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机器学习预测了美国成年人使用SHAP解释暴露于挥发性有机化合物的骨关节炎风险

Shanbin Zheng¹, Jiaqing Zhu¹, Xun Cao¹

¹Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, 210029, China.

Scientific reports

|November 11, 2025

概括

挥发性有机化合物 (VOC) 暴露与骨关节炎 (OA) 风险有关. 特定的VOC代谢物DHBMA和HMPMA被确定为使用机器学习的新风险因素,建议新的预防策略.

科学领域:

环境健康环境健康
毒理学毒理学毒理学
流行病学流行病学

背景情况:

对挥发性有机化合物 (VOC) 的广泛暴露与慢性疾病有关.
挥发性有机化合物暴露与骨关节炎 (OA) 风险之间的联系尚不清楚.

研究的目的:

调查VOC代谢物与OA风险之间的关联.
确定影响OA发展的关键VOC代谢物.
开发一个基于VOC暴露的OA风险的预测模型.

主要方法:

对3683名美国成年人的国家健康和营养检查调查 (NHANES) 数据 (2011-2018) 的分析.
通过尿路代谢物和自我报告的OA状态评估17种VOCs.
机器学习模型的应用,包括线性差异分析 (LDA) 和夏普利添加式解释 (SHAP) 的解释.

主要成果:

在LDA模型中,AUC达到0.755,证明了有效的OA风险预测.
在SHAP分析中,N-Acetyl-S-(3,4-dihydroxybutyl) -l-cysteine (DHBMA) 和N-Acetyl-S-(3-hydroxypropyl-2-methyl) -l-cysteine (HMPMA) 被确定为OA风险的重要预测因素.
在这些VOC代谢物和OA风险之间观察到非线性剂量反应关系,在人口亚组中一致.

结论:

集成VOC暴露数据的机器学习模型可以有效预测OA风险.
DHBMA和HMPMA被确定为OA的新风险因素.
研究结果支持将环境暴露纳入OA风险预测和制定有针对性的预防策略.

关键词:

机器学习是机器学习.挥发性有机化合物的代谢物.骨关节炎是一种骨关节炎.沙普利添加剂的解释

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