Characterization of inflammatory bowel disease with urinary metabolic profiling. - Williams, H; Cox, I; Walker, D; North, B; Patel, V; Marshall, SE; Jewell, D; Ghosh, S; Thomas, H; Teare, J; Jakobovits, S; Zeki, S; et al. (2009)
OBJECTIVES: Distinguishing between the inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC) is important for both management and prognostic reasons. Discrimination using noninvasive techniques could be an adjunct to conventional diagnostics. Differences have been shown between the intestinal microbiota of CD and UC patients and controls; the gut bacteria influence specific urinary metabolites that are quantifiable using proton high-resolution nuclear magnetic resonance (NMR) spectroscopy. This study tested the hypothesis that such metabolites differ between IBD and control cohorts, and that using multivariate pattern-recognition analysis, the cohorts could be distinguished by urine NMR spectroscopy. METHODS: NMR spectra were acquired from urine samples of 206 Caucasian subjects (86 CD patients, 60 UC patients, and 60 healthy controls). Longitudinal samples were collected from 75 individuals. NMR resonances specific for metabolites influenced by the gut microbes were studied, including hippurate, formate, and 4-cresol sulfate. Multivariate analysis of all urinary metabolites involved principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA). RESULTS: Hippurate levels were lowest in CD patients and differed significantly between the three cohorts (P<0.0001). Formate levels were higher and 4-cresol sulfate levels lower in CD patients than in UC patients or controls (P=0.0005 and P=0.0002, respectively). PCA revealed clustering of the groups; PLS-DA modeling was able to distinguish the cohorts. These results were independent of medication and diet and were reproducible in the longitudinal cohort. CONCLUSIONS: Specific urinary metabolites related to gut microbial metabolism differ between CD patients, UC patients, and controls. The emerging technique of urinary metabolic profiling with multivariate analysis was able to distinguish these cohorts.