Daniel Sprockett1, Natalie Fischer2, Rotem Sigall Boneh3, Dan Turner4, Jarek Kierkus5, Malgorzata Sladek6, Johanna C Escher7, Eytan Wine8, Baruch Yerushalmi9, Jorge Amil Dias10, Ron Shaoul11, Michal Kori12, Scott B Snapper13,14, Susan Holmes15, Athos Bousvaros13, Arie Levine3,16, David A Relman1,2,17. 1. Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, California, USA. 2. Division of Infectious Diseases & Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA. 3. Pediatric Gastroenterology and Nutrition Unit, Wolfson Medical Center, Holon, Israel. 4. The Juliet Keidan Institute of Pediatric Gastroenterology & Nutrition, Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel. 5. Department of Gastroenterology, Hepatology, Feeding Disorders and Pediatrics, The Children's Memorial Health Institute, Warsaw, Poland. 6. Department of Pediatrics, Gastroenterology and Nutrition, Jagiellonian University Medical College, Cracow, Poland. 7. Department of Pediatric Gastroenterology, Erasmus MC-Sophia Children's Hospital, Rotterdam, the Netherlands. 8. Division of Pediatric Gastroenterology and Nutrition, Department of Pediatrics, University of Alberta, Edmonton, Canada. 9. Pediatric Gastroenterology Unit, Soroka University Medical Center, and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel. 10. Department of Pediatrics, Hospital de Sao Joao, Porto, Portugal. 11. Pediatric Gastroenterology Unit, Ruth Children's Hospital, Rambam Medical Center, Haifa, Israel. 12. Pediatric Day Care Unit, Kaplan Medical Center, Rehovot, Israel. 13. Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, Massachusetts, USA. 14. Division of Gastroenterology, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA. 15. Department of Statistics, Stanford University, Stanford, California, USA. 16. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 17. Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA.
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. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation 2019.
RCT Entities:
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. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation 2019.
Entities:
Keywords:
antibiotics; disease remission; microbiota; pediatric Crohn’s disease; random forest model
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