预测中国中老年人的心脏代谢多发病轨迹：来自全球老龄化和成人健康队列研究的见解

摘要(中文译文)

预测心脏代谢多病共存（CMM）的能力可显著促进对具有风险的中国中老年人群的识别与干预。本研究旨在基于多维风险因素，采用集成机器学习方法构建CMM进展轨迹的预测模型。数据来自世界卫生组织在中国开展的全球老龄化与成人健康研究（SAGE），涵盖2007年至2019年期间，共纳入4，518名参与者。心脏代谢疾病（CMDs）的发病信息通过自报调查收集。CMM定义为至少存在两种CMD，包括高血压、糖尿病、心绞痛、卒中和肥胖。采用多状态模型检验多维因素对从健康状态向单一CMD转变、继而向CMM转变的影响，随后构建了这些转变的预测模型。随访期间，52.19%的初始健康个体发生了一种心脏代谢疾病（CMD），其中15.61%进展为CMM。女性、低人均GDP、不健康行为、高PM浓度、低湿度和低温被确定为从健康到CMD以及从CMD到CMM进展过程中的共同风险因素。此外，在健康向CMD转变过程中，高龄、低教育水平和体能受损为独立风险因素。值得注意的是，在CMD向CMM转变过程中，认知功能（IC）受损被确定为独立影响因素。利用这些关键预测因子，构建了整合五种机器学习算法的堆叠集成模型，该模型预测从健康向CMD转变的曲线下面积（AUC）值为0.89（95% CI：0.88-0.91），预测从CMD向CMM进展的AUC值为0.76（95% CI：0.72-0.82）。总之，本研究表明，基于多维因素的堆叠集成模型可有效预测CMM的进展。本研究不仅识别了不同转变阶段的特异性风险因素，还凸显了机器学习在改善早期风险分层和为老龄化人群预防CMM的针对性干预提供依据方面的潜力。

Abstract

The ability to predict cardiometabolic multimorbidity (CMM) could significantly facilitate the identification of and intervention for middle-aged and older Chinese adults at risk. This study aimed to develop a prediction model for CMM progression trajectories based on multidimensional risk factors using an ensemble machine learning approach. Data from 4,518 participants were obtained from the World Health Organization's Study on Global AGEing and Adult Health (SAGE) in China, covering the period from 2007 to 2019. Information on the incidence of cardiometabolic diseases (CMDs) was collected via self-reported surveys. CMM was defined as the presence of at least two CMDs, including hypertension, diabetes, angina, stroke, and obesity. A multi-state model was used to examine the influence of multidimensional factors on the transition from health to a single CMD and subsequently to CMM. Predictive models for these transitions were then developed. During follow-up, 52.19% of initially healthy individuals developed one cardiometabolic disease (CMD), among whom 15.61% progressed to CMM. Female, low GDP per capita, unhealthy behaviors, elevated PM concentrations, low humidity, and low temperatures were identified as shared risk factors across the progression from health to CMD and from CMD to CMM. Moreover, in the health-to-CMD transition, older age, low educational level, and physical fitness impairment emerged as independent risk factors. Distinctively, in the CMD-to-CMM transition, IC impairment was identified as an independent influencing factor. Using these key predictors, a stacking ensemble model incorporating five machine learning algorithms was developed, and the model demonstrated area under the curve (AUC) values of 0.89 (95% CI: 0.88-0.91) for predicting the transition from health to CMD, and 0.76 (95% CI: 0.72-0.82) for predicting progression from CMD to CMM. In conclusion, this study demonstrates that a stacking ensemble model based on multidimensional factors can effectively predict the progression of CMM. Our study not only identified distinct risk factors for different transitional stages but also highlighted the potential of machine learning to improve early risk stratification and inform targeted interventions for preventing CMM in the aging.

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