Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes.

ABSTRACT

OBJECTIVE: To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features. RESEARCH DESIGN AND METHODS: We used an interpretable machine learning framework to identify the type 2 diabetes-related gut microbiome features in the cross-sectional analyses of three Chinese cohorts one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1 n = 203, 48 cases; cohort 2 n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS-type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832). RESULTS: The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23-1.33), 1.23 (1.13-1.34), and 1.12 (1.06-1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further confirmed the MRS-type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship. CONCLUSIONS: Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.