Inflammatory bowel diseases · 2026
Metagenomic Sequencing Reveals Distinct Gut Microbiome Profiles in Therapy-Naïve de Novo Pediatric Inflammatory Bowel Disease.
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Vermeer Eva, Prins Femke M, Hidding Iwan J, Jagt Jasmijn Z, de Jonge Robert, Benninga Marc A, Gacesa Ranko, Weersma Rinse K, de Boer Nanne K H, de Meij Tim G J
Аннотация
BACKGROUND AND AIMS: Microbiome 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.
METHODS: We 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.
RESULTS: Alpha 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).
CONCLUSIONS: We 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.
METHODS: We 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.
RESULTS: Alpha 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).
CONCLUSIONS: We 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.