Multidimensional analysis of the association between metabolic abnormality combinations and depressive symptoms in overweight and obese populations: Cross-sectional evidence from two multinational cohorts.

OBJECTIVE

Metabolic diseases and psychiatric disorders comorbidity are increasingly becoming a hot topic of current research. This study aimed to explore the multidimensional associations and heterogeneity characteristics of metabolic abnormality combinations and depressive symptoms in overweight and obese populations.

METHODS

Based on data from two multinational cohort studies, the Health and Retirement Study (HRS) in the United States and the English Longitudinal Study of Ageing (ELSA) in the United Kingdom, a total of 11,288 participants with a BMI ≥ 25 kg/m were included. Symptom network models were used to analyze the direct associations between metabolic indicators and depressive symptoms. Linear regression was employed to quantify the dose-response effect of different metabolic abnormality combinations on depression scores. Interaction analysis was extracted to examine the interactions between diabetes, hypertension, and central obesity on depression scores. Logistic regression was implemented to analyze the relationships between specific depressive symptoms and metabolic abnormality combinations. Latent class analysis (LCA) was conducted to identify subtypes of overweight/obese individuals with depressive symptoms and their corresponding metabolic abnormalities.

RESULTS

Symptom network analysis revealed that 'felt depressed' was the core symptom node. Linear regression found the group associated with central obesity increased Center for Epidemiologic Studies Depression Scale-8 items (CES-D8) score, and the combination of 'diabetes+hypertension+central obesity' had the most significant impact on somatic symptoms. The interaction analysis found no significant multiplicative or additive interaction effects of the three metabolic diseases on depression scores. On specific depressive symptoms, 'Everything an effort' was significantly associated with multiple metabolic abnormality combinations. LCA identified five subtypes of depression, among which the 'diabetes+hypertension' combination was significantly related to the Action-Dysregulation Group, while the Anhedonia Group was primarily driven by central obesity.

CONCLUSION

The conclusion suggests that metabolic abnormalities affect depressive symptom dimensions through specific pathways, highlighting the need for targeted psychological intervention strategies based on metabolic characteristics.