Fig. 4: Gut microbial features explaining glucose intolerance.
a, Bi-network showing the top 300 MGS–metabolite pairs with the largest absolute SHAP values based on a force-directed algorithm. b, Network analysis of the node degree and betweenness centrality for those top 300 MGS–metabolite pairs. c, Bidirectional causal inference using mediation analyses to estimate the proportions of effect mediated by hippurate between H. microfluidus and B. wexlerae. d, Dot plot showing the significant correlations between plasma hippurate and H. microfluidus and B. wexlerae, respectively, in the discovery cohort (Spearman ρ correlation analyses, raw two-sided P < 2.2 × 10−16). e, Replication of the Hominifimenecus-hippurate-Blautia associations in a Chinese cohort (Spearman ρ correlation analyses, two-sided raw P < 2.2 × 10−16). f, Top ten metabolites identified as important features in the 2-h OGTT, FBG HbA1c, fasting insulin, HOMA-IR or FINDRISC (n = 49 in total) based on the GBDT models; metabolites were order according to their SHAP values (reflecting feature importance) to 2-h OGTT levels. The lifestyle features (purple) including both the physical activity levels (as measured by steps per day) and dietary components or MGSs (green) with the maximum or minimum SHAP values for each metabolite are also shown on the right. Metabolites were colored using pathway annotations, including those involved in amino acid, lipid, carbohydrate and xenobiotic metabolism as in a.
