Zhang Gege, Dong Sijie, Wang Li
Xuzhou Medical University, Xuzhou, Jiangsu, China.
The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
BMC Public Health. 2025 May 23;25(1):1904. doi: 10.1186/s12889-025-23075-7.
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.
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.
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.
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.
心血管代谢疾病（CMD）在中老年人群中的发病率持续上升，不仅影响身体健康，还显著增加了患抑郁症的风险。本研究旨在构建一个机器学习模型，以预测中老年CMD患者患抑郁症的风险，并识别关键风险因素。
基于2018年至2020年中国健康与养老追踪调查（CHARLS）的数据，纳入了4477名年龄在45岁及以上的患者。采用LASSO回归筛选风险因素，并使用三种机器学习算法——逻辑回归（LR）、随机森林（RF）和XGBoost——构建预测模型。使用ROC曲线、校准曲线和决策曲线评估模型的性能。
研究发现了几个与抑郁症显著相关的风险因素，包括残疾状况、疼痛、退休状况、慢性病数量、教育水平、年龄、性别、居住地点、生活满意度、对未来的乐观态度以及自评健康状况。女性（56%）、农村居民（64%）、残疾人士、未退休者（85%）和患有慢性病的人（73%）中抑郁症的发病率显著更高。LR模型表现出最佳的预测性能，AUC为0.69。关键预测因素包括自评健康、居住情况、教育水平、性别、疼痛、生活满意度、年龄和对未来的期望。
本研究基于逻辑回归开发了一种抑郁症风险预测模型，为中老年CMD患者的心理健康干预提供了重要参考。识别和干预高危人群对于提高患者的生活质量至关重要。
