Using nationally representative data collected in the United States, we examined the complex relationships between depression (MDD), weight status outcomes, SES, food insecurity, demographic and lifestyle factors including physical activity and dietary intakes using both linear regression and structural equation models. We observed several key findings: First, we found a different association between weight status outcomes and depression in young US women and men. BMI and depression were associated only among women but not among men, which is consistent with some previous studies (Carpenter et al., 2000
; Dragan and Akhtar-Danesh, 2007
; Heo et al., 2006
; Istvan et al., 1992
; Onyike et al., 2003
), although some other studies found no gender difference in the BMI-depression association (Herva et al., 2006
; Simon et al., 2006
). In general, we found that MDD was associated with higher BMI and with morbid obesity (OR=2.2 to 2.9) among young U.S. women compared to their non-depressed counterparts. Several recent studies have also replicated those findings (Carpenter et al., 2000
; Dragan and Akhtar-Danesh, 2007
; Istvan et al., 1992
; Onyike et al., 2003
; Simon et al., 2006
), though others found no or an inverse significant association particularly among older adults (Kuriyama et al., 2006
; Li et al., 2004
) . In contrast, we found an inverse association between MDD and overweight and morbid obesity among men (OR=0.2 to 0.4). Our findings and those of others should be carefully interpreted in light of the fact that loss of or enhanced appetite are possible symptoms inherent in the definition of depression in most of the scales and diagnostic tools used including CIDI (American Psychiatric Association, 1994
; de Wit et al., 2009
Second, our multivariate linear regression models indicated that BMI was inversely related to SES (mainly income) among women but not among men, independently of MDD. Moreover, MDD was associated with both higher BMI and lower PA, among women, independently of SES factors. This finding has been replicated elsewhere (Dragan and Akhtar-Danesh, 2007
). Improved dietary quality, as measured by HEI, was not associated with MDD independently of SES among both men and women. In multivariate logistic regression models, MDD was not significantly associated with overweight or obesity but only with morbid obesity among women. Gender, but not SES, moderated the MDD-binary adiposity associations.
Third, our SEM models indicate that lower food insecurity, lack of MDD and higher PA were one mechanism by which higher SES was associated with lower BMI, particularly among women. The direct effect of MDD on BMI was not significant, indicating that PA is the main mediator that explained the association between MDD and adiposity among women as well as NH blacks. Among men, NH white and NH black subjects, SES had no direct association with BMI. However, MDD among men (as was the case among women and NH blacks) was related to lower PA which in turn was associated with reduced BMI. In addition, the direct association of MDD with BMI among men was a marginally inverse one (p<0.10). For either gender, SEM models did not indicate that dietary quality was a significant mediator between MDD and BMI. Stratified analysis also indicated that MDD and HEI were inversely associated independently of SES and food insecurity among NH whites and among women.
Finally, another important finding is the lower prevalence of MDD among minority ethnic groups compared to NH White, despite the inverse association between SES and MDD and the lower SES experienced in minority ethnicities. This may suggest that NH whites are more likely to elicit depressive symptoms than other ethnic groups, independently of SES factors, though another possible explanation is that NH White may have better access to medical services increasing their chance for MDD diagnosis.
Several biological mechanisms have been suggested to explain the association between obesity and depression. For example, leptin resistance may contribute to alterations of affective status. Leptin resistance could occur at several levels, including impaired transport of leptin across the blood–brain barrier, reduced function of the leptin receptor, and defects in leptin signal transduction (Lu, 2007
; Munzberg and Myers, 2005
). This would give rise to a causal pathway in which depression is directly caused by leptin resistance which in turns alters appetite and in turn increasing the risk of obesity. Another suggested mechanism is hypercortisolemia which is associated with stress and depression, and in turn was shown to be associated with greater fat deposits (particularly in the abdominal region) and with the metabolic syndrome (Vogelzangs et al., 2007
; Weber-Hamann et al., 2002
; Young, 2004
). Finally, depressed subjects are often prescribed anti-depressant medication which enhances appetite and thus, overconsumption of food (Fava et al., 2005
These alternative mechanisms might explain at least part of the associations we detected between morbid obesity and MDD among women in logistic regression models and the inverse relationship between MDD and HEI observed in Whites and in women in SEM models. However, PA had a major mediating role in the positive association between MDD and BMI among women and NH blacks, indicating that sedentary behaviors resulting from elevated depressive symptoms may be explaining higher adiposity in those two groups. It is worth noting that the association between mood and obesity may apply to other disorders particularly bipolar depression (Alciati et al., 2007
; Hasler et al., 2004
; McElroy et al., 2004
; Pickering et al., 2007
The present study has several strengths. First, the sample was nationally representative of young adults in the United States. Second, DSM-IV criteria were applied to obtain diagnosis of major depressive disorder which is the gold standard and allows for better comparability with previous literature. Third, stratified analysis was carried out to identify gender and ethnic differences in patterns of association. Fourth, this is one of the very few attempts to study the association between BMI and depressive symptoms using SEM models (Dragan and Akhtar-Danesh, 2007
; Stunkard et al., 2003
) and the first to test pathways linking depression to SES, lifestyle factors and BMI outcomes. Finally, we used a recently released USDA overall dietary quality measure that reflects several aspects of healthy eating based on the new 2005 Dietary Guidelines (U.S. Department of Agriculture (USDA), 2005
). In addition, NHANES data allowed us to study PA as well as dietary intake and quality, in particular, using self-reported measures both simple ones and ones computed using METs×hrs/week for PA (Cheng et al., 2007
; Lagerros and Lagiou, 2007
Limitations of our study included its cross-sectional nature, which impedes ascertainment of causality. Second, depression data was only collected for a relatively small sample of young US adults aged 20–39 years. This limited stratified analyses by ethnicity, age, or SES groups to detect effect modification by these variables. Third, measurement error in study participants’ self-reported dietary intake and PA would be of concern. Random errors may bias the findings toward the null while systematic measurement errors could bias the results toward either direction depending on the nature of the errors. Fourth, the HEI is a reflection of overall dietary quality though some of its components and related dietary/nutrient intake patterns may have a greater weight than others in explaining the MDD-HEI association. In fact, a sensitivity analysis correlating HEI with various food groups and nutrients showed that HEI was most strongly associated with higher total fruit (r=0.47), lower added sugar (r=−0.36), fiber (r=0.36), and vitamin C (r=0.31) intakes. Fifth, many of the SEM path coefficients were between non-continuous variables and thus interpretability of their values was less evident. However, their statistical significance and the direction of the association were of main interest in this study. Sixth, some of the findings using our regression analysis models were discrepant from those in SEM models. This may be due to adjustment for sampling design complexity in the former but not in the latter models. Finally, while CIDI is an improvement over other measures of depression, it has its limitations and validity of lay interviews may be constrained by the amount of probing.
In conclusion, our study suggested major gender differences in the pathways linking SES, MDD (indicates depression) and lifestyle factors to body weight outcomes. Hence, SES, mental health and lifestyle interventions among men and women may have different effects on their adiposity. Among both genders and among NH blacks, however, PA seems to play a major role in explaining the relationship between MDD and BMI, which indicate the potential effectiveness of PA interventions among depressed subjects in reducing the risk of obesity. Moreover, our finding that a large part of the effect of SES on MDD is mediated by food insecurity (specifically among women and among “other ethnic groups”) suggests that availability and access to food assistance programs may help reduce MDD risk that is associated with low SES. More research is needed to clarify the causal relationship between MDD and lifestyle factors, especially PA, and how they interact with SES and other demographic factors in affecting obesity. The mechanism by which depression and adiposity may affect each other must be studied in depth using advanced statistical models and physiological markers that would help explain reduced PA and the tendency to have poorer quality diets – which may be direct outcomes of self-neglect and fatigue associated with depression -- among depressed women with morbid obesity. A good understanding of the pathways between MDD and adiposity will help develop effective future interventions.