A study on the impact of chronic diseases and depressive symptoms comorbidity on the risk of cognitive impairment in middle-aged and older adults people based on the CHARLS database

Objective This study aimed to investigate the combined impact of comorbid chronic diseases and depressive symptoms on the risk of cognitive impairment in middle-aged and older adults populations. It also explored the interaction mechanisms and provided scientific evidence for cognitive health interventions. Methods Data from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS) were used, including 16,890 participants aged 45 years and older. Overlap weighting was applied to control for confounding factors such as gender, age, BMI, ADL, and smoking status. Multivariate logistic regression models assessed the combined effect of chronic diseases and depressive symptoms on cognitive impairment risk. Subgroup analyses were conducted to explore gender, age, and education level differences. Sensitivity analyses, including propensity score matching (PSM) and E-value estimation, were performed to evaluate the robustness of the findings. Results The coexistence of chronic diseases and depressive symptoms significantly increased the risk of cognitive impairment, with an adjusted odds ratio (OR) of 1.22 (95% CI: 1.14–1.31, p < 0.05). Subgroup analyses revealed that this combined effect was more pronounced in males, individuals aged ≥60 years, and those with lower education levels (elementary school or below). Overlap weighting effectively balanced baseline characteristics, while sensitivity analyses and E-value calculations confirmed the robustness of the results. Conclusion Comorbid chronic diseases and depressive symptoms exert a significant cumulative effect on cognitive impairment risk in middle-aged and older adults populations through complex interaction mechanisms. This study addresses a research gap and provides evidence for personalized disease management and psychological interventions. Future research should further explore the mechanisms of these interactions and validate the findings in diverse populations to enhance generalizability.