Abstract Background Inflammatory Bowel Disease (IBD), which comprises Crohn's Disease (CD) and Ulcerative Colitis (UC), is a disorder in which complex interactions between genetic, environmental, and microbial factors trigger alterations in the immune intestinal response. On the other hand, nutrient catabolism from the diet carried out by the microbiota results in the formation of metabolites that also impact immune responses on the intestinal mucosa. Considering that IBD patients have altered microbiota compared to healthy controls (HC) and that the urinary metabolic profile of an individual is the result of a combination of genetic, dietary, and microbial components, we propose the hypothesis that the urinary metabolic profile distinguishes IBD patients from HC. The aim of the study was to compare metabolomic analysis of urine samples from HC and IBD patients. Methods 27 patients were included [HC (n=10), UC (n=10), and CD (n=7)]. Clinical IBD activity was evaluated with partial Mayo score (UC) and Harvey-Bradshaw index (CD). Metabolites in urine were measured using gas chromatography-mass spectrometry (GC-MS). Metabolomic analysis and machine learning were performed using the MetaboAnalyst software. PLS-DA was used for multivariate analysis, and clustering separation was performed using hierarchical clustering. To identify and interpret patterns of metabolite concentration changes in a biologically meaningful way we perform Over Representation Analysis. Results In total, 62 organic acids were detected. Comparing UC and HC revealed 11 significantly altered metabolites (9 increased and 2 decreased). Main increased metabolites comprised lactic acid (p=0.0015), pyroglutamic acid (p=0.0147), isocitric acid (p=0.0325), and phosphoric acid (p=0.0091). Interestingly, the value of adipic and suberic acids were higher in remission vs. active patients. Furthermore, adipic acid was higher in UC patients with proctitis compared to left-side colitis/extensive colitis. Comparing CD and HC revealed only 2 significantly altered metabolites, glycolic acid (p=0.0214), and lactic acid (p=0.0111). Oxalic acid was more concentrated in CD patient with perianal disease. Finally, propionic and phosphoric acid were able to discriminate UC from CD patients. The most affected metabolic pathways were related to energy metabolism and oxidative stress, including the Warburg effect (p=0.0016), gluconeogenesis (p=0.0106), pyruvate metabolism (p=0.0197) and glutathione metabolism (p=0.0039) (Fig.1). Conclusion We found that the urinary metabolic profile of IBD patients differs from the HC, particularly for UC patients. These metabolites profile could be used to improve diagnosis and monitoring in patients with IBD.