|Author:||Sorting Last Post on Top ↓ Message:|
Depressive Mood and Abdominal Fat Distribution in Overweight Premenopausal Women
Objective: There is increasing evidence that depressive mood is associated with central obesity, but little is known about the association between depression and abdominal fat distribution. This study investigated this relationship in premenopausal women.
Research Methods and Procedures: We recruited 101 overweight premenopausal women who had no eating disorders as defined using the DSM IV criteria. Depressive mood was assessed using Zung's Self-Rating Depression Scale (SDS). Areas of visceral (VAT) and subcutaneous (SAT) adipose tissue at the level of vertebral body L4L5 were measured using computed tomography. Associations of VAT, SAT, and the ratio of VAT to SAT with natural logarithmic transformation [(ln)]SDS were evaluated using linear regression. Anthropometric indices and physical fitness were also measured. Information on socioeconomic status, education level, and alcohol and smoking habits was obtained using self-administered questionnaires. A hospital nutritionist assessed nutritional status. All of these factors were adjusted for as possible confounding factors in the analyses.
Results: The (ln)SDS score showed a positive association with the area of VAT, even after adjusting for the confounders mentioned above (p 0.01). BMI, waist circumference, maximal oxygen uptake, and age were also associated with the area of VAT (all p 0.05). In contrast, the (ln)SDS score was not associated with SAT (p 0.10).
Discussion: We showed that depressive mood is associated with VAT, not with SAT, in overweight premenopausal women. These findings may explain some of the association between depression and coronary heart disease. More studies are needed to elucidate the causal relationship.
Obesity and depression
Both obesity and depression are becoming increasingly prevalent and are associated with numerous complications, including hypertension and coronary heart disease (1,2). Although the underlying mechanisms of the effects of obesity on these complications are well documented, those of depression are unclear and controversial.
Interestingly, recent studies have shown that obesity and depression are associated with each other and that this association is clearer in persons with central obesity (3,4,5,6). This has drawn much attention because it may help to explain findings linking depression to coronary heart disease and other metabolic complications. However, these studies have some limitations: 1) most of the studies did not fully control for the effects of other important confounding variables, such as exercise and diet (3,4,5,6), and 2) most of them did not consider the distribution of visceral adipose tissue (VAT)1 and subcutaneous adipose tissue (SAT), which have different clinical implications.
Central obesity clearly has greater health hazards than peripheral obesity. In addition, in persons with a similar degree of central obesity, those with more visceral fat have more metabolic complications and a greater risk of coronary heart disease than do those with subcutaneous fat (7,8). When considering the association of depression with central obesity and the risk of coronary heart disease, it is postulated that depressive mood is associated more with VAT than with SAT. However, few studies have investigated this.
This study examined the association between depression and abdominal fat distribution in overweight premenopausal women after controlling for possible confounding variables, such as diet and exercise.
Premenopausal women with a BMI 25 kg/m2 were recruited at the health promotion center at Ilsan-Paik Hospital in Korea. In March 2003, 303 women visited this center. All women were asked about their menstrual history and were classified as premenopausal if they had regular menstrual periods during the previous year. We excluded persons who were pregnant, postmenopausal, or had irregular menses, Cushing's syndrome, or polycystic ovarian disease (n = 148). We also excluded those with an eating disorder defined using the DSM IV criteria (9) and those taking oral hypoglycemic agents, insulin, or thyroid, lipid-lowering, antihypertensive, or estrogen agents (n = 38). In all, 117 women were selected, but 16 refused to participate in the study, so 101 participants were finally recruited.
The degree of obesity was assessed using BMI, waist circumference (WC), and waist to hip ratio (WHR). Height and body weight were measured using a digital scale, with the examinee wearing a light gown. WC was measured by a single well-trained examiner to the nearest 0.1 cm with a tape measure at the midpoint between the lower costal margin and iliac crest. During WC measurement, the examinees were asked to relax and exhale, while standing with their feet 25 to 30 cm apart. The examiner was careful not to put pressure on the soft tissues by pulling the tape measure too tightly and kept it parallel to the floor. Total body fat percentage was assessed using a body fat impedance analyzer (Inbody; Biospace, Seoul, Korea). The examinees were asked not to eat or drink for 2 hours beforehand. Abdominal fat was assessed from computed tomography scans (Somatom Plus 4; Siemens, Forchheim, Germany) taken at the level between the fourth and fifth lumbar vertebral bodies. Abdominal fat was defined as the area corresponding to the pixel range from 190 to 30 Hounsfield units (10). VAT and SAT were measured. Fat inside the peritoneum was considered VAT, and that between the dermis and muscle fascia was considered SAT. From these two measures, the ratio of VAT to SAT (VSR) was calculated.
Depressive symptoms were assessed using Zung's Self-Rating Depression Scale (SDS), which consists of 20 items. Each item is rated on a four-point frequency scale ranging from "none or a little of the time" to "most or all of the time" (11,12). The Korean version of Zung's SDS has been shown to be reliable and valid (13).
Nutrition and Exercise Capacity
A trained hospital nutritionist interviewed all of the subjects using the 24-hour recall method to get information on total calorie intake, the proportions of lipids and carbohydrates, minerals, and dietary habits.
Maximal oxygen uptake (VO2max) was obtained by having the subjects exercise on a cycle ergometer (TKK3070 Active 10; Takei Co., Tokyo, Japan) for 13 minutes at gradually increasing intensity to a submaximal heart rate. During this time, the heartbeat was monitored, and built-in software calculated the anticipated VO2max. Back muscle strength was assessed using a muscle strength scale (TKK 5102; Takei Co.). Before the test, the length from the handle to the footplate was adjusted so that the subject could reach the handle when she bent her back by 30ฐ in the standing position with her heels together and toes 15 cm apart. In this position, the subject was asked to extend her back gradually, increasing her strength. The back muscle strength was measured twice in kilograms. Muscle endurance was assessed using the number of sit-ups done in 30 seconds on a sit-up board (TKK5102; Takei Co.), with the subject's knees flexed and both hands holding the neck. A forward bending board (TKK 5103; Takei Co.) was used to examine flexibility. The subjects were asked to bend forward with their knees extended and fingers downward. This action was done twice, and the maximum distance reached by the fingertips was measured in centimeters.
Because the SDS scores were positively skewed, we transformed this score using the natural logarithm before the analyses to improve the approximation to a Gaussian distribution. Pearson's correlation coefficient was used to examine the relationship between the natural logarithmic transformation [(ln)]SDS score and anthropometric measurements, including WC, BMI, WHR, total body fat percentage, VAT, and SAT. Multiple stepwise linear regression analyses were used to evaluate the relationships between (ln)SDS score and abdominal fat distribution (VAT, SAT, and VSR) after adjusting for possible confounding variables, such as diet and exercise capacity. Backward stepwise methods were used in the process of model selection. The p value of the covariates was set at 0.10 for removal in the linear regression model.
The model analyzed age, (ln)SDS score, and education level as continuous variables. Income level was obtained from the average monthly household income in Korean currency (10,000 won units). Pack-years of smoking were calculated and analyzed as a continuous variable. Daily alcohol consumption was calculated from the weekly or monthly frequency and amount of alcohol intake. Marital status was dichotomized: married or unmarried. Occupation was also dichotomized: housewife or salaried worker. Because BMI and WC are too highly correlated (r = 0.800, p 0.001) to be included in one model together, separate models were used. The level of significance was chosen as p 0.05.
The study subjects were overweight women with a BMI 25 kg/m2. Most of them had 12 years of education (94.1%), and more than one-half of them were salaried workers (61.4%). The smoking rate, including ex-smokers, was 19.8%. The proportion of study subjects that consumed alcohol was 53.5%, but most were light drinkers (15 g/d; Table 1). After log-transformation, (ln)SDS score had a Gaussian distribution (Kolmogorov-Smirnov method, p 0.01, data not shown). The areas of SAT and VAT also showed Gaussian distributions (Kolmogorov-Smirnov method, p = 0.200 and 0.074, respectively).
In the multiple linear regression analyses (Table 2), BMI (p 0.001) and WC (p = 0.001) had the most significant associations with VAT, followed in order by age (p 0.001) and depression (p = 0.003 in model I; p = 0.003 in model II). VO2max was also a significant parameter and was inversely associated with the amount of VAT.
BMI (p 0.001) and WC (p 0.001) were both significantly associated with SAT, as they were with VAT (Table 2). Age was negatively associated with SAT (p = 0.001 in model III; p 0.001 in model IV). Physical activity was not associated with SAT in either model III (p = 0.067) or model IV (p = 0.075). The (ln)SDS score was not associated with SAT (p = 0.826 in model III; p = 0.398 in model IV).
VSR was associated with age (p 0.001) and (ln)SDS score (p 0.001;Table 2).
Meta-analyses have shown that depressed persons have a relative risk of 1.64 (95% confidence interval = 1.29 to 2.08) for coronary heart disease compared with nondepressed ones (14). Several cohort studies have reported similar relative risk values of 1.023.36 (15,16,17). It remains unclear how depressive symptoms affect coronary heart disease and other complications, although several explanations have been put forward. Depression is linked to type 2 diabetes (18,19), hypertension (20), and low high-density lipoprotein-cholesterol (21), all of which are major risk factors for coronary heart disease. Moreover, a patient with depression tends to have poorer health behavior, e.g., they are less active (22).
After adjusting for these variables, it has been reported that depressive mood increases the risk of coronary heart disease independently (23,24). This suggests that there is another mechanism that explains the association between depressive mood and coronary heart disease. Our analyses revealed that depressive mood had a positive association with VAT but no significant association with SAT. Excess VAT is now regarded as an important risk factor for coronary heart disease and related metabolic complications (25,26). Therefore, this association between depressive mood and VAT could help to explain the relationship between depressive mood and coronary heart disease. Several interesting studies have reported that patients with depression have increased levels of cytokines, such as interleukin-6 (27,28), which are important mediators of coronary heart disease. The increased interleukin-6 was explained as resulting from increased visceral adiposity (29). Although this explanation is not the full mechanism for the causal effect of depression on coronary heart disease, it supports this relationship.
The underlying mechanisms explaining the association between depressive mood and obesity are unclear. Some have proposed that hypothalamic-pituitary-adrenal (HPA) axis dysregulation evokes this association (30,31), but evidence supporting this explanation is unclear and controversial. There is also some epidemiological evidence supporting the relationship between depressive mood and obesity. Barefoot et al. (32) and others (33,34) have shown in large-scale longitudinal cohort studies that depressed persons are at increased risk of obesity. These epidemiological findings imply that depression triggers visceral obesity, even though the mechanism is not clear. Further studies are needed to elucidate this association.
We limited our study subjects to premenopausal women. It is well known that visceral adiposity increases with age. Premenopausal women tend to develop central obesity dominated by subcutaneous fat, whereas postmenopausal women have visceral fat predominantly. These changes are thought to result from the fact that muscle mass decreases and hormonal status changes to the android type with age (35,36). Previous studies examined older men or postmenopausal women. Because aging increases the chance of having both central obesity and depression (37), associations between these two variables may have been confounded by age and status of menopause in those studies. Therefore, we restricted our study subjects to premenopausal women.
Our study has some limitations. Although it showed that depressed overweight women had increased visceral adiposity, the cross-sectional design of this study limited its ability to establish a causal relationship. Another limitation was that we could not conduct detailed analyses using the outcome variable of major depression defined by DSM. The reported prevalence of major depression is 3% in Korea (38), and we could find only four subjects with major depression. Therefore, we could not conduct this kind of analysis because of lack of enough statistical power. These limitations necessitate further studies of this issue to understand the causal relationships more clearly.
1 Nonstandard abbreviations: VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue; WC, waist circumference; WHR, waist to hip ratio; VSR, ratio of VAT to SAT; SDS, Self-Rating Depression Scale;VO2max, maximal oxygen uptake; (ln), natural logarithmic transformation.
Topof pageHernandez-Ono, A., Monter-Carreola, G., Zamora-Gonzalez, J., et al (2002) Association of visceral fat with coronary risk factors in a population-based sample of postmenopausal women. Int J Obes Relat Metab Disord 26: 3339. | Article |
1. Chan, J. M., Rimm, E. B., Colditz, G. A., Stampfer, M. J., Willett, W. C. (1994) Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 17: 961969. | Article | PubMed | ISI | ChemPort |
2. Rosmond, R., Lapidus, L., Marin, P., Bj๖rntorp, P. (1996) Mental distress, obesity and body fat distribution in middle-aged men.Obes Res 4: 245252. | PubMed |
3. Istvan, J., Zavela, K., Weidner, G. (1992) Body weight and psychological distress in the NHANES I. Int J Obes Relat Metab Disord 16: 9991003. | PubMed | ChemPort |
4. Roberts, R. E., Kaplan, G. A., Shema, S. J., Strawbridge, W. J. (2000) Are the obese at greater risk for depression? Am J Epidemiol 152: 163170. | Article | PubMed | ISI | ChemPort |
5. Rไikk๖nen, K., Matthews, K. A., Kuller, L. H. (1999) Anthropometric and psychosocial determinants of visceral obesity in healthy postmenopausal women. Int J Obes Relat Metab Disord 23: 775782. | Article | PubMed |
6. Rimm, E. B., Stampfer, M. J., Giovannucci, E., et al (1995) Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Am J Epidemiol 141: 11171127. | PubMed | ISI | ChemPort |
7. Prineas, R. J., Folsom, A. R., Kaye, S. A. (1993) Central adiposity and increased risk of coronary artery disease mortality in older women. Ann Epidemiol 3: 3541. | PubMed | ChemPort |
8. American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders 4th ed. American Psychiatric Association Washington, DC.
9. Tokunaga, K., Matsuzawa, Y., Ishikawa, K., Tarui, S. (1983) A novel technique for the determination of body fat by computed tomography. Int J Obes Relat Metab Disord 7: 437445.
10. Zung, W. (1965) A self-rating depression scale. Arch Gen Psychiatry 12: 6370. | PubMed | ChemPort |
11. Zung, W. (1986) Self-rating depression scale, and depression status inventory In: Satorious, N Ban, T eds.. Assessment of Depression 221231. Springer-Verlag New York.
12. Shin, H. C., Kim, C. H., Park, Y. W., et al (2000) Continuous variable for all analyses. Validity of Zung's self-rating depression scale: detection of depression in primary care. J Korean Acad Fam Med 21: 131703.
13. Rugulies, R. (2002) Depression as a predictor for coronary heart disease: a review and meta-analysis. Am J Prev Med 23: 5161. | Article | PubMed | ISI |
14. Ariyo, A. A., Haan, M., Tangen, C. M., et al (2000) Depressive symptoms and risks of coronary heart disease and mortality in elderly Americans. Cardiovascular Health Study Collaborative Research Group. Circulation 102: 17731779. | PubMed | ISI | ChemPort |
15. Pratt, L. A., Ford, D. E., Crum, R. M., Armenian, H. K., Gallo, J. J., Eaton, W. W. (1996) Depression, psychotropic medication, and risk of myocardial infarction. Prospective data from the Baltimore ECA follow-up. Circulation 94: 3123
Show hospitality to strangers for, by doing that, some have entertained angels unawares.
Never stop helping others because others think that they abuse you. That stranger can be Jesus and you lose the opportunity to serve, even in something simple.