Decoding IBD progression: a dynamic biomarker atlas for personalized disease stratification.

backgroundAccurate staging is pivotal for tailoring treatment intensity, optimizing resource allocation, and improving long-term patient outcomes in IBD. The intestinal microbiota and transcriptional profiles emerge as critical determinants in IBD staging, demonstrating promise as non-invasive biomarkers for predicting disease progression and informing personalized therapeutic strategies.

methodsWe recruited 97 participants (IBD patients and healthy controls) at the First Affiliated Hospital of Chongqing Medical University, collecting fecal and serum samples for integrated multi-omics analysis. Microbial community profiling was performed via 16S rRNA sequencing, and host transcriptomic landscapes were characterized using RNA-seq. Stage-specific microbial signatures were identified using NetMoss, a network-based microbial community analysis tool, while differential gene expression across IBD stages was determined by Boruta feature selection coupled with a recursive SVM classifier (REF-SVM). Cross-omics correlations between gut microbiota abundances and host gene expression were evaluated to map microbiota-host interactions. For predictive modeling, seven machine learning algorithms were trained on microbial and transcriptomic features, with a Stacking Classifier meta-ensemble employed to integrate predictions and optimize classification accuracy for IBD staging. This pipeline enabled the discovery of microbial biomarkers, stage-specific transcriptional markers, and robust multi-omics models for disease stratification.

resultsThis study enrolled 97 participants (74 IBD patients, 23 healthy controls) and identified significant clinical dysregulations (P < .05) in albumin, cholesterol, erythrocyte sedimentation rate, CRP, and fecal calprotectin in IBD cohorts. Fecal samples from 57 participants underwent 16S rRNA sequencing (mean depth 42.86 Mbp/sample, 92.4% OTU annotation efficiency), revealing reduced microbial α-diversity (Chao1/Shannon indices, P < .05) in IBD patients, alongside stage-specific taxonomic shifts, including Bacilli/Gammaproteobacteria enrichment and Bacteroidia/Clostridia alterations. NetMoss-based biomarker discovery pinpointed discriminative taxa (Bifidobacterium.catenulatum and Bacteroldes.fragilis in remission, Streptococcus.gallolyticus, Veillonella.atypica and Clostridium.butyricum in mild, Blautia.obeum in moderate, and Bacteroides. Uniformis in severe IBD). Parallel RNA-seq analysis of 72 samples (18,673 genes detected, 97.89% alignment rate) uncovered 161 differentially expressed genes, with stage-specific markers (YIPF4 and GIMAP6 in remission, HBB and FKBP5 in mild, TUBB1 in moderate, ALAS2, MMRN1 and IGFBP2 in severe IBD) enabling 82.4% staging accuracy via a REF-SVM classifier. Multi-omics integration revealed functional microbial-host interactions, such as Bacteroides.fragilis associations with GIMAP6/YIPF4 and Blautia.obeum with TUBB1. Integrated models demonstrated strong predictive performance (AUCs: microbial = 0.79, transcriptional = 0.80), highlighting the clinical utility of multi-omics biomarkers for IBD stratification.

conclusionsThis study establishes a multi-omics framework for non-invasive IBD staging, linking gut microbiota dynamics to host transcriptional reprogramming. The integration of NetMoss-derived biomarkers and machine learning enhances precision in stratifying disease severity, offering actionable insights for therapeutic interventions. These findings underscore the clinical potential of combining microbial and transcriptomic data to transform IBD management.