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

Journal: BMC public health
PMID: 40025455

Abstract

BACKGROUND: 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.

Authors

  • Jingjing Liu
    School of Electro-Mechanical Engineering, Xidian University, Xi'an 710071, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhangdaihong Liu
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK.
  • Yibin Zhou
    Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, 215011, China.
  • Xiaoguang Li
    Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.

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