Zhejiang Da Xue Xue Bao Yi Xue Ban · Apr 29, 2026

Associations between multimorbidity patterns and depressive symptoms among middle-aged and older adults: a latent class analysis based on CHARLS data

By Sun Y, Zhao C, Peng S et al.

OBJECTIVES: To examine the association between different multimorbidity patterns and depressive symptoms among middle-aged and older adults. METHODS: Data were obtained from 491 middle-aged and older adults with multimorbidity in the 2020 China Health and Retirement Longitudinal Study (CHARLS). Latent class analysis (LCA) was used to identify distinct multimorbidity patterns. Models with one to four classes were fitted sequentially, and the optimal model was selected based on a comprehensive evaluation of the model fit indices and interpretability. Logistic regression was applied to assess the associations between latent classes and depressive symptoms, adjusting for sex, age, marital status, residence, income status, and number of chronic conditions. RESULTS: The prevalence of depressive symptoms among individuals with multimorbidity was 37.1%. Three multimorbidity patterns were identified: a cardiometabolic pattern, a digestive-musculoskeletal pattern, and a respiratory pattern. After adjusting for covariates, significant differences in depressive symptoms were observed across patterns. Compared with the cardiometabolic pattern, individuals in the digestive-musculoskeletal pattern had a higher likelihood of depressive symptoms (OR=1.795, 95%CI: 1.14-2.82, P=0.011), while no significant difference was found for the respiratory pattern (OR=1.234, 95%CI: 0.70-2.14, P=0.458). In addition, having four or more chronic conditions (OR=1.996, 95%CI: 1.15-3.47, P=0.014) and being aged ≥70 years (OR=2.817, 95%CI: 1.24-6.68, P=0.015) were significantly associated with higher odds of depressive symptoms. CONCLUSIONS: Multimorbidity patterns are associated with depressive symptoms among middle-aged and older adults, among which the digestive-musculoskeletal pattern is particularly linked to a higher risk of depression.

doi.org/10.3724/zdxbyxb-2025-0615Open accessPubMed 42478176Populations