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

The ASEMD pipeline successfully integrates ML models, epidemiological methods, and spatial analysis techniques, providing a robust framework for understanding the complex interactions between APs and MDs. We also identified specific APs, including <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>P</mml:mi> <mml:msub><mml:mi>M</mml:mi> <mml:mn>10</mml:mn></mml:msub> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>C</mml:mi> <mml:mi>O</mml:mi></mml:mrow> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>S</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:math> , as being strongly linked to higher rates of diabetes, dyslipidemia, and hypertension in central and northern cities. Future region-specific public health strategies or interventions, especially in those areas with high pollutant levels, are needed to mitigate air pollution's impact on metabolic health.