Using Genetic Variation to Predict and Extend Long-term Kidney Transplant Function.

Abstract

Renal transplantation has transformed the life of patients with end-stage renal disease and other chronic kidney disorders by returning endogenous kidney function and enabling patients to cease dialysis. Several clinical indicators of graft outcome and long-term function have been established. Although rising creatinine levels and graft biopsy can be used to determine graft loss, identifying early predictors of graft function will not only improve our ability to predict long-term graft outcome but importantly provide a window of opportunity to therapeutically intervene to preserve graft function before graft failure has occurred. Since understanding the importance of matching genetic variation at the HLA region between donors and recipients and translating this into clinical practise to improve transplant outcome, much focus has been placed on trying to identify additional genetic predictors of transplant outcome/function. This review will focus on how candidate gene studies have identified variants within immunosuppression, immune response, fibrotic pathways, and specific ethnic groups, which correlate with graft outcome. We will also discuss the challenges faced by candidate gene studies, such as differences in donor and recipient selection criteria and use of small data sets, which have led to many genes failing to be consistently associated with transplant outcome. This review will also look at how recent advances in our understanding of and ability to screen the genome are starting to provide new insights into the mechanisms behind long-term graft loss and with it the opportunity to target these pathways therapeutically to ultimately increase graft lifespan and the associated benefits to patients.

DOI: 10.1097/TP.0000000000000836

Cite this paper

@article{Simmonds2015UsingGV, title={Using Genetic Variation to Predict and Extend Long-term Kidney Transplant Function.}, author={Matthew J. Simmonds}, journal={Transplantation}, year={2015}, volume={99 10}, pages={2038-48} }