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

开发预后算法来识别 2 型糖尿病 ESKD 高风险受试者

• 批准号: 10693928
• 负责人: Andrzej S Krolewski
• 金额: $ 72.5万
• 依托单位: JOSLIN DIABETES CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693928
• 关键词: Age; Albuminuria; Algorithms; Antibodies; Biological Assay; Biological Markers; Characteristics; Chronic Kidney Failure; Chronic Kidney Insufficiency; Clinic; Clinical; Clinical Trials; Cohort Studies; Costs and Benefits; Country; Data; Development; Diabetes Mellitus; Diabetic Nephropathy; Discrimination; End stage renal failure; Follow-Up Studies; Goals; Immunoassay; Impairment; Individual; Intervention; Kidney; Kidney Diseases; Laboratories; Measures; Modeling; Molecular; Non-Insulin-Dependent Diabetes Mellitus; Online Systems; Outcome; Participant; Patient Care; Patients; Performance; Prevalence; Preventive therapy; Prognostic Marker; Proteins; Proteomics; Renal function; Research; Research Personnel; Risk; Sampling; Serum; Serum Proteins; Specificity; Specimen; Techniques; Testing; Time; United States National Institutes of Health; biomarker identification; biomarker panel; candidate marker; cardiovascular risk factor; clinical care; clinical predictors; cohort; diabetic; diabetic patient; disorder risk; experience; follow-up; high risk; improved; loss of function; multiplex assay; non-diabetic; novel therapeutics; participant enrollment; patient stratification; prediction algorithm; prevent; primary outcome; prognostic algorithm; prognostic model; prognostic performance; prognostic tool; progression risk; risk prediction; risk prediction model; secondary outcome; sex; success

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.

项目摘要/摘要 随着美国和其他国家糖尿病患病率的上升，正在进行的研究工作可以找到 生物标志物允许鉴定具有高终阶段肾脏疾病风险的糖尿病患者 （ESKD）。在NIH和JDRF的支持下，我们已经确定了21种显着的血清蛋白 在乔斯林肾脏研究中，与肾功能丧失和ESKD的风险增加有关，并且具有 开发了一个临时的奥林克多路复用测定法（所谓的乔斯林肾脏面板[JKP]）来测量这些 生物标志物。初步数据强烈表明，JKP的一部分可以显着提高 当添加到GFR和Allbuminuria中时，患有2型糖尿病（T2D）受试者的ESKD风险。在这个 提案，我们旨在在其他环境中验证这些初步发现，以发展改进 ESKD风险预测的算法。我们打算使用现有数据和标本来实现这些目标 来自有和没有T2D的个体，来自1个。慢性肾功能不全队列（CRIC）研究；和2 控制糖尿病（ACCORC）试验中心血管风险的行动及其后续研究手风琴。我们的 具体目的是：1：确定乔斯林肾脏面板中21个生物标志物中最有用的信息 并在预测ESKD风险时，评估其性能，并将其添加到GFR和蛋白尿 患有T2D和慢性肾脏疾病的受试者。我们将在基线中测量JKP的21种蛋白质 来自约1,500名CRIC参与者的血清标本T2D，将与GFR一起使用这些数据 蛋白尿将开发和内部验证多标记预后算法，以预测ESKD的风险 （主要结果）或ESKD的复合材料和/或肾功能损失（次要结果） 随访10年。 2：评估从CRIC到T2D个人的发现的普遍性 肾功能的较广泛。我们将从病例的基线血清标本中测定JKP。 〜2,000个协议/手风琴参与者的队列样本，并将使用这些数据调查 在CRIC中对具有不同特征的糖尿病患者建立的预测算法的概括性。这 在AIM 1中开发的预后模型和AIM 2中的外部验证将用于构建基于网络的基于网络 在临床环境中估算ESKD的10年风险的肾脏风险计算器。 3：评估 肾脏风险计算器从糖尿病患者转移到非糖尿病肾脏疾病。我们将 测量来自CRIC的〜1,700名非糖尿病受试者的基线血清样品中的21个JKP生物标志物 研究并将评估AIM 2中开发的肾脏风险计算器的性能，以预测风险 非糖尿病肾脏疾病患者的ESKD和ESKD/50％的肾功能损失。提议 研究很有可能导致开发改进分层的预后工具 糖尿病患者根据其发展为ESKD的风险。这将是一个很大的进步 优化患者护理并提高新的ESKD预防干预措施的临床试验效率。