chapter 37 of this volume). This is in spite of a rich diversity of medical and behavioral individual- and communitybased interventions and wide-ranging policy change. This pandemic poses serious challenges not only to the health community, but also to society as a whole: the personal, societal and economic costs tied to it are extremely high. It is increasingly recognized that the patterns of food overconsumption and physical inactivity driving the obesity pandemic are rooted in the way modern industrialized society operates. It has created environmental conditions that overwhelm a biology evolved for dramatically different conditions. These challenges have been compounded by the radical transformational force of globalization, which has created a world where modern affluent economies, developing economies and emerging markets, and the least developed countries are all part of the same global system, where tradition and modernity intersect as never before (Dubé et al., 2008a). Globalization has accelerated the spread of ideas, information and cultural changes, and unleashed tremendous potential for individual, economic and social growth and developments. It has also significantly impacted health, as economic development and industrial progress are tied to increases in overweight and obesity rates (Cutler et al., 2003). This poor alignment between biology, markets and society is reflected not only in the rise of obesity and chronic diseases, but also in issues regarding food and nutrition security, poverty and health inequity, as well as child development and mental health. Our current environment presents an important challenge to human biology – one that will only continue to grow, unless we revisit some of the fundamental ways in which our society operates. These include: 1. The ways in which we – as individuals, families and communities – live, consume, invest, and take care of our children

2. The ways in which we – as educational, health, media and business organizations – produce, promote, trade and provide goods and services to individuals, families and communities 3. The ways in which we – as trade institutions, investment markets and governments – maintain the present health and economic divide that shapes the arena where individuals, families, communities and organizations evolve. Cutting-edge science from both the biology and the society sides of the equation is crucial to this effort, as are creative thinking and sustained commitment and action from all stakeholders around the world, at the local, national and global levels. An unprecedented convergence of interests, in the wake of the financial downturn, can yield breakthrough novel and more effective pathways for individual behavioral change as well as social and business innov­ation. Challenges and opportunities lie at new frontiers of transdisciplinary and cross-sectoral science; in novel behavioral change interventions; in new mind-sets and methods for organiz­ational decision-making; in public and private investment in business and social innovation; and in breakthrough institutional entrepreneurship for better balanced policy, governance, and government. Only this can pave the way toward a vision of present and future economy and society that biology can more sustainably withstand. More concretely, it means that: 1. Health and public health professionals, organizations and systems must galvanize individual and societal action by all actors in society. They must develop the necessary expertise and capabilities to provide their counterparts in education, agriculture, business, media, urban planning, and transportation, with guiding principles, frameworks for action and the best available evidence regarding the health impacts of policies and actions.

The Brain-to-Society model of motivated choice

2. Professionals, organizations and systems from all sectors that shape the current environment must mainstream health into their respective everyday and strategic activities, in a manner that is compatible and sustainable from the perspective of their primary sectors of activities. 3. Professionals, organizations and systems in the whole of society must singly and jointly engage in breakthrough and integrative innovation in science, policy and action to make healthy choices the default option for individuals. This handbook is based on the conviction that it is possible to reap the many benefits of modern economic development worldwide, without paying the high toll of obesity and its chronic disease consequences. In the rest of this Introduction, we present the Brain-to-Society model of motivated choice as the overarching conceptual framework of this handbook. We then provide an overview of the book. All in all, this collection assembles the scientific foundations for the proposed model as well as the multi-level and multi-sector components of the Whole-of-Society changes needed to curb the obesity pandemic.

The Brain-to-Society model of motivated choice The Brain-to-Society model of motivated choice (Daniel et al., 2008; Dubé et al., 2008b) is a broad integrative approach to understanding, mapping, modeling, and ultimately guiding in a more adaptive direction, the pathways by which brain systems (considering the genetic background and psychological predispositions) and society systems (through the familial, organizational and collective choices and policies in health, social and economic domains that shape environments) singly and jointly determine

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individual choices in domains of motivated adaptive behaviors. Motivated adaptive behaviors are cue-induced processes, shaped by human evolution and tied to biological drivers, that span a wide range of choices – for example, from food and companionship to strategies for minimizing physical and psychological discomfort (Kalivas and Volkow, 2005; Dubé et al., 2008b). At a basic level, sensory and emotional systems all have single and combined roles in food choice and eating. These signals interact with environmental cues in complex ways to define the motivational or “reward” value of food. Some of these biologically-driven processes are also involved in the less adaptive case of addiction (Volkow and O’Brien, 2007). Yet, on another level, humans have the capacity to regulate behavior in a flexible and goal-directed manner through deliberate and effortful acts of will power and self-control. They can overcome maladaptive cue-induced impulses and allow more adaptive choice alternatives. This capacity is linked to executive control systems, which include inhibition, decision-making, goal selection and planning, and are enabled by more recently evolved brain systems. These are also sensitive to environmental conditions (Diamond, 2009). The BtS model of motivated choice views individual choice as the outcome of the complex and dynamic relationships between biology and psychology, shaping choice and behavior, in response to environmental cues and taking into account both the immediate context and internalized life-course information called upon by the immediate context. These cue-induced processes operate on different timescales, and through a diversity of mechanisms that all together define how the brain acts as a command center for choice and behavior. Thus, the brain systems and society systems, which underlie the organizational and collective choices that in turn shape environment, are all part of the same system guiding individual choice (Figure 1).

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Figure 1.  The Brain-to-Society model of motivational choice.

The choice architecture and what it means for obesity prevention The BtS model of motivated choice also assumes that the default value of individual choice – i.e., the easiest and most natural option – is modulated by organizational and collective choices, which form the choice architecture within which individual choice occurs. The choice architecture emerges from the single and combined consequences of the choices made by governments, businesses, civil society, and community organizations that operate in health and non-health domains at local, national and global levels. Accounts of the biologically challenging qualities of the present choice

architecture are manifold (Wansink, 2006; Dubé et al., 2007): 1. There is excessive reliance on information and education in individual-level intervention 2. Nutrition and health are not sufficiently integrated into school, work and community activities and environments 3. Nutrition and health have not sufficiently penetrated innovation, value-chain and strategic activities in agriculture, food and other business sectors 4. The power of commercial and social marketing and media has not been shifted toward the promotion of healthy eating 5. Rural, industrial, economic and social development has thus far not paid sufficient

A Whole-of-Society approach to obesity prevention

attention to the challenges imposed to biology by environment 6. Policy changes that could lead to progress lie outside of the health jurisdiction 7. There is a lack of convergence in policy and action between developed and developing countries, between health and economic activities, and between the local, national and global levels of decisions 8. The political will as well as the health and development budgets devoted to the promotion of healthy eating are insufficient. In such a context, where the needed changes to create a protective choice architecture lie outside the traditional purview of health, health and public health professionals, organizations and systems, armed with insufficient means and limited power, cannot continue to promote healthy lifestyles if all other social and economic actors and individuals passively maintain a relative status quo. Conversely, health cannot continue to be managed from the outside, without a sophisticated understanding of the complex mechanisms, motives and success criteria that guide the decision-making and action of these non-health actors. As such, to transform the choice architecture into one that supports healthy lifestyles, both health and non-health actors must converge to lead effective changes. The research program underlying the BtS model deploys cutting-edge concepts and methods from neuroscience and systems sciences with the latest advances in behavioral and social sciences to better understand individual food choices in the contexts of biology and environment. While the brain suffers from decision-making shortcomings and self-control challenges in the face of a plentiful environment, it also possesses a unique capacity for selfpreservation, empathy and creativity, with the power to foster innovation, entrepreneurship and leadership. The approach that emerges from

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this book relies on the assumption that society systems, just like brain systems, are more amen­ able to changes than previously thought. Both systems can be changed in order to better sustain healthy lifestyle behaviors.

A Whole-of-Society approach to obesity prevention The BtS model of motivated choice calls for a dramatically different approach to population health that permeates its traditional boundaries and builds novel competencies and capacities in both health and non-health domains of activities. The Whole-of-Society (WoS) approach to obesity prevention reflects the fact that the neces­sary changes are woven into the everyday lives of individuals, communities, organizations, markets and societies. It goes beyond current “whole-of-government” approaches, which have called for the integration of healthy public policies within all sectors that contribute to lifestyle (education, agriculture, and industry and trade). As comprehensive as they may be, traditional governmental policies and programs alone cannot reach the scale, scope and speed of changes needed to reverse current obesity and chronic disease trends. The BtS model is motivated by the need to go beyond crossgovernmental efforts to harness the power of individuals themselves, communities and businesses, and of other social and economic actors. This approach brings together recent developments in science and the best models and practices from the fields of population health and global health, with breakthrough advances from the key sectors that shape the environment in which individual lifestyle choices are made: food and agriculture, education, media, finance, management, law, politics and economics. The aim is to better equip the population health and healthcare community to serve as catalysts

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and leaders in promoting policy convergence in economic domains, and, conversely, to better equip non-health policy-makers and strategists to place health on their agenda. Therefore, the WoS approach is: 1. Transdisciplinary – scientists, researchers, decision-makers and strategists from all fields work together to develop shared conceptual and methodological frameworks and strategies for policy change and innovation, which not only integrate but also transcend their respective disciplinary perspectives 2. Multi-sectoral – actors from public agencies, communities and the private sector, from all social and economic domains that contribute to lifestyles, are mobilized to place health on their strategic agenda 3. Multi-level – individuals, policy-makers and strategists whose decisions at the community, municipal, provincial/state, national, transnational and global levels influence the environment in which individual choice is made are involved.

Handbook overview Part 1 of this two-part handbook, “From Brain to Behavior”, provides the scientific foundations of the Brain-to-Society model of motivated choice. Part 2, “From Society to Behavior: Policy and Action”, then lays down the foundations of a Whole-of-Society approach to population health. In moving “From Brain to Behavior”, Section A of Part 1 examines sensory, reward, and other biological systems that explain how energy has become “delight” for living species. Section B shifts to executive control systems, which are unique to mankind, and also addresses selfcontrol challenges in the modern world of plenty, in particular when wired-in, non-adaptive

predispositions are culturally reinforced. The contributions in Section C move beyond brain systems driving behavior to examine more broadly other biological systems that impact energy balance and body weight, including genetics and epigenetics. Section D offers integrative and multi-level perspectives on eating, energy balance and body-weight regulation. Finally, in Section E, existing approaches to individual behavior changes are revisited in light of this more sophisticated understanding of the biological, motiv­ ational and rational bases of individual food choice and its relationship to energy balance and body weight. In Part 2, “From Society to Behavior: Policy and Action”, the emphasis shifts to the organizational and collective choices that shape the environment in which individual choice is made. Section A begins by laying out the needs and challenges in policy and action to prevent obesity, in both developed and developing countries. Section B focuses on the economy as a core agent shaping policy and action. The set of contributions in Section C covers policy and action to shift the drivers of food supply and demand in a healthy direction. In Section D, we then look to scaling up policies and actions to create family, school, community and social networks that support healthy individual choices. The socio-economic health gradient is examined in Section E, and finally, in Section F, existing broad societal approaches to obesity prevention are analyzed and the potential of systems science is introduced. The concluding chapter sets new frontiers in science, policy and action, introduces the Whole-of-Society approach to obesity prevention, and highlights the new models of capitalism and society that can support it.

References Cutler, D. M., Glaeser, D. L., & Shapiro, J. M. (2003). Why have Americans become more obese? Journal of Economic Perspectives, 17(3), 93–118.

References

Diamond, A. (2009). The interplay of biology and the environment broadly defined. Developmental Psychology, 45(1), 1–8. Dubé, L., Kouri, D., Fafard, K., & Sipos, I. (2007). Childhood obesity: A societal challenge in need of health public policy. Report on Policy Implication of the Health Challenge 2007. Think Tank for Canada. Dubé, L., Shetty, P., Webb, P., Fresco, L., McKnight, W., & Hawkes, C. (2008a). Framing Paper. Prepared for the Gates Foundation Workshop: From Crisis to a New Convergence of Agriculture, Agri-Food and Health. Held in Montreal, Quebec, November 8–9, 2008. Dubé, L., Bechara, A., Böckenholt, U., Ansari, A., Dagher, A., Daniel, M., De Sarbo, W. S., Fellows, L. K., Hammond, Ross, A., Huang, T. T.-K., Huettel, S., Kestens, Y.,

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Knäuper, B., Kooreman, P., Moore, D. S., & Smidts, A. (2008b). Towards a brain-to-society systems model of individual choice. Marketing Letters, 19, 323–336. James, P. (2006). Presentation offered during the 2006 Mcgill Health Challenge Think Tank. Held in Montreal, Quebec, October 25–27, 2006. Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry, 162(8), 1403–1413. Volkow, N. D., & O’Brien, C. P. (2007). Issues for DSM-V: Should obesity be included as a brain disorder? American Journal of Psychiatry, 164(5), 708–710. Wansink, B. (2006). Mindless eating: Why we eat more than we think. New York, NY: Bantam Books.

C H A P T E R

1 The Pleasures and Pains of Brain Regulatory Systems for Eating Jaak Panksepp Department of VCAPP, College of Veterinary Medicine, Washington State University, Pullman, WA, USA

o u t l i n e 1.1 Introduction

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1.2 Satiety Agents versus Aversion-Inducing Agents 6 1.3 Various Methodologies to Evaluate Affective Change in Pre-Clinical Appetite Research

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1.5 Conclusion

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1.1  Introduction

Diverse neuropeptidergic details of this circuitry have now been clarified (Horvath and Diano, 2004; Broeberger, 2005; Konturek et al., 2005; Gao and Horvath, 2007; Coll et al., 2008). In brief, there are complex neuropeptide-based neural networks that are able to gauge the energy status of the organism, and to adjust foraging and eating behavior accordingly. This network is constructed of hypothalamic neuropeptides, such as hypocretin/orexin, neuropeptide Y and agouti-related peptide, -melanocyte-stimulating hormone, and melanin-concentrating hormone;

All basic survival needs of the body are represented in genetically ingrained circuits concentrated in subcortical visceral regions of the brain. Energy balance is regulated by a strict equation (Figure 1.1) that has recently been illuminated in great detail. For many decades, abundant evidence has indicated that medial hypothalamic regions, concentrated especially in the arcuate nucleus, contain major detectors for long-term homeostatic energy balance (Panksepp, 1974).

Obesity Prevention: The Role of Brain and Society on Individual Behavior

1.4 Conditioned Taste Aversions – From Animal Models to Human Brain Analysis?



2010 Elsevier Inc. © 2010,



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Figure 13.2  Changes in preference for the flavors of yoghurts consumed in low-fat (striped column) or high-fat (solid column) form by children. Source: Adapted from Kern et al. (1993), with permission.

acquired liking is not restricted to one source of nutrients, with studies to date showing changes both with carbohydrate and fat as energy sources. These data fit well with the broad observation that energy-dense foods (that is, foods rich in the major macronutrients) are generally the most liked (Stubbs and Whybrow, 2004). Further evidence of the broad significance of FCL as an explanation for acquired flavor-liking can be seen in the strength of acquired liking for the flavor of drinks that contain substances with psychoactive consequences, such as alcohol and caffeine – preferences that counteract the normal aversive reaction to the bitter taste of caffeine and alcohol. In humans, a wealth of research has shown clear and enduring increases in liking for the flavor of drinks that have been paired with ingestion of caffeine (see, for example, Rogers et al., 1995; Yeomans et al., 1998, 2005b; Tinley et al., 2003; Dack and Reed, 2008), showing that the effects of FCL extend beyond an ability to acquire liking for the flavors of nutrient-dense foods.

13.4.3  Flavor–flavor models of evaluative conditioning Evaluative conditioning (EC) involves transfer of affective value from a known liked or

­ isliked stimulus to a second, novel ­ stimulus d (Field and Davey, 1999; De Houwer et al., 2001). In the case of flavor-based learning, such changes in liking are usually interpreted within an associative learning framework based on the principles of Pavlovian conditioning, where repeated pairing of a previously hedonically neutral flavor or flavor component (interpreted as a Pavlovian CS) with a second flavor or flavor element that is already liked or disliked (interpreted as the UCS) results in transfer of liking to the previously neutral flavor CS (see Figure 13.1b). There are now many published examples of both acquired flavor-disliking (Baeyens et al., 1990; Dickinson and Brown, 2007; Wardle et al., 2007) and -liking (Zellner et al., 1983; Yeomans et al., 2006; Brunstrom and Fletcher, 2008) based on laboratory-based studies of flavor–flavor associations in humans. In terms of understanding the nature of ­flavor– flavor learning, one variation of flavor–flavor learning, where the CS is a food-related odor and the US is a taste (Stevenson et al., 1995, 1998, 2000a; Stevenson, 2003; Stevenson and Boakes, 2004), has proved particularly valuable since it helps define the different flavor elements more clearly than do studies that use more mixed flavors as CS. The typical design of these olfactory conditioning studies is relatively simple: odors are first presented orthonasally (i.e., sniffed) on their own, and evaluations of various sensory characteristics, including those using gustatory descriptors (e.g., sweetness, sourness, ­ saltiness etc), along with hedonic ratings, are made. The odor is then experienced repeatedly paired with a taste stimulus (e.g., 10% sucrose to give a sweet US) in a number of disguised training ­trials. Finally, the odor is re-evaluated orthonasally. The consistent finding was that ratings of the degree to which the odor possessed the sensory dimension related to the trained US increased. For example, when an odor was paired with sucrose, the rated sweetness of the odor post-training was consistently higher than it was before training started (Stevenson et al.,

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13.4  Flavor-preference learning

13.4.4  Social acquisition of flavor-liking Social learning may contribute to acquisition of flavor-liking in two different ways. Social facilitation refers to modified behavior due to

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1995, 1998), even though the sucrose was not present when odors were rated orthonasally. EC would predict that odors paired with sweet tastes would become liked, but whereas some studies report increased liking for sweet-paired odors (Yeomans and Mobini, 2006; Yeomans et al., 2006, 2007), many of the earlier studies failed to find these effects (Stevenson et al., 1995, 1998, 2000a, 2000b). However, increased liking would only be predicted if the individual under test actually rated the sweet US as pleasant, and since there are individual differences in rated evaluation of sweet tastes (Looy et al., 1992; Looy and Weingarten, 1992), a simple explanation for the variability in these findings is that those studies that failed to find increased liking did not have sufficient sweet-likers to support this change – a suggestion supported by clear findings of increased liking when participants are preselected to be sweet-likers (Yeomans and Mobini, 2006; Yeomans et al., 2006, 2008a, 2009b, 2009c). This is illustrated in Figure 13.3, where changes in rated pleasantness and sweetness are shown in relation to classification of sweet-liking. Note that while in Figure 13.3b acquired sweetness was seen regardless of liker status, changes in odor pleasantness (Figure 13.3a) depended critically on hedonic evaluation of the 10% sucrose solution used during odor–taste pairing. Overall, development of a dislike for flavor components consistently paired with an aversive flavor US appears more robust than does acquired liking for a flavor paired with a second liked flavor element. Flavor–flavor learning, therefore, appears an important element of human flavor-preference development, although, as with FCL, more research is needed to determine the full scope and importance of flavor–flavor associations.

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Figure 13.3  Changes in the rated (a) pleasantness and (b) sweetness of odors rated orthonasally following repeated retronasal exposure to the same odor paired with 10% sucrose either by sweet-likers (solid bars) or by sweetdislikers (open bars). Source: Adapted from Yeomans et al. (2006), with permission.

the mere presence of others (Guerin, 1993). In the context of food, social facilitation has been shown to influence eating in ways that may influence flavor preference development. People reliably consume more when in groups than alone (De Castro, 1990; De Castro et al., 1990; Redd and De Castro, 1992). This social facilitation of meal-size may lead to acquired preference if the increased intake includes novel items, where exposure alone may enhance liking, perhaps reinforced further by the post-ingestive effects of the meal. Direct evidence for social facilitation of food preferences has been reported in species other than humans, such as capuchin monkeys (Visalberghi and Addessi, 2000). The

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acquisition of liking for the burning ­ sensation of spicy food by Mexican children could be interpreted as evidence of social facilitation effects: Mexican children are exposed to these foods in the context of meals where they are consumed by adults (Rozin and Schiller, 1980; Rozin, 1982), and the mere presence of others may reduce neophobia and so promote acceptance and later liking. Many other studies report increased acceptance of, and reduced neophobia towards, unfamiliar foods by children when exposed to these foods in a social context (Birch, 1980). A more recent study confirms the role of social facilitation: children showed neophobic responses to unfamiliar foods which were reduced by the presence of an adult consuming that food (Addessi et al., 2005). A more powerful social influence on flavorliking acquisition may be through social modeling (also referred to as observational learning or social imitation). Here, the observation by one individual of a second individual who is consuming and enjoying a food may lead to increased liking for the same food by the observer. Thus, children showed increased acceptance of an unfamiliar food when an adult was eating that food than when an adult merely offered the food to them (Harper and Sanders, 1975). Similarly, the presence of an enthusiastic teacher who modeled food acceptance was highly effective in encouraging repeated consumption and increased acceptance of novel foods by children (Hendy and Raudenbush, 2000). Combining observation of a peer consuming a food with positive social reinforcers has also proved an effective method of enhancing children’s preferences for less preferred foods, such as vegetables (Horne et al., 1995, 2004). Enhanced intake of foods through social modeling by peers may be particularly influential on development of food likes in children (Horne et al., 2004; Romero et al., 2009). Overall, social learning is clearly an important element in flavor preference development, which seems to operate primarily by reducing

neophobia and so allowing more direct flavorlearning (FFL and FCL) to occur.

13.5  Different learning mechanisms interact to enhance flavor-liking Although experimental studies have been able to establish multiple mechanisms through which flavor-liking may be acquired, the typical experimental study examines one putative mechanism while ensuring that as many alternative influences as possible are controlled for. Thus, for example, studies examining effects of multiple exposures of a flavor paired with ingestion of some form of nutrient typically run control groups exposed to the flavor alone (Kern et al., 1993). However, in real-life it is clear that flavor-liking for foods is likely to develop through multiple mechanisms at the same time. Consider, as an illustrative example, how liking for the flavor of chocolate might be acquired. Most chocolates consumed in Western society are sweetened, and our innate tendency to like sweet tastes should predispose us to find chocolate to be acceptable on first exposure. Our first exposure will confirm that the food is not poisonous, leading to reduced neophobia for the food through learned safety. It is also likely that our first exposure to chocolate will occur in the presence of others, and observation that other people are consuming it will further help reduce neophobic reactions through social facilitation. Also, if we observe pleasurable responses to consuming chocolate by people who we trust, this in turn may enhance liking through social modeling. The pairing of unique chocolate flavor elements with sweetness would be predicted to enhance liking through flavor– flavor associations, and, once ingested, the highfat and -sugar content of chocolate, along with small amounts of caffeine, should all promote flavor-liking through FCL. Thus, in the case of

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13.6  Liking and intake: the role of palatability in overeating

liking for foods like chocolate, which has been reported as the food most often named as a craved item (Hetherington and MacDiarmid, 1993; Gibson and Desmond, 1999; Parker et al., 2006), we can see plausible influences of all the major learning elements described in this chapter working together to generate a strong acquired like. Several experimental studies have confirmed how learning mechanisms interact to modify flavor-liking. For example, when a novel flavor was paired with sweetness (FFL), energy (FCL), or sweetness and energy (FFL and FCL), the largest increase in liking was seen where the opportunity for both associations was present, with smaller increases with either FFL or FCL alone (Yeomans et al., 2008a). Likewise, the increased liking for a drink flavor by association with caffeine consumption was enhanced when training was in a sweet context (where a flavor– sweet association could add to the flavor– caffeine association: Figure 13.4), but was retarded when caffeine was consumed in a bitter context (Yeomans et al., 2007). Thus, FFL and

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Figure 13.4  Rated pleasantness of drink flavors before and after repeated pairing with caffeine (hashed bar) or placebo (open bar), and with added sweetness (aspartame), bitterness (quinine) or no added flavoring (water). Source: Adapted from Yeomans et al. (2007), with permission.

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FCL appear to have additive effects on acquisition of flavor-liking. However, social effects seem to interact with FCL to generate liking (Jansen and Tenney, 2001), since the effectiveness of a social model in enhancing food preferences in children was greater when the ingested food was high energy than low energy, implying a synergistic effect between social reinforcement and post-ingestive effects. Overall, these studies provide clear predictions about the situations where flavor-liking will develop, and these models are consistent with actual observations of flavor preferences. The critical question now is how these acquired likes may modify food intake and so be a risk factor for overeating and consequent weight gain.

13.6  Liking and intake: the role of palatability in overeating Why does understanding the basis of flavorliking matter to obesity? The answer lies in the role of flavor hedonics as a driver of short-term food intake. Many studies in humans and other animals have established a clear relationship between hedonic evaluation of a food and consequent intake (Nasser, 2001; Sorensen et al., 2003; Yeomans et al., 2004a; Westerterp, 2006). Since it is harder to evaluate hedonic evaluation in nonhuman animals, this discussion concentrates on the human literature. The simplest studies take the same food and modify its flavor, either by adding a disliked component (or note in sensory terms) or by adding liked flavor elements. The outcome is very clear: a change in liking produces a predictable change in intake, with a linear relationship between hedonic evaluation and overall consumption (Yeomans et al., 2004a). In relation to short-term overconsumption, this implies that liking drives overeating and so may be a significant risk factor for development of obesity. Indeed, many people have suggested

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that the availability of energy-dense palatable food has been a major environmental component which has fostered the rapid increase in obesity (Wansink, 2004; Ulijaszek, 2007). It is also notable that intake does not decrease reliably as energy density decreases: it appears that, in the short-term, it is the volume of food that is regulated, leaving a risk of passive overeating as the energy density of our diet increases (Westerterp, 2006). As energy density is also enhanced with greater liking and so may actively drive overconsumption, it is easy to see how the combined active and passive overconsumption of energydense food greatly increases the risk of obesity. Until recently, what was not clearly known was whether this active overconsumption was also seen for acquired flavor likes. In terms of the mechanism through which palatability drives short-term intake, our understanding has increased at both the phenomenological and biological levels of explanation. Thus, there is clear evidence that increased flavor-liking leads to short-term increases in desire to eat (the experience of hunger). This appetizer effect (Yeomans, 1996; Figure 13.5) Very

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offers a behavioral description of how evaluation of sensory quality modulates internal appetitive state and so alters short-term intake. Pharmacological studies have offered some insights into the biological basis of how liking enhances appetite. For example, blockade of opioid receptors both reduces food pleasantness (Drewnowski et al., 1989; Yeomans et al., 1990) and abolishes the appetizer effect (Yeomans and Gray, 1997). However, all of these types of study rely on contrasts between foods varying in immediate palatability, without consideration of whether this is a consequence of learned liking.

13.7  Acquired liking as a driver of overeating The previous discussion clearly shows that liking drives short-term intake, but was based on analyses of either manipulation or variation in liking on intake. Since most liking for flavors is acquired, one interpretation of these findings is that acquired liking then is a driver of intake. Two recent studies in our laboratory suggest this is the case. First (Yeomans et al., 2008a), liking and voluntary intake of a highly novel food (a fruit sorbet) was tested before and after the same flavor was associated with energy (provided by the non-sweet carbohydrate maltodextrin), sweetness (aspartame), or energy and sweetness (sucrose). Exposure to the same flavor paired with sucrose (i.e., where both flavor– sweetness and flavor–energy associations could be made) resulted in a large increase in liking for the flavor in the sorbet context and an increase in voluntary intake (Figure 13.6). In a different learning model, people evaluated and consumed a low-energy soup on separate days before and after repeated experience of the same soup either unaltered or with its flavor enhanced by monosodium glutamate (MSG: Yeomans et al., 2008b). As predicted by evaluative condition (EC), the greater liking for the

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13.8 Individual differences in learning

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Figure 13.6  Changes in (a) intake, (b) liking and (c) sweetness of a novel-flavored sorbet after experiencing the same flavor paired with ingestion of sweetness and energy (sucrose: SUC), energy alone (maltodextrin: MALT), sweetness alone (aspartame: ASP) or unaltered (exposure control: EXP), along with an unexposed control condition. Source: Adapted from Yeomans et al. (2008a), with permission.

MSG-enhanced version during the training sessions transferred to the soup alone, resulting in increased liking, an enhanced appetizer effect, and greater intake at post-training. Both these examples provide unequivocal confirmation that acquired liking can act as a driver of shortterm intake.

13.8  Individual differences in learning An important observation in relation to the recent increases in the incidence of obesity is that there are large phenotypic variations in whether individuals who are exposed to the modern, obesogenic environment become obese, with a significant proportion remaining lean. Thus, some people are susceptible to gaining significant weight in a weight-promoting environment, but others are resistant to weight gain (Blundell and Cooling, 2000; Blundell et al., 2005; Carnell and Wardle, 2008). There are myriad factors that may contribute to this variability, many of which are reviewed elsewhere

in this book. In the present context, it is notable that the idea that over-responsiveness to hedonic cues has been reported in obese participants (Nisbett, 1968; Price and Grinker, 1973; Rissanen et al., 2002), and has been cited as a factor underlying overeating (Drewnowski et al., 1985; Nasser, 2001; Sorensen et al., 2003; de Graaf, 2005). Moreover, it has recently been argued that in terms of external food cues driving short-term intake, a distinction can be made between normative cues such as portion size, and sensory cues such as palatability, with most people sensitive to normative cues but the obese over-responsive to sensory cues (Herman and Polivy, 2008). Thus, acquired flavor likes may be significant contributors to overeating and consequent obesity. Since the major argument of this chapter is that acquired flavor-liking may be a ­ significant driver of short-term overconsumption, one pos­ sible source of individual differences in response to food cues may relate to the extent to which individuals learn flavor-based associations, either through FCL or FFL. To date, no studies have specifically contrasted these learning mechanisms between obese and ­normal populations;

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however, a number of studies have started to identify significant differences in ability to learn through both FFL and FCL in subsets of normalweight individuals, some of which may contri­ bute to weight gain and later lead to increased body weight. The first studies to report individual differences in flavor-based learning did so in relation to dietary restraint (Brunstrom et al., 2001, 2005) – the tendency to self-restrict food intake in order to control body-weight. People who score high on measures of restraint do so because they are either trying to lose weight, or are aware that their unrestrained behavior places them at risk of gaining weight. Thus, in the absence of studies with obese patients, studies with restrained eaters might identify deficits in flavor-based learning that may be relevant to our understanding of obesity. Crucially, these studies reported that women who scored higher on a questionnaire measure of dietary restraint (restrained eaters) were insensitive to FFL (Brunstrom et al., 2001, 2005) and FCL (Brunstrom and Mitchell, 2007). In the original FFL study (Brunstrom et al., 2001), women were presented with a novel drink flavor (CS) followed by consumption of small sweets, with different contingencies between different flavors and the frequency with which sweet reinforcers were presented. Subsequent liking for the drink flavors increased as a function of the contingent relationship with sweet presentation in unrestrained eaters, but did not differ between restrained eaters. A second series of studies (Brunstrom et al., 2005) extended these findings. It was found that restrained women tended to experience increased liking for flavors that were least frequently paired with delivery of sweets, in contrast to unrestrained women who showed strongest liking for the flavors most frequently paired with sweet delivery. These effects were replicated in a further study where the CS consisted of pictures rather than flavors (Brunstrom et al., 2005). Finally, the most recent study examined acquired liking for flavors through

a­ ssociation with energy in a test of FNL, and again found impaired learning in restrained but not unrestrained participants (Brunstrom and Mitchell, 2007). Alongside restraint, a second measure also seems to identify significant individual differences in response to foods. The Three Factor Eating Questionnaire disinhibition scale (TFEQ-D) has been shown to reliably measure a number of aspects of eating that may increase the risk of becoming obese (Bryant et al., 2008). In relation to appetite, high scores on the TFEQ-D have been shown to be a better predictor than restraint of eating in response to stress (Oliver and Huon, 2001; Haynes et al., 2003), and to be associated with a heightened appetite response to palatability (Yeomans et al., 2004b) and greater selection of high-fat and sweetened foods (Lahteenmaki and Tuorila, 1995; Contento et al., 2005; Bryant et al., 2006). Many studies also report a positive association between TFEQ-D scores and BMI (Williamson et al., 1995; Provencher et al., 2003, 2004; Bellisle et al., 2004; Hays and Roberts, 2008). In relation to flavor-based learning, we recently reported that scores on the TFEQ-D were found to predict the extent to which women acquired liking for a flavor paired with sweetness in an FFL paradigm (Yeomans et al., 2009a: Figure 13.7). Notably, in that study, it was not an ability to acquire flavor-liking per se that was impaired, since all women acquired a dislike for a flavor paired with an unpleasant taste (the bitter taste of quinine). What the study indicated was that women scoring high on the TFEQ-D showed a greater increase in liking for the sucrose-paired flavor despite no differences in actual liking for sucrose between groups. Thus, the TFEQ-D appears to measure some aspect of overexpression of hedonic response, which in turn appears to be a risk factor for overeating. Although research into individual differences in the tendency to acquire flavor-liking is still at an early stage, the outcome of the few studies reported to date does suggest that differences in the way individuals acquire flavor-liking may

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references

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Figure 13.7  Changes in pleasantness of a novel odor paired either with a pleasant sweet taste (sucrose: open bars) or with an unpleasant bitter taste (quinine: solid bars) by sweet-liking women who scored either high or low on the disinhibition scale of the Three Factor Eating Questionnaire. Source: Adapted from Yeomans et al. (2009a), with permission.

make people more or less at risk of overeating, and so becoming obese. Future studies are needed to confirm these findings in obese groups.

13.9  Summary Multiple learning mechanisms operate together to allow humans to identify safe and nutritious foods from the huge variety of potential food items in our environment. Social factors are likely to be key to our initial exposure to foods, and such exposure helps us rapidly to learn what is safe. Innate and previously learned flavor preferences direct our liking for associated novel flavors, and once ingested, the experience of nutrient and other effects of food constituents becomes associated with the flavors, leading to powerful acquired liking for energy-dense foods. Liking itself, including acquired flavor likes, is a short-term driver of food intake. In environments where food was scarce, this would have meant that the ­consumer took maximum advantage of rare but

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highly nutritious food sources. However, in the modern world, where such foods are abundant, the ability of acquired likes to drive intake is a risk factor for overeating and obesity. What remains less clear is the extent to which individual variation in response to the sensory quality of foods may help explain phenotypic variation in the tendency to become obese. Emerging evidence that women who are prone to overeat also show heightened responses in acquiring flavor likes, in addition to a large body of literature suggesting that obese individuals over-respond to hedonic food cues, suggests that liking may be a key factor in explaining individual differences in obesity risk.

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C H A P T E R

14 Biopsychological Factors and Body Weight Stability Jean-Philippe Chaput and Angelo Tremblay Department of Social and Preventive Medicine, Laval University, Quebec City, Canada

o u t l i n e 14.1  Introduction

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14.2  I s Knowledge-based Work a   Potential Determinant of the   Current Obesity Epidemic?

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14.3  Is Short Sleep Duration a   Potential Determinant of the   Current Obesity Epidemic?

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14.1  Introduction The maintenance of an adequate body weight is a major determinant of the survival of higher organisms, including mammals. Body-weight and body-composition stability over long periods of time require that energy intake matches energy expenditure. In human adults, there are mechanisms partly influenced by heredity that balance

Obesity Prevention: The Role of Brain and Society on Individual Behavior

14.4  W  eight Loss: Not Always Beneficial   for the Psychological Health 184 14.5  P  hysical Activity and Diet:   What is the Impact on Body-  weight Stability?

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energy intake and expenditure. Body-weight regulation requires the maintenance of not only energy balance but also nutrient balance – i.e., the mixture of fuel oxidized must be adjusted to match the composition of fuel mix ingested (Flatt, 1987). Because protein and carbohydrate reserves stored in adults vary relatively little, body-weight regulation mainly concerns adipose tissue mass. The chronic imbalance between energy intake

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and expenditure results in changes in the adipose tissue mass. Therefore, body-weight regulation implies that adipose tissue mass is “sensed” and leads to appropriate responses in individuals who maintain body weight and body composition constant during prolonged periods of time. A variety of factors determine body-weight balance and regulation, and the size of the adipose tissue mass is not subjected to a strict set point. Many individuals, whether lean or obese, maintain their body weight within small limits during long periods of time. If energy intake exceeds expenditure by 1 percent daily for 1 year, the result would be approximately 9000 kcal stored, or 1.15 kg of body weight (Rosenbaum et al., 1997). The mean weight gain of the average American between 25 and 55 years of age is about 9 kg, which represents a mean excess of circa 0.3 percent ingested calories over energy expenditure (Rosenbaum et al., 1997). The high precision of energy balance maintenance is achieved by several regulatory loops. Many pathways participate in homeostatic responses that tend to maintain adequate fuel storage. The combined responses that control energy intake and expenditure to maintain energy homeostasis have conferred a survival advantage to humans. Food is increasingly available, and advances in technology and transportation have reduced the need for physical activity. These two environmental changes challenge body-weight regulation, and contribute to the increasing prevalence of obesity worldwide. Beyond the “Big Two” factors (physical inactivity and poor diet), recent research has emphasized the potential roles of additional environmental factors in contributing to the obesity epidemic (Keith et al., 2006), including: sleep debt endocrine disruptors l reduction in variability in ambient temperature l decreased smoking l l

l l l l l l

pharmaceutical iatrogenesis changes in distribution of ethnicity and age increasing gravida age intrauterine and intergenerational effects assortative mating and floor effects body mass index-associated reproductive fitness.

The list is not exhaustive. Public health practitioners and clinicians need to take these into account when looking at anti-obesity policies and actions. In spite of a growing number of works in this field, the obesity crisis rages on. This suggests that the obesity problem is multifaceted, and requires a combination of therapies in order to be managed. This chapter focuses on two phenomena characterizing our modern society and that challenge body-weight stability: (1) the increase of knowledge-based work (KBW) in daily labor as well as in leisure time; and (2) the reduction of sleeping time. In addition, we discuss the psychological impact of dieting and weight loss, which may impede the success of diet/physical activity clinical interventions. Finally, integrative comments and novel insights are provided.

14.2  Is knowledge-based work a potential determinant of the current obesity epidemic? Technological changes have brought about a progressive shift away from physically demanding tasks to knowledge-based work (KBW), which solicits an enhanced cognitive demand (Mitter, 1999). This modern transition has also redefined the notion of “fatigue at work”, which is now more of a psychosomatic nature (such as a burnout) than physical exhaustion (Iacovides et al., 2003). From a physiological standpoint, KBW represents a type of activity that relies on the brain,

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14.2 Is knowledge-based work a potential determinant of the current obesity epidemic?

Energy expenditure (kJ/45 min)

Chaput and Tremblay (2007) undertook an interventional study with female students to evaluate the impact of KBW on feeding behavior and spontaneous energy intake, using a crossover design. They used a two-session protocol including an ad libitum buffet which was preceded by either a 45-minute cognitive task (reading a document and writing a 350-word summary on a computer) or a 45-minute resting period (in the sitting position). As shown in Figure 14.1, the mean energy expenditure of the two conditions was comparable (13 kJ difference), whereas the mean ad libitum energy intake in the KBW group task exceeded that in the resting group by 959 kJ (P  0.01). Furthermore, the

300 270 240 210 180 150 120 90 60 30 0

∆ = 13 kJ

Control

KBW

∆ = 959 kJ *

6000 Energy intake (kJ)

which essentially utilizes glucose for its energy metabolism. Physical activity solicits skeletal muscle metabolism, which, to a significant extent, relies on fat metabolism. In addition, tasks requiring a significant cognitive demand are more likely to be confounded with neurogenic stress, which is known to promote a positive energy balance (Akana et al., 1994; Pijlman et al., 2003). In humans, many observations support the idea that an increase in KBW and/or stress promotes excess energy intake. Indeed, it was found, for instance, that the increased workload associated to the preparation of an NIH grant application was associated with a high energy intake and percent energy from fat compared to a lower workload period (McCann et al., 1990). Also, Wardle and colleagues (2000) found that high workload periods in a department store – 47 hours of work over 7 days, with a high level of perceived stress – were related to higher energy, saturated fat and sugar intakes compared to low workload periods (32 hours of work per week). Other studies have shown that overtime hours are positively correlated with 3-year changes in body mass index (BMI) and waist circumference (Nakamura et al., 1998). Moreover, the excess weight gain in spouse caregivers of individuals with Alzheimer’s disease was also associated with increased energy intake compared to spouses in the control group (Vitaliano et al., 1996). The impact of stress on spontaneous feeding has been studied under well-standardized laboratory conditions. Macht (1996) demonstrated that subjective hunger motivation was potentiated by emotional stress when energy intake was low in the preceding hours. Epel and colleagues (2001) observed that stress-induced cortisol reactivity was associated with increased energy intake after a first stress session. This is consistent with Wallis and Hetherington (2004), who reported that chocolate consumption increased by 15 percent after a cognitive task (Stroop Test) as compared to a control session.

181

5000 4000 3000 2000

Control

KBW

Figure 14.1  Energy expenditure of rest (control) and knowledge-based work (KBW) and spontaneous energy intake in a buffet-type meal offered after the completion of each task. Data are expressed as mean  standard error of the mean (SEM); *significantly different from control value (P  0.01). Source: Adapted from Chaput and Tremblay (2007).

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subjects did not compensate for the ad libitum buffet by eating less during the rest of the day. This suggests a net caloric surplus. Dallman and colleagues (2003) have suggested that the overconsumption of food may be perceived as a reaction, whereby eating serves as a consolation and/or compensation for emotional stress. According to these authors, people eat “comfort food” in an attempt to reduce the activity of the stress-response network. Beyond this interpretation, other data suggest that KBW can be viewed as its own specific entity, producing certain physiological effects that promote a positive energy balance, independently of the emotional stress with which it is occasionally paired. In this regard, KBW produced plasma glucose and insulin instability (defined as the sum of absolute changes between each time of blood collection at every 15 minutes) 2.2 and 8.3 times greater, respectively compared to the resting activity (Tremblay and Therrien, 2006). Furthermore, Chaput and colleagues (2008a) recently reported in another experimental study that cognitive work acutely induced an increase in spontaneous energy intake and promoted increased fluctuations in plasma glucose and insulin levels. According to the glucostatic theory of appetite control1, energy intake may be triggered with the goal of restoring glucose homeostasis (Mayer, 1953; Chaput and Tremblay, 2009a). Interestingly, Chaput and Tremblay (2009b) also observed that mental work solicited by computer-related activities produced an increase in cortisol levels, which was related to a compensatory increase in caloric intake. This observation is in line with the results from Epel and colleagues (2001), who found that high cortisol reactors (defined as the increase from baseline to stress levels of salivary

cortisol) consumed significantly more calories and more high-fat, sweet foods on the stress day compared with low reactors, but consumed similar amounts on the control day. Thus, computer-related activities represent a particular type of sedentary activities that are stressful and biologically demanding. According to Tremblay and colleagues (2009), this type of activity cannot be in any way considered a restful activity, and deserves to be counterbalanced by an adequate physical activity regimen. As opposed to KBW, physical exercise enhances the accuracy and cell sensitivity to numerous hormones and substrates (Tremblay and Therrien, 2006). Consequently, the progressive shift from physically demanding tasks to KBW, which necessitates cognitive demand, has changed the biological requirements of the human organism. It is therefore noteworthy to focus on the impact of cognitive tasks and their potential effect on the control of food intake. Taken together, these observations suggest that activities requiring significant cognitive demand favor overconsumption of foods and body-weight gain. Moreover, acute effects of KBW suggest that this work modality might promote a greater positive energy balance in comparison to what would be expected from a sedentary activity. This adds a new component to sedentary lifestyles, made more harmful when one is subjected to mental stress. It also raises an additional obstacle in the fight against obesity, in that KBW is now the modern way of working. The orexigenic effect of mental work implies, too, that modern societies might be in a conflictual state, as KBW could significantly handicap the ability to spontaneously match energy intake and expenditure, and thus promote weight gain.

1

More than 50 years ago, Jean Mayer proposed that changes in blood glucose concentrations or arteriovenous glucose differences are detected by glucoreceptors that affect energy intake. According to this theory, an increase in blood glucose concentrations results in increased feelings of satiety, whereas a drop in blood glucose concentrations has the opposite effect.

1.  From Brain to Behavior

14.3 Is short sleep duration a potential determinant of the current obesity epidemic?

14.3  Is short sleep duration a potential determinant of the current obesity epidemic? Reduced sleeping time has become a widespread phenomenon driven by the demands and opportunities of the modern “24-hour” society. Not surprisingly, reports of fatigue and tiredness are more frequent today than a few decades ago (Bliwise, 1996). Over the course of the second half of the twentieth century, the dramatic increase in the incidence of obesity appears to have paralleled the progressive decrease in the duration of self-reported sleep (Flegal et al., 1998; Van Cauter et al., 2005). Consequently, many researchers have suggested that our “cavalier attitude” toward sleep could be partly responsible for our expanding waistlines. Indeed, a good night’s sleep, an activity that should ideally occupy about onethird of our lives, is an integral part of a “good health package”. It is therefore relevant to ask whether the current emphasis on poor diet and

lack of exercise omits the importance of sleep in the battle against obesity, thereby hindering individuals’ ability to maintain a healthy body weight. Chaput and colleagues (2006a) reported a dose–response relationship between short sleeping hours and childhood overweight/obesity. The risk for overweight/obesity in children reporting sleeping 8–10 hours per night was 3.45 times greater than for those who reported 12–13 hours per night. As seen in Figure 14.2, short sleep duration was the most important determinant of the potential risk to overweight/ obesity in children. Other studies examined the sleep–body weight association in children, and the conclusions were concordant with the Chaput and colleagues (2006a) findings (Gupta et al., 2002; Sekine et al., 2002; Von Kries et al., 2002; Reilly et al., 2005). In adults, short sleep duration also predicted an increased risk of being overweight or obese (Hasler et al., 2004; Spiegel et al., 2004; Taheri et al., 2004; Gangwisch et al., 2005; Vorona et al., 2005; Chaput et al., 2007, 2008b). Importantly, it was shown that the neuroendocrine control of appetite

Low total family income

Physical inactivity

Low parental educational level

Long hours of TV watching

Parental obesity

Short sleep duration

4 3.5

Odds ratio

3 2.5 2 1.5 1 0.5 0

Figure 14.2 

183

Relationship between potential risk factors and childhood overweight/obesity.

Source: Adapted from Chaput et al. (2006a).

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14.  Biopsychological Factors

was affected as plasma levels of the anorexigenic hormone, leptin, were decreased. Levels of the orexigenic hormone, ghrelin, increased (Spiegel et al., 2004; Taheri et al., 2004). Hence, these neuro­ endocrine changes were associated with increased hunger and appetite, which may lead to overeating and weight gain. Other large-scale studies also showed that both short and long sleeping durations are independently linked to an increased risk of coronary events, symptomatic diabetes and mortality (Ayas et al., 2003a, 2003b; Patel et al., 2004; Tamakoshi and Ohno, 2004). In these studies, the cut-off point of minimal mortality and related events was at 7 hours of sleep daily. Thus, there may be an “optimal sleeping time” for the prevention of common diseases and premature death. However, the mechanisms behind these associations are not fully understood, and the effects of long sleep duration on body weight and/or other health outcomes appear to be different from those associated with shorter sleep duration. Besides decreased leptin and increased ghrelin levels, physiological data in adults suggest that short-term partial sleep restriction leads to striking alterations in metabolic and endocrine functions, including decreased glucose tolerance, insulin resistance, increased sympathetic tone, elevated cortisol concentrations, and elevated levels of pro-inflammatory cytokines (Spiegel et al., 1999; Vgontzas et al., 2000; Taheri et al., 2004). Thus, one could speculate that a chronic lack of sleep represents a stress factor stimulating appetite, promoting weight gain and impairing glycemic regulation, with a subsequently increased risk of impaired glucose tolerance and, eventually, type II diabetes. However, a good night’s sleep is different for each individual, and is subject to a broad range of potential confounding variables. Consequently, many experts doubt that more sleep, natural or drug-induced, can be the answer to successful weight loss. Once a person is overweight, poor sleep and uncontrolled

appetite could become part of a vicious cycle; obesity might make it hard to sleep, and poor sleep might make it harder to lose weight. Instead, researchers have focused on identifying individuals with “high-risk” sleeping patterns, in order to prevent weight-related problems before they arise. An early warning sign, such as altered leptin concentration, might alert physicians that the body is suffering more than is immediately obvious. In addition, it may be useful to identify children who do not sleep enough and to encourage parents to change these sleeping habits. Future research needs to examine the effect of short sleeping duration on appetite, food intake and obesity. These studies should use an interventional study design to establish the cause-and-effect relationship behind sleep duration and obesity. They should also examine the effects of restricted sleep on both sides of the energy balance equation, with the use of objective measures for sleep duration and quality. It may thus be demonstrated that the rise of obesity in many societies around the world is partly linked to sleep deprivation. Future studies can also examine whether increasing sleep to 7 or 8 hours per night can help individuals lose weight or prevent weight gain. This may prove to be a pleasurable way to control obesity.

14.4  Weight loss: not always beneficial for the psychological health From a psychosocial perspective, overweight and obesity adversely affect the quality of life. They carry a social stigma that may contribute to higher rates of anxiety, depression and low selfesteem (Puhl and Brownell, 2001; Kottke et al., 2003; McElroy et al., 2004). Depression may contribute to weight gain and obesity and, vice versa, obesity may contribute to depression (Wyatt et al., 2006). From a weight-loss standpoint, it

1.  From Brain to Behavior

14.4  Weight loss: not always beneficial for the psychological health

is realistic to say that the association between obesity and metabolic complications generally constitutes the main argument in justifying weight-reducing programs: the aim is to improve the metabolic risk profile of obese individuals. However, benefits to physical health should not be associated with detrimental effects on mental health or psychological wellbeing. In this regard, the psychological effects that accompany weight loss in obese individuals are of high importance in order to understand the psychological barriers to weight loss, and the optimal management of obesity. The majority of the evidence in this field of research shows the beneficial impact of weight reduction on mental wellbeing and health-related quality of life (Rippe et al., 1998; Fine et al., 1999; Fontaine et al., 1999; Kaukua et al., 2002; Karlsson et al., 2003). However, they fail to mention the possible psychological costs associated with weight loss, reflected by a destabilization of body homeostasis. Such negative psychological costs require a cautionary approach to weight reduction. As shown in Figure 14.3, Chaput and Tremblay found that depression symptoms increased significantly after a weight

Figure 14.3  Clinical threshold of depression. Evolu­ tion of depression symptoms, measured with the Beck Depression Inventory (BDI), over the course of a progressive body-weight loss program that consisted of a supervised diet and exercise clinical intervention. *significantly different from baseline mean score (P  0.05); **P  0.01. Source: Adapted from Chaput et al. (2005; 2006b).

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loss of 10 kg (Chaput et al., 2005; dynamic weight-loss phase). These symptoms were more pronounced at the static weight-loss phase, the plateau (Chaput et al., 2006b). Furthermore, the increase in the symptoms of depression was associated with an increased restraint of eating (Chaput et al., 2005, 2006b). This psychobiological phenomenon was observed concomitantly with a significant decrease in resting energy expenditure, and a significant increase in hunger and a desire to eat (Chaput et al., 2006b). In another recent study, Chaput and colleagues (2008c) linked the increase in depression symptoms with glucose homeostasis and thyroid function. Specifically, the significant increase in depression symptoms observed after an average loss of 11.2 percent of initial body weight induced via energy restriction (700 kcal/day) and an aerobic exercise program was shown to be highly associated with hypoglycemia at the end of an oral glucose challenge, and with a decrease in total triiodothyronine (T3) and free thyroxine (fT4) levels. Such results are not surprising, as glucose is the main substrate of the brain and thyroid function is related to metabolism, which represent effects that may influence mood and wellbeing as well as the perception of “body energy”. This suggests that weight loss up to a certain level has the potential to destabilize body homeostasis and induce a psychobiological vulnerability favoring weight regain. For health professionals, these observations indicate that body-weight management should maintain a reasonable balance between the health benefits associated with weight loss and the potential negative consequences for the control of energy intake and expenditure. Furthermore, various psychological and physio­ logical adaptations make body-weight maintenance following weight loss difficult, and render the individual vulnerable to weight regain. In this context, patients may need to accept a more modest weight-loss outcome (Foster et al., 1997).

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14.5  Physical activity and diet: what is the impact on body-weight stability? An individual looking to lose weight through a healthy diet and regular physical activity usually asks how much weight can be feasibly lost. There is no straightforward answer to this question, as it depends on numerous factors. From a physiological standpoint, the most realistic answer compares the loss of the regulatory impact on fat balance that occurs with weight loss with the gain in the regulatory impact on fat balance that can be promoted by a healthy lifestyle. This assumption means that the fat compartment contains beneficial molecules that aim to fight against a further weight gain. Indeed, fat gain facilitates the maintenance of body homeostasis because of an increased hormonal gradient which favors the regulation of energy balance. An increase in plasma free-fatty acids, fat oxidation, sympathetic nervous system activity, insulinemia at euglycemia, and leptin­ emia are all adaptations that contribute to promote body-weight stability over time (Tremblay and Doucet, 2000; Chaput and Tremblay, 2009a). Simply put, the increase in body fatness is accompanied by neuroendocrine adaptations that favor an increase in energy expenditure and a decrease in energy intake. Accordingly, if one wants to maintain a reduced-obese state, the stimulating effects of a healthy lifestyle on the regulatory processes should, theoretically, be equivalent to what is lost with body mass reduction. Up to now, we have not been able to promote weight losses exceeding 10–12 percent of the initial body weight without inducing metabolic and behavioral changes compromising the ability to maintain subsequent long-term weight stability. It is thus likely that individuals cannot continue to lose weight without more demanding activity and diet changes than those displayed at the end of the program, when the plateauing occurred.

The failure to adhere to healthy lifestyle habits following weight loss leaves the patient with two possible strategic choices in regards to maintaining subsequent body-weight stability. The first is a self-imposed energy restriction, irrespective of the hunger sensations that may be perceived. This option may be counterproductive in the long term; Drapeau and colleagues (2003) observed greater weight gain over time in women that displayed restraint behaviors. The second scenario is to simply not adhere to a healthy lifestyle, the consequence of which will be weight regain as the body will attempt to re-equilibrate the energy and fat balance. Consequently, the reduced-obese individual wishing to maintain the new morphological status is left with very few alternatives. In fact, the only real and valid option is to improve body functionality by healthy activity and diet habits, and thus to compensate for the loss of physiological impact of the decrease in body fat. However, even if a person displays an exemplary discipline in the implementation of a healthy lifestyle, the resulting beneficial impact is not limitless. Body-weight management imposes systematically a balance between the expectations of an individual and what his or her biology can tolerate in terms of lifestyle changes. In some cases, the management of this balance may be complicated by the increased practice of KBW and/or short sleep duration.

14.6  Conclusion and perspectives The modern world demands less energy, and is characterized by an improved quality of life. Modernity has thereby provided numerous products and services contributing to the comfort and wellbeing of people. Beside the obvious positive changes related to the health status and life expectancy of individuals, it has

1.  From Brain to Behavior

References

contributed to considerable gains in labor efficiency and productivity. However, this environment challenges body-weight stability, as decreased sleeping time and increased KBW provide stimuli that can induce a positive caloric balance over time. As described in this chapter, this new reality can partly explain the current obesity epidemic, and also mitigates the potential outcomes of a diet–physical activity weight-reducing program. In this context, an increased level of body fat might be necessary to maintain body-weight stability.

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C H A P T E R

15 Nutrition, Epigenomics and the Development of Obesity: How the Genome Learns from Experience John C. Mathers Human Nutrition Research Centre, Institute for Ageing and Health, Newcastle University, Newcastle on Tyne, UK

o u tl i ne 15.1 The Basics of Epigenetics and Epigenomics

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15.5 An Epigenetic Basis for Developmental Programming of Obesity? 197

15.2 Epigenetic Marks During Development and Aging 193

15.6 Physical Activity, Epigenetic Markings and Obesity 197

15.3 Nutritional Epigenomics

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15.7 Concluding Comments

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15.4 Epigenetics and Brain Function

196

Acknowledgments

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15.1  The basics of epigenetics and epigenomics The human DNA sequence defines biologi­ cal capacity in that it determines the genes, and the functionality of those genes, which can be expressed by the individual. However, although all nucleated cells in a person contain exactly the same genomic sequences, the diversity of structure

Obesity Prevention: The Role of Brain and Society on Individual Behavior

and function in different cells and tissues is mani­ fested by the expression of characteristically different consortia of genes. This cellular differen­ tiation is programmed, at least in part, by epigen­ etic mechanisms that regulate the expression of genes over long periods of time. Epigenetics is the science of chromatin modifications responsible for such altered regulation of gene expression occurring in the absence of changes in the pri­ mary DNA sequence. In other words, epigenetics

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© 2010, 2010 Elsevier Inc.

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is a mechanism which facilitates phenotypic plasti­ city in the context of a fixed genotype. Chromatin can be considered as “smart packaging” which, in addition to helping package the  2 m of DNA into the nucleus (diameter  20 m), carries a sophisticated pattern of marks that regulate chro­ matin structure, DNA accessibility and transcrip­ tion of specific sequences. The best understood epigenetic mark is the covalent addition of a methyl group at the 5 position in a cytosine residue when this precedes a guanine residue – a so-called CpG dinucleo­ tide. The prevalence of the CpG dinucleotide in the human genome is much less than would be expected, and these dinucleotides tend to cluster in DNA domains known as CpG islands which are characterized by high G  C and CpG con­ tents (Bird, 2002). About 50 percent of human genes have CpG islands (CGI) in their promoter regions, sometimes extending into the first exon. The cytosines in these regulatory regions of genes are usually unmethylated, in contrast with cytosine residues elsewhere in the genome which are heavily methylated. Indeed, mam­ malian genomes are dominated by methylated DNA, with unmethylated domains (largely CGI) accounting for only 1–2 percent of the total (Suzuki and Bird, 2008). This divergent methyl­ ation landscape reflects the functionality of the individual DNA sequences with unmethylated promoters allowing transcription of the associated gene, whereas methylated regions are transcrip­ tionally silent. At its simplest, DNA methylation acts as a transcriptional switch which is in the “on” position when the CpG island is unmethyl­ ated and signals “off” when methylated. Within the nucleus, DNA is packaged by sophisticated wrapping around an octet of glob­ ular proteins known as histones i.e. two copies of each histone H2A, H2B, H3 and H4. These histones host further epigenetic marks in the form of post-translational chemical modifica­ tion of amino acid residues, including acetyla­ tion and ubiquitination of lysine residues,

­ hosphorylation of serines, and methylation of p lysine and arginines (Berger, 2007). In all, there are more than 100 distinct post-translational modifications of histones (Kouzarides, 2007). Individual histone modifications and patterns of modifications, described as histone decoration, constitute a histone code (Jenuwein and Allis, 2001) which, in conjunction with DNA meth­ ylation status, regulates the expression of asso­ ciated genes (Bernstein et al., 2007). Although there is some dispute about how inclusive the term “epigen­etics” should be, many in the field consider that the density of nucleosome packing along DNA, the presence of proteins that recog­ nize methylated DNA or modified histones, and higher-level topological organization of these elements into complex structures (Berger, 2007) contribute to the complexity of epigenetic infor­ mation (Feinberg, 2008). The term “epigenome” describes the totality of epigenetic marks in a given cell under specified conditions, and “epi­ genomics” is the science (and technology for the study) of genome-wide epigenetic marks. If epigenetic marks are important in defining over long time periods the complement of genes that characterize specific cell types, then it is evident that there must be mechanisms for sus­ taining patterns of epigenetic information across cell generations. For example, when a hepato­ cyte divides, its daughters “need to know” that they are liver cells rather than kidney or bone cells; the tissue of origin is remembered (LaddAcosta et al., 2007). Indeed, in mitotic tissues, this hypothesis would predict that epigenetic features characteristic of individual stem cells would be recapitulated in the progeny of those stem cells. This prediction holds, and the phe­ nomenon is best exemplified in the intestinal mucosa, where the patterns of DNA methylation differ between individual crypts. This reflects the diversity of methylation patterns in the stem cells populating those crypts (Kim and Shibata, 2004). The molecular mechanism responsible for “memorization” of DNA methylation marks

1.  From Brain to Behavior

15.2  Epigenetic marks during development and aging

through mitosis is well understood. During semi-conservative replication, DNA methylation transferase 1 (DNMT1) uses the parental strand as a template to methylate the daughter strand, with S-adenosyl methionine (SAM) acting as the methyl donor. In contrast, the molecular mechanisms for memorization of histone modi­ fications remain obscure. No enzyme has been identified that recognizes chromatin modifica­ tions in the parental cell and reproduces them in the daughter cells (Feinberg, 2008). However, it appears that histones segregate randomly dur­ ing mitosis so that each daughter cell acquires some of the marked proteins, which then spread to the newly deposited histones (Hatchwell and Greally, 2007).

15.2  Epigenetic marks during development and aging Each individual’s DNA sequence is fixed at conception, but their epigenetic state, as indi­ cated by DNA methylation patterns, changes throughout the life-course. The most dra­ matic of these changes occur very early after a highly methylated sperm fuses with a relatively unmethylated egg. In the first few cell divisions, the new individual undergoes genome-wide demethylation, which erases parental methyla­ tion marks for all genomic sequences with the exception of imprinted genes (Reik, 2007) – that is, genes which are expressed in a parent-oforigin-specific manner. Between the morula and the blastocyst stages there is genome-wide de novo methylation, with tissue-specific methyl­ ation patterns emerging later in embryonic development (Reik, 2007; Feinberg, 2008). In embryonic stem (ES) cells, there appears to be a novel chromatin-based mechanism for maintain­ ing pluripotency through which expression of developmentally-important transcription factors

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is regulated epigenetically by “bivalent domains”, which silence these genes in ES cells but keep them poised for activation (Bernstein et al., 2006). When compared with the tsunami-like remodeling of the epigenetic landscape seen in early embryonic life, DNA methylation patterns (and, by inference, other epigenetic marks) are relatively stable following birth. However, there is substantial evidence that these epigenetic pat­ terns continue to evolve over the life-course. A good illustration of this evolution is provided by Fraga’s study of monozygotic twins, which found that members of twin pairs were epigen­ etically indistinguishable when young but epi­ genetic portraits (DNA methylation patterns and histone acetylation) diverged with age (Fraga et al., 2005). These epigenetic differences in older twin pairs were reflected in greater dif­ ferences in gene expression (Fraga et al., 2005), suggesting that the greater epigenetic hetero­ geneity may have functional consequences. Epigenetics is emerging as an important field for those studying the biology of aging and agerelated diseases because of the potential functional consequences of the changes in epigenetic marks that accumulate with age (Fraga and Esteller, 2007). Studies of aging cells in culture, of animal models, and of older humans indicate that, in general, genomic DNA becomes progressively demethylated with age. In contrast, some genes (for example, some tumor suppressor genes and other DNA defense genes) become silenced by promoter methylation (Fraga and Esteller, 2007). Until recently, the understanding of aging’s effects in humans was handicapped by the restriction of cross-sectional studies which cannot provide infor­ mation on intra-individual changes in epigenetic marks over time. A study of an Icelandic cohort in whom DNA was collected 11 years apart, and that of a Utah (USA) cohort sampled 16 years apart (Bjornsson et al., 2008), has changed this land­ scape. This study showed that genome-wide DNA methyl­ation changed in a substantial proportion of each cohort, with individuals showing both gains

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15.  Nutrition, Epigenomics and Obesity

and losses of cytosine methylation (Bjornsson et al., 2008). In contrast, previous studies found little evi­ dence of age-related changes in methylation of the human IGF2/H19 locus (Heijmans et al., 2007) and of human chromosomes 6, 20 and 22 (Eckhardt et al., 2006). However, both of these studies were cross-sectional and, because some individuals gain DNA methylation whilst others become relatively hypomethylated with age, the process of averag­ ing degrees of methylation for each age group is likely to obscure individual age effects (Bjornsson et al., 2008). Further, studies of epigenetic markings at an individual cell level may provide novel insights into the development of age-related frailty. Changes in DNA methylation over the lifecourse may not occur equally in all cells within a tissue – that is, aging may increase the extent of epigenetic mosaicism within a tissue. Since copying of DNA methylation patterns across cell generations is much less well-policed than is the primary sequence, methylation patterns may drift over time, leading to greater inter­ cellular divergence in methylation patterns within a tissue with age. By expanding HMEC cells from 1 to 106 followed by bisulfite sequen­ cing, Ushijima and colleagues (2003) quantified epigenetic error rates for a panel of genes and reported a mean of 0.1 percent “errors” per site per cell generation. This increased heterogen­eity in epigenetic markings with time may contri­ bute to the greater cell-to-cell variation in gene expression that is observed in cardiomyocytes of older mice (Bahar et al., 2006). The obser­ vation that this greater cell-to-cell variation appeared to be random (i.e., differed between genes within a cell) (Bahar et al., 2006) is con­ sistent with the mechanism of epigenetic drift over time. Increased cell-to-cell diversity in epi­ genetic marking with age may have important functional consequences and, at a tissue level, may explain some of the reduction in speed and magnitude of response to stimuli (loss of homeostasis) that characterizes aging and the ­development of frailty (Figure 15.1).

Young

Large, unified response

Old

Reduced, variable response

Figure 15.1  Conceptual functional consequences of increased inter-cellular heterogeneity in promoter methyla­ tion and subsequent silencing of a gene age in a given tissue. , unmethylated gene, expression; , methylated gene, no expression. Source: Mathers and Ford (2009).

15.3  Nutritional epigenomics There is indisputable evidence that nutritional exposures contribute to phenotypic plasticity and, indeed, that exposures early in life can have profound effects on health decades later. As mechanisms that play a significant role in orches­ trating the complex interplay between nutrition (and other lifestyle exposures) and the genome that determines individual phenotype, epigen­ etic processes are strong candidates. In other words, it is proposed that epigenetic markings (1) allow phenotypic plasticity in a fixed geno­ type, and (2) connect environmental exposures with gene expression and function (Feinberg, 2007). To help focus research attention on the key processes likely to be involved in linking envi­ ronmental (nutritional) exposure with altered phenotypes, we developed the simple concep­ tual model of the “4Rs of epigenomics” (Figure 15.2; Mathers and McKay, 2009). This model pro­ poses that nutritional exposures are “Received” and “Recorded” by epigenetic mechanisms, and that the environmentally determined epigenetic marks are “Remembered” across succeeding cell generations. Sometime later, the consequences of earlier environmental exposures are “Revealed”

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15.3  Nutritional epigenomics Environment (diet)

Receive and Record

Remember

Reveal

Time

Figure 15.2  The four Rs of epigenomics. Conceptual model of the key processes through which altered epigenomics markings as a result of nutritional expo­ sures are Received, Recorded, Remembered and Revealed. Source: Mathers (2008), reproduced with permission from Cambridge University Press.

as altered gene expression, which translates into changes in cellular and tissue function (Mathers and McKay, 2009). There is only fragmentary understanding of the mechanisms through which nutritional exposures are received and recorded as novel epigenetic marks (the first two “Rs”), yet the list of food components which modulate DNA methylation and histone decoration is expand­ ing (for reviews, see Arasaradnam et al., 2008; Mathers and Ford, 2009). In many cases, the functional consequences of the altered epigen­ etic marks are not known and the field is ripe for the systematic study of the relationships between specific epigenetic marks and transcrip­ tional responses (the fourth “R”). The impact of maternal nutrition on epigenetic markings, gene expression and phenotype is probably best exemplified by studies in the viable yel­ low agouti (Avy) mouse (Waterland and Jirtle, 2003). The offspring of mouse dams fed a diet enriched with methyl donors (folate, vitamin B12, choline and betaine) during pregnancy are more likely to have mottled or pseudo-agouti coats (rather than yellow coats) and a reduced risk of being obese (Waterland and Jirtle, 2003). The molecular mechanism for these effects

195

appears to involve greater methylation of the cryptic promoter in the proximal end of the Avy intracisternal A-particle (Waterland and Jirtle, 2003). Intriguingly, similar effects are seen when the diet of the mouse dam is supplemented with genistein, which is not a methyl donor. This epi­ genetic remodeling does not seem to be driven by availability of methyl groups (Dolinoy et al., 2006). The rapid increase in obesity prevalence in the past few decades is consistent with a hypothesis of transgenerational amplification of adiposity, which might be mediated by the effects of maternal adiposity on birth weight and subsequent adult adiposity (Lawlor et al., 2007). Recent data suggest that maternal ­obesity in Avy mice induces transgenerational amplification of obesity. This adverse effect, however, can be ameliorated by supplement­ ing the dams with dietary methyl donors (Waterland et al., 2008). Importantly, the effects in these mice were independent of epigenetic changes at the Avy locus (Waterland et al., 2008). The search for epigenetic mechanisms will need to be widened to include, for example, genes in pathways regulating food intake and/ or energy expenditure. These results also pro­ vide proof of concept that the putative cycle of transgenerational amplification of ­ obesity might be broken by readily implemented nutri­ tional interventions. The mandatory fortifica­ tion of staple foods with folic acid (one of the methyl donors used in the mouse studies) in the US, Canada and elsewhere has resulted in significant increases in folate status of the whole population, including women of childbearing age (Pfeiffer et al., 2005). This “­natural” experiment provides an opportunity to test the hypothesis that maternal methyl donor ­supplementation per se is effective in reducing the risk of obesity in the offspring by examin­ ing the relationships between maternal and offspring adiposity before and since the wide­ spread fortification of baked goods with folic acid in 1996.

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15.4  Epigenetics and brain function Investigation of epigenetically-mediated mechanisms in the brain is in its early stages, but it is already apparent that epigenetic marks are important for brain structure and func­ tion. For example, Rett syndrome (RTT), the single gene disorder caused by mutations in the gene encoding methyl-CpG-binding pro­ tein 2 (MeCP2 – located at chromosome Xq28), presents a progressive loss of developmen­ tal milestones associated with aberrant gene expression (Feinberg, 2008). In the healthy state, MeCP2 selectively binds CpG dinucleotides and mediates transcriptional repression through interaction with histone deacetylase and the corepressor SIN3A. The loss of this repression is the mechanism underlying the pathogenesis of RTT (Amir et al., 1999). Recent analysis of DNA methylation signatures in the human brain has shown that different brain regions (cere­ bellum, cerebral cortex and pons) are distin­ guished by characteristically different patterns of DNA methylation (Ladd-Acosta et al., 2007). Differences between brain regions within indi­ viduals were much greater than those between individuals due to potential confounders includ­ ing age, sex, post-mortem interval or cause of death. These authors suggested that epigenetic signatures may, in part, determine brain func­ tional programs (Ladd-Acosta et al., 2007). To date, there has been little research on the effects of altered supply of specific nutrients on brain epigenetic marks. However, a recent publication reported that long-term feeding of a diet low in methyl donors caused genomic DNA hyper­ methylation in the rat cortex which was associ­ ated with reduced expression of DNMT1 and increased expression of the de novo DNA methyl transferase DNMT3A (Pogribny et al., 2008). There is substantial proof of principle that environmental factors program gene expression in the brain, that this occurs through epigenetic

mechanisms, and that the sequelae are both long-lasting and important for health. In a rat model, high-quality maternal care characterized by licking, grooming and arched back nursing in the first week of life (“good” mothers) produces offspring with reduced fearfulness and more modest hypothalamo-pituitary-adrenal (HPA) responses to stress (Weaver et al., 2004). In this model, whole genome transcriptomic analy­ sis of hippocampal tissue revealed more than 900 genes that were differentially expressed between the adult offspring of “good” and “poor” mothers (Weaver et al., 2006). Maternal care was associated with alterations in the pat­ tern of methylation of the glucocorticoid recep­ tor (GR – also designated NR3C1, for nuclear receptor sub-family 3, group C, member 1) gene and altered histone acetylation within the hip­ pocampus which became apparent within the first week of life and persisted into adulthood (Weaver et al., 2004). Importantly, these aberrant epigenetic marks could be reversed by crossfostering. In addition, central infusion of tricho­ statin A (a histone deacetylase inhibitor) ablated the effects of maternal care on histone acetylation, DNA methylation, GR expression, and HPA responses to stress (Weaver et al., 2004). These findings support the hypothesis that epigenetic processes in the brain provide a mechanism through which maternal care influences longterm responses to stress in the offspring (Weaver et al., 2004). Interestingly, “good” maternal care resulted in demethylation of very specific CpG sites corresponding with the nerve growth factor-inducible protein A (NGFI-A) transcrip­ tion factor response element in exon 17 of the GR promoter (Weaver et al., 2004). A recent study in mother–infant pairs pro­ vides support for the hypothesis of environ­ mental “programming” of the HPA axis by maternal factors in humans (Oberlander et al., 2008). Methylation of specific CpG residues in the potential NGIF-A consensus binding site within exon 17 of the glucocorticoid receptor gene (NR3C1) in neonatal cord (venous) blood

1.  From Brain to Behavior

15.6  Physical activity, epigenetic markings and obesity

mononuclear cells correlated with exposure to maternal depression in the third trimester of pregnancy (Oberlander et al., 2008). Importantly, this increased methylation correlated posi­ tively with HPA stress reactivity assessed as the change in salivary cortisol concentration in response to a non-noxious stressor (Oberlander et al., 2008). Given the lower risk of childhood (Arenz et al., 2004) and perhaps adult (Owen et al., 2005) obesity among those who have been breastfed, it is tempting to speculate that the nature of maternal care in the early post-natal period may have profound effects on adult health through altered programming of behaviors mediated by epigenetic mechanisms in the brain.

15.5  An epigenetic basis for developmental programming of obesity? There is now strong evidence from both obser­ vational studies in humans and experiments in animal models that nutritional insults during intrauterine and early post-natal development enhance the risk of increased adiposity later in life. Intriguingly, both maternal under-nutrition (leading to low birth weight) and maternal obes­ ity (associated with greater birth weight and adiposity) increase risk of childhood and adult obesity (Taylor and Poston, 2007). Whether simi­ lar molecular and cellular mechanisms underlie the phenotypic convergence resulting from these two contrasting adverse nutritional exposures remains to be discovered, but it seems likely that both cause hypothalamic “malprogramming” (Plagemann, 2005). The adipokine leptin appears to be the dominant factor, providing the brain with long-term information about the status of energy reserves in adipose tissue by binding to the leptin receptor in the hypothalamus and acti­ vating the JAK–STAT and other signal transduc­ tion pathways (Badman and Flier, 2005). There

197

is growing evidence that leptin concentrations in the early post-natal period may play a central role in hypothalamic programming (reviewed by Taylor and Poston, 2007). For example, oral dos­ ing with physiological amounts of leptin during the suckling period in rats resulted in reduced body fat content in adulthood, and altered hypothalamic expression of a number of genes involved in leptin signaling (Pico et al., 2007). Of particular interest was the lower expression of suppressor of cytokine signaling 3 (SOCS3), an important mediator of leptin resistance, which may produce enhanced sensitivity to leptin in the regulation of food intake (Pico et al., 2007). Since leptin is present in human breast milk but not in infant formula, it is possible that leptin supply during breast-feeding may contribute to the “protection” against obesity (Pico et al., 2007) seen among those who have been breast­ fed (Arenz et al., 2004). The mechanism through which leptin (or other exposures) alters SOCS3 expression remains to be discovered. However, this may involve an epigenetic mechanism, since the SOCS3 gene contains a large CpG island extending from the promoter region into exon 2, and aberrant methylation is associated with altered expression of the gene and disruption of JAK–STAT signaling (Niwa et al., 2005). Given the centrality of the hypothalamus in the control of food intake, there is an a priori case that epi­ genetic dysregulation of expression of appetite regulatory genes and/or of associated receptors and signaling cascades may play an important role in the programming of obesity.

15.6  Physical activity, epigenetic markings and obesity In contrast with the expanding body of evi­ dence that dietary factors have wide-ranging effects on epigenetic marks and, in so doing, may modulate risk of obesity, very little is known

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about the impact of physical activity on the epi­ genome or, conversely, about how epigenetically altered regulation of gene expression might influ­ ence willingness to undertake (or capacity for) physical activity. However, in the cancer field, there is epidemiological evidence for associa­ tions between physical activity and gene methyl­ ation. In a study of promoter methylation of a panel of six genes in colonic tumors, the number of methylated CpG islands increased with age but, perhaps surprisingly, fewer were methylated in those with higher BMI (Slattery et al., 2007). This study found no relationship between level of physical activity and number of methylated genes, but there was evidence that those report­ ing high physical activity had a lower risk of both CIMP-low and CIMP-high tumors (CIMP  CpG island methylator phenotype) (Slattery et al., 2007). The widespread genomic derangements in tumors make it difficult to ascribe causality to such observations, and studies of non-tumor tis­ sue can be more informative. The likelihood of promoter hypermethylation of the tumor sup­ pressor gene APC in non-malignant breast tissue is inversely related to recent and lifetime meas­ ures of physical activity (Coyle et al., 2007). Given that physical activity appears to lower the risk of breast cancer, and that the loss of function of APC (by promoter methylation or mutation) is mecha­ nistically important in tumor development, these data support the hypothesis that physical activity might be protective through reducing the likeli­ hood that aberrant epigenetic marking will dis­ able key defense genes. The mechanism(s) through which physical activity appears to impact on epigenetic mark­ ings are not understood, but inflammation is potentially a critical mediator. There is good evi­ dence that ulcerative colitis (a common type of inflammatory bowel disease) is associated with higher methylation of several genes in the human colonic mucosa (Issa et al., 2001), and chronic gas­ tric inflammation is accompanied by increased methylation of several genes (Kang et al., 2003). More recently, global DNA hypermethylation in

peripheral blood leukocytes was correlated with chronic systemic inflammation (based on circu­ lating concentration of C-reactive protein) and shown to be associated significantly with both allcause and cardiovascular disease mortality even after adjustment for age, inflammation, and other risk factors (Stenvinkel et al., 2007). Since lack of physical activity and obesity are each associated with a chronic inflammatory state (Handschin and Spiegelman, 2008) it is reasonable to suppose that both may have effects on epigenetic marks, and disentangling cause from consequence will be a considerable challenge. Currently, there is major interest in the role of the power­ ful transcriptional co-activator PGC1 (peroxi­ some-proliferator-activated receptor  (PPAR) co-activator 1) as the master down-regulator of inflammation in response to exercise (Handschin and Spiegelman, 2008), and it will be important to discover whether expression of the gene encod­ ing PGC1 is epigenetically regulated.

15.7  Concluding comments The topology of the epigenomic landscape provides a sophisticated and long-lasting set of signals for regulating gene expression in a given cell under particular circumstances, and across cell generations. However, these epigenetic marks are plastic and respond to environmental exposures, including diet. It is therefore prob­ able that epigenetic processes are a major mecha­ nism through which nutrition modulates health throughout the life-course. Technologies for char­ acterizing the epigenomics landscape are readily available (Esteller, 2007) and developments in this area are expected to accelerate. Epigenetics has been identified by the National Institutes of Health as an emerging frontier of science (http://nihroadmap.nih.gov/epigenomics/). In contrast with the rapid advances in under­ standing of the role of epigenetics in the etiology of cancers (Esteller, 2008), there has been little

1.  From Brain to Behavior

References

research on epigenetic mechanisms in the devel­ opment of obesity, and the field is open for novel investigations of, for example, how expression of the genes responsible for regulating food intake and energy expenditure are controlled epigeneti­ cally. Small differences in expression sustained over long periods of time would be expected to have profound effects on energy balance and, therefore, risk of obesity. The tools, including bioinformatics approaches (McKay et al., 2008), necessary to support research on nutritional epi­ genomics and obesity are there to be used.

Acknowledgments Nutritional epigenomics research in my laboratory is funded by the BBSRC and EPSRC through the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN) (BB/C008200/1), by the BBSRC (grant no. BH081097) and by NuGO “The European Nutrigenomics Organisation; linking genomics, nutrition and health research” (NuGO; CT2004-505944), which is a Network of Excellence funded by the European Commission’s Research Directorate General under Priority Thematic Area 5, Food Quality and Safety Priority, of the Sixth Framework Programme for Research and Technological Development. Further informa­ tion about NuGO and its activities can be found at http://www.nugo.org.

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Niwa, Y., Kanda, H., Shikauchi, Y., Saiura, A., Matsubara, K., Kitagawa, T., Yamamoto, J., et al. (2005). Methylation silencing of SOCS-3 promotes cell growth and migration by enhancing JAK/STAT and FAK signalings in human hepatocellular carcinoma. Oncogene, 24, 6406–6417. Oberlander, T. F., Weinberg, J., Papsdorf, M., Grunau, R., Misri, S., & Devlin, A. M. (2008). Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant corti­ sol stress responses. Epigenetics, 3, 97–106. Owen, C. G., Martin, R. M., Whincup, P. H., Davey-Smith, G., Gillman, M. W., & Cook, D. G. (2005). The effect of breastfeeding on mean body mass index throughout life: A quantitative review of published and unpublished observational evidence. American Journal of Clinical Nutrition, 82, 1298–1307. Pfeiffer, C. M., Caudill, S. P., Gunter, E. W., Osterloh, J., & Sampson, E. J. (2005). Biochemical indicators of B vita­ min status in the US population after folic acid fortifi­ cation: Results from the national health and nutrition examination survey 1999–2000. American Journal of Clinical Nutrition, 82, 442–450. Pico, C., Oliver, P., Sanchez, J., Miralles, O., Caimari, A., Priego, T., & Palou, A. (2007). The intake of physiologi­ cal doses of leptin during lactation in rats prevents obes­ ity in later life. International Journal of Obesity (London), 31, 1199–1209. Plagemann, A. (2005). Perinatal programming and func­ tional teratogenesis: Impact on body weight regulation and obesity. Physiology and Behavior, 86, 661–668. Pogribny, I. P., Karpf, A. R., James, S. R., Melnyk, S., Han, T., & Tryndyak, V. P. (2008). Epigenetic alterations in the brains of Fisher 344 rats induced by long-term administration of folate/methyl-deficient diet. Brain Research, 1237, 25–34. Reik, W. (2007). Stability and flexibility of epigenetic gene regulation in mammalian development. Nature, 447, 425–432. Slattery, M. L., Curtin, K., Sweeney, C., Levin, T. R., Potter, J., Wolff, R. K., Albertsen, H., & Samowitz, W. S. (2007). Diet and lifestyle factor associations with CpG island methylator phenotype and BRAF mutations in colon cancer. International Journal of Cancer, 120, 656–663. Stenvinkel, P., Karimi, M., Johansson, S., Axelsson, J., Suliman, M., Lindholm, B., et al. (2007). Impact of inflam­ mation on epigenetic DNA methylation – a novel risk factor for cardiovascular disease? Journal of Internal Medicine, 261, 488–499. Suzuki, M. M., & Bird, A. (2008). DNA methylation land­ scapes: Provocative insights from epigenomics. Nature Reviews Genetics, 9, 465–476. Taylor, P. D., & Poston, L. (2007). Developmental program­ ming of obesity in mammals. Experimental Physiology, 92, 287–298.

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C H A P T E R

16 The Role of Early Life Experiences in Flavor Perception and Delight Julie A. Mennella and Gary K. Beauchamp Monell Chemical Senses Center, Philadelphia, PA, USA

o u tl i n e 16.1 Introduction

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16.1  Introduction Food is much more than a source of calories, since its flavor can signal nutrient sources, provide pleasure (or pain) and, through experience, be identified with one’s family, community and culture. The pleasure experienced upon ingestion of a food is a complex process mediated by the chemical senses (taste and smell and irritant

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Acknowledgments

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properties of foods) in the periphery and then multiple brain substrates, which are remarkably well conserved phylogenetically (Berridge and Kringelbach, 2008). The degree to which the chemicals that stimulate these flavor senses are liked or disliked is determined by innate or inborn factors, learning and experience, and the interactions among these. In essence, these senses, which are already well-developed at birth (for review, see Ganchrow and Mennella, 2003),

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function as gatekeepers throughout one’s life. They control one of the most important decisions an animal makes – whether to reject a foreign substance or take it into the body. Furthermore, these senses function to inform the gastrointestinal system about the quality and quantity of the impending rush of nutrients. Although the modernization and industrialization of the food supply has produced many benefits, unanticipated consequences from eating diets rich in sugars, salt and fats have become increasingly commonplace (Gidding et al., 2009). Excessive intake of foods containing high amounts of salt or sugars (and consequently foods that taste salty and sweet) causes or exacerbates a number of illnesses. For example, high intake of salt has been linked to hypertension in some individuals, and there is a broad, but not universal, agreement that decreasing salt intake on a population-wide basis could save many lives (Hooper et al., 2004). Similarly, excessive intake of refined sugars has been linked to the metabolic syndrome and, perhaps less persuasively, to obesity (Reed and McDaniel, 2006). Thus, it is recommended that both adults and children limit the amount of salt and simple sugars; minimize excessive intakes of energy, saturated fat, trans fat and cholesterol; and favor diets rich in vegetables and fruits, whole grains, low- and non-fat dairy products, legumes, fish and lean meat (Gidding et al., 2005; Lichtenstein et al., 2006). Despite such recommendations, neither adults nor their children are complying. The 2004 Feeding Infants and Toddlers Study in the US alarmingly revealed that while toddlers were more likely to be eating fruits than vegetables, one in four did not even consume one vegetable on a given day (Briefel et al., 2006; Mennella et al., 2006). Instead, they, like older children (Siega-Riz et al., 1998; Mannino et al., 2004; Nicklas et al., 2004; Schmidt et al., 2005), were more likely to eat fatty foods such as French fries, sweet- and salty-tasting snacks and sweet beverages, and less likely to eat

bitter-tasting vegetables (Briefel et al., 2006; Mennella et al., 2006). None of the top five vegetables consumed by toddlers was a dark green vegetable (Mennella et al., 2006). Not only is the consumption of fruits and vegetables generally low in pediatric populations (Briefel et al., 2006; Mennella et al., 2006), but acceptance of these foods is difficult to enhance beyond toddlerhood (Wardle et al., 2003a, 2003b). Moreover, despite participation in high-quality dietary intervention programs, snacks, desserts and pizza continue to contribute heavily in the diets of elementary school students (Van Horn et al., 2005). One reason for why it is difficult to alter children’s dietary intake is the remarkably potent rewarding properties of the flavors of foods. This chapter will focus on the biological imperatives that shape food and flavor likes and dislikes, and will take a developmental approach since, although some changes in preference occur during adolescence, many food preferences are firmly in place by the time a child reaches the age of 3 years (Resnicow et al., 1998; Skinner et al., 2002a; 2002b; Cooke et al., 2004; Nicklaus et al., 2004, 2005). Because the senses of taste and smell are the major determinants of whether young children will accept a food (that is, they eat only what they like (Birch, 1998)), these senses take on even greater significance in understanding the bases for food choices in children than they do for adults. In what follows, it will be argued that the type of foods preferred or rejected by children reflects their basic biology. We focus on the ontogeny of sweet, salty and bitter tastes because these tastes have been most extensively studied, are directly involved in choices of specific foods of concern (for example, sweet and salty snacks, green vegetables), and exhibit age-related changes in function. Although flavors associated with fats and fatty acids may also be detected, in part, by the sense of taste, there is insufficient evidence to review the ontogeny of fat taste. However, given children’s preference

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16.2  Flavor and the ontogeny of the senses

(Fisher and Birch, 1995) and the rewarding properties of fats (see, for example, Johnson et al., 1991), this is certainly a research topic worthy of further investigation. The inherent plasticity of the chemical senses and how, as a consequence of post-natal maturation and early life experiences, developmental processes act to ensure that a child is not restricted to a narrow range of foodstuffs by virtue of few preferences and strong aversions for foods will be discussed. First, though, the chapter begins by providing a basic understanding of taste, smell and chemical irritation, the differences between them, and how they interact to produce the overall impression of a food which we define as its flavor.

16.2  Flavor and the ontogeny of the senses The perceptions arising from the senses of taste, smell and chemical irritation combine in the oral cavity to determine flavor. These perceptions are often confused and misappropriated (Rozin, 1982), with such olfactory sensations as vanilla, fishy, chocolate and coffee being erroneously attributed to the taste system per se when, in fact, much of the sensory input is due to retronasal olfaction (see below).

16.2.1  Taste The taste system is attuned to a small number of perceptual classes of experience, the so-called basic tastes, each of which specifies crucial information about nutrients or dangerous substances. This small number of primary taste qualities (e.g., sweet, salty, bitter, sour and savory or umami) is detected by specialized receptors on the tongue, other parts of the oral cavity and even in the gastrointestinal system (Bachmanov and Beauchamp, 2007; Egan and Margolskee, 2008). These basic tastes either stimulate intake (sweet, salty and savory) or inhibit it (bitter and

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perhaps sour) when ingested within a generally restricted range of concentrations. Major progress has been made in identifying the initial events in taste recognition (for more extensive reviews, see Chandrashekar et al., 2006; Kim et al., 2006; Bachmanov and Beauchamp, 2007; Lumpkin and Caterina, 2007; Katz et al., 2008). It appears that two different strategies have evolved to detect taste molecules. For salty and sour tastes, it is widely believed that ion channels serve as receptors. Here, H (sour) and Na (salty) ions interact with channels in the taste cell membrane. The cell is then activated, and sends an electrical message to the brain. However, for both of these taste qualities the molecular identity of the receptors and their exact mechanisms are still unknown. For sweet, umami and bitter tastes, G-proteincoupled receptors (GPCRs) appear to play the most prominent roles. These GPCRs bind taste molecules in a sort of lock-and-key mechanism, thereby activating the taste cell to send an electrical message to the brain. For sweet and umami, a family of three GPCRs, named T1R1, T1R2 and T1R3, act in pairs (T1R1  T1R3 for umami and T1R2  T1R3 for sweet) to detect molecules imparting these taste qualities. Other GPCRs may also be involved. A substantially larger family of GPCRs, the T2Rs (n  25), constitutes the bitter receptors. From an evolutionary perspective, these taste qualities likely evolved to detect and reject that which is harmful and to seek out and ingest that which is beneficial. It has been hypothesized that the small number of taste qualities evolved because of the functional importance of the primary stimuli (e.g., sugars, sodium chloride, amino acids and protein, organic acids, bitter toxins) in nutrient selection, especially in children. Preference for salty and sweet tastes is thought to have evolved to attract us to minerals and to energy-producing sugars and vitamins, respectively. Rejection of bitter-tasting and irritating substances evolved to protect the animal from being poisoned and the plant producing

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these chemicals from being eaten (Jacobs et al., 1978; Glendinning, 1994). However, while bitter tastes are innately disliked, with experience people may come to like certain foods that are bitter, particularly some vegetables, and foods and beverages with pharmacologically active bitter compounds, such as caffeine or ethanol.

16.2.2  Olfaction The organization of the olfactory system reflects the need to recognize a wide range of odors and to discriminate one odor from another. In fact, the olfactory receptors are encoded by the largest mammalian superfamily of genes (Buck and Axel, 1991). In contrast to the taste system, there are thousands of diverse odor qualities. Volatile molecules (odorants) bind to olfactory receptors located on a relatively small patch of tissue high in the nasal cavity. Odor molecules can reach these receptors by entering the nostrils during inhalation (orthonasal route) or traveling from the back of the oral cavity toward the roof of the nasal pharynx (retronasal route). It is this retronasal stimulation arising from the molecules of foodstuffs that leads to many of the flavor sensations we experience during eating. Although there is some evidence that certain odors may be innately biased in a positive or negative direction (Khan et al., 2007), individual experiences largely determine how much a person likes or dislikes the odor component of a food or beverage flavor. Through experiences, odors acquire personal significance (Epple and Herz, 1999; Mennella and Forestell, 2008; Mennella and Garcia, 2000). Memories evoked by odors are more emotionally charged and resistant to change than those evoked by other sensory stimuli (Herz and Cupchik, 1995; Epple and Herz, 1999). The unique processing of olfactory information (Cahill et al., 1995) and the olfactory system’s immediate access to the neurological substrates underlying non-verbal

aspects of emotion and memory (Royet and Plailly, 2004) help explain the large emotional component of food aromas. This, coupled with the recent finding that the most salient memories formed during the first decade of life will likely be olfactory in nature (Willander and Larsson, 2006), explains how food aromas can trigger memories of childhood, and why flavors and food aromas experienced during childhood remain preferred and can, to some extent, provide comfort.

16.2.3  Chemical irritation Sensations resulting from chemicals stimulating receptors and free nerve endings of the trigeminal and vagal nerves lead to oral, nasal and pharyngeal sensations such as pain, heat, coolness, tingling, tickle and itch. Recent research has shown that a family of transient receptor potential (TRP) channels is involved in detecting many of these chemicals (Bautista et al., 2006; Liman, 2007). These channels also respond to actual heat and cooling. While “irritating” sensations are critical in food and flavor acceptability, and most likely have a huge impact on acceptance by children, there is virtually no research on their ontogeny. Thus, the remainder of this chapter focuses only on taste and smell.

16.2.4  Ontogeny of the flavor senses Both taste and olfactory systems are welldeveloped and functioning before birth (for review, see Ganchrow and Mennella, 2003). The anatomical substrates mediating the detection of taste stimuli make their first appearances at around the seventh or eighth week of gestation, and by the thirteenth to fifteenth weeks, the taste bud in which taste receptor cells arise begins to morphologically resemble the adult bud, except for the cornification overlying the papilla (Bradley and Stern, 1967; Bradley and

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Mistretta, 1975). Taste receptor cells are capable of conveying gustatory information to the central nervous system by the last trimester of pregnancy. This information is available to systems organizing sucking, facial expressions, and other affective behaviors. With regard to olfaction, the olfactory bulbs and receptor cells needed to detect olfactory stimuli have attained adult-like morphology by the eleventh week of gestation (Humphrey, 1940; Pyatkina, 1982). Olfactory marker protein, a biochemical correlate of olfactory receptor functioning, has been identified in the olfactory epithelium of human fetuses at 28 weeks of gestation (Chuah and Zheng, 1987). Because the external nares (nostrils) are opening between the sixteenth and twenty-fourth gestational weeks, there is a subsequent continual movement of amniotic fluid through the nasal passages such that, by the last trimester of pregnancy, the fetus inhales more than twice the volume of amniotic fluid it swallows. The chemical composition of this fluid, and hence its flavor, changes constantly, in part because of the passage of food flavors from the maternal diet (Hepper, 1988; Mennella et al., 1995; Schaal et al., 2000). Even in air-breathing organisms, volatile molecules must penetrate the aqueous mucus layer covering the olfactory epithelium to reach receptor sites on the cilia. Thus, there is no fundamental distinction between olfactory detection of airborne versus waterborne stimuli during fetal life.

16.3  Taste and development From the perspective of taste development, what children like to eat (e.g., sweet cereals, desserts, salty snacks) and do not like to eat (e.g., green vegetables) is not surprising. Children are programmed, through the sense of taste, to like foods and beverages that taste sweet or salty, and to dislike bitter ones (Cowart et al., 2004).

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16.3.1  Sweet taste Intense liking for sweet taste is evident early in ontogeny. Within the first few hours of life, consistent, quality-specific facial expressions such as smiling and relaxation of facial muscles are elicited when infants taste sweet­tasting solutions (Steiner, 1977; Rosenstein and Oster, 1988). This suggests that the liking for sweet reflects basic human biology, and is not solely a product of modern-day technology and advertising. For infants and children around the world, the general rule seems to be the sweeter the better (for review, see Liem and Mennella, 2002; Mennella, 2008). Preferences for sweets remain heightened throughout childhood (Beauchamp and Moran, 1984; Mennella et al., 2005; Pepino and Mennella, 2005a) and early adolescence (Desor et al., 1975), but then decline to adult levels during late adolescence (Desor and Beauchamp, 1987). In a cross-sectional study that measured sweet preference in more than 750 participants, 50 percent of the children and adolescents, but only 25 percent of the adults, selected a 0.60-M sucrose concentration as their favorite solution. To put this in perspective, a 0.60-M sucrose concentration is equivalent to approximately 12 spoonfuls of sugar in 230 ml of water (an 8-ounce glass), whereas a typical cola is about half of this sucrose (or sucrose equivalent) concentration. Making foods, beverages and even medications taste sweet can increase both liking and acceptance by children (Filer, 1978; Beauchamp and Moran, 1984; Sullivan and Birch, 1990). This strong preference for sweet tastes may have an ecological basis. At birth, a sweet-liking may help to ensure the acceptance of sweet-tasting mother’s milk. As children begin to eat solid foods, their sweet preference attracts them to foods, such as fruits, that are associated with energy-producing sugars, minerals and vitamins. Although strong evidence is lacking, it has been suggested that such preferences evolved to solve a basic nutritional problem

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of attracting children to sources of high energy during periods of maximal growth (Simmen and Hladik, 1998; Drewnowski, 2000; Coldwell et al., 2009). Although the liking for sweet-tasting substances is inborn, the degree to which early experiences alter or modulate sweet preferences later in life is largely unknown. Longitudinal studies revealed that babies who were routinely fed sweetened water during the first months of life exhibited a greater preference for sweetened water when tested at 6 months (Beauchamp and Moran, 1982) and then again at 2 years of age (Beauchamp and Moran, 1984) when compared to those who had little or no experience with sweetened water. Similarly, a more recent crosssectional study on 6- to 10-year-old children revealed that such feeding practices may have longer-term effects on the preference for sweetened water than previously realized (Pepino and Mennella, 2005a). However, there are no compelling data suggesting that repeated exposure to sugar water results in a generalized heightened hedonic res­ ponse to sweetness (Beauchamp and Moran, 1984). Rather, the context in which the taste exp­erience occurs is an important factor. Through familiarization, children develop a sense of what should, or should not, taste sweet (Beauchamp and Cowart, 1985). The cultures in which children live and their early-life experiences enable them to develop a sense of how foods should taste. If the goal is to limit consumption of sweet foods and beverages, children’s preferences for sweetness may not be the only barrier. Sweet-liking may also have its roots in how sweets make children feel. A small amount of a sweet solution placed on the tongue of a crying newborn can blunt expressions of pain and calm both preterm and full-term infants who have been subjected to painful events such as heel stick or circumcision, presumably via the involvement of the endogenous opioid system (Blass and Hoffmeyer, 1991; Barr et al., 1999).

Afferent signals from the mouth, rather than gastric or metabolic changes, appear to be responsible for the analgesic properties of sugars (Barr et al., 1999; Ramenghi et al., 1999; Bucher et al., 2000). The ability of sweets to reduce pain continues during childhood (Miller et al., 1994; Pepino and Mennella, 2005b), and the more children like sucrose, the better it works in increasing pain tolerance during the cold pressor test (Pepino and Mennella, 2005b). Thus, it is important to realize that trying to limit consumption of sweet-tasting foods and beverages may be more difficult for some children or certain ethnic groups (Desor et al., 1975; Bacon et al., 1994; Pepino and Mennella, 2005a) because of individual differences in the inherent hedonic value of sweet tastes and how sweets make a person feel.

16.3.2  Salt taste Children’s avidity for salt is more complex and less well understood than that for sweets. A liking for salt water relative to plain water is not evident at birth (Steiner, 1977; Rosenstein and Oster, 1988). Young infants (2–4 months of age) did not detect and differentiate salt solutions from plain water. Rather, the ability to detect salty tastes appears to develop later; it is in most children around 4–5 months of age that a preference begins to be observed (Beauchamp et al., 1986). Moreover, to a greater extent than that observed for sweet taste, the degree of avidity for salt seems to be affected by individual experiences, beginning in utero (Crystal and Bernstein, 1995, 1998; Stein et al., 2006). For example, severe maternal emesis can have an enduring influence on an offspring’s response to salty tastes (Crystal and Bernstein, 1995; Leshem, 1999). Similarly, several behavioral measures related to salty taste preference have been found to be invers­ely related to birth weight over the first 4 years of life (Stein et al., 2006). Because it

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is generally accepted that excess salt intake can lead to or exacerbate hypertension, we speculate that one mechanism predisposing to high salt intake is the heightened preferences caused by in utero events common to lower birth-weight babies, although the mechanisms underlying this effect of body weight are not known. Like sweet tastes, children prefer substantially higher levels of salt than do adults, and adding salt to many foods can drive consumption (Beauchamp and Moran, 1984; Beauchamp et al., 1994). Factors responsible for this age-related difference are not known. Nevertheless, we do know that salt-liking and preference in infants and young children are regulated to some extent by prior dietary exposure. For example, bottlefed infants exhibit higher salt preferences than do breastfed infants (Beauchamp and Stein, 2008), perhaps due to the greater amounts of sodium in formula relative to breast milk. Other evidence indicates that infants who are fed starchy foods (that likely also contain substantial amounts of salt) early in life have elevated salt preferences compared to infants whose early supplemental feedings do not contain these high-salt foods (Beauchamp and Stein, 2008). The findings relating preference for salty taste with amount of exposure were correlational, and hence do not prove cause and effect. However, studies on adults revealed that the experimental manipulation of salt intake can alter salt-taste perception and preference (Bertino et al., 1982; Beauchamp et al., 1990). When total salt intake is reduced over a substantial period of time, adults prefer lower levels of salt and perceive a given level of salt as being more intense. This taste change, which takes 2 to 3 months, can be rapidly reversed when individuals are returned to their typical dietary salt level (Beauchamp et al., 1990). In conclusion, salty taste preferences begin to be observed at about 4 months of age, and are apparently more plastic than are sweet preferences. Nevertheless, our knowledge of how early exposures impact later preferences and

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intake remains incomplete. It will be important to determine whether early exposure to lowersalt foods can help protect the developing child from excess intake later in life.

16.3.3  Bitter taste A rejection of bitter compounds is common across many phyla, and is thought to reflect the need to avoid consuming toxic compounds. There are, however, many species diff­ erences in sensitivities to bitter compounds and the number of different bitter receptors that are expressed (Go, 2006); these differences are thought to reflect differences in ecological niches and food choices. It is generally assumed that the existence of multiple bitter receptors (there are approximately 25 in humans; Chandrashekar et al., 2000; Mueller et al., 2005) reflects the wide structural variability of bitter compounds, which in turn reflects the evolution of protective compounds by plant species. Plants do not “want” to be eaten, and animals do not “want” to be poisoned. Thus, a strong rejection of bitterness by children is evolutionarily prudent: children may be at particular risk from the ingestion of toxic, bitter compounds. Rejection of bitter tastes is evident early in life, although there seem to be differences based on the bitter compound tested. For example, while human infants respond with highly negative facial expressions to concentrated quinine, significant rejection of urea does not occur until a few weeks after birth (Kajuira et al., 1992). A different developmental timetable for rejecting different bitter compounds may reflect the multiple controls of bitterness sensation that develop at different rates (Margolskee, 2002). Moreover, the 25 different bitter receptors, each likely responsive to one or several structurallyrelated bitter compounds, could be expressed at different times during development. One of the predominant flavor characteristics of the prototypical healthy foods – vegetables – is

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their bitterness. Indeed, many of the apparent health-related benefits of consuming vegetables come precisely from bitter ingredients such as glucosinolates, which at low levels are healthful but at higher levels can be harmful. However, there is a great deal of individual differences in how sensitive people are to specific bitter compounds. The classic example of genetic differences in taste sensitivity is for phenylthiocarbamide (PTC) and the related chemical 6-npropylthiouracil (PROP). Some people can detect these compounds at low concentrations, whereas others need much higher concentrations, or cannot detect them at all (Kim et al., 2003; Bufe et al., 2005; Hayes et al., 2008). The gene TAS2R38, variants which accounts for the majority of this taste polymorphism, codes for one member of the family of taste receptors that respond to bitter stimuli. Recently, it was discovered that variation in this bitter receptor specifically regulates adults’ bitterness perception of cruciferous vegetables known to contain PTC-like glucosinolates (e.g., turnips, broccoli, mustard greens) (Sandell and Breslin, 2006). Children are not only more likely to experience a strong bitter taste from PTC and PROP, but are also more sensitive to it, detecting it at lower concentrations than adults (Blakeslee, 1932; Karam and Freire-Maia, 1967; Anliker et al., 1991; Mennella et al., 2005). This agerelated change in sensitivity for PROP was recently shown to be affected by sequence diversity in the bitter taste receptor TAS2R38 gene. Children who were heterozygous for the common form of this receptor were more sensitive to the bitterness of PROP than were adults with this same form (Mennella et al., 2005). Like sweet and salt preference, the timing of the shift from child-like to adult-like PROP perception occurs during adolescence (Mennella et al., 2010). The age-related change in bitter perception is likely to have a broad impact because of the high allele frequencies of the taster and non-taster haplotypes in the human population.

One effective strategy in reducing the bitterness of certain foods, and thereby increasing their acceptability, is to add salt. This may partly explain the ubiquitous use of salt in cooking evident in many cultures. Sodium salts, particularly sodium chloride (i.e., table salt), impart a desirably salty taste to foods (Kemp and Beauchamp, 1994). One mechanism underlying this increase in palatability may be the suppressing activity of sodium on bitter taste by a mechanism that is still obscure. There are substantial compoundspecific differences in the effectiveness of salt in inhibiting bitterness, presumably reflecting the wide array of bitter compounds and the multiple receptor-transductive pathways for bitterness. Salt also enhances the intensity of sweetness, presumably by blocking bitterness and thereby releasing sweetness from suppression (Breslin and Beauchamp, 1997). Furthermore, like adults (Kroeze and Bartoshuk, 1985; Breslin and Beauchamp, 1995; Keast and Breslin, 2002), the perceived bitterness of some bitter compounds is reduced when such compounds are mixed with sodium salts in children (Mennella et al., 2003). Perhaps a little salt may go a long way in getting children to accept the taste of bitter vegetables. Childhood may represent a time of heightened bitter-sensitivity. As will be discussed in the next section, children’s acceptance of bittertasting foods such as leafy green vegetables can be facilitated with early and repeated exposure (Gerrish and Mennella, 2001; Forestell and Mennella, 2007; Mennella et al., 2008). However, it may be harder to ensure that children who are particularly sensitive to compounds in bitter vegetables are exposed to these, in comparison with bitter-insensitive children. An absence of early exposure to bitterness may, in turn, affect the development of their taste system. In rodents, early taste deprivation remodels the central nervous system (Mangold and Hill, 2007), and experience with bitterness during early life changes bitter taste preferences in adulthood (Harder et al., 1989).

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16.4  Learning about food flavors

16.4  Learning about food flavors The flavor of food is comprised of much more than the basic tastes of sweetness, sourness, bitterness, saltiness and umami or savoriness. The contribution to the overall flavor of the volatile odors of foods, perceived retronasally, is crucial for identifying foods. During the past two decades, a growing body of data has suggested that early experiences with these food volatiles serve as the foundation for lifelong habits. That is, in contrast to taste preferences, preferences for volatile flavor compounds detected by the sense of smell retronasally are generally more highly influenced by experiences, with those occurring early in life being particularly salient (Bartoshuk and Beauchamp, 1994). The sensory environment in which fetuses live, the amniotic sac, changes as a function of the mother’s food choices, since dietary flavors are transmitted and flavor amniotic fluid (Hepper, 1988; Mennella et al., 1995; Schaal et al., 2000). Prenatal experiences with food flavors, which are transmitted from the mother’s diet to the amniotic fluid, lead to greater acceptance and enjoyment of these foods during weaning. This flavor-learning continues when infants are breast-fed, since human milk is composed of volatile flavors which directly reflect the foods, spices and beverages ingested or inhaled (e.g., tobacco) by the mother (Mennella and Beauchamp, 1991, 1993, 1996). In common with other mammals (for review, see Mennella, 2007), early exposure leads to greater liking and acceptance. For example, infants whose mothers ate more fruits and vegetables during pregnancy and lactation were more accepting of these foods during weaning (Mennella et al., 2001; Forestell and Mennella, 2007). That amniotic fluid and breast milk share a commonality in flavor profiles with the foods eaten by the mother suggests that breast milk may “bridge” the experiences with volatile flavors

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in utero to those with solid foods. More­over, the sweetness and textural properties of human milk, such as viscosity and mouth-coating, vary from mother to mother, thus suggesting that breast-feeding, unlike formula feeding, provides the infant with the potential for a rich source of other variations in chemosensory experiences. The types and intensity of flavors experienced in breast milk may be unique for each infant, and serve to identify the culture to which the child is born and raised. In other words, the flavor principles of the child’s culture are experienced prior to their first taste of solid foods. When infants are exposed to a flavor in the amniotic fluid or breast milk and are tested sometime later, the exposed infants accept the flavor more than infants without such experience (Mennella et al., 2001). This pattern makes evolutionary sense, since the foods that a woman eats when she is pregnant and nursing are precisely the ones that her infant should prefer. All else being equal, these are the flavors that are associated with nutritious foods, or at least foods she has access to, and hence the foods to which the infant will have the earliest exposure. In a recent study, it was shown that breast-feeding conferred an advantage when infants first tasted a food, but only if their mothers regularly eat similar tasting foods (Forestell and Mennella, 2007). If their mothers eat fruits and vegetables, breast-fed infants will learn about these dietary choices by experiencing the flavors in their mother’s milk, thus highlighting the importance of a varied diet for both pregnant and lactating women (Forestell and Mennella, 2007). These varied sensory experiences with food flavors may help explain why children who were breastfed were found to be less “picky” (Galloway et al., 2003) and more willing to try new foods (Sullivan and Birch, 1994; Mennella and Beauchamp, 1996), which in turn contributes to greater fruit and vegetable consumption in childhood (Skinner et al., 2002a; Cooke et al., 2004; Nicklaus et al., 2005). Formula

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feeding, quite a new innovation in human infant eating practices, differs from breast milk in that it lacks sensory variety and does not reflect the foods the mother consumes. There is not enough known about how this lack of flavor experience impacts later food choices, but it is reasonable to hypothesize that formula-fed children are at a nutritional disadvantage. Nevertheless, recent research revealed that once infants, regardless if they are breast- or formula-fed, are weaned to solid foods, acceptance can be facilitated by different types of early dietary experience. One type of experience entailed repeated dietary exposure to a particular vegetable or fruit for at least 8–9 days (Sullivan and Birch, 1994; Gerrish and Mennella, 2001; Forestell and Mennella, 2007; Mennella et al., 2008). Like children (Birch and Marlin, 1982), infants ate significantly more of the fruit or vegetable to which they were repeatedly exposed. Merely looking at the food does not appear to be sufficient, since children have to experience the flavor of the food to learn to like it (Birch et al., 1987). Another type of dietary experience does not require actual exposure to the target fruit or vegetable, but rather experience with a variety of flavors. Infants who were repeatedly exposed to a different starchy vegetable each day ate as many carrots after the exposure as did infants who were repeatedly exposed to carrots (Gerrish and Mennella, 2001). Similarly, repeated dietary experience with a variety of fruits enhanced acceptance of a novel fruit, but had no effect on the infants’ acceptance of green vegetables (Mennella et al., 2008). Because rejection of bitter taste is largely innate (Kajuira et al., 1992), infants may need actual experience with bitter taste, more exposures, or a different type of variety experience to enhance acceptance of green vegetables. Additional experimental studies, as well as randomized nutrition interventions that focus on maternal dietary habits and infantile dietary experiences, are needed to better understand how liking for the taste of foods develops (Lucas, 1998).

16.5  Concluding remarks The child’s basic biology, a consequence of a long evolutionary history, does not predispose the child to favor low-sugar, low-sodium and vegetable-rich diets. The sensory and biological considerations reviewed herein shed light on why it is difficult to make lifestyle changes in young children, and why it is difficult for children to eat nutritious foods when these foods do not taste good to them. We cannot easily change the basic ingrained biology of liking sweets and avoiding bitterness. If this is the bad news, the good news arises from our growing knowledge of how, beginning very early in life, sensory experience can shape and modify flavor and food preferences. In other words, what we can do is modulate children’s flavor preferences by providing early exposure, starting in utero, to a wide variety of healthy flavors, and moderating exposure to salt. To this end, the pregnant and nursing mother should widen her food choices to include as many flavorful and healthy foods as possible. Infants of women who do not breastfeed should be exposed repeatedly to a variety of foods, particularly fruits and vegetables, from an early age. Further, mothers should be encouraged to focus on their infants’ willingness to eat the food, and not just the facial expressions made during feeding. They should also be made aware that, with repeated dietary exposure, it may take longer to observe changes in facial expressions than intake (Forestell and Mennella, 2007). Also, infant formula manufacturers should be encouraged to provide lower-salt infant formula that contains flavors of the foods that children will be weaned to (e.g., fruits, vegetables). These experiences, combined with provision of infants and children with nutritious foods and flavor variety as well, should maximize the chance that they will select and enjoy a more healthy diet. Moreover, many of these preferences may last throughout

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REFERENCES

the entire lifespan, and can help overcome the reluctance to consume vegetables. The best predictor of what children eat is whether they like the taste of the food (Resnicow et al., 1998). The reward systems that encourage us to seek out pleasurable sensations and the emotional potency of food- and flavor-related memories initiated early in life together play a role in the strong emotional component of food habits. An appreciation of the complexity of early feeding, and a greater understanding of the cultural and biological mechanisms underlying the development of food preferences, will aid in our development of evidence-based strategies and programs to improve the diets of our children.

Acknowledgments The preparation of this manuscript and much of the research described herein was supported by NIH Grant HD37119 from the National Institutes of Health, USA. We thank Dr Allison Ventura Rubenstein for helpful comments on the manuscript.

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Reed, D. R., & McDaniel, A. H. (2006). The human sweet tooth. BMC Oral Health, 6(Suppl. 1), S17. Resnicow, K., Smith, M., Baranowski, T., Baranowski, J., Vaughan, R., & Davis, M. (1998). 2-year tracking of children’s fruit and vegetable intake. Journal of the American Dietetic Association, 98, 785–789. Rosenstein, D., & Oster, H. (1988). Differential facial responses to four basic tastes in newborns. Child Devevlopment, 59, 1555–1568. Royet, J. P., & Plailly, J. (2004). Lateralization of olfactory processes. Chemical Senses, 29, 731–745. Rozin, P. (1982). “Taste-smell confusions” and the duality of the olfactory sense. Attention, Perception & Psychophysics, 31, 397–401. Sandell, M. A., & Breslin, P. A. (2006). Variability in a tastereceptor gene determines whether we taste toxins in food. Current Biology, 16, R792–R794. Schaal, B., Marlier, L., & Soussignan, R. (2000). Human foetuses learn odours from their pregnant mother’s diet. Chemical Senses, 25, 729–737. Schmidt, M., Affenito, S. G., Striegel-Moore, R., Khoury, P. R., Barton, B., Crawford, P., et al. (2005). Fast-food intake and diet quality in black and white girls: The National Heart, Lung, and Blood Institute Growth and Health Study. Archives of Pediatrics and Adolescent Medicine, 159, 626–631. Siega-Riz, A. M., Carson, T., & Popkin, B. (1998). Three squares or mostly snacks – What do teens really eat? A sociodemographic study of meal patterns. Journal of Adolescent Health, 22, 29–36. Simmen, B., & Hladik, C. M. (1998). Sweet and bitter taste discrimination in primates: Scaling effects across species. Folia Primatologica (Basel), 69, 129–138. Skinner, J. D., Carruth, B. R., Bounds, W., Ziegler, P., & Reidy, K. (2002a). Do food-related experiences in the first 2 years of life predict dietary variety in school-aged children? Journal of Nutrition Education and Behavior, 34, 310–315. Skinner, J. D., Carruth, B. R., Wendy, B., & Ziegler, P. J. (2002b). Children’s food preferences: A longitudinal analysis. Journal of the American Dietetic Association, 102, 1638–1646. Stein, L. J., Cowart, B. J., & Beauchamp, G. K. (2006). Salty taste acceptance by infants and young children is related to birth weight: Longitudinal analysis of infants within the normal birth weight range. European Journal of Clinical Nutrition, 60, 272–279. Steiner, J. E. (1977). Facial expressions of the neonate infant indicating the hedonics of food-related chemical stimuli. In J. M. Weiffenbach (Ed.), Taste and development: The genesis of sweet preference (pp. 173–188). Washington, DC: US Government Printing Office. Sullivan, S. A., & Birch, L. L. (1990). Pass the sugar, pass, the salt: Experience dictates preference. Developmental Psychobiology, 26, 546–551. Sullivan, S. A., & Birch, L. L. (1994). Infant dietary experience and acceptance of solid foods. Pediatrics, 93, 271–277.

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REFERENCES

Van Horn, L., Obarzanek, E., Friedman, L. A., Gernhofer, N., & Barton, B. (2005). Children’s adaptations to a fat-reduced diet: The Dietary Intervention Study in Children (DISC). Pediatrics, 115, 1723–1733. Wardle, J., Cooke, L. J., Gibson, E. L., Sapochnik, M., Sheiham, A., & Lawson, M. (2003a). Increasing children’s acceptance of vegetables; a randomized trial of parent-led exposure. Appetite, 40, 155–162.

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Wardle, J., Herrera, M. L., Cooke, L., & Gibson, E. L. (2003b). Modifying children’s food preferences: The effects of exposure and reward on acceptance of an unfamiliar vegetable. European Journal of Clinical Nutrition, 57, 341–348. Willander, J., & Larsson, M. (2006). Smell your way back to childhood: Autobiographical odor memory. Psychonomic Bulletin, 13, 240–244.

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C H A P T E R

17 Implications of the Glycemic Index in Obesity Julia M.W. Wong1,2, Andrea R. Josse1,2, Livia Augustin3, Nishta Saxena1,2, Laura Chiavaroli1,2, Cyril W.C. Kendall1,2 and David J.A. Jenkins1,2,4 1

Clinical Nutrition & Risk Factor Modification Center, St. Michael’s Hospital, Toronto, Canada 2 Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada 3 Unilever Health Institute, Unilever Research and Development, Vlaardingen, The Netherlands 4 Department of Medicine, Division of Endocrinology and Metabolism, St Michael’s Hospital, Toronto, Canada

o u t l i n e 17.1 Introduction

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17.5 GI and Obesity

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17.2 The Concept of the Glycemic Index

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17.6 GI and Diabetes

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17.3 Mechanisms of Action

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17.7 GI and Cardiovascular Disease

225

17.8 Conclusion

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17.4 Effects of Low GI Foods on Appetite, Food Intake and Satiety 222

17.1  Introduction The growing prevalence of obesity in adults and children is an important public health concern, as these individuals are at greater risk of developing chronic diseases such as coronary

Obesity Prevention: The Role of Brain and Society on Individual Behavior

heart disease (CHD) and diabetes. Nutritional strategies to combat this growing concern have never been more important. The current recommendation of high-carbohydrate diets to help manage weight (Klein et al., 2004) has recently been challenged as the number of people who are classified as overweight (body mass index

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(BMI) 25 kg/m2) or obese (BMI 30 kg/m2) (WHO, 2000) continues to rise. Alternative dietary approaches have emerged which vary in their total calories, macronutrient (carbohydrate, fat, and protein) content, energy density and glycemic index, as well as portion control (Klein et al., 2004). However, at the center of this debate are the metabolic effects of carbohydrates: these diets focus on decreasing the total carbohydrate content and increasing the intake of protein. However, the nature of the carbohydrate may be important – that is, slow-release carbohydrates or low glycemic-index (GI) foods versus fast-release carbohydrates or high GI foods. Evidence suggests that there are metabolic advantages to increasing low GI food consumption, and that their consumption should be advised over that of high GI foods.

foods based on the rate of carbohydrate absorption as determined by its postprandial glycemic response compared to a reference standard (Jenkins et al., 1981, 1984). Thus, the GI differentiates carbohydrate-rich foods that result in a lower postprandial blood glucose rise (i.e., low GI foods) from those that produce a larger postprandial blood glucose rise (i.e., high GI foods). As a result, the GI is considered a specific pro­ perty of the food itself and differs from the term “glycemic response”, which is an individual’s change in blood glucose after ingestion of the food (Wolever, 2006). Many starchy staples of traditional cultures have a lower GI, including pasta, some wholegrain breads, cracked wheat or barley, some rices, dried peas, beans and lentils (Jenkins et al., 1980, 1986; Thorne et al., 1983) (Tables 17.1 and 17.2). In cultures such as the Pima Indians and the

17.2 THE CONCEPT OF THE GLYCEMIC INDEX It was traditionally believed that postprandial blood glucose responses were determined by the carbohydrate chain length, often referred to as simple or complex carbohydrates. Over time, increasing experimental evidence has questioned this classification and given rise to the concept of the GI. It suggests, as an extension to the dietary fiber hypothesis first proposed by Burkitt and Trowell (Burkitt and Trowell, 1977), that certain carbohydrates, by virtue of their rate of digestibility and absorption, may provide a strategy to prevent and manage chronic diseases such as diabetes and CHD (Jenkins and Jenkins, 1995). The GI is defined as the incremental area under the blood glucose response curve (IAUC) elicited by a 50-g available carbohydrate portion of a food, expressed as a percentage of the response after the consumption of 50 g of anhydrous glucose or white bread (Wolever, 2006). In other words, it is a numerical classification of carbohydrate

Table 17.1  Glycemic indices of some traditional and contemporary foods Food

GI*

Traditional foods Pasta (spaghetti)

60

Pumpernickel bread

58

Cracked wheat

68

Pearled barley

36

Parboiled rice

68

Beans

39–55

Lentils

36–42

Chickpeas

39

Contemporary foods White bread

101

White bagels

103

Instant mashed potatoes

122

Glutinous white rice

132

Corn flakes

116

*

GI values are based on white bread as the reference food, which has a glycemic index of 100. Source: Adapted from Foster-Powell et al. (2002).

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17.3 Mechanisms of action

Table 17.2  Classification of foods based on GI values*† Low GI (78 or less)

Medium GI (79–99)

High GI (100 or more)

Grains:

Grains:

Grains:

Barley

Wheat roti

White bread

Pasta/noodles

Brown rice

White rice

Fruits & vegetables:

Fruits & vegetables:

Fruits & vegetables:

Strawberries, raw

Pineapple, raw

Banana, ripe

Orange, raw

Grapes, raw

Watermelon

Peaches, raw and canned in natural juice

Pumpkin, boiled

Potato, baking (Russet)

Carrots, boiled

Potato, new/ white

Rice noodles

Extensive research has led to the compilation of data into comprehensive international GI food tables, which have greatly facilitated research and clinical applications of this concept (Foster-Powell et al., 2002; Atkinson et al., 2008). Furthermore, the concept of glycemic load (GL) has been developed to assess the total glycemic impact of the diet. The GL is the product of the dietary GI and the amount of available dietary carbohydrate in a food or diet (Salmeron et al., 1997a).

17.3  Mechanisms of action It has been suggested that the metabolic effects of low GI foods relate to their rate of absorption in the gut (Figure 17.1). Low GI foods are absorbed at slower rates, which in turn results in a lower rise in postprandial blood

Glucose

Australian Aborigines, the relatively recent shift from traditional low GI foods to high GI foods may partially explain the increasing rates of diabetes among these populations (Thorburn et al., 1987; O’Dea, 1991; Boyce and Swinburn, 1993). The GI of traditional common starch foods may also have been affected by recent changes in food processing and manufacturing that reflect a changing consumer demand (Bjorck et al., 2000).

Sweet potato, boiled Sweet corn Other:

Other:

Other:

Legumes

Popcorn

Rice cakes

Chickpeas

Potato crisps

Soda crackers

(a)

Time

Glucose

Kidney beans Lentils Soya beans Milk, skim & full fat Yogurt *

GI values are based on white bread as the reference food, which has a glycemic index of 100. † Canadian values where available. Conversion: 70/100 to glucose scale Source: Adapted from Foster-Powell et al. (2002) and Atkinson et al. (2008).

(b)

Time

Figure 17.1  Hypothetical effect of feeding diets with a (a) low or (b) high glycemic index on gastrointestinal glucose absorption and postprandial blood glucose. Source: Reproduced from Jenkins et al. (2002), with permission.

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glucose and insulin levels. Many factors may influence the rate of carbohydrate absorption of a food and, therefore, its GI value. These include the rate of digestion (Jenkins et al., 1981; Englyst et al., 1999) and transit time (Englyst et al., 1992), food form (physical form, particle size), type of preparation (cooking method and processing) (Haber et al., 1977; O’Dea et al., 1980; Jenkins et al., 1982a; Sheard et al., 2004), ripeness (Englyst and Cummings, 1986), nature of the starch (predominance of amylose or amylopectin) (Wursch et al., 1986; Sheard et al., 2004), monosaccharide components, presence of antinutrients such as -amylase inhibitors (Isaksson et al., 1982; Yoon et al., 1983), and the amount and type of fiber, fat and protein content (Thorne et al., 1983; Krezowski et al., 1986). The metabolic effect of a reduced rate of absorption has been demonstrated in studies of healthy individuals as well as of people with type 2 diabetes, and when carbohydrates are ingested slowly over a prolonged period of time. For example, when a glucose solution was sipped at an even rate over 180 minutes in comparison to the same amount of glucose taken as a bolus, a marked decrease in insulin secretion and lower serum free fatty acid (FFA) levels were observed (Jenkins et al., 1990). This improvement was also observed after consuming low GI foods. This may be due in part to a sustained tissue insulinization, a suppressed FFA release and the absence of counter-regulatory endocrine responses (Wolever et al., 1988; Jenkins et al., 1990; Ludwig et al., 1999), hence resulting in minimal hormonal fluctuations. Over time, glucose is removed from circulation at a faster rate and blood glucose concentrations return toward baseline despite continued glucose absorption from the gut. This results in an improved postprandial peak and incremental area under the glucose curve. Other studies have demonstrated an improved “second meal” effect, such that an intravenous glucose tolerance test shows a more rapid uptake of glucose (increased KG) after sipping than after the bolus

drink (Jenkins et al., 1990). The improved postprandial glycemia of the second meal may be related to the prolonged suppression of FFA levels (Jenkins et al., 1982b).

17.4  Effects of low GI foods on appetite, food intake and satiety It has been proposed that low GI foods have properties that may make them potentially bene­ ficial for weight control. These include the ability to promote satiety and delay hunger, reduce fluctuations in glycemia and insulinemia, promote higher rates of fatty acid oxidation, and minimize the decline in metabolic rate during energy restriction (McMillan-Price and BrandMiller, 2006). However, the reverse has also been observed in acute studies. High, not low, GI foods have been associated with satiety and reduced food intake (Anderson and Woodend, 2003a). This is observed in studies where subjects are given various preloads and short-term (e.g., 1–2 hours) food intake is measured after consumption of the preload (Holt et al., 1995; Woodend and Anderson, 2001; Anderson et al., 2002). Satiety may be increased in the short term with the rapid increase in blood glucose after the intake of high GI foods, whereas the intake of low GI foods may be more effective in sustaining satiety in the long-term (Anderson and Woodend, 2003b; van Amelsvoort and Weststrate, 1992). Over 50 years ago, the glucostatic theory first suggested a link between blood glucose concentrations and appetite sensations. More specifically, high blood glucose utilization was considered to signal satiety and the termination of feeding, whereas low blood glucose utilization was believed to trigger the onset of feeding (Mayer, 1955). This theory continues to generate interest, as seen by a growing number of studies still exploring this concept. Proponents of the theory agree that meal initiation is dependent on

1.  From Brain to Behavior

17.4  Effects of low GI foods on appetite, food intake and satiety

the transient declines in blood glucose (i.e., patterns in blood glucose) (Campfield and Smith, 2003), whereas opponents support the theory that low GI foods are more satiating because of their lower rate of digestion and absorption from the gut, by modulating the appetite-controlling gut hormones, and not just to postprandial gly­cemia alone (Holt et al., 1992; Jenkins et al., 1982c). Reviews of short-term studies of GI and appetite generally demonstrate that increased satiety, delayed return of hunger or decreased ad libi­ tum food intake after the consumption of low compared with high GI foods, as measured by visual analog scales or subsequent meal intakes (Ludwig, 2000; Roberts, 2000; Ebbeling and Ludwig, 2001). However, others have found no consistent association between GI, appetite and food intake (Raben, 2002), and it has also been reported that, acutely, high GI foods are more satiating (Anderson and Woodend, 2003a). It is worth noting that a number of these studies did not completely control for differences in the test diets; differences in variables such as energy density, macronutrient content or palatability may or may not have affected the results (Roberts, 2000). In another study, the effect of high, medium or low GI breakfast meals on subsequent ad libi­ tum meal intake was investigated in obese teenage boys (Ludwig et al., 1999). It was observed that voluntary energy intake was significantly reduced by 53 percent and 81 percent after the medium and low GI meals respectively, compared to the high GI meal. These results suggested that low GI meals had a greater effect on satiety and subsequent food intake compared to an isocaloric high GI meal. High GI foods tend to increase the rate of carbohydrate absorption, cause large blood glucose and hormonal (insulin/glucagon) fluctuations and, together with reduced satiety, promote excess food intake over time (Haber et al., 1977; Ludwig et al., 1999). Many studies looking at appetite, satiety and food intake were conducted in the short term, and may not be indicative of what might occur in the long term. Further studies will need to be

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conducted to determine whether these effects are observed in the long term. The gastrointestinal tract releases a number of regulatory peptide hormones that influence certain physiological processes, including gut motility and short-term feelings of hunger and satiety. These gut hormones include ghrelin, peptide YY, cholecystokinin (CCK), pancreatic polypeptide, amylin, glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1 (GLP-1), oxyntomodulin, gastrin and secretin (Murphy and Bloom, 2006). Two specific hormones, GIP and GLP-1, have important effects on insulin action (Drucker, 2007), which may be relevant to the inclusion of low GI foods for the treatment of hyperinsulinemic conditions (e.g. metabolic syndrome, first stages of type 2 diabetes) and in diabetes prevention. There is some evidence indicating that slowly absorbed carbohydrates induce a lower acute response in GIP and GLP-1 (Jenkins et al., 1982b, 1990; Juntunen et al., 2002), but this does not appear to affect pancreatic polypeptide responses (Jenkins et al., 1990). The GI of a meal has also been found to be inversely associated with the perception of satiety and CCK levels, a hormone involved in appetite suppression (Holt et al., 1992). Furthermore, the addition of viscous fiber in the form of beans (Bourdon et al., 2001) or barley (Bourdon et al., 1999) has been shown to increase postprandial CCK responses. This suggests a possible role for the gastric volume and the bulkiness of food in the maintenance of appetite suppression. In clinical trials, dietary adherence is often an important consideration when relating findings to everyday practices. There are many factors that affect dietary adherence, including taste – also known as palatability (Lloyd et al., 1995; Glanz et al., 1998; Brekke et al., 2004). Many studies assessing the palatability of low GI diets in comparison to high GI diets have shown that they are equally acceptable (Jimenez-Cruz et al., 2003, 2004; Ebbeling et al., 2007; Jenkins et al., 2008; Wolever et al., 2008). Furthermore, not only is there a lack of studies demonstrating poor acceptability

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17. glycemic index and obesity

of low GI diets, but some studies also show they are the preferred diet choice (Gilbertson et al., 2001; Barnard et al., 2009). For example, the study by Gilbertson and colleagues (2001) looked at glycemic control in children with type 1 diabetes given dietary advice on following either a low GI diet or a carbohydrate-exchange diet. A clear preference for the low GI dietary regimen by both the children and their parents who experienced the two types of dietary advice was demonstrated by quality of life questionnaires, and was the choice of diet continued after completion of the study (Gilbertson et al., 2001).

17.5  GI and obesity Low GI diets may play a potential role in body-weight regulation in that the type of carbo­ hydrate may be more important than the total amount. This is supported by a number of popu­ lation studies. In a seasonal variation of blood cholesterol study of 572 individuals, the GI was associated with a higher body mass index, thereby suggesting that the type of carbohydrate is important in determining its effect on body weight (Ma et al., 2005). Similarly, the EURODIAB Complications Study of over 3000 individuals with type 1 diabetes found that a lower GI diet was associated with lower levels of waist-to-hip ratio and waist circumference (Toeller et al., 2001). Several studies have looked at the effects of low GI weight-loss diets on body weight or composition, compared to a high GI diet. Slabber and colleagues (1994) compared two energy-restricted diets of either high or low GI in healthy obese females for 12 weeks in a parallel study (n  30), followed by some subjects crossing over to the alternate treatment for another 12 weeks (n  16) after a washout period. Both diets resulted in a significant reduction in weight after the parallel study (9.3 kg low GI vs 7.4 kg high GI), but after the crossover study, the low GI diet resulted in a greater reduction in body weight than did the high GI diet (7.4 kg v. 4.5 kg respectively, P  0.04)

(Slabber et al., 1994). Bouché and colleagues (2002) looked at 11 healthy men who were randomized into a 5-week low or high GI diet in a crossover design. Body weight remained comparable between the two diets after the intervention periods. However, the low GI diet resulted in a greater reduction in body fat mass (700 g reduction) and an increased lean body mass as measured by dual-energy X-ray absorptiometry (DEXA). The reduction in body fat mass was mainly attributable to a decline in trunk fat (Bouché et al., 2002). Similarly, a study of 14 obese adolescents who received an energy-restricted low GI and low GL diet for 6 months followed by a 6-month follow-up demonstrated a significant reduction in both body weight (at 12 months) and fat mass (at 6 and 12 months), as measured by DEXA, as compared to the energy-restricted lowfat diet group (Ebbeling et al., 2003). Despite these positive findings of the effects of low GI diets on body weight and composition, some studies have shown no benefit (Frost et al., 2004; Sloth et al., 2004; Ebbeling et al., 2005, 2007). At this time, there is no consensus as to the effect of low GI diets on body weight or composition. However, low GI diets may still reduce risk factors for CHD and diabetes, which are often present in those who are overweight or obese (Grundy et al., 2004). This issue needs to be addressed in long-term studies with large sample sizes and well-controlled dietary interventions where only the GI differs. Care should be taken to ensure that the intervention diets are matched in palatability, energy density, fiber content and macronutrient composition (Sloth and Astrup, 2006).

17.6  GI and diabetes Several studies have looked at dietary GI in relation to the risk and management of type 2 diabetes. Large prospective cohort studies investigating the association between GI and the risk of type 2 diabetes have found a positive relation,

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17.7  GI and cardiovascular disease

where higher dietary GI resulted in increased diabetes risk (Salmeron et al., 1997a, 1997b; Hodge et al., 2004; Schulze et al., 2004). However, this was not observed in the Iowa Women’s Health Study and the Atherosclerosis Risk in Communities (ARIC) Study (Meyer et al., 2000; Stevens et al., 2002). The first found no association between GI and GL with type 2 diabetes; this is possibly because of the inclusion of an elderly cohort, which could have introduced a selection bias (Meyer et al., 2000). The ARIC Study also observed no association; this may relate to the dietary assessment tool used, which was not specifically designed to assess GI (Stevens et al., 2002). Two recent meta-analyses summarizing the effects of low GI diets on risk factors for diabetes and CHD demonstrated a significant reduction in fructosamine and hemoglobin A1c (HbA1c) in those receiving low GI diets (Kelly et al., 2004; Opperman et al., 2004), but no significant changes in blood glucose or insulin (Kelly et al., 2004). One meta-analysis of 14 randomized controlled trials comparing low GI diets to conventional or high GI diets and glycemic control in individuals with diabetes found that the low GI diets were able to reduce glycated proteins by 7.4 percent and HbA1c by 0.43 percent compared to high GI diets (Brand-Miller et al., 2003). The studies included in this meta-analysis were either of a randomized crossover or a parallel design, of 12 days to 12 months in duration (mean: 10 weeks), and comprised a total of 356 subjects. Subsequent studies have been consistent with the results of this meta-analysis (Jimenez-Cruz et al., 2003; Rizkalla et al., 2004), though there is an indication that larger and longer low GI studies have not found the benefit in glycosylated protein (Wolever et al., 2008). In addition to the positive effects of low GI foods on the treatment of diabetes, drug therapies that reduce the rate of glucose absorption have also been shown to be effective in the control of diabetes and its complications. Use of acarbose, an -glucosidase enzyme inhibitor which converts the diet into a low GI diet, at a dosage

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of 100 mg three times daily in subjects with type 2 diabetes in the UK Prospective Diabetes Study (UKPDS), resulted in significantly lower HbA1c compared to placebo at 3 years (Holman et al., 1999). This improvement in glycemic control was comparable to that achieved by monotherapy with a sulfonylurea, metformin or insulin. In the STOP-NIDDM trial, subjects with impaired glucose tolerance were randomized to receive either 100 mg of acarbose three times daily or a placebo (Chiasson et al., 2002). For those on acarbose, there was a 25 percent reduction in the risk of progression of diabetes and a significant increase in reversion of impaired glucose tolerance to normal glucose tolerance.

17.7  GI and cardiovascular disease Epidemiological and clinical studies assessing the role of GI on the development of cardiovascular disease (CVD) have shown that low GI diets are associated with reduced CVD risk, possibly suggesting a protective role. The Nurses’ Health Study of over 75,000 women demonstrated a direct positive relation between fatal and non-fatal myocardial infarction, and GI and GL. The association of dietary GI and GL with CHD risk was more prominent in those with a BMI  23 kg/m2, suggesting that dietary GI may be more important in those with a greater BMI who may also have a greater degree of insulin resistance (Liu et al., 2000). Similarly, a high carbohydrate intake or, more specifically, a high GI diet tended to be positively associated with atherosclerotic progression in postmenopausal women (Mozaffarian et al., 2004). However, the Zutphen Study of older men (van Dam et al., 2000) observed no significant association of GI or GL with CHD, possibly due to the smaller sample size and the age of the cohort at baseline. Drug therapies that reduce the rate of glucose absorption have also been shown to be effective in reducing the risk of CVD.

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The STOP-NIDDM trial demonstrated that de­creasing postprandial hyperglycemia with acarbose was associated with a 49 percent relative risk reduction in the development of cardiovascular events and a 2.5 percent absolute risk reduction in subjects with impaired glucose tolerance. Furthermore, acarbose was associated with a 34 percent relative risk reduction in new cases of hypertension and a 5.3 percent absolute risk reduction (Chiasson et al., 2003). Numerous studies have explored the effect of low GI diets on CHD risk factors. Epidemiologi­ cal studies have shown that a low GI dietary pattern is associated with lower serum triglycerides and/or higher serum HDL cholesterol levels, suggesting that low GI diets may help preserve HDL-C (Frost et al., 1999; Ford and Liu, 2001; Liu et al., 2001; Slyper et al., 2005). Furthermore, in the Women’s Health Study, GI was positively associated with C-reactive protein (CRP) (Liu et al., 2002). The effects of low GI diets in clinical trials on major risk factors for CVD have been summarized in recent meta-analyses (Kelly et al., 2004; Opperman et al., 2004); 15 or 16 clinical trials were included in each analysis, and these trials varied in terms of subjects’ disease classification (healthy, with CHD, or with type 1 or 2 diabetes). It was found that low GI diets resulted in no change in HDL-C, triglycerides and LDL-C compared to high GI diets. However, improvements in total cholesterol were observed (Kelly et al., 2004; Opperman et al., 2004), with greater reductions in those with a higher baseline level (Opperman et al., 2004). Interestingly, the observed improvement in HDL-C in epidemiological studies is not consistent with the clinical trials. Nonetheless, despite the appearance of only a weak effect of low GI diets on CHD risk factors, it was concluded that the studies conducted to date were short term, of poor quality, and small in sample size. Therefore, there is a need for more well-designed randomized controlled trials of adequate power and duration to assess the effect of low GI diets on CHD (Kelly et al., 2004). Other clinical trials have

started to investigate new and emerging risk factors for CHD. Plasminogen activator inhibitor-1 (PAI-1) levels were reduced on a low GI diet in subjects with type 2 diabetes (Jarvi et al., 1999; Rizkalla et al., 2004). A low GL diet compared to a low-fat diet during weight loss found marked improvements in heart disease risk factors such as insulin resistance, TG levels, CRP and blood pressure while on the low-GL diet (Pereira et al., 2004).

17.8  Conclusion The habitual consumption of low GI foods in the context of a high-carbohydrate diet may help to reduce the risk of obesity, type 2 diabetes and heart disease. Drastic dietary changes may result in short-term health benefits, but long-term compliance is often an issue. Despite continuing controversy, the concept of the GI may still have great clinical implications if it can be easily incorporated into dietary and lifestyle modification strategies to help in the selection of better quality starchy foods. Moreover, if lower GI foods were to contribute to greater satiation, reduced postprandial glycemia and/or insulinemia, bodyweight reduction or change in composition, these attributes may help to reduce the risk of CHD and diabetes. More long-term efficacy and effectiveness studies are required to better determine the potential health benefits of low GI diets.

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C H A P T E R

18 Characterizing the Homeostatic and Hedonic Markers of the Susceptible Phenotype

1

John Blundell1, Eleanor Bryant2, Clare Lawton1, Jason Halford3, Erik Naslund4, Graham Finlayson1 and Neil King5 Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK 2 Centre for Psychology Studies, University of Bradford, Bradford, UK 3 Psychology Department, University of Liverpool, Liverpool, UK 4 Clinical Sciences, Danderyd Hospital, Karolinska Istitutet, Stockholm, Sweden 5 Institute of Health and Innovation, Queensland University of Technology, Brisbane, Australia

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18.2 Susceptible and Resistant Phenotypes 232 18.3 What Would a Susceptible Phenotype Look Like? 18.4 What Level of Analysis is Appropriate? 18.5 Appetite is Not Rocket Science – It is More Complicated

Obesity Prevention: The Role of Brain and Society on Individual Behavior

233 233 234

18.6 Diversity, Susceptibility and Homeostasis

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18.7 Hedonics: The Importance of Liking and Wanting

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18.8 Comparing Susceptible and Resistant Phenotypes

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18.9 Resistance to Weight Loss – The Other Side of Susceptibility

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18.10 Conclusions

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18.  Characterizing the Susceptible Phenotype

18.1  The approach Recently, after 50 years of concentrated research on the mechanisms underlying energy homeostasis, there has developed increased interest in the potency of non-homeostatic influences on appetite control and body-weight regulation. This is often expressed as the relationship between hedonic and homeostatic processes (see, for example, Saper et al., 2002; Berthoud, 2004, 2006; Blundell and Finlayson, 2004). To express the force of non-regulatory eating, the term “hedonic hunger” has been proposed (Lowe and Butryn, 2007). These conceptualizations have shifted the understanding of poor weight control towards the idea that hedonic processes overcome homeostatic regulation. Indeed, for some time it has been recognized that the homeostatic system operates asymmetrically, easily permitting overconsumption but more strongly defending against under-eating and weight decrease. The system therefore appears to be “permissive” of weight gain, and the term “passive obesity” has been used in a recent influential report to convey this idea (Foresight Report, 2007). This, in turn, reflects the concept of “passive overeating” (Blundell et al., 1996). The first stage in understanding susceptibility and resistance is to decide what questions to ask. Inevitably, susceptibility and ­ resistance will not be uni-dimensional constructs that apply universally and can be categorically defined. Different clusters of susceptibility ­ (individuals sharing patterns of physiology and behavior) can be envisaged (and demonstrated). Therefore, susceptibility will exist in several forms, or subtypes. These subtypes – or phenotypes – can  be an appropriate target for research. They define a construct between the truly universal or nomothetic approach, and the truly individual or idiographic approach (Allport, 1937). Different susceptible phenotypes can exist in

parallel, and “obesogenic” environments exploit this ­ susceptibility. This chapter will describe an approach to studying susceptibility to weight gain (and its partner construct, resistance to weight loss).

18.2  Susceptible and resistant phenotypes An obesogenic environment clearly encourages weight gain and obesity. However, not all people living in an obesigenic culture become obese: some remain of normal weight, or lean. Considerable individual differences exist in the capacity of people to succumb to weight gain or to resist it. This implies the existence of a spectrum of proneness or vulnerability within a population (for model, see Ravussin and Kozak, 2004). Along this spectrum it is possible to identify clusters of individuals who are susceptible and clusters who are resistant; we have called these contrasting groups “phenotypes” because they can be defined according to particular markers. Characterizing the ways in which these phenotypes differ can shed light on the particular biological and behavioral features, and their responsiveness to the environment, that encourage weight gain. Obesity is a heterogeneous entity. There is a need to differentiate between groups of obese individuals by assessing what risk factors predispose them to becoming obese and, in some cases, what characteristics prevent them from losing weight. Could the identification of a susceptible phenotype help in the prevention of obesity? The first stage concerns how to detect a suscep­ tible phenotype. By definition, a susceptible ­person is gaining weight or has already become obese. It is more difficult to detect a person in the process of weight gain than it is to identify, for instance, someone who has already attained a BMI of 35. However, the stratification of BMI

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18.4  What level of analysis is appropriate

that is ­ commonly recognized (normal, 20–24.9; overweight, 25–29.9; obese, 30; super obese, 40) should not be regarded as definitive. This handy classification, now a rule of thumb, is based solely on the accumulation of risk factors and morbidity; it has no grounding in etiology, and nothing to say about causation. Therefore, a susceptible phenotype can exist at any BMI, and reflects the capacity of a person to persistently gain weight even after accumulating a significant amount of fat. However, the easiest first step in characterizing susceptibility would be to study someone with a high BMI. Such identifying features could, though, be a consequence of obesity as well as a cause. Therefore, longitudinal studies of weight-gaining individuals are necessary. Studies already carried out have identified predictors (moderators) of weight gain (see, for example, Hays et al., 2002; Dykes et al., 2004). In order to be of use in the prevention of obesity, it is necessary to identify markers of susceptibility in a lean or normal-weight person; at this stage, a strategy to oppose or offset the susceptibility features could be initiated. Such markers can be identified.

18.3  What would a susceptible phenotype look like? The idea of susceptibility implies a set of biobehavioral processes that favor the achievement of a positive energy balance. In simple terms, this means the promotion of overconsumption together with a sedentary lifestyle, which conjointly would lead to an increased accretion of energy. Although these features often coexist, the weight of evidence suggests that increased energy intake is more pernicious and of greater potency. Therefore, the major features of a susceptible phenotype relate to an excessive food intake

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in an obesogenic environment. The phenotypic expression of this feeding could take the form of extended eating periods (defective satiation leading to large meals), frequent initiation or rapid re-initiation of eating episodes (weak postprandial satiety), binge-eating episodes (features of weak satiation and satiety), heightened sensitivity to the pleasurable aspects of eating, tendency to seek high-energy dense foods, etc. A susceptible phenotype may not show all of these features. Just as there are multiple routes to weight gain and obesity, there will be subgroups of susceptible phenotypes (Blundell and Cooling, 2000). The common element among susceptible individuals is the expression of a poorly restrained willingness to eat.

18.4  What level of analysis is appropriate? It should be clear that the ideology of psychobiology contains the belief that susceptibility incorporates a genetic component. Therefore, the susceptible phenotype will be associated with specific polymorphic markers of particular genes related to weight gain or obesity itself (see Bouchard, 2008). At one level, therefore, there will be a genetic analysis of susceptibility. This genetic “explanation”, however, may be distant from the observed expression of susceptibility that can be studied in research units or be managed in clinical or public health settings. Psychobiology implies a two-way bridge between physiology and the environment, with the bridge reflecting behavior itself. Therefore, one way of defining susceptibility (and, by implication, resistance) is through the architecture of behavior and the proximal processes that influence this behavior. This approach gives susceptibility a form that is an accessible target for physiological (pharmacological), behavioral and public health approaches to dealing with the obesity epidemic.

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18.5  Appetite is not rocket science – it is more complicated

sweet foods. There are two reasons why this form of overconsumption is difficult to manage. The first is the likelihood that the selection of foods with these properties (fattiness and sweetness) connotes a biologically useful, It is often remarked that appetite control energy-yielding capacity which humans would is not rocket science; indeed, it is much more be genetically predisposed to find attractive. complicated than that. The reason for believ-  The second is the unwillingness of humans ing this apparent absurdity concerns predict- to relinquish a potent source of pleasure. The ability. Whereas physical science embodies  importance of such food choices in susceptible sufficient predictability to enable a rocket to be people is therefore both subconscious (medisent to a distant planet, most of us cannot pre- ated via implicit predispositions) and condict what we are going to eat for the next meal scious (perceived loss of rewarding stimulus) (or how much). In an obesogenic environment, (Finlayson et al., 2008). An important demonthe act of eating is unpredictable because the stration (by means of visual evoked potenenvironment contains a huge range of possibili- tials) has been that the brain has the capacity ties creating a tapestry of eating opportunities, to detect and recognize the fat content of foods requiring choice. This is part of the legacy of within 150–200 ms of exposure to visual images humans being omnivores. For omnivores, food of foods (Toepel et al., 2008). choice is not an option, it is an obligation – and it extends the range of edible foods beyond the limits of optimal nutrition. 18.6  Diversity, susceptibility Coupled with this lack of predictability and homeostasis is the tremendous diversity in the expressed forms of eating behavior. This is apparent when Homeostasis is an inherent property of a comparing dietary profiles and patterns of eating among different geographical and cultural biologically regulated system. One of the rearegions, yet there is also equal diversity within sons why humans (as omnivores) are successethnic or social groups. Consequently, the eat- ful is because whatever the profile of foods ing behavior of humans is characterized by consumed (from the huge range available), the huge individual variability; there is no univer- biological system can adapt. Therefore, greatly sal, normal pattern, nor is there any unique divergent patterns of eating are biologically pathological pattern. An attribute of eating viable – the physiological and biochemical that can be predicted with some certainty is processes operate to maintain the system. This that it will be enjoyable. Although there are means that behavioral adaptation (to dietary exceptions to this rule, food is a common and possibilities) is not always necessary. Thus, potent source of pleasure. This is made possible behavioral regulation of food choice, although because of the links between sensory receptors feasible, is not an imperative. However, behav(mainly sight, taste and smell) and the neu- ioral regulation of internal states is clearly an ral pathways mediating liking and wanting. adaptive strategy that serves a homeostatic purIndependent of the dietary profile and the top- pose, since behavior can be initiated and termiographical pattern, eating normally generates a nated to optimize biological requirements. In hedonic response that can be extremely potent. the control of ­appetite, this motivated behavior One feature of the susceptible phenotype is the takes the form of an increase in drive (hunger) high hedonic response, especially to fatty and in response to signals of need (low glycogen ­ 

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18.7  Hedonics: the importance of liking and wanting

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levels or an empty stomach), and a positive 18.7  Hedonics: the inhibition of eating (satiation) and the mainimportance of liking tenance of inhibition (satiety) in response to and wanting signals of repletion. In a closed or tightly controlled situation the operation of this biobehavJust as homeostatic processes can be analyzed ioral system can be demonstrated. However, in some situations (see, for example, Levitsky,   to reveal components such as orexigenic drive 2005), eating appears to be unregulated by (hunger), satiation and satiety, hedonic pro-  internal signals and dominated by environmen- cesses also have a structure that can be dissected.  tal stimuli (such as portion size). Data of this The fundamental processes of “liking versus type are often used to argue that the biological wanting” inform current theory and research regulation of eating is irrelevant to the obesity (Berridge, 1996). Liking and wanting have the epidemic. There are many examples of eating logical status of hypothetical constructs that being initiated in the absence of a drive (hun- mediate between a neuropsychological process ger), and of eating persisting in the presence of and a directive behavior. These processes can be inhibitory satiety signals. Therefore, although investigated in humans (Finlayson et al., 2007a), the homeostatic system displays regulatory and have a dominant role to play in food preferproperties, these mediating processes can be ences. Consequently, they also play a key role in readily over-ridden. Susceptibility can involve a susceptibility to overeating. Many people would assume that liking and pattern of eating that operates with weak influwanting are identical phenomena, both of which ence of homeostatic regulation. It has been authoritatively stated that “a well- signify a positive attraction to food. In behavioknown response in nutrition research and prac- ral terms, we assume that a change in liking will tice is the dramatic variability in inter-individual lead to proportional adjustments in wanting response to any type of dietary intervention” and, likewise, differences in wanting will pre(Ordovas, 2008: S40). The difference between dict changes in liking. This would be the natural susceptible and resistant individuals reflects the view of a layperson. However, there are strong spectrum of this inter-individual responsive- grounds for recognizing that liking and wanting ness. In research, it is clearly possible to work can be clearly dissociated, and constitute diswith the variance itself. Sectioning the variance tinct identities. This means that they have much and working with subunits (phenotypes) is a greater resolving potential for understanding manageable and transparent approach (Blundell the role of hedonics on eating and, therefore, on overconsumption. Thus, the importance of likand Cooling, 1999). Therefore, on theoretical grounds, suscep- ing versus wanting reflects the functional sigtibility to weight gain is likely to involve weak nificance of these two distinguishable processes, homeostatic regulation (that would permit a operating within the hedonic domain, for overready initiation of eating and a weak inhibition) consumption and weight regulation in humans. A reasonable proposal is that wanting rather  and a potent hedonic influence (strong attraction to energy-dense foods and a dispropor- than liking may be the crucial process in maintionately strong liking and wanting for specific taining an obese state. For this to be confirmed,  foods). These attributes would be expressed it is necessary that wanting and liking can through enduring traits (reflecting biologically be dissociated. This is clearly shown in the based predispositions) and through episodically parallel field of research on chronic drug oscillating states (such as hunger sensations) abusers which shows that repeated drug-  taking behavior and strong motivation to obtain (see, for example, Blundell et al., 2008).

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a “fix” (wanting) can occur in the absence of any pleasant sensations (liking) during ingestion (Lamb et al., 1991). Moreover, food-liking is often a rather stable characteristic within an individual, and appears relatively uninfluenced by increasing weight status (Cox et al., 1999). The implication is that liking may be important in establishing the motivational properties of food, but once these are formed it is the up-regulation of wanting in an obesogenic environment – insensitiv- Figure 18.1  Frequency distribution of BMI for highity to homoeostatic signals but over-reactivity to fat consumers, defined by the absolute amount of fat conexternal cues – that promotes overconsump- sumed after the database had been cleaned by the exclusion tion by influencing what, and how much, is of implausible reports. The distribution for low-fat consumers also shows wide variation in BMI, but only one person eaten from moment to moment (Finlayson et al.,   reached a BMI of 30. 2007b). Source: Adapted from Macdiarmid et al. (1996). This situation in humans is a parallel of the phenomenon seen in rats habitually exposed to a potentially weight-inducing, high-fat diet (Levin et al., 1989). Susceptible individuals show a clusVariability can begin with preferences for, ter of characteristics when appetite regulation is and selection of, particular foods in the habit- challenged. There is a relatively weak suppresual diet. In an environment that contains a sur- sion of hunger in response to consumed high-fat feit of all types of foods, people “choose” to eat foods, suggesting weak, fat-induced satiety sigquite diverse ranges of foods within a single naling (Blundell et al., 2005). This may involve culture (of course, there are major intercultural CCK, PYY or some other gut peptide. A weak differences). For example, high-fat and low- satiation response leads to larger meals. These fat phenotypes have been identified (Cooling factors suggest variable strength (impairment) and Blundell, 2000). Habitual low-fat diets of homeostatic signaling systems. Other studappear to confer a resistance to weight gain ies indicate a weak compensation to high-fat (Macdiarmid et al., 1996), a characteristic also loads related to insulin resistance (Speechly and shown by successful weight-losers (Klem et al., Buffenstein, 2000) and poor com­pensation to 1997), although one must note that a low-fat, enforced overconsumption (Cornier et al., 2004). Evidence also points to a differential responhigh-carbohydrate diet may not be beneficial for all (particularly for some obese, highly sed- sivity in the hedonic processes influencing eatentary people). However, clear variability can ing (Blundell and Finlayson, 2004). There is be demonstrated in the response to a high-fat a preference for high energy-dense over low diet. Although a high fat intake is a potent risk ­  energy-dense foods (Westerterp-Plantenga et al., factor for weight gain, the relationship between 1998), and an increased wanting for high-fat the preference for high-fat foods and weight foods under postprandial satiation conditions gain is not a biological inevitability (Blundell (Le Noury et al., 2004). Long-standing evidence and Macdiarmid, 1997). Some people habitually points to a link between adiposity and sensory consuming a high-fat diet are obese (susceptible) preference for fat (Mela and Sacchetti, 1991). Susceptible high-fat phenotypes also report whilst others are lean (resistant) (Figure 18.1).

18.8  Comparing susceptible and resistant phenotypes

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18.9  Resistance to weight loss – the other side of susceptibility

more dramatic hedonic responsivity to foods than do lean people (Blundell et al. 2005). A high binge-eating score on the Binge Eating Scale (BES) (Gormally et al., 1982) is also a feature of susceptibility and, even in normal-weight women, is associated with an increased liking for all foods and an increased wanting for sweet, high-fat foods (Blundell and Finlayson, 2008). Susceptible and resistant individuals also differ in the strength of certain traits measured by psychometric tests. Men defined as susceptible on a habitual high-fat diet score much higher on traits of Disinhibition and Hunger (but not Restraint) on the Three Factor Eating Questionnaire (TFEQ) than do resistant (same age) men. Such individuals can be regarded as opportunistic eaters who are often in a state of high readiness to eat, and also are likely to be easily provoked into eating by environmental triggers (for a review, see Bryant et al., 2007). The trait of Disinhibition is also associated with weight gain or obesity in large-scale surveys (Hays et al., 2002; Dykes et al., 2004) and smaller intervention studies (Lawson et al., 1995). There is, moreover, presumptive evidence that this trait has a genetic basis (Steinle et al., 2002; Bouchard et al., 2004) and may be linked to the GAD-2 gene. Other evidence suggests that the Disinhibition trait is associated with fasting levels of leptin and adiponectin which may influence the tonic control of appetite (Blundell et al., 2008). Consequently, individuals susceptible to weight gain appear to display a portfolio of risk factors which, acting together, make such people extremely vulnerable in the obesogenic environment.

18.9  Resistance to weight loss – the other side of susceptibility It may be claimed that the rate of increase in the prevalence of obesity is driven by three intrinsic features: the susceptibility of people to

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gain weight, the failure of people to maintain weight loss, and the resistance to lose weight. Therefore, the natural increase in weight gain combined with the failure (of those already obese) to lose or maintain weight will contribute to the overall increase in obesity. The resistance to lose weight is different from the failure to maintain lost weight, which reflects a susceptibility to weight (re)gain. When groups of people are subjected to weight-loss regimes, the physiological system generates automatic compensatory metabolic processes to adjust for the energy deficit (Rosenbaum et al., 2005). The system can also make behavioral adjustments by up-regulating the orexigenic drive and increasing energy intake (Heini et al., 1998). This can be demonstrated very clearly by the response to imposed and supervised exercise regimes (King et al., 2008). In a scientifically controlled study, obese people participating in a fully supervised 12-week program of exercise showed an average weight loss of 3.3 kg. However, the most remarkable effect was the diversity of individual responses, which ranged from a loss of 14 kg to a weight gain of 2 kg, despite individuals achieving similar levels of exercise-induced energy expenditure (Figure 18.2). This type of diversity in response to imposed exercise was noted many years ago (see, for example, Bouchard, 1994; Bouchard and Rankinen, 2001) but apparently overlooked by  most researchers. It follows that any interpretation based on the average weight-loss response would obliterate the true response of the individuals doing the exercise. “This kind of variation is an example of normal biological  diversity … and is beyond measurement error and day-to-day variation” (Rankinen and Bouchard, 2008: S47). It reflects the degree of individual variation in physiologically and behavioral adaptive processes. In the investigation by King and colleagues (2008), the design of the study permitted the source of variability in the compensatory response to be identified and measured. In those individuals who were

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Figure 18.2  Individual changes in body weight and body fat at the end of the mandatory exercise program. BW, body weight; FM, fat mass. Source: Adapted from King et al. (2008).

“resistant” to the theoretical weight loss, the exercise increased the orexigenic drive, reflected in a persistently high hunger, accompanied by an increased food intake, a preference for high energy-dense fatty foods and a relative aversion for low energy-dense foods (fruit and vegetables) (Caudwell et al., 2009). In turn, these food preferences represent an altered hedonic response to exercise (Finlayson et al., 2009). The characteristics of this “resistant” phenotype are still under investigation, but clearly illustrate the diversity of the human psychobiological response. Inter-individual variability is a dominant feature of both nutritional and exercisebased interventions.

is readily apparent. Investigation of the spectrum reveals clusters of individuals who can be termed susceptible phenotypes, and clusters that are resistant. Scientific comparison  between these contrasting phenotypes is a legitimate and powerful approach that can throw light on the way in which bio-social processes influence individual behavior. The susceptible phenotype is a suitable target for scientific study and for management of clinical and public health programs, and early identification of a susceptible phenotype in children (see, for example, Carnell and Wardle, 2008) would be very valuable.

References 18.10  Conclusions The heterogeneity of the human response to interventions that impact on energy balance and weight regulation is a demonstrable fact. The existence of a spectrum of susceptibility

Allport, G. W. (1937). Personality: A psychological interpretation. New York, NY: Holt Rinehart & Winston. Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience and Biobehavioral Reviews, 20, 1–25. Berthoud, H. R. (2004). Neural control of appetite: Crosstalk between homeostatic and non-homeostatic systems. Appetite, 43, 315–317.

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C H A P T E R

19 The Carnivore Connection: Crosspopulation Differences in the Prevalence of Genes Producing Insulin Resistance Stephen Colagiuri1, Scott Dickinson1 and Jennie Brand-Miller2 1

Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, and School of Molecular and Microbial Biosciences, University of Sydney, NSW, Australia

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19.2 The Evolution of Insulin Resistance

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19.3 Determinants of Insulin Resistance 19.3.1 Physiological Determinants 19.3.2 Pathological Determinants

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19.1  Background Although insulin has a number of metabolic effects, insulin resistance is usually defined as a state in which physiological levels of insulin have a decreased biological action on plasma glucose. Glucose uptake by skeletal muscle and adipose tissue, and suppression of hepatic glucose production, are affected. To maintain normoglycemia in the insulin-resistant state,

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19.3.3 Genetic Determinants

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excessive compensatory increases in insulin are required which may eventually lead to a decline in and exhaustion of insulin-producing pancreatic beta cells, the development of glucose intolerance, and, finally, type 2 diabetes (Polonsky, 1999). Insulin resistance is associated with a constellation of traits other than glucose intolerance, including visceral obesity, dyslipid­ emia, hypertension, and a prothrombotic state (Reaven, 1988). Epidemiological studies consistently show an independent association between

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insulin resistance and risk of cardiovascular ­disease (McFarlane et al., 2001). The term ­insulin resistance is often used interchangeably with decreased insulin sensitivity or reduced insulin action. There are wide differences in the ability of insulin to mediate glucose disposal among individuals. Insulin sensitivity is a continuous variable, and distinguishing normal and abnormal insulin sensitivity in individuals is difficult because there is no uniform quantitative definition of what constitutes insulin resistance. Individuals are considered insulin resistant if they have normal glucose tolerance but lie in the most insulin-resistant quartile of a given population (Reaven et al., 1993). In individuals with normal glucose tolerance, insulin resistance can vary by four- to ten-fold, with some measures of resistance not differing substantially from those of people with impaired glucose tolerance (IGT) or type 2 diabetes (Reaven et al., 1989; Clausen et al., 1996). However, those subjects in the most insulin-resistant quartiles are generally significantly more obese and less glucose tolerant compared with more insulinsensitive individuals (Clausen et al., 1996). Also, those with insulin resistance but normal glucose tolerance are hyperinsulinemic compared with insulin sensitive controls, allowing insulinresistant individuals to overcome the defect in the short term. Quantitative comparisons of insulin resistance across racial/ethnic groups are difficult because of the need to take into account the effects of age, gender, weight, physical fitness, and glucose tolerance. However, some data support real differences. Comparative data are available for Mexican Americans (Haffner et al., 1992) and Australian Aborigines (Proietto et al., 1992) consistent with the view that insulin resistance is more common in individuals without diabetes in these populations. AfricanAmericans maintain glycemia following a carbohydrate load by producing a much larger insulin response, two- to three-fold greater than

that seen in matched European Caucasians (Osei and Shuster, 1994). African-Americans also have lower adiponectin levels than Caucasians, which is associated with insulin resistance (Osei et al., 2005). Asian Indians are more insulin resistant than matched European Caucasians, as demonstrated by reduced rates of glucose disposal adjusting for confounding variables (Chandalia et al., 1999). Dickinson and colleagues (2002) studied 60 lean, healthy young adults from five racial/ethnic groups (European Caucasians, Chinese, Southeast Asians, Asian Indians and Arabic Caucasians) and assessed glucose and insulin responses following a 75-g carbohydrate meal, and insulin sensitivity by HOMA or the hyperinsulinemic euglycemic clamp technique. While mean fasting glucose concentrations were similar among the groups, Southeast Asian and Chinese subjects showed markedly higher postprandial glycemia than did European Caucasians, with 1.5- to 2.0-fold higher mean incremental areas under the glucose curve. The groups also differed significantly in insulin sensitivity, with European Caucasians being the most sensitive, whereas Southeast Asians were the most resistant. The results were not explained by differences in sex, age, BMI or birth weight. The variation in insulin resistance across ethnic groups could be due to genetic and/or biochemical differences, and in the absence of definitive data it is not currently possible to separate these influences.

19.2  The evolution of insulin resistance Several theories have been proposed to explain the current high prevalence and population differences in insulin resistance and the associated type 2 diabetes, both of which increase in populations transitioning from a traditional lifestyle. The “thrifty genotype”

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19.2  The evolution of insulin resistance

hypothesis was first proposed by Neel (1962), and postulates the presence of genetic traits that had survival value for hunter-gatherers, allowing the ability to go without food for extended periods of time. A “thrifty” metabolism made for efficient storage of fat during times of plenty, providing an energy buffer during scarcity. Such a gene(s) would now be detrimental, with the abundant food supply and lack of physical activity predisposing to obesity. Subsequently, it was proposed that insulin resistance was the phenotypic expression of the thrifty genotype (O’Dea, 1991). In the presence of modern-day constant food supply, insulin resistance would result in hyperinsulinemia and eventual diabetes. Diamond (1992) described it as the “collision of our old hunter-gatherer genes with our new twentieth-century life style”. The “not-so-thrifty genotype” was suggested by Reaven (1998), who hypothesized that the purpose of insulin resistance was not to increase fat storage, as Neel suggested, but to spare the proteolysis of muscle tissue during periods of famine. Hales and colleagues (1991) proposed a “thrifty phenotype” to explain metabolic adaptations to allow survival of a malnourished fetus. The hypothesis, based on anthropometric records of infants, associates poor early fetal and infant growth with insulin resistance and the later development of type 2 diabetes and other metabolic abnormalities. Subsequently, it has been suggested that low birth weight may be genetically determined (Poulsen et al., 1997). Hattersley and Tooke (1999) proposed the “fetal insulin” hypothesis, which argues against the thrifty phenotype and in favor of genetically determined insulin resistance resulting in low insulin-mediated fetal growth in utero as well as insulin resistance later in adult life. They propose that low birth weight, hypertension, IGT and eventual diabetes are the phenotypic expression of the insulin-resistant genotype. Brand-Miller and Colagiuri developed the “carnivore connection” hypothesis to explain

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the evolution of genes predisposing to insulin resistance (Miller and Colagiuri, 1994; Colagiuri and Brand-Miller, 2002), and proposed a critical role for the quantity and quality of diet­ ary carbohydrate in the evolution of insulin resistance and hyperinsulinemia. Insulin resistance offered survival and reproductive advantages during the Ice Age, which dominated the last two million years of human evolution and which was characterized by low-carbohydrate, high-protein diets (Richards et al., 2000). While carbohydrate was scarce, compensatory hyperinsulinemia would not have been needed to maintain normal glucose tolerance. Dietary carbohydrate increased beginning about 10,000 years ago, following the end of the last Ice Age and the development of agriculture. Traditional carbohydrate foods have a low glycemic index (GI) and produce only modest postprandial increases in plasma insulin. However, beginning with the Industrial Revolution, there is now a constant supply of highly refined high GI carbohydrate in modern diets, resulting in excessive postprandial hyperinsulin­emia, exposing the disadvantages of the insulin resistance genotype and predisposing to type 2 diabetes and other metabolic abnormalities. The situation has been further aggravated over the past 60 years by the explosion in the range of available convenience and “fast foods”, which expose most populations to caloric intakes far in excess of energy requirements. This overconsumption has been responsible for the increased prevalence of obesity in Western and developing societies, and an important factor in determining the prevalence of insulin resistance in any population. The carnivore connection also offers an explanation for the relative insulin sensitivity of European Caucasians. These theories are based on the assumption of the advantage of insulin resistance for reproduction and survival during periods of famine thought to have been common through human evolution. Extensive evidence now shows that

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starvation was in fact not common in prehistoric hominids or among modern hunter-­gathers (Cordain et al., 1999). However, evidence supports natural selection to a mixture of famines and seasonal food shortage in the postagricultural era mediated through fertility, rather than viability selection (Prentice et al., 2008). Speakman (2008) challenges this view, proposing instead the “drifty gene” hypothesis which puts forward possible scenarios based on random unselected genetic drift. The relative contribution of environmental and genetic influences to insulin sensitivity remains unclear. While the molecular basis of these theories remains unknown, the relative roles played by genetic and environmental factors will continue to be the subject of intense debate.

19.3  Determinants of insulin resistance Insulin action is influenced by physiological, pathological and genetic factors (Figure 19.1).

19.3.1  Physiological determinants Insulin resistance increases with age. This trend, however, diminishes when adjustments are made for the effect of BMI, body composition, and physical activity (Ferrannini et al., 1996; Basu et al., 2003). Physical activity increases insulin sensitivity, an effect that can be demonstrated after 4–6 weeks of intensive training (Koivisto et al., 1986). Diet also influences insulin action. Epidemiological studies suggest an association between high saturated fat intake and reduced insulin sensitivity in humans (Marshall et al., 1997; Mayer-Davis et al., 1997) while animal studies demonstrate that diets high in fat, particularly saturated fat, lead to insulin resistance (Lee et al., 2006). A study in women with advanced CVD awaiting bypass surgery (Frost et al., 1998) showed an improvement in glucose tolerance and insulin sensitivity after 4 weeks on a low GI diet (compared with a high GI diet). In overweight, middleaged men, Brynes and colleagues (2003) demonstrated that HOMA-insulin resistance increased significantly on a high GI diet compared with a macronutrient-matched low GI diet.

Evolutionary environment - food availability and type - reproduction and fertility

Genes

Genetic selection

Physiologic determinants - age - diet - physical activity - pregnancy

Genetic drift

Pathologic determinants - overweight/obesity Insulin action

Figure 19.1  Determinants of insulin action.

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19.3  Determinants of insulin resistance

Insulin sensitivity decreases during ­pregnancy (Reece et al., 1994) and may result in gestational diabetes, a common and increasing problem, especially in women of non-European background (Kaaja and Greer, 2005).

19.3.2  Pathological Determinants Regardless of glucose tolerance, body weight has a major influence on insulin sensitivity. An increase in body weight of 35–40 percent above the normal range results in an insulin sensitivity decline of 30–40 percent (DeFronzo and Ferrannini, 1991). The worldwide increasing rates of overweight and obesity are a major determinant of individual and population insulin resistance. Insulin resistance, in the context of obesity, is the most common risk factor for type 2 diabetes and metabolic abnormalities (Eckel et al., 2005). There is a strong association between abdominal adiposity and insulin resistance (Abate et al., 1995; Cnop et al., 2002; Wagenknecht et al., 2002) for any level of total body fat; the subgroup of individuals with excess intra-abdominal fat has a substantially greater risk of having insulin resistance (Despres and Lemieux, 2006). Several mechanisms may result in obesityrelated insulin resistance and have provided a focus for the search for the genetic determinants of insulin resistance. Impaired non-esterified fatty acid (NEFA) metabolism is an important contributor to insulin resistance in the viscerally obese (Despres et al., 1990; Pouliot et al., 1992; Chan et al., 1994; Folsom et al., 2000; Hayashi et al., 2008). Adipose tissue not only stores and mobilizes lipids, but also releases a number of cytokines and pro-inflammatory molecules. The macrophage infiltration in adipose tissue in the obese is likely to play a role in the inflammatory profile characteristic of people with abdominal obesity (Weisberg et al., 2003), and may be responsible for obesity-related insulin resistance (Wellen and Hotamisligil, 2005). Other possible mechanisms

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include endoplasmic reticulum stress (Ozcan et al., 2004) leading to up-regulation of JNK, which in turn suppresses insulin action through inhibition of the insulin receptor substrate-1 and its associated downstream signaling pathways. Insulin-resistant subjects have elevated levels of lipid in their skeletal muscle cells compared with matched insulin-sensitive subjects. Falholt and colleagues (1988) observed a relationship between insulin resistance and intramyocellular lipid (IMCL) independent of overweight or obesity. Storlien and colleagues (1991) fed rats a high-fat diet and found that mean muscle tri­ glyceride accumulation was inversely correlated with insulin sensitivity, suggesting involvement of the intracellular glucose–fatty acid cycle. In Pima Indians without diabetes, skeletal muscle triglycerides were inversely correlated with insulin sensitivity even after controlling for all other measures of fat (Pan et al., 1997). With the availability of magnetic resonance spectroscopy to measure IMCL, more data have emerged showing significant associations between skeletal muscle triglycerides and insulin resistance (Jacob et al., 1999; Krssak et al., 1999; Perseghin et al., 1999; Virkamaki et al., 2001). However, this relationship was not observed in a group of South Asian subjects, but was present in a European Caucasian control group (Forouhi et al., 1999). Shulman proposed a unifying hypothesis for a number of forms of human insulin resistance. He suggested that an accumulation of intracellular fatty acid metabolites in muscle or liver, whether by increased caloric intake or by a failure of mitochondrial fatty acid oxidation, could produce an insulin-resistant state (Shulman, 2000) through activation of a serine/threonine kinase cascade, downstream activation of IB kinase- (IKK-) and c-JUN NH2-terminal protein kinase (JNK-1), phosphorylation at serine sites on insulin receptor substrate-1 (IRS-1) and decreased activation of glucose transport due to the inability of serine-phosphorylated forms of IRS-1 to associate with phosphatidylinositol 3-kinase (PI3K) (Shulman, 2004).

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19.3.3  Genetic Determinants Since insulin sensitivity varies between individuals and populations even when standardized for confounders, it is likely that there is a contributing genetic component. This is supported by familial clustering (Lillioja et al., 1987), twin studies (Newman et al., 1987; Mayer et al., 1996; Narkiewicz et al., 1997) and other lines of evidence (Rewers and Hamman, 1995; Abate et al., 1996; Rique et al., 2000; Malecki and Klupa, 2005). However, the precise genetic determinants of the more common form of insulin resistance remain unclear, and it is likely that multiple genes in various combinations are responsible.

19.4  Candidate genes and cross-population genetic differences There are several possibilities where genetic variation and candidate genes could play a role, including the insulin receptor, cellular signaling and glucose metabolism. Studies have highlighted some population-specific genes associated with insulin action. Pima Indians without diabetes show a familial aggregation of insulin sensitivity suggestive of a single gene with a co-dominant mode of inheritance (Bogardus et al., 1989) linked to chromosomal markers on 4q (Prochazka et al., 1993). This region is also linked to insulin resistance and 2-h plasma insulin concentrations in Mexican Americans (Prochazka et al., 1993; Mitchell et al., 1995). The FABP2 (a protein that binds saturated and unsaturated long-chain fatty acids) is linked to this chromosomal region and is expressed in the epithelial cells of the small intestine, and likely plays a role in the absorption of fatty acids. A missense mutation of FABP2 (Ala54Thr) has been identified, and has an allele frequency of 0.29 in Pima Indians,

0.34 in Japanese, 0.31 in Caucasians, 0.28 in Finns, and 0.14 in indigenous Canadians. In Pima Indians, the Ala54Thr variant was associated with both reduced insulin sensitivity and elevated fasting insulin levels (Baier et al., 1995). The 3-adrenergic gene is mainly expressed in visceral adipose tissue, where it plays an important role in lipid metabolism (Walston et al., 1995). A missense mutation in the gene (Trp64Arg) is associated with early onset of type 2 diabetes, overweight (visceral fat accumulation), and insulin resistance (Sakane et al., 1997). Pima Indians homozygous for Trp64Arg have a higher predisposition to early onset of type 2 diabetes, a higher BMI and lower resting metabolic rate (Walston et al., 1995). Finns, heterozygous for this mutation, show earlier onset of type 2 diabetes and decreased glucose disposal rates (Widen et al., 1995). PPARy has two isoforms determined by differential splicing of the gene on chromosome 3p25. PPARy1 is present in most tissues, whereas PPARy2 is predominantly expressed in adipose tissue. While both positive (Deeb et al., 1998) and negative associations (Meirhaeghe et al., 2000) between the gene and insulin resistance have been reported, recent studies show that substitution of proline to alanine at position 12 in the y2-specific exon (Pro12Ala) is associated with significantly less insulin resistance (Ek et al., 2001; Gonzalez-Sanchez et al., 2002; Helwig et al., 2007). Insulin resistance has been linked with the ectoenzyme plasma cell membrane glycoprotein-1 differentiation antigen (PC-1) in humans where levels of PC-1 are elevated two- to threefold in key tissues (Frittitta et al., 1996, 1997). PC-1 binds to the insulin receptor but does not block the ability of insulin to bind the receptor; instead, it interferes with insulin-induced autophosphorylation of the receptor and tyrosine kinase activation (Maddux and Goldfine, 2000). Abate and colleagues (2003) reported that the PC-1 K121Q polymorphism was associated with insulin resistance in Asian Indians compared with Caucasians.

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19.4  Candidate genes and cross-population genetic differences

The insulin receptor substrate-1 gene (encoded on chromosome 2q36 as a single exon) plays a role in determining insulin resistance. Several IRS-1 gene mutations have been identified in humans, but only G971R and Gly972R appear to have an association with obese subjects with type 2 diabetes (Clausen et al., 1995; Baroni et al., 2004). Horikawa and colleagues (2000) identified a susceptibility gene for type 2 diabetes in Mexican Americans on chromosome 2q in association with the gene encoding cysteine protease calpain-10. Although the exact role of calpain-10 in insulin resistance remains controversial, it appears to affect glucose uptake pathways in skeletal muscle (Otani et al., 2004) and adipose tissue (Paul et al., 2003). Adiponectin is encoded by the gene ADIPOQ and is involved in glucose and lipid metabolism; it is decreased in insulin-resistant states (Yamauchi et al., 2001; Bajaj et al., 2004). Adiponectin acts through its receptors ADIPOR1 and ADIPOR2, with ADIPOR2 being the main isoform for the insulin-sensitizing effects in human skeletal muscle (Civitarese et al., 2004). Polymorphisms in the ADIPOQ gene have been studied in various populations, including Caucasians and Japanese, and suggest that gene variation predisposes to insulin resistance (Gu et al., 2004; Nakatani et al., 2005). The conversion from pre-diabetes to type 2 diabetes in the STOP-NIDDM trial was predicted by SNP 45 and SNP 276 polymorphisms of the ADIPOQ gene (Zacharova et al., 2005). Damcott and colleagues (2005) found an association between ADIPOR1 and ADIPOR2 variants and type 2 diabetes in an Old Order Amish population, while Stefan and colleagues (2005) showed that a variation in ADIPOR1 may affect insulin sensitivity in Europeans. Studies from Canada suggest that insulin resistance may be a significant inherited trait contributing to the onset of type 2 diabetes (Hegele et al., 2003). Linkage of type 2 diabetes to chromosome 20q12-q13.1 has been

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reported (Klupa et al., 2000; Permutt et al., 2001). Localized in this region is the transcription factor hepatic nuclear factor (HNF)-4, which plays a critical role in the regulation and expression of genes essential to the normal functioning of the liver, pancreas and gut (Stoffel and Duncan, 1997). Hegele and colleagues (1999) identified a population-specific HNF-1 G319S mutation among a group of Oji-Cree Indians in Northern Canada which conferred susceptibility to type 2 diabetes. Individuals carrying this mutant gene had significantly higher post-challenge plasma glucose levels and fasting hyperinsulinemia suggestive of insulin resistance. Other studies suggest a major locus on chro­ mosome 6 (near marker D6S403) strongly influ­encing plasma insulin concentrations and insulin resistance in Mexican Americans (Duggirala et al., 2001); two diabetes susceptibility loci on chromosome 6q associated with insulin resistance and insulin secretion in Finns (Watanabe et al., 2000; Shtir et al., 2007) and, in the same region, diabetes in both Pima Indians (Hanson et al., 1998) and Japanese (Iwasaki et al., 1999). Insulin levels and body fat in the Quebec Family Study were linked to a region on chromosome 1p32-22 (Chagnon et al., 2000). In a study involving 2684 Asian Indians from the UK (Chambers et al., 2008), a genome-wide association study found a significant association between four SNPs in the MC4R gene and insulin resistance (HOMA-IR) with the association with the SNP rs12970134 persisting after adjusting for waist circumference, BMI and body mass. Several syndromes of insulin resistance based on single mutations of genes have been described. Over 50 mutations in the insulin receptor gene (located on chromosome 19p13.213.3) have been reported and are associated with severe insulin resistance and hyperinsulinemia (Taylor et al., 1992). These syndromes result in severe outcomes, including intrauterine growth retardation, fasting hypoglycemia and death, within the first year of life (Mercado et al., 2002).

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19.5  Conclusion Insulin resistance is common, and is implicated in a number of metabolic abnormalities, particularly the development of type 2 diabetes. Differences in insulin resistance are apparent across populations, and likely contribute to the differences in prevalence of diabetes and other metabolic abnormalities. While insulin action is influenced by many factors, including age, diet, physical activity and especially body weight, ethnic/racial differences also exist, implying underlying genetic variations. Various theories have been proposed to explain the evolution of variations in insulin resistance and the interaction with the modern-day environment. The carnivore connection hypothesis is based on the evolutionary changes in the quantity and quality (GI) of dietary carbohydrate and the advantages of insulin resistance for reproduction and during times when dietary carbohydrate, rather than energy, was scarce. These theories will remain speculative, however, until progress is made in identifying the specific molecular and genetic basis for population and individual differences in insulin action. Although our genetic make-up may exacer­ bate the impact of our current lifestyle, finding individual and societal solutions to combat these evolutionary changes is proving challenging. Without a major global catastrophe we are unlikely to be able to turn back the clock, and neither would many want to, considering the benefits of modernization. Increasing physical activity is arguably the most amenable way of increasing an individual’s insulin sensitivity, especially when coupled with appropriate dietary changes. Increased attention to urban design and providing individuals with the opportunity to exercise is fundamental. However, effective and sustainable strategies to address the excessive and inappropriate energy intake are more problematic.

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Gonzalez-Sanchez, J. L., Serrano Rios, M., Fernandez Perez, C., Laakso, M., & Martinez Larrad, M. T. (2002). Effect of the Pro12Ala polymorphism of the peroxisome proliferator-activated receptor gamma-2 gene on adiposity, insulin sensitivity and lipid profile in the Spanish population. European Journal of Endocrinology, 147(4), 495–501. Gu, H. F., Abulaiti, A., Ostenson, C. G., et al. (2004). Single nucleotide polymorphisms in the proximal promoter region of the adiponectin (APM1) gene are associated with type 2 diabetes in Swedish caucasians. Diabetes, 53(Suppl. 1), S31–S35. Haffner, S. M., Stern, M. P., Watanabe, R. M., & Bergman, R. N. (1992). Relationship of insulin clearance and secretion to insulin sensitivity in non-diabetic Mexican Americans. European Journal of Clinical Investigation, 22(3), 147–153. Hales, C. N., Barker, D. J., Clark, P. M., et al. (1991). Fetal and infant growth and impaired glucose tolerance at age 64. British Medical Journal, 303(6809), 1019–1022. Hanson, R. L., Ehm, M. G., Pettitt, D. J., et al. (1998). An autosomal genomic scan for loci linked to type II diabetes mellitus and body-mass index in Pima Indians. American Journal of Human Genetics, 63(4), 1130–1138. Hattersley, A. T., & Tooke, J. E. (1999). The fetal insulin hypothesis: An alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet, 353(9166), 1789–1792. Hayashi, T., Boyko, E. J., McNeely, M. J., Leonetti, D. L., Kahn, S. E., & Fujimoto, W. Y. (2008). Visceral adiposity, not abdominal subcutaneous fat area, is associated with an increase in future insulin resistance in Japanese Americans. Diabetes, 57(5), 1269–1275. Hegele, R. A., Cao, H., Harris, S. B., Hanley, A. J., & Zinman, B. (1999). The hepatic nuclear factor-1alpha G319S variant is associated with early-onset type 2 diabetes in Canadian Oji-Cree. The Journal of Clinical Endocrinology and Metabolism, 84(3), 1077–1082. Hegele, R. A., Zinman, B., Hanley, A. J., Harris, S. B., Barrett, P. H., & Cao, H. (2003). Genes, environment and Oji-Cree type 2 diabetes. Clinical Biochemistry, 36(3), 163–170. Helwig, U., Rubin, D., Kiosz, J., et al. (2007). The minor allele of the PPARgamma2 pro12Ala polymorphism is associated with lower postprandial TAG and insulin levels in non-obese healthy men. The British Journal of Nutrition, 97(5), 847–854. Horikawa, Y., Oda, N., Cox, N. J., et al. (2000). Genetic ­variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nature Genetics, 26(2), 163–175. Iwasaki, N., Wang, Y.Q., Cox, N.J., Ogata, M. and Iwamoto, Y. (1999) A genome-wide screen for type 2 diabetes susceptibility genes in Japanese. Paper presented at the 2nd

Research Symposium on the Genetics of Diabetes. San Jose. Jacob, S., Machann, J., Rett, K., et al. (1999). Association of increased intramyocellular lipid content with insulin resistance in lean nondiabetic offspring of type 2 diabetic subjects. Diabetes, 48(5), 1113–1119. Kaaja, R. J., & Greer, I. A. (2005). Manifestations of chronic disease during pregnancy. Journal of the American Medical Association, 294(21), 2751–2757. Klupa, T., Malecki, M. T., Pezzolesi, M., et al. (2000). Further evidence for a susceptibility locus for type 2 diabetes on chromosome 20q13.1-q13.2. Diabetes, 49(12), 2212–2216. Koivisto, V. A., Yki-Jarvinen, H., & DeFronzo, R. A. (1986). Physical training and insulin sensitivity. Diabetes/ Metabolism Reviews, 1(4), 445–481. Krssak, M., Falk Petersen, K., Dresner, A., et al. (1999). Intramyocellular lipid concentrations are correlated with insulin sensitivity in humans: A 1H NMR spectroscopy study. Diabetologia, 42(1), 113–116. Lee, J. S., Pinnamaneni, S. K., Eo, S. J., et al. (2006). Saturated, but not n-6 polyunsaturated, fatty acids induce insulin resistance; role of intramuscular accumulation of lipid metabolites. Journal of Applied Physiology, 100(5), 1467–1474. Lillioja, S., Mott, D. M., Zawadzki, J. K., et al. (1987). In vivo insulin action is familial characteristic in nondiabetic Pima Indians. Diabetes, 36(11), 1329–1335. Maddux, B. A., & Goldfine, I. D. (2000). Membrane glycoprotein PC-1 inhibition of insulin receptor function occurs via direct interaction with the receptor alphasubunit. Diabetes, 49(1), 13–19. Malecki, M. T., & Klupa, T. (2005). Type 2 diabetes mellitus: From genes to disease. Pharmacological Reports, 57(Suppl), 20–32. Marshall, J. A., Bessesen, D. H., & Hamman, R. F. (1997). High saturated fat and low starch and fibre are associated with hyperinsulinaemia in a non-diabetic population: The San Luis Valley Diabetes Study. Diabetologia, 40(4), 430–438. Mayer, E. J., Newman, B., Austin, M. A., et al. (1996). Genetic and environmental influences on insulin levels and the insulin resistance syndrome: An analysis of women twins. American Journal of Epidemiology, 143(4), 323–332. Mayer-Davis, E. J., Monaco, J. H., Hoen, H. M., et al. (1997). Dietary fat and insulin sensitivity in a triethnic population: The role of obesity The Insulin Resistance Atherosclerosis Study (IRAS). The American Journal of Clinical Nutrition, 65(1), 79–87. McFarlane, S. I., Banerji, M., & Sowers, J. R. (2001). Insulin resistance and cardiovascular disease. The Journal of Clinical Endocrinology and Metabolism, 86(2), 713–718. Meirhaeghe, A., Fajas, L., Helbecque, N., et al. (2000). Impact of the peroxisome proliferator activated receptor

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C H A P T E R

20 Neuroanatomical Correlates of Hunger and Satiaty in Lean and Obese Individuals Angelo Del Parigi Senior Medical Director, Medical Affairs, Pfizer Inc., New York, NY, USA

o u tline 20.1 Physiology of Hunger and Satiety in Human Eating Behavior

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Eating behavior in humans is not a stereotypical behavior driven only by the need to compensate for acute changes in energy status. It is clear that emotional, cognitive and cultural factors play a major role in the initiation and termination of an eating episode. To put it simply, a negative energy balance is sufficient but not necessary to initiate eating. However, homeostatic, hedonic and cognitive controls of eating behavior are intimately intertwined. Their separation as discrete neurophysiological processes is, in fact, supported by theoretical principles rather than by empirical evidence (Berthoud and Morrison, 2008).

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20.1  Physiology of hunger and satiety in human eating behavior Hunger and satiety are at the crossroads of this complex interplay between metabolic and non-metabolic factors regulating human eating behavior. In fact, energy balance is continuously monitored by the brain through multiple endocrine and neural mechanisms, which include long- and short-term signals of changes in energy stores, and changes in energy currency, respectively. On this dynamic background which steers

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the individual toward the decision to start eating or not, or to stop eating or not, the information from the external environment, either sensorial (such as the sight, smell or taste of food) or social (such as the availability of a scheduled “break” for lunch) may in fact act as triggering factors. If we add additional layers of complexity that pertain to cultural, psychological and environmental constraints related to the logistics of food consumption, and/or to the projected body image, and/or to the choice of specific foods (for example, inducted by a commercial or by religious beliefs), we encompass the multitude of factors which define the rhythm and the gears of human eating behavior. Although a simple model of meal control might suggest that feeding terminates when a sufficient quantity of nutrients has been ingested to meet individual nutritional needs, it is clear that the normal rate of eating indicates that meal termination occurs too early to reflect absorption of the ingested nutrients. The satiety response can actually be resolved in different chronological phases (the so-called “satiety cascade”) characterized by different underlying phenomena, mainly sensorial and cognitive, leading to the actual termination of a meal (i.e., satiation), or post-ingestive and post-absorptive phases, supporting the duration of fasting intervals between meals – that is, the properly defined satiety (Blundell and Tremblay, 1995). As such, satiation defines the discrete transition from eating to fast, while satiety characterizes the period of fast that follows. During this interval, as satiety declines, the subjective feeling of the drive to eat reaches the threshold of conscious appreciation, which is what is generally defined as hunger. In fact, operationally, satiety can be defined as the state of hunger suppression. Although fasting is a common denominator of both satiety and hunger, satiety is associated with a feeling of comfort and low desire for food, whereas hunger is associated with discomfort and high desire for food. Furthermore, in normal conditions, while satiety

is mainly a digestive-metabolically driven pro­ cess, where the gastrointestinal processing of the alimentary bolus and consequent absorption of nutrients and elicited hormonal responses are the leading factors, hunger can also be triggered by externally or internally generated cues, such as the sight or smell of food or the desire for prompt gratification – for example, in stressful life conditions. Consistent with the view of protracted overeating, or eating in excess of metabolic needs, as the leading contributing factor to weight gain and obesity, dysregulation of hunger and/or satiety appears to be a plausible working hypothesis for the understanding of the pathophysiology of obesity. In fact, the search for the biological underpinnings of a positive energy imbalance and weight gain has been intensely focusing on the molecular signatures of hyperphagia or overeating in animals and in humans. It is beyond the scope of this chapter to review the evidence accrued on the molecular pathways associated with weight gain. Suffice it to say that many catabolic and anabolic signals have been identified and tested in rodent models of obesity, and that overwhelming evidence supports the notion that weight gain and obesity are associated with neurofunctional aberrations (Bray, 2004). However, the translation of this experimental evidence to common forms of human obesity has been disappointing. Part of this loss in translation is likely due to the limitations of access to the brain for scientific experiments in humans.

20.2  Functional neuroimaging evidence One of the few available options for a noninvasive exploration of the in vivo biology of the human brain is offered by functional neuro­ imaging, which, depending on the technique of choice, measures different proxies for changes in local neural activity or receptor binding,

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ultimately allowing for the identification of regional brain responses to specific stimulations. As such, functional neuroimaging has also been used for the investigation of brain responses to food-related stimuli in the attempt to identify neurobiological patterns associated with different states of eating behavior and different metabolic conditions, including obesity and the risk for obesity. In this context, hunger and satiety have been investigated to identify possible neurofunctional markers of these conditions in obese and normal-weight individuals, and assess their importance to the pathophysiology of obesity. From a theoretical/behavioral standpoint, such an approach could contribute to answering the atavistic and always stimulating question: is overeating (and the consequent weight gain) induced by enhanced feelings of hunger, or by a weakened satiety response, or by both? From a methodological/experimental point of view, such an approach would rather test the question: are there neurofunctional markers of overeating in obese individuals expressed at a scale that can be investigated with functional neuroimaging? Within this theoretical and methodological framework, a pioneering neuroimaging program was designed and implemented at the National Institute for Diabetes, Digestive and Kidney Diseases (NIDDK) branch in Phoenix, Arizona, USA, using positron emission tomography (PET) and 15O-water to measure changes in regional cerebral blood flow (rCBF), a marker of local neural activity. This technique works by measuring the effect of the intravenous administration of a dose of 15O-water, which is a tracer conveyed and distributed to tissues throughout the body by the arterial blood flow. This tracer rapidly diffuses through the blood–brain barrier, making it suitable for the measurement of rCBF. The spatial resolution of this technique is limited by the precision of the localization of the positron emitting nucleus (1- to 6-mm radius). On the other hand, the short half-life of 15O (122.24 seconds)

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makes it possible to acquire multiple images during a single scanning session, allowing for each person to serve as his or her own control, which eliminates a series of confounders. For example, the assessment of a change in local neural activity in response to a stimulus is implemented by subtracting the map of rCBF associated with the application of the stimulus from the map of rCBF associated with the baseline condition. Automated algorithms transpose each individual PET subtraction map onto a standardized stereotactical space, perform group data analysis, and generate statistical parametric maps of statedependent changes in rCBF (Acton and Friston, 1998). When superimposed onto an MRI scan of the same subject, these maps allow precise identification of the neuroanatomical location of the change in neuronal activity subject by subject. We have used PET and 15O-water to study the brain responses to hunger (after a 36-hour fast) and to early satiety (in response to a liquid meal providing 50 percent of the resting energy requirements) in normal weight, obese and postobese men and women (Del Parigi et al., 2002a, 2004, 2005; Gautier et al., 2000, 2001; Tataranni et al., 1999; Tataranni and Del Parigi, 2003). Subjects were admitted to the clinical research unit of the NIDDK in Phoenix for approximately 1 week. On admission, all subjects were placed on a weight-maintaining diet (50 percent carbohydrate, 30 percent fat, 20 percent protein). Body composition was assessed by dual energy X-ray absorptiometry (DPX-l, Lunar, Madison, WI), and resting energy expenditure, after a 12-hour overnight fast, was measured for 45 minutes by using a ventilated hood system (DeltaTrac, SensorMedics, Yorba Linda, CA). Extreme abnormalities in eating behavior were excluded by using the Three-Factor Eating Questionnaire (Stunkard and Messick, 1985) which estimates three major dimensions of eating behavior – dietary restraint, a measure of cognitive control over eating behavior; disinhibition, a measure of susceptibility to sensory and emotional cues; and hunger, a measure of

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sensitivity to physiological cues. The imaging session took place after a 36-hour fast, during which only water and non-caloric, noncaffeinated beverages were provided. First, we obtained a structural MRI of the head, to rule out gross abnormalities and provide anatomical information for the precise localization of the functional findings. Soon afterwards, the functional session began. A PET transmission scan using a 68Germanium/ 68 Gallium ring source was performed to correct subsequent emission images for radiation attenuation. Next, the preparation of the subject for the functional imaging session continued with the insertion of a plastic extension tube into the mouth to the middle of the tongue, while the subject was lying supine on the PET table. This apparatus was then connected to a peristaltic pump (IMED 980, Imed, San Diego, CA), set to deliver, over a period of 25 minutes, a liquid formula meal (Ensure-Plus 1.5 kcal/ml, RossAbbott Laboratories, Columbus, OH) providing 50 percent of the previously measured daily resting energy expenditure. Two 1-minute PET scans were performed right before starting the administration of the liquid meal (i.e., with the subject hungry) and two PET scans were collected right after the administration of the liquid meal (i.e., with the subject sated), with intervals of 10 minutes between scans. For each scan, a 50-mCi intravenous bolus of 15O-labeled water was injected. To eliminate possible confounding factors such as tactile stimulation of the tongue and motor neuron activity, swallowing was consistently induced by administering 2 ml of water before each of the four PET scans. During each scan, subjects rested still in the supine position, the head immobilized in a custom-made solidified foam helmet, and were asked to keep their eyes closed and pointing forward. Subjective ratings of hunger and satiety were recorded after each PET scan, using a 100-mm visual analog scale (Lawton et al., 1993). Blood samples were also collected immediately after each scan for the measurement of plasma glucose, free

fatty acids, insulin and leptin concentrations. To familiarize each subject with the experimental setting and minimize the risk of learning-related artifacts, the feeding procedure was practiced on the research ward before PET scanning. PET images were reconstructed with an inplane resolution of 10 mm full width at halfmaximum (FWHM), and a slice thickness of 5 mm FWHM. We used this approach to seek the answer to three main experimental questions: 1. Can the functional neuroanatomical correlates of hunger and satiety be imaged in humans? 2. Are there selective differences in the brain responses to meal consumption between obese and normal-weight individuals? 3. What is the pathophysiological relevance, if any, of these differences? In regard to the first question, our results demonstrated that the administration of a satiating meal to hungry individuals was associated with increased neural activity in the prefrontal cortex (generally involved in the top-down control of behavior, especially inhibiting inappropriate response tendencies) and decreased neural activity in several limbic and paralimbic areas (regions involved in a wide array of functions spanning metabolic, affective and motivational processes), and cerebellum. Among the limbic/paralimbic areas, we observed decreased activity in response to the meal in the insular cortex (a visceral sensory area also involved in processing food craving (Pelchat et al., 2004a)), the anterior cingulate (selectively involved in response to noxious stimuli (Craig et al., 1996)) and the orbitofrontal cortex (involved in cross-sensorial processing). Some of these findings were also replicated in a study of the changes in brain activity related to eating solid food (Small et al., 2001). Taken together, these findings not only demonstrated the feasibility of a neuroimaging study applied to obesity-related questions, but also showed that hunger and early satiety

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are associated with specific regional brain responses. These regional responses clustered in a hunger-related domain, encompassing areas involved in responses to emotional cues and sensory information as well as to metabolic changes. Several of these areas were also implicated by other neuroimaging studies in responses to other forms of urges, such as thirst (Denton et al., 1999) and hunger for air (Brannan et al., 2001). Conversely, early satiety appeared to be associated with the activation of only one brain region, the prefrontal cortex, an area that reached the greatest phylogenetic expansion in humans and functionally presides over cognitive processing of information, including topdown control over behavioral responses. In light of these results and the evidence that all these major regional brain domains are reciprocally interconnected, we postulated that the prefrontal cortex, through efferent inhibitory projections to the limbic and paralimbic areas, exerts inhibiting effects on eating by suppressing the hunger-related activation of these brain areas. As an aside, the presence of a distributed, and possibly redundant, network of brain areas activated by hunger seems to support the common notion that the control of energy balance is inherently biased, favoring anabolic processes such as food intake (Schwartz et al., 2003). In regard to the second question, we observed that obese individuals respond to hunger and early satiety with greater changes in some of the limbic/paralimbic areas and in the prefrontal cortex, respectively, compared to normal-weight individuals. Specifically, in obese compared to normal-weight individuals, we observed that limbic and paralimbic areas, including the orbitofrontal cortex, insula and hippocampus, showed a greater activity in response to hunger, whereas dorsal and ventral prefrontal areas showed a greater activity in response to satiety. These differences were generally consistent in men and women (Del Parigi et al., 2002b). However, in men only, the hunger response in the hypothalamus was attenuated in obese

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c­ ompared to normal-weight individuals (Gautier et al., 2000). A similar observation was reported in lean and obese individuals in response to the ingestion of a glucose solution (Matsuda et al., 1999). We also found significant associations between postprandial changes in plasma insulin, glucose and FFA, and postprandial changes in neural activity in several brain regions, which suggests that hormones and metabolites might contribute to the generation of postprandial neural responses. In some instances, the correlations between postprandial changes in hormones/ metabolites and neural activity were in opposite directions in obese and normal-weight individuals (Del Parigi et al., 2002a). To answer the third and most challenging question, we recruited post-obese individuals who had successfully achieved and maintained a normal body weight by lifestyle changes despite a past of morbid obesity (BMI  35) (Del Parigi et al., 2004). Anthropometrically, these individuals showed a normal-weight phenotype, not different from the normalweight group previously studied, while their past as formerly obese individuals indicated a high susceptibility to weight gain, constantly counteracted by an intense physical activity regimen and actively controlled dietary intake. Although in a cross-sectional fashion, we planned the study of these formerly obese individuals in order to explore functional similarities in the brain responses to hunger and satiety between a group of obese-prone and a group of currently obese individuals to be interpreted as putative markers for neurofunctional signatures of predisposition to weight gain and obesity. Similarities between postobese and obese were actually observed only in the posterior hippocampus, which exhibited a similar decrease of neural activity in the obese and post-obese groups, whereas in the normalweight group the regional activity increased (Del Parigi et al., 2004). The hippocampus is a brain region implicated in many cognitive phenomena, chiefly related to mnemonic and

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learning processes, but it is also rich in receptors of metabolic signals and has been associated with food craving by another neuroimaging study which reported a response in an overlapping hippocampal area (Pelchat et al., 2004b). While suggestive of putative neurofunctional markers of increased risk for weight gain and obesity, this finding still awaits testing in properly designed longitudinal studies in individuals at high risk for obesity before and after gaining weight. Until then, the assessment of the pathophysiological relevance of the neurofunctional differences observed between obese and normal-weight individuals remains exploratory. In conclusion, we believe that the use of functional neuroimaging in the search for the neurofunctional underpinnings of eating behavioral differences between obese and normal weight individuals has proven to be both feasible and useful. The study of the neurofunctional correlates of hunger and satiety is one of the viable experimental settings in pursuing a better understanding of the neurobiology of eating behavior and its aberrations. We have reported a series of exploratory findings that have been partially confirmed in independent studies. These observations have generated hypotheses that are amenable to proper testing in longitudinal studies, where the pathophysiological importance of obesity-related neural abnormalities can be determined and offer the rationale for new investigational targets for the pharmaco­therapy of obesity.

References Acton, P. D., & Friston, K. J. (1998). Statistical parametric mapping in functional neuroimaging: Beyond PET and fMRI activation studies. European Journal of Nuclear Medicine, 25, 663–667. Berthoud, H. R., & Morrison, C. (2008). The brain, appetite, and obesity. Annual Review of Psychology, 59, 55–92. Blundell, J. E., & Tremblay, A. (1995). Appetite control and energy (fuel) balance. Nutrition Research Reviews, 8, 225–242. Brannan, S., Liotti, M., Egan, G., Shade, R., Madden, L., Robillard, R., Abplanalp, B., Stofer, K., Denton, D., & Fox, P. T. (2001). Neuroimaging of cerebral activations

and deactivations associated with hypercapnia and hunger for air. Proceedings of the National Academy of Sciences USA, 98, 2029–2034. Bray, G. A. (2004). Obesity is a chronic, relapsing neurochemical disease. International Journal of Obesity and Related Metabolic Disorders, 28, 34–38. Craig, A. D., Reiman, E. M., Evans, A., & Bushnell, M. C. (1996). Functional imaging of an illusion of pain. Nature, 384, 258–260. Del Parigi, A., Gautier, J. F., Chen, K., Salbe, A. D., Ravussin, E., Reiman, E., & Tataranni, P. A. (2002a). Neuroimaging and obesity: Mapping the brain responses to hunger and satiation in humans using positron emission tomography. Annals of the New York Academy of Sciences, 967, 389–397. Del Parigi, A., Chen, K., Gautier, J. F., Salbe, A. D., Pratley, R. E., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2002b). Sex differences in the human brain’s response to hunger and satiation. The American Journal of Clinical Nutrition, 75, 1017–1022. Del Parigi, A., Chen, K., Salbe, A. D., Hill, J. O., Wing, R. R., Reiman, E. M., & Tataranni, P. A. (2004). Persistence of abnormal neural responses to a meal in postobese individuals. International Journal of Obesity and Related Metabolic Disorders, 28, 370–377. Del Parigi, A., Pannacciulli, N., Le, D. N., & Tataranni, P. A. (2005). In pursuit of neural risk factors for weight gain in humans. Neurobiology of Aging, 26(Suppl. 1), 50–55. Denton, D., Shade, R., Zamarippa, F., Egan, G., Blair-West, J., McKinley, M., & Fox, P. (1999). Correlation of regional cerebral blood flow and change of plasma sodium concentration during genesis and satiation of thirst. Proceedings of the National Academy of Sciences USA, 96, 2532–2537. Gautier, J. F., Chen, K., Salbe, A. D., Bandy, D., Pratley, R. E., Heiman, M., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2000). Differential brain responses to satiation in obese and lean men. Diabetes, 49, 838–846. Gautier, J. F., Del Parigi, A., Chen, K., Salbe, A. D., Bandy, D., Pratley, R. E., Ravussin, E., Reiman, E. M., & Tataranni, P. A. (2001). Effect of satiation on brain activity in obese and lean women. Obesity Research, 9, 676–684. Lawton, C. L., Burley, V. J., Wales, J. K., & Blundell, J. E. (1993). Dietary fat and appetite control in obese subjects: Weak effects on satiation and satiety. International Journal of Obesity and Related Metabolic Disorders, 17, 409–416. Matsuda, M., Liu, Y., Mahankali, S., Pu, Y., Mahankali, A., Wang, J., DeFronzo, R. A., Fox, P. T., & Gao, J. H. (1999). Altered hypothalamic function in response to glucose ingestion in obese humans. Diabetes, 48, 1801–1806. Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004a). Images of desire: Food-craving activation during fMRI. Neuroimage, 23, 1486–1493. Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004b). Images of desire: Food-craving activation during fMRI. Neuroimage, 23, 1486–1493.

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Schwartz, M. W., Woods, S. C., Seeley, R. J., Barsh, G. S., Baskin, D. G., & Leibel, R. L. (2003). Is the energy homeo­ stasis system inherently biased toward weight gain? Diabetes, 52, 232–238. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & JonesGotman, M. (2001). Changes in brain activity related to eating chocolate: From pleasure to aversion. Brain, 124, 1720–1733. Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29, 71–83.

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Tataranni, P. A., & Del Parigi, A. (2003). Functional neuro­ imaging: A new generation of human brain studies in obesity research. Obesity Reviews, 4, 229–238. Tataranni, P. A., Gautier, J. F., Chen, K., Uecker, A., Bandy, D., Salbe, A. D., Pratley, R. E., Lawson, M., Reiman, E. M., & Ravussin, E. (1999). Neuroanatomical correlates of hunger and satiation in humans using positron emission tomography. Proceedings of the National Academy of Sciences USA, 96, 4569–4574.

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C H A P T E R

21 Neuroendocrine Stress Response and Its Impact on Eating Behavior and Body Weight Beth M. Tannenbaum1, Hymie Anisman2 and Alfonso Abizaid2 1

McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada 2 Institute of Neuroscience, Carleton University, Ottawa, Canada

o u t l ine 21.1 Introduction

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21.5 Peripheral Signals Regulating Energy Balance 21.5.1  Leptin 21.5.2  Insulin 21.5.3  Ghrelin

21.4 Imaging Studies in Humans

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21.2 Hypothalamo-Pituitary-Adrenal Axis 262

21.1  Introduction There is considerable overlap between the physiological systems that regulate food intake and those that mediate stress responses, and stressful events may influence food ingestion. Considering that the ability of an organism to mount an effective defensive response is highly dependent on available energy stores (i.e., the

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mobilization and utilization of blood glucose), it is not surprising that overlapping mechanisms exist among stress and consummatory systems. That said, under certain conditions it may be adaptive for processes that stimulate defensive behaviors to inhibit those relating to ingestive processes – for example, it would clearly be counterproductive for an organism facing a threat from predators to engage in a search for food.

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Under non-stressful conditions, individuals have the opportunity to adopt healthy eating practices and maintain a healthy body weight. However, under stressor conditions, particularly if these are chronic and unremitting, the wear and tear on biological and behavioral coping methods may be excessive (McEwen, 2007), and shifts may occur to food consumption patterns in regard to both quality and quantity. In order to maintain “allostasis”, the processes by which homeostasis is maintained in the face of stressors, numerous short-term adaptive coping strategies can be employed, although some of these may have negative long-term repercussions. In rodents, stressors typically result in reduced food intake. However, in humans there are marked individual differences: some individuals reduce their consumption, whereas others increase ingestion (particularly carbohydrate snacks). Some of these individuals may employ eating as a coping method, even if it is an ineffective or counterproductive one. Indeed, this coping strategy may involve a shift from the consumption of healthy foods to the overconsumption of “comfort foods” that are typically high in calories, fat and sugar, and low in nutritional value. As individuals turn to “comfort foods” to alleviate stress, the continued failure to cope with stressors may promote the development of obesity (Laitinen et al., 2002). This drive for comfort is associated with a shift from the homeostatic/allostatic system (dependent on energy stores and nutritional status) to the nonhomeostatic or reward-seeking system (invol­ved with the motivational aspects of eating). The “homeostatic” and “non-homeostatic” controls on food intake and energy expenditure are achieved through coordination between the hypothalamus, the brainstem and various limbic areas. However, if pleasure is experienced after the consumption of high-sugar/high-fat foods, the hedonic response might be capable of over-riding homeostasis/allostasis, and result in an elevated appetite and a drive to overeat “pleasurable” calories (McEwen, 2007).

The present chapter addresses some of the current research findings in both animal and human populations that have elucidated how and why food consumption patterns can be altered under stressor conditions. It is suggested that cortisol (or corticosterone in rodents) and several metabolic hormones, released under stress and anxiety conditions, are linked to changes in metabolic function. Moreover, through repeated experiences, individuals may learn that eating high-caloric foods can reduce some of the unpleasant effects of stress and thus, with further stressor encounters, individuals may “self-medicate” through eating “comfort foods”.

21.2  Hypothalamo-pituitaryadrenal axis The activation of the hypothalamo-pituitaryadrenal (HPA) axis is comprised of a network of regions that span both the central and peripheral nervous systems. In response to stressors, corticotrophin releasing hormone (CRH) is released from cells in the paraventricular nucleus (PVN) of the hypothalamus. CRH acts on the anterior pituitary corticotrophs to stimulate the synthesis and release of adrenocorticotropic hormone (ACTH), which then acts on the adrenal cortex to stimulate the release of cortisol (humans) or corticosterone (animals). Cortisol regulates its own levels via a series of negative-feedback loops at both brain and pituitary sites. Excessive and/or chronic stressors, possibly through actions on HPA functioning, can adversely impact a variety of physiologic functions and behavioral outputs, such as growth, reproduction, glucose metabolism (insulin resis­ tance and type 2 diabetes), immunocompetence, deposition of body fat, atherosclerosis, hippo­ campal atrophy, and depression (Kyrou et al., 2006). This makes negative-feedback efficacy essential, but under conditions of chronic stress

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21.3  Stress and food intake: it is not all homeostatic or automatic

the ability of the axis to “shut-off” or dampen circulating cortisol levels is often compromised, and the diurnal rhythms associated with stressors may be disturbed (Michaud et al., 2008). In addition to the hypothalamus, the amyg­ dala plays a fundamental role in mounting effective stress responses. It contains many of the peptides implicated in stress and anxiety regulation, such as CRH (Behan et al., 1996). However, in contrast to the hypothalamus, corticosterone stimulates CRH release in the amyg­ dala, which then affects anxiety/fear responses (Makino et al., 1994) as well as the regulation of food intake and appraisal. Like the amygdala, medial prefrontal cortical regions (mPFC) (i.e., the anterior cingulate gyrus, the subcallosal gyrus and the orbitofrontal cortex), appear to be involved in the memory of the emotional valence of stimuli, and thus play an active role in the inhibition of fear responses mediated by the amygdala (Pignatti et al., 2006; Petrovich et al., 2007). Therefore, the response to stress is highly complex and recruits varied brain regions that serve and support both the endocrine and cognitive aspects of the axis.

21.3  Stress and food intake: it is not all homeostatic or automatic Animals exposed to repeated stressors typically eat less, and therefore weight gain is limited. Yet, under some conditions, they also show increased consumption of palatable foods and liquids, accompanied by reduced HPA axis activity (Pecoraro et al., 2004; la Fleur et al., 2005). As indicated earlier, intake of “comfort foods” reduces HPA axis activity and promotes the activation of brain circuits implicated in reward-seeking behaviors (Dallman et al., 2003). In line with the view that “comfort foods” have positive effects, when provided to chronically stressed rats they may negate stress-induced

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abnormalities of cortisol and dopamine (DA) functioning (Dallman et al., 2006). In effect, it may be that the inhibition of neuroendocrine responses to stressors in rats eating “comfort foods” is explained by the interplay between the negative effects of chronic stressors and the positive effects of “comfort foods” on inputs to the ventral tegmental area (VTA) nucleus accumbens reward network – brain sites critically involved in dopamine DA regulation, the primary neuro­ chemical implicated in responses to reward and addiction. As such, DA modulation may be responsible for regulating the reward or reinforce­ ment necessary to enhance feeding as seen in obesity (Berridge, 1996). These reward-related areas are also activated in response to drugs of abuse (McQuade et al., 2004), and it is conceivable that the underlying brain mechanisms associated with stressor-provoked eating are similar to those that ultimately result in the compulsive drug consumption seen in addiction (Volkow and O’Brien, 2007). Data from human studies support the idea that stressors can enhance caloric intake as a means to cope with stressful events (Anisman et al., 2008). Daily hassles were associated with increased consumption of high-fat/high-sugar snacks, and with a reduction in the frequency of main meals and the consumption of veget­ ables. Interestingly, psychosocial stressors elicited hyperphagic responses in subjects, whereas physical stressors caused hypophagic responses (O’Connor et al., 2008). There appear to be premorbid features that predict the impact of stressors on eating and weight gain. Specifically, it was reported that among students followed over a 12-week stressful period, dietary restraint decreased and their body mass index (BMI) increased (Roberts et al., 2007). Further, those with the highest dietary restraint scores were those with the highest initial BMI and lowest daily salivary cortisol secretion. This is consistent with evidence that restrained eaters struggle to control food intake and weight, as well as with the predictions of the dietary restraint

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model; namely, that reliance on cognitive control over eating, rather than physiological cues, renders dieters vulnerable to uncontrolled eating. Paralleling such findings, healthy medical students who had higher urinary cortisol and insulin during academic exams had identified themselves as stress eaters and showed greater weight gain than non-stress eaters during a stressful academic period (Epel et al., 2004). Others showed that laboratory stressors (unsolvable anagrams, Trier Social Stress Test) were associated with increased caloric intake (and, in particular, fat) (Zellner et al., 2006), especially in those who showed substantial cortisol responses to the stressor (Epel et al., 2001).

21.4  Imaging studies in humans Brain imaging studies have been instrumental in elucidating the functional network that controls appetite, identifying the specific brain regions differentially involved in hunger and satiety in humans (Tataranni et al., 1999). For example, hunger was associated with increased regional cerebral blood flow (rCBF) in the hypothalamus, the anterior cingulate cortex, and the insular and orbitofrontal cortices (IC and OFC), whereas satiety was associated with changes in the prefrontal cortex (PFC) (Tataranni et al., 1999). It has been postulated that the PFC has inhibitory effects on the hypothalamic regions that regulate hunger in humans, thus promoting meal termination. In effect, there is decreased activity of the ‘‘hunger’’ areas when the individual is satiated. Since the PFC is known to have inhibitory projections to these areas, termination might be provoked by the “anorexigenic” PFC down-regulating neuronal activity in the orexigenic CNS regions. PET analyses indicate that gastric distension (as a mechanic visceral stimulus to simulate satiety) provokes activation of the inferior frontal

gyrus (a component of the PFC) (Le et al., 2006). This suggests that this region plays a pivotal role as a convergence zone for processing foodrelated/visceral stimuli, and for the coordination of states of appetite and satiety. In addition, it appeared that the amygdala was involved in the coordination of appetitive behaviors (Baxter and Murray, 2002; Cardinal et al., 2002; Holland and Gallagher, 2004). Specifically, it is thought that the amygdala, through interactions with the OFC, signals the hedonic value of a stimulus or object (Holland and Gallagher, 2004). By interacting with posterior visual areas, this region may be important in defining the salience of biologically relevant stimuli (LaBar et al., 2001). The processing of hunger and satiety cues appears to be contingent on the inherent reward value of the food, the individual’s motivational state, and other factors that could influence motivational processes (such as stressor exper­ iences). In this regard, it was found that when students were highly motivated to eat chocolate (and rated the chocolate as being highly pleasant), rCBF increased in the medial OFC and IC. Conversely, rCBF in the PFC and lateral OFC increased with satiety as the chocolate became less pleasant (Small et al., 2001). Killgore and colleagues (2003) likewise tested whether images of high-caloric foods would have greater motivational salience in the amygdala and PFC relative to images of low-caloric foods, as measured by fMRI (Killgore et al., 2003). They found activation of the amygdala irrespective of the caloric content of the food image, but significant activation in the PFC following the presentation of high-caloric foods. Given these data and those supporting the inhibitory role of the PFC on food intake (Del Parigi et al., 2007), it is likely that the inhibition of food reward is probably a goal of this prefrontal-orbitofrontal loop. Thus, in addition to the differential recruitment of brain areas in conditions of satiety and hunger, the neural activity in these areas can be modulated by the incentive value of food stimuli

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21.5  Peripheral signals regulating energy balance

(i.e., the inherent reward value) as well as differences in motivational state. Several studies have shown that there is overlap between brain regions associated with food intake and in anticipation of or in response to stressful stimuli. For example, anticipation of public speaking was associated with activation in the hippocampus (HC)/amygdala (Tillfors et al., 2001). Subjects asked to solve difficult mathematical problems showed increased activation of the ventral right prefrontal cortex (rPFC) and insula/putamen, as measured by perfusion MRI (Wang et al., 2007). rPFC activation persisted well beyond the termination of the stressor task, suggesting a heightened state of vigilance or emotional arousal (Wang et al., 2005). Based on several PET studies, it also appears that subcortical DA was increased in response to physiological (Adler et al., 2000) and psychological (Pruessner et al., 2004) stressors. For instance, Pruessner and colleagues reported that a mental arithmetic stressor produced brain activations involving the occipital, parietal and motor cortex. The most profound effect of the stressor, however, seemed to involve a deactivation across a network of limbic structures, including the hippocampus, amygdala, insula, hypothalamus, ventral striatum, medioorbitofrontal cortex and posterior cingulate cortices (Pruessner et al., 2008). Interestingly, indi­viduals who reacted to the stressor with a significant increase in circulating cortisol showed the greatest deactivation in the aforementioned brain regions (Pruessner et al., 2008). These results suggest that this set of limbic system structures shows high activity during nonstressful states, serving as a threat-detecting system. The system constantly scans the environment for signs of incoming danger or threat. Once such a condition is met, the activity of this system is actively curtailed to initiate the alarm response consisting of hormonal and physiological responses of the HPA and other axes to allow adaptation of the organism in response to the threat. As such, one mechanism for acute

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stress-induced changes in food consumption may be a redirecting of resources normally available for feeding processes towards basic survival needs. However, under conditions of chronic stress, continuously elevated adrenal glucocorticoids, through a compromised alarm system, may be associated with a shift towards hedonic feeding patterns.

21.5  Peripheral signals regulating energy balance The brain regions discussed here appear to be fundamental in integrating both internal and external cues, promoting appropriate physiological and behavioral responses to maintain homeostasis. Some of these brain regions are also influenced by peripheral hormones which are fundamental in determining consumption and satiety.

21.5.1 Leptin The discovery of leptin, the protein encoded by the Ob gene, could be considered among the most important research findings in the field of energy balance. Produced primarily by adipocytes, leptin is secreted into the circulation and targets the brain and peripheral organs to ultimately decrease food intake, increase energy expenditure and reduce adiposity (Zhang et al., 1994; Campfield et al., 1995; Halaas et al., 1995). Mutation of this gene or the gene encoding the leptin receptor results in morbid obesity, insulin resistance and infertility (Zhang et al., 1994; Chen et al., 1996). In addition to being a metabolic hormone, leptin also acts as a modulator of the HPA axis and might influence the effects of stressors on systems other than those regulating energy homeostasis (Lu et al., 2006). Indeed, leptin targets critical brain regions responsible for the regulation of the HPA axis, such as the

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hippocampus, the brainstem and the hypothalamic PVN (Hakansson et al., 1998; Hosoi et al., 2002). Leptin receptors are also found in regions where corticosterone and CRH may affect food intake and energy balance. For example, leptin targets the raphe nuclei and the VTA in the midbrain, where it modulates the activity and release of 5-HT and DA, respectively (Finn et al., 2001; Fernandez-Galaz et al., 2002; Clark et al., 2006; Fulton et al., 2006; Hommel et al., 2006). The presence of receptors in these regions also suggests that leptin may directly influence reward-seeking behaviors, and affective tone, related to feeding. Support for this idea comes from studies where leptin promoted anhedonia reflected by an in­crease in the reward threshold (i.e., reduced value of the reward) among rats responding to rewarding electrical stimulation from the lateral hypo­ thalamus (Fulton et al., 2000). Like several other hormones, leptin is influenced by stressors (Konishi et al., 2006). It has been suggested that, through actions on reward mechanisms, it might contribute to stressrelated pathology such as depression (Anisman et al., 2008). In fact, in rodents the depressivelike behavioral disturbances introduced by a chronic stressor could be antagonized by systemic or intrahippocampal (but not hypothalamic) leptin administration (Lu et al., 2006). However, the data concerning leptin variations in relation to depression in humans have been inconsistent (Deuschle et al., 1996; Kraus et al., 2001; Atmaca et al., 2002; Westling et al., 2004; Kauffman et al., 2005; Eikelis et al., 2006; Jow et al., 2006; Otsuka et al., 2006; Pasco et al., 2008), and the source for these inconsistencies is uncertain. However, these may have been related to variability of the features of depression across individuals. While some display typical features (e.g., reduced eating and sleeping), in atypical depression, symptoms may be comprised of reverse neurovegetative features (e.g., increased eating, sleeping). More research is needed to obtain a clearer picture of the specific role of leptin on depressive disorders. Given the relation

between leptin, CRH and glucocorticoid pro­ cesses, it can be suggested that leptin contributes to the different stressor-provoked changes of ingestion evident in depressive illness.

21.5.2  Insulin The role of insulin in the regulation of energy balance is well established, and it is suggested that its interactions with glucocorticoids, leptin, ghrelin and cytokines play a critical role in the development of obesity and metabolic anomalies seen after continuous exposure to stressful events (Landsberg, 2001). Like leptin, insulin secretion from the pancreas is increased in animals exposed to stressors (Black, 2006; Innes et al., 2007). Acutely, insulin increases glucose utilization in the periphery and targets the hypothalamic ARC to reduce NPY synthesis and ultimately decrease food intake (Woods et al., 1996). Nevertheless, chronic stressor exposure leads to insulin insensitivity through a number of mechanisms that may be mediated by elevated glucocorticoid action (Black, 2006). One reason for this is that insulin resistance is ameliorated by adrenalectomy (Saito and Bray, 1984; Duclos et al., 2005). Interestingly, insulin depletion also prevents some of the obesogenic effects of glucocorticoids, particularly those where stressors may promote increased consumption of high-caloric foods (la Fleur et al., 2004). Thus, in the absence of insulin, animals with elevated corticosterone levels may not experience the soothing effects of “comfort foods” (la Fleur et al., 2004). Given that dopamine cells in the VTA express insulin receptors (Figlewicz et al., 2003), it is possible that insulin targets midbrain dopamine cells to sensitize them to the action of glucocorticoids, thereby enhancing food-seeking behaviors.

21.5.3  Ghrelin Ghrelin is a stomach-derived peptide that has generated considerable attention because, unlike

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other peripheral signals, ghrelin stimulates food intake, increases adiposity and decreases metabolic rate (Kojima et al., 1999; Tschop et al., 2000). The effects of ghrelin, like those of glucocorticoids, leptin and the other hormones discussed to this point, occur both centrally and peripherally (Kojima and Kangawa, 2005). Within the hypothalamus, ghrelin receptors are concentrated in the VMH and ARC, where they target primarily NPY/AGRP neurons to modulate its orexigenic properties (Nakazato et al., 2001). Ghre­lin also directly stimulates orexin cells in the LH as well as cells in the PVN, VMH, DMH and SCN, suggesting a large degree of complexity in its effects within the hypothalamus (Guan et al., 1997; Toshinai et al., 2003; Zigman et al., 2006). In addition to its hypothalamic actions, ghrelin also targets extrahypothalamic structures that overlap with targets of glucocorticoid action, including the hippocampus, the brainstem and the midbrain VTA, the substantia nigra and the raphe nuclei (Guan et al., 1997; Abizaid et al., 2006; Zigman et al., 2006). Microinfusion of ghrelin into the VTA leads to increased food intake, whereas microinfusion of antagonists decreased compensatory food intake after a fast (Guan et al., 1997; Carlini et al., 2004; Naleid et al., 2005; Abizaid et al., 2006; Zigman et al., 2006). Interestingly, ghrelin injections increase food-related imagery and stimulate the activity of reward pathways in human subjects, including the PFC and amygdala. This further implicates ghrelin in appetitive responses to incentive cues (Schmid et al., 2005; Malik et al., 2008). Peripherally, ghrelin targets the pituitary to enhance the release of growth hormones and stress hormones such as ACTH and pro­ lactin (Arvat et al., 2001; Stevanovic et al., 2007). Ghrelin also stimulates the release of corticosterone, an effect mediated by the increases in the release of ACTH (Stevanovic et al., 2007). It is notable that although adrenalectomy reduces food intake and body weight, the orexigenic effects of ghrelin are not affected by this manipulation, supporting the idea that ghrelin does

not promote food intake through the stimulation of corticosterone secretion (Proulx et al., 2005). In this regard, ghrelin also stimulates the proliferation of adipocytes, which might underlie the obesogenic effects of this hormone (Kim et al., 2004; Zwirska-Korczala et al., 2007). There is evidence that ghrelin levels fluctuate in response to acute and chronic stressors (Kristenssson et al., 2006; Ochi et al., 2008), but little is known about the potential role of ghrelin in the metabolic alterations that follow continuous exposure to stressors. In humans, an acute stressor (the Trier Social Stress Test; TSST) increases plasma ghrelin and cortisol levels, although the post-stress increase in the urge to eat found to occur in some individuals was unrelated to acute changes in plasma ghrelin levels (Rouach et al., 2007). Nevertheless, little is known about ghrelin responses to chronic stressors and their possible interactions. There are several potential mechanisms by which ghrelin and corticosterone might influence metabolic processes. These include the interaction of stressor-induced corticosterone and ghrelin action on the melanocortin system to modulate sympathetic outflow; VTA and substantia nigra functioning to regulate motivational aspects of feeding; and actions at the hippocampus to regulate feedback mechanisms that keep HPA activity in check.

21.6  Conclusion The obesity epidemic is often viewed as the outcome of an inherited genetic predisposition to store energy in the form of adipose tissue in combination with sedentary lifestyles. The current review offers stress as a possible factor in the generation of obesity and metabolic syndrome. Here, we have reviewed evidence that acute activation of the HPA axis affects brain and peripheral organs to affect appetite. Continued stimulation of this system results in

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severe energetic dysregulation leading to obesity, insulin resistance, cardiovascular disease and early death. There are numerous mechan­ isms that engender these pathological conditions, including effects on brain regions that regulate metabolism, autonomic function, and behavioral processes that include motivational, cognitive and affective behaviors. Particular emphasis has been placed upon regulatory and autonomic processes. However, a therapeutic agent might be developed on the basis of an understanding of processes linking stress and the soothing effects of high-caloric foods, as well as a better understanding regarding the contribution of corticosterone in relation to the feeding, metabolic and rewarding effects of leptin, insulin, dopamine and ghrelin. It could be speculated that behavioral and cognitive-based therapies aimed at reducing stress, as well as modifying behavior via programs that diminish the incentive value of high-calorie diets while enhancing the incentive value of physical activity as a means of reducing stress, may prove to be effective clinical tools to reduce obesity. It has been shown that physical activity is associated with the release of so-called “feel-good” endorphins in frontolimbic brain structures that may mediate some of the therapeutically beneficial consequences of exercise on depression, stress and anxiety in patients. As such, interventions introducing physical activity into one’s daily life could serve to promote the same “reward” as high-calorie foods, but without the detrimental health consequences in the long term. In time, the observed health benefits of increased physical activity may serve as motivation to adopt more balanced and nutritious eating patterns as an adjunct to an overall healthier lifestyle. Thus, in order to reduce the physical, mental and economic costs of the current obesity epidemic, it is imperative that preventative strategies that involve a remodeling of the notion of “eating for pleasure” are introduced early on and promoted throughout the lifespan.

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C H A P T E R

22 Eating Behavior and Its Determinants: From Gene to Environment John M. de Castro College of Humanities and Social Sciences, Sam Houston State University, Huntsville, TX, USA

o u t l i n e 22.1  Introduction

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22.2  Genes

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22.6 Discussion

22.1  Introduction A large set of compelling evidence has already been presented in regard to the role of physiology in the control of food intake and body weight. That is, however, only part of the story. The environment also plays a major role. In fact, the environment may be the single most important influence on intake in the short term, but also in the long-term control of body weight. However, distinctions between physiology and environment are no longer as clear as originally thought, since they appear to interact in subtle and important ways to control intake and body size (de Castro, 2004a).

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There are significant clues that physiology plays a less important role than currently acknowledged. First, if physiology were in complete control, then body weight would remain relatively stable. This is not in fact the case. On an individual level, significant body-weight changes can occur at any age and be maintained (Pearcey, 2000). At the population level, there has been a marked increase in body weight over the past several decades (Flegal, 1999; Mokdad et al., 1999; Flegal, et al., 2001; Ogden et al., 2002). Second, if the physiology were completely responsible for regulation, then daily intake should be fairly constant. This is also not the case: the total food energy intake fluctuates widely from day to day

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(Morgan et al., 1987; Tarasuk and Beaton, 1991; de Castro, 1998; Hartman et al., 1999). It might be possible that regulation occurs across days such that intake on one day affects intake on subsequent days – except that, again, this is not the case. There is no significant relationship between one day’s food intake and that on the subsequent day, and only very weak relationships to the intake two and three days later (de Castro, 1998, 2000). Hence, neither body size nor daily intake appears to be tightly regulated, suggesting that environmental influences have a wide range in which to affect intake.

22.2  Genes Genes have marked influences on body size and composition (Bouchard et al., 1985, 1986; Stunkard et al., 1986, 1990; Bouchard, 1991; Hewitt et al., 1991; de Castro, 1993a; Allison et al., 1996). It stands to reason that if body size is influenced by genes, then the ingestion of nutrients that underlies the development and maintenance of body mass should also show genetic influences. We investigated this notion by studying the food and fluid intakes of adult twins living independently in their normal environments. Detailed eating behaviors along with contextual and psychological variables were measured with a 7-day diet-diary technique (de Castro, 1994a, 1999a, 2006a). Twins were required to make detailed records of their intake in a pocket-sized diary for 7 consecutive days; in addition, they were asked to record their feelings and the nature of the environmental context. This procedure has been shown to have reasonable levels of reliability and validity (de Castro, 1994a, 1999a, 2006a). A heritability analysis of the twins eating behavior revealed that 42 percent of the variance in daily intake, independent of body size, was accounted for by inheritance (de Castro, 1993b). In addition, carbohydrate, fat, protein, alcohol

and water intakes were all significantly affected by inheritance in a manner that was, to some extent, independent of the overall daily intake (de Castro, 1993b). Since daily intake occurs in the form of meals, it follows that inheritance should also affect meal intake. Indeed, independent of the level of overall intake, heredity accounted for 28 percent of the variance in the meal sizes and for 34 percent of the variance in the meal frequencies (de Castro, 1993a). These findings demonstrate that genes have pervasive influences on body size and intake, including separate and independent effects on height, weight, overall and macronutrient intake, meal size, and meal frequency. One way that the physiology can affect intake is by influencing gastrointestinal physiology. Indeed, the fuller the stomach at the beginning of the meal, the smaller size of the meal eaten (de Castro et al., 1986). Genes appear to affect not only the amount of food in the stomach, but also the degree of restraint on intake exerted by stomach filling. Genetic influences have been found to affect the amount estimated to be in the stomach before and after meals (de Castro, 1999b) (Figure 22.1). When the relationship between stomach content and the size of the meals is established for each individual, the slope of the relationship provides a measure of how responsive that individual is to his or her stomach content. Genetic influences have also been demonstrated for the slope of the relationship between stomach content and the amount eaten (de Castro, 1999b) (Figure 22.1). Hence, how full the stomach is at the start of the meal and how big a suppressive effect the stomach content has on subsequent intake are both significantly influenced by genes.

22.3  The environment Although genes clearly influence intake, the majority of the variance in intake is accounted

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Heritability analysis of factors affecting intake Mean variable values %of the variance accounted for

Heredity

Individual environment

Familial environment

100% 80% 60% 40% 20% 0%

Stomach content

Hunger

%Morning intake

%Afternoon intake

%Evening intake

#of people

%of the variance accounted for

Regression slopes 100%

Heredity

Restraint

Density

Palatability

Difference slopes

Individual environment

Familial environment

80% 60% 40% 20% 0% Stomach content

Hunger

#of people

Morningafternoon

Density

Morningevening

Afternoonevening

Hi-Lo palatability

Figure 22.1  Heritability analysis of factors affecting intake. The proportion of the variance in the factor means (upper panel) and the slopes of the relationships between the factors and the meal size (lower panel) that could be accounted for by the individual environment (white), family environment (striped) and heredity (black) in the linear structural modeling heritability analysis of the twin data.

for by environment. While inheritance accounts for 42 percent of the variation in overall intake, 58 percent is due to environment. While inheritance accounts for 28 percent and 34 percent, respectively, of the variation in meal size and meal frequency, 72 percent and 64 percent, respectively, is due to environment. In addition, as we will see, the influence of genes is, at least in part, due to gene–environment interactions and genetic influences on environmental selections. Some of the most important environmental effects on behavior derive from social influences

and food intake is no exception. When people eat with other people, they eat, on average, 44 percent more than when they eat by themselves (de Castro and de Castro, 1989). The degree of social facilitation of food intake is related to the number of people present. When one other person is present, 33 percent more is eaten, while 47 percent, 58 percent, 69 percent, 70 percent, 72 percent and 96 percent increases were associated with two, three, four, five, six, and seven or more people, respectively (de Castro and Brewer, 1992). In addition, these social influences are greater when family and

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friends are present than with other types of eating companions (de Castro, 1994b). An important but rarely recognized environmental influence on intake is the time of day. Over the course of the day, the amount eaten in meals increases (de Castro, 1987) and the amount of time before the next meal decreases. Hence, the period of satiety produced per unit of food energy ingested declines over the day, becoming quite low by late evening. This decrease in the satiating effect of food over the course of the day can lead to differing intakes, depending on when food is ingested. When a large proportion of intake is eaten in the morning, lower overall intake occurs over the day. In contrast, when a high proportion of intake is eaten late in the day, there is a higher overall intake for the day (de Castro, 2004b). Not only does the time of day make a difference in intake; so too does the time of the week, with substantially larger intakes on weekend days (de Castro, 1991a). This extends even to the season of the year, with significantly greater intakes in the fall (de Castro, 1991b). Psychological phenomena are also major influences on intake. The greater the level of subjective hunger, the more will be eaten; the more that is eaten, the lower the level of hunger (de Castro and Elmore, 1988). To some extent, the influence of hunger appears to be independent of the stomach content. In addition, many humans attempt voluntarily to establish control over their food intake, in a phenomenon labeled dietary restraint. Higher restraint is associa­ ted with lower overall intake, smaller meals and lower fat intake (de Castro, 1995). In addition to the social environment, the physical environment also has important effects upon the amounts ingested (Stroebele and de Castro, 2004a). When eating while watching television, people tend to eat more over the day (Stroebele and de Castro, 2004b). In addition, people eat substantially more in restaurants than they do at home or at work (de Castro et al., 1990; Stroebele and de Castro, 2004a, 2006).

The characteristics of the foods consumed are also important determinants of the amounts ingested. Palatability is a hypothetical construct that stands for the stimulus qualities of a substance that affects its acceptability (Rogers, 1990). The more palatable a food, the greater the amount consumed, with highly palatable meals being 44 percent larger than neutral or unpalatable meals (de Castro et al., 2000). The energy density of the diet ingested is significantly associated with intake (Yao and Roberts, 2001): the more energy per gram of food, the more total energy is ingested in the meal (de Castro, 2004c, 2005). Thus, the social, temporal, psychological and dietary environments have substantial impacts on the amounts ingested in meals and over a day.

22.4  Genes–environment interactions Obviously, the control of intake is a complex phenomenon involving a myriad of physiological and environmental variables that independently and interactively influence intake. Classically, genes have been viewed as primarily determining anatomical structure. It appears that inheritance has much more subtle and complex effects, and can influence the environments that an individual chooses to occupy and the impact of these environments on intake. The influence of genes on the social facilitation of intake was explored by analyzing the relationship between the number of people present and the meal intake of twins, reported in the 7-day diet diaries. Interestingly, there were significant inheritance effects not only on the number of eating companions at meals, but also on the choice of the companions, accounting for over 25 percent of the variance in the likelihood of eating with family, friends and a spouse (de Castro, 1997) (see Figure 22.1). This observation is quite remarkable, and clearly

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22.4  Genes–environment interactions

muddies the distinction between inheritance and environment. Genes appear not only to affect physiology, but also the kind of environments that people choose to occupy. The genetic influence, however, extends beyond simply impacting the choice of environments. It also appears to impact the magnitude of the effect of these environments on intake. The twin data showed that heredity affected the extent to which intake was increased by the presence of other people. The correlation between the number of people present and the amount eaten was significantly heritable (de Castro, 1997). More significant, however, was that the slope of the regression between the number of people present and meal size was also considerably affected by inheritance (see Figure 22.1). The slope of the regression can be viewed as a measure of the responsiveness of the individual to social effects. These data indicate that genetic factors affect not only the number and types of people at meals, but also the impact of these companions on intake. This is a remarkable intrusion of genes into environmental influences on intake. Looking in a similar way at the heritability of time-of-day associations with intake, the twin data revealed the significant influences of genes on the time of day at which people choose to eat. Some people eat a larger portion of their daily intake in the morning, others do so in the afternoon and yet others in the evening, and these proportions were found to be heritable (de Castro, 2001a) (see Figure 22.1). The differences in the proportions of intake ingested during different periods of the day can be used as a metric of the individual’s responsiveness to time of day. The twin data showed that the differences in the proportions of intake eaten during the morning and afternoon, the morning and evening, and the afternoon and evening were significantly heritable (see Figure 22.1). This indicates that inheritance affects not only the time of day at which people choose to eat, but also the impact of that time selection on intake. As with social

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facilitation, the results with the diurnal rhythms of intake indicate that genes affect environmental choices and their impact on intake. Genes also appear to influence the psychological state of the individual and the effect of that state on intake. The twins were asked to rate their level of hunger before and after meals. Heritability analysis of these data revealed that the subjective levels of hunger at which individuals initiate a meal and at which they finish the meal are significantly heritable (de Castro, 1999c) (see Figure 22.1). The strength of the relationship between hunger and intake provides a metric of the individual’s responsiveness to the subjective state of hunger. Using this measure, the twin data suggest that this responsiveness is also heritable. Significant heritabilities are present for both the correlation and the slope of the regression between hunger and meal size (see Figure 22.1). The relationship between the amount eaten during the meal and the change in the level of subjective hunger produced by that intake provides a measure of individuals’ psychological responsiveness to their intake. The twin data for this measure indicate that this responsiveness is heritable. Significant heritabilities were calculated for both the correlation and the slope of the regression between meal size and change in subjective hunger. Thus, heredity has a variety of significant influences on the hunger–intake relationship, including how hungry the individual is at the start of the meal and how big of an effect that hunger has on subsequent intake, as well as how hungry individuals are when they have finished eating and how big an impact intake has on changing perceived hunger. Individuals’ tendency to restrain their energy intake, the psychological characteristic of cognitive restraint, also appears to be affected by genes. Cognitive restraint was measured by having the twins complete the three-factor eating questionnaire (Stunkard and Messick, 1985). An analysis of these data revealed significant genetic effects, accounting for 44 percent of the

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variance in cognitive restraint (de Castro and Lilenfeld, 2005) (see Figure 22.1). Hence, it ap­pears that individuals inherit a tendency to restrain their intake. Characteristics of the food, such as its attractiveness and palatability, appear also to be affected by inheritance, as well as their impact on the amounts eaten. Twins were asked to rate the attractiveness of the food before and after the meals. Heritability analysis of these data indicated significant heritability, accounting for 23 percent of the variance in the palatability ratings before the meal (de Castro, 2001b) (see Figure 22.1). Similarly, significant heritability was found for the amounts ingested in both low-palatability and high-palatability meals. A metric of the responsiveness of the individual to palatability is the difference in the amount ingested between meals of low and high palatability. This measure was also found to be significantly heritable (see Figure 22.1). The dietary energy density is another characteristic of the food that appears to be affected by inheritance. Analysis of the twin data revealed that dietary energy density is a highly heritable factor, with inheritance accounting for over 40 percent of the variance (de Castro, 2006b) (see Figure 22.1). Thus, the preferred diet density, which in turn has a major influence on intake, appears to be affected by genes. Hence, the data suggest that genes influence not only how much is eaten in a meal, but also the preferred palatability of the food, the reactivity of the individual to that palatability, and the selected level of dietary energy density of the food.

these factors and the magnitude of their impacts on intake appear to be affected by genes. Given this level of complexity, it is difficult to comprehend how all of these simultaneously present variables are combined to result in some form of control of intake. In order to summarize all of these variables’ influences on intake, we developed the general model of intake regulation (de Castro and Plunkett, 2002) (Figure 22.2). The conceptual system of the model includes the assumption that intake is affected by a wide range of physiological and environmental factors. Each of the factors is assumed to account for only a small portion of the variance in intake. In addition, the level and impact of these factors can vary from individual to individual, and these individual differences are affected by heredity. In the model, factors are sorted into two sets, labeled as uncompensated (primarily environmental) and compensated (primarily physiological) factors. A key difference between these types of factors is that compensated factors have negative-feedback loops with intake, simultaneously affecting and being affected by intake,

General intake regulation model

Uncom pensated factors U

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22.5  A general model of intake regulation It is clear from the data reviewed above that food intake is affected by a large array of physiological, environmental, social, psychological and dietary factors. Both the preferred level of

Figure 22.2  General intake regulation model. The general intake regulation model, wherein intake (I) is controlled by two sets of factors; compensated factors (Ci) that both affect and are affected by intake via negativefeedback loops, and uncompensated factors (Ui) that affect but are not affected by intake. Inheritance affects the system by determining the preferred level for intake, and compensated and uncompensated factors also by determining the level of impact of the factors on intake (W).

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22.5  A general model of intake regulation

while uncompensated factors affect intake, but are not affected by intake. Each of the factors is assumed to have preferred levels that are in turn influenced by heredity. The model further specifies that each factor has a particular magnitude of influence on intake. These impact factors, with weights varying between 1 and 1, are assumed to differ among individuals and be affected by heredity. The general model of intake regulation is a descriptive system. In order to ascertain if the model can produce predicted outcomes that parallel intake and body-weight changes seen in the natural environment, a computer simulation was implemented. The simulation was designed to test the model’s response to changes similar to those that occur in the natural environment, and individual differences in responsiveness to environmental changes. For this simulation, an instantiation of the model was implemented, with overall daily food energy as the intake variable. It was found that the model’s behavior could be well represented by a rather simple instantiation that included only four hypothetical uncompensated factors and four hypothetical compensated factors, in addition to body weight. The parameterization of the model was arbitrary, except that it was specified that the sum of all

of the positive and negative weights would be equal to zero (de Castro, 2006b). The model’s response to a simulated change in the environment was investigated by doub­ ling the level of one uncompensated factor. In response to the change, initially the body weight became unstable and oscillated at a markedly higher level before stabilizing and sett­ ling at a 7 percent higher body weight (Figure 22.3). The model then maintained this new body weight as long as no further changes occurred. Subsequently, the model’s response to differences in individual responsiveness was investigated. The weighting factor was manipulated in conjunction with the doubling of the uncompensated factor, as above. When the weighting factor was low, the doubling of the uncompensated factor produced only a small increase in body weight. However, when the weighting factor was large, the model’s output reflected a large increase in body weight (Figure 22.3). The output body weight was found to depend upon both the amount of increase in the level of the uncompensated factor and the magnitude of the weighting factor. Hence, the model predicted that a sustained change in the environment would trigger a sustained change in body weight; its magnitude would depend on

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Figure 22.3  Model output after doub­ling uncompensated factor with varying weights. Results of a computer simulation of the general intake regulation model in response to a doubling of one uncompensated factor with seven different levels of impact weights. Four hypothetical compensated factors and four hypothetical uncompensated factors with varying weights were set to produce a stable output from the model of 60-kg body weight. One uncompensated factor’s level was doubled. Seven simulations were performed with differing weights for the doubled uncompensated factor.

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the individual’s inherited responsiveness to the factor.

22.6  Discussion It should be clear from the above review that the control of food intake regulation is a very complex phenomenon that defies simple description. It involves large numbers of factors and processes that not only act individually, but also interact. This should, however, come as no surprise. Food intake is so essential to the survival of the individual that there must have been extraordinary evolutionary pressures to produce control mechanisms with great flexibility and adaptability, able to operate in a wide variety of environments and conditions. This suggests that the genetic underpinnings of the system would be multifaceted and deeply involved in multiple mechanisms, from physiological to social and environmental. This is exactly what we observe. It is surprising, however, that genes and the environment are so intertwined. Historically, genes were perceived as influencing anatomical structure, while the environment had independent effects. However, the reviewed findings suggest that inheritance influences not only physiology, but also the environment and the responsiveness of the individual to the environment. These genetic influences on the envi­ ronment probably are the result of the operation of inherited psychological characteristics. For example, the preferred number of people present at meals may well result from an inherited extraversion or sociability factor (Saudino et al., 1997). These factors would then tend to prompt the individual to seek out preferred levels of companionship. Inborn differences in circadian oscillators (Kolker and Turek, 1999) or in the gustatory system (Matsunami et al., 2000) might explain how genes affect the time of day that people choose to eat, and their preferred

palatability levels. Nevertheless, whether direct or indirect, genes have the capacity to affect the selection of environments that an individual chooses to occupy, and also the impact those environments might have on that individual’s behavior. The level of food intake is a complex integ­ ral of the effects of a large number of influences. The general model of intake regulation is a useful heuristic to represent the complexity of intake regulation. It includes the ideas that some of the significant influences on intake originate in the environment, some from heredity, and many from the interaction of heredity and environment; some have negative-feedback loops with intake, while others do not. The simulations of the model suggest an explanation for how individual and societal changes may underlie large changes in the incidence of obesity. The model’s simulation results suggest that when an individual’s environment changes, there would be commensurate changes in intake and body weight. Indeed, body-weight changes occur most frequently during the late teens to late twenties (Pearcey, 2000). During this time, large chronic changes in environment occur: individuals leave home, enter college, marry, have children and begin careers. The model well predicts that such changes in the environment would be paralleled by changes in body weight, as observed in reality. The recent societal increase in body weight (Flegal, 1999; Mokdad et al., 1999, 2003; Flegal et al., 2000; Ogden et al., 2002) has been paralleled by unprecedented changes in the environment for both energy expenditure and intake. The modern world has produced a marked reduction in activity and thereby energy expenditure. The model would predict that this would be an “obesogenic” environment (Ravussin and Bouchard, 2000), resulting in a new, higher level for body weight. In addition, the eating environments have been markedly altered, with increases over the past few decades in dietary energy densities, portion sizes, palatability, variety and availability of

1.  From Brain to Behavior

REFERENCES

copious quantities of attractive foods, restaurant eating, breakfast-skipping and shifting of intake to the evening, television watching including incessant advertisements for food, home delivery, and attractive pre-prepared foods (Stroebele and de Castro, 2004). The model would predict that these changes would be more than sufficient to prompt an obesity epidemic (Hill and Peters, 1998). A further strength of the model is that it includes mechanisms than can account for individual differences in responsiveness. When individuals are immersed in new environments, some react and gain weight, while others appear unaffected. The model includes inborn responsiveness to environmental factors, and the simulations indicate that differences in responsiveness can markedly alter the effect of a change in the environment. This could also serve as a hypothesis to explain certain eating disorders. Inheritance appears to be an influence on the development of anorexia nervosa (Lilenfeld and Kaye, 1998; Bulik et al., 2000; Klump et al., 2001a, 2001b). From the ­perspective of the model, anorexia nervosa may be conceptualized as an inherited tendency to high levels of dietary restraint and a high inherited responsiveness to that restraint. The model also suggests what the nature of effective weight-control strategies might be in an individualized program that first detects what factor(s) the individual is most responsive to and then alters the level of these factors as required. The model clearly shows, however, that the changes must be maintained. Changes in intake and weight will only remain as long as the changes in the environment remain. As soon as the environment reverts to its prior condition, so too will body weight. Hence, the model predicts what is often observed: weight quickly reverts to its prior level when the dietary strategy is terminated. This strategy is applicable not only to weight loss, but also to weight gain – frequently desired in the elderly and in recovery from illness.

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In conclusion, the control of intake in freel­ iving humans is affected by a myriad of genetic, physiological, dietary, psychological, social and cultural variables. Each of these influences has large individual differences in both level and responsiveness. The general model of intake regulation provides an integrated and comprehensive account of how all these pieces might fit together to produce the level of intake and body weight in an individual and in populations.

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and fluid intakes of free-living humans. Appetite, 23, 179–192. de Castro, J. M. (1995). The relationship of cognitive restraint to the spontaneous food and fluid intake of free-living humans. Physiology & Behavior, 57(2), 287–295. de Castro, J. M. (1997). Inheritance of social influences on eating and drinking in humans. Nutrition Research, 17, 631–648. de Castro, J. M. (1998). Prior days intake has macronutrients specific delayed negative feedback effects on the spontaneous food intake of free-living humans. Journal of Nutrition, 28, 61–67. de Castro, J. M. (1999a). Measuring real world eating behavior. Progress in Obesity Research, 8, 215–221. de Castro, J. M. (1999b). Inheritance of pre-meal stomach content influences on eating and drinking in free-living humans. Physiology & Behavior, 66, 223–232. de Castro, J. M. (1999c). Inheritance of hunger relationships with food intake in free living humans. Physiology & Behavior, 67(2), 249–258. de Castro, J. M. (2000). Macronutrient selection in free feeding humans: Evidence for long term regulation. In H. R. Berthoud & R. J. Seeley (Eds.), Neural control of macro­ nutrient selection (pp. 43–59). New York, NY: CRC Press. de Castro, J. M. (2001a). Heritability of diurnal changes in food intake in free-living humans. Nutrition, 17(9), 713–720. de Castro, J. M. (2001b). Palatability and intake relationships in free-living humans: Influence of heredity. Nutrition Research, 21(7), 935–945. de Castro, J. M. (2004a). The control of eating behavior in free-living humans. In E. M. Stricker & S. C. Woods (Eds.), Handbook of the behavioral neurobiology, Vol. 14. Neurobiology of food and fluid intake (pp. 469–504). New York, NY: Plenum. de Castro, J. M. (2004b). The time of day of food intake influences overall intake in humans. Journal of Nutrition, 134, 104–111. de Castro, J. M. (2004c). Density and intake relationships in the eating behavior of free-living humans. Journal of Nutrition, 134, 335–341. de Castro, J. M. (2005). Stomach filling may mediate the influence of dietary energy density on the food intake of free-living humans. Physiology & Behavior, 86(1–2), 32–45. de Castro, J. M. (2006a). Varying levels of food energy selfreporting are associated with between group but not within subjects differences in food intake. Journal of Nutrition, 36, 1382–1388. de Castro, J. M. (2006b). Heredity influences the dietary energy density of free-living humans. Physiology & Behavior, 87, 192–198. de Castro, J. M., & Brewer, E. M. (1992). The amount eaten in meals by humans is a power function of the number of people present. Physiology & Behavior, 51, 121–125.

de Castro, J. M., & de Castro, E. S. (1989). Spontaneous meal patterns in humans: Influence of the presence of other people. American Journal of Clinical Nutrition, 50, 237–247. de Castro, J. M., & Elmore, D. K. (1988). Subjective hunger relationships with meal patterns in the spontaneous feeding behavior of humans: Evidence for a causal connection. Physiology & Behavior, 43, 159–165. de Castro, J. M., & Lilenfeld, L. (2005). The influence of heredity on dietary restraint, disinhibition, and perceived hunger in humans. Nutrition, 21(4), 446–455. de Castro, J. M., & Plunkett, S. (2002). A general model of intake regulation. Neuroscience and Biobehavioral Reviews, 26(5), 581–595. de Castro, J. M., McCormick, J., Pedersen, M., & Kreitzman, S. N. (1986). Spontaneous human meal patterns are related to preprandial factors regardless of natural environmental constraints. Physiology & Behavior, 38, 25–29. de Castro, J. M., Brewer, M., Elmore, D. K., & Orozco, S. (1990). Social facilitation of the spontaneous meal patterns of humans is independent of time, place, alcohol, or snacks. Appetite, 15, 89–101. de Castro, J. M., Bellisle, F., Dalix, A. M., & Pearcey, S. (2000). Palatability and intake relationships in free-living humans: Characterization and independence of influence in North Americans. Physiology and Behavior, 70, 343–350. Flegal, K. M. (1999). The obesity epidemic in children and adults: Current evidence and research issues. Medicine and Science in Sports and Exercise, 31(Suppl. 11), S509–S514. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2000). Prevalence and trends in obesity among US adults 1999–2000. Journal of the American Medical Association, 288(14), 1723–1727. Hartman, A. M., Brown, C. C., Plamgren, J., Pietinen, P., Verkasalo, M., Myer, D., et al. (1999). Variability in nutrient and food intakes among older middle-aged men. American Journal of Epidemiology, 132, 999–1012. Hewitt, J. K., Stunkard, A. J., Carroll, D., Sims, J., & Turner, J. R. (1991). A twin study approach towards understanding genetic contributions to body size and metabolic rate. Acta Geneticae Medicae et Gemellologiae, 40, 133–146. Hill, J. O., & Peters, J. C. (1998). Environmental contributions to the obesity epidemic. Science, 280, 1371–1374. Klump, K. L., Kaye, W. H., & Strober, M. (2001a). The evolving genetic foundations of eating disorders. Psychiatric Clinics of North America, 24(2), 215–225. Klump, K. L., Miller, K. B., Keel, P. K., McGue, M., & Iacono, W. G. (2001b). Genetic and environmental influences on anorexia nervosa syndromes in a population-based twin sample. Psychological Medicine, 1(4), 737–740. Kolker, D. E., & Turek, F. W. (1999). The search for circadian clock and sleep genes. Journal of Psychopharmacology, 13(4, Suppl. 1), S5.

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Rogers, P. J. (1990). Why a palatability construct is needed. Appetite, 14, 167–170. Saudino, K. J., Pedersen, N. L., Lichtenstein, P., McClearn, G. E., & Plomin, R. (1997). Can personality explain genetic influences on life events? Journal of Personality and Social Psychology, 72(1), 196–206. Stroebele, N., & de Castro, J. M. (2004a). The influence of ambience on food intake in humans. Nutrition, 20, 821–838. Stroebele, N., & de Castro, J. M. (2004b). Television viewing is associated with an increase in meal frequency in humans. Appetite, 42, 111–113. Stroebele, N., & de Castro, J. M. (2006). Influence of physiological and subjective arousal on food intake in humans. Nutrition, 22(10), 996–1004. Stunkard, A. J., & Messick, S. (1985). The Three-Factor Eating Questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29, 71–83. Stunkard, A. J., Foch, T. T., & Hrubec, Z. (1986). A twin study of human obesity. Journal of the American Medical Association, 256, 51–54. Stunkard, A. J., Harris, J. R., Pedersen, N. L., & McClearn, G. E. (1990). The body-mass index of twins who have been reared apart. New England Journal of Medicine, 322, 1483–1487. Tarasuk, V., & Beaton, G. H. (1991). The nature and individuality of within-subject variation in energy intake. American Journal of Clinical Nutrition, 54, 464–470. Yao, M., & Roberts, S. B. (2001). Dietary energy density and weight regulation. Nutrition Reviews, 59(8 Pt 1), 247–258.

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C H A P T E R

23 The Molecular Regulation of Body Weight: The Role of Leptin, Ghrelin and Hypocretin John J. Medina Department of Bioengineering, University of Washington, Seattle, WA, USA

o u tl i n e 23.1 Introduction

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23.1  Introduction In most mammals, energy homeostasis is modulated by two groups of signaling mechanisms. The first group is comprised of short-term signals arising from the gastrointestinal system that provide information about individual meal intake. The second group is comprised of collections of long-term signals arising from adiposity hormones. These provide information about overall energy stores. The interactions between

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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these signaling mechanisms inform the organism about energy supply and expenditure, and profoundly influence consumptive behavior and the perception of satiation. The human brain assists in maintaining energy homeostasis by integrating information about the body’s current energy needs with an unrelenting analysis of its energy stores (Schwartz, 2001). From appetite creation to satiation, complex interlocking neuronal circuits have evolved to modulate both the consuming and the expending aspects of feeding behavior.

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These circuits are subject to a wide variety of environmental influences, including emotional, social and cognitive factors. Such influences can be modulated by the rewarding (and punishing) aspects of food consumption (Berthoud, 2004). The signals derived from these systems’ interactions are exerted at the cellular level. The up-to-the-minute energy requirements of mammalian cells, for example, are satisfied by circulating glucose levels in the vasculature. The body goes to great lengths to ensure those concentrations of glucose are kept within specific and firmly regulated limits. To maintain such tight ranges, physiological processes must exert strict control over energy homeostasis. In aggregate, the ideal adult state exists where energy expenditure equals energy uptake, resulting in the maintenance of a relatively stable body weight. A disruption in this energy balance sheet can result in sustained bi-directional weight problems, pathological examples of which can include morbid obesity and anorexia nervosa. A great deal of progress has occurred in our understanding of the molecular interactions involved in regulating appetite. These advances promise to increase our genetic understanding of both overconsumptive and underconsumptive food-related behaviors. This chapter describes three well-characterized proteins involved in both the maintenance and disruption of the energetic balancing act.

23.2  Leptin, ghrelin and hypocretin Many appetite-specific neuronal circuits are found within the brainstem and hypothalamus. These regions respond to information about energy homeostasis through an interrelated network of hormonal signals arising from tissues throughout the body (Morton et al., 2006). Three of the best characterized signals are leptin, ghrelin and hypocretin. Circulating leptin,

a hormone generated by adipose tissue, provi­ des information about overall energy stores. Ghrelin, a protein secreted by the gut, communicates with the arcuate nucleus of the hypo­ thalamus and functions as a short-term meal initiation signal. Hypocretin (also called orexin) has a wide variety of functions. While it is a powerful stimulator of feeding behavior, mutations in its sequence are also responsible for narcolepsy, a disorder of arousal (Faraco et al., 1999). Hypocretin’s involvement in disparate physiological roles serves as an important example of the multi-functionality of most hormones involved in the maintenance of energy homeo­ stasis. In the following sections, we will briefly summarize the effects of these three proteins on the creation of feeding behaviors and the maintenance of energy balance in adult humans.

23.3  Leptin protein Isolating the leptin protein, first characterized more than a decade ago (Zhang et al., 1994), was pivotal in the quest to characterize appetite regulation at the level of the gene. Leptin’s overall function is to provide the brain with information about the body’s energy supplies, and it is specifically involved in mediating sensations of satiety (Meister, 2000). The protein is also involved in a variety of physiological processes. Leptin is encoded by the human obese gene OB, a sequence found on chromosome 7 (7q31.3) (Isse et al., 1995). The gene spans over 18 kb, and is composed of two introns and three exons. The protein, which possesses a putative signal sequence, is comprised of 166 amino acids (Gong et al., 1996). Though leptin is produced in abundance in adipose tissue, it is also found in smaller quantities in the heart, stomach, placenta, and mammary epithelium (Klok et al., 2006). Its expression profile is fairly simple: the protein synthesized in adipocytes consists of a single mRNA species (Masuzaki et al., 1995).

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23.3  Leptin protein

The metabolic effects of leptin are mediated through the leptin receptor. The receptor is encoded by the OBR gene (also called LEPR gene), localized to chromosome 1 (1p31) (Chung et al., 1996). The sequence of the receptor is much larger than its ligand’s, comprising18 exons and 17 introns. The fully processed protein is of 1162 amino acids. In contrast to the OB gene, the OBR sequence gives rise to a number of splice variants. One species, the OBRb variant, has a large intracellular domain, retains full signaling capability, and is widely expressed throughout the human brain (Campfield et al., 1996). There are particularly high concentrations of this receptor in the hypothalamus and cerebellum (Burguera et al., 2000), though it has also been found in other tissues such as the stomach, the vasculature and the placenta.

23.3.1  Leptin function Leptin is probably best known for its function in maintaining energy homeostasis, a role most thoroughly characterized in laboratory animals (Pelleymounter et al., 1995). Leptin exerts its effects by controlling regulatory feedback mechanisms that cause the brain to inhibit food intake. It thus plays a powerful role in the normal regulation of body weight in laboratory animals (Halaas et al., 1995). Montague and colleagues (1997) first demonstrated an energy-balancing role for leptin in humans by examining the metabolic history of a pair of morbidly obese children. They subsequently characterized explicit examples of congenital leptin deficiency: the patients presented a normal birth weight, but rapidly developed severe obesity. Consumptive behavior was associated with impaired satiety and accompanying hyperphagia. The associated chromosomal deficiency turned out to be a homozygous frameshift mutation in the OB gene. Other researchers showed a higher prevalence of obesity in patients heterozygous for the same (Farooqi et al., 2001).

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While much of the research literature indicates that leptin plays a role in the long-term maintenance of energy homeostasis, other research shows that leptin functions in the regulation of short-term food intake as well (Sobhani et al., 2000). The protein may even play a role in the adaptation to energy deprivation, such as that experienced during dieting (Weigle et al., 1997). Because of this expanding role, its potential involvement in disease states such as anorexia nervosa is under active investigation (Chan and Mantzoros, 2005).

23.3.2  Leptin mechanism of action Leptin is released into the vasculature by adipose tissues as a direct function of their energy stores (Golden et al., 1997). Once released, leptin penetrates the blood–brain barrier, gaining access to the neural tissues that control appetite. Leptin provides information about the body’s energy supplies by associating with leptin receptors found throughout the hypothalamus, particularly the arcuate nucleus (Saahu, 2004). Once bound, the protein exerts a wide variety of effects on hypothalamic neurons, including the expression of a variety of orexigenic and anorexigenic neuro­ peptides (Schwartz et al., 1996). Once expressed, the interaction of these peptides with specific hypothalamic cell populations supplies the brain with information about both feeding status and energy supplies. The result is appetite regulation. Clinical experiments designed to assess reac­ tions of patients with naturally occurring leptin deficiencies to exogenously supplied leptin have found correlated behavioral changes (Kolaczynski et al., 1996). These treated patients show changes in ingestive and non-ingestive behaviors resulting in a decreased appetite. A concomitant weight loss occurs. There is even an increase in physical activity, which may be related to the weight loss. Other researchers have demonstrated just the opposite effect: circulating leptin levels are positively associated

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with determining resting metabolic rates in humans (healthy, non-obese males) (Jørgensen et al., 1998). Exactly how those interactions affect human appetite regulation is a contentious issue. For instance, a number of researchers do not find that vascular leptin levels are associated with overall metabolic efficiency in either obese or non-obese patients – something one might predict, given its role in maintaining energy homeostasis (Licinio et al., 2004).

23.4  Ghrelin protein The biological effects of leptin are counterbalanced by those of ghrelin. Whereas leptin induces weight loss by stimulating sensations of satiety, ghrelin affects metabolic balance by providing appetite-stimulation signals (Cummings et al., 2001). Human prepro-ghrelin is encoded by the GHRL gene, a sequence located on chromosome 3 (3p25-26) (Kojima et al., 1999). The gene spans a small distance – about 5 kb – and is composed of three introns and four exons (Ueno et al., 2005). The unprocessed protein is of 117 amino acids, though its fully processed form is whittled to 28 amino acids (Kojima et al., 1999). Ghrelin is produced in abundance in the stomach – the tissue from which it was originally isolated – though the peptide has also been found in the adrenal cortex, the ovaries and the pancreas (Tortorella et al., 2003; Gaytan et al., 2005). Ghrelin is additionally produced by neurons in a wide variety of regions in the brain, including the pituitary and various regions within the hypothalamus (Cowley et al., 2003). The metabolic effects of ghrelin are mediated through the ghrelin receptor, the growth hormone secretagogue GHS-R. The gene for this receptor is also located on chromosome 3 (3q26.2), spans 4 kb, and is comprised of two exons and one intron (McKee et al., 1997). There

are two mRNA moieties processed from the primary GHS-R transcript, GHS-R1a (Howard et al., 1996) and GHS-R1b (Petersenn et al., 2001). The GHS-R1a variant is 366 amino acids in length and the GHS-R1b is estimated to be 289 amino acids (Petersenn et al., 2001). GHSR1a is found in a wide variety of tissues, including the infundibular hypothalamus and the human pituitary (Howard et al., 1996). It has been further identified in the testis (Gaytan et al., 2004) and ovaries (Gaytan et al., 2005). It should be noted that GHS-R1b has never been isolated from in vivo tissues (Petersenn et al., 2001).

23.4.1  Ghrelin function Several lines of evidence support the hypothesis that ghrelin functions as a short-term mealinitiation signal in humans. Preprandial elevation of ghrelin, for example, corresponds with a rise in self-reported hunger scores in non-obese human volunteers (Cummings et al., 2004). Exogenously supplied infusions of ghrelin in both non-obese and obese subjects induce perceptions of hunger, and result in elevated food intake (Wren et al., 2001). Stimulatory effects on gastric emptying have been positively correlated with increasingly elevated levels of ghrelin as well (St-Pierre et al., 2003). In laboratory rats, this elevation negatively affects short-term energy expenditure (Tschöp et al., 2000). Though a similar finding awaits confirmation in human subjects, the role of ghrelin in the short-term perception of hunger is unambiguous. Mounting evidence suggests that ghrelin additionally functions in the maintenance of longterm energy balance. Laboratory rodents exposed to daily doses of ghrelin become obese, increasing the amounts of adipose tissue by inhibiting the animal’s ability to utilize fat (Tschöp et al., 2000). A similar mechanism may operate in humans. Human BMI is inversely correlated with levels of ghrelin; circulating levels of this

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23.5  Hypocretin protein

hormone increase when obese patients begin to lose weight (Hansen et al., 2002). Patients with Prader-Willi syndrome (a genetic condition characterized by an insatiable appetite and accompanying weight gain) possess circulating ghrelin levels that are dramatically elevated when compared to healthy controls (Paik et al., 2004). Plasma ghrelin levels are lower in patients who have undergone gastrectomy; this decrease is thought to be the primary reason why such procedures result in weight loss (Ariyasu et al., 2001). Ghrelin levels decrease when patients diagnosed with anorexia nervosa begin to gain weight (Otto et al., 2001). Given such data, understanding the role of ghrelin in such pathologies is an area of intense investigation. Though ghrelin may exert long-term regulatory effects, these data must be examined in light of findings in animals that were deliberately constructed without a functional ghrelin gene (knockout animals, homozygous for ghrelin mutation). These mice enter adulthood with normal body composition, are of typical size, possess normal food intake, demonstrate unremarkable growth rates, and show no obvious changes in feeding behavior (De Smet et al., 2006). These data suggest that ghrelin, unlike leptin, is not crucial for the overall maintenance of energy homeostasis.

23.4.2  Ghrelin mechanism of action The concentration of ghrelin is highly regulated. Indeed, whether or not ghrelin is secreted by the stomach depends primarily on the organism’s nutritional state (Ariyasu et al., 2001). There are both preprandial (Cummings et al., 2004) increases and postprandial (Tschöp et al., 2001a) decreases, and they appear to exhibit diurnal variation (De Smet et al., 2006). The target appears to be, at least in part, specific hypothalamic nuclei (Cowley et al., 2003). A number of pathways have been proposed to explain the appetite-stimulating effects of ghrelin. One popular explanation hypothesizes that upon secretion from the stomach,

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ghrelin migrates through the blood–brain barrier and finds its way to various hypothalamic targets, where it exerts its biological functions (Banks et al., 2002). Another explanation posits more localized control: this theory considers the finding that regions in the hypothalamus are capable of synthesizing endogenous ghrelin. It is argued that the hypothalamus simply responds to metabolic intermediaries and the protein is produced locally (Cowley et al., 2003). A final pathway proposes that ghrelin bypasses the vasculature, at least in part, and exerts its brain-related effects through the vagal nerve and nucleus tractus solitarus (Ueno et al., 2005). Whatever pathway is the correct explanation, it is clear that ghrelin exerts meal-initiating signals by controlling the expression of various hypothalamic peptides. These include NPY (Nakazato et al., 2001), AgRP (Kamegai et al., 2001) and hypocretin, also called orexin (Toshinai et al., 2003). Because of their opposing roles, it is tempting to consider that leptin and ghrelin interact directly and complementarily in maintaining energy supply. It has been hypothesized, for example, that leptin or leptin-induced signals lead to the inhibition of ghrelin secretion by the stomach (Yildiz et al., 2004). This notion, at least in humans, is not unambiguously confirmed. One study has demonstrated a negative correlation between ghrelin and leptin concentration in fasting obese patients (Tschöp et al., 2001b). Another study examining obese pediatric populations showed no such correlation (Ikezaki et al., 2002).

23.5  Hypocretin protein Hypocretin has a functionally opposing role to the meal-initiation role of ghrelin, appearing to be involved in the regulation of feeding behavior, specifically with an increase in consumptive behaviors. Its exact role in energy homeostasis is not universally accepted. It serves, however, as a canonical example of the extent to which proteins

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involved in energy homeostasis can be multifunctional (Siegel, 2004). Among other things, mutations in the hypocretin gene cause human narcolepsy (Peyron et al., 2000). The isolation of hypocretin was simultaneously announced in two papers in 1998, 4 years after leptin had been characterized. De Lecea and colleagues termed their protein hypocretin (de Lecea et al., 1998). The name was a fusion of the hypothalamus, its neuroanatomic region of expression, and secretin, the gastric peptide with which hypocretin appeared to possess structural homology. Two novel mRNAs synthesized in the hypothalamus were isolated, putatively encoding two peptides termed Hcrt-1 and Hcrt-2. Independently, Sakurai also described the isolation of two novel peptides (Sakurai et al., 1998). These moieties were termed orexin-A and orexin-B, after the term orexis, which refers to desire or appetite. Though the terms are synonymous, the peptides will be referred to as hypocretin throughout this chapter. The human hypocretin gene is located on chromosome 17q21 (Preti, 2002). It is a relatively short sequence, composed of two exons and an intervening exon. Hcrt-1 peptide consists of 33 amino acid residues, and is folded back on itself in a classic “hairpin” which is held in place by disulfide bonds. Hcrt-2 is a peptide consisting of a linear chain of 28 amino acids. Both peptides are generated from a single, large peptide, preprohypocretin (de Lecea et al., 1998; Sakurai et al., 1999). The molecules are extraordinarily conserved, with sequences essentially identical in humans, pigs, dogs, sheeps, cows, rats and mice (Sakurai, 2004). The metabolic effects of hypocretin are mediated through the hypocretin receptors. Two have been isolated, which for purposes of this chapter will be labeled type 1 Hcrt receptors and type 2 Hcrt receptors. The mammalian receptors share 65 percent amino acid identity with each other, though they have different binding profiles. Type 1 binds with greatest affinity to Hcrt-1 peptide, while type 2 binds Hcrt-1 and Hcrt-2 with equal affinity (Zhu et al., 2003).

Not surprisingly, neurons expressing hypocretin and attendant receptors are concentrated in the hypothalamus. A dense collection of Hcrt neurons is found in the dorsomedial hypothalamic nucleus, medial to the fornix (Thannickal et al., 2000). These cells project widely throughout the rest of the brain. Other densely innervated Hcrt-positive regions include the raphe nuclei of the brainstem and locus coeruleus. Less densely innervated regions include the neocortex and limbic regions of the brainstem (Peyron et al., 1998; van den Pol, 1999). Hcrt-1 and Hcrt-2 receptors are also found throughout these innervated regions, but their expression patterns receptors can be remarkably tissue-specific. Receptor 1 is found mostly in the anterior olfactory nucleus, the cingulate cortex, the anterior hypothalamus and the locus coeruleus. Receptor 2 is found in much smaller quantities in those regions. Hcrt-2 distribution patterns are predominant in the medial septal nucleus, the hippocampal CA3 field and the arcuate nucleus of the hypothalamus (Trivedi et al., 1998; Marcus et al., 2001). Hcrt-expressing neurons are found outside the central nervous system. The protein, and its attendant receptors, have been isolated from the intestines and the pancreas (Siegel, 2004), the testis and the adrenal tissue (Jöhren et al., 2001). Determining its biological role in these diverse tissues remains an intense area of investigation.

23.5.1  Hypocretin function As mentioned, hypocretin turns out to have an astonishing array of functions besides the regulation of feeding behavior. Indeed, some researchers have hypothesized that it plays no direct role in energy homeostasis, serving rather as a hormone involved in mediating motor activity during periods of waking and sleeping (Siegel, 2004). Support for this notion came a year after its isolation, when a group of researchers demonstrated that mutations in the Hcrt peptide system

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23.6  Concluding remarks

were responsible for narcolepsy in dog populations. These mutations could occur in the Hcrt receptor gene (Lin et al., 1999) or in the Hcrt gene itself (Chemelli et al., 1999). The non-primate symptoms were remarkably similar to human narcolepsy, and the role of Hcrt in human sleep dysfunction was eventually established (Peyron et al., 2000). These data have uncovered a complex relationship between sleep/wake states and feeding. From an evolutionary perspective, the purpose of this linkage may be to arouse the subject to resupply lowering energy stocks. Hypocretin has now been shown to mediate a wide range of human behaviors, including sympathetic activation (Chen et al., 2000), HPAmediated stress responses (Date et al., 2000), reward-seeking and chemical addiction (Harris et al., 2005), as well as eating behaviors. The subsequent discussion will focus on the role of hypocretin in appetite regulation.

23.5.2  Hypocretin mechanism of action The biological effects of hypocretin are mediated in part by a short 36 amino acid neurotransmitter called Neuropeptide Y (NPY) (Ganjavi and Shapiro, 2007). NPY peptide has been shown to be a powerful stimulator of feeding behavior. The interaction between hypocretin and NPY may represent a potentially critical regulatory point of energy-balancing control, clarifying the indirect role hypocretin may play in energy homeostasis. Several lines of evidence support this conclusion. Neuroanatomical studies revealed that hypocretin’s positive axons were connected to NPY-laden neurons in the arcuate nucleus of the hypothalamus (many NPY-laden neurons actually express hypocretin) (Rauch et al., 2000). When exogenous hypocretin was applied directly to the rodent arcuate nucleus, NPY expression was transiently increased. Other work with NPY antagonists in the presence of hypocretin showed alterations in consumptive behavior. If the animal was pre-treated with

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NPY antagonist, the appetite-stimulating effect of exogenously supplied hypocretin was attenuated (López et al., 2002). Researchers investigating the molecular biology of the previously described leptin have shed light on the relationship between hypocretin and NPY. Increases in leptin expression were found to be associated with a decrease in the synthesis of NPY. This resulted in an inhibition of feeding behavior. Neurons carrying hypocretin were shown to express leptin receptors, and may be regulated by leptin. Indeed, leptin appears to inhibit the activity of hypocretinproducing neurons (Kok et al., 2002). Behavioral work has shown that decreasing hypocretin levels dramatically decreases consumptive behavior by down-regulating NPY, which in turn may be a result of low circulating leptin levels (Hara et al., 2001).

23.6  Concluding remarks The isolation of leptin, ghrelin and hypocretin has profoundly deepened our understanding of how mammalian energy homeostasis is regulated at the molecular level. Irregularities in the interactions between the physiological systems in which these proteins interact may profoundly influence the development of human obesity. Specific abnormalities in the expression of any one of these protein systems may also shed light on consumptive disorders such as anorexia nervosa. These ideas are complemented by data demonstrating that the brain processes stimuli related to eating in a manner similar to how it responds to other addictive stimuli (Volkow and Wise, 2005). These addictive tendencies may have strong genetic roots (Ball, 2008). There is also great potential for components of these systems to serve as therapeutic targets in the design of future medications, though so far the results are uneven. Treating patients with leptin, for example, shows great

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promise for patients with leptin deficiencies, but little for obese patients without such deficiencies (Westerterp-Plantenga et al., 2001). Taken together, the view leptin, ghrelin and hypocretin affords us demonstrates the incredibly complex nature of maintaining energy homeo­ stasis. Fortunately, for the vast majority of us, the action steps we need to ensure a healthy balance are much simpler than the substrates that afford us the luxury. As with so many disorders involving food intake, lifestyle changes involving diet regulation and exercise promotion are the simplest and most effective of the many treatments proposed to address pathologies related to energy balance (Orzano and Scott, 2004). As we increase our understanding of these mechanisms, we will be in a much more powerful position to show how medical intervention can work hand in hand with these lifestyle changes. For the few of us for whom low-fat diets and sufficiently robust exercise programs are not enough, this is fortunate news indeed.

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Saahu, A. (2004). Leptin signaling in the hypothalamus: Emphasis on energy homeostasis and leptin resistance. Frontiers in Neuroendocrinology, 24, 225–253. Sakurai, T. (2004). Roles of orexin/hypocretin in regulation of sleep/wakefulness and energy homeostasis. Sleep Medicine Reviews, 9(4), 231–241. Sakurai, T., Amemiya, A., Ishii, M., Matsuzaki, I., Chemelli, R. M., Tanaka, H., et al. (1998). Orexins and orexin receptors: A family of hypothalamic neuropeptides and G proteincoupled receptors that regulate feeding behavior. Cell, 92(4), 573–585. Schwartz, M. W. (2001). Brain pathways controlling food intake and body weight. Experimental Biology and Medicine, 226, 978–981. Schwartz, M. W., Peskind, E., Raskind, M., Boyko, E. J., & Porte, D., Jr. (1996). Cerebrospinal fluid leptin levels: Relationship to plasma levels and to adiposity in humans. Nature Medicine, 2(5), 589–593. Siegel, J. M. (2004). Hypocretin (orexin): Role in normal behavior and neuropathology. Annual Review of Psychology, 55, 125–148. Sobhani, I., Bado, A., Vissuzaine, C., Buyse, M., Kermorgant, S., Laigneau, J. P., et al. (2000). Leptin secretion and leptin receptor in the human stomach. Gut, 47(2), 178–183. St-Pierre, D. H., Wang, L., & Taché, Y. (2003). Ghrelin: A novel player in the gut–brain regulation of growth hormone and energy balance. News in Physiological Sciences, 18, 242–246. Thannickal, T. C., Moore, R. Y., Nienhuis, R., Ramanathan, L., Gulyani, S., Aldrich, M., Cornford, M., & Siegel, J. M. (2000). Reduced number of hypocretin neurons in human narcolepsy. Neuron, 27(3), 469–474. Tortorella, C., Macchi, C., Spinazzi, R., Malendowicz, L. K., Trejter, M., & Nussdorfer, G. G. (2003). Ghrelin, an endogenous ligand for the growth hormone-secretagogue receptor, is expressed in the human adrenal cortex. International Journal of Molecular Medicine, 12(2), 213–217. Toshinai, K., Date, Y., Murakami, N., Shimada, M., Mondal, M. S., Shimbara, T., et al. (2003). Ghrelininduced food intake is mediated via the orexin pathway. Endocrinology, 144(4), 1506–1512. Trivedi, P., Yu, H., MacNeil, D. J., Van der Ploeg, L. H., & Guan, X. M. (1998). Distribution of orexin receptor mRNA in the rat brain. FEBS Letters, 438(1-2), 71–75. Tschöp, M., Smiley, D. L., & Heiman, M. L. (2000). Ghrelin induces adiposity in rodents. Nature, 407(6806), 908–913. Tschöp, M., Wawarta, R., Riepl, R. L., Friedrich, S., Bidlingmaier, M., Landgraf, R., & Folwaczny, C. (2001a). Post-prandial decrease of circulating human ghrelin levels. Journal of Endocrinological Investigation, 24(6), RC19–RC21. Tschöp, M., Weyer, C., Tataranni, P. A., Devanarayan, V., Ravussin, E., & Heiman, M. L. (2001b). Circulating ghrelin

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C H A P T E R

24 Energy Balance Regulation: Complex Interplay between the Autonomic and Cognitive/Limbic Brains to Control Food Intake and Thermogenesis Denis Richard and Elena Timofeeva Centre de Recherche de l’Institut universitaire de Cardiologie et de Pneumologie de Québec, Canada

o u tli n e 24.1  Introduction

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24.3.2 The LH in Energy Homeostasis 305 24.3.3 The Brainstem as a Key Relay in Energy Homeostasis 307 24.3.4 The Ventral Striatum and the Brain Reward System in the Regulation of Energy Balance 308

24.2 The Regulation of Energy Balance 300 24.2.1 Energy Expenditure and Brown Adipose Tissue Thermogenesis 300 24.3 Brain Pathways Involved in the Control of Food Intake and Thermogenesis 301 24.3.1 The ARC–PVH Axis in Food Intake and Thermogenesis Control 303

24.1  Introduction The escalating prevalence of obesity together with the rising awareness of the detrimental impact of this condition on health and health costs have considerably stimulated research related to the etiology and complications of excess

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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fat deposition. Investigation of the main factors causing obesity has led to appreciable progress in our understanding of the respective and integrated roles of the environment and genetics in the development of this condition. Obesity largely results from complex gene–environment interactions (O’Rahilly and Farooqi, 2006; Speakman,

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2006; Wardle et al., 2008). The “obesogenic” milieu in which we live, characterized by a sedentary lifestyle, excess ingestion of energy-dense palatable food, stress, and pollution, proves to be particularly obesity-inducing in individuals who are genetically predisposed to positive energy balance. Genes that promote obesity might not only be energy-conserving genes acquired through evolution to combat periods of food shortage (Speakman, 2006), but all those genes (“thrifty” or not) that are involved in the overall adaptation/ inadaptation to the changes imposed by the “modern lifestyle” and whose under- or over­ expression potentially leads to overeating and other obesity-promoting behaviors. Excess fat deposition results from an imbalance between energy intake and energy expenditure, the two inescapable determinants of energy (or fat) balance. The understanding of the complex controls exerted on these determinants is therefore essential to decipher the etiology of obesity, and to envision effective behavioral or pharmacological strategies to prevent or reverse excess fat deposition. In recent years, major progress has been made in understanding the key metabolic systems and brain circuitries involved in the regulation of energy balance. This chapter aims at reviewing the role of certain systems involved in energy-balance regulation. It focuses on key axes and pathways implicated in the integrated controls of both food ingestion and regulatory/adaptative thermogenesis.

24.2  The regulation of energy balance Given the stability of body energy stores in response to attempts to change energy balance (such as food deprivation, overfeeding and excess physical activity), it has been argued that energy balance is regulated (Keesey and Corbett, 1984; Cabanac, 2001; Levin, 2006). The process of regulation is particularly efficient at

maintaining constant energy stores in the pro­ cess of reducing fat reserves. Excess fat stores are fiercely “defended”, which certainly sets hurdles in any attempts to tackle obesity. Fat losses are associated with an increase in hunger or appetite and a reduction in adaptive thermogenesis, a potential regulatory component of energy expenditure (Tremblay et al., 2007).

24.2.1  Energy expenditure and brown adipose tissue thermogenesis The impact of the expenditure component on energy balance is particularly meaningful in laboratory rodents, in which a strong regulatory control is exerted through the sympathetic nervous system (SNS) on brown adipose tissue (BAT), a potent effector of thermogenesis (Cannon and Nedergaard, 2004; Sell et al., 2004; Landsberg, 2006), whose role in energy expenditure in humans might be more important than previously anticipated (Nedergaard et al., 2007; Ravussin and Kozak, 2009). In contrast to white adipocytes, brown fat cells are highly adapted to dissipate chemical energy in the form of heat (Cannon and Nedergaard, 2004; Sell et al., 2004). The thermo­ genic power of BAT is conferred by the presence of uncoupling protein-1 (UCP1) (Nicholls, 2008). UCP1 is part of a subfamily of mitochondrial transporters also including UCP2 and UCP3, with which it shares homology of sequence (Bouillaud et al., 2001; Ricquier, 2005). UCP1 is unique to brown adipocytes, and is found in the inner mitochondrial membrane. It is the archetypical UCP, generating heat by “uncoupling” ATP synthesis from cellular respiration. Active UCP1 allows the dissipation of the electrochemical gradient, which is generated across the inner membrane by the electron transport along the respiratory chain, and which is normally used to generate ATP. Mitochondrial uncoupling prevents ATP synthesis, and energy is instead given off as heat, substrates being efficiently

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24.3  Brain pathways involved in the control of food intake and thermogenesis

catabolized without the respiratory constraint of ATP (Nicholls and Locke, 1984). BAT is very well vascularized so that the heat produced can travel throughout the body. UCP1 activity is driven by the activation of the SNS, whose post-ganglionic neurons densely innervate brown adipocytes. SNS-mediated UCP1 activity is governed by neurons (Bartness et al., 2005) found in brain structures implicated in energybalance regulation. In rodents, BAT thermogenesis not only allows for cold adaptation, but also contributes to energy balance regulation; food deprivation reduces SNS-mediated UCP1 activity, whereas excess food ingestion increases it. Up until recently, there was the general consensus that BAT was not present in significant amounts in adult humans. However, researchers/ clinicians using positron emission tomography and computed tomography (PET/CT) primarily to detect tumors demonstrated that some adipose tissue sites can capture significant amounts of the PET tracer F-18 fluoro­deoxyglucose (FDG, a glucose analog taken up by high-glucoseusing cells such as cancer cells) and these were not tumors but brown fat depots (Nedergaard et al., 2007). As typical white fat tissue does not capture FDG, one has to deduce that the deposits represent BAT sites. We recently calculated that between 6 and 7 percent of patients scanned for tumors show sites of intense FDG uptake in cervical, clavicular and spinal areas in deposits characterized by CT as being fat. The prevalence of brown fat was higher in female than in male subjects, and decreased as a function of age, body mass and ambient temperature (D. Richard, E. Turcotte and A. Carpentier, personal communication, 2009). The demonstration that BAT can exist in substantial amounts in certain individuals has rejuvenated interest in the role played by adaptive thermogenesis in humans (Ravussin and Kozak, 2009). Adaptive thermogenesis describes the changes in energy expenditure in response to alterations in energy balance resulting from

301

excess energy intake/energy gain or energy restriction/energy loss that are not attributable to changes in body mass or body composition. There is evidence that adaptive, or regulatory, thermogenesis could play a role in energy balance regulation and obesity in humans (Tremblay et al., 2007; Wijers et al., 2009), particularly in conditions leading to energy deficits (Dulloo, 2006). In laboratory rodents, it is clear that excessive energy consumption of palatable and energy-dense food items selected from the human obesogenic diet (the so-called “cafeteria diet”) induced BAT thermogenesis to limit excess energy deposition (Rothwell and Stock, 1979; Richard et al., 1988).

24.3  Brain pathways involved in the control of food intake and thermogenesis Regulation of energy balance (and hence regulation of fat mass and body weight) is determined by controls exerted on both food intake and thermogenesis (Saper et al., 2002; Blundell, 2006; Morton et al., 2006; Berthoud, 2007; Abizaid and Horvath, 2008; Adan et al., 2008; Berthoud and Morrison, 2008; Crowley, 2008) (Figure 24.1). The brain is critically involved in those complex controls, which are achieved through harmonized crosstalk between autonomic (hypothalamus and brainstem) and cognitive/limbic (hippocampus, amygdala, striatum and cortex) brain circuitries. The hypothalamus and the brainstem are key structures involved in the involuntary control of food intake and thermogenesis in response to changes in energy stores. The limbic structures are known to support functions such as emotion, learning, memory, pleasure, olfaction, vision and taste. The “autonomic” and “cognitive/limbic” brains work inseparably in regulating energy balance (Berthoud and Morrison, 2008). For instance, the strength of hedonic stimuli related to food is influenced by

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the deficit and surfeit of energy; palatable food items are undeniably more appetizing in conditions of energy restriction. It also has to be pointed out that the pleasurable signaling associated with the ingestion of palatable energy-dense foods inherent to an obesogenic environment has the power of distorting the autonomic controls exerted on food intake and energy expenditure (Berthoud and Morrison, 2008). The control of food intake and energy expenditure is insured by interconnected neurons

expressing varied receptor types and produ­cing diverse peptides or classic neurotransmitters that have been grouped into “anabolic” and “catabolic” mediators (Schwartz et al., 2000). Those mediators are found in nuclei such as the arcuate nucleus of the hypothalamus (ARC), paraventricular hypothalamic nucleus (PVH), lateral hypothalamus (LH), nucleus of the tractus solitarius (NTS), nucleus accumbens (NAcc), ventral tegmental area (VTA), amygdala and cortex. All these nuclei are tied to each other to

Limbic/cognitive

Autonomic

(Cortex, striatum, amygdala)

(Hypothalamus, brainstem)

Catabolic

Catabolic

Anabolic

Anabolic

NPY/AgRP -MSH/CART 5 HT MCH Orexins

Endocannabinoids Opioids Dopamine

Circulating signals Catabolic Anabolic Leptin (tonic) Insulin (tonic)

Ghrelin (episodic) Corticosteroids (tonic) GLP1/PYY (episodic) Nutrients (episodic)

Heat

Food energy Fat stores

Organ and cell metabolism Physical activity Regulatory thermogenesis

Figure 24.1  Overview of the regulation of energy balance presenting the main brain regions and chemical mediators involved in the control of food intake and energy expenditure. The stability of energy stores depends on controls exerted on both energy intake and energy expenditure. These controls are done by interconnected neurons comprised in the cognitive/limbic (cortex, striatum, amygdala) and autonomic (hypothalamus, brainstem) brains. These neurons express various receptor types and produce different peptides and classic neurotransmitters that have been grouped into “anabolic” and “catabolic” mediators. The neurons involved in control of energy intake and energy expenditure are influenced by peripheral hormones or other chemicals capable of informing brain cells on the status of the energy stores as well as on the nutritional status. These hormones or chemicals can be anabolic and catabolic, and have further been categorized as producing tonic (long-term) or episodic (short-term) effects. Gastrointestinal hormones produce episodic signals that mainly relate to the nutritional status. The controls exerted on regulatory (adaptive) thermogenesis are of major importance, in particular in small mammals.

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24.3  Brain pathways involved in the control of food intake and thermogenesis

form pathways that control the intake as well as the expenditure of energy. They comprise ­ neurons that produce energy-balance-­influencing mediators such as neuropeptide Y (NPY), agouti-related peptide (AgRP), alpha-melanocyte-stimulating hormone (-MSH), cocaineand amphetamine-regulated transcript (CART), melanin-concentrating hormone (MCH), orexins, endocannabinoids, opioids, dopamine and serotonin. The production of these diverse molecules is modulated by short- and long-term signals that inform the brain about the status of the energy stores and energy fluxes. Whereas leptin and insulin are recognized as the main long-term tonic signals, the gastrointestinal hormones ghrelin, peptide tyrosine-tyrosine (PYY), cholecystokinin (CCK) and glucagon-like peptide 1 (GLP-1) are known as short-term or episodic regulatory signals (Woods, 2005; Blundell, 2006). Circulating nutrients, including glucose, lipids and amino acids, are also sensed by brain “catabolic” and “anabolic” neurons (Obici and Rossetti, 2003; Lam et al., 2005; Potier et al., 2009). The peripheral signals have also been described as being “catabolic” and “anabolic”.

24.3.1  The ARC–PVH axis in food intake and thermogenesis control The ARC forms, with the PVH, perhaps the most important duet in the autonomic regulation of energy balance (Schwartz et al., 2000; Williams et al., 2001; Jobst et al., 2004; Elmquist et al., 2005; Morton et al., 2006; Adan et al., 2008). It consists of a tiny nucleus found in the basomedial hypothalamus just above the median eminence and adjacent to the third ventricle, in a position slightly caudal to the PVH (Williams et al., 2001). The ARC comprises two populations of neurons strongly involved in the control of energy intake and energy expenditure, and it integrates peripheral signals that influence energy homeostasis. One population of neurons synthesizes proopiomelanocortin (POMC)

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and CART, whereas the other synthesizes NPY and AgRP. POMC and CART are catabolic peptides, whereas NPY and AgRP are anabolic. The POMC/CART and NPY/AgRP neurons innervate both the descending and the parvocellular neurosecretory divisions of the PVH (Elmquist et al., 2005), through which nucleus they may influence energy homeostasis. The peptide CART is also likely an important player in the regulation of energy balance (Larsen and Hunter, 2006; Vrang, 2006). In the ARC, CART is co-localized with POMC in catabolic neurons. Similar to POMC, CART exerts anorexigenic effects. CART is also found in the retrochiasmatic area (RCh), which is seen as a hypothalamic structure distinct from the ARC (Vrang, 2006). CART neurons in the RCh have projections to the intermediolateral column (IML) (Elias et al., 1998) and could therefore represent a brain site for the autonomic action of the adipocyte-derived hormone leptin. Indeed, the RCh CART neurons, which also express POMC, have been reported to be involved in the leptin-mediated activation of the sympathetic outflow to BAT (Vrang, 2006). The melanocortin system in energy homeostasis POMC/CART neurons exert their catabolic effects in large part via -MSH, a peptidergic fragment ensuing from POMC cleavage. Within the brain, -MSH binds to melanocortin 3 (MC3R) and 4 (MC4R) receptors with which it essentially constitutes, together with AgRP, the “metabolic” melanocortin system (Adan et al., 2006; Butler, 2006; Ellacott and Cone, 2006). The functional significance of both MC3R and MC4R in energy homeostasis has been validated in Mc3r (Chen et al., 2002) and Mc4r (Huszar et al., 1997) knockout mice. Mc3r ablation seemingly enhances visceral fat accretion (Chen et al., 2002), whereas Mc4r disruption causes a significant and widespread body fat deposition (Huszar et al., 1997; Butler, 2006). In humans,

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Mc4r deficiency represents one of the most commonly known monogenic forms of obesity; up to 5 percent of severely obese patients apparently have pathogenic Mc4r mutations (Farooqi and O’Rahilly, 2006). The excessive fat deposition occurring in Mc4r knockout mice has been found to result from an increase in energy intake and a decrease in energy expenditure (Ste Marie et al., 2000; Butler, 2006). MC4R is endogenously antagonized by AgRP, which is solely expressed in the ARC neurons, where it is co-synthesized with NPY (Hahn et al., 1998). AgRP is over­ expressed in obese mice (Shutter et al., 1997) and, when injected centrally, it increases food intake (Rossi et al., 1998) and reduces energy expenditure (Asakawa et al., 2002), similar to synthetic melanocortin receptor antagonists. The MC4R is expressed in the three major parts of the PVH (parvocellular, magnocellular and descending divisions) (Kishi et al., 2003), and the role of the PVH in the hypophagia mediated by the MC4R has been ascertained (Balthasar et al., 2005). Reactivation of MC4R in the PVH by cre-recombinase appears sufficient to correct the hyperphagia seen in Mc4r null allele mice (loxTB Mc4r mice) (Balthasar et al., 2005), and microinjections of MC4R ligands into the PVH reduce food intake without apparently causing aversive effects (Giraudo et al., 1998). In ­addition, the MC4R seems to govern thermogenic effects. Transneuronal tract-tracing experiments have established a clear link between the PVH neurons expressing the MC4R and BAT (Voss-Andreae et al., 2007; Song et al., 2008), and such a link appears functional, as PVH microinjections of MC4R agonists elevate oxygen consumption (Cowley et al., 1999) and BAT temperature (Song et al., 2008). Peripheral metabolic influences on the ARC–PVH axis The role played by the ARC–PVH axis in energy balance regulation is strongly modulated by adipostatic signals such as leptin (Friedman, 2009) and insulin (Woods and D’Alessio, 2008),

and by acute satiety/hunger signals such as PYY (Karra et al., 2009), GLP1 (Chaudhri et al., 2008), oxyntomodulin (Wynne and Bloom, 2006) and ghrelin (Cummings and Overduin, 2007; Wiedmer et al., 2007). Nutrients also appear to be important modulators of the ARC activity (Woods and D’Alessio, 2008; Potier et al., 2009). The adipocyte-derived hormone leptin is certainly one of the most important controllers of the ARC–PVN axis. Leptin acts in the brain, into which it is actively transported, and where it binds to its long-form receptor (Ob-Rb) (Myers et al., 2008). It unquestionably exerts part of its catabolic action at the levels of the ARC, where, through the STAT3 signaling cascade, it reduces the production of NPY and AgRP while stimulating synthesis of POMC (Farooqi and O’Rahilly, 2008). Importantly, leptin also acts early in life as a trophic signal that stimulates ARC axon outgrowth to the PVH (Bouret et al., 2004). Novel pathways converting metabolic signals in the ARC and PVH Recent studies have highlighted the importance of AMP-activated protein kinase (AMPK) (Lage et al., 2008), mammalian target of rapamycin complex 1 (mTORC1) (Kahn and Myers, 2006; Woods et al., 2008), and forkhead box O1 (FoxO1) (Kim et al., 2006) in converting metabolic signals into anorectic (appetite-­suppressing) responses in the hypothalamus. AMPK is seen as a gauge of cellular energy status (Hardie, 2007). High and low levels of AMPK activity in the ARC stimulate and repress food intake, respectively (Lage et al., 2008). In keeping with this, expression and activity of AMPK in the ARC are increased by orexigenic stimuli such as fasting, ghrelin and cannabinoids, and are decreased by re-feeding and leptin (Kahn et al., 2005). AMPK may exert various actions to alter energy balance, including an inhibition exerted on mTORC1 (Kahn and Myers, 2006). mTORC1 has been clearly located in the ARC POMC and NPY/AgRP neurons, and its activation causes

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24.3  Brain pathways involved in the control of food intake and thermogenesis

anorexia (Cota et al., 2006a). mTORC1 can be activated by nutrients such as the branchedchain amino acid leucine (Cota et al., 2006a), as well as by insulin and leptin, two activators of phosphoinositide 3-kinase (PI3K) (Kahn and Myers, 2006). Activation of PI3K in turn leads to the phosphorylation of FoxO1 (Kido et al., 2001), which triggers its nuclear exclusion and its proteosomal degradation. Degradation of FoxO1 is catabolic, as it represses the orexigenic AgRP gene (Kim et al., 2006; Kitamura et al., 2006). It is still unclear whether FoxO1 also modulates NPY and POMC (Kim et al., 2006; Kitamura et al., 2006). AMPK, mTORC1 and FoxO1 are novel players in the brain regulation of energy balance. They appear to be of particular importance in the brain sensing of nutrients such as long chain fatty acids and branched-chain amino acids.

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that in turn results from a decreased activity of the NPY/AgRP neurons, which are particularly stimulated by the shortage of food (Sanacora et al., 1990). Also supporting a function for brain UCP2 in energy metabolism is the work from Parton and colleagues (Parton et al., 2007) suggesting that the genetic deletion of Ucp2 prevents obesity-induced loss of glucose sensing. Very recently, UCP2 was shown to be critical in the ARC action of the hormone ghrelin (Andrews et al., 2008), the production of which is stimulated by energy restriction (Cummings et al., 2002). The UCP2-dependent action of ghrelin on ARC NPY/AgRP neurons is apparently driven by a fatty acid oxidation pathway involving AMPK and free radicals that are scavenged by UCP2. The role of UCP2 in controlling reactive oxygen species (ROS) has been demonstrated (Arsenijevic et al., 2000).

ARC UCP2 in energy balance regulation UCP2 constitutes, together with UCPI, one of the main members of a subfamily of mitochondrial transporters/exchangers. In the brain, Ucp2 mRNA is distributed in a pattern that foretells neuroendocrine, behavioral, autonomic and neuroprotective functions (Richard et al., 1998, 1999, 2001). It is, for instance, expressed (1) within the neuroendocrine PVH; (2) within the ARC, in neurons co-expressing NPY and AgRP (Coppola et al., 2007) and expressing POMC (Parton et al., 2007); (3) in the brainstem within neurons controlling the sympathetic and parasympathetic nervous system (Richard et al., 1998, 1999, 2001); and (4) in the hippocampus (Clavel et al., 2003) and other regions sensitive to excitotoxicity. Recent animal studies have provided sound evidence for a role for brain UCP2 in energy homeostasis (Coppola et al., 2007; Parton et al., 2007; Andrews et al., 2008). UCP2 has been demonstrated to be involved in the rebound feeding induced by fasting (Coppola et al., 2007). Indeed, compared to wild-type mice, Ucp2/ mice exhibit reduced eating following fasting

24.3.2  The LH in energy homeostasis The LH has a long-established role in the regulation of energy balance (Anand and Brobeck, 1951; Berthoud, 2007). It is involved in food intake as well as in energy expenditure. More recently, the LH has been suggested to be a bridge between the cognitive/limbic and autonomic brain areas involved in energy balance regulation (Berthoud, 2006, 2007). The neurons comprised in this region indeed link the hypothalamus with the NAcc and VTA, two key parts of the brain reward system. The LH could also likely participate in the regulation of energy balance through descending projections to the brainstem and spinal cord areas involved in autonomic functions (Oldfield et al., 2002; Morrison, 2004). The LH control on energy expenditure has recently been further supported by data demonstrating the link between the LH and BAT. Transneuronal labeling experiments have indeed established a clear poly­ synaptic link between melanin-concentrating hormone (MCH) and orexin neurons and BAT (Oldfield et al., 2002, 2007; Zheng et al., 2005).

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The MCH system in the regulation of energy balance Evidence is accumulating to suggest that the MCH system is implicated in the regulation of energy balance (Nahon, 2006; Pissios et al., 2006). Indeed, chronic treatment with MCH (Gomori et al., 2003) and MCH overexpression (Ludwig et al., 2001) lead to obesity and an increased susceptibility to high-fat feeding, whereas antagonism of MCHR1 (Shearman et al., 2003) or ablation of MCH (Shimada et al., 1998), MCH neurons (Alon and Friedman, 2006) and MCHR1 (Chen et al., 2002; Marsh et al., 2002) promotes leanness. MCH mRNA-expressing cells are concentrated within the LH and the adjacent zona incerta (ZI). These neurons project from the LH to the rest of the brain (Bittencourt et al., 1992) in regions expressing the MCHR1 (Hervieu et al., 2000), the functional MCH receptor in rats and mice. Two subpopulations of MCH neurons have been characterized, based on the presence or absence of a CART co-localization (Cvetkovic et al., 2004). The MCH/CART cells, described as MCH type A neurons, project to the caudal brainstem and the spinal cord, whereas the MCH/CART cells, referred to as MCH type B neurons, terminate in the forebrain. MCH type A neurons likely fulfill autonomic functions and are also likely involved in the control of energy expenditure, as they are polysynaptically linked to BAT (Oldfield et al., 2002, 2007). In fact, more than 50 percent of the LH MCH neurons surrounding the fornix (the bundle of axons crossing the LH) are infected following the injection in BAT of the transneuronal retrograde tracer pseudorabies virus (PRV), a marker used to map the SNS outflow to BAT (Oldfield et al., 2002). MCH Type B neurons could also participate in the thermogenic function of MCH, as they connect with the ARC– PVH axis and the dorsal brainstem (Cvetkovic et al., 2004). Meanwhile, the type B neurons are likely to play a role in the control of food intake,

as they project to the NAcc, whose role in the rewarding effects of food and other substances is acknowledged (Pecina, 2008; Carlezon and Thomas, 2009). Injection of MCH in the NAcc increases food intake (Georgescu et al., 2005; Guesdon et al., 2009). There is strong evidence for the involvement of MCH not only in the control of food intake but also in the control of energy expenditure. In the leptin-deficient ob/ob mouse, deletion of MCH induces a dramatic fat loss without any food intake reduction (Segal-Lieberman et al., 2003). Mchr1 disruption (Chen et al., 2002; Marsh et al., 2002) even leads to leanness despite hyperphagia. The increased energy expenditure caused by deletion of MCH in ob/ob mice is accompanied by increases in both metabolic rate and locomotor activity (Segal-Lieberman et al., 2003). However, the observation that the enhanced metabolic rate (seen throughout the day) of the Mch/ob/ob mice is not totally paralleled by an increase in locomotor activity (seen only at night) suggests that the augmented energy expenditure cause by deletion of MCH (Segal-Lieberman et al., 2003) is not solely due to an increase in physical activity, which is consonant with recent results showing the inability of an i.c.v. injection of MCH to reduce locomotion in rats (Guesdon et al., 2009). Another determinant of MCH-induced energy expenditure could certainly be BAT thermogenesis, as Mch/ ob/ob mutants exhibited an increase in BAT expression of UCP1. In rats and mice, the effects of MCH, including those on energy metabolism, are mediated through the MCHR1, which appears to be the sole MCH receptor expressed in those species. However, humans (Hill et al., 2001; Rodriguez et al., 2001), monkeys (Fried et al., 2002), dogs and ferrets (Tan et al., 2002) also express a second MCH receptor, referred to as MCHR2. Not much is currently known about this receptor except that its highest mRNA levels are found in the frontal cortex, amygdala and NAcc (Wang et al., 2001), which suggests that MCHR2 could

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24.3  Brain pathways involved in the control of food intake and thermogenesis

be involved in the cognitive, motivational and hedonic (non-homeostatic) aspects of feeding. The orexin system in the regulation of energy balance Another LH neuronal entity liable to affect the regulation of energy balance is the orexin system. Orexin-producing neurons are concentrated in the posterior LH, from where they extend dorsally, medially and laterally from the fornix (Swanson et al., 2005). Their localization is rather similar to that of the MCH neurons, even though they form an entirely distinct population of cells. The orexin neurons project to the entire neuraxis excluding the cerebellum (Peyron et al., 1998; Date et al., 1999). There are two orexins: orexin A and orexin B. They are issued from the same precursor, and act on two distinct receptors (OX1R and OX2R). The brain distribution of the orexin receptors is in good correspondence with the orexin neuronal projections. Among other areas, orexinimmunoreactive nerve endings have been located in the ARC (OX2R), VTA, NAcc shell (OX2R), caudal raphe and locus coeruleus (LC) (OX1R) (Peyron et al., 1998; Date et al., 1999). Similar to MCH neurons, orexin cells have been separated into two populations, based on their locations, projections and functions (Harris and Aston-Jones, 2006). One population, which would be mainly involved in reward, includes LH neurons projecting to the VTA. The other, which would be implicated in arousal, comprises perifornical/dorsomedial-nucleus neurons sending projections to the brainstem. The stimulation of the orexin system increased appetite (Sakurai et al., 1998), reward (Harris et al., 2005), body temperature (Yoshimichi et al., 2001), BAT SNS drive (Yasuda et al., 2005) and locomotor activity (Kotz, 2006). All those effects are consonant with the action of orexins in waking and arousal (Sakurai, 2005; Matsuki and Sakurai, 2008; Ohno and Sakurai, 2008). Food deprivation and ghrelin stimulate orexin neurons, whereas

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leptin and glucose exert opposite effects (Ohno and Sakurai, 2008). Ablation of orexin neurons is associated with the development of a lateonset obesity that occurs even in the presence of hypophagia (Hara et al., 2001), strongly suggesting the stimulating effect of orexin neurons on energy expenditure.

24.3.3  The brainstem as a key relay in energy homeostasis The brainstem, which includes the hindbrain (pons and medulla oblongata) and the midbrain, forms, with the hypothalamus, a key autonomic arm in the regulation of energy balance. The brainstem comprises the NTS, which, together with the area postrema (AP) and the motor nucleus of the vagus nerve, composes the dorsal vagal complex (DVC). The DVC integrates peripheral signals conveyed by cholecystokinin, PYY and GLP1, which are gastrointestinal hormones significantly influencing energy balance regulation (Moran, 2006; Murphy and Bloom, 2006; Cummings and Overduin, 2007). The action of these hormones is exerted on vagal afferents, which terminate in the DVC or at the level of the AP, one of the circumventricular organs (devoid of a blood–brain barrier). The AP expresses key gastrointestinal hormone receptors (Fry and Ferguson, 2007). The key role of the DVC (and hindbrain in general) in the regulation of energy balance has been advocated by Grill and colleagues (Grill and Kaplan, 2001; Grill, 2006), who carried out elegantlydesigned experiments with decerebrated rats (rats subjected to a complete transection of the neuroaxis at the mesodiencephalic juncture) to decisively demonstrate the brainstem implication in the effects of leptin (Harris et al., 2007), melanocortins (Williams et al., 2000) and ghrelin (Faulconbridge et al., 2005). In addition to integrating peripheral inputs, the brainstem comprises nuclei, such as the caudal raphe, periaqueductal gray (PAG),

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pontine reticular nuclei, lateral paragigantocellular nucleus, and locus ceruleus (LC), that are likely involved in SNS-mediated thermogenesis (Bamshad et al., 1999; Cano et al., 2003; Morrison et al., 2008). These nuclei are capable of relaying information from the hypothalamus to the IML, from which originate SNS pre­ganglionic neurons involved in the control of BAT thermogenesis.

24.3.4  The ventral striatum and the brain reward system in the regulation of energy balance The ventral striatum is constituted of the olfactory tubercle and the NAcc, which has emerged as a crucial structure in the control of food intake (Salamone, 1994). Being part of the limbic system, the NAcc comprises two distinct sections: the core and shell (Heimer et al., 1997). The NAcc forms, with the VTA, the mesolimbic pathway, also referred to as the mesolimbic reward pathway. This pathway comprises dopaminergic neurons originating from the VTA, which terminates in the NAcc. These dopaminergic neurons likely play a role in the motivational component of reward (referred to as “incentive salience” or “wanting”) (Berridge and Robinson, 2003; Finlayson et al., 2007), and they do not appear to be essential in sensing the hedonic value or the “liking” aspects of food (Pecina et al., 2003). The opioid system and the brain reward system The main endogenous opioids are -endorphin, enkephalins, and dynorphins. They are widely found in the brain, and act through three receptor types:  (-endorphin, met- and leuenkephalins),  (met- and leu-enkephalins) and  (dynorphin). The opioid system plays a key role in the hedonic response to food (Levine and Billington, 2004; Cota et al., 2006b; Pecina et al.,

2006). Brain administration of opioid receptor agonists and antagonists has been shown to exert site-specific effects on feeding. Injection of the -opioid receptor agonist DAMGO in the NAcc shell markedly increases the hedonic value of sweetness (Pecina and Berridge, 2005). The NAcc shell expresses the -opioid receptor as well as the CB1 receptor (Fusco et al., 2004; Matyas et al., 2006), which are part of systems capable of strong interactions (Fattore et al., 2004; Robledo et al., 2008). The cannabinoid system in the brain reward system The endocannabinoid system is essentially composed of two receptors, namely the cannabinoid 1 and 2 (CB1 and CB2) receptors, and of “on-demand” produced endocannabinoids (the most notable being anandamide and 2-arachidonoylglycerol (2-AG)) (Di Marzo, 2008). The endocannabinoid system appears to be genuinely involved in energy balance regulation; it exerts actions on both energy intake and energy expenditure, and its activity is affected by the status of the fat stores in a way to prevent any major oscillations in the fat reserves. Administration of cannabinoid receptor agonists such as the plant-derived cannabinoids delta-9-tetrahydrocannabinol (9-THC), the active substance of the cannabis plant, caused hyperphagia and increased the preference for palatable food (Brown et al., 1977; Williams et al., 1998). These effects are mediated through the CB1 receptor, whose genetic ablation (Cota et al., 2003; Ravinet Trillou et al., 2004) or antagonism (Arnone et al., 1997; Jbilo et al., 2005) reduces energy deposition; CB2 receptor antagonism is without any apparent effect on energy balance (Wiley et al., 2005). CB1 receptor antagonism, which expectedly has no influence on ingestive behavior in CB1/ mice (Di Marzo et al., 2001), not only creates hypophagia but also seemingly stimulates energy expenditure (Doyon et al., 2006; Herling et al., 2008; Kunz et al., 2008). Food

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deprivation (Kirkham et al., 2002) and obesity (Di Marzo et al., 2001) elevate endocannabinoid levels in the hypothalamus. There is sound evidence to suggest that the endocannabinoid system can influence food intake by acting on the ventral striatum. The endocannabinoids apparently control the activity of the dopaminergic neurons of the mesolimbic reward pathway that project from the VTA to the ventral striatum (Matyas et al., 2008). The NAcc expresses a considerable amount of the CB1 receptor protein (Fusco et al., 2004; Matyas et al., 2006), and it is known to produce anandamide and 2-AG. Functionally, intra-NAcc injections of anandamide, 2-AG, fatty acid amide hydrolase (FAAH) inhibitors (anandamide inactivator), and inhibitors of anandamide uptake, all increase food intake (Kirkham et al., 2002; SoriaGomez et al., 2007). In addition, ventral striatum synthesis of the two main endocannabinoids is induced by food deprivation and normalized by re-feeding (Kirkham et al., 2002). Moreover, whereas food deprivation increases 2-AG levels only in the hypothalamus, it raises the levels of both anandamide and 2-AG in the limbic forebrain (Kirkham et al., 2002), revealing the relative importance of the brain reward system and the NAcc in the regulation of energy balance. The role of the ventral striatum and the brain reward system in the anabolic action of the endocannabinoids is further supported by series of experiments that have demonstrated that CB1 receptor agonism can enhance the preference for palatable foods such as sucrose (Brown et al., 1977) and fat (Koch, 2001), and increase food intake in sated rats by increasing the duration and number of meals (Williams and Kirkham, 2002). Also, it has been demonstrated that CB1 receptor antagonism can blunt the conditionedplace preference for food (Chaperon et al., 1998), the selective preference for sucrose (Arnone et al., 1997), the reinforcing and motivational properties of a chocolate-flavored beverage (Maccioni et al., 2008), and the desire for sweets and highfat food (Scheen et al., 2006). Altogether, these

experiments point to a role of endocannabinoids on the “liking”/”wanting” aspects of ingestive behavior (Williams and Kirkham, 2002). In fact, endocannabinoids appear to influence both the “liking” and “wanting” for food. Similar to -opioid receptor agonists, endogenous ligands of the CB1 receptor, such as anandamide, appear to be capable of enhancing sweet reward when injected in the NAcc shell (Mahler et al., 2007). The influence of the endocannabinoids on the “wanting” aspect of food (Thornton-Jones et al., 2005) is supported by the observation that the cannabinoid antagonism reduces the intra NAcc release of dopamine that is induced by a novel, highly palatable food (Melis et al., 2007). Finally, it appears worth mentioning that the effects of endocannabinoids on the brain reward system implicate the hypothalamus (Soria-Gomez et al., 2007) and the brainstem (DiPatrizio and Simansky, 2008); this is not unexpected, considering the link existing between the limbic/cognitive and autonomic circuitries controlling food intake.

24.4  Conclusion The regulation of energy balance depends on complex crosstalk between the autonomic and cognitive/limbic brain circuitries that control energy intake and energy expenditure. The “metabolic autonomic brain” includes (1) various hypothalamic structures, among which the ARC, PVH and LH are prominent; and (2) the brainstem, which per se constitutes a major afferent/efferent relay for metabolic signals. The ARC comprises the NPY/AgRP and POMC/ CART neurons, whose respective anabolic and catabolic roles in energy balance have been acknowledged. Both NPY/AgRP and POMC/ CART neurons project to the PVH, probably the most important brain neuroendocrine structure, which forms, with the ARC, one of the most notable circuitries in energy homeostasis.

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The PVH comprises a division referred to as the descending division, whose neurons project to the brainstem and the spinal cord to control food intake and energy expenditure. Both the hypothalamus and the brainstem harbor receptors for the main catabolic and anabolic hormones informing about the energy stores and the nutritional status. The LH hosts neurons, such as those expressing MCH, which are in a good position to interface the autonomic and cognitive/limbic brains. MCH neurons not only autonomically control food intake and BAT thermogenesis, but also influence the activity of the ventral striatum, a main component of the brain reward system. The latter, whose activity is also modulated by peripheral metabolic hormones such as leptin and ghrelin, plays a major role in energy balance, as it integrates various hedonic/anhedonic signals capable of reinfor­ cing or blunting behaviors modulating energy homeostasis.

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C H A P T E R

25 Stealth Interventions for Obesity Prevention and Control: Motivating Behavior Change Thomas N. Robinson Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine; Center for Healthy Weight, Stanford University School of Medicine and Lucile Packard Children’s Hospital at Stanford, Stanford, CA, USA

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25.1  Motivation for behavior change Most past behavioral and lifestyle interventions to prevent and treat obesity have produced relatively modest and non-sustained effects on weight (Summerbell et al., 2003, 2005). Even state-of-the-art behavioral programs, that successfully reduce weight or weight gain during treatment, are generally followed by regain of some, if not all, of the lost weight. One possible

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reason is insufficient attention to motivational factors related to eating, physical activity and sedentary behaviors (Robinson, 2001). Cognitive social learning models of behavior indicate that motivational processes are key to influencing behavior (Deci and Ryan, 1985; Bandura, 1986, 1997). Many medical and public health interventions tend to emphasize outcome-based incentives for behavior change, such as reducing or maintaining weight, physical appearance, becoming more fit, and reducing risks of future chronic diseases – the eventual outcomes

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of adopting a healthy diet, increased physical activity and decreased sedentary behaviors. Because the beneficial outcomes of changing eating and activity behaviors often lag in time behind the behaviors, they may not be sufficient motivators to adopt, generalize and maintain new behaviors (Bandura, 1986, 1997). Our substantial clinical and research experiences in designing, implementing and evaluating obesity prevention and treatment interventions suggest that it may be more important to emphasize motivation for participating in the interventions themselves – the process of behavior change. To produce behavior changes over time, it is necessary that participating in those behaviors (the activities making up the behavior change process) is rewarding. Lack of sufficient attention to this process motivation may be one explanation for the difficulty that many children and adults have initiating and sustaining behavior changes to maintain or reduce their weight over time.

25.2  Self-efficacy Social cognitive theory specifies perceived self-efficacy, a belief in one’s capabilities to perform the specific actions required to produce expected attainments, as a primary determinant of behavior change (Bandura, 1986, 1997). While interventions focusing on outcomes may contribute to increased motivation for behavior change, conversely they also may undermine perceived self-efficacy for behavior and weight change when payoffs are not immediately forthcoming. Even when weight changes are immediate, biological homeostatic mechanisms tend to protect against a reduced weight, making additional incremental loss or maintenance more difficult (Friedman and Halaas, 1998; Schwartz et al., 2003). Thus, the potential incentive value of continued weight loss and its associated benefits become less salient. Unless other rewards are recognized, this reduced salience diminishes the

motivational impact and self-efficacy for behavior change, and individuals will likely start to regain or reaccelerate weight gain as is observed in traditional weight-loss programs (Wadden et al., 2005). Another potential outcome of intervention is no weight loss or continued gain in weight. If this occurs, individuals using weight as a primary motivator would likely have low perceived self-efficacy to perform the behaviors required to control their weight. Therefore, emphasizing an outcome goal could actually be detrimental to long-term weight control and obesity prevention, as unsuccessful attempts would further reinforce individuals’ perceived inability to lose or control their weight. In contrast, emphasizing process motivators in the design of prevention and treatment interventions is more likely to enhance selfefficacy for behavior change, thereby resulting in behavior change and weight control. To do so, it is necessary to specifically design prevention and treatment interventions that are motivating in themselves (the process of behavior change). Process motivators are the factors that elicit and sustain attention to and persistence in an activity. Table 25.1 contrasts examples of outcome motivations typically used in medical and public health interventions versus process motivations that increase intrinsic motivation for participating in the process of behavior change, based on research on intrinsic motivation and our own observations (Robinson and Borzekowski, 2006; Lepper et al., 2008).

25.3  Stealth interventions Accepting the strategy of emphasizing process motivators over outcome motivators then begs the question: if motivations are not tied to health outcomes, does a health behavior change intervention need to look, taste, sound, feel or smell like a health behavior change intervention? In fact, if it is the process that is important,

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Table 25.1  Examples of outcome and process motivators for physical activity and eating behaviors Outcome motivators

Process motivators

Obesity/weight loss

Fun

Diabetes

Taste

Hyperlipidemia

Choice

Hypertension

Control

Risk for cardiovascular diseases and/or cancer

Goals

Other chronic diseases

Curiosity

Personal appearance

Challenge Teamwork Competition Pride, sense of accomplishment Anticipated parent, peer, or social approval/ disapproval Social interaction

interventions can be designed to focus on intrinsically motivating characteristics of behaviors without appearing to be directly related to obesity, physical activity, nutrition, or any aspect of health at all, to be successful. Such interventions are referred to here as stealth interventions, because the primary emphasis is on maximizing the incentive value of the intervention activities themselves rather than their resulting healthrelated outcomes (Robinson and Sirard, 2005). From the perspective of participants, they may not adopt improved health-related behaviors for the purpose of reducing their weight or weight gain, but reduced weight or weight gain instead become beneficial “side effects” of their participation. This approach was central to designing effective screen-time reduction interventions for children and families (Robinson, 1999; Robinson et al., 2003; Robinson and Borzekowski, 2006). Intervention activities were designed to enhance the intrinsic motivation for reducing screen time by emphasizing

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challenge, curiosity, perceived choice and control, fantasy and contextualization, perceived individualization, cooperation and competition, social interaction, positive emotions, and anticipated peer and adult approval as integral to the process of reducing screen time (Robinson and Borzekowski, 2006; Lepper et al., 2008). This model stands in sharp contrast to the more traditional approach of trying to persuade persons to change their behaviors to achieve health and social outcomes, such as weight loss, or cholesterol or blood pressure reduction (Robinson and Borzekowski, 2006). The stealth intervention approach can be taken one step further by structuring environments and intervention activities to enhance the motivational value of behaviors that are directly health promoting. An example is the use of dance to promote physical activity among young girls. The original “Dance for Health” program was developed as a medical student public service and research project (Flores, 1995). The objective was to provide a motivating and active alternative to traditional physical education (PE) classes. To evaluate its efficacy, 81 seventh-grade children (mean age 12.6 years, 43 percent Latino and 44 percent AfricanAmerican, 49 girls) were randomized to either aerobic dance (treatment) or their usual PE classes (control). Both were delivered during the regular PE periods of 40–50 minutes, 3 times per week, for a 12-week period. The dance classes were led by Stanford undergraduate and graduate students. Each dance period was designed to include about 30 minutes of moderate- to high-intensity aerobic dance. Popular music was selected for dance routines developed by the instructors. Usual PE included standard PE instruction and playground activities, led by the school PE teacher. Assessments were completed by trained staff, blinded to treatment assignment, at baseline and after 12 weeks. Girls randomized to dance classes significantly reduced their body mass index (BMI) and resting heart rate, a measure of aerobic fitness, compared to

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girls in standard PE. There were no significant differences between groups for boys, possibly because co-ed. dance was not as motivating for boys at this age. However, this is still one of the few PE interventions to ever show benefits in body composition, and suggests that designing PE classes around a highly motivating activity can result in sufficient levels of physical activity to reduce weight gain. The success of the Dance for Health project, with its emphasis on process motivators rather than outcome motivators, led to the design of after-school ethnic dance classes to promote physical activity among pre-adolescent girls (Robinson et al., 2003). Physical activity falls dramatically as girls enter adolescence (Kimm et al., 2002). Experience has demonstrated that it is extremely difficult to persuade girls to exercise, even to dance, for the purpose of improving their health and preventing future chronic diseases. In contrast, it was found that many girls vigorously exerted themselves as part of a dance class if it was designed to maximize the immediate incentive value of participation. For the Stanford GEMS trial, both theory and extensive formative research (Kumanyika et al., 2003) were used to design intrinsically motivating intervention activities to encourage low-income African-American girls to participate in a dance class (Robinson et al., 2003). This approach resulted in an intervention that linked dance to the girls’ cultural heritage and included popular social dances, periodic performances for family, friends and community events, choreography, set and costume design, popular teachers, safe and supervised after-school care, and homework assistance. Notably, it did not overtly emphasize physical activity, obesity or health. Girls participated in dance classes primarily because it was an enjoyable and rewarding experience, not because it would improve their fitness, weight or health. From the perspectives of the girls and their parents, improved fitness, weight and health were side effects of participating in a dance class (Robinson et al., 2003).

In the Stanford GEMS pilot randomized controlled trial, 65 African-American girls aged 8–10 years, from 61 families/households, were enrolled from low-income areas of East Palo Alto and Oakland, CA. The treatment intervention included five family-based lessons to reduce television, videotape and video-game use, and the GEMS Jewels after-school dance program (emphasizing traditional African dance, step and hip-hop, and time for homework and mentoring). Dance groups were offered 5 days per week at three different neighborhood community centers. The comparison group received an active-placebo state-of-the-art information-based health education program. Compared to girls randomized to the health education condition, girls in the after-school dance and televisionreduction treatment condition showed trends toward reduced BMI, reduced waist circumference and reduced television viewing, in just 12 weeks (Robinson et al., 2003). This same approach can also be applied in the context of weight control among overweight and obese children. An example is the Stanford SPORT (Sports to Prevent Obesity Randomized Trial) team sports program for overweight children (Weintraub et al., 2008). Team sports can be highly motivating for some children, but are often avoided by overweight children (who may, for example, not want to be “picked last” or the slowest person on the field). However, when we designed a soccer team to specifically cater to the needs of overweight children, they enthusiastically participated in moderate and vigorous activity. We emphasized the process motivators from Table 25.1 with many of the rewarding features of team sports that are unrelated to preexisting skills and high levels of competition, such as simply belonging to a team, wearing a uniform, receiving attention from young adult coaches, opportunities to see improvement in their skills, and even some friendly competition with other similarly skilled players/teams or family members. In a test of Stanford SPORT, 21 overweight or obese (BMI  85th percentile

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for age and sex on the 2000 CDC BMI reference) fourth- and fifth-grade children were enrolled and randomized to 6 months of either afterschool soccer, or health education. Of the 21 enrolled children, 14 (66 percent) had never previously participated in a sports team. Soccer was initially offered 3 days per week, but this was increased to 4 days per week at the request of participating children and parents. Children randomized to the active placebo health education condition received a 25-session, state-of-the-art information-based nutrition and health education intervention consisting of weekly after-school meetings conducted by health educators. All 21 children (100 percent) completed the study. We found medium to large beneficial effects in BMI, age- and sex-adjusted BMI (BMI-Z), total daily physical activity, and time spent in moderate physical activity and vigorous physical activity (measured by accelerometers) at both 3-month and 6-month follow-up. All 9 children (100 percent) randomized to the soccer group compared to 5 of 12 children (42 percent) randomized to the health education group had lower BMI Z-scores at both 3 and 6 months. At 6-month follow-up, 8 of 9 children (89 percent) in the soccer program stated that they wanted to continue to play on a soccer team (Weintraub et al., 2008).

25.4  Social and ideological movements as stealth interventions to change health behaviors As illustrated by the examples above, the stealth intervention approach encourages looking outside of traditional avenues for promoting physical activity and reducing energy intake to prevent and treat obesity. In doing so, we observe that some of the most compelling examples of widespread, dramatic and sustained behavior changes occur in the context

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of social and ideological movements. Some of the most obvious examples are the behaviors associated with religious traditions. For example, Seventh Day Adventist, Mormon, Hindu, Jewish and many other religious traditions include significant dietary prescriptions that many followers are able to follow throughout their life-course, despite a powerful food environment promoting contrary behaviors. At an extreme, some individuals sacrifice their lives as martyrs for their religious beliefs (Atran, 2003). Compare that to the difficulty many people have in following even comparatively small changes in their diet or activity behaviors for purposes of reducing their weight, cholesterol or blood pressure levels. Religious traditions, however, are not the only examples where participants endorse, adopt and sustain dramatic changes in their behaviors. Analogous to religious movements, similar dramatic and durable behavior changes can be seen as part of social justice, civil rights, environmental, anti-tobacco, political and other social movements. Think, for example, of the motivations encountered among vegetarians. In pediatric practice, it is not unusual to meet adolescents who have adopted vegetarianism because of their concern for animal welfare or to support environmental sustainability. In contrast, it is rare to meet a teenager who has adopted a vegetarian diet for its health benefits. Participation in these traditions and movements may be motivating because of their spiritual, social, economic, self-esteem enhancing, self-actualizing, or other qualities associated with the processes of belonging to such a social movement. Based on these observations, social and ideological movements may represent a potentially powerful stealth intervention model for changing obesity-related behaviors. Observations about social movements in general, and the anti-tobacco movement in particular, have triggered suggestions about harnessing the power of social movements to combat obesity (Dietz and Robinson, 2008). Some have focused on building a new anti-obesity movement de

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novo using the lessons learned from retrospective explanations of successful prior social movements (Economos et al., 2001). However, retrospective explanations of success may or may not be relevant to prospectively building a successful movement, as they are limited by the conceptual models and perspectives of those of us who are looking back. To date, obesity and appeals to improve health have not proven sufficiently compelling or motivating to stimulate a social movement to combat obesity. An alternative strategy, however, is to identify existing and emerging social/ideological movements that share behavioral goals with those for weight control. It may be possible to integrate efforts to control obesity into social movements that are already proving to be highly motivating to significant segments of the population. We have identified a number of existing social and ideological movements that include goal behaviors which overlap with some of the goal behaviors of obesity prevention and control. These movements, therefore, have the potential to produce obesity prevention and reduction as side effects, as discussed with other forms of stealth interventions. A number of these are illustrated in Table 25.2. These examples show that many existing movements from across the political, cultural and socio-economic spectrum share behavioral goals consistent with obesity prevention and control. Considering obesity prevention and treatment via social movements that are not overtly driven by concerns regarding obesity or health may be a particularly powerful stealth intervention. An additional benefit of promoting change through ideological and social movements is their potential to influence behavior and public policy to promote further change through the simultaneous actions of multiple societal sectors – family, government, markets and civil society. Attempting to reduce obesity by allying with and working through these larger social and ideological movements becomes the ultimate expression of the stealth intervention approach. If successful, reductions in individual

and population levels of obesity may occur without having to persuade patients or the public to change their eating and activity behaviors for purposes of attaining and maintaining a healthy weight – an approach that has proven ineffective to date. Linking obesity to existing ideological and social movements does not preclude turning obesity prevention and control into a social movement of its own, of course. Studying the characteristics of successful social movements may help us promote a similar movement around obesity, and this should also be pursued (Economos et al., 2001; Dietz and Robinson, 2008). The same principles, emphasizing process motivators that are inherent in the stealth intervention approach, will still apply.

25.5  Conclusion Stealth interventions can take multiple forms. As demonstrated in interventions to reduce children’s screen time (Robinson, 1999; Robinson et al., 2003; Robinson and Borzekowski, 2006), emphasis is placed on incentives for the process of behavior change rather than outcomes, by using frames to enhance intrinsic motivation such as perceived choice and control, individualization, fantasy and contextualization, challenge, curiosity, and cooperation and competition (Robinson and Borzekowski, 2006; Lepper et al., 2008). These design features make the process of behavior change rewarding, easy and desirable rather than a sacrifice or burden, as “diets” and “exercise” are often perceived to be. These same approaches can be applied to the design of other nutrition and physical activity interventions to raise the incentive value of participation, rather than trying to persuade participants to change through reason and logical arguments. Taking stealth interventions to the next level is to identify or structure healthpromoting environments and activities that are

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Table 25.2  Social and ideological movements with behavioral goals that overlap with the goals of obesity prevention and control Social movements

Overlapping behaviors

Environmental sustainability/climate change: reducing global warming and climate change, sustainable agriculture, organic farming, slow food, eating locally (locavores), water conservation, recycling/waste reduction, improving air quality

Greater consumption of fresh fruits and vegetables and reduced intake of meat, poultry, and processed and packaged foods that are transported over long distances; less automobile use, more walking, bicycling and mass-transit use

Food safety: infections (e.g., E. coli O157:H7, bovine spongiform encephalopathy/mad cow disease) and potentially harmful additives (e.g., contaminants in imported food)

Less meat consumption and greater consumption of locally grown fruits and vegetables

Human rights/social justice: workers’ rights, poor working conditions among fast-food workers and suppliers (e.g., slaughterhouses, meatpacking, farm workers), food justice, access to fresh fruits and vegetables in low-income areas

Lower fast-food, meat and poultry consumption, less processed foods, more fruits and vegetables from farmers’ markets, Community Supported Agriculture (CSA), boycotts of fast-food chains

Anti-globalization: movements by farmers, labor unions, human rights groups, nationalists, etc. supporting local economies and against corporate and cultural globalization

Greater consumption of locally grown foods, lower consumption of fast food and imported foods, lower patronage of multinational food chains and producers

Animal protection

Lower meat and poultry consumption, vegetarianism

Anti-consumerism: movements to reduce the impact of a consumer culture, advertising and marketing

Lower consumption of heavily advertised and marketed fastfood and snack foods/convenience foods; less screen media use

Cause-related fundraising: to raise awareness and money for charitable causes such as cancer or AIDS research and services (e.g., Team-in-Training).

Walk-a-Thons, door-to-door fundraising, training and participation in distance races, triathlons, long distance walks and bike rides, etc.

Energy independence/reduced dependence on foreign oil

Less automobile use, more walking, bicycling and masstransit use; greater consumption of locally grown fruits and vegetables; lower consumption of packaged and processed foods transported over long distance

Youth violence and crime prevention

Participation in sports programs to reduce youth involvement in crime/gangs (e.g., midnight basketball leagues, police sports leagues), participation in neighborhood policing/neighborhood watch

National security: movements to promote a physically fit military

Participation in military training or military reserve programs involving physical activity

Political action: as part of the movements listed above or others

Increased physical activity and displacement of sedentary behavior from door-to-door campaigning, public demonstrations, marches, etc.

motivating in themselves. As shown, after-school ethnic dance classes for girls can redefine sustained moderate to vigorous exercise (with its perspiration, fatigue and soreness) as a highly rewarding and fun, cultural, social, artistic and political

activity. These interventions are designed such that, from the perspective of the participants, physical activity, diet and weight changes are positive side effects of their participation, rather than the primary motivators. Finally, the next

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logical step for the stealth intervention approach is to harness the motivational appeal of existing and emerging social and ideological movements, to achieve greater and more sustained effects on behavior. The stealth intervention approach has a number of important advantages as a model for obesity prevention and control. First, it is theorybased. The guiding conceptual framework has its origins in theories of motivation and behavior that are supported by substantial empirical research, including social cognitive theory (Bandura, 1986, 1997), self-determination theory (Deci and Ryan, 1985), and basic and applied experimental research in intrinsic motivation (Lepper et al., 2008). Second, the stealth intervention approach can be applied at all levels of intervention – individual, family, school, workplace, community, social and/or environmental – overcoming the limitations of models that focus on the level of intervention rather than the process of behavior change. Third, it is solution-oriented (Robinson and Sirard, 2005), emphasizing the discovery of solutions rather than causes of obesogenic behaviors, allowing intervention designers to entertain potential solutions that would not be identified from traditional etiological thinking. Finally, the stealth intervention approach prompts the possibility of building new alliances and synergies with other, previously unrelated, movements and causes that share similar behavioral goals. In this way, available resources, expertise and experience can be shared, potentially strengthening all efforts to produce greater and more expedient individual and society-wide changes.

References Atran, S. (2003). Genesis of suicide terrorism. Science, 299, 1534–1539. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman and Company.

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and selfdetermination in human behavior. New York, NY: Plenum Publishing Co. Dietz, W. H., & Robinson, T. N. (2008). What can we do to control childhood obesity? Annals of the American Academy of Political and Social Science, 615, 222–224. Economos, C. D., Brownson, R. C., DeAngelis, M. A., Foerster, S. B., Foreman, C. T., Gregson, J., et al. (2001). What lessons have been learned from other attempts to guide social change? Nutrition Reviews, 59, S40–S56. Flores, R. (1995). Dance for health: Improving fitness in African American and Hispanic adolescents. Public Health Reports, 110, 189–193. Friedman, J. M., & Halaas, J. L. (1998). Leptin and the ­regulation of body weight in mammals. Nature, 395, 763–770. Kimm, S. Y., Glynn, N. W., Kriska, A. M., et al. (2002). Decline in physical activity in black girls and white girls during adolescence. New England Journal of Medicine, 347, 709–715. Kumanyika, S., Story, M., Beech, B. M., et al. (2003). Collaborative planning process for formative assessment and cultural appropriateness in the girls health enrichment multi-site studies (GEMS): A retrospection. Ethnicity and Disease, 13, S15–S29. Lepper, M. R., Master, A., & Yow, W. Q. (2008). Intrinsic motivation in education. In M. L. Maehr, S. A. Karabenick, & T. C. Urdan (Eds.), Advances in motivation in education: Vol. 15 (pp. 521–556). Bingley: Emerald Group Publishing Limited. Robinson, T. N. (1999). Reducing children’s television viewing to prevent obesity. Journal of the American Medical Association, 282, 1561–1567. Robinson, T. N. (2001). Population-based obesity prevention for children and adolescents. In F. E. Johnston, & G. D. Foster (Eds.), Obesity, growth and development: Vol. 3 (pp. 129–141). London: Smith-Gordon and Company Limited. Robinson, T. N., & Borzekowski, D. L. G. (2006). Effects of the SMART classroom curriculum to reduce child and family screen time. Journal of Communication, 56, 1–26. Robinson, T. N., & Sirard, J. R. (2005). Preventing childhood obesity: A solution-oriented research paradigm. American Journal of Preventive Medicine., 28, 194–201. Robinson, T. N., Killen, J. D., Kraemer, H. C., et al. (2003). Dance and reducing television viewing to prevent weight gain in African-American girls: The Stanford GEMS pilot study. Ethnicity and Disease, 13, s65–s77. Schwartz, M. W., Woods, S. C., Seeley, R. J., Barsh, G. S., Baskin, D. G., & Leibel, R. L. (2003). Is the energy homeostasis system inherently biased toward weight gain? Diabetes, 52, 232–238. Summerbell, C. D., Ashton, V., Campbell, K. J., Edmunds, L., Kelly, S., & Waters, E. (2003). Interventions for treating

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obesity in children Art. No. CD001872. DOI: 10.1002/ 14651858.CD001872. Cochrane Database of Systematic Reviews, 3. Summerbell, C. D., Waters, E., Edmunds, L. D., et al. (2005). Interventions for preventing obesity in children Art. No.: CD001871. DOI: 10.1002/14651858.pub2. The Cochrane Database of Systematic Reviews, 4.

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Wadden, T. A., Sternberg, A., Letizia, K. A., et al. (2005). Behavioral treatment of obesity. Psychiatric Clinics of North America., 28, 151–170. Weintraub, D. L., Tirumalai, E. C., Haydel, K. F., et al. (2008). Team sports for overweight children: The Stanford Sports to Prevent Obesity Randomized Trial (SPORT). Archives of Pediatrics and Adolescent Medicine, 162, 232–237.

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C H A P T E R

26 From Diets to Healthy and Pleasurable Everyday Eating Lyne Mongeau Department of Social and Preventive Medicine, University of Montreal, Quebec, Canada

o u t l i n e 26.1 The Diet Zeitgest

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26.2 A New Weight Paradigm

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26.1  The diet Zeitgest Eating is obviously a complex and central behavior to human life, but it is important not to neglect one’s relationship with the body as an influence on eating. To fully understand and effectively intervene in weight- and food-related problems, the issues linked to obesity must of course be examined, but also those associated with the quest for thinness, prevalent in modern societies. This section will outline the origins as well as the various aspects of life underlying the quest for thinness and weight loss. The quest for thinness and the world of diets are not flukes of history. Their origins are numerous and complex. While women’s bodies have often been targeted by transformations and diktats, never in the course of history have

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26.3 The New Paradigm’s Contribution to Solving the Obesity Epidemic 337

women’s bodies been so thin (Hubert, 2004). While not explaining everything (Stearns, 1997; Sobal and Maurer, 1999), there are some fundamental factors that coherently represent the rise of the obsession with thinness and the oppression of overweight (Figure 26.1). In Figure 26.1, the religious precedent represents the base upon which rests three pillars of the rise of the thin ideal: (1) the industrial era and consumer society; (2) fashion and design; and (3) medicine and dietetics. These three factors contributed to the transformation of lifestyles, which progressively became more sedentary and urbanized and were the object of a major process of liberalization in various domains. The obsession with thinness being primarily a female concern, the gender aspect is represented by the horizontal arrow in the

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© 2010, 2010 Elsevier Inc.

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Moral question restraint

prejudice

Progressive transformation of lifestyles

The industrial era and consumer society -publicity -media

Fashion and design -nature -nudity -ready-made clothing

Medicine and dietetics

The status of women -motherhood -sexuality

Religious morality

Figure 26.1  Illustration of the elements responsible for the rise of the obsession with thinness and the oppression of overweight. Reproduced from: Côté, D. and Mongeau L., (2005), Le programme Choisir de maigrir? Guide pour les intervenantes et les intervenants. Montreal: ÉquiLibre, Groupe d’action sur le poids. Reprinted and translated with permission.

figure, because it transcends eras and has not yet been resolved. Finally, at the top there is the crux of the issue, represented by the moral question, which is, in turn, manifested on an individual basis by self-control and restraint, and on a collective basis by prejudice. Religious imperatives have regulated life since the beginnings of time, and help to explain both physical representations of the body as well as the relationship with food. Greek wisdom advocated moderation, and Christian revulsion of appetite is stark. Fasting was a highly valued ritual during the Middle Ages, sustained by puritan Protestantism. Gluttony is one of the Seven Deadly Sins, a vice according to others, because people who are gluttonous eat more than is naturally required. Overweight is not so much condemned as is the nature of the relationship with food. In fact, the Bible ignores fat, and saints are usually represented as thin. In spite of this, historians have noted that the theme of self-sacrifice has long been associated with a belief that resisting food is a

sign of sainthood. Even recently, different religious practices pertaining to eating restrictions are still engaged in, such as forbidding children from eating, withholding dessert from them as punishment, doing penance and fasting on certain days of the Catholic calendar (Stearns, 1997; Fischler, 2001). Therefore, for many centuries humans deprived themselves willingly of food, but never with the aim of losing weight. The first pillar (Figure 26.1) is comprised of the far-reaching social transformations brought about by the Industrial Revolution and the market economy. At the beginning of the twentieth century, due to the unprecedented rise in productivity, tensions arose between capitalism’s profit imperative, and the Judaeo-Christian values of frugality and the importance of being parsimonious (Austin, 1999). In this regard, ecclesiasts denounced the new infatuation for consuming, describing it as frivolous and greedy. Gradually, their efforts to keep the faithful on the “straight and narrow” lessened. They encouraged listening to good music and

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26.1  The diet Zeitgest

buying novels. The array of products available for purchase grew, and marketing, until then strictly informational, began appealing to emotions (Stearns, 1997). To dissipate the tension, it was necessary to de-standardize the self-sufficient attitude of pre-Industrial families who would make themselves whatever they needed (Austin, 1999). The increased availability of products coupled with strategies to promote consumption profoundly modified domestic necessities and routines. This was the birth of “good housekeeping” (Ehrenreich and English, 1982). Individuals became attached to the act of purchasing per se (Stearns, 1997), and “shopping” was born. With the assault of electronic media and the influence of advertisements, the consumer world became even more important. Among the suddenly available array of products related to weight and the body we find healthy, “light” and ready-made foods. These products illustrate particularly the resolution of the Judaeo-Christian dilemma, reconciling consumption and abstinence. “Light” food is the perfect example: large quantities, but with fewer calories (Austin, 1999). In addition, manu­ factured products were found to be helpful to women who were now in employment, and thus had less time to prepare meals. Finally, the relationship with the body was also affected by the advent of the consumer era, referring here to the fashion and design pillar. The physical capabilities of the body are less and less needed and valued, as they are replaced by machines. Conversely, the body has become a selling tool in itself, an object of purchasable transformations (Hubert, 2004): slimming products and services, beauty and body objects, plastic surgery, cosmetic treatments and massage services, clothing and other accessories, etc. It represents the decline of the body as subject and the rise of the body as object: it can even be defined the “body project” (Brumberg, 1998). These products represent a significant share of the consumer market, referred to here as the “body industries”. Therefore, industrialization and the

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new social conditions associated with it significantly influenced the modern relationship individuals have with food, their bodies and their weight. The second pillar represents fashion and design. Feminists often blame fashion as being one of the factors that has oppressed and subordinated women. While fashion certainly justified various harmful practices, such as corsets, feetbinding, the lengthening of the neck, etc., nevertheless, it has never been as destructive as today (Seid, 1994). Three principal elements related to fashion seem to have significantly influenced the rise of the cult of thinness: the corset, nudity, and the standardization of sizes linked to the development of ready-made clothing. While the use of corsets goes back to the dawn of time, the end of the eighteenth and the beginning of the nineteenth century marked the start of their demise. Two major influences encouraged this demise: the return to nature, and the freedom of movement needed to practice certain sports (Histoire de la lingerie, n.d.). While the corset was not very comfortable, it was not so much its use as its disappearance that affected the emergence of restraint eating. The corset compensated for body shapes that were too rounded or not rounded enough. Without the corset, it is the body itself that must respond to beauty ideals. This change imposed constraints of a difference nature on women: to alter the body from the inside. The progressive undressing of the body added other constraints on women’s bodies (Hubert, 2004): the naked body now has to be perfect in itself, because it is exposed to all. In the past, women approached beauty ideals by manipulating clothing and jewelry and modifying what they wore. Today, because of the nudity imposed by fashion, the body itself has become the subject of manipulations (Seid, 1994). The sales of beauty products, hair dyes, antiwrinkle creams, etc., have reached new heights. Physical modifications are no longer limited to transforming the body externally, changing

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body weight or using benign and temporary solutions such as massages, wraps or other such frivolities. It is literally transformed, altered permanently, by removing or adding a little something. Plastic and cosmetic surgery is no longer a medical discipline dedicated to the correction of accidental flaws or birth deformities; it constitutes a medico-cosmetic alternative, the provision of one service among many others in the world of physical transformations, a consumer good. The third element, ready-made clothing, made its appearance in the US in 1870. From then on, clothing was no longer tailored individually; the body had to fit into the clothing (Stearns, 1997). Yet the history of ready-made clothes has more to do with democratizing designer wear, too expensive for the poorer segments of society, than with constraining the body. Neither was the standardization of clothing aimed at encouraging thinness; it was rather a temporal coinci­ dence. Since fat was under attack, the advent of standard sizes and the public context in which people tried clothes (the store versus the home) naturally promoted an awareness of the body. Characteristics of the clothing industry, such as mass production, marketing principles, fashion magazines, etc., molded what we know today of fashion. Women’s clothing is often designed by male designers, to a greater or lesser degree mindful of women’s figures. Interestingly, clothes that hug curves are more difficult to adjust. The medical and dietetic discourse constitutes the final pillar upon which rests the focus on thinness. The causes of obesity are numerous, and our knowledge of them has evolved greatly. Explanations underpin the recommended treatments, and for obesity these alternate between biology and behavior. They rarely escape a certain moral judgment, which shifts back and forth between individual and collective responsibility (Bray, 1990). The quantity, variety and nature of the recommendations found in the literature for losing weight are astounding: encouraging

sweating, using laxatives, controlling passionate impulses and the frequency of sexual intercourse, bathing every 8 hours and rubbing oneself down with flannel, taking cold baths, taking hot baths, ingesting soap, eating only one meal per day – to give but a few examples. In regard to food, recommendations are just as varied and contradictory: drink the least possible/ as much as possible, eat as little/as much meat as possible, fruit-based diets, milk-based diets, chew each mouthful of food 32 times, etc. (Apfeldorfer, 2000). Spread over the course of a few decades, each of these recommendations or treatments benefited from a credible context. During the nineteenth century, public receptivity to such dietetic recommendations was also attributable to a more constricting form of dress, as well as to the emergence of very strict table etiquette for the upper class – etiquette that called for a certain degree of restraint. Therefore, even at that time weight control was popular. To medical and dietetic recommendations were added other weight-controlling products and gadgets. Subsequent to the nineteenth century’s miracle solutions, the twentieth century, through women’s magazines, saw the marketing and promotion of various slimming strategies. For example, “Rengo”, advertised in the Pittsburg Press in 1908, promised the loss of 1 pound per day, playing upon the embarrassment caused by obesity. The slogan urged clients not to wait: “Now – do not wait until you are a disgusting, frightful sight” (Stearns, 1997). Dietetics fads were decried by the medical and scientific communities, which were increasingly worried about related health risks. In addition, lifestyle changes were already manifesting themselves through the increasing inactivity of the population, which soon raised alarm in medical circles. On the other hand, the medicalization of women’s health offered doctors the opportunity to target fat surplus in middle- to upper-class women (Ehrenreich and English, 1982; Kohler Riessman, 1998). The rhetoric argued for the need to develop diets based on

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26.1  The diet Zeitgest

scientific facts, rather than on various tricks and gadgets (Stearns, 1997). In a culture where weight and appearance were growing in importance, weight control soon became the focal point of the new profession of dietitian/nutritionist. Recommendations for better weight control had two goals: health and esthetics (Parham, 1999). Parham (1999) examined the training curriculum of dietitians/ nutritionists during the first century of the profession’s existence. When the profession first began to develop, nutritional knowledge was limited to the role of nutrients responsible for energy provision, but concerns regarding obesity became more and more important. With the aim of influencing health, training focused on the knowledge of food and its related physiological processes; public demand, on the other hand, focused on weight control and maintenance. Restraint eating, self-sacrifice, and tracking calorie intake soon became the center of the dietetic approach, hence the rather pejorative label of “diet police”. In the 1960s and 1970s, with the support of scientific evidence, restraint eating slowly fell out of favor. Major articles reported the ineffectiveness of recommended approaches (Stunkard and McLaren-Hume, 1959; Stunkard and Penick, 1979), and others called into question the very issue of obesity treatment (Wooley et al., 1979; Wooley and Wooley, 1984; Brownell, 1993; Brownell and Rodin, 1994). Paradoxically, this launched a whole new series of dietary recommendations, also based on restraint eating: fasting, very low-calorie diets, surgery, medication and aerobic exercise (Parham, 1999). After a few relative successes, the long-term effectiveness of these various strategies was also deemed to be mixed (NIH, 1993). It was only in 1995 that the Institute of Medicine declared that the success criterion for a weight-loss program was a weight loss of 5 percent of the original weight, maintained for a year (IOM, 1995). Then, in 1998, the National Institutes of Health (NIH) recommended losing 10 percent of the original weight over the course of 6 months (NHLBI,

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2000). These recommendations constituted a first step in the medical community towards moderation in regard to weight control. While the role of women has been mentioned several times in the description of the three pillars, Figure 26.1 illustrates that issues of gender transcend this history. Are weight-related problems so much the problems of women? While there have been recent reports documenting body image problems in males, weight remains primarily a female issue (Striegel-Moore et al., 2008). While the angle with which feminist ques­ tions are treated varies considerably from one author to another, there is a consensus regarding the issue of gender and the rise of thinness (Seid, 1994; Stearns, 1997). Historically seen as “the weaker sex”, in the nineteenth century, women began to fight for the right to vote, and sought to erase ancient perceptions of their bodies, their supposed weakness and vulnerability, which made them unfit for political power, education or employment (Weitz, 1998). However, the emancipation of women corresponded with the rise of the ideal of thinness (Orbach, 1978; Stearns, 1997). Women entering the workforce, the control of reproduction and sexual liberalization are all factors that contributed to modifying the relationship women had with their bodies. Along with fashion, the consumer era, and the decline in popularity of and investment in motherhood and domesticity, thinness brought back the idea of the fragile body, the body object and the esthetic body (Weitz, 1998). Many feminists do not support this correlational hypothesis, arguing that it was a reaction against the feminist movement, or a conspiracy to bring back women to these more traditional ideas. One of the reactionary elements is precisely the heightened pressure to control one’s appearance, thereby keeping women slaves to their bodies and moving them away from power (Wolf, 1991; Weitz, 1998). The final element of Figure 26.1 pertains to public opinion about weight. It is effectively the cement which binds all the elements together,

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and confers to the explanation its global nature. As mentioned previously, public opinion has traditionally oscillated between acceptance and judgment. While Hippocrates, the father of medicine, was reluctant to discuss the causes of obesity, this was certainly not the case for Galien, who stated: “The hygienic art promises to maintain in good health those who obey it; but to those who are disobedient, it is just as if it did not exist at all” (Bray, 1990: 911). It is a clear indication that Galien perceived obesity as the irrefutable proof of someone’s “inadequacy”. The nineteenth century saw the rise of negative perceptions of obesity and fatness (Stearns, 1997). As an example, the magazine Living Age wrote: “Fat is now regarded as an indiscretion and almost as a crime” (Stearns, 1997: 22). These prejudices soon morphed into a true phobia of fat, a conviction that animal fat, in all its forms – on the body, in the blood, on the plate – is dangerous. Concurrently, Americans began perceiving themselves as too fat, as continuously becoming fatter, eating too much, eating the wrong foods, too sedentary, and therefore flabby. They saw themselves as an ailing group, even if their life expectancy kept improving. In fact, the greatest fear they had was of becoming physically and psychologically “soft”. The most powerful and pernicious aspect of the phobia of fat is that being fat is as shameful as being dirty – as though you could become thin as easily as you could become clean (Seid, 1994). Fat prejudices have been found in every segment of the population, as well as in many groups of professionals (Crandall and Biernat, 1990). Discrimination against fat people exists in the workplace as well as in healthcare ser­vices (Puhl and Heuer, 2009). In their day-to-day lives, obese people are subjected to mockery. They are seen as lazy, stupid and unpleasant (Wadden and Phelan, 2002). If thinness symbolizes success and power, obesity means failure and powerlessness. Obesity therefore constitutes an issue of social power (Breseman et al., 1999). In fact, considering how far our societies

have come in eliminating prejudice, some believe that obesity is its last stronghold (Sobal and Stunkard, 1989; Andreyeva et al., 2008). The main discourse about weight can be summarized in three points: (1) overweight is always unhealthy; (2) overweight is mainly caused by a lack of individual self-control in regard to food and physical activity; and (3) any person who wants to be thin, can be. Self-control is an impor­ tant point because it represents the basis of prejudice and discrimination, as well as the primary argument of the weight-loss industry – i.e., that obesity can be eliminated if the individual takes the appropriate steps (Brownell, 1991; Ritenbaugh, 1991; Brown and Bentley-Condit, 1998). This idea fits well in the puritan values of individualism and individual willpower. In fact, according to Seid (1994), this belief system has fostered prejudice, and has turned the quest for thinness into almost a religion. Therefore, the fear of overweight feeds the desire for thinness. But how can we hold such negative opinions of our fellow human beings, who simply possess a different physical appearance? How can esthetic norms lead us to such extremes? A passing dietetic trend might have been accompanied by particular fashion requirements, but the effects there would have been short term. According to Stearns (1997), the new anxiety with regard to weight is not only an issue concerning health or even appearance; it has also deeply affected American conscience, to the point that the obsession continues to grow. Deepseated causes must have justified this truly moral condemnation. It is in self-control research, in the equation between laziness and obesity and in the disgust with people who cannot respect the values of self-control, individual will and effort that one finds the last link, the crux of the issue. Stearns (1997) hypothesizes that as liberalization spread through various spheres of life – sexuality, consumption, hobbies – feelings of guilt increased, and had to be contained by the establishment of new restraints. Physical and dietary discipline gave the impression that one was compensating

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26.2  A new weight paradigm

for the indulgence linked to other pleasures, was paying lip service to religious morality. Fischler (2001: 391) summarizes this idea well: “It is tempting to see in today’s diets a laicized version of yesterday’s fasts. Thinness has appeared to be a modern form of sainthood that can only be attained through restriction”. Therefore, the moral stance is manifested by a series of personal selfconstraints which allow for inner peace, followed by powerful moral judgment of others, to express one’s disapproval of hedonism.

26.2  A new weight paradigm Obesity is not a simple pathology that occurs in the presence of an easily definable condition. On the contrary, we gain weight for a multitude of reasons, and, less well-known, as individuals attempt to lose weight through various strategies, the problem becomes more complicated because physiological, behavioral and psychological homeostasis is disrupted. The problem’s recurrence is spectacular, and, after over half-acentury of serious efforts to find a way to attain successful and sustained weight loss, results remain mixed (Ayyad and Anderson, 2000; Jeffery et al., 2000; Anderson et al., 2001; Curioni and Lourenco, 2005; Shaw et al., 2005; Tsai and Wadden, 2005; Franz et al., 2007). Among researchers and practitioners, this failure has led to a deep questioning of obesity treatments and has even raised doubts regarding the need to lose weight (Wooley et al., 1979; Wooley and Wooley, 1984; Garner and Wooley, 1991; Brownell, 1993; Brownell and Rodin, 1994). This is one of the motivations behind developing a new vision of weight loss, and constitutes the first assumption of the new paradigm:

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(1) Weight-loss methods are ineffective. The two others are: (2) Weight loss methods are unhealthy; and (3) Commonly-held beliefs, past and present, about the causes and consequences of obesity are incorrect (Mongeau, 2005). The same line of questioning also emerged within the feminist movement (Stearns, 2004) and fed these assumptions. In the 1970s, various workshops led women to reflect upon the obsession with physical appearance, weight and food (Orbach, 1978; Hirschmann and Munter, 1988). The feminist movement evolved in two ways with regard to weight. First, they fed into a movement for the protection of the rights of overweight/obese people and the more general acceptance trend. Rights organizations, such as the National Association to Advance Fat Acceptance (NAAFA) (formerly the National Association to Aid Fat Americans), founded in 1969, fights primarily against inequalities, stigmatization and discrimination against overweight/ obese people, and aims at creating a society in which obese people can live with dignity and equality (NAAFA, 2008). The second evolutionary trend that adopted the feminist movement was to develop an intervention approach, focused on individual wellbeing, which aimed at rehabilitating the relationship between food and body image. Participants were asked to reconnect with hunger and satiety signals, as well as to relearn to respect their tastes, while eliminating restrictive eating. It is effectively giving oneself back the right to eat (Hirschmann and Munter, 1988). Between 1975 and today, other programs have been developed around the world.1 This trend was strongly driven by the development of restraint-eating theory, pioneered by Herman and Polivy (Herman and Mack, 1975; Herman and Polivy, 1984). This theory stipulated

1 

Bacon et al., 2002; Rapoport et al., 2000; Diet Free Forever (Steinhardt et al., 1999); Sbrocco et al., 1999; Undieting (Hetherington and Davies, 1998); Goodrick et al., 1998; Tanco et al., 1998; Beyond Dieting (Ciliska, 1990); The Solution Method (Mellin et al., 1997); If Only I Were Thin (Robinson and Bacon, 1996); Overcoming Overeating (Steinhardt and Nagel, 1995); HUGS, a Non-Diet Lifestyle Program (Omichinski and Harrison, 1995); Eat for Life (Carrier et al., 1994); Undieting (Polivy and Herman, 1992); Roughan et al., 1990; McNamara, 1989; What About Losing Weight? (Mongeau, 2005).

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that individuals in a state of cognitive restraint impose upon themselves rigid limits to regulate their dietary intakes, which are determined by rules and beliefs regarding “good” foods and the permitted quantities. Removed from the restraint framework, they are completely destabilized and become incapable of managing their dietary intake. They then eat without control until they feel ill (Herman and Polivy, 1984). Hence, cognitive restraint is about replacing a dietary behavior regulated by outside criteria with dietary behaviors planned and determined according to cognitive criteria, or with dietary behaviors modeled to specific diets (Herman and Polivy, 1980). Regarding the use of weight-loss products and methods, their consequences on health depend upon the length, nature, method and scope of the self-imposed restriction (Gregg and Williamson, 2002). They can be short term and minor but, with severe dietary restrictions and/ or frequent use, consequences may be major: cardiac arrhythmia, electrolytic imbalances (Gregg and Williamson, 2002), biliary calculations (Liddle et al., 1989; Weinsier et al., 1995), and loss of bone density (Langlois et al., 2001; Ensrud et al., 2003). In addition, many of the natural weight-loss products contain ingredients that may be toxic and/or may lead to serious health problems, some fatal (INSPQ, 2008). In a recent scientific report, the Institut national de santé publique du Québec questioned the safety and utility of many weight-loss products and methods and, given the vulnerability of individuals excessively concerned by their weight, called into doubt the capacity of this industry to adequately protect the health of users, especially in the current social context which idealizes thinness (INSPQ, 2008).

While some benefits of successful weight loss have been recorded (INSPQ, 2008), they only last as long as the weight loss is maintained. This weight maintenance has been shown to be very difficult (Wing and Phelan, 2005). Therefore, faced with the risks related to weight loss and the odds against maintaining the loss, what is the net benefit? This is where the paradigm’s second assumption, that weight loss is unhealthy, comes in. Finally, the last assumption refers to the discourse on the causes and consequences of obesity. The explosion of media articles and reports on the obesity epidemic that summarize research results without qualifying them contributes to the third assumption. As for the consequences of obesity, there is a clear link between real obesity and various illnesses, but the link between obesity and mortality as well as the consequences of overweight are not so clear (McGee, 2005; Romero-Corral et al., 2006; Flegal et al., 2007). Numerous factors enter the causal equation between weight and health or longevity, which are not always taken into account or well understood. Several research results suggest that, independent of weight, individuals with a good cardio-respiratory condition present a reduced morbidity and mortality compared to unfit individuals (Ross and Katzmarzyk, 2003; LaMonte and Blair, 2006). Considering the diverse points presented above, the new paradigm promotes “Health-atEvery-Size” (HAES), and represents a new way of conceiving weight problems. According to the definition of this paradigm,2 the vision that emerges completely breaks with the former approach. Based on the premise that it is best to improve one’s health and to honor one’s body, the new paradigm supports individuals in the adoption of new lifestyle habits to improve their

2 

According to Kuhn (1972), a paradigm is the system of beliefs, recognized values and techniques common to a particular scientific community. It consists of a framework of assumptions which defines what a problem is, a solution, and a method which molds and guides the researchers’ work. Guided by a new paradigm, researchers adopt new instruments and look to new directions. More importantly still, during scientific revolutions researchers discover new and different things, even though they are studying previously examined questions with familiar tools.

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26.3  The new paradigm’s contribution to solving the obesity epidemic

health and wellbeing rather than encouraging weight loss at any cost. The paradigm encourages the acceptance and respect of the natural diversity of physical forms, suggests eating in a flexible manner, responding to hunger and satiety signals, finding pleasure in an active body, and becoming more physically active (Bacon, 2008). Globally, the new paradigm allows indi­viduals to potentialize their health independently of their weight, which is difficult to change. The process is based on the exploration, knowledge and understanding of all facets of the weight problem. By focusing on self-esteem and acceptance, intervention empowers individuals to change what can be changed, and to accept what cannot be changed (Berg, 1999). The success of this process is measured more by the accomplishment of personal goals and the evaluation of wellbeing rather than exclusively in terms of the weight lost. Since the approach is non-directive and focuses on empowerment, it allows participants to learn how to take charge of their health; health practitioners are then partners or facilitators in the process. Different programs based on this paradigm have been evaluated. They tend to improve the relationship with food, body image, selfesteem and self-efficacy, and to reduce rates of depression and emotional eating (Ciliska, 1990; McFarlane, 1999; Miller and Jacob, 2001; Foster and McGuckin, 2002; Bacon et al., 2005; Mongeau, 2005; Provencher et al., 2007, 2009). These results occur in the absence of significant weight loss, and therefore cannot be attributed to this. Therefore, these results suggest that the changes are the fruit of the active principles of this approach.

26.3  The new paradigm’s contribution to solving the obesity epidemic To halt the obesity epidemic, there exists an important consensus on the necessity of adopting

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a mixed approach, composed of macrosocial and individual measures (James, 1995; Swinburn et al., 1999; Nestle and Jacobson, 2000; French et al., 2001; Kumaniyka et al., 2002). In the case of public health, the World Health Organization (2000) recommends the development of an obesity management strategy that covers a complete continuum of actions, prioritizing the following points in particular: 1. Actions that influence the weight of the entire population and aim to modify social, cultural, political and physical elements of the environment 2. Actions targeted to those individuals with weight-related problems and that aim at: • weight maintenance • the treatment of complications • weight loss. Can we conceive of possibilities whereby human individuals can pull themselves out of the great dependence on external solutions to find a path which fits them and will allow them to re-establish control over their life and health? In what way is this new paradigm a guide on the path towards a better balance between a healthy weight and a pleasurable way of life? What can this new paradigm bring to the fight against obesity? Psychology theory has recently undergone a paradigm shift (Bandura, 2001). From a rather mechanized vision, the conception of human behavior has evolved towards a vision of individuals as control agents of their own life (Baranowski et al., 2002). A high level of functional consciousness implies a deliberate manipulation of information with the aim of selecting, building, regulating and evaluating the course of actions. This is actualized by an intentional mobilization and a productive use of a semantic and pragmatic process of activities, goals and other future events. Therefore, building upon this vision of behavioral self-determination, the intervention model proposed by the new paradigm integrates the fundamental elements

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of awareness, self-control, and enlightened analysis of information. In addition, elements from the feminist approach and the theory of empowerment favor the convergence towards key concepts of self-confidence (self-esteem and self-efficacy). It is by considering the principles and values upon which public health actions are based (Donnan, 2001; MSSS, 2003) that we may hope to reconcile public health strategies and the new paradigm. In this regard, it is interesting to recall these principles. The Programme national de santé publique du Québec 2003–2012 (MSSS, 2003) presents the ethical values and principles guiding the practice of public health. First, promoting the common good is a value that occupies a central place in public health. Health, well­being, safety, and a healthy environment represent especially targeted common goods. The promotion of the common good involves defending the long-term interest of a population which is sometimes in the grip of individual and temporary preferences. Charity and no-harm are two ethical principles that cannot be ignored, and which enlighten the concept of common good. Charity requires that the benefits of an intervention be evaluated in relation to the harm it may cause. It highlights the importance of protecting the population against the misdeeds, iatrogenic or other, that may flow from certain interventions or inaction. Finally, they reflect, particularly, principles of another nature: doubt and precaution. Doubt invites practitioners periodically to question evidence, even solid evidence, in the planning of interventions. The principle of precaution guards against inaction when health risks are not well known or well characterized or, again, when the knowledge underpinning interventions is incomplete, whereas the risks are serious and irreversible. If charity and precaution invite action, no-harm and doubt invite prudence. Even though the new paradigm was not developed in response to a change of vision in public health intervention, but with regard to

individual-level intervention, the convergence between these assumptions and the values they underpin, and the principles and values of public health, is striking. This fact opens the door to concerted action between public health actors and supporters of the new weight paradigm. The cooperation between these two groups of actors could favor, for instance, respect of the psychosocial dimensions of the issue, core components of the new paradigm. Individuals’ autonomy being a common principle of both groups of actors, a favored educational strategy could be based on the empowerment of individuals and on social support. The Governmental Action Plan on the promotion of healthy lifestyle habits and the prevention of weight-related problems 2006–2012, Investir pour l’avenir (Investing for the Future; MSSS, 2006), developed in Quebec, Canada, is an illustration of the convergence between a public health strategy and the new paradigm. The latter made its appearance among nutrition professionals in Quebec in the 1980s, and has gradually inscribed itself into the practice of a significant number of them. Twenty years later, some of these professionals, now public health practitioners, have proposed a vision of public health that incorporates many elements of this paradigm (ASPQ, 2003, 2005). Within the Action Plan, we find specific elements from this mix of visions. For example, the use of the term “weight-related problems” rather than “obesity” takes into account both sides of the problem: overweight, and an excessive preoccupation with weight. Respect for different body shapes is one of the objectives of the Action Plan. Many actions aim at better controlling the weight-loss industry – a protection measure along the same lines as the control measures imposed on infectious agents in the environment. Finally, the plan also warns against a perverse consequence: we must not foster a population-wide weight psychosis and worsen the obsession regarding food. Similarly, the plan warns against increasing stigmatization.

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References

What, then, is the best approach to fighting obesity efficiently, while maintaining the pleasures of eating? Certainly not restrictive diets that, in addition to removing pleasure, pose risks to health and wellbeing, without offering a high probability of weight mainten­ance. Interventions based on the new paradigm, which permits the reconciliation between the body and food, activity and fun, coupled with societal interventions which can make healthy choices easy and unhealthy choices more difficult (Milio, 1986), offer the best hope.

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C H A P T E R

27 Resisting Temptations: How Food-Related Control Abilities can be Strengthened through Implementation Intentions Christine Stich1, Philip J. Johnson2 and Bärbel Knäuper2

1

Population Health, Prevention and Screening Unit, Cancer Care Ontario, Toronto, Canada 2 Department of Psychology, McGill University, Montreal, Canada

o u t l i n e 27.1 Introduction 27.2 The Motivational Nature of Food 27.2.1  The Rewarding Effects of Food 27.2.2 The Rewarding Effects of Food and Attention 27.2.3 The Rewarding Effects of Food and Eating Habits

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27.1  Introduction The past decade has witnessed a rise in the investigation of self-regulation, in particular in regard to the study of the processes underlying self-regulation failure and success in eating and weight control. Successful behavioral

Obesity Prevention: The Role of Brain and Society on Individual Behavior

27.3 Food-Related Control Abilities 27.3.1 Attention and Inhibitory Behavioral Control 27.3.2  Implementation Intentions 27.3.3 Implementation Intentions and Eating Behavior 27.3.4 Replacing Unhealthy Habits Through Implementation Intentions

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control of eating involves the inhibition of the impulse to eat, particularly if it conflicts with long-term goals, such as eating more healthily or losing weight (Baumeister et al., 2007). If an individual lacks the inhibitory control necessary to resist temptation, he or she will succumb to it (Westling et al., 2006). Lapses in self-regulation

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are thus an indication of a breakdown in inhibitory control. Some researchers suggest that, in the domain of eating behavior, being overweight and obese are linked to a lack of inhibitory control, and, moreover, this lack might have crucial consequences for the development, maintenance and treatment of obesity in children and adults (see, for example, Nederkoorn et al., 2006a, 2006b, 2007). For example, using a stop-signal task to assess inhibitory control in children, Nederkoorn and colleagues (2006a) found that obese children undergoing a weight-loss treatment showed less inhibitory control than normal-weight children. They also demonstrated that less successful children in response inhibition lost less weight in the program (Nederkoorn et al., 2006a). The researchers also compared obese women with normal-weight women and found that the for­ mer were less likely to demonstrate inhibitory control (Nederkoorn et al., 2006b). Like inhibition control, attention control processes are necessary for successful self-regulation (Mischel et al., 1989; Mischel and Ayduk, 2004; Rueda et al., 2005). For example, in their classic work on delayed gratification in children, Mischel and colleagues (Mischel and Ebbesen, 1970) showed that when 4-year-olds were provided with rewarding stimuli (cookies), attention paid to the rewards substantially decreased their ability to delay gratification – to wait for the reward. If not exposed to rewards, children waited on average over 11 minutes; however, if exposed to rewards, they waited less than 6 minutes. The ability to delay gratification has important implications, not only for successful self-regulation with food, but also in other domains, such as cognitive, academic, and social competences. Mischel’s delay of gratification paradigm in children has been shown to be predictive of various indices of self-regulation later in life. For instance, 4-year-old children willing to wait longer before receiving their reward were over 10 years later described by their parents as adolescents who were more academically and socially competent than their peers, more able to tolerate frustration and cope maturely with stress,

and better able to resist temptation. Moreover, the time that 4-­year-old children were willing to wait before receiving the reward was shown to be significantly related to their verbal and quantitative scores on the Scholastic Aptitude Test (Mischel et al., 1989). It should be noted that, depending on the rewarding effects of stimuli (such as cookies in Mischel’s paradigm), some stimuli are more attractive than others, thus producing an attentional bias (Waters et al., 2003). Consequently, when addressing people’s attention and inhibitory control abilities, it is also important to take into account the rewarding effects of the stimuli, as these will determine the degree to which such stimuli tax individuals’ attention and inhibitory control abilities. We first review the motivational nature of food, and in particular high-fat/high-sugar food, and its consequences for attention to food and the development of (maladaptive) eating habits. We then discuss how certain self-control strategies might help people control their food intake. We focus here particularly on implementation intentions, and speculate how better planning with implementation intentions could help people replace unhealthy eating habits with healthier ones.

27.2  The motivational nature of food 27.2.1  The rewarding effects of food In a relatively new theoretical approach to understanding food intake, Berridge (1996) suggests that “wanting” food and “liking” food are possibly two separate components affecting food intake, as they have distinct underlying brain substrates. In other words, although liking food is usually associated with wanting food, the two concepts can also be separated (Berridge, 1996; Berridge and Robinson, 2003). For example, the sight of a cake in a bakery can draw in an individual, causing an intense desire

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27.2  The motivational nature of food

(“want”) for the cake, even if that individual does not necessarily expect to “like” the cake very much (Winkielman and Berridge, 2003). According to Berridge (1996), “liking” refers to the hedonic preference for food, to its perceived pleasantness or palatability, and thus represents an affective component of food. In contrast, “wanting” food refers to the incentive or reward associated with food, and corresponds more closely to appetite or craving. As such, wanting represents a motivational component of eating (Berridge, 1996). At the same time, even though food intake is influenced by both liking and wanting, sensory information such as the appearance, smell and taste of food is transformed into attractive and desired incentives that motivate food intake through the attribution of rewarding effects, which are guided by associative learning (Berridge and Robinson, 2003). Hence, once a specific food item is associated with rewarding effects, the mere perception of its attributes, such as appearance or smell, can motivate eating (e.g., Weingarten and Elston, 1990; Fedoroff et al., 2003; Tiggemann and Kemps, 2005).

27.2.2  The rewarding effects of food and attention As noted earlier, research has shown that stimuli with rewarding effects can seize an individual’s attention (Mischel and Ayduk, 2004), and therefore people may develop an attentional bias or hyper-vigilance toward such stimuli (Waters et al., 2003). It has been found for a wide range of stimuli, such as smoke-related stimuli in smokers (e.g., Sharma et al., 2001; Waters et al., 2003), alcohol stimuli in alcoholics (e.g., Sharma et al., 2001; Noël et al., 2007) and mood-congruent stimuli in manic and depressed individuals (Murphy et al., 1999). With respect to clinical populations, attentional bias for food stimuli has been found in both individuals with anorexia and those with bulimia nervosa (for review, see Faunce, 2002).

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Such a bias has also been demonstrated in individuals without an eating pathology. For instance, using a cued-target, covert attention paradigm, Leland and Pineda (2006; Study 1) found an attentional bias for food-related words in non-clinical normal-weight participants. In this study, participants were presented with food-related and neutral cue words. By appearing in the same or opposite hemifield, these cues served as either valid (75 percent) or invalid (25 percent) predictors of target (rectangles) location. Supporting the hypothesis that visual selective attention is biased by food-related stimuli, results showed that the effect of valid cues was larger for trials in which food words were cues than for trials in which neutral words were cues. Looking further to the motivational salience of food stimuli, we recently found an attentional bias for visual food stimuli. Using a version of a go/no-go task to investigate inhibitory control for food stimuli, we found that participants paid more attention to and showed a higher decision bias for food rather than for control stimuli. Specifically, participants responded faster to food pictures than to control pictures (landscapes), and tended to respond more to food than control pictures (Stich et al., 2008). The question then arises as to whether specific types of food possess particular rewarding effects. As mentioned earlier, the rewarding effects of food (i.e., wanting) have been shown to have different underlying brain substrates than its palatability (i.e., liking). The two processes, however, are most likely interrelated when it comes to food intake (Berridge, 1996). Hence, individuals want to eat food they find palatable (e.g., Epstein et al., 2003). Moreover, foods that taste good (i.e., are more palatable) are often those high in fat and/or sugar. In contrast, foods that are low in fat and/or sugar are often perceived as unpalatable (Drewnowski, 1998). Research has confirmed that, when given the choice between energy-dense snack foods and fruits and vegetables, most individuals choose

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snack foods (e.g., Goldfield and Epstein, 2002). Such findings suggest that people have a preference for palatable, energy-dense foods, and therefore they might show a higher attentional bias toward energy-dense foods. However, to our knowledge, research has yet to explore the relationship between types of food in terms of their palatability and energy density, and attentional bias.

27.2.3  The rewarding effects of food and eating habits The aforementioned findings of attentional bias to food stimuli are especially important, as food stimuli can serve as cues to activate well-practiced routines or habits (e.g., Sayette, 1999; Verplanken and Wood, 2006). In general, people acquire habits slowly, based on the covariation between features of contextual cues and responses that they repeatedly experience (Wood and Neal, 2007). Habits are mediated by memory representations of the cue–response link, which operate automatically (Tiffany, 1990), without intention, effort or conscious awareness (Shiffrin and Schneider, 1977). This way, once a habit is formed, the behavioral response is merely triggered by the perception of the cues. Because responses to contextual cues can occur intentionally or unintentionally, habits can be formed without being mediated by goals (Wood and Neal, 2007). One way in which habits can be formed is in contexts in which people repeatedly experience rewards for a specific response (e.g., Neal et al., 2006; Wood and Neal, 2007). For example, when regularly eating chips in front of the TV, this behavior can develop into a habit because eating in itself is rewarding. Over time and with sufficient repetition, a person will automatically associate TV-watching with eating chips, even if these are not available. Illustrating the automaticity associated

with habits, laboratory research and real-world studies have repeatedly demonstrated the facilitating effect of contextual cues on speed and acc­uracy of responses (for an overview, see Wood and Neal, 2007). The self-regulatory process of permanently over-riding or inhibiting habitual and automatic rewarding responses requires conscious effort (Baumeister et al., 1994). Thus, when a goal (e.g., losing weight) conflicts with a habitual response (e.g., eating chips in front of the TV), a person’s self-regulatory capacities will determine whether the goal or the habitual response will prevail. The undesirable habit will be inhibited only if sufficient self-regulatory capacities are available (Wood and Neal, 2007). Overall, research suggests that if foods associated with high rewarding effects are readily available, they will seize individuals’ attention. Food can activate eating habits via its appearance, smell and taste, as well as through the context in which its consumption frequently occurs. The rewarding effects of food in combination with eating habits might lead to pre-potent approach tendencies that make inhibitory control challenging. Exerting control to inhibit attention and habitual eating responses depends on the availability of self-regulatory capacities. Uninhibited, and in combination with a sedentary lifestyle, such pre-potent approach tendencies may contribute to the overconsumption of foods and, ultimately, to overweight and obesity, especially when the food consumed is energy dense.

27.3  Food-related control abilities 27.3.1  Attention and inhibitory behavioral control The task of controlling habitual food-related responses involves (1) attention control (Mischel

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and Ayduk, 2004) and (2) inhibitory control (Nederkoorn et al., 2006a). Attention control refers to the ability to keep oneself from getting distracted by attention-grabbing, goal-irrelevant stimuli, and to focus cognitive resources on goal-relevant behavior (e.g., avoiding fatty foods or sweet desserts and eating vegetables instead; Gollwitzer and Sheeran, 2006). As illustrated by ample research findings, failing to control attention, and therefore to inhibit the distraction through tempting stimuli, can greatly undermine goal pursuit (e.g., Kuhl, 1981; Gollwitzer and Schaal, 1998; Gollwitzer and Sheeran, 2006; also see Mischel and Ayduk, 2004). Inhibitory control is needed to inhibit or stop the execution of behavioral responses once a pre-potent or habitual behavior is initiated (e.g., Logan et al., 1997; Gollwitzer and Sheeran, 2006). In contexts where an undesired behavior can be avoided completely, inhibitory control refers to the inhibition of behavior in response to stimuli. For example, in the context of smoking cessation, it can mean avoiding stores where cigarettes are sold, not buying a package of cigarettes when seeing it in the store, or even inhibiting the act of lighting a cigarette, in order to avoid the occurrence of the undesired behavior (i.e., smoking). Inhibitory control with food, however, is more challenging, because individuals need to eat on a regular basis. Thus, instead of simply avoiding any behavior related to the tempting stimuli, people rather need to inhibit the consumption and overconsumption of particular types of food (e.g., energy-dense foods). In sum, to successfully abstain from giving in to eating temptations, both attention control and inhibitory behavioral control are necessary. Particularly in a world where food stimuli with significant rewarding effects are readily available, successful behavioral control entails the inhibition of consumptive responses to appetitive food stimuli (attention control), as well as the inhibition of behavioral impulses (inhibitory control) that would lead to an overconsumption of calories.

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27.3.2  Implementation intentions As mentioned, engaging in self-regulatory control processes such as attention and inhibitory behavioral control requires a significant amount of self-regulatory effort (e.g., Baumeister et al., 1994; Wood and Neal, 2007). For these control efforts to be successful over time, their activation needs to be transformed from a non-automatic, conscious and effortful process into an automatic response (Mischel and Ayduk, 2004). The conversion of effortful self-regulatory processes into automatic ones lies at the center of Gollwitzer’s research on implementation intentions (Gollwitzer, 1993, 1996, 1999). Research has shown that by forming implementation intentions, people can avoid giving in to temptations elicited by food. Implementation intentions are volitional strategies that can be used to translate behavioral intentions into actual behavior. While behavioral intentions (e.g., “I intend to eat a low-fat diet”) express people’s motivation to engage in a certain behavior, implementation intentions specify the steps to be followed in order to translate intentions into behavior. They do this by specifying the conditions, such as the critical environment or context, under which a target behavior will be performed. Implementation intentions are concrete if the plan of action specifies when, where and how one will perform a behavior in order to achieve a specific goal: “If situation Y arises, then I will perform goal-directed response Z!” (Gollwitzer, 1993, 1999; for review, see Gollwitzer and Sheeran, 2006). Forming implementation intentions is effective because it is a conscious commitment to perform a goal-directed behavior (ignore) when encountering certain critical cues (dessert; Gollwitzer, 1999). This commitment creates a strong mental link between the critical situation/ cue and one’s intended behavior. Thus, instead of relying solely on motivation and willpower, implementation intentions allow individuals to

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partly delegate control of their behavior to the situation and, as a result, the intended behavior is carried out quasi-automatically (Gollwitzer, 1999; Gollwitzer and Sheeran, 2006). Research has shown that specifically forming “if–then” links helps to partly delegate control of behavior to critical cues in the situation, thereby making action initiation immediate, efficient, and absent of conscious intent (e.g., Gollwitzer and Brandstätter, 1997; Aarts and Dijksterhuis, 2000; Brandstätter et al., 2001; Webb and Sheeran, 2004). Successfully establishing this “if–then” plan assists in activating effective self-­regulatory behavior, even under stressful or cognitively demanding situations (Gollwitzer, 1999; Gollwitzer and Sheeran, 2006). The formation of implementation intentions has been shown to be impressively effective. In a recent meta-analysis of effects of implementation intentions, Gollwitzer and Sheeran (2006) performed 94 independent tests, and found that implementation intentions had a positive effect of medium-to-large magnitude (d  0.65) on goal attainment. For health-related behaviors, a medium effect size was found. Particularly in the area of eating behaviors, research has shown that implementation intentions are effective in increasing the consumption of fruits and vegetables (Kellar and Abraham, 2005; Armitage, 2007), reducing fat intake (Armitage, 2004; Luszczynska et al., 2007a) and increasing healthy eating (Verplanken and Faes, 1999). More recently, implementation intentions have been found to enhance weight reduction among overweight and obese people (Luszczynska et al., 2007b). Gollwitzer and Sheeran (2006) observed implementation intentions to be effective in every step of goal pursuit. Namely, they are effective in promoting the initiation of goalstriving, shielding ongoing goal pursuits from unwanted influences, disengaging from failing courses of action, and, finally, conserving capability for future goal-striving. Such effectiveness is even more remarkable considering the sheer simplicity of the implementation intention

instructions employed in these studies, and that they are usually administered just once in standard implementation intention experiments. Finally, the effects of implementation intentions have been shown to be relatively enduring. For instance, they were still present after 48 hours between implementation intention formation and cue encounter (see Gollwitzer, 1999). In intervention studies, they have been found to be effective in promoting health behavior over a period of up to 6 months (Luszczynska, 2006). Furthermore, Sheeran and Orbell (1999) suggested that once the behavior initiated through implementation intentions has been performed regularly over a longer period of time (e.g., 3 weeks), it becomes habitual in nature. Thus, the effects of implementation intentions should not diminish greatly over longer periods of time. This, however, is an empirical question, and further studies are needed to test the long-term effects of implementation intentions on the maintenance of health behaviors (Luszczynska, 2006).

27.3.3  Implementation intentions and eating behavior With respect to eating behavior, while many people initiate dieting behaviors, the majority of diets are brief and unsuccessful (Stotland et al., 1991; Jeffery et al., 2000). The cardinal problem in this area is not goal initiation, but goal completion. As noted previously, people are derailed, in part, because of lack of attention and/or inhibitory behavioral control in the face of incentive food or food cues. Thus, to attain weight loss or healthy eating goals, people’s self-regulatory task is to shield their goal-striving from unwanted influences. Research by Gollwitzer and colleagues suggests that implementation intentions can prevent “derailment” and thus protect goalstriving from unwanted influences by suppressing (1) unwanted attention responses (e.g., “If I see something delicious, but unhealthy on the menu, then I will ignore it”) and (2) unwanted

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behavioral responses (e.g., “If I am tempted to eat a delicious, but energy-dense dessert, I will not eat it”; Gollwitzer and Sheeran, 2006). In Gollwitzer and Sheeran’s (2006) meta-analysis, all studies on the inhibition of unwanted attention responses and unwanted behavioral responses showed strong beneficial effects of implementation intentions. Forming implementation intentions to suppress unwanted attention toward food should prevent the individual from paying attention (the original behavioral response) to appetitive but unhealthy stimuli (i.e., the cue; Gollwitzer and Sheeran, 2006), and thus should increase attention control. More precisely, depending on the situation and on the individual’s personal eating goals, suppressing unwanted attention by forming implementation intentions should allow the individual to (1) ignore (new behavioral response) food (cue) or, when eating is appropriate, to (2) focus on healthy food alternatives or limit portion sizes (new behavioral responses). Looking at the effectiveness of implementation intentions in shielding goalstriving from distraction in eating behavior, Achtziger and colleagues (2008) posited that implementation intentions help people ignore thoughts about high-fat snacks that they are craving, resulting in better diet adherence. As illustrated earlier, it may not always be sufficient to inhibit unwanted attention responses to food in order to avoid eating, or eating too much. It is often also necessary to inhibit unwanted behavioral responses (see also Gollwitzer and Sheeran, 2006). Forming implementation intentions to inhibit unwanted behavioral responses should increase inhibitory behavioral control, and should also prevent unhealthy eating behaviors. Again, depending on the individual’s personal goals, it should allow that individual to avoid certain food or to eat less in general. Hence, implementation intentions could be used to replace the original behavioral responses to specific food cues (e.g., eating) with new, healthy and intended responses (e.g.,

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eating fruits instead of high-caloric snacks). Thus, the same attention-grabbing food cues that, in the absence of implementation intentions, would lead to self-regulatory failure could also be used as critical cues when forming implementation intentions. Future research needs to investigate in which format implementation intention-based interventions aimed at inhibiting unwanted attention and behavioral responses to food should be administered in the general population, in order to effectively promote healthy eating and weight loss, and to prevent obesity.

27.3.4  Replacing unhealthy habits through implementation intentions The automatic activation of habitual respon­ ses is key to habit persistence. After all, behavior modification approaches have long recognized that habits initiated by cues can be used for intervention. These approaches change undesired behavior, such as addictions, by altering the very contexts in which such maladaptive behaviors occur. Furthermore, people are typically encouraged to limit their exposure to critical cues with the potential to activate undesired habits. However, such attempts to avoid critical-cue exposure might require a significant amount of self-regulatory effort (Wood and Neal, 2007). Implementation intentions could be used to replace habitual behavioral responses to specific cues (e.g., eating chips when watching TV) with new, healthy and intended responses (e.g., ignoring the chips, eating healthy snacks instead) (cf. Verplanken, 2005). While the cues remain the same, when paired with an appropriate goal-directed response they serve to activate effective self-regulatory behavior. Like habits, implementation intentions allow individuals to partly delegate control over their behavior to the situation. No conscious effort is needed, and the behavior is carried out

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quasi-automatically when the relevant cue is encountered (Gollwitzer, 1999). Implementation intentions and habits bear important similarities (Sheeran et al., 2005). For instance, in both situations the cue is linked to the activation of behavior; once the cue is encountered, the behavior is activated without conscious effort (Verplanken and Faes, 1999). However, unlike habits, which are acquired slowly through repeated covariation of cue and response, implementation intentions are formed consciously and instantaneously (Gollwitzer, 1999; Sheeran et al., 2005; Verplanken and Wood, 2006). Because implementation intentions are associated with the automatic initiation of action, they can create what Gollwitzer (1999) calls “instant habits”, which can result in habitual behavior over time (Gollwitzer and Schaal, 1998). Future research needs to show whether, similar to habit formation, the rehearsal of implementation intentions increases the strength of cue–response link – whether they can slowly be turned into habits. If so, rehearsing implementation intentions would enhance their impact on behavioral responses (Sheeran et al., 2005). Since implementation intentions and habits share similar features, it seems plausible that, with sufficient repetition, the cue–response association formed initially through implementation intentions could help people form new, healthier habits (Verplanken, 2005; Verplanken and Wood, 2006). In sum, forming implementation intentions to inhibit unwanted attention and behavioral responses to food cues could be an important step in changing people’s unhealthy eating habits.

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Armitage, C. J. (2004). Evidence that implementation intentions reduce dietary fat intake: A randomized trial. Health Psychology, 23, 319–323. Armitage, C. J. (2007). Effects of an implementation intentionbased intervention on fruit consumption. Psychology & Health, 22, 917–928. Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at self-­regulation. San Diego, CA: Academic Press. Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The strength model of self-control. Current Directions in Psychological Science, 16, 251–255. Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience and Biobehavioral Reviews, 20, 1–25. Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neuroscience, 26, 507–513. Brandstätter, V., Lengfelder, A., & Gollwitzer, P. M. (2001). Implementation intentions and efficient action initiation. Journal of Personality and Social Psychology, 81(5), 946–960. Drewnowski, A. (1998). Energy density, palatability, and satiety: Implications for weight control. Nutrition Revi­ews, 56, 347–353. Epstein, L. H., Truesdale, R., Wojcik, A., Paluch, R. A., & Raynor, H. A. (2003). Effects of deprivation on hedonics and reinforcing value of food. Physiology & Behavior, 78, 221–227. Faunce, G. J. (2002). Eating disorders and attentional bias: A review. Eating Disorders, 10, 125–139. Fedoroff, I., Polivy, J., & Herman, C. P. (2003). The specificity of restrained versus unrestrained eaters’ responses to food cues: General desire to eat, or craving for the cued food? Appetite, 41, 7–13. Goldfield, G. S., & Epstein, L. H. (2002). Can fruits and vegetables and activities substitute for snack foods? Health Psychology, 21, 299–303. Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. European Review of Social Psychology, 4, 141–185. Gollwitzer, P. M. (1996). The volitional benefits of planning. In P. M. Gollwitzer & J. A. Bargh (Eds.), Linking cogni-­ tion and motivation to behavior: The psychology of action (pp. 287–312). New York, NY: Guilford Press. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493–503. Gollwitzer, P. M., & Brandstätter, V. (1997). Implementation intentions and effective goal pursuit. Journal of Persona­ lity and Social Psychology, 73, 186–199. Gollwitzer, P. M., & Schaal, B. (1998). Metacognition in action: The importance of implementation intentions. Personality and Social Psychology Review, 2, 124–136. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social ­Psy­chology, 38, 69–119.

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C H A P T E R

28 The Dieter’s Dilemma: Identifying When and How to Control Consumption Ayelet Fishbach1 and Kristian Ove R. Myrseth2 1

Booth School of Business, University of Chicago, Chicago, IL, USA ESMT European School of Management and Technology, Berlin, Germany

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o u t l i n e 28.1  Introduction 28.2 A Two-Stage Model of Self-Control: Identification Versus Resolution

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28.1  Introduction Drawn to plentitudes of tempting foods, the dieters’ challenge to restrict consumption is two-fold. Not only must dieters employ the force of will to steer clear from temptation, but they must also know when such efforts are appropriate in the first place. Clearly, people need to eat, and, unlike other temptations (e.g., cigarettes, drugs and alcohol), abstinence is not a solution. The question is then when and under what circumstances should people exercise restraint? Having one extra sandwich alone will not incur serious costs even for the strict dieter, but having the extra sandwich every day

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28.2.1 The First Stage: Conflict Identification 28.2.2 The Second Stage: Choice Resolution 28.3  Conclusions

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may. Indulging in one chocolate alone will not cause significant problems for most dieters, but regular consumption may. Knowing when to exercise restraint is as important as knowing how to exercise restraint, and these two challenges together constitute the dieters’ dilemma. In this chapter, we review research on the two stages of the dieter’s dilemma. We first distinguish between the different challenges associated with each stage for success and failure at self-control. Subsequently, we review the research on conflict identification, focusing on factors that increase the dieter’s tendency to identify a self-control problem when facing tempting foods. Thereafter, we discuss the second stage, focusing on the role of

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counteractive self-control processes in promoting the pursuit of dieting and health goals.

28.2  A two-stage model of self-control: identification versus resolution While the psychological literature to date has mainly focused on the manifestations and mechanisms of self-control (Mischel et al., 1989; Baumeister et al., 1994, Loewenstein, 1996; Fishbach and Trope, 2007), a precondition for selfcontrol is that individuals perceive a self-control conflict and hence the necessity to harness temptation. Of course, there are circumstances in which the person will have no issue with recognizing a potential problem of indulging. We could imagine a gourmet diner faced with a delicious dessert, knowing that having that dessert could trigger a dangerous allergic reaction; she should not have it. In this case, the capacity to exercise self-control efforts determines the diner’s likelihood of resolving the conflict in favor of the goal to stay healthy (and alive). In other circumstances, however, recognizing conflict may not prove obvious. For example, another dieter could be facing the same dessert, though without allergy concerns. Having this one dessert alone will but trivially affect his

health, but having desserts in general may prove detrimental (e.g., Rachlin, 2000). The likelihood that the dieter indulges in dessert, therefore, depends jointly on his (1) identifying choice conflict and (2) invoking effective self-control strategies given conflict identification. Of course, the problem of identification for the dieter is commonplace. It characterizes most consumption decisions about food because the cost of eating too much on a single occasion usually is trivial. On the basis of this analysis, we propose a two-stage model of self-control to describe the dieter’s dilemma (Myrseth and Fishbach, 2009a). According to this model (see Figure 28.1), individuals facing a tempting stimulus either will identify self-control conflict or not (Stage 1). If selfcontrol conflict is identified, the individual will employ self-control processes to promote goalpursuit over indulgence in temptation (Stage 2). However, if self-control conflict is not initially identified, the individual will choose temptation without invoking self-control processes. The individual who has identified a conflict may then succeed to stay clear of temptation, in which case we have successful goal pursuit. Alternatively, self-control processes may fall short, and we have a classic case of acrasia: lacking command over oneself. Although the individual’s failure in applying effective self-control strategies after identifying a conflict will yield outcomes

Stage 1: Conflict identification

Stage 2: Conflict resolution Successful selfcontrol strategies (restraint)

Identify selfcontrol conflict Facing temptation

Do not identify conflict (indulging)

Figure 28.1  The two-stage model of self-control.

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Unsuccessful strategies (indulging)

28.2  A two-stage model of self-control: identification versus resolution

similar to those of the person who failed to identify conflict (Stage 1), the etiologies of the two are distinct. These distinct etiologies for success and failure at self-control are consequential for understanding and improving goal pursuit.

28.2.1  The first stage: conflict identification The problem of identification arises only in circumstances under which the cost of a single indulgence is trivial (or epsilon). We conceptually distinguish between “malignant” and “epsilon cost” temptation. The former is characterized by potentially serious costs associated with unit consumption (e.g., sugar for the dieter with diabetes); the latter is characterized by trivial costs (e.g., sugar for the dieter with no diabetes). Specifically, the unit consumption cost of epsilon temptation is trivial, but the extended consumption cost may prove quite serious. Epsilon cost temptation is distinct from malignant temptation by virtue of its ambiguous threat to goal pursuit (during Stage 1, Figure 28.1). The individual facing malignant temptation likely will identify self-control conflict, but conflict identification in the face of epsilon cost temptation is less clear. For most people, a serving of tempting food represents an epsilon cost temptation: there are trivial costs associated with consuming a serving of food, but potentially serious costs following extended consumption. Therefore, the question of conflict identification is central to understanding healthy eating. We propose two conditions necessary for the dieter to identify self-control conflict in the face of epsilon cost temptation. In general, the dieter must view the choice opportunity in relation to multiple similar choice opportunities. This interrelated choice frame has two key properties that distinguish it from its counterpart, the isolated frame: 1. Width: The interrelated frame must be wide, such that the individual sees multiple choice opportunities

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2. Consistency: The individual expects to make similar choices for each of the multiple opportunities. Successful resolution of the dieter’s dilemma depends on the width of the frame, and people make healthier food choices when they consider a wide (vs narrow) time frame (Read et al., 1999a; Rachlin, 2000). For example, when making a snack choice for the entire week, people may choose healthier options than when making a separate decision each day of the week (Kudadjie-Gyambi and Rachlin, 1996; Read et al., 1999b). We have recently demonstrated the effect of a wide frame in a study that manipulated the mere perception of the time frame as wide versus narrow (Myrseth and Fishbach, 2009b). Participants in our study approached a food stand offering free carrots and chocolates, unaware that they were participating in a study. The poster adjacent to the food stand announced either “Spring Food Stand” (the wide frame), or “April 12 Food Stand” (the narrow frame). We found that those who approached the “Spring” food stand took more carrots (the healthy option) and fewer chocolates (the tempting option) than did those who approached the “April 12” food stand. This is because “Spring” activated a wider time frame than a specific spring day, increasing the likelihood that participants approaching this stand considered the present choice in light of similar future opportunities. Thus, they more likely identified self-control conflict between staying healthy and indulging than did those approaching the “April 12” stand. However, adopting a wide time frame is not sufficient for identifying self-control conflict. In addition, individuals should see themselves making similar choices across multiple opportunities, leading them to highlight the same important goal across these opportunities. Research on the dynamics of self-regulation addresses this second criterion for conflict identification (Fishbach and Dhar, 2005; Fishbach et al., 2006;

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Fishbach and Zhang, 2008; Koo and Fishbach, 2008). This research examines how frames of goal pursuit influence patterns of self-regulation. These frames can lead either to a highlighting dynamic of choice, by which pursuit of the overriding goal is chosen across choice opportunities, or to a balancing dynamic, by which goal pursuit and indulgence in temptation are balanced across opportunities. Specifically, this work suggests that choices consistent with goal pursuit may signal either greater commitment to a goal or progress toward this goal. For example, after choosing to forego unhealthy food, individuals may conclude either that they are more committed to their health goals or that they have made progress on the health goals. The two possible inferences from the same choice, to eat healthy food, will have opposite consequences for subsequent course of action. As shown in Figure 28.2, a “commitment frame” leads to a dynamic of “highlighting” the important goal, whereas a “progress frame” leads to a dynamic of “balancing” this goal and short-term temptation. In a commitment frame, for example, choosing to eat healthy food increases the likelihood that a person will make another healthy choice at the next opportunity, because the strength of the health goal increases (high commitment). Conversely, choosing to eat unhealthy food decreases the likelihood of making healthy choices because the strength of the health goal decreases (low commitment). In contrast, in the progress frame, choosing to eat healthy food decreases the likelihood that a person will make another healthy choice,

because the strength of the partially fulfilled goal decreases (high progress). Conversely, choosing to eat unhealthy food increases the likelihood of making a healthy choice because the strength of an unfulfilled goal is high (low progress). In a study that tested this model, Fishbach and colleagues (2006) manipulated the frame of healthy behaviors for gym users by priming (or not) super-ordinate health goals. They hypothesized that when gym users consider the overall meaning of their workout for their super-ordinate health goals, they will infer their personal commitment from their level of exercise, and so subsequent food consumption would be consistent with initial performance through a dynamic of highlighting (i.e., greater exercise – healthier eating). In contrast, when gym users focus on the action itself (no health goal reminder), they infer their level of progress from their level of exercise, and subsequent behavior would compensate for initial performance through a dynamic of balancing (i.e., greater exercise – unhealthier eating). Consistent with these predictions, when gym users were reminded of the super-ordinate health goal, those who learned that they exercised more than others intended to eat healthier food than did those who learned that they exercised less (a highlighting dynamic). In contrast, when gym users were not reminded of the super-ordinate goal, those who learned that they exercised less than others intended to eat healthier food than did those who learned that they exercised more (a balancing dynamic). It appears that when the super-ordinate health goal is salient and conflict

Goal Commitment

Highlighting a focal goal and inhibiting alternative goals

Goal Progress

Balancing between a focal goal and alternative goals

Figure 28.2  Dynamics of self-regulation.

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28.2  A two-stage model of self-control: identification versus resolution

is identified, thinking about exercise achievement may further reinforce commitments to the goal of maintaining good health, thus promoting consistent healthy behavior. However, when the super-ordinate goal is not salient and conflict not identified, thinking about exercise achievement, paradoxically, may reduce healthy food consumption because people engage in a balancing dynamic of choice, whereby consistency of choice is not expressed across opportunities. In another study that tested how inferences of goal progress may allow individuals to indulge, Fishbach and Dhar (2005) manipulated perceived goal progress by asking participants to indicate the discrepancy between their current and ideal weight on scales with endpoints 5 lb or 20 lb. The same discrepancies in absolute terms (e.g., 3 lb) would appear larger in the former than in the latter case, and so participants were expected to infer more goal progress for the 20-lb scale than for the 5-lb scale. Accordingly, those indicating discrepancies on the 20-lb scale subsequently were more likely to choose an unhealthy chocolate over a healthy apple. That is, learning that one is closer to one’s ideal weight reduces efforts at healthy eating when one does not identify a self-control conflict between eating healthily or not. Further studies find that merely thinking about future goal-pursuit influence present choice (Zhang et al., 2007). For example, when considering future workouts, gym users may conclude either that they are more committed to their health goal, or that they will make progress toward the goal. These inferences will have opposite implications for what they presently decide to eat. People will indulge less in unhealthy foods when interpreting future workouts as commitment to health goals, but more when interpreting future workouts as progress toward health goals. Moreover, to the extent that people are optimistic and believe more goal attainment is achieved in the future than in the past (Weinstein, 1989; Newby-Clark et al., 2000; Buehler et al., 2002), future ­ expectations

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may have greater impact than past behaviors on present choice. For example, considering the intention to exercise in the future, more than considering past exercise, increased healthy food consumption in the commitment frame but reduced it in the progress frame. Research on the dynamics of self-regulation further shows that presenting alternatives as competing versus complementary influences whether individuals adopt, a highlighting or balancing dynamic of choice (Fishbach and Zhang, 2009). According to this research, presenting goal- and temptation-related options (e.g., healthy and unhealthy food) apart in two separate choice sets, versus together in one choice set (e.g., in two different bowls or in the same bowl), determines whether individuals perceive them as conflicting versus complementary. When the options are apart, they seem conflicting and thus promote a highlighting dynamic of choice; when the options are together, they seem complementary and hence promote a balancing dynamic of choice. In a highlighting dynamic, individuals employ selfcontrol processes to secure goal pursuit. In a balancing dynamic, however, they proactively postpone goal pursuit in favor of instant gratification. That is, when individuals plan to balance between complementary alternatives, they do not see themselves making the same choice in the future, and so there is no self-control conflict. Therefore, they choose to indulge presently, with the intention to choose goal-pursuit later. To demonstrate these effects, Fishbach and Zhang (2008) presented healthy and unhealthy food items in one of three formats: (1) together in one image, to induce a sense of complementarity and a dynamic of balancing; (2) in two separate images, to induce a sense of competition and a dynamic of highlighting; or (3) in two separate experimental sessions, as a control condition (see Figure 28.3). They selected healthy and unhealthy food items that were similarly positive when evaluated independently (e.g., fresh tomatoes vs a cheeseburger). As expected,

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Together

Apart

Figure 28.3  Presenting foods together or apart.

presenting these items together, in one image, increased liking for unhealthy foods because the items appeared complementary. However, presenting them apart, in separate images, increased liking for healthy foods because the items appeared conflicting. In another study, these researchers measured liking for healthy and unhealthy courses on a restaurant menu. The courses were presented either together on one menu (e.g., “garden salad” and “chili cheese fries” were on the same appetizer list), or apart, in two separate parts of a menu (e.g., one side included the “garden salad” and the other included the “chili cheese fries” on the appetizer lists). The researchers found that mixing these courses together on a single menu rendered them complementary and increased the value of unhealthy courses. However, presenting them separately, on two parts of a menu, rendered them conflicting and increased the value of healthy courses. Moreover, when healthy and unhealthy foods were mixed together, fewer participants chose a healthy entrée than chose a healthy dessert. This is consistent with a balancing dynamic of choice, where immediate gratification takes precedent over subsequent goal-pursuit. However, when healthy and unhealthy foods were separated on the menus into distinct sections, most participants chose both a healthy entrée and a healthy dessert, consistent with a highlighting dynamic of choice. Similar to reminding people of their super­ordinate goals, presenting alternatives apart, as conflicting with each other, facilitates successful self-control by helping people identify a conflict

between maintaining good health and indulging. When items are presented together and seem complementary, people fail to perceive a self-control problem in the present choice, leaving goal-pursuit for the future (“I start my diet tomorrow”). It follows that people’s concern with weight-­watching should positively predict choice of healthy items when these items are presented apart from unhealthy items, signaling conflict between important goals and temptations, but not when these items are presented together with unhealthy items, signaling no conflict. To examine this idea, Fishbach and Zhang (2008) offered participants a choice between a chocolate bar and a bag of baby carrots. They found that when the options were presented apart, in two different piles, more participants chose the healthy carrots than when the options were presented together. More importantly, when the chocolates and carrots were presented apart, participants’ concern with weight-watching positively predicted their choice of carrots over chocolate. That is, dieters preferred carrots more than did non-dieters. However, when the foods were mixed into the same pile, participants’ concern with weight-watching did not predict choice, because they failed to identify the choice set as a self-control conflict. Therefore, they did not adhere to their goals. In summary, research reviewed here demonstrates that identification of self-control conflict, first of all, depends, on a wide frame. That is, the individual must consider making multiple choices, such that the cost of yielding to temptation appears potentially high. A wide frame, however, is not sufficient for identifying selfcontrol conflict. In addition, individuals must perceive a choice pattern that highlights one type of choice and promotes consistency.

28.2.2  The second stage: choice resolution To the extent that self-control conflict is identified upon presentation of temptation, the

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28.2  A two-stage model of self-control: identification versus resolution

individual will exert self-control. Then, ­successful goal-pursuit will depend on the effectiveness of self-control strategies. In this section, we address the nature of the self-­control strategies from the standpoint of counteractive control theory (Trope and Fishbach, 2000; Fishbach and Trope, 2005; Myrseth et al., 2009). This theory describes the processes by which individuals offset (i.e., counteract) the influence of temptations on goal-pursuit. According to counteractive control theory, self-control strategies involve asymmetric shifts in motivational strength: an increase in motivation to pursue a goal and a reduction in motivation to pursue temptation. Such asymmetric shifts may result from behavioral strategies. For example, facing the tempting presence of cigarettes, alcohol or fattening food, people may choose to skip purchase opportunities for these items or maintain only a small supply, thereby constraining the availability of temptation (Thaler and Shefrin, 1981; Schelling, 1984; Ainslie, 1992; Wertenbroch, 1998). Because self-control is a process of asymmetric response, people also increase the availability of goal-related items. For example, individuals ­ maintain a large supply of healthy products and take advantage of purchase opportunities to pre-commit themselves to goalrelated choices: some purchase gym membership for the entire year, or buy extra supplies of healthy foods. Behavioral self-control strategies act directly on the physical availability of choice alternatives. Other self-control strategies act on the psychological representation of the choice alternatives, and involve selective attention, encoding, and interpretation of these alternatives. For example, research finds that people promote goal-pursuit by forming “cool” or abstract representations of temptations, thereby reducing their appeal (Metcalfe and Mischel, 1999; Mischel and Ayduk, 2004; Kross et al., 2005; Fujita et al., 2006). Correspondingly, it is possible people promote goal-pursuit by forming a “hot” or concrete representation of goal-consistent behavior.

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Furthermore, self-control involves changes in the valuation of goals and temptations. That is, individuals experiencing self-control conflict counteractively bolster the value of goals and dampen the value of temptation. For example, Trope and Fishbach (2000) document that students bolster the value of studying for an important exam when they consider tempting leisure. More recently, Myrseth and colleagues (2009) explored the parallel devaluation of temptation in the domain of food consumption. They show that individuals devaluate tempting foods when these interfere with dieting goals. Specifically, Myrseth and colleagues examined how availability of tempting food influences their evaluation. They argue that individuals with weight-watching goals, before choosing between healthy and unhealthy food, will dampen their valuation of unhealthy food relative to that of healthy food. However, this pattern should attenuate after choosing, when tempting foods no longer threaten dieting goals. For example, the dieter contemplating the dessert menu in a restaurant will perceive the napoleon as more appealing when the dessert cart is in the kitchen than when it is in front of her; the unavailable napoleon is less threatening to her dieting goals. In support of this analysis, Myrseth and colleagues found that individuals choosing chocolates bars over health bars valued chocolates less than health bars before but not after they had made their choice. Once they had chosen the health bars, chocolates no longer represented a threat to their weight-watching goals, and participants did not employ counteractive valuation. Asymmetric shifts need not be of conscious, deliberative nature. That is, in contrast to common belief that self-control is an explicit response requiring conscious deliberation and executive processing resources (Mischel, 1996; Muraven and Baumeister, 2000), some self-­control responses involve non-conscious modes of operation (Moskowitz et al., 1999; Fishbach et al., 2003; Amodio et al., 2004; Gollwitzer et al., 2005;

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Fishbach and Shah, 2006). These non-­conscious responses are efficient, and characterize successful self-regulators more than they do unsuccessful ones (Ferguson, 2008). For example, successful dieters are more likely to exhibit implicit self-control responses than do those who fail to follow their dietary goals (Fishbach et al., 2003). Non-conscious counteractive control takes several forms. One is that individuals alter the implicit value of goal- and temptation-related alternatives when goal-pursuit conflicts with indulging in temptation. Outside their conscious awareness, individuals boost the value of the goal while dampening the value of temptation (Fishbach et al., 2010). For example, in the presence of cues for unhealthy foods, Fishbach and colleagues find that individuals concerned with weight-watching increase the implicit positivity of concepts related to healthy alternatives (e.g., fruit, vegetable) by associating them with positive concepts. In another study, these authors find that weight-watching individuals further decrease the implicit positivity of concepts related to unhealthy foods (e.g., candy, cake) by associating them with negative concepts. Nonconscious processes, which boost the value of healthy foods while devaluing unhealthy foods, may increase the likelihood that individuals choose to eat healthy foods. Another form of implicit counteractive control entails changes in the accessibility of goals and temptations (Fishbach et al., 2003). Individuals shore up their goals by activating related constructs in response to interfering temptations, and by inhibiting tempting constructs in response to cues for the over-riding goal (see also Shah et al., 2002). For example, success in weight-watching entails activating concepts related to dieting when encountering a tempting chocolate cake, and inhibiting thoughts about fatty food when exercising. Fishbach and colleagues (2003) illustrated the former point with a subliminal sequential priming procedure (Fazio et al., 1995; Bargh et al., 1996). The more important weight-watching was to participants,

the faster they recognized words relating to weight-watching (e.g., diet) upon subliminal priming of concepts related to conflicting temptation (e.g., chocolate). This pattern held only for weight-watchers, who were generally successful at maintaining their weight, suggesting that the implicit operations facilitate pursuit of health and weight-watching goals. Another technique for promoting goal pursuit over temptation is to keep distance from tempting objects, but maintain physical proximity to objects that facilitate goal-pursuit (Thaler and Shefrin, 1981; Schelling, 1984; Ainslie, 1992). For example, anticipating problems posed by a previous romantic partner, people may move to a different location or maintain close proximity to others who help them cope. This asymmetrical response, to approach goals and avoid temptations, also occurs at the non-conscious level. To demonstrate this effect, Fishbach and Shah (2006) investigated dieters’ response time with a joystick for pulling words toward themselves (i.e., approaching) and pushing words away from themselves (i.e., avoiding). They found that committed dieters were quicker to push the joystick (avoiding) in response to temptationrelated words (such as chocolate or sweets) than in response to goal-related words (such as slim or shape). That is, they automatically avoided the temptation. Not only do self-control processes act at the basic level of approach and avoidance, but approaching goals and avoiding temptation also improves goal-pursuit. Accordingly, Fishbach and Shah (2006) demonstrated that participants who completed a joystick task, in which they responded to unhealthy food stimuli by pushing (avoiding) and to fitness stimuli by pulling (approaching), later expressed stronger preferences for healthy foods than did those who completed the opposite task (i.e., responded to food stimuli by pulling and to fitness-stimuli by pushing). These results suggest that simply expressing subtle behavior consistent with one’s weight-watching or health goals may prove

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28.3  Conclusions

sufficient to strengthen the goals and thus to increase goal-consistent behavior. That is, dieters who practice pushing away the dessert plate may increase their frequency of choosing healthy over unhealthy foods. In summary, this section has reviewed the processes of self-control. In line with counter­ active control theory, we propose that self-control involves asymmetric changes in motivational strength. We further propose that individuals more likely to succeed at goal-pursuit are better at employing these self-control strategies. Notably, individuals apply self-control strategies only to the extent that they have identified a self-control conflict. Specifically, research on counteractive control finds that self-control is elicited only when important goals are perceived to conflict with temptations, and when external mechanisms are not in place to ensure goal pursuit (Fishbach and Trope, 2005). Thus, the perception that temptation threatens goal pursuit (Stage 1) is necessary to activate subsequent self-control processes.

28.3  Conclusions The dieter’s dilemma has two components. First, individuals facing tempting foods either identify or not conflict between indulging and pursuing weight-watching or other health goals. Second, if they have identified conflict in the first stage, they will subsequently draw on self-control strategies to restrict consumption. If their strategies are successful, then they exercise restraint. However, if their strategies fall short, they will indulge, as will they if they do not identify conflict in the first place. We thus identify two distinct etiologies of indulgence in tempting foods: namely, the absence of selfcontrol conflict, and the failure of self-control strategies. While a significant proportion of psychological research to date has focused on implementation of self-control strategies (see, for example,

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Thaler and Shefrin, 1981; Mischel et al., 1989; Baumeister et al, 1998), the nature of self-control dilemmas for dieters entails that conflict identification often is not trivial. This is because one act of indulgence will have little impact on one’s overall success at maintaining good health; only if this act is repeated across many opportunities may it seriously undermine the health goal. In this type of dilemma, conflict identification in the face of epsilon cost temptation will depend on the frame of the choice opportunity. Specifically, it will depend on whether the frame is wide, capturing multiple choice opportunities, or narrow, capturing a single opportunity, and whether the individual perceives that the same choice will be made for each opportunity. For example, the question of having one bitesized chocolate alone may not be sufficient to activate self-control strategies for most dieters, but the prospect of regularly having this opportunity probably is, though only to the extent that the present choice is thought to be the same as future ones. The second component of the dieter’s dilemma involves the implementation of selfcontrol pro­cesses. In line with research on counteractive control (e.g., Fishbach and Trope, 2007), we propose that the essence of these self-control operations involves an asymmetric motivational response: increasing the motivational strength of the goals (e.g., weight-watching) while decreasing the motivational strength of indulging in temptation (e.g., eating dessert). For example, to resist the chocolate, the dieter can elaborate on what makes healthy eating valuable, while undermining the perceived appeal of the chocolate. We conclude that the problems of overeating may not be mere problems of acting against one’s better judgment, but also problems of determining better judgment in the first place. Better understanding of the etiology of successful weight-watching and health maintenance would lay the groundwork for better remedies against excessive indulging.

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C H A P T E R

29 Lifestyle Change and Maintenance in Obesity Treatment and Prevention: A Self-determination Theory Perspective Heather Patrick1, Amy A. Gorin2 and Geoffrey C. Williams1 1

Departments of Medicine and of Clinical and Social Psychology, University of Rochester, Rochester, NY, USA 2 Department of Psychology, University of Connecticut, Storrs, CT, USA

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29.6 Potentional Limitations of Current Interventions: An SDT Perspective 370

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29.1  Introduction As discussed elsewhere in this volume, obesity has become a serious public health problem, with both short- and long-term physical and psychological consequences. In recent years, rates of overweight and obesity have soared in developed and developing countries. In the United States

Obesity Prevention: The Role of Brain and Society on Individual Behavior

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alone, some 66 percent of adults are overweight or obese, as are 17 percent of adolescents and 19 percent of children (National Center for Health Statistics, http://www.cdc.gov/nchs/fastats/overwt. htm). The key to obesity prevention is lifestyle change: improving dietary intake and increasing physical activity. To date, several interventions have been developed to target these behaviors and

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increase weight-loss success. Behavioral wei­ght control, consisting of education about nutrition and physical activity and instruction in key behavioral techniques (e.g., self-monitoring and problem-solving), is the treatment of choice for overweight to moderately obese individuals (BMI 25–40 kg/m2) (Wing, 2002). Weight losses average out at 0.5 kg per week, for a total weight loss of 9.7 kg over 6 months of behavioral weight loss treatment (roughly 8–10 percent of initial body weight) (Wing and Phelan, 2009). However, despite these strong initial results, the long-term impact is disappointing. Unhealthy eating and exercise habits resurface within weeks to months of completing the treatment and, as a result, only 60–70 percent of weight lost during treatment is maintained at 1-year post-treatment, and nearly all weight is regained within 3–5 years (Perri et al., 2001). One reason for this is that existing weight-loss programs largely ignore the potentially crucial element of motivation for sustained behavioral change. Self-determination theory (SDT) (Deci and Ryan, 2000; Ryan and Deci, 2000a) is a general theory of human motivation that addresses the importance of motivation in behavior change and its maintenance. The purpose of this chapter is to describe the general tenets of SDT, to discuss applications of SDT to the lifestyle changes relevant to weight loss, and to provide suggestions for future research and interventions to prevent obesity.

29.2  Self-determination theory One of the key assumptions of self-determination theory is that human beings are naturally oriented toward growth, health and development. However, social-contextual circumstances may facilitate or impede this natural process of motivation and self-governance. Thus, self-determination theory offers an organismic dialectic perspective

on human motivation, which acknowledges the interplay between the person and the situation in various behavioral contexts. SDT posits that all humans possess three basic psychological needs: competence, relatedness and autonomy. The need for competence involves the need to feel optimally challenged in one’s endeavors and to feel capable of achieving desired outcomes. Relatedness pertains to the need to feel close to and understood by important others. Finally, autonomy refers to the need to feel volitional, as the originator of one’s actions. When these needs are met, people evidence optimal motivation and improved physical and psychological outcomes (Ryan and Deci, 2000a). SDT distinguishes between autonomous and controlled motivations. Autonomous motivation is characterized by feelings of choice, volition and self-integration. Controlled motivation is characterized by feelings of pressure, guilt, obligation and self-fragmentation. Importantly, SDT allows for this distinction between autonomous and controlled motivation at the individual differences level or personality level while also acknowledging the role of the broader social milieu in supporting or thwarting optimal motivation in particular domains (e.g., regular physical activity). Thus, according to SDT, individuals may be oriented toward relatively more or less autonomous (or controlled) functioning. The social surround may support or thwart optimal (i.e., autonomous) motivation within individuals in these same circumstances. SDT also addresses how, through the process of internalization, health behavior may be both changed and maintained.

29.3  Self-regulation In addition to its focus on psychological needs, SDT also speaks to self-regulation of behaviors. According to the theory, behaviors may be regulated in relatively more autonomous ways, in that behaviors may be engaged

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29.4 Need-supportive contexts

in because they are fun, interesting, and inherently enjoyable. In contrast, other behaviors may be regulated in relatively more controlled ways, in that they are engaged in because of inter- or intrapersonal pressure, such as demands, evaluation, pressure and guilt. A key distinction between autonomous and controlled behaviors is that the former are engaged in for the sake of the behavior itself, whereas the latter are en­gaged in for some separable outcome. People engaging in behaviors for more autonomous reasons experience greater interest, excitement and confidence for the target behavior. They subsequently evidence enhanced performance, persistence and creativity, as well as heightened vitality, self-esteem and general wellbeing (Deci and Ryan, 1995; Ryan et al., 1995a; Sheldon et al., 1997; Nix et al., 1999; Ryan and Deci, 2000b). Some behaviors may be inherently unenjoyable, or may be enacted largely for some separ­ able outcome. For many, lifestyle change may be experienced as such. This may be because the behavior itself is unpleasant (e.g., muscle soreness and stiffness associated with beginning a new exercise routine) or because the behavior is simply a means to some other end (e.g., changing one’s eating habits for the purpose of losing weight). Ryan and Connell (1989) thus proposed a continuum of motivation to address the fact that behaviors may be engaged for some separ­ able outcome, but they may be relatively more or less integrated with the broader sense of self and thus relatively more or less autonomous. The least autonomous form of self-regulation is external regulation. Externally regulated behaviors are engaged in to gain some reward or avoid a punishment. For example, someone may change her diet to get her spouse to stop nagging her about her eating habits. Introjected regulation refers to behaviors that are engaged in to avoid guilt or shame, or to gain the approval of others. For example, someone may begin an exercise regimen because he is embarrassed about his weight and feels guilty about not taking better care of his health. Identified behaviors

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are enacted because the goal is important or valued by the person. For example, someone may try to improve his eating habits because he values eating in a healthy manner. Finally, integrated regulation refers to behaviors that are integrated to one’s sense of self and are concordant with one’s values. For example, an individual may begin exercising regularly because it is consistent with his goals for healthy eating and of losing 10 kg, and these goals are consistent with his overall value for health. It is the integration with other autonomously- and innately-held values that leads to greater levels of motivation for making and sustaining these behaviors. A growing body of research has demonstrated the importance of autonomous self-regulation for a range of health behaviors. The more autonomously motivated patients are for a health behavior, the more adherent they are to treatment, and the better their health outcomes. This finding has emerged in mandatory treatment for alcohol use (Ryan et al., 1995b), participation in a weight-loss program (Williams et al., 1996), diabetes self-management (Williams et al., 1998a), adherence to medication prescriptions (Williams et al., 1998b), and tobacco abstinence (Williams et al., 1999, 2002; Williams and Deci, 2001). Thus, one key way in which researchers, clinicians and policy-makers may positively influence obesity outcomes is by facilitating the process of internalization in the people with whom they attempt to intervene.

29.4  Need-supportive contexts SDT maintains that motivation is dynamic. Thus, relatively less autonomous forms of selfregulation can become more autonomous through the process of internalization. Although a primary assumption of SDT is that individuals are inherently oriented toward health and optimal

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motivation and may thus naturally tra­verse thr­ough the internalization process, the theory also acknowledges the role of the social context in supporting or thwarting internalization. Optimal motivation and internalization arise out of needsupportive contexts (Ryan and Deci, 2000a). Traditionally, need-support has been studied within “vertical” relationships, where one person is in a position of authority over another (e.g., doctors and patients, parents and children, teachers and students, etc.), though more recent research has begun examining need-support in horizontal relationships between equals (e.g., romantic partners, peers, etc.). In the context of lifestyle change related to weight loss, both types of relationships may offer support in ways that facilitate or impede the process of internaliz­ ation. The concept of need-support represents an interpersonal climate whereby one takes the perspective of another into consideration, asks the individual what he or she wants to achieve, provides relevant information and opportunities for choice, encourages the other to accept personal responsibility for his or her behavior, and refrains from judgment or evaluation when asking about past behavior. In healthcare contexts (i.e., practitioner–patient interactions), this also involves providing clear recommendations, encouraging questions, providing meaningful rationale for treatment recommendations and satisfactory answers to questions, and acknowledging that the patient does not have to change or accept an unwanted treatment. Thus, needsupport involves actively engaging individuals in a discussion about their health and health behaviors with minimal pressure, judgment and control (Ryan, 1993; Reeve, 1998; Williams, 2002). A growing body of research has provided evidence for the critical role of need-support in facilitating autonomous self-regulation for health behaviors. For example, adolescents who were provided a need-supportive message about choosing not to start smoking were more autonomously motivated not to smoke compared to those who received a more controlling

message. Those who were more autonomously motivated not to smoke, in turn, smoked less frequently and less intensely 4 months after the intervention (Williams et al., 1999). In other research, students enrolled in a gymnastics class reported a more intrinsic interest in and greater intentions to continue participating in the class when they perceived their instructor as need-supportive (Goudas et al., 1995). When coaches were seen as need-supportive, competitive swimmers experienced greater autonomous motivation for the sport and were more likely to persist in it (Pelletier et al., 2001). A similar set of findings emerged for competitive gymnasts with regard to perceived need-support from parents and coaches (Gagne et al., 2003). In addition to having need-supportive healthcare providers, who have little direct contact with individuals outside the clinical setting, having need-supportive family members appears to confer health benefits. Williams et al. (2006) demonstrated that increases in perceived need support from important others (e.g., family members) were associated with increases in autonomous self-regulation and perceived competence, as well as better outcomes in a smoking cessation and dietary intervention trial. Interestingly, need-support from important others provided variance distinct from need-­support from healthcare providers, suggesting that both make independent contributions to health outcomes. When allowed to compete for variance, need-support from both healthcare providers and important others uniquely contributed to abstinence from tobacco. In dietary outcomes (e.g., percent calories from fat), the importantothers measure accounted for a greater percent of the variance than the healthcare-providers measure. In ongoing work, Gorin and colleagues (2009) have found that increases in perceived need-support from another adult in the home are associated with increases in autonomous self-regulation for weight control. These increases in autonomous self-regulation predict weight losses over that same time period (controlling

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29.5  SDT and weight loss

for relevant baseline values), providing more evidence of the crucial role family members can play in the weight-loss process.

29.5  SDT and weight loss To date, very little research has focused on SDT applications to weight loss and healthy eating. However, an impressive body of evidence is emerging regarding SDT applications to leisure time physical activity – an important lifestyle change relevant to weight loss. In the first study to apply SDT to weight loss, Williams and colleagues studied severely obese patients enrolled in a 26-week, medically-supervised, very lowcalorie weight loss program (Williams et al., 1996). Additionally, patients attended weekly meetings during which they met with a medical practitioner and participated in a group counseling session. Results indicated that those who were more autonomously motivated for weightloss treatment were more likely to attend treatment sessions and evidenced greater weight loss throughout the treatment program (as indicated by body mass index; BMI). Importantly, autonomous motivation for treatment was associated with greater weight-loss maintenance and better maintained physical activity 2 years post-intervention. Thus, autonomous motivation for weight-loss treatment was associated with both initiated and maintained weight loss and behavioral outcomes. Patients in the Williams et al. (1996) study also completed measures of perceived need-support from treatment practitioners. Perceiving one’s treatment practitioners as more need-supportive was associated with more autonomous reasons for treatment, better treatment attendance, and greater long-term reduction in BMI. Further, path analyses indicated that the link between perceived needsupport and treatment attendance and weightloss outcomes was mediated by autonomous self-regulation. Those who experienced greater

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need-support were more treatment-adherent and had more sustained weight-loss outcomes because of the influence of need-support on autonomous self-regulation. Thus, both need-support and auto­ nomous self-regulation are important to weightloss initiation and maintenance. In a randomized controlled trial examining the impact of weight loss on urinary incontinence in over 300 overweight women, Gorin and colleagues (2008) explored whether baseline levels of autonomous and controlled selfregulation, and changes in self-regulation over 6 months, were associated with 6-month weightloss outcomes. Controlling for baseline weight, the results suggest that higher levels of controlled self-regulation at study entry were associated with worse weight-loss outcomes, whereas better weight-loss outcomes were associated with increases in autonomous self-regulation and decreases in controlled self-regulation over the 6-month period. Silva and colleagues (2008) recently reported on the development of an SDT-based random­ized controlled trial for weight loss among over­weight individuals. Participants were randomly assigned to either the SDT weight-loss intervention or a general health improvement intervention. The intervention period is 1 year, and is followed by a 2-year, no intervention followup. The SDT weight-loss intervention consists of 30 2-hour group sessions conducted with groups of up to 30 participants. Participants received information from physical activity, nutrition and behavior-change specialists about the lifestyle changes needed to achieve weight loss. Those in the general health intervention attended a similar number of sessions, but their session content was based on a series of 3- to 6-week long educational topics, including preventive nutrition, self-care, stress management and effective communication skills, among others. Preliminary analyses suggest that the SDT-based intervention resulted in greater increases in physical activity, improvement in dietary intake, and better weight-loss outcomes. Importantly,

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participants in the SDT-based intervention evidenced more autonomous self-regulation for physical activity and weight loss, which independently predicted the relevant behavior change outcomes (Patrick et al., 2009). While relatively little attention has been given to SDT applications to weight loss and dietary changes, much research has focused on SDT approaches to physical activity, with a recent focus on recreational physical activity in adults. Those who have more autonomous self-regulation for exercise are more ready to initiate exercise and report enjoying exercise more (Mullan and Markland, 1997; Mullan et al., 1997). Thogersen-Ntoumani and Ntoumani (2006) found that those who had more autonomous self-regulation for recreational exercise had fewer periods of relapse into a sedentary lifestyle, and greater intentions to continue exercising. Autonomous self-regulation has also been associated with consistently exercising over a period of 6 months (Matsumoto and Takenaka, 2004) and participation in moderatelevel physical activity consistent with public health recommendations (Standage et al., 2008). Together, this set of findings supports the importance of autonomous self-regulation in recreational physical activity initiation (Mullan and Markland, 1997) and recreational physical activity consistency and maintenance (Matsumoto and Takenaka, 2004; Thogersen-Ntoumani and Ntoumani, 2006). These findings can inform obesity interventions as they speak to the underlying mechanisms of lifestyle changes necessary to achieve and maintain weight loss.

29.6  Potentional limitations of current interventions: an SDT perspective As noted previously, one reason that weightloss maintenance has remained elusive may be the failure of these interventions to consider

the critical role of motivation in health behavior change and maintenance. One key issue may be the focus on weight loss as an outcome. While weight loss is an important outcome because of its potential impact on health, the focus on weight loss inherently creates a circumstance whereby lifestyle change becomes a means to an end. That is, changes to one’s levels of physical activity and quality of dietary intake are enacted primarily for the purpose of losing weight, and not necessarily for the inherent value or pleasure of the activities themselves. Thus, lifestyle change is enacted based on relatively less autonomous self-regulation. When lifestyle change is engaged for some separable outcome – particularly when that outcome is clearly measureable (e.g., lose 10 kg) – the outcome attains especially significant meaning. If the outcome is not achieved, then it is easy to infer that the lifestyle change “didn’t work” and is therefore not useful to continue. Yet the focus on separable, measurable outcomes may also explain why even those who achieve initial weight loss may discontinue lifestyle change and thus regain weight. Once the outcome has been achieved, there is no need to continue the utilitarian lifestyle changes. In many ways, failing to sustain lifestyle change may be even more problematic than failure to maintain weight loss. Research indicates that the health benefits of lifestyle change – particularly physical activity – are independent of weight or weight status (US Department of Health and Human Services, 2008). Another potential problem with a focus on weight loss is that, for many, weight loss as a goal is in large part about one’s physical appearance or impressing others. These are goals that are relatively extrinsic or external to the self. This is in contrast to goals about health and personal growth which are more intrinsic or internal to the self (Kasser and Ryan, 1993, 1996). Weight loss is a prime example of the conflict between people’s inherent value for health and societal and cultural values for image and appearance. Extrinsic goals about image and appearance can

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29.7 Directions for future research based on SDT

further thwart autonomous self-regulation, as they create additional pressure for the individual to attain outcomes that may not be feasible and that are beyond that which is required for improved health. The result of repeated efforts to lose weight and then regain it may be an amotivated state whereby individuals are unable to muster the energy needed to initiate, much less sustain, health behavior change.

29.7  Directions for future research based on SDT The past three decades have seen substantial improvement in the efficacy of interventions for weight loss. Yet much remains to be done to better elucidate the characteristics of these interventions that are likely to foster not only weight-loss initiation but also sustained weight loss and lifestyle change over time. As a broad theory of human motivation, SDT is uniquely positioned to address many of these issues, particularly through the development of interventions that target the social-contextual characteristics likely to facilitate autonomous self-regulation. These interventions may target healthcare practitioners as well as the broader social context within which individuals interact on a more consistent basis, such as family and friends. SDT may also address how interventions can be framed with a focus on health – rather than an exclusive focus on weight loss – to support optimal motivation. While it is unlikely that weight-loss interventions can fully get away from weight loss as a goal, practitioners can work with patients to facilitate a focus on health as part of that goal. Importantly, interventions that focus on the health benefits of lifestyle change in the form of improved dietary intake and increased physical activity independent of weight loss may be particularly beneficial. Indeed, increasing evidence suggests that health benefits from these lifestyle

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changes occur much sooner than weight loss. For example, engaging in moderate levels of physical activity for 30 minutes a day on 5 or more days a week has been shown to improve various health outcomes, including decreasing heart disease and diabetes risks. However, engaging in more vigorous levels of physical activity and for longer periods of time is required to achieve weight loss (Jakicic et al., 2001). There is also increased evidence regarding the weight loss needed to achieve health benefits. Specifically, weight loss of 5–10 percent results in improved health with respect to cardiovascular disease and diabetes (Diabetes Prevention Program Re­s­earch Group, 2002; Knowler et al., 2002; Look AHEAD Research Group, 2007). For overweight or obese individuals, this amount of weight loss is unlikely to change their weight status and move them from being classified as overweight to normal weight based on BMI. Modest weight loss may also fail to result in substantial changes to one’s physical appearance, which may be frustrating and disheartening for many patients, particularly when the energy behind wanting to lose weight centers on a desire to look better. According to SDT, practitioners can support patients’ needs by acknowledging their interest in and concern about weight. Thus, we do not recommend that practitioners ignore weight concerns or minimize their patients’ interest in weight loss as an outcome. Beyond considering the patients’ perspective on the importance of weight loss, practitioners may serve to further facilitate autonomous self-regulation by working with patients to shift their focus to their innate tendency toward health. Although motivation for health may not necessarily be at the forefront of all patients’ minds as they embark on weight-loss efforts, SDT maintains that this energy source exists and can be activated. While concern for achieving a particular weight or an interest in one’s image will likely not dissipate completely, practitioners may be better positioned to facilitate autonomous self-regulation and work with patients to achieve long-term

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lifestyle change and maintenance by bringing to the forefront the patient’s inherent value for health. Research is needed on how best to sustain this focus on health in the face of conflicting societal and cultural messages. Much of the research to date examining SDT in healthcare settings has focused on the importance of need-support as provided by healthcare practitioners. This is indeed an important part of any intervention focused on motivating health behavior change and its maintenance. However, given that eating and physical activity are behaviors that occur almost entirely in the home, work and community contexts, and not in the clinical environment, need-support from family members and other members of patients’ social network is needed. Without proper training, and despite the best of intentions, family and friends may provide help and support in controlling ways that undermine autonomous self-regulation and sabotage the very success these efforts are intended to promote. For example, Goldsmith and colleagues (2006) found that cardiac patients attempting weight loss and other lifestyle changes described talking with their spouse about their health behavior changes as “pressure”, “control”, “demands”, “poli­cing” and “gatekeeping”. If perceived as control­ling, important others’ attempts at providing support may backfire. In a study with cardiac rehabilitation patients, Franks and colleagues (2002) found that social control from important others (e.g., trying to the stop the other from doing unhealthful things) was associated with worse adherence to lifestyle goals at 6 months. An additional study indicated that young adults attempting to lose weight reported significantly more weight loss when they perceived their family and friends as need-supportive, but not when they perceived that support to be controlling (Powers et al., 2009). The need to develop and evaluate interventions specifically designed to improve the social context of weight loss is supported by both SDT and these empirical findings.

In addition to being a serious public health problem, obesity and its related health behaviors are extraordinarily complex, with roots in individual, familial/relational, cultural–societal and healthcare contexts. While the decision to make lifestyle changes and to sustain them ultimately rests with individuals and their willingness to put forth effort to these ends, there is much that can be done within the social-contextual environment to facilitate these efforts. Healthcare practitioners – whether they are physicians, nurses, nutritionists/dietitians, personal trainers or other professionals – are uniquely positioned to provide need-support to patients in ways that facilitate optimal motivation. They can do this by acknowledging their patients’ perspectives, including their ambivalence about health behavior change, providing clear recommendations about patients’ health and their behavior, and providing a rationale for treatment recommendations. Although healthcare practitioners play a critical role in facilitating patients’ motivation, it is also important to develop interventions that teach laypeople – particularly family members and friends – how to be need-supportive with important others who are trying to make lifestyle changes and lose weight. Intervening with family and friends who are part of a patient’s daily life and routine is crucial to enhancing the effects of need-supportive treatment environments and creating sustainable interventions. Finally, while it is unlikely that social and cultural contexts will dramatically shift away from a focus on image, appearance and weight, interventions that also draw attention to our innate value for health will serve to facilitate long-term lifestyle change maintenance by harnessing this inherent energy source that will catalyze optimal motivation and autonomous self-regulation.

References Deci, E. L., & Ryan, R. M. (1995). Human autonomy: The basis for true self-esteem. In M. Kernis (Ed.), Efficacy, agency, and self-esteem (pp. 31–49). New York, NY: Plenum Press.

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Williams, G. C. (2002). Improving patients’ health through supporting the autonomy of patients and providers. In E. L. Deci & R. M. Ryan (Eds.), Handbook of selfdetermination research (pp. 233–254). Rochester, NY: University of Rochester Press. Williams, G. C., & Deci, E. L. (2001). Activating patients for smoking cessation through physician autonomy support. Medical Care, 39(8), 813–823. Williams, G. C., Grow, V. M., Freedman, Z. R., Ryan, R. M., & Deci, E. L. (1996). Motivational predictors of weight loss and weight-loss maintenance. Journal of Per­ sonality and Social Psychology, 70(1), 115–126. Williams, G. C., Freedman, Z. R., & Deci, E. L. (1998a). Supporting autonomy to motivate glucose control in patients with diabetes. Diabetes Care, 21(10), 1644–1651. Williams, G. C., Rodin, G. C., Ryan, R. M., Grolnick, W. S., & Deci, E. L. (1998b). Autonomous regulation and adherence to long-term medical regimens in adult out­patients. Health Psychology, 17(3), 269–276. Williams, G. C., Cox, E. M., Kouides, R., & Deci, E. L. (1999). Presenting the facts about smoking to adolescents: The effects of an autonomy supportive style. Archives of Ped­ iatrics and Adolescent Medicine, 153(9), 959–964. Williams, G. C., Gagné, M., Ryan, R. M., & Deci, E. L. (2002). Facilitating autonomous motivation for smoking cessation. Health Psychology, 21, 40–50. Williams, G. C., McGregor, H. A., Sharp, D., Kouides, R. W., Levesque, C., Ryan, R. M., et al. (2006). A selfdetermination multiple risk intervention trial to improve smokers’ health. Journal of General Internal Medicine, 21(12), 1288–1294. Wing, R. R. (2002). Behavioral weight control. In T. A. Wadden & A. J. Stunkard (Eds.), Handbook of obesity treatment (pp. 301–316). New York, NY: Guilford Press. Wing, R. R., & Phelan, S. (2009). Behavioral treatment of obesity. In R. H. Eckel (Ed.), Obesity: An academic basis for clinical evaluation and treatment (pp. 415–435). New York, NY: Lippincott Williams & Wilkins.

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C H A P T E R

30 Nutritional Genomics in Obesity Prevention and Treatment Branden R. Deschambault, Marica Bakovic and David M. Mutch Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada

o u t l i ne 30.1 Background

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30.5 The Role of Gene Expression

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30.2 The Genetics of Obesity

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30.3 Nutritional Genomics

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30.6 From Bench to Bedside: Predicting Outcome

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30.4 The Role of Gene Polymorphisms

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30.7 Outlook

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30.1  Background

illustrated by NHANES, an important increase in the prevalence of childhood obesity has also The prevalence of both obesity (BMI  30 kg/m2) occurred during the past 50 years (Ogden et al., and morbid obesity (BMI   40 kg/m2) have 2007). Taken together, obesity is now considered an now established a firm foothold in our society epidemic that has placed an unsustainable burden (Sturm, 2007). This trend is visible in data from on social and public health programs around the the National Health and Nutrition Examination world. Indeed, the metabolic abnormalities and Surveys (NHANES) in the United States, where chronic disease risks associated with obesity (Field a shift in BMI measurements in the upper per- et al., 2001; Calle and Thun, 2004), which include centiles was observed in adult men and women type 2 diabetes, hypertension, heart disease between NHANES II (1976–1980) and NHANES and certain cancers, reinforces the urgency with III (1999–2004) (Ogden et al., 2007). As further ­ which public health agencies must innovatively

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respond to curb this epidemic. Current public health initiatives designed to address the increasing prevalence of obesity, albeit in their infancy, tend to provide general exercise, nutritional and lifestyle recommendations (McKinnon et al., 2009). Such recommendations can yield positive and encouraging results; however, for any one individual in a population there is considerable variability in response to these lifestyle recommendations. As such, pioneering methods to address the obesity epidemic must come in the form of a paradigm shift away from medical and nutritional recommendations based on research from genetically diverse populations exposed to highly heterogeneous lifestyles, towards more personalized approaches in which the genetic make-up and lifestyle are considered for each individual. The field of nutritional genomics exemplifies this paradigm shift (Khoury et al., 2007). It is now becoming more common that people are familiar with such terms as “personalized nutrition” and “nutrigenomics”, partly because of the technological advances made in the post-genomic era and partly because commercial laboratories have begun to market genotyping services to the public (Morin, 2009). However, while the framework for this conceptual progression has been insightfully drafted (Agurs-Collins et al., 2008), its realization requires further delineation of how gene–gene and gene– ­environment interactions are able to impact health. There is a considerable ongoing effort in the scientific community to identify and catalog those genes that affect parameters associated with body weight, such as the regulation of food intake, energy expenditure, lipid and glucose metabolism, and adipose tissue development. Genetic studies have revealed familial aggregation (Allison et al., 1996a) and heritability for quantitative traits associated with obesity, such as BMI (Allison et al., 1996b), total and regional adiposity (Malis et al., 2005), and waist– hip ratio (Rose et al., 1998). Heritability has also been documented for numerous facets of eating behavior (Tholin et al., 2005). Such efforts have revealed that the obese phenotype is highly

heterogeneous, and varies in genetic complexity from person to person. Monogenic obesity, which stems from a single dysfunctional gene, is characterized by an extremely severe phenotype that presents itself in childhood and is often associated with additional behavioral, developmental and endocrine disorders; however, monogenic obesity accounts for less than 5 percent of the severe obesity cases (Mutch and Clement, 2006). Rather, the more common polygenic form of obesity is the result of numerous genes (i.e., up to several hundred) each having a minor contribution to the overall phenotype. Moreover, these genes not only interact with each other (gene–gene), but are also sensitive to environmental factors (gene–environment). Thus, defining the genetic component of common obesity continues to prove challenging.

30.2  The genetics of obesity In the ongoing search for genetic variants that independently predispose certain individuals to common obesity, a variety of modern populationlevel approaches have been employed. These approaches vary in scope and resolution, and can be broadly divided into genome-wide studies (hypothesis-generating) and candidate gene studies (hypothesis-driven) (Hirschhorn and Daly, 2005; Li and Loos, 2008) (Figure 30.1). Genomewide studies use linkage mapping or association approaches to survey the entire genome for unknown variants that correlate with a particular phenotypic endpoint or incidence of disease (Hirschhorn and Daly, 2005; Balding, 2006). In this regard, the researcher is not looking at a particular gene a priori, but rather hypothesizes that unrecognized genes and/or gene variants are associated with a phenotype of interest and remain to be discovered. In contrast, hypothesisdriven studies are based on association or resequencing approaches, and can vary in scale from individual single nucleotide polymorphisms

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30.2  The genetics of obesity

Hyphenate generating approach

Genome-wide linkage analyses (populations of related individuals)

Gene expression profiling (microarrays) Genome-wide association studies (unrelated individuals–cases & controls)

Highly penetrant genes/loci (monogenic obesity)

Associated gene variants (polygenic obesity)

Differentially expressed genes (polygenic obesity)

Hyphenate driven approach

Transgenic animal models

Candidate genes

Transfected in vitro systems

Functional studies large population validation studies

Figure 30.1  Relationship between hypothesis-generating and hypothesis-driven research. Candidate genes can be identified via a multitude of interconnected approaches. To definitively demonstrate that a candidate gene has a real role in obesity requires epidemiological studies as well as functional cellular and animal transgenic models.

(SNPs) to fine mapping of candidate regions (typically 1–10 Mb) (Hirschhorn and Daly, 2005; Balding, 2006). In other words, the researcher aims to demonstrate that a particular gene or chromosomal region is significantly associated with a phenotype of interest. Hypothesis-driven candidate gene association studies in humans are typically designed to validate functional data obtained using transgenic animal and cellular models or positional data (Risch and Merikangas, 1996; Hirschhorn and Daly, 2005; Balding, 2006). By genotyping allelic variants (i.e., SNPs), the association between a gene and an obesity-related trait can be evaluated in cases versus controls at the population level (Balding, 2006; Li and Loos,

2008). The 2005 Human Obesity Gene Map update reported associations between 127 candidate genes and obesity-related phenotypes (Rankinen et al., 2006); however, conflicting results have been reported for several of these genetic variants. The poor replication of associations has been attributed to insufficient sample sizes, low frequencies (a function of ethnicity) and modest effects associated with causal alleles (Balding, 2006; Li and Loos, 2008), leading some to speculate on the excess of false-positive associations for complex diseases (Lohmueller et al., 2003). Fortunately, recent studies have managed to sample tens of thousands of subjects to provide further insight regarding some previous conflicting results. For example, the association

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between obesity and the K121Q polymorphism (rs1044498) in the ectoenzyme nucleotide pyrophosphate phosphodiesterase (ENPP1) gene, which codes for a transmembrane glycoprotein capable of inhibiting insulin receptor-mediated signal transduction, has not been validated using larger cohorts (Meyre et al., 2007; Seo et al., 2008). Thus, the candidate gene approach remains a useful confirmatory tool that is capable of either substantiating or refuting the role of a specific gene in polygenic obesity. Linkage studies were the first genome-wide hypothesis-generating approach used in the initial searches for human obesity susceptibility loci or genes. Genome-wide linkage analysis requires the recruitment of related individuals for investigation of co-segregation of genetic markers with disease phenotype. The first genome-wide linkage scan for an obesity-related quantitative trait loci (QTL) was published in 1997, and identified an association between a region at chromosome 11q21-q22 and percentage body fat in Pima Indians (Norman et al., 1997). Since this initial study, the number of published linkage scans has increased considerably and highlighted promising candidate genes for further in-depth analyses. In 2005, the final update to the Human Obesity Gene Map reported 253 QTLs derived from 61 genome-wide linkage scans, 15 of which were replicated in 3 or more studies (Rankinen et al., 2006); however, the genes or gene variants related to these replicated QTLs have not yet been discovered (Li and Loos, 2008). While replicated results tend to alleviate doubts regarding the validity of previous findings, a rank-based meta-analysis of 37 genome-wide linkage scans was unable to achieve convincing evidence linking BMI or obesity to any of the QTLs evaluated (Saunders et al., 2007), despite generating substantial statistical power (31,000 individuals, 10,000 families). It has been suggested that genome-wide linkage scans are better suited to the discovery of highly penetrant gene variants, such as those underlying the monogenic and clinically severe forms of obesity that exist in a common chromosomal background amongst related

individuals (Hirschhorn and Daly, 2005; Li and Loos, 2008). As such, Li and Loos recently postulated that genome-wide association will replace genome-wide linkage as the hypothesis-generating tool of choice for the identification of genes underlying common obesity (Li and Loos, 2008). Genome-wide association scans (GWAS) have been made possible due to technological advancements, as well as initiatives such as the International HapMap Consortium. Indeed, the HapMap initiative has resulted in a public database describing over 10 million human SNPs (International HapMap Consortium et al., 2007) and has also identified the subset of markers that best capture genetic variability in humans (Daly et al., 2001), referred to as tag SNPs. An important caveat with regard to this impressive database is that it has been generated using genetic information from “only” four populations: Nigerian, Japanese, Chinese and American. Thus, while this genetic sampling provides an excellent starting point, the tag SNPs identified in these populations may not provide the same degree of information in other populations. In 2006, the first GWAS was published, and described an association in obese children and adults with a common SNP (rs7566605) in the insulin-induced gene 2 (INSIG2) (Herbert et al., 2006). Although the authors replicated this association in four separate samples, subsequent independent investigation in large, populationbased studies (i.e., 20,000 individuals) has failed to confirm these initial findings (Dina et al., 2007; Loos et al., 2007a; Rosskopf et al., 2007). In 2007, the Welcome Trust Case-Control Consortium used a GWAS approach to search for genetic variants that predispose individuals to type 2 diabetes (T2D) (Frayling et al., 2007). A cluster of SNPs in the fat mass and obesity associated (FTO) gene region were found to be strongly associated with T2D; however, when data were adjusted for BMI the association was lost. In other words, this positioned FTO as a gene predisposing people to obesity rather than T2D

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30.3  Nutritional genomics

(Frayling et al., 2007). The association between FTO variants and BMI was found in each of the 13 cohorts used (Frayling et al., 2007), and later in 3 additional and independent cohorts (Scuteri et al., 2007). A more recent GWAS identified common variants near the melanocortin 4 receptor (MC4R) gene (rs17782313, rs17700633) which were associated with fat mass, weight and risk of obesity (Loos et al., 2008). Two subsequent studies have confirmed the associations between FTO and MC4R variants, and BMI, as well as identifying previously unrecognized associations between other sequence variants and obesity traits (Thorleifsson et al., 2009; Willer et al., 2009). Despite the exciting progress in our ability to identify gene variants associated with obesity-related traits, it remains important to recognize the phenotypic impact of such variants. For example, the Genetic Investigation of Anthropometric Traits (GIANT) consortium noted that individuals (n  178) with 13 or more “standardized” (weighted by relative effect size) BMI-increasing alleles were only 0.59 kg/m2 heavier than an average individual in the cohort studied (n  14,409) (Willer et al., 2009). This finding highlights the modest effects typically associated with common obesity-predisposing variants, and reinforces the complexity of such a task. Other possible contributors to the established heritability of BMI that are now being explored are epistasis (Dong et al., 2005), epigenetics (Campion et al., 2009) and copy number variations/polymorphisms (Willer et al., 2009). Ultimately, complex traits such as common obesity are seldom solely dictated by genetic factors. Rather, it is more likely that genetic factors will determine the susceptibility of individuals to their surrounding “obesogenic” environment – i.e., an environment favoring energy abundance and storage versus energy expenditure (Bouchard and Rankinen, 2007; Chung and Leibel, 2007). It is this concept that has reinforced the research community’s interest in unraveling gene–gene and gene–environment interactions, with the

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goal of identifying those factors that are causative for the increased prevalence of obesity. In the context of common obesity, the environmental challenges that have typically garnered the most attention for their potential interaction with genetic factors include dietary composition, smoking, and physical activity. While the term “interaction” may mean different things to different people, we use the term here to describe a deviation from the additive or multiplicative effects of combining genotype and environment factors (Loos et al., 2007b). The remainder of this chapter will be focused on diet–gene interactions as they pertain to the study of human obesity.

30.3  Nutritional genomics When searching for the culprit underlying the dramatic increase in obesity, it is perhaps easiest to point the “finger of blame” at diet. Although our dietary habits and the composition of our foods have changed considerably over the past century, it is evident that not everyone in society who has experienced an energy surplus has gained weight similarly. So how can we explain the existence of these inter-individual differences? It is the field of nutritional genomics that has taken center stage in an attempt to answer this question. The term “nutritional genomics” is best considered as an umbrella term that describes two distinct but highly related disciplines: nutrigenetics and nutrigenomics; however, a quick search in published literature reveals that the abridged term “nutrigenomics” is also used as an umbrella term. Irrespective of the term used, both subdisciplines describe the study of diet–gene interactions; however, nutrigenomics and nutrigenetics tend to approach the question from different perspectives. The former describes a global functional response to a nutrient or diet at the level of gene expression, protein expression or, more recently, metabolite abundance. The latter is concerned with understanding the role of genetic

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variation in modifying an individual’s response to a dietary stimulus (Mutch et al., 2005). Both approaches are starting to yield insight into how diet–gene interactions may alter an individual’s susceptibility to gaining weight. Furthermore, it is expected that nutritional genomics research will generate knowledge that will assist clinicians eventually to assess and predict those patients susceptible to weight gain and, more importantly, help tailor lifestyle modifications that will improve an individual’s propensity for weight loss.

30.4  The role of gene polymorphisms Diet–genotype interactions in the context of obesity have been explored using either observational or intervention approaches, where the focus has been on candidate genes related to energy balance and/or adipogenesis. Observational approaches have been previously used to reveal interactions between lifestyle factors and polymorphisms in genes coding for leptin (LEP), the -adrenoceptor (ADRB) family, uncoupling proteins (UCPs), peroxisome proliferator-activated receptor-alpha and -gamma (PPAR, PPAR), the interleukin 6 receptor (IL6R), and numerous others that are associated with obesity-related traits (recently reviewed by Qi and Cho, 2008). While the aforementioned review highlights promising diet–genotype interactions and their influence on weight parameters, a major limitation with some of the studies discussed is the small number of subjects used to identify associations. As such, it is imperative that these exciting preliminary findings are replicated in much larger populations. Nevertheless, these studies have generated novel hypotheses that suggest diet can influence obesity differently based on genetic variants. Several studies have begun to explore the impact of high energy intake on obesity risk in subjects with particular gene variants. For example, Miyaki and colleagues studied the interaction

between the adrenergic beta-3 receptor (ADRB3) and high energy intake in 295 Japanese males. ADRB3 is a gene predominantly expressed in adipose tissue, and is involved in the regulation of lipolysis and thermogenesis. The authors found that the ADRB3 Trp64Arg polymorphism (rs4994) was not systematically associated with obesity; however, the authors did find that subjects with the Trp64Arg polymorphism and the highest level of energy intake displayed an increased risk for obesity (OR  3.37; 95% CI  1.12–10.2) (Miyaki et al., 2005). Interestingly, a recent study examined FTO gene variants in order to determine whether dietary energy density predisposes children to an increased fat mass several years later, and whether this predisposition is related to FTO variant status. The authors were unable to identify an association between dietary energy density and FTO polymorphisms that influence fat mass later in life (Johnson et al., 2009). Taken together, these studies are encouraging, as they reflect the importance of incorporating nutritional genomics into ongoing research programs; however, it will be important to independently verify these results (both positive and negative results) in larger populations. While the interaction between energy intake and genetic variation has revealed intriguing results, other researchers have begun to assess interactions between dietary macronutrient content and genetic variation, and their potential impact on obesity risk. Similar to ADRB3, the ADRB2 gene is involved in the regulation of adipose tissue lipolysis, and its activity is downregulated in subcutaneous fat of obese subjects (Rasmussen et al., 2003). The ADRB2 Gln27Glu polymorphism (rs1042714) was shown to cause aberrant agonist-mediated down-regulation of receptor expression (Green et al., 1994). Addition­ally, the ADRB2 Gln27Glu variant was shown to modify the probability of obesity in relation to carbohydrate intake (Martinez et al., 2003). Although the incidence of obesity was not affected by the polymorphism, the authors found that women with the Gln27Glu variant

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30.5  The role of gene expression

consuming a higher carbohydrate intake ( 49 percent total energy) displayed significantly elevated fasting insulin levels (P   0.01) and a greater risk for obesity (OR  2.56) when compared to female Gln27Glu carriers with a lower carbohydrate intake. These trends were not observed in female Gln27Gln homozygotes, or in men. The increased propensity for obesity in female Gln27Glu carriers was hypothesized to reflect established alterations in fat oxidation in obese Glu27Glu homozygotes (Macho-Azcarate et al., 2003) that potentially contribute to a hyperlipidemic state and thereby set the stage for high carbohydrate consumption to induce insulin resistance and subsequent hyperinsulinemia (Marti et al., 2008). The transcription factor PPAR2 plays an important role in the regulation of adipocyte differentiation and is the predominant isoform in adipose tissue. The PPAR2 Pro12Ala polymorphism was investigated for interactions with total fat intake and dietary fatty acid intake, as well as the ratio of dietary polyunsaturated to saturated (PUFA:SFA) fats. This line of thinking stemmed from research exploring PPAR-regulated highfat diet induced adipocyte hypertrophy and insulin resistance in mice, where PPAR heterozygous null mice were found to be resistant to high-fat diet-induced obesity (Kubota et al., 1999). Additionally, fatty acids and their derivatives are thought to be the endogenous ligands for PPARs (Jump, 2004). It was therefore intriguing to speculate that the PPAR2 Pro12Ala variant may be a candidate genetic factor influencing an individual’s metabolic response to dietary fat. The Nurses’ Health Study cohort revealed an inverse association between monounsaturated fat (MUFA) intake and BMI, but only among carriers of the Pro12Ala variant-allele (Memisoglu et al., 2003). In the Quebec Family Study, the Pro12Ala variant was shown to modulate the interaction between diet SFA and various anthropometric measures, such as BMI and waist circumference (Robitaille et al., 2003). Additionally, Pro12Ala carriers consuming a diet with a low PUFA: SFA

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ratio had an increased BMI, while the opposite was found true when the PUFA: SFA ratio was high (Luan et al., 2001). As the examples above indicate, evidence ex­ists demonstrating that genetic polymorphisms can modulate the effect of diet on an individual’s risk for developing an obesity-related trait (Figure 30.2a); however, identifying single diet–gene interactions (already a challenge!) is merely scratching the surface when it comes to unraveling the impact of diet on the risk of obesity. Foods are not single nutrients, but complex mixtures of macronutrients, micronutrients and non-nutritive phytochemicals. Furthermore, numerous cultural, social, familial and environmental factors mean that a particular food in one country may be very different in another. As such, a diet–gene interaction observed in one cohort may not be observed in another cohort because of the aforementioned factors. Genetic differences between populations will also be a major factor that affects the independent replication of diet–gene interactions. Thus, while current nutritional genomic studies tend to make conclusions based on data stemming from the study of a single gene variant, the future challenge will be to integrate and consider all of these data simultaneously.

30.5  The role of gene expression Investigating the role of genetic variants on an individual’s susceptibility to weight gain, both independently and in conjunction with diet, will remain an important component in deciphering the etiology of common obesity. It is also crucial to consider the role of gene expression in the development of obesity, as well as the different influences that dietary components will have on modulating gene expression (Figure 30.2b). The advent and integration of microarray technologies, which enables the

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t/ or sp ion n a Tr iffus d

Nutrient

Obesity-related trait (e.g., BMI)

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Protein

35 Intracellular pool

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mRNA

Ligand

32 TF

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TF

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5’regulatory sequence

Homozygote WT carrier

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pre-mRNA DNA Pol

Phenotypic response (e.g., adipogenesis, lipolysis, etc.)

Expression (+/-)

Gene

Minor allele carrier Low level

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Alternate metabolic fates

High level

Nucleus

Exposure to dietary factor

(b)

Figure 30.2  Diet–gene interactions from nutrigenetic and nutrigenomic perspectives. (a) Hypothetical data illustrating a gene–lifestyle interaction in the context of obesity. This example represents one possible relationship between the level of exposure to a dietary factor and the phenotypic outcome (e.g., BMI). (b) Crude mechanism by which a nutrient could contribute to the regulation of gene expression, by acting as a ligand for nuclear transcription factors which interact with regulatory sequences upstream of target genes, thereby potentially altering cellular activities. TF, transcription factor; DNA Pol, DNA polymerase.

analysis of global gene expression, has greatly facilitated this effort (Mutch, 2006). In recent years, microarray studies have revealed that the adipose tissue transcriptome is responsive to weight loss induced by lifestyle and surgical interventions, and can thus be used to identify both differentially expressed genes and predictors (Figure 30.3). For instance, participants in the European project NUGENOB (NUtrient-GENe interactions in human OBesity) demonstrated that the consumption of a 10-week, low-energy diet in obese women, irrespective of the carbohydrate and fat content, led to modest, yet significant (P  0.001 for both moderate- and low-fat diets), weight loss. Furthermore, over 100 genes were identified as differentially regulated when comparing adipose tissue gene expression before and after weight loss, where genes involved in polyunsaturated fat biosynthesis were down-regulated (e.g., fatty acid synthase, stearoyl coenzyme A desaturase 1 (SCD1), and fatty acid desaturase 1 and 2) (Dahlman et al.,

2005). The authors suggested this may be important for depletion of lipids from human fat cells during the consumption of a low-energy diet. In an alternate study, inflammation-related genes were down-regulated in obese subjects who lost weight in response to the 28-day consumption of a very low calorie diet (VLCD; 800 kcal/day) (Clement et al., 2004). The transcriptional changes induced by the VLCD led to a “normalization” of the expression profiles in obese subjects (i.e., the expression profiles tended to resemble those of lean subjects). The transcriptome is sensitive not only to changes in total energy intake, but also to the macronutrient composition in diet; however, the NUGENOB consortium demonstrated that energy restriction, rather than variations in the dietary content of carbohydrate and fat, was the dominant factor influencing gene expression following the 10-week consumption of lowenergy diets (Capel et al., 2008). Nevertheless, such knowledge has prompted the suggestion that understanding why individuals respond

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(a)

Which genes discriminate cases from controls?

vs.

(Differentially expressed genes)

Controls

Cases

Candidate genes

(b)

Which genes predict a response? 1. Identify ‘predictors’ with Training Set (9/10 available data) 2. Verify accuracy of ‘predictors’ in test set (1/10 available data)

Responders and non-responders known (Training set)

Unknown sample (Test set)

Figure 30.3  Identifying candidate genes with microarrays. Using supervised bioinformatic algorithms, microarrays can be used to identify both discriminators and predictors. Discriminators, or differentially expressed genes, are identified by comparing one group (e.g., obese subjects) to another group (e.g., lean subjects). Predictors are identified using a two-step approach in which a classifier (i.e., genes considered to be predictive) is built using 90 percent of the subjects (responders and non-responders) and then tested in a blinded manner using the remaining 10 percent of the subjects. If the classifier is valid, responders and non-responders in the remaining 10 percent of the subjects will be correctly classified and predictive genes are identified.

differently to weight-loss diets varying in macronutrient composition will pave the way for personalized nutrition. As previously mentioned, both the obesity phenotype and response to lifestyle interventions demonstrate considerable inter-individual variability. While gene variants influence this variability, gene expression has also been found to play an important role in shaping an individual’s response to an “obesogenic” environment. Indeed, it can be hypothesized that the existence of a coordinated transcriptional response that favors energy expenditure over energy storage in times of overfeeding may provide a degree of “resistance” to weight gain. Several studies have been published in which the authors have

attempted to address the utility of gene expression to predict changes in weight. For example, the transcriptomic response to short-term energy surplus in lean and obese men was recently investigated by Shea and colleagues. Sampling abdominal subcutaneous adipose tissue before and after a 7-day hypercaloric diet, the authors found 45 genes were differentially expressed in response to the diet, of which 6 of them were also different between lean and obese individuals (Shea et al., 2009). These six genes, which revealed an interaction between adiposity and the hypercaloric diet, included transferrin, transaldolase 1, cathepsin C, insulin receptor substrate 2, pyruvate dehydrogenase kinase isozyme 4 and SCD1. The authors suggested

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this group of genes may constitute a protective molecular profile in lean individuals that prevents excessive weight gain despite a significant surplus of energy. A study by Koza and colleagues found that mice could be characterized as “high gainers” and “low gainers” as early as 6 weeks, and that these phenotypic differences persisted during times of diet-induced weight gain or dietinduced weight loss (Koza et al., 2006). Gene expression profiling in mouse adipose tissue and hypothalamus revealed important differences between high and low gainers, most notably in the Wnt signaling pathway. The authors concluded that this differential expression profile may serve to predict whether mice will be high or low gainers. A human study performed by Mutch and colleagues examined whether subcutaneous adipose tissue gene expression could be used to predict subjects who will respond to a low-calorie diet (LCD) by losing weight (8–12 kg) versus those subjects who fail to lose weight (4 kg) (Mutch et al., 2007). The authors demonstrated that the global gene expression profiles of responders and non-responders could be distinguished, but the use of predictive bioinformatic methods failed accurately to predict responders from non-responders. When such studies are considered, it appears that gene expression profiles do differ between individuals who lose weight during a dietary intervention versus those subjects who do not; however, further work is required before it will be possible to predict weight loss with a high degree of accuracy.

30.6  From bench to bedside: predicting outcome An ultimate goal of nutritional genomic re­search is to predict a particular outcome. In other words, a clinician, using genetic knowledge, will be able to make informed lifestyle recommendations for a patient that will help reduce

disease risk and/or improve treatment efficacy. The search for reliable predictors for successful weight loss is ongoing, and focus over the past decade has begun to consider genetic make-up (Moreno-Aliaga et al., 2005). Current methods to identify successful responders to a weight-loss intervention tend to be based on weight loss during an initial evaluation phase (Finer et al., 2006); however, there is an increasing interest in exploring the utility of candidate gene variants to predict a patient’s response to hypo­caloric or macronutrient-restricted diets (Martinez et al., 2008). Indeed, support for gene variants related to appetite control (leptin and melanocortin pathway genes), energy expenditure (UCPs, ADBRs), lipid metabolism (PLIN) and adipogenesis (PPAR2) is substantiated by the various epidemiological studies previously discussed. The UCP gene family (UCP1-3) can modulate energy expenditure by uncoupling respiration and phosphorylation at the mitochondrial inner membrane. The UCP1 proton transporter is predominantly expressed in brown adipose tissue, and the -3826A/G polymorphism (alternatively referred to as Bcl-1) was previously found to modify the recovery to long-term positive energy balance (Ukkola et al., 2001). Furthermore, subjects homozygous for the Bcl-1 allele lost significantly less weight following a 10-week LCD intervention than did heterozygotes, who in turn lost less weight than wild-type homozygotes (P   0.05) (Fumeron et al., 1996). Another study further demonstrated a potential role for UCP1 haplotype in determining the magnitude of weight lost after a 1-month VLCD (Shin et al., 2005). Interestingly, research suggests a possible synergistic effect for the UCP1 Bcl-1 and ADRB3 Trp64Arg polymorphisms, which confers an additional resistance to weight loss during periods of negative-energy balance that is not observed in those with only one of the variants (Kogure et al., 1998). Evidence also exists for the related UCP2 and UCP3 genes, where polymorphisms and certain haplotypes can modify an individual’s response to a VLCD,

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30.7  Outlook

in terms of body and fat mass reduction (Cha et al., 2006; Yoon et al., 2007). These findings suggest that polymorphisms in UCPs may be of considerable interest for predicting diet-induced weight loss. The perilipin (PLIN) protein is an important regulator of lipase activity in adipocytes, and inhibits triglyceride catabolism until phosphory­ lated by catecholamine-driven signal transduc­tion pathways (Brasaemle et al., 2009). The 13041A/G (rs2304795) and 14995A/T (rs1052700) SNPs in the PLIN gene were previously shown to be associated with waist circumference and percentage body fat in a gender-specific manner (Qi et al., 2004). In another study, Corella and colleagues found that subjects with the 11482G/A polymorphism (rs894160) minor allele were resistant to weight loss, even with a 1-year LCD intervention (Corella et al., 2005). Furthermore, genetic variation at this locus in general has been shown to influence weight loss after both caloric restriction (12-week) (Jang et al., 2006) and the consumption of a VLCD (6-week) (Soenen et al., 2009). Thus, studies are emerging which demonstrate that using specific gene polymorphisms to predict diet-induced weight loss in individuals may not be wishful thinking, yet translating this knowledge from the laboratory bench to the actual clinic requires further validation and replication studies. Indeed, it must be determined whether predictive SNPs are broadly applicable in the general population or whether they are best suited for particular subsets of the population (e.g., ethnic-specific, male versus female, adult versus children, diseased versus healthy, etc.).

30.7  Outlook As the body of research presented above demonstrates, genetic factors acting both independently and in conjunction with lifestyle factors play a notable role in the etiology of obesity. More importantly, this research has begun to

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provide an explanation for the heterogeneous response of individuals to energy imbalances stemming from diet and/or alternate lifestyle factors. Recent progress has revealed that other factors influencing genetic variation, such as epigenetics (Campion et al., 2009) and copy number variants (Sha et al., 2009; Willer et al., 2009), also have an important role and need to be considered in future nutritional genomic research. Furthermore, the recent concept of “genetical genomics”, an approach combining DNA sequence variation with gene expression data, may identify susceptibility genes that are causative, rather than responsive, for disease traits (Mehrabian et al., 2005; Schadt et al., 2005). Incorporating this knowledge and approach into future nutritional genomics research will also be of paramount importance. Despite this encouraging progress, a deal of skepticism remains regarding the utility of nutritional genomics research in the fight against the obesity epidemic; however, as discussed in the preceding sections, this relatively new axis of nutritional sciences has provided glimpses of its potential to contribute to substantial advances in the clinical prediction of individuals susceptible to weight gain and/or loss, as well as the synchron­ ization of weight-loss diets with the genetic make-up of individual obese persons. While nutritional genomics is inherently translational research (i.e., moving research from bench to bedside), there is considerable controversy regarding the appropriateness and timing for the mass dissemination and commercialization of this research. Although all parts of the spectrum (i.e., from the researcher to the medical/ nutritional practitioner to the consumer) are aware of nutritional genomics and recognize its potential, there remains a paucity of scientific studies in which genetic information has been used to drive nutritional counseling. To the best of the authors’ knowledge, only a single research paper has been published along this line. In 2007, a personalized energy-restricted diet (i.e., nutrigenetic testing complemented

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by specific dietary advice and/or supplement consumption to account for deficiencies) was implemented in a clinical trial, and compared to a generic energy-restricted diet (Arkadianos et al., 2007). In other words, some subjects simply ate a common energy-restricted diet, whereas other subjects consumed an energyrestricted diet in accordance with genetic information. The authors clearly stated the intention was not to design personalized weight loss diets per se; rather, they sought to optimize a subject’s nutrient intake in light of the current understanding of genomic variation and diet–gene interactions to better motivate these individuals for compliance and weight loss. A panel of SNPs related to folic acid metabol­ism, phase II enzyme detoxification, oxidant balance, bone health, inflammation and lipid metabolism were genotyped, as opposed to the polymorphisms modulating obesity risk discussed in the present chapter. Nevertheless, results indicated that those in the “nutri­genetictested” group were significantly more likely to maintain weight loss in the longer term (300 days) as compared to the “non-tested” group (OR  5.74; 95% CI  1.74–22.52). There was also a significant decrease after 90 days (P  3  106) in fasting glucose levels amongst “nutrigenetic-tested” subjects with baseline levels 100 mg/dl. Although encouraging, such a result will need to be replicated in larger cohorts prior to the widespread implementation of nutritional genomics research as a means to combat obesity. While the aforementioned study was a joint endeavor between medical practitioners and a commercial company, most nutritional genomic companies provide direct-to-consumer tests. In 2006, the Government Accountability Office in the United States released a report stating that directto-consumer nutrigenetic tests for chronic disease risks were misleading, and provided ambiguous and medically unproven dietary recommendations (Kutz, 2006). There is also a consensus in the scientific community that the potential for

nutritional genomics in chronic disease risk reduction and personalized weight-loss regimens requires considerably more research into diet–gene interactions, and must therefore be approached with caution at the present time (Arab, 2004; Adamo and Tesson, 2007; Rimbach and Minihane, 2009). Although the causality between diet–gene interactions and chronic disease requires further scientific validation, evidence suggests there may be an alternate benefit of discussing genetics during consultations with obese individuals. For instance, a study found that providing a one-session consultation on weight management to obese subjects, in which genetic information was included (i.e., heredity, twin studies), led to novel insights on weight problems, and improved a subjective rating of negative mood regarding obesity (Rief et al., 2007). Similarly, in individuals with a family history of obesity, this type of consultation resolved some feelings of self-blame, and led to more achievable weight-loss goals in subjects with and without familial predisposition (Conradt et al., 2009). Taken together, nutritional genomics fills an important niche in the scientific foundation of a “Brain-to-Society” systems approach for addressing the obesity epidemic. It is clear that common obesity is a multi-factorial disease that has direct links with aspects of biochemistry, nutrition, sociology and psychology, amongst others. While nutritional genomics generates critical information regarding diet–gene interactions, it is only by considering all aspects simultaneously that we can hope to alleviate the current obesity epidemic.

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C H A P T E R

31 Physical Activity for Obese Children and Adults Ross Andersen and Catherine Sabiston Department of Kinesiology and Physical Education, McGill University, Montreal, Canada

o u t l i n e 31.1 Introduction

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31.2 Adults and Physical Activity

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31.3 Physical Activity and Young People 392 31.4 Linking Physical Activity and Obesity 393 31.4.1 Sedentary Activities and Obesity 393 31.4.2 Lifestyle Physical Activity 393 31.5 The Model 31.5.1 Environmental Factors 31.5.2 Self-perceptions, Attitudes and Beliefs

394 395 395

31.1  Introduction The obesity epidemic has become one the leading health problems in developed countries around the world. This is a public health challenge, and countries such as Canada, Brazil and Mexico are experiencing dramatic increases in the prevalence of obesity. Recent data from the National Center

Obesity Prevention: The Role of Brain and Society on Individual Behavior

31.5.3 Social Context

396

31.6 Supporting Overweight Individuals in Overcoming real and/or Perceived Barriers to Physical Activity 397 31.7 Outcomes 31.7.1 Physical Outcomes 31.7.2 Psycho-emotional Outcomes 31.7.3 Social Outcomes

397 397 398 398

31.8 Fit or Fat

399

31.9 Conclusion

399

for Health Statistics (NCHS) show that more than 33 percent of Americans are overweight, and 34 percent are obese (Ogden et al., 2006). More than 6 percent are “extremely” obese. In the US, over one-third of adults (or 72 million people) were classified as obese in 2005–2006, as reported by the NCHS (Ogden et al., 2006). The causes of the obesity epidemic are complex and multifaceted. Clearly, at a population

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31.  Physical Activity and obesity

level, an energy imbalance is necessary for such widespread increases in body mass index (BMI) to occur. While much has been written about the increased energy intake as the root cause of the epidemic, it is critical to examine the energy expenditure component of the energy balance equation at the same time.

31.2  Adults and physical activity The Surgeon General has reported that a sedentary lifestyle is hazardous to our health, and a growing body of scientific evidence supports recommendations of increasing physical activity to lose weight and maintain good health (Hagan et al., 1986; Klem et al., 1997; Schoeller, 1999; Department of Health and Human Services and US Department of Agriculture, 2005). Despite this knowledge, few adults perform enough physical activity to derive health benefits from it. Most exercise scientists agree that performing at least three bouts of vigorous exercise per week can result in significant health benefits (Haskell et al., 2009). Recently, it has become apparent that the health benefits of physical activity may be achieved at intensities that are lower than those traditionally recommended. Many countries are now encouraging people to accumulate moderate intensity activity throughout the day if they cannot exercise vigorously. The American College of Sports Medicine has recommended that obese individuals accumulate between 200 and 300 minutes of moderate intensity physical activity per week to enhance long-term weight management (Jakicic et al., 2001). This is similar to the recommendation from the Institute of Medicine (IOM) that suggests doing 60 minutes of moderate intensity physical activity per day for weight management (Institute of Medicine, 2002). The International Association for the Study of Obesity (IASO) also advocates 45–90 minutes of

physical activity per day to control body weight (Saris et al., 2003). Nonetheless, obese adults are more likely to be sedentary and not participate in leisure time activity than are their leaner counterparts (Shields and Tremblay, 2008). A recent report found that 19 percent of obese men and only 16 percent of obese women met minimum public health recommendations for physical activity (Centers for Disease Control and Prevention (CDC), 2000). Moreover, in community settings, obese individuals are more likely to choose passive versus active options to ambulate and commute (Andersen et al., 2006). It is paradoxical that 62 percent of obese men and 57 percent of obese women report attempting to use physical activity to lose weight (CDC, 2000).

31.3  Physical activity and young people The World Health Organization (WHO) recom­ mends that school-age children engage in moderate to vigorous physical activity for at least 60 minutes per day (World Health Organization, 2009). Appropriate physical activity may help children better manage their weight, and has been associated with the promotion of healthy development (Floriani and Kennedy, 2008). Despite the numerous physiological and psychological benefits of active living for young people, levels of physical activity are decreasing around the globe. The following outlines key reasons why physical activity has been engineered out of many young people’s lives: more opportunities for sedentary leisure time (television, Internet, video games); lack of time; greater pressure for academic performance and increased amounts of homework; less recess, intramural and after-school sports in the schools; lack of safe places to exercise; reduced active commuting to and from school; changes in neighborhood design that result in reduced physical activity; increased parental concerns

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31.4 Linking physical activity and obesity

about child safety; unskilled children not being encouraged to remain in sports activity; unskilled children feeling embarrassed to exercise in front of peers; and more dual-career families (Floriani and Kennedy, 2007, 2008).

3.4 3.1 29.2

Watching TV Watching time-shifted TV

8.1

Time on the Internet

31.4  Linking physical activity and obesity Inactivity and overweight/obesity have been identified as significant health risks (Physical Activity Guidelines Advisory Committee, 2008). While inactivity and overweight/obesity do not necessarily co-occur, they have been linked. Increasing physical activity has been identified as one way of battling the obesity epidemic (Wing, 1999). We propose that increasing physical activity, in particular lifestyle activity, and reducing time spent in sedentary activities are strategies to help offset the public health risks associated with obesity across the lifespan.

31.4.1  Sedentary activities and obesity A direct association between the hours of tele­vision watched and BMI or body fatness in American children has been reported (Andersen et al., 1998a). Others have found that time spent playing video games, in front of a computer and, to lesser extent, reading are also related to an increased prevalence of obesity. This link has been made primarily as a result of the low energy expenditure that occurs during sedentary behaviors. It has also been reported that children who watch 5 or more hours of television per day consume on average 200 kcal per day more than their counterparts who watch 1 hour or less of television per day (Crespo et al., 1998). We suspect that television watching may be a cue to eat for many overweight individuals. Recent data from the Neilson Organization (Figure 31.1) have reported that the average American adult watches over 127 hours of television and browses

Watching video on the Internet

153.2

Watching video on a mobile phone

Figure 31.1  Average monthly hours among American adults doing sedentary activities. Source: Nielson Three Screen Report (2008).

the Internet for 26 hours per month. This represents 22 percent of all hours each month – or 39 percent of typical waking hours are spent in front of a screen. Epstein and colleagues (1997) have developed a treatment for sedentary overweight children that encourages them to reduce the time spent engaging in sedentary activities. Children in these studies are given television allowances and are taught to limit the time they spend on the Internet and playing video games. They are also encouraged to look for opportunities to walk or cycle to and from school. While this model has not been tested in adult populations, it may offer promising results.

31.4.2  Lifestyle physical activity Many investigators have also demonstrated that traditional vigorous exercise may not be the optimal way to help sedentary overweight individuals adopt more active lifestyles (Andersen et al., 1998b, 1999; Jakicic et al., 2001). This is particularly true if they do not enjoy or are not able to perform traditional vigorous, continuous exercise. Lifestyle physical activity encourages patients to look for opportunities to accumulate moderate-intensity physical activity throughout the

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day (Andersen, 1999; Andersen et al., 1999). It has been found that sedentary overweight adults often feel that this may be a do-able way to begin increasing their physical activity. Moreover, patients who begin increasing their activity with lifestyle activity seem to gain confidence in their ability to exercise, and over time begin to transition into a more traditional, moderate-to-vigorous exercise program. Jakacic and colleagues have also found that accumulating short 10-minute bouts of aerobic exercise may offer obese adults a suitable alternative to traditional uninterrupted exercise (Jakicic et al., 1999). This is important, given that a perceived lack of time remains the top reason that sedentary overweight individuals report for not participating in regular activity.

31.5  The model The model depicted in Figure 31.2 has been developed by the authors to summarize physical activity motivation and health behavior change models – e.g., the expectancy–value model (Eccles, 1983); self-determination theory (Deci and Ryan, 1985); social cognitive theory (Bandura, 1997); the social-ecological model (Bronfenbrenner, 1977); and theory of planned behavior (Ajzen and Madden, 1986) – as well as empirical evidence demonstrating direct and mediating relationships as outlined. This non-linear model describes the individual, social and environmental factors that influence physical activity in overweight individuals. Positive changes, as a result of ­physical activity

Weight status Antecedents Self-perceptions, attitudes, beliefs

Physical environment

Social context

Physical activity Moderators -diet -sedentary behavior

Psychoemotional

Physical/ biological Outcomes

Figure 31.2  Model relating weight status and physical activity.

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Social

31.5  The model

intervention, have also impacted these factors; hence the feedback loop depicted in the model (Kahn et al., 2002; Gallagher et al., 2006). The outcomes of physical activity are classified as having psycho-emotional, social and physical underpinnings.

31.5.1  Environmental factors The relationship between weight status and physical activity levels may be explained in part by environmental factors. For example, neighborhood features, such as lower perceptions of safety and characteristics that preclude walking, have been linked to higher body weight. Neighborhoods with higher walkability indices (grid-like structures with less cul-de-sacs and more street connectivity and intersections, the presence of sidewalks, and perceptions of safety) tend to promote physical activity and support a greater number of active transportation than low-walkability neighborhoods (Gordon-Larsen et al., 2006; Smith et al., 2008; Spence et al., 2008). There is a lower prevalence of overweight in these safe and walkable neighborhoods, which tend to be located in more urban living areas (Joens-Matre et al., 2008). Notwithstanding the walkability index, there is also lower prevalence of overweight in urban compared to rural areas. Furthermore, access to facilities and opportunities can be a barrier to increased physical activity levels for overweight individuals (Gallagher et al., 2006; Holt et al., 2008). Strategies to increase safe play spaces and to provide specific sports and physical activity programs to overweight youth appear to be effective in reducing weight and enhancing health and well-being (Farley et al., 2007; Weintraub et al., 2008). Greater (or increased awareness of) opportunities for physical activity among overweight individuals are necessary. One approach may be to promote lifestyle physical activity. Integrating 30–60 minutes, 5–7 days per week, of lifestyle physical activity has adaptive physical and

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psychological outcomes, including reduced anxiety and depression, increased self-esteem, and reduced fatigue (Andersen et al., 1999). Environmental manipulations, such as simple signs and visual prompts encouraging physical activity (for example, taking the stairs instead of the elevator), appear to be beneficial in increasing lifestyle physical activity (Andersen et al., 1998b; Rees, 2007). Other environmental attributes include the development and implementation of institutional policies, and community-level program development. Physical activity opportunities that enable overweight individuals to exercise together may also be particularly important.

31.5.2  Self-perceptions, attitudes and beliefs Understanding the individual-level factors that are linked to physical activity participation among overweight/obese individuals is challenging and overwhelming. Nonetheless, most emphasis has been placed on self-perceptions such as self-concept, self-efficacy, perceptions of competence, enjoyment, intrinsic motivation and interest in physical activity, and perceived barriers and drawbacks of exercising. Specifically, individuals who have weaker perceptions of the physical self and higher weight concerns are more likely to use physical activity as weight management (Page and Fox, 1997). Self-esteem tends to be directly associated with physical activity levels (Dunn et al., 2001), and overweight individuals may be more likely to report lower self-esteem than their healthyweight counterparts (Pesa et al., 2000). Perceptions of competence/physical activity self-efficacy and/or beliefs of enjoyment and interest have shown some of the strongest associations to behavior both in theory (Bandura, 1997; Eccles and Wigfield, 2002) and in empirical evidence (Deci and Ryan, 1985; van der Horst et al., 2007; Sabiston and Crocker, 2008). Unfortunately, overweight persons report lower

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perceptions of competence and generally feel less pleasure while exercising, and thus exhibit lower enjoyment beliefs (Garcia-Bengoechea et al., 2010). Once physically active, overweight individuals have reported empowering increases in perceptions of competence and self-concept that foster increased physical activity participation and greater confidence to try new activities (Gallagher et al., 2006; Sabiston et al., 2009). This relationship is illustrated in Figure 31.2 by the circular nature of the model. Research from the Australian Longitudinal Study on Women’s Health reports that depressive symptoms are higher among women who are overweight or obese as well as among more sedentary individuals (Ball et al., 2008). Physical activity can promote and maintain mental health by protecting individuals from depression and anxiety (Paffenberger et al., 1994; Motl et al., 2004). Physical activity may also be used as a treatment for non-clinical depressive symptoms and diagnosed depression (North et al., 1990; Craft and Landers, 1998). Overweight individuals face unique socialcontext barriers, such as stereotypes, embarrassment, body-image concerns, time barriers and lack of motivation, and obstacles (e.g., weather, access, support) are commonly reported (Godin et al., 1986; Gallagher et al., 2006). Efforts aimed at reducing the perception and/or existence of these barriers for overweight individuals are necessary. Simply engaging in structured physical activity programs seems to reduce the extent of reported barriers (Gallagher et al., 2006), thus providing some justification for the circular nature of the model.

31.5.3  Social context The social context includes others’ beliefs and behaviors regarding physical activity and weight status. Most individuals need to perceive strong social-normative beliefs about exercising in order to engage in the behavior. Drawing

from the sport and exercise literature, social support primarily consists of the network of providers (who) as well as the types of strategies provided (what) (Rees, 2007). For example, having physically active friends, co-workers and family members who can act as role models has a positive impact on physical activity levels for youth and adults, independent of weight status (Sallis et al., 2000). In fact, overweight youth reported that a barrier to their activity levels was not having someone with whom to engage in physical activities (Zabinski et al., 2003). The functions of social support are important factors in promoting physical activity throughout the lifespan for overweight individuals (Trost et al., 2003, Sabiston et al., 2009). Providers of physical activity-related support may provide information (i.e., advice, suggestions and guidance), emotional strategies such as encouragement and praise, esteem functions (i.e., comfort, concern, and care), and tangible support, which includes providing instrumental and practical assistance (such as gym memberships, equipment, etc.). Socially-created barriers, such as stereotypes that overweight individuals are lazy and unmotivated, and weight discrimination/fat biases may act as barriers to physical activity among overweight individuals (Ball et al., 2000; Zabinski et al., 2003; Davison et al., 2008). Furthermore, overweight individuals who internalize these stereotypes themselves are at risk of poor psychosocial functioning and wellbeing (Davison et al., 2008). Overweight girls and women report embarrassment and body image as additional factors hindering their engagement in physical activity (Hooper and Veneziano, 1995; Treasure et al., 1998; Zabinski et al., 2003). Taken together, social support and a strong social-normative belief can promote physical activity, whereas stereotypes and fat stigmatiz­ ation may act as deterrents to activity among overweight individuals. In addition to the direct effect that social factors may have on physical

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31.7  Outcomes

activity, they also indirectly influence behavior by affecting an individual’s self-perceptions, attitudes and beliefs about exercise (Brustad, 1996). Taken together, the factors outlined in the model are strong correlates of physical activity behavior. The following outlines ways to support overweight individuals in overcoming barriers to being physically active, and in turn increase the likelihood of engaging in physical activity.1 1. Since changing the physical environment is difficult and costly, it is important to help overweight individuals become aware of available opportunities and reframe their conception of environmental factors. An intention implementation plan has been demonstrated to be very efficient in overcoming barriers. Individuals write down barriers that they encounter or anticipate encountering in their neighborhood, and prepare a plan for what they will do when this barrier is encountered. It usually reads as follows: “If... [barrier], then... [plan]”. 2. Using visual cues and prompts to remind individuals to exercise is also an effective

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strategy. The key is to use personal photos and affirmations, rather than unrealistic magazine photos or impersonal slogans. 3. Seeking social support and using available resources, whether a trainer in a gym, an Internet website, or a colleague or friend, has been found to be helpful. Another strategy is identifying exercise partners. 4. Highlighting the positive emotions that result from exercise, rather than those of guilt, shame and embarrassment that keep individuals from being physically active, can also help overcome barriers. 5. Finally, it is crucial to help individuals set SMART (Specific, Measureable, Attainable, Realistic, and Time-based) goals.

31.5.4  Outcomes Physical activity confers physical, mental (psychological and emotional) and social health benefits (World Health Organization, 2009). The physical benefits of exercise include (but are not limited to) the reduced risk of cardiovascular disease, ischemic stroke, diabetes, various

1

In order for healthcare providers to help overweight patients adopt more active lifestyles, it is always helpful to know how much physical activity they are currently engaging in. Direct measures of physical activity tend to be the most accurate, since they do not rely on patient recall. While unpractical for most clinical settings, the best technique to measure physical activity remains the water technique (i.e., the individual consumes a stable isotope to examine hydrogen and carbon utilization over a period of time to calculate total energy expenditure). A more practical approach may be to use motion sensors (i.e., accelerometers and pedometers). The accelerometers can measure activity in two or three different planes, using sensors that can reflect speed and intensity of effort. They are more costly (approximately US $350) than pedometers, and are often used more commonly in research settings. In contrast, pedometers are available in most sporting goods stores and essentially measure the number of steps taken per day. Approximating the individual’s stride length and multiplying this value by the number of steps taken will calculate daily walking distance. Good quality devices can be purchased for about US $30, and they have been found to provide motivation to patients. Patients should be encouraged to wear a pedometer for a week to get a baseline activity assessment. They should then strive to increase their daily step count by 1000 steps per day each week until they reach 10,000 steps per day. Finally, questionnaires have also been used to assess physical activity. Several valid and reliable questionnaires are available for use in a written format or administered by a trained clinician. As with all self-report questionnaires, an inability to recall past physical activity episodes can lead to problems of inaccuracy and lack of precision.

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types of cancers, and osteoporosis (Ford and Mokdad, 2008). Men and women who are physically active also have a 20–35 percent reduced relative risk of death (Macera and Powell, 2001; Hu et al., 2004; Myers et al., 2004). Moreover, physical activity leads to improved body composition (i.e., reduction in adiposity and weight control), which in turn further improves the already mentioned physical health outcomes (Tremblay et al., 1990; Warburton et al., 2006).

31.5.5  Psycho-emotional outcomes As stated above, overweight individuals often have poorer psychological profiles than their healthy-weight counterparts and chronically ill individuals (Ball et al., 2008). Physical activity benefits are often most effective for those individuals who have the worst mentalhealth profiles – providing justification for the enhanced need to help overweight individuals become more physically active. Additionally, increases in physical activity may be protective against depressive symptoms (Fox, 2000). In addition to alleviating mental health problems such as depression and anxiety, physical activity also appears to influence stress levels. Research has shown that physically active individuals are more likely to have dampened stress reactions in general. If they experience stressful events, their stress levels tend to return to baseline faster than those of individuals who are not physically active (Buckworth and Dishman, 2002; Boutcher et al., 2009). Physical activity can also be a coping mechanism used to deal with stress. Since overweight individuals tend to have lower levels of self-esteem, greater bodyimage disturbance and overall heightened selfpresentation issues, physical activity can also confer positive effects on these body-related affects and cognitions (Treasure et al., 1998). Specifically, when weight loss or perceptions of increased aerobic capacity and muscular strength are reported as a result of physical activity participation,

individuals tend to have increases in perceptions of physical self-concept. This association is depicted by the feedback loop in the model. Furthermore, with the identity shift that may be experienced as a result of regular physical activity participation, there may be the opportunity for overweight individuals to experience psychological growth. Whereas the concept of positive psychological growth has been studied in populations who have suffered trauma (i.e., cancer, death, abuse) (Tedeschi and Calhoun, 2004), it can be speculated that overweight individuals who lose weight and/or become physically active also experience this growth. The concept suggests that as overweight individuals struggle with their weight and attempt to become physically active, changes to their physical self-concept and perceptions (i.e., feelings of being more muscular, having more energy and endurance, and reduced weight) will lead to psychological growth (Tedeschi and Calhoun, 2004). This means realizing new possibilities, developing personal strength and empower­ ment, a new appreciation for life, and new or stronger relationships with others (Tedeschi and Calhoun, 2004). More research is needed to better understand the experiences of psychological growth for overweight individuals.

31.5.6  Social outcomes The idea of developing new or stronger relationships with others through exercise is the final outcome of the model. Generally, individuals appear to benefit most from group-based interventions as compared to individual, home-based programs or information/education efforts (van der Horst et al., 2007). Regardless of the nature of physical activity, be it groups or individual participation, the behavior appears to enhance social normative beliefs, and provide individuals with greater support networks and the ability to seek social support when needed. Moreover, when accomplished in groups, it enhances connectedness among participants (Kayman et al., 1990).

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References

31.6  Fit or fat It is well known that excess weight and physical inactivity are independently related to mortality. However, many public health scientists have argued that it is important to consider fitness and levels of physical activity when evaluating the health risks of obesity. A recent report examined the association between physical activity and overweight status to evaluate whether activity can reduce the adverse impact of a higher BMI on coronary heart disease (CHD) (Weinstein et al., 2008). Women were classified as active if they met public health guidelines and engaged in 30 minutes or more of moderate activity most days of the week, including brisk walking or jogging. Women who engaged in less were classified as inactive. After adjusting for confounding variables, inactive normal-weight women had a slightly (8 percent) higher risk of CHD than those who were fit and active. Conversely, the risk of developing CHD was 54 percent more likely among overweight women and 88 percent more likely for obese women compared to their normal-weight active counterparts. The data show that overweight and obese women can considerably alter the risk of heart disease by remaining physically active. This is particularly important for those obese individuals who may not be ready to lose weight. However, an active lifestyle did not entirely eliminate the risks of obesity, which reinforces the importance of active living combined with maintaining a healthy body weight.

31.7  Conclusion In conclusion, the obesity epidemic is now one of the greatest public health challenges facing healthcare professionals in developed countries around the globe. In addition to sensible meal planning, it is critical to examine strategies

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to encourage both youth and adult patients to adopt more active lifestyles and reduce sedentary activities. Accumulating appropriate amounts of activity may help to promote healthier living, even if weight status is not changed.

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Zabinski, M. F., Saelens, B. E., Stein, R. I., Hayden-Wade, H. A., & Wilfley, D. E. (2003). Overweight children’s barriers to and support for physical activity. Obesity Research, 11, 238–246.

1.  From Brain to Behavior

C H A P T E R

32 Economic Growth as a Path Toward Poverty Reduction, Better Nutrition and Sustainable Population Growth* T.N. Srinivasan Yale University, New Haven, CT, USA; Stanford Center for International Development, Stanford University, Stanford, CA, USA

o u t l i ne 32.1 Introduction and a Definition of Terms

407

32.2 What is Needed to Accelerate and Sustain Growth?

409

32.4 The Case of Undernutrition and Obesity

413

Acknowledgments

416

32.3 Country Case Study: China and India 410

32.1  Introduction and a definition of terms The late nineteenth century was the glorious period for the first wave of globalization. There were no restrictions on capital and labor movements, and neither passports nor visas were

required then. The world was one of free movement of goods, people and capital. The gold standard insured that there were no exchange risks. It was a period during which global growth exploded. It all came to an abrupt end with World War I. John Maynard Keynes’ Economic Consequences of Peace, written after the Treaty of Versailles, highlights the glories of the first wave.

*

The following is an edited transcript of a talk given by Dr. T. N. Srinivasan during the 2006 McGill Health Challenge Think Tank, hosted in Montreal, Canada, October 25–27, 2006.

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407

© 2010, 2010 Elsevier Inc.

408

32.  Globalization, growth and poverty reduction

Between the two World Wars, the global economic system broke down with the abandonment of the gold standard and competitive devaluation of currencies, the erection of tariff barriers exemplified by the infamous Smoot-Hawley tariffs in the US, and the collapse of world trade and capital flows, all of which contributed to the Great Depression and to World War II. With the intention of preventing the disaster which befell the world between the two World Wars, institutions were set up, among which are the International Monetary Fund and the World Bank. The attempt to set up an International Trade Organization in 1948 failed, and it finally succeeded only in 1995, when the World Trade Organization (WTO) was established. Between 1948 and 1995, the General Agreement on Tariffs and Trade (GATT) governed trade. This chapter will focus primarily on globalization, because it has been a major force in eliminating poverty in many regions of the world. Indeed, trade and globalization have been major elements in the saga that have led us to where we are today. We will examine the relationship between globalization and poverty reduction as it pertains to developing countries across time. The instrumentality of sustained and rapid growth for poverty reduction cannot be denied. The only known effective method to reduce poverty across the world over time has been the acceleration of economic growth. The late Nobel laureate Jan Tinbergen – a saint among economists and economist among saints – said this beautifully: While the main aims of the new strategy are of a multiple character, with important social elements in it, the most important single figure representing the set of aims is the rate of growth in real product of the developing world. This is because production is the source of financing social measures, because production implies elements such as food, housing, education and other social services, because employment is directly dependent on the volume of production envisaged and because more equal income distribution can be more easily attained from a high than from a low average income. (Tinbergen, 1971: 12)

There are several links in the globalization– growth–poverty reduction chain, which this chapter will address in later sections. Not all links are present all the time or in all countries, and therefore threshold effects are possible. Their good effects cannot be realized until some threshold, by way of infrastructure, for instance, is reached. Not all links are unidirectional. Some can improve poverty reduction while others, coming the other way, can be offsetting influences. The assertion, without taking the context into account, that globalization is either all bad or all good, is misleading and simplistic. From an economic perspective, there are national poverty lines based on the situation in different countries, but the concept of US $1 per day poverty line at Purchasing Power Parity (PPP) exchange rates has become the most popular. This is, however, far too crude an index to describe a poverty situation. It purportedly compares poverty across countries, adjusting for the differences in the purchasing power of national currencies. However, this adjustment is crude, since the poor usually pay different prices than the rest of the population within each country, and it does not take into account the variation in prices and poverty across regions within large countries such as China and India. Measuring poverty within countries raises other issues as well. If you compare data from household surveys with national income data, there are always some discrepancies between the two for legitimate reasons, such as differences in coverage and data collection and estimation methods, and necessarily biases in one or both. In general, there are no convincing reasons to conclude that one source is better than the other. Growth according to one may not be the same as growth according to the other. Moreover, in the case of poverty alleviation, growth of particular sectors (such as agriculture) is far more important than growth of other sectors or of the overall economy.

2.  From Society to Behavior: Policy and Action

32.2 What is needed to accelerate and sustain growth?

Poverty is a socio-political as well as an economic concept. Adam Smith knew it very well when he talked about necessities and luxuries. He argued that a linen shirt was a necessity for the Englishmen of his day, even though it would have been a luxury for less privileged individuals. Poverty cannot be disassociated from its historical, social and political contexts. However, most of the literature on the subject focuses on an economic notion of poverty.

32.2  What is needed to accelerate and sustain growth? There are essentially three sources of growth. The first source is through the use of more inputs into production, such as land, labor, capital – and, more specifically, human capital, in terms of education, skills, etc. The second is improving the productive efficiency of the inputs. The third is the innovation that creates new products, new uses for existing products, and again improves the efficiency of input use. Innovation has been a very important source of economic growth. There are limits to increasing use of inputs, such as capital, labor and exhaustive resources. For example, China saved 55 percent of its GDP in 2007 according to the World Bank, but there will come a time when the Chinese will want to consume rather than save more than 50 percent of their output. However, even if high savings and investment can be sustained, the marginal returns to investment diminish rapidly. The same is true with respect to labor. The only source of growth that is sustainable is productivity-raising innovation. It is therefore important to focus on policies and institutions that encourage productivity growth. Globalization contributes to all three sources of growth. Opening up to trade increases the efficiency of resource allocation by exploiting national comparative advantage. In this case,

409

one country specializes in something that it is more successful at producing than another is; the latter, in turn, specializes in the production of other things at which it is better than the former. The two countries exchange, and trade becomes mutually beneficial. Openness to flows of capital, labor, natural and other resources augment their domestic availability. If we have too little capital, allowing capital to come in from elsewhere increases the capital for our productive uses at home and reduces its cost. The same is true for skilled and unskilled labor flowing in from poorer to richer countries: their rewards are raised above what they would have been without such flows. Openness to technology flows ensures that fruits of productivity-raising and growth-sustaining innovation anywhere are available everywhere, provided one does not create institutional bottlenecks that impede the flow of technology. Finally, institutions, both domestic and international, matter. Policies as well distortions in markets and the functioning of institutions, both domestic and international, can limit the operation of the globalization–growth link. Globalization and growth reinforce each other. Trade is a handmaiden of growth – that is to say, faster growth means greater trade. Trade as an engine of growth means that greater trade accelerates growth; thus, there is a two-way relationship between trade and growth. When we look at poverty at the individual or household level, its main determinants are resource endowments of poor households and their returns. In countries such as India, where 60 percent of the population still depends on agriculture for its employment and where almost 70 percent of the population lives in rural areas, access to land is a major issue. Access to employment and productive employment of labor are also important. Capital certainly is relevant. Access to resources through market and non-market transactions between households and institutions is important. The institutions in the financial system that

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32.  Globalization, growth and poverty reduction

relate to credit markets play significant roles. Infrastructure – transportation and communications – are also crucial in facilitating the distribution and marketing of a product. Resource allocation within households also has important repercussions for growth and poverty. The discrimination against female children in developing countries’ households is quite well-known. Whether a female child is fed as well as her male sibling, whether she is allowed to go to school and, if so, whether she will be given the same degree of education as the male child, matters. These decisions take place at the individual household level, rather than at the governmental level, and the government’s ability to influence them is limited.

32.3  Country case study: China and India This section will focus on China and India as case studies to illustrate the arguments made above. According to Maddison (2009), in 2006 China and India together represented 2.4 billion people, a little less than 40 percent of the global population, and 23 percent of global GDP at 1990 PPP dollars. Their share of global population in 2030 will fall to 33 percent, and

in global GDP will rise to 37 percent. Almost half the world’s poor lived in China and India in 2005, according to the World Bank. Growth in both accelerated only after they opened their economies and began globalizing. China’s globalization started in 1978, while India’s began hesitantly in the mid-1980s, and more resolutely and systemically after 1991. Before turning to their experience, let us review the trends in growth of world trade and output after 1950. The GATT was established after World War II, following eight rounds of multilateral trade negotiations under its auspices that resulted in trade barriers being brought down from 15 percent or more in the early 1950s to about 5 percent in 2007. As can be seen in Table 32.1, trade expanded rapidly. From 1950 to 1963 and 1963 to 1973, trade grew by 8 percent and 9 percent respectively per year, compared to only 6 percent growth of output. The 1973 and the 1980 oil shocks, followed by the 1980s debt crisis, caused export and output growth to slacken during 1973 and 1990. The former fell to 4 percent; the latter to 3 percent. Since the 1990s, the modern era of globalization that started in the late 1980s has revived annual growth rate of exports to around 6 percent, although output growth has not yet revived, remaining below 3 percent per year.

Table 32.1  Growth in world merchandise exports and output (average annual percentage change in volume terms) Agriculture Exports

Output

Fuels Exports

Manufacture Output

Exports

Output

Total Exports

Output

1950–1963

4.2

3.0

7.0

5.0

8.5

6.8

7.9

5.8

1963–1973

4.0

2.8

8.0

5.0

11.2

7.8

9.0

6.0

1973–1990

2.2

2.2

0.5

1.0

5.8

3.2

4.0

30

1990–2000

4.0

2.0

3.9

1.9

6.1

2.2

6.0

2.1

2000–2007

4.0

2.5

3.5

1.5

6.5

4.0

5.5

3.0

Sources: WTO (2006: Chart I.1); WTO (2009: Table 1.1).

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32.3  Country case study: China and India

Table 32.2  Share in world merchandise trade by region and economy (percent) 1948

1953

1973

1983

2007

Exports

Output

Exports

Output

Exports

Output

Exports

Output

Exports

Output

North America

28.1

18.5

24.8

20.5

17.3

17.2

16.8

18.5

13.6

19.4

US

21.7

13.0

18.8

13.9

12.3

12.3

11.2

14.3

8.5

14.5

Europe

35.1

45.3

39.4

43.7

50.9

53.3

43.5

44.2

42.4

43.4

South and Central America

11.3

10.4

9.7

8.3

4.3

4.4

4.4

3.8

3.7

3.3

Africa Asia China

7.3

8.1

6.5

7.0

4.8

3.9

4.5

4.6

3.1

2.6

14.0

13.9

13.4

15.1

14.9

14.9

19.1

18.5

27.9

25.3

0.9

0.6

1.2

1.6

1.0

0.9

1.2

1.1

8.9

6.8

Japan

0.4

1.1

1.5

2.8

6.4

6.5

8.0

6.7

5.2

4.4

India

2.3

2.3

1.3

1.4

0.5

0.5

0.5

0.7

1.1

1.6

Source: WTO (2009: Tables 1.6 and 1.7).

Table 32.2 highlights the level of globalization of different continents and regions in the past 50 years. We can see that China’s share of world merchandise exports dropped in 1948, just before the Communists took over, to less than 1 percent, and remained around 1 percent until the 1980s. In 2007, China was the second largest exporter in the world, with a share of 8.9 percent. India, on the other hand, had a 2.3 percent share of world exports in 1948. Assiduously following an import substitution strategy for 30 years, it brought it down to 0.5 percent in 1983. Since the 1980s India has slowly reintegrated with the world economy, and its 2007 share of world export is only 1.1 percent. The lesson here is that policies matter. If a country is going to forego integration for 30 years, it will pay a price: China began opening up in 1978, but in India, this only started in 1991. Tables 32.3a and 32.3b feature, respectively, growth in GDP and poverty (share of population living on less than $1.25 a day at 2005 purchasing power parity) over the three decades since 1980 in low- and middle-income countries (LMICs). China has achieved a spectacular 10 percent growth per year on average since the 1980s, as compared to India’s 6 percent. During

Table 32.3a  Growth of GDP (percent per year, average), for low- and middle-income countries 1980–1990*

1990–2000

2000–2007**

7.9

8.5

8.1

10.3 5.5 5.7 1.7

10.6 5.5 5.9 2.5

10.3 7.3 7.8 5.1

East Asia and Pacific China South Asia India Sub-Saharan Africa

Sources: *World Bank (2006: Table 4.1); **World Bank (2009a: Table 4.1).

Table 32.3b  Poverty (share of people living on less than $1.25 a day; percent)

East Asia and Pacific China South Asia India Sub-Saharan Africa World

1981

1990

2005

77.7 84.0 59.4 59.8 53.4 52.2

54.7 60.2 51.7 51.3 57.6 42.0

16.8 15.9 40.3 41.6 50.9 25.3

Source: *World Bank (2009a: Table 2.8).

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32.  Globalization, growth and poverty reduction

2000–2007, India inched towards a 7.8 percent rate of growth, but still lags behind China. Turning to poverty, 77.7 percent (84 percent) of East Asia’s (China’s) population lived on less than $1.25 per day in 1981. That figure came down significantly to only 16.8 percent (15.9 percent) in 2005. South Asia’s (India’s) poverty rate has moved from 59.4 percent (59.8 percent) to 40.3 percent (41.6 percent) during the same period. The sad story is still Sub-Saharan Africa. Its rate of growth has accelerated from 1.7 percent during 1980–1990 to 5.1 percent during 2000–2007, in part due to the effects of globalization. Yet there has been very little poverty reduction from its 1981 level of 53.4 percent, when the region had a lower poverty rate compared to the regions of East and South Asia, to 50.9 percent in 2005 – the highest among the three. Looking at the development of China and India in greater depth, let us start with the first wave of globalization in the late nineteenth century, and then move to selected features for GDP, GDP growth and poverty in the postWorld War II era. The data are presented in Tables 32.4a–d. During the first wave of globaliz­ ation (1870–1918), China was in turmoil due to numerous conflicts, such as the Boxer Rebellion and the Opium Wars. India, on the other hand, had come under British direct rule about a decade earlier, which had brought some peace and stability. India benefited from the first wave of globalization whereas China did not, so that by 1913, China’s GDP per capita at $552 was only

82 percent of India’s $673. When China and India began their current growth era after World War II, China’s per capita income at $448 was still significantly lower, at 72 percent of India’s $619. China was merely catching up with India during the entire Mao era. The Mao era was associated with the disasters of a famine that killed 30 million, and of the Great Leap Forward and the Cultural Revolution. India has not exper­ ienced disasters of corresponding magnitude since Independence in 1947. When Deng Xiao Ping opened up the Chinese economy in 1978, the Chinese began globalizing and China took off. India continued to lag behind, because it did not begin to globalize seriously until the 1990s. In India, according to the official data, from the 1950s to the early 1980s, annual growth stagnated at around 3.75 percent and the proportion Table 32.4a  GDP per capita at Purchasing Power Parity exchange rates China

India

1870

530

533

1913

552

673

1950

448

619

1973

838

853

1990

1871

1309

2006

6048

2598

2030

17,394

7472

Sources: Maddison (2008: Table 12) for 1870–1990; Maddison (2009a: Table 5a) for 2006 and 2030.

Table 32.4b  Growth of real GDP (average, percent per year) 1950–1980

1980–1990

1990–2000

2000–2007

2008–2009

China

4.39*

10.3

10.6

9.4

6.5†

India

3.75**

5.7*

6.2

5.9†

6.0





Sources: *Maddison (2009b: Table 6) for 1952–1978; **Author’s estimates; World Bank (2006: Table 4.1);  World Bank (2009a: Table 4.1); †World Bank (2009b). GDP in constant 2000 dollars. The data for China are for 2009, and GDP is in 2000 PPP dollars. 2.  From Society to Behavior: Policy and Action

413

32.4  The case of undernutrition and obesity

of poor in the population fluctuated at around 50 percent. There was no downward trend. Only as annual growth began accelerating to 5.7 percent in the 1980s, 5.9 percent in the 1990s and 7.8 percent since 2000, did poverty begin declining in India. The latest figure for 2004–2005 shows that 27.5 percent of the Indian population is below the very modest national poverty line. Tables 32.5a–c present globalization measures for China and India at different points in time, respectively featuring the share of trade in goods in GDP, foreign capital inflows, and tariff barriers. As can be seen, the share of traded goods – imports, and exports in GDP – in 2007 was 67.8 percent for China and 30.8 percent for India. India’s tariff barriers continue to be higher than China’s. In terms of foreign capital inflows, China is, once again, far ahead of India. Table 32.4c  Poverty (proportion of population below poverty line) for China 1981

1990

1996

2002

2005

China (New National Poverty Line)*

52.8

22.2

9.8

7.3

5.2

China (World Bank)** $1.25/day Poverty Line

84.0

60.2

36.4

28.4

15.9

India (World Bank) ** $1.25/ day Poverty Line

59.8

51.3

46.6

43.9

41.6

Sources: *Chen and Ravallion (2007: Table 4); **World Bank (2009a: Table 2.8).

That is again another measure of the integration with respect to capital.

32.4  The case of undernutrition and obesity Poverty in India, China and other poor countries remains the primary determinant of undernourishment, stunting and wasting. Figure 32.1 brings together data from the National Family Health Survey from developing countries, in regard to under- and over-nourishment in India. The curve to the left is the distribution curve for Indian children, and the curve to the right is the “international reference” curve. As can be seen, comparing the weight-for-age distribution for children under the age of 3 years in India to the global reference population, the problem for India is on the left-hand side of the distribution, with the emergence of mild overweight at the other extreme of the distribution. The children are found predominantly under the “severe underweight” and “moderate underweight” headings. Globalization has led to moderate progress in reducing undernourishment. Figures 32.2 and 32.3 reveal that the reduction in prevalence of undernutrition during the 1990s in India was modest to moderate, whether measured in terms of prevalence variation for moderate or for severe underweight. Looking at the distribution quintiles, the wealthiest show a 36 percent prevalence in 1992–1993. The figure is almost double (61 percent) for the poorest.

Table 32.4d  Poverty (proportion of population below poverty line) for India India (Official)

1951–1952 1961–1962 1973–1974 1977–1978 1983

1987–1988 1993–1994 1999–2000 2004–2005

Rural India

47.4

47.2

55.7

53.1

45.7

39.1

37.3

27.1

28.3

Urban India

35.5

43.6

48.0

45.2

46.8

38.2

32.4

23.6

25.7

Combined

45.3

46.5

54.1

51.3

44.5

38.9

36.0

26.1

27.5

Sources: Datt (1998, 1999); MOF (2009).

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32.  Globalization, growth and poverty reduction

Table 32.5a  Measure of global integration: share of trade in goods in GDP China

India

1950–1951

11.5

1960–1961

10.3

1970–1971

6.9

1980–1981

13.4

1990

32.5*

China

13.1*

1990–1991

14.6

2000–2001

22.5

2007

Table 32.5b  Measure of global integration: foreign capital inflows

67.8*

30.8*

Sources: India MOF (2006); *World Bank (2006, 2009a: Table 6.1).

India

1990

2007

1990

2007

Gross Private Capital 2.5 (% of GDP) Gross Foreign Direct 1.2 Investment (% of GDP) Foreign Direct 3.48 Investment ($ billion) Portfolio Investment ($ billion):   Bonds 0.48  Equity 0

10.0

0.8

5.9

4.8

0.1

3.1

0.24

23.0

0.15 0

8.2 35.0

138.4

1.72 18.5

Sources: World Bank (2006: Tables 6.1, 6.8); World Bank (2009a: Tables 6.1 and 6.11).

Table 32.5c  Measure of global integration: tariff barriers (all products) China 1992

India 2007

1990

2005

Simple Mean Tariff

40.4

8.9

79.0

17.0

Weighted Mean Tariff

32.1

5.1

56.1

13.4

Share of Lines with International Peaks

77.6

14.9

92.4

15.4

Sources: World Bank (2006: Table 6.7); World Bank (2009a: Table 6.8).

Normal distribution curve (international reference) Distribution curve for indian children

Moderate underweight

Mild overweight Moderate overweight

Server underweight

–6.0

–5.0

–4.0

–3.0

–2.0

–1.0

.0

1.0

2.0

3.0

4.0

5.0

6.0

Source: Calculated from NFHS data

Figure 32.1  Weight-for-age distribution: children aged under 3 years in India compared to the global reference population. Source: Gragnolati et al. (2005).

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415

32.4  The case of undernutrition and obesity

80

73

69

Percentage of children

70 60

68

53 47

50

47

49

45

46

40 30

25

22

23

18

20

18

10

15 3

3

0 1992

1998

1992

Underweight

1998

1992

Stunting Mild

Moderate

1998 Wasting

Severe

Source: Underweight figures calculated directly from NFHS I and NFHS II data; other figures obtained from StatCompiler DHS (ORC Macro 2004). Note: Figures are children under the age of three

Figure 32.2  A modest reduction in the prevalence of undernutrition during the 1990s. Source: Gragnolati et al. (2005).

% Rural children undernourished

80 70 60 50 40 30 20 10

Underweight Severe underweight

1996− 97

1995− 96

1991− 92

1988− 90

1974− 79

1996− 97

1995− 96

1991− 92

1998− 90

1974− 79

0

Stunting Moderate underweight

Source: WHO Global Database on Child Growth and Malnutrition (WHO 2004a); original data from NNMB (1974–79, 1988–90, 1991–92), DWCD (1995–96) and Vijayaraghavan and Rao (1996–97). Note: Prevalence is not strictly comparable across time periods since each round of surveys used different sampling methodologies and calculated prevalence across different age groups.

Figure 32.3  Trends in the prevalence of underweight and stunting among children aged under 5 years in rural India Source: Gragnolati et al. (2005).

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32.  Globalization, growth and poverty reduction

Table 32.6  Underweight, stunting and wasting, by global region (2000) Percentage of under-5s (2000) suffering from: Region

Underweight

Stunting

Wasting

6

14

2

Africa

24

35

8

Asia

28

30

9

India

47

45

16

Bangladesh

48

45

10

Bhutan

19

40

3

Maldives

45

36

20

Nepal

48

51

10

Latin America and Caribbean

Pakistan

40

36

14

Sri Lanka

33

20

13

22–27

28–32

7–9

All developing countries

Source: Gragnolati et al. (2005: Table 3).

Table 32.6 shows similar prevalence variation across countries. In conclusion, while the global upward obesity trends draw attention to the double burden of over- and undernutrition in LMICs, it is also extremely important that attention continues to be paid to the many forms of undernourishment that continue to exist in developing countries.

Acknowledgments I acknowledge with thanks the permissions of the following: M. Gragnolati, his co-authors and the World Bank for data from their paper for the World Bank; Angus Maddison for data from his unpublished (2009a) and published (2009b) papers, and his paper in the Asian Economic Policy Review; Gaurav Datt for data from his unpublished papers of 1998 and 1999; Shaohua Chen and Martin Ravallion and the Journal of Development Economics for their paper of 2007; Ministry of

Finance, Government of India for data from the Economic Survey for 2005–2006 and 2008–2009; Wiley Science for data from Angus Maddison’s papers published in the Asian Economic Policy Review, and The Review of Income and Wealth; United Nations Institute for Training and Research (UNITAR) to quote Jan Tinbergen for his lecture at UNITAR in 1971; World Bank for data from World Development Indicators for 2006 and 2009; and the World Trade Organization for data for International Trade Statistics, 2006 and 2008.

References Chen, S., & Ravallion, M. (2007). China’s (uneven) progress against poverty. Journal of Development Economics, 32(1), 1–42. Datt, G. (1998). Poverty in India and Indian states: An update Working Paper 47. Washington, DC: International Food Policy Research Institute. Datt, G. (1999). Has poverty in India declined since the economic reforms? Washington DC: World Bank. Gragnolati, M., Shekar, M., Das Gupta, M., Bredenkamp, C., & Lee, Y.-K. (2005). India’s undernourished children: A call for reform and action. Washington DC: World Bank. Maddison, A. (2008). Shares of the rich and the rest in the world economy: Income divergence between nations, 1820-2030. Asian Economic Policy Review, 3, 67–82. Maddison, A. (2009a August). The world economy in 2030: A quantitative assessment. Utrecht: paper presented at International Economic History Association. Maddison,   A. (2009b July). Measuring the economic perform­ ance of transition economies: Some lessons from Chinese exper­ ience, the review of income and wealth, Series 55, (1). MOF. (2006). Economic Survey, 2005–06. New Delhi: Ministry of Finance. MOF. (2009). Economic Survey, 2008–09. New Delhi: Ministry of Finance. Tinbergen, J. (1971). Towards a better international order. New York, NY: United Nations Institute for Training and Research Lecture Series 2. World Bank. (2006). World development indicators. Washington, DC: World Bank. World Bank. (2009a). World development indicators. Washington, DC: World Bank. World Bank. (2009b). Prospects for the global economy. Washington, DC: World Bank. WTO. (2006). International trade statistics 2005. Geneva: World Trade Organization. WTO. (2009). International trade statistics 2008. Geneva: World Trade Organization.

2.  From Society to Behavior: Policy and Action

C H A P T E R

33 The Human Agent, Behavioral Changes and Policy Implications Transcribed Remarks by Daniel Kahneman Center for Health and Well-Being, Princeton University, Princeton, NJ, USA

o u t l i n e 33.1 The Economic and Psychological view of Human Nature

417

33.2 Culture as an Economic Externality 418 33.3 A Psychologist’s Explanation of Behavior

419

33.5 An Argument for some Paternalism

420

418

33.1  The economic and psychological view of human nature Proponents of the Chicago school of thought perceive the human agent as completely rational. In their seminal article “A theory of rational addiction”, Becker and Murphy (1988) present a human agent who anticipates everything and chooses between two options as a function of their derived utility. The agent, in principle, knows he is going to become addicted because

Obesity Prevention: The Role of Brain and Society on Individual Behavior

33.4 Happiness, or the Power of Human Adaptability

he is discounting the future, and discounting the future at the interest rate. For example, if we look at the case of a 15-year-old who is considering losing a year at the age of 70, discounted at 5 percent per year, that year of life is not worth much. When one discounts the future at 5 percent per year, one can rationalize a great deal of rather unhealthy behaviors. It is a powerful idea, but also perhaps a questionable one too. There is a fundamental difference that shows itself here between the economic analysis – or the Chicago economic analysis – and what most psychologists feel to be the case. Psychologists

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33.  The Human Agent, Behavioral Changes and Policy Implications

and economists adopt very different perspectives of the mainsprings of behavior and decisions. Psychologists tend to take a retrospective view of the consequences of action. For example, looking at someone who began smoking at a young age, and suffering and regretting it later, psychologists would say that it was not worth it. This retrospective view of the whole arc stretches from the decision to the consequences. An economist’s perspective is solely based on the point of decision. Theirs is a forward-looking perspective. It is generally agreed that people discount very sharply. On the one hand, if one assumes that the human agent is rational, one accepts his discount rate. On the other, if one takes the retro­ spective view, one does not. An issue with the economic point of view is that it does not take regret into consideration. We return to the example of the person who began smoking young, but now hates it, cannot quit, and regrets ever starting. According to the Chicago school of thought, that person is perceived in the same way as a person who goes to a restaurant, eats a meal, and then refuses to pay. There is very little sympathy for regret. When one takes the retrospective view – the view that, in the author’s opinion, most people are inclined to take – regrets matter. There is a lot of regret for different kinds of behavior in which people engage early in life and that have delayed consequences.

33.2  Culture as an economic externality Considering the human agent in all its imperfect complexity also calls for examining choice and behavior in their cultural context, with culture having the potential for both protective and aggravating effects. Using “culture” as an explanation for a behavior is often seen as an admission that we are dealing with something we do not know how to change. In a study conducted with Fischler and other colleagues, we surveyed

the wellbeing of women in two cities: 800 women in Columbus, Ohio, and 800 women in Rennes, France (Kahneman et al., 2009). Results showed a striking difference in the body mass index (BMI) of these women: the average BMI in the Columbus group was of 28; in Rennes, it was 23. The authors suggested a few reasons which, altogether, may explain this significant discrepancy. For instance, they found that French women walk more, approximately 36 minutes per day, whereas American women only walk approximately 20 minutes per day. The authors also looked at women’s use of time and activities to which they paid attention. They found that French women spent more time at the table than American women. The difference was not great, however. What dramatically differed, though, was the incidences of focal eating. Sixty percent of the French women surveyed reported that, when eating, it was the main activity in which they engaged. Only 30 percent of American women reported eating as a focal activity. Researchers also looked at the time French mothers spent with their children. There were two distinct peaks at meal times. Clearly, these mothers were with their children when the children were eating. Nowhere else in Europe did the researchers come across this behavior. It was a cultural difference unique to France, which in this case appears to have had a protective effect against nutritionally poor eating habits and high BMI.

33.3  A psychologist’s explanation of behavior How do psychologists explain behavior? First, psychologists tend to avoid intentions as explanations of behavior. Behavior is, as much as possible, a result of the environment, human biology (what is colloquially known as “instinctive behaviors”) or, sometimes, culture, related to historical and cultural learning interpretations. How behavior is explained by psychologists has

2.  From Society to Behavior: Policy and Action

33.4  Happiness, or the power of human adaptability

important implications on how they think about controlling and changing behavior. One crucial psychological observation in explaining behavior – which sharply distingui­ shes the psychological point of view from the economic one – is the huge impact of factors that should not matter: namely, inconsequential details. Psychologists call this the “framing effect”. Bertrand and colleagues conducted a study in South Africa, where they convinced banks to mail 55,000 loan offers to customers who had previously taken out loans (Bertrand et al., 2005). Loan offers had different interest rates, but also had other variations – for example, some of the promotional literature had pictures of a man, some of a woman. The power of including a picture was worth 4 percent of interest rates. It demonstrates the relative powers of factors that rationally do matter (e.g., financial information) and factors that do not matter (e.g., a picture). It certainly puts into perspective the role of communication in conveying important nutritional information. Another example of this is demonstrated in Brian Wansink’s research. In one of these studies examining eating behavior, a jar of candy was placed within reach of participants. For another group, it was placed 6 feet away. Participants in the former group ate significantly more than those in the latter. Automatic behaviors requiring little thought are engaged in differently than those requiring decisions about physical effort and displacement.

33.4  Happiness, or the power of human adaptability A final factor to consider in examining the full psychological complexity of the human agent is happiness. However, applying notions of happiness to public health problems turns out to be extremely difficult because things do not work the way we would like them to work,

419

with variation in psychological wellbeing and happiness hardly mapping objective reality. A significant proportion of our population lives in appalling conditions, dreadful health or other circumstances. We would expect them to be unhappy, yet they are not. They have adapted to the worst of conditions. The differences between healthy and unhealthy individuals are not as stark as what one would expect. Studying happiness does not provide much support for interventions. With colleagues from around the world, we have begun studying happiness and wellbeing in the hopes of measuring, among other things, the burden of disease, and thereby providing justification for intervention. We found that people are so good at adapting to circumstances that the differences are much smaller than they would have to be in order to justify interventions. Those who become blind or paraplegic are, inevitably, devastated, yet they eventually recover. It has been found that they get as much pleasure as anyone else in listening to a joke, eating a meal or interacting with their grandchildren. It is therefore difficult to bring happiness to bear on health problems and thereby justify intervention. Returning to the comparison of women in Columbus, Ohio and Rennes, France, reported earlier, Fischler and colleagues found a correl­ ation between BMI and life satisfaction. While there was little correlation between BMI and average mood, women with high BMI tended to be less satisfied with their lives. One reason for this is that BMI is largely associated with other problems, such as low income, low education and low status. Keeping income and education constant, the association between BMI and life satisfaction essentially vanishes. In the same study, women were also posed a 32-item questionnaire about what gives them “joy” in life: How much joy do you get from different aspects of life? From spiritual experiences? Gardening? Sex? Eating with friends? Results showed that a high BMI was clearly

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33.  The Human Agent, Behavioral Changes and Policy Implications

correlated with a constriction in the sources of joy. This remains the case when keeping status as a constant. Women with higher BMI find less joy in their weight (the correlation here is of 0.45), in the physical condition, in their physical activities, in their looks, in taking walks, in nature and the outdoors, in community activities, in gardening, etc. As for the sources that gave high-BMI women more joy than to their leaner counterparts, two significant differences were identified: television and pets. As one can see, there is extreme diversity in human happiness, and it is evident that people can cope, making scientific research on happiness and wellbeing particularly challenging.

33.5  An argument for some paternalism Kurt Lewin, one of the great twentieth-century psychologists, looking at the complexity of behavioral change, suggested that the question “How do we get people to do what we want them to do?” was wrong. He rephrased it: “Why are they not already doing what we want them to do?” This small but powerful difference alters the way one thinks about behavioral change. The first question, “How do we get people to do what we want them to do?”, implies argument, explanations, incentives and threats. The second, “Why are they not already doing what we want them to do?”, directs the attention to something entirely different; namely, how do you change the situation of those making the choices so that they become more likely to do what it is they are supposed to be doing? As a result, one ends up with very different prescriptions. Lewin talks about driving forces that push people where one wants them to go, and restraining forces that prevent them from going where one does not want them to go. He suggests that one should focus on limiting

the restraining forces rather than on limiting the driving forces. In the context of obesity, this concept may be applied to the issue of enlisting industry cooperation in such a way as it is easy for corporations to act in the public interest. At the individual level, while not explaining why people prefer French fries to broccoli, it certainly does suggest an interesting avenue in attempting to change eating behaviors so that people come to prefer broccoli to French fries. By focusing on creating conditions that help people do what they are supposed to do, we come to an argument in favor of some paternalism. In this regard, the contrast between the US and the rest of the world is striking: most nations take a fair amount of paternalism for granted. The US, on the other hand, apologizes for it. There is a deep sense that individuals have the right to operate without any interference from government. It is very apparent that Americans are conditioned to worry about paternalism and intervention. Paternalism is often seen as a phenomenon whereby adults are treated as if they were children. The main justification for paternalism is that humans have self-control problems. The gurus of behavioral economics spoke of the human agent, as viewed in behavioral economics, as having bounded rationality, bounded selfishness (unlike the economic man) and bounded self-control. It is those with bounded self-control who tend to justify paternalism and interventions. The main concept driving paternalism is the idea that people want to behave differently from the way they are inclined. In addition, they are willing to pay for it. One only has to look at the popularity of small packages of sweets to see that people are paying to help with a selfcontrol problem. Paternalism is on the rise in the US, and behavioral economics is having a significant impact on this. Behavioral economics does not so much provide an argument for paternalism as undermine arguments against paternalism.

2.  From Society to Behavior: Policy and Action

References

References Becker, G. S., & Murphy, K. M. (1988). A theory of rational addiction. Journal of Political Economy, 96(4), 675–700. Bertrand, M., Karlan, D. S., Mullainathan, S., Shafir, E., Zinman, J. (2005) What’s psychology worth? A field exp­e­ riment in the consumer credit market. Yale University Economic Growth Center Discussion Paper No. 918.

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Kahneman, D., Schkade, D. A., Fischler, C., Krueger, A. B., & Krilla, A. (2009). The structure of well-being in two cities: Life satisfaction and experienced happiness in Columbus, Ohio; and Rennes, France. In E. Diener, J. F. Helliwell, & D. Kahneman (eds.), International differences in well-being. New York, NY: Oxford University Press.

2.  From Society to Behavior: Policy and Action

C H A P T E R

34 The Four Pillars of the Industrial Machine: Can the Wheels be Steered in a Healthier Direction? William Bernstein efficientfrontier.com, North Bend, OR, USA

o u t l i n e 34.1 Introduction

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34.2 Malthus’ World

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34.3 How Nations Become Wealthy

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34.4 The Progress of Economic Development

426

34.5 Measuring Economic Development

429

The bourgeoisie, during its rule of scarce one hundred years, has created more massive and more colossal productive forces than have all preceding generations together. Karl Marx, Communist Manifesto

34.1  Introduction Around 1820, the world tilted on its economic axis. Before that date, the life of the average per­ son on the planet improved little, if at all, from

Obesity Prevention: The Role of Brain and Society on Individual Behavior

34.6 The 2 Percent Productivity Cruise Control

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34.7 The Obesity Connection

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34.8 The Way Forward

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Acknowledgments

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year to year, decade to decade, or even century to century. Then, in the early nineteenth century, the world slowly became a much richer place. Material wellbeing, as measured by world per capita GDP, began to increase at 2 percent per year, meaning that, for the past two centuries, the life of the child has been approximately twice as prosperous as that of the parent, doub­ ling the standard of living once every 36 years (Maddison, 2001). This improvement in the material condition of mankind resulted largely from developments

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34.  The Four Pillars of the Industrial Machine

in manufacturing productivity owing to advanced technologies, which were in turn effected by four institutional pillars: secure property rights, scientific rationalism, efficient capital markets, and advanced communications and transport mechanisms. The same factors that caused improved manufacturing productiv­ ity also resulted in a revolution in agricultural technology. This has been a two-edged sword: on the one hand, the cheap, high-energy food­ stuffs available from these technologies have mitigated world hunger; unfortunately, these same foodstuffs have also triggered a world­ wide obesity epidemic.

6

Population (millions)

424

4

2 40

50

60

70

80

90

Real per capita GDP (1865 = 100)

Figure 34.1  The Malthusian trap in England 1265–1595. Data from Anderson (1996) and Clark (2001).

34.2  Malthus’ world Before 1820, the regime described by Thomas Malthus operated with its inexorable logic. In his harsh world, a nation’s food supply, along with its population, grew slowly, if at all, so that, in the short term, the standard of living was inversely proportional to the number of mouths to feed. Were population to increase, there would not be food enough to go around. Prices would rise, while wages and the standard of living in general would fall. If, on the other hand, the population were suddenly to fall, as happened in the mid-fourteenth century in the wake of the Black Death, the survivors’ food supply, wages and standard of living would rise dramatically. History, as well as Malthus’ life experi­ence, had burned this sequence of events into his consciousness. Figure 34.1 plots the per cap­ ita GDP of England from 1255 to 1545 versus population. The thin, crescent-shaped distribu­ tion of the data points depicts the “Malthusian Trap”, as summarized by historian Phyllis Deane (1979): When population rose in pre-industrial England, product per head fell: and, if for some reason (a new

technique of production or the discovery of a new resource, for example, or the opening up of a new market), output rose, population was not slow in fol­ lowing and eventually leveling out the original gain in incomes per head.

In this eternal cycle, food output might rise, but population followed in lock step, dooming mankind to a near-subsistence existence. Paradoxically, soon after Malthus immortal­ ized this grim state of affairs, it abruptly came to an end in Western Europe. Figure 34.2 shows that, sometime around 1600, a bulge developed in the crescent, and Figure 34.3 shows that, after 1800, population cleanly broke out of the crescent, never again to return to starvation’s edge. The escape from the trap was made possible not by an increased birth rate, but rather by a 40 percent drop in the death rate, the result of rapidly improving living stand­ ards born out of vigorous economic growth (Ashton, 1967). The nature of that growth changed dramati­ cally in the centuries following 1600. Initially the growth was “extensive”, consisting of a significant increase in the size of the national economy caused purely by population growth,

2.  From Society to Behavior: Policy and Action

34.3  How nations become wealthy

34.3  How nations become wealthy

6 Population (millions)

1655

1705

1615

4

Before 1600 After 1600

2 40

50

60

70

80

90

Real per capita GDP (1865 = 100)

Figure 34.2  The trap breaks down 1600–1705. Data from Anderson (1996) and Clark (2001).

Population (millions)

20

1865

Before 1710 After 1710

15

1845 1825

10 `

1715

1785

5 0 40

60

425

80

100

Real per capita GDP (1865 = 100)

Figure 34.3  Breaking out of the trap 1715–1865. Data from Anderson (1996) and Clark (2001).

unaccompanied by any real improvement in the wealth or material comfort of the aver­ age citizen. In other words, for the first time, the economy mustered just enough growth to keep pace with population. By the nineteenth century growth had become “intensive”, out­ pacing even the human urge to reproduce, with advances in per capita income and an increase in wellbeing at the individual level (Jones, 1988).

Beginning around 1820, the pace of economic advance picked up noticeably, making the world a better place in which to live. What happened? An explosion in technology, the likes of which had never been seen before. New technology is the powerhouse of per capita economic growth; without it, significant increases in productivity and consumption do not occur. Four things are needed to develop new technologies: 1. Property rights. Innovators and tradesmen must rest secure that the fruits of their labors will not be arbitrarily confiscated, by the State, by criminals or by monopolists. The assurance that a person can keep most of his or her just reward is the right that guarantees all other rights, though the right to property is never absolute. Even the most economically libertarian governments, such as in Singapore and Hong Kong, levy some taxes, enforce some form of eminent domain and maintain some restrictions on commercial freedom of action. A government that fails to control inflation or maintain proper banking and property controls, such as Brazil in the 1980s or present-day Zimbabwe, steals as surely as, and on a much greater scale than, a common thief. 2. Scientific rationalism. Economic progress depends on the development and commercialization of ideas. Their creation requires an intellectual framework that supports the inventive process – an infrastructure of rational thought which relies on empirical observation and on the mathematical tools that support technologic advance. The scientific method that we take for granted in the modern West is a relatively new phenomenon. Only in the past 400 years have Western peoples

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34.  The Four Pillars of the Industrial Machine

freed themselves from the dead hand of the totalitarian, Aristotelian mindset, and even today, in areas of Africa, Asia and the Middle East, honest intellectual inquiry is at risk from the forces of state and religious tyranny. 3. Capital markets. The large-scale production of new goods and services requires vast amounts of money from others – “capital”.1 Even if property and the ability to innovate are secure, capital is still required to develop schemes and ideas. Since almost no entrepreneur has enough money to massproduce his or her inventions, economic growth is impossible without substantial capital from outside sources. Before the nineteenth century, society’s best, brightest and most ambitious had scant access to the massive amounts of money necessary to transform their dreams into reality. 4. Fast and efficient communications and transportation. The final step in the creation of new technologies is their advertisement and distribution to buyers hundreds or thousands of miles away. Even if entrepreneurs possess secure property rights, the proper intellectual tools and adequate capital, their innovations will languish unless they quickly and cheaply put their products into the hands of consumers. For example, only two centuries ago did sea transport become safe, efficient and cheap. Land transport followed suit 50 years later, with the invention of the steam engine. Of the four factors, property rights fell into place first in early medieval England and Holland. Scientific rationalism followed hard on

the heels of Francis Bacon’s Novum Organum in 1620, and the modern capital markets saw their origins in Amsterdam and London around the same time. The final piece of the puzzle, the trans­ port and communications revolutions, resulted from the advent of steam and telegraph in the late eighteenth and early nineteenth centuries, respectively, unleashing the floodgate of global prosperity and, along with it, an epidemic of global obesity. Not until all four of these factors – property rights, scientific rationalism, effective capital markets and efficient transport/communication – are in place can a nation prosper. The absence of even one factor endangers economic progress and human welfare; kicking out just one of these four legs will topple the table of a nation’s bounty. This occurred in eighteenth-century Holland with the British naval blockade, in the world’s Communist states with the loss of property rights, and in much of the Middle East with the absence of capital markets and Western rationalism. Finally, and most tragically of all, in most of present-day Africa, all four factors remain essentially absent.

34.4  The progress of economic development The portrait economic historian Angus Maddison and others painted of economic development is as stunning as it was unex­ pected. The lot of the average individual, meas­ ured as real per capita GDP, did not change at all during the first millennium after the birth of Christ. Over the next 500 years, between 1000 and 1500, things did not get much better.

1 

The term “capital” is fraught with economic meaning. Economists frequently employ a broad definition of the term, encompassing human capital, knowledge, or “intellectual” capital, as well as physical capital such as plant and equipment. Here, “capital” is defined in the narrowest possible sense: money available for investment.

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34.4  The progress of economic development 3%

$1,000

1%

8 99

3 19

73

–1

97

0 19

50

–1

95

3 19

13

–1

91

0 18

70

–1

87

0 18

20

–1

82

0 00

(a)

–1

50

2000

15

1500

–1

1000

00

500

10

0

00

0%

$100

10

(a)

2%

0–

Per-capita GDP (1990$)

$10,000

Per-capita GDP (1990$)

$10,000

(b)

2% $1,000

1%

$100 1700

1750

1800

1850

1900

1950

2000

Figure 34.4  (a) World per capita GDP (inflation adjusted). (b) World per capita GDP (inflation adjusted). Data from Maddison (2001).

0% 0–1000

1000–1500

(b)

1500–1820

1820–1998

Years A.D.

Figure 34.5  (a) Annualized growth of world per cap­ ita GDP. (b) Annualized per capita world GDP growth. Data from Maddison (2001).

Figure 34.4(a & b), which plots world per capita GDP2 since the dawn of the Common Era, brings the welfare of the average person into sharp focus. Before 1820, there was only minus­ cule material progress from decade to decade and century to century. After 1820, the world steadily became a more prosperous place. Figure 34.5(a & b), which summarizes the aver­ age annual growth in world real per capita GDP,

displays the breakout occurring around 1820 from a different viewpoint. Once again, prior to 1820, there was little improvement in the mater­ ial welfare of the average human. At the height of the Greek and Roman peri­ ods, the “urbanization ratio” – the proportion of the population living in cities of more than 10,000, an excellent barometer of an econo­ my’s margin over subsistence – was estimated

2 

Economists have found it easy to criticize Maddison’s estimates of income and production from centuries ago. After all, how can he be sure that the annual per capita GDP of Japan at the birth of Christ was $400 in current dollars, rather than $200 or $800? Maddison himself concedes the point: “To go back earlier involves use of weaker evidence, greater reliance on clues and conjecture. If you want to measure economic progress over the centuries, you first must ask, ‘How much money is necessary to sustain a subsistence level of existence?’“ Maddison’s answer was that, in an underdeveloped nation in 1990, that amount was about $400 per year. Next, economic historians use whatever data they can find to determine what percentage of the population existed at this level. A society in which nearly 100 percent of the population is engaged in farming and that does not export any substantial amount of its agricultural products lives, by definition, very close to Maddison’s $400 per year subsistence level. It is highly artificial to assign, as he did, a $400 per capita GDP to Europe in AD 1, China in 1950, or modern day Burkina Faso, but doing so at least provides economic historians with a working standard against which to measure economic growth (Maddison, 2001).

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34.  The Four Pillars of the Industrial Machine

at only several percent. In 1500, the largest city in Europe was Naples, with 150,000 inhabit­ ants; only 865,000 Europeans lived in cities of more than 50,000, or about 1 percent of the total European population; only 6 percent lived in towns of more than 10,000. In the great civili­ zations of Asia, which during the medieval era were far more advanced than those in Europe, the percentage of the population engaged in agriculture was even closer to 100 percent, and the opulence of the tiny ruling elites did not much raise the overall level of prosperity. So it seems likely that before 1500 the world’s overall per capita GDP was close to a subsist­ ence level. Even in the US until as late as 1920, fully 70 percent of the working population was employed on farms. Today, that figure stands at 1 percent. Europe did produce some economic growth after the fall of the Roman Empire. The early medieval period saw the switch from a two-crop to a three-crop rotational system; the invention of the horseshoe and horse collar, water mill and windmill; and the replacement of the twowheeled cart with the four-wheeled variety (Jones, 1987). Economic historians, however, disagree about just when these changes began to result in growth, with estimates ranging from the eighth to the fifteenth century. These advances, although they produced growth, merely resulted in increases in popula­ tion, leaving the wellbeing of the average citi­ zen unchanged. The wide range of opinion on the dating of the renaissance of growth in the post-Roman world is proof enough that per capita growth (the best measure of the well­being of the individual) cannot have been sub­ stantial or sustained. Yet, before 1820, there were hints of the com­ ing prosperity. Maddison estimates that around AD 1500, European per capita GDP averaged $774, with Renaissance Italy clocking in at $1100 (Maddison, 2001). Italy’s relative pros­ perity would not last long. After 1500 it would

$100,000 Per-Capita GDP (1990$)

428

$10,000 England Japan

$1,000

China

$100 1500

1600

1700

1800

1900

Figure 34.6  Growth versus starting wealth.

2000

Data from Maddison (2001).

s­ tagnate, while Holland began to experience persistent, if sluggish, economic growth. Around the same time, however, Britain’s growth rate began to increase as well, although more slowly than Holland’s. The Glorious Revolution of 1688 brought a stable constitutional monarchy to England, along with a Dutch king, the cream of Holland’s financiers, and Dutch advances in the capi­ tal markets from across the North Sea. It took more than a century for English growth to accelerate rapidly following these events. Not until the middle of the nineteenth century did the average Englishman live better than the average Dutchman, and even then only after the crippling of Holland’s economy by dec­ ades of British naval blockade, followed by the destruction of the Dutch Republic by Napoleon Bonaparte. The British seeded their overseas colonies not only with their people but, more critically, with their legal, intellectual and financial institutions. The great economic transformation did not begin to spread to the rest of Europe and Asia until much later. Its effects were highly uneven there, as depicted in Figure 34.6, with the “takeoffs” of England, Japan and China occurring in 1820, 1870 and 1950, respectively.

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34.6  The 2 percent productivity cruise control

34.5  Measuring economic development The beauty of examining a very long his­ torical sweep is that it “washes out” even large uncertainties about growth. For example, over a period of 1000 years, if we overestimated the beginning or ending per capita GDP by a fac­ tor of two, this would entail an error of just 0.07 percent per year in the annual growth rate. Put another way, world per capita GDP growth since the birth of Christ could not possibly have been as high as 0.5 percent, since if it were, per capita GDP would have grown from $400 in current dollars to over $8.6 million by the year 2000! So we can be certain that, for most of this period, growth was indeed very close to zero. Putting it yet a third way, even the most wildly optimistic estimates suggest no more than a doubling or tripling in per capita global GDP between AD 1 and AD 1000, versus the eightfold increase in the 172 years following 1820. During this same 172-year period, per capita GDP in the UK grew 10-fold, and in the US, 20-fold.

34.6  The 2 percent productivity cruise control The vigor of modern economic growth astounds. Throughout the 1800s, real per capita GDP growth in what is now called the devel­ oped world gradually accelerated to about 2 percent per year, then maintained that pace throughout the entire turbulent twentieth cen­ tury. Table 34.1 lists the growth of real per capita GDP in 16 nations during the twentieth century, dividing them into nations that were physically ravaged by world war or civil war, and those that were not.

Table 34.1  Annualized per capita GDP growth, 1900–2000 Per capita GDP growth War-damaged Belgium

1.75%

Denmark

1.98%

France

1.84%

Germany

1.61%

Italy

2.18%

Japan

3.13%

Netherlands

1.69%

Spain Average for war-damaged countries

1.91% 2.01%

Not war-damaged Australia

1.59%

Canada

2.17%

Ireland

2.08%

Sweden

1.96%

Switzerland

1.72%

United Kingdom

1.41%

United States

2.00%

Average for countries not war-damaged

1.85%

Source: Bernstein and Arnott (2003).

Notice how tightly around 2 percent the growth rates cluster – 13 of the 15 nations increased their per capita GDP between 1.6 percent and 2.4 per­ cent per year. It is as though an irresistible force – a sort of economic cruise ­control – propelled their productivity upwards at almost exactly 2 percent per year – not faster and not slower. Notice also the similarity between the average growth rates of the war-torn and non-war-torn nations. The devastation of war does no long-term damage to the economies of developed nations. Figure 34.7 displays another fascinating char­ acteristic of Western economies – the wealthiest advanced nations of 1900 tended to grow the

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34.  The Four Pillars of the Industrial Machine

Annualized per capita GDP growth, 1900-2000

3.0%

2.5%

2.0%

1.5%

1.0% $1,000

$2,000

$3,000

$4,000

$5,000

Per capita GDP in 1900 (constant 1990$)

Figure 34.7  Per capita GDP versus US (US  100%). Data from Maddison (2001).

100% 80% 60%

Germany

40%

economic machinery during World War II is clearly visible at the left edge of Figure 34.8. Japan began World War II with a per capita GDP 40 percent of the US value; by the war’s end, it had fallen to just 15 percent. Germany’s per capita GDP fell from 80 percent to 40 per­ cent of that of the US during the same period. By 1990, however, both nations had completely recovered. The Western growth machine reduces the catastrophe of conquest to a mere historical hiccup. Within just two decades, Japanese and German economies completely recovered their former prosperity relative to the US. In the fol­ lowing 20 years, Japan’s relative per capita GDP doubled yet again. The beginning of the nineteenth century did not transform every corner of the world. At first, only Europe and its New World offshoots pros­ pered. Nonetheless, over the ensuing 200 years, the Western variety of growth spread over the rest of the globe.

Japan

20% 0% 1940

1950

1960

1970

1980

1990

Figure 34.8  Per capita GDP (inflation adjusted). Data from Maddison (2001).

slowest, while the least wealthy tended to grow the fastest. In other words, the per capita wealth of the most advanced nations tends to converge. Japan, which started out the twentieth century as the poorest of the nations listed, saw its pro­ ductivity grow at 3 percent per year, while the leader in 1900, Great Britain, grew at only 1.4 percent per year. The most spectacular example of the resil­ iency of Western economies – the tendency to “catch up” – is provided by examining the per capita GDP of Germany and Japan from 1940 to 1990. The devastation of the Axis Powers’

34.7  The obesity connection As advanced technologies have made work­ ers more productive, societies wealthier and manufactured products cheaper, so too have these four pillars of economic growth applied to agriculture. At its birth, the US needed more than two-thirds of its population to feed itself; now, 1 percent feeds not only the nation, but also much of the rest of the world. Conversely, whereas a century ago the average American family spent more than half its income on food, today that figure stands at less than 10 percent. While almost all agricultural products have become much cheaper in real terms, it stands to reason that the prices of the most highly processed foods – the high-energy sug­ ars and fats largely responsible for the mod­ ern obesity ­ epidemic – have fallen the most.

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34.8  The way forward

One commodity, sucrose,3 has fallen in price more, and over a longer period, than almost any other. Also, in virtually every part of the world, its per capita consumption has increased stead­ ily over the course of recorded history. It nicely illustrates the progress of economic growth in agriculture (Mintz, 1986). During the medieval period, sugar was con­ sidered a “fine” spice, as rare and expensive as its four trade cousins: cloves, nutmeg, mace and cinnamon. Economic historians estimate that during the fifteenth century, European per cap­ ita consumption was just one teaspoon per year (Hobhouse, 1986). Sugar’s mass production and consumption only began around 1500, with the invention of the three-cylinder mill, which could be driven by water or animal power. With it, the important problem of crushing sugar cane was remediated. A second problem, the lack of fuel, which had resulted from the deforestation of the Middle East, Europe, and the Atlantic Islands, was resolved by the discovery of the New World’s endless forests. By the time of Columbus’s transatlantic voy­ ages, cane had just been transplanted to the Spanish Canaries, from which his expeditions were staged. It quickly spread throughout the New World tropics, and touched off an explo­ sion of cane production that powered much of the world economy for the next three centu­ ries. The “sugar belt” of the New World, which

431

spread from Northern Brazil to Surinam and up the Caribbean chain all the way to Cuba and even the Louisiana Delta, attracted large ­numbers of European settlers lured by the rela­ tively short transatlantic passage, the lack of organized Native opposition, and agricultural profits unimaginable in their homelands. Over the next five centuries, improvements in growing, refining and transportation technol­ ogies for sugar have made it a bulk commod­ ity that sells so cheaply that it is given scarcely a second thought. Today, the average American consumes 66 pounds per year and the average European consumes 87 pounds – a perfect pub­ lic health storm born of the marriage of a highly addictive foodstuff and the genius of modern industrial productivity (FAO, 2006; CIA, 2008; US Census Bureau, 2008).

34.8  The way forward In the unlovely jargon of economics, the global obesity crisis is a classic “externality”, in which the same market forces that have yielded an everincreasing global prosperity impose unintended costs on society at large, not unlike industrial pollution. It is not difficult to propose solutions, none of which are mutually exclusive: taxa­ tion of corporations that produce energy-dense foods, taxation of the end products themselves,

3

 The cane plant, Saccharum officinarum, requires a frost-free growing season of about 12–18 months, steady and copi­ ous rainfall or irrigation, and year-round temperatures averaging more than 70°F. Cane harvesting and the subse­ quent extraction of pure, granulated sugar from the cut stalks is hot, backbreaking work that consumes vast amounts of both fuel and human effort. The production of sugar is as much an industrial process as an agricultural one. It occurs in three stages. First, the cane is crushed to release the sweet cane juice. For millennia, this was accomplished with crude and inefficient mortar-and-pestle devices, and made cane juice a luxury product, even where abundant slave labor was available. Next, the sweet juice has to be “reduced” by boiling it down to a concentrated sucrose solution, a process that consumes massive amounts of fuel. Finally, the solution is repeatedly heated and cooled in a refining process that separates out the sugar into granules of purity ranging from clear crystalline rocks to a brown residue – treacle or molasses – that cannot be further crystallized. This final process – sugar refining – not only con­ sumes yet more fuel, but also requires great skill – so much so that during the colonial age, it was accomplished mainly in the advanced industrial centers of Europe (Galloway, 1977).

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34.  The Four Pillars of the Industrial Machine

intensive public education, and subsidies of lowenergy density foods, to name but a few. An analysis of the relative merits of these pos­ sibilities is well beyond the scope of this chapter, but a good starting point is the classic model of externalities proposed by University of Chicago economist Ronald Coase,4 who explored the arcana of government regulation of conflicts among private parties. Consider, for example, a corn farm adjacent to a cattle ranch. The cattle wander, as is their wont, onto the corn farm and eat the farmer’s crop. This is a classic “negative externality”, similar to industrial pollution or a noisy neighbor. Coase realized that there were two possible ways to settle this sort of conflict. The first and most obvious way required the cattleman to pay for the damage. The second and less intuitive way allowed the cattle rancher to request payment from the farmer in exchange for fencing his cattle. In the first case, the lia­ bility is the cattleman’s; in the second, it is the farmer’s. Coase’s genius lay in the realization that it did not matter who initially “owned” the liabil­ ity. In each case, the end result would be the same – an identical amount of money would change hands, just in different directions. Economically, the two possible outcomes were equivalent (Coase, 1960). In the Coase para­ digm, only three things matter: 1. That ownership and liability be clearly defined 2. That property and liability can be bought and sold at will 3. That the expenses of negotiating, selling and enforcement are low. Coase’s third condition is critical. When many parties are involved, negotiation costs can be high. Such is the case with the obesity crisis,

in which the damage is suffered by hundreds of millions of people and is produced by a rela­ tively small number of large corporations. (The same is true of atmospheric pollution.) The problem, then, is that having millions of citizens negotiate the payment of damages with a myriad of food companies (or payment from millions of citizens to the companies to produce healthier foods) is impossibly cumbersome. Thus, the best economic model we have to deal with the crisis strongly suggests that governments must intervene (McMillan, 2002). Just how they do so is one of the most important inter­ national public policy questions of our era.

Acknowledgments The author would like to thank Atlantic Monthly Press and McGraw-Hill Inc. for grant­ ing permission for the adaptation of material from A Splendid Exchange and The Birth of Plenty for use in this chapter.

References Anderson, M. (1996). British Population History from the Black Death to the Present Day. Cambridge: Cambridge University Press. Ashton, T. S. (1967). The industrial revolution. Oxford: Oxford University Press. Bernstein, W. J., & Arnott, A. D. (2003). Earnings growth: The two-percent dilution. Financial Analysts Journal, 59(5), 51. Central Intelligence Agency. (2008). The 2008 World Fact Book. Online. Available: https://www.cia.gov/library/­ publications/the-world-factbook/index.html/. Clark, G. (2001). The secret history of the Industrial Revolution. Working Paper. Coase, R. H. (1960). The problem of social cost. Journal of Law and Economics, 3, 1–44.

4

 Coase’s name is known mainly among economists and lawyers. The paper, “The Problem of Social Cost”, Journal of Law and Economics, is one of the most cited articles in economic literature. In 1991, he was awarded the Nobel Prize in Economics for this and related work.

2.  From Society to Behavior: Policy and Action

References

Deane, P. (1979). The first industrial revolution. Cambridge: Cambridge University Press. Food and Agriculture Organization. (2006). Sugar. The Food Outlook. Online. Available: http://www.fao.org/­ documents/show_cdr.asp?url_file/docrep/009/ J7927e/j7927e07.htm/. Galloway, J. H. (1977). The Mediterranean sugar industry. Geographical Review, 67(2), 182–188. Hobhouse, H. (1986). Seeds of change. New York, NY: Harper and Row. Jones, E. L. (1987). The European miracle. Cambridge: Cambridge University Press.

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Jones, E. L. (1988). Growth recurring. Ann Arbor, MI: University of Michigan Press. Maddison, A. (2001). The world economy: A millennial perspective. Paris: OECD Press. McMillan, J. (2002). Reinventing the bazaar. New York, NY: W.W. Norton. Mintz, S. W. (1986). Sweetness and power. New York, NY: Penguin. US Census Bureau. (2008). US POPClock Projection. Online. Available: http://www.census.gov/population/www/ popclockus.html/.

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C H A P T E R

35 Libertarian Paternalism: Nudging Individuals toward Obesity Prevention Laurette Dubé James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management, McGill University, Montreal, Canada

o u tline 35.1  Introduction 35.2 Biases and Shortcomings in Human Decision-Making 35.2.1 Status quo and Default Bias 35.2.2 Limited Cognitive Capacity 35.2.3 Neglect of Consequences of Distributed Choices and Concreteness Bias 35.2.4 Present-biased Preference and Hyperbolic Discounting 35.2.5 Prediction Failure

435 436 436 437 437 437 437

35.1  Introduction At the root of obesity and chronic diseases lie, as argued by Julian Le Grand, the “giants of excess”. These include the excess consumption of alcohol, tobacco, drugs, high-fat, high-salt and

Obesity Prevention: The Role of Brain and Society on Individual Behavior

35.2.6 Reference Dependence and Loss Aversion 438 35.2.7 Vulnerability to Framing Effects 438 35.3 On Libertarian Paternalism

438

35.4  Libertarian Paternalism Applied 35.4.1 Resetting the Default Option 35.4.2 Immediate Rewards and Pre-commitment

440 440

35.5  Limitations and Conclusion

440

440

high-sugar foods, as well as the increase of sedentary activities. They emerged from industrialization and economic development, a phenomenon which is also responsible for tremendous social and economic benefits. Industrialization has primarily flourished in market economies, rooted in the power of individual creativity, self-interest and

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35.  Libertarian Paternalism AND Obesity Prevention

freedom of choice. Modern society evolved within the clear boundaries of a two-pronged institutional framework: on the one hand, market mechanisms align supply and demand; on the other, social and political governance prevents/addresses market failures and takes care of the social domains of health and human development. Standard approaches to policy-making and regulation assume the full rationality of individual agents, be these consumers, firms or other organizations. In this context, policy-making and regulation lie outside the purview of individual decision-making and market mechanisms (Camerer et al., 2003). It is assumed that (1) decision-makers have well-defined preferences or goals and make decisions to maximize these preferences; (2) preferences reflect the true costs and benefits of available options; and (3) in situations that involve uncertainty, decision-makers have well-formed beliefs about how the uncertainty will resolve itself and such probabilistic assessments will be updated in light of new information according to Bayes’ law (Camerer et al., 2003). Government regulation can take a variety of forms; some aim at redistribution, others to counteract externalities. The form that concerns us here is paternalistic regulation, which is designed to help on an individual basis: it treads on individual sovereignty by forcing, or preventing, choices for the individual’s own good. Behavioral economists have suggested that libertarian paternalism (also called asymmetric paternalism) can better guide individual choices (Camerer et al., 2003; Thaler and Sunstein, 2003). Relying on a sophisticated understanding of human biases and shortcomings, such interventions and policies aim at “nudging” individuals and organizations to act in their own and in society’s best interest while preserving freedom of choice. In societies where large proportions of the population are overweight or obese, traditional and rationality-based interventions and policies have obviously failed. An argument can be made in favor of a libertarian paternalistic approach to obesity prevention.

The next section reviews biases and shortcomings most relevant to lifestyle behavior choices, presents key premises of libertarian paternalism vis-à-vis traditional policy approaches, and examines applications of libertarian paternalism to behavior change strategies and policy interventions addressing obesity.

35.2  Biases and shortcomingS in human decision-making The emerging field of behavioral economics has highlighted that human decision-making is the end product of multiple competing motives, reasons, considerations and perspectives. This body of evidence has unraveled systematic ways in which individuals make less-than-optimal choices, revealing that individuals (1) rarely exhibit rational expectations, (2) fail to update their forecast according to Bayesian rules, (3) use heuristics that ignore utility maximiz­ ation, and/or (4) shift their preferences and choices as a function of contextual changes in the ways options or outcomes are presented. These decision biases and pitfalls may help explain why and how individuals engage (or do not) in obesity-promoting lifestyle behaviors. Here, we briefly review the most relevant.

35.2.1  Status quo and default bias Changing any state imposes physical, cognitive and, in some cases, emotional costs that human beings intuitively tend to avoid by mere inertia. As a result, status quo or default options have a powerful impact on decision-making. According to status quo and default biases, even if an individual knows the best course of action, what is automatic and/or what has always been done in the past represents the path of least resistance (Lowenstein et al., 2008). In the modern environment of plenty, where high-calorie, high-fat and

2.  From society to behavior: policy and action

35.2 Biases and shortcomings in human decision-making

high-sugar food options and sedentary activities are ubiquitous, the current default options (i.e., those that are made most natural by the choice architecture) appear to be setting the stage for obesogenic behavior.

35.2.2  Limited cognitive capacity Individuals have processing difficulties which can mean cognitive overloads when too many options are presented to them (Ratner et al., 2008). It has also been found that when humans deliberate excessively about a decision, they are more likely to focus too much on less important criteria, to develop an attachment to certain options, and later to feel a sense of loss toward the unchosen options and a lower sense of post-choice satisfaction.

35.2.3  Neglect of consequences of distributed choices and concreteness bias Lifestyle decisions are made several times a day, every day. The marginal impact of each decision is negligible. Indeed, in regard to eating, no single indulgence has a discernable effect on weight; it is only in aggregate that their cumulative consequences are manifested (Hernstein and Prelec, 1991). Most individuals are motivated by action that produces measurable, tangible benefits. Actions that do not produce tangible progress toward a goal are less motivating (Weber and Chapman, 2005). For many behaviors that undermine health, factors working against adherence (such as time costs) are tangible. Adverse outcomes, such as long-term risks, are intangible and often delayed.

437

ones (Ratner et al., 2008). Hyperbolic discounting implies that agents are relatively far-sighted when making trade-offs between rewards at different times in the future, but pursue immediate gratification when available (O’Donaghue and Rabin, 1999). Loewenstein (1996) attributes present biases to the overwhelming power of visceral factors, such as hunger, cravings or emotions felt at the time of decision. Such factors distort the perceived utility of choice alternatives, increasing the utility of options that alleviate visceral factors while decreasing that of options unrelated to visceral factors, such as long-term body weight or health consequences. In the context of inter-temporal choice, people exhibit dynamic inconsistency, valuing present consumption much more than future consumption. In other words, people have self-control problems. This explains why many behavioral patterns that undermine health involve immediate bene­ fits (such as the pleasure of eating a high-caloric food or the convenience of eating processed food) or immediate costs (such as the inconvenience of taking a drug or undergoing a medical procedure), coupled with delayed, and often uncertain, benefits. Caring less about the future than the present can be rational, but most individuals place much greater weight on the present than would follow from a consistent tendency to discount the future. A major consequence is that behavior is not consistent over time: decisionmakers do not make the decision they expected they would (when they evaluated the decisions in prior periods) when the actual time arrives. This account for both self-control and procrastin­ ation (Camerer et al., 2003).

35.2.5  Prediction failure 35.2.4  Present-biased preference and hyperbolic discounting Individuals place disproportionate weight on present costs and benefits relative to future

A stream of literature on affective forecasting documents several common mistakes people make when predicting how they will feel in the future, underestimating the degree of emotional adaptation to changes in their lives (Gilbert and

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Ebert, 2002). People are also poor at predicting what they will do, overestimating their ability to commit to a plan of action (for example, I will renounce dessert and start exercising tomorrow) (Zauberman, 2003).

35.2.6  Reference dependence and loss aversion Violating expected utility theory, the valuation of choice options is examined in relation to the reference point considered as the status quo against which the choice options are assessed. A given distance from the reference point that represents a loss looms larger than a gain of the same magnitude.

1981), in which professionals and policy-makers are asked to choose between two alternative programs to combat the outbreak of an Asian disease (Box 35.1), respectively presented in a “gain” (i.e., outcomes expressed in terms of number of lives saved) and a “loss” (i.e., outcomes expressed in terms of number of live lost). Although there is no substantial difference between the two versions, in the “gain” version of the problem a substantial majority of respondents favor Program A, indicating risk aversion. In the “loss” version of the problem, a clear majority favor Program B, the risk-seeking option. Numerous framing effects have also been found in how individuals respond to commercial marketing and healthpromoting communications.

35.3  On libertarian paternalism

35.2.7  Vulnerability to framing effects Preferences are affected by variations of irrele­ vant features of options and outcomes. One of the best demonstration of framing effects is the Asian Disease Problem (Tversky and Kahneman,

Libertarian paternalism brings together two otherwise contradictory concepts. Libertarianism refers to the concept by which individual freedom

Box 35.1

Th e A sia n D is e as e P r o bl e m Imagine that the United States is preparing for an outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:

two-thirds probability that no people will be saved. Which one of the two programs would you favor?

Version Gain Frame

If Program A is adopted, 400 people will die. If Program B is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die. Which one of the two programs would you favor?

If Program A is adopted, 200 people will be saved. If Program B is adopted, there is a one-third probability that 600 people will be saved and a

Version Loss Frame

Source: Tversky and Kahneman (1981).

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35.3  On libertarian paternalism

is maximized and the presence and intervention of the state is minimized or completely eliminated. The individual is viewed as a completely rational agent who is forward-looking, and whose choices are made to maximize utility and self-interest (Murphy, 2006). Paternalism, however, refers to the concept by which the State makes decisions on behalf of individuals for their own good. It assumes that the individual is incapable of making his or her own decisions, and must therefore be taken care of by a “fatherly” State. Libertarianism is based on a false assumption and two misconceptions. The false assumption is that individuals always make choices that are in their best interest – an assumption that has been found time and time again to be false. The first misconception is that the choices made by government or other actors shaping the environment have no effect on the choices other individuals and/or organizations make. The second misconception is that paternalism always involves coercion (Camerer et al., 2003). As illustrated in the following example, the choice of the order in which to present food items does not coerce anyone to do anything, but may influence an individual’s choice. Consider the problem facing the director of a company cafeteria who discovers that the order in which food is arranged influences the choice that people make. To simplify, consider three alternatives: the director could (1) make choices that she thinks would make the customers better off; (2) make choices at random; or (3) maliciously choose the items he or she thinks would make the customers as obese as possible. Option 1 appears to be paternalistic, which it is, but would anyone advocate option 2 or 3 (Thaler and Sunstein, 2003: 175)? From the perspective of libertarian paternalism, a policy counts as “paternalistic” if it is selected with the goal of influencing the choices of affected parties in a way that will make those parties better off (Thaler and Sunstein, 2003). “Better off” is to be measured as objectively as possible. Libertarian paternalism accounts for the possibility that

439

individuals could improve inferior choices if they had complete information, unlimited cognitive abilities, and no lack of will power. Since this is usually not the case, it is assumed that a form of paternalism cannot be avoided, and that the altern­ atives to it (such as choosing options that make people worse off) are unattractive. Libertarian paternalism helps individuals who are prone to making irrational decisions, while imposing minimal or no restrictions on and not harming those making informed, deliberate decisions. In the purest cases, people behaving sub-optimally are benefited without imposing any costs on those behaving optimally (Camerer et al., 2003). Crucial tenets of libertarian paternalism are to (1) shift behavior in a self-interested direction without abridging individuals’ freedom to choose and (2) help those behaving in a selfdestructive fashion without distorting the decisions of those behaving in a self-interested fashion (Down et al., 2009). The overall aim is to find policies that maximize the health and wellbeing of individuals and society while minimizing the effects on autonomy and freedom of choice (Le Grand, 2008). While some criticize libertarian paternalism as the first step towards government control (Goldstein, 2008), it offers a vision whereby the government “nudges” individuals and organizations in the healthiest direction. Reasoning, judgment, discrimination and self-control are seen as burdens which the State can help lighten. Indeed, some (Loewenstein and O’Donoghue, 2006) go so far as to argue that government and the law have a responsibility to take into account how the choice architecture impacts human behaviors and emotions, and the choices that follow from these. In the context of obesity and the “giants of excess”, by changing the incentive structure that is faced by individuals and organizations we can bring the costs from unhealthy activities (or the benefits from healthy ones) back from the future, and/or reduce the benefits from unhealthy activities (or reduce the costs of healthy ones) in the present (Le Grand, 2008).

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35.4  Libertarian paternalism applied Modifying the choice architecture in which individuals make health-related decisions can yield behavioral changes. Various studies an­c­hored in the field of behavioral economics have attempted to demonstrate the effectiveness of such an approach to driving behaviors.

35.4.1  Resetting the default option Novel research has yielded insights into the importance of the default option. Benartzi and Thaler (2007), for instance, showed that introducing some small design features in the way 401(k) plans are presented can go a long way to overcoming faulty biases and heuristics in retirement savings behavior. They highlighted behavioral research that illustrated common individual behaviors related to choosing a retirement savings plan: individuals were usually slow to join advantageous plans, were unlikely to make frequent changes, and adopted naïve diversification strategies. Yet small changes to the retirement savings plan, such as automatic enrollment, precommitment to synchronize pay raises and savings increases, and simplifying the investment selection process, can go a long way in optimizing an employee’s 401(k) (Benartzi and Thaler, 2007). Automatic enrollment to a retirement savings program meant an enrollment increase from 49 percent to 86 percent (Madrian and Shea, 2001; Choi et al., 2003). It was also found that employees saved more if the employer automatically deposited a significant share of the salary in a retirement savings plan than if the default option placed the onus with the employee.

35.4.2  Immediate rewards and pre-commitment Volpp and colleagues (2008) conducted a study to determine whether common decision

errors, such as prospect theory, loss aversion and regret, could be used to design effective weight-loss interventions. Participants engaged in a 16-week weight-loss program that combined monthly weigh-ins, a lottery incentive program and a deposit contract. The study was designed upon the following assumptions: (1) that there is a significant incentive value to small rewards and punishments; (2) that people are motivated by past rewards and the prospect of future rewards; (3) that people are emotionally attracted to the small probabilities of large rewards; and (4) that the desire to avoid regret is a potent force in decision-making under risk–loss aversion. The use of economic incentives produced significant weight loss during the intervention; the mean weight loss was 5.5 kg. It ought to be noted, however, that the weight loss was not sustained following the intervention (and the end of the incentives). Down and colleagues (2009) conducted a study in which they assessed the effects of information versus a paternalistic intervention that made healthier sandwiches more convenient to order. While there was no effect when customers were presented with calorie information or daily calorie recommendations, the convenience manipulation had a strong impact on sandwich choice, such that participants were more likely to choose a low-calorie sandwich when it was more convenient to do so. This was found in both dieters and non-dieters. Also significant, it was found that while the provision of calorie information may influence the choice of some people, it could also have the perverse effect of promoting greater calorie consumption.

35.5  Limitations and conclusion While libertarian paternalism offers significant promise in addressing major societal problems, care must be taken when tweaking the choice architecture. Ariely and colleagues (2009) demonstrated the complexity of the choice architecture

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References

and the risks involved in attempting to “nudge” individual and organizational behaviors. They studied the incentives behind charitable donations, and found that monetary incentives interacted negatively with image motivation, and therefore diluting charitable behavior. Indeed, monetary incentives had no effect on pro-social and charitable behavior because of the negative perception of the incentive (Ariely et al., 2009). Therefore, challenges in order to address the choice architecture supporting and promoting unhealthy eating and lifestyle behaviours lie in judiciously matching role, context and measure.

References Ariely, D., Bracha, A., & Meier, S. (2009). Doing good or doing well? Image motivation and monetary incentives in behaving prosocially. American Economic Review, 99(1), 544–555. Benartzi, S., & Thaler, R. H. (2007). Heuristics and biases in retirement savings behavior. Journal of Economic Perspectives, 21(3), 81–104. Camerer, C., Issacharoff, S., Lowenstein, G., O’Donaghue, T., & Rabin, M. (2003). Regulation for conservatives: Behavioral economics and the case for “asymmetric paternalism”. University of Pennsylvania Law Review, 151(3), 1211–1254. Choi, J., Laibson, D., Madrian, B. C., & Metrick, A. (2003). Optimal defaults. American Economic Review, 93(2), 180–185. Down, J. S., Loewenstein, G., & Wisdom, J. (2009). Strategies for promoting healthier food choices. American Economic Review: Papers & Proceedings, 99(2), 159–164. Gilbert, D. T., & Ebert, J. E. (2002). Decisions and revisions: The affective forecasting of changeable outcomes. Journal of Personality and Social Psychology, 82(4), 503–514. Goldstein, E. R. (2008). When nudge comes to shove. Chronicle of Higher Education, 54(35), B10–B11. Hernstein, R. J., & Prelec, D. (1991). Melioration: A theory of distributed choice. Journal of Economic Perspectives, 5(3), 137–156.

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Le Grand, J. (2008). The giants of excess: A challenge to the nation’s health. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(4), 843–856. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65(3), 272–292. Loewenstein, G., & O’Donoghue, T. (2006). We can do this the easy way or the hard way: Negative emotions, selfregulation, and the law. The University of Chicago Law Review, 73(83), 183–206. Loewenstein, G., Brennan, T., & Volpp, K. G. (2008). Asymmetric paternalism to improve health behaviors. Journal of the American Medical Association, 298(20), 2415–2417. Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401k participation and saving behavior. Quarterly Journal of Economics, 116(4), 1149–1187. Murphy, K. (2006). Obesity and the Economic Man. Presen­ tation given during the McGill Health Challenge Think Tank, hosted in Montreal, Canada, 2006, October 25–27. O’Donaghue, T., & Rabin, M. (1999). Doing it now or later. American Economic Review, 89(1), 103–124. Ratner, R. K., Soman, D., Zauberman, G., Ariely, D., Carmon, Z., Keller, P. A., et al. (2008). How behavioral decision research can enhance consumer welfare: From freedom of choice to paternalistic intervention. Marketing Letters, 19, 383–397. Thaler, R. H., & Sunstein, C. R. (2003). Libertarian paternalism. American Economic Review, 93(2), 175–179. Tversky, A., & Kahneman, D. (1981). The framing of decision and psychology of choice. Science, 453–458. Volpp, K. G., John, L. K., Troxel, A. B., Norton, L., Fassbender, J., & Loewenstein, G. (2008). Financial incentive-based approaches for weight loss: A ran­ domized trial. Journal of the American Medical Association, 300(22), 2631–2637. Weber, B. J., & Chapman, G. B. (2005). Playing for peanuts: Why is risk seeking more common for low-stakes gambles? Organizational Behavior and Human Decision Process, 97, 31–46. Zauberman, G. (2003). The intertemporal dynamics of consumer lock-in. Journal of Consumer Research, 30(3), 405–419.

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C H A P T E R

36 The Current State of the Obesity Pandemic: How We Got Here and Where We Are Going* Philip James International Association for the Study of Obesity, and   International Obesity Task Force, London, UK

o u t l i n e 36.1 The Current State of the Obesity   Pandemic 36.1.1 World Data on Overweight, Obesity and their Related Chronic Diseases

36.1.2 Regional Data on Overweight and Obesity

445 445

36.1  The current state of the obesity pandemic 36.1.1  World data on overweight, obesity and their related chronic diseases According to the World Health Organization, approximately 1.6 billion adults were overweight

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36.2 How Did We Get Here?

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36.3  The Complexity of the Problem

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in 2005, and 400 million adults were obese. Furthermore, the WHO estimates that by 2015, these numbers will rise to 2.3 billion and 700 million respectively.1 Even more worrisome is the growing trend of overweight/obesity among children. It is estimated that 287 million children are overweight and 74 million are obese around the world.2 Furthermore, whereas once overweight and obesity were considered

* The following is based on two presentations given by Dr. Philip James during the 2006 and 2007 editions of the McGill Health Challenge Think Tank, which were hosted respectively in Montreal, October 25–27, 2006 and November 7–9, 2007. 1  http://www.who.int/mediacentre/factsheets/fs311/en/index.html 2  International Obesity Task Force estimates made in 2006.

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36.  Current State of the Obesity Pandemic

Global Totals 2002 Obese: 356 million O/wt ≥ 25: 1.4 billion

35 USA

% Obese (BMI =>30 kg/m2)

30 25 Finland

20

Sweden (Goteborg)

15 10 5 0 1970

2015 Obese: 704 million O/wt ≥ 25: 2.3 billion

England

Cuba

Australia Brazil

Norway (Tromsø)

1975

1980

1985

1990

Japan 1995

2000

2005

Year

Figure 36.1  Escalating obesity rates in adults. Source: IOTF (2006).

a problem only in high-income countries, they are now dramatically on the rise in low- and middle-income countries, particularly in urban settings.3 Figure 36.1 illustrates current overweight/ obesity trends for adults in various countries, based on data collected from various sources by the International Obesity Task Force (IOTF). Except for Cuba, where the collapse of Soviet aid together with the US economic blockade created major but temporary food shortages, every other country shows a growing rate of overweight/ obesity, with the sharpest inclines being in the US, England and Australia. The only country, other than Cuba, where the trend has seemingly leveled off is in Finland. (Indeed, Finland has engaged in strong anti-obesity action in recent years and has, for instance, successfully lowered their fat intake from 43 percent to nearly 30 percent.4

This intake, however, is still too high given the degree of Finnish physical inactivity.) Figure 36.2 shows the prevalence of overweight in children within WHO regions. While still a relatively minor problem in most of Africa, information from South Africa shows that as these countries develop, they too develop the problem of overweight and obesity. This trend is further emphasized in other developing countries, such as the Caribbean Islands, other Middle East countries, for example in Bahrain, and in the Pacific Islands. Analyses highlight the link between economic development and urbanization, and the rise of overweight and obesity (Ezzati et al., 2005). Furthermore, as economic development occurs, there is a shift away from overweight being a disease of the rich and successful: in most middleincome countries, the overweight and obese

3 

http://www.iotf.org/database/documents/GlobalPrevalenceofAdultObesityOctober2009v2.pdf http://www.ktl.fi/attachments/suomi/esittely/organisaatiokaaviot/pj_esityksia/warsova.ppt

4 

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447

36.1  The current state of the obesity pandemic

45 40

% Over weight

35 30 25

Boys Girls

20 15 10

WHO africa region

WHO south east asia region

WHO Western Pacific

WHO Eastern Mediterranean Region

WHO European Region

USA

Chile

Brazil

Portugal

Spain

England

Czech Republic

Bahrain

Iran

Saudi Arabia

Australia

New Zealand

China

Japan

Thailand

India

Sri Lanka

Algeria

South Africa

00

Mali

05

WHO Americas Region

IOTF 200 6

Figure 36.2  Childhood overweight (including obesity) by WHO region. Source: IOTF (2006).

members of society are found among the poorer segments (Monteiro et al., 2007). Overweight/obesity has profound repercussions on health, and is directly linked to cardiovascular diseases, cancer, and type 2 diabetes. Childhood obesity is also now recognized as a major amplifier of disease, with a much higher chance of premature death and early disability in adulthood. Across the world, in both developed and developing countries, obesity’s related chronic diseases place a huge burden on societies so that now the WHO finds that cardiovascular disease is the world’s main cause of death, killing approximately 17 million people every year with the majority of these deaths occurring in low- and middle-income countries (LMICs) – not the affluent West (Lopez et al., 2006). Diabetes itself affects 246 million people throughout the world, and this figure, it is expected, will reach 380 million by 2050. By then,

80 percent of diabetes cases will be in LMICs – countries that do not necessarily have the means to deal with such a widespread problem (Diabetes Atlas, 2003). Figure 36.3 shows that cardiovascular disease is the leading cause of death in these LMICs. Also of note is the number of deaths attributed to cardiovascular disease in LMICs – just over 10 million deaths per year, compared with just over 2 million deaths in high-income countries. This again emphasizes the scale of the problem in developing countries, which have a hopelessly inadequate health system and socio-economic infrastructure to tackle the diseases properly. The underlying major risk factors for such diseases in the affluent world are smoking, high blood pressure and overweight/ obesity; in LMICs, the major killers are childhood underweight, unsafe sex and high blood pressure (Lopez et al., 2006). Nevertheless, in these LMICs, smoking, high blood cholesterol levels,

2.  From Society to Behavior: Policy and Action

448

36.  Current State of the Obesity Pandemic

Low- and Middle-income countries Cause

High-income countries

Deaths (millions)

% total deaths

Cause

Deaths (millions)

% total deaths

1.

Ischemic heart disease

5.70

11.8

Ischemic heart disease

1.36

17.3

2.

Cerebrovascular disease

4.61

9.5

Cerebrovascular disease

0.76

9.9

3.

Lower respiratory infections

3.41

7.0

Trachea, bronchus & lung cancers

0.46

5.8

4.

HIV/AIDS

2.55

5.3

Lower respiratory infections

0.34

4.4

5.

Perinatal conditions

2.49

5.1

Chronic obstructive pulmonary disease

0.30

3.8

6.

Chronic obstructive pulmonary disease

2.38

4.9

Colon and rectal cancers

0.26

3.3

7.

Diarrhoeal diseases

1.78

3.7

Alzheimer's & other dementias

0.21

2.6

8.

Tuberculosis

1.59

3.3

Diabetes mellitus

0.20

2.6

9.

Malaria

1.21

2.5

Breast cancer

0.16

2.0

10

Road traffic accidents

1.07

2.2

Stomach cancer

0.15

1.9

Amplified by excess weight gain

Figure 36.3  The ten leading causes of death in low- and middle/high-income countries. Source: Lopez et al., (2006); reproduced with permission of the World Health Organization.

high alcohol and low fruit and vegetable intakes with overweight/obesity still come into the top 10 primary risk factors. So it is notable that in both developed and developing countries, these risk factors and therefore the principal causes of death and disability are preventable. Yet only a fraction (usually 1 percent) of national budgets for health are devoted to prevention. If the direct medical costs are not enough of an incentive for action, the financial costs to society should raise serious alarms. Figure 36.4 illustrates the financial impact of chronic diseases amplified by overweight/obesity (WHO, 2005). The sheer population sizes of India and China, compounded by their rapid economic development, mean that costs will be enormous in

coming years. In the next 10 years, China is going to be handicapped by at least $100 billion, which amounts to 2 percent of its GDP. Figure 36.5 looks at the costs of different degrees of excess weight in a study from the US (Arterburn et al., 2005). The curvilinear line indicates the increasing cost as one gets fatter. Of note, an interesting feature is that although we know that when one is very fat costs are very high, the graph shows that the biggest absolute cost to the United States arises not in the group of exceptionally obese people with a BMI of over 40, but from those who have only modest levels of overweight (BMI from 25 to 30). Their individual extra cost is small, but for the society as a whole it is huge. The implication

2.  From Society to Behavior: Policy and Action

449

36.1  The current state of the obesity pandemic

600

Projected foregone national income due to heart disease, stroke and diabetes in selected countries, 2005–2015

International dollars (billions)

500 400 300 200 100 0

Brazil

Canada

China

India

Nigeria Pakistan Russian United United federation kingdom republio of tanzania

Figure 36.4  The financial impact of chronic diseases amplified by obesity. Source: WHO (2005), reproduced with permission of the World Health Organization.

Per capita costs $

18

5000

16

4500

14

4000 3500

12

3000

10

2500 08

2000

06

1500

04

1000

02

500

00

40

BMI

Figure 36.5  The costs of different degrees of excess weight in the USA ($56 billion). Source: Arterburn D et al., (2005).

2.  From Society to Behavior: Policy and Action

0

Per capita costs $

Total excess expenditure $ billion

Total excess expenditure $ billion

450

36.  Current State of the Obesity Pandemic

1985– 1989

1995–1999

1990– 1994

2000–2008

% Obesity