Abstract
Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by recurrent abdominal pain and altered bowel habits. While gut microbiota alterations and metabolic disturbances have been implicated in IBS, their potential role in classification and patient stratification remains unclear. This study aimed to evaluate the potential of integrating gut microbiota profiling and urinary metabolomics for improved IBS classification. Fifty-four participants (27 healthy controls, 27 IBS patients) were recruited for gut microbiota and urinary metabolite analysis. Gut microbiota composition was assessed via 16S rRNA gene sequencing, and urinary metabolites were profiled using gas chromatography-mass spectrometry (GC-MS). Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of gut microbiota, urinary metabolites, and their combined model. LEfSe analysis identified that Clostridia and Prevotella as enriched in IBS patients, whereas Bacteroidales and Faecalitalea were predominant in healthy individuals. Urinary metabolite analysis revealed significant alterations in metabolite profiles, with IBS patients exhibiting elevated fructose levels and trends of increased serine, mannose, and galactose. ROC curve analysis demonstrated that urinary metabolomics (AUC = 0.65) outperformed gut microbiota profiling (AUC = 0.54), while a combined approach integrating both datasets achieved the highest predictive accuracy (AUC = 0.74). These findings indicate that integrating urinary metabolomics with gut microbiota profiling may provide preliminary insights into IBS classification. Given the small sample size, potential risk of overfitting, absence of external validation, and possible dietary or medication confounding, the observed performance of the combined approach should be regarded as hypothesis-generating rather than confirmatory. Nevertheless, the results highlight the potential utility of integrative omics strategies in IBS research, underscoring the need for validation in larger, sex-balanced, and clinically diverse cohorts to establish their robustness and broader relevance.