Mucosal microbiome is predictive of pediatric Crohn's disease across geographic regions in North America

• Type of Material: Article
• Authors: Rajesh Shah, Baylor Health Care System; Kristi Hoffman, Baylor College of Medicine; Lee Denson, Cincinnati Children's Hospital Medical Center; Subramaniam Kugathasan, Emory University; Richard Kellermayer, Baylor College of Medicine
• Language: English
• Date: 2023-01-01
• Publisher: Emory University Libraries
• Publication Version: Final Published Version
• Copyright Statement: © 2023 Shah R et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.12688/f1000research.108810.2
• Title of Journal or Parent Work: F1000Research
• Volume: 11
• Start Page: 156
• End Page: 156
• Grant/Funding Information: The RISK consortium was supported by the Crohn’s and Colitis Foundation.; RK was supported by philanthropic donations through the Gutsy Kids Fund led by the Brock Wagner and Klaasmeyer families.
• Keywords: Feces; Microbiota; machine learning; inflammatory bowel disease; Crohn Disease; microbiome; Humans; Crohn’s disease; Child; Intestinal Mucosa
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Background: Patients with Crohn's disease (CD) have an altered intestinal microbiome, which may facilitate novel diagnostic testing. However, accuracy of microbiome classification models across geographic regions may be limited. Therefore, we sought to examine geographic variation in the microbiome of patients with CD from North America and test the performance of a machine learning classification model across geographic regions. Methods: The RISK cohort included 447 pediatric patients with CD and 221 non-inflammatory bowel disease controls from across North America. Terminal ileum, rectal and fecal samples were obtained prior to treatment for microbiome analysis. We divided study sites into 3 geographic regions to examine regional microbiome differences. We trained and tested the performance of a machine learning classification model across these regions. Results: No differences were seen in the mucosal microbiome of patients with CD across regions or in either the fecal or mucosal microbiomes of controls. Machine learning classification algorithms for patients with CD performed well across regions (area under the receiver operating characteristic curve [AUROC] range of 0.85-0.91) with the best results from terminal ileum. Conclusions: This study demonstrated the feasibility of microbiome based diagnostic testing in pediatric patients with CD within North America, independently from regional influences.