ABSTRACT

ASSOCIATIONS AMONG AGE, PHYSICAL ACTIVITY, INSULIN SENSITIVITY, RESISTIN, ENDOTHELIN-1, ADIPONECTIN AND IGF-1 LEVELS

by Caitlyn Alyse Thomas

A variety of cytokines, including resistin, adiponectin, endothelin-1 (ET-1) and insulin- like growth factor-1 (IGF-1), are thought to influence adipose and endothelial tissue function. The PURPOSE of this study was to assess the relationships among age, physical activity level, insulin sensitivity and resistin, adiponectin, ET-1 and IGF-1 levels in healthy young and older adults. METHODS: A sample of 20 young (age: 21.0±1.2y) and 20 older (age: 68.4±4.0y) adults was used. The following were assessed: habitual physical activity level (PA); body composition; and fasting serum levels of resistin, adiponectin, ET-1, IGF-1, glucose and insulin. The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. RESULTS: PA level was not correlated with any protein, glucose or insulin levels. There were no significant differences in resistin and adiponectin levels between young and old subjects (p = 0.056; p = 0.81). There were significant differences in ET-1 and IGF-1 levels between young and old subjects (p= 0.01; p= 2.15 x 10-7). Adiponectin and IGF-1 were inversely correlated with BMI (p = 0.004; p= 0.002). HOMA-IR was positively correlated with BF% (p = 0.015). CONCLUSIONS: Resistin, adiponectin, ET-1, IGF-1 and PA levels do not appear to be predictive of insulin sensitivity in young or older adults.

ASSOCIATIONS AMONG AGE, PHYSICAL ACTIVITY, INSULIN SENSITIVITY, RESISTIN, ENDOTHELIN-1, ADIPONECTIN AND IGF-1 LEVELS

A Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Science

by

Caitlyn Alyse Thomas

Miami University

Oxford, Ohio

August 2018

Advisor: Kyle L. Timmerman, Ph.D.

Reader: Kevin D. Ballard, Ph.D.

Reader: Michael W. Crowder, Ph.D.

©2018 Caitlyn Alyse Thomas

This thesis titled

ASSOCIATIONS AMONG AGE, PHYSICAL ACTIVITY, INSULIN SENSITIVITY, RESISTIN, ENDOTHELIN-1, ADIPONECTIN AND IGF-1 LEVELS

by

Caitlyn Alyse Thomas

has been approved for publication by

College of Education, Health and Society

and

Department of Kinesiology and Health

______Kyle L. Timmerman, Ph.D. Thesis Advisor

______Kevin D. Ballard, Ph.D.

______Michael W. Crowder, Ph.D.

Table of Contents Page

LIST OF TABLES ...... iv LIST OF FIGURES ...... v DEDICATION ...... vi ACKNOWLEDGMENTS ...... vii CHAPTER INTRODUCTION ...... 1 METHODS ...... 4 RESULTS ...... 7 DISCUSSION ...... 9 REFERENCES ...... 24

iii

List of Tables

Page

TABLE 1. Subject Background ...... 16 TABLE 2. Average Protein Levels of Young and Old Subjects ...... 16 TABLE 3. Correlation with Physical Activity Level within Subject Age Groups ...... 17 TABLE 4. Correlation with BMI Controlling for Age and Gender ...... 17 TABLE 5. Correlation with HOMA-IR Controlling for Age and Gender ...... 17 TABLE 6. Correlation with HOMA-IR Controlling for Age, Gender and BMI ...... 18 TABLE 7. Correlation with Body Fat Percentage Controlling for Age and Gender ..... 18

iv

List of Figures

Page

FIGURE 1. Average Resistin Concentration for Young and Old Subjects ...... 19 FIGURE 2. Average Adiponectin Concentration for Young and Old Subjects ...... 19 FIGURE 3. Average Endothelin-1 Concentration for Young and Old Subjects ...... 20 FIGURE 4. Average IGF-1 Concentration for Young and Old Subjects ...... 20 FIGURE 5. Average HOMA-IR for Young and Old Subjects ...... 21 FIGURE 6. Correlation Between IGF-1 and BMI ...... 21 FIGURE 7. Correlation Between Adiponectin and BMI ...... 22 FIGURE 8. Correlation Between Body Fat Percentage and HOMA-IR ...... 22 FIGURE 9. Correlation Between Body Fat Percentage and IGF-1 ...... 23

v

Dedication

I would like to dedicate my thesis to my parents. They have always supported me and taught me that success is a state of mind that is achieved through consistent hard work.

vi

Acknowledgments

Thank you to my committee members, Dr. Kevin Ballard and Dr. Michael Crowder, for giving their time, knowledge and expertise in this study.

Thank you to all of the undergraduate and graduate students for your support and assistance in this study.

Last, but not least, thank you to my thesis advisor Dr. Kyle Timmerman. I greatly appreciate all of your time, encouragement and guidance during this study. You have been an influential mentor and I have enjoyed working together. Thank you for helping me grow into a better researcher.

vii

CHAPTER 1 Introduction

Insulin resistance is the result of a decreased response in peripheral tissues to insulin 1. Approximately 30.3 million people in the United States suffer from insulin resistance 2. Insulin sensitivity is commonly estimated using the homeostasis model assessment of insulin resistance (HOMA-IR) 3–5. HOMA-IR is a convenient method of calculating insulin sensitivity by multiplying fasting plasma insulin by fasting plasma glucose and dividing by the constant 22.5 3. In adults, a HOMA-IR value above 2.5 is considered to be an indicator of insulin resistance 5. The primary causes of insulin resistance include, but are not limited to, the following: overweight/obesity; physical inactivity; high blood glucose (hyperglycemia); and aging 2. Evidence suggests a correlation between inflammation and insulin resistance 1. The secretion of pro-inflammatory factors increase with increasing adiposity 6, leading to a variety of health concerns including insulin resistance and cardiovascular disease 1,6,7. A variety of cytokines, including resistin, adiponectin, endothelin-1, and insulin-like growth factor-1, are thought to influence adipose and endothelial function and have emerged as novel predictors of cardiovascular disease and insulin resistance 1,6,8–13. Resistin is a 12-kDa polypeptide belonging to the family of cysteine-rich C-terminal domain proteins that are similar to the proteins found in the inflammatory family 14. Due to its inflammatory nature, resistin could be related to insulin resistance and cardiovascular disease 15. Resistin concentration has been found to be altered in obese subjects, suggesting its relation to obesity-linked disorders such as insulin resistance 8,16–19. Equivocal results regarding the relationship between resistin, age, and physical activity level have been found in humans. One study found that resistin levels did not differ between young- and old-aged subjects 8, while another study found elevated resistin levels in elderly compared with middle-aged subjects 20. Increased levels of resistin were found in healthy middle-aged men following an 8-week exercise program 21 and in elite athletes 22. In contrast, no significant changes in resistin levels were observed in obese adolescents following a 9-month exercise intervention 16. Previous studies have shown that resistin secretion is influenced by ET-1 levels 23,24. ET- 1 is a peptide that is primarily released from vascular endothelial cells, but it can also be produced by vascular smooth muscle cells (VSMC), cardiomyocytes, leukocytes, macrophages,

1 and neurons. At normal levels ET-1 functions as a vasoconstrictor that contributes to vascular tone, but at elevated levels it is also pro-inflammatory and promotes VSMC proliferation 9. While ET-1 is best known for its role in cardiovascular disease (mainly hypertension), it is also critical for neurological function, pulmonary physiology, fluid and electrolyte transport, autoimmune disorders, and cancer biology 25. Because ET-1 contributes to hypertension and cardiovascular disease, the effect of short and long-term physical activity on ET-1 levels has been studied 26–29. Plasma and skeletal muscle ET-1 levels were found to be higher in old sedentary subjects when compared to young sedentary and old active subjects after an 8-week aerobic exercise intervention 26. Differing results were found in other studies where 8-week and 12-week exercise interventions were used, respectively. These studies concluded that ET-1 levels in old male subjects did not differ before and after the intervention 28,29. Adiponectin is a 30-kDa adipokine 30 involved in glucose and lipid homeostasis. Adipocyte differentiation and insulin production stimulate the secretion of adiponectin 31. A decreased concentration of adiponectin has been associated with insulin resistance and hyperinsulinemia. When adipocytes become large there is a decrease in the production and secretion of adiponectin, an insulin-sensitizing hormone, thus leading to insulin resistance in obese subjects 31. Previous studies have investigated the effects of physical activity, gender and caloric restriction on adiponectin levels 30,32–34, but little is known about the possible influence of aging on adiponectin 33,35. It has been concluded that exercise interventions 30,32,36 and weight loss 33,34 significantly increase adiponectin levels. Studies have also shown gender differences with higher adiponectin levels found in females compared to males 32,35,36. Insulin-like growth factor-1 (IGF-1) is a peptide consisting of 70 amino acids with a molecular weight of 7649 Da. IGF-1 has a structure similar to that of insulin, which gives it the ability to bind to insulin receptors 37. Production of IGF-1 occurs primarily in the liver, but it can also be produced by adipocytes, which suggests IGF-1 has both autocrine and paracrine effects. The main biological effect of IGF-1 is growth stimulation and cell differentiation, but it has also been found to have insulin-like effects in vivo and in vitro 38. IGF-1 plays a role in muscle growth, hypertrophy, and maintenance 37. Exercise aids in maintaining muscle mass and function and also decreases the rate of sarcopenia 8,37,39. In the study by McMahon et al. (2014), the combination of exercise and IGF-1 treatment, but neither

2 treatment alone, increased muscle mass in mice 39. Previous research has also shown that there is a clear age-related decrease in IGF-1 levels in human and rodent blood 8,39,40. However, equivocal results have been reported regarding the influence of IGF-1 administration on muscle mass and function in older adults 39,40. While there are significant findings in the research regarding resistin, ET-1, adiponectin, and IGF-1, some obvious and contradictory gaps are present. Based on previous research there are differing conclusions about the relationship between resistin and body composition 22. On a broader scale, the research findings have shown equivocal results regarding the relationships among resistin, age, body composition, and exercise 8,20,21,41. Despite the emerging evidence suggesting that resistin, ET-1, adiponectin, and IGF-1 may be important predictors of insulin resistance, few researchers have examined the impact of age and habitual physical activity level on these proteins within the same study. In combination, knowledge regarding these four protein levels and their associations among age, physical activity level and measures of insulin resistance could predict the potential development of chronic diseases, such as insulin resistance and cardiovascular disease. Thus, the purpose of this proposed study was to assess the relationships among resistin, ET-1, adiponectin, and IGF-1 levels, age, habitual physical activity level and measures of insulin sensitivity in healthy younger and older adults. The primary goal of this study was to determine if resistin, ET-1, adiponectin, and IGF-1 levels differ between old and young adults. Based on previous reports, we hypothesized that resistin and endothelin-1 levels would be higher, and adiponectin and insulin-like growth factor- 1 levels would be lower, in older compared to younger adults. The second goal of this study was to assess the potential association between physical activity level and serum resistin, ET-1, adiponectin, and IGF-1 levels within groups. We hypothesized that higher physical activity level within old and young subjects would be inversely correlated with serum resistin and ET-1 levels, but positively correlated with adiponectin and IGF-1 levels. The third goal of this study was to assess the potential association between resistin, ET-1, adiponectin, and IGF-1 levels and HOMA-IR. We hypothesized that higher HOMA-IR would be positively correlated with resistin and endothelin-1 levels, but inversely correlated with adiponectin and insulin-like growth factor- 1 levels.

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CHAPTER 2 Methods

Subjects/Study Design:

Fasting blood samples were obtained from 20 younger and 20 older subjects collected as part of previous studies in this laboratory. Fasting blood glucose levels were collected and analyzed using the Cholestec analyzer. Exclusion criteria for both younger and older subjects included the following: physical dependence; active cancer; significant cardiovascular, metabolic, or pulmonary disease; recent treatment with anabolic or corticosteroids; alcohol or drug abuse; tobacco use; and BMI <20 kg/m2 or BMI > 30 kg/m2. All subjects were determined to be relatively healthy by the above criteria. All of the procedures outlined in this study were approved by Miami University’s Institutional Review Board (IRB). For this comparative/associational study subjects were grouped by age as either older (≥ 65 years) or younger (18-30 years) to explore the relationships among resistin, adiponectin, ET-1, and IGF-1 levels and age. As part of their participation in a previous study, all subjects completed physical activity questionnaires that estimated their leisure time physical activity level in kcal/week. Their body composition was also measured as part of their participation. Stored serum samples were utilized to assess resistin, adiponectin, ET-1, IGF-1, and insulin levels in the subjects of this study.

Physical Activity Questionnaires:

Physical activity frequency and intensity were determined in older subjects using the Community Healthy Activities Model Plan for Seniors (CHAMPS). This questionnaire has been validated for the estimation of frequency and intensity of total and moderate-to-vigorous physical activity (kcal/week) 42,43. In young adults, physical activity frequency, duration and intensity were estimated using the International Physical Activity Questionnaire (IPAQ). As with the CHAMPS questionnaire, the IPAQ allows for physical activity to be quantified in kcal/week for younger subjects 44. The use of these questionnaires allowed us to determine if physical activity level within groups was

4 associated with serum resistin, adiponectin, ET-1 and IGF-1 levels. Two types of questionnaires were used in the previous study subjects participated in. The IPAQ is used with younger subjects, while the CHAMPS is primarily used with older subjects.

Height, Weight and Body Composition:

Height and weight of all subjects were measured and their body composition was determined using a bioelectrical impedance analyzer (BIA). The anthropometric parameters of each subject were entered into the Tanita scale. Fat-free mass and fat mass were estimated based on the time it took for an imperceptible electric current to pass from one pole of the BIA unit to the other. This method is based on the premise that lean tissue (muscle, connective tissue, and bone) conducts electricity more efficiently than fat tissue.

Enzyme-Linked Immunosorbent Assay (ELISA):

Solid-phase sandwich ELISA kits from R & D Diagnostics were used for the detection and quantification of serum resistin (ng/mL), adiponectin (ng/mL), endothelin-1(pg/mL), IGF-1 (ng/mL), and insulin (µIU/mL). The ELISA technique is commonly used for the detection of proteins. All samples were run in duplicate, and compared to standard curves of known concentrations for each protein assessed. The resistin ELISA required a 5-fold dilution of serum samples. The manufacturer tested three samples of known concentration (0.60, 2.26, 4.72 ng/mL) 20 times to assess intra-assay precision. The coefficients of variation (CVs) for the resistin ELISA ranged between 3.8-5.3%. The adiponectin ELISA required a 100-fold dilution of serum samples. Three samples of known concentration (12.5, 45.3, 91.5 ng/mL) were tested 20 times to assess intra-assay precision. The CVs for the adiponectin ELISA were determined to be 3.3, 2.8, and 5.0%, respectively. The ET-1 ELISA did not require any dilution of serum samples. Three samples of known concentration (3.00, 7.34, 14.7 pg/mL) were tested 20 times to assess intra-assay precision. The CVs for the ET-1 ELISA were determined to be 4.0, 2.3, and 1.9%, respectively. The IGF-1 ELISA required 20 µL of serum sample be pretreated with 380 µL of

5

Pretreatment A solution (included with ELISA kit). The samples were vortexed and incubated for 10 min. Then 50 µL of sample was added to 200 µL of Pretreatment B solution (included with ELISA kit) and assayed immediately. Three samples of known concentration (0.490, 1.243, 2.373 ng/mL) were tested 20 times to assess intra-assay precision. The CVs for the insulin-like growth factor-1 ELISA were determined to be 3.5, 4.3, and 4.3%, respectively. The insulin ELISA did not require any dilution of serum samples. Three samples of known concentration (66.8, 158, 299 pmol/L) were tested 20 times to assess intra-assay precision. The CVs for the insulin ELISA were determined to be 3.9, 3.7, and 4.0%, respectively.

Data Analysis:

Prior to analysis, all data were assessed for assumptions of normality and equal variance and all data were found to be normally distributed. An independent sample t-test was utilized to test for age-related differences in serum resistin, adiponectin, ET-1, IGF-1, HOMA-IR, and PA levels. Partial correlations were performed to determine if physical activity level and serum resistin, adiponectin, endothelin-1, and IGF-1 were correlated within groups (controlling for age, gender, and BMI). Partial correlations were performed to determine if BMI and serum resistin, adiponectin, endothelin-1 and IGF-1 were correlated (controlling for age and gender). Partial correlations were performed to determine if HOMA-IR level and serum resistin, adiponectin, endothelin-1, IGF-1, moderate-to-vigorous physical activity level (MVPA), body fat percentage (BF%), and fasting blood glucose level were correlated (controlling for age, gender and BMI). Significance was set to p < 0.05.

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CHAPTER 3 Results

Subject Background:

A convenience sample of 40 subjects was used for this study (Table 1). Twenty young subjects (10 male, 10 female) were included, with ages ranging from 19-24 years and an average age of 21.0 ± 1.2 years. Twenty old subjects (6 male, 14 female) were included, with ages ranging from 62-76 years and an average age of 68.4 ± 4.0 years. The average BMIs of the subject groups were 24.3 ± 4.5 kg·m-2 (young) and 25.5 ± 3.1 kg·m-2 (old). The average HOMA- IR levels of the subject groups were 1.53 ± 0.62 (young) and 2.08 ± 1.06 (old).

Relationship Between Age and Resistin, Adiponectin, ET-1 and IGF-1 Levels:

No significant difference was found between groups for serum resistin (p = 0.056), although resistin levels tended to be higher in young compared to older adults. No significant difference was found between groups for serum adiponectin (p = 0.81). ET-1 levels were significantly higher in older subjects when compared to younger subjects (p = 0.01). Serum IGF- 1 levels were significantly lower in older subjects when compared to younger subject (p = 2.15 x 10-7). There was no significant difference between young and old subjects resistin/IGF-1 ratio (p = 0.94). Fasting blood glucose levels in older subjects were significantly higher compared to younger subjects (p = 1.77 x 10-5). No significant difference was found between insulin levels (p = 0.32) in young and old subjects (Table 2). HOMA-IR levels were significantly higher in older compared to young adults (p = 0.05) (Table 2).

Correlation Between Physical Activity Level and Resistin, Adiponectin, ET-1 and IGF-1 Levels:

No correlation was found between physical activity level and resistin (p = 0.70), adiponectin (p = 0.82), ET-1 (p = 0.11), and IGF-1 (p = 0.80) levels in older subjects while controlling for age and gender (Table 3). No correlation was found between physical activity

7 level and resistin (p = 0.91); adiponectin (p = 0.96); ET-1 (p = 0.53); IGF-1 (p = 0.79) levels in young subjects while controlling for age and gender (Table 3). Young subjects completed the IPAQ and older subjects completed the CHAMPS, thus correlations between groups was not possible.

Correlation Between BMI and Resistin, Adiponectin, ET-1 and IGF-1 Levels:

No correlation was found between BMI and resistin (p = 0.83) or ET-1 (p = 0.99) levels while controlling for age and gender (Table 4). An inverse correlation was found between BMI and adiponectin (p = 0.002) and BMI and IGF-1 (p = 0.026) levels while controlling for age and gender (Table 4).

Correlation Between HOMA-IR, Resistin, Adiponectin, ET-1 and IGF-1 Levels, MVPA, BF% and Fasting Blood Glucose:

No correlation was found between HOMA-IR and resistin (p = 0.104), adiponectin (p = 0.502), ET-1 (p = 0.258), and IGF-1 (p = 0.291) levels while controlling for age and gender (Table 5). No correlation was found between HOMA-IR and MVPA levels within young and older subjects (young, p = 0.333; older, p = 0.818) while controlling for age, gender and BMI (Table 6).

Correlation Between Body Fat Percentage and Resistin, Adiponectin, ET-1 and IGF-1 Levels:

Body fat percentage was not correlated with resistin (p = 0.621); adiponectin (p = 0.070); or ET-1 (p = 0.659) while controlling for age and gender. A significantly inverse correlation was found between IGF-1 (p = 0.041) and body fat percentage (Table 7) while controlling for age and gender.

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CHAPTER 4 Discussion

The current study investigated the relationship among age, physical activity level and measures of insulin sensitivity and serum resistin, adiponectin, ET-1 and IGF-1 levels in healthy younger and older adults. The primary goal of this study was to determine if serum resistin, ET- 1, adiponectin and IGF-1 differ between younger and older subjects. The second goal of this study was to assess the potential association between physical activity level within groups and serum resistin, ET-1, adiponectin and IGF-1 levels. The third goal of this study was to assess the potential association between resistin, ET-1, adiponectin and IGF-1 and HOMA-IR. In combination, knowledge regarding these four protein levels and their associations among age, physical activity level and measures of insulin resistance could predict the potential development of chronic diseases, such as insulin resistance and cardiovascular disease. The hypothesis that resistin and ET-1 levels would be higher in older adults was partially supported. The results of the current study found no significant difference in resistin levels between younger and older adults (Table 2, Figure 1), which is supported by one previous study 8. Conversely, Mohty et al. (2010) investigated the age-related changes in resistin levels in unhealthy middle-aged and older adults, and reported that the older subjects had significantly higher resistin levels compared to the middle-aged subjects 20. Further research is necessary to determine the relationship between resistin and age in healthy and clinical populations in order to confirm how resistin levels change with age. Our results showed that ET-1 levels were significantly higher in older adults (Table 2, Figure 3), with similar results reported in previous studies (24,39). The Van Guilder et al. (2007) study showed elevated ET-1 levels in healthy older adults, while Nyberg et al. (2013) showed a significant difference between younger and older sedentary subjects ET-1 levels. The current study cannot be fully supported by the Nyberg et al. (2013) results due to the differences in subject population. Our study utilized physically active younger and older adults, while the Nyberg et al. (2013) study had the following subject groups: sedentary younger and older; physically active older. Including a sedentary younger and older subject group in our study would have potentially shown the effects of habitual physical activity on ET-1 levels.

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With respect to adiponectin and IGF-1, it was hypothesized that both levels would be lower in older adults. This hypothesis was partially supported by the findings of our study. No significant difference in adiponectin levels was discovered between younger and older adults (Table 2, Figure 2). The association between age and adiponectin levels are still unclear, with conflicting reports in the literature 33,35. Obata et al. (2013) reported young healthy subjects had significantly lower adiponectin levels compared to older healthy subjects 35. Although this finding differs from the results of the current study, the young subject population in the Obata et al. (2013) study were 40-50 years. This is much older than the young subject population (19-22 years) of our study thus, we would have expected to find a larger difference between our young and older subjects adiponectin levels. Obata et al. (2013) also considered the combined effects of age and diabetes on adiponectin levels. Both diabetic and healthy subjects showed significantly higher adiponectin levels with age, but diabetic adiponectin levels remained significantly lower compared to healthy subjects 35. It was concluded that the higher adiponectin levels found in older healthy and diabetic adults was due to a lessened activity of binding substances to adiponectin, thus a reduced clearance rate of adiponectin 35. Adequate levels of adiponectin are important, due to its ability to decrease insulin resistance, which contributes to hyperinsulinemia and atherosclerosis 31,35. The results of our study showed significantly lower IGF-1 levels in older compared to younger adults (Table 2, Figure 4) and is supported by previous research 8,39,40,46. Using a larger subject group with similar characteristics to this current study, Bucci et al. (2013) showed significantly lower IGF-1 levels in older compared to younger adults 8. According to previous studies, the resistin/IGF-1 ratio is associated with metabolic syndrome in older adults 8,47. When this ratio was calculated using our data (Table 2), no significant difference was found between younger and older adults (p = 0.94). This result is not supported by the previous findings of Bucci et al. (2013), where older adults had a significantly higher resistin/IGF-1 ratio compared to younger adults 8. The larger sample size (n = 412) used in the Bucci et al. (2013) study potentially contributes to the difference in results with our study (n = 40). The second goal of this study was to assess the potential association among physical activity level within groups and serum resistin, ET-1, adiponectin and IGF-1 levels. The hypothesis that higher physical activity level within younger and older subjects was inversely correlated with serum resistin and ET-1 was not supported (Table 3). Most of the previous

10 studies investigating the effects of physical activity on resistin levels utilized an exercise intervention protocol 16,21, with only one study utilizing subjects’ habitual physical activity levels 22. Our study utilized habitual physical activity levels and found no significant correlation between MVPA and resistin levels. The results reported in previous research regarding the relationship between physical activity level and resistin are equivocal. The effects of 9-months of aerobic exercise training (5 days/week, 45-60 min/session) on obese adolescents showed no significant changes in resistin levels 16. However, after an 8-week aerobic exercise intervention (4 days/week, 35 min/session) healthy middle-aged men showed a significant increase in resistin levels 21. Lastly, when elite endurance athletes were compared to lean sedentary adults and overweight adults (with normal glucose tolerance and impaired glucose tolerance), it was found that the elite athletes had significantly higher resistin levels 22. While all of these studies, including the current study, used subjects of various ages and characteristics, there is still no clear trend regarding the effect of physical activity on resistin levels. Future studies should investigate the longitudinal effects of habitual physical activity on resistin levels. In the current study, ET-1 levels were not significantly correlated with physical activity level (Table 3). Variable results have been reported in previous studies on the effect of physical activity on ET-1 levels 26,27,29,45. In contrast with our study, previous studies employed exercise intervention programs. Nyberg et al. (2013) reported ET-1 levels decreased in hypertensive subjects to the level of normotensive subjects after an 8-week exercise intervention (2-3 days/week, 60 min/session) 26. This is in contrast to the findings of Thijssen et al. (2007), who found no changes in ET-1 levels in older men after an 8-week cycling intervention (3 days/week, 30 min/session) 29. Nyberg et al. (2013) also investigated the effects of habitual physical activity on ET-1 levels. Their results showed older sedentary subjects had significantly higher ET-1 levels when compared young sedentary and older active subjects 26. Based on the previous studies, including a sedentary subject group in the current study would broaden the difference in physical activity level and potentially better show its effects on ET-1. The hypothesis that higher physical activity level within younger and older subjects was positively correlated with adiponectin and IGF-1 levels was not supported. The findings of our study indicated no significant correlation between physical activity and adiponectin levels (Table 3). But there was a significant inverse correlation between adiponectin levels and BMI (Table 4) and a correlation with BF % that was trending towards significance (Table 8). Some previous

11 studies have shown a significant increase in adiponectin levels after an exercise intervention 32,36, with one showing no significant change in adiponectin levels 30. All of the previous studies saw a significant decrease in BMI and BF % and significant correlations between BMI, BF % and adiponectin levels 30,32,36. The primary difference between our study and previous studies is that the subjects experienced weight loss in the previous studies. An increase in adiponectin level after exercise is commonly seen after a significant weight loss 30,32,36. Our subjects did not experience weight loss, which could be the reason adiponectin levels did not differ significantly between young and old subjects and were not correlated with physical activity level (Table 2, Table 3). We found no correlation between IGF-1 and physical activity levels within young and old subject groups (Table 3). Previous studies have proven ambiguous on whether physical activity affects IGF-1 levels 48–51. A significant increase in IGF-1 levels was reported by Vega et al. (2010) after healthy subjects performed an acute bout of resistance exercise 48. However, many other studies found no significant change in IGF-1 levels after a resistance exercise intervention 49–51. The latter studies are more in agreement with our study, given no significant correlation was found between IGF-1 and physical activity level. Kraemer et al. (1999) found no significant differences in IGF-1 levels when comparing healthy, physically active younger (30 y/o) and older (62 y/o) adults 50. Our subjects were considered habitually active based on the IPAQ (average 3000 MET-min/wk) and CHAMPS (average 1000 kcal/wk) scoring (Table 1). While the subject characteristics were similar to Kraemer et al. (1999), our results showed significantly higher IGF-1 levels in older subjects, thus highlighting the inconsistency of the results reported in the literature 48,50. The third goal of this study was to assess the potential association among resistin, ET-1, adiponectin and IGF-1 and HOMA-IR. The hypothesis that higher HOMA-IR will be positively correlated with resistin and ET-1, but inversely correlated with adiponectin and IGF-1 levels weas not supported (Table 5). But our results indicated a significant difference between young and old subjects HOMA-IR levels (p = 0.05, Table 2). Initially, resistin was thought to influence insulin action thus associating it with insulin resistance 14,52, but previous studies have failed to provide a clear relationship between resistin levels and insulin resistance 14,16,22,52. Gueugnon et al. (2012) reported higher insulin levels, HOMA-IR and resistin levels in obese adolescents when compared to healthy adolescents 16.

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But, in agreement with our results, no correlation was found between resistin and measures of insulin resistance (HOMA-IR) 16. When studying resistin levels in elite athletes, Perseghin et al. (2006) found elevated resistin levels and no association with measures of insulin sensitivity 22. While vastly different subject groups were used in each of these studies, Gueugnon et al. (2012) and Perseghin et al. (2006) both concluded that resistin does not have an association with insulin resistance 16,22. These findings are in agreement with the current study, which found no significant correlation between HOMA-IR and resistin levels in healthy young and older subjects (Table 5). Hivert et al. (2008) reported results that conflicted with Gueugnon et al. (2012) and Perseghin et al. (2006). Associations were discovered among adiponectin, resistin and insulin resistance 53. Significant direct correlations were shown between HOMA-IR and BMI, and HOMA-IR and resistin, and a significant inverse correlation with HOMA-IR and adiponectin. The inverse correlation between HOMA-IR and adiponectin was stronger in subjects with metabolic syndrome compared to subjects without metabolic syndrome. It was also determined that the prevalence of insulin resistance decreased with higher adiponectin levels and increased with higher resistin levels, in subjects with or without metabolic syndrome 53. These findings support that there is a relationship among insulin resistance, resistin and adiponectin. Adiponectin secretion is stimulated by insulin 31, thus its association with insulin resistance is supported in the literature 31,35,54–56. Kern et al. (2003) investigated the relationship between adiponectin levels and insulin resistance in obese, non-insulin resistant subjects. In agreement with the current study, Kern et al. (2003) reported an inverse correlation between adiponectin levels, BMI and BF %. However, unlike the current study, a significant positive correlation was found between adiponectin and insulin sensitivity 56. In order to observe the relationship between adiponectin and insulin sensitivity in the absence of obesity, Kern et al. (2003) matched subjects who were insulin sensitive and insulin resistant for BMI and age. It was discovered that insulin resistant subjects had significantly lower adiponectin levels than insulin sensitive subjects 56. These findings were further supported by Yamamoto et al. (2002), where a significant inverse correlation between adiponectin and HOMA-IR was reported in healthy middle-aged adults 35. The subjects in the current study were considered insulin sensitive with a normal BMI. These subject characteristics could have led to the non-significant correlation between adiponectin and HOMA-IR.

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Endothelial dysfunction is commonly induced by type I and type II diabetes 57. In subjects with insulin resistance, endothelial function is compromised, resulting in an over production of vasoconstrictors, such as ET-1 57,58. Piatti et al. (2000) studied the relationship between ET-1 and measure of insulin resistance in subjects with varying degrees of insulin resistance. It was reported that the subjects with impaired glucose tolerance (high HOMA-IR) also had the highest ET-1 levels compared to subjects with normal glucose tolerance 59. ET-1 levels were also positively correlated with all measures of insulin resistance: insulin; HOMA-IR; triglycerides; body weight; BMI; and waist-to-hip ratio 59. Another study reported a significant decrease in whole-body glucose uptake when healthy subjects were administered insulin and synthetic ET-1 60. The previous study by Piatti et al. (2000) also found no correlation between ET-1 and HOMA-IR in their healthy subjects 59, therefore supporting our finding of no correlation between ET-1 levels and HOMA-IR in healthy subjects. However, including an insulin resistant subject group in future studies could show correlations between ET-1 and HOMA-IR. Due to the similarities in structure, IGF-1 has the ability to exert insulin-like effects in adipose tissue 61. While the exact mechanisms are not well known, it is known that IGF-1 has the ability to stimulate glucose uptake 61. While the current study found no correlation between IGF- 1 levels and HOMA-IR (Table 5), previous studies have reported different results 62–64. Teppala et al. (2010) investigated the association between IGF-1 and insulin resistance finding that decreased IGF-1 levels were positively associated with insulin resistance 64. When a longitudinal study was performed by Sandhu et al. (2002), it was discovered that middle-aged subjects (45-65 y) with IGF-1 levels below the median (<152 µg/L) had a significantly higher risk of developing insulin resistance 63. When older adults (> 65 y) were studied, no association was found between IGF-1 levels and insulin resistance, suggesting that this relationship is not the same in middle- aged and older adults 62. These previous studies led to the investigation of age-related differences in IGF-1 levels and HOMA-IR in our study, but no significant correlations were found (younger, p = 0.785; older, p = 0.320; collectively, p = 0.676). To be able to better compare with previous research, the current study would need to include an insulin resistant group, which would potentially show a significant correlation between IGF-1 levels and insulin resistance. Due to the use of a convenience sample from a previous study, some limitations are present in the current study. Different measures of moderate-to-vigorous physical activity were

14 used for younger and older subjects. The IPAQ was used to measure MVPA in younger subjects and the CHAMPS was used to measure MVPA in older subjects. Because of the use of different questionnaires, comparison of physical activity levels across subject groups was not possible. Ideally, in future studies, the same MVPA questionnaire would be used as well as an objective measure of physical activity, such as an accelerometer. Another limitation of our study was the use of a small sample size (n = 40). In future studies, a larger sample size should be used, with a clinical subject group including insulin resistant, overweight/obese, and sedentary subjects. In conclusion, age was associated only with higher levels of ET-1 and lower levels of IGF-1, thus suggesting age is not predictive of resistin or adiponectin levels. Our second hypothesis indicates that habitual physical activity level does not effect resistin, ET-1, adiponectin or IGF-1 levels. Adiponectin and IGF-1 levels were found to be inversely associated with BMI of both younger and older subjects proposing a relationship to obesity. Lastly, HOMA- IR was significantly associated to BF %, but not to resistin, ET-1, adiponectin or IGF-1 levels. This implies that in healthy subjects resistin, ET-1, adiponectin and IGF-1 levels are not predictive of insulin resistance, but BF % is predictive of insulin resistance.

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TABLE 1. Subject Background Young Subjects Old Subjects Male n = 10 n = 6 Female n = 10 n = 14 Average Age 21.0 ± 1.2y 68.4 ± 4.0y Age Range 19-24 62-76 BMI 24.3 ± 4.5 kg·m-2 25.5 ± 3.1 kg·m-2 BF % 22.03 ± 10.58 36.70 ± 5.40 HOMA-IR 1.53 ± 0.62 2.08 ± 1.06 MVPA 2926 ± 2563 (MET-min/wk) 1719 ± 1291(kcal/wk) Data are mean ± standard deviation. Abbreviations: BMI, body mass index; BF %, body fat percentage; HOMA-IR, homeostasis model assessment of insulin resistance; MVPA, moderate- to-vigorous physical activity; MET-min/wk, metabolic equivalent task minute per week; kcal/wk, kilocalories per week.

TABLE 2. Average Protein Levels of Young and Old Subjects Protein Young Subjects Old Subjects p-value Resistin 3.49 ± 0.97 ng/mL 2.97 ± 0.69 ng/mL 0.056 Adiponectin 101.4 ± 61.7 ng/mL 106.0 ± 59.4 ng/mL 0.81 ET-1 1.89 ± 0.52 pg/mL 2.34 ± 0.55 pg/mL 0.01* IGF-1 157.3 ± 47.1 ng/mL 79.88 ± 28.26 ng/mL 2.15 x 10-7** Resistin/IGF-1 Ratio 0.042 ± 0.089 0.043 ± 0.021 0.94 Fasting Blood Glucose 76.7 ± 11.8 (mg/dL) 91.7 ± 6.8 (mg/dL) 1.77 x 10-5** Insulin 8.00 ± 2.67 (uIU/mL) 9.13 ± 4.21 (uIU/mL) 0.32 HOMA-IR 1.53 ± 0.62 2.08 ± 1.06 0.05* * p < 0.05; ** p < 0.01. Data are mean ± standard deviation. Abbreviations: ET-1, endothelin-1; IGF-1, insulin-like growth factor-1; ng/mL, nanograms per milliliter; pg/mL, picograms per milliliter; mg/dL, milligrams per deciliter; uIU/mL, microinternational units per milliliter.

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TABLE 3. Correlation with Physical Activity Level within Subject Age Groups Controlling for Age and Gender Protein Old Subjects MVPA Young Subjects MVPA Resistin r = -0.10; p = 0.70 r = -0.03; p = 0.91 Adiponectin r = 0.10; p = 0.82 r = -0.01; p = 0.96 ET-1 r = 0.39; p = 0.11 r = -0.16; p = 0.53 IGF-1 r = -0.06; p = 0.80 r = 0.07; p = 0.79 Fasting Blood Glucose r = -0.08; p = 0.74 r = -0.28; p = 0.26 Insulin r = 0.05; p = 0.84 r = -0.18; p = 0.47 * p < 0.05. Abbreviations: ET-1, endothelin-1; IGF-1, insulin-like growth factor-1; MVPA, moderate-to-vigorous physical activity.

TABLE 4. Correlation with BMI Controlling for Age and Gender Protein BMI Correlation Resistin r = -0.037; p = 0.83 Adiponectin r = -0.36; p = 0.026* ET-1 r = 0.002; p = 0.99 IGF-1 r = -0.48; p = 0.002* * p < 0.05. Abbreviations: ET-1, endothelin-1; IGF-1, insulin-like growth factor-1; BMI, body mass index.

TABLE 5. Correlation with HOMA-IR Controlling for Age and Gender Protein HOMA-IR Correlation Resistin r = 0.261; p = 0.114 Adiponectin r = -0.167; p = 0.316 ET-1 r = 0.188; p = 0.258 IGF-1 r = 0.070; p = 0.676 * p < 0.05. Abbreviations: ET-1, endothelin-1; IGF-1, insulin-like growth factor-1; HOMA-IR, homeostatic model assessment of insulin resistance.

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TABLE 6. Correlation with HOMA-IR Controlling for Age, Gender and BMI Measure HOMA-IR Correlation MVPA Young: r = -0.250; p = 0.333; Old: r = -0.06; p = 0.818 BF% r = 0.397; p = 0.015* * p < 0.05. Abbreviations: MVPA, moderate-to-vigorous physical activity level; BF%, body fat percentage; HOMA-IR, homeostatic model assessment of insulin resistance.

TABLE 7. Correlation with Body Fat Percentage Controlling for Age and Gender Measure BF % Correlation Resistin r = 0.083; p = 0.621 Adiponectin r = -0.297; p = 0.070 ET-1 r = 0.074; p = 0.659 IGF-1 r = -0.333; p = 0.041* * p < 0.05. Abbreviations: Abbreviations: ET-1, endothelin-1; IGF-1, insulin-like growth factor- 1; BF %, body fat percentage.

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4 Young Old 3.5

3

2.5

2

(ng/mL) 1.5

1 Mean Resistin Level Level Mean Resistin 0.5

0

FIGURE 1. Average Resistin Concentration for Young and Old Subjects. Error bars represent the standard error of the mean.

140 Young Old 120

100

80

60 (ng/mL) 40

20 Mean Adiponectin Level Level Mean Adiponectin 0

FIGURE 2. Average Adiponectin Concentration for Young and Old Subjects. Error bars represent the standard error of the mean.

19

3 Young Old

2.5

* 1 Level Level 1

- 2

/mL) 1.5

pg ( 1

0.5 Mean Mean Endothelin

0

FIGURE 3. Average Endothelin-1 Concentration for Young and Old Subjects. Error bars represent the standard error of the mean.

180 Young Old 160

140

120 *

1 Level Level 1 100 -

80 (ng/mL) 60

Mean Mean IGF 40

20

0

FIGURE 4. Average Insulin-like Growth Factor-1 Concentration for Young and Old Subjects. Error bars represent the standard error of the mean.

20

Young Old

3 *

2.5

IR - 2

1.5 HOMA

1

0.5

0

FIGURE 5. Average HOMA-IR for Young and Old Subjects. Error bars represent standard error of the mean.

300 r = -0.48; p = 0.002

250

200

150

1 (ng/mL)1 -

IGF 100

50

0 15 20 25 30 35 40 BMI (kg/m2)

FIGURE 6. Correlation Between IGF-1 and BMI Controlling for Age and Gender

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350 r = -0.36; p = 0.026

300

250

200

150

100 Adiponectin Adiponectin (ng/mL)

50

0 15 20 25 30 35 40 BMI (kg/m2)

FIGURE 7. Correlation Between Adiponectin and BMI Controlling for Age and Gender

7 r =0.39, p = 0.015

6

5 IR

- 4

3 HOMA

2

1

0 0 10 20 30 40 50 60 Body Fat Percentage

FIGURE 8. Correlation Between Body Fat Percentage and HOMA-IR Controlling for Age, Gender and BMI. r = 0.39; p = 0.015

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300 r = -0.333; p = 0.041

250

200

150

1 (ng/L)1 -

IGF 100

50

0 0 10 20 30 40 50 60 Body Fat Percentage

FIGURE 9. Correlation Between Body Fat Percentage and IGF-1 Controlling for Age and Gender.

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