Development of Prognostic Algorithms to Identify Subjects at High Risk of ESKD in Type 2 Diabetes

项目总结/摘要 随着美国和其他国家糖尿病患病率的上升，有一项正在进行的研究工作， 生物标志物允许识别处于终末期肾病高风险的糖尿病患者 （ESKD）。在NIH和JDRF的支持下，我们鉴定了21种血清蛋白， 与Joslin肾脏研究中肾功能丧失和ESKD风险增加相关， 开发了一种临时OLINK多重检测试剂盒（所谓的Joslin Kidney Panel [JKP]）来测量这些 生物标志物。初步数据强烈表明，JKP的一个子集可以显着提高能力， 当加入GFR和白蛋白尿时，预测2型糖尿病（T2 D）受试者的ESKD风险。在这 我们的目标是在其他环境中验证这些初步研究结果，以制定改进的 ESKD风险预测算法。我们打算利用现有的数据和标本来实现这些目标 来自患有和不患有T2 D的个体，来自1.慢性肾功能不全队列（CRIC）研究; 2.的 控制糖尿病心血管风险行动（雅阁）试验及其随访研究ACCORDION。我们 具体目的是：1：确定Joslin肾脏检测试剂盒中21种生物标志物中信息量最大的一种 并评估他们的表现，当加入GFR和蛋白尿，在预测ESKD风险， 2型糖尿病和慢性肾病受试者。我们将在基线中测量JKP的21种蛋白质 来自约1，500名CRIC T2 D参与者的血清标本，并将这些数据与GFR和 白蛋白尿，以开发和内部验证预测ESKD风险的多标志物预后算法 （主要结局）或ESKD和/或50%肾功能丧失（次要结局）的复合终点 10年随访。2：评价CRIC的研究结果对T2 D患者的普遍性， 更广泛的肾功能。我们将在一个病例的基线血清样本中检测JKP- 约2，000名雅阁/ACCORDION参与者的队列样本，并将使用这些数据调查 CRIC中内置的预测算法对具有不同特征的糖尿病患者的可推广性。的 在目标1中开发并在目标2中得到外部验证的预测模型将用于建立一个基于网络的 肾脏风险计算器，用于估计临床环境中ESKD的10年风险。3：评价 肾脏风险计算器从糖尿病肾病到非糖尿病肾病的可转移性。我们将 测量来自CRIC的约1，700名非糖尿病受试者的基线血清样本中的21种JKP生物标志物 研究，并将评估目标2中开发的肾脏风险计算器在预测风险方面的性能 ESKD和ESKD/非糖尿病肾病患者50%肾功能丧失。拟议 研究很有可能导致开发用于分层的改进的预后工具。 糖尿病患者根据其进展为ESKD的风险。这将是一个巨大的进步， 优化患者护理和提高新ESKD预防干预措施临床试验的效率。

PROJECT SUMMARY/ABSTRACT With the rising prevalence of diabetes in the US and other countries, there is an ongoing research effort to find biomarkers allowing the identification of patients with diabetes at high risk of end stage kidney disease (ESKD). With support from NIH and JDRF, we have identified 21 serum proteins that were significantly associated with increased risk of kidney function loss and ESKD in the Joslin Kidney Study, and have developed an ad hoc OLINK multiplex assay (so called Joslin Kidney Panel [JKP]) to measure these biomarkers. Preliminary data strongly suggest that a subset of the JKP can significantly improve the ability to predict ESKD risk in subjects with type 2 diabetes (T2D) when added to GFR and allbuminuria. In this proposal, we aim to validate these preliminary findings in other settings, in order to develop improved algorithms for ESKD risk prediction. We intend to accomplish these goals using existing data and specimens from individuals with and without T2D from 1. the Chronic Renal Insufficiency Cohort (CRIC) Study; and 2. the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and its follow-up study ACCORDION. Our Specific Aims are: 1: To identify the most informative of the 21 biomarkers in the Joslin Kidney Panel and evaluate their performance, when added to GFR and albuminuria, in predicting ESKD risk among subjects with T2D and chronic kidney disease. We will measure the 21 proteins of the JKP in baseline serum specimens from ~1,500 CRIC participants with T2D, and will use these data together with GFR and albuminuria to develop and internally validate multi-marker prognostic algorithms predicting the risk of ESKD (primary outcome) or the composite of ESKD and/or 50% loss of kidney function (secondary outcome) during 10 years of follow-up. 2: To evaluate the generalizability of findings from CRIC to T2D individuals with a broader spectrum of kidney function. We will assay the JKP in baseline serum specimens from a case- cohort sample of ~2,000 ACCORD/ACCORDION participants and will use these data to investigate the generalizability of the predictive algorithms built in CRIC to diabetic patients with different characteristics. The prognostic models developed in Aim 1 and externally validated in Aim 2 will be used to build a web-based Kidney Risk Calculator for the estimation of the 10-year risk of ESKD in a clinical setting. 3: To evaluate the transferability of the Kidney Risk Calculator from diabetic to non-diabetic kidney disease. We will measure the 21 JKP biomarkers in baseline serum samples from ~1,700 non-diabetic subjects from the CRIC study and will assess the performance of the Kidney Risk Calculator developed in Aim 2 in predicting the risk of ESKD and ESKD/50% kidney function loss in patients with non-diabetic kidney disease. The proposed research has a high likelihood of resulting in the development of improved prognostic tools for the stratification of patients with diabetes according to their risk of progression to ESKD. This would be a great advancement for optimizing patient care and for improving the efficiency of clinical trials of new ESKD-preventing interventions.