通过机器学习方法重新审视成功的衰老,以量化慢性疾病的影响
Nan He1, Xiangchun Xu2, Haocheng Wang3
1Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Scientific reports
|November 17, 2025
概括
慢性疾病,如高血压,病和关节炎显著影响成功的衰老,影响认知和身体健康. 将这些条件纳入老龄化定义可以改善对老年人的评估和干预措施.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 成功的衰老通常被定义为没有疾病,残疾,抑郁症和高认知/社会功能.
- 许多常见的慢性疾病,如高血压和关节炎,被排除在传统的成功衰老指标之外.
- 这种排除限制了对老龄人口的综合评估和有针对性的干预措施.
研究的目的:
- 量化多种慢性疾病对成功衰老及其核心组成部分的预测影响.
- 通过纳入更广泛的慢性疾病范围,完善现有的成功衰老框架.
- 为老年人制定有针对性的健康干预提供证据.
主要方法:
- 利用了2011-2015年中国健康与退休长度研究 (CHARLS) 的数据.
- 应用了六个机器学习模型,在超参数调整后选择XGBoost进行预测和解释.
- 使用夏普利增量解释 (SHAP) 和累积局部效应 (ALE) 进行模型解释性和特征重要性分析.
主要成果:
- 高血压,病和关节炎被确定为成功衰老的最有影响力的预测因素.
- 睡眠时间也是影响成功预测衰老的重要特征.
- 病和关节炎与抑郁症状和认知功能相关;高血压与身体功能相关.
结论:
- 传统上不包括在成功衰老定义中的慢性疾病显著影响衰老结果.
- 扩大成功衰老的定义,包括更广泛的慢性疾病,提高了个人评估.
- 这些发现支持有针对性的健康干预措施,并提高对健康衰老至关重要的因素的认识.
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