#3275 Urinary metabolomics evaluation in sensitized kidney transplant recipients

Abstract Background and Aims Chronic kidney disease is a global public health problem and treatment options for patients with end-stage kidney disease are either dialysis or kidney transplantation. Kidney transplant recipients (KTRs) require long term monitoring of allograft function which is traditionally based on serial measurements of serum creatinine and proteinuria. However, when changes in these variables become evident, histological alterations might be irreversible. It is therefore of outmost importance to develop non-invasive, sensitive biomarkers to timely monitor allograft function. As kidneys are organs with a very high metabolic rate, a metabolomics approach could provide insights for early detection of systematic changes in KTRs and has the potential for monitoring allograft function. Method In this cross-sectional study we analyzed the urine metabolomics fingerprint of KTRs with Nuclear Magnetic Resonance (NMR) spectroscopy. We studied eleven KTRs with preformed or de novo donor specific antibodies (DSA+) and nine KTRs without donor specific antibodies (DSA−). Urine samples were collected and centrifuged, the supernatant was collected from each sample and stored at −80 °C until NMR analysis. Analysis of the samples was performed using a Bruker Avance III HD 700 MHz NMR spectrometer equipped with a cryogenically cooled TCI probe. For each urine sample one-dimensional 1H NMR NOESY presat experiment and a two-dimensional (2D) J-resolved experiment was performed. In multivariate statistical analysis the method of Principal Component Analysis (PCA) was used and the Partial Least Squares-Discriminant Analysis (PLS-DA) was chosen as the main supervised method for reducing the number of variables. Results Baseline kidney function (eGFR) of DSA(+) and DSA(−) KTRs was similar (57 ± 27.3 vs. 63 ± 22 ml/min/1.73 m2, P = 0.54). Following multivariate statistical analysis with PCA and PLS-DA, variable importance in projection (VIP) scores ranging from 3.5 to 2.2 suggested that 1-Methylnicotinamide, glucose, histamine, 2-Hydroxyisovalerate, citrate, threonate, myo-inositol are the most important variables in descending order. Univariate analysis was further performed on the successfully identified metabolites, targeting only the non-overlapping metabolites’ peaks for integration and non-parametric Kruskal–Wallis test, followed by a false discovery rate (FDR) correction was conducted. In total, 21 metabolites were detected and all of them were expressed with higher intensity in the DSA(–) group. None of these metabolites, however, was found to be statistically significant, with a p-value ranging from 0.082 in formate to 0.934 in acetate. Conclusion Urine metabolites including 1-Methylnicotinamide, glucose, histamine, 2-Hydroxyisovalerate, citrate, threonate and myo-inositol tend to differ between KTRs with and without DSAs, even though no statistical significance was ascertained in this study.