Unveiling metabotype clustering in resveratrol, daidzein, and ellagic acid metabolism: Prevalence, associated gut microbiomes, and their distinctive microbial networks

Abstract

The gut microbiota (GM) produces different polyphenol-derived metabolites, yielding high interindividual variability and hampering consistent health effects. GM metabotypes associated with ellagic acid (urolithin metabotypes A (UMA), B (UMB), and 0 (UM0)), resveratrol (lunularin -producers (LP) and non-producers (LNP)), and daidzein (equol-producers (EP) and non-producers (ENP)) are known. However, individual polyphenol-related metabotypes do not occur individually. In contrast, different combinations coexist (i.e., metabotype clusters, MCs). We report here for the first time these MCs, their distribution, and their associated GM in adult humans (n = 127) after consuming for 7 days a nutraceutical (pomegranate, Polygonum cuspidatum, and red clover extracts) containing ellagitannins + ellagic acid, resveratrol, and isoflavones. Urine metabolites (UHPLC-QTOF-MS) and fecal microbiota (16S rRNA sequencing) were analyzed. Ten MCs were identified: LP + UMB + ENP (22.7%), LP + UMA + ENP (21.3%), LP + UMA + EP (16.7%), LP + UMB + EP (16%), LNP + UMA + ENP (11.3%), LNP + UMB + ENP (5.3%), LNP + UMA + EP (3.3%), LNP + UMB + EP (2%), LNP + UM0 + EP (0.7%), and LNP + UM0 + ENP (0.7%). Sex, BMI, and age did not affect the distribution of metabotypes or MCs. Multivariate analysis (MaAslin2) revealed genera differentially present in individual metabotypes and MCs. Network analysis (MENA) showed the taxa acting as module hubs and connectors. Compositional and functional profiling, alpha and beta diversities, topological network features, and GM modulation by the nutraceutical differed depending on whether the entire cohort or each MC was considered. The nutraceutical did not change the composition of LP + UMA + EP (the most robust GM with the most associated functions) but increased its network connectors. This pioneering approach, joining GM’s compositional, functional, and network features in polyphenol metabolism, paves the way for identifying personalized GM-targeted strategies to improve polyphenol health benefits.