Prevention of Type 2 Diabetes

Derek LeRoith Editor

Prevention of Type 2 Diabetes From Science to Therapy

Editor Derek LeRoith, MD, PhD Department of Medicine Mount Sinai School of Medicine New York, NY, USA

ISBN 978-1-4614-3313-2 ISBN 978-1-4614-3314-9 (eBook) DOI 10.1007/978-1-4614-3314-9 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012936664 © Springer Science+Business Media New York 2012 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

Epidemiological data demonstrate very convincingly that there is a world-wide epidemic of obesity. This epidemic has driven a similar increase in cases of metabolic syndrome and eventually Type 2 diabetes. Furthermore, a very worrisome increase in obesity and Type 2 diabetes is occurring in the adolescence. The health risks associated with these epidemics are well known and indeed affect most organs and tissues of the human body. A critical point of discussion has been how to deal with the obesity epidemic and therefore prevent the increase in Type 2 diabetes with all its ramifications. This book has been complied to discuss the various aspects of both the understanding of the pathophysiology and ways to implement prevention. It deals with the epidemic in adults as well as children, it discusses local, national, and international programs set up to prevent diabetes and also the recent trials using changes in lifestyle and certain oral medications that were shown to either prevent, delay, or suppress the onset of Type 2 diabetes. The chapters are all brought to the reader by world experts and we believe that the reader will find valuable information in the book. New York, NY, USA

Derek LeRoith

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Acknowledgement

My thanks to the authors for their contributions. Derek Leroith

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Contents

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Prevention of Type 2 Diabetes; from Science to Therapies ................ Emily Jane Gallagher and Derek LeRoith

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Pathophysiology: Loss of b-Cell Function ........................................... Ele Ferrannini and Andrea Mari

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Pathophysiology of Insulin Resistance: Implications for Prevention ......................................................................................... Shamsa Ali and Vivian A. Fonseca

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Epidemiology Including Youth Through Adulthood and Socioeconomic Impact .................................................................... Helen Looker

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Prediabetes Genes in Pima and Amish ................................................ Leslie J. Baier

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Predicting Diabetes ................................................................................ Rachel Dankner and Jesse Roth

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Screening for Prediabetes and Diabetes ............................................... Amir Tirosh

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Neuropathy in Prediabetes and the Metabolic Syndrome.................. Aaron I. Vinik and Marie-Laure Nevoret

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Interventional Trials to Prevent Diabetes: Diabetes Prevention Program ............................................................................... Vanita R. Aroda and Robert E. Ratner

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Decreasing Postprandial Plasma Glucose Using an a-Glucosidase Inhibitor in Subjects with IGT for the Prevention of Type 2 Diabetes Mellitus: The STOP-NIDDM Trial ....................................................................... Jean-Louis Chiasson, Markku Laakso, and Markolf Hanefeld

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Contents

Da Qing, Finnish DPP, Tripod, and Dream: Lifestyle and Thiazolidinediones in the Prevention of Diabetes............................................................................................... Mariela Glandt and Zachary Bloomgarden

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Community Approaches to Diabetes Prevention ................................ Ann Albright and David Williamson

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Think Locally, Act Locally, Extend Globally: Diabetes Prevention Through Partnerships with Local Communities........................................................................ Carol R. Horowitz and Brett Ives

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Global Challenge in Diabetes Prevention from Practice to Public Health ...................................................................................... Peter E.H. Schwarz

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Index ................................................................................................................

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Contributors

Ann Albright, PhD, RD Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA Shamsa Ali, MD Department of Medicine, Section of Endocrinology, Tulane University Health Science Center, New Orleans, LA, USA Vanita R. Aroda, MD MedStar Health Research Institute, Hyattsville, MD, USA Georgetown University School of Medicine, Washington, DC, USA Leslie J. Baier, PhD Diabetes Molecular Genetics Section, Phoenix Epidemiology and Clinical Research Center, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA Zachary Bloomgarden, MD Department of Medicine, Mount Sinai School of Medicine, New York, NY, USA Jean-Louis Chiasson, MD Department of Medicin, Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM), Université de Montréal, Montréal, QC, Canada Rachel Dankner, MD, MPH Unit for Cardiovascular Epidemiology, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, School of Public Health, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Patient Oriented Research, The Feinstein Institute for Medical Research, Manhasset, North Shore, New York Ele Ferrannini, MD Department of Internal Medicine, University of Pisa School of Medicine, Pisa, Italy Vivian A. Fonseca, MD, FRCP Department of Medicine, Section of Endocrinology, Tulane University Health Science Center, New Orleans, LA, USA xi

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Contributors

Emily Jane Gallagher Division of Endocrinology Diabetes and Bone Disease, Mount Sinai Medical Center, New York, NY, USA Mariela Glandt, MD Medical Director, Diabetes Medical Center, Tel Aviv, Israel Markolf Hanefeld, MD, PhD Centre for Clinical Studies, Dresden, Germany Carol R. Horowitz, MD, MPH Departments of Health Evidence and Policy and Medicine, Mount Sinai School of Medicine, New York, NY, USA Brett Ives, MSN, NP, CDE Division of Endocrinology, Diabetes, and Bone Diseases, Department of Medicine, Mount Sinai School of Medicine, New York, NY, USA Markku Laakso, MD, PhD Department of Medicine, University of Eastern Finland, Kuopio, Finland Derek LeRoith, MD, PhD Division of Endocrinology Diabetes and Bone Disease, Mount Sinai Medical Center, New York, NY, USA Helen Looker, MD Division of Endocrinology Diabetes and Bone Disease, Mount Sinai Medical Center, New York, NY, USA Medical Research Institute, Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, Level 7, University of Dundee, Ninewells Hospital & Medical School, Dundee, Scotland, UK Andrea Mari, PhD Institute of Biomedical Engineering, National Research Council, Padova, Italy Marie-Laure Nevoret, MD Strelitz Diabetes Center for Endocrine and Metabolic Disorders and Neuroendocrine Unit, Eastern Virginia Medical School, Norfolk, VA, USA Robert E. Ratner, MD MedStar Health Research Institute, Georgetown University School of Medicine, Washington, DC, USA Jesse Roth, MD Laboratory of Diabetes & Metabolic Disorders, Elmezzi Graduate School of Molecular Medicine, The Feinstein Institute for Medical Research, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York Endocrinology Division, Department of Medicine, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA Peter E.H. Schwarz, MD Abteilung Prävention und Versorgung des Diabetes, Medizinische Klinik III, Universitätsklinikum Carl Gustav Carus, Technischen Universität Dresden, Dresdon, Germany Amir Tirosh, MD, PhD Division of Endocrinology, Diabetes and Hypertension, Brigham and Women’s Hospital and Harvard School of Public Health, Boston, MA, USA

Contributors

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Aaron I. Vinik, MD, PhD, FCP, MACP Strelitz Diabetes Center for Endocrine and Metabolic Disorders and Neuroendocrine Unit, Eastern Virginia Medical School, Norfolk, VA, USA David Williamson, PhD Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

Chapter 1

Prevention of Type 2 Diabetes; from Science to Therapies Emily Jane Gallagher and Derek LeRoith

Introduction At the beginning of the twentieth century, a newly diagnosed diabetic patient had a life expectancy of 44 years. Little could be offered to treat diabetes except for dietary restriction and starvation, and death from hyperglycemic coma was common. After the discovery of insulin in 1921, the average life expectancy in patients with diabetes rapidly increased to 61 years, death rates from coma declined, while death from cardiovascular disease, gangrene, and renal complications began to increase [1]. Diabetes research, for many years to follow, focused on understanding the pathophysiology behind diabetes and its complications, and the development of new treatments and technologies to improve the care of diabetic patients. Improvements in sanitation in the early 1900s resulted in an increase in life expectancy for the entire population, and by 1933, it was already recognized that the risk of developing diabetes increased with advancing age. In addition to advancing age, it was also noted that genetics and obesity contributed to one’s risk of developing diabetes. The social changes at the time allowed easier access to food, and the transition of labor from manual to mechanical in both urban and rural societies led greater numbers of people becoming overweight. Therefore, as the population was living longer and becoming more overweight, it was predicted that a rise in the incidence of diabetes was inevitable [2]. By the end of the twentieth century, diabetes was being described as a global epidemic. Life expectancy continued to rise; the social changes leading to overconsumption of food and an increasingly sedentary lifestyle caused the rates of obesity to escalate worldwide (Fig. 1.1). As diabetes and its complications could be effectively managed with modern medicine, the cost of diabetes care was growing annually. Therefore, in the twenty-first century, the focus of diabetes care is

E.J. Gallagher • D. LeRoith, MD, PhD (*) Division of Endocrinology Diabetes and Bone Disease, Mount Sinai Medical Center, 1 Gustave L. Levy Place, New York, NY 10029, USA e-mail: [email protected] D. LeRoith (ed.), Prevention of Type 2 Diabetes: From Science to Therapy, DOI 10.1007/978-1-4614-3314-9_1, © Springer Science+Business Media New York 2012

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Fig. 1.1 Factors contributing to the increased prevalence of type 2 diabetes mellitus

shifting toward diabetes prevention. In 1933, Joslin commented that diabetes is not a contagious disease and is a disease for the doctor to treat, rather than the state, the city, or the boards of health. However, given the extent of the current diabetes epidemic, professional societies, health boards, and government are becoming increasingly involved in diabetes prevention and there have been suggestions that the epidemic should be addressed in a similar manner to the outbreak of an infectious disease [3].

The Scope of the Diabetes Epidemic In 1998, the World Health Organization (WHO) predicted that the global diabetes epidemic would affect 154 million people by the year 2000 [4]. The actual rise in diabetes, however, surpassed this projection, and the most recent figures from the WHO suggest that, by the year 2030, more than 366 million people, or 4.4% of all adults worldwide, will have diabetes. India is expected to have the greatest number of individuals with diabetes (79.4 million), followed by China, the USA, Indonesia, and Pakistan [5]. The WHO estimates are more conservative than those of the International Diabetes Federation (IDF) that anticipates a rise in the number of people with diabetes to 438 million by 2030 [6]. It is conceivable, however, that the actual increase in diabetes may surpass even these projections, due to the aging population, the growing obesity epidemic, the increasing rates of T2DM in children and adolescents, progressive urbanization, and the rise in prevalence of T2DM in developing countries. In parallel with the increase in diabetes prevalence, the age of onset of T2DM has been decreasing. T2DM has been traditionally a disease of adulthood, but in recent

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years there has been an increase in the prevalence of T2DM in children and adolescents. While in general T1DM is still more common than T2DM in this age group, certain ethnic groups, including the Pima Indians in Arizona, have very high rates of T2DM in adolescents, and in Japanese school children T2DM is now more common than T1DM [7, 8]. A greater prevalence of T2DM in younger adults will result in an increased rate of T2DM in women of childbearing age, increasing the risk of congenital anomalies and neonatal complications. In Pima Indians, the offspring of women with diabetes during pregnancy were more obese than the offspring of those without T2DM and over 70% of the offspring of diabetic mothers had T2DM at 25–34 years of age [9]. Therefore, T2DM begets T2DM in this vicious cycle of metabolic derangement. Along with the increasing prevalence of diabetes, mortality from diabetes-related conditions has also been increasing. From 2000 to 2010, it increased from 5.2 to 6.8% and this figure is expected to climb further over the next 10 years [10, 11]. In addition to increasing mortality, diabetes is also associated with significant morbidity. Diabetes is the leading cause of blindness among adults aged 20–74 years in the United States and is also the leading cause of chronic kidney disease. Neuropathy affects 60–70% of people with diabetes and more than 60% of nontraumatic amputations occur in those with diabetes. Severe periodontal disease affects more than a third of people with diabetes. Furthermore, people with diabetes have a risk of heart disease and stroke that is 2–4 times greater than those without diabetes [7]. These complications can cost an individual in life expectancy and quality of life, and cost a nation in healthcare expenses. Globally, diabetes-related healthcare costs accounted for approximately 11.1% of the total healthcare expenditure in 2010. This economic cost is expected to rise from USD 376 billion to USD 490 billion between the year 2010 and 2030 [12]. Medical expenses for those with diabetes are 2.3-fold higher than for those without diabetes and 2–5.5 times higher in diabetic patients with complications, compared to those with diabetes with no complications [13]. Of the total economic cost of diabetes in the United States, approximately a third is indirect cost, due to disability, loss of work and premature mortality [7]. With the growing prevalence of diabetes in children and adolescents, there is enormous concern that there will be great morbidity and early mortality in the young population from the development of diabetes-related complications at a younger age [14]. Therefore, preventing or delaying the onset of T2DM has become the subject of many recent studies as well as the focus of healthcare policy and national and international agencies.

Diabetes and Prediabetes Most studies have focused on the prevention of T2DM in those at highest risk of developing the condition, specifically those with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT). Six to ten percent of individuals with IGT alone progress to T2DM every year, while the 6-year cumulative incidence of T2DM for those with both IFG and IGT is up to 65% [15]. In the United States, 79 million

4 Table 1.1 Current definitions of diabetes ADA 2011 Fasting plasma ³126 glucose (mg/dL) Two-hour plasma ³200 glucose during a 75 g OGTT (mg/dL) A1c ³6.5%

E.J. Gallagher and D. LeRoith

WHO 2011 IDF 2006 ³126 ³126

AACE/ACE 2010 ³126

³200

³200

³200

³6.5%

Not Not recommended recommended as primary diagnostic test

Additional criteria

Random plasma glucose of ³200 mg/dL classic symptoms of hyperglycemia or hyperglycemic crisis ADA American Diabetes Association; WHO World Health Organization; IDF International Diabetes Federation; AACE American Association of Clinical Endocrinologist; ACE American College of Endocrinology

people or 35% of the population aged 20 years and older have “pre-diabetes” (IFG or IGT or both), while 50% of those over the age of 65 years have prediabetes (NIH Statistics 2011). In addition to being associated with an increased risk of developing T2DM, prediabetes confers an increased risk of cardiovascular mortality [16–18]. The current diagnostic criteria for the diagnosis of diabetes from the American Diabetes Association (ADA), WHO, and the American Association of Clinical Endocrinologists/American College of Endocrinology (AACE/ACE) are outlined in Table 1.1. All three committees endorse the use of fasting plasma glucose (FPG) and the 2 h 75 g oral glucose tolerance test (OGTT) for the diagnosis of diabetes, with repeat testing to confirm the diagnosis in the absence of unequivocal hyperglycemia [19]. The ADA additionally adopted the use of A1c to diagnose diabetes in nonpregnant adults in 2010. They recommend that the A1c test should be performed using a method that is certified by the National Glycohemoglobin Standardization Program (NGSP) and an assay that is standardized to the Diabetes Control and Complications Trial (DCCT) reference assay. An A1c ³6.5% is diagnostic of diabetes according to the current ADA criteria [19]. In 2011, the WHO also recommended that an A1c of ³6.5% be used to diagnose diabetes, provided that the tests and assays are standardized to the international reference values, and that no condition is present that impairs its accurate measurement (e.g., hemoglobinopathies, certain drugs, increased red cell turnover) [20]. The AACE/ACE position statement from 2010 does not support the use of A1c as a primary test to diagnose diabetes, but states that it may be considered an additional optional diagnostic criterion [21]. Prediabetes is defined by the ADA as IFG, with a FPG level of 100–125 mg/dL (5.6–6.9 mmol/L), or IGT with a 2 h plasma glucose value of 140–199 mg/dL

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Table 1.2 Current definitions of prediabetes ADA WHO/IDF AACE/ACE Impaired fasting glucose (mg/dL) 100–125 110–125 100–125 Impaired glucose tolerance (plasma 140–199 140–199 140–199 glucose 2 h after 75 g oral glucose tolerance test) (mg/dL) A1c 5.7–6.4% Not recommended Not recommended ADA American Diabetes Association; WHO World Health Organization; IDF International Diabetes Federation; AACE American Association of Clinical Endocrinologist; ACE American College of Endocrinology

(7.8–11 mmol/L) after a 75 g OGTT, or an A1c level of 5.7–6.4% (Table 1.2). The current cutoff value for IFG was modified by the ADA in 2003, reducing it from a FPG level of 110 mg/dL (6.1 mmol/L) to 100 mg/dL (5.6 mmol/L). The AACE and ACE use the same IFG (100–125 mg/dL) and IGT (140–199 mg/dL) levels as the ADA to define prediabetes. The WHO and IDF define IFG as 110–125 mg/dL (6.1–6.9 mmol/L) rather than 100 mg/dL (5.6 mmol/L) as defined by the ADA, AACE, and ACE (Table 1.1). The WHO uses the term “intermediate hyperglycemia” instead of prediabetes, to describe glucose levels between normoglycemia and diabetes [22]. The use of A1c to diagnose prediabetes was added to the criteria used to define prediabetes, as individuals with A1c levels between 5.5 and 6% have a 5-year incidence of diabetes of 9–25%, and an A1c of 6–6.5% have a 5-year risk of 25–50% [19]. However, its use as a diagnostic tool for diabetes and prediabetes remains controversial. Recent studies have demonstrated that there are significant discrepancies in the number of individuals classified as having prediabetes, depending on which criteria are used to define the condition [23, 24]. Both of these studies report a low sensitivity for A1c in the diagnosis of prediabetes. Based on the 2005–2008 NHANES data, of those individuals with IGT, only 58.2% had a fasting glucose level of 100– 125 mg/dL, 23.4% had a fasting glucose of 110–125 mg/dL, and 32.3% had an A1c of 5.7–6.4% [24]. However, using OGTT as the “gold standard” for diagnosing diabetes is also controversial, as A1c is highly correlated with the risk of retinopathy, and therefore, although A1c may detect a different population, it is not necessarily inferior [25]. The arguments for and against using A1c for the diagnosis of diabetes and prediabetes are outlined in Table 1.3. A1c is a more convenient than fasting glucose or an OGTT, as the patient does not have to fast or suffer the inconvenience of the 2 h OGTT. There is less intraindividual variation with the A1c related to stress and illness, when compared to plasma glucose. It has less variation over a short period of time (coefficient of variation, CV = 3.6%), compared to FPG (CV 5.7%) or 2 h postprandial glucose (CV = 16.6%). A1c is also a better predictor of complications than plasma glucose and is a better reflection of overall glycemia than a plasma glucose, which reflects glucose only at a particular point in time. Additionally, A1c has less preanalytic variability and the NGSP assay is regulated and standardized to the DCCT, while plasma glucose may decrease prior to analysis and the assay is not internationally standardized. However, A1c diagnoses less people

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Table 1.3 Advantages and disadvantages of A1c for diagnosing diabetes and prediabetes Advantages of using A1c Disadvantages of using A1c • More convenient • Missing certain cases will lead to lost opportunities at prevention • Less intraindividual variability • More expensive • Easier to follow up baseline and subsequent readings • Limited availability of standardized tests in many countries • Better predictor of complications than fasting plasma glucose or postprandial glucose • Variation in correlation with mean glucose in some ethnic groups • More preanalytical stability • Altered A1c with certain medical • Reflects long-term control, not a point in time conditions, medications • Greater testing due to more convenience, • Need more research to validate therefore more overall cases diagnosed range for prediabetes

with prediabetes and therefore will lead to missed opportunities to impact their future risk of developing diabetes. A1c is a more expensive assay than plasma glucose and although most developed countries have a standardized assay, labs in many developing countries do not, and point of care testing is not recommended by the ADA due to lack of standardization; therefore, doctors and other healthcare professionals need to be certain that the lab processing their blood samples has a standardized assay before using A1c as a screening test. Many factors are emerging apart from glucose levels that influence A1c, including ethnicity, pregnancy, blood loss, hemoglobinopathies, anemias, certain medications, and chronic diseases. A1c is not recommended to diagnose diabetes in pregnant women. It is important to be aware of these limitations before using A1c to diagnose diabetes or prediabetes and so the diagnostic test of choice should be individualized for the patient [26, 27]. In addition, if A1c measurements become widely used to define prediabetes, an apparent decline in the prevalence of prediabetes may result.

Obesity, Insulin Resistance, and Diabetes One of the major driving forces behind the diabetes epidemic is the escalating obesity epidemic. Obesity rates are ever increasing due to sedentary lifestyle and caloric excess. In the United States, over a third of the population are obese, while over two thirds are overweight or obese [28]. Increasing body mass index (BMI) is associated with increased prevalence of diabetes in all ethnic groups. However, results of the NHANES III and the 1999–2004 NHANES show that the Mexican American and Non-Hispanic Black population were more likely than the Non-Hispanic White population to develop diabetes in the normal and overweight BMI categories [29]. Similarly, Asian Americans are more likely to develop T2DM than White Americans, despite having a lower BMI. In addition to a possible genetic predisposition of

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certain ethnicities to developing T2DM, Asian Americans are more likely to have greater visceral adiposity than their White counterparts for any given BMI [30]. Increased visceral adiposity is associated with insulin resistance, and insulin resistance is known to be an important factor underlying the development of T2DM [31, 32]. Therefore, the global spread of obesity will not affect all populations equally; certain populations such as South Asians will be at higher risk of developing T2DM, with lesser degrees of obesity [30, 33].

Preventing Type 2 Diabetes Many intervention studies have demonstrated that lifestyle modification in the setting of a clinical trial is at least as effective as pharmacological therapy for reducing the progression from prediabetes or the metabolic syndrome to T2DM. In a meta-analysis of studies examining the effect of lifestyle, diabetic medication, and antiobesity medication on the cumulative incidence of diabetes over 5 years, the number needed to treat (NNT) to prevent or delay one case of diabetes was 6.4 for lifestyle, 10.8 for antidiabetic medication, and 5.4 for orlistat [34]. Bariatric surgery has also been reported to decrease the prevalence of prediabetes and T2DM [35]. Therefore, treating individuals at risk for T2DM with lifestyle intervention, pharmacotherapy, or surgery can potentially delay or prevent the onset of T2DM. The increasing morbidity and mortality associated with diabetes, in addition to the rising diabetes-related healthcare expenditure, has led to the recognition of T2DM as a major public health concern. The IDF, WHO, ADA, the National Cholesterol Education Program—Third Adult Treatment Panel (NCEP-ATP III), along with representatives from every continent convened in Lisbon, Portugal in 2006 to create a consensus statement on T2DM prevention. In this statement, they proposed that T2DM prevention strategies should not only be targeted toward those individuals at high risk of developing T2DM, but also the general population. Those at higher risk of developing T2DM should be identified and lifestyle modification strategies should be advised, with the possible addition of pharmacological agents if lifestyle modification fails to achieve the desired results. Targeting the general population should go beyond the scope of the healthcare sector; governments should be involved to establish health policy initiatives related to transportation and urban planning to promote physical activity; food pricing and advertising should promote healthy eating. Education programs need to target children and adults to raise awareness of the risk of diabetes to the whole population and to help people understand the importance of healthy eating, maintaining a healthy weight, and exercising regularly. This consensus statement implores governments to change policies in order to empower people to improve their physical activity and develop healthy eating habits [36]. Others have suggested that the epidemic of obesity and T2DM is not only related to lack of exercise and poor nutrition but also to a host of medical and environmental factors in a predisposed person that contribute to disease development. They therefore suggest that a wider approach needs to be taken to resolve the multiple causes of obesity and

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diabetes [37]. Overall, diabetes prevention is becoming a priority for healthcare professionals and governments. The best method of identifying those at highest risk and the best method of prevention of the disease and its complications are the subject of intense research and heated debate.

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20. WHO Consultation. Use of glycated haemoglobin (HbA1C) in the diagnosis of diabetes mellitus. 2011. http://www.who.int/cardiovascular_diseases/report-hba1c_2011_edited.pdf. Accessed May 2011. 21. American Association of Clinical Endocrinologists Board of Directors; American College of Endocrinologists Board of Trustees. American Association of Clinical Endocrinologists/ American College of Endocrinology Statement on the use of hemoglobin A1c for the diagnosis of diabetes. Endocr Pract. 2010;16(2):155–6. 22. WHO. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF consultation. Geneva, Switzerland: WHO Press; 2006. 23. Mann DM, Carson AP, Shimbo D, Fonseca V, Fox CS, Muntner P. Impace of HbA1c screening criteria on the diagnosis of pre-diabetes among US adults. Diabetes Care. 2010;33(10):2190–5. 24. James C, Bullard KM, Rolka DB, Geiss LS, Williams DE, Cowie CC, et al. Implications of alternative definitions of prediabetes for prevalence in US adults. Diabetes Care. 2011;24(2):387–91. 25. Buse JB. Screening for diabetes and prediabetes with proposed A1c-based diagnostic criteria. Diabetes Care. 2010;33(12):e174. 26. Bonora E, Tuomilehto J. The Pros and Cons of diagnosing diabetes with A1c. Diabetes Care. 2011;34 Suppl 2:S184–90. 27. Malkani S, Mordes JP. Implications of using hemoglobin A1c for diagnosing diabetes mellitus. Am J Med. 2011;124:395–401. 28. Flegal KM, Carroll M, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303(3):235–41. 29. Zhang Q, Wang Y, Huang ES. Changes in racial/ethnic disparities in the prevalence of type 2 diabetes by obesity level among US adults. Ethn Health. 2009;14(5):439–57. 30. Lee JWR, Brancati FL, Yeh HC. Trends in the prevalence of type 2 diabetes in Asians versus whites. Diabetes Care. 2011;34:353–7. 31. Hutley L, Prins JB. Fat as an endocrine organ: relationship to the metabolic syndrome. Am J Med Sci. 2005;330(6):280–9. 32. Reaven GM. Banting Lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595–607. 33. Lear SA, Humphries SK, Birmingham CL. The use of BMI and waist circumference as surrogates of body fat differs by ethnicity. Obesity. 2007;15:2817–24. 34. Gilles CL, Abrams KR, Lambert PC, Cooper NJ, Sutton AJ, Hsu RT, et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ. 2007;344(7588):299–307. 35. De la Cruz-Munoz N, Messiah SE, Arheart KL, Lopez-Mitnik G, Lipshultz SE, Livingstone A. Bariatric surgery significantly decreases the prevalence of type 2 diabetes mellitus and prediabetes among morbidly obese multiethnic adults: long term results. J Am Coll Surg. 2011;212:505–13. 36. Alberti KGMM, Zimmet P, Shaw J. International diabetes federation: a consensus on type 2 diabetes prevention. Diabet Med. 2007;24:451–63. 37. Egger J, Dixon J. Non-nutrient causes of low-grade, systemic inflammation: support for ‘a canary in the mineshaft’ view of obesity in chronic disease. Obes Rev. 2011;12:339–45.

Chapter 2

Pathophysiology: Loss of b-Cell Function Ele Ferrannini and Andrea Mari

Introduction The pathophysiology of prediabetes is a direct extension of the physiology of glucose control. In fact, all evidence indicates that progression from normoglycemia to dysglycemia to frank hyperglycemia occurs along a continuum not just of plasma glucose concentrations but also of underlying mechanisms. Therefore, the pathophysiology of prediabetes can be described equally well as shifts in glucose tolerance category and in terms of continuous changes in glucose parameters [1]. The glucose system is highly homeostatic, swings in plasma glucose concentrations rarely exceeding 3 mmol/L (54 mg/dL) in normal subjects. At any given time, the plasma glucose concentration represents the balance between entry of glucose into and exit from the circulation via cellular metabolism or excretion: excessive release or defective removal (or combinations of the two) will result in rising glucose levels. Entry and exit of glucose are subject to multiple regulatory mechanisms, with insulin and glucagon principally controlling entry and insulin governing exit. The role of the endocrine pancreas in the pathophysiology of prediabetes can therefore be reduced to the following questions: Are there changes in b-cell or a-cell function (or sensitivity to these hormones)? What consequences do these changes have for glucose homeostasis? A preliminary consideration is the unique organization of the insulin/glucagon system. For many protein and nonprotein hormones, action is modulated by at least one, often two, hierarchical hormonal feedback pathways (e.g., CRH and ACTH hormone for cortisol, GnRH and gonadotrophins for sex steroids). In these cases,

E. Ferrannini, MD (*) Department of Internal Medicine, University of Pisa School of Medicine, Via Roma, 67, 56100 Pisa, Italy e-mail: [email protected] A. Mari, PhD Institute of Biomedical Engineering, National Research Council, Padova, Italy D. LeRoith (ed.), Prevention of Type 2 Diabetes: From Science to Therapy, DOI 10.1007/978-1-4614-3314-9_2, © Springer Science+Business Media New York 2012

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sensitivity is provided by the circulating hormone concentrations acting upon specific hormone receptors located on target tissues as well as on the master gland of the feedback loop (e.g., the pituitary). In the case of insulin and glucagon, there is no pituitary or hypothalamic relay; target tissues control secretion directly. Thus, the circulating concentrations of substrates (mostly glucose, but also amino acids, free fatty acids, and ketone bodies), which result from insulin action on intermediary metabolism in different tissues, feed signals back to the b-cell and the a-cell. Sensitivity gating is provided by insulin and glucagon receptors on target tissues. An additional level of regulation is autocrine/paracrine in nature, i.e., insulin receptors on the b-cell and the a-cell, respectively.

b-Cell function A normal b-cell integrates multiple hormonal and substrate inputs to mount a secretory response precisely geared at limiting plasma glucose excursions [2]. The chain of events leading from stimulation of biosynthesis, processing, packaging, and release of the hormone is highly complex and tightly regulated at multiple steps. Therefore, it is not surprising that the repertoire of in vivo b-cell responses is ample; correspondingly, no single clinical test of insulin secretion captures the overall ability of b-cells to govern glucose homeostasis. However, modes of insulin secretory response can be categorized into two main groups, static and dynamic, simply on the basis of the time course of stimulation. By this criterion, static properties are those that represent adaptation to chronic or prolonged stimuli, such as fasting hyperglycemia, obesity, and insulin resistance; dynamic properties are those that determine the response to acute stimulation. Fasting insulin concentration and fasting secretion rate are the primary static parameters, reflecting the b-cell secretory setpoint. In addition, in nondiabetic subjects the total amount of insulin released over a specified period of time is directly related to fasting insulin secretion, presumably also reflecting the level of the setpoint: Fig. 2.1 shows this covariation in a large group of individuals in whom the deconvolution technique was used to reconstruct secretion rates from plasma C-peptide concentrations [3]. Dynamic properties can be investigated in response to a variety of acute stimuli, the most popular being an intravenous glucose bolus (IVGTT), a hyperglycemic clamp, and oral glucose (OGTT) or mixed meal administration. Analysis of these clinical tests has been performed in a large number of variants, employing diverse doses and timing of the stimulus, sampling schedules, and data analysis. Timehonored empirical indices are the acute insulin response to an IVGTT or to a hyperglycemic plateau (AIR, as the sum of the incremental plasma insulin or C-peptide concentrations over the first 8 min following the glucose challenge) [4], and the insulinogenic index on the OGTT (as the ratio of plasma insulin increments at 30 min to the corresponding plasma glucose increments). It is now recognized that the use of intravenous or oral glucose as stimulus may yield different, even contrasting, information on b-cell function [5]. More insight into in vivo dynamics of insulin secretion

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Fig. 2.1 Relationship between fasting insulin secretion rate and total insulin output (over the 2 h following the ingestion of 75 g of glucose) in nondiabetic subjects (n = 1,318). Unpublished data from the RISC study [43]

can be gained with the use of mathematical models. We have developed and validated one such model [6], which incorporates the main features of the in vitro response of isolated rodent or human islets to glucose stimulation: glucose sensitivity, rate sensitivity, and potentiation. Glucose sensitivity is the dose–response of insulin release to glucose concentrations; variably modeled (e.g., as a Michaelis–Menten function), the slope of this relationship measures the ability of b-cells to sense glucose, synchronize, and rev up their insulin secretion accordingly. Rate sensitivity is the ability to sense the speed of change of glucose concentrations and to further augment insulin release proportionally. Potentiation is the fact that pre-exposure to glucose enhances the response to glucose itself; this phenomenon can be also thought of as a priming effect or a glucose memory. In the isolated perfused pancreas, the timing of successive glucose applications is crucial for glucose potentiation: if the time interval between two stimulating glucose levels is too long, potentiation vanishes, if too short potentiation regresses to inhibition [7]. Each of these three dynamic properties of b-cell function can be demonstrated to be present in vivo in man. We shall illustrate their emergence and their role in prediabetes by using data from the RISC study [3]. This cohort of women and men of European descent, ranging in age between 30 and 60 years, were carefully phenotyped using the hyperinsulinemic euglycemic clamp technique, an IVGTT, and an OGTT. Based on the latter, the majority of the participants had normal glucose tolerance (fasting glucose 27 kg/m2 [5]. Similarly, waist circumferences cutoffs of 80 and 90 cm, respectively, were indicative of moderate and marked increase in risk, both in men and in women. The persons with IGT identified in the survey formed the cohort for the long-term follow-up and intervention Da Qing study. Of the 577 persons with IGT who were randomized in the study, 530 completed the intervention. Their baseline age was 45, BMI 25.8 kg/m2, and fasting and 2-h blood glucose 5.6 and 9.0 mmol/L. Treatment assignments were made on a clinicwide basis (rather than differently assigning individuals within a given clinic) to a control group and to groups with diet alone, to exercise alone (primarily walking), and to both diet and exercise interventions; over 6 years, the incidences of 2-h glucose >200 were 15.7, 10, 8.3, and 9.6, and incidences of fasting glucose >140 mg/ dL were 9.6, 3.7, 5.3, and 5.5 per 100-patient years in the four groups, respectively. Cumulatively, diabetes developed in 66, 47, 44, and 45% of the members of the respective groups [6]. Based on the 2-h glucose, and on both fasting and 2-h glucose, the interventions led to 36–47% and to 29–33% reductions in diabetes, respectively. The exercise intervention was particularly effective in persons with BMI < 25, with diabetes developing in 60% of controls, 38% of those with diet, 26% with exercise, and 35% with both interventions, while overweight participants developed diabetes among 72% of controls, 48% with diet alone, 51% with exercise alone, and 53% with diet plus exercise. Baseline fasting and 2-h insulin measurements were performed in 284 of the 577 persons in the trial [7]. Those with higher insulin levels had lesser responses to all the interventions. The diet plus exercise intervention trended to be particularly useful for this group. Fascinating studies reported 20-year follow-up results of all but 26 of the 577 persons randomized in the DaQing interventions. The reduction in diabetes development seen in the intervention vs. control groups at the end of 6 years was maintained through the subsequent 14 years, with very high cumulative diabetes development rates of 80% vs. 93% [8]. The authors note that there was little difference in weight-change between the intervention and control groups, suggesting a different mechanism of prevention from that operative in the “Finnish Diabetes Prevention Study” and in the US Diabetes Prevention Program [9], where weight loss appeared to be the principal mechanism of benefit. There was a trend to reduction in cumulative CV mortality at 12.5% vs. 17.4%, with two thirds of the deaths being attributed to stroke and the remainder to heart disease. First CVD events

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occurred in 39% of those in the lifestyle groups vs. 42% of controls. A1c was 7.3% vs. 7.8%, and severe retinopathy occurred in 9.2% vs. 16.2%, respectively, but there were no differences in neuropathy and nephropathy [10].

Conclusions Diet, exercise, and the combination of both lifestyle interventions in Chinese persons with prediabetes reduced the absolute risk of diabetes by approximately 20%, without a major effect on weight, and, interestingly, appearing particularly to reduce risk in insulin-deficient rather than insulin-resistant persons. In 20-year follow-up, the absolute diabetes development rate was 13% lower among those persons originally randomized to the lifestyle interventions, and their level of glycemic control appeared to be better than among the original control population. The population had high likelihood of diabetic complications, particularly retinopathy and CVD, with reduction in rates of severe retinopathy among those undergoing lifestyle interventions and a trend to reduction in CV mortality.

Finnish Diabetes Prevention Study Another study of lifestyle intervention to prevent type 2 diabetes in high-risk individuals was the Finnish Diabetes Prevention Study. In addition to the main goal of assessing the efficacy of an intensive diet-exercise program in preventing or delaying type 2 diabetes mellitus in subjects with IGT, it also aimed to evaluate the effects of the intervention program on cardiovascular risk factors and to assess the determinants for the progression to diabetes in persons with IGT. Recruitment began after a pilot study in 1993 and was completed in May 1998. The study subjects were recruited in five different centers in Finland through population screenings with special emphasis on high-risk groups such as those with obesity or first-degree relatives of patients with type 2 diabetes. In the study, 523 overweight subjects with IGT ascertained by two OGTTs were randomized to either a control or intervention group. IGT was defined as a plasma glucose concentration of 140–198 mg/dL (7.8–11.0 mmol/L) 2 h after the oral administration of 75 g of glucose in subjects whose plasma glucose concentration after an overnight fast was less than 140 mg/dL, based on criteria adopted by the WHO in 1985. Participants also had to be between 40 and 64 years of age at randomization and had to be overweight with BMI > 25 kg/m2 to be eligible for the study. Subjects who already had a diagnosis of diabetes (except for gestational diabetes) or were already involved in a vigorous exercise program and those with other diseases were excluded. The subjects in the control group received general information at the start of the trial about the lifestyle changes necessary to prevent diabetes and about annual follow-up visits, but no specific individualized programs were offered to them. A 3-day food record was filled out once a year. The subjects in the intervention group had

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seven sessions with a nutritionist during the first year and a visit every 3 months thereafter. The dietary goals of the intervention were (1) reduction in weight of 5% or more, (2) reduction in total intake of fat to less than 30% of energy consumed, (3) reduction in intake of saturated fat to less than 10% of energy consumed, and (4) increase in fiber intake to at least 15 g/1,000 kcal. The patient filled out a 3-day food record before the first appointment, and every 3 months thereafter. After 6 months, the use of a very-low-calorie diet (VLCD) for 2–5 weeks or as a substitute for one to two meals per day was considered to boost weight loss. Subjects were also guided individually to increase their physical activity. The physical activity goal was to achieve moderate exercise for at least 30 min/day. Endurance exercise (such as walking, jogging, swimming, aerobic ball games, or skiing) was recommended as a way to increase aerobic capacity and improve cardiorespiratory fitness. Supervised, progressive, individually tailored, circuit-type resistance-training sessions were also offered with the aim of improving large muscle group functional capacity and strength. Participants were instructed to perform a moderate to high number of repetitions and to take a break of 15–60 s between the stations on the circuit. The study subjects completed the validated Kuopio Ischaemic Heart Disease Risk Factor Study 12-month Leisure Time Physical Activity (LTPA) questionnaire at baseline and at every annual visit. All participants had an annual OGTT, fasting lipid panel, medical history, and physical examination with measurements of height, weight, waist circumference, and systolic and diastolic blood pressure. Of the 522 subjects in the study, 265 were randomized to the intervention group with intensive diet-exercise counseling and 257 were randomized to the control group. The two groups were similar at baseline with mean age 55 and BMI 31.1 kg/m2. The mean baseline fasting plasma glucose was 109 mg/dL and mean plasma glucose 2 h after the 75 g oral glucose load was 160.3 mg/dL without significant difference between the groups. Of the participants, 10% in the intervention group and 8% in the control group were lost to follow-up, with median follow-up of 4 years. The primary outcome was development of diabetes, as measured by OGTT, repeated for confirmation if diagnostic of diabetes. Diabetes was diagnosed in a total of 86 subjects—27 in the intervention group and 59 in the control group, with incidences of 32 and 78 cases per 1,000 person-years in the intervention and control groups, respectively, a 58% reduction. The intervention was effective for both sexes, with diabetes decreasing 63 and 54% in men and in women, respectively. The study included a number of secondary outcomes. Mean weight reduction was 4.5 kg in the intervention group and 1.0 kg in the control group at 1 year. Some regain of weight appeared during the following 2 years. Excluding intervention group participants on VLCD, whose weight reduction was 6.2–7.0 kg at year 1 and 4.8–7.2 kg at year 3, mean weight reduction was 4.1–4.3 kg at year 1 and 3.2–4.5 kg at year 3. During the first year, the prevalences of abdominal obesity, IFG, elevated blood pressure, and low HDL cholesterol decreased significantly in the intervention group, although that of hypertriglyceridemia was unchanged. In the control group, only the prevalence of hypertension decreased. From baseline to study-end, a significant decrease in the prevalence of abdominal obesity, elevated blood pressure, low HDL

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cholesterol, and elevated triglycerides was observed in the intervention group, but only low HDL cholesterol improved in controls. The lifestyle intervention reduced abdominal obesity, adjusted for age, sex, and baseline value [11]. The prevalence of metabolic syndrome decreased during the first year from 74.0 to 58.0% vs. from 74.0 to 67.7% in the intervention and control groups, respectively. At the end of the study, 62.6% of subjects in the intervention group and 71.2% of subjects in the control group had metabolic syndrome, which corresponds to an age- and sex-adjusted odds ratio of 0.62 [12]. A subset of patients were studied in detail to determine the impact of the 4-year lifestyle intervention on insulin sensitivity and insulin secretion, endeavoring to determine which factor was more important in reducing the incidence of diabetes. All of these patients were studied in the Kupio clinic, where in addition to the OGTT, 87 participants at baseline and 52 participants at the end of the study underwent a frequently sampled intravenous glucose tolerance test (FSIGT). In this subset, at the end of the study, the intervention group lost on average 3.5 kg more than the control group and had a reduction in waist circumference 2.4 cm greater than that of the control group. There were strong correlations between the 4-year changes in insulin sensitivity and in weight. In the entire group, insulin sensitivity improved by 64% among those in the highest tertile of weight loss, but deteriorated by 24% in those who gained weight (lowest tertile). The acute insulin response declined significantly in the control group [13]. Thus, both improvement in insulin sensitivity and protection against the reduction in insulin secretion appeared to play roles in the benefit of the intervention. Cardiovascular mortality and morbidity were monitored through computerized register linkage to two nationwide health registers: the Hospital Discharge Register and the Causes of Death Register, using the national personal identification number. After a median follow-up time of 10.2 years, there was no statistically significant difference in cardiovascular mortality between the two groups, with 57 new cardiovascular events in the intervention group and 54 in the control group. Men and women had a similar CVD incidence, whether the groups were analyzed separately or combined. There were no significant differences between the intervention and control groups in coronary artery angioplasty, in by-pass surgery, or in treatment for dyslipidemia and blood pressure [14]. All participants in the study who had not developed diabetes were invited to take part in the postintervention follow-up, with yearly nurse visits during which the same procedures were carried out as during the intervention period. No specific diet or exercise counseling was provided during this 3-year follow-up. Those who originally participated in the intervention vs. control groups had diabetes incidence rates of 4.6 vs. 7.2 per 100 person-years, a 36% reduction in relative risk [15]. Post hoc analyses were carried out to assess the determinants for the progression to diabetes. Even after adjustment for other risk factors, dietary fat and fiber intake were significant predictors of sustained weight reduction and of progression to type 2 diabetes [16]. Greater levels of moderate-to-vigorous LTPA were associated with decreased likelihood of developing metabolic syndrome and an increased likelihood of its resolution, after adjustment for changes in dietary intakes of total and

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saturated fat, fiber, and energy, and change in BMI. The increase in moderateto-vigorous LTPA was most strongly associated with improvement of glycemia [17]. Another post hoc analysis found long sleep duration to be associated with increased risk of type 2 diabetes. In the control group, participants who slept more than 9 h had greater rates of developing diabetes, while patients in the intervention group implementing lifestyle changes did not show an effect of sleep duration on the incidence of diabetes. The lifestyle intervention resulted in similar improvement in body weight, insulin sensitivity, and immune mediator levels regardless of sleep duration [18]. Another study examined which individual components of the comprehensive lifestyle intervention were most likely to reduce subclinical inflammation, which confers increased risks of type 2 diabetes, cardiovascular disease, neurodegenerative disorders, and other age-related chronic diseases. C-reactive protein and interleukin-6 levels, thought to represent the best characterized pro-inflammatory risk factors for type 2 diabetes, were compared at baseline and 1 year after follow-up in a subsample of 406 of the participants, both inflammatory markers decreasing with the lifestyle intervention. The decrease was predicted by increases in fiber intake and in moderate-to-vigorous LTPA, but not by increase in total LTPA or by changes in carbohydrate or fat intake [19]. A post hoc analysis showed that the higher the baseline risk for diabetes, the greater the risk reduction achieved during the intervention, so that those who were most at risk benefitted the most. Risk was calculated using the FINDRISC questionnaire, a validated screening tool for undiagnosed type 2 diabetes, dysglycemia, and the metabolic syndrome [20]. In participants with low baseline risk, the risk of developing diabetes was low whether they were in the interventional or control group. Participants at high risk lowered the risk if they were in the intervention group, actively dieting and exercising, while those at high risk who were randomized to the control group had a very high incidence rate of diabetes. The study also showed that the intervention was most effective among the oldest (age 61 years) individuals, with a relative risk reduction of 64% compared with that in the control group. This was similar to the findings in the US Diabetes Prevention Program, where the participants in the oldest age-group (60–85 years at baseline) achieved the largest risk reduction [21]. A recent study investigated whether a family history of diabetes or genetic variants of type 2 diabetes modulated the decreased incidence of diabetes achieved with lifestyle changes [22]. As of today, 30 genetic variants have been found to be associated with an increased risk of type 2 diabetes [23–25]. Generally, each variant has a limited effect, since the risk may increase by 10–15% per copy of each risk allele, with the exception of TCF7L2, which has a more pronounced influence, increasing the risk 1.4-fold [26]. These known genetic variants account for only 10% of the genetic basis of type 2 diabetes and seem to have limited ability in predicting the development of type 2 diabetes[27, 28]. The study found that, at 4-year follow-up, those participants with a family history of diabetes seemed to have a lower incidence of diabetes than those with no family history. However, this difference between the two groups disappeared when the entire 7-year follow-up was

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analyzed, showing that the effect of diet and exercise was significant both among those with and without a family history of diabetes. The study did not find aggregation of known genetic risk variants in persons with a family history of diabetes, and genetic risk variants did not modify the incidence of diabetes. Two substudies provided supportive evidence for the involvement of genetic variation as a modifier in the effect of lifestyle changes. One showed that genetic variation in adiponectin, found in the ADIPOQ locus, contributed to variation in body size and serum adiponectin concentrations and may also modify the risk of developing type 2 diabetes [29]. Another study showed that patients who were homozygous for a polymorphism of the hepatic lipase gene were less likely to benefit from the lifestyle intervention, and therefore were more likely to develop diabetes (13% vs. 1% in subjects who had at least one normal allele), suggesting that polymorphism of the hepatic lipase gene is a risk factor for type 2 diabetes [30].

Conclusions The Finnish Diabetes Prevention Study showed that a moderate degree of weight loss achieved through diet and exercise led to a very significant change in the risk of developing diabetes. Twenty-two patients with IGT need to be treated in order to prevent one case of diabetes. This very feature-rich study offered a plethora of insights and consistently highlights the importance of diet and exercise for prevention. The intervention was particularly effective in those at the highest risk. It is possible that many of the results of the study are less robust than they would be in a nonstudy, real-world situation, as the control arm also received advice and received consistent medical follow-up. Although there was no difference in cardiovascular mortality, the study was not powered to examine this effect. More time might be required to see changes in this endpoint. Furthermore, the study showed that the mortality in both arms of the study was low compared to mortality in individuals with IGT in the general population, a not uncommon finding among participants in randomized controlled clinical trials. It is also notable that the study had a very low dropout rate, compared to most weight loss studies, suggesting that patients at risk for diabetes are willing to participate in an intensive prevention program when given the opportunity.

TRIPOD/PIPOD The TRoglitazone In Prevention Of Diabetes (TRIPOD) study investigated the development of diabetes in 266 high-risk Hispanic women with previous gestational diabetes treated with either troglitazone or placebo [31]. The women were identified through chart reviews and patient interviews from Los Angeles County Women’s

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and Children’s Hospital from August 1995 to May 1998. Women met the inclusion criteria if they were older than 18 years of age, had gestational diabetes mellitus within the previous 4 years, and were willing to use effective contraception. Exclusion criteria included evidence of chronic disease, serum alanine aminotransferase concentration >1.5 times the laboratory upper normal, or known diabetes. Women also were excluded if they had a sum of five OGTTs plasma glucose concentrations >625 mg/dL predicting a >70% 5-year risk of diabetes. Enrolled subjects received dietary advice and were advised to walk for 30 min 3 days each week. Patients underwent a FSIGT within 4 weeks of the screening OGTT to assess baseline insulin sensitivity and pancreatic b-cell function, and then were randomized to receive troglitazone 400 mg/day or placebo in a double-blind fashion. Fasting glucose was measured at 3-month intervals and OGTTs were performed annually to detect diabetes. Measurements of height, weight, sitting blood pressure, fasting serum lipids, and carotid intima-media thickness (cIMT) were performed at the times of OGTTs. The trial was scheduled to continue until August 2000, but was terminated in March 2000, when troglitazone was withdrawn from the market after reports of hepatotoxicity. At that time, 79% of 105 subjects active in TRIPOD had not reached their annual OGTT visit for the year 2000. They were notified of their treatment status, asked to discontinue study medications, and scheduled for an end-of-trial OGTT. Development of diabetes was the primary study endpoint. During the median follow-up of 30 months, average annual diabetes incidence rates in women who returned for follow-up were 12.1 and 5.4% in the placebo and troglitazone groups, respectively, a 55% reduction. Treatment with troglitazone significantly improved insulin sensitivity, while acute insulin response remained the same, resulting in a significant improvement in disposition index from baseline and vs. placebo, suggesting improved b-cell compensation for insulin resistance [32]. The most responsive group to intervention were those who early in the study, while on troglitazone, showed the greatest reduction in insulin resistance and fall in insulin secretion to a glucose challenge. The Pioglitazone In Prevention Of Diabetes (PIPOD) study was conducted as a single arm, open-label follow-up study for those participants who had completed the TRIPOD study. OGTTs were performed annually on pioglitazone and at the end of the 6-month postdrug washout. Intravenous glucose tolerance tests (IVGTTs) for assessment of insulin sensitivity and b-cell function were conducted at baseline, after 1 year on pioglitazone, and at the end of the postdrug washout [33]. Incidence rates of diabetes were calculated from 86 women (42 from the active treatment arm of the TRIPOD study) who had at least one follow-up visit after enrollment. Overall, 11 of them had diabetes at one or more OGTTs during a median of 35.9 months of pioglitazone treatment. Average annual incidence rates of diabetes were 5.2% during pioglitazone treatment and 4.6% during the entire observation period, including the post drug washout. Similar to the TRIPOD study, the risk was lowest in women with the largest reduction in total IVGTT insulin area after 1 year of treatment. The study showed that pioglitazone stopped the decline in b-cell

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function that occurred during placebo treatment in the TRIPOD study. Pioglitazone also maintained the stability of b-cell function that occurred during troglitazone treatment in the TRIPOD study. A follow-up study reported results on the progression of subclinical atherosclerosis, measured by cIMT in the women in PIPOD who did not develop diabetes. The study analyzed the 61 women who completed at least one annual follow-up cIMT measurement. Pioglitazone treatment was associated with a significant reduction in the rate of progression of cIMT compared to rates that had been observed in the same individuals during placebo treatment in the TRIPOD study. Pioglitazone also maintained a persistently low cIMT progression rate in women who had been on troglitazone treatment in TRIPOD and pioglitazone in PIPOD. The impact of pioglitazone on cIMT was not explained by alterations in body weight, blood pressure, or circulating concentrations of glucose, insulin, or standard lipids [34].

Conclusion Both troglitazone and pioglitazone treatment given to Hispanic women with prior gestational diabetes were associated with stable pancreatic b-cell function and a relatively low rate of diabetes. The lowest rate of diabetes occurred in association with the greatest reduction in insulin secretory demands during the first year of treatment. Pioglitazone was also shown to slow progression of subclinical atherosclerosis as measured by cIMT. The findings from these two trials suggest that thiazolidinedione drugs may modify the natural history of progression to type 2 diabetes in high-risk Hispanic patients.

DREAM (Diabetes REduction Assessment with Ramipril and Rosiglitazone Medication) In the DREAM trial, 5,269 persons with prediabetes were randomized to receive ramipril 15 mg/day, rosiglitazone 8 mg/day, both, or neither. Of these, 35% had isolated IGT, 14% had isolated IFG, and 51% had both. At semiannual OGTTs, diabetes was diagnosed if two consecutive plasma glucose levels within a 3-month period exceeded 125 mg/dL fasting or 199 mg/dL at 2 h [35]. In total, 24,872 individuals in 21 countries were screened with OGTT over 2 years; 14,661 were women, with glycemic abnormality 3% more likely per child born, 14% more likely in those with a history of eclampsia or preeclampsia, 21% more likely in those with history of irregular menses, and 53% more likely in those with history of gestational diabetes. There was 5% greater likelihood per year of age, and 9% greater likelihood among those of non-European ancestry [36].

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Patients were followed in the study for a median of 3 years. The likelihood of diabetes was not significantly reduced by ramipril, but the drug increased the likelihood of regression to normoglycemia by 16%, with 2-h glucose 135 vs. 141 mg/dL in those receiving vs. not receiving the agent [37]. Among those receiving vs. not receiving rosiglitazone, diabetes developed in 11% vs. 25%. There was regression to normoglycemia in 51% vs. 30%, no significant change in atherosclerotic events, and 0.5% vs. 0.1% likelihood of congestive heart failure [38]. The glycemic benefit of rosiglitazone was greater among those of Latino ethnicity and lesser among South Asians, although all ethnic groups showed significant reduction in diabetes development [39]. Interestingly, ramipril was neither associated with improvement nor even with lesser degrees of worsening of albuminuria or estimated glomerular filtration rate (eGFR), while rosiglitazone was associated with a trend to reduced likelihood of deterioration in eGFR, and significant 18% reduction in albuminuria progression and 20% improvement in the composite renal endpoint of first occurrence of progression of albuminuria, decreased eGFR by ³30%, or dialysis or transplantation [40]. The single nucleotide polymorphism rs6123045, in the nuclear factor of activated T-cells cytoplasmic calcineurin-dependent 2 (NFATC2) gene, was significantly associated with development of edema in the rosiglitazone-treated group, although neither with cardiovascular endpoints nor with congestive heart failure [41]. The endothelium-derived vasoconstrictive factor endothelin-1 induces calcineurin activity, and rosiglitazone suppresses nuclear translocation of NFATc4 and enhances the association of PPARg with NFATc4/calcineurin in isolated cardiomyocytes. This genetic finding may improve our understanding of thiazolidinedione-induced edema. A subset of 1,425 participants in the trial had serial measurement of cIMT. There was a trend to slower progression in patients receiving rosiglitazone, with significant reduction in the average of two IMT measurements in the common carotid far wall; ramipril failed to change either measure [42]. It should be noted that there were low levels of carotid atherosclerosis at baseline, and that demonstrating regression of such subclinical disease would not be expected to occur during the trial. Another interesting subset analysis showed marked improvement in b-cell function with rosiglitazone, either as measured with the fasting proinsulin-to-C-peptide ratio, or with the change from baseline to 30 min in insulin divided by that in glucose, in turn divided by the homeostasis model assessment of insulin resistance; this was particularly the case in patients with elevation in the baseline 2-h glucose, and lesser improvement was seen in those with isolated IFG; ramipril did not improve any of these measures [43]. A 1–2-year posttreatment follow-up was performed on a representative subset of 47% of participants in the study. Those previously receiving rosiglitazone remained heavier, with BMI 31 vs. 30 kg/m2, and 12% vs. 7% had edema; those previously receiving ramipril had 5/2 mmHg lower blood pressure. Among those randomized to rosiglitazone vs. placebo, 12% vs. 26% had developed the primary endpoint of diabetes or death while on treatment, 19% vs. 31% after an initial washout period, and 27% vs. 39% at the end of the 1–2-year follow-up period, suggesting that a subset of patients had durable diabetes prevention with and following use of the agent [44]. Ramipril did not show a significant long-term effect on glycemia.

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Conclusions Thiazolidinedione treatment had major effect in preventing diabetes in this trial as in TRIPOD, with evidence of durable posttrial benefit. The drugs may both reduce insulin resistance and improve b-cell function. The expected side effect of edema was seen, and an explanatory gene polymorphism was found, without evidence of increase in atherosclerotic endpoints and with modest suggestion of antiatherosclerotic benefit. There was also evidence in this relatively low-risk population of renal benefit of the treatment. Surprisingly, no benefit was seen with a high dose of ramipril either in preventing diabetes or in reducing renal or cardiovascular complications. Given the cost-effectiveness of lifestyle intervention and the evidence of fluid retention as a side effect with rosiglitazone, its practical use in diabetes prevention was questioned at the time of the study [45], although thiazolidinedione use in patients at the higher ranges of prediabetes, perhaps based on A1c values, was suggested to potentially be of benefit [46].

Summary In the DaQing study, lifestyle intervention not only reduced the development of diabetes, but, at 20-year follow-up, improved glycemic control in participants, reduced diabetic retinopathy, and showed a trend to reduction in CVD. The Finnish Diabetes Prevention Study showed similar benefit in reducing development of diabetes, suggesting this to be a feasible and effective approach. Both troglitazone and pioglitazone, given in TRIPOD to women who had had gestational diabetes, were associated with diabetes prevention, with a suggestion of reduction in otherwise progressive b-cell failure. Rosiglitazone was similarly effective in DREAM, with evidence of durable posttrial benefit, and with a suggestion of reduction in intermediate renal and atherosclerosis endpoints. Taken together, these trials suggest that measures to reduce development of diabetes are possible and should be strongly considered by healthcare planners to prevent the burden of disease which is otherwise likely to ensue.

References 1. Cowie CC, Rust KF, Ford ES, Eberhardt MS, Byrd-Holt DD, Li C, et al. Full accounting of diabetes and pre-diabetes in the U.S. population in 1988–1994 and 2005–2006. Diabetes Care. 2009;32:287–94. 2. International Diabetes Federation. Fact sheet: impaired glucose tolerance (IGT); 2011. http:// www.idf.org/fact-sheets/impaired-glucose-tolerance. Accessed 31 March 2011. 3. Hu YH, Pan XR, Liu PA, Li GW, Howard BV, Bennett PH. Coronary heart disease and diabetic retinopathy in newly diagnosed diabetes in Da Qing, China: the Da Qing IGT and Diabetes Study. Acta Diabetol. 1991;28:169–73.

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4. Pan XR, Hu YH, Li GW, Liu PA, Bennett PH, Howard BV. Impaired glucose tolerance and its relationship to ECG-indicated coronary heart disease and risk factors among Chinese. Da Qing IGT and diabetes study. Diabetes Care. 1993;16:150–6. 5. Li G, Chen X, Jang Y, Wang J, Xing X, Yang W, et al. Obesity, coronary heart diseaase risk factors and diabetes in Chinese: an approach to the criteria of obesity in the Chinese population. Obes Rev. 2002;3:167–72. 6. Pan XR, Li GW, Hu YH, Wang XJ, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. Diabetes Care. 1997;20: 537–44. 7. Li GW, Hu YH, Yang WY, Jiang YY, Wang JP, Xiao JZ, et al. Effects of insulin resistance and insulin secretion on the efficacy of interventions to retard development of type 2 diabetes mellitus: the DA Qing IGT and Diabetes Study. Diabetes Res Clin Pract. 2002;58:193–200. 8. Li GW, Zhang P, Wang JP, Gregg EW, Yang WY, Gong QH, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008;371:1783–9. 9. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al.; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. 10. Gong Q, Gregg EW, Wang J, An Y, Zhang P, Yang W, et al. Long-term effects of a randomised trial of a 6-year lifestyle intervention in impaired glucose tolerance on diabetes-related microvascular complications: the China Da Qing Diabetes Prevention Outcome Study. Diabetologia. 2011;54:300–7. 11. Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al.; Finnish Diabetes. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343–50. 12. Ilanne Parikka P, Eriksson JG, Lindstrom J, et al. Effect of lifestyle intervention on the occurrence of metabolic syndrome and its components in the Finnish Diabetes Prevention Study. Diabetes Care. 2008;31:805–7. 13. Uusitupa M, Lindi V, Louheranta A, Salopuro T, Lindstro J, Tuomilehto J; Finnish Diabetes Prevention Study Group. Changing lifestyles of people with impaired glucose tolerance 4-year results from the Finnish Diabetes Prevention Study. Diabetes. 2003;52:2532–8. 14. Uusitupa M, Peltonen M, Lindström J, Aunola S, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, et al.; Finnish Diabetes Prevention Study Group. Ten-year mortality and cardiovascular morbidity in the Finnish Diabetes Prevention Study—secondary analysis of the randomized trial. PLoS One. 2009;4:e5656. 15. Lindström J, Ilanne Parikka P, Peltonen M, Aunola S, Eriksson JG, Hemiö K, et al.; Finnish Diabetes Prevention Study Group. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet. 2006;368:1673–9. 16. Lindström J, Peltonen M, Eriksson JG, Louheranta A, Fogelholm M, Uusitupa M, et al. Highfibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia. 2006;49:912–20. 17. Ilanne-Parikka P, Laaksonen DE, Eriksson JG, Lakka TA, Lindstr J, Peltonen M, et al.; Finnish Diabetes Prevention Study Group. Leisure-time physical activity and the metabolic syndrome in the Finnish diabetes prevention study. Diabetes Care. 2010;33:1610–7. 18. Tuomilehto H, Peltonen M, Partinen M, Lavigne G, Eriksson JG, Herder C, et al.; Finnish Diabetes Prevention Study Group. Sleep duration, lifestyle intervention, and incidence of type 2 diabetes in impaired glucose tolerance: the Finnish Diabetes Prevention Study. Diabetes Care. 2009;32:1965–71. 19. Herder C, Peltonen M, Koenig W, Sütfels K, Lindström J, Martin S, et al.; Finnish Diabetes Prevention Study Group. Anti-inflammatory effect of lifestyle changes in the Finnish Diabetes Prevention Study. Diabetologia. 2009;52:433–42.

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20. Makrilakis K, Liatis S, Grammatikou S, Perrea D, Stathi C, Tsiligrosa P, Katsilambros N. Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece. Diabetes Metab. 2011;37:144–51. 21. Schulze MB. Determinants for the effectiveness of lifestyle intervention in the Finnish Diabetes Prevention Study: response to Lindstrom et al. Diabetes Care. 2008;31:857–62. 22. Uusitupa MI, Stančáková A, Peltonen M, Eriksson JG, Lindström J, Aunola S, et al. Impact of positive family history and genetic risk variants on the incidence of diabetes the Finnish Diabetes Prevention Study. Diabetes Care. 2011;34:418–23. 23. McCarthy MI, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:164–71. 24. Grant RW, Hivert M, Pandiscio JC, Florez JC, Nathan DM, Meigs JB. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia. 2009;52:2299–305. 25. Voight BF, Scott LJ, Steinthorsdottir V, et al.; MAGIC Investigators; GIANT Consortium. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–89. 26. Grant RW, Hivert M, Pandiscio JC, Florez JC, Nathan DM, Meigs JB. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia. 2009;52:2299–305. 27. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359:2220–32. 28. Cornelis MC, Qi L, Zhang C, et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann Intern Med. 2009;150:541–50. 29. Todorova B, Kubaszek A, Pihlajamäki J, Lindström J, Eriksson J, Valle TT, et al. The G-250A promoter polymorphism of the hepatic lipase gene predicts the conversion from impaired glucose tolerance to type 2 diabetes mellitus: the Finnish Diabetes Prevention Study. J Clin Endocrinol Metab. 2004;89:2019–23. 30. Siitonen N, Pulkkinen L, Lindström J, Kolehmainen M, Eriksson JG, Venojärvi M, et al. Association of ADIPOQ gene variants with body weight, type 2 diabetes and serum adiponectin concentrations: the Finnish Diabetes Prevention Study. BMC Med Genet. 2011;12:5. 31. Buchanan TA, Xiang AH, Peters RK, Kjos SL, Marroquin A, Goico J, et al. Preservation of pancreatic beta-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk Hispanic women. Diabetes. 2002;51:2796–803. 32. Buchanan TA, Xiang AH, Peters RK, Kjos SL, Berkowitz K, Marroquin A, et al. Response of pancreatic beta-cells to improved insulin sensitivity in women at high risk for type 2 diabetes. Diabetes. 2000;49:782–8. 33. Xiang AH, Peters RK, Kjos SL, Marroquin A, Goico J, Ochoa C, et al. Effect of pioglitazone on pancreatic beta-cell function and diabetes risk in Hispanic women with prior gestational diabetes. Diabetes. 2006;55:517–22. 34. Xiang AH, Hodis HN, Kawakubo M, Peters RK, Kjos SL, Marroquin A, et al. Effect of pioglitazone on progression of subclinical atherosclerosis in non-diabetic premenopausal Hispanic women with prior gestational diabetes. Atherosclerosis. 2008;199:207–14. 35. Gerstein HC, Yusuf S, Holman R, Bosch J, Pogue J; DREAM Trial Investigators. Rationale, design and recruitment characteristics of a large, simple international trial of diabetes prevention: the DREAM trial. Diabetologia. 2004;47:1519–27. 36. McDonald SD, Yusuf S, Sheridan P, Anand SS, Gerstein HC; Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication Trial Investigators. Dysglycemia and a history of reproductive risk factors. Diabetes Care. 2008;31:1635–8. 37. DREAM Trial Investigators, Bosch J, Yusuf S, Gerstein HC, Pogue J, Sheridan P, et al. Effect of ramipril on the incidence of diabetes. N Engl J Med. 2006;355:1551–62. 38. DREAM (Diabetes REduction Assessment with ramipril and rosiglitazone Medication) Trial Investigators, Gerstein HC, Yusuf S, Bosch J, Pogue J, Sheridan P, et al. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet. 2006;368:1096–105.

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39. Boyko EJ, Gerstein HC, Mohan V, Yusuf S, Sheridan P, Anand S, et al.; DREAM Trial Investigators. Effects of ethnicity on diabetes incidence and prevention: results of the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial. Diabet Med. 2010;27:1226–32. 40. DREAM Trial Investigators, Dagenais GR, Gerstein HC, Holman R, Budaj A, Escalante A, et al. Effects of ramipril and rosiglitazone on cardiovascular and renal outcomes in people with impaired glucose tolerance or impaired fasting glucose: results of the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial. Diabetes Care. 2008;31:1007–14. 41. Bailey SD, Xie C, Do R, Montpetit A, Diaz R, Mohan V, et al.; DREAM Investigators. Variation at the NFATC2 locus increases the risk of thiazolidinedione-induced edema in the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) study. Diabetes Care. 2010;33:2250–3. 42. Lonn EM, Gerstein HC, Sheridan P, Smith S, Diaz R, Mohan V, et al.; DREAM (Diabetes REduction Assessment with ramipril and rosiglitazone Medication) and STARR Investigators. Effect of ramipril and of rosiglitazone on carotid intima-media thickness in people with impaired glucose tolerance or impaired fasting glucose: STARR (STudy of Atherosclerosis with Ramipril and Rosiglitazone). J Am Coll Cardiol. 2009;53:2028–35. 43. Hanley AJ, Zinman B, Sheridan P, Yusuf S, Gerstein HC; Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication (DREAM) Investigators. Effect of Rosiglitazone and Ramipril on {beta}-cell function in people with impaired glucose tolerance or impaired fasting glucose: the DREAM trial. Diabetes Care. 2010;33:608–13. 44. DREAM On (Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication Ongoing Follow-up) Investigators, Gerstein HC, Mohan V, Avezum A, Bergenstal RM, Chiasson JL, et al. Long-term effect of rosiglitazone and/or ramipril on the incidence of diabetes. Diabetologia. 2011;54:487–95. 45. Tuomilehto J, Wareham N. Glucose lowering and diabetes prevention: are they the same? Lancet. 2006;368:1218–9. 46. Davidson MB. Clinical implications of the DREAM Study. Diabetes Care. 2007;30:418–20.

Chapter 12

Community Approaches to Diabetes Prevention Ann Albright and David Williamson

Introduction Preventing type 2 diabetes is a public health challenge that cannot be met by the clinical care sector acting alone. It requires complimentary and shared public health and clinical approaches that together achieve more than each can accomplish unaided (Fig. 12.1). The clinical sector must be involved in assessing patients’ risk for type 2 diabetes, discussing risk status with patients and their support network, referring (or encouraging) high-risk patients to participate in proven, communitybased structured lifestyle programs, and, where necessary, prescribing medications for those at risk for and treating those who go on to develop diabetes. Glasgow et al. has defined a public health approach to diabetes as “a broad, multidisciplinary perspective that is concerned with improving outcomes in all people who have (or are at risk for) diabetes, with attention to equity and the most efficient use of resources in ways that enhance patient and community quality of life” [1]. The role of the public health sector is described by the Ten Essential Public Health Services ([2]; Fig. 12.2). When applied specifically to diabetes prevention, public health services include such actions as monitoring diabetes risk; informing, educating, and empowering people about prediabetes; mobilizing partnerships to reduce new cases of diabetes; linking people to proven diabetes prevention services; and

A. Albright, PhD, RD (*) Division of Diabetes Translation, Centers for Disease Control and Prevention, 2877 Brandywine Road, Williams Building/4770 Buford Highway, MS K-10, Atlanta, GA 30341, USA e-mail: [email protected] D. Williamson, PhD Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA 203 D. LeRoith (ed.), Prevention of Type 2 Diabetes: From Science to Therapy, DOI 10.1007/978-1-4614-3314-9_12, © Springer Science+Business Media New York 2012

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Fig. 12.1 Prevention of type 2 diabetes. The community–clinic partnership model. Elements in the clinical component are adapted from the Chronic Care Model, MacColl Institute for Healthcare Innovation. The elements listed in this figure are not intended to be all-inclusive, but to provide information on the kinds of elements contributed by each sector and shared across sectors (provided by the Centers for Disease Control and Prevention (CDC), Division of Diabetes Translation)

developing policies that support individual risk reduction and community change that makes it easier to practice healthy behaviors. Since diabetes risk progresses along a continuum from low risk to high risk, it is important that effective interventions exist along the continuum. Most of the evidence currently available for diabetes prevention involves those at high risk for type 2 diabetes. There is some evidence that provides insights into broader population risk reduction. Such a reduction may help those at high risk maintain healthy behaviors and prevent others from moving to high-risk status. This chapter examines evidence for community approaches to diabetes prevention and describes current national efforts in the United States to bring together the clinical and community (public health) sectors to prevent type 2 diabetes in high-risk persons.

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1. 2. 3. 4. 5. 6. 7.

Monitor health status to identify community health problems. Diagnose and investigate health problems and health hazards I the community. Inform, educate, and empower people about health issues. Mobilize community partnerships to identify and solve health problems. Develop policies and plans that support individual and community health efforts. Enforce laws and regulations that protect health and insure safety. Link people to needed personal health services and ensure the provision of health care when otherwise unavailable. 8. Ensure a competent public health and personal health care workforce. 9. Evaluate effectiveness, accessibility, and quality of personal and population-based health services. 10. Research for new insights and innovative solutions to health.

Fig. 12.2 The Ten Essential Public Health Services, developed by the Core Public Health Functions Steering Committee, describes the public health activities that should be undertaken in all communities [2]

Lessons and Questions from U.S. Diabetes Prevention Translation Research Several published research studies, carried out in real-world settings, have implemented modified versions of the lifestyle intervention developed by the US Diabetes Prevention Program (DPP) clinical research trial. These studies have very similar goals. For example, Aldana et al. ([3], p. 499) state, “The purpose of this study was to determine if the U.S. National Institutes of Health Diabetes Prevention Program (DPP) could be successfully implemented in a worksite setting.” Such studies are translation research studies, defined as “… applied research that strives to translate the available knowledge and render it operational in clinical and public health practice” ([4], p. 1794). Several of these studies were recently described [5]. It has been pointed out that “The availability of health-related interventions now in the marketplace exceeds by a considerable margin our societal ability to afford them” ([6], p. v). This observation suggests that economic costs are an important factor limiting wide-scale adoption of effective clinical and public health interventions. Therefore, a major focus of translation research for diabetes prevention is how best to use limited resources to deliver lifestyle intervention, while ensuring that weight loss is adequate to significantly decrease future incidence of—and health costs associated with—type 2 diabetes. To address this challenge, translation research studies use diverse approaches that affect key factors related to wide-scale implementation of diabetes prevention. These factors include participants’ diabetes risk; lifestyle curriculum; duration and intensity of intervention; attendance; body weight, diet, and physical activity monitoring; type of intervention staff; and weight loss achieved.

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A formal systematic review of all available diabetes prevention translation research studies is beyond the scope of this chapter. Instead, our purpose is to highlight some of the lessons learned, and still being learned and practical questions being raised, by examining eight US studies that represent varied approaches to translating the DPP clinical trial lifestyle intervention [3, 6–13]. We have organized this narrative review along the key factors listed above. For comparison we list the original DPP trial in the first row of each table describing various aspects of the studies (Tables 12.1–12.3).

Basic Characteristics of the Translation Research Studies The studies took place in diverse settings including community-based organizations such as Ys (also known as YMCAs) and churches, community-based outpatient clinics, health maintenance organizations, and worksites (Table 12.1). Sample sizes for the studies were generally modest, ranging from 8 participants to 295, with most studies having fewer than 100 participants. The studies’ “evaluation periods” (elapsed time between first and last outcome measurement) ranged from 3 months to 1 year. For some studies, the evaluation period was the same as the duration of the lifestyle intervention, while, for others, the evaluation period was substantially longer. Loss to follow-up of study participants during the evaluation period ranged from 0 to 43%, with most studies losing about 20% of their participants. The typical study participant was a moderately obese woman in her early-to-mid 50s. As in most weight studies, men were in the minority in nearly all studies. Since men and women are at similar risk of developing diabetes, more applied research is clearly needed on how to recruit and retain men for lifestyle intervention programs.

Factors Important for Broad and Effective Implementation Participants’ Diabetes Risk The proportion of study participants with diagnosed prediabetes ranged between 8 and 100%, with only three of the studies having 100% of participants with prediabetes (Table 12.1). Only one study [8] relied on a physician-reported diagnosis of prediabetes (based on either the fasting blood test or 2-h glucose tolerance test). The remaining seven studies performed their own diagnostic tests; three used capillary (“finger-stick”) blood tests (one was non-fasting [7]) and three administered fasting plasma blood tests. Only one study [3] used the oral glucose tolerance test. All of the studies that performed their own diagnostic tests also used some form of riskfactor screening test to reduce the number of participants that required the more costly and less convenient diagnostic blood tests (not shown in table).

Table 12.1 Characteristics of some US research studies that have translated the Diabetes Prevention Program (DPP) trial lifestyle intervention Loss to follow-up Duration over total of evaluation Na evaluation period (% prediabetes) Diagnosis of prediabetes First author Setting period (%) Age Men (%) Knowler; DPP Academic clinical 1,079 (100) Fasting plasma blood test 2.8 Years 6.5 50.6 32 Research centers and OGTT Group Ackermann Semi-urban YMCA 46 (100) Random capillary blood 1 Year 15 56.5 50 glucose test Amundson Local outpatient 295 (52) Primary care provider 1 Year 17 53.6 20 clinic, community diagnosis of impaired health center, fasting glucose or YMCA impaired glucose tolerance Aldana Worksite of local 35 (89) Oral glucose tolerance 1 Year 0 nr 34 employer test Boltri African American 8 (100) Fasting capillary blood 6 Month 0 nr nr church glucose test Seidel Distinct neighbor88 (42) Fasting plasma blood 3 Month 43 54 16 hoods in poor glucose test urban community Kramer Urban and rural 93 (46) Fasting plasma blood 3 Month for part 22 54.8 19 primary care glucose test of sample, practices 12 month for remainder McBride Local Health 37 (8) Fasting plasma blood 11 Month 18 51.9 41 Maintenance glucose test organizations Davis-Smith African American 10 (100) Fasting capillary blood 1 Year 20 nr 30 church glucose test nr Not reported a In lifestyle arm of study 35.7

37.4

35.7

nr

31.6

32.0

35.9

32.0

Mean BMI at baseline 33.9

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Table 12.2 Characteristics of lifestyle interventions in some US translation research studies that have translated the DPP trial lifestyle intervention # Core Physical sessions Attendance Weight Diet activity Lifestyle intervenFirst author Curriculum Format (weeks) (%) Post-core monitoring monitoring monitoring tion staff Staff training Knowler; DPP Individual 16 (24) 15 (95) Monthly 1:1 Each Participants From Registered Not described DPP (1:1) or group session recorded particidietitians, Research sessions 7 days of pant logs masters level Group dietary exercise intake physiologists, each psychologists, week in health booklet educators Ackermann Closely Group 16 (20) 9 (57) Monthly Each nr nr YMCA staff with 2½-Day training modeled (8–12 group session associate or by former after DPP memsessions bachelor degree DPP trial bers) or equivalent staff training/ certification in exercise or health Amundson Closely Group 16 (16) 13 (83) Monthly Each From From Registered 2-Day training modeled (8–34 group session participarticidietitians by former after DPP memsessions pant logs pant logs DPP trial bers) staff Aldana Closely Group and 16 (24) 11 (67) Monthly Measured From Logs and Registered nurses nr modeled 1:1 group by nurse participedomand certified after DPP sessions monthly pant logs eter health educator

6 (7)

12 (12)

(12– 15)

5 (78)

nr

8 (67)

6 (52)

12 (12)

12

10 (62)

16 (16)

None

Monthly group sessions for part of sample only Monthly group and 1:1 sessions

None

None

Each session

Frequency not reported

Selfmonitored in charts twice weekly

Frequency not reported Charted weekly nr

Weekly exercise logs

A dietitian and an exercise specialist

One, 60-min training session

nr

2-Day training by former DPP trial staff Registered nurses, 2-Day training health educator, by staff that registered originally dietitian, and developed exercise DPP specialist

“Volunteer medical One training personnel” session

Registered dietitian, clinical exercise physiologist From From “Healthcare participarticiprofessional pant logs pant logs (HCP)”

Participant weekly food records

From Monitoring particibooks pant logs and pedometer

Logs and From pedomparticieter pant logs

nr

nr Not reported (this does not mean the activity did/did not occur, only that it was not reported in the publication)

Modeled Group (10 after DPP members)

Davis-Smith

Group (size nr)

Modified DPP

Closely Group (8 modeled memafter DPP bers) Modified Group DPP (5–13 members) Modified Group DPP (size nr)

McBride

Kramer

Seidel

Boltri

nr Not reported a Personal communication with Dr. Ronald Ackermann

Table 12.3 Weight loss outcomes during the “core sessions” component of some US translation research studies that have translated the DPP trial lifestyle intervention Time between first and last # Core sessions “Core” weight measure First author (weeks) (weeks) Weight loss (kg) Weight loss (%) ³7% Loss (%) ³5% Loss (%) Knowler; DPP Research Group 16 (24) 24 6.5 7 50 nr Ackermann 16 (20) 24 5.5 6 36 59a Amundson 16 (16) 16 6.7 6.7 45 67 Aldana 16 (24) 24 2.9 3.3 nr nr Boltri 16 (16) 16 3.4 3.6 nr nr Seidel 12 (12) 12 nr nr 26 46 Kramer 12 (14) 12 3.4 3.5 24 52 McBride 12 (12) 12 5.0 4.6 nr nr Davis-Smith 6 (7) 24 4.0 3.8 nr nr

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Because the over-arching goal for public health translation of diabetes prevention is to ensure that biologically effective lifestyle programs are provided at a sustainable economic cost, participants’ risk status is of great importance. Persons with prediabetes have annual risks of developing diabetes that can be 10–15 times higher than people with normal glucose levels [14]. Because persons with normal glucose levels have substantially lower risk for future diabetes—and diabetes-related health costs—diabetes prevention programs that include participants with normal glucose levels are much less likely to save money [15]. It is unlikely that third party payers (private health insurers, employers, or federal, state, and local governments) will pay for new health interventions, such as diabetes prevention, unless there is confidence that the intervention will at least pay for itself by reducing future healthcare costs. The practical reality, however, is that most persons with prediabetes do not know they have it [16]. Diagnosis requires access to a health professional and medical laboratory. Therefore, an important challenge of community-based lifestyle intervention programs is how to effectively partner with the clinical sector to accurately and conveniently identify potential lifestyle intervention participants with prediabetes.

Lifestyle Curriculum and Intervention Format All studies reported using versions of the original DPP lifestyle intervention curriculum that had been modified for use in group settings (Table 12.2). Some studies reported modifying the sequence of topics introduced during the 16-week “core” phase, such as introducing the topic of physical activity earlier [10, 11], as well as introducing calorie-counting at the same time that fat-gram counting is discussed [10, 11]. Only one study [3] reported offering both individual and group sessions. Reported group sizes ranged from 5 [10] to 34 [8], with most studies having groups between 8 and 12 participants. Two studies did not report group sizes [11, 12]. It is likely that differences among the curricula were much smaller than the similarities. Indeed, the DPP trial curriculum was developed for a very diverse study group (45% ethnic minorities) and had a very similar impact on reduction in diabetes incidence regardless of ethnic background [17]. To rapidly scale-up diabetes prevention in the United States, especially in nonacademic settings where research is not the main objective, access to a standardized, easily available lifestyle curriculum is essential.

Duration and Intensity of Intervention Like the DPP trial, the intervention period in most studies had two phases, a “core” and a “post-core” phase (Table 12.2). The core phase in these studies consisted of intensive group sessions, often held weekly; the subsequent post-core phase consisted of less intensive group sessions held monthly. The purpose of the post-core

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phase was intended to help participants improve and maintain weight loss, dietary, and physical activity behaviors learned during the core phase. The studies generally reported that group sessions lasted about 1 h (not shown). All of the studies included a core phase. Four of the studies had 16 core sessions, as did the DPP trial, and these were offered over a 16–24-week period; three studies included 12 core sessions offered over a 12–15-week period; and one study included 6 core sessions, offered over a 7-week period. Core sessions met no more frequently than weekly. Five of the eight studies included post-core phases with monthly sessions. Depending on the core phase’s duration, the post-core sessions occurred over the remaining 6–9 months. The impact on weight loss and economic costs of offering post-core sessions more or less frequently than monthly has not, to our knowledge, been studied in translation research. In addition, the utility and economic implications of offering post-core sessions for additional periods beyond the initial year of lifestyle intervention has not been studied in translation research. Only one of the four studies that used fewer than 16 core sessions explicitly reported a reason, and this was to reduce the cost of the intervention [11]. An additional reason may be for the convenience of the participants, which might increase session attendance and ultimately weight loss.

Attendance The absolute number of core sessions attended was highest in those studies with 16 sessions, ranging from 9 to 13 sessions attended. Mean attendance in studies offering 12 sessions ranged between 6 and 8, and, in the study with 6 core sessions, average attendance was 5 sessions. These limited data suggest that offering more core sessions will mean more attendance. Attendance has important implications for weight loss, which we discuss later in this chapter.

Body Weight, Diet, and Physical Activity Monitoring A key component of successful behavioral weight loss programs is self-monitoring of body weight, diet, and physical activity, which involves daily weighing and daily logs filled out by participants [18]. Further monitoring occurs during program sessions when participants are weighed and their diet and physical activity logs are reviewed by lifestyle intervention staff. Self-monitoring was one of the most emphasized components of the original DPP trial lifestyle intervention [19]. All but two of the studies reported that self-monitoring of weight, diet, and physical activity were included in the lifestyle intervention; three of the studies reported that pedometers were used, in addition to paper logs, for physical activity monitoring.

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Community Approaches to Diabetes Prevention

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Intervention Staff Healthcare professionals (HCPs), including physicians, certified diabetes educators, registered dietitians, nurses, and exercise specialists, can all play key roles in diabetes prevention in high-risk persons. HCPs can assess risk status through screening and diagnostic tests commonly performed in the clinical setting. HCPs can help patients who are identified with prediabetes interpret test results and understand future implications of their risk status. Importantly, HCPs can ensure that these high-risk patients are actively and effectively referred to community-based lifestyle intervention programs in the local community. On follow-up visits, HCPs can review progress of the high-risk patients who participate in the community-based lifestyle intervention program and reinforce improvements in body weight and dietary behaviors that have been achieved. For those high-risk patients in whom lifestyle intervention is, for whatever reason, not effective, timely decision-making about starting pharmacotherapy is also under the purview of the HCP. Substantial improvements in the cost-effectiveness of the DPP lifestyle intervention are made by offering the intervention in group, rather than individual, format [20]. Further reductions in the cost of the lifestyle intervention can occur when less expensive staff deliver the intervention. The original DPP trial employed staff that had Master’s degrees in clinical disciplines (Table 12.2). In contrast, one of the translation studies used regular employees of a local YMCA who had Associate or Bachelor degrees in health-related areas [7]. The other studies commonly employed dietitians, nurses, health educators, or exercise specialists. Some studies reported using “volunteer medical personnel” or “healthcare professionals,” not otherwise described. The study that used Y staff to deliver the intervention reported that “… the hourly wage of Y group instructors was approximately half that of behavioral experts in the DPP” [7]. In a separate publication, the same study reported the total 1-year cost for the lifestyle intervention including supplies, personnel time, and program administration was $275–325/participant [21]. The study by Kramer et al. [11], which used HCPs, including registered dietitians, nurses, health educators, and exercise specialists, to deliver the intervention, estimated the cost/participant of a 1-year program with 12 core sessions and 9 post-core sessions was about $300. None of the other studies reported program costs. It is noteworthy that the DPP trial lifestyle intervention reported that the first year cost was $1,399/participant [20]. Six of the eight studies reported training of intervention staff. Training ranged in duration from a single 60-min session to 2½ days. Four of the studies used expert staff from the original DPP study to conduct training. Academic or clinical certification may not be necessary to be an effective “lifestyle coach.” Rather, empathy and group leadership skills may be more important attributes for effective lifestyle intervention [22]. If so, further reductions in program cost might be achieved, without reducing effectiveness, by using intervention staff without clinical qualifications or college degrees. To effectively scale-up diabetes prevention in the United States, an accessible and cost-effective national system must be developed to train the large numbers of lifestyle intervention staff. This effort requires that a significant cadre of “master trainers” be developed to train intervention staff.

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Weight Loss Achieved The DPP trial found that weight loss was the single most important factor in reducing the incidence of diabetes in high-risk persons, with those losing 5 kg or more experiencing a 58% reduction in 3-year diabetes incidence [23]. In addition, after statistically adjusting for changes in diet and physical activity, the DPP trial found that for every 1 kg of weight loss there was a 16% reduction in risk of developing diabetes, but the impact of diet and physical activity was not statistically significant after adjusting for weight loss. This occurred because weight loss is the key variable that mediates reduction in diabetes incidence brought about by improvements in diet and physical activity. In the absence of significant weight loss, however, physical activity still had an impact on diabetes risk, albeit somewhat less than losing 5 kg or more. Among the 30% of DPP trial lifestyle participants who lost