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

项目摘要/摘要 随着美国和其他国家糖尿病患病率的上升，一项正在进行的研究努力找到 生物标志物可用于识别具有终末期肾病高危风险的糖尿病患者 (ESKD)。在NIH和JDRF的支持下，我们已经鉴定出21种显著 在Joslin肾脏研究中，与肾功能丧失和ESKD风险增加有关，并已 开发了一种特殊的OLINK多重分析(所谓的Joslin肾脏小组[JKP])来测量这些 生物标志物。初步数据有力地表明，JKP的一个子集可以显著提高 预测2型糖尿病(T2D)受试者合并GFR和蛋白尿时的ESKD风险。在这 我们的目标是在其他情况下验证这些初步调查结果，以便制定更好的 ESKD风险预测算法。我们打算利用现有的数据和样本来实现这些目标 来自1.慢性肾功能不全队列(CRIC)研究的患有和不患有T2D的个体；以及2. 控制糖尿病心血管风险的行动(ACCORD)试验及其随访研究手风琴。我们的 具体目标是：1：确定Joslin肾脏小组21个生物标志物中最具信息量的 并评估当加入GFR和蛋白尿时，它们在预测ESKD风险方面的表现 T2D和慢性肾脏疾病的受试者。我们将在基线上测量JKP的21个蛋白质 来自约1,500名患有T2D的CRIC参与者的血清样本，并将与GFR和 蛋白尿开发和内部验证预测ESKD风险的多标记预测算法 (主要结果)或ESKD和/或50%肾功能丧失的组合(次要结果) 经过10年的随访。2：评估CRIC的研究结果对T2D个体的普适性 肾功能的范围更广。我们将检测一个病例的基线血清样本中的JKP- 对约2,000名ACCORD/手风琴参与者进行队列抽样，并将使用这些数据来调查 CRIC中建立的预测算法对不同特征的糖尿病患者的普适性。这个 在AIM 1中开发并在AIM 2中进行外部验证的预测模型将用于建立基于Web的 肾脏风险计算器，用于在临床环境中估计ESKD的10年风险。3：评估 肾脏风险计算器在糖尿病肾病和非糖尿病肾病之间的可转移性。我们会 检测CRIC约1700名非糖尿病受试者基线血清样本中的21种JKP生物标志物 研究并将评估在目标2中开发的肾脏风险计算器在预测风险方面的性能 在非糖尿病肾病患者中，ESKD和ESKD/50%肾功能丧失的比例。建议数 研究很有可能导致改进的分层预后工具的开发 根据糖尿病患者进展为ESKD的风险进行评估。这将是一个巨大的进步 优化患者护理，提高新的ESKD预防干预措施的临床试验效率。

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.