Stressful Life Events and the Metabolic Syndrome: The Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia Study

The Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia Study is a population-based study in the Botnia region of Western Finland. The study was designed to obtain accurate estimates of prevalence and risk factors for diabetes, impaired glucose tolerance (IGT), and the metabolic syndrome in the population aged 18–78 years and to use this information for prediction and prevention of the disease. The current study was initiated in 2004 in five centers (Närpes, Malax-Korsnäs, Korsholm, Vasa, and Jakobstad). Using the population registry we selected a random sample of subjects aged 18–78 years (96,000 subjects) representing on average 9% of the population. The aim was to include altogether 5,000 individuals. This article reports data from the first 3,621 persons (1,712 men and 1,899 women) of the 6,079 invited (60%). Of them, 17 had at least one of the components of the metabolic syndrome missing, and 192 did not fill in the stressful life events questionnaire. Altogether, 3,407 (1,618 men and 1,789 women) participants had complete data available on the components of the metabolic syndrome and life events. They were younger and more educated, reported higher alcohol intake, had a family history for diabetes less often, and met the ATP III/IDF criteria for metabolic syndrome less often compared with the entire group (P < 0.001; no differences were found in sex distribution or frequency of smoking and regular exercise, P > 0.31). The participants gave their written informed consent. The study protocol was approved by the ethics committee of Helsinki University Hospital.

The subjects participated in an oral glucose tolerance test (OGTT) by ingesting 75 g of glucose after a 12-h overnight fast. Subjects with known diabetes who were taking antidiabetes medication or with fasting plasma glucose >10 mmol/l did not take part in the OGTT (n = 19). During the OGTT, blood samples for plasma glucose and serum insulin were drawn at 0, 30, and 120 min. The diagnosis of diabetes was based on the results from the OGTT or a history of previously known diabetes. The diagnosis of diabetes was based on the World Health Organization criteria (5). Thus, subjects with a fasting plasma glucose ≥7.0 mmol/l and/or 2-h plasma glucose ≥11.0 mmol/l during an OGTT were considered to have diabetes. Subjects with fasting plasma glucose between 6.1 and 6.9 mmol/l were considered to have impaired fasting glucose (IFG) and subjects with fasting plasma glucose <7.0 mmol/l and 2-h plasma glucose between 7.8 and 11.0 mmol/l were considered to have IGT (5).

Waist circumference was measured with a soft tape midway between the lowest rib and the iliac crest when the subject was standing. Fasting blood samples were drawn for the measurement of HDL cholesterol and triglycerides. Two blood pressure recordings were obtained from the right arm of a sitting subject after 10 min of rest, and the mean value was calculated.

We used the ATP III (4) and the IDF criteria (6) to define the metabolic syndrome. According to the ATP III criteria at least three of the following five criteria have to be met: waist circumference ≥102 cm in men and ≥88 cm in women, serum triglycerides ≥1.7 mmol/l and HDL cholesterol <1.0 mmol/l in men and <1.3 mmol/l in women, IFG/IGT or diabetes, and blood pressure ≥130/85 mmHg and/or use of antihypertensive medication. The IDF clinical criteria include waist circumference ≥94 cm in men and ≥80 cm in women and an additional two of the following criteria: serum triglycerides ≥1.7 mmol/l, HDL cholesterol <1.03 mmol/l in men and <1.29 mmol/l in women, blood pressure ≥130/85 mmHg and/or use of antihypertensive medication, or fasting plasma glucose ≥5.6 mmol/l.

In addition to the components defined by the ATP III and the IDF, we extended our analyses to using the homeostasis model assessment of insulin resistance (HOMA_IR) [(fasting plasma glucose × fasting insulin)/22.5] (14) as an additional index in insulin resistance and BMI (weight in kilograms divided by the square of height in meters, measured with subjects in light indoor clothing and without shoes) as an additional index of general obesity as outcomes.

Plasma glucose during the OGTT was measured with a glucose dehydrogenase method (HemoCue, Ängelholm, Sweden) and serum insulin by a fluoroimmunoassay (Delphia; Perkin-Elmer Finland, Turku, Finland). Serum HDL cholesterol and triglyceride concentrations were analyzed by an enzymatic method on a Konelab 60i analyzer (Thermo Electron Oy, Vantaa, Finland).

The subjects completed a questionnaire consisting of 15 stressful life events (Table 2). All questions concerned life events known to be major stressors (12,15,–17). The subjects were asked to evaluate the occurrence and stressfulness of these events (0, not occurred; 1, not at all stressful; 2, mildly stressful; 3, moderately stressful; and 4, extremely stressful) during the past 12 months. For the analyses, the measurement scale was dichotomized by contrasting moderately and extremely stressful events (hereafter called “extremely stressful life events”) with events that were not at all or mildly stressful or had not occurred at all (hereafter called “no stressful life events”) (12).

The subjects self-reported their weekly alcohol consumption (grams per week), current smoking (yes vs. no or former smoker), regular exercise (yes vs. no), level of education (less than high school, high school or college graduate, or degree beyond college), and family history of known diabetes (yes vs. no) in at least one first-degree relative (father, mother, or sibling).

Logistic regression analyses, odds ratios (OR), and 95% CIs were computed to examine associations between stressful life events and the metabolic syndrome. Multiple linear regression analyses, unstandardized regression coefficients, and 95% CIs were computed to examine associations between stressful life events and HOMA_IR, waist circumference, BMI, triglycerides, HDL cholesterol, systolic blood pressure and diastolic blood pressure, and logistic regression analyses to examine associations with IFG and IGT. The associations were adjusted for the mediating and confounding factors. Because the unadjusted and fully adjusted models resulted in essentially similar results, we present the fully adjusted associations only. Finally, because associations between psychosocial factors and the metabolic syndrome may be moderated by sex (9,10), we tested whether any of the associations varied for men and women by including an interaction term, “sex × extremely stressful life event” in the models. Tests of moderation by sex were also supported by our own data demonstrating a preponderance for women to report more life events as extremely stressful in all of the measured life domains (supplementary Tables A1 and A2, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-1027/DC1). In no instance was there a significant sex interaction term (P > 0.07) (data not shown). For this reason, we report the results in both sexes combined.

Table 1 shows that the agreement rate between the ATP III and the IDF criteria was high. Consequently, the ATP III and the IDF criteria resulted in similar differences between the groups meeting and not meeting the metabolic syndrome in biological, sociodemographic, and lifestyle characteristics and in family history of diabetes. Therefore, characteristics of the sample are presented according to the ATP III criteria only (Table 1).

Table 2 shows that the odds for having the metabolic syndrome according to the ATP III, the IDF, or both criteria were significantly higher among participants who had experienced extremely stressful life events in finance-related (ongoing financial strain, severe financial strain, threat of unemployment, or personal bankruptcy), work-related (troubles with coworkers or beginning a new job), and health-related domains (concern over own or child's ability to cope with stress). Further, the odds for having the metabolic syndrome according to the ATP III and the IDF criteria were significantly higher among participants who had experienced at least one stressful life event in the finance-related domain and at least three events in any of the life domains (Table 3).

Analyses of the components of the metabolic syndrome indicated that participants who had experienced stressful life events in the finance-, work-, and health-related domains or had experienced at least three events in any of the life domains displayed higher levels of HOMA_IR, waist circumference, BMI, and triglycerides and had a higher odds for having IGT (Table 4). Stress did not associate significantly with IFG, HDL cholesterol, or blood pressure (data not shown).

The key finding in the present study is that individuals who reported extremely stressful life events within finance- and work-related life domains had significantly higher odds for having the metabolic syndrome. The risk was further increased according to accumulation of stressful finance-related events and according to having at least three events in any of the life domains we measured, namely finance, work, social relationships, health, and housing. Accumulation of stressful finance- and work-related life events and having at least three stressful events in any of the life domains also associated significantly with insulin resistance, obesity, and triglycerides. The associations were not confounded by sex, age, lifestyle, or family history of diabetes.

Our findings are in agreement with previous studies (9,,,–13) by showing that major stress-related events associate with an increased risk of having the metabolic syndrome. However, our findings further suggest that of the life events we measured, those relating more specifically to finance and work seemed the most harmful. Although chronic work stress has been associated with an increased risk of having the metabolic syndrome in the Whitehall II study (11), the other existing studies have not specifically focused on work-related stress, and none of the studies focused on finance-related stress, precluding direct comparisons among the studies.

Our findings with the different components of the metabolic syndrome agree well with the work and hypothesis put forward by Björntorp (8). According to Björntorp, an hypothalamic-pituitary-adrenocortical axis abnormality may contribute to both insulin resistance and abdominal obesity with lipids and blood pressure being the secondary complications. In addition to alcohol and smoking, among the major triggers of this chain of events is psychosocial stress of a “defeat,” “helpless” type (8). Although the cross-sectional nature of our study precludes any inferences about causality, our finding that major stressful life events associated most closely with indexes of insulin resistance, obesity, and triglycerides is interesting. To our knowledge, one previous study has shown that accumulation of non–work-related stressful life events was associated with increased risk of obesity in men and with higher waist-to-hip ratio in women and men but not with fasting insulin concentrations (17), findings that are at least partly in line with the current ones.

Apart from the hypothalamic-pituitary-adrenocortical axis abnormality, other mechanisms that may underlie these associations include autonomic nervous system changes and inflammatory activity (4,8,18). Alterations in these physiological systems are linked with the metabolic syndrome, insulin resistance, and obesity (19). By inducing changes in lifestyle, stress may associate with metabolic changes through smoking, alcohol use, and physical inactivity. Our associations were not, however, affected by controls of lifestyle and neither were the associations affected by educational attainment or by a family history of diabetes. Finally, stress may induce changes in other psychosocial risk factors, such as sleep pattern (20) and depression (21). Because poor sleep and depression are associated with the metabolic syndrome (12,22), insulin resistance (23,24), and diabetes (24,25), these may provide additional pathways through which stress is related to the metabolic syndrome. Furthermore, we cannot rule out genetic pathways. Although specific genetic markers cannot be designated, these may relate to glucocorticoids, catecholamines, and inflammatory markers or an as yet unknown novel genetic marker.

The strengths of this study lie in the population-based study design and detailed clinical examination and measurement of stressful life events and their severity across various life domains. All of these strengths contribute significantly to the existing literature. None of the prior studies testing associations between stress and the metabolic syndrome have been population-based, restricting the external validity of the findings. None of the prior studies has measured insulin and glucose after an oral glucose challenge, precluding more precise definitions of the metabolic syndrome and focus on glucose tolerance. Except for one study (12), none of the studies has measured the perceived severity of the stressful life events. Different events may pose different experiences in different individuals, thus sorting out the perceived severity of different events (not equating events per se, such as death of a spouse or death of a pet) is important. In addition, our study offered sufficient power to test the sex specificity of the associations. Although the association between some psychosocial factors and metabolic syndrome has been reported to be different in men than in women (9,10), our data did not reveal any such differences.

Apart from a cross-sectional study design, another limitation of the study is that the sample is composed of whites only. Thus, the findings may not generalize to groups with other ethnic backgrounds. Furthermore, 5.9% of our study population were excluded because of missing information, the major reason being missing data in the life events questionnaire (5.3%). Those with full information, compared to those without, were younger, were more educated, consumed more alcohol, had a family history of diabetes less frequently, and met the criteria for metabolic syndrome less frequently. However, a bias toward inclusion of younger, more educated, and healthier participants might diminish rather than increase our ability to detect significant associations. We measured occurrence and severity of stressful life events the past 12 months. Yet, we cannot determine the precise timing and duration of the life events and hence cannot address the temporal relationships between stress and health in any further detail. Stressful life events and psychiatric disorders, such as major depression, are associated (21). Because we did not have data available on psychiatric disorders, we cannot rule out the possibility that these may explain the associations. Finally, although all the associations were adjusted for level of education as a proxy of social position, a possibility remains that for a specific subgroup adjustment for level of education may not have been sufficient to capture the overlap that may exist in social position and some of the stressful events.

To summarize, our study shows that extremely stressful life events, particularly those related to finance and work, are associated with increasing odds of having the metabolic syndrome and with having higher degrees of insulin resistance, obesity, and triglycerides. Our study was conducted before the global economic crisis. Thus, if finance- and work-related troubles play a role in the pathogenesis increasing the risk for the metabolic syndrome, we can only speculate that over the next decade we may see an increase in the prevalence of the metabolic syndrome and associated disease.

The PPP-Botnia study has been supported by grants from the Sigrid Juselius Foundation, Folkhälsan Research Foundation, Nordic Center of Excellence in Disease Genetics, Signe and Ane Gyllenberg Foundation, Swedish Cultural Foundation in Finland, Finnish Diabetes Research Foundation, Foundation for Life and Health in Finland, Finnish Medical Society, Ministry of Education, Paavo Nurmi Foundation, Perklén Foundation, Ollqvist Foundation, and Närpes Health Care Foundation. The study has also been supported by the Municipal Health Care Center and Hospital in Jakobstad, Health Care Centers in Vasa, Närpes, and Korsholm.

L.G. has been a consultant for and served on advisory boards for sanofi-aventis, GlaxoSmithKline, Novartis, Merck, Tethys Bioscience, and Xoma and received lecture fees from Lilly and Novartis. No other potential conflicts of interest relevant to this article were reported.

The skillful assistance of the Botnia Study Group is gratefully acknowledged.