Gut Microbial Signatures Associated With Clinical Remission in Inflammatory Bowel Disease Treated With Biologics: A Comprehensive Multi‐Cohort Analysis

ABSTRACT Background and Aims The relationship between gut microbiota and biological treatment response in inflammatory bowel disease (IBD) remains incompletely understood. We sought to characterize microbial signatures associated with clinical remission and develop a prediction model for clinical remission. Methods We analyzed 16 S rRNA gene sequencing data from two independent public cohorts ( n = 231) treated with biologics (infliximab: n = 23; adalimumab: n = 22; ustekinumab: n = 186). Microbial diversity and taxonomic compositions were compared between the remission and non‐remission groups. Random Forest algorithm was employed to construct a prediction model using differential genera and clinical features, with performance evaluated through cross‐validation. The model was further validated in a local cohort ( n = 29). Results Significant differences in alpha and beta diversity were observed between the remission and non‐remission groups ( p < 0.05). MaAsLin2 analysis identified 25 differentially abundant genera ( p < 0.05). Among these, we selected the top 10 genera with highest importance scores ( Parabacteroides_B_862066 , Agathobaculum , Ruminococcus_E , Sutterella , Clostridium_R_135822 , Hominilimicola , Onthenecus , Butyricimonas , Bariatricus , Hominenteromicrobium ) to build the Random Forest model, notably all enriched in remission patients. The model demonstrated robust predictive performance for clinical remission (AUC: 0.895), which was further validated in the local cohort (AUC: 0.750). Conclusion There is a relationship between gut microbial signatures and biological treatment outcomes in IBD patients. A predictive model based on gut microbiota composition may help stratify patients for treatment response. Further investigation of microbiome modulation strategies may enhance therapeutic efficacy.