通过使用可解释机器学习,探索暴露于per-和多基基物质与类风湿性关节炎风险之间的关系
Zhi Li1, Xinping Xu2, Ke Zhang3
1Nanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Nanjing, Jiangsu, China.
Frontiers in public health
|June 18, 2025
概括
机器学习模型预测环境PFAS暴露的类风湿性关节炎风险. perfluorooctane硫酸 (PFOS) 和MPAH显示非线性风险模式,有助于预防策略.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 类风湿性关节炎 (RA) 是一种慢性自身免疫性疾病.
- 环境暴露,如和多基物质 (PFAS),影响RA风险.
- 以前的研究表明PFAS和RA之间存在联系,但缺乏可解释的预测模型.
研究的目的:
- 开发和验证一个可解释的机器学习模型,以基于PFAS暴露来预测类风湿关节炎风险.
- 确定关键的PFAS预测因子,并了解它们与RA风险的非线性关联.
- 创建一个公开可访问的工具来评估个人RA风险.
主要方法:
- 从国家健康和营养检查调查 (2003-2018) 中对11,705名参与者的分析.
- 使用AUC,精度,灵敏度,特异性和F1分数对12个机器学习算法的评估.
- 使用夏普利添加式解释 (SHAP) 识别关键预测因子,并检查与部分依赖图和LOWESS曲线的非线性关系.
主要成果:
- CatBoost模型表现出卓越的性能 (AUC:0.82,准确度:74%,F1得分:0.62).
- perfluorooctane硫酸 (PFOS) 和MPAH被确定为重要的预测因素.
- PFOS显示RA风险增加超过15.10 ng/ml (U形),MPAH在0.22 ng/ml,与非线性暴露-反应关系.
结论:
- 机器学习有效地预测了与PFAS暴露相关的类风湿性关节炎风险.
- 确定了非线性暴露-反应模式,可以了解导致RA的环境因素.
- 开发的基于网络的风险计算器为公共卫生和临床应用提供了实用的工具.
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