Fecal Microbial Community Profiling Allows Discrimination of Phenotype and Treatment Response in Pediatric Crohn's Disease and Ulcerative Colitis - An International Meta-Analysis

Background and aims The pathophysiology of pediatric inflammatory bowel disease (PIBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), is not entirely understood. Dysregulation of the intestinal microbiome is recognized as both a disease-driving and a potential therapeutic target. This study aimed to systematically analyze gut microbiome compositions and its applicability as a biomarker for disease progress and treatment response. Methods Bibliographic and nucleotide databases were searched. Raw 16S-rRNA sequencing reads were subjected to a uniform downstream dada2/phyloseq pipeline to extract taxonomy, community structure, and abundance information. Patient metadata were extracted from publications, and study authors were contacted for further details if required. Results Twenty-six studies comprising 3956 stool samples (CD 41%, UC 36%, 23% healthy) were included in the analyses. Median age of individuals was 12 (interquartile range 4). Sex distribution was comparable. Alpha diversity was reduced between the healthy and both UC and CD treatment-naïve groups (P < .001) and further reduced with increasing clinical disease activity. Beta diversity revealed altered community structure in treatment-naïve children with PIBD (P < .001). This alteration remained in patients in clinical remission (P < .001). Machine learning models discriminated between treatment-naïve patients with CD or UC with an area under the receiver operating characteristics curve (AUROC) of 98%. Microbial communities differed between patient responders versus nonresponders to treatment (P < .001). Further, microbial community profiling distinguished treatment response (eg, steroid, nutrition, or TNFα) with AUROCs of 82%-90%. Conclusions Gut microbial community structure is substantially altered in active and inactive PIBD and may be utilized as a biomarker for differentiating PIBD subtype and predicting treatment response.