ArticleInflammatory bowel diseases2026
Metagenomic Sequencing Reveals Distinct Gut Microbiome Profiles in Therapy-Naïve de Novo Pediatric Inflammatory Bowel Disease.
Article in Inflammatory bowel diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular Mechanisms Underlying the Higher Prevalence of Anemia in Crohn's Disease Compared with Ulcerative Colitis: A Systematic Review.International journal of molecular sciences · 2026Pooled it
- Microbial and functional shifts between flare and remission in a single-center cohort of children with inflammatory bowel disease.World journal of clinical pediatrics · 2026Article
- From Multi-Omics to Molecular Causality: A Five-Tier Evidence Framework for the Single-Bacterium-Metabolite Axis in IBD.Microorganisms · 2026Review
- Advances in non-invasive biomarkers for pediatric inflammatory bowel disease diagnosis.World journal of gastroenterology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
aimsMicrobiome studies reveal distinct microbial differences in inflammatory bowel disease (IBD), indicating its potential role in pathophysiology and as a noninvasive diagnostic biomarker. This study aims to profile the gut microbiome in children with IBD, compared to both healthy controls (HC) and controls with gastrointestinal symptoms (CGI), and to assess the potential of microbiome profiles as noninvasive diagnostic markers for de novo treatment-naïve pediatric IBD, and as early predictive markers for therapy response.
methodsWe analyzed baseline fecal samples and clinical data from 103 therapy-naïve children with IBD, 75 CGI, and 356 age and sex matched HC. Metagenomic sequencing was performed, and prediction models assessed diagnostic potential and prediction of induction therapy response at 3 months.
resultsAlpha diversity progressively decreased from HC to CGI (P < .001) and decreased even further in IBD patients (P = .0056). Beta diversity analysis showed significant clustering differences (P < .001, R2 = 0.045). Differential abundance analysis revealed 116 species differing between HC and IBD, and 30 species between CGI and IBD. Prediction models based on microbiome features accurately distinguished IBD from HC (area under the curve [AUC] = 0.96) and from CGI (AUC = 0.71). However, these models were outperformed by clinical features, such as fecal calprotectin. Microbiome-based prediction of response to induction therapy in general showed limited accuracy (AUC = 0.63), as well as for response to nutritional induction therapy (AUC = 0.67).
conclusionsWe observed profound gut microbiome differences between de novo, therapy-naïve pediatric IBD patients and controls. While microbiome profiles hold promise for improving diagnostic precision, their predictive value for therapy response seems limited.
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