Circulating Apoptotic Endothelial Cells and Apoptotic Endothelial Microparticles Independently Predict the Presence of Cardiac Allograft Vasculopathy

ObjectivesMaintenance of endothelial homeostasis may prevent the development of cardiac allograft vasculopathy (CAV). This study investigated whether biomarkers related to endothelial injury and endothelial repair discriminate between CAV-negative and CAV-positive heart transplant recipients.BackgroundCAV is the most important determinant of cardiac allograft survival and a major cause of death after heart transplantation.MethodsFifty-two patients undergoing coronary angiography between 5 and 15 years after heart transplantation were recruited in this study. Flow cytometry was applied to quantify endothelial progenitor cells (EPCs), circulating endothelial cells (CECs), and endothelial microparticles. Cell culture was used for quantification of circulating EPC number and hematopoietic progenitor cell number and for analysis of EPC function.ResultsThe EPC number and function did not differ between CAV-negative and CAV-positive patients. In univariable models, age, creatinine, steroid dose, granulocyte colony–forming units, apoptotic CECs, and apoptotic endothelial microparticles discriminated between CAV-positive and CAV-negative patients. The logistic regression model containing apoptotic CECs and apoptotic endothelial microparticles as independent predictors provided high discrimination between CAV-positive and CAV-negative patients (C-statistic 0.812; 95% confidence interval: 0.692 to 0.932). In a logistic regression model with age and creatinine as covariates, apoptotic CECs (p = 0.0112) and apoptotic endothelial microparticles (p = 0.0141) were independent predictors (C-statistic 0.855; 95% confidence interval: 0.756 to 0.953). These 2 biomarkers remained independent predictors when steroid dose was introduced in the model.ConclusionsThe high discriminative ability of apoptotic CECs and apoptotic endothelial microparticles is a solid foundation for the development of clinical prediction models of CAV.