Developing and validating prognostic metabolomic signatures of diabetic kidney disease

项目摘要/摘要 基本原理。糖尿病是导致肾脏疾病的主要原因，占美国约2000万人的40% 成人慢性肾脏疾病病例。然而，糖尿病患者之间有很大的异质性。 关于肾脏疾病的发展。因此，迫切需要确定预后因素。 生物标记物可以为未来肾脏疾病提供早期和可靠的证据，以便高危患者能够 接受最佳医疗护理。现有的临床、蛋白质组和基因组标记并不一致 准确预测肾功能下降。代谢组学，一种对药物最终产物的系统评价 体液中的细胞功能，有可能告知慢性疾病的生理和病理影响。 代谢组学分析与先进的定量方法相结合可以在临床上发挥关键作用 糖尿病肾病的有用预后标志。然而，随着计算方法的发展， 足够的严谨性已经落后于进行大规模定量代谢组学的技术能力。在……里面 这项建议旨在解决糖尿病肾病研究中的这一计算差距。目标。我们会 实施严格的计算方法以确定可靠的预后代谢物+临床+遗传 糖尿病肾病进展的征兆。具体地说，我们的目标是(I)测试以前的 签名，并应用最先进的分析技术和新的统计方法来识别新的 用于预测肾脏疾病进展的多变量代谢物集合；(Ii)量化共同调节的模式 糖尿病肾病中的代谢物，并在网络生物学中开发新的工具来发现新的酶， 与糖尿病肾病进展有关的蛋白质、代谢物和分子途径； 测试这些模型是否可以在独立的前瞻性队列中准确地预测肾脏疾病的进展。 方法：研究方法。使用来自大型预期队列的临床、遗传和代谢数据，这些数据来自1200种不同的、良好的 对于典型的2型糖尿病患者，我们将应用统计方法进行变量选择(例如， 惩罚性回归)和机器学习方法(例如，随机森林)，这些方法在 高维设置，以识别肾脏疾病进展的健壮和节俭的信号。我们 将量化代谢物之间的共同调节模式并推断与糖尿病肾脏有关的生物学途径 疾病。在整个建模过程中，将采用严格的培训-验证范例，以便 提高模型的重现性，减少偶然发现。冲击力。这项工作的一个主要产品将是 临床实用的糖尿病肾功能高危患者识别算法的研究 拒绝。我们的发现还将提供对肾功能障碍标志物的洞察，并阐明可能的 治疗糖尿病肾病的治疗目标，从而潜在地指导未来临床的设计 审判。

PROJECT SUMMARY/ABSTRACT Rationale. Diabetes is a leading cause of renal disease, accounting for 40% of the estimated 20 million US adult cases of chronic kidney disease. There is, however, substantial heterogeneity across diabetic patients with regards to development of kidney disease. Hence, there is an urgent need to identify prognostic biomarkers that can provide early and reliable evidence of future kidney disease, so that high-risk patients can receive optimal medical care. Existing clinical, proteomic and genomic markers do not consistently nor accurately predict kidney function decline. Metabolomics, a systematic evaluation of the end-products of cellular function in fluids, has the potential to inform physiological and pathological effects of chronic diseases. Metabolomic analysis combined with advanced quantitative methods could play a key role in building clinically useful prognostic signatures of diabetic kidney disease. Yet, development of computational methods with adequate rigor has lagged behind the technical capacity to perform large scale quantitative metabolomics. In this proposal we aim to address this computational gap in diabetic kidney disease research. Aims. We will implement rigorous computational methods to identify robust prognostic metabolite + clinical + genetic signatures of diabetic kidney disease progression. Specifically, we aim to (i) test the accuracy of previous signatures, and apply state-of-the-art analytic techniques and novel statistical methods to identify new multivariate metabolite sets for predicting kidney disease progression; (ii) quantify patterns of co-regulation of metabolites in diabetic kidney disease, and develop new tools in network biology to discover novel enzymes, proteins, metabolites, and molecular pathways which are implicated in diabetic kidney disease progression; (iii) test if these models can accurately predict kidney disease progression in independent prospective cohorts. Methods. Using clinical, genetic and metabolomic data from large prospective cohorts of > 1200 diverse, well- characterized patients with Type 2 diabetes, we will apply statistical methods for variable selection (e.g., penalized regression), and machine learning methods (e.g., random forest), which are known to perform well in the high-dimensional setting, to identify robust and parsimonious signatures of kidney disease progression. We will quantify inter-metabolite co-regulation patterns and infer biological pathways implicated in diabetic kidney disease. Throughout the modeling process, a rigorous training-validation paradigm will be adopted in order to improve reproducibility of models and reduce chance findings. Impact. A major product of this work will be the development of a clinically useful algorithm for identifying diabetic patients at high-risk for kidney function decline. Our findings will also provide insight into markers of renal dysfunction, and elucidate possible therapeutic targets for treating diabetic kidney disease, thus potentially informing the design of future clinical trials.