Metabolomics Study for Identification of Potential Biomarkers of Long-Term Survival in Kidney Transplantation Recipients.

C.-D. Kim, H.-Y. Jung, R.-H. Kim, S.-M. Park, J.-H. Park, K.-H. Lee, E.-S. Lee, K.-Y. Kim, J.-Y. Choi, J.-H. Cho, S.-H. Park, Y.-L. Kim.

Division of Nephrology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Korea

Meeting: 2017 American Transplant Congress

Abstract number: D269

Keywords: Graft survival, Kidney transplantation

Session Information

Session Name: Poster Session D: Long Term Kidney Outcomes

Session Type: Poster Session

Date: Tuesday, May 2, 2017

Session Time: 6:00pm-7:00pm

Presentation Time: 6:00pm-7:00pm

Location: Hall D1

Background: The development of new strategies to improve long-term survival outcome after kidney transplantation is also becoming important. Although current diagnosis of allograft dysfunction relies on serum creatinine concentration and biopsy, they are nonspecific indicators of allograft function. Therefore, noninvasive, sensitive, and specific biomarkers for the prediction of long-term survival are needed. The aim of this study is to discover potential biomarkers for long-term survival in KTRs using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Methods: We used the metabolic approach to explore the change of metabolites in the serum of KTRs. A total of 414 KTRs aged > 18 years at 6 kidney centers in the Republic of Korea were recruited. Among them, 24 patients who met criteria of long-term good survival (LGS) and 10 patients who diagnosed biopsy-proven chronic antibody-mediated rejection (CAMR) were selected for discovery set. After quantile normalization with chromatographic data, multivariate statistical analysis was performed using SIMCA 14. We attempted to analyze metabolic profiling with LGS and CAMR groups.

Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot showed a separation between two groups in the principal component. In the corresponding loading plot, 344 metabolites responsible for the separation observed in the score plot were identified (Variable influence on projection ≥ 1.0). Then we selected 54 metabolites to compare mass to charge by searching web database (HMDB). By comparing mass chromatogram patterns, 11 endogenous metabolites were finally identified.

Conclusions: We found serum metabolites which differ between LGS and CAMR groups. Further studies should focus on validating of these metabolites and setting a model to predict long-term survival in KTRs, and investigating its relationship to specific underlying pathophysiology.

CITATION INFORMATION: Kim C.-D, Jung H.-Y, Kim R.-H, Park S.-M, Park J.-H, Lee K.-H, Lee E.-S, Kim K.-Y, Choi J.-Y, Cho J.-H, Park S.-H, Kim Y.-L. Metabolomics Study for Identification of Potential Biomarkers of Long-Term Survival in Kidney Transplantation Recipients. Am J Transplant. 2017;17 (suppl 3).

To cite this abstract in AMA style:

Kim C-D, Jung H-Y, Kim R-H, Park S-M, Park J-H, Lee K-H, Lee E-S, Kim K-Y, Choi J-Y, Cho J-H, Park S-H, Kim Y-L. Metabolomics Study for Identification of Potential Biomarkers of Long-Term Survival in Kidney Transplantation Recipients. [abstract]. Am J Transplant. 2017; 17 (suppl 3). https://atcmeetingabstracts.com/abstract/metabolomics-study-for-identification-of-potential-biomarkers-of-long-term-survival-in-kidney-transplantation-recipients/. Accessed May 19, 2025.

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