与人类生物衰老加速相关的环境化学因素:可解释的机器学习研究
1Changchun University of Chinese Medicine, 1035 Boshuo Road, Jingyue Street, Nanguan District, Changchun City, Jilin Province, China.
Archives of gerontology and geriatrics
|October 25, 2025
概括
这项研究发现,糖尿病和BMI等临床因素比环境化学物质暴露更强烈地预测生物衰老加速. 机器学习识别了特定的化学物质,如烯,但突出了复杂的相互作用,而不是简单的预测改进.
科学领域:
- 环境健康 环境健康
- 老年学是一门学科.
- 计算生物学 计算生物学
背景情况:
- 生物衰老加速 (BA-Accel) 与健康结果有关.
- 环境化学物质暴露是衰老的潜在调节者.
- 了解这些关联需要先进的分析方法.
研究的目的:
- 通过机器学习研究78种环境化学物质与生物衰老加速之间的关联.
- 确定BA-Accel的关键化学贡献者及其作用模式.
- 评估化学物质暴露对衰老速度的预测效用.
主要方法:
- 在NHANES数据 (n=1693) 上使用可解释机器学习 (ML) 框架.
- 分析了78种环境化学品和两个BA-Accel措施 (KDM-BA-Accel,PhenoAge-Accel).
- 使用沙普利添加式解释 (SHAP) 进行特征重要性和相互作用分析.
主要成果:
- ML模型显示预测性能中等 (R2 0.20-0.27);化学物质暴露没有比临床因素改善预测.
- 糖尿病和BMI是BA-Accel的重要预测因素.
- SHAP确定了血液中的二烯,尿中的二烯代谢物和尿中的二烯-3作为关键化学物质,揭示了非线性关系和相互作用.
结论:
- 单一的化学物质暴露对个体衰老速度的预测价值有限.
- ML方法有效地识别了混合暴露中的关键化学物质和复杂相互作用.
- 调查结果可以为环境暴露和衰老的公共卫生风险管理策略提供信息.
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