Tu1763 Nuclear Magnetic Resonance (NMR)-Base Urine Metabolomics Analysis of Patients With Clostridium difficile Infection

Abstract

Background: Clostridium difficile infection (CDI) is a common hospital acquired infections with significant morbidity and mortality. The risk of recurrent CDI is approximately 20%, 40% and up to 80% after the first, second, and third episode, respectively. Furthermore, recurrent CDI is associated with intestinal dysbiosis, characterized by reduced biodiversity and expansion of potentially pathogenic bacteria. The novel science of metabolomics provides a powerful tool to characterize vast numbers of metabolites in biological fluids with complex compositions and has recently yielded several clinically important diagnostic tests in cases where the human intestinal microbiota is altered. Aim: the aim of this pilot project was to examine the urine metabolomics profile in patients with CDI to determine if a unique metabolic profile was associated with recurrent infections. Method: Spot urine samples were prospectively collected from 31 patients (male 19, mean age 55 years) with CDI: 12 during 1st episode, 5 during 2nd episode, 5 during 3rd episode, and 9 with > 4th episode. Onedimensional NMR spectra were acquired using an Oxford 600MHz NMR instrument with a VNMRJ software. The 1H NMR spectrum of each urine sample was analyzed using Chenomx NMRSuite v7.6 (Chenomx Inc., Edmonton, AB). Multivariate statistical analysis was performed by using Simca P+ v 12.0.1 from Umetrics (Uema, Sweden). Results: Using 69 metabolites, a two-component orthogonal partial least squares (OPLS) model for 1, 2, 3 or at least 4 episodes of CIDwas built, with R2Y= 0.471 andQ2= 0.12 (model's predictability of data in 7-fold cross validation). Based on OPLS model, clustering was observed among patients with 1 episode, 2 episodes and 3 or more episodes of CDI, respectively as shown in figure 1. Conclusion: This is the first study to demonstrate that NMR urine metabolomics has the ability to distinguish patients with various frequencies of recurrent CDI. Urine metabolomics has the potential to become an accurate, noninvasive and inexpensive diagnostic test for predicting recurrent CDI, and can help clinicians in risk stratification and selection of the most appropriate therapy for each patient.