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Thareja, Garvita
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Middle Tennessee State University
Approximately 7.1% of individuals are affected by depression in North America annually. Depression is a severe form of mental illness characterized by upset mood, loss of self-esteem, sadness, lack of interest in performing daily activities and lack of pleasure. Severe depression can lead to functional limitations and even mortality. Conversely, timely diagnosis can lead to early intervention and longevity. Although prior research has documented that depression is a major public health problem, few studies have examined the association between metabolic syndrome and depression from a preventive perspective. Of the limited studies, there are unclear results pertaining to the direction of the relationship. In addition, there is a paucity of information on individuals living with chronic illnesses, such as osteoarthritis. Moreover, meta-analyses conducted in the past utilized limited demographic characteristics of the participants. Also, nationally representative studies rarely have examined disparities in demographics (e.g., marital status, education) using a prospective research design. To our knowledge, no studies have considered social isolation while measuring depression among individuals with osteoarthritis. Such descriptive research can provide a foundation for future analytical studies. Therefore, the purpose of this study was to address the mentioned gaps in the literature by using nationally and publically available data to conduct a meta-analysis and use Generalizing Estimating Equations (GEE) to examine the association between metabolic syndrome and depression. In order to conduct the meta-analysis, we searched various databases such as PsycINFO, MEDLINE Complete, CINAHL Complete, ScienceDirect, SportDiscuss, and JEWL from the year 1999-2019. Search criteria and keywords such as “Metabolic Syndrome”, “Syndrome x”, “insulin resistance syndrome”, “depression”, and a combination of these keywords after the equivalent-subject expander were used to identify potentially relevant terms. Using inclusion criteria, the initial search was narrowed down to thirteen articles. For the meta analysis, comprehensive meta-analysis (CMA) software was used. For the Generalizing Estimating Equations (GEE), data from the Osteoarthritis Initiative (OAI), an existing National Institute of Health (NIH) database, were used. Participants (N = 2643) had severe osteoarthritis or were at risk of developing it. The majority of included participants were White (85.9%) and women (57.3%) with an average age of 60 years. Metabolic syndrome was measured using confirmatory factor analysis (CFA) of systolic blood pressure, diabetes, BMI, and waist circumference variables that were available in the OAI database. Depression was measured using the Center for Epidemiological Scale for depression (CES-D) from the OAI database. Data were measured at baseline, 24 months, 72 months, and at 96 months. Associations were adjusted for age, race, gender, education, marital status, social isolation, baseline depression, and time between visits. SPSS (v25) was utilized to conduct the GEE analysis. Results from the meta-analysis indicate that there is a significant association between METS and depression. In total, 17 effect sizes were calculated from 13 studies. Statistical heterogeneity between studies was not significant (I2 = 38.71%, df = 16 and p=.053), indicating that studies were compatible. Due to insignificant heterogeneity, a fixed-effect model was used. Individuals who live with METS are 1.14 times more likely to have depression when compared with individuals without METS. . Results from the GEE analysis indicated that there is no significant association between METS and depression among individuals living with osteoarthritis across time (B = .285, p = .302). The overall model intercept was significant (B = 4.24, p = .000). Among other predictors, education, marital status, social isolation, baseline depression, and time (years) between the visits were found to be significantly associated with depression. When controlling all other significant predictors, positive associations with depression over time were found for marital status (B = .280, p = .017), baseline depression (B = .653, p = .000), and years (B = .333, p = .000). Negative associations with depression over time were found for education (B = -.206, p = .009) and social isolation (B = -.1.469, p = .000). Results from both meta-analysis and GEE provide a foundation for future research and have practical implications for the treatment of depression. Clinicians should note the prevalence of depression and consider these issues in treatment, planning, and implementation.