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 The prevalence and public health implications of being overweight and obese constitute a national health priority. Most North American adults are overweight, and a rising proportion are categorically obese (1). MDD and BD are also highly prevalent chronic medical disorders with an estimated combined lifetime prevalence of 10% to 15% (2). Both MDD and BD are associated with an increased prevalence of somatic disorders (for example, diabetes mellitus and coronary artery disease) (3). Obesity and MDs are clinically and pathoetiologically heterogeneous (5). Over the past several decades, the lifetime prevalence for obesity and MDs has significantly increased in both children and adults (6). This rapid increase in the estimated lifetime prevalence for each chronic disorder offers epidemiologic support for shared environmental risks (7). Results from several cross-sectional and longitudinal studies indicate that obesity and MDs frequently cooccur (8). Moreover, several obesity-associated medical disorders may occur more frequently and manifest at a relatively earlier age in individuals with MDs (compared with the general population) (9,10). The association between obesity and MDs strengthens support for common moderating and mediating variables for both phenotypes (11). To our knowledge, there are no previous population-based investigations attempting to simultaneously estimate the prevalence of obesity in both BD and MDD. We hypothesized that indices of excess weight would be more prevalent in individuals screening positive for an MDE or an ME. We further aimed to examine factors that may moderate the obesity–MD covariation. MethodsThe CCHS 1.2 was conducted by Statistics Canada (www.statcan.ca/english/freepub/82-617-XIE). Respondents were residents of private dwellings. Dwellings were sampled with a multistage stratified cluster design. Most interviews (86%) were conducted in person, the remainder by telephone. The responding sample totaled 36 984 people aged 15 years or over. The response rate was 77%. The data were weighted to represent the household population in the 10 Canadian provinces in 2002. The survey collected information on lifetime and past 12-month prevalence of various mental disorders, using the WMH-CIDI. The WMH-CIDI was designed to be administered by lay interviewers and is generally based on diagnostic criteria outlined in the DSM-IV (1). (The questionnaire used for the CCHS is available at www.statcan.ca/English/concepts/heatlh/cycle1.2/index.htm). Details of the specific algorithms used to define MDD and BD are available in a published report (12). The survey collected information on self-reported height and weight, previously diagnosed medical disorders, access to and use of mental health care services, admissions to hospitals and use of medications, and disability associated with mental health. The survey also collected information on determinants and correlates of mental health, such as sociodemographic information, income, and level of education, and level of leisure time physical activity. According to DSM-IV-TR criteria, MD was defined as a lifetime occurrence of either an MDE or an ME. The prevalence of MD in the household population aged 15 years or over was estimated by appropriately weighting the sample proportion screening positive for MD. The association of MD with obesity was examined with weighted bivariate tabulations using BMI cutpoints established by the WHO (13). Sex-specific multivariate regression models were fitted to further examine the relation between obesity and MD while controlling for other demographic variables (for example, sex, age, education, income, marital status, level of leisure time physical activity, illicit drug dependency, alcohol dependency, functional dependency [that is, dependency on help from others to carry out activities of daily living], and hospitalization for a mental health problem within the past year). Further analyses explored the possible relations between MD and selected classes of psychotropic medication. All statistical analyses were performed with SAS statistical software, release 9.1. To account for the complex sampling design of the CCHS, coefficients of variation were calculated on estimates and significance of differences between estimates according to the bootstrap technique. The level of statistical significance was defined as P < 0.05. ResultsData from the CCHS indicate that 13% of Canadians aged 15 years or over screen positive for an MD (that is, symptoms consistent with an MDE or an ME) (Table 1). This proportion of positive screens extrapolates to about 3.3 million Canadians. The prevalence of MD among individuals who were categorically obese was higher in married individuals than in those who were single, divorced, separated, or widowed. MD also varied with income: individuals at the lowest level of income had nearly twice the likelihood (22%) of MD, compared with those at the highest level (12%).
Individuals screening positive for an MD were more likely to be obese than the general population: 19% of people with an MD were obese, compared with 15% without an MD (P < 0.05). The average BMI for the general population was 25.53. For individuals screening positive for MDE or ME, the average BMI was 25.96 (P < 0.001) (data not shown). In nearly all examined subpopulations, the likelihood of obesity was higher in individuals with lifetime MD (Table 1). Differences were especially pronounced in individuals at lower socioeconomic levels. For example, in the lowest level of income group, the prevalence of obesity was nearly 26% in individuals with lifetime MD, compared with 14% in those without MD. Sex-specific tabulations revealed that within all 3 obesity classes the prevalence of obesity was higher for female respondents with MD. In male respondents, however, no significant differences emerged in obesity with MD (Figures 1 and 2).
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To study the relation between MD and obesity while controlling for sociodemographics, we employed multiple logistic regression. In preliminary analysis, the OR differed significantly by sex, and consequently, separate models were fitted for the sexes. In male respondents, once we controlled for the effects of sociodemographic factors, chronic disease, alcohol dependency, illicit drug dependency, recent hospitalization, functional dependency, and physical activity level, the odds of obesity showed no significant association with MD (Table 2).
Significant associations emerged for level of education. Male respondents without a college or university education had higher odds of obesity, compared with postsecondary graduates. The odds of obesity were elevated in relation to prevalent chronic disease, but lowered in relation to illicit drug dependency in male respondents. As expected, moderate or intense physical activity was negatively related to obesity. In female respondents, the odds of obesity were modestly, but significantly, elevated in relation to the presence of an MD (95%CI, 1.0 to 1.5, OR 1.2) (Table 2). Associations between education and obesity, however, were less powerful in female respondents than in male respondents. Chronic disease and activities of daily living dependency were positively associated with obesity, while illicit drug dependency and physical activity were negatively associated with obesity. Further analyses studied the possible relation between psychotropic medication use and obesity. The CCHS asked respondents about their use of selected categories of medications in the past year, including hypnotics, anxiolytics, mood stabilizers, antidepressants, and antipsychotics. As indicated in Table 3, in individuals screening positive for lifetime MD, antidepressants, followed by anxiolytics, were used most commonly. Under 2% of individuals with MD reported using antipsychotic medication.
We examined use of each of these drug categories in relation to obesity in multiple logistic regression modelling. Only antipsychotics were significantly associated with obesity (P < 0.05). In female respondents, in the fully controlled model (as shown in Table 2), the odds of obesity in association with this class of drugs was 3.1 (95%CI,1.7 to 5.7, P < 0.05) (data not shown). In male respondents, the data suggested a similar relation (95%CI, 1.0 to 5.5, OR 2.3), with a trend toward statistical significance (P = 0.06). DiscussionThis is the first investigation evaluating excess weight among respondents in Canada who screen positive for an MD. Results indicate that female respondents who screen positive for MD have a significantly higher likelihood of being obese. The use of antipsychotic medications was also associated with obesity (14). A significant database documents similarities between obesity and MDs in phenomenology, comorbidity, family history, and pharmacologic treatment response (2,3). Recent reviews of this topic have concluded that MDs and obesity are separate but related disorders, with distinct but overlapping pathophysiologies. There are likely multiple affective disorder–obesity covariations in the population (5,6). For example, both conditions are more frequently reported in individuals with lower socioeconcomic status and lower levels of education (7–10). Neither the guidelines for the treatment of MDs nor those for the treatment of obesity disorders contain any specific recommendations for screening of the other condition. The presence of obesity in MD populations, notably in individuals with BD, is associated with a more severe illness, as indicated by more frequent affective episodes, greater severity of index presentation, higher recurrence vulnerability, comorbid medical disorders, and suicidal behaviour (11,15–17). This investigation was unable to address the association of obesity with illness course variables (2). This investigation has several methodologic factors that limit interpretations and inferences that can be drawn from it. The WMH-CIDI is a validated screening tool for psychopathology in population-based epidemiologic studies. It was devised for use in epidemiologic and transcultural fields, can be used by nonmedical staff, has a modular structure, and simultaneously gives diagnoses according to the ICD-10 and the DSM-IV-TR, covering 39 and 32 diagnoses respectively on Axis I (18). A clinical reappraisal study suggested that the WMH-CIDI was able to detect an ME characterized by euphoria, grandiosity and the ability to maintain energy without sleep; however, it was less sensitive at detecting irritable and dysphoric forms of mania (that is, mixed states) (19). This suggests that the WMH-CIDI may underestimate the overall prevalence of BD. The proportion of individuals screening positive for an MDE or an ME is commensurate with other recently conducted regional and crossnational studies with lifetime aggregate estimates of about 10% to 25% (20–23). Most respondents screening positive for an MD were not taking psychotropic medications. These results are consistent with medication compliance studies, which indicate that a relatively small proportion of diagnosed and treated MD patients will be compliant with medication for at least 1 year (24–27). Our data also indicate that MDs with comorbid obesity are more frequent in married individuals, compared with single, widowed, divorced, and separated individuals. This is inconsistent with other epidemiologic studies. We also report on the lifetime occurrence of an MD and current presence of obesity. We have no way of knowing whether the individuals screening positive for MD were currently affectively ill. Anthropometric indices were determined by self-report. In general, respondents, particularly women, tend to underestimate their actual weight and overestimate their height (28). This reporting bias suggests that our estimates of obesity may be conservative. Analyses were not adjusted for the potential presence of psychiatric disorders in the general population control group. Given the methodology of this study, we were unable to determine the directionality of the association between affective disorders and obesity (29–31). Although it is likely that the prevalence of mental disorders is higher in individuals who are institutionalized or homeless, the CCHS only collected information from the household-dwelling population. Therefore, estimates of prevalence can only be generalized to this population and probably underestimate the prevalence in the entire population. The extent to which such underascertainment may have biased the results of the analysis is unknown. While it is preferable to stratify respondents according to the elapsed interval since their last affective episode, it was not possible to do so with the CCHS data. We agree with other commentators, that clinicians should be encouraged to screen for indices of excess weight and associated morbidity (for example, diabetes mellitus) in patients (notably women) who screen positive for an affective disorder (33). Although not the focus of this investigation, other study results support routine screening of psychiatric disorders in individuals who are obese (32–35). There is a need to develop and test specific interventions for individuals with comorbid affective disorders and obesity. The field would also benefit from research attempting to ascertain the mediators and moderators of the association between MDs and excess weight (5). Funding and SupportThis research received no funding or support. References1. American Psychiatric Association. Practice guideline for the treatment of patients with major depressive disorder (Revision). Am J Psychiatry 2000;157(Suppl 4):1–45. 2. McElroy SL, Kotwal R., Malhotra S, Nelson EB, Keck PE, Nemeroff CB. Are mood disorders and obesity related? A review for the mental health professional. J Clin Psychiatry 2004;65:634–51. [quiz]. 3. Stunkard AJ, Faith MS, Allison, KC. Depression and obesity. Biol Psychiatry 2003;54:330–7. 4. McIntyre RS, Konarski JZ, Misener VL, Kennedy SH. Bipolar disorder and diabetes mellitus: epidemiology, etiology, and treatment implications. Ann Clin Psychiatry 2005;17:82–93. 5. Faith MS, Matz PE, Jorge MA. Obesity-depression associations in the population. J Psychosom Res 2002;53:935–42. 6. Dong M, Giles WH, Felitti V J, Dube SR, Williams JE, Chapman DP, and Anda RF. Insights into Causal Pathways for Ischemic Heart Disease: Adverse Childhood Experiences Study. Circulation 9-28-2004;110:1761–6. 7. Goodman E, Slap GB, Huang B. The public health impact of socioeconomic status on adolescent depression and obesity. Am J Public Health 2003;93:1844–50. 8. Faith MS, Manibay E, Kravitz M, Griffith J, Allison DB. Relative body weight and self-esteem among African Americans in four nationally representative samples. Obes Res 1998;6:430–7. 9. Stevens J, Kumanyika SK, Keil JE. Attitudes toward body size and dieting: differences between elderly black and white women. 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A 40-year perspective on the prevalence of depression: the Stirling County study. Arch Gen Psychiatry 2000;57:209–15. 22. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, and others. The epidemiology of major depressive disorder: results from the national comorbidity survey replication (NCS-R). JAMA 2003;289:3095–105. 23. Hirschfeld RM, Calabrese JR, Weissman MM, Reed M, Davies MA, Frye MA, and others. Screening for bipolar disorder in the community. J Clin Psychiatry 2003;64:53–9. 24. Johnson RE, McFarland BH. Lithium use and discontinuation in a health maintenance organization. Am J Psychiatry 1996;153:993–1000. 25. Keller MB, Hirschfeld RM, Demyttenaere K, Baldwin DS. Optimizing outcomes in depression: focus on antidepressant compliance. Int Clin Psychopharmacol 2002;17:265–71. 26. Masand PS. Tolerability and adherence issues in antidepressant therapy. Clin Ther 2003;25:2289–304. 27. Lin EH, Von Korff M, Ludman EJ, Rutter C, Bush TM, Simon GE, and others. Enhancing adherence to prevent depression relapse in primary care. Gen Hosp Psychiatry 2003;25:303–10. 28. Black DR, Taylor AM, Coster DC. Accuracy of self-reported body weight: stepped approach model component assessment. Health Educ Res 1998;13:301–7. 29. Hasler G, Pine DS, Gamma A, Milos G, Ajdacic V, Eich D, and others. The associations between psychopathology and being overweight: a 20-year prospective study. Psychol Med 2004;34:1047–57. 30. Sullivan M, Karlsson J, Sjostrom L, Backman L, Bengtsson C, Bouchard C, and others. Swedish obese subjects (SOS)—an intervention study of obesity. Baseline evaluation of health and psychosocial functioning in the first 1743 subjects examined. Int J Obes Relat Metab Disord. 1993;17:503–12. 31. Black DW, Goldstein RB, Mason EE. Prevalence of mental disorder in 88 morbidly obese bariatric clinic patients. Am J Psychiatry 1992;149:227–34. 32. Wadden TA, Stunkard AJ. Psychopathology and obesity. Ann NY Acad Sci 1987;499:55–65. 33. McElroy SL, Frye MA, Suppes T, Dhavale D, Keck PE Jr, Leverich GS, and others. Correlates of overweight and obesity in 644 patients with bipolar disorder. J Clin Psychiatry 2002;63:207–13. 34. Friedman MA, Brownell KD. Psychological correlates of obesity: moving to the next research generation. Psychol Bull 1995;117:3–20. 35. McIntyre RS, Mancini DA, Pearce MM, Silverstone P, Chue P, Misener VL, and others. Mood and psychotic disorders and type 2 diabetes: a metabolic triad. Can J Diabetes 2003;29:122–32. Author(s)Manuscript received October 2005, revised, and accepted January 2006. 1.Assistant Professor, Department of Psychiatry and Pharmacology, University of Toronto, Toronto, Ontario; Head, Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Ontario. 2.PhD Candidate, Institute of Medical Science, University of Toronto, Toronto, Ontario. 3.Senior Analyst, Health Statistics Division, Statistics Canada, Ottawa, Ontario. 4.Research Coordinator, Mood Disorders Psychopharmacology Unity, University Health Network, Toronto, Ontario. 5.Psychiatrist in Chief, Department of Psychiatry, University Health Network, Toronto Ontario; Professor, Department of Psychiatry, University of Toronto, Toronto, Ontario; Institute of Medical Science, University of Toronto, Toronto, Ontario. Address for correspondence: Dr RS McIntyre, Department of Psychiatry, University of Toronto, 399 Bathurst Street, Toronto, ON, M5T 2S8 e-mail: roger.mcintyre@uhn.on.ca
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