To Örebro University

INTRODUCTION: We aimed to determine the diagnostic and prognostic potential of baseline microbiome profiling in inflammatory bowel disease (IBD).

METHODS: Participants with ulcerative colitis (UC), Crohn's disease (CD), suspected IBD, and non-IBD symptomatic controls were included in the prospective population-based cohort Inflammatory Bowel Disease in South-Eastern Norway III (third iteration) based on suspicion of IBD. The participants donated fecal samples that were analyzed with 16S rRNA sequencing. Disease course severity was evaluated at the 1-year follow-up. A stringent statistical consensus approach for differential abundance analysis with 3 different tools was applied, together with machine learning modeling.

RESULTS: A total of 1404 individuals were included, where n = 1229 samples from adults were used in the main analyses (n = 658 UC, n = 324 CD, n = 36 IBD-U, n = 67 suspected IBD, and n = 144 non-IBD symptomatic controls). Microbiome profiles were compared with biochemical markers in machine learning models to differentiate IBD from non-IBD symptomatic controls (area under the receiver operating curve [AUC] 0.75-0.79). For UC vs controls, integrating microbiome data with biochemical markers like fecal calprotectin mildly improved classification (AUC 0.83 to 0.86, P < .0001). Extensive differences in microbiome composition between UC and CD were identified, which could be quantified as an index of differentially abundant genera. This index was validated across published datasets from 3 continents. The UC-CD index discriminated between ileal and colonic CD (linear regression, P = .008) and between colonic CD and UC (P = .005), suggesting a location-dependent gradient. Microbiome profiles outperformed biochemical markers in predicting a severe disease course in UC (AUC 0.72 vs 0.65, P < .0001), even in those with a mild disease at baseline (AUC 0.66 vs 0.59, P < .0001).

CONCLUSIONS: Fecal microbiome profiling at baseline held limited potential to diagnose IBD from non-IBD compared with standard-of-care. However, microbiome shows promise for predicting future disease courses in UC.