HT-MMIOW: A Hypothesis Test approach for Microbiome Mediation using Inverse Odds Weighting

TL;DR

This paper introduces HT-MMIOW, a new hypothesis testing method using inverse odds weighting to assess microbiome mediation effects, effectively handling high-dimensional, correlated, and compositional microbiome data.

Contribution

The paper presents a novel inverse odds weighting approach for microbiome mediation analysis that addresses high dimensionality and data complexity, improving power and flexibility.

Findings

Identified microbiome mediating effect between maternal antibiotics and childhood allergy.

Demonstrated method's effectiveness through simulation studies.

Applied method to real microbiome data revealing significant mediation.

Abstract

The human microbiome has an important role in determining health. Mediation analyses quantify the contribution of the microbiome in the causal path between exposure and disease; however, current mediation models cannot fully capture the high dimensional, correlated, and compositional nature of microbiome data and do not typically accommodate dichotomous outcomes. We propose a novel approach that uses inverse odds weighting to test for the mediating effect of the microbiome. We use simulation to demonstrate that our approach gains power for high dimensional mediators, and it is agnostic to the effect of interactions between the exposure and mediators. Our application to infant gut microbiome data from the New Hampshire Birth Cohort Study revealed a mediating effect of 6-week infant gut microbiome on the relationship between maternal prenatal antibiotic use during pregnancy and incidence of childhood allergy by 5 years of age.