代谢功能障碍相关脂肪性肝病老年人冠心病和中风的危险因素：一项全国性队列研究

摘要(中文译文)

患有代谢功能障碍相关脂肪性肝病的老年人心血管疾病（CVD）风险升高，然而传统模型（如Framingham风险评分）依赖线性假设，且是针对较年轻人群开发的。非线性机器学习方法可能改善这一高危人群的风险预测。本研究利用韩国国民健康保险服务数据，开展了一项基于人群的队列研究，对67 575名年龄≥65岁、患有代谢功能障碍相关脂肪性肝病的成年人（2009—2010年评估）进行随访，观察2011年至2019年新发CVD、冠心病和卒中情况。Cox比例风险（CoxPH）模型用于识别事件时间预测因子，而基于极端梯度提升（XGBoost）的生存模型用于探索非线性关联并预测9年CVD风险。CoxPH和XGBoost模型均优于Framingham风险评分，其中CoxPH在验证集中的区分度略优于XGBoost。两种模型识别出的主要预测因子大体相似。对于冠心病，两种模型均将Charlson合并症指数、抗血小板药物使用和血清肌酐列为最重要的预测因子之一。在CoxPH模型中，Charlson合并症指数的解释相对风险最高（0.031），XGBoost模型亦将Charlson合并症指数按Shapley加性解释值排在首位（0.159）。对于卒中，两种模型均将年龄、Charlson合并症指数和抗血小板药物使用列为前3位预测因子，其中年龄居首（解释相对风险，0.065；Shapley加性解释，0.239）。总CVD也观察到类似结果。在患有代谢功能障碍相关脂肪性肝病的老年人中，CoxPH与生存XGBoost表现出相似的预测性能，并识别出传统模型（如Framingham风险评分）未强调的关键预测因子。这些发现凸显了将基于较年轻人群的工具应用于患有代谢功能障碍相关脂肪性肝病的老年人时的局限性，并支持开发针对性的CVD风险模型。

Abstract

Older adults with metabolic dysfunction-associated steatotic liver disease have elevated cardiovascular disease (CVD) risk, yet conventional models, such as the Framingham Risk Score, rely on linear assumptions and were developed for younger populations. Nonlinear machine learning approaches may improve risk prediction in this high-risk group. Using Korean National Health Insurance Service data, this population-based cohort study followed up 67 575 adults, aged ≥65 years, with metabolic dysfunction-associated steatotic liver disease (assessed 2009-2010) for incident CVD, coronary heart disease, and stroke from 2011 to 2019. Cox proportional hazards (CoxPH) models identified time-to-event predictors, whereas an extreme gradient boosting (XGBoost)-based survival model explored nonlinear associations and predicted 9-year CVD risk. Both CoxPH and XGBoost models outperformed the Framingham Risk Score, with CoxPH showing slightly better discrimination than XGBoost in the validation sets. The 2 models identified broadly similar leading predictors. For coronary heart disease, both identified Charlson comorbidity index, antiplatelet agent use, and serum creatinine among the most important predictors. In the CoxPH model, the Charlson comorbidity index had the highest explained relative risk (0.031), and the XGBoost model also ranked Charlson comorbidity index first by Shapley Additive Explanations value (0.159). For stroke, both models identified age, Charlson comorbidity index, and antiplatelet agent use as the top 3 predictors, with age ranked first (explained relative risk, 0.065; Shapley Additive Explanations, 0.239). Similar findings were observed for total CVD. In older adults with metabolic dysfunction-associated steatotic liver disease, CoxPH and survival XGBoost showed similar predictive performance and identified key predictors not emphasized in traditional models, like the Framingham Risk Score. These findings underscore limitations of applying younger-population-based tools to older adults with metabolic dysfunction-associated steatotic liver disease and support the development of tailored CVD risk models.

如何引用

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