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Quantifying the relation between gut microbiome and body weight can provideinsights into personalized strategies for improving digestive health. In thispaper, we present an algorithm that predicts weight fluctuations using gutmicrobiome in a healthy cohort of newborns from a previously published dataset.Microbial data has been known to present unique statistical challenges thatdefy most conventional models. We propose a mixed effect Dirichlet-treemultinomial (DTM) model to untangle these difficulties as well as incorporatecovariate information and account for species relatedness. The DTM setup allowsone to easily invoke empirical Bayes shrinkage on each node for enhancedinference of microbial proportions. Using these estimates, we subsequentlyapply random forest for weight prediction and obtain a microbiome-inferredweight metric. Our result demonstrates that microbiome-inferred weight issignificantly associated with weight changes in the future and its non-trivialeffect size makes it a viable candidate to forecast weight progression.

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