Meredith F. MacKay. Copyright by Meredith F. MacKay 2008

Transcription

1 Evaluating alternate anthropometric measures as predictors of incident type 2 diabetes mellitus (T2DM): The Insulin Resistance Atherosclerosis Study (IRAS). By Meredith F. MacKay A thesis submitted in conformity with the requirements for the degree of Master s of Science Nutritional Sciences University of Toronto Copyright by Meredith F. MacKay 2008

2 Evaluating alternate anthropometric measures as predictors of incident type 2 diabetes mellitus (T2DM): The Insulin Resistance Atherosclerosis Study (IRAS). Abstract Meredith F. MacKay Master s of Science Department of Nutritional Sciences University of Toronto 2008 The goal of this study was to compare different anthropometric measures in terms of their ability to predict T2DM and to determine whether predictive ability was modified by ethnicity. Anthropometrics were measured at baseline on 1073 non-hispanic Whites (nhw), African Americans (AA) and Hispanics (HA), of which 146 developed T2DM after 5.2 years. Logistic regression models were used with areas under the receiver operator characteristic curve (AROC) comparing the prediction of models. Overall, there was no clear distinction between measures of overall and central obesity in terms of T2DM prediction. Waist-height ratio (AROC=0.678) was the most predictive measure, followed by BMI (AROC=0.674). Results were similar in nhw and HA, although, in AA, central adiposity measures best predicted T2DM. Measures of central and overall adiposity predicted T2DM to a similar degree, except in AA where central measures were most predictive. ii

3 Acknowledgments Thank you to my family for always being so supportive and encouraging me to set goals and helping me follow them through and thank you to my supervisor Dr. Hanley for always being there with support and advice when I was unsure of my next step. iii

4 Table of Contents Acknowledgments... iii Table of Contents... iv List of Tables... viii List of Figures... ix List of Appendices... x Chapter 1 Introduction and Literature Review Introduction... 1 Type 2 diabetes mellitus (T2DM) has become a worldwide epidemic and a major public health burden in the 21 st century (1, 2). It is one of the most common non-communicable diseases and the fifth leading cause of death in the world (2-4). The IDF estimated that in 2003 the global prevalence of T2DM (among year olds) was 194 million with the expectation that this number would increase to 333 million by 2025 (2). This figure is in agreement with the World Health Organization (WHO) estimate that 366 million people would have T2DM by 2030 (5) T2DM: Overview and epidemiology Definition and diagnostic criteria Prevalence Complications Risk factors Non-modifiable risk factors Modifiable risk factors Diabetes Risk Scores Obesity and diabetes Directly measured adiposity and T2DM Visceral adiposity Subcutaneous adiposity Body composition assessment iv

5 5.1 Direct measures of body composition and adipose tissue Indirect measurements of body composition BMI Waist circumference (WC) Waist-height ratio (WHtR) Waist-hip ratio (WHR) Hip circumference (HC) Waist circumference manipulations Skinfold thickness Percent (%) body fat from bioelectrical impedance analysis (BIA) Rationale Objectives Hypothesis Research questions Chapter 2 Study Design and Methods The Insulin Resistance Atherosclerosis Study (IRAS) Design, subject characteristics and follow-up participation Baseline measurements Anthropometric and blood pressure measurements Oral Glucose Tolerance Testing Laboratory procedures Standardization and reliability of study procedures Diabetes Risk Scores Statistical analysis Evaluation of distributions and necessary transformations Univariate analysis of baseline characteristics v

6 12.3 Association of baseline anthropometric measures with incident diabetes mellitus Independent variables Dependent variables Interaction testing Multiple logistic regression models and odds ratios Areas under the receiver operator characteristic curve to compare the predictive ability of models Chapter 3 Manuscript: Prediction of Type 2 Diabetes using alternate anthropometric measures: The Insulin Resistance Atherosclerosis Study Introduction Methods Laboratory procedures Standardization and reliability of study Statistical analysis Results Discussion Chapter 4 Discussion Summary Pathogenic mechanism of visceral adipose tissue Anthropometric measurement and Diabetes Mellitus: Previous literature Relative prediction of anthropometry in the context of more complex models Comparisons to previous literature Explaining discrepancies between AROC and OR values Clinical and public health recommendations Strengths and limitations Conclusion References or Bibliography vi

7 Appendices vii

8 List of Tables Table Table Table Table Table Table Table Table Table Table Table Table viii

9 List of Figures Figure Figure Figure Figure Figure ix

10 List of Appendices Appendix Appendix x

11 1 Chapter 1 Introduction and Literature Review 1 Introduction Type 2 diabetes mellitus (T2DM) has become a worldwide epidemic and a major public health burden in the 21 st century (1, 2). It is one of the most common non-communicable diseases and the fifth leading cause of death in the world (2-4). The IDF estimated that in 2003 the global prevalence of T2DM (among year olds) was 194 million with the expectation that this number would increase to 333 million by 2025 (2). This figure is in agreement with the World Health Organization (WHO) estimate that 366 million people would have T2DM by 2030 (5). There are many risk factors that contribute to the development of T2DM - both non-modifiable, such as family history, ethnicity and perinatal factors (6), as well as modifiable, such as obesity, dietary patterns, physical activity, smoking, coffee and alcohol consumption. Obesity is the best described risk factor for T2DM (2). However, central obesity plays a role in the risk of T2DM independently of overall obesity, and has been shown to be a more significant risk factor than overall obesity in some studies (7). There are many methods used for the measurement of obesity and body fat distribution. Direct methods of assessing body fat distribution include computed tomography (CT), magnetic resonance imaging (MRI) and various other methods, all of which are labor-intensive and costly, and as such are not applicable in large epidemiological studies or in regular clinical practice (8,

12 2 9). Obesity and fat distribution are therefore often determined by anthropometric measurements, such as body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-tohip ratio (WHR), waist-to-height ratio (WHtR), skinfold thickness measurement as well as bioelectrical impedance analysis (BIA) (10). It is not known however, which of these anthropometric variables best predicts T2DM across genders and ethnic groups. 2 T2DM: Overview and epidemiology 2.1 Definition and diagnostic criteria Diabetes Mellitus refers to a group of metabolic diseases that share the common characteristic of hyperglycemia, which may be the result of deficient insulin secretion, poor insulin function or both (11). This leads to ineffective action of insulin on target tissues, causing anomalies in the metabolism of carbohydrate, fat and protein (11). Symptoms include: polyuria, polydipsia, weight loss, sometimes with polyphagia, and blurred vision, although the disease may be asymptomatic in its early stages (11). A joint World Health Organization (WHO) and International Diabetes Federation (IDF) taskforce in 2005 implemented updated criteria for the diagnosis and classification of diabetes mellitus (12). Current criteria for diabetes diagnosis include one of the following (11): 1) symptoms of diabetes in addition to a plasma glucose concentration of greater than or equal to 11.1mmol/l (200mg/dL); 2) a fasting (no caloric intake for at least 8 hours) plasma glucose level of greater than or equal to 7.0 mmol/l (126mg/dl); or 3) a 2-hour plasma glucose level greater

13 3 than or equal to 11.1 mmol/l (200mg/dl) measured after a 75-gram oral glucose tolerance test (OGTT ) (11). The above diabetes diagnostic and classification criteria were derived from epidemiological studies of prevalence and incidence of diabetes-specific microvascular complications, which result from sustained levels of hyperglycemia (12). Hyperglycemia leads to microvascular damage, causing a lower quality of life due to a lower life expectancy and a greater morbidity from diabetic complications (12). 2.2 Prevalence The global community is on the verge of a joint pandemic of obesity and diabetes (13). Diabetes is becoming an epidemic in developed and especially in developing countries as the already high prevalence is expected to increase significantly over the course of the next few decades (2). The WHO estimated the global diabetes prevalence in 2000 to be 171 million (14). The IDF estimated in 2003 that 194 million people had diabetes worldwide and this number is expected to grow to 333 million by 2025 (2) and 366 million by 2030 (14). These accelerated rates are attributed to enormous increases in the prevalence rates of T2DM in developing countries (5). Type 2 diabetes is also considered to be the world s fifth leading cause of death by the WHO (4). There is a gender difference in diabetes prevalence, as females have prevalence rates that are nearly 10% higher than that of males around the globe (2). There are also vastly different prevalence estimates between countries and populations in the world, due primarily to ethnic differences (2). In adults (30-64y), the highest prevalence of T2DM in the world occurs among

14 4 the Pima Indian adult population in the U.S. (50%), followed by the Nauruans of the South Pacific (41%) (15). However, in some older, more traditional communities within developing countries, T2DM prevalence rates are lower than 3% (15). Rates in European, Asian, Hispanic and Arabic populations vary between three and twenty percent (15). 2.3 Complications In most developed countries T2DM is a leading cause of morbidity and mortality (2). If undiagnosed or untreated, a perpetual state of hyperglycemia often causes long-term micro- and macrovascular damage (11). Microvascular diabetic complications include retinopathy which may lead to adult-onset blindness, nephropathy causing kidney failure, peripheral neuropathy leading to foot ulcers and potential limb amputation, and autonomic neuropathy leading to gastrointestinal and genitourinary complications. In addition, macrovascular complications are common and include sexual dysfunction (11) and cardiovascular disease, as 75% of all T2DM patients die from atherosclerotic complications (16), and are at a 2 to 4 times greater risk of coronary artery disease than those free of diabetes (17). Other severe and potentially fatal consequences of diabetes include hyperglycemia and ketoacidosis or nonketotic hyperosmolar syndrome (11). 2.4 Risk factors The current epidemic of type 2 diabetes has been recognized as the result of a genetically predisposed population (18) that is progressively more sedentary and a victim of the westernized calorie dense diet (4, 11, 19, 20). Approximately half of the risk of developing T2DM has been attributed to environmental exposures and the other half to genetics (21), making it a consequence of both modifiable and non-modifiable risk factors.

15 5 Despite the complex web of etiology surrounding T2DM, it is an obesity-dependent disease with obesity as its number one risk factor (3). Obesity, overall as well as centrally distributed adiposity, is a well established risk factor for T2DM (22-26), with diabetes risk shown to increase in a dose-response fashion (7). Central fat, a marker of excess visceral adipose tissue (VAT), and upper-body nonvisceral fat are the greatest source of metabolic complications that lead to development of diabetes (20, 22) Non-modifiable risk factors Ethnicity Prevalence rates of T2DM are greater in African American, Hispanic, Native American, Asian and Pacific Island populations compared to non-hispanic white populations (2, 27). African Americans have been shown to have a two to three times greater prevalence of T2DM than non- Hispanic whites (28), which could be attributed to ethnic variation in body fat distribution (27). However, Filipino, Japanese, and Chinese populations also show a higher prevalence of T2DM than non-hispanic white populations, despite having similar regional adipose distribution (29). These ethnic variations in T2DM prevalence may be indicative of ethnic differences in quantities of VAT, although despite the greater diabetes prevalence in African Americans, studies have shown non-hispanic White populations to have more VAT than African Americans at similar levels of obesity (30, 31). African American men have less visceral and more subcutaneous adipose tissue than non-hispanic white men at any corresponding level of obesity (30), and African American women are more insulin resistant than non-hispanic white women when

16 6 matched for age, WHR and obesity level. However, the association between insulin sensitivity and VAT is strong and significant in both groups (31). Nondiabetic African-Americans and nondiabetic Hispanics have a higher degree of insulin resistance and a higher acute insulin response than nondiabetic non-hispanic whites, suggesting that a greater insulin resistance might be largely responsible for the higher prevalence of T2DM in these ethnic populations (32). Hispanic subjects have a higher prevalence of T2DM than non- Hispanic whites, even after adjusting for a greater degree of adiposity in the Hispanic subjects, thus Hispanic subjects present a lower glucose tolerance and a higher degree of hyperinsulinemia than would be expected from their degree of obesity (33). Other Non-Modifiable Risk Factors In addition to ethnicity, other non-modifiable risk factors for T2DM include: 1) a family history of the disease, defined as one or more first degree relatives with T2DM, 2) a history of gestational diabetes, 3) a history of elevated blood glucose levels or impaired glucose tolerance (21), and 4) fetal under-nutrition and low birth weight (34). Family history is an independent risk factor for T2DM as well as for its precursors (38). Epidemiological studies have shown that those with a family history of T2DM manifest risk factors such as defective insulin action (35), glucose intolerance, dyslipidemia, weight gain and poor beta cell function (36) earlier in life than those without a family history (35) and are more likely to develop the disease themselves, with the risk increasing if both parents are affected (37). Epidemiological studies have also shown a higher incidence of T2DM later in life for those born with a low birth weight, which was

17 7 confirmed by animal studies that showed poor nutrition to the fetus increased future risk of the metabolic syndrome and T2DM (34) Modifiable risk factors Obesity is the most critical risk factor in the development of T2DM and it is modifiable through lifestyle changes (39). Obesity and its association with diabetes will be discussed in depth in subsequent pages (see section 5 Obesity and Diabetes). Improvements in glucose tolerance have been related both to increasing levels of physical activity as well as weight reduction, as both are known to independently reduce T2DM risk (40). With thirty minutes of daily physical activity and a five percent reduction in body weight (41), incident T2DM was reduced by 55% in high risk individuals (42). Diet is another modifiable risk factor for T2DM development, outside of its role in weight management (43). Prospective studies have shown higher intakes of monounsaturated or polyunsaturated fats to have beneficial effects on glucose metabolism and insulin sensitivity, while diets rich in saturated and trans fats have been shown to decrease insulin sensitivity and detrimentally affect glucose metabolism (44). The protective effects of whole grain and the benefits of cereal fiber consumption have also been documented (43, 46). Diets high in dietary fibre decrease the insulinemic response to other dietary carbohydrates by slowing the absorption of carbohydrates (47). Some observational studies have shown a protective effect from regular consumption of coffee on the risk of T2DM (45).

18 8 Alcohol consumption and cigarette smoking are also risk factors for T2DM development (48, 49). They affect fasting glucose indirectly via their effects on obesity and also have a direct effect on glucose via physiological factors related to insulin secretion and insulin resistance (50, 51). Heavy smokers tend to be insulin resistant and show a correlation with higher prevalence rates of hyperglycemia (52), hyperinsulinemia as well as dyslipidemia compared to nonsmokers (50). Prospective studies have shown smoking to predict T2DM (53). Moderate alcohol consumption is correlated to greater insulin-mediated glucose uptake, lower plasma glucose and insulin, which may contribute to a lower disease risk (51). In contrast, high alcohol consumption is associated with a twofold increase in T2DM risk (49), and abstainers are also at increased risk (49). Possible explanations for this U-shaped relationship include: 1) moderate alcohol consumption increases HDL cholesterol while high consumption increases body weight, triglycerides and blood pressure (54), and 2) moderate alcohol consumption has antiinflammatory effects (the benefits for which will be described in a subsequent section (5a Visceral Adiposity) as well as insulin sensitizing effects resulting in lower plasma insulin concentrations (54). 3 Diabetes Risk Scores T2DM is an asymptomatic disease in its early stages and as such can remain undiagnosed for many years following its onset due to the gradual nature of its development (55). At diagnosis, many patients already exhibit many of its complications, with half showing some form of diabetic tissue damage, 20% exhibiting retinopathy, and 14% exhibiting peripheral vascular disease. Early detection has many benefits: 1) decreased risk of cardiovascular disease complications which allows for more effective risk management, 2) prevention of microvascular complications (56). A further benefit to diabetes risk scores is the potential delaying or even

19 9 preventing the onset of T2DM with lifestyle interventions and weight management (41, 57, 58). Prevention of T2DM is now considered a national priority and an important component of Canada s diabetes strategy (59). Efforts have been made to create diabetes risk scores to identify those at high risk of developing T2DM, or identify those who already unknowingly manifest the disease. By identifying underlying indicators of T2DM before the clinical signs and symptoms of the disease present through a simple risk analysis based on easily obtained information, or by self-administered questionnaires, the number of individuals required to endure glucose measurement diagnosis, more specifically oral glucose tolerance tests, could be minimized (60, 61). Variables used in current risk scores include those obtained through clinical visits or those obtained through self-administered questionnaires (see Table 1). Many scores have been developed using a full model as well as a more quickly and easily performed clinical model (62). The full models contain more numerous and more complex variables, while the clinical models reduce the number of variables and include those used routinely in clinical practice, making them more easily calculated (62). Most risk scores use BMI (56, 61, 62, 65) as the anthropometric variable to classify obesity, however some use WC (63) or BMI and WC together (64, 66). However, it remains unknown whether alternate anthropometric measurements would provide greater predictive power in the context of variables typically used in these risk scores.

20 10 Table 1.1. Summary of current diabetes risk score model variables Risk Factors Lindstrom* (64) Schulze (63) Glumer (61) Stern** (62) Griffin (56) Baan*** (65) Aekplakorn (66) Age 45-54= =3 (yrs) x = = = = 18 (yrs) x Age (yrs) x Per 5yr group, starting 55yr= = = =1 50=2 Sex Female=0 Male = 4 Female = Male=0 Female= Male = 0 Female=0 Male=5 Female=0 Male=2 BMI (kg/m 2 ) > 25-30=1 >30=3 <25 = =7 30=15 (kg/m 2 ) x 0.07 <25= =0.699x BMI =1.97xB MI 30= x BMI 1.Obese ( 30)=5 Nonobese=0 <23=0 23,<27.5 = = 5 WC (cm) Men, 94- <102; Women, 80- <88 =3 Men, 102; Women 88=4 (cm) x 7.4 Male <90; female<80 = 0 Men 90; Women 80=2 PA (hrs/week) x -2 Inactive leisure time= 6 Active leisure time= 0 Fam Hx No parents=0 Parent=7 No=0 Parent/ sib= None=0 Parent/ sib= Parent+ sib= No=0 Parent/sib= 2 Hypertension Rx=2 Yes=46 No=0 Yes=10 No=0 Rx = Rx=4 Yes=2 No=0 Ethnicity HDL SBP FBG MA= nhw=0 (mg/dl) x (mm Hg) x (mg/dl) x Height (cm) x= - 2.4

21 11 Alcohol 20-40g/d = - 20 Smoking Dietary: Former=24 Current=64 Coffee (>150g/d)= -4 Whole grains Yes= -9 Red Meat (>150g/d)= 49 None=0 Ex= Current= Hx EBG Hx of Diabetes=5 Abbreviations: Body mass index (BMI), waist circumference (WC), physical activity (PA), family history (Fam Hx),Mexican American (MA), non-hispanic White (nhw), Prescription medication (Rx), high density lipoprotein (HDL), systolic blood pressure (SBP), fasting blood glucose (FBG), History of elevated blood glucose (Hx EBG). For examples of score calculation see Appendix 1. *Lindstrom: Concise model. **Stern: Clinical model ***Baan: Predictive Model 1 (PM1). 4 Obesity and diabetes Obesity, a condition in which there is an excess of stored adipose tissue in the body (67), is the result of a growing exposure, or an overexposure, to environmental and behavioral risk factors, such as physical inactivity, poor diet and stress, all of which affect the phenotype of those who are genetically susceptible to obesity (68). Unfortunately, the current generation is entering adulthood with levels of obesity that have never been seen before (13). Obesity is considered a global public health as well as an economic crisis (15). Over 300 million people around the world are obese (13), and according to the WHO, obesity has reached epidemic levels in the 21 st century and is now the most common nutritional disorder in the western world (67). The coinciding of both the obesity epidemic and that of T2DM has led to the term diabesity, to stress the importance of the relationship between the two diseases (3).

22 12 Obesity is strongly associated with risk factors for metabolic diseases (67) and is the best described risk factor for T2DM (23, 24, 69). It is also associated with dyslipidemia, hypertension and some cancers (7). Obesity is associated with early characteristics of T2DM development, such as impaired glucose tolerance (70), and is thought to be causally linked to T2DM through insulin resistance (2, 5, 11). Weight gain caused by excessive fat accumulation leads to a resistance to insulin through changes in endocrine activity (2), specifically through the increased release of non-esterified fatty acids and glycerol, hormones (e.g. leptin and adiponectin), and pro-inflammatory cytokines (TNF-α, IL-6) (71, 72) which in turn lead to an increased demand for the production of insulin in the pancreas (2, 5). If this process is coupled with poorly functioning pancreatic beta cells, or the decreased insulin production which occurs with age, then blood glucose levels cannot be controlled (73) which eventually leads to the development of diabetes (2). In other words, diabetes arises when the pancreas is unable to sustain the body s requirement for insulin (74). 4.1 Directly measured adiposity and T2DM Adipose tissue is found throughout the body and its amount and distribution are affected by age, gender, ethnicity, genetics, diet, physical activity, endocrine status and pharmacological agents (8, 75). Adiposity and its distribution contribute to disease risk (26, 76) as higher rates of disease are found in subjects with centrally distributed adipose tissue, which supports the notion that the assessment of body fat distribution is critical in determining the risk for metabolic diseases (11, 25, 77-79).

23 13 As is summarized in table 1.2, there have been a number of studies documenting the associations between obesity, determined by direct measures including computed tomography (CT) and magnetic resonance imagine (MRI), and T2DM and related disease risk factors. These associations are modified by the location of fat in the body, and results tend to show a greater risk of disease for an upper/central body distribution of adipose tissue rather than a lower body distribution of adiposity. As described in table 1.2, the majority of studies find central adiposity to be significantly associated with T2DM and associated risk factors (80-84), while some have found peripheral or subcutaneous adiposity to play a more prominent role in insulin resistance and insulin-stimulated glucose disposal (85, 86) and still others have found no effect (87) from either distribution or have found that both distributions are equally correlated to T2DM (88). Insulin resistance and increased plasma triglycerides also have a documented correlation with central obesity and there is a demonstrated link between the site of fat distribution and adipocyte structure, lipolytic activity and the metabolic profile (78, 89). Studies of variations in total body fat have documented that all variance observed in insulinmediated glucose disposal (Rd) is not accounted for by total body fat or overall obesity alone, as Rd values appear to reach a maximum threshold at a body fat content of approximately 30% (85), suggesting other risk factors such as regional adiposity, physical activity and genetics may also play important roles in disease risk and development (85). Diabetes risk has been shown to increase in a dose-response manner with increased abdominal obesity (4, 25, 79, 80, 90). Even patients who are not classified as obese by traditional standards (e.g. BMI) but have a greater accumulation of body fat in the abdominal area show the metabolic effects of obesity, including an increased risk of T2DM (11). Clinical evidence has shown that diabetes is more strongly

24 14 associated with measures of central obesity than with measures of overall obesity (89, 91, 92). Many (22, 23, 69, 80, 87, 93-96), but not all (97) studies have shown this relationship to be independent of overall obesity, as the latter did not find any additional predictive ability of central adiposity after adjusting for overall obesity (see table 1.3). The above discussion as well as the studies outlined in table 1.2 taken together, illustrate that the site of adipose tissue distribution is vitally important to determine disease risk (98). However, there remains some controversy in the literature over the relationships between the different adipose tissue distribution sites and their relative contributions to the etiology of T2DM (85, 87, 99). Adipose tissue located viscerally has been shown to increase the risk of diabetes development (100) and precede the onset of T2DM (80), whereas, the more peripherally distributed subcutaneous adipose tissue has not been shown to have the same strength of association (100), and both have shown independent relationships with insulin resistance (101). These inconsistencies may be partly due to ethnic variations in adipose tissue distribution (30, 31, 33, 102). At similar levels of obesity (as measured by BMI) and similar distributions of increased central adiposity (as measured by WHR), African American women have less visceral fat than non-hispanic white women (31, 103). African American men also have less visceral adipose tissue (VAT) and more subcutaneous adipose tissue (SAT) than similarly obese non- Hispanic white men (30).

25 Visceral adiposity Visceral adipose tissue (VAT) is defined as the adipose tissue which is found deep within the body cavity, around the body s internal organs, in the intrathoracic, intraabdominal and intrapelvic areas (8). Visceral adipose tissue has been shown to be an important risk factor for development of the metabolic syndrome, insulin resistance and T2DM (25, 80). The amount of VAT is an important determinant for a subject s risk of metabolic complications, as large amounts of VAT are associated with disruptions in glucose and lipid metabolism, which increase the risk of T2DM (87, 89). Subjects with excess VAT show a greater incidence of coronary heart disease, and this effect is consistent in both genders and is independent of overall obesity ( ). Visceral adiposity shows a negative association with insulin sensitivity - as VAT increases, insulin sensitivity decreases (in other words, insulin resistance increases) (107). Viscerally obese patients comprise a subgroup of generally obese patients that represent the highest glycemic and insulin responses from an oral glucose dose and thus are at the highest risk for the development of diabetes (96). Visceral adipose tissue leads to insulin resistance by decreasing the insulin-mediated disposal of glucose (95, 108). Viscerally located adipose tissue plays a metabolically active role in the development of insulin resistance through an increased portal release of free fatty acids (FFAs) (42, 109). Visceral adipocytes have a higher lipolytic activity, increasing the influx of FFAs released into portal circulation, (110) which negatively affects the hepatic metabolism of insulin

26 16 (42). It is necessary to differentiate between various visceral adipose tissue types, namely retroperitoneal and intraperitoneal, as only intraperitoneal fat drains directly into the portal system causing an increase in circulating FFAs (85). Visceral adipose tissue is also known to release adipokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and adiponectin (111). Obesity is characterized by mild, low-grade, chronic inflammation (111), thus circulating levels of TNF-α and IL-6, markers of inflammation, are increased in the obese, while levels of adiponectin, which has anti-inflammatory effects, are decreased, leading to a proinflammatory state (111). It is hypothesized that this state of chronic inflammation plays a causal role in the development of T2DM (111). TNF- α and IL-6 have also been shown to impair insulin signaling pathways; adiponectin has shown a strong and consistent inverse relationship with insulin resistance (112). Surgical removal of visceral adipose tissue in animal models has documented significant improvements in both peripheral and hepatic insulin sensitivity and glucose tolerance (113). There is also a gender difference in diabetes risk from visceral fat accumulation such that increasing visceral fat has shown to increase a women s risk of developing diabetes by three times, and increase a man s risk by a more modest 30% (114) Subcutaneous adiposity Subcutaneous fat is adipose tissue that has been deposited beneath the skin s surface and comprises approximately twice as great a proportion of total body fat as does the viscerally located, detrimental, intraperitoneal fat that increases portal free fatty acid circulation (85).

27 17 Obese subjects with a subcutaneous, accumulation of adiposity tend to not display an increased risk of metabolic disease (99). Some studies documenting the association between diabetes risk and subcutaneous adiposity have shown subcutaneous fat to be at least as important visceral fat in the etiology of insulin resistance (see table 1.2) (85, 86). This discrepancy may be explained by the discovery of pockets of deep subcutaneous fat that have been associated with insulin resistance and observed to act in a manner more similar to visceral fat, whereas the more superficially located subcutaneous fat shows the traditionally weak associations with insulin resistance (115). Surgical removal of subcutaneous adipose tissue in animal models observed no effect on glucose tolerance (113). As well, liposuction of subcutaneous adipose tissue in human subjects, in both patients with T2DM and those without T2DM, showed no improvements to insulin sensitivity in skeletal muscle, the liver or adipose tissue (116). It also had no significant effect on other metabolic disease markers of blood pressure, fasting plasma glucose, insulin, and lipid concentrations and concentrations of plasma markers of inflammation and insulin resistance (TNF-α, IL-6 and adiponectin) (116). These studies support the hypothesis that subcutaneous fat plays a lesser role in the etiology of insulin resistance and T2DM than does visceral fat.

28 18 Table 1.2. Review of the association of direct measures of body fat distribution to type 2 diabetes mellitus and related metabolic disorders. Paper Study type Subjects Variables Findings Sparrow, Cross- 41 men IV: CT scan Body fat distribution, 1986 (83) sectional DV: Glucose tolerance significant correlate of glucose tolerance. Greater upper body fat and greater ratio of upper:lower body fat significantly correlated to increased 2-h serum glucose, even after adjusting for age and BMI. Intra-abdominal fat is significantly correlated to 2-h glucose. Fujioka, Cross- 15 males, 31 IV: SAT, intraabdominal The V/S ratio 1987 (81) sectional females. Mean fat (CT scan) VAT: SAT significantly correlated age 42y Ratio (V/S ratio) with plasma glucose DV: fasting plasma area under the curve. glucose level, area under plasma glucose concentration curve. Abate, 1995 Cross- 39 males IV: Total body adiposity Subcutaneous truncal fat (85) sectional (30 Caucasian, (hydrodensitometry), plays important role in 4 African- regional adiposity (MRI), insulin resistance. American, 2 Hispanic, 2 Asian, and SFT. DV: Insulin sensitivity Intra/retro-peritoneal fat plays a lesser role. 1 Egyptian), mean age

29 19 47yr. Ross, 1996 Cross- 40 females IV: VAT and SAT VAT associated with (82) sectional distribution, skeletal plasma insulin and muscle (MRI) glucose variables DV: Plasma insulin and glucose levels. independent of SAT and skeletal muscle Goodpaster, Cross- 26 males and IV: body composition Rd negatively correlated 1997 (86) sectional 28 females (DEXA, CT), aerobic with FM, VAT, SAT, fitness, insulin sensitivity. and thigh fat DV: Insulin-stimulated -SAT and VAT similar glucose disposal (Rd). association with IR -SAT significant after adjusting for VAT, converse not found. Boyko, 2000 Prospective (6, 520 Japanese- IV: plasma glucose, C- Greater VAT precedes (80) 10yr) Americans peptide, and fasting development of T2DM, insulin, OGTT, independent of fasting abdominal, thoracic, thigh insulin, insulin fat areas (CT), BMI, and secretion, glycemia, insulin secretion total and regional DV: Incident T2DM adiposity and family history of diabetes. Smith, 2001 Cross- 199 males and IV: DEXA, CT Total body fat is a major (88) sectional females DV: Metabolic variables contributor to the metabolic risks of obesity, with VAT, and deep SAT also significant. Hayashi, 2003 (84) Prospective (10yr) 128 Japanese American IV: plasma glucose and insulin, fat areas by CT, VAT increases risk of IGT, independently of IR, insulin secretion and other fat depots.

30 20 insulin secretion and IR DV: Incident IGT Wagenknecht, 2003 (100) Fox, 2007 (117) Crosssectional Crosssectional 999 Hispanic, 458 African American males and females 3001 males and females IV: VAT and SAT measured by CT scan DV: S I, AIR, DI IV: VAT and SAT by CT scan VAT and SAT = both strong independent relationship with IR, VAT stronger correlation to S I. SAT correlated AIR VAT and VAT+SAT (interaction) inversely correlated with DI VAT more strongly correlated with most metabolic risks than SAT DV: BP, FPG, TG, HDL, DM, MetS, IFG. Tong, 2007 (118) Prospective (10yrs) 457 Japanese- American males and females IV: VAT and SAT by CT scan. DV: MetS (NCEP VAT accumulation independently predicts MetS. definition) Abbreviations: IV (independent variable), DV (dependent variable), MRI (magnetic resonance imaging), SFT (skinfold thickness), CT (Computed tomography), VAT (visceral adipose tissue), SAT (subcutaneous adipose tissue), FM (fat mass), IR (insulin resistance), DEXA (dual energy x-ray absorptiometry), UWW (underwater weighing), HC (hip circumference), WTR (waist to thigh ratio), WHR (waist to hip ratio), IGT (impaired glucose tolerance), WC (waist circumference), S I (insulin sensitivity), AIR (acute insulin response), DI (disposition index), BP (blood pressure), DM (diabetes mellitus), FPG (fasting plasma glucose), TG (triglycerides), IFG (impaired fasting glucose), MetS (metabolic syndrome),

31 21 5 Body composition assessment Body composition assessment refers to the quantification of the fat and fat-free mass (FFM) in the human body (9), which is used to provide information on disease risk (68). At the molecular level, the human body is composed of lipids, water, proteins, minerals and carbohydrates. Of those, lipids are the most frequently measured in studies of obesity and are most often stored in the body as triglycerides (120). The principles of body composition are based on a model in which the body is broken down into 2 compartments: Fat (stored triglyceride), which is hydrophobic and considered to have a fixed density of g/cc at 37 C (121), and fat free mass (122), which is considered to have a constant density of 1.1g/cc at 37 C and a water content of 72-74% (assumed to be constant at 73%) (121). Since body water is not present in stored triglycerides and water occupies a relatively fixed fraction of FFM, it has been adopted as the index upon which body composition is determined (123). Total body water (TBW) is made up of extracellular (45%) and intracellular (55%) water and its quantities vary between individuals of differing adiposity (124). 5.1 Direct measures of body composition and adipose tissue Body composition and body fat distribution can be measured directly and accurately by several methods, including underwater weighing (UWW), densitometry, computerized tomography (CT), magnetic resonance imaging (MRI), and isotope dilution (ID) (120, 123), all of which can be complex and expensive (120). ID uses labeled water [ 3 H 2 O, D 2 O and H 18 2 O (125)] to determine a subject s exact measure of TBW (120). From a known TBW content, fat free mass and fat mass can be calculated (120). UWW submerges a subject underwater, air is expired from the lungs, and residual lung volume is determined. Body volume is calculated as the difference between dry and submerged weights, with the residual lung volume subtracted (126). Dual-

32 22 energy x-ray absorptiometry (DEXA) has been validated against both of the above standards of body composition analysis (127). DEXA is based on the principle that the differences in density and chemical composition of body tissues will attenuate x-rays differently (128). These three described methods of analysis are the most accurate techniques to measure body composition but are expensive, require a qualified technician and are not usually portable (129). Imaging techniques provide an in vivo analysis of body fat distribution with high accuracy and reproducibility (120) and are the most precise and reliable method for qualifying and quantifying VAT (8). MRI is considered by some to be the gold standard of VAT quantification because of its high accuracy and noninvasiveness (8). CT scans are also noninvasive and accurately estimate VAT, but are less favorable than MRI as they require radiation exposure (120). Abdominal ultrasound and echocardiography also provide good readings and easily calculated estimations of VAT at a lower cost than the above imaging techniques, but may be highly variable depending on the quality of the machine and skill of the technician (8). These are direct and accurate but very costly methods of determining body composition and body fat distribution and require either a qualified administrator or technician, or the use of nonportable equipment, and thus are not practical in epidemiological and clinical settings (8, 9, 129, 130).

33 Indirect measurements of body composition Anthropometry is defined as the measurement of body weight and body dimensions. It is used to reflect body fat and body fat distribution in large epidemiological studies or in clinical settings (131). It is a fast, easy, inexpensive and very widely used method of estimating body fat distribution (120, 131). Anthropometry is used to assess growth and development as well as to assess the risk of chronic disease associated with obesity and adipose tissue distribution (68). It is frequently used in epidemiologic and pathophysiological research involving health risk, health outcomes, body fat distribution, overweight and obesity (68), as it provides surrogate measures of adiposity and regional fat distribution (120). A disadvantage of anthropometry is the technical skill required by the clinician to maintain both the accuracy and reproducibility of the measurements and to minimize the errors of prediction (120). However, even with highly trained personnel, any indirect measure of body composition or adipose tissue distribution will result in errors of prediction (132). The validity of anthropometric measurements also tends to vary depending on a subject s age, sex, and ethnicity (133). The two most widely used anthropometric measurements to classify obesity and central adiposity are body mass index (BMI) and waist circumference (WC), respectively (13, 134), although there remains controversy regarding which anthropometric measure provides the greatest predictive ability for disease risk (135). Table 1.3 provides a summary of findings from prospective studies on the associations of various anthropometric measurements with incident T2DM. Most studies found measures of central adiposity to be more significantly associated with T2DM, with waist circumference usually being a stronger predictor than waist-to-hip ratio. However, some studies found BMI to be the strongest predictor of T2DM with no added

34 24 predictive benefit from adding further measures of central adiposity (table 1.3), while others found predictive benefits from adding the waist circumference measure to BMI (136). Waist-tohip ratio (WHR) was found to be significantly associated with T2DM, but only when not compared to other anthropometric measures. Hip circumference (HC) and thigh circumference (TC) have been found to be associated with a lower risk of DM (137), while another study found no anthropometric measure to be clearly superior in T2DM prediction (138). In studies that have compared measures according to ethnicity, results have been inconsistent. Waist-to-thigh ratio seems to play a role in T2DM in the Pima Indian population (97, 139), but not in any other ethnic subgroup analysis, although BMI was equally as predictive in that population and there was no demonstrated benefit to adding other anthropometric measures to the model (97). In one Hispanic population study, anthropometry was found to not be significant after including glucose and insulin in the model (140), while others found central obesity to play a role in T2DM prediction (90). In African American and non-hispanic white population studies there were no anthropometric measures found to be superior in predicting T2DM (141). When accounting for gender, there are mixed results on whether central adiposity or overall obesity measures are more predictive for women (23, 25, 42, 142), while for men WC is more predictive than WHR (42, 143). Taken together, previous literature in the area of prediction of T2DM using anthropometric measures (as shown in table 1.3), especially within ethnic subgroups, highlight the inconsistency and the need for more research in the area.

35 BMI The most common measure of overall obesity is Quetelet s Index, also known as body mass index (BMI) which is measured according to the following formula: weight (kg) / height (m) 2 (120, 127). A higher BMI is associated with increased fat stores, those both centrally and peripherally located, and also with increased musculature but not necessarily with increased VAT (144). BMI is used to classify protein energy malnutrition (120) and to classify obesity internationally by the WHO according to the following categories (validated for Caucasian populations): Normal weight ( kg/m 2 ), overweight ( kg/m 2 ) and obese (>30 kg/m 2 ) (67, 127). BMI estimations can be further developed according to a subject s age, gender and ethnicity (127). A potential limitation to BMI measurements is despite the fact that it accounts for height, it does not account for leg length and it has been shown that differences in leg length or body build account for a different relationship between BMI and percent body fat (234). However, it is inexpensive and easily administered but has recently been shown to be less accurate in predicting disease than other measures of body fat estimation (145) as it provides no information as to the distribution of adipose tissue (67) Waist circumference (WC) Measures of central obesity provide additional predictive information for the risk of disease, especially T2DM, beyond that which is provided by measures of overall obesity. This is especially the case among those in the upper extremes of central obesity distributions (24). Waist circumference (WC) is an excellent indicator of abdominal adiposity (146) and is an inexpensive, strongly correlated surrogate measure of the amount of visceral adipose tissue (10, 147). It is also strongly related to abdominal subcutaneous fat, total abdominal fat as well as total body fat (148). It is used in metabolic syndrome definitions and diabetes risk scores as a marker of central obesity (149). There are conflicting findings on whether WC is the single most accurate

36 26 anthropometric measure of central obesity; however a majority of studies find WC to be more closely associated with central obesity as measured by CT scan than BMI, waist-hip ratio, sum of skinfolds, subscapular-triceps ratio, sagittal abdominal diameter, and %body fat (30, 104, 150), and more predictive of metabolic disease risk and thus preferred over other anthropometric measures (92, 143, 151, 152), especially those measuring overall obesity (25, 90, 138, 147). It is hypothesized that despite the various suggested WC cut-off values, the girth measurement would be most effective if treated as a continuous variable, with health risk increasing as the WC value increases, rather than as a categorical value as it is currently usually presented (153). However, using a cut point may be more appropriate in a clinical setting. Also, there is some controversy as to the ideal location of the waist circumference measurement. Currently, there is no consensus for the optimal measurement protocol and no scientific rationale for any of the protocols recommended by principal health authorities, i.e. WHO (156) or National Institutes of Health (NIH) (157). There are three protocols which predominate the literature: measurement at the umbilicus, at the midpoint between the rib and iliac crest and the minimal waist circumference (158). The WHO s guidelines measure the midpoint between the lower border of the rib cage and the iliac crest (156). The NIH guidelines are to measure the superior border of the iliac crest (157). Both use bony landmarks as a guide for measurement placement. A recent review of WC measurements by outcome (158) showed that in prospective associations of WC with cardiovascular disease (morbidity and mortality), measurement at the umbilicus was found to be significantly associated with the outcome more often than other measurement protocols. In prospective associations with T2DM, measurement at the midpoint was found to be significantly associated with T2DM most often, followed by umbilicus then

37 27 minimum WC. However, the finding of the review was that WC measurement protocol was found to have no substantial influence on the association of WC with morbidity and mortality (158) Waist-height ratio (WHtR) WC has been challenged as the best predictor of disease as it does not account for differences in body stature (150) and the ratio of waist circumference to height (WHtR) has the potential to be a more effective predictor of diabetes, cardiovascular disease, mortality, and intraabdominal fat (159). The waist-to-height-ratio (WHtR) is a measure of disease risk that is considered by some to be more sensitive than BMI due to its closer association with central obesity, and more sensitive than WC because of its adjustment for stature (160). Stature has been shown to be negatively correlated to metabolic risk factors (161), thus it is important to adjust for the protective effect of height when considering disease risk (149, 160), as subjects with identical WC and BMI measurements but different heights, may have a different risk for hyperglycemia, hypertension and other metabolic diseases (162). The risk for myocardial infarction has been documented to be greater in short than in tall men and women (163, 164). With WHtR, the same cut-off value has the potential to be used for all ethnic groups, gender and ages which would simplify the communication of messages for prevention and health risk (149, 160), and the WHtR may also be applicable to children ( ).

38 Waist-hip ratio (WHR) Waist-to-hip-ratio (WHR) is the ratio determined when a subject s WC is divided by their hip circumference (HC), and has been used frequently to identify those subjects with a greater upper body versus lower body distribution of obesity and also as a surrogate method of estimating central adiposity (92). WHR is associated with increased central fat distribution, increased VAT, decreased thigh muscle and reduced physical fitness (144). Some studies show a strong correlation of WHR to VAT (146), while others show no association (151), which could be explained by the attenuation of the apparent relationship between VAT and WHR after controlling for age and total adiposity (146). WHR has been shown in most cases to be a weaker predictor of CAD, T2DM and dyslipidemia than BMI (168). Many studies have contrasted WHR and WC in T2DM prediction (see table 1.3) with WC usually being more significantly predictive of T2DM (42, 90, 136, 143, 169), however some studies have found a greater prediction of T2DM from WHR than with WC (69, 142) Hip circumference (HC) A greater WHR has been used to indicate excessive VAT in terms of disease risk; however, a large WHR could also be due to a smaller hip circumference (HC) rather than a large WC (170). Studies have begun investigating the independent effects of large waist and small HC on glucose levels and diabetes risk (137, ). A larger HC has been consistently associated with lower glucose levels as well as a lower risk of T2DM development, independent of waist WC, even after adjusting for age and BMI (137, 170, 171). A larger HC has also been associated with lower cardiovascular disease risks (171, 172).

39 29 Variations in HC have been attributed to differences in bone structure, pelvic width, gluteal muscle as well as subcutaneous gluteal fat (172). Narrow hips may reflect less subcutaneous fat, which would be beneficial, although narrow hips may also reflect gluteal muscle atrophy (172), which would detrimental. The specific mechanisms behind the protective effect of a larger hip circumference are unknown (172), although there are several hypotheses: 1) larger hips could indicate greater gluteal skeletal muscle and thus greater insulin clearance from the muscles (174), 2) larger hips could be indicative of a larger skeletal frame, which has a protective effect on metabolic risk factors (161), and 3) a larger hip circumference could indicate more femoral fat mass, which protects in addition to the effects of greater muscle mass. Fat in the femoral or gluteal area has been hypothesized to be less sensitive to lipolytic stimulus, as lipoprotein lipase activity is greater in femoral subcutaneous adipose fat versus visceral fat (172) Waist circumference manipulations Manipulations of anthropometric measurements include the Conicity index (CI) and Abdominal Volume Index (AVI), both of which use WC in a mathematical formula: CI = WC/[0.109 (weight/height)], with WC measured in meters, weight in kilograms and height in meters (172). The value is the result of a conversion of units of volume and mass to units of length and is thus a constant (172). AVI = [2WC (WC-HC) 2 ]/1000, where HC is hip circumference, both HC and WC are measured in centimeters, and the 2cm and 0.7cm values are constant (173).

40 30 It has been argued that ratios are inappropriate risk indicators, as they are more difficult to interpret biologically, are less sensitive to weight gain, and contain inherent statistical limitations (26). In addition, WC was still found to be a more effective predictor of risk and of biochemical markers of T2DM than CI or AVI (151) Skinfold thickness Skinfold (SF) thickness measurements are a simple method of estimating the fat content and fat distribution of the human body in epidemiologic and clinical studies (152, 175). It is an acceptable method of estimating body fatness, as approximately 40-60% of the total fat in the human body is found in the subcutaneous region (133). There are some underlying assumptions to acknowledge when using skinfold thickness to estimate body composition: 1) the thickness of subcutaneous fat represents a constant proportion of the subject s total body fat, and 2) the sites measured reflect this average thickness (123). Interestingly, neither of these assumptions has been proven true (123). The precision of the measure is highly dependent on the skill of the technician as well as the site selection (123). It is recommended that SF thickness be measured at four sites: subscapular, suprailiac, biceps and triceps (176). Although it is possible to estimate % body fat and make predictions from measurements of SF thickness at only one or two sites, the use of all four sites provide a more accurate representation of the regional distribution of fat, and using fewer sites increases the potential for greater measurement variability (176). The subscapular skinfold site is

41 31 representative of a central pattern of adipose tissue, while the triceps skinfold represents a peripheral distribution of adipose tissue (92). The triceps is the most frequently measured site as it is easily accessible, reproducible and has the ability to measure differences among subjects (133). The ratio of central to peripheral skinfold thickness measures, indicated by the subscapular-to-triceps ratio (STratio) (92, 104) is a more effective measure of disease risk than any individual skinfold measurement, and a larger the ratio indicates a greater risk (177). SF thickness measurements can also be summed to indicate overall adiposity (107, 172). There are several equations used to predict body fat mass from SF measurements, either alone or in combination with other anthropometric measurements (175). The most frequently used SF equations are from Durin and Womersley: % fat = [(4.95/density)-4.5] x 100 (176), with body density determined according to Siri s 1956 formula (178), Jackson: Db= x S x S x age (S 1 = triceps + suprailiac + thigh skinfolds) (179), and more recently, equations from Peterson: For men: %BF= (age x ) - (height x ) + (sum4 x ) - (sum4 2 x )

42 32 For women: %BF= (age x ) + (BMI x ) (height x ) + (sum4 x ) - (sum4 2 x ), sum4 is the sum of the triceps, subscapular, suprailiac and midthigh skinfold thicknesses (180). There are some documented disadvantages to SF thickness measurements, including a loss of accuracy in older and more obese subjects. It was originally developed as a technique to estimate body composition in normal weight populations and has not been properly validated in obese populations (144). In addition to the skill required from the technician to maintain precision, accuracy and precision may be lost when measuring obese subjects if the skinfold measure is greater than 40mm (144, 181). At birth the majority of fat is subcutaneous, thus a skinfold measurement has greater accuracy to predict total body fat in younger subjects (182). With age, a greater proportion of body fat is distributed viscerally, since skinfold thickness measures subcutaneous fat, it becomes a less reliable indicator of total body fat with advancing age (182). Skinfold thickness measurements also have some documented disadvantages versus body circumferences: 1) the average reliability of circumferences is significantly greater than that of skinfolds (183), and 2) circumferences can be measured even on very obese subjects, while skinfold thickness measurements can not be measured when the skinfold is thicker than is possible to measure on the calipers being used (184).

43 Percent (%) body fat from bioelectrical impedance analysis (BIA) BIA is a noninvasive, accurate and inexpensive method of determining body composition ( ) that has been validated by densitometry (188), isotope dilution (189, 190) and DEXA (191, 192) in normal and obese individuals. Subjects lay in a supine position with arms and legs abducted and not touching the body. Electrodes are placed on the dorsal surface of the right hand and foot (132). A localized 50kHz current is administered and resistance (the inverse of conductivity) and reactance are measured to the nearest ohm (132, 193). The resistance of tissues to the current is referred to as the impedance value, and the opposition of tissue membranes to the current is the reactance (187). Total body water is estimated by measuring the opposition of tissues to the flow of a current, the reactance (187). Lean tissue contains more electrolytes than does adipose tissue, thus there is a difference in ionic content providing different conductivity to the current (193). Lean tissue, rich in water and electrolytes, provides less resistance to the current than lipid-rich adipose tissue, which provides greater resistance to the current (120). BIA uses the principle that impedance to electrical current flow is related to the volume of the conductor (in the case of BIA, the conductor is the human body) and the square of the conductors length (height) (194). A standardized protocol must be used when administering BIA (187) as many factors may influence the results. Body position, hydration status, food/beverage consumption, ambient air and skin temperature, recent physical activity and the conductance of the examination table have all been shown to have an effect on the validity, reproducibility and precision of the impedance measurement (187).

44 34 There are several formulas from which to determine % body fat from the resistance and reactance measurements, the most common of which are (127): Lukaski: FFM = 0.73 x stature 2 / resistance x reactance x weight 4.03 (132), Segal: FFM = x stature x resistance x weight x age (195), as well as formulae provided by the manufacturer of the RJL electrical impedance system, RJL Equipment Company, Mt. Clemons, Montana. The Lukaski formula has been found to be inaccurate and imprecise compared to DEXA and shown to not provide information as to changes in % body fat during periods of weight loss (127). Segal, referred to as the standard adult equation (186), also underestimates % body fat compared to DEXA, however, it is significantly less biased than the Lukaski formula and since resistance contributes less of the method variance, this formula is more resistant to meaningless variations of the instrument and is thus more clinically applicable (127). The manufacturer s formulae have been shown to grossly overestimate lean body mass, especially in obese subjects, and the overestimation increases with obesity, when compared to hydrostatic weighing (193, 195). The accuracy of % body fat estimates from BIA has also been shown to vary depending on the weight of the subject; BIA tends to overestimate % body fat in lean subjects and underestimate % body fat in overweight subjects (127, 129). Since BIA was also developed in a normal weight

45 35 population, the underlying assumption of biological constancy is not maintained in obese subjects as their body fluid distribution and body geometry are distorted, leading to the underestimation of fat mass (127). BIA has also been validated in type 1 diabetes, using a variation of the RJL method, with proximal rather than distal attachment of electrodes, as well as type 2 diabetes subjects (196, 197) and thus can be a useful anthropometric tool in both of these populations. Table 1.3. Review of the association of anthropometric measures with T2DM. Paper Study Design Subjects Variables Findings Ohlson, 1985 (69) Prospective 792 men, 54 years IV: WHR WHR positively (13.5yr) old DV: Incident T2DM and significantly associated with risk T2DM Lundgren, 1989 (23) Prospective (12yr) 1462 Women IV: BMI, SFT,WHR DV: incidence T2DM BMI, SFT and WHR significantly associated with incident T2DM. Haffner, 1990 Prospective (8yr) 474 Mexican IV: BMI, STratio Neither significant (140) Americans DV: Incident T2DM when adjusted for glucose and insulin. Chan, 1994 (24) Prospective (5yr) 51,529 U.S. male health professionals IV: BMI, WC, WHR DV: Incident T2DM -WC better indicator than WHR. -BMI dominant

46 36 risk factor for T2DM. Warne, 1995 (139) Prospective (6yr) 290 male and 443 female Pima Indians IV: BMI, WC, thigh circumference, WTR, weight, and percentage body fat DV: incident T2DM BMI, WC, TC, WTR, %BF all predicted T2DM. - WTR most predictive for men -BMI most predictive for women. -TC was worst predictor for both. Carey, 1997 (25) Prospective (8yr) 43,581 women, Nurses Health Study Wei, 1997 (90) Prospective (7yr) 721 Mexican Americans IV: BMI, WC, WHR DV: Incident T2DM IV: Body weight, BMI, WC, HC, WHR, triceps and subscapular SFT DV: Incident T2DM BMI, WHR, and WC independent predictors of T2DM WC strongest predictor. Stevens, 2001 Prospective (9yr) 12,814 African IV: BMI, WC, Equivalent in (141) American and WHR T2DM predictive white men and DV: incident ability. women T2DM Sargeant, 2002 (138) Prospective (4yr) 290 men and 438 women IV: BMI, WC, WHtR, WHR DV: Incident All were independent predictors of T2DM. None clearly superior.

47 37 T2DM. Snijder, 2003 Prospective (6yr) 1357 men and IV: hip and thigh HC and TC (137) women circumferences. associated with DV: Incident T2DM. lower risk of T2DM, independent of BMI, age, WC. Tulloch-Reid, Prospective Pima Indian: 624 IV: BMI, WC, TC, BMI and WHtR 2003 (97) (5.25yr) men and 990 HC, WHR, WTR best predictors of women DV: Incident diabetes in men, T2DM - BMI, WHtR, WC, and WTR best predictors in women. - BMI excellent predictor T2DM not improved by adding other measures. Rosenthal, 2004 Nested case 57,130 women IV: BMI, WC, HC, Central obesity is a (142) control WHR stronger risk factor DV: Association with T2DM for diabetes than overall obesity. Meisinger, 2006 Prospective (9yr) 3055 men and IV: BMI, HC, WC should be (136) 2957 women WHR, WC measured in DV: Incident T2DM addition to BMI to assess the risk of type Wang, 2005 (143) Prospective (13yr) men IV: BMI, WC, WHR DV: Incident -WC better predictor than WHR.

48 38 DPP, 2006 (42) Prospective (3yr) 3234 men and women T2DM IV: WC, HC, SFT, BMI WC best predictor T2DM in both sexes. DV: incident T2DM Abbreviations: IV (independent variable), DV (dependent variable), BMI (Body mass index), WC (waist circumference), WHR (waist to hip ratio), SFT (skinfold thickness), STratio (subscapular to triceps skinfold thickness ratio), MetS (metabolic syndrome), SAD (sagittal abdominal diameter), CT (computed tomography), UWW (underwater weighing), VAT (visceral adipose tissue), SAT (subcutaneous adipose tissue), MRI (magnetic resonance imaging), WHtR (waist to height ratio), HC (hip circumference), TC (thigh circumference), WTR (waist to thigh ratio), %BF (percent body fat). 6 Rationale Obesity is the best described risk factor of T2DM. The majority of epidemiologic and clinical studies to date have used indirect anthropometric measures such as BMI and body circumferences to study the association of body mass with T2DM, with BIA and skinfolds being used much less frequently. There is a lack of systematic data from studies comparing a wide range of different anthropometric measures in the prediction of T2DM. Further, the degree to which these alternate anthropometric indices predict T2DM differently by gender and ethnicity has received very limited attention. This is an important issue given the ongoing debate regarding the appropriateness of recommended anthropometric measures in metabolic syndrome definitions and various anthropometric cut points across genders and various ethnicities. For example, a variety of anthropometric obesity indices are used in Metabolic Syndrome definitions: NCEP uses central adiposity as a component of the definition, requiring subjects to have a WC >102cm for men and >88cm for women; IDF requires central obesity in its definition and uses ethnic specific WC cut-offs, unless overall obesity (BMI) is >30kg/m 2 ; while the WHO uses either central obesity or overall obesity as a component of their definition, defining it as

49 39 WHR >0.85 for women and >0.90 for men, unless BMI >30kg/m 2, in which case obesity is assumed. As was seen in the variety of diabetes risk scores discussed previously, some use central adiposity measures, such as WC while others use overall obesity measures, such as BMI. There is no consistency in the obesity requirements for any of these scores and definitions, thus the utility of anthropometric measures in T2DM prediction, especially across gender and ethnicity, requires additional research. 7 Objectives The objectives of this study are: 1. To compare different anthropometric measures, including BMI, waist and hip circumferences, WHR, WHtR, the sum of triceps and subscapular skinfold thicknesses, the ratio of these skinfold measurements (STratio), and % body fat from BIA in terms of their ability to predict incident T2DM. a. To further determine whether these measures predict DM differently across three ethnic groups (African American, Hispanic and non-hispanic White) and by gender. 2. To determine which anthropometric measures provide greater predictivity in the context of variables used in current diabetes prediction models, which include to clinical markers of blood pressure and family history.

50 40 8 Hypothesis Anthropometric indices that reflect a more central distribution of adiposity will have a greater ability to predict incident type 2 diabetes, across gender and ethnicity, than those measures which reflect overall obesity. Anthropometric measures will provide additional predictive information on top of that provided by clinical markers of blood pressure and family history, in the context of variables used in current diabetes risk models. 9 Research questions 1. Do anthropometric measures that reflect central obesity have greater predictive ability in terms of incident diabetes than those reflecting overall obesity? 2. Are the predictive abilities of these alternate anthropometric indices modified by ethnicity and/or gender? 3. Do alternate anthropometric measures provide greater prediction in the context of current diabetes risk score variables than the anthropometric measures currently used?

51 41 Chapter 2 Study Design and Methods 10 The Insulin Resistance Atherosclerosis Study (IRAS) IRAS is a large epidemiological study designed to cross-sectionally and prospectively investigate the associations between insulin resistance, diabetes and cardiovascular disease in a triethnic cohort of American adults (197). Details of the IRAS study have been presented in detail previously (197). Aspects of the study relevant to the current project are described below. The recruitment goal for IRAS was 1600 participants, to be comprised equally of men and women, three ethnic groups (African American, Hispanic and non-hispanic White) and three glucose tolerance groups (normal, impaired glucose tolerance [IGT], and diabetes) (197). At the time of recruitment, the most populous minority groups in the United States were African American and Hispanic; they also represented ethnicities at a greater health risk for the above mentioned diseases than the non-hispanic White group. Beginning in 1992, subjects were recruited from four clinical settings across the United States. In Los Angeles and Oakland, California, African American and non-hispanic white subjects were recruited from a nonprofit health maintenance organizations (197). In Colorado, from the San Luis Valley Diabetes Study (197) and in San Antonio, Texas, from the San Antonio Heart Study, Hispanics and non- Hispanic white subjects were recruited from ongoing population-based epidemiologic studies (197). The IRAS protocol was approved by the local ethics review committees for each centre and all subjects completed written informed consent (169, 197).

52 42 IRAS excluded individuals that exhibited any of the following criteria: 1) treatment with corticosteroids in the past six months; 2) insulin treatment in the past 5 years; 3) severely limited caloric intake (<800kcal/day); 4) decompensated congestive heart failure; 5) decompensated emphysema or chronic lung disease; 6) unstable angina; 7) active treatment for cancer; 8) seizure disorder or epilepsy; 9) kidney dialysis, transplant or renal failure; 10) serious illness within the past month (e.g. heart attack, major surgery); 11) pregnancy; or 12) cognitive or psychological dysfunction (197) Design, subject characteristics and follow-up participation The baseline IRAS cohort is comprised of individuals ranging in age from years, of which 56% are women. The cohort includes 37% non-hispanic White, 34% Hispanic and 29% African American, based on self-reported ethnicity. Those subjects with a normal glycemic response comprised 44% of the cohort, while 23% had IGT and 33% had diabetes (197). Baseline measurements occurred between October 1992 and April 1994 (197), with follow-up examinations performed an average of 5.2 years later, between 1998 and Follow-up exams were conducted according to the same protocol as the baseline examinations (198). Of the 1626 subjects who participated at baseline, 1313 (80.1%) also attended the follow-up examination (169). Those attending the follow-up examinations were not significantly different from those that did not attend in terms of ethnicity, sex, baseline glucose tolerance status and BMI (all analysis p>0.32) (198).

53 43 At baseline, 537 subjects were confirmed by oral glucose tolerance testing (OGTT) to have diabetes (198) and thus were excluded from the present analysis; 1,088 individuals were confirmed to be free of diabetes at baseline. At the follow-up examination, 148 cases of incident type 2 diabetes were diagnosed by OGTT. Only those subjects that were non-diabetic at baseline and with all baseline anthropometric measures available were included, thus the total number of subjects for the current study was 1073, including 430 non-hispanic white, 282 African American and 361 Hispanic subjects. There were 469 male and 604 female subjects, 715 had normal glucose tolerance (NGT), 358 had impaired glucose tolerance (IGT) and of those with IGT, 224 had impaired fasting glucose (IFG). There were 146 incident cases diagnosed at follow-up Baseline measurements The baseline examinations lasted approximately four hours and included anthropometric measures, blood collection and oral glucose tolerance tests (OGTT) (197). Participants were asked to fast for 12 hours prior to the examination, refrain from alcohol and exercise for 24 hours and not smoke the morning of the visit (197). Each subject completed an OGTT to determine glucose tolerance status, based on World Health Organization criteria (199) and baseline anthropometric measurements (described in detail below, section 1.b.i.) were performed (197). Information concerning age, gender, ethnicity, family history (defined as at least one first degree relative), and current medication use was acquired through self-report (200).

54 Anthropometric and blood pressure measurements Resting blood pressure (systolic and fifth-phase diastolic) was measured, with the subject seated, three times after a five minute rest with a standard mercury sphygmomanometer, and the average of the second and third readings was used in the analysis (197). Hypertension was defined as a systolic blood pressure greater than or equal to 140mmHg and/or diastolic blood pressure greater than or equal to 90mmHg or currently taking antihypertensive medications (201). Anthropometric measurements of height, body weight, waist and hip girths, triceps and subscapular site skinfold thicknesses and bioelectrical impedance were conducted (197) following a standardized protocol (202), with the participant standing barefoot and in lightweight clothing (169). Weight was measured with a calibrated beam balance scale to the nearest 0.1 kg and height was measured with a steel measuring tape, marked in centimeters, hung vertically on the wall at a right angle to the floor with zero at the base board, to the nearest 0.1cm (202). Girths were measured using a flexible steel tape measure on bare skin, in duplicate to the nearest 0.1cm (202). Waist circumference (WC) was measured during midrespiration at the minimum circumference between the iliac crest and 10 th rib and hip circumference (HC) was measured at the maximum circumference of the gluteus (202). Waist-to-hip ratio (WHR) was defined as waist circumference (cm) divided by hip circumference (cm), while waist-to-height ratio (WHtR) was defined as waist circumference (cm) divided by height (cm). BMI was defined as weight (kg) divided by height (m 2 ). BMI categories were determined according to 1998 National Heart, Lung and Blood Institute (NHLBI) cut points: normal weight, ; overweight, ; and obese if BMI>29.9 kg/m 2 (8).

55 45 Figure 2.1. Descriptive guide for location of waist circumference measure: at the minimum circumference between the iliac crest and 10 th rib. Source: IRAS anthropometry manual Skinfold (SF) thicknesses were measured according to a standardized protocol (204), using Lange skinfold calipers (202). Four different skinfold calipers are commonly used in anthropometric research literature, Adipometer, Harpenden, Holtain and Lange; Lange is the most commonly used, and is appropriate to measure obese subjects, whereas some other calipers are not (133). Skinfold thickness was measured at the triceps and subscapular sites (197), which are the two most frequently measured sites on the body (133). The site over the triceps is measured most commonly as it is the easiest to access, is the most reproducible and thus yield the most precise measurements (204). Triceps skinfold thickness was measured on the back of the arm, directly over the muscle at the midpoint of the arm. The midpoint of the arm was located by measuring the distance between the

56 46 olecranon process and acromial process while the arm was flexed at a right angle, and the midpoint was marked. The measure was taken with the arm hanging at the subjects side with the skinfold pinched and measured vertically (202). With shoulders relaxed and arms hanging at the subjects side, the subscapular site was measured just below the inferior angle of the scapula; it was measured at a downward and outward angle, following the natural fold of the skin (202). The sum of skinfold thickness can be used as an estimation of overall obesity, while the ratio of the subscapular-to-triceps sites (also referred to as the centrality index) is used to determine the ratio of central to peripheral adiposity (177), with a larger ratio indicating a greater central distribution of adiposity. Both SF thicknesses were measured on the right side of the body in triplicate and averaged in analysis, to reduce variability (197, 202). Figure 2.2. Descriptive guide to caliper placement for skinfold thickness measures. Source: IRAS anthropometry manual