¹Department of Medical Biotechnology, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
²Acibadem Labmed Clinical Laboratories, Istanbul, Turkey
³Department of Medical Biochemistry, Faculty of Medicine, Acibadem University, Istanbul, Turkey
⁴Acibadem Bursa Hospital Central Clinical Laboratory, Bursa, Turkey
⁵Acibadem Bursa Hospital, Department of Nephrology and Organ Transplantation, Bursa, Turkey
⁶Acibadem Bursa Hospital, Department of Urology and Organ Transplantation, Bursa, Turkey
⁷Ege University Faculty of Medicine, Department of Nephrology, İzmir, Turkey DOI : 10.29228/jrp.451 The ability to monitor patients plays a major role in the success of kidney transplants. However, transplant monitoring still depends on relatively outdated, inadequate technologies. The aim of this study was to reveal the metabolomic profile of the kidney allograft using the metabolomic screening technique and to identify specific eGFRbased biomarkers to monitor individuals with different levels of post-transplantation graft dysfunction. In the current study, urine samples from 131 unique kidney transplant recipients were collected and analyzed by ultra-high performance liquid chromatography and benchtop QTof mass spectrometer (Xevo G2 XS QTof). Acquired data were first pre-processed by Progenesis QI 2.3 (Nonlinear Dynamics, Waters, UK). Putative annotation was performed against the HMDB database following multivariate statistical analysis. Post-transplant biomarker panels that can distinguish stages of renal dysfunction were created by combining the significant markers and taking their ratios. Overall, 8 metabolites were significantly altered within three groups of kidney transplant recipients:4,5-Dihydroorotic acid, N2- Succinyl-L-glutamic acid 5-semialdehyde, Valyl-Arginine, Pantothenic acid, L-phenylalanyl-L-hydroxyproline, MG(0:0/24:0/0:0), QYNAD and 12-Hydroxy-13-O-D-glucuronoside-octadec-9Z-enoate as biomarker candidates (p<0.05). The ratio of 4,5-Dihydroorotic acid to Pantothenic acid (panel-1) can be used to monitor kidney function. Specifically, these metabolite ratios were found to be more sensitive to changes in kidney function than panel-2, which consisted of 7 metabolites, excluding QYNAD, of the 8 major metabolites. Our results may contribute to the monitoring of kidney transplant patients based on post-transplant eGFR-based kidney function stages, thus providing a method for the early evaluation and monitoring of the kidney transplant recipient after transplantation for kidney transplant patient management. Keywords : Urine Metabolites; metabolome profiling; metabolomics; UPLC/ESI/QTOF-MS/MS; Kidney Transplantation