Synergistic effects of depressed mood and obesity on long-term cardiovascular risks in 1510 obese men and women: results from the MONICA–KORA Augsburg Cohort Study 1984–1998



To examine the contribution of depressed mood in obese subjects on the prediction of a future coronary heart disease event (CHD).


A prospective population-based cohort study of three independent cross-sectional surveys with 6239 subjects, 45–74 years of age and free of diagnosed CHD, stroke and cancer. During a mean follow-up of 7 years, 179 CHD events occurred among men and 50 events among women.


A total of 737 (23%) male and 773 (26%) female subjects suffering from obesity (BMI 30 kg/m2).


Body weight determined by trained medical staff following a standardized protocol; standardized questionnaires to assess subsyndromal depressive mood and other psychosocial features.


The main effect of obesity to predict a future CHD (hazard ratio, HR=1.38, 95% CI 1.03–1.84; P=0.031) and the interaction term of obesity by depression (HR=1.73, 95% CI 0.98–3.05; P=0.060) were borderline significant, both covariate adjusted for multiple risk factors. Relative to the male subgroup with normal body weight and no depression, the male obese group with no depression was not at significantly increased risk for CHD events (HR=1.17, 95% CI 0.76–1.80; P=0.473) whereas CHD risk in males with both obesity and depressed mood was substantially increased (HR=2.32, 95% CI 1.45–3.72, P>0.0001). The findings for women were similar, however, not significant probably owing to lack of power associated with low event rates. Combining obesity and depressed mood resulted in a relative risk to suffer from a future CHD event of HR 1.84 (95% CI 0.79–4.26; P=0.158).


Depressed mood substantially amplifies the CHD risk of middle-aged obese, but otherwise apparently healthy men. The impact of depression on the obesity risk in women is less pronounced.


Obesity is strongly associated with traditional cardiovascular disease risk factors, including high blood pressure and diabetes mellitus,1 but also with novel risk factors, mainly pro-inflammatory cytokines,2 which all support the biological plausibility of a strong link between obesity and atherosclerosis. However, epidemiological studies have not consistently corroborated an association between obesity and long-term coronary heart disease (CHD) risks. Recently, Dyer et al.3 analyzed data from 33 (mainly) population-based long-term follow-up studies and found relationships between body weight and CHD risk, which included direct associations, nonsignificant positive and negative associations, J- and U-shaped associations and one significant inverse association.

Methodological problems3, 4 may contribute to these inconsistencies including failure to control for the confounding effect of cigarette smoking, inappropriate adjustment for risk factors that represent important biological effects of obesity and the inclusion of early CHD events that may not account for a direct relationship between obesity and CHD end points.3, 5 Additionally, several investigators have proposed undefined confounding risk factors associated with obesity and CHD risk to explain this ‘apparent paradox’.4

Depression may be a possible candidate factor to be considered as a significant effect modifier in the population-based relationship between obesity and CHD risk for two major reasons: depressive mood is an independent behavioral risk factor for subsequent coronary events in apparently healthy subjects.6, 7 Depressed mood, mainly in adolescents, also increases the risk for subsequent obesity even after controlling for baseline body weight.8 Although obesity, by itself, does not appear to be systematically associated with psychopathological outcomes, subgroups of obese individuals have been identified who suffer substantially from psychiatric disorders, especially depression.9, 10, 11 Thus, the depression risk may not be equally distributed among obese subjects and, in consequence, may contribute inconsistently to a subsequent CHD risk.

In this study, we sought to identify obese individuals who also suffered from depressed mood and to investigate whether the combination of obesity and depression constitutes a particular ‘at risk’ population for future CHD events.

Patients and methods


The presented data were derived from the population-based MONICA (MONItoring trends and determinants in CArdiovascular disease) Augsburg studies (Germany) as part of the multinational WHO MONICA project.12 Altogether 13 428 persons (6725 men, 6703 women, response 77%) aged 25–74 years, randomly drawn from the general population, participated in at least one of three independent population-based surveys, conducted in 1984/85 (S1), 1989/90 (S2) and 1994/95 (S3). The psychosocial data set followed recommendations given by the MONICA steering committee.13

In the KORA follow-up study (, the vital status was assessed for all participants of the three MONICA surveys in 1998. During the observation period, 772 participants (531 men, 241 women) had died. Vital status could not be assessed for 56 persons (31 men, 25 women) who had moved to unknown locations.

Study group

A total of 12 888 subjects with psychosocial data were available. Based on the assumption of a low probability of adverse CHD events, 5417 subjects younger than 45 years were excluded. A total of 823 subjects had missing values on depression status, and body mass index (BMI) data were missing in 50 subjects. Subjects with missing values on depression status were older, more likely to be women, to live alone and had a lower educational level than subjects included into the study (χ2 test, all P<0.001). Furthermore, participants with prevalent CHD or stroke (n=207) or a history of cancer (n=128) at baseline were excluded. Also, subjects with a BMI<18.5 kg/m2(n=24) were excluded. Thus, a total of 6239 subjects (3238 men and 3001 women) were included in the present analysis. Of those, 1723 (28%) subjects were from survey S1, 2262 (36%) from S2 and 2253 (36%) from S3.

The cohort of 6,239 subjects in the present analysis was followed for an average of mean 7.1 (4.0 s.d.) years (minimum 0.11 to maximum 13.7 years) (median: 7.8 years, interquartile range (IQR) 6.8 years). During this observational period, 179 coronary events occurred in the male group, of which 94 events were fatal and 85 non-fatal, and 50 events occurred in the female group of which 22 events were fatal and 28 non-fatal.

Risk factor assessment at baseline

Body mass index was calculated as weight in kilograms divided by height in square meters. Body height and body weight were determined by trained medical staff following a standardized protocol. According to the recommendations of the WHO, obesity was defined as BMI 30 kg/m2.

A non-fasting venous blood sample was collected from all participants in a supine resting position. Total serum cholesterol (mg/dl) and high-density lipoprotein cholesterol (HDL-C) were measured by enzymatic methods (CHOD-PAP, Boehringer Mannheim, Germany).

A regular smoker was defined as a subject who currently smoked at least one cigarette per day. Blood pressure was measured on the right arm in a sitting position using a Hawksley random-zero sphygmomanometer adhering to the WHO MONICA protocol.12

Education level was categorized into ‘low’ (<12 years of schooling) and ‘high’ (12 years of schooling). Alcohol consumption was classified into three categories: non-drinkers (0 g/day), intake of 0.1–39.9 and >40 g/day. To assess physical activity, participants were classified as ‘active’ during leisure time if they regularly participated in sports and if they were active for at least 1 h per week in summer and winter.

Depressive symptomatology was assessed using a subscale from the von Zerssen affective symptom check list.14 The DEpression and EXhaustion subscale (DEEX scale) combines eight items (fatiguability, tiredness, irritability, loss of energy, difficulty in concentrating, inner tension, nervousness, anxiety) ranging from 0 to 3, leading to a Likert-like scoring range of 0–24. Subjects in the top tertile of the depressive symptom distribution were considered as index group for subjects with a depressed mood. Sex-specific cutoff points were applied.15

Social support was assessed according to the Social Network Index (SNI) initially designed for the Alameda county study16 comprising marital status, contact with friends and relatives, the index of close contacts and activities in groups. The combination of all components allows a comprehensive rating from 1 (low SNI) to 4 (high SNI). Self-perceived health was directly assessed by one interview question with a poor health condition as highest rating. Happiness was assessed with a one-item rating scale ranging from 1 (very happy) to 6 (very unhappy). The instrument was applied in S2 and S3. Sleep complaints were items concerning difficulties initiating sleep (DIS) and difficulties maintaining sleep (DMS) and were adopted from the Uppsala Sleep Inventory.17

Study end points

Study end points were a (non-) fatal acute myocardial infarction and sudden cardiac death. The WHO MONICA diagnostic categories were based on symptoms, cardiac enzymes (creatine kinase, aspatate aminotransferase, lactate dehydrogenase) and typical ECG changes.12 Cases were identified via the MONICA Augsburg coronary event registry and censored at the 75th year of age.18 Death certificates were obtained from the local health departments and were coded for the underlying cause of death using the ninth revision of the International Classification of Diseases (ICD-9, 410–414, 798).

Statistical analysis

Means or proportions for baseline demographic and clinical characteristics were computed for the study participants stratified by depressed mood and obesity. Significance of differences in means was tested with F-tests, and significance of differences in proportions was tested by χ2-test. All tests were performed two sided.

The major goal of the present study was to assess possible joint effects of obesity and depressed mood on the prediction of a future fatal and non-fatal coronary event. To this end, we first examined the main single effects of obesity by running crude and adjusted Cox proportional hazard models19 for both variables with the time to event as the dependent variable and then tested the interaction term of both variables in crude and adjusted models.

The main analysis consisted of proportional hazard models to examine the combined effects of obesity and depression for men and women separately by examining the hazard ratios for all subgroup combinations derived from obesity and the depressed mood group relative to the remaining body weight and non-depressed group. In each model, variables were entered simultaneously. Crude models were adjusted for age and survey (S1, S2, S3). All other models were further adjusted for total cholesterol, cigarette smoking and systolic blood pressure, education, alcohol consumption and physical activity. The assumption of proportionality was assessed by fitting models stratified by risk factor categories, then plotting the log (−log (survival)) curves for each risk factor to check parallelism by visual inspection. The assumption of a linear relationship between a continuous variable and the incidence of a coronary event was assessed by univariate Cox proportional hazard models including the variable in simple and square-routed form in the regression equation. The effects of square roots of the continuous variables (age, cholesterol, blood pressure) were not significant, indicating linearity of these variables. P-values for the Cox models were based on Wald statistic. A P-value 0.05 was considered significant. Statistical analyses were performed using SPSS (version 12.0).


Among the total of 3238 men in the present study, a subpopulation of 737 (23%) individuals were identified with manifest obesity. Among 3001 women, a total of 773 (26%) individuals suffered from obesity. In the obese subpopulation, 233 (32%) men and 244 (32%) women were considered to be depressed.

The baseline characteristics of the obese study participants stratified by depression and male sex are shown in Tables 1 and 2. We compared obese subjects with and without depression and found no statistical differences in CHD risk factors. However, depressed subjects reported a significantly more impaired self-perceived health and a lower level of happiness. Moreover, they suffered significantly more from sleeping disorders. The risk factor distribution in women followed comparable pattern.

Table 1 Baseline demographic and clinical characteristics of obese male study participants (n=737, aged 45–74 years), stratified by depression
Table 2 Baseline demographic and clinical characteristics of obese female study participants (n=773, aged 45–74 years), stratified by depression

We analyzed the main effects of obesity and the interaction term of obesity × depression on the prediction of a future coronary event as a prerequisite to investigate the major study aim. Obesity was predictive in the crude model, adjusted for age and survey (HR=1.48, 95% CI 1.12–1.96; P<0.006), and covariate adjusted for multiple risk factors (HR=1.38, 95% CI 1.03–1.84; P=0.031). The interaction term of obesity × depression disclosed a borderline significant interaction effect in the adjusted model after multiple risk factor adjusting (HR=1, 73, 95% CI 0.98–3.05; P=0.060). Both findings, the independent ability of obesity to predict a future coronary event and its significant interaction with depression, were considered as basis to look for possible joint effects of both conditions.

Figure 1 shows the survival curves of four subgroup combinations derived from two levels of body weight and two levels of depressed mood in men. All findings were adjusted for multiple CHD risk factors.

Figure 1

Survival curves of 3238 men, stratified in groups with four subgroup combinations derived from two levels of body weight and two levels of depression. Non-adi, non-D: male subgroup with normal (and moderately heightened) body weight and no depression (n=1662); non-adi, Dep: male subgroup with normal body weight and depression (n=839 men); adi, non-D: male subgroup with obesity and no depression (n=504 men); adi, Dep: male subgroup with obesity and depression (n=233 men).

Relative to the subgroup with normal (and moderately heightened) body weight and no depressed mood (n=1662 men), non-obese body weight and depressed mood in 839 men (HR=1.26, 95% CI 0.88–1.80; P=0.209) entailed no significant results as did the combination of obesity/no depression in 504 men (HR=1.17, 95% CI 0.76–1.80; P=0.473). However, when combining obesity and depression in 233 men, the relative risk to suffer from a future CHD event mounted to HR=2.32 (95% 1.45–3.72; P>0.0001).

The findings for women (Figure 2) were similar, however not significant mainly owing to substantially lower event rates: relative to the female subgroup with (sub)normal body weight (BMI >30 kg/m2) and no depression (n=1433), the combination of (sub) normal body weight and depressed mood in 795 women (HR 0.76, 95% CI 0.35–1.69 P=0.506) and obesity and no depression in 529 women (HR 0.95, 95% CI 0.42–2.16; P=0.898) entailed no significant results. However, combining obesity and depression in 244 women resulted in a relative risk to suffer from a future CHD event of HR=1.84 (95% 0.79–4.26; P=0.158) after adjustment for multiple CHD risk factors.

Figure 2

Survival curves of 3001 women, stratified in groups with four subgroup combinations derived from two levels of body weight and two levels of depression. Non-adi, non-D: female subgroup with normal (and moderately heightened) body weight and no depression (n=1433); non-adi, Dep: female subgroup with normal body weight and depression (n=795); adi, non-D: female subgroup with obesity and no depression (n=529); adi, Dep: female subgroup with obesity and depression (n=244).


Obesity in adulthood substantially reduces life expectancy20 and is one of the classical risk factors associated with increased mortality risk from CHD and ischemic stroke.21 However, some prospective population-based studies failed to confirm the independent influence of obesity on fatal outcomes.22, 23, 24, 25, 26, 27 Among others, undefined confounding risk factors associated with obesity and CHD risk might account for these inconsistencies. We assumed that negative affectivity, basically depression, may play a crucial role to elucidate the gap between biological plausibility and clinical outcomes of obesity.

The major finding of the present study is to show that depression substantially contributes to the risk of a fatal or non-fatal future CHD event in obese, but otherwise apparently healthy subjects, particularly in men. According to the data of the present analysis, subjects with either low depression and obesity or low body weight and depression exhibited comparable event risks. The independent influence of obesity on the incidence of CHD, which has been proven in the present data analysis, attenuated substantially when controlling for depression. In other words, obese subjects who do not suffer from a depressed mood may be better protected against the deleterious effects of obesity.

To the best of our knowledge, this is the first study to elucidate the impact of depression on the long-term course of obesity. Some cross-sectional studies have found a direct relationship between mood and obesity28, 29, 30, 31, 32, 33, 34, 35, 36 whereas the association was not entirely confirmed or even rejected in other studies pointing to obesity as related to low levels of depression and anxiety.37, 38, 39

Reasons as to why a depressed mood exerts such an apparently deleterious effect on obese subjects are not known so far. Against expectation, the excess risk of depressed obese subjects cannot be explained by a greater burden of relevant CHD risk factors in the depression group. Any association between depression and coronary events has to be mediated by physiological factors. It is most likely that differences in activation states of the hypothalamic-pituitary-adrenal axis40, 41 or neuro-endocrine overstimulation of CRP or fibrinogen2, 42, 43 might yield differences between depressed and non-depressed patients. However, in the present analysis, we did not control for them, as they may be involved as mediating pathways.

Strength and limitations of the study

The strength of the present study is primarily its prospective design, and the cohort based on a random sample of the general population. The sample size allows extensive controlling of confounding variables.

However, there are also limitations that need to be addressed. In the present study, a subsyndromal depressive mood was assessed by the DEEX scale, which is among the less rigorous options to assess depressive mood although a recent reexamination of its validity and reliability is promising.15 Clinically, the DEEX scale identified symptoms of reduced vitality, weakness and ‘vital exhaustion’44 but without a negative self-concept and feelings of guilt feelings,45 which provides sufficient coverage for all important facets of subsyndromal depressive mood of negative affectivity in the context of cardiovascular medicine.46, 47 Additionally, depression was measured at one time point, so that transient states of depression could not be distinguished from persistent states.

In the present study, the assessment of obesity was based on BMI, which is a surrogate for percent body fat.48, 49 The inability of BMI to distinguish between subcutaneous and intra-abdominal fat stores may underestimate the neuro-immunological properties of obesity, as the accumulation of intra-abdominal fat in particular is thought to promote endocrine abnormalities.2, 42, 43


In conclusions, the present study shows that the CHD event risk of middle-aged obese, but otherwise apparently healthy men was substantially amplified by a depressed mood state whereas the CHD risk of obese men without depression corresponds approximately with the risk of non-obese populations after controlling for CHD risk factors.


  1. 1

    Wilson PW, D'Agostino RB, Sullivan L, Parise H, Kannel WB . Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med 2002; 162: 1867–1872.

  2. 2

    Festa A, D'Agostino Jr R, Williams K, Karter AJ, Mayer-Davies EJ, Tracy RP et al. The relation of body fat mass and distribution to markers of chronic inflammation. Int J Obes 2001; 25: 1407–1415.

  3. 3

    Dyer AR, Stamler J, Garside DB, Greenland P . Long-term consequences of body mass index for cardiovascular mortality: the Chicago Heart Association Detection Project in Industry study. Ann Epidemiol 2004; 14: 101–108.

  4. 4

    Manson JE, Colditz GA, Stampfer MJ, Willett WC, Rosner B, Monson RR et al. A prospective study of obesity and risk of coronary heart disease in women. N Engl J Med 1990; 29: 882–889.

  5. 5

    Lee IM, Manson JE, Hennekens CH, Paffenbarger Jr RS . Body weight and mortality. A 27-year follow-up of middle-aged men. JAMA 1993; 270: 2823–2828.

  6. 6

    Rugulies R . Depression as a predictor for coronary heart disease. A review and meta-analysis. Am J Prev Med 2002; 23: 51–61.

  7. 7

    Wulsin LR, Singal BM . Do depressive symptoms increase the risk for the onset of coronary disease. A systematic quantitative review. Psychosom Med 2003; 65: 259–267.

  8. 8

    Goodman E, Whitaker RC . A prospective study of the role of depression in the development and persistence of adolescent obesity. Pediatrics 2002; 110: 497–504.

  9. 9

    Faith MS, Matz PE, Jorge MA . Obesity–depression associations in the population. J Psychosom Res 2002; 53: 935–942.

  10. 10

    Roberts RE, Deleger S, Strawbridge WJ, Kaplan GA . Prospective association between obesity and depression: evidence from the Alameda County Study. Int J Obes 2003; 28: 5143–5521.

  11. 11

    Dong C, Sanchez LE, Price RA . Relationship of obesity to depression: a family-based study. Int J Obes 2004; 28: 790–795.

  12. 12

    WHO MONICA Project Principal Investigators (prepared by H Tunstall-Pedoe). Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation 1994; 90: 583–612.

  13. 13

    WHO MONICA Project Principal Investigators,MONICA Psychosocial Optional study. Suggested Measurement Instruments. Regional Office for Europe, 1989.

  14. 14

    Zerssen Dv . Die Beschwerden-Liste Klinische Selbstbeurteilungsfragebögen aus dem Münchner Psychiatrischen Informationssystem (Psychis). Weinheim (Beltz), 1976.

  15. 15

    Ladwig KH, Marten-Mittag B, Baumert J, Doering A, Loewel H . Case-finding for vital exhaustion and depressive mood in the general population: reliability and validity of a symptom driven diagnostic scale. Results from the MONICA Augsburg Study. Ann Epidemiol 2004; 14: 332–338.

  16. 16

    Seeman TE, Kaplan GA, Knudsen L, Cohen R, Guralnik J . Social network ties and mortality among the elderly in the Alameda County. Am J Epidemiol 1987; 126: 714–723.

  17. 17

    Mallon L, Broman JE, Hetta J . Sleep complaints predict coronary artery disease mortality in males: a 12-year follow-up study of a middle-aged Swedish population. J Int Med 2002; 251: 207–216.

  18. 18

    Loewel H, Lewis M, Hoermann A, Keil U . Case finding, data quality aspects and comparability of myocardial infarction registers: results of a south German register study. J Clin Epidemiol 1991; 44: 249–260.

  19. 19

    Cox DR, Oakes D . Analysis of Survival Data. Chapman & Hall: London, 1984.

  20. 20

    Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L . Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann Intern Med 2003; 138: 24–32.

  21. 21

    Klein S, Burke LE, Bray GA, Blair S, Allison DB, Pi-Sunyer X et al. Clinical implications of obesity with specific focus on cardiovascular disease: a statement for professionals from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism: endorsed by the American College of Cardiology Foundation. Circulation 2004; 110: 2952–2967.

  22. 22

    McGee DL, Reed DM, Yano K, Kagan A, Tillotson J . Ten-year incidence of coronary heart disease in the Honolulu Heart Program. Relationship to nutrient intake. Am J Epidemiol 1984; 119: 667–676.

  23. 23

    Wilcosky T, Hyde J, Anderson JJ, Bangdiwala S, Duncan B . Obesity and mortality in the lipid research clinics program follow-up study. J Clin Epidemiol 1990; 43: 743–752.

  24. 24

    Yao CH, Slattery ML, Jacobs Jr DR, Folsom AR, Nelson ET . Anthropometric predictors of coronary heart disease and total mortality: findings from the US Railroad Study. Am J Epidemiol 1991; 134: 1278–1289.

  25. 25

    Jousilahti P, Tuomilehto J, Vartiainen E, Pekkanen J, Puska P . Body weight, cardiovascular risk factors, and coronary mortality. 15-year follow-up of middle-aged men and women in eastern Finland. Circulation 1996; 93: 1372–1379.

  26. 26

    Dorn JM, Schisterman EF, Winkelstein Jr W, Trevisan M . Body mass index and mortality in a general population sample of men and women. The Buffalo Health Study. Am J Epidemiol 1997; 146: 919–931.

  27. 27

    Yao CH, Slattery ML, Jacobs Jr DR, Folsom AR, Nelson ET . Anthropometric predictors of coronary heart disease and total mortality: findings from the US Railroad Study. Am J Epidemiol 1991; 134: 1278–1289.

  28. 28

    Gray RS, Fabsitz RR, Cowan LD, Lee ET, Welty TK, Jablonski KA et al. Relation of generalized and central obesity to cardiovascular risk factors and heart disease in a sample of American Indians: the Strong Heart Study. Int J Obes 2000; 24: 849–860.

  29. 29

    Hopkinson G, Bland RC . Depressive syndromes in grossly obese women. Can J Psychiatry 1982; 18: 24–28.

  30. 30

    Istvan J, Zavela K, Weidner G . Body weight and psychological distress in NHANES I. Int J Obes 1992; 16: 999–1003.

  31. 31

    Hassan MK, Joshi AV, Amonkar MM . Obesity and health related quality of life: a cross-sectional analysis of the US population. Int J Obes 2003; 27: 1227–1232.

  32. 32

    Siegel JM, Yancey AK, McCarthy WJ . Overweight and depressive symptoms among African-American women. Prev Med 2000; 31: 232–240.

  33. 33

    Carpenter KM, Hasin DS, Allison DB, Faith MS . Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health 2000; 90: 251–257.

  34. 34

    Roberts RE, Kaplan GA, Shema SJ, Strawbridge WJ . Are the obese at greater risk for depression? Am J Epidemiol 2000; 152: 935–942.

  35. 35

    Roberts RE, Strawbridge WJ, Deleger S, Kaplan GA . Are the fat more jolly? Ann Behav Med 2002; 53: 935–942.

  36. 36

    Onyike CU, Crum RM, Lee HB, Lyketsos CG, Eaton WW . Is obesity associated with major depression? Results from the Third National Health and Nutrition Examination Survey. Am J Epidemiol 2003; 158: 1139–1147.

  37. 37

    Crisp AH, McGuiness B . Jolly fat: relation between obesity and psychoneurosis in the general population. BMJ 1976; 1: 7–9.

  38. 38

    Hallstrom T, Noppa H . Obesity in women in relation to mental illness, social factors and personality traits. J Psychosom Res 1981; 25: 75–82.

  39. 39

    Palinkas LA, Wingard DL, Barrett-Connor E . Depressive symptoms in overweight and obese older adults: a test of the ‘jolly fat’ hypothesis. J Psychosom Res 1996; 40: 59–66.

  40. 40

    Katz JR, Taylor NF, Goodrick S, Perry L, Yudkin JS, Coppack SW . Central obesity, depression and the hypothalamo-pituitary-adrenal axis in men and postmenopausal women. Int J Obes 2000; 25: 246–251.

  41. 41

    Weber-Hamann B, Hentschel F, Kniest A, Deuschle M, Colla M, Lederbogen F et al. Hypercortisolemic depression is associated with increased intra-abdominal fat. Psychosom Med 2002; 64: 274–277.

  42. 42

    Miller GE, Stetler CA, Carney RM, Freedland KE, Banks WA . Clinical depression and inflammatory risk markers for coronary heart disease. Am J Cardiol 2002; 90: 1279–1283.

  43. 43

    Ladwig KH, Marten-Mittag B, Löwel H, Döring A, Koenig W . Influence of depressive mood on the association of CRP and obesity in 3205 middle aged healthy men. Results from the MONICA Augsburg Study. Brain Behav Immun 2003; 17: 268–275.

  44. 44

    Appels A, Mulder P . Excess fatigue as a precursor of myocardial infarction. Eur Heart J 1988; 9: 758–764.

  45. 45

    Wassertheil-Smoller S, Applegate WB, Berge K, Chang CJ, Davis BR, Grimm Jr R et al. Change in depression as a precursor of cardiovascular events. Arch Internal Med 1996; 156: 553–561.

  46. 46

    Schulz R, Beach SR, Ives DG, Martire LM, Ariyo AA, Kop WJ . Association between depression and mortality in older adults: the Cardiovascular Health Study. Arch Internal Med 2000; 160: 1761–1768.

  47. 47

    Kop WJ, Gottdiener JS, Tangen CM, Fried LP, McBurnie MA, Walston J et al. Inflammation and coagulation factors in persons &gt;65 years of age with symptoms of depression but without evidence of myocardial ischemia. Am J Cardiol 2002; 89: 419–424.

  48. 48

    Smalley KJ, Knerr AN, Kendrick ZV, Colliver JA, Owen OE . Reassessment of body mass indices. Am J Clin Nutr 1990; 52: 405–408.

  49. 49

    Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB . How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143: 228–239.

Download references


The MONICA–KORA Augsburg study is financed by the GSF-National Research Center for Environment and Health. We are grateful to the MONICA Augsburg teams, which have conducted the studies and identified the clinical outcomes of the survey participants. We also thank Andrea Schneider for data handling and quality control. We thank Ulrich Keil, MD, who initiated the MONICA Study Augsburg as first principal investigator.

Author information

Correspondence to K-H Ladwig.

Rights and permissions

Reprints and Permissions

About this article


  • epidemiology
  • risk factors
  • depressed mood
  • cardiovascular disease

Further reading