ArticleInflammatory bowel diseases2020
Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.
Article in Inflammatory bowel diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Fecal Microbial Community Profiling Allows Discrimination of Phenotype and Treatment Response in Pediatric Crohn's Disease and Ulcerative Colitis-An International Meta-Analysis.Inflammatory bowel diseases · 2025Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Current perspectives and future horizons in predicting response to nutrition therapy in children with Crohn's disease - a narrative review.Pediatric research · 2026Review
- Predictors of using biologics within one year after EEN therapy in pediatric Crohn's disease.Translational pediatrics · 2026Article
- Microbial predictors of sustained clinical remission following the Crohn's disease exclusion diet in adults and microbiome shifts toward healthy pediatric controls.Crohn's & colitis 360 · 2026Article
- Artificial Intelligence in Pediatric Inflammatory Bowel Disease: Applications in Diagnosis, Monitoring, and Therapeutic Decision-Making.Children (Basel, Switzerland) · 2026Review
- Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.Journal of pediatric gastroenterology and nutrition · 2026Article
- Baseline gut microbiome features associated with fecal calprotectin response to exclusive enteral nutrition in pediatric Crohn's disease.Frontiers in pediatrics · 2026Article
- A prediction model for genetic cholestatic disease in infancy using the machine learning approach.Journal of pediatric gastroenterology and nutrition · 2025Article
- Therapeutic mechanisms of exclusive enteral nutrition in Crohn's disease.Seminars in immunopathology · 2025Review
- Artificial intelligence in inflammatory bowel disease.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Review
- Age, inflammation, alkaline phosphatase, and coronary artery calcification in firefighters.BMC cardiovascular disorders · 2025Article
- Tryptophan metabolites profile predict remission with dietary therapy in pediatric Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.PLoS computational biology · 2024Article
- Mechanisms of Action of Exclusive Enteral Nutrition and Other Nutritional Therapies in Crohn's Disease.Nutrients · 2024Review
- Dietary interventions in Crohn's disease: A simple solution to refractory disease.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Article
- Enteral nutrition promotes the remission of colitis by gut bacteria-mediated histidine biosynthesis.EBioMedicine · 2024Article
- Interplay between inflammatory bowel disease therapeutics and the gut microbiome reveals opportunities for novel treatment approaches.Microbiome research reports · 2023Article
- Unexpectedly high incidence of Kawasaki Disease in a Canadian Atlantic Province- an 11-year retrospective descriptive study.Pediatric rheumatology online journal · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks.
methodsA total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels.
resultsMicrobial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus.
conclusionsOur machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.