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评估多环境化学物质暴露与代谢综合征的关联:一种机器学习方法

Yehoon Jo¹, Mi-Yeon Shin², Sungkyoon Kim¹

¹Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea; Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.

Environment international

|April 25, 2025

概括

环境化学物质暴露,如PFOA和PFOS,与代谢综合征 (MetS) 风险有关. 机器学习模型将这些化学物质确定为关键预测因素,强调需要综合预防策略.

科学领域:

环境健康环境健康
毒理学毒理学毒理学
代谢性疾病是一种代谢性疾病.

背景情况:

代谢综合征 (MetS) 是一个日益严重的全球健康问题,与心血管疾病和2型糖尿病有关.
环境化学物质暴露越来越多地被认为是通过破坏代谢过程而对MetS发展的潜在贡献者.

研究的目的:

通过机器学习研究环境化学物质暴露与MetS风险之间的关联.
确定MetS的关键环境预测因素,并了解暴露-反应模式.

主要方法:

利用了来自韩国国家环境健康调查 (KoNEHS) 第4轮 (2018-2020年) 2,960名参与者的数据.
应用了8个机器学习算法,包括多层感知子 (MLP) 和随机森林 (RF),以预测MetS风险.
雇佣的夏普利添加式拓展 (SHAP) 和部分依赖地块 (PDP) 用于模型解释.

主要成果:

在MLP和RF模型中,预测性表现强 (AUC=0.79,F1分数=0.82).
和多醇基物质 (PFAS),特别是PFOA和PFOS,被确定为MetS风险的重要预测因素,除了年龄和BMI.
分析显示了线性和非线性暴露-反应关系,表明某些化学物质的潜在值影响.

结论:

环境暴露,特别是PFAS,是MetS风险评估中的重要因素.
机器学习模型提供了强大的预测能力和对化学代谢相互作用的洞察力.
研究结果支持将环境因素纳入气候变化防治,并主张采取监管措施减少有害暴露.

关键词:

化学物质暴露化学物质暴露科内希斯 (KoNEHS) 是一个机器学习是机器学习.代谢综合征是代谢综合征.在PFAS中,有很多方法.

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