探索挥发性有机化合物暴露与慢性病之间的关联:可解释机器学习方法的证据
Liyan Jiang1,2, Hongling Wang1, Yang Xiao1,2
1Department of Laboratory Medicine, The Second Affiliated Hospital of Guilin Medical University, Guilin, China.
Renal failure
|June 24, 2025
概括
尿液中的挥发性有机化合物 (VOC) 与慢性病 (CKD) 风险有关. N-Acetyl-S-(3,4-dihydroxybutyl) -L-cysteine (DHBMA) 成为一个关键预测因子,表明环境VOCs影响CKD发展.
科学领域:
- 环境健康 环境健康
- 代谢学 代谢学 代谢学
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 慢性病 (CKD) 影响全球近7亿人,如挥发性有机化合物 (VOCs) 等环境因素越来越被认为是潜在的贡献者.
- 传统的线性方法可能低估了VOC和CKD之间的复杂相互作用,需要先进的分析方法.
研究的目的:
- 调查尿液中特定的VOC代谢物与患CKD的风险之间的关联.
- 应用可解释的机器学习模型来识别CKD风险的关键VOC预测因素.
主要方法:
- 来自国家健康和营养检查调查 (2011-2020) 的15种尿液VOC代谢物的分析.
- 使用多变量逻辑回归,LASSO和机器学习模型 (LR,RF,XGBoost,KNN,MLP).
- 采用了夏普利添加式解释 (SHAP) 来提高模型的可解释性.
主要成果:
- 发现CEMA,DHBMA,HMPMA和PGA与CKD风险有显著的积极关联.
- XGBoost模型显示出强大的预测能力,DHBMA通过SHAP分析被确定为主要预测因子.
- 对于AAMA和CYMA在更高度下发现了不良关联.
结论:
- 尿液中的特定VOC代谢物,特别是DHBMA,与CKD风险有显著的相关性.
- 环境中的VOC暴露在CKD病变发生过程中起着重要作用.
- 结果可以指导开发针对性预防策略的CKD.
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