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使用可解释的机器学习框架构建和验证基于新型营养物质的衰老风险指数:来自两个基于人口的研究的结果

Rui Qiang Li¹, Ting Yu Lu¹, Jiao Wang¹

¹School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, Guangdong Province, China.

Nutrition journal

|December 30, 2025

概括

使用机器学习开发了一种新的饮食衰老风险指数 (DARI),用于评估衰老. 较高的DARI分数与加速衰老,增加死亡风险和较低的α-Klotho水平有关,突出显示了饮食在衰老中的作用.

科学领域:

老年学是指老年学的学科.
营养科学营养科学
生物统计学生物统计学

背景情况:

人口老龄化是一个日益严重的全球性问题.
饮食因素与衰老有关,但缺乏表型衰老的特定指数.

研究的目的:

开发和验证表型衰老的饮食指数得分.
评估饮食衰老风险指数 (DARI) 与健康结果之间的关联.

主要方法:

使用可解释的机器学习 (适应弹性网,XGBoost,随机生存森林) 与SHAP值.
使用来自广州生物银行队列研究 (GBCS) 和NHANES的数据构建和验证了饮食衰老风险指数 (DARI).
定义加速衰老是从表型年龄的残留物回归到时间年龄.

主要成果:

较高的DARI分数与较老的表型年龄相关,并在GBCS中增加加速衰老的风险.
较高的DARI与较高的全因死亡风险有关,特别是在吸烟者中.
在NHANES中,较高的DARI与较低的α-Klotho水平有关,这是衰老的标志物.

结论:

使用机器学习开发并验证了一种新的DARI,提供了全面的饮食衰老措施.
DARI有效地捕捉了营养摄入对表型衰老的影响.
有一个在线工具可用于在人口研究中应用DARI.

关键词:

加快年龄的加速.所有原因的死亡率.机器学习机器学习营养素营养素营养素现型年龄现型年龄

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