Spatial mixed models for assessing environmental exposure effects on the microbiome

2026-06-16

AI Summary

The mechanisms by which environmental exposures, such as air pollution, influence the microbiome remain poorly understood, and existing analytical methods struggle to simultaneously account for spatial dependence across geographic regions and ecological correlations among microbial taxa. This study proposes a novel spatial hierarchical modeling framework that, for the first time, integrates conditional autoregressive priors to jointly model both spatial and ecological dependency structures, coupled with Bayesian inference for effect estimation and feature selection. Simulation studies demonstrate that the proposed approach substantially outperforms current methods in terms of statistical power, false discovery control, and estimation accuracy. Applied to a real-world cohort exposed to PM₂.₅, the method successfully identifies both known and putative microbial genera potentially mediating the health effects of air pollution, offering a more robust analytical tool for environmental microbiome research.

Abstract

The influence of environmental exposures, such as air pollution, on human health has become increasingly recognized. A growing body of evidence suggests that the microbiome may mediate these effects, explaining the relationship between the environment and host biology. However, the impact of environmental exposures on the microbiome is not yet fully understood, and statistical modeling in this context is challenged by complex dependency structures. In particular, microbiome data exhibit spatial dependencies across sampling regions as well as ecological correlations among microbial taxa, which, if ignored, can substantially reduce detection power, leading to missed true signals. We introduce a novel spatial mixed modeling framework for microbiome data that accounts for both region-level spatial dependency and taxon-level ecological dependency using conditional autoregressive priors. Through simulations, we demonstrate that this framework outperforms existing methods that ignore such dependencies, by achieving high detection power in feature selection while maintaining low false positive rates and reduced mean squared error in estimation. Applied to two real studies-data from Food and Microbiome Longitudinal Investigation study and lung microbiome dataset-with fine particulate matter (PM_2.5) exposures, our model identified genera, which are known to be involved in pollution-related health outcomes, as well as novel taxa that may mediate host responses to air pollution. This novel approach offers a powerful and flexible tool for uncovering biologically meaningful associations in complex environmental data.

Problem

• microbiome
• environmental exposure
• spatial dependency
• ecological correlation
• statistical modeling

Innovation

• spatial mixed model
• microbiome
• environmental exposure
• conditional autoregressive prior
• ecological dependency