THE EFFECTS OF OBESITY ON THE RELATIONSHIPS AMONG INSULIN LIKE GROWTH FACTOR-1 AND MARKERS OF DIABETES. Samin Moham, RD. A Thesis

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1 THE EFFECTS OF OBESITY ON THE RELATIONSHIPS AMONG INSULIN LIKE GROWTH FACTOR-1 AND MARKERS OF DIABETES Samin Moham, RD A Thesis Submitted to the Graduate College of Bowling Green State University In partial fulfillment of the requirements For the degree of MASTERS OF FAMILY & CONSUMER SCIENCES June 2010 Committee: Martha Sue Houston, PhD, RD, Advisor Younghee Kim, PhD, RD Nancy Boudreau, PhD

2 ii ABSTRACT Martha Sue Houston, PhD, Advisor PURPOSE: The purpose of this study was to examine the relationships among diabetes, obesity and serum insulin like growth factor 1 (IGF-1) concentrations in a large nationally representative sample of civilian, non-institutionalized adults in the United States. More specifically, the influence of obesity on the relationship among IGF-I and markers of diabetes was examined. METHODS: The sample population utilized in this study was data from the Third National Health and Nutrition Examination Survey (NHANES III), which is a nationally representative sample of civilian non-institutionalized persons conducted by the National Center for Health and Prevention from 1988 through Serum IGF-1 levels were collected from participants 20 years of age and older at a mobile examination center morning session after an average overnight fast of approximately eleven hours. Participants were excluded if they were pregnant, lactating, or had incomplete data, resulting in a study population of 2430 males (47%) and 2727 females (53%). Measures of diabetes included the examination of responses to a series of questions from the questionnaire section of the NHANES III. In addition to the responses to questions, other markers of glucose control were used in this study including blood glucose, glycated hemoglobin levels, and homeostatic measure of insulin resistance (HOMA_IR). Measures of obesity included body mass index, body weight, total body fat, and waist to hip ratio. Differences in serum IGF-I across groups categorized by fasting plasma glucose and body mass index were analyzed by ANOVA and Tukey s multiple comparison test. The relationships among markers of diabetes, markers of obesity and serum IGF-I were examined with multiple regression models that

3 iii included age, gender, race-ethnicity. The Statistical Analysis System (SAS) software (version 9.1, Cary, NC and SUDAAN (Release 5.50, Research Triangle Park, NC) were utilized for all statistical analysis. RESULTS: Age-adjusted serum IGF-1 were significantly less in men but not women classified as diabetic compared to participants with normal blood glucose or pre-diabetes. In both men and women, across all race/ethnic groups, age-adjusted IGF-I concentrations were significantly lower with obesity (BMI > 35). A series of regression models found that age, gender and race/ethnicity are strong determinants of serum IGF-I concentrations. However, diabetes and obesity had significant and independent effects on IGF-I. Thus the negative relationship between serum IGF-I and diabetes was not totally explained by the presence of obesity. CONCLUSION: The role of IGF-I in the etiology and/or possible treatment of diabetes is an important topic of basic and clinical research. In the present study of a large, diverse population, the complexities of the relationships among IGF-I adiposity and impaired glucose metabolism were evident. The presence of obesity and diabetes, independently result in lower serum IGF-I concentrations even when accounting for the influences of age, gender and race/ethnicity. The mechanisms of these effects and the clinical implications of lower circulating IGF-I on morbidity and mortality await further investigation.

4 iv TABLE OF CONTENTS Page CHAPTER 1: INTRODUCTION... 1 CHAPTER 2: REVIEW OF LITERATURE... 2 THE IGF AXIS... 2 IGF Binding Proteins... 3 VARIABLES THAT AFFECT IGF LEVELS... 4 Gender... 4 Race... 4 Age... 5 THE LINK BETWEEN NUTRITION AND IGF... 5 Indicator of Energy and Protein Status... 5 Dietary Intakes... 6 Thyroid hormone... 7 DIABETES OVERVIEW... 7 THE LINK BETWEEN DIABETES AND IGF Comparison with Insulin... 9 Therapeutic implications OBESITY PREVALENCE AND DISEASE PROCESS THE LINK BETWEEN IGF-1 AND MEASURES OF BODY COMPOSITION STATEMENT OF PROBLEM STATEMENT OF SIGNIFICANCE RESEARCH QUESTIONS CHAPTER 3: METHODOLOGY NHANES III STUDY POPULATION... 14

5 v EXCLUSION CRITERIA LABORATORY DATA DEMOGRAPHIC DATA AND CLASSIFICATIONS STATISTICAL ANALYSIS CHAPTER 4: RESULTS PARTICIPANTS CHARACTERISTICS MULTIPLE REGRESSION ANALYSES CHAPTER 5: DISCUSSION AND CONCLUSIONS ANSWERS TO RESEARCH QUESTIONS COMPARISONS WITH EXISTING LITERATURE CONCLUSIONS AND RELEVANCE OF RESULTS RECOMMENDATIONS FOR FUTURE STUDIES REFERENCES... 33

6 vi LIST OF TABLES Number Name Page 1 Table 1: Age adjusted serum IGF-1 levels by diabetes status Table 2: Age adjusted serum IGF-1 levels by BMI category Table 3: Age adjusted percent glycated hemoglobin levels by BMI category.25 4 Table 4: Regression analysis: The relationship among IGF-1, demographic variables, and markers of diabetes and obesity Table 5: Multiple regression without adjustments for age, gender, and race/ethnicity..27

7 1 CHAPTER 1: INTRODUCTION The insulin-like growth factors (IGFs) are small polypeptides with high sequence similarity to insulin that control the growth of multiple cell types. These insulin-like growth factors stimulate multiple cellular responses that are related to growth, including synthesis of DNA, RNA, and cellular protein. Insulin-like growth factor-1 (IGF-1) and insulin-like growth factor-ii (IGF-II) signal cells that adequate nutrient is available in the cell s environment for the cell to enter the s phase of the cell division cycle (2). The complex system that cells use to communicate with their physiologic environment is sometimes referred to as the IGF axis. This so-called axis consists of two cell-surface receptors (IGF1R and IGF2R), two ligands (IGF-1 and IGF-2), and a family of six high-affinity IGF binding proteins (IGFBP 1-6).

8 2 CHAPTER 2: REVIEW OF LITERATURE The IGF Axis Insulin-like growth factors (IGF) are polypeptides which are central mediators of physiological growth (3). These growth factors have a high sequence similarity and structural similarity to insulin, hence being named insulin-like. Insulin-like growth factor-1 is a peptide that is mainly produced in the liver in response to growth hormone (GH) secretion, and it mediates many of the anabolic actions of growth hormone through cell surface IGF-1 receptors (IGF-IR). GH is produced in the pituitary gland, is released into the blood stream, which then stimulates the liver to produce IGF-1. IGF-1 then stimulates systemic body growth. The activity of IGF-1 is affected by specific IGF-binding proteins, whose activity is in turn regulated by phosphorylation, proteolysis, polymerization, and cell association (4). IGF-1 deficient people who are categorized as having Laron syndrome (or Laron s dwarfism) are treated with recombinant IGF-1. The IGF-I peptide circulates in the plasma and has endocrine effects in a number of target tissues (5). Whereas insulin is mainly a metabolic hormone, IGFs promote proliferation, survival, and/or differentiation in most cell types. However, IGFs still retain insulin-like metabolic activity. The dysfunctional regulation of these factors has been linked to several disease states, such as cancer. Circulating IGF-1 levels are relatively low in childhood, peak at puberty and slowly decline throughout adult life (6). Investigations have shown that genetic manipulation leading to the suppression of neuro-endocrine IGF-I signaling prolongs the mammalian cell lifespan (5). IGF-I is one of the main mediators of the actions of growth hormone (GH) (5). It has been wellestablished that both GH and IGF-I are critically important for normal human growth (7). Recent studies have identified children with mutations that disrupt the IGF-I or IGF-I receptor who also

9 3 have significant growth failure that is not responsive to GH, and exhibit a large range of other abnormalities that suggest a much broader role for IGF-I in tissue development than only an agent for the actions of GH (7). IGF-1 is also able to act in an autocrine and paracrine fashion and is secreted by almost all cell types (5). Such conditions as critical illness, poorly controlled diabetes, malnutrition, cirrhosis and hypothyroidism may result in low levels of circulating IGF (5). It should be noted, however, that IGF-II expression is not regulated by growth hormone or nutritional status to any great extent (6). IGF Binding Proteins IGF signaling is modulated by a family of six structurally related binding proteins (IGFBP 1-6). These binding proteins bind IGF with high affinity, but they do not bind insulin. The majority of circulating IGF-I is bound to insulin-like growth factor-binding protein (IGF-BP)-3, a large protein forming complex that is unable to cross the endothelium (5). IGFBP-3 is the predominant circulating IGFBP (6). IGFBP-3 can form a complex with either IGF-I or IGF-II to form a ternary complex in the circulation which carries roughly 75% of circulating IGFs. The half-life of IGFs is greatly increased within ternary complexes since they cannot leave the circulation. About 25% of circulating IGFs are bound to other IGFBPs in binary complexes. IGFBPs usually inhibit IGF actions because they bind IGFs with higher affinity than the IGF-I receptor. However, in the case of IGFBP-3, IGF actions are promoted instead of inhibited (6). A few other proposed functions of the IGF binding proteins are the prolongation of the plasma half-life of IGF-1 and IGF-2, control of the rate of IGF transport from the vascular compartment, and regulation of the interaction between IGF-1 and the type 1 IGF receptor on the cell surface (8).

10 4 Variables that Affect IGF Levels Gender Gender is one of the variables with which the level of circulation IGF-1 varies. In comparison with males, females tend to have lower levels of IGF-1 and higher levels of IGFBP-3 (9). Other than the type of hormones each sex produces, this difference may be due to other factors that cause women to have lower levels of IGF-1. One study showed women who used oral contraceptives had lower levels of IGF-1 and increased levels of IGFBP-3 when compared to non-users (10). Likewise, IGF-1 levels vary with reproductive factors such as breastfeeding, and hormone replacement therapies such as estrogen (11). For example, a history of breastfeeding is associated with higher levels of IGFBP-3 (11). Thusly, the difference in the levels of IGF within women is due to the varying uses of hormone therapies. Race IGF-1 levels have been known to vary with race and ethnicity. In the CARDIA Male Hormone Study, samples of black and white male participants were examined for the relationship between IGF-1 and IGFBP-3 with different lifestyle factors to see if there was any resulting difference (12). One relationship found has been that white men have a higher IGF-1 and IGF-BP3 concentration compared to black men (12). In another study, it appeared that people of Latino ethnicity had lower levels of both IGF-1 and IGF-BP3 compared to other races. IGF-1 levels varied not only by racial/ethnic group but also by BMI category in a race-specific manner (13). IGF-1 levels declined with increasing obesity status most dramatically in Latinos and Japanese (13).

11 5 Age In prepubertal children serum IGF-1 concentrations are related to linear growth and body size, that is, well nourished but small and slow-growing children have lower serum IGF-1 concentrations. During progression through puberty the pubertal stage influences IGF-1 status (14). Girls reach the highest serum IGF-1 concentrations during Tanner stage IV around the timing of menarche. If measures of body size during puberty are controlled for, the pubertal stage remains the most important predictor of the serum IGF-1 concentration. Following completion of puberty there is a decrease of the serum IGF-1 concentrations over several years. Any use of the serum IGF-1 concentration as a measure of nutritional status in humans would therefore have to take into account these developmental changes (14). The Link between Nutrition and IGF Indicator of Energy and Protein Status IGF-1 seems to be one of the many markers of nutritional status in humans (8). During fasting in normal volunteers, IGF-1 levels decrease within 24 hours, mimicking changes in nitrogen balance (8). This decrease in IGF-1 is also seen in protein calorie malnutrition such as marasmus, kwashiorkor, anorexia nervosa, and AIDS. The extent to which IGF-1 decreases during fasting depends on the extent of the nutritional deficit as noted by serum albumin concentrations, weight deficit, or loss of body cell mass (8). Likewise, upon repletion of energy and protein, IGF-1 levels are restored to nearly 70% of the pre-fasting values in 5 days (8). When rats are fasted for three days, they experience a significant reduction in serum IGF-1. During refeeding, hepatic growth hormone binding increased at approximately the same rate as the serum IGF-1 concentrations (2). In fasted rats, steady-state hepatic IGF-1 mrna levels decrease in comparison with well-fed controls, thus indicating that decreased transcription of IGF-1

12 6 mrna may be the mechanism mediating the decrease in blood IGF-1 concentrations (2). In contrast, dietary protein restriction in rats appears to lower serum IGF-1 concentrations. Rats exposed to a dietary protein restriction of 5% instead of the normal 15% protein diet show consistent reductions of serum IGF-1 concentrations, but only modest reductions in GH binding. This showed that the low IGF-1 concentration was not mediated via reduced GH binding. Protein restriction also caused reduction of hepatic IGF-1 mrna along with the decline in serum IGF-1, which suggests that the changes in serum IGF-1 are partially due to transcriptional events (2). Other studies have shown that fasting induces significant reductions in IGF-1 mrna in nonhepatic tissues such as kidney, muscle, gut, and brain, indicating that these changes are not restricted to hepatic IGF-1 mrna (2). It should be noted that this relationship between IGF-1 levels and protein status is not observed in critically ill patients (8). Alterations in IGF metabolism are also observed with deficiencies in zinc, specific amino acids, manganese, vitamin A, vitamin D, calcium, iodine, magnesium, potassium, and copper (8). Dietary Intakes Some studies have also concluded that the type of diets individuals consume also affects the serum IGF-1 levels. In the literature reviewed, three studies confirmed a positive association between the intakes of animal protein and IGF-1 levels (11,15,16). Vegan women had a 13% lower mean serum IGF-1 concentration than both meat-eater (women) and vegetarian women (15). Dietary intervention studies have also found a dairy milk supplement to increase IGF-1 levels in adults by 10% (15). Other studies have confirmed possible effects calcium may have on serum IGF-1 concentrations as well. In one study examining both men and women, calcium intake was positively associated with IGF-1 levels (17). In this same study, other micronutrients

13 7 with which IGF-1 levels showed a positive correlation with were fiber and soy protein (17), further confirming the notion that the type of dietary protein source affects IGF-1 levels as concluded by studies mentioned above. With reference to other dietary factors, studies found no particular relationship between vitamin supplement intake and IGF-1 concentrations. Intakes of total energy, alcohol, total fat and carbohydrate intake were also not associated with IGF-1 levels according to one study examining year old male participants (18) and another study examining both male and female participants with a mean age of 61 (17). Thyroid hormone Another relationship between IGF and nutritional status is that as IGF-1 levels decrease, thyroxine (T3) levels decrease as well (19). This idea is confirmed by the observation that in animals with hypothyroidism, IGF-1 levels are decreased, which suggests the possible regulation of IGF-1 by thyroid hormone (8). This could suggest a possible relationship between hypothyroidism or hyperparathyroidism and a possible association with IGF-1 serum concentrations. Diabetes Overview Diabetes has been defined as two different types. Type 1 diabetes is a condition where the beta cells of the pancreas do not produce enough insulin to sustain the needs of the individual to store glucose into body cells. Type 2 diabetes is a condition where the individual s pancreas may produce enough insulin, and in addition the individual becomes resistant to the insulin being produced, such that after a time, there needs to be more insulin produced for the same or even lesser response to high blood sugar levels. The latter type (2) has been in the forefront of investigation in the past decade, mainly due to the growing concern that it is occurring in

14 8 younger people every year (20). Type 2 diabetes has traditionally been viewed as a disorder of adults, most commonly seen in those who are middle-aged and elderly (20). Type 2 diabetes is an emerging problem especially in those ethnic populations known to have high prevalence of type 2 diabetes during adulthood (20). The prevalence of diabetes is on the rise, with the total number of cases projected to increase from 171 million in 2000 to 366 million by 2030 (21). Several studies have demonstrated that the progression of type 2 diabetes can be delayed with lifestyle modifications or the use of pharmacotherapy in subjects with pre-diabetes (21). The link between diabetes and IGF-1 The relationship between diabetes and IGF-1 levels has been explored in the scientific community. A Dutch study showed that carriers of a 192 bp polymorphism in the IGF-1 gene promoter had higher circulating IGF-1 levels and lower risks of developing type 2 diabetes than non-carriers of this allele (6). The proposed mechanism for this was a cytosine-adenosine repeat 1 kb upstream from the transcription site of the IGF-1 gene. It was not concluded whether this polymorphism itself is involved in the regulation of IGF-1 expression or whether it flags another polymorphism in the promoter region functionally involved in IGF-1 expression. Furthermore, it has been established that circulating IGF-1 and IGFBP-3 levels are decreased in poorly controlled diabetic patients and in diabetic rat models (6). GH levels are increased in diabetic patients, so that GH resistance underlies the decrease in IGF-1 levels (6). Another study states that patients with insulin-dependent diabetes have some hepatic resistance to GH, with elevated serum GH levels and reduced IGF-1 levels (22). Even subtle changes in IGF-1 can be indicative of several factors according to some studies. IGF-1 levels in the upper normal range are associated with reduced blood pressure, increased insulin sensitivity, and reduced prevalence of diabetes mellitus (22). Epidemiological studies have suggested that IGF-1 levels in the lower

15 9 normal range are associated with an increased risk of ischemic heart disease and stroke (22). Free IGF-1 levels have also been suggested to have a protective role in the development of atherosclerosis (22). However, the consistency that has been seen in the link between type 2 diabetes and IGF-1 levels has not been seen in reviewing the literature concerning the link between type 1 diabetes and IGF-1 levels. One study contributes the discrepancy to differences in assay technologies (6). Comparison with Insulin Serum insulin concentrations decline with dietary restriction. Diabetic rats that are insulin-deficient have decreased levels of IGF-1 and decreased liver GH binding (23). This suggests that insulin may have a role in the regulation of serum IGF-1 concentrations through the changes in liver GH binding (8). Insulin also induces the stimulatory effect of GH and amino acids on IGF-1 production (24). Insulin-like growth factors produce rapid metabolic changes and play a key role in preadolescent growth. Both IGF-I and IGFBP-1 levels are regulated at least in part by insulin (5). IGF-I levels can also have an effect on insulin sensitivity, and some studies suggest that IGF-I and IGFBP-1 levels may be altered in insulin resistance states and type 2 diabetes (25). Lower levels of IGF-I can be indicative of poor glycemic control in patients with type 2 diabetes (26). In addition, studies in recombinant human insulin-like growth factor-i (rhigf-i) infusion have shown that it improves glucose tolerance and lipid profile in patients with type 2 diabetes, decreases insulin requirements in type 1 DM, and improves insulin sensitivity and lipid profile in the normal population(5). IGF-I may also contribute to adjustments in glucose disposal through its effects on insulin secretion as well as insulin sensitivity, although its role in this regard is controversial (5). Some studies of pancreatic IGF-I have suggested that it may be important for

16 10 islet cell growth (27), whereas others conversely have shown that pancreatic- specific deficiency of IGF-I may protect against development of enlarged pancreatic islets and type 2 diabetes (28). Therapeutic implications The hypoglycemic effects of IGF-1 are mediated by the IGF-1 receptor rather than the insulin receptor (6). Therefore, it was postulated that IGF-1 can forgo the insulin resistance of patients with type 2 diabetes. The efficacy of IGF-1 treatment has been confirmed by some short term and long term studies (6). In addition to having intrinsic hypoglycemic properties, IGF-1 increases insulin sensitivity and decreases GH levels (6). IGF-1 treatment has a number of doselimiting side effects such as edema, jaw pain, headaches, Bell s palsy and hypoglycemia. Thus, an IGF/IGFBP-3 complex has been used which reduces insulin requirements in patients with type 1 diabetes with minimal side effects as seen in a short term study (29). Regulatory agencies have also been concerned about the possibility of IGF-1 treatment accelerating diabetic retinopathy (6). Recombinant human IGF-I and rhigf-1/igfbp-3 complex are also potential targets for other chronic inflammatory or nutritional disorders such as Crohn s disease, juvenile chronic arthritis or cystic fibrosis (30). Gene therapy trials performed on animal models have important practical limitations in humans, including variation in the level of gene expression differing in the transition from animal models to humans (30). In the last few years, two companies (Tercica and Insmed) compiled enough clinical trial data to seek FDA approval in the United States. In August 2005, the FDA approved Tercica's IGF-1 drug, Increlex TM, as replacement therapy for severe primary IGF-1 deficiency based on clinical trial data from 71 patients (31). In 2008, Tran and colleagues found that IGF-1R is a potential therapeutic target for gastrointestinal stromal tumors (32).

17 11 Obesity Prevalence and Disease Process One of the major associative disorders of type 2 diabetes is obesity. Other health consequences of obesity include its association with other significant disease states such as hypertension, dyslipidemia, metabolic syndrome, atherosclerosis, degenerative joint disorders, obstructive sleep apnea, and even certain cancers (33). The prevalence of obesity in the United States has been rising and is reaching epidemic proportions with one-third of the population being obese (BMI> 30 kg/m2) and two-thirds of the population being overweight (33). Obesity is also associated with cardiovascular disease, which is the leading cause of death in the United States (33). The Link between IGF-1 and Measures of Body Composition The relationships among IGFs, chronic nutritional status, body composition, and adiposity are complex and have been difficult to categorize. Studies among healthy adults have reported a null association (34), (35), a positive association (36,37), an inverse association (38,39), and a non-linear association between BMI and IGF-1 levels (13). The data on the relationship between IGFBP-3, the main binding protein of IGF-1, and obesity are also inconsistent (40). In 2002, Lukanova and colleagues examined 445 men and 391 women in a combined sample from three case-control cancer studies nested in the Northern Sweden Health and Disease Cohort (41). They argued that there is a nonlinear relationship between IGF-I and indices of body fat such as body mass index (BMI). They observed that there are decreased levels of IGF-I levels seen in prolonged fasting (as seen in patients with anorexia nervosa) and short-term fasting of normal-weight subjects (41). In contrast, in obese subjects, total plasma IGf-1 levels either remained stable or slightly decreased in comparison to IGF-1 levels of normal weight subjects (41). These observations suggested to the authors a nonlinear relationship of

18 12 circulating IGF-I with body fatness, with positive association between IGF-I and BMI in comparatively lean subjects, but an inverse relationship at higher BMI levels (41). However, according to another study, serum growth hormone binding protein (GHBP), IGF-1, and IGFBP- 3 are all significantly decreased in low weight patients with anorexia nervosa and return to nearly normal levels with refeeding (42). A review article discussing the roles of each IGF states that low circulating IGF-II levels predict weight gain and obesity in humans and that IGF-II levels increase with higher current fat mass index and waist-hip ratio (30). The same review does not state a clear relationship between body weight and IGF-1, but does state that plasma IGF-1 correlates with body size where constitutionally tall children had elevated IGF-1 levels (30). This review further reports that circulating IGF-1 levels are significantly lower in individuals with metabolic syndrome (30). One study sampling Korean men found no significant differences in circulating levels of the total IGF-1 and IGFBP-3 observed between the control and obese subjects (43). In the same study, free IGF-1 levels were significantly elevated in obese subjects, and the ratio of free to total IGF- 1 was also higher in obese subjects (43). Another study concluded that decreased IGF-II expression leads to increased fat deposition and obesity in mice, further adding that IGF-II is likely to affect fat metabolism rather than feeding behaviors (30). The short-term overfeeding of normal-weight women also resulted in an increase of IGF-I levels (44). By contrast, in obese subjects, total plasma IGF-I levels either remain stable or slightly decrease in comparison to IGF-I levels of normal-weight subjects (45). In Western, well-nourished populations, there are slightly negative correlations between serum IGF-I and the degree of adiposity as determined by BMI, or percent of body fat (46). However,

19 13 the overall picture of the relationship, between obesity and IGF-1 levels is still rather hazy and inconclusive according to the literature thus far. Statement of Problem The link between obesity and insulin-like growth factors has not been conclusive according to the literature reviewed above. There is significant debate concerning the relationship between IGF-1 and markers of adiposity or obesity with no consensus being reached in the scientific community. Statement of Significance Thus far, there has been no study examining the results of the NHANES III data sets in reference to IGF levels and obesity along with insulin resistance. This would be a novel endeavor in terms of possible associations between two conditions that commonly occur together in the same individuals (diabetes and obesity) and IGF concentrations. Furthermore, the use of the NHANES III data allows for an accurate representation of the US population since the sample size is larger than average for a given study. Research Questions 1. What are the differences in IGF levels among participants with DM, insulin resistance and normal blood glucose? 2. Does the presence of obesity affect the relationship between IGF-1 and markers of diabetes and blood glucose control?

20 14 CHAPTER 3: METHODOLOGY NHANES III The data used came from the National Health and Nutrition Examination Survey III (NHANES III), which is nationally representative of the US population between 1988 and NHANES is a national survey designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. It was conducted by the national center for Health Statistics (NCHS), a faction of the Centers for Disease Control and Prevention (CDC). The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests. The sample for the survey is selected to represent the U.S. population of all ages. To produce reliable statistics, NHANES over-samples persons 60 and older, African Americans, and Hispanics. All participants visit with a physician, during which dietary interviews and body measurements are included for everyone. All but the very young have a blood sample taken and will have a dental screening. In general, the older the individual, the more extensive the examination was. Study Population The participants were non-institutionalized individuals aged 20 years and older. Informed consent was obtained from all subjects or a parent/guardian. All the procedures were approved by the NCHS Institutional Review Board. Dietary and demographic information was collected by trained interviewers at the time of data collection. The race-ethnicity variable was self-reported and categorized as Non-Hispanic White or Caucasian, Non-Hispanic black, Mexican American or other. Physical exams included blood

21 15 pressure, standing height measurements, and weight in kilograms. The body mass index (BMI) of each individual was calculated from these measurements. A random subsection of NHANES III participants aged 20 and over, was selected to attend a morning session at the mobile examination center (MEC), in which blood samples were collected after an overnight fast. Subjects reporting hemophilia or recent cancer chemotherapy treatment were excluded. Surplus serum from this collection was stored for future analysis. Exclusion Criteria The present study includes all subjects for which serum IGF protein data was available from the analysis of stored surplus serum (n = 5157). Due to gestational alterations in IGF protein levels, pregnant and lactating women were excluded from the current study. It is assumed that the subjects were not in critical illness during the time of sampling since the national health and nutrition examination survey does not survey the chronically ill or hospitalized. Although IGF-1 levels have been known to vary with the presence of malignancies, participants who had been diagnosed with cancer were not excluded from this study. The reason for this inclusion was that NHANES III did not have very detailed information regarding cancer. There were only two questions, the first being have you ever been diagnosed with skin cancer and the next being have you ever been diagnosed with any other kinds of cancer. It was decided at the early stages of this study that these two questions were not sufficient enough in detail to exclude those who may have had cancer in the past and are now in remission, or what kind of other cancer was in question. Participants with cardiovascular disease were also not excluded from the study because the present study aims to examine those at risk for diabetes mellitus and/or obesity. All NHANES III data collected are publicly available on the NCHS website.

22 16 Laboratory Data Participants were instructed to fast for hours before the morning examination at the MEC. Fasting compliance and duration of the fast were self-reported. Serum samples were stored under appropriate refrigeration (4-8 ºC) or frozen (-20 ºC) until shipped to analytical laboratories for testing. Reference methods for measuring analytes in blood and urine are described in the Laboratory Procedures Used for NHANES III (DHHS manual). Serum IGF-1 and IGFBP-3 were measured from the stored surplus samples using standard laboratory protocols IGF-1 ELISA (catalog # ) and IGFBP-3 IRMA (catalog #6600) by a single technician at Diagnostic Systems Laboratories, Inc (Webster, TX). The NHANES III IGF protein information was released in September Demographic Data and Classifications Diabetes Mellitus (DM) was defined as fasting plasma glucose levels greater than 126 mg/dl and/or a yes response to any of the following interview questions: Have you ever been told by a doctor that you have diabetes or sugar diabetes?, Other than during pregnancy, has a doctor ever told you that you have diabetes or sugar diabetes?, Are you currently taking insulin?, Are you currently taking diabetes pills to lower your blood sugar? Subjects were categorized as having pre-diabetes if the fasting plasma glucose concentration was greater than or equal to 100 mg/dl but less than 126 mg/dl. Subjects were categorized as having normal blood sugar if the fasting plasma glucose concentration was less than or equal to 100 mg/dl. The World Health Organization as well as the American Diabetes Association both maintain that the diagnostic criteria for diabetes mellitus should be held at blood glucose levels above 126 mg/dl, and impaired fasting glucose to be between 100 to 126 mg/dl (47). Since an individual can be

23 17 have diabetes but practice good blood glucose control, three different markers of diabetes were used in the present study. In addition to fasting blood glucose concentrations, the percent glycated hemoglobin levels were also considered, since it is considered as one of the gold standards for the diagnosis of diabetes when appropriately used (48). Another marker of diabetes that has been examined in the present study is the homeostatic model assessment of insulin resistance (HOMA_IR), which is a method used to quantify insulin resistance and beta cell function (49). These three methods are compared with one another to see if there is any variety in the relationship between markers of diabetes and IGF-1 levels (49). As part of the physical examination, participants height and weight was also measured, from which a body mass index (BMI) was calculated. Four BMI categories were used in the present study: BMI less than 25, BMI between 25 and 29.9, BMI between 30 and 34.9, and BMI greater than 35. Another method by which body composition can be measured is bioelectrical impedance (BIA) which determines the electrical impedance, or opposition to the flow of an electric current through body tissues which can then be used to calculate an estimate of total body water (TBW). TBW can be used to estimate fat-free body mass and, by difference with body weight, total body fat (50). No single test being a sole valid measure of body composition, these various measures of body composition and obesity were compared in the present study. Statistical Analysis Analysis of the NHANES III data was conducted following the recommendations in the Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey, NHANES III (1988 to 1994). Logistic and multiple linear regression models were adjusted for age, gender, and race/ethnicity to determine the association between IGF-1 and insulin resistance and between this association and varying markers of obesity.

24 18 Data were downloaded from the NCHS website into Statistical Analysis System (SAS) software (version 9.1, Cary, NC). Analytical guidelines and appropriate weighing, outlined in the NHANES III documentation was followed. Initial exploratory analysis was conducted in SAS; However, final models and analyses were performed using SUDAAN (Release 5.50, Research Triangle Park, NC). Statistical procedures were also used for the analysis of data from complex sample surveys. Subjects were included in the data analysis only if they were not pregnant or lactating and had an IGF-1 protein value from the surplus serum collected at the morning session of the MEC (n=5157). If any subjects were missing data for all the variables in question, they were excluded as well. Multivariate regression analyses were performed to compute the geometric mean concentrations of IGF-1 for various anthropometric measures such as body mass index, fasting blood gluose and glycated hemoglobin levels. Adjustments were made for age, gender, and race/ethnicity. Analyses were done for the entire sample population as well as separately for males and females since there is a significant interaction between gender and IGF-1 levels as noted in the review of literature. All tests of significance were two-sided and p-values of less than 0.05 were considered statistically significant. Tukey s single-step multiple comparison procedure was used to find which means are significantly different from one another.

25 19 CHAPTER 4: RESULTS Participants Characteristics The data generated from this study includes 2430 males (47%) and 2727 females (53%) who attended the morning session at the MEC, all aged 20 years and older. The majority of the population was non-hispanic white (45%) followed by non-hispanic black and Mexican Americans (both 27%). Participants were only included if they had complete data for the following variables: IGF-1, blood glucose, insulin levels, hemoglobin A1C (Hbg A1C), weight, BMI, and a calculated waist to hip ratio. Descriptive characteristics of the study population are presented in tables 1, 2, and 3. Table 1 shows the IGF-1 levels across three race/ethnicity groups separated by sex for three levels of insulin resistance: normal, pre-diabetes, and diabetes. IGF-1 levels were higher in males than they were in females, which is consistent with previous findings in the review of literature. 69% of all females and 57% of all males were in the normal blood glucose level range of <100 mg/dl. For all females and all males, as blood sugar increased, IGF-1 levels decreased across the categories for markers of diabetes, except in both the male and female non-hispanic black population. In male non-hispanic blacks, those with blood glucose greater than 126 mg/dl had the highest IGF-1 levels (298 ng/ml), followed by those with normal blood glucose (288 ng/ml), and finally those with pre-diabetes had the lowest IGF-1 levels (269 ng/ml). In the female non- Hispanic blacks, the trend was ordered differently in that those in the normal blood glucose group had the highest IGF-1 levels (285 ng/ml), followed by those in the diabetes group (283 ng/ml), and finally those in the pre-diabetes group had the lowest IGF-1 levels (263 ng/ml). There was no significance in the difference found in females with regard to IGF-1 levels across the three diabetes groups. However, there was a significant difference between specific male

26 20 subgroups across the three diabetes groups, except for Mexican Americans. For all males, those in the normal and pre-diabetes groups had similar IGF-1 levels, and those in the pre-diabetes and diabetes group had similar IGF-1 levels, but those in the normal and diabetes groups had significantly different IGF-1 levels. Interestingly, the percentage of black women and the percentage of Mexican women who were diabetic were much higher than the percentage of white women who were diabetic (12%, 13%, and 7% respectively). Also, these are the same two groups where the p-values are less than 0.05 and thus significant, but where no difference was seen using Tukey s multiple comparisons approach. IGF-1 levels were significantly lower in diabetic men compared to men with normal plasma glucose concentrations. Pre-diabetic and diabetic women had lower but not statistically different serum IGF-1 concentrations compared to women with normal plasma glucose. The lack of statistical significance among the groups in women may be related to the small sample size of diabetic women. Table 2 shows the glycated hemoglobin levels of all males and females in the three race/ethnicity groups across four BMI categories (adjusted for age). When looking only at females, the majority was in the category of BMI less than 25 (39%); however, when looking only at males, the majority of males were in the BMI category of (41%). There was a direct relationship between glycated hemoglobin levels and BMI, in that as BMI increased across the four groups, glycated hemoglobin also increased. The only time there was a slight drop in glycated hemoglobin from the previous BMI category was in non-hispanic white males in BMI to BMI above 35, although this was not statistically significant. In females, of the three race/ethnicity groups, non-hispanic black females had the highest glycated hemoglobin levels across all BMI categories, and non-hispanic white females had the lowest. However, for males,

27 21 the highest glycated hemoglobin levels in the first BMI category (<25) was in the Mexican American, and the highest glycated hemoglobin levels in the following three BMI categories were in the non-hispanic black males. Table 3 shows IGF-1 levels across four BMI categories, <25, , , and >35. For all males and females, there was an inverse relationship between BMI and IGF-1 levels, where as BMI increased, IGF-1 levels decreased (p<0.0001). There were significant differences seen across both genders in the separation of IGF-1 levels across BMI groups in contrast with the previous table where the separation of IGF-1 levels was across diabetes groups. In non-hispanic black males, there was a deviation from this general trend since the highest IGF-1 levels were seen in the second BMI category ( ) instead of the expected first (<25), however, this difference was not significant. Multiple Regression Analyses Regression models including IGF-1 levels, one marker of diabetes, and one marker of obesity were examined in various combinations and pairings. IGF-1 was the dependent variable and the other variables (markers of diabetes, markers of obesity, and demographic variables such as age, gender, race/ethnicity) were the independent variables. These results are shown in table 4. The first model shows the adjustments for age, gender, and race/ethnicity without any variables introduced (R 2 =0.273). The models which follow either have one or two variables introduced into the regression analysis. Models 1-3 show an inverse relationship between markers of diabetes and IGF-1 levels. There was significance reported for all measures of diabetes (blood glucose, hemoglobin A1C, and HOMA_IR). When combining one marker of obesity with a marker of diabetes in combination with IGF-1 levels, the original inverse relationship between IGF-1 levels and diabetes markers still held to be significant for all except two instances. The

28 22 only combinations where the B-coefficients were not significant were when Hgb A1C was examined along with BMI and total body fat (Models 13 and 14). There were overall gains in variability in models 1-19, from model 0 as the reference point, even though the increase in R 2 was only The models with the highest R 2 were those using blood glucose/total body fat (model 10) and HOMA_IR/total body fat (model 18). However, the majority of the variability in the models can be explained by the demographic variables (age, gender, race/ethnicity). This is shown in table 5, where one can see that without the adjustments for age, gender, and race/ethnicity, the R 2 is a fraction of what it would be if these variables were adjusted for as they are in table 4. There is still a significant relationship between the IGF-1 levels and markers of obesity and diabetes; however, a small portion of the variability is explained by these models, as seen in table 5.

29 23 Table 1 Age adjusted serum IGF-1 mean levels (n) by diabetes status Normal Pre-Diabetes <100 mg/dl mg/dl 272 ± ± 4 All participants (5157) (3263) (1395) Diabetes >126 mg/dl 260 ±7 (499) p-value All Females (2727) NH 1 White (1255) NH Black (789) Mexican (683) 257 ± 3 (1883) 254±4 (898) 285±6 (553) 250±4 (432) 253± 5 (577) 253±6 (272) 263±8 (143) 230±9 (162) 250 ±12 (267) 246±15 (85) 283±15 (93) 227±10 (89) ± 4 a 284± 4 ab All Males (2430) (1380) (818) 289 ± 5 a 287± 5 ab NH White (1073) (576) (391) 288 ± 6 ab 269 ± 7 a NH Black (631) (405) (170) 267 ± ± 7 Mexican (726) (399) (257) Means in ng/ml ± Standard Error (SE) a, b Means with different superscripts are statistically different 1 NH refers to non-hispanic 268 ± 7 b (232) 266 ± 8 b (106) 298 ± 9 b (56) 245 ± 15 (70)

30 24 Table 2 Age adjusted percent glycated hemoglobin levels (n) by BMI category BMI <25 BMI BMI All participants (5157) 5.20 ± 0.02 a 5.27 ± 0.03 a 5.54 ± 0.06 b (1975) (1857) (840) BMI ± 0.07 b (485) p-value All Females (2727) NH 1 White (1255) NH Black (789) Mexican (683) 5.16±0.02 a (1067) 5.13±0.02 a (603) 5.33±0.06 a (245) 5.20±0.04 a (219) 5.22±0.04 a (850) 5.15 ±0.05 a (369) 5.60±0.08 b (245) 5.38±0.06 ab (236) 5.41±0.04 b (461) 5.37 ±0.05 b (172) 5.62±0.09 b (152) 5.54±0.09 b (137) 5.73 ±0.09 c (349) 5.68 ±0.11 b (111) 6.05±0.24 b (147) 5.68±0.11 b (91) ±0.03 a 5.33±0.03 a All Males (2430) (908) (1007) 5.21±0.04 a 5.29±0.03 ab NH White (1073) (408) (454) 5.36±0.05 a 5.60±0.05 b NH Black (631) (258) (232) 5.44±0.08 a 5.49±0.05 a Mexican (726) (242) (321) Percent Glycated Hemoglobin ± Standard Error (SE) BMI, Body Mass Index 1 NH refers to non-hispanic a, b, c Means with different superscripts are statistically different 5.68±0.11 b (379) 5.68±0.14 c (157) 5.73±0.07 b (95) 5.54±0.14 a (127) 5.70±0.13 b (136) 5.62±0.15 bc (54) 6.17±0.19 b (46) 5.88±0.15 a (36)

31 25 Table 3 Age adjusted serum IGF-1 mean levels (n) by BMI category BMI <25 BMI BMI All Participants (5157) 277 ± 3 a 272 ± 4 a 266 ± 5 a (1975) (1857) (840) BMI ± 6 b (485) p-value All Females (2727) NH 1 White (1255) NH Black (789) Mexican (683) 263 ±4 a (1067) 259 ±5 a (603) 300±7 a (245) 270±7 a (219) 256±5 a (850) 252 ±6 a (369) 285±9 a (245) 247±6 b (236) 256 ±6 a (461) 255±7 a (172) 281±8 a (152) 226±11 bc (137) 222 ±7 b (349) 223±9 b (111) 240±7 b (147) 193±12 c (91) ±4 a 288 ±5 a All Males (2430) (908) (1007) 295±5 a 290 ±5 a NH White (1073) (408) (454) 283±6 a 294±8 a NH Black (631) (258) (232) 269±7 a 269±5 ab Mexican Males (726) (242) (321) BMI, Body Mass Index Means in ng/ml ± Standard Error (SE) a, b, c Means with different superscripts are statistically different 1 NH refers to non-hispanic 276 ±8 a (379) 278±9 ab (157) 280±11 ab (95) 243±8 bc (127) 242±9 b (136) 244 ±11 b (54) 240±12 b (46) 224±10 c (36)

32 26 Table 4 Regression analysis: The relationships among IGF-1, demographic variables, and markers of diabetes and obesity Models β-coefficients Age -3.17** -3.08** -3.06** -3.09** -3.15** -3.08** -3.09** -2.83** -3.09** -3.03** -3.04** -2.80** -3.08** -3.02** -3.03** -2.78** -3.11** -3.06** -3.06** -2.84** Gender Male 21.38** 22.87** 22.33** 22.44** 29.07** 22.65** 16.39** 36.04** 29.37** 23.43** 17.68** 35.74** 29.20** 23.12** 17.21** 35.75** 27.64** 22.91** 17.67** 33.99** Female Race/Ethnicity White Black Mexican ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Markers of Diabetes Blood Gluc -0.23** -0.17** -0.14* -0.15* -0.15* Hgb A1C -6.44** -4.51* * HOMA_IR -0.17** -0.11** -0.07* -0.08* -0.11** Markers of Obesity Body Wt -0.53** -0.48** -0.49** -0.38** BMI -2.04** -1.89** -1.94** -1.72** TBF -1.15** -1.08** -1.10** -0.97** W: H Ratio -147** -135** -139** -120** Model p-value < < < < < < < < < < < < < < < < < < < < Model R BMI, Body Mass Index; Hgb A1C, glycated hemoglobin; HOMA_IR, homeostatic model assessment of insulin resistance; TBF, Total Body Fat; W:H, Waist:Hip * indicates statistical significance 0.05, and ** indicates statistical significance 0.01 Note: In the row where there are no β-coefficients listed, the ** indicate that the whole variable (i.e. race/ethnicity) is statistically significant (p 0.01)

33 27 Table 5 Multiple regression without adjustments for age, gender, or race/ethnicity Models 1a 2a 3a 4a 5a 6a 7a 9a Β-Coefficients Markers of Diabetes Blood Gluc -0.69** -0.55** Hgb A1C ** HOMA_IR -0.33** Markers of Obesity Body Wt BMI -3.18** -2.49** TBF -1.95** W: H Ratio -247** Model p-value < < < < < < < < Model R BMI, Body Mass Index; Hgb A1C, glycated hemoglobin; HOMA_IR, homeostatic model assessment of insulin resistance; TBF, Total Body Fat; W:H, Waist:Hip * indicates statistical significance 0.05, and ** indicates statistical significance 0.01