Metabolic syndrome and depression: evidence from a cross-sectional study of real-world data in Japan.

BACKGROUND: Both metabolic syndrome (MetS) and depression are high priority health problems, especially for working age. Numerous studies have explored the link between metabolic syndrome and depression; however, not all of them have consistently demonstrated an association. The objective of this study was to determine whether there is an association between MetS and depression by analyzing extensive real-world data (RWD). METHODS: Our data was drawn from insurance claims and health checkups of local government officials across all prefectures in Japan except for Tokyo in the 2019 fiscal year. According to the number of months with diagnosis of depression and prescription of antidepressants, the study participants were classified into the following categories: Certainly not Depression (CN), Possibly not Depression (PN), Possible Depression (PD), and Certain Depression (CD). Associations between MetS and its components-visceral obesity, hypertension, hyperlipidemia, and diabetes- and these categories of depression were analyzed by logistic regression. RESULTS: The depression categories of the 130,059 participants were as follows: CN 85.2%; PN 6.9%; PD 3.9%; and CD 4.1%. For men, the adjusted odds ratio (AOR) for MetS were PN 0.94 (95% CI: 0.86-1.02), PD 1.31 (1.19-1.43), and CD 1.63 (1.50-1.76), with reference to CN. For women, AOR of MetS were PN 1.10 (0.91-1.32), PD 1.54 (1.24-1.91), and CD 2.24 (1.81-2.78). Among the MetS components, visceral obesity, hyperlipidemia, and diabetes were significantly associated with depression categories. CONCLUSIONS: In this study, we found a significant association between MetS and depression, this association being similar to that previously reported. Our findings provide robust evidence for linkage between MetS and depression, suggesting that analysis of RWD is useful for providing concrete evidence.

Quantitative Definition of Low-Health-Interest Populations by Using Regression Trees: A Nationwide Internet Survey in Japan.

BACKGROUND: Reducing health disparities is a public health issue. Identification of low-health-interest populations is important, but a definition of people with low health interest has not yet been established. We aimed to quantitatively define low-health-interest populations. METHODS: A nationwide cross-sectional internet survey was conducted in 2022. We compiled regression tree (RT) analyses with/without adjustment for age, sex, and socioeconomic status with the 12-item Interest in Health Scale (IHS, score range 12-48) as an explanatory variable and the 10 composite health behaviors as a dependent variable. We defined the first IHS branching condition from the root node as a lower-health-interest group and the terminal node with the lowest health behaviors as the lowest-health-interest group. RESULTS: The mean IHS value of 22,263 analyzed participants was 32.1 ± 5.6; it was higher in females and in those who were aged over 45 years, had a high education, a high income, or a spouse. The first branching condition was IHS 31.5, and the terminal node branched at 24.5, before/after adjustment for covariates. CONCLUSIONS: We determined the cutoff values of the IHS as <32 for a lower-health-interest group and <25 for the lowest-health-interest group. Using these cutoffs might enable us to reveal the characteristics of low-health-interest populations.