Globally, the prevalence of depression and obesity are important public health concerns, with an estimated 350 and 500 million people suffering, respectively. A recent meta-analysis of 17 community-based cross-sectional studies among adults showed a positive overall association between depression and obesity. Depression and obesity are disorders of stress with dysregulation of the stress management system [Figure 1]. Obesity is a systemic disorder, leading to increase in cortisol, leptin, insulin levels resulting in hypothalamic pituitary adrenal axis dysregulation, and insulin resistance, which can further induce inflammation and worsen depression. There is a dose-dependent relationship between the both, that is, with higher body mass index (BMI) the risk of clinical depression increases.
Obesity and depression run in the vicious cycle [Figure 2]. When an individual is in a depressed mood state, it is also associated with anhedonia and sedentary lifestyle along with dietary changes. On the other hand, obese patients have higher levels of leptin hormone, which is known to cause clinical depression. Depression and obesity are both associated with social stigma and low self-esteem. The adverse health and social consequences are significant when depression and obesity cooccur. Symptoms of depression in obese patients are strongly associated with poor quality of life, particularly social functioning and mental health. Further, obesity coupled with depression has significant economic implications, owing to reduced participation in the labor force and absenteeism.
There are limited data on the prevalence of comorbid obesity and depression in the general practice setting. The frequency with which comorbid depression and obesity occur is important because of the implications for its detection and management. For example, some somatic criteria for the diagnosis of depression, such as sleep problems, fatigue, or changes in appetite, could be confounded with manifestations of obesity. Depressed people with overweight are difficult to manage; however, little decrease in their body weight had remarkably helped improving their depression. While interventions for weight management, such as exercise and lifestyle modification may also be helpful in managing depression, there are no set protocols for providing integrated treatment of these health conditions.
This study aimed to examine the risk of depression among overweight and obese individuals in a large station of Armed Forces and associate depression with other risk factors.
MATERIALS AND METHODS
This is a cross-sectional (prospective) descriptive study conducted in the general outpatient department (OPD) of a large station of Armed Forces.
The study population was overweight and obese personnel registered in polyclinic (calculated according to the BMI) who were ≥18 years of age and who fulfilled both inclusion criteria. Inclusion criteria were all personnel whose BMI was ≥25 and who were willing to participate in the study. From a previous study, the prevalence of depression among obese participants was 23%, which sample size was calculated to be 106 taking the absolute error of margin of 8% with 95% confidence interval (CI).
After taking their informed consent, data were collected by self-administered/interviewer assisted (in case of insufficient ability) Patient Health Questionnaire (PHQ-9) to assess the risk of depression. The PHQ-9 has been extensively used by medical personnel in the primary care setting. A total score of ≥10 to classify depression is considered the best balance between sensitivity (88%) and specificity (88%) compared to mental health professional assessment, and this cutoff value was used for this study.
Self-administered data were obtained for age category, rank, service years, education status, marital status, status of living with family, past field postings, and habit of drinking alcohol.
Body mass index
Participants were asked to report their weight to the nearest kilogram and their height to the nearest centimeter. BMI was calculated, and World Health Organization definitions applied to categories overweight (25–29.9 kg/m2) and obese (>30 kg/m2). Overweight and obese individuals were included in the study.
The PHQ-9 was used to assess the risk of depression. It is scored on a 27-point scale. Severity of risk of depression was graded according to the following scores: (0–4) – no risk; score (5–10) – mild risk; score (10–14) – moderate risk; and score (>15) – severe risk of depression.
Data entry and statistical analysis were conducted using the Statistical Package for the Social Sciences program version 21 (IBM Corporation, Chichago USA). Descriptive analysis was performed including frequencies, percentages, and confidence intervals. Binary logistic regression model was used to examine association between predictor variables (as captured by data capturing form) and risk of depression. The main model consists of the following predictor variables: demographic variables such as age, service years, rank, education, marital status, living status, and habit of drinking alcohol. Results were expressed as odds ratio (OR) and 95% CIs. In this study, the level of significance was set at P < 0.05 for all the analyses.
A total of 106 study population including 71 (67%) overweight, and 35 (33%) obese were evaluated. The mean age ± standard deviation of patients was 37.73 ± 9.3 years with a minimum age of 25 years and maximum age of 56 years. Among the study population, 82 (77.4%) were of the rank sergeant and below and 24 (22.6%) were warrant officers and above with mean service years of approximately 18 years. More than half of the study population was graduates (75.5%). Almost 80% of individuals were married, and 74.5% of them were living with family. A majority of the respondents (63%) had the habit of drinking alcohol. Among the study population, 67% of them were overweight, and 33% of them were obese with a mean BMI of 27.9 kg/m2 [Table 1].
As per this study, of the individuals assessed, 13 (12%) individuals fulfilled the criteria for risk of moderate depression, 58 (54%) for mild depression, and 35 (33%) individuals had no risk for clinically significant depression. Thus almost half of individuals with overweight or obese were ailing from the risk of mild-to-moderate depression. There was no individual with risk of severe depression. This study is in concurrence with a previous study which found overweight increased the risk of onset of depression. The prevalence of depression in overweight and obese individuals can be associated with factors such as stigmatization, low self-esteem, and body dissatisfaction which can contribute to or exacerbate depressive illness. Conversely, features of depression may also cause or exacerbate weight problems and obesity.
On assessing the individuals whether they had any difficulty in job, about 53% of individuals had some difficulty in job, about 20% of them had moderate difficulty in job, and about 25% of them had no difficulty in job [Table 2].
The likelihood of depression was most strongly associated with BMI (OR: 6.33; 95% CI: 2.94–13.64; P < 0.001) followed by age (OR: 1.12; 95% CI: 1.08–3.7; P = 0.008), living single (OR: 1.31; 95% CI: 1.06–1.54; P = 0.004), and the habit of drinking alcohol (OR: 1.24; 95% CI: 1.04–1.64; P = 0.041). There was a positive association between weight gain and depression in a previous study also. Advancing age was associated with depression which was also shown in earlier studies.
Obesity and depressive disorders are common morbidities with overlapping pathophysiology whose coexistence is associated with adverse health outcomes. In fact, many overweight/obese patients exhibited weight loss after the treatment of depression.
This study found that large number (54%) of obese patients had a risk of mild depression, and few (12%) had a risk of moderate depression. As per recent literature, the individuals with risk of mild depression to be treated in community setting itself with lifestyle modification, dietary advice, and involvement of pleasure-seeking activities. Whereas individuals with risk of moderate depression need to be referred to psychological counselor for further investigation and counseling sessions.
The data regarding the prevalence of depression in patients with overweight and obese individuals from India are scarce. However, a previous study from the USA reported that obese were significantly more likely to be depressed than those who were either normal or underweight.
Regression analysis from the previous studies identified more educated, unmarried group of individuals with worse mental health. However, the likelihood of depression was not significant with education status and marital status in this study. This study also showed no significant association with service years, rank, and field postings. The most likely explanation for these disparate results relates to differences in the populations studied and the procedures used, particularly the measures of depression.
Exploration into the most effective ways of discovering the potential comorbidity of obesity and depression will help to assist in developing interventional approaches to effectively target these conditions simultaneously or sequentially.
The PHQ-9 has been shown to have high sensitivity and specificity in the general practice setting. However, it is a screening tool and does not provide a clinical diagnosis of depression. Therefore, there is likely to be some degree of measurement error in the rates of depression reported for each BMI category. Cross-sectional nature of this study precluded an examination of the alternate hypothesis that depression increases the risk for obesity.
It is recommended that every individual with overweight/obesity should once be screened by the PHQ-9 questionnaire by the authorized medical attendant at the OPD level and should be analyzed for the risk of depression. Individuals who are detected to have moderate-to-severe risk of depression should not only undergo lifestyle modifications but also be enrolled in a combined comprehensive obesity and depression control program, which will include the systemic series of counseling sessions by a trained psychological counselor and should be followed up till the individual is benefited. The benefit of interpersonal and psychotherapy may also be given by referring them to psychiatry centers. This will improve not only the overall functional status including mental, emotional, and social well-being but also their health perception and quality of life. It will also prevent cognitive dysfunction, increased days of work absence, early retirement, higher dependence on disability welfare and correspondingly, and improvement in depressive symptoms leading to increase in work productivity.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
1. World Health Organization. . Depression
Fact Sheet No. 369. 2012Last Accessed on 2018 Mar 30 Geneva World Health Organization Available from: http://wwwwhoint/mediacentre/factsheets/fs369/en/indexhtml
2. World Health Organization. . Obesity
Fact Sheet No. 311. 2013Last Accessed on 2018 Mar 30 Geneva World Health Organization http://wwwwhoint/mediacentre/factsheets/fs311/en/
3. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, et al Overweight
, and depression
: A systematic review and meta-analysis of longitudinal studies Arch Gen Psychiatry. 2010;67:220–9
4. Bornstein SR, Schuppenies A, Wong ML, Licinio J. Approaching the shared biology of obesity
: The stress axis as the locus of gene-environment interactions Mol Psychiatry. 2006;11:892–902
5. Hryhorczuk C, Sharma S, Fulton SE. Metabolic disturbances connecting obesity
Front Neurosci. 2013;7:177
6. Patten SB, Williams JV, Lavorato DH, Modgill G, Jetté N, Eliasziw M. Major depression
as a risk factor for chronic disease incidence: Longitudinal analyses in a general population cohort Gen Hosp Psychiatry. 2008;30:407–13
7. Dixon JB, Dixon ME, O'Brien PE. Depression
in association with severe obesity
: changes with weight loss Arch Intern Med. 2003;163:2058–65
8. Karakus MC. Obesity
with Comorbid Depression
and Early Retirement IHEA 2007 6th World Congress: Explorations in Health Economics Paper. 2007Last accessed on 2018 Mar 03 Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=992241
9. Markowitz S, Arent SM. Understanding the relation between obesity
: Causal mechanisms and implications for treatment Clin Psychol (New York). 2008;15:1–20
10. Jantaratnotai N, Mosikanon K, Lee Y, McIntyre RS. The interface of depression
Obes Res Clin Pract. 2017;11:1–10
11. Carey M, Small H, Yoong SL, Boyes A, Bisquera A, Sanson-Fisher R. Prevalence of comorbid depression
in general practice: A cross-sectional survey Br J Gen Pract. 2014;64:e122–7
12. Gilbody S, Richards D, Brealey S, Hewitt C. Screening for depression
in medical settings with the Patient Health Questionnaire (PHQ): A diagnostic meta-analysis J Gen Intern Med. 2007;22:1596–602
13. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression
severity measure J Gen Intern Med. 2001;16:606–13
14. World Health Organization. BMI Classification.Last accessed on 2018 Mar 03 Geneva World Health Organization Available from: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html
15. Noppa H, Hällström T. Weight gain in adulthood in relation to socioeconomic factors, mental illness and personality traits: A prospective study of middle-aged women J Psychosom Res. 1981;25:83–9
16. Kendrick T, Dowrick C, McBride A, Howe A, Clarke P, Maisey S, et al Management of depression
in UK general practice in relation to scores on depression
severity questionnaires: analysis of medical record data BMJ. 2009;338:b750
17. Roberts RE, Kaplan GA, Shema SJ, Strawbridge WJ. Are the obese at greater risk for depression
? Am J Epidemiol. 2000;152:163–70
18. Reed DB. The relationship between obesity
and psychological general well-being in United States women Diss Abstr Int. 1985;46:3791