ArticleFrontiers in nutrition2026
Resistant starch selectively depletes a putative pathobiont-enriched gut microbial module: evidence from multiple dietary fiber intervention cohorts.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Dietary fiber, particularly resistant starch (RS), has been proposed to modulate the gut microbiota and immune microenvironment; however, individual intervention studies yield inconsistent results due to inter-study heterogeneity. This study aimed to identify reproducible gut microbiota responses to RS across multiple independent cohorts and explore the ecological basis by which RS shapes microbial community structure. Methods: Gut microbiota composition was analyzed in 586 paired fecal samples collected from healthy adults in multiple independent cohorts undergoing RS dietary interventions. All datasets were processed through a unified bioinformatics pipeline to minimize technical variability. Differential abundance analysis, co-occurrence network analysis, and validation against inflammatory bowel disease (IBD) populations were performed to identify reproducible microbial responses and assess their potential clinical relevance. Results: RS supplementation was associated a consistent reduction in alpha diversity and a reproducible, unidirectional taxonomic response: 22 specific taxa were consistently depleted across cohorts, including putative immunostimulatory pathobionts such as Conclusion: Across cohorts, RS intake was consistently associated with a selective ecological filter that consistently depletes a set of microbial taxa including putative immunostimulatory pathobionts, providing a mechanistic basis for its proposed role in dietary modulation of the gut immune microenvironment. These consistently depleted taxa may serve as candidate biomarkers to identify individuals most likely to benefit from RS-based dietary fiber interventions, while their utility for predicting individual responsiveness before intervention remains to be tested.
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