Key result
A logistic regression model demonstrated the best predictive performance for identifying depression risk in middle-aged and elderly patients with cardiovascular metabolic diseases, achieving an AUC of 0.69.
Why the study?
Cardiovascular metabolic diseases increasingly affect middle-aged and elderly populations and significantly raise depression risk, prompting the construction of a machine learning model to predict depression risk and identify key risk factors.
Observational (n=4,477)
Yes
A logistic regression model utilizing 11 clinical and demographic factors can moderately predict the risk of depression in middle-aged and elderly patients with cardiovascular metabolic diseases, facilitating early psychological interventions.
May aid depression risk stratification in CMD patients; leaves open prospective validation before clinical use.
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. METHODS: Based on data from the China Health and Retirement Longitudinal Study (CHARLS) from 2018 to 2020, 4,477 patients aged 45 and above were included. LASSO regression was used to screen for risk factors, and three machine learning algorithms-logistic regression (LR), random forest (RF), and XGBoost-were employed to build predictive models. The performance of the models was evaluated using ROC curves, calibration curves, and decision curves. RESULTS: The study found several risk factors significantly associated with depression, including disability status, pain, retirement status, number of chronic diseases, education level, age, gender, place of residence, life satisfaction, optimism about the future, and self-rated health status. The incidence of depression was significantly higher among women (56%), rural residents (64%), individuals with disabilities, non-retirees (85%), and those with chronic illnesses (73%). The LR model demonstrated the best predictive performance, with an AUC of 0.69. Key predictive factors included self-rated health, residence, education level, gender, pain, life satisfaction, age, and hope for the future. CONCLUSION: This study developed a depression risk prediction model based on logistic regression, providing important references for psychological health interventions in middle-aged and elderly patients with CMD. Identifying and intervening in high-risk populations is crucial for improving patients' quality of life.
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Zhang et al. (2025) conducted an observational in Cardiovascular metabolic diseases and depression (n=4,477). Risk factors for depression (e.g., self-rated health, residence, education, gender, pain) vs. Patients without the respective risk factors was evaluated on Predictive performance (AUC) for depression risk (AUC 0.69). A logistic regression model demonstrated the best predictive performance for identifying depression risk in middle-aged and elderly patients with cardiovascular metabolic diseases, achieving an AUC of 0.69.
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