36 Background: Current screening and surveillance strategies for Barrett’s esophagus are inadequate. More reliable tools are needed. A unique urinary metabolomic signature could fill this niche. We applied metabolomic techniques to identify urinary metabolites capable of facilitating in the diagnosis of Barrett’s esophagus. Methods: Urine samples from patients with histologically confirmed Barrett’s esophagus (n=32) and normal, healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted the detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial-least squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of patients with Barrett’s esophagus and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing the concentrations of 59 metabolites in the urine of healthy volunteers and patients with Barrett’s esophagus. Those metabolites contributing the most class discriminating information included 3-hydoxybutyrate, adipate and choline. Clear distinction between patients with Barrett’s esophagus and healthy controls was noted when PLS-DA was applied to the data set. Model parameters for both the goodness of fit R2, and the predictive capability Q2, were high (R2 = 0.96; Q2 = 0.90). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Conclusions: Urinary metabolomics identified a discrete signature associated with Barrett’s esophagus compared to healthy controls. This profile has the potential to aid in diagnosis and the development of new therapeutic targets.
Implications of Recent Revelations from Basic and Clinical Studies of Barrett’s Esophagus for Screening and Surveillance Strategies
