通过健康的社会和环境决定因素预测中老年心血管-肾脏-代谢综合征患者的抑郁风险：一种使用来自中国的纵向数据的可解释机器学习方法。
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.
作者信息
Xu Xinyi, Li Xinru, Li Xiyan, Xue Benli, Zheng Xiao, Xiao Shujuan, Yang Lingli, Zhang Xinyi, Chen Chengyu, Zheng Ting, Li Yuyang, Wang Yanan, Han Jianan, Wu Haoran, Zhang Mengjie, Liao Yanming, Bai Siyi, Zeng Nan, Zhang Chichen
机构信息
School of Public Health, Southern Medical University, Guangzhou, China.
School of Marxism, Southern Medical University, Guangzhou, China.
出版信息
J Health Popul Nutr. 2025 Jun 4;44(1):187. doi: 10.1186/s41043-025-00897-0.
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 NO 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 NO represented 10.09% of the total effect (average), indicating that NO 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 NO 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.
背景
心血管-肾脏-代谢（CKM）综合征是一种全身性疾病，其特征是心血管系统、慢性肾脏病和代谢风险因素之间存在病理生理相互作用。在中国，中老年患者中CKM的患病率相对较高。目前的研究缺乏对健康的社会和环境决定因素对CKM患者抑郁症影响的探索。
目的
本研究旨在通过健康的社会和环境决定因素构建中老年CKM患者抑郁症风险预测模型。
方法
本研究纳入了3220名参与者，这些参与者来自中国健康与养老追踪调查（CHARLS）的三波调查。使用10种机器学习模型构建了中老年CKM患者抑郁症风险预测模型。此外，还分析了该人群中一氧化氮（NO）在关节炎与抑郁结局之间的中介作用。
结果
利用CHARLS的纵向队列数据构建了一个可解释的机器学习模型框架，以预测中老年CKM患者的抑郁症风险。随机森林（RF）模型在预测训练集方面表现出强大性能，而极端梯度提升（Xgboost）模型表现出优异的泛化能力。关节炎的存在对抑郁结局有显著的独立影响，平均直接效应为-8.5559。关节炎对抑郁结局的总效应为-9.5162。NO的中介效应占总效应的10.09%（平均），表明NO在关节炎与抑郁结局之间起中介作用。
结论
基于2011年至2015年CHARLS纵向数据，开发了中老年CKM患者抑郁症风险预测模型。使用SHAP框架提供机器学习模型解释。需要针对健康的社会和环境决定因素的干预策略。潜在策略包括加强城市绿化以降低NO水平，将CKM作为特殊门诊慢性病进行整合以减轻患者经济负担，以及关注CKM患者的关节炎和消化系统疾病治疗。
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