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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:563-569 (2007)
© 2007 The Gerontological Society of America

Psychosocial Risk Factors and the Metabolic Syndrome in Elderly Persons: Findings From the Health, Aging and Body Composition Study

Nicole Vogelzangs, Aartjan T. F. Beekman, Stephen B. Kritchevsky, Anne B. Newman, Marco Pahor, Kristine Yaffe, Susan M. Rubin, Tamara B. Harris, Suzanne Satterfield, Eleanor M. Simonsick and Brenda W. J. H. Penninx

1 Department of Psychiatry and Institute for Research in Extramural Medicine, VU University Medical Center, Amsterdam, The Netherlands.
2 Wake Forest University School of Medicine, Winston-Salem, North Carolina.
3 University of Pittsburgh, Pennsylvania.
4 University of Florida and Veteran's Affaires Medical Center, Gainesville.
5 University of California, San Francisco.
6 National Institute on Aging, Bethesda, Maryland.
7 University of Tennessee, Memphis.
8 National Institute on Aging, Baltimore, Maryland.

Address correspondence to Nicole Vogelzangs, MSc, Department of Psychiatry and EMGO Institute, VU University Medical Center, Oldenaller 1, 1081 HJ Amsterdam, The Netherlands. E-mail: nicolev{at}ggzba.nl


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Psychosocial factors have been associated with metabolic abnormalities that increase the risk of cardiovascular disease and diabetes. This study investigated the cross-sectional relationship between psychosocial risk factors and the metabolic syndrome in a community-based sample of older persons.

Methods. Participants were 2917 persons aged 70–79 years enrolled in the Health, Aging and Body Composition study. Depressive and anxiety symptoms, negative life events, and inadequate emotional support were assessed, and a summary psychosocial risk index was calculated. Metabolic syndrome was defined as three or more of the following criteria: abdominal obesity, high triglycerides, low high-density lipoprotein (HDL) cholesterol, high fasting glucose, and high blood pressure.

Results. Negative life events and inadequate emotional support increased the odds of having metabolic syndrome after adjustment for demographic and lifestyle variables (odds ratio [OR] per life event = 1.13, 95% confidence interval [CI] = 1.05–1.22; OR = 1.35, 95% CI = 1.10–1.66, respectively). The relationship between depressive symptoms and metabolic syndrome was only found in white (OR per standard deviation [SD] = 1.11, 95% CI = 1.01–1.23), but not in black (OR per SD = 0.97, 95% CI = 0.86–1.11) persons. Anxiety symptoms were significantly associated with metabolic syndrome in men (OR per SD = 1.13, 95% CI = 1.00–1.28), but not in women (OR per SD = 0.98, 95% CI = 0.89–1.08). Moreover, a higher score on the psychosocial risk index was associated with an increased probability of having the metabolic syndrome (OR = 1.30, 95% CI = 1.12–1.52).

Conclusions. In the elderly population, different psychosocial risk factors are associated with a higher prevalence of the metabolic syndrome. Whether reduction or better management of psychosocial risk factors can improve the metabolic profile remains to be demonstrated.

CARDIOVASCULAR disease (CVD), diabetes, and affective disorders rank high among the leading disorders causing distress, disability, and mortality (1). There is increasing evidence that these disorders are linked and that clustering of these disorders presents one of the most challenging problems for public health, especially in later life. For instance, depression and anxiety have been shown to increase the risk of new CVD events, new coronary heart disease (CHD) events, and cardiac mortality (2–4). Also, patients with newly diagnosed type 2 diabetes are more likely to have a history of depression than are people free of diabetes (5). In addition to affective disorders, other stressors known to contribute to psychological distress have been associated with CVD and diabetes. Both lack of emotional support and experience of major stressful life events have been shown to subsequently increase the risk of CVD, CHD, and type 2 diabetes (6–8). These findings suggest that, although depression, anxiety, emotional support, and stressful life events are distinct entities, they can all be seen as psychosocial risk factors for developing CVD or diabetes.

Emerging evidence suggests that part of the link between these psychosocial risk factors and CVD and diabetes may operate through the metabolic syndrome (9), a clustering of several CVD risk factors including (i) abdominal obesity, (ii) hypertriglyceridemia, (iii) low high-density lipoprotein (HDL) cholesterol, (iv) hypertension, and (v) hyperglycemia. According to the National Cholesterol Education Program Adult Treatment Panel III, a person has the metabolic syndrome if three or more of these conditions are present (10). Using these criteria, the Third National Health and Nutrition Examination Survey estimated a prevalence of 23.7% of the metabolic syndrome among all U.S. adults, and a prevalence of 42.0% among adults aged 70 years and older (11). A number of studies has shown that individuals with the metabolic syndrome have an increased risk of cardiovascular morbidity and mortality (12–15). Furthermore, persons with metabolic abnormalities have shown an enlarged risk of incident diabetes, with increasing risks in those exhibiting more abnormalities (15).

Psychosocial risk factors, which might include both exposure to stressors, e.g., life events, and experienced psychological distress, such as depression and anxiety, have been linked to individual components of the metabolic syndrome, including insulin resistance, high blood pressure, abdominal obesity, and lipid abnormalities (8,16,17). Such a link could be due to the fact that psychological stress can result in sensitization of the hypothalamo–pituitary–adrenal (HPA) axis (18,19), elevated inflammation (20), and inhibition of sex steroid secretion (21,22), all of which may induce metabolic abnormalities (23–25). A combination of psychosocial risk factors might dysregulate these biological systems even more.

A first step in unraveling the relationship between psychosocial risk factors and the metabolic syndrome is to explore their cross-sectional association. In addition, it is important to know whether psychosocial risk factors co-occur with the metabolic syndrome. Treatment of both metabolic syndrome and psychosocial stress may be less effective if accompanying problems are ignored. Until now, few studies have examined the link between psychosocial risk factors and the metabolic syndrome directly, especially in old age. The present study investigates the association between psychosocial risk factors, as indicated by depression, anxiety, recent life events, and experienced inadequate emotional support, and the metabolic syndrome in a large cohort of well-functioning older men and women.


   METHODS
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 Abstract
 Methods
Results
 Discussion
 References
 
Study Population
Participants were from the Health, Aging and Body Composition (ABC) study, a prospective cohort study of 3075 well-functioning white and black elders, aged 70–79 years. Participants were recruited in 1997 and 1998, drawn from a sample of Medicare-eligible beneficiaries residing in the areas surrounding Pittsburgh, Pennsylvania, and Memphis, Tennessee. Individuals were excluded if they (i) were incapable of communicating, (ii) reported difficulty with walking for one-quarter mile, walking up 10 steps, or performing activities of daily living, (iii) had active cancer treatment in the past 3 years, or (iv) had plans to move out of the area. Baseline measurements were used for the present study. Baseline data on metabolic syndrome were missing for 40 participants, and 118 participants had missing data on psychosocial risk indicators, leaving 2917 participants for the present analysis. Persons with missing data were more often black than those in the present study (p =.007), but they did not differ in terms of age, sex, and education. All participants signed an informed written consent, approved by the institutional review boards of the clinical sites.

Measurements
Baseline characteristics.-- Demographic characteristics included age, sex, and race (white or black). Educational level was measured continuously on a scale from 1 (grade 1) to 18 (doctoral degree), indicating the highest level of education completed. Income was measured by dividing persons into yearly income groups: <$10,000, $10,000–$25,000, $25,000–$50,000, and ≥$50,000. Because there were many missing values on this variable, missing value was included as a separate group. Furthermore, some lifestyle characteristics were assessed: smoking status (non-, former, or current), current alcohol use (drinks > 1 drink per day or not), and physical activity (sum of weight training, high and medium intensity exercise, and aerobic dance [in kcal/kg/wk]). Baseline presence of CVD (including stroke or transient ischemic attack, myocardial infarction, angina pectoris, percutaneous transluminal coronary angioplasty, or coronary artery bypass grafting) and diabetes was adjudicated using standardized algorithms considering various sources of information: self-report, medication use, clinical examination findings, and medical claims data from the former Health Care Financing Administration.

Metabolic syndrome.-- Metabolic syndrome was defined, following the National Cholesterol Education Program Adult Treatment Panel III guidelines (10), as meeting at least three of the following five criteria: (i) abdominal obesity (waist circumference >102 cm in men and >88 cm in women), (ii) hypertriglyceridemia (triglyceride level of ≥150 mg/dL), (iii) low HDL cholesterol (<40 mg/dL in men and <50 mg/dL in women), (iv) high blood pressure (systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg, or currently using antihypertensive medication), (v) high fasting glucose (≥110 mg/dL or currently using antidiabetic medication). Waist circumference and blood pressure were both averaged over two measurements. All medication regularly taken in the past 2 weeks were brought in, recorded, and coded according to the Iowa Drug Information System (26). Use of antidiabetic and antihypertensive drugs was ascertained from this inventory. Lipid and fasting glucose levels were measured after an overnight fast. In addition to presence of metabolic syndrome, in line with others (27), the number of metabolic components was used as an index of severity of metabolic abnormalities.

Psychosocial Risk Factors
Depressive symptoms.-- Depressive symptoms were measured with the 20-item Center for Epidemiologic Studies Depression (CES-D) scale assessing depressive symptoms in the previous week (28). This scale, ranging from 0 to 60, has been widely used in older populations and has been shown to be a valid instrument [(29), internal consistency was high: Cronbach's {alpha} = 0.81].

Anxiety symptoms.-- Anxiety symptoms were measured using three items from the anxiety subscale of the validated Hopkins Symptom Checklist (30). The items were: "During the past week, have you felt nervous or shaky inside?, ... tense or keyed up? ... fearful?" Possible responses were "no," "a little," "quite a bit," and "extremely." An anxiety symptoms score was calculated, ranging from 0 to 9, as used before (31). The Cronbach {alpha} of these three items was 0.61.

Negative life events.-- The occurrence of seven common and important negative life events during the past year was assessed and summed. Life events included (i) close friend or family had a serious accident or illness, (ii) spouse or partner died, (iii) (grand)child, close friend, or relative died, (iv) pet died, (v) relationship with close friend or family changed for the worse, (vi) participant or family has been assaulted or robbed, and (vii) close friend or family has been arrested or in trouble with the law.

Inadequate emotional support.-- Participants who reported they could have used "some" or "a lot" more emotional support than they received in the past year were considered to have inadequate emotional support. This measure gives an indication of the subjective need for emotional support, which has been associated with the development and progression of CHD (7).

Psychosocial risk index.-- The above four measures have all individually been shown to be psychosocial risk factors for developing CVD and diabetes (2–8). Although little is known about the underlying biological mechanisms, it might be hypothesized that these psychosocial variables have a similar or, at least to some extent, an additive biological impact. In consequence, it might be that persons who have high scores on more than one psychosocial risk factor have an even greater biological dysregulation and therefore are even more at risk for developing the metabolic syndrome, CVD, or diabetes. Therefore, the above psychosocial factors were combined to calculate a summary psychosocial risk index. Depressive symptoms were standardized into a continuous variable ranging from 0 to 1 by dividing each individual score by the maximum possible CES-D score (i.e., 60). Similar standardization was done for anxiety symptoms (divided by 9) and life events (divided by 7). Inadequate emotional support was kept as a dichotomous 0–1 measure. Then, the four 0–1 measures were summed to attain an overall continuous psychosocial risk index, ranging from 0 to 4. The four psychosocial risk factors were associated with each other (all p <.001): The highest correlation was found between depression and anxiety (Spearman correlation = 0.40), and the lowest correlations were found with negative life events (between 0.09 and 0.14).

Statistical Analysis
Chi-square and t test statistics were used to assess differences in psychosocial risk factors between participants with or without metabolic syndrome and with or without a metabolic abnormality. For all following analyses, age, sex, race, education, income, smoking status, alcohol use, and physical activity were used as covariates. Logistic regression analyses were conducted to assess the association between metabolic syndrome and each of the psychosocial risk factors. Because a few studies reported sex differences in the link between psychosocial risk factors and cardiovascular outcomes (2,32), sex interactions were explored and also race interactions were tested. To examine if there was a linear relationship between psychosocial risk factors and number of metabolic syndrome components, linear regression was used. To test whether psychosocial risk factors were independently associated with the metabolic syndrome, a logistic regression analysis was conducted including all four psychosocial risk factors at the same time. Furthermore, we checked whether the link between psychosocial risk factors and metabolic syndrome was dependent on the presence of CVD or diabetes by testing for interactions with CVD or diabetes, respectively. In addition, we ran stratified analyses for persons with and without CVD.


   RESULTS
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 Methods
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Discussion
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Baseline characteristics of the study population are shown in Table 1. The mean age of the 2917 participants included in this study was 73.6 years (standard deviation [SD] = 2.9); 51.5% were women, 41.1% were black, and 38.6% of the participants had the metabolic syndrome. Overall, levels of psychosocial risk were low.


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  		Table 1. Baseline Characteristics.

 
As shown in Table 2, metabolic syndrome was associated with depressive symptoms, negative life events, inadequate emotional support, and the psychosocial risk index. Furthermore, although levels of psychosocial risk were rather low for the total sample, in general they appeared to be somewhat higher in participants with metabolic abnormalities, although differences were small and only some were statistically significant.


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  		Table 2. Association Between Psychosocial Risk Factors and Metabolic Syndrome in Older Men and Women (N = 2917).

 
We then conducted logistic regression analyses to calculate the odds ratio (OR) of metabolic syndrome for the different psychosocial risk factors before and after adjustment (Table 3). Also, sex and race interactions were explored by examining Sex and Race x Psychosocial risk factor interaction terms. Before adjustment, depressive symptoms modestly increased the odds of the metabolic syndrome, but this was no longer significant after adjustment (OR per SD increase = 1.06, 95% confidence interval [CI] = 0.98–1.14; p =.16). However, a trend was found for a Race x Depressive symptoms interaction (p =.10): The odds of metabolic syndrome increased with increases in depressive symptoms in white (OR per SD increase = 1.11, 95% CI = 1.01–1.23; p =.03), but not in black (OR per SD increase = 0.97, 95% CI = 0.86–1.11; p =.67) persons. Although there was no overall increase in odds of metabolic syndrome with increases of anxiety symptoms (OR per SD increase = 1.03, 95% CI = 0.96–1.11; p =.44), a trend for a Sex x Anxiety symptoms interaction was identified (p =.08). Men had a 13% increased odds of metabolic syndrome per SD increase in anxiety symptoms score (OR = 1.13, 95% CI = 1.00–1.28; p =.05). For women, anxiety symptoms did not elevate the odds of metabolic syndrome (OR per SD increase = 0.98, 95% CI = 0.89–1.08; p =.73). Experiencing a negative life event did increase the odds of metabolic syndrome by 13% (OR = 1.13, 95% CI = 1.05–1.22; p =.001). Also, people who experienced inadequate emotional support had a 35% increased odds of having the metabolic syndrome (OR = 1.35, 95% CI = 1.10–1.66; p =.005) compared to people with adequate emotional support. Furthermore, when the four psychosocial risk factors were combined into one overall psychosocial risk index, the odds of metabolic syndrome was 1.30 per point increase on the psychosocial risk index (95% CI = 1.12–1.52; p =.001). For the index, no statistically significant sex or race interactions were found (all p >.15). The results of the linear regression analyses of the relationship between psychosocial risk factors and number of metabolic abnormalities are also shown in Table 3 and are in line with the results for the dichotomous measure of metabolic syndrome.


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  		Table 3. Association Between Psychosocial Risk Factors and Metabolic Syndrome and Number of Metabolic Abnormalities (N = 2917).

 
When all psychosocial risk factors were included in the same model, it appeared that negative life events and inadequate emotional support were still independently associated with the metabolic syndrome (OR = 1.12, 95% CI = 1.04–1.21; p =.004 and OR = 1.31, 95% CI = 1.05–1.62; p =.02, respectively). In contrast, associations for depressive and anxiety symptoms were weakened and no longer statistically significant, although there was a trend for association between depressive symptoms and the metabolic syndrome in whites (OR = 1.10, 95% CI = 0.98–1.23; p =.11) and between anxiety symptoms and number of metabolic abnormalities in men (ß =.051, p =.09).

Table 4 shows the results of logistic regression analyses testing the association between psychosocial risk factors and the metabolic syndrome, stratified by prevalent CVD status. The prevalence of the metabolic syndrome was 36.4% among persons without CVD (N = 2216) and 45.5% among those with CVD (N = 701). Among persons without CVD, the link between psychosocial risk factors and the metabolic syndrome was very similar as compared to the overall sample, and was significant for depressive symptoms in whites (p =.03), negative life events (p =.006), inadequate emotional support (p =.02), and the psychosocial risk index (p =.005). There was no evidence for a CVD x Psychosocial risk factor interaction (all pinteraction >.15). No Diabetes x Psychosocial risk factor was found either (all pinteraction >.15).


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  		Table 4. Association Between Psychosocial Risk Factors and Metabolic Syndrome.

 

   DISCUSSION
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 Abstract
 Methods
 Results
 Discussion
References
 
This study showed that distinct psychosocial risk factors were associated with the metabolic syndrome in a large cohort of well-functioning older persons. We found that older people who experienced negative life events and inadequate emotional support had an increased prevalence of the metabolic syndrome. For depression, an association with the metabolic syndrome was found only in whites and not in blacks. Anxiety symptoms appeared only to be associated with the metabolic syndrome in men, but not in women. Furthermore, when these psychosocial factors were combined into a psychosocial risk index, persons with a higher risk index had a higher probability of having the metabolic syndrome.

Our study is one of the first to test the relationship between different psychosocial risk factors and the metabolic syndrome in a population-based cohort of older people. Most studies so far have examined the association between psychosocial factors and the metabolic syndrome indirectly, by studying individual metabolic components. Only few studies were able to test this relationship directly. Räikkönen and colleagues (33) reported an association between depressive symptoms, tension, and anger and the metabolic syndrome in a population-based cohort of 425 middle-aged women. Kinder and colleagues (34) found that young women, but not men, with a history of depression were twice as likely to have the metabolic syndrome. In our study, a psychosocial risk index including depressive and anxiety symptoms, negative life events, and inadequate emotional support showed a linear relationship with the odds of having the metabolic syndrome. This finding suggests that persons with high values on these measures have a higher risk for the metabolic syndrome. However, strongest associations with the metabolic syndrome were found for negative life events and inadequate emotional support, and these associations appeared to be independent of the associations of other psychosocial risk factors. Associations between depressive and anxiety symptoms and the metabolic syndrome were modest and not consistent across race and sex.

In addition, the results of our study show that associations between psychosocial risk factors and the metabolic syndrome seemed more consistent than the associations with individual components of the metabolic syndrome. These results may suggest that psychosocial risk factors may lead to not just one metabolic abnormality, but have a more widespread effect on metabolism and lead to a cluster of metabolic abnormalities, such as present in the metabolic syndrome. Furthermore, our results show that the relationship between psychosocial risk factors and the metabolic syndrome is not due to presence of CVD. When CVD patients were excluded, the found associations remained, which indicates that the link between psychosocial risk factors and the metabolic syndrome in our study does not just reflect a consequence of underlying clinical CVD.

In our study, a trend for a race interaction for depressive symptoms was found. This finding is in contradiction to those in the study of Kinder and colleagues (34) where no interaction for race was found. Furthermore, we found a trend for sex interaction for anxiety, which has not been described before. The prevalence of most psychosocial risk factors was rather similar for whites versus blacks and only slightly higher for women than for men, indicating that differences in prevalence cannot explain the interaction effects observed. Because we had a large study sample and only found trends for interaction, these may be chance findings and further research should confirm and explain the interaction effects observed in this research.

There may be different pathways through which psychosocial risk factors are related to the metabolic syndrome and, although the psychosocial risk factors used in this study are rather distinct concepts, these pathways may be similar. Psychosocial factors have shown to activate the HPA axis resulting in higher cortisol concentrations (18,19) which, in turn, are associated with metabolic abnormalities such as obesity, insulin resistance, and high blood pressure (35). Another pathway may be through inflammatory processes. Psychosocial risk factors, especially depression, have shown to be associated with increased levels of inflammatory markers, such as interleukin-6 and C-reactive protein (20). Inflammatory markers have been linked to obesity, lower HDL levels, and higher triglycerides and fasting glucose concentrations (24). A third pathway could be through sex steroid hormones, as low levels of testosterone and dehydroepiandrosterone sulfate (DHEA-S) have been associated with major depression (21,22). Alternatively, low levels of sex steroid hormones have been linked to various metabolic abnormalities (25). However, evidence for these pathways remains indirect, and additional longitudinal research is needed to investigate these processes further. In addition to these biological pathways, it may be that people with a high psychosocial risk have poorer health habits, e.g., they exercise less or have more fat intake, thereby increasing their metabolic risk (9). However, we did adjust for some lifestyle variables; this adjustment did not affect the results much.

Our study has some limitations. First, causal inferences are limited due to the cross-sectional design. A longitudinal design would be more appropriate to explore the direction of the relationship between psychological distress and the metabolic syndrome. However, in our study, metabolic syndrome was only remeasured after 5 years. Due to dropout (15%), missing values (19%), and exclusion of persons with baseline metabolic syndrome (27%), there was only a limited and likely very selective subsample left for longitudinal analyses. In addition, due to aging and frailty processes among the oldest old, several counteractive body composition changes exist (e.g., loss of fat mass), which could contaminate a possibly existing link between psychosocial risk factors and incidence of metabolic syndrome. Consequently, a longitudinal approach that examines the incidence of metabolic syndrome may be better studied in a younger cohort where frailty and selective survival play no major role yet. What can be said about causality is that, in our study, one of the strongest associations with metabolic syndrome was found for negative life events in the past year, which generally have a rather random and independent occurrence (e.g., death or illness of family member, violence, relative in trouble with the law). Consequently, for this association, the most likely pathway is that exposure to negative life events is associated with unfavorable metabolic changes. A second limitation is that our population was relatively healthy, with a low prevalence of depressive symptoms. The mean CES-D score was 4.7, and only 4.6% met the CES-D cutoff of 16 for clinically relevant depression. This percentage is low compared to other elder population-based studies (between 10% and 15%) (3,36). Restriction of range in the markers for psychosocial risk may have weakened the associations found. However, despite this limitation an association with the metabolic syndrome was found. Our study also had some important strengths, including the access to a large cohort of elderly persons from the community, allowing sufficient statistical power to examine the association between psychosocial risk factors and the metabolic syndrome and allowing exploration of sex and race differences. Furthermore, both psychosocial risk and the metabolic syndrome were examined in detail, allowing assessment of associations of components of both. Moreover, a well-accepted definition of the metabolic syndrome was used, enhancing the comprehensibility and interpretability of our results.

We believe that our findings support the idea that there is an important relationship between a variety of psychosocial risk factors and the metabolic syndrome, which are both common in later life. These results contribute significantly to the recent discussion implicating pathways from psychosocial factors through the metabolic syndrome to CVD and diabetes. A next step would be to test the hypothesis as to the mechanisms underlying the link between psychosocial risk factors and the metabolic syndrome. Knowing these mechanisms may lead to more effective and integrated efforts to prevent and treat cardiovascular disorders, diabetes, depression, and anxiety, which co-occur in later life.


   Acknowledgments
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 Discussion
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This work was supported by National Institute on Aging (NIA) contract numbers N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106 and in part by the Intramural Research Program of the National Institutes of Health, NIA. Data analyses were supported by grant 1R01-HL972972 from the National Heart, Lung, and Blood Institute (NHLBI).


   Footnotes
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Decision Editor: Luigi Ferrucci, MD, PhD

Received March 27, 2006

Accepted September 1, 2006


   References
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