ArticleMicrobiome2018
Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease.
Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
64 citing papers in PubMed, 3 syntheses or guidelines pooled it, 127 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
- Metagenomic analysis of gut microbiota in non-treated plaque psoriasis patients stratified by disease severity: development of a new Psoriasis-Microbiome Index.Scientific reports · 2020Pooled it
- Gut microbial signatures and stability are associated with a co-diagnosis of endometriosis and inflammatory bowel disease.iScience · 2026Article
- Gut microbiota as a potential predictor of therapeutic response in adults with Crohn's disease: A systematic review.World journal of gastrointestinal pathophysiology · 2025Article
- Tissue and stool microbiome in pediatric inflammatory bowel disease patients: diversity differs in patients with relapsing and non-relapsing Crohn's disease.Gut pathogens · 2025Article
- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
- Comparison of different microbiome analysis pipelines to validate their reproducibility of gastric mucosal microbiome composition.mSystems · 2025Article
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- BodyMeta: A comprehensive database for microbes under various pathological and physiological conditions.Computational and structural biotechnology journal · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Gut-microbiota-based ensemble model predicts prognosis of pediatric inflammatory bowel disease.iScience · 2024Article
- Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.Cell reports methods · 2024Article
- SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.PLoS computational biology · 2024Article
- Dietary bile acids supplementation decreases hepatic fat deposition with the involvement of altered gut microbiota and liver bile acids profile in broiler chickens.Journal of animal science and biotechnology · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Gut metabolome and microbiota signatures predict response to treatment with exclusive enteral nutrition in a prospective study in children with active Crohn's disease.The American journal of clinical nutrition · 2024Article
- Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression.bioRxiv : the preprint server for biology · 2023Article
- Diagnosis of Crohn's disease and ulcerative colitis using the microbiome.BMC microbiology · 2023Article
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials.Journal of Crohn's & colitis · 2023Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundCrohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.
resultsWe sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.
conclusionsWe demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.
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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.