Mixed Effect Dirichlet-Tree Multinomial for Longitudinal Microbiome Data and Weight Prediction

TL;DR

This paper introduces a novel mixed effect Dirichlet-tree multinomial model to analyze longitudinal microbiome data, enabling improved weight prediction in newborns by capturing microbial composition and related covariates.

Contribution

The paper presents a new statistical model that addresses microbiome data challenges, incorporating covariates and species relatedness for better weight prediction.

Findings

Microbiome-inferred weight correlates significantly with future weight changes.

The proposed model enhances inference of microbial proportions through empirical Bayes shrinkage.

Using microbiome data improves weight prediction accuracy.

Abstract

Quantifying the relation between gut microbiome and body weight can provide insights into personalized strategies for improving digestive health. In this paper, we present an algorithm that predicts weight fluctuations using gut microbiome in a healthy cohort of newborns from a previously published dataset. Microbial data has been known to present unique statistical challenges that defy most conventional models. We propose a mixed effect Dirichlet-tree multinomial (DTM) model to untangle these difficulties as well as incorporate covariate information and account for species relatedness. The DTM setup allows one to easily invoke empirical Bayes shrinkage on each node for enhanced inference of microbial proportions. Using these estimates, we subsequently apply random forest for weight prediction and obtain a microbiome-inferred weight metric. Our result demonstrates that microbiome-inferred weight is significantly associated with weight changes in the future and its non-trivial effect size makes it a viable candidate to forecast weight progression.