• Tai Yeon Koo, Jong Cheol Jeong, Yonggu Lee, Kwang-Pil Ko, Kyoung-Bun Lee, Sik Lee, Suk Joo Park, Jae Berm Park, Miyeon Han, Hye Jin Lim, Curie Ahn, and Jaeseok Yang.
• From the Transplantation Center, Seoul National University Hospital, Seoul (TYK, HJL, CA, JY); Department of Pathology, Seoul National University Hospital, Seoul (K-BL); Department of Nephrology, Ajou University School of Medicine, Suwon (JCJ); Department of Cardiology, Sungae Hospital, Seoul (YL); Department of Preventive Medicine, Gachon University of Medicine and Science, Incheon (K-PK); Department of Nephrology, Chonbuk National University Hospital, Jeollabuk-do (SL); Department of Nephrology, Inje University Busan Paik Hospital, Busan (SJP); Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine (JBP); and Department of Internal Medicine (MH, CA), Seoul National University College of Medicine, Seoul, Republic of Korea.
• Medicine (Baltimore). 2016 Mar 1; 95 (11): e3076.

AbstractSeveral recipient biomarkers are reported to predict graft dysfunction, but these are not useful in decision making for the acceptance or allocation of deceased donor kidneys; thus, it is necessary to develop donor biomarkers predictive of graft dysfunction. To address this issue, we prospectively enrolled 94 deceased donors and their 109 recipients who underwent transplantation between 2010 and 2013 at 4 Korean transplantation centers. We investigated the predictive values of donor urinary neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and L-type fatty acid binding protein (L-FABP) for reduced graft function (RGF). We also developed a prediction model of RGF using these donor biomarkers. RGF was defined as delayed or slow graft function. Multiple logistic regression analysis was used to generate a prediction model, which was internally validated using a bootstrapping method. Multiple linear regression analysis was used to assess the association of biomarkers with 1-year graft function. Notably, donor urinary NGAL levels were associated with donor AKI (P = 0.014), and donor urinary NGAL and L-FABP were predictive for RGF, with area under the receiver-operating characteristic curves (AUROC) of 0.758 and 0.704 for NGAL and L-FABP, respectively. The best-fit model including donor urinary NGAL, L-FABP, and serum creatinine conveyed a better predictive value for RGF than donor serum creatinine alone (P = 0.02). In addition, we generated a scoring method to predict RGF based on donor urinary NGAL, L-FABP, and serum creatinine levels. Diagnostic performance of the RGF prediction score (AUROC 0.808) was significantly better than that of the DGF calculator (AUROC 0.627) and the kidney donor profile index (AUROC 0.606). Donor urinary L-FABP levels were also predictive of 1-year graft function (P = 0.005). Collectively, these findings suggest donor urinary NGAL and L-FABP to be useful biomarkers for RGF, and support the use of a new scoring system based on donor biomarkers to facilitate decision-making in acceptance and allocation of deceased donor kidneys and contribute to maximal organ utilization.

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