TReg, CD4 Tem, and CD8 Temra fail to predict acute cellular rejection in living-donor renal allograft recipients

[摘要] Since it was pioneered successfully, renal transplantation remains the only option for patients with stage-5 chronic, severe, and end-stage renal disease for whom dialysis treatment complications preclude its continued use.Currently, and historically, the supply of suitable living-donor allografts is far less than their clinical need, and this gap cannot be offset by transplanting available cadaveric kidneys.Immuno-suppressive and -induction therapies have been used to impair the recipients’ immune response against the allograft. Without such therapies many recipients’ immune systems would reject the organ, causing the patient to again experience renal failure.It is therefore incumbent upon the public health community to ascertain the most effective treatment modality for transplanted organs to ensure that these limited resources are utilized in the most efficient manner possible.Thymoglobulin and Basiliximab induction therapies are two induction treatments available clinically for kidney transplantation.Within this study, patients undergoing treatment using either of these induction-agents had their circulating T cell phenotypes analyzed and compared.The goal of this study was to produce a statistical model, based on Generalized Estimating Equation methodology, that would predict episodes of acute cellular rejection (ACR) between the day of the transplant and up to one year after transplantation.The selection of potential covariates was based upon previously identified T cell markers.Due to small sample size, missing data, and both left- and right-truncation of the data, the model was not able discriminate between patients that underwent ACR from those that did not based upon the a priori markers of interest. However, the analysis did identify different memory T cell proportions that may be predictive of ACR in patients treated with Thymoglobulin or Basiliximab and warrants further study.The public health impact of a predictive model would be to increase the quality and duration of life in individual patients and reduce the burden of End Stage Renal Disease (ESRD) within the US population as a whole.

[发布机构] the University of Pittsburgh