Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning

作者：Jun Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang · 发表于：BMC Public Health · 年份：2025 · DOI：10.1186/s12889-025-22077-9 · 被引用次数：11 · 研究领域：Health, Environment, Cognitive Aging、Air Quality and Health Impacts、Data-Driven Disease Surveillance

Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living standards, posing significant public health challenges. The MDs are influenced by a complex interplay of genetic factors, lifestyle choices, and socioeconomic conditions. Additionally, environmental pollutants, particularly air pollutants (APs), have attracted increasing attention for their potential role in exacerbating these MDs. However, the impact of APs on the MDs remains unclear. This study introduces a novel machine learning (ML) pipeline, an Algorithm for Spatial Relationships Analysis between Exposome and Metabolic Diseases (ASEMD), to analyze spatial associations between APs and MDs at the prefecture-level city scale in China. The ASEMD pipeline comprises three main steps: (i) Spatial autocorrelation between APs and MDs is evaluated using Moran’s I statistic and Local Indicators of Spatial Association (LISA) maps. (ii) dimensionality reduction and spatial similarities identification between APs and MDs clusters using Principal Component Analysis (PCA), k-means clustering, and Jaccard index calculations, further validated through spatial maps. (iii) AP exposure is adjusted by demographic and lifestyle confounders to predict MDs using machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LightGBM, and Multi-Layer Perceptron (MLP)). SHAP values are employed to identify key adjusted...