Developing and validating prognostic metabolomic signatures of diabetic kidney disease

开发和验证糖尿病肾病的预后代谢组学特征

基本信息

批准号：
9923450
负责人：
Loki Natarajan
金额：
$ 33.45万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9923450
关键词：
Accounting Address Adopted Adult Albuminuria Algorithms American Bioinformatics Biological Biological Markers Biology Caring Cell physiology Cessation of life Chronic Disease Chronic Kidney Failure Chronic Kidney Insufficiency Clinical Clinical Trials Collaborations Complications of Diabetes Mellitus Computing Methodologies Data Data Set Development Diabetes Mellitus Diabetic Nephropathy Diagnosis Disease Disease Management Disease Progression Enzymes Evaluation Funding Future Genomics Genotype Goals Healthcare Heterogeneity Hospitalization Kidney Kidney Diseases Laboratories Lead Link Liquid substance Longitudinal cohort Medical Medical Genetics Methods Modeling Molecular Non-Insulin-Dependent Diabetes Mellitus Pathologic Pathway interactions Patients Pattern Physiological Pima Indian Play Process Prognostic Marker Prospective cohort Proteomics Publishing Recommendation Regulation Renal function Reporting Reproducibility Research Risk Sampling Sampling Studies Statistical Methods Statistical Models Techniques Testing Training Type 2 diabetic Urine Validation Work bioinformatics tool biological heterogeneity chemical association cohort comorbidity design diabetic diabetic patient genetic signature genomic biomarker high dimensionality high risk improved innovation insight kidney dysfunction machine learning method metabolome metabolomics model development mortality multidimensional data nephrogenesis network models novel open source personalized medicine predictive modeling predictive signature predictive test premature prognostic prognostic signature prospective protein metabolite random forest targeted treatment therapeutic target tool urinary

项目摘要

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.

项目摘要/摘要理由。糖尿病是肾脏疾病的主要原因，占美国2000万估计的40％成人慢性肾脏疾病病例。但是，糖尿病患者之间存在很大的异质性关于肾脏疾病的发展。因此，迫切需要识别预后可以提供未来肾脏疾病的早期可靠证据的生物标志物，以便高危患者可以获得最佳医疗服务。现有的临床，蛋白质组学和基因组标志物并非一致或准确预测肾功能下降。代谢组学，对最终产物的系统评估流体中的细胞功能有可能为慢性疾病的生理和病理作用提供信息。代谢组分析与高级定量方法结合使用，可以在临床上构建中起关键作用糖尿病肾脏疾病的有用预后特征。然而，开发与足够的严格落后于执行大规模定量代谢组学的技术能力。在我们的建议旨在解决糖尿病肾脏疾病研究中的计算差距。目标。我们将实施严格的计算方法来识别强大的预后代谢物 +临床 +遗传糖尿病肾脏疾病进展的特征。具体来说，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颖的统计方法来识别新的用于预测肾脏疾病进展的多元代谢物集；（ii）量化共同调节的模式糖尿病肾脏疾病中的代谢物，并开发网络生物学的新工具，以发现新型酶，与糖尿病肾脏疾病进展有关的蛋白质，代谢产物和分子途径；（iii）测试这些模型是否可以准确预测独立前瞻性队列中的肾脏疾病进展。方法。使用临床，遗传和代谢组数据，来自> 1200种不同的前瞻性人群表征2型糖尿病患者，我们将应用统计方法进行可变选择（例如，惩罚回归）和机器学习方法（例如随机森林），已知在高维环境，以确定肾脏疾病进展的强大和简约的特征。我们将量化与糖尿病肾脏有关的量子间共同调节模式和推断生物学途径疾病。在整个建模过程中，将采用严格的培训验证范式以进行提高模型的可重复性并减少机会发现。影响。这项工作的主要产品将是开发一种临床上有用的算法，用于鉴定高危肾脏功能的糖尿病患者衰退。我们的发现还将提供对肾功能障碍标记的见解，并阐明可能治疗糖尿病肾脏疾病的治疗靶标，因此有可能告知未来临床的设计试验。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Host variables confound gut microbiota studies of human disease.

DOI：
10.1038/s41586-020-2881-9
发表时间：
2020-11
期刊：
Nature
影响因子：
64.8
作者：
Vujkovic-Cvijin I;Sklar J;Jiang L;Natarajan L;Knight R;Belkaid Y
通讯作者：
Belkaid Y

Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data.

DOI：
10.1111/biom.13481
发表时间：
2022-09
期刊：
BIOMETRICS
影响因子：
1.9
作者：
Jiang, Lingjing;Haiminen, Niina;Carrieri, Anna-Paola;Huang, Shi;Vazquez-Baeza, Yoshiki;Parida, Laxmi;Kim, Ho-Cheol;Swafford, Austin D.;Knight, Rob;Natarajan, Loki
通讯作者：
Natarajan, Loki

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.

DOI：
10.1186/s12859-023-05171-w
发表时间：
2023-02-20
期刊：
BMC bioinformatics
影响因子：
3
作者：
通讯作者：

Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry.

DOI：
10.3390/metabo11100671
发表时间：
2021-09-30
期刊：
Metabolites
影响因子：
4.1
作者：
Saito R;Hirayama A;Akiba A;Kamei Y;Kato Y;Ikeda S;Kwan B;Pu M;Natarajan L;Shinjo H;Akiyama S;Tomita M;Soga T;Maruyama S
通讯作者：
Maruyama S