S-K Lee1, B K Kim2, H-Y Jung1, J-H Cho1, S-H Park1, Y-L Kim1, S-Y Kim2, Y-R Yoon2, B H Chung3, S-H Lee4, C-D Kim5. 1. Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Korea. 2. Department of Biomedical Science, BK21 Plus KNU Bio-Medical Convergence Program for Creative Talent, Cell and Matrix Research Institute and Clinical Trial Center, Kyungpook National University Graduate School and Hospital, Daegu, Korea. 3. Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. 4. Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, Korea. 5. Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Korea. Electronic address: drcdkim@knu.ac.kr.
Abstract
BACKGROUND: The recent progress and appropriate use of immunosuppressive drugs have considerably improved the short-term survival in kidney transplantation recipients (KTRs). 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 was to discover potential biomarkers for long-term survival in KTRs through the use of liquid chromatography-tandem mass spectrometry. METHODS: We used the metabolic approach to explore the change of metabolites in the serum of KTRs. Twenty-four KTRs with long-term good survival (LGS) and 10 KTRs with chronic antibody-mediated rejection (CAMR) were included in this study. After quantile normalization with chromatographic data, multivariate statistical analysis was performed. We attempted to analyze metabolic profiling with LGS and CAMR groups. RESULTS: The orthogonal partial least-squares discriminant analysis score plot showed a separation between 2 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). We then selected 54 metabolites to compare mass with charge by searching a web database, and 11 compounds were identified. CONCLUSIONS: We found metabolites in serum that differ in LGS and CAMR groups. Further studies are needed to figure out potential metabolomic biomarkers to predict long-term survival in KTRs.
BACKGROUND: The recent progress and appropriate use of immunosuppressive drugs have considerably improved the short-term survival in kidney transplantation recipients (KTRs). 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 was to discover potential biomarkers for long-term survival in KTRs through the use of liquid chromatography-tandem mass spectrometry. METHODS: We used the metabolic approach to explore the change of metabolites in the serum of KTRs. Twenty-four KTRs with long-term good survival (LGS) and 10 KTRs with chronic antibody-mediated rejection (CAMR) were included in this study. After quantile normalization with chromatographic data, multivariate statistical analysis was performed. We attempted to analyze metabolic profiling with LGS and CAMR groups. RESULTS: The orthogonal partial least-squares discriminant analysis score plot showed a separation between 2 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). We then selected 54 metabolites to compare mass with charge by searching a web database, and 11 compounds were identified. CONCLUSIONS: We found metabolites in serum that differ in LGS and CAMR groups. Further studies are needed to figure out potential metabolomic biomarkers to predict long-term survival in KTRs.