Machine Learning Guided Identification of Host Microbiome Metabolic Biomarkers for Precision Noninvasive Diagnosis of Irritable Bowel Syndrome and Differential Classification from Inflammatory Bowel Disease

Abstract

Irritable Bowel Syndrome (IBS) and Inflammatory Bowel Disease (IBD) represent two major gastrointestinal disorders that frequently share overlapping clinical manifestations, including abdominal pain, altered bowel habits, bloating, and impaired quality of life. Despite advances in endoscopic imaging, histopathological assessment, and serological testing, accurate differentiation between IBS and IBD remains a substantial clinical challenge, particularly during early disease stages and in patients presenting with mild or atypical symptoms. The increasing recognition of the gut microbiome as a critical regulator of host metabolism, immune signaling, and intestinal homeostasis has created new opportunities for developing noninvasive diagnostic biomarkers capable of improving diagnostic precision. This study proposes a machine learning-guided multi-omics framework for the identification of host–microbiome metabolic biomarkers that can distinguish IBS from IBD using fecal microbiome composition, microbial functional pathways, and metabolomic profiles. A comprehensive analytical workflow integrating microbiome sequencing data, microbial metabolic signatures, and host-associated metabolites was developed to identify discriminative biomarker panels. Multiple machine learning algorithms including Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Elastic Net models were evaluated for feature selection and disease classification. Biomarker prioritization was based on predictive contribution, biological relevance, and reproducibility across independent cohorts. The proposed framework identified distinct microbial and metabolic patterns associated with each disease phenotype. IBS was characterized by alterations in microbial fermentation pathways, short-chain fatty acid metabolism, and functional microbial heterogeneity, whereas IBD exhibited pronounced signatures related to inflammatory metabolism, bile acid dysregulation, immune-associated microbial networks, and reduced microbial diversity. Integration of microbiome and metabolomic variables substantially improved diagnostic performance compared with single-omics approaches. Machine learning-driven feature ranking revealed several host–microbiome interaction markers with strong discriminatory capacity for differential diagnosis. The findings demonstrate the potential of combining microbiome-derived metabolic biomarkers with advanced machine learning approaches to establish a precision medicine strategy for noninvasive gastrointestinal disease diagnosis. This integrative framework may contribute to earlier disease recognition, reduction of unnecessary invasive procedures, improved patient stratification, and personalized therapeutic decision-making in functional and inflammatory bowel disorders.