Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes
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=1832, 270 cases) and two validation cohorts (cohort 1: n=203, 48 cases; cohort 2: n=7009, 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 non-T2D participants, and assessed the correlation between the MRS and host blood metabolites (n=1016). We transferred human faecal 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=1832).
RESULTS The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per one 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 3 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 confirms 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.