Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China

Abstract

Abstract Background Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM in middle-aged and elderly patients is relatively high. The current research lacks an exploration into the impact of social and environmental determinants of health on depression in CKM patients. Objective This study aims to construct a depression risk prediction model for middle-aged and elderly CKM patients by social and environmental determinants of health. Methods In this study, 3220 participants were included and collected from three waves of the China Health and Retirement Longitudinal Study (CHARLS). A depression risk prediction model for middle-aged and elderly CKM patients was constructed by using 10 machine learning models. Additionally, the mediating effect of NO2 between arthritis and depression outcomes was analyzed in this population. Results An interpretable machine learning model framework was constructed to predict depression risk in middle-aged and elderly CKM patients using the longitudinal cohort data from CHARLS. The RF model demonstrated strong performance in predicting the training set, and the Xgboost model exhibited excellent generalization ability. The presence of arthritis showed a significant independent effect on depression outcomes, with an average direct effect of − 8.5559. The total effect of arthritis on depression outcomes was − 9.5162. The mediating effect of NO2 represented 10.09% of the total effect (average), indicating that NO2 serves as a mediator between arthritis and depression outcomes. Conclusions A depression risk prediction model for middle-aged and elderly CKM patients was developed based on the CHARLS longitudinal data from 2011 to 2015. The SHAP framework was used to provide machine learning model explanations. Intervention strategies that address social and environmental determinants of health are needed. Potential strategies include enhancing urban greening to reduce NO2 levels, integrating CKM as a special outpatient chronic disease to alleviate the financial burdens of patients, and focusing on the treatment of arthritis and digestive diseases in CKM patients.

Keywords

• Cardiovascular-Kidney-Metabolic syndrome
• Random forest
• Mediation analysis
• Health management