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Metabolic syndrome and psychosocial factors

ProQuest Dissertations and Theses, 2009
Dissertation
Author: Maureen P Tweedy
Abstract:
Metabolic syndrome is a constellation of risk factors, including abdominal obesity, hypertriglyceridemia, low HDL cholesterol, high blood pressure, and high fasting glucose, that commonly cluster together and can result in cardiovascular disease. The prevalence of metabolic syndrome and the components that comprise the syndrome vary by age and by racial/ethnic group. In addition, previous research has indicated that the risk factors contributing to metabolic syndrome may be exacerbated by exposure to perceived stress. This study utilized data from the 2002, 2004, and 2006 Health and Retirement Study (HRS) and National Health and Nutrition Examination Survey (NHANES) data sets. It was hypothesized that depression and anxiety (conceptualized as stress in this study) increase the risk of presenting with metabolic syndrome while social support decreases the risk of metabolic syndrome. While results of cross-sectional analysis do not indicate a significant relationship between depression and metabolic syndrome ( t = -.84, ns), longitudinal analysis does indicate a significant relationship between depression and metabolic syndrome over time (t = -5.20, p <.001). However, anxiety is not significantly related to metabolic syndrome when the relationship is examined through cross-sectional analysis (t = -1.51, ns) and longitudinal analysis (χ2 = 13.83, ns). Similarly, social support is not significantly related to metabolic syndrome when examined in cross-sectional (χ2 = .63, ns) and longitudinal ( t = 1.53, ns) analysis. Although level of stress is not significantly related to metabolic syndrome as a whole, there is a significant relationship between stress and both triglyceride level (t = -2.94, p = .003) and blood glucose level (t = -3.26, p = .001).

iii TABLE OF CONTENTS

LIST OF TABLES ................................................................................................. iv CHAPTER I: INTRODUCTION ............................................................................. 1 CHAPTER II: METHOD ...................................................................................... 23 CHAPTER III: RESULTS .................................................................................... 31 CHAPTER IV: DISCUSSION ............................................................................. 57 APPENDIX ....................................................................................................... 139 REFERENCES ................................................................................................. 143

iv LIST OF TABLES Table 1: Comparison of Results ......................................................................... 58 Table 2: HRS Gender x Probable Metabolic Syndrome ..................................... 74 Table 3: HRS Race/Ethnicity x Probable Metabolic Syndrome .......................... 75 Table 4: HRS Race/Ethnicity x Gender x Probable Metabolic Syndrome ........... 76 Table 5: HRS Age and Probable Metabolic Syndrome....................................... 77 Table 6: HRS Coupleness and Probable Metabolic Syndrome .......................... 78 Table 7: HRS Education and Probable Metabolic Syndrome ............................. 79 Table 8: HRS Education Categories and Probable Metabolic Syndrome ........... 80 Table 9: HRS Change in Health Status .............................................................. 81 Table 10: HRS Change in Health Status Between Cycles and Probable Metabolic Syndrome ........................................................................................................... 82 Table 11: HRS Meeting Criteria for Metabolic Syndrome Across Cycles ........... 83 Table 12: HRS Pearson Correlations: Psychosocial Variables x Meeting Criteria for Probable Metabolic Syndrome ........................................................................... 84 Table 13: HRS Pearson Correlations x Gender: Psychosocial Variables x Meeting Criteria for Probable Metabolic Syndrome .......................................................... 85 Table 14: HRS Pearson Correlations x Race/Ethnicity: Psychosocial Variables x Meeting Criteria for Probable Metabolic Syndrome ............................................ 87 Table 15: HRS Pearson Correlations x Race/Ethnicity and Gender: Psychosocial Variables x Meeting Criteria for Probable Metabolic Syndrome ......................... 90 Table 16: HRS Mean Depression Scores x Probable Metabolic Syndrome ....... 96

v Table 17: HRS Change in Meeting Criteria for Probable Metabolic Syndrome and Mean Depression Scores ............................................................................................. 97 Table 18: HRS Change in Meeting Criteria for Probable Metabolic Syndrome and Mean Depression Scores ............................................................................................. 98 Table 19: HRS Change in Overall Depression Level x Metabolic Syndrome ..... 99 Table 20: HRS Perception of Control of One’s Life .......................................... 100 Table 21: HRS Change in Sense of Control ..................................................... 101 Table 22: HRS Emotional Support ................................................................... 102 Table 23: HRS Change in Emotional Support .................................................. 103 Table 24: HRS Depression x Emotional Support.............................................. 104 Table 25: HRS MANOVA Gender Differences in Psychosocial Variables ........ 104 Table 26: HRS MANOVA Race/Ethnicity Differences in Psychosocial Variables105 Table 27: HRS MANOVA Gender and Race Interactions in Metabolic Syndrome105 Table 28: NHANES Gender x Metabolic Syndrome ......................................... 106 Table 29: NHANES Race/Ethnicity x Metabolic Syndrome .............................. 107 Table 30: NHANES Race/Ethnicity x Gender x Metabolic Syndrome............... 108 Table 31: NHANES Age x Metabolic Syndrome ............................................... 109 Table 32: NHANES Education x Metabolic Syndrome ..................................... 109 Table 33: NHANES Marital Status x Metabolic Syndrome ............................... 110 Table 34: NHANES Couple status x Metabolic Syndrome ............................... 111 Table 35: NHANES Household income x Metabolic Syndrome ....................... 112 Table 36: NHANES Waist Circumference x Metabolic Syndrome .................... 114 Table 37: NHANES Waist circumference and Metabolic Syndrome ................ 114

vi Table 38: NHANES HDL x Metabolic Syndrome .............................................. 115 Table 39: NHANES HDL and Metabolic Syndrome .......................................... 115 Table 40: NHANES Triglyceride Level x Metabolic Syndrome ......................... 116 Table 41: NHANES Triglyceride Level and Metabolic Syndrome ..................... 116 Table 42: NHANES Systolic Blood Pressure x Metabolic Syndrome ............... 117 Table 43: NHANES Diastolic Blood Pressure x Metabolic Syndrome .............. 117 Table 44: NHANES Blood pressure Criteria and Metabolic Syndrome ............ 117 Table 45: NHANES Fasting Plasma Glucose x Metabolic Syndrome .............. 118 Table 46: NHANES Fasting Plasma Glucose x Metabolic Syndrome .............. 118 Table 47: NHANES Lacking Food and Metabolic Syndrome ........................... 119 Table 48: NHANES Covered x Health Insurance ............................................. 119 Table 49: NHANES Change in Health Status x Metabolic Syndrome .............. 120 Table 50: NHANES Saw Mental Health Professional ....................................... 120 Table 51: NHANES Mean Depression Score ................................................... 121 Table 52: NHANES Stress Index...................................................................... 121 Table 53: NHANES Emotional Support ............................................................ 122 Table 54: NHANES Pearson Correlations ........................................................ 123 Table 55: NHANES Pearson Correlations x Gender ........................................ 124 Table 56: NHANES Pearson Correlations x Race/Ethnicity ............................. 126 Table 57: NHANES Pearson Correlations x Race/Ethnicity and Gender ......... 129 Table 58: NHANES MANOVA Gender Differences in physical Components of Metabolic Syndrome ......................................................................................................... 135

vii Table 59: NHANES MANOVA Race Differences in physical Components of Metabolic Syndrome ......................................................................................................... 135 Table 60: NHANES MANOVA Gender and Race Interactions in Physical Components of Metabolic Syndrome......................................................................................... 136 Table 61: NHANES MANOVA Gender Differences in Psychosocial Variables 136 Table 62: NHANES MANOVA Race Differences in Psychosocial Variables .... 137 Table 63: HANES MANOVA Gender and Race Interactions in Metabolic Syndrome ......................................................................................................................... 137 Table 64: Summary of Hierarchical Regression Analyses for Variables Predicting Physical Components of Metabolic Syndrome ................................................. 138 Table A.1: HRS Questions Related to Psychosocial Factors ........................... 140 Table A.2: HRS Questions Related to Health ................................................... 141 Table A.3: NHANES questions Related to Psychosocial Factors ..................... 141 Table A.4: NHANES Laboratory Measures ...................................................... 142

1 CHAPTER I INTRODUCTION

Metabolic syndrome is a constellation of risk factors that commonly cluster together and can result in cardiovascular disease. Individuals with metabolic syndrome also appear to be susceptible to other medical conditions including fatty liver, cholesterol gallstones, asthma, sleep disturbances, polycystic ovary syndrome, and some types of cancer (Grundy, Brewer, Cleeman, Smith, & Lenfant, 2004). The syndrome was first described by Reavan (1988) who named the constellation syndrome X. The cluster of risk factors has also been known as “the insulin resistance syndrome” and “the deadly quartet” (Eckel, Grundy, & Zimmet, 2005). In more recent years “metabolic syndrome” is the term most commonly used in order to avoid the implication that insulin resistance is the central cause of the associated risk factors (Grundy et al., 2004). However, no standard definition for metabolic syndrome has been established. The clinical definition of metabolic syndrome has varied over time and by authoring agency. However, the three most commonly used definitions include aspects related to obesity, diabetes and cardiovascular disease. While there are two types of diabetes, no definition of metabolic syndrome differentiates between Type I or Type II diabetes. Most research appears to focus only on Type II diabetes; for this reason, all discussions of diabetes in this paper will refer to Type II diabetes. In 1998, the World Health Organization (WHO) proposed a definition of metabolic syndrome that included specific criteria. According to this WHO definition, an individual must be diagnosed with diabetes, impaired glucose tolerance, impaired fasting glucose or insulin resistance, as well as two or more of the following: high blood pressure (≥160/90 mmHg), hyperlipidemia (triglyceride concentration ≥150 mg/dL and or high-density lipid (HDL)

2 cholesterol <35 mg/dL in men and <39 mg/dL in women), central obesity (waist-to-hip ratio >0.90 in men or >0.85 in women and/or body mass index (BMI) >30 kg/m²), or microalbuminuria (urinary albumin excretion rate ≥20 µg/min or an albuine-to-creatinine ratio ≥20 mg/g) (Ford & Giles, 2003). More recently, in 2002, the Third Report of the National Cholesterol Education Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III [ATP III]) has established a similar, but not identical, definition. According to this ATP III definition, an individual can be classified as having metabolic syndrome if three or more of the following criteria are met: abdominal obesity (waist circumference >102 cm in men and >88 cm in women), hypertriglyceridemia (triglyceride concentration ≥50 mg/dL), low HDL cholesterol (<40 mg/dL in men and <50 mg/dL in women), high blood pressure (≥130/85 mg Hg), or high fasting glucose (≥110 mg/dL) (Ford & Giles, 2003). Other risk factors included in this definition, but not with specific criteria, are a proinflammatory state (elevated C-reactive protein (CRP) and a prothrombotic state (increased plasma plasminogen activator inhibitor (PAI)-1 and fibrinogen). In addition, in the ATP III definition of metabolic syndrome, other non-metabolic factors such as physical inactivity, advanced age, diet, cigarette smoking, and family history of premature coronary heart disease are considered risk factors for metabolic syndrome but are not specifically defined (Grundy, et al., 2004). Yet a third definition of the metabolic syndrome was released by the International Diabetes Federation (IDF) in 2005 (Wyne, 2005). While similar to the above WHO and ATP-III definitions, the IDF criteria was developed to stress the role played by insulin resistance and central obesity in the development of the metabolic syndrome. In addition, the IDF definition is the only definition that contains ethnic-specific criteria for waist circumference measurement.

3 According to the IDF definition of the metabolic syndrome, an individual can be considered to have metabolic syndrome if he/she has central obesity (waist circumference >94 cm for European men and >80 cm for European women, with ethnicity-specific values for other groups) plus two of the following factors: increased triglyceride level (> 150 mg/dL or undergoing specific treatment for increased triglycerides), reduced HDL cholesterol (< 40 mg/dL in men and <50 mg/dL in women or undergoing specific treatment for reduced HDL cholesterol), hypertension (systolic blood pressure >130 mm Hg or diastolic blood pressure > 85 mm Hg or undergoing treatment for previously diagnosed hypertension), or increased fasting plasma glucose (> 100 mg/dL or previously diagnosed type 2 diabetes). Ethnic-specific values for waist circumference have been established for the following groups: European (males: ≥ 94 cm, females: ≥ 80 cm), South Asian (based on a Chinese, Malay and Asian- Indian population: males: ≥ 90 cm, females: ≥ 80 cm), and Japanese (males: ≥ 95 cm, females: ≥ 90 cm). For ethnic South and Central American groups, the IDF has suggested that South Asian recommendations be applied until more specific data are available. The IDF has recommended also that European data be used for Sub-Saharan African, and Eastern Mediterranean and Arab populations until more specific data are available. The IDF has additionally recognized that in the United States the ATP III values for waist circumference (males ≥ 102 cm, females ≥ 88) are likely to continue to be used for clinical purposes. Prevalence and Ethnicity Distribution of Metabolic Syndrome Previous research of the prevalence of the metabolic syndrome in the United States has indicated that prevalence varies according to the definition of metabolic syndrome and among various ethnic groups. Ford and Giles (2003) analyzed data from the Third National Health and Nutrition Examination Study (NHANES III), a cross-sectional health examination

4 study of a nationally representative sample of the United States population that was carried out from 1988 to 1994. Using the ATP III definition, the age-adjusted prevalence among 8,608 participants was 23.9%, and using the WHO definition, this age-adjusted prevalence was 25.1%. Among all the participants in the study, 86.2% of those who met either of the two diagnostic criteria were considered to have metabolic syndrome under both definitions (i.e., diagnostic overlap). Because of slight differences in the two definitions, using the ATP III criteria, 6.2% were considered to have metabolic syndrome; however, when the WHO criteria is applied, these same individuals are not considered to have metabolic syndrome. Conversely, using WHO criteria 7.6% were classified as having metabolic syndrome while these same individuals were not considered to have metabolic syndrome when ATP III criteria is used. Of those individuals who were classified as having metabolic syndrome under the ATP definition but not under the WHO definition, 89.0% did not have hyperglycemia and were not insulin resistant but did meet at least two of the WHO criteria. On the other hand, of those who had metabolic syndrome under the WHO definition but not under the ATP III definition, 82.4% had two of the ATP III criteria. Perhaps more significantly, notable differences were observed for some ethnic groups (Ford & Giles, 2003). For Caucasian groups, the WHO prevalence estimates were similar to the ATP III estimates, however, WHO estimates were higher than ATP III estimates for other ethnic groups. African American men presented with the largest difference: using ATP III criteria, 16.5% were considered to have metabolic syndrome, while under WHO criteria, almost 25% (24.9%) were considered to have metabolic syndrome. Diabetes appears to play a strong role in the development of metabolic syndrome. It is estimated that 68.6% of the total population of diabetics have metabolic syndrome (Lin & Pi-

5 Sunyer, 2007). Of this total, prevalence among Caucasians is 69.9%, among African Americans 64.8%, and among Mexican Americans 62.4%. The prevalence of metabolic syndrome in nondiabetics is much lower; among Mexican Americans, prevalence is 29.5%, among Caucasians 24%, and among African Americans 16.5%. These prevalence rates suggest the strong role played by the presence of diabetes in the development of metabolic syndrome. Gender and age also appear to influence the prevalence of metabolic syndrome. Ford, Giles, and Dietz (2002) found that prevalence increases as individuals grow older, but then appears to decrease slightly at approximately age 70 and older. The researchers do not speculate on possible causes for the small decline in metabolic syndrome that occurs in older adults, but it is possible that some individuals with metabolic syndrome die of cardiovascular disease prior to age 70. In addition, while age-adjusted prevalence was similar for men and women in the total population, prevalence was higher in women than in men among both African Americans and Mexican Americans. Insulin Resistance in Metabolic Syndrome Insulin resistance occurs when body cells are unable to utilize the insulin that is produced by the pancreas and results in increased glucose, triglycerides and fatty acids in the blood. While the presence of fatty acids in the blood can stimulate the secretion of insulin, prolonged excessive concentration of fatty acids causes a decrease in insulin secretion (Eckel et al., 2005). While insulin resistance can exist at any level of body fat content, resistance tends to increase as body fat percentage increases (Grundy et al., 2004). In addition, insulin resistance appears to provide a modest contribution to the associated development of hypertension in metabolic syndrome (Eckel et al., 2005).

6 Obesity and Abnormal Body Fat Distribution in Metabolic Syndrome While there are multiple risk factors for metabolic syndrome, obesity appears to be a key factor and is considered by some researchers to be the “predominant driving force behind” (Grundy, 2007) metabolic syndrome. Abdominal obesity specifically appears to play a role in metabolic syndrome as excess adipose tissue contributes to an increase in the presence of fatty acids and a resulting increase in triglycerides. Obesity may also cause an increase of blood pressure, as there is excess leptin, a hormone that triggers increased sympathetic activity of the cardiovascular system. Leptin resistance also appears to be stimulated by the increased presence of glucosteroids and is strongly associated with obesity because of impaired signals of hunger satiety (Björntorp, Holm, & Rosmond, 2000). Cardiovascular Components of Metabolic Syndrome It is difficult to separate cardiovascular components of metabolic syndrome from the insulin resistance and obesity components; this is a result of the complicated interrelationship between these component metabolic syndrome factors. Even though difficult to parcel out, it is important to note the multiple cardiovascular factors that play a role in the diagnosis of metabolic syndrome. While some research has indicated that there is not a strong relationship between elevated blood pressure and metabolic syndrome (Meigs, 2000), other studies have suggested that there is a strong association between obesity and hypertension (Grundy et al., 2004). In addition, Wyne (2005) has noted that insulin can stimulate the production of angiotensin II, a hormone that increases blood pressure. This suggests that hypertension may act as a “surrogate marker for hyperinsulinemia” (Wyne, 2005) in individuals who are at risk of developing diabetes or metabolic syndrome.

7 Dyslipidemia is another cardiovascular component that is important in all of the definitions of metabolic syndrome. Elevated triglycerides and the associated decreased HDL levels can contribute to the development of atherosclerosis (Wyne, 2005). Increased concentration of insulin in the blood may also contribute to the increased level of triglycerides present in individuals with metabolic syndrome (Grundy et al., 2004). Other research has indicated that individuals with metabolic syndrome frequently present with increased heart rate (Pannier, Thomas, Eschwege, Bean, Benetos, Leocmach, et al., 2006), yet another possible contributor to cardiovascular disease when present with other cardiovascular risk factors. A proinflammatory state and a prothrombotic state are also frequently associated with metabolic syndrome (Grundy et al., 2004). A proinflammatory state refers to an elevated level (> 3mg/L) (Wyne, 2005) of C-reactive protein (CRP) and is considered a risk factor for cardiovascular disease. A proinflammatory state may also be linked to the presence of obesity because excess adipose tissue releases inflammatory cytokines that may in turn produce increased levels of CRP. A prothrombotic state occurs when there are elevated levels of plasma plasminogen activator inhibitor and fibrogen (Grundy et al., 2004), both of which produce increased clotting in the blood and are related to cardiovascular diseases. Because levels of fibrogen increase in response to increased release of cytokinines, it is possible that the inflammatory and prothrombotic states are interrelated. Allostasis and Allostatic Load Allostasis is defined as a dynamic process in which an organism is able to achieve stability through adaptation (McEwen & Wingfield, 2003). That is, as the environment around an organism changes, the organism, in this case the human body, achieves a stability of physiological systems by adapting to both predictable and unpredictable changes, such as

8 social interactions, disease, and weather, in the surrounding environment. During allostasis, various neural, neuroendocrine, and neuroendocrine-immune systems are activated to allow the body to adapt (McEwen, 1998). If these systems are not activated too frequently and are able to be activated and deactivated properly, the body is able to cope efficiently with the various adaptational challenges that confront it. However, there may be occasions when the allostatic systems are either activated too frequently or do not perform correctly; this condition has been described by McEwen as “allostatic load” or the “price of adaptation” (McEwen, 1998). This can result in a gradual decline in the body’s ability to maintain its various systems within the normal ranges at both rest and when responding to environmental challenges (Karlamangla, Singer, McEwen, Rowe, & Seeman, 2002). In terms of physiological presentation, allostatic load has been operationalized (Seeman, McEwen, Row, & Singer, 2001) as a collection of ten components which assess the functioning of multiple systems including metabolic processes, the sympathetic nervous system, and the cardiovascular system. Of these ten components, four have been found to also be main components utilized to determine the presence or absence of metabolic syndrome. These four components are: systolic and diastolic blood pressure, HDL level, and abdominal obesity. The presence of hypertension, abdominal obesity, and decreased HDL level can therefore indicate that an individual is experiencing allostatic load. There are two different types of allostatic load. Type 1 allostatic overload occurs when energy demand exceeds energy supply (McEwen & Wingfield, 2003), including energy stored within various body systems. For example, when an individual is confronted with a decrease in food availability but must continue to perform his/her usual activities, Type 1 allostatic overload results. In comparison to Type 1 allostatic overload, when energy demands are not exceeded

9 yet the body continues to ingest or store as much or more energy than it needs, Type 2 allostatic overload results. This type of overload may be seen when an individual consumes a diet rich in fats, increases food consumption as a way of coping with stress, or has metabolic imbalances that predispose the individual to fat deposition in body tissues (McEwen & Wingfield, 2003). Events which are perceived to be stressful and which induce behavioral and/or physiological reactions can also contribute to allostatic overload. Individuals who are more reactive to aversive changes in the environment will tend to have less stability of the adaptive systems while individuals who have better psychological coping skills or buffers will tend to have less reactivity and increased stability of the adaptive systems (McEwen, 1998). Model of Mechanism of Allostatic Control While the activation of the various systems associated with allostasis has a protective function in the short term, over longer time periods as in a sustained allostatic state, prolonged activation of neural, neuroendocrine, and neuroendocrine-immune systems can have damaging effects. For example, in times when increased energy is needed in order for the body to react to a perceived stressor in the environment, proteins and lipids are converted to more easily-utilized carbohydrates by glucocorticosteroids. (Glucocorticosteroids are hormones that promote the conversion of proteins and lipids to carbohydrates which are more easily broken down to supply energy to muscles and allow the body to initiate a behavioral response such as running.) By this same mechanism, glucocorticosteroids also enable the body to replenish energy reserves after the period of running has been completed. In addition, glucocorticosteroids act on structures within the brain to promote food-seeking behavior. In this way, glucocorticosteroids regulate behaviors that influence energy expenditure and intake (McEwen & Wingfield, 2003). However, in a situation where there is inactivity and a lack of

10 energy expenditure, such as when there is a perceived stress that does not require a behavioral response, chronically elevated glucocorticosteroid levels can reduce the normal action of insulin to facilitate glucose uptake. As a result, both insulin levels and glucose levels in the blood rise, and in combination with the increased level of glucosteroid levels, foster the development of atherosclerotic plaques in coronary arteries as well as the depositing of body fat in tissues (McEwen & Wingfield, 2003). Thus, the presence of increased blood glucose level, increased triglycerides in the blood, and obesity can indicate not only metabolic syndrome but also serve as an indication that an individual is experiencing allostatic load in response to inability to adapt to the environment or prolonged exposure to an environment perceived to be stressful. Glucocorticosteroids also work in conjunction with the nervous system to influence an individual’s reactions to various situations. According to McEwen & Wingfield (2003), the nervous system acts to determine how “stressful” a particular situation is to the individual and then determines what the behavioral and physiological reactions to the situation will be. Within the brain, strong emotional responses to the stressful situation can result in the formation of memories. Glucocorticosteroids can act through intracellular receptors to assist in the establishment of long-lasting memories of situations and the individual’s responses. While this can be an adaptive function at times, it can also be maladaptive when there is repeated stress over days or when, as a result of allostatic load, glucocorticosteroid levels remain high for an extended period. In this case, the result can be atrophy of neurons in the hippocampus with resulting shrinkage of the hippocampus. Impaired function of the hippocampus can result in decreased accuracy of contextual memory. Impaired contextual memory has been

11 hypothesized to cause the individual to perceive as stressful situations that might ordinarily be non-stressful if contextual memory was intact (McEwen, 1998). Cortisol and Stress Hormones It has been hypothesized that increased activity of the hypothalamic-pituitary-adrenal (HPA) axis in response to perceived stressors in the environment is associated with increased production of glucocorticosteroids, specifically cortisol (Björntorp, et al., 2000; Pasquali, Vicennati, Cacciari, & Pagotto, 2006). The HPA axis is made up of feedback interactions between the hypothalamus, the pituitary gland, and the adrenal gland. As part of the neuroendocrine system, the HPA acts to control reactions to environmental stressors. The hypothalamic region functions to process incoming signals that are transmitted through the endocrine and/or autonomic nervous system. The hypothalamic region influences a variety of mechanisms, such as temperature regulation and changes in energy balance, to ensure that homeostasis is maintained. However, there are frequently threats to an organism’s homeostasis. Such threats include external threats such as toxins, infections, and physical trauma (Björntorp, et al., 2000), as well as perceived threats, often called “stress” in research literature. The hypothalamus is activated when a stressor is perceived and both the sympathetic nervous system and the HPA axis are activated. The sympathetic nervous system (SNS) is part of the autonomic nervous system and functions without conscious control of the organism. The SNS regulates stress response commonly known as the “fight or flight response.” During this response adrenaline and noradrenalin are released and primarily activate the cardiovascular system. Research suggests that individuals who are unable to cope effectively with long-term adverse stressors are likely to also present with increased HPA axis activity in conjunction with

12 increased sympathetic nervous system activity (Pasquali, et al., 2006). Specifically, chronic sympathetic nervous system activation has been linked to the development of hypertension while chronic activation of the HPA axis has been shown to have an effect on the endocrine and metabolic systems (Björntorp et al., 2000). When the HPA axis is activated, glucocorticosteroid hormones, specifically, cortisol are released into the blood. The elevated level of free cortisol in turn results in increased fat deposition especially in the central/abdominal region of the body. In addition, an extended increase in HPA axis activity is associated with insulin resistance. Because of the strong association between abdominal obesity, abnormalities in HPA axis activity, and insulin resistance (Pasquali et al., 2006), abdominal obesity and measures of insulin resistance are frequently used as indicators that there is an alteration in HPA axis activity over time and that there is a risk for the development of metabolic syndrome. Because individuals react not only to physical stressors, but also to perceived stressors, psychosocial factors can play a role in hypothalamic arousal and activation of the HPA axis. The way that individuals interpret perceived stressors influences their reactions to stress. For example, when faced with persistent stressors, individuals may react with either helplessness or hypervigilance, reactions that are strongly associated with feelings of anxiety. In addition, neurochemical changes that occur in the brain can result in cognitive impairment or depression (McEwen, 1998). Depression and Anxiety Research has indicated that psychological conditions such as depression and anxiety have a negative impact on physical health and can play a contributing role in the development or exacerbation of physical illnesses (Balon, 2006). This concept is not new; Weitz (1983),

13 writes that the relationship between the mind and body and disease have been noted “in the writings of philosophers and physicians dating back to antiquity.” More recently, almost three decades ago, insurance companies noticed a correlation between psychological distress and health insurance claims. That is, those individuals who experienced greater subjective stress accounted for a larger percentage of claims. In response, some large firms implemented programs to decrease employees’ psychological distress in an attempt to decrease insurance costs (Pelletier & Frecknall, 1989). More recently, Glannon (2003) has indicated that psychological conditions such as depression and anxiety have an effect on both neurological and endocrine systems of the body. Other research has indicated that stress can reduce the activity of the immune system, and furthermore, that coping mechanisms can influence the magnitude of this effect (Myers and Benson, 1992). Furthermore, in a review of research spanning a decade, Pelletier (1992) found that psychological factors appear to be significant in both health and chronic disorders. In addition, Pelletier indicates that psychologically-based interventions can be as effective as medically-based treatments. Regarding specific illnesses, Weitz (1983) found a correlation between negative psychological states and the development and progression of cancer. Depression is a mood disorder characterized by a number of symptoms. The essential feature of a depressive disorder is “either depressed mood or the loss of interest or pleasure in nearly all activities” (Diagnostic and statistical manual of mental disorders (4 th ed., text revision), 2000, p. 369 (DSM-IV-TR)). In addition, the depressed individual must experience at least four of the following symptoms: changes in appetite or weight, changes in sleep, changes in psychomotor activity, decreased energy, difficulty thinking, concentrating, or making decisions, feelings of worthlessness or guilt, or recurrent thoughts of death or suicidal ideation,

Full document contains 158 pages
Abstract: Metabolic syndrome is a constellation of risk factors, including abdominal obesity, hypertriglyceridemia, low HDL cholesterol, high blood pressure, and high fasting glucose, that commonly cluster together and can result in cardiovascular disease. The prevalence of metabolic syndrome and the components that comprise the syndrome vary by age and by racial/ethnic group. In addition, previous research has indicated that the risk factors contributing to metabolic syndrome may be exacerbated by exposure to perceived stress. This study utilized data from the 2002, 2004, and 2006 Health and Retirement Study (HRS) and National Health and Nutrition Examination Survey (NHANES) data sets. It was hypothesized that depression and anxiety (conceptualized as stress in this study) increase the risk of presenting with metabolic syndrome while social support decreases the risk of metabolic syndrome. While results of cross-sectional analysis do not indicate a significant relationship between depression and metabolic syndrome ( t = -.84, ns), longitudinal analysis does indicate a significant relationship between depression and metabolic syndrome over time (t = -5.20, p <.001). However, anxiety is not significantly related to metabolic syndrome when the relationship is examined through cross-sectional analysis (t = -1.51, ns) and longitudinal analysis (χ2 = 13.83, ns). Similarly, social support is not significantly related to metabolic syndrome when examined in cross-sectional (χ2 = .63, ns) and longitudinal ( t = 1.53, ns) analysis. Although level of stress is not significantly related to metabolic syndrome as a whole, there is a significant relationship between stress and both triglyceride level (t = -2.94, p = .003) and blood glucose level (t = -3.26, p = .001).