Novel Biomarkers of Chronic Kidney Disease in Children

项目摘要。候选人我是耶鲁大学的一名儿科肾病学家，致力于改善 慢性肾脏病（CKD）患儿的治疗。我申请的目的是获得指导性的研究培训 开发一种生物标志物增强的儿童CKD进展风险预测模型，用于未来的临床研究。 审判这项研究将建立在我之前的培训基础上，重点是急性肾损伤后CKD的风险 以及使用生物标志物来预测儿童的肾脏预后。这份提案将为我提供 高级统计学、生物标志物方法学和儿科CKD的学习和正式教学课程。我会 还高度重视发展建立有效合作所需的专业技能， 科学写作，并获得资金来支持我的研究。为了完成我的既定计划，我得到了 我的高素质的主要共同导师（奇拉格帕里克博士和苏珊弗思）和指导委员会（博士。 林海群、尤金·夏皮罗和普拉萨德·德瓦拉詹）在肾损伤领域拥有跨学科专业知识 生物标志物、转化研究、生物统计学和儿科CKD。这种多学科的指导沿着 Parikh博士的应用转化研究计划的高技能培训环境将使我能够 进行我提议的研究，并建立一个独立资助的研究项目。 项目儿童CKD的进展导致终末期肾病（ESRD），这与 死亡率比一般儿科人群高30 - 150倍。传统的生物标记物，血清 在临床试验中，肌酐和蛋白尿被用于预测CKD的进展，尽管两者都相关 CKD的进展和对干预措施的反应较差。有许多候选疗法 但随着对血清肌酐和蛋白尿的持续依赖，临床试验可能会继续失败。 儿童CKD生物标志物领域是一个非常有前途的研究领域， 目前投入的资金。预测CKD的进展将使临床医生能够更好地进行随访，转诊， 为家庭提供更好的指导。更重要的是，最佳的生物标志物和风险 预测模型可以代替蛋白尿和血清肌酐在生物标志物指导的临床试验。我们计划 从基线测量肾损伤、炎症、修复和纤维化的尿液和血清生物标志物 入选儿童CKD（CKiD）队列的869名CKD儿童的样本，并确定其 与纵向测量的GFR下降和事件ESRD的关系。生物标志物的最佳组合 加上来自2/3的CKiD患者的临床变量将产生预测CKD进展的风险预测模型。 我们的风险预测模型将在1/3的CKiD患者体内验证纵向GFR下降， 在AKI队列评估、系列评价和后续后遗症的124名儿童中进行外部评估。 开发CKD进展的风险预测模型可以改变范式，改变临床护理， 儿童CKD

Project Summary. Candidate. I am a pediatric nephrologist at Yale University dedicated to improving outcomes for children with chronic kidney disease (CKD). The goal of my application is to obtain mentored research training to develop a biomarker-augmented risk prediction model of pediatric CKD progression for use in future clinical trials. This research will build upon my prior training, which focused on the risk of CKD after acute kidney injury and the use of biomarkers to predict renal outcomes in children. This proposal will provide me with hands-on learning and formal didactic coursework in advanced statistics, biomarker methodology, and pediatric CKD. I will also intensely focus on developing the professional skills necessary for establishing effective collaborations, scientific writing, and obtaining funding to support my research. To accomplish my stated plan, I have the support of my highly qualified primary co-mentors (Drs. Chirag Parikh and Susan Furth) and mentoring committee (Drs. Haiqun Lin, Eugene Shapiro, and Prasad Devarajan) with interdisciplinary expertise in the fields of kidney injury biomarkers, translational research, biostatistics, and pediatric CKD. This multidisciplinary mentorship along with the highly skilled training environment at Dr. Parikh's, Program of Applied Translational Research will allow me to conduct my proposed research and establish an independently funded research program. Project. Progression of CKD in children leads to end stage renal disease (ESRD), which is associated with mortality rates 30-150 times higher than the general pediatric population. The traditional biomarkers, serum creatinine and proteinuria, are used to predict progression of CKD in clinical trials even though both correlate poorly with the progression of CKD and the response to interventions. There are numerous candidate therapies for CKD, but with a continued reliance on serum creatinine and proteinuria, clinical trials will likely continue to fail. The field of CKD biomarkers in children is a very promising area of research with a small amount of resources invested to date. Predicting progression of CKD will allow clinicians to better time follow-up, referral for transplant, and provide better guidance to families. More importantly, an optimal panel of biomarkers and risk prediction model can replace proteinuria and serum creatinine in biomarker guided clinical trials. We plan to measure urine and serum biomarkers of kidney injury, inflammation, repair, and fibrosis from the baseline samples of the 869 children with CKD enrolled in the CKD in Children (CKiD) cohort and determine their relationship with longitudinal measured GFR decline and incident ESRD. The optimal combination of biomarkers plus clinical variables from 2/3rd's of the CKiD patients will yield a risk prediction model to predict CKD progression. Our risk prediction model will be validated for longitudinal GFR decline, internally in 1/3rd of the CKiD patients, and externally in the 124 children of the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI cohort. Developing a risk prediction model of CKD progression can be paradigm changing, transforming clinical care for children with CKD.