Bacteroides- and Prevotella-enriched gut microbial clusters associate with metabolic risks

Abstract

The gut microbiome plays a critical role in human health through its influence on numerous physiological functions such as metabolism and immunity, with disruptions in microbial communities increasingly linked to metabolic disorders. In a large-scale cohort study in Japan, we investigated the association between gut microbiome profiles and metabolic health. Using 16S rRNA gene sequencing, four-enterotype clustering revealed that the Bacteroides 2 (B2) enterotype was associated with lower alpha-diversity and increased risk of metabolic diseases, particularly obesity (OR = 1.51) and hypertension (OR = 1.49). Refined seven-enterotype clustering further stratified the Ruminococcus, Prevotella, and Bacteroides enterotypes into distinct subtypes, uncovering a novel high-risk Prevotella 2 (P2) enterotype associated with nearly two-fold increased risk of obesity and diabetes mellitus. The B2 and P2 enterotypes were characterized by reduced abundance of beneficial short-chain fatty acid (SCFA) producers (Faecalibacterium, Anaerostipes) and enrichment of opportunistic pathogens (Fusobacterium and Veillonella for B2, Megamonas and Megasphaera for P2). Microbial metabolic influence network analysis revealed enterotype-specific interaction patterns, with R1, R2, and P1 enterotypes demonstrating cooperative production of SCFAs and other metabolites, while B enterotypes displayed synergy in the production of a range of sugar compounds. These findings underscore the utility of refined enterotype clustering as a powerful tool to reveal previously unrecognized gut microbial patterns linked to metabolic risk. By identifying B2 and the newly characterized P2 enterotypes as high-risk microbial profiles, this study opens new avenues for microbiome-based stratification and early intervention in metabolic disease management.

Keywords: Bacteroidetes; Enterotype; Metabolic disease; Microbiome; Prevotella.

Fig. 1

Bacteroides 2 enterotype associates with metabolic risks. A Principal coordinate analysis plot based on the Bray–Curtis dissimilarity index. Each point represents an individual sample, colour-coded by four enterotypes; Bacteroides 1 (B1), Bacteroides 2 (B2), Ruminococcus (R), and Prevotella (P). B Violin plots showing the distribution of alpha diversity (i.e., Shannon Diversity Index) across different enterotypes. Boxes represent the interquartile range (IQR; Q1 and Q3), with the line inside marking the median. Wilcoxon test followed by pairwise comparison with Bonferroni correction was conducted. *; p value < 2.2 × 10^–308. C Stacked bar plots illustrating the relative abundance of bacterial genera. The thirteen most abundant genera are colour-coded as indicated in the legend and all other genera are grouped in “Others”. D The Linear discriminant analysis Effect Size (LEfSe) plots identifying distinct gut bacterial genera in each enterotype. E Stacked bar plots illustrating the relative abundance of participants with (Cases) and without (Control) cardiometabolic risks (χ² = 105.867, df = 3, p = 8.5 × 10^–23). F Heatmap of odds ratios from logistic regression analysis showing associations between different gut microbiome enterotypes (B1, B2, R and P) and metabolic risks. Metabolic conditions assessed include obesity (OB), hypertension (HT), dyslipidaemia (DL), and diabetes mellitus (DM). *; p value < 0.05

Fig. 2

Seven-enterotype clustering. A Sankey diagram illustrating the reclassification of gut microbiome enterotypes from four clusters (left) to seven clusters (right). The width of each path represents the proportion of samples transitioning from the four-cluster classification to the seven- cluster classification. B Principal coordinate analysis plot using the Bray–Curtis dissimilarity index reveals the degree of separation among seven enterotypes. Each point represents an individual sample, colour-coded by enterotypes. C Violin plots showing the distribution of alpha diversity across seven different gut microbiome enterotypes. Boxes represent the interquartile range (IQR; Q1 and Q3), with the line inside marking the median. *; p value < 2.2 × 10^–308. D Stacked bar plots illustrating the relative abundance of bacterial genera in each of the seven enterotypes. The thirteen most abundant genera are colour-coded as indicated in the legend and all other genera are grouped in “Others”.

Fig. 3

Prevotella 2 enterotype associates with metabolic risks. A The LEfSe analysis was performed to identify distinct gut bacterial genera in each enterotype. B, C, F Violin plots showing the centred log ratio- transformed relative abundance of each taxa across the seven enterotypes. Each violin plot displays the distribution of abundance values within each enterotype. D Stacked bar plots illustrating the relative abundance of participants with (Cases) and without (Control) metabolic risks factor across different enterotypes (χ² = 105.053, df = 6, p = 2.21 × 10^–20). E Heatmap of odds ratios from logistic regression analysis showing associations between different gut microbiome enterotypes and metabolic risks, which includes obesity (OB), hypertension (HT), dyslipidaemia (DL), and diabetes mellitus (DM). *; p value < 0.05

Fig. 4

Microbial metabolic influence network (MIN) analysis shows association between metabolites and enterotypes. For the metabolites that showed differences in total metabolic specific influence (TMSI) index between enterotypes, TSMI index were compared using a heatmap. A Metabolites involved in cooperative interspecies interactions. B Metabolites involved exclusively in competitive interspecies interactions.