The effect of genetic polymorphism in complement proteins on clinical renal transplant outcome

肾衰竭患者的数量正在增加，透析患者每年增加7%。对于大多数患者来说，最好的治疗方法是肾移植。不幸的是，对移植肾脏的需求超过了可用的数量，移植等待名单每年都在增加。移植失败率稳定，需要再次进行透析或另一次移植，使这种情况变得更糟。越来越多的证据表明，调整药物治疗可以减缓移植肾损伤的进展。不幸的是，我们往往很晚才认识到损害正在发生，此时可能已经有严重的不可逆的肾损害。理想情况下，我们将能够在早期阶段，在发生重大损害之前识别出处于风险中的患者，并在此时改变治疗方法。这项建议的主要目的是开发一种方法，通过这种方法可以预测移植时后期问题的风险。研究表明，个体之间在遗传水平上的差异会影响移植后的结果。然而，这项工作尚未转化为改善病人护理。我们计划利用这些遗传差异来预测肾移植后患者的病程，并确定那些导致移植物损伤的高风险或低风险患者。这将使我们能够改变监测的强度，甚至使用药物治疗来防止损害的发展。本申请中提出的工作将定义这种类型分析的强大程度，从而形成该领域未来临床试验的基础。

The number of people with kidney failure is increasing, with a 7% per year rise in patients on dialysis. For the majority of these patients the best treatment is kidney transplantation. Unfortunately the demand for transplant kidneys exceeds the number available and the transplant waiting list increases yearly. This situation is made worse by a steady rate of transplant failure requiring a return to dialysis or another transplant.There is increasing evidence that modifying drug treatment can slow the progression of damage to a transplanted kidney. Unfortunately we are often late to recognise that damage is occurring at which point there may already be significant irreversible renal damage. Ideally we would be able to identify patients at risk at an early stage, prior to the development of significant damage, and alter treatments at this point. The main objective of this proposal is to develop a method by which risk of later problems can be predicted at the time of transplantation.Studies have shown that differences between individuals at a genetic level can influence the outcome after transplantation. However, this work has yet to translate into improvements in patient care. We plan to use these genetic differences to predict the course of patients following kidney transplantation and to identify those patients at high or low risk of problems leading to graft damage. This would allow us to alter the intensity of monitoring and even use drug treatments to prevent damage developing. The work proposed in this application will define how powerful this type analysis can be and therefore form the basis of future clinical trials in this area.