Treatment-specific composition of the gut microbiota is associated with disease remission in a pediatric crohn's disease cohort

Daniel Sprockett, Natalie Fischer, Rotem Sigall Boneh, Dan Turner, Jarek Kierkus, Malgorzata Sladek, Johanna C. Escher, Eytan Wine, Baruch Yerushalmi, Jorge Amil Dias, Ron Shaoul, Michal Kori, Scott B. Snapper, Susan Holmes, Athos Bousvaros, Arie Levine, David A. Relman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The beneficial effects of antibiotics in Crohn's disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD and the use of microbiota features as classifiers or predictors of disease remission. Methods: 16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct random forest models to classify remission or predict treatment response. Results: Both MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random forest models constructed from microbiota profiles before and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (area under the curve [AUC], 0.879; 95% confidence interval, 0.683-0.9877; sensitivity, 0.7778; specificity, 1.000; P < 0.001). A random forest model trained on pre-antibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC, 0.8; P = 0.24). Conclusions: MET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.

Original languageEnglish
Pages (from-to)1927-1938
Number of pages12
JournalInflammatory Bowel Diseases
Volume25
Issue number12
DOIs
StatePublished - 1 Dec 2019

Funding

FundersFunder number
**Department of Pediatric Gastroenterology
Chan Zuckerburg Biohub Microbiome Initiative
Children’s Memorial Health Institute
Department of Microbiology & Immunology
Department ofPediatrics, University ofAlberta
Erasmus MC-Sophia Children’s Hospital, Rotterdam
Juliet Keidan Institute of Pediatric Gastroenterology & Nutrition, Shaare Zedek Medical Center
Rambam Medical Center, Haifa, Israel
Ruth Children’s Hospital
Sackler School of Medicine, Tel Aviv University
Soroka University Medical Center
Stanford University School of Medicine, Stanford, California, USA
Thomas C. and Joan M. Merigan Endowment
Wolfson Medical Center, Holon, Israel
National Science FoundationDGE-114747
National Institutes of Health
National Institute of General Medical SciencesT32GM007276
Brigham and Women's Hospital
Stanford University
School of Medicine, Stanford University
Harvard Medical School
Boston Children's Hospital
Leona M. and Harry B. Helmsley Charitable Trust
Uniwersytet Jagielloński Collegium Medicum
Department of Medicine, Georgetown University
‡Pediatric Gastroenterology and Nutrition Unit

    Keywords

    • Antibiotics
    • Disease remission
    • Microbiota
    • Pediatric Crohn's disease
    • Random forest model

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