Prediction of irritable bowel syndrome by integrating urine metabolites and gut microbiota

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder characterized by abdominal discomfort and altered bowel habits. Although alterations in gut microbiota and metabolic disturbances have been associated with IBS, their diagnostic relevance remains uncertain. This project aims to investigate whether combining fecal microbiota profiling via 16S rRNA sequencing with urinary metabolomic analysis using GC-MS can improve classification of IBS patients. By integrating microbiome and metabolite data, this study seeks to identify novel non-invasive biomarkers and contribute to a better understanding of the pathophysiology of IBS, ultimately aiding in more accurate diagnosis and personalized treatment approaches.