Growth Differentiation Factor 15 Is Superior to Troponin I in the Evaluation of Kidney Transplant Candidates

Abstract

<b><i>Introduction:</i></b> Pretransplant cardiac troponin I (cTNI) has demonstrated its predicting value in survival after kidney transplant. Growth differentiation factor 15 (GDF-15) is a biomarker currently studied as a predictor of mortality and cardiovascular events (CVE) in different scenarios. The aim of this study was to compare the utility of these two biomarkers in the prediction of events after kidney transplant. <b><i>Methods:</i></b> We included 359 kidney transplants performed in our center between 2005 and 2015. cTNI and GDF-15 were measured on stored serum samples obtained pretransplant. <b><i>Results:</i></b> Median GDF-15 was 5,346.4 pg/mL, and cTNI was 5.6 ng/L. After follow-up, 77 (21.5%) patients died, and the incidence of cerebrovascular accident (CVA), acute coronary syndrome (ACS), and major adverse CVEs (MACE) was 6.38%, 12.68%, and 20.56%, respectively. Patients were stratified in tertiles according to GDF-15 and cTNT levels. By multivariate cox regression analysis including both biomarkers and different clinical characteristics, we found a significant relation between GDF-15 and mortality, CVAs, and MACE (highest tertile hazard ratio [HR] 2.2 95% confidence interval [CI] [1.2–4.1], <i>p</i> = 0.01, HR 9.7 CI 95% [2.2–43.1], <i>p</i> = 0.003 and HR 2.7 CI 95% [1.4–5.1], <i>p</i> = 0.002). On the contrary, posttransplant ACS was related to cTNI (highest cTNI tertile HR 3.2 CI 95% [1.5–7.3], <i>p</i> = 0.003). <b><i>Discussion:</i></b> Our study indicates the potential utility of GDF-15 as a mortality and CVE predictor after kidney transplant and its superiority compared to cTNI. By contrast, probably due to its tissue specificity, cardiac troponin showed a stronger correlation with acute coronary events. Although more studies are needed to confirm our findings, these two molecules could be used in conjunction with other tools to predict adverse events after transplant and ideally find strategies to minimize them.