Abstract
INTRODUCTION: Despite the effectiveness of infliximab in treating Crohn's disease (CD), up to 40 % of patients fail to respond adequately. OBJECTIVES: This study aimed to identify predictive biomarkers of primary non-response to infliximab in treatment-naïve CD patients by characterizing baseline gut microbiome-metabolome interactions and to validate their mechanistic role in driving therapeutic resistance. METHODS: In a prospective cohort of 100 CD patients initiating infliximab therapy and 49 healthy controls, we performed longitudinal 16S rRNA sequencing and untargeted metabolomics on pre-/post-treatment fecal samples. Machine learning (twelve algorithms including K-Nearest Neighbors, Linear Discriminant Analysis, Naive Bayes, and LightGBM) identified predictive microbial and metabolic features, with findings experimentally validated through fecal microbiota transplantation (FMT) in a murine TNBS-induced colitis model. RESULTS: Non-responders at baseline demonstrated significant microbial dysbiosis marked by β-diversity variation, depletion of Bifidobacterium, Blautia, and Lachnospiraceae, and enrichment of Escherichia/Shigella. Metabolomic profiling identified 179 differentially abundant metabolites, including deficiencies in taurochenodeoxycholic acid (TCDCA) and perturbations in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Among single-omics models, the microbiome-based Linear Discriminant Analysis achieved optimal performance (test AUC = 0.805), surpassing metabolomics-only (best AUC = 0.634) and integrated multi-omics approaches (best AUC = 0.779). SHAP analysis revealed Bifidobacterium as the dominant protective predictor, with its depletion strongly associated with non-response. Mechanistically, MIMOSA2 analysis linked Bifidobacterium catenulatum to TCDCA production, while FMT from non-responders exacerbated murine colitis through Treg depletion and M1 macrophage polarization, confirming microbiome-driven immune dysregulation. CONCLUSIONS: These findings establish gut microbiome composition, particularly Bifidobacterium abundance, as a critical determinant of anti-TNF response in CD, mediated through bile acid-dependent regulation of Treg/M1 macrophage homeostasis. While multi-omics integration did not enhance predictive performance, microbiome-based machine learning models offer clinically actionable biomarkers for treatment stratification, providing a roadmap for precision therapy to overcome biological resistance in inflammatory bowel disease.