Presented by: Yiqing Wang
Background: Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder, yet the role of diet and gut microbial communities in the pathophysiology of IBS is not fully understood. Thus, we investigated the interplay between dietary risk factors and specific taxonomic and functional groups in IBS subtypes.
Methods: We drew data from the Personalized Responses to Dietary Composition Trial (PREDICT) 1 study, a single-arm, single-blind intervention study of 969 UK adults aged 18-65 years using standardized meals to predict individual metabolic response to foods. Participants were classified into non-IBS and IBS subtypes (IBS-C, constipation; IBS-D, diarrhea; IBS-M, mixed) according to Rome III criteria. Habitual diet was assessed using a food frequency questionnaire and gut microbiome by metagenomic sequencing of stool samples.
Results: Participants with IBS (172, 18%) were predominantly female, younger, and attained higher levels of education than those without IBS (Figure 1A). Participants with IBS-D (49, 5%) more frequently consumed healthy plant-based foods compared to other participants, as well as higher levels of animal-based foods but lower levels of lactose than those without IBS (Figure 1B). Gut microbiome composition differed slightly by IBS subtype, as reflected by nominally lower microbial diversity in IBS-D (Figure 2 A-B). Using linear regression adjusted for a wide range of host factors, we identified several taxa and functional pathways associated with specific IBS subtypes, including slight increases in typically pro-inflammatory taxa during IBS-C (e.g. Ruminococcus gnavus, Escherichia coli) and loss of typical gut strict anaerobes during IBS-D (e.g Faecalibacterium prausnitzii), explaining the overall lower diversity. Although limited by the available population size, these taxa showed intriguing evidence of interaction with dietary risk factors in association with IBS subtypes (Figure 2C). For example, while the predicted probability for IBS-D based on a multivariable-adjusted binomial model was lower at higher levels of Faecalibacterium prausnitzii when fiber intake was low, this association was reversed as fiber intake increased. Although outperformed by other host factors, as expected for the notoriously multifactorial etiology of IBS, gut microbial taxa and functional pathways were significant independent machine learning predictors for distinguishing participants with IBS-D from those with IBS-C or no IBS (Figure 2D).
Conclusions: We identified variations in gut microbial composition, function, and diet-microbiota interactions specific to IBS subtypes. Our findings may provide insights into microbiome-aware dietary interventions for IBS treatment. Further longitudinal studies are needed to confirm our results.