Background Pediatric inflammatory bowel disease (IBD) is an emergent health challenge globally, marked by complex interactions between host genetics, immune responses, and the gut microbiome. Despite advances in sequencing and computational approaches, robust, clinically translatable biomarkers remain limited, especially for Asian pediatric populations. This study aimed to integrate network-based microbial community analysis with machine learning (ML) to identify treatment-responsive bacterial taxa and predictive biomarkers in Chinese children with IBD. Methods A prospective cohort of 50 children (30 IBD, 20 non-IBD controls) was enrolled at a tertiary hospital in Beijing, China. Fecal samples underwent 16 S rRNA sequencing, and clinical data were systematically recorded. Microbial diversity, taxonomic shifts, and co-occurrence networks were analyzed using QIIME 2, SparCC, and Cytoscape. Four ML classifiers (Random Forest, Logistic Regression, XGBoost, and Voting Classifier) were developed, with SHAP used for model interpretability. Decision curve analysis (DCA) assessed clinical net benefit. Results Children with IBD demonstrated significantly reduced Shannon diversity (3.55 ± 0.58 vs. 4.20 ± 0.46, p = 0.003) and observed operational taxonomic units (OTUs) (134 ± 30 vs. 188 ± 40, p = 0.001) compared to controls. Beta diversity (Bray–Curtis, PERMANOVA p = 0.001) and (Non-metric multidimensional scaling) NMDS revealed distinct community clustering. IBD networks were fragmented, with fewer edges (85 vs. 120, p = 0.02) and lower clustering coefficient (0.25 vs. 0.40, p = 0.01). Key taxa such as Faecalibacterium (3.2% ± 2.1 vs. 8.5% ± 4.0, p = 0.0005) were depleted in IBD, while Escherichia–Shigella (6.8% ± 5.5 vs. 1.2% ± 2.0, p = 0.002) and Veillonella (2.5% ± 2.2 vs. 0.5% ± 0.8, p = 0.010) were enriched. ML models achieved high accuracy (XGBoost: 98%, Voting Classifier: 97%), with SHAP analysis confirming these taxa and inflammation markers (CRP/ESR, calprotectin) as top predictors. DCA demonstrated clear net benefit for ML-guided diagnosis. Conclusions Network-based and ML-integrated analysis of the gut microbiome in pediatric IBD identified reproducible, treatment-responsive biomarkers and delivered accurate, interpretable prediction. This integrative approach supports precision diagnostics and monitoring in pediatric IBD.

Network and machine learning integration reveals gut microbiome biomarkers in pediatric IBD

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2025年12月22日

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Yixing Luo,
Yang Yang

摘要

Background

Pediatric inflammatory bowel disease (IBD) is an emergent health challenge globally, marked by complex interactions between host genetics, immune responses, and the gut microbiome. Despite advances in sequencing and computational approaches, robust, clinically translatable biomarkers remain limited, especially for Asian pediatric populations. This study aimed to integrate network-based microbial community analysis with machine learning (ML) to identify treatment-responsive bacterial taxa and predictive biomarkers in Chinese children with IBD.

Methods

A prospective cohort of 50 children (30 IBD, 20 non-IBD controls) was enrolled at a tertiary hospital in Beijing, China. Fecal samples underwent 16 S rRNA sequencing, and clinical data were systematically recorded. Microbial diversity, taxonomic shifts, and co-occurrence networks were analyzed using QIIME 2, SparCC, and Cytoscape. Four ML classifiers (Random Forest, Logistic Regression, XGBoost, and Voting Classifier) were developed, with SHAP used for model interpretability. Decision curve analysis (DCA) assessed clinical net benefit.

Results

Children with IBD demonstrated significantly reduced Shannon diversity (3.55 ± 0.58 vs. 4.20 ± 0.46, p = 0.003) and observed operational taxonomic units (OTUs) (134 ± 30 vs. 188 ± 40, p = 0.001) compared to controls. Beta diversity (Bray–Curtis, PERMANOVA p = 0.001) and (Non-metric multidimensional scaling) NMDS revealed distinct community clustering. IBD networks were fragmented, with fewer edges (85 vs. 120, p = 0.02) and lower clustering coefficient (0.25 vs. 0.40, p = 0.01). Key taxa such as Faecalibacterium (3.2% ± 2.1 vs. 8.5% ± 4.0, p = 0.0005) were depleted in IBD, while Escherichia–Shigella (6.8% ± 5.5 vs. 1.2% ± 2.0, p = 0.002) and Veillonella (2.5% ± 2.2 vs. 0.5% ± 0.8, p = 0.010) were enriched. ML models achieved high accuracy (XGBoost: 98%, Voting Classifier: 97%), with SHAP analysis confirming these taxa and inflammation markers (CRP/ESR, calprotectin) as top predictors. DCA demonstrated clear net benefit for ML-guided diagnosis.

Conclusions

Network-based and ML-integrated analysis of the gut microbiome in pediatric IBD identified reproducible, treatment-responsive biomarkers and delivered accurate, interpretable prediction. This integrative approach supports precision diagnostics and monitoring in pediatric IBD.