Mediation Analysis with Compositional Exposures

Thomas M. Menino Convention & Exhibition Center 

Microbe-derived metabolites are key mediators in the interactions between a host and their microbiome. Understanding the causal links between the gut microbiome, its metabolites, and a clinical outcome provides new potential for understanding, preventing and treating microbiome-associated diseases. While many studies have focused on the mediating role of the microbiome, few have specifically explored the role of bacterial metabolites as mediators, with the microbiome itself acting as the exposure. This shift in perspective introduces unique methodological challenges, particularly due to the high dimensionality and compositional structure of microbiome data. To address these challenges, we propose a latent variable mediation framework that captures variation in microbial composition through a microbial balance, defined as the log-ratio between two unknown subsets of taxa. This balance serves as a latent scalar exposure and simplifies the estimation of the overall indirect effect at the community level, while simultaneously identifying specific taxa that contribute to the overall direct and indirect effects. This article covers the model's estimation and inference, and illustrates its real-world application using data from a randomized controlled crossover feeding trial.

compositional data

balances

mediation analysis

Bayesian inference