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使用机器学习对空气污染物暴露及其与代谢疾病相关性的空间分析

Jingjing Liu¹, Chang Liu¹, Zhangdaihong Liu²

¹School of Public Health, Shanghai Jiao Tong University School of Medicine, 227 South Chongqing Road, Shanghai, 200025, China.

BMC public health

|March 2, 2025

概括

空气污染与糖尿病和高血压等代谢疾病的增加有关. 一个新的机器学习管道,ASEMD,确定了在中国推动这些健康风险的特定污染物.

科学领域:

环境健康环境健康
流行病学流行病学
机器学习机器学习

背景情况:

糖尿病,高血压和脂质失调等代谢性疾病 (MDs) 正在引起越来越多的公众健康问题.
遗传,生活方式和社会经济因素影响MDs.
空气污染物 (APs) 在恶化MDs中的作用越来越被认可,但尚未完全理解.

研究的目的:

引入一条新的机器学习 (ML) 管道,即暴露体和代谢疾病 (ASEMD) 之间的空间关系分析算法.
在中国的县级城市规模上分析AP和MD之间的空间关联.
在对混因素进行调整后,确定与MDs相关的特定AP.

主要方法:

该ASEMD管道评估空间自相关性 (摩兰的I,LISA地图) 和识别集群 (PCA,k-means,Jaccard指数).
ML模型 (XGBoost,RF,DT,LightGBM,MLP) 使用调整后的AP暴露数据预测MDs的风险.
SHAP值识别了与MD相关的关键AP,模型性能通过10倍交叉验证进行评估.

主要成果:

在AP和糖尿病,脂质失调和高血压的患病率之间发现了显著的空间相关性.
APs有效地预测了MDs风险 (AUROC:糖尿病为0.890,失脂症为0.877,高血压为0.710).
像PM2.5,PM10和NO2这样的特定AP与糖尿病,脂质失调和高血压有很强的相关性.

结论:

ASEMD管道为研究AP-MD相互作用提供了一个强大的框架.
特定的污染物,包括PM2.5,PM10和NO2,与中国中部和北部更高的MD率密切相关.
需要特定区域的公共卫生战略,以减轻空气污染对代谢健康的影响.

关键词:

空气污染物空气污染物机器学习机器学习代谢性疾病代谢性疾病

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