一个可解释的机器学习模型预测了不同环境化学物质暴露对抑郁症的互动和累积风险
Gang Luo1, Wei Xu1, Yuyang Sha1
1Center for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China.
Translational psychiatry
|November 1, 2025
概括
环境化学混合物 (ECM) 有助于抑郁风险. 这项研究使用机器学习来识别关键的ECM和途径,如氧化应激,将它们与抑郁症联系起来,帮助有针对性的预防.
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:
- 人类每天都会遇到各种环境化学混合物 (ECM),这可能会影响心理健康.
- 之前的研究重点是个别的ECM,因此对抑郁风险的累积和相互作用影响未得到充分研究.
研究的目的:
- 开发一个可解释的机器学习 (ML) 模型来预测ECM的抑郁风险.
- 为了发现ECM和内源代谢物/影响抑郁症的蛋白质之间的相互作用.
- 为了确定与抑郁症相关的关键环境化学物质暴露.
主要方法:
- 利用了来自1333名成年人的NHANES 2011-2016数据,分析了血清和尿液ECM.
- 使用PHQ-9分数评估抑郁症,并使用9个ML模型,包括随机森林.
- 应用沙普利添加物解释 (SHAP) 对于特征重要性和途径识别中介网络分析.
主要成果:
- 一个随机森林模型在预测ECM的抑郁风险方面取得了高准确性 (AUC:0.967,F1:0.91).
- 血清中的,血清中的和尿中的2-二被确定为重要的预测因素.
- 氧化应激和炎症成为ECM和抑郁症之间的关键调解途径.
结论:
- 一种可解释的ML方法有效地阐明了抑郁症的累积环境风险.
- 确定了特定的ECM和途径,为复杂的化学健康相互作用提供了洞察力.
- 这些发现可以为环境相关抑郁症的有针对性的干预和预防策略提供信息.
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