Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation.

ABSTRACT: The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associ

SUBMITTER: Aasmets O 

PROVIDER: S-EPMC8573957 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature