ArticleEBioMedicine2025
De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.
Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDetermining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States.
methodsUsing aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation between these clusters with chronic disease geography (defined as groups of counties with high chronic disease prevalence rates) was calculated. Disease-pollution correlation matrices were used together with network analysis to identify the strongest disease-pollution relationships. Results were compared to LISA, Moran's I, univariate, elastic net, and random forest regression.
findingsaPEER produced 68,820 human interpretable maps with distinct pollution-derived regions, and acetaldehyde/benzo(a)pyrene was found to be strongly associated with hypertension (J = 0.5316, p = 3.89 × 10
interpretationaPEER identified a pollution-defined geographical region associated with chronic disease, highlighting the role of aPEER in epidemiological and geospatial analysis, and exposomics in understanding chronic disease geography.
fundingThis work was primarily funded by the BPHC, NHLBI (R03 HL157890) and the CDC, and this work was funded in part by grants from the NIH (U01 HG007691, R01 HL155107, and HL166137), the American Heart Association (AHA24MERIT1185447), and the EU (HorizonHealth 2021 101057619) to JL.
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