1H NMR-based metabolic profiling of human serum before and after renal transplantation.

Renal transplant success is closely tied to the ability to monitor transplant recipients. However, transplant monitoring still depends on relatively dated technologies. Thus, we applied a novel method of proton nuclear magnetic resonance (NMR)-based metabolomics to investigate the altered metabolic pattern in serum, seeking to identify biomarkers involved to different periods of renal transplant patients. Serum was obtained from 28 healthy controls (class 4) and from 20 renal transplant patients in different periods: pretransplant (class 1) and on the 1st (class 2) and the 7th day (class 3) after transplantation. After performing proton NMR spectroscopy, multivariate pattern recognition was applied to cluster the groups and establish disease-specific metabolite biomarker profiles. Compared with class 4, 19 different peaks and 10 potential biomarkers were identified in class 1, class 2, and class 3 (p < 0.0001). Nevertheless, there was no obvious difference between class 1, class 2, and class 3 (p > 0.05). Partial least squares-discriminant analysis models were able to identify patients with sensitivity and specificity of 98.7% and 95.4%, respectively. These results not only indicate that this novel method has sufficient sensitivity to distinguish renal transplant patients from controls but also identify biomarkers to monitor graft function, which could be developed to a clinically useful diagnostic tool.