可解释机器学习的洞察力,了解PFAS暴露与糖尿病之间的关联
Cui Wang1, Xinping Xu2, Shuai Luo3
1Nanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Jiangsu, China.
Ecotoxicology and environmental safety
|June 24, 2025
概括
和多基基物质 (PFAS) 暴露与糖尿病 (DM) 风险有关. 机器学习模型将PFOA识别为保护性,而PFOS和PFNA增加了风险,为糖尿病预防策略提供了信息.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 数据科学数据科学数据科学
背景情况:
- 糖尿病 (DM) 构成了全球卫生挑战,其与和多基物质 (PFAS) 暴露的关联尚未得到充分理解.
- 之前没有开发过机器学习 (ML) 模型,以基于PFAS暴露水平来预测DM风险.
研究的目的:
- 开发和验证基于PFAS暴露的机器学习模型,以预测基于PFAS暴露的糖尿病风险.
- 确定与DM风险相关的特定PFAS化合物,并阐明它们的预测贡献.
主要方法:
- 分析国家健康和营养检查调查 (NHANES) 数据,来自10471名参与者 (2003-2018).
- 对12个ML模型进行比较,LightGBM因其卓越的性能而被选中 (AUC=0.84).
- 应用变量重要性,部分依赖分析 (PDA),夏普利添加式扩展 (SHAP) 和LOWESS平滑进行详细分析. 使用Gradio.com开发了一个基于Web的计算器.
主要成果:
- perfluorooctanoic acid (PFOA) 是最强的预测因子,与DM风险有负面关联,其保护值为2.48 ng/mL.
- perfluorooctane sulfonic acid (PFOS) 和 perfluorononanoic acid (PFNA) 显示出与DM风险的积极关联,在低PFOA水平下具有轻微的协同效应.
- SHAP分析证实了PFAS的主要保护作用,揭示了各种PFAS和DM风险之间的非线性关系. 开发的计算器有助于临床风险评估.
结论:
- 新的机器学习洞察力揭示了PFAS暴露和糖尿病之间复杂的关联.
- 研究结果表明,PFOA可能具有对DM的保护作用,与PFOS和PFNA等其他PFAS的关联相反.
- 建议进行进一步的前性验证,结果可能为未来的糖尿病预防环境卫生政策提供信息.
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