Type 1 Diabetes - Complications, Pathogenesis, and Alternative Treatments

TYPE 1 DIABETES – COMPLICATIONS, PATHOGENESIS, AND ALTERNATIVE TREATMENTS Edited by Chih-Pin Liu Type 1 Diabetes – Com...

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TYPE 1 DIABETES – COMPLICATIONS, PATHOGENESIS, AND ALTERNATIVE TREATMENTS Edited by Chih-Pin Liu

Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments Edited by Chih-Pin Liu

Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Sandra Bakic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright illustrart, 2011. Used under license from Shutterstock.com First published November, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from [email protected]

Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments, Edited by Chih-Pin Liu p. cm. ISBN 978-953-307-756-7

free online editions of InTech Books and Journals can be found at www.intechopen.com

Contents Preface IX Part 1

Diabetes Mellitus and Complications

1

Chapter 1

The Study of Glycative and Oxidative Stress in Type 1 Diabetes Patients in Relation to Circulating TGF-Beta1, VCAM-1 and Diabetic Vascular Complications 3 Vladimir Jakus, Jana Kostolanska, Dagmar Michalkova and Michal Sapak

Chapter 2

Diet, Lifestyle and Chronic Complications in Type 1 Diabetic Patients 21 S.S. Soedamah-Muthu, S. Abbring and M. Toeller

Chapter 3

Lipid Disorders in Type 1 Diabetes Bruno Vergès

Chapter 4

Prevalence of Type 1 Diabetes Correlates with Daily Insulin Dose, Adverse Outcomes and with Autoimmune Process Against Glutamic Acid Decarboxylase in Adults 61 Mykola Khalangot, Vitaliy Gurianov, Volodymir Kovtun, Nadia Okhrimenko, Viktor Kravchenko and Mykola Tronko

Part 2

Psychological Aspects of Diabetes

45

83

Chapter 5

Type I Diabetes in Children and Adolescents Laura Nabors, Phillip Neal Ritchey, Bevin Van Wassenhove and Jennifer Bartz

85

Chapter 6

Inadequate Coping Attitudes, Disordered Eating Behaviours and Eating Disorders in Type 1 Diabetic Patients 95 Ricardo V. García-Mayor and Alejandra Larrañaga

Chapter 7

Predictors of Adherence, Metabolic Control and Quality of Life in Adolescents with Type 1 Diabetes 119 M. Graça Pereira, A. Cristina Almeida, Liliana Rocha and Engrácia Leandro

VI

Contents

Chapter 8

Part 3 Chapter 9

Contributing Factors to Poor Adherence and Glycemic Control in Pediatric Type 1 Diabetes: Facilitating a Move Toward Telehealth 141 Joseph P. H. McNamara, Adam M. Reid, Alana R. Freedland, Sarah E. Righi and Gary R. Geffken Perspectives of Diabetes Pathogenesis

159

Fulminant Type 1 Diabetes Mellitus in IRS-2 Deficient Mice 161 Toshiro Arai, Nobuko Mori and Haruo Hashimoto

Chapter 10

Obesity in the Natural History of Type 1 Diabetes Mellitus: Causes and Consequences 181 Fernando Valente, Marília Brito Gomes and Sérgio Atala Dib

Chapter 11

Meta-Analysis of Genome-Wide Association Studies to Understand Disease Relatedness 199 Stephanie N. Lewis, Elaine O. Nsoesie, Charles Weeks, Dan Qiao and Liqing Zhang

Chapter 12

Cytokine-Induced -Cell Stress and Death in Type 1 Diabetes Mellitus 213 Lisa Vincenz, Eva Szegezdi, Richard Jäger, Caitriona Holohan, Timothy O’Brien and Afshin Samali

Chapter 13

A Novel L-Arginine/L-Glutamine Coupling Hypothesis: Implications for Type 1 Diabetes 241 Paulo Ivo Homem de Bittencourt Jr. and Philip Newsholme

Part 4

Identification and Monitoring of Diabetes Mellitus 285

Chapter 14

Diabetes Type 1 and 2: What is Behind a Classification? 287 Adriana Mimbacas and Gerardo Javiel

Chapter 15

The Enlarging List of Phenotypic Characteristics That Might Allow the Clinical Identification of Families at Risk for Type 1 Diabetes 305 Elena Matteucci and Ottavio Giampietro

Chapter 16

Altering Trends in the Epidemiology of Type 1 Diabetes Mellitus in Children and Adolescents 323 Elisavet Efstathiou and Nicos Skordis

Chapter 17

Genetic Testing of Newborns for Type 1 Diabetes Susceptibility – The MIDIA Study 337 Kjersti S. Rønningen

Contents

Part 5

Alternative Treatments for Diabetes

363

Chapter 18

Potentials and Limitations of Bile Acids and Probiotics in Diabetes Mellitus 365 Momir Mikov, Hani Al-Salami and Svetlana Golocorbin-Kon

Chapter 19

Role of Vitamin D in the Pathogenesis and Therapy of Type 1 Diabetes Mellitus 403 Agustin Busta, Bianca Alfonso and Leonid Poretsky

Chapter 20

Honey and Type 1 Diabetes Mellitus 423 Mamdouh Abdulrhman, Mohamed El Hefnawy, Rasha Ali and Ahmad Abou El-Goud

Chapter 21

Fatty Acid Supply in Pregnant Women with Type 1 Diabetes Mellitus 437 Éva Szabó, Tamás Marosvölgyi and Tamás Decsi

Chapter 22

Therapeutic Modelling of Type 1 Diabetes 461 Nilam Nilam, Seyed M. Moghadas and Pappur N. Shivakumar

VII

Preface Diabetes is a leading cause of death in many countries affecting both the young and old at an alarming rate worldwide. Due to the destruction of insulin-producing beta islet cells by the body’s own immune system, type 1 diabetes is the most common metabolic and endocrinal disease among children. Finding a cure for this disease has been a major challenge for both basic science and clinical investigators. Owing to rapid research progress in the field, we now have a better knowledge in understanding the cellular and molecular basis responsible for diabetes, the associated complications, and alternative treatments for this chronic inflammatory disease. This book is intended as an overview of recent progress in type 1 diabetes research worldwide, with a focus on different research areas relevant to this disease. These include: diabetes mellitus and complications, psychological aspects of diabetes, perspectives of diabetes pathogenesis, identification and monitoring of diabetes mellitus, and alternative treatments for diabetes. In preparing this book, leading investigators from several countries in these five different categories were invited to contribute a chapter to this book. We have striven for a coherent presentation of concepts based on experiments and observation from the authors own research and from existing published reports. Therefore, the materials presented in this book are expected to be up to date in each research area. Complications, such as those lead to severe cardiovascular diseases, caused by diabetes due to chronic inflammation have attracted intensive research in recent years. This section contains four chapters that discuss diabetes mellitus-caused complications due to the effect of glycative and oxidative stress, varied diet and life style, lipid disorders, and daily insulin doses. Better knowledge in these areas may help those who are affected by diabetes to reduce disease-associated long-term complications and to improve their life quality. Type 1 disease usually occurs at young age and can have significant impact on those affected. Therefore, managing this disease becomes a very challenging daily task for the patients themselves and for their close family members. Frustration may occur while patients deal with their disease conditions, making adherence and proper glycemic control a challenge. To help address these challenges, four chapters discuss psychological aspects of diabetes, which provide updated and useful information regarding psychological factors related to diabetes in children and adolescents, how to

X

Preface

avoid inadequate coping attitudes and maintain good eating behaviors, and how to improve adherence, metabolic control, and quality of life. While many aspects of how type 1 diabetes may occur are discussed elsewhere, five chapters are included in this book to address some unique or alternative perspectives of diabetes pathogenesis. For example, a chapter discusses the effect of IRS-2 gene on fulminant type 1 diabetes in animal models. Another chapter discusses the role of obesity in the natural history of type 1 diabetes. Other factors, such as cytokinesinduced beta cell death and the effect of L-Arg/L-Glu coupling on diabetes, are the topics of two other chapters respectively. Aided by recent progress in genomic studies, the fifth chapter discusses the use of meta-analysis of genome-wide association studies to better understand disease relatedness. Due to the nature of the disease, it is not an easy task to identify disease risk or susceptibility early in life. The classification between type 1 vs. type 2 diabetes has also become less clear nowadays as increasing number of adults can develop symptoms of type 1 diabetes. Four chapters provide insightful discussion addressing this important issue. A chapter discusses evidence showing altering trends in the epidemiology of type 1 diabetes in children and adolescents. Thanks to recent research progress and better understanding of diabetes pathogenesis, a chapter discusses a more comprehensive list of phenotypic markers that may help clinical identification of families at risk. Another chapter suggests that genetic testing of newborns may also be a possibility to help identify disease susceptibility and distinguish different types of diabetes. Finding a cure for type 1 diabetes remains a major challenge and ultimate goal for diabetes basic and clinical research. Currently, insulin injection is the gold standard for disease treatment. The use of insulin pump therapy has provided continuous subcutaneous insulin infusion for patients. A chapter provides an overview of mathematical modeling of using pump therapy as a management strategy for diabetes therapy. In seeking better treatments for this disease, recent studies suggest that it may be promising approaches using alternative medicine in therapies. Four chapters discuss findings in this area and suggest new aspects of using alternative medicine approaches to treat or ameliorate the disease conditions. These include the potential use of bile acid and probiotics, vitamin D, honey and fatty acids. Finally, we would like to thank all the authors who have contributed to this book. While there is no doubt that this book may have omitted some important findings in diabetes field, we hope the information included in this book will be useful for both basic science and clinical investigators. We also hope that diabetes patients and their family will benefit from reading the chapters in this book.

Dr. Chih-Pin Liu The Beckman Research Institute of the City of Hope, Duarte, California, USA

Part 1 Diabetes Mellitus and Complications

1 The Study of Glycative and Oxidative Stress in Type 1 Diabetes Patients in Relation to Circulating TGF-Beta1, VCAM-1 and Diabetic Vascular Complications 1Institute

Vladimir Jakus1, Jana Kostolanska2, Dagmar Michalkova1 and Michal Sapak3

of Medical Chemistry, Biochemistry and Clinical Biochemistry, Faculty of Medicine, Comenius University, Bratislava 2Children Diabetological Center of the Slovak Republic, 1st Department of Pediatrics, Comenius University and University Hospital for Children, Bratislava 3Institute of Medical Immunology, Faculty of Medicine, Comenius University, Bratislava Slovakia

1. Introduction Type 1 diabetes mellitus (T1DM) is one of most frequent autoimmune diseases and is characterized by absolute or nothing short of absolute endogenous insulin deficiency which results in hyperglycemia that is considered to be a primary cause of diabetic complications (DC) (Rambhade et al., 2010). T1DM leads to various chronic micro- and macrovascular complications. Diabetic nephropathy is a major cause of morbidity and mortality in patients with DM. Microvascular disease is the main determinant in the development of late complications in DM. Persistent hyperglycemia is linked with glycation, glycoxidation, and oxidative stress (Aronson, 2008; Negre-Salvayre et al., 2009). During glycation and glycoxidation there are formed early, intermediate and advanced glycation products via Maillard reaction, glucose autoxidation and protein glycation. Accumulation of advanced glycation end products (AGEs) has several toxic effects and takes part in the development of DC, such as nephropathy (Kashihara et al, 2010), neuropathy, retinopathy and angiopathy (Peppa & Vlassara, 2005; Yamagishi et al., 2008; Goh & Cooper, 2008; Karasu, 2010). Higher plasma levels of AGEs are associated also with incident cardiovascular disease and all-cause mortality in T1DM (Nin et al., 2011). AGEs are believed to induce cellular oxidative stress through the interaction with specific cellular receptors (Ramasamy et al., 2005; Boulanger et al., 2006; Yamagishi, 2009; Mosquera, 2010). On the other side, carbonyl stress-induced tissue damage is caused by AGE precursors formed by hyperglycaemia, hyperlipidemia, nonenzymatic glycation, peroxidation of lipids and metabolic processes.

4

Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments

It has been suggested that the chronic hyperglycaemia in diabetes enhances the production of reactive oxygen species (ROS) from glucose autoxidation, protein glycation and glycoxidation, which leads to tissue damage (Son, 2007). Also, cumulative episodes of acute hyperglycaemia can be source of acute oxidative stress. A number of studies have summarized the relation between glycation and oxidation (Boyzel et al., 2010). The overproduction of ROS leads to oxidative modification of biologically important compounds and damage of them. Uncontrolled production of ROS often leads to damage of cellular macromolecules (DNA, lipids and proteins). Some oxidation products or lipid peroxidation products may bind to proteins and amplify glycoxidation-generated lesions. Lipid peroxidation of polyunsaturated fatty acids, one of the radical reaction in vivo, can adequately reflect increased oxidative stress in diabetes. Advanced oxidation protein products (AOPP) are formed during oxidative stress by the action of chlorinated oxidants, mainly hypochlorous acid and chloramines. In diabetes the formation of AOPP is induced by intensified glycoxidation processes, oxidant-antioxidant imbalance, and coexisting inflammation (Piwowar, 2010a, 2010b). AOPP are supposed to be structurally similar to AGEs and to exert similar biological activities as AGEs, i.e. induction of proinflammatory cytokines in neutrophils, as well as in monocytes, and adhesive molecules (Yan et al., 2008). Accumulation of AOPP has been found in patients with chronic kidney disease (Bargnoux, et al., 2009). Further possible sources of oxidative stress are decreased antioxidant defenses, or alterations in enzymatic pathways. Diabetes is associated also with inflammation (Navaro & Mora, 2006; Wautier et al., 2006; Devaraj et al., 2007; Hartge et al., 2007; Fawaz, et al., 2009 ; Van Sickle et al., 2009; Nobécourt et al., 2010). ROS are implicated also in the pathogenesis of the inflammatory response to ischemic-reperfusion which is exacerbated in diabetes. Oxidative stress during reperfusion is markedly balanced in diabetes and this appears to results from increased leukocyte recruitment and a higher capacity of diabetic leukocytes to generate ROS in response to stimulation. Several adhesion molecules are expressed on endothelial cells and participate in leukocyte adhesion to the endothelium. These molecules are important for monocyte– endothelium interaction in the initiation and progression of atherosclerosis. The monocytemacrophage is a pivotal cell in atherogenesis. Cellular adhesion molecules mediate attachment and transmigration of leukocytes across the endothelial surface and are thought to play a crucial role in the early steps of atherogenesis (Seckin et al., 2006). Adhesion molecule VCAM-1 is not expressed under baseline conditions but is rapidly induced by proatherosclerotic conditions in rabbits, mice, and humans, including in early lesions. Initially, it is unclear whether VCAM-1 is simply a marker for atherogenesis or whether it acts in this disease pathway. AGEs promote VCAM-1 expression and atheroma formation in rabbits (Vlassara et al., 1995) and in cultured human endothelial cells (Schmidt et al., 1995). These results suggest the involvement of AGEs in the accelerated coronary atherosclerosis on diabetes (Zhang et al., 2003). Plasma concentrations of VCAM-1 are elevated also in T1DM patients with microalbuminuria and overt nephropathy (Schmidt et al., 1996; Clausen et al, 2000). Diabetic nephropathy is characterized by specific morphological changes including glomerular basement membrane thickening, mesangial expansion and glomerular and tubulointersticial sclerosis. The first clinical manifestation of diabetic nephropathy is microalbuminuria, defined as a urinary albumin excretion rate of 20 to 200 microgram/min. Growth factor TGF-beta1 is one of profibrotic cytokines and is important mediator in the pathogenesis of diabetic nephropathy (Goldfarb & Ziyadeh, 2001; Schrijvers et al., 2004; Wang et al., 2005; Wolf & Ziyadeh, 2007). TGF-beta1 stimulates production of extracellular

The Study of Glycative and Oxidative Stress in Type 1 Diabetes Patients in Relation to Circulating TGF-Beta1, VCAM-1 and Diabetic Vascular Complications

5

matrix components such as collagen-IV, fibronectin, proteoglycans (decorin, biglycan). TGFbeta1 may cause glomerulosclerosis and its one of the causal factor in myointimal hyperplasia after baloon injury of carotid artery. It mediates angiotensin-II modulator effect on smooth muscle cell growth. Besides profibrotic activity, TGF-beta1 has immunoregulatory function on adaptive immunity too. AGEs induce connective tissue growth factor-mediated renal fibrosis through TGF-beta1-independent Smad3 signalling (Zhou et al., 2004; Chung et al., 2010). The present study investigates the relationship between diabetes complications presence, diabetes control (represented by actual levels of HbA1c (HbA1cA) and mean of HbA1c during the last 2 years (HbA1cP)), early glycation products (fructosamine (FAM)), serum advanced glycation end products (s-AGEs), lipid peroxidation products (LPO), advanced oxidation protein products (AOPP), profibrotic cytokines and adhesive molecules in patients with T1DM. We wanted to find a relationship of DC to glycative and oxidative stress parameters, circulating (serum) TGF-beta1 and soluble VCAM-1. Further, we aimed to compare measured parameters in groups –DC, one with DR, with DR combined with another DC and one with only DC another than DR and their combinations. The further aim of the present study was to evaluate if monitoring of circulating FAM, HbA1c, s-AGEs, AOPP, LPO in patients with T1DM could be useful to predict the diabetic complications development.

2. Study design and methods 2.1 Patients and design The studied group consisted of 46 children and adolescents with T1DM regularly attending the 1st Department of Pediatrics, Children Diabetological Center of the Slovak Republic, University Hospital, Faculty of Medicine, Comenius University, Bratislava. They had T1DM with duration at least for 5 years. One of children was obese (BMIc 97 percentile) and three of them were of overweight (BMIc about 90 percentile). The file was divided into two subgroups: 20 persons without DC (-DC) and 46 those with them (+DC). Then the file of +DC patients was divided into several subgroups according to particular complications: the patients only with retinopathy, those with neuropathy combined with another kinds of DC and those with other than retinopathy to compare the parameters of glycative and oxidative stress and cytokines in each mentioned subgroups. The urine samples in our patients were collected 3 times overnight, microalbuminuria was considered to be positive when UAER was between 20 and 200 microgram/min in 2 samples. No changes (fundus diabetic retinopathy) were found by the ophtalmologist examining the eyes in subject without retinopathy. Diabetic neuropathy was confirmed by EMG exploration using the conductivity assessment of sensor and motor fibres of peripheral nerves. The controls file consists of 26 healthy children. The samples of EDTA capillary blood were used to determine of HbA1c and serum samples were used to determine of FAM, s-AGEs, AOPP and VCAM-1. The samples of serum were stored in -18°C/-80°C. 2.2 Parameter analysis 2.2.1 Determination of UAER UAER was determined by means of immunoturbidimetric assay (Cobas Integra 400 Plus, Roche, Switzerland), using the commercial kit 400/400Plus. The assay was performed as a part of patients routine monitoring in Department of Laboratory Medicine, University Hospital, Bratislava.

6


2.2.2 Determination of fructosamine For the determination of fructosamine we used a kinetic, colorimetric assay and subsequently spectrophotometrical determination at wavelength 530 nm. We used 1-deoxy1-morpholino-fructose (DMF) as the standard. Serum samples were stored at -79°C and were defrost only once. This test is based on the ability of ketoamines to reduce nitroblue tetrazolium (NBT) to a formazan dye under alkaline conditions. The rate of formazan formation, measured at 530 nm, is directly proportional to the fructosamine concentration. Measurements were carried out in one block up to 5 samples. To 3 ml of 0.5 mmol/l NBT were added 150 microliters of serum and the mixture was incubated at 37°C for 10 minutes. The absorbance was measured after 10 min and 15 min of incubation at Novaspec analyzer II, Biotech (Germany). 2.2.3 Determination of glycated hemoglobin HbA1c HbA1c was determined from EDTA capillary blood immediately after obtained by the lowpressure liquid chromatography (LPLC) (DiaSTAT, USA) in conjunction with gradient elution. Before testing hemolysate is heated at 62-68°C to eliminate unstable fractions and after 5 minutes is introduced into the column. Hemoglobin species elute from the cationexchange column at different times, depending on their charge, with the application of buffers of increasing ionic strength. The concentration of hemoglobins is measured after elution from the column, which is then used to quantify HbA1c by calculating the area under each peak. Instrument calibration is always carried out when introducing a new column set procedure (Bio-RAD, Inc., 2003). 2.2.4 Determination of serum AGEs Serum AGEs were determined as AGE-linked specific fluorescence, serum was diluted 20fold with deionized water, the fluorescence intensity was measured after excitation at 346 nm, at emission 418 nm using a spectrophotometer Perkin Elmer LS-3, USA. Chinine sulphate (1 microgram/ml) was used to calibrate the instrument. Fluorescence was expressed as the relative fluorescence intensity in arbitrary units (A.U.). 2.2.5 Determination of serum lipoperoxides Serum lipid peroxides were determined by iodine liberation spectrophotometrically at 365 nm (Novaspec II, Pharmacia LKB, Biotech, SRN). The principle of this assay is based on the oxidative activity of lipid peroxides that will convert iodide to iodine. Iodine can then simply be measured by means of a photometer at 365 nm. Calibration curves were obtained using cumene hydroperoxide. A stoichiometric relationship was observed between the amount of organic peroxides assayed and the concentration of I3 produced (El-Saadani et al., 1989). 2.2.6 Determination of serum AOPP AOPP were determined in the plasma using the method previously devised by Witko-Sarsat et al. (1996), modified by Kalousova et al. (2002). Briefly, AOPP were measured by spectrophotometry on a reader (FP-901, Chemistry Analyser, Labsystems, Finland) and were calibrated with chloramine-T solutions that in the presence of potassium iodide absorb at 340 nm. In standard wells, 10 microliters of 1.16 M potassium iodide was added to 200

The Study of Glycative and Oxidative Stress in Type 1 Diabetes Patients in Relation to Circulating TGF-Beta1, VCAM-1 and Diabetic Vascular Complications

7

microliters of chloramine-T solution (0–100 micromol/l) followed by 20 microliters of acetic acid. In test wells, 200 microliters of plasma diluted 1:5 in PBS was placed to cell of 9 channels, and 20 microliters of acetic acid was added. The absorbance of the reaction mixture is immediately read at 340 nm on the reader against a blank containing 200 microliters of PBS, 10 microliters of potassium iodide, and 20 microliters of acetic acid. The chloramine-T absorbance at 340 nm being linear within the range of 0 to 100 micromol/l, AOPP concentrations were expressed as micromoles per liter of chloramine-T equivalents. 2.2.7 Determination of TGF- beta1 Quantitative detection of TGF- beta1 in serum was done by enzyme linked immunosorbent assay, using human TGF-beta1 ELISA-kit (BMS249/2, Bender MedSystem). Brief description of the method: into washed, with anti-TGF-beta1 precoated microplate were added prediluted (1:10) sera (100 microliters) and “HRP-Conjugate” (50 microliters) as a antihuman-TGF-beta1 monoclonal antibody and incubated for 4 hour on a rotator (100rpm). After microplate washing (3 times) “TMB Substrate Solution” (100 microliters) was added and was incubated for 10 minutes. Enzyme reaction was stopped by adding “Stop Solution” (100 microliters). The absorbance of each microwell was readed by HumaReader spectrophotometer (Human) using 450 nm wavelength. The TGF-beta1 concentration was determined from standard curve prepared from seven TGF-beta1 standard dilutions. Each sample and TGF-beta1 standard dilution were done in duplicate. 2.2.8 Determination of serum soluble form of adhesion molecule VCAM-1 For serum soluble form of VCAM-1 (sVCAM-1) estimating we used bead-based multiplex technology and Athena Multi-LyteTM Luminex 100 xMAP (multi-analyte profiling) analyser. We used RnD systems manufacturer kits: „Human Adhesion Molecule MultiAnalyte Profiling Base Kit“ and „Fluorokine® MAP Human sVCAM-1/CD106 Kit“. Analyte-specific antibodies are pre-coated onto color-coded microparticles. Microparticles, standards and samples are pipetted into wells and the immobilized antibodies bind the analytes of interest. After washing away any unbound substances, a biotinylated antibody cocktail specific to the analytes of interest are added to each well. Following a wash to remove any unbound biotinylated antibody, streptavidin-phycoerythrin conjugate (Streptavidin-PE), which binds to the captured biotinylated antibody, is added to each well. A final wash removes unbound Streptavidin-PE and the microparticles are resuspended in buffer and read using the Luminex analyzer. One laser is microparticle-specific and determines which analyte is being detected. The other laser determines the magnitude of the phycoerythrin-derived signal, which is in direct proportion to the amount of analyte bound (R&D Systems, Inc. 2010). 2.2.9 Statistical analysis Shapiro-Wilk test was performed to the test the distribution of all continuous variables. The variables with normal distribution were compared by one way ANOVA test followed by Bonferroni´s post-test and the results was expressed as mean ± SD. Since the evaluated variables did not have normal distribution, we compared them with Kruskal–Wallis nonparametric analysis of variance (ANOVA) followed by Bonferroni´s post-test and the results was expressed as median (1st quartile, 3rd quartile). The Fisher´s test was used to compare the subgroups in regard to diabetic retinopathy and other complications presence/absence. Pearson´s test with correlation coefficient r or Spearman´s one with Spearman's rank correlation coefficient R in case of small count of variables were then used to evaluate the

8


association between parameters described within the text, in all studied patients and in diabetic and non-diabetic subgroups. P values less than 0.05 were accepted as being statistically significant. All statistical analyses were carried out using Excel 2003, Origin 8 and BioSTAT 2009.

3. Results Clinical and biochemical characteristics of the patients with T1DM without and with diabetic complications and controls (CTRL) are reported in Table 1. Age (yrs.) DD (yrs.) HbA1cA (%) FAM (mmol/l) s-AGEs (A.U.) AOPP (micromol/l) LPO (nmol/ml) TGF-beta1 (ng/ml) VCAM-1 (ng/ml) a b

CTRL 9.0(6.1,14.0) 5.0 ± 0.3 1.67 ± 0.31 54.9 ± 9.9 58.8(52.0, 71.8) 100(88, 110) 3.30 ± 3.41 12.6 ± 3.7

n 26 0 18 24 22 11 10 8 15

T1DM -DC 14.4(12.4, 17.9)ab 6(5.5, 8.1)b 8.3 ± 1.4 ab 2.64 ± 0.38 ab 64.4 ± 10.1 ab 43.3(42.6, 60.4) 106(105, 161) 5.9 ± 4.14b 17.1 ± 3.1

n 20 20 20 20 20 17 19 10 19

T1DM +DC 16.4(15.1, 17.6)a 10.0(7.9, 12.9) 10.4 ± 1.4a 3.06 ± 0.48a 71.8 ± 11.6 78.2(49.5, 114.6) 127(109, 152)a 10.49 ± 4.55a 17.4 ± 3.3a

N 26 26 26 26 24 20 17 16 26

significant difference in comparison with CTRL significant difference in comparison with +DC group T1DM

Table 1. Clinical and biochemical characteristics of the patients with T1DM and controls As seen, HbA1c and FAM were significantly elevated in both diabetic groups in comparison with controls and also in +DC vs. –DC those. Serum AGEs were significantly elevated in +DC compared to –DC and also to controls, but the difference between –DC and controls was not significant. The levels of AOPP were evidently higher in +DC compared to controls, but the difference was not significant. The levels of LPO were significantly elevated in +DC vs. controls, the differences between both diabetic groups and between –DC vs. controls were not significant. The levels of TGF-beta1 similarly to s-AGEs were significantly elevated in +DC compared to –DC and also to controls, but the difference between –DC and controls was not significant (Fig. 1). In terms of the VCAM-1 values, only between +DC and controls there were found significant difference there (Fig. 2). The levels of TGF-beta1 are significantly elevated in +DC compared to –DC (10.49 ± 4.55 vs. 5.9 ± 4.14 ng/ml, p0.05).

10


1200

VCAM-1 (ng/ml)

1100 1000 900 800 700 600 5

6

7

8

9

10

11

12

HbA1cA (%)

Fig. 3. The linear correlation between VCAM-1 and HbA1cA DD HbA1cA HbA1cP FAM (yrs.) Age (yrs.) 0.205 -0.267 -0.232 -0.328 DD (yrs.) N 0.152 -0.008 0.326 HbA1cA (%) N 0.748∆ 0.676∆ HbA1cP (%) N 0.433 FAM N (mmol/l) s-AGEs (A.U.) AOPP (micromol/l) LPO (nmol/ml) TGF-beta1 (ng/ml)

-0.030 -0.256 0.217 0.302

0.130† -0.193† -0.298† -0.400†

TGFbeta1 0.334 0.406† -0.001 0.036† -0.398 -0.588† -0.507∆ -0.042†

0.134

0.081†

-0.260 -0.224†

0.630∆

N

0.197†

0.270

0.068

N

0.671†∆ 0.477†

-0.447†

N

0.200†

-0.404

N

-0.578†

s-AGEs AOPP

LPO

0.006

VCAM-1 -0.478∆ 0.089 0.653∆ 0.501∆

Table 2. The relationships between the parameters in patients with T1DM without diabetic complications († R, ∆ p25kg/m2 and clinical diagnosis of type 2 diabetes were overweight or obese with mutations in one or more of the analyzed genes. Our results indicated that insulin resistance in patients with complex diagnosis may be explained by the occurrence of a mutation in one or more of the analyzed single nucleotide polymorphisms (SNPs).

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5. Importance of genetics for the clinician In the last 15 to 20 years, clinicians have been concerned with grasping the increasing complexity of this disease, with a gradual worldwide increase of its prevalence that has turned it into a pandemic disease. The latest evidence shows that, despite correcting their lifestyle, we cannot always achieve good metabolic control in patients in complex clinical situations. There is a population, which is probably formed by most of our patients, with clinical features where their phenotype is a good reflection of their genotype; but we are finding with increasingly frequency clinical cases that are difficulty to classify with the current criteria. In these cases, a high percentage of patients had severe difficulties with their metabolic control. It is precisely here where we need to carry out a proper genetic diagnosis, and eventually an immunologic one, to allow us a broader view of their pathology. Several clinical observations and systematic studies have shown that classical type 1 diabetes, whether in children, late onset in adults, or individuals over 65, can coexist in the same individual with "classic" type 2 diabetes where insulin secretion deficiency and insulin resistance are detected simultaneously (Serrano Rios, 2009). This group of patients is usually referred to a diabetes specialist because the primary care physician cannot decide about or control them. Once reached this stage and after correcting the variables that affect proper metabolic control, such as nutritional plan and regular physical activity, we can see that many of these patients keep having a poor metabolic control. These patients are usually overweight and / or obese with a very erratic response to anti-diabetic drugs alone or in combination, both between them and with insulin. Complying with the algorithms, we almost always end up giving insulin to our patients, but in many cases this is probably done too late. This was analyzed by many authors that described as final: “therapeutic inertia”. They are usually described as patients with a poor adherence to the treatment plan. Also, on average they start insulin treatment before the classical diabetes type 2 patients. Thus, we are planning to deepen into genetic typing, in order to see if it may help us to understand the etiopathogenesis of these patients, and why they do not have the expected response to the drug treatment. As stated above, these patients surprisingly had a genotype that does not agree with their phenotype. This was what allowed us as clinicians to begin to understand these facts and to find an explanation (albeit partial) of the poor outcome of each patient. What are the issues that the clinician should consider for further study of certain patients? a. Obese patients showing good response to insulin during intercurrence: many of these patients had an intercurrent disease, and with the temporary insulin treatment they achieved a good control (especially in early stages of diagnosis) that may be explained by an improvement in glucotoxicity and / or moderate insulinopenia. In these patients the insulin will be removed based on these myths: (1) Insulin is "ineffective"; (2) Insulin injection increases cardiovascular diseases and hyperinsulinemia; and (3) Insulin causes weight gain. The reluctance we see in these patients insulin is based on misguided or questionable in view of the genetic results we are finding and the matching clinical trials. b. Poor response to insulin sensitizers: in particular thiazolidinedione but also biguanides. c. Poor response to secretagogues: it is usually attributed to glucotoxicity, but how much influence does drug response have?

Diabetes Type 1 and 2: What is Behind a Classification?

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d. e.

Obese patients without dyslipidemia or other elements of metabolic syndrome. Overweight or obese type 2 diabetes patients with hypoglycemia episodes, especially at night. f. Type 2 Diabetic patients with microangiopathic complications preceding or concomitant to the macrovascular complications. Already, Nolan and Murphy (2001) posed an approach to the phenotypic and metabolic characterization of insulin-resistant patients, impaired glucose tolerance, or type 2 diabetes, and the use of glutamic acid decarboxylase antibodies (GADA), genetic markers, and models to estimate the insulin-resistance should be considered. These authors discuss the utility of using genetic markers based on population studies for type 2 diabetes mellitus. In what it has to do with the poor response to treatment, we must remember that both therapeutic inefficiency and drug toxicity, which have been seen in some individuals, have been frequently observed. Due to the presence of some drug metabolizing enzymes, drugs can participate as inhibitors or inducers of these enzymes, thereby their variation in activity between individuals. This variability in enzyme activity may reflect the existence of mutations in their genes.

6. Conclusion: What is happening? The above mentioned points will lead us to review the different mechanisms that may have taken place in the evolutionary processes leading to the current status of this disease. We will consider some possible situations. a. Researchers are considering the way natural selection is currently operating in humans. The concept of “the survival of the strongest individual” perhaps is no longer valid in the 21st century. Quintana Murci et al. (2007), at the Pasteur Institute (Paris), have looked for answers regarding the mechanisms of human evolution by comparing whole genomes of different populations. They analyzed more than 2.8 million genetic markers in different populations from different ethnic groups collected in the HAPMAP project. They found that 582 genes were subject to “strong selective pressures” during the last 60,000 to 10,000 years. Some of these genes are strongly associated to external features (e.g.: hair, skin color); others are to the response to pathogenic agents or drugs; and others to diseases with different incidences between populations, like diabetes, obesity, or hypertension. Barreiro pointed out that ”it is the first time that it can be demonstrated, concerning the whole genome that natural selection participates in the differentiation of the populations” This work is not only useful to satisfy our curiosity, but also to aid in the identification of genes implied in different diseases (Barreiro et al., 2005; 2008). Well defined since the XV century, clinical knowledge on diabetes gradually increased, and in the end two major distinct types of this pathology were described (1 and 2). In order to understand the current increase of chronic diseases, it is necessary to consider the important relationship between human feeding and human evolution. The regular offer of food that seemed to help human evolution so greatly in the past is also generating a great amount of diseases and their corresponding incapacities (like hemiplegics, aphasia, amputations, etc.). This fact is a true evolutionary paradox (Insua & Fuks, 2003). Initially, we must consider the importance of the neutralization of positive selective pressure introduced by the availability of nutrients as consequence of human civilization. A good

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example of the relationship between nutritional factors, diabetes, and population genetics is Szathmary’s hypothesis (Marrodán, 2000) for explaining the high incidence of diabetes in several Amerindian populations (USA and Canada), either in reservations or in those adapted to western life. Diabetes is a genetic-based disease whose manifestation is partly favored by excessive carbohydrate consumption. Possibly, several individuals with a specific genotype would produce insulin faster when faced with higher glucose levels than others; and they would also store this glucose as glycogen or fat more efficiently. This genotype would have been positively selected in a nutritional environment where periods of abundant or shortage of foods oscillate in a critical form. But this capacity for a faster answer to carbohydrate stimulus has a biological cost when food intake is constant. Under this situation, genes increasing insulin production are no longer beneficial to the individual because their carriers become obese, exhausting the physiological capacity of the pancreas, and leading to the subsequent development of diabetes (Marrodán, 2000; Harris, 2002). Variations in diabetes or obesity genes imply that adaptation to fasting was also an important selective agent. Quintana-Murci et al. (2007) pointed out that insulin-regulating genes have been positively selected. Thus, for instance, the ENPP1 gene has a mutation protecting against obesity and type II diabetes. This variant is present in 90% of non-African individuals and it is almost absent in African ones. The susceptible genotype may have been selected in these populations because unusually frequent fasting periods may have taken place during the initial colonization of 'new worlds'. The abovementioned non-insulin dependent diabetes mellitus has shown a strong genetic component that may include a 'thrifty' genotype(s) (Neel 1962; Zimmet et al., 1990). The 'thrifty' genotype(s) may have once allowed founding populations to survive both 'feast' and 'famine' conditions for several generations. Individuals carrying these genes would have had an increased efficiency for energy extraction (nutrition) from environmental scarce resources. During times of abundance those individuals with this predisposition would store more energy than those lacking it. When the progress of human civilization assured continuous fat-rich and fiber-poor diets, and a sedentary lifestyle, the 'thrifty' genotype(s) became disadvantageous, leading to obesity, increased insulin resistance, beta cell misbalance, and finally diabetes (Wendorf, 1991; 1992). What was a selective advantage in past environments is currently, for most people in industrialized countries, an undesired condition. The result is obesity, diabetes, and the metabolic syndrome. For many years, diabetes was considered as a lethal or near-fatal disease by death simply by complications or by difficulties in the reproductive stages, both for men and women. More recently —the use of insulin is a landmark in this subject— reproductive problems and some of their related complications have been solved. In conclusion, evolutionary or Darwinian medicine considers that many contemporary diseases are associated to incompatibility between current human lifestyles and environments, and those under which human biology was shaped. As the observed difference between the incremental rates of both civilization and evolution is so great, most human evolutionary changes took place when our ancestors were gatherer-hunters. Thus, many characteristics and conducts that had adaptive value in the past may currently have non-adaptive value. Medicine has always tried to improve and look for the patient’s cure. It really improved people’s health, but in this process populational issues that are beyond the epidemiologic point of view were overlooked. This medical conduct may be explained by


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the lack of information, or misinterpretation, of the importance of the genetic components of the disease. But this disregarding could be considered also as having iatrogenic elements: we improve the current patient’s quality of life, but on the other hand we hamper that of future persons. This process implies the emergence of currently unknown entities. New discoveries have allowed life extension for affected people, with the subsequent appearance of new pathological complications that were not seen before simply because affected individuals passed away before their onset. b. Are we witnessing a new type of diabetes, called “double o hybrid” by some authors? We believe that the phenomenon we are watching is simply another expression of the multifactorial nature of this disease. When we analyze populations with an ethnic mixture or of different ethnic origins, we begin to get a glimpse of the products of genetic admixture. This leads us to find a higher proportion of problematic patients that are difficult to classify because, when examining their phenotype, they are affected by certain genes that are masked by others. Hence, we can see families where, according to traditional classification, both entities (type 1 and type 2) coexist. This fusion would be associated with a new and intermediate phenotype (Tuomi, 2005). There are a few studies identifying patients where both type of diabetes overlap (Libman &. Becker, 2003; Pozzilli & Guglielmi, 2009); moreover, Pozzilli and Gugliemi (2007) place this entity in the middle of the double “rainbow” (made up of type 1 and type 2 diabetes). The “accelerator hypothesis” is a theory that shares this vision. It is a singular, unifying concept, which states that type 1 and type 2 diabetes are the same insulin-resistance disorder set against different genetic backgrounds. This hypothesis does not deny the role of autoimmunity, only its primacy in the process. It distinguishes type 1 and 2 diabetes only by tempo, the faster tempo reflecting the (inevitably) earlier presentation in the more susceptible genotype (Wilkin, 2009). Recognition that susceptibility arises through the combination of multiple genetic pathways influencing hazardous factors in a nonlinear manner suggests that a 'decanalization' process contributes to the epidemic nature of common genetic diseases. The evolution of the human genome, combined with a marked environmental and cultural perturbation in the past two generations, might lead to the uncovering of cryptic genetic variations that are a major source of disease susceptibility (Gibson, 2009). This would be also favored by others processes such as an increase of life expectancy and fertility of affected individuals, the globalization phenomena, and increased admixture of different ethnic groups when compared with the past. The last phenomenon is clearly seen in Latin America and mainly in the Uruguayan population as it was stated above. Regardless of all the arguments presented in this chapter, we think that it is extremely important to introduce the genetic risk profile into the present diabetes classification criteria. This will clearly improve our capability of distinguishing between different types of diabetes or specific presentations. The effects of genetic traits in diabetes had been studied for decades, but few consistent risk factors have been well established. Currently, most of large scale studies on candidate genes do not combine the analysis of both types of diabetes. Establishing the association between genotype and phenotype would allow a deeper insight into the pathogenesis of the disease. Screening of associated anomalies and the possibility of anticipating future outcomes would be consequently improved. While for many countries, especially in Latin-America, individual genetic diagnosis can be very expensive to implement, we must realize that we are facing a multifactorial disease.

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Thus, although classifications may be useful, they only have relative value. We must keep an open mind to the fact that there are patients that do not fall in any of them, and we must remember that genetics is at the base of diabetes, as there are multiple genes that interact both with the environment and between them. These interactions can result in a somewhat "liar" phenotype. In the preceding sections we saw how mutations in a few genes associated to insulin resistance may mask the presence and/or action of genes causing autoimmune disorders. Moreover, if we take into account the modifications observed with genome scanning, where there are millions of Single Nucleotide Polymorphisms, it is virtually impossible to make a phenotype-based classification. Although we are aware that understanding the pathogenesis of hyperglycemia or the basis for an effective treatment may be deemed as more important than knowing the type of diabetes we are dealing with, we are currently persuaded that the distinguishing between different types or presentation forms of diabetes based on genetic information is an important task that has turned into our great challenge.

7. Acknowledgment This chapter could not have been written without the valuable collaboration of graduate and postgraduate students that are part of our research group (in alphabetic list): Br. Matías Fabregat, B.S. Joaquina Farías, MSc. Mariana Fernández, B.S. Ana Laura Reyes, Br. Jorge Souto, MSc. Pilar Zorrilla.

8. References ADA (2004). American Diabetes Association. Diagnosis and Classification of Diabetes Mellitus, American Diabetes Association. Diabetes Care Vol. 27 (s1), (January, 2004), pp.55-60, ISSN 0149-5992 ADA (2010). American Diabetes Association (2010). Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, Vol.33, (January, 2010), pp.S62-S69; doi:10.2337/dc10-S062, ISSN 0149-5992. ALAD (2010). Asociación Latinoamericana de Diabetes. Guías ALAD de diagnóstico control y tratamiento de la Diabetes Mellitus Tipo 2. pp 1-78 http://www.revistaalad.com.ar/guias/GuiasALAD_DMTipo2_v3.pdf. Alberti, K.G. & Zimmet, P.Z. (1998). For the WHO Consultation: Definition, diagnosis and classification of diabetes mellitus and its complications. I. Diagnosis and classification of diabetes mellitus: provisional report of a WHO Consultation. Diabetes Medicine, Vol.15, Nº7, (July 1998), pp.539 –553, ISSN 1464-5491. Almind, K.; Bjorbaek, C.; Vestergaard, H.; Hansen, T.; Echwald, S. & Pedersen, O. (1993) Amino-acid polymorphisms of insulin-receptor substrate-1 in non-insulindependent diabetes-mellitus. Lancet, Vol.342, Nº 8875, (October 1993), pp.828-832, ISSN 1474-4422. Almind, K.; Inoue, G.; Pedersen, O. & Kahn, C.R. (1996) A common amino acid polymorphism in insulin-receptor substrate-1 causes impaired insulin signalling evidence from transfection studies. Journal Clinical Investigation, Vol.97, Nº11, (Jun 1996), pp.2569-2575, ISSN 1365-2362 Altshuler, D.; Hirschhorn, J.N.; Klannemark, M.; lindgren C.M.; Vohl, M.C.; Nemesh, J.; Lane, C.R.; Schaffner, S.F.; Bolk, S.; Brewer, C.; Tuomi, T.; Gaudet, D.; Hudson, T.J.;


299

Daly, M.; Groop, L. & Lander, L.S. (2000). The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genetics Vol.26, Nº.1, (September 2000), pp.76-80. ISSN 1061-4036. Aubert, J.; Champigny, O.; Saint-Marc P.; Negrel, R.; Collins, S.; Ricquier, D. & Ailhaud, G. (1997). Up-regulation of UCP-2 gene expression by PPAR agonists in preadispose and adipose cell. Biochemical Biophysical Research Communications, Vol.238, Nº2, (September 1997), pp.606-611, ISSN. 1090-2104. Auwerx, J. (1999). PPARgamma, the ultimate thrfty gene. Diabetologia, Vol. 42, Nº.9, (September 1999), pp.1033-1049, ISSN 0012-186X Banting, F.G.; Best, C.H.; Collip, J.B.; Campbell, W.R. & Fletcher, A.A. (1922). Pancreatic extract in the treatment of diabetes mellitus. Canadian Medical Association Journal, Vol. 12, Nº3, (March 1922), pp.141–146, ISSN. 0008-4409 Barreiro, L.B.; Patin, E.; Neyrolles, O.; Cann, H.M.; Gicquel, B. & Quintana-Murci, L. (2005). The heritage of pathogen pressures and ancient demography in the human innate immunity CD209/CD209L region. American Journal of Human Genetic, Vol.77, Nº5, (August 2005), pp. 869-886, ISSN 1537-6605. Barreiro, L.B.; Laval, G.; Quach, H.; Patin, E. & Quintana Murci, L. (2008). Natural selection has driven population differentiation in modern humans Nature genetics, Vol.40, Nº.3, (February 2008), pp.340-345, ISSN 1061-4036. Bell, Ch.G.; Walley, A.J. & Froguel Ph. (2005). The genetics of human obesity. Nature Reviews Genetics, Vol. 6, Nº.3, (March 2005), pp.221-234, doi:10.1038/nrg1556, ISSN 10614036 Cardoso, H.; Crispino, B.; Mimbacas, A. & Cardoso, E. (2004). A low prevalence of cystic fibrosis in Uruguayans of mainly European descent. Genetics Molecular Research Vol.3, Nº.2, (March 2004) pp.258-263 ISSN 1676-5680. Carmona, M.C.; Valmaseda, A.; Iglesias, R.; Mampel, T.; Viñas, O.; Giralt, M. & Villaroya, F. (1998). 9-cis retinoic acid induces the expression of uncoupling protein-2 gene in brown adipocytes. Federation of European Biochemical Societies, Vol.441, Nº.3, (December 1998), pp.447-450, ISSN 1873-3468. Chawla, A.; Schwarz, E.J.; Dimaculangan, D.D. & Lazar, M.A. (1994) Peroxisome proliferator-activated receptor (PPAR) gamma: adipose-predominant expression and induction early in adipocyte differentiation. Endocrinology, Vol. 135, Nº2, (August 1994), pp.798-800, ISSN. 0013-7227. Clausen, J.; Hansen, T.; Bjoerbaek, C.; Echwald, S.; Urhammer, S.; Rasmussen, S.; Andersen, C.; Hansen, L.; Almind, K.; Winther, K.; Haraldsdottir, J.; Borch-Johnsen, K. & Pedersen, O. (1995). Insulin resistance: Interactions between obesity and a common variant of the insulin receptor substrate-1. Lancet, Vol.346, Nº8972, (August 1995), pp.397–402, ISSN 0140-6736. Dabelea, D.; Hanson, R.; Bennett, P.H.; Roumain, J.; Knowler, W.C. & Pettitt, D.J. (1998). Increasing prevalence of type 2 diabetes in American Indian children. Diabetologia, Vol.41, Nº8, (August 1998), pp. 904—910, ISSN 0012-186X. Das, S.K. & Elbein, S.C. (2006). The Genetic Basis of Type 2 Diabetes. Cellscience, Vol.2, No.4, (August 1994), pp.100–131, ISSN 1742-8130. De Dios A. & Frechtel G. Ruiz M. (2011). Genética molecular de la diabetes y sus complicaciones. In: Diabetes mellitus, M. Ruiz & M.L. Ruiz Morosini, pp.27-31, Acadia, ISBN 978-987-570-153-3, Buenos Aires, Argentina. Deeb, S.S.; Fajas, L.; Nemoto, M.; Pihlajamäki, J.; Mykkänen, L.; Kuusisto, J.; Laakso, M.; Fujimoto, W & Auwerx, J. (1998). A Pro12Ala substitution in PPARgamma2

300


associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nature Genetics, Vol.20, No.3, (November 1998), pp. 284-287, ISSN 1061-4036. Dobson, M. Nature of the urine in diabetes. Medical Observations and Inquiries, 1776, Vol.5. pp.298–310 In: Goldfine ID & Youngre JF. (1998). Contributions of the American Journal of Physiology to the discovery of insulin. Endocrinology Metabolism Vol.37, (February 1998), E207–E209, 1998, ISSN 94143-161 DPP (Clinical Trils.gov ID nº: NCT00004992). NIH Publication No. 06–5099 http://ndep.nih.gov/media/DPP_FactSheet.pdf, July 2008, http://diabetes.niddk.nih.gov/dm/pubs/preventionprogram/ Fabregat, M.; Javiel, G.; Vitarella, G. & Mimbacas, A. (2010). Estudio de frecuencias génicas del SNP -23Hph1 como segundo loci de susceptibilidad en la población con diabetes en el Uruguay. XIV Congreso de la Asociación Latinoamericana de Diabetes (ALAD), Santiago de Chile, Chile, 7-11 Noviembre http://www.congresoaladchile2010.com/pdfs/trabajos_modalidad_oral.pdf . Farias, J.; Fabregat, M.; Reyes, A.L.; Souto, J.; Fernandez, M.; Javiel, G.; Vitarella, G. & Mimbacas, A. (2010). El perfil genético como herramienta en la identificación de pacientes con Doble Diabetes. V Congreso Uruguayo de Endocrinología y Metabolismo (SUEM) Montevideo 2-4 de Setiembre, Montevideo, Uruguay http://www.congresoaladchile2010.com/pdfs/trabajos_modalidad_poster.pdf Federici, M.; Pertrone, A.; Porzio, O.; Bizzarri, C.; Lauro, D.; Alfonso, R.; Patera, I.; Cappa, M.; Nistico, L.; Baroni, M.; Sesti, G.; Di Mario, U.; Lauro, R. & Buzzetti, R. (2003) The Gly9723Arg IRS-1 Variant Is Associated with Type 1 Diabetes in Continental Italy. Diabetes,Vol.52, Nº3, (March 2003), pp.887–890, ISSN. 0012-1797 Fernández, M.; Acosta, M.; Airaudo, C.; Fernández, J.; Ferrero, R.; Javiel, G.; Pena, A.; Simonelli, B.; Soto, E.; Vitarella, G. & Mimbacas. A. (2009). Estudio comparativo de prevalencia del gen de la ECA en muestras de diabéticos y población general. Revista Médica del Uruguay, Vol.25, Nº2 (June 2009), pp. 110-115, ISSN 1688-0390. Fernández, M.; Javiel, G.; Vitarella, G. & Mimbacas, A. (2010) Estudio preliminar de test genético moleculares sencillos que permitan determinar si el paciente tendrá una buena respuesta al tratamiento inicial con metformina. XIV Congreso de la Asociación Latinoamericana de Diabetes (ALAD), Santiago de Chile, Chile, 7-11 Noviembre http://www.congresoaladchile2010.com/pdfs/trabajos_modalidad_poster.pdf. Freeman, H. & Cox, R.D. (2006). Type-2 diabetes: a cocktail of genetic discovery. Human Molecular Genetics, Vol. 15, Nº2, (July 2006), pp. R202–R209, ISSN 1460-2083. Gascue, C.; Mimbacas, A.; Sans, M.; Gallino, J.P.; Bertoni, B.; Hidalgo, P. & Cardoso, H. (2005). Frequencies of the four major Amerindian mtDNA haplogroups in the population of Montevideo, Uruguay. Human Biology, Vol.77, Nº.6, (December 2005), pp. 873-878, ISSN 0018-7143, Copyright 48201-1309 Gibson, G. (2009). Decanalization and the origin of complex disease. Nature Review Genetic, Vol.10, Nº2, (February 2009), pp.134-140, ISSN 1471-0056. Hansen, L.; Ekstrøm C.T.; Tabanera y Palacios, R.; Anant, M.; Wassermann, K. & Reinhardt, R.R. (2006). The Pro12Ala variant of the PPARG gene is a risk factor for Peroxisome Proliferator-Activated Receptor Agonist-Induced edema in type 2 diabetic patients. The Journal of Clinical Endocrinology & Metabolism, Vol.91, Nº.9, (September 2996), pp.3446–3450, ISNN 1945-7197


301

Harris, M. (2002). Buenos para comer. Enigmas de la Alimentación, Alianza Editorial S.A., pp.1336, ISBN: 84-206-3977-X Hathout, E.H.; Thomas, W.; El-Shahawy, M.; Nahab, F. & Mace, J.W. (2001). Diabetic autoimmune markers in children and adolescents with type 2 diabetes. Pediatrics, Vol. 107, Nº.6, (June 2001), pp. 1-4, ISSN 1098-4275 Himsworth, A. (1936) Diabetes mellitus: its differentiation into insulin-sensitive and insulininsensitive types. Lancet Vol.227, Nº5864, (January 1936), pp.127-130, ISSN 14744465 Howard, G.; O'Leary, D.; Zaccaro, D.; Haffner, S.; Rewers, M.; Hamman, R.; Selby J.V.; Saad, M.F.; Savage, P. & Bergman,R. (1996). Insulin sensitivity and atherosclerosis. Circulation, Vol.93, Nº10, (May 1996), pp.1809-1817, ISSN 1524-4539 Imait, Y.; Fusco, A.; Suzuki, Y.; Lesniak, M.A.; D’Alfonso, R.; Sesti, G.; Bertoli, A.; Lauro, R.; Accili, D. & Taylor; S.I. (1994). Variant sequences of Insulin Receptor Substrate-1 in patients with noninsulin-dependent Diabetes Mellitus. Journal of Clinical Endocrinology and Metabolism, Vol.79, Nº.6, (December 1994), pp.1655-1658, ISSN 1945-7197 Insúa, M.F. & Fuks, K. (2011). Evolución humana y dieta Introducción: Paradoja evolutiva Nutriinfo.com.ar http://nutrinfo.com/pagina/info/evolucio.html (14/2/2011). Ito, E.; Ozawa, S.; Takahashi, K.; Tanaka, T.; Katsuta, H.; Yamaguchi, S.; Maruyama, M.; Takizawa, M.; Katahira, H.; Yoshimoto, K.; Nagamatsu, S. & Ishida, H. (2004). PPAR-gamma overexpression selectively suppresses insulin secretory capacity in isolated pancreatic islet through induction of UCP-2 protein. Biochemical and Biophysical Research Communications Vol.324, Nº22, (November 2004), pp.810-814. ISSN 1090-2104 Kang, E.S.; Park, S. Y.; kim, H.J.; Ahn, C.W.; Cha, B.S.; Lim, S.K.; Nam. C.M. & Lee, H.C. (2005). Effects of Pro12Ala polymorphism of peroxisome proliferator-activated receptor 2 gene on rosiglitazone response in type 2 diabetes. Clinical Pharmacology and Therapeutics, Vol.78, Nº2, (August 2005), pp.202-208, ISSN 1532-6535 Kolb, T. & Mandrup-Poulsen, T. (2005)An immune origen of type 2 diabetes? Diabetologia, Vol.48, (March 2005), pp.1038–1050. DOI 10.1007/s00125-005-1764-9n Libman, I.M. & Becker, D.J. (2003). Coexistence of type 1 and type 2 diabetes mellitus: double diabetes? Pediatric Diabetes, Vol.4, (March 2003), pp. 110-113. ISSN 1399543X Marchetti, P.; Lupi, R.; Federici, M.; Marselli, L.; Masini, M.; Boggi, U.; Del Guerra, S.; Patanè, G.; Piro, S.; Anello, M., Bergamini, E.; Purrello, F.; Lauro, R. & Mosca, F. (2002) Insulin secretory function is impaired in isolated human islets carrying the Gly9723Arg IRS-1 polymorphism. Diabetes, Vol.51, Nº5, (May 2002), pp.1419–1424, ISSN 1939-327X Marrodán, M.D. (2000). La alimentación en el contexto de la evolución biocultural de los grupos humanos. Zairak, Vol.20, pp.109-121 ISSN 1137-439X. Mercado, M.M.; McLenithan, J.C.; Silver, K.D. & Shuldiner, A.R. (2002). Genetics of Insulin Resistance. Current Diabetes Reports, Vol.2, Nº1, (February 2002), pp.83–95, ISSN 1539-0829 Mimbacas, A.; Pérez-Bravo, F.; Hidalgo, P.C.; Javiel, G.; Pisciottano, C.; Grignola, R.; Jorge, A.M., Gallino, J.; Gasagoite, J. & Cardoso, H. (2003). Association between type 1 diabetes and DQB1* alleles in a case-control study conducted in Montevideo, Uruguay. Genetics and Molecular Research, Vol.2, Nº1, (September 2002), pp. 29-35, ISSN 1676-5680.

302


Mimbacas, A.; Pérez-Bravo, F.; Santos, J.L.; Pisciottano, C.; Grignola, R.; Javiel, G.; Jorge, A.M. & Cardoso, H. (2004). The association between HLA-DQ genetic polymorphism and type 1 diabetes in a case-parent study conducted in an admixed population. European Journal of Epidemiology, Vol.19, Nº10, (March 2004), pp. 931934, ISSN 0393-2990 Mimbacas, A.; Trujillo, J.; Gascue, C.; Javiel, G. & Cardoso, H. (2007). Prevalence of vitamin D receptor gene polymorphism in a Uruguayan population and its relation to type 1 diabetes mellitus. Genetics and Molecular Research, Vol. 6, Nº3, (July 2007), pp. 534542, ISSN 1676-5680. Mimbacas, A.; García, L.; Zorrilla, P.; Acosta, M.; Airaudo, C.; Ferrero, R.; Pena, A.; Simonelli, B.; Soto, E.; Vitarella, G.; Fernández, J. & Javiel, G. (2009). Genotype and phenotype correlations in diabetic patients in Uruguay. Genetics and Molecular Research, Vol.8, Nº4, (September 2009), pp. 1352-1358, ISSN 1676-5680. Mimbacas, A.; Fernández M.; Souto, J.; Farias, J.; Reyes, A.; García, L.; Acosta, M.; Airaudo, C.; Ferrero, R.; Simonelli, B.; Soto, E.; Vitarella, G.; Fernández, J. & Javiel, G. (2010). Genotype and phenotype correlations in diabetic patients in Uruguay. The 1st Latin American Congress: Controversies to Consensus in Diabetes, Obesity and Hypertension (CODHy). Hilton Hotel, Buenos Aires, Argentina, March 11-14. Mimbacas, A.; Vitarella G.; Souto, J.; Reyes, AL.; Farías, J.; Fernández, M.; Fabregatm M. & Javiel G. (2011). The phenotype mask the genotype: a posible new diabetes expression. Journal of Pediatric Genetic. In press. Neel, J.V. (1962). Diabetes mellitus: a "thrifty" genotype rendered detrimental by 'progress'? American Journal of Human Genetics, Vol.14, Nº 4, (December 1962), pp.353-362. ISSN 1537-6605. Neufeld, N.; Raffael, L.; Landon, C.; Chen, Y. & Valdheim, C. (1998). Early presentation of type 2 diabetes in Mexican-American youth. Diabetes Care Vol.21, Nº1, (January 1998), pp. 80-86, ISSN 1935-5548 Nolan J.J. & Murphy E. (2001). ¿Existe la intolerancia oral a la glucosa y, en tal caso, qué hay que hacer?. In: Diabetes Aspectos difíciles y controvertidos. G. V. Gill, J.C. Pickup & G. Williams, pp.25-39, Ars Médica, Barcelona, ISBN 0-632-05324-0, España. Patlak, M. (2002). Combating diabetes. Federation of American Societies for Experimental Biology Journal (FASEB), Vol. 16, Nº.14,(December 2002), pp.1853, ISSN 1530-6860. Patlak, M. (2002). New weapons to combat an ancient disease: treating diabetes. Breakthroughs in Bioscience, Federation of American Societies for Experimental Biology Journal (FASEB), (December 2002), pp. 1-13, ISSN 1530-6860 Pinhas-Hamiel, O.; Dolan, L.M.; Daniels, S.R.; Standiford, D.; Khoury, R. & Zitler, P. (1996) Increased incidence of non-insulin dependent diabetes mellitus among adolescents. The Journal of Pediatric, Vol.128, Nº5, (May 1996), pp. 608-615, ISSN 1097-6833. Pinhas-Hamiel, O. & Zeitler, P. (1999). The importance of a name. The New England Journal of Medicine, Vol.340, (May 1999), pp. 1418-1421, ISSN 0028-4793 Pinhas-Hamiel, O.; Dolan, L.M. & Zeitler, P. (1997). Diabetic ketoacidosis among obese African-American adolescents with NIDDM. Diabetes Care, Vol.20, Nº4, (April 1997), pp. 484-486, ISSN 0149-5992 Porzio, O.; Federici, M.; Hribal, M.L.; Lauro, D.; Accili, D.; Lauro, R.; Borboni, P. & Sesti, G. (1999). The Gly972Arg amino acid polymorphism in IRS-1 impairs insulin secretion in pancreatic β cells. The Journal Clinical Investigation, Vol.104, Nº3, (August 1999), pp.357–364, ISSN 1558-8238 (


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Pozzilli, P. & Buzzetti, R. (2007). A new expression of diabetes: double diabetes Trends in Endocrinology and Metabolism, Vol.18, Nº.2, (January 2007), pp.52-57, ISSN 1879-3061 Pozzilli, P.; Guglielmi, C.; Pronina, E.; Petraikina, E. (2007). Double or hybrid diabetes associated with an increase in type 1 and type 2 diabetes in children and youths. Pediatric Diabetes, Vol.8, Nº.S9, (December 2007), pp.88–95. ISSN 1399-5448 Pozzilli, P. & Guglielmi C. (2009). Double Diabetes: A Mixture of Type 1 andType 2 Diabetes in Youth. Endocr Dev. Basel, Karger, 2009, vol 14, pp 151–166 In Endocrine Involvement in Developmental Syndromes, Cappa M, Maghnie M, Loche S, Bottazzo GF ISBN: 978-3-8055-9042-6 retrieved from http://content.karger.com/ Quintana-Murci, L.; Alcaïs, A.; Abel, L. & Casanova, J.L. (2007). Immunology in natura: clinical, epidemiological and evolutionary genetics of infectious diseases. Nature Immunology, Vol.8, Nº11, (November 2007), pp.1165-1171, ISSN 1529-2908 Reyes, A.L.; Farias, J.; Fernández, M.; Souto, J.; Fabregat, M.; Javiel, G.; Vitarella, G. & Mimbacas, A. (2010). La genética como potente herramienta para el diagnóstico y clasificación de pacientes diabéticos de clínica compleja. XIV Congreso de la Asociación Latinoamericana de Diabetes (ALAD), Santiago de Chile, Chile, 7-11 Noviembre http://www.congresoaladchile2010.com/pdfs/trabajos_modalidad_poster.pdf Rotimi, C.N. & Jorde, L.B. (2010). Ancestry and disease in the age of genome medicine. New England Journal of Medicine, Vol.363, Nº16, (October 2010), pp.1551-1558, ISSN 0028-4793. Ruiz M. (2011). Etiopatogenia de la diabetes tipo 2. In: Diabetes mellitus, M. Ruiz & M.L. Ruiz Morosini, 27-31, Acadia, ISBN 978-987-570-153-3, Buenos Aires, Argentina Sans, M.; Figueiro, G.; Ackermann, E.; Barreto, I.; Egaña, A.; Bertoni, B.; PoittevinGilmet, E.; Maytia, D. & Hidalgo, P.C. (2011). Mitochondrial DNA in Basque-descendants from the city of Trinidad, Uruguay: Uruguayan- or Basque-like population?. Human Biology, Vol.83, No1, (in press) Scott, C.; Smith, J.; Cradock, M. & Pihoker, C. (1997). Characteristics of youth-onset noninsulin dependent diabetes mellitus and insulin-dependent diabetes mellitus at diagnosis. Pediatrics, Vol.100, Nº1, (July 1997), pp.84-91, ISSN 0031-4005 Serrano Ríos M. (2009). Etiopatogenia de la diabetes mellitus tipo 1. In: La diabetes mellitus en la práctica clínica, F.J. Tëbar Massó & F. Escobar Jiménez, pp.31-45, Editorial Panamericana, ISBN978-84-7903-450-4, Madrid. Sesti, G.; Federici, M.; Hribal, M.L.; Lauro, D.; Sbraccia, P. & Lauro, R. (2001). Defects of the insulin receptor substrate (IRS) system in human metabolic disorders. The Federation of American Societies for Experimental Biology Vol.15, Nº12, (October 2001), pp.2099-2111, ISSN 1530-6860 Sigal, R.J.; Doria, A.;Warram J.H. & Krolewski, A.S. (1996) Codon 972 polymorphism in the insulin receptor substrate-1 gene, obesity, and risk of noninsulin-dependent diabetes mellitus. Journal Clinical Endocrinoly and Metabolism, Vol.81, Nº.4, (April, 1996), pp. 1657-1659, ISSN 1945-7197 Soto, E.; Mimbacas, A.; Gascue, C.; Javiel, G.; Ferrero, R.; Vitarella, G. & Cardoso, H. (2004). Asociación entre hiperhomocisteinemia, cardiopatía isquémica y diabetes tipo 2. Revista Uruguaya de Cardiología, Vol.19, pp.101-106, ISSN 0797-0048 Souto,J.; Javiel, G.; Fernández, J. & Mimbacas A. (2010). Contribución de la Genética en la toma de decisiones terapéuticas, a propósito de 2 casos clínicos. XIV Congreso de la Asociación Latinoamericana de Diabetes (ALAD), Santiago de Chile, Chile, 7-11

304


Noviembre http://www.congresoaladchile2010.com/pdfs/trabajos_modalidad_poster.pdf Stumvoll, M.; Wahl, H.G.; Löblein, K.; Becker, R.; Machicao, F.; Jacob, S. & Häring H. (2001) Pro12Ala Polymorphism in the Peroxisome Proliferator–Activated Receptor-g2 gene is associated with increased antilipolytic insulin sensitivity. Diabetes Vol.50, Nº4, (April 2001), pp.876–881, ISSN 0012-1797 Tuomi, T. (2005). Type 1 and Type 2 Diabetes What do they have in common? Diabetes, Vol. 54, NoS2, (December 2005), pp.S40-S45, ISSN 0012-1797 Villaroya, F.; Iglesisas, R. & Giralt, M. (2007), PPARs in the control of Uncoupling Gene expression. Hindawi Publishing Corporation Peroxsimone proliferator-activated receptors research, Vol. 2007, (October 2006), pp74364-74376, ISSN 1637-4765. Wendorf, M. & Goldfine, I.D. (1991). Archaeology of NIDDM: excavation of the "thrifty" genotype. Diabetes, Vol.40, Nº2, (February 1991), pp.161-165, ISSN 0012-1797 Wendorf, M. (1992). Archaeology and the "Thrifty" Non Insulin Dependent Diabetes Mellitus (NIDDM) Genotype. Advances in peritoneal dialysis. Vol.992, Nº8, pp.2012077, ISSN. 1197-8554 Wilkin, T.J. (2009). The accelerator hypothesis: a review of the evidence for insulin resistance as the basis for type I as well as type II diabetes. International Journal of Obesity, Vol.33, Nº7, (July 2009), pp.716-726, ISSN 1476-5497 Yen, C.J.; Beamer, B.A.; Negri, C.; Silver, K.; Brown, K.A.; Yarnall, D.P.; Burns D.K., Roth, J. & Shuldiner, A.R. (1997). Molecular scanning of the human peroxisome proliferator activated receptor gamma (hPPAR gamma) gene in diabetic Caucasians: identification of a Pro12Ala PPAR gamma 2 missense mutation. Biochemical Biophysical Research Community. Vol.18, Nº2, (December 1997), pp.270-274, ISSN 1090-2104 Yip, J.; Facchini, F.S. & Reaven, G.M. (1998). Resistance to insulin-mediated glucose disposal as a predictor of cardiovascular disease. Journal Clinical Endocrinology Metabolism, Vol.83, Nº.8, (August 1998), pp.2773-2776, ISSN 1945-7197 Zhang, Y.; Wat, N.; Stratton, I.M.; Warren-Perry, M.G.; Orho, M.; Groop, L. & Turner, R.C. (1996). UKPDS 19: heterogeneity in NIDDM: separate contributions of IRS-1 and beta 3-adrenergic-receptor mutations to insulin resistance and obesity respectively with no evidence for glycogen synthase gene mutations. UK Prospective Diabetes Study. Diabetologia, Vol.39, Nº12, (December 1996), pp.1505-1511, ISSN 0012-180X Zhang, F.; Lavan, B.E. & Gregoire, F.M. (2007). Selective Modulators of PPAR-γ Activity: Molecular Aspects Related to Obesity and Side-Effects. Peroxsimone proliferatoractivated receptors research, (2007), pp.1-7, doi:10.1155, ISSN 1687-4765 Zimmet, P.; Dowse, G. & Finch, C. (1990). The Epidemiology and natural history of NIDDM -lessons from the South Pacific. Diabetes Metabolism Reviews, Vol.6, Nº2, (March 1990), pp.91-124, ISSN 0742-4221 Zorrilla, P.; Mimbacas, A.; Gascue, C.; Javiel, G. & Cardoso, H. (2006). Prevalencia del polimorfismo I/D del gen de la enzima convertidora de angiotensina (ECA) en la población de Montevideo. Revista Médica del Uruguay, Vol.12, Nº.1, (Octubre 2005), pp.17-21 ISSN 1688-0390.

15 The Enlarging List of Phenotypic Characteristics That Might Allow the Clinical Identification of Families at Risk for Type 1 Diabetes Elena Matteucci and Ottavio Giampietro

Department of Internal Medicine, University of Pisa Italy 1. Introduction Type 1 diabetes is a chronic metabolic disease whose aetiology and pathogenesis remain not completely understood. Current criteria for the diagnosis of diabetes are: 1) haemoglobin A1c ≥ 6.5% (assayed using a method that is certified by the National Glycohemoglobin Standardization Program, NGSP, and standardised or traceable to the Diabetes Control and Complications Trial, DCCT, reference assay), 2) fasting plasma glucose (FPG) ≥ 126 mg/dl, 3) 2-hour plasma glucose ≥ 200 mg/dl during an oral glucose tolerance test (OGTT, 75 g), 4) a random plasma glucose ≥ 200 mg/dl (American Diabetes Association, 2011). The classification of diabetes includes: type 1 diabetes, type 2 diabetes, other specific types of diabetes due to other causes, and gestational diabetes mellitus. Type 2 diabetes, which is usually associated with obesity and older age, results from insulin resistance and progressive failure of pancreatic beta-cell function. Type 1 diabetes, which has usually an abrupt onset in younger people, is an organ-specific autoimmune disease characterised by absolute insulin deficiency resulting from beta-cell destruction. However, autoimmunity may not be the primary cause: environmental triggers are believed to precipitate type 1 diabetes in genetically susceptible individuals (van Belle et al., 2011). The overall incidence of type 1 diabetes is increasing; the majority of the increase is observed in the youngest age group, which also appeared to be the heaviest (Evertsen et al., 2009). Indeed, the accelerator hypothesis (Wilkin, 2009) suggests that type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds. Three processes could variably accelerate the loss of beta cells through apoptosis: constitution, insulin resistance, and autoimmunity. None of these accelerators leads to diabetes without excess weight, which causes an increase in insulin resistance and, thus, the weakening of glucose control. In turn, the glucotoxicity accelerates beta-cell apoptosis directly and by inducing beta-cell immunogens and autoimmunity in genetically predisposed subjects. Insulitis is commonly observed in recent-onset type 1 diabetes, but it does not uniformly affect all insulincontaining islets (differences in islet function?). It has been suggested that under increased insulin demand (puberty, adolescence, high sugar intake, etc.) a population of islets may be more prone to dysfunction or death, thereby attracting antigen presenting cells and

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promoting insulitis in susceptible individuals (Rowe et al., 2011). In a genome-wide association study, 41 distinct genomic locations provided evidence for association with type 1 diabetes in the meta-analysis (Barrett et al., 2009). The Type 1 Diabetes Genetics Consortium (T1DGC) has recruited families with at least two siblings who have type 1 diabetes in order to identify genes that determine an individual’s risk of type 1 diabetes. T1DBase is the web-based resource focused on the genetics and genomics of type 1 diabetes susceptibility (https://www.t1dgc.org) that provides the updated table of human loci associated with type 1 diabetes (Table 1). Chromosome 1p13.2 1q31.2 2q12 2q24.2 2q33.2 3p21.31 4q27 5p13 6p21.31 6p21.33 6q15 6q23.3 6q25.3 10p15.1 10p15.1 11p15.5 12q13.2 12q13.3 12q24.12 15q25.1 16p13.3 18p11.21 18q22.2 21q22.3 22q13.1

Gene of interest Protein tyrosine phosphatase, non-receptor type 22 Regulator of G-protein signalling 1 Interleukin 18 receptor accessory protein Interferon induced with helicase C domain 1 Cytotoxic T-lymphocyte-associated protein 4 Chemokine (C-C motif) receptor 5 Interleukin 2 Major histocompatibility complexes similar to BTB and CNC homology 1, basic leucine zipper transcription factor 2 similar to Tumor necrosis factor, -induced protein 3 T-cell activation Rho GTPase-activating protein Interleukin 2 receptor,  Protein kinase C,  Insulin II

Abbreviation PTPN22 RGS1 IL18RAP IFIH1 CTLA4 CCR5 IL2 HLA-B, -A, -DRB1, -DQB1, -DPB1 BACH2 TNFAIP3 TAGAP IL2RA PRKCQ INS

Kinesin family member 5A

KIF5A

Protein tyrosine phosphatase, non-receptor type 2 CD226 antigen

PTPN2 CD226

(from: http://t1dbase.org/page/PosterView/display/poster_id/386)

Table 1. Human loci associated with type 1 diabetes. With regards to the causative environmental triggers that have been implicated in the pathogenesis of type 1 diabetes, they have been recently reviewed (van Belle et al., 2011; Vehik & Dabelea, 2011) and include particularly viral infections, gut microbic flora and other bacteria, early life feeding patterns, wheat proteins, and vitamin D.

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2. Identifying individuals at risk for type 1 diabetes In Europe, the number of adults with diabetes was expected to reach 55.2 million (8.5% of the adult population) in 2010; about 112,000 children and adolescents were estimated to have type 1 diabetes mellitus (http://www.diabetesatlas.org/content/europe). Most diabetic cases are complex diseases resulting from interactions between genetic and environmental determinants in genetically predisposed individuals. Empirical evidence suggests a architecture of many genetic loci with many variants of small effect (Wray & Goddard, 2010). Genome-wide association studies have suggested that the majority of susceptible loci have small contributions to phenotypic variation and therefore there should be a large number of susceptibility loci involved in the genetic basis of complex diseases (consistent with the polygenic model). Moreover, the differentiation of sporadic and familial cases has implied that most complex diseases are genetically heterogeneous. Family history has a high positive predictive value, but a low negative predictive value. Yang et al. (2010) have shown that 1) the proportion of sporadic cases depends on disease prevalence and heritability of the underlying liability scale, and 2) a large proportion of sporadic cases is expected under the polygenic model due to the low prevalence rates of common complex genetic diseases. Thus, the causal mechanisms cannot be inferred from the observed proportion of sporadic cases alone. The prediction of disease risk to relatives from many risk loci or markers requires a model that combines the effects of these loci. The constrained multiplicative, Odds and Probit models fitted data on risk to relatives, but it is difficult to distinguish between them until genetic variants that explain the majority of the known genetic variance are identified (Wray & Goddard, 2010). Hence, genetic risk modelling to derive prediction of individual risk and risk to relatives are still difficult to reconcile. In most individuals with autoimmune type 1 diabetes, beta cell destruction is a chronically progressive and very slow process that starts long before overt disease. During this “silent” phase, autoantibodies are produced and self-reactive activated lymphocytes infiltrate the islets of Langerhans (Rowe et al., 2011). Autoantibodies that target self-antigens in the insulin-secreting beta cells of the pancreas include: islet cell autoantibodies (ICA), insulinoma-associated antigen-2 antibodies (IA-2A), antibodies against the related antigen IA-2 beta (IA-2), insulin autoantibodies (IAA), autoantibodies to the 65kDa isoform of glutamic acid decarboxylase 65 (GADA), and the recently identified autoantibodies to the zinc transporter 8 (ZnT8A) (Table 2). Islet autoantibodies are potent tools for the prediction of type 1 diabetes and are the basis for recruitment in prevention trials and immunointervention trials. In the general childhood population in Finland, one-time screening for GADA and IA-2A was capable of identifying about 60% of those individuals who will develop type 1 diabetes over the subsequent 27 years; both positive and negative seroconversions occurred over time reflecting a dynamic process of beta cell autoimmunity, but positivity for at least two diabetes-associated autoantibodies represented in most cases a point of no return (Knip et al., 2010). So far, however, the place of autoantibody-based risk assessment in routine clinical practice is limited because no proven therapeutic interventions is available for people at high risk of progression to type 1 diabetes. Until therapies modulating the disease process become available, the benefit to individual patients is questionable - awareness of risk is rather useless or even stressful - and diabetes antibody testing does not yet have a role in clinical care (Bingley, 2010). It is considered likely that islet-related autoantibodies are not directly pathogenetic, whereas autoreactive CD4 and CD8 T cells mediate beta cell damage.

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Therefore, standardised autoantibody screenings should be combined with the detection of autoreactive T cells. Unfortunately, none of the currently available T cell assays satisfies all the features of a good assay: small blood sample required, simplicity, specificity, low intraand inter-assay variability (Fierabracci, 2011). Notwithstanding recent developments based on immunosorbent spot and immunoblotting techniques, the International Workshops of the Immunology Diabetes Society concluded that T cell results are still inconclusive and novel approaches are currently being investigated. In conclusion, it may be that in the future combination screening predicts type 1 diabetes clinical onset, but actually genetic risk, serum autoantibody profiling and T cell assays are uneconomical when applied in the general population. Autoantibodies against 38-kDa glycated islet cell membraneassociated protein 51-kDa aromatic-L-amino-acid decarboxylase 52-kDa rat insulinoma Aminoacyl-tRNA synthetase Carbonic anydrase II Carboxypeptidase H Chymotrypsinogen-related 30-kDa pancreatic DNA topoisomerase II Ganglioside GM2-1 Gangliosides GM1, 2, 3, etc. Glucose type-2 transporter Glutamic acid decarboxylase Heat shock proteins Insulin Insulinoma-associated antigen-2 Insulinoma-associated antigen 2 Islet cell Islet cell surface Proinsulin Zinc transporter 8

Abbreviation

Method

GLIMA

Immunoprecipitation

AADC

Immunoprecipitation

52-kDa RIN ARS CA II CPHA

Immunoblot ELISA* ELISA* Radiobinding assay Immunoblot analysis

TopIIA GM2-1 GLUT2 GADA HSP IAA IA-2A IA-2 ICA ICSA PIAA ZnT8

ELISA* and Western blot Indirect immunoperoxidase technique *ELISA Western blot Radiobinding assay, ELISA* *ELISA Radiobinding assay Radiobinding assay, ELISA* Radiobinding assay Indirect immunofluorescence Radiobinding assay Radiobinding assay Radiobinding assay

* Enzyme linked immunosorbent assay

Table 2. List of islet autoantibodies detected in type 1 diabetes (modified from Winter & Schatz, 2011).

3. Phenotyping type 1 diabetes families Translational research aims to integrate basic life science (genomics, transcriptomics, proteomics, and metabolomics) with insights gained from clinical experience to comprehensively study complex biological system and complex human diseases. Translation requires, among others, methods that relate molecular and cellular phenotypes


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to clinical characteristics (Bebek et al., 2011). Indeed, the correlation between quantitative phenotypes and traits allows for a more efficient use of the genetic information; hence the importance of accurate family phenotyping studies. Unaffected family members can contribute as much to the analysis as individuals with the disease diagnosis. For example, the finding of cognitive deficits in individuals with schizophrenia and in the clinically unaffected relatives of these individuals suggested that these deficits are part of the innate underlying distinct differences that make some individuals vulnerable to schizophrenia. Examining these complementary biological phenotypes in genetic studies has been found to provide valuable information about the pathway that connects genotype to clinical disease (Almasy et al., 2008). Similarly, large-scale genetic fine mapping and genotype-phenotype associations implicated polymorphisms in the IL2RA region in type 1 diabetes: IL2RA type 1 diabetes susceptibility genotypes were associated with lower circulating levels of the biomarker, soluble IL-2RA (Lowe et al., 2007). However, despite the theoretical advantages of quantitative trait analysis and testing of multiple plausible domains, some matters have emerged since quantitative traits may not be the most relevant phenotypes to investigate in search for the genetic etiology of disease. Identifying the “best” phenotype for genetic studies needs to survey family members and examine coexisting features and familial segregation patterns. A focus on careful assessment of the most genetically relevant phenotypes has been recommended (Brzustowicz & Bassett, 2008). Over the years, our research efforts have sought primarily to gain a comprehensive understanding of the common phenotypic elements that characterise families with a sporadic case of type 1 diabetes. Here we provide a research-based overview of these familial peculiarities that include multifaceted, easily detectable, clinical perturbations: physical (BMI), cardiovascular (blood pressure response to exercise and circadian blood pressure pattern), biochemical (fasting plasma glucose, HbA1c, lipids, homeostasis model assessment of insulin sensitivity, plasma markers of oxidative damage), cellular (cellular markers of oxidative damage, transplasma membrane electron transport systems, mitochondrial membrane potential), and immunological (lymphocyte subsets).

4. Body weight in type 1 diabetes families According to epidemiological findings and the accelerator hypothesis, the prevalence of overweight in preadolescent children is increasing, it tracks into adulthood and may increase diabetes and cardiovascular disease risk in adulthood. The risk of childhood obesity seems to increase with exposure to diabetes or cigarette smoke in utero, high birth weight, rapid weight gain in infancy, and shorter breastfeeding duration. The Diabetes Autoimmunity Study in the Young (DAISY) examined longitudinally 1,718 children from birth that were at increased risk for type 1 diabetes (Lamb et al., 2010). Gender, diabetes exposure in utero, size for gestational age, weight gain in the first year of life, and total breastfeeding duration (inverse) showed significant association with higher childhood BMI. Mediation analysis suggested that 1) the protective effect of breastfeeding duration on childhood BMI was largely mediated by slower infant weight gain, and 2) the increased risk of higher childhood BMI associated with exposure to diabetes in utero was partially explained by greater birth size. Maternal obesity before pregnancy and weight gain during pregnancy significantly predicted increased risk of persistent multiple positivity for islet autoantibodies in offspring with high genetic susceptibility for type 1 diabetes (Rasmussen et al., 2009). A systematic review and meta-analysis (12 studies) indicated that high birth

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weight and increased weight gain during the first year of life were associated with an increased risk of type 1 diabetes in later life (Harder et al., 2009). Metabolic demand and insulin resistance have been suggested to be involved in the development of type 1 diabetes (Evertsen et al., 2009; Wilkin, 2009), but the evidence is not consistent across the studies. In 1650 prospectively followed children of mothers or fathers with type 1 diabetes (BABYDIAB cohort), islet autoantibodies-positive children were not insulin resistant (based on homeostasis model assessment of insulin resistance, HOMA-IR) and did not have increased BMI around and early after seroconversion (Winkler at al., 2009). In this study, of 777 children with HOMA-IR measurements, 84 developed islet antibodies during the study: analysis of HOMA-IR by age showed no significant difference between islet autoantibody-positive and islet autoantibody-negative children, with a tendency towards a lower HOMA-IR in the antibody-positive children compared with the antibody-negative children. In a primary school health program in Pisa we screened 869 primary school children (448 M, 421 F, mean age 1185 months): height, weight, four skinfolds, and four circumferences were measured; a family-reported questionnaire was used to determine family composition, history, and lifestyle (Giampietro et al., 2002). The percentages of children who could be considered overweight (BMI  95th percentile of age- and sex-specific National Health and Nutrition Examination Survey I, NHANES I, reference data) were boys, 10.0%, and girls, 9.3%. It emerged that offspring BMI was correlated with birth weight, parental BMI and scholarship level, children blood pressure, and hours per day spent in television viewing. Family history for diabetes was associated with higher BMI, skinfold thickness at the subscapular area (SSF), waist circumference, and upper thigh. Family history for hypertension was associated with higher SSF/skinfold thickness at the triceps area (TCF) ratio. We concluded that anthropometric and anamnestic data on child and family yield more accurate estimates of risk profile: fat distribution seems relevant for metabolic and cardiovascular disorders. Since our initial investigations on type 1 diabetes families, we found that first degree relatives’ BMI tended to be higher when compared with healthy control subjects who had no first-degree relative with type 1 diabetes, although the difference did not always reach statistical significance (Matteucci & Giampietro, 2000a; Matteucci et al. 2004a, 2004b; Matteucci et al. 2006). In recent years, on the contrary, the difference in BMI between unaffected siblings of type 1 diabetic probands and healthy control subjects has reached the statistical significance (Figure 1, Matteucci et al., 2010). This finding probably reflects the trend toward increasing body weight and obesity in the general population, declining physical activity and unhealthy dietary habits that we have documented (Matteucci et al., 2004b, 2007, 2008). However, the emerging difference in BMI between unaffected relatives and control subjects suggests that additional factors are operative in type 1 diabetes families, which remain unknown. The single nucleotide polymorphism rs9939609 in the fat mass and obesity associated gene (FTO) region on chromosome 16q12, which increases the risk of childhood obesity and type 2 diabetes, did not alter susceptibility to type 1 diabetes (Field et al., 2007). Although increased early growth was associated with disease risk in various European populations, any role of infant feeding in this association remained unclear (EURODIAB Substudy 2 Study Group, 2002). Scientific evidences suggested associations of allelic variations in the Vitamin D receptor gene and phenotypes related to body weight, glucose homeostasis, diabetes and


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its vascular complications (Reis et al., 2005). Whatever the case, our data in adult members of type 1 diabetes families highlight that the ‘familial’ predisposition to overweight remains throughout life.

Fig. 1. Body mass index (BMI) in control subjects, type 1 diabetic patients and their siblings (Matteucci et al., 2010).

5. Familial cardiovascular abnormalities Diabetes and hypertension are strongly associated although the role of glycaemia in promoting hypertension is a matter of debate (Invitti, 2003). HbA1c variability predicts not only incident microalbuminuria and progression of established renal disease but also cardiovascular disease events in patients with type 1 diabetes (Wadén et al., 2009). Moreover, HbA1c concentration predicts cardiovascular disease and all-cause mortality in adults without diabetes (Khaw et al., 2004). In healthy non-diabetic and non-hypertensive men, fasting plasma glucose is independently associated with blood pressure at rest and during exercise and development of elevated blood pressure after 7-years follow-up (Biornholt et al., 2003). Usually, in type 1 diabetes families, parental hypertension has been associated with diabetic nephropathy in adult and young offspring (Viberti et al., 1987; Marcovecchio et al., 2010), but the familial/hereditary factors that have an impact on diabetic nephropathy have not been so far identified. In a large homogeneous population from the Finnish Diabetic Nephropathy study, a cluster of parental hypertension, cardiovascular disease, cardiovascular mortality, and type 1 diabetes was associated with diabetic nephropathy in offspring with type 1 diabetes. It seemed that the more the traits clustered in family, the higher the risk for diabetes nephropathy (Thorn et al., 2007). In this regard it is noteworthy that enhanced sodium/lithium countertransport and sodium/hydrogen exchange had been suggested to predict diabetic nephropathy (Walker et al., 1990; Ng et al, 1990). However, we found evidence contradicting this favourite hypothesis. Indeed, our data demonstrated convincingly that sodium/hydrogen exchange activity was significantly higher in type 1 diabetes with no difference among the two groups

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of diabetic patients with and without nephropathy. Moreover, enhanced sodium/hydrogen exchange activity was also a common feature of nondiabetic first-degree relatives of type 1 diabetic patients with no difference among the corresponding groups of relatives. The association between antiport activities of diabetic probands and their relatives suggested that the altered activity of the transporter was primarily determined by familial factors whose nature remained to be clarified (Matteucci & Giampietro, 2000b). Generally, the observation of raised arterial blood pressure in relatives of type 1 diabetes patients was based on history, a single measurement of arterial blood pressure, or a 24-h ambulatory record; we were first to evaluate the response to ergometer exercise (Matteucci et al., 2006). Blood pressure response to exercise had been evaluated as a predictor of future hypertension and cardiovascular disease (Sharabi et al., 2001). Moreover, the heritability for resting blood pressure and blood pressure response to exercise was under investigation (An et al., 2000). We identified an abnormal blood pressure response to exercise testing not only in type 1 diabetic probands but also in asymptomatic normotensive non-diabetic relatives of type 1 diabetics, in which it was associated with indices of metabolic syndrome and oxidative damage. Furthermore, in healthy normotensive non-diabetic control subjects without family history of type 1 diabetes, strong associations were found 1) between resting systolic blood pressure and fasting plasma glucose as well as fasting plasma insulin levels, and 2) between systolic blood pressure response to exercise and HbA1c levels (Matteucci et al., 2006). In a recent study, we performed 24-hour ambulatory blood pressure monitoring in type 1 diabetes families with the primary aim of investigating the circadian variability of blood pressure and the ambulatory arterial stiffness index in healthy siblings of type 1 diabetes patients vs healthy control subjects who had no first-degree relative with type 1 diabetes (Matteucci et al., 2010). Secondary aims of the study were to explore the influence of both cardiovascular autonomic function and erythrocyte electron transfer activity as oxidative marker on the ambulatory blood pressure profile. Indeed, human erythrocytes possess a transplasma ferricyanide reductase activity (measured as the erythrocyte velocity of ferricyanide reduction) that transfers reducing equivalents from intracellular reductants to extracellular oxidants (Matteucci & Giampietro, 2000c) and belongs to the ubiquitous transplasma membrane electron transport systems. Transplasma membrane electron transport activities have been related to the regulation of vital cellular processes and to the pathogenesis of various human disorders (Lane & Lawen, 2009) and exist also in endothelial cells where they have been suggested to regulate redox status and possibly atherogenesis through regulation of haeme oxygenase-1 expression (Lee et al., 2009). We found that systolic blood pressure midline-estimating statistic of rhythm and pulse pressure were higher in type 1 diabetes patients and correlated positively with diabetes duration and the rate of oxidant-induced erythrocyte electron transfer to extracellular ferricyanide. Autonomic dysfunction was associated with diastolic blood pressure ecphasia and increased ambulatory arterial stiffness index. Siblings had higher BMI (Figure 1), lower insulin sensitivity (Figure 2), larger systolic blood pressure amplitude (Figure 3), and higher ambulatory arterial stiffness index than controls. Daytime systolic blood pressure was positively, independently associated with BMI and erythrocyte electron transfer to extracellular ferricyanide. Among non-diabetic people, there was a significant correlation between ambulatory arterial stiffness index and fasting plasma glucose. We concluded that siblings of type 1 diabetes patients exhibited a cluster of sub-clinical metabolic abnormalities associated with consensual perturbations in blood pressure variability. Moreover, our


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findings supported, in a clinical setting, the proposed role of transplasma membrane electron transport systems in vascular pathobiology.

Fig. 2. Homeostasis model assessment of insulin sensitivity (HOMA-IS) in the same study groups (Matteucci et al., 2010).

Fig. 3. Systolic blood pressure amplitude (SBP-Amplitude) in the same study groups (Matteucci et al., 2010).

6. Biochemical phenotype and redox balance in type 1 diabetes relatives Our studies over the years have linked family history of type 1 diabetes (first-degree kinship) with multiple biochemical abnormalities. Since 2000 we documented metabolic perturbations in nondiabetic relatives: parents differed from age-matched control subjects in the higher plasma concentrations of glucose and Lipoprotein (a); their fibrinogen was borderline but did not reach any statistical significance; in turn, siblings of type 1 diabetes

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patients differed from age-matched control subjects in the higher levels of Lipoprotein (a) (Matteucci et al., 2000a). In the same study, we investigated the redox status and antioxidant defences in these families. The premises were the following:  enhanced levels of free radicals found in diabetes mellitus and impaired glucose tolerance has long been assumed to be related to chronically elevated glucose levels (Baynes and Thorpe, 1999; Vijayalingam et al., 1996),  oxidative stress was suggested to play a primary role in the pathogenesis of diabetes and its complications but Authors still discussed whether oxidation preceded the appearance of complications or it merely reflected their presence (Baynes and Thorpe, 1999). We suggested an alternative hypothesis, i.e. that oxidative stress preceded diabetes mellitus. In the case, indirect evidence for increased oxidative stress could be also detectable in nondiabetic relatives of type 1 diabetic patients. In order to provide evidence of a familial imbalance between radical production and antioxidant defences, we investigated indices of glucose and lipid metabolism, markers of plasma and cell lipid peroxidation, a novel marker of oxidant-induced protein damage, and the effects of oxygen radicals on erythrocytes of patients with type 1 diabetes and their relatives. We measured blood creatinine, glucose, HbA1c, cholesterol, triglycerides, Lipoprotein (a), fibrinogen, malondialdehyde, and advanced oxidation protein products. Erythrocyte response to oxidative stress (3-hincubation at 37°C with or without a radical generating system) was evaluated by measuring erythrocyte glutathione, erythrocyte malodialdehyde, and haemolysis. Plasma and erythrocyte malodialdehyde were found to be significantly elevated in diabetics and relatives than in controls. Basal erythrocyte glutathione was lower in diabetics and incubations of cells caused in diabetics a decrease in erythrocyte glutathione of lesser degree than in control subjects, while a significant increase in haemolysis. Among relatives, haemolysis was increased both at baseline and after incubation. Plasma malodialdehyde was associated with blood glucose, creatinine, and fibrinogen; basal erythrocyte malodialdehyde with plasma Lipoprotein (a), fibrinogen, and plasma malodialdehyde. Basal erythrocyte glutathione content correlated with serum glucose and erythrocyte malodialdehyde production. In that occasion, we were pioneers of the research on redox balance in type 1 diabetes families. We presented first evidence that markers of lipoprotein metabolism (Lipoprotein (a)), oxidative stress (plasma and erythrocyte malodialdehyde), and cellular fragility (haemolysis) are abnormal in non diabetic relatives of type 1 diabetics supporting the view that familial elements even precede diabetes. It seemed reasonable that the same biologic markers considered major predictors of cardiovascular disease could also trace familial susceptibility to type 1 diabetes, just as they have been associated with the development of type 2 diabetes (Matteucci et al., 2000a). Based on the finding of elevated circulating markers of lipid peroxidation and increased cellular fragility, we decided to complete and integrate our investigation with further biochemical measurements of possible first-chain initiating or stimulating factors in order to evaluate, in the same families, the contribution of extracellular antioxidants to the increased oxidative stress. We also aimed to understand the eventual relationship between oxidative stress and the abnormal sodium/hydrogen exchange activity previously observed


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(Matteucci et al., 2001). We were unable to find out any abnormalities in circulating metal ions (such as iron, transferrin, ferritin, copper, and ceruloplasmin) or extracellular antioxidant defences (such as serum uric acid, albumin, bilirubin,) that could favour oxidative stress in non-diabetic relatives of type 1 patients. On the contrary, we confirmed our previous finding of a generalised increase in sodium/hydrogen exchange activity. The rate of amiloride-sensitive hydrogen efflux from erythrocytes was significantly associated with both erythrocyte glutathione content and some markers of radical-induced damage such as plasma advanced oxidation protein products and malondialdehyde, erythrocyte osmotic fragility, and erythrocyte malondialdehyde accumulation under oxidative stress. Hence, this additional study provided the first in vivo demonstration of a significant association between oxidative stress and sodium/hydrogen exchange upregulation. The familiarly overactive sodium/hydrogen exchange itself could be viewed as further evidence pointing to the presence in these families of a redox disequilibrium where oxidation seems to be prevailing. Taken into account that:  mitochondria are the cellular site of oxidation-reduction reactions and energy transfer processes; mitochondrial dysfunction is believed to play a role in the development of diabetes and its complications because of the active generation of free radicals (Maiese et al., 2007),  a reactive oxygen species-mediated long-term 'memory' of hyperglycaemic stress has been reported in the mitochondria of endothelial cells (Ihnat et al., 2007), but impairment of mitochondrial function has been also observed in subjects with family history of type 2 diabetes before the onset of impaired glucose tolerance (Petersen et al., 2004), in the last step of our research we measured the mitochondrial membrane potential in peripheral blood granulocytes from type 1 diabetic patients and their unaffected siblings using the mitochondrial indicator 5,5’,6,6’-tetra chloro-1,1’,3,3’-tetraethylbenzimidazolylcarbocyanine iodide (JC-1) in conjunction with flow cytometry (Matteucci et al., 2011). This was the first study to examine mitochondrial membrane potential of circulating leukocytes in type 1 diabetes families and to document consistent evidence for mitochondrial hyperpolarisation that was highest in type 1 diabetic patients and intermediate in their siblings. Fasting plasma glucose was the only correlate of leukocyte mitochondrial membrane potential. Confirming previous observations in type 1 diabetes families, siblings had fasting plasma glucose slightly higher than control subjects yet lower HbA1c levels. The combination of higher mean fasting plasma glucose, lower homeostasis model assessment of insulin sensitivity (HOMA-IS) and lower HbA1c levels suggested that siblings had both impaired basal glucose clearance rate and enhanced insulin-stimulated muscle glucose disposal. We hypothesised that in type 1 diabetes families, radical-induced mitochondrial membrane potential oscillations may be synchronized toward polarized states. The positive association between mitochondrial membrane potential oscillations and fasting plasma glucose within the range from normal to dysglycemic conditions suggested that hyperglycaemic challenge implied increased glucose metabolisation, enhanced oxidant formation and hyperpolarisation of the mitochondrial membrane. It is noteworthy that succination of proteins, which is an irreversible chemical modification of cysteine by the Krebs cycle intermediate fumarate, is increased by hyperpolarisation of

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the inner mitochondrial membrane and develops in concert with mitochondrial and oxidative stress in diabetes (Frizzell et al., 2011).

7. Immunological functions in type 1 diabetes families Although type 1 diabetes is a T-cell–mediated autoimmune disease, until a few years ago relatively few studies have attempted to associate T-cell autoreactivity with disease progression, in comparison with efforts directed on monitoring autoantibodies, and those that have been performed were largely limited to CD4 T-cells (Roep, 2008). Currently, islet epitope-specific CD8 T cells are believed to have a pivotal role in the destruction process. Unfortunately, monitoring multiple epitope-specific CD8 T cell populations poses many technical problems. Recently, monitoring of CD8 T cells reactive to beta-cell-derived antigens has been performed using the combinatorial quantum dot technique, which has been validated using peripheral blood cells from recent-onset type 1 diabetic patients, their siblings, and control subjects (Velthuis et al., 2010). Moreover, during the progression of autoimmune diabetes, memory autoreactive regulatory CD8 T cells can be expanded that could effectively suppress the expansion of dominant and subdominant effectors (Khadra et al., 2010). Increasing evidence shows the significance of CD4 and CD8 regulatory T cells, expressing the marker CD25 or IL-2 receptor, in autoimmune disease models. On the contrary, very few study have dealt with the role of CD23 or low affinity IgE receptor. In 2004, given that abnormalities in redox balance clustered in type 1 diabetes families and the intracellular redox status seemed to modulate immune function, we aimed to investigate the relationship between oxidative stress and immunologic features. We measured oxidative markers, serum pro-inflammatory cytokines, soluble cytokine receptors, and subsets of peripheral blood lymphocytes (by varying combinations of CD4, CD8, CD23, and CD25) from type 1 patients, low-risk (i.e. without underlying islet autoimmunity) non-diabetic first-degree relatives of diabetic patients, and healthy subjects (Matteucci et al., 2004a). In these families, protein and lipid oxidation was confirmed from reduced sulfhydryl groups, increased advanced oxidation protein products, increased plasma and erythrocyte malondialdehyde. Relatives had decreased counts of monocytes, of cells coexpressing CD23 and CD25, and of CD25+ cells in peripheral blood. Patients with type 1 diabetes had similar defects and, in addition, showed decreased counts of peripheral CD4+CD8+ lymphocytes and increased serum levels of soluble receptors for IL-6 and IL-2. This was the first demonstration of leukocyte abnormalities in low-risk T1DM relatives, also presenting signs of oxidative stress. Moreover, our study reported first evidence that the oxidative stress observed in type 1 diabetes families was correlated to immunological hallmarks suggestive of different immunoregulatory mechanisms. A crucial question remained open: did the alteration in immune functions follow the altered intracellular redox status or vice versa? More recently, we have characterised CD26 expression of T cell subsets in patients with type 1 diabetes because 1) high expression of CD26 among CD8+ T cells has been suggested to be a marker of effective long-term memory T cell formation typical of acute resolved viral infections (Ibegbu et al., 2009), and 2) an increased risk of persistent viral infections, such as hepatitis C (HCV), was reported among diabetic patients (Lonardo et al., 2009). No significant difference was seen in percentages or absolute numbers of CD4+CD26+, CD4+CD26-, CD8+CD26+, and CD8+CD26- between type 1 diabetes and control people.


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However, the fluorescence intensity of CD26 expression on CD8+ lymphocytes revealed a significant decrease in type 1 diabetic patients compared with control subjects. Mean fluorescence of CD8+CD26+ cells was inversely correlated with the absolute number of CD4+CD26- cells (Matteucci et al., 2010). We interpreted the finding (low expression of CD26 among CD8+ T cells in type 1 diabetes) as indicating a defect in successfully developed longterm memory CD8+ T cells or in CD8+ T cells activation, even though the negative association with the number of CD4+CD26- T cell does not support a recent activation of peripheral T cells. We intend to continue research in this field in consideration of the immunomodulating role of the multifunctional CD26 (Ohnuma et al., 2011).

8. Concluding remarks Today, there is a great need to integrate molecular biology with whole organ physiology. Findings from molecular and cellular studies must be brought back to intact organ systems without loosing the physiological context (Königshoff et al., 2011). This is especially true in the field of metabolic diseases where the study of individual proteins and signalling pathways in detail may not be easily translated to the intact organism. Taken into account the enlarging list of phenotypic characteristics that might allow the early clinical identification of families possibly at risk for sporadic cases of type 1 diabetes, many questions await an answer. We suggest the two main (in our opinion) issues.

Fig. 4. Some of the potential mechanisms linking metabolic syndrome and T cell maintenance. First question: may insulin-resistance be the common denominator of the observed familial peculiarities? And therefore, second question: could an early correction of one/some of

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these common clinical abnormalities modify the natural history of the disease and thence its epidemiology? The data above summarised suggest to consider also alternative ways beyond the traditional immuno-based interventions so far extensively investigated in the field of type 1 diabetes. There is increasing attention to the role of metabolic syndrome and immune responses as well as to the relation between the immune and neuroendocrine systems (Figure 4). The adipocyte-derived proinflammatory hormone leptin can affect the survival and proliferation of autoreactive CD4 T cells (Matarese et al., 2008; Galgani et al., 2010). Immune and neuroendocrine systems have bidirectional communications (Kelley et al., 2007; Berczi et al., 2009). Growth hormone and ghrelin are expressed in immune cells, which in turn bear receptors for these hormones (Hattori, 2009). Leptin, ghrelin, insulin-like growth factor 1, insulin-like growth factor binding protein 3, and cytokines regulate both thymopoiesis and maintenance of T cells. Therefore, elucidation of metabolic syndrome, T cell metabolism, hormones, and microbiota may lead to new insights into the maintenance of proper immune responses (Hsu & Mountz, 2010). At the present state of knowledge and given the current diabetes epidemic, it would seem reasonable that proper, more realistic, public health interventions (by general and family practitioners) are designed that address general issues such as feeding, lifestyle, overweight, ‘borderline’ blood pressure, impaired fasting glucose, etc. These health interventions, beyond the conventional boundaries that have for so long limited the visual field, might have a favourable cost-benefit ratio.

9. References Almasy, L., Gur, R.C., Haack, K., Cole, S.A., Calkins, M.E., Peralta, J.M., Hare, E., Prasad, K., Pogue-Geile, M.F., Nimgaonkar, V., Gur, RE. (2008). A genome screen for quantitative trait loci influencing schizophrenia and neurocognitive phenotypes. The American Journal of Psychiatry, Vol. 165, N. 9: 1185–1192. American Diabetes Association. (2011). Standards of medical care in diabetes-2011. Diabetes Care, Vol. 34, N. Supplement 1: S11-S61. An, P., Rice, T., Pérusse, L., Borecki, I.B., Gagnon, J., Leon, A.S., et al. (2000). Complex segregation analysis of blood pressure and heart rate measured before and after a 20-week endurance exercise training program: the HERITAGE Family Study. American Journal of Hypertension, Vol. 13, N. 5 Pt1: 488-497. Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Plagnol, V., Pociot, F., Schuilenburg, H., Smyth, D.J., Stevens, H., Todd, J.A., Walker, N.M., Rich, S.S.; Type 1 Diabetes Genetics Consortium. (2009). Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, Vol. 4, N. 6: 703-707. Baynes, J.W., Thorpe, S.R. (1999). Role of oxidative stress in diabetic complications. A new perspective on an old paradigm. Diabetes, Vol. 48, N. 1: 1-9. Bebek, G., Koyutürk, M., Chance, M.R., Price, N.D. (2011). Integrative –omics for translational sciente. In: Biocomputing 2011. Proceedings of Pacific Symposium, date of access 2011-03-17, available from: . Berczi, I., Quintanar-Stephano, A., Kovacs, K. (2009). Neuroimmune regulation in immunocompetence, acute illness, and healing. Annals of the New York Academy of Sciences, Vol. 1153: 220-239.


319

Bingley, P. J. (2010). Clinical application of diabetes antibody testing. The Journal of Clinical Endocrinology and Metabolism, Vol. 95, N. 1: 25-33. Biornholt, J., Erikssen, G., Kjeldsen, S.E., Bodegård, J., Thaulow, E., Erikssen, J. (2003) Fasting blood glucose is independently associated with resting and exercise blood pressure and development of elevated blood pressure. Journal of Hypertension, Vol. 21, N. 7: 1383-1389. Brzustowicz, L.M., Bassett, A.S. (2008). Phenotype matters: the case for careful characterization of relevant traits. The American Journal of Psychiatry, Vol. 165, N. 9: 1196–1198. EURODIAB Substudy 2 Study Group. (2002). Rapid early growth is associated with increased risk of childhood type 1 diabetes in various European populations. Diabetes Care, Vol. 25, N. 10: 1755-1760. Evertsen, J., Alemzadeh, R., Wang, X. (2009). Increasing incidence of pediatric type 1 diabetes mellitus in Southeastern Wisconsin: relationship with body weight at diagnosis. PLoS ONE, Vol. 4, N. 9: e6873. Field, S.F., Howson, J.M., Walker, N.M., Dunger, D.B., Todd, J.A. (2007). Analysis of the obesity gene FTO in 14,803 type 1 diabetes cases and controls. Diabetologia, Vol. 50, N. 10: 2218-2220. Fierabracci, A. (2011). The potential of multimer technologies in type 1 diabetes prediction strategies. Diabetes/Metabolism Research and Reviews, Vol. 27, N. 3: 216-229. Frizzell, N., Lima, M., Baynes, J.W. (2011). Free Radical Research, Vol. 45, N. 1: 101-109. Galgani, M., Procaccini, C., De Rosa V., Carbone, F., Chieffi, P., La Cava, A., Matarese, G. (2010). Leptin modulates the survival of autoreactive CD4+ T cells through the nutrient/energy-sensing mammalian target of rapamycin signaling pathway. Journal of Immunology, Vol. 185, N. 12: 7474-7479. Giampietro, O., Virgone, E., Carneglia, L., Griesi, E., Calvi, D., Matteucci, E. (2002). Anthropometric indices of school children and familiar risk factors. Preventive Medicine, Vol. 32, N. 5: 492-498. Harder, T., Roepke, K., Diller, N., Stechling, Y., Dudenhausen, J.W., Plagemann, A. (2009). Birth weight, early weight gain, and subsequent risk of type 1 diabetes: systematic review and meta-analysis. American Journal of Epidemiology, Vol. 169, N. 12: 14281436. Hattori, N. (2009). Expression, regulation and biological actions of growth hormone (GH) and ghrelin in the immune system. Growth hormone and IGF Research, Vol. 19, N. 3: 187-197. Hsu, H.C., Mountz, J.D. (2010). Metabolic syndrome, hormones, and maintenance of T cells during aging. Current Opinion in Immunology, Vol. 22, N. 4: 541-548. Ibegbu, C.C., Xu, Y.X., Fillos, D., Radziewicz, H., Grakoui, A., Kourtis, A.P. (2009). Differential expression of CD26 on virus-specific CD8(+) T cells during active, latent and resolved infection. Immunology, Vol. 126, N. 3:346-353. Ihnat, M.A., Thorpe, J.E., Kamat, C.D., Szabó, C., Green, D.E., Warnke, L.A., et al. (2007). Reactive oxygen species mediate a cellular ‘memory’ of high glucose stress signalling. Diabetologia, Vol. 50, N. 7: 1523-1531. Invitti, C. (2003). Can glycaemia affect blood pressure? Journal of Hypertension, Vol. 21, N. 7: 1265-1267.

320


Kelley, K.W., Weigent, D.A., Kooijman, R. (2007). Protein hormones and immunity. Brain, Behavior, and Immunity, Vol. 21, N. 4: 384-392. Khadra, A., Tsai, S., Santamaria, P., Edelstein-Keshet, L. (2010). On how monospecific memory-like autoregulatory CD8+ T cells can blunt diabetogenic autoimmunity: a computational approach. Journal of Immunology, Vol. 185, N. 10: 5962-5972. Khaw, K.-T., Wareham, N., Bingham, S., Luben, R., Welch, A., Day, N. (2004). Association of hemoglobin A1c with cardiovascular disease and mortality in adults: the European Prospective Investigation into cancer in Norfolk. Annals of Internal Medicine, Vol. 141, N. 6: 413-420. Knip, M., Korhonen, S., Kulmala, P., Veijola, R., Reunanen, A., Raitakari, O.T., Viikari, J., Akerblom, H.K. (2010). Prediction of type 1 diabetes in the general population. Diabetes Care, Vol. 33, N. 6: 1206-1212. Königshoff, M., Uhl, F., Gosens, R. (2011). From molecule to man: integrating molecular biology with whole organ physiology in studying respiratory disease. Pulmonary Pharmacology & Therapeutics, doi:10.1016/j.pupt.2011.02.002. Lamb, M.M., Dabelea, D., Yin, X., Ogden, L.G., Klingensmith, G.J., Rewers, M., Norris, J.M. (2010). Early-life predictors of higher body mass index in healthy children. Annals of Nutrition & Metabolism, Vol. 56, N. 1: 16-22. Lane, D.J., Lawen, A. (2009). Ascorbate and plasma membrane electron transport-Enzymes vs efflux. Free Radical Biology & Medicine, Vol. 47, N. 5:485-495. Lee, S., Li, R., Kim, B., Palvolgyi, R., Ho, T., Yang, Q.Z., Xu, J., Szeto, W.L., Honda, H., Berliner, J.A. (2009). Ox-PAPC activation of plasma membrane electron transport (PMET) system increases expression of heme oxygenase 1 (HO-1) in human aortic endothelial cell (HAEC). Journal of Lipid Research, Vol. 50, N. 2:265-274. Lonardo, A., Adinolfi, L.E., Petta, S., Craxì, A., Loria, P. (2009). Hepatitis C and diabetes: the inevitable coincidence? Expert Review of Anti-Infective Therapy, Vol. 7, N. 3:293-308. Lowe, C.E., Cooper, J.D., Brusko, T., Walker, N.M., Smyth, D.J., Bailey, R., Bourget, K., Plagnol, V., Field, S., Atkinson, M., Clayton, D.G., Wicker, L.S., Todd, J.A. (2007). Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes. Nature Genetics, Vol. 39, N. 9: 1074-1082. Maiese, K., Morhan, S.D., Chong, Z.Z. (2007). Oxidative stress biology and cell injury during type 1 and type 2 diabetes mellitus. Current Neurovascular Research, Vol. 4, N. 1: 6371. Marcovecchio, M.L., Tossavainen, P.H., Acerini, C.L., Barrett, T.G., Edge, J., Neil, A., Shield, J., Widmer, B., Dalton, R.N., Dunger, D.B. (2010). Maternal but not paternal association of ambulatory blood pressure with albumin excretion in young offspring with type 1 diabetes. Diabetes Care, Vol. 33, N. 2: 366-371. Matarese, G., Procaccini, C., De Rosa V. (2008). The intricate interface between immune and metabolic regulation: a role for leptin in the pathogenesis of multiple sclerosis? Journal of Leukocyte Biology, Vol. 84, N. 4: 893-899. Matteucci, E., Consani, C., Masoni, M.C., Giampietro, O. (2010). Circadian blood pressure variability in type 1 diabetic subjects and their nondiabetic siblings – influence of erythrocyte electron transfer. Cardiovascular Diabetology, 9: 61 (http://www.cardiab.com/content/9/1/61).


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Matteucci, E., Ghimenti, M., Consani, C., Di Beo, S., Giampietro, O. (2010). About CD26 CD8 lymphocytes in type 1 diabetes. Scandinavian Journal of Immunology, Vol. 71, N. 2: 123-124. Matteucci, E., Ghimenti, M., Consani, C., Masoni, M.C., Giampietro, O. (2011). Exploring mitochondrial membrane potential in type 1 diabetes families. Cell Biochemistry and Biophysics, Vol. 59, N. 2: 121-126. Matteucci, E., Giampietro, O. (2000a). Oxidative stress in families of type 1 diabetic patients. Diabetes Care, Vol. 23, N. 8, 1182-1186. Matteucci, E., Giampietro, O. (2000b). Erythrocyte sodium/hydrogen exchange activity and albuminuria in families of type 1 diabetic patients. Diabetes Care, Vol. 23, N. 3, 418420. Matteucci, E., Giampietro, O. (2000c). Transmembrane electron transfer in diabetic nephropathy. Diabetes Care, Vol. 23, N. 7, 994-999. Matteucci, E., Giampietro, O. (2001). Oxidative stress in families of type 1 diabetic patients: further evidence. Diabetes Care, Vol. 24, N. 1, 167-168. Matteucci, E., Giampietro, O. (2007). Central Italy: unexpected macro- and micro-nutrient deficiencies in regular diet of residents from Pisa province. The potential role of medical education in young people; In: Malnutrition in the 21th century, Editor Vesler L.W., pp. 127-145, Nova Science Publishers, Hauppauge NY, U.S.A. Matteucci, E., Giampietro, O. (2008). Central Italy: physical activity of female residents from Pisa province, In: Progress in exercise and women’s health research, Editor Coulter J. P., pp. 241-253, Nova Science Publishers, Hauppauge NY, U.S.A. Matteucci, E., Malvaldi, G., Fagnani, F., Evangelista, I., Giampietro, O. (2004a). Redox status and immune function in type I diabetes families. Clinical and Experimental Immunology, Vol. 136, N. 3: 549-554. Matteucci, E., Passerai, S., Mariotti, M., Fagnani, F., Evangelista, I., Rossi, L., Giampietro, O. (2004b). Dietary habits and nutritional biomarkers in Italian type 1 diabetes families: evidence of unhealthy diet and combined-vitamin-deficient intakes. European Journal of Clinical Nutrition, Vol. 59, N. 1: 114-122. Matteucci, E., Rosada, J., Pinelli, M., Giusti, C., Giampietro, O. (2006). Systolic blood pressure response to exercise in type 1 diabetes families compared with healthy control individuals. Journal of Hypertension, Vol. 24, N. 9: 1745-1751. Ng, L.L., Simmons, D., Frighi, V., Garrido, M.C., Bomford, J., Hockaday, T.D. (1990). Leukocyte Na+/H+ antiport activity in type 1 (insulin-dependent) diabetic patients with nephropathy. Diabetologia, Vol. 33, N. 6: 371-377. Ohnuma, K., Hosono, O., Dang, N.H., Morimoto, C. (2011). Dipeptidyl peptidase in autoimmune pathophysiology. Advances in Clinical Chemistry, Vol. 53: 51-84. Petersen, K.F., Dufour, S., Befroy, D., Garcia, R., Shulman, G.I. (2004). Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes. The New England Journal of Medicine, Vol. 350, N. 7: 664-671. Rasmussen, T., Stene, L.C., Samuelsen, S.O., Cinek, O., Wetlesen, T., Torjesen, P.A., Rønningen, K.S. (2009). Maternal BMI before pregnancy, maternal weight gain during pregnancy, and risk of persistent positivity for multiple diabetes-associated autoantibodies in children with the high-risk HLA genotype: the MIDIA study. Diabetes Care, Vol. 32, N. 10: 1904-1906.

322


Reis, A.F., Hauache, O. M., Velho, G. (2005). Vitamin D endocrine system and the genetic susceptibility to diabetes, obesity and vascular disease. A review of evidence. Diabetes & Metabolism, Vol. 31, N. 4: 318-325. Roep, B.O. (2008). Islet autoreactive CD8 T-cells in type 1 diabetes: licensed to kill? Diabetes, Vol. 57, N. 5: 1156. Rowe, P.A., Campbell-Thompson, M.L., Schatz, D.A., Atkinson, M.A. (2011) The pancreas in human type 1 diabetes. Seminars in Immunopathology, Vol. 33, N. 1: 29-43. Sharabi, Y., Ben-Cnaan, R., Hanin, A., Martonovitch, G., Grossman, E. (2001). The significance of hypertensive response to exercise as a predictor of hypertension and cardiovascular disease. Journal of Human Hypertension, Vol. 15, N. 5: 353-356. Thorn, L.M., Forsblom, C., Fagerudd, J., Pettersson-Fernholm, K., Kilpikari, R., Groop, P.H.; FinnDiane Study Group. (2007). Clustering of risk factors in parents of patients with type 1 diabetes and nephropathy. Diabetes Care, Vol. 30, N. 5 :1162-1167. van Belle, T.L., Coppieters, K.T., von Herrath, M.G. (2011). Type 1 diabetes: etiology, immunology, and therapeutic strategies. Physiological Reviews, Vol. 91, N. 1: 79-118. Vehik, K., Dabelea, D. (2011). The changing epidemiology of type 1 diabetes: why is it going through the roof? Diabetes/Metabolism Research and Reviews, Vol. 27, N. 1: 3-13. Velthuis, J.H., Unger, W.W., Abreu, J.R., Duinkerken, G., Franken, K., Peakman, M., Bakker, A.H., Reker-Hadrup, S., Keymeulen, B., Drijfhout, J.W., Schumacher, T.N., Roep, B.O. (2010). Simultaneous detection of circulating autoreactive CD8+ T-cells specific for different islet cell-associated epitopes using combinatorial MHC multimers. Diabetes, Vol. 59, N. 7: 1721-1730. Viberti, G.C., Keen, H., Wiseman, M.J. (1987). Raised arterial pressure in parents of proteinuric insulin dependent diabetics. British Medical Journal, Vol. 295, N. 6597: 515–51. Vijayalingam, S., Parthiban, A., Shanmugasundaram, K.R., Mohan, V. (1996). Abnormal antioxidant status in impaired glucose tolerance and non-insulin-dependent diabetes mellitus. Diabetic Medicine, Vol. 13, N. 8: 715-719. Wadén, J., Forsblom, C., Thorn, L.M., Gordin, D., Saraheimo, M., Groop, P.H.; Finnish Diabetic Nephropathy Study Group. (2009). A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes, Vol. 58, N. 11: 2649-2655. Walker, J.D., Tariq, T., Viberti, G. (1990). Sodium-lithium countertransport activity in red cells of patients with insulin-dependent diabetes and nephropathy and their parents. British Medical Journal, Vol. 301, N. 6753: 635-638. Wilkin, T.J. (2009) The accelerator hypothesis: a review of the evidence for insulin resistance as the basis for type I as well as type II diabetes. International Journal of Obesity, Vol. 34, N:1: 210-211. Winkler, C., Marienfeld, S., Zwilling, M., Bonifacio, E., Ziegler, A.G. (2009). Is islet autoimmunity related to insulin sensitivity or body weight in children of parents with type 1 diabetes? Diabetologia, Vol. 51, N. 10: 2072-2078. Wray, N.M., Goddard, M.E. (2010). Multi-locus models of genetic risk of disease. Genome Medicine, Vol. 2 (N. 2): 10. Yang, J., Visscher, P.M., Wray, N.M. (2010). Sporadic cases are the norm for complex disease. European Journal of Human Genetics, Vol. 18, N. 9: 1039-1044.

16 Altering Trends in the Epidemiology of Type 1 Diabetes Mellitus in Children and Adolescents Elisavet Efstathiou and Nicos Skordis

Paediatric Endocrine Unit, Department of Paediatrics, Makarios Hospital, Nicosia Cyprus 1. Introduction Diabetes mellitus is a group of metabolic diseases characterised by chronic hyperglycemia resulting from defects in insulin secretion and/or insulin action, or both [1]. The history of diabetes dates back to 1550 BC as the polyuric states were described in an Egyptian papyrus, where treatment was given with a four day decoction of bones, wheat, grain, grit and earth. The term diabetes was coined by Aretaeus of Cappadocia in the 2nd century AD for conditions causing increased urine output. The sweet taste of diabetic urine was noted in the 5th century AD by Indian physicians and in 1776, Matthew Dobson confirmed that diabetic serum and urine contained sugar. The revolution in the history of Diabetes was the discovery of insulin by Banting, Best and colleagues in 1922 (http://wwunix.oit.umass.edu /~abhu000/diabetes/index.html). Type 1 diabetes mellitus (T1DM) is one of the most common endocrine metabolic disorders in children and adolescence worldwide with serious acute and chronic complications. It has been proven that T1DM represents the ending result of an autoimmune destruction of the pancreatic islet beta cells in genetically susceptible individuals exposed to certain but still unclear environmental factors. The precise cause of T1DM is not known. However, multiple genetic and environmental risk factors seem to play an important role in the genesis of the disease. The genetic background is complex and difficult to be explained by the involvement of HLA gene region alone. On the other hand viral and nutritional factors changing continuously from country to country, may contribute to the etiology of T1DM. There is no doubt that monitoring temporal trends and incidence of T1DM contribute to the international effort to determine the exact pathogenesis of the disease and it is of critical public health importance. All these temporal trends in the incidence of T1DM have provided significant clues for understanding the disease, most likely reflecting environmental changes more than genetic changes and detecting the factors that implicated in this increase. In this chapter we review the changing trends in the epidemiology of T1DM and we present data on the rising incidence of T1DM in Greek Cypriot population.

2. Incidence-changing trends The prevalence of T1DM greatly varies between different countries, within countries, and between different ethnic populations. The global variation of the incidence of T1DM is

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evaluated by grouping the populations with very low (20/100.000 per yr) incidence [2]. The different annual incidence rates of T1DM comparing different countries of the world (0.1 to 57.6 per 100000) are displayed in figure 1 [1]. The highest incidence is observed in the Scandinavian countries, where Finland has the highest one reported while there is a gradual decrease in countries located closer to equator [3]. However in some areas such as Puerto Rico, Kuwait and Sardinia there is an unexplained highly increased incidence [4]. The lowest incidence in the world is observed in China, where an enormous geographic variation in the development risk is observed [5]. A long time ago, during the 5th century BC, Hippocrates described diabetes as a ‘rare condition’ while later on Arataeus the Cappadocian described it as ‘not being frequent among men’ (http://wwunix.oit.umass.edu/~abhu000/diabetes/index.html). Nowadays the incidence of T1DM increases dramatically throughout the world and it is estimated that it may reach the status of an epidemic in the 21st century [6]. A number of 37 studies from 27 countries confirmed the increased incidence for the period 1960-96 in T1DM with an upward tendency in another 12 countries. The global average annual increase was 3.0% per year with a more pronounced relative increase in the populations with lower incidence [7]. If these trends continue, the number of new cases T1DM in children younger than five years of age may double in some regions between 2005 and 2020 and prevalent cases in children under 15 years will rise by 70 % [8]. The need for rigorous epidemiological studies to monitor the trends of T1DM in children less than 15 year of age led to the creation of the World Health Organization ( WHO) sponsored Diabetes Mondiale (DIAMOND) [2] Project and the EURODIAB study [9]. The data from the WHO project for the incidence of T1DM worldwide DIAMOND showed a large geographic variability. This study group was based on 43,013 cases of T1DM from a study population of 84 million children aged 14 year old or less during the period 1990-1999 in 114 populations from 57 countries. During this time the average annual increase in incidence was 2.8% (95% CI 2.4%–3.2%) with a slightly higher rate during 1995 to 1999, 3.4% (95% CI 2.7%–4.3%) than during 1990 to 1994, 2.4% (95% CI 1.3%–3.4%). An increase in the incidence of T1DM was observed in the populations studied (4.0% in Asia, 3.2% in Europe, and 5.3% in North America) with the exception of Central America and the West Indies, where T1D is less prevalent, and where the trend was a decrease of 3.6% [10]. It is of interest that several reports have shown an increase in the incidence of T1DM worldwide. This tendency implicates an increasing influence of environmental trigger factors against a background of genetic susceptibility. The geographic and ethnic variations mirror the prevalence of susceptibility genes or that of contributing environmental factors, or both. Nevertheless this increasing incidence rate in such a short period cannot be solely attributed to genetic shifts. The EURODIAB ACE study group examined the trends in the incidence of T1DM from 1989 to 1994. The study was based on 16,362 cases of T1D in 44 European centres and Israel covering a population of 28 million children [9]. There were enormous variations in the annual incidence rate with 3.2/100,000 person-years in the Former Yugoslav Republic of Macedonia to 40.2/100,000 person-years in two regions of Finland. During this time the annual increase in the incidence rate of T1D was 3.4% (95% CI 2.5%–4.4%) although the rate of increase was noted to be higher in some central European countries. The rates of increase were found to be the highest in the youngest age group: ages 0 to 4 years (6.3%, 95% CI 1.5%–8.5%), 5 to 9 years (3.1%, 95% CI 1.5%–4.8%), and 10 to 14 years (2.4%, 95% CI 1.0%–3.8%).

Altering Trends in the Epidemiology of Type 1 Diabetes Mellitus in Children and Adolescents

325

Fig. 1. Mean Annual Incidence rates for T1DM comparing different countries in the world as seen in reference 1. Furthermore one of the most notable and recent, in the United States, includes a populationbased study of incidence rates of T1DM from 10 study locations by The SEARCH for Diabetes in Youth Study. The Search Group found an overall incidence of T1DM in children 0–19 of 24.3 per 100,000 person years with the highest rates observed among the 5–9 and 10– 14 age groups with rates of 22.9 and 33.9 per 100,000 respectively [11]. A recent study from Saudi Arabia over an 18 year period, has shown an average incidence of 27.52/100000/year increasing from 18.05/100000/year in the first 9 years of the study period to 36.99/100000/year in the next 9 years [12]. Significant increase in incidence of T1DM was also observed in Lower Silesia during the period 2000-2005 with an increase from 10.43/100000 in 2000 to 13.49/100000 in 2005 [13]. Additionally in Scotland two studies by Patterson and co-workers [14-15] have shown an increasing incidence from 13, 8/100000/year between 1968 and 1976 and up to

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21,0/100000/year from 1977 to 1983 for children aged less than 19 year old. Another study for the same population found that the incidence of T1DM had increased from 22,7/100000 in 1984 to 26,0/100000 in 1993 and this increase of about 2% a year, though small, is statistically significant and the effect over 10 years is a large increase [16]. An important increase in incidence of T1DM was also observed in Saxony between the five year periods 1999-2003 and 2004-2008 with estimated rates 15.7/100000 and 19.2/100000 respectively [17]. Ιn our study we have ascertained the mean annual incidence on T1DM in the Greek Cypriot population during the period 1990 - 1999 in children younger than 15 years of age. During this period the incidence of T1DM was 10.76 /100000[18]. In order to identify an increase in the incidence of T1DM in our country, as occurred in the majority of the populations worldwide, we had performed an analysis of the newly diagnosed cases until the end of the year 2004. There was a statistically significant increase in the incidence during the period 2000-2004 with an estimated mean overall incidence 11.9/100000[19]. We had subsequently extended this work by adding the new cases of the five year period 2005 – 2009 in order to document this rising trend by comparing the incidence between the two decades (1990-1999 vs 2000-2009). We have observed a rising trend of the mean incidence from 10.76/100.000 at the first decade (1990-1999) of the study up to 14.4/100.000 at the second decade (2000-2009). According to Wilcoxon two-sample test this increasing trend of incidence during the 20 years analysed is statistically significant (p-value=0.0091). The mean incidence rate for each 5 year- period in accordance with the population data (population below the age of 15yr) is presented in figure 2. The overall mean incidence of T1DM, in the Greek Cypriot population was 12.46/100000 during the twenty-year period 1990-2009. This raised incidence classifies Cyprus among the countries with high incidence of T1DM.

Fig. 2. Different annual incidence rates during five year periods.

Altering Trends in the Epidemiology of Type 1 Diabetes Mellitus in Children and Adolescents

327

Additionally this rising incidence was more pronounced among children who manifested the disease before the age of 5yr. Table 1 shows the percentage of newly diagnosed T1DM cases expressed as age group in accordance with the international standards. Period at Diagnosis 1990-1999 2000-2009 1990-2009

0-4 n 31 55 86

% 19.0 (13.0-25.0) 26.4 (20.4-32.4) 23.2 (18.9-27.5)

Age Group (years) 5-9 n % 66 40.5 (33.0-48.0)** 77 37.0 (30.4-43.6)* 143 38.5 (33.5-43.5)**

n 66 76 142

10-14 % 40.5 (33.0-48.0)** 36.5 (30.0-43.0)* 38.3 (33.4-43.2)**

Table 1. Age of diagnosis in total and in the three age groups. Binomial test performed to compare proportions compared to “0-4 year” age group: * p

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