Multimorbidity Clusters and Depressive Symptom Networks in Old Age: Evidence From China.

OBJECTIVE: To identify patterns of chronic disease multimorbidity in older Chinese adults, examine variables associated with the link between multimorbidity and depression, and characterize depressive symptom network features. METHODS: We analyzed cross-sectional data from the China Health and Retirement Longitudinal Study including 2,117 adults aged ≥60 years. Fourteen physician-diagnosed chronic conditions were subjected to exploratory factor analysis to identify latent multimorbidity clusters. Logistic regression was used to examine correlates of depression. For each cluster, Mixed Graphical Models estimated partial correlations among depressive symptoms assessed by the 10-item Center for Epidemiologic Studies Depression Scale and selected correlates, with centrality and bridge indices computed to characterize network structure. RESULTS: Tetrachoric factor analysis identified two stable clusters-a metabolic-vascular cluster (hypertension, dyslipidemia, diabetes, stroke) and a visceral-neurodegenerative cluster (liver disease, kidney disease, arthritis, memory-related disorders). Ten variables were significantly associated with depression (area under the curve=0.734, 95% confidence interval 0.708-0.793), with cognitive impairment showing the strongest association. Network analyses indicated that effort-related and motivational symptoms were central across clusters, whereas cognitive impairment consistently exhibited the highest bridge strength linking multimorbidity-related factors and depressive symptoms. CONCLUSION: Multimorbidity in older adults forms clinically meaningful clusters associated with distinct depressive symptom network characteristics. Cognitive impairment emerged as the most prominent bridge-related correlate. Findings are associative and hypothesisgenerating. Longitudinal and interventional studies are warranted.