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揭示尿路异性雌激素对成年人慢性病的影响:机器学习模型

Bowen Zhang¹, Liang Chen², Tao Li¹

¹Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China; Laboratory of Mitochondrial Metabolism and Perioperative Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, China; Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.

Ecotoxicology and environmental safety

|February 23, 2025

概括

暴露于诸如酸盐, parabens 和之类的异性雌激素 (XEs) 与慢性病 (CKD) 有关. 机器学习模型确定了特定的尿路XE代谢物,如甲基 (MeP),作为CKD风险的关键预测因素.

科学领域:

环境健康环境健康
腎臟病學 (nephrology) 是一種醫學專業.
毒理学毒理学毒理学
医疗保健中的机器学习

背景情况:

慢性病 (CKD) 是一个重要的全球健康问题.
暴露于环境化学物质,称为异性雌激素 (XEs),包括酸盐, parabens 和,已越来越多地与不良健康结果相关.
XEs对CKD病原体的特定贡献仍然是一个需要进一步研究的领域.

研究的目的:

开发和验证可解释的机器学习 (ML) 模型,用于预测CKD.
确定主要的 Estrogen (XE) 尿中代谢物,这些代谢物作为CKD的显著预测因素.
探索在美国成年人群中特定的XE代谢物和CKD风险之间的关系.

主要方法:

利用了来自国家健康和营养检查调查 (NHANES) 数据库 (2007-2016) 的数据.
开发并比较了四种ML算法 (随机森林,XGBoost,KNN,SVM) 和用于CKD预测的后勤回归.
为了模型的可解释性,使用了沙普利增量解释 (SHAP) 和部分依赖图 (PDP).

主要成果:

XGBoost显示了CKD的最高预测准确度,AUC为0.817.
通过SHAP分析识别的关键预测性尿路代谢物包括甲基 (MeP),单--糖) 甲酸盐 (MCNP) 和三酸盐 (TCS).
根据PDP分析,MeP的积极影响,MCNP的负面影响以及TCS在特定范围内对CKD预测的混合影响.

结论:

尿液中异性雌激素 (XE) 代谢物,特别是MeP,MCNP和TCS,是慢性病 (CKD) 的重要预测因素.
可解释的ML模型突出了个性化CKD护理策略的潜力,重点是XE暴露管理.
管理尿中甲基 (MeP) 水平可能对有效的CKD控制至关重要,这表明了潜在的治疗点.

关键词:

慢性脏疾病慢性脏疾病机器学习机器学习帕拉本类药物可以类化合物类化合物甲酸的含量甲酸的含量异性雌激素是一种异性雌激素.

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