Gut microbiota signatures differentiate trajectory-defined response phenotypes and predict self-management outcomes...

Heterogeneity in symptom presentation and treatment response in irritable bowel syndrome (IBS) remains poorly understood. The gut microbiota may contribute to this variability, but its role in shaping symptom trajectories and responses to self-management interventions is unclear.

To identify symptom trajectory phenotypes and determine whether gut microbiota composition and function distinguish these phenotypes and predict multidimensional responses to pain self-management interventions in young adults with IBS.

Ancillary data analysis from a randomized control trial ( NCT03332537 ).

Participants with longitudinal data (n = 62) were analyzed using longitudinal k-means clustering (KML) based on trajectories of measures in IBS quality of life (QOL), Brief Pain Inventory (BPI), and psychoneurological outcomes (anxiety, applied cognition, depression, fatigue, global health, positive affect, and sleep disturbance) over 12 weeks. Baseline differences between clusters were assessed with Wilcoxon rank-sum tests, and longitudinal changes were evaluated with linear mixed models. Gut microbiota composition and predicted functional pathways were compared between phenotypes. Bayesian Additive Regression Trees (BART) models were used to identify baseline microbial taxa and pathways predictive of longitudinal changes in QOL, BPI pain interference, and severity.

Two distinct trajectory-defined response phenotypes were identified: a Constrained Response Phenotype (Phenotype A, n = 35) and an Adaptive Multidomain Response Phenotype (Phenotype B, n = 27). At baseline, Phenotype B showed lower pain severity and interference, but higher levels of anxiety, depression, and fatigue compared to Phenotype A. Over 12 weeks, both phenotypes showed improvements in pain outcomes (all p < 0.05), but only Phenotype B demonstrated broad improvements across psychoneurological domains and QOL (all p < 0.05). Phenotype A exhibited more limited improvements and worsening in several psychoneurological domains. Gut microbiota functional pathways differed between phenotypes, including pathways related to xenobiotic degradation, amino acid metabolism, bile secretion, and immune-related processes (all raw p < 0.05), although these did not remain significant after multiple testing correction. Machine learning models identified distinct, phenotype-specific microbial predictors of intervention response. In Phenotype A, genera such as Alistipes and Sutterella were consistently identified across models, whereas in Phenotype B, predictors included Phascolarctobacterium , Collinsella , and Parabacteroides . Functional pathways also differed between phenotypes, suggesting distinct microbiome-linked mechanisms underlying symptom trajectories and responses to pain interventions.

Young adults with IBS exhibit distinct multidimensional response phenotypes that are associated with differential clinical and microbiome profiles. Baseline gut microbiota composition and functional capacity demonstrate phenotype-specific predictive signatures of treatment response, supporting a microbiome-informed framework for stratifying patients and advancing personalized self-management strategies in IBS.

□ Substantial heterogeneity exists in irritable bowel syndrome symptoms and treatment response, with variability across pain, psychological distress, and quality of life domains.□ Gut microbiota composition and function are linked to IBS pathophysiology, including associations with pain sensitivity, inflammation, and brain-gut signaling.□ Self-management interventions (e.g., mindfulness, behavioral strategies) can improve IBS symptoms, but responses are inconsistent and difficult to predict.

□ Distinct longitudinal symptom trajectory phenotypes were identified, separating individuals with pain-predominant and limited psychoneurological improvement from those with lower pain and greater multidomain psychoneurological improvement.□ Gut microbiota composition and functional profiles varied between trajectory-defined clusters, indicating a biological foundation for differences in symptom patterns.□ Machine learning models showed that gut microbiota features predict pain severity, interference, quality of life, and their longitudinal changes, supporting microbiome-based stratification for self-management outcomes.