Developing and validating prognostic metabolomic signatures of diabetic kidney disease

项目概要/摘要 理由。糖尿病是肾脏疾病的主要原因，占美国估计2000万人的40%。 成人慢性肾脏病病例。然而，在糖尿病患者中存在显著的异质性 与肾脏疾病的发展有关。因此，迫切需要确定预后 生物标志物，可以提供早期和可靠的证据，未来的肾脏疾病，使高危患者可以 获得最佳的医疗护理。现有的临床、蛋白质组学和基因组学标记并不一致， 准确预测肾功能下降。代谢组学是对代谢产物的系统评价， 液体中的细胞功能，有可能告知慢性疾病的生理和病理影响。 代谢组学分析结合先进的定量方法可以在临床构建中发挥关键作用 糖尿病肾病的有用的预后标志。然而，计算方法的发展， 足够的严格性已经落后于进行大规模定量代谢组学的技术能力。在 本提案旨在解决糖尿病肾病研究中的这一计算缺口。目标。我们将 实施严格的计算方法，以确定可靠的预后代谢物+临床+遗传 糖尿病肾病进展的标志。具体而言，我们的目标是（i）测试以前的准确性 签名，并应用最先进的分析技术和新的统计方法，以确定新的 用于预测肾脏疾病进展的多变量代谢物集合;（ii）量化 代谢物在糖尿病肾病，并开发新的工具，在网络生物学发现新的酶， 涉及糖尿病肾病进展的蛋白质、代谢物和分子途径;（iii） 测试这些模型是否可以在独立的前瞻性队列中准确预测肾脏疾病的进展。 方法.使用来自大型前瞻性队列的临床，遗传和代谢组学数据，这些队列包括> 1200个不同的，良好的- 特征化的2型糖尿病患者，我们将应用统计方法进行变量选择（例如， 惩罚回归），以及机器学习方法（例如，随机森林），已知在 高维设置，以识别肾脏疾病进展的稳健和简约特征。我们 将量化代谢物间的协同调节模式，并推断糖尿病肾脏相关的生物学途径。 疾病在整个建模过程中，将采用严格的培训-验证范例，以便 提高模型再现性并减少偶然发现。冲击这项工作的一个主要成果将是 开发一种用于识别肾功能高危糖尿病患者的临床有用算法 下降我们的研究结果也将提供深入了解肾功能不全的标志物，并阐明可能的 治疗糖尿病肾病的治疗靶点，从而可能为未来的临床设计提供信息。 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.