Virtual metabolomics as a discovery tool for novel cardiometabolic disease biology

虚拟代谢组学作为新型心脏代谢疾病生物学的发现工具

基本信息

批准号：
10606582
负责人：
Jane F Ferguson
金额：
$ 54.29万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10606582
关键词：
Acceleration Acute Address African African ancestry Asian Automobile Driving Biological Biological Assay Biological Markers Biology Blood Blood specimen Cardiac Cardiometabolic Disease Cardiovascular Diseases Clinical Collection Communities Consumption Coronary Arteriosclerosis Cost Savings DNA Diabetes Mellitus Disease Disease Progression Electronic Health Record Electronic Medical Records and Genomics Network Emerging Technologies Epidemiology European European ancestry Event Generations Genetic Genotype Gout Heritability High Density Lipoprotein Cholesterol Individual LDL Cholesterol Lipoproteins Link Measurable Measurement Measures Metabolic Diseases Metabolism Modification Morbidity - disease rate Non-Insulin-Dependent Diabetes Mellitus Obesity Outcome Pathologic Pathway interactions Peripheral Vascular Diseases Phenotype Plasma Population Prevention strategy Process Registries Research Research Design Research Personnel Risk Risk Marker Sample Size Sampling Single Nucleotide Polymorphism Source Stroke Target Populations Testing Therapeutic Time Underrepresented Populations Urate Validation Vulnerable Populations biobank biomarker discovery burden of illness cardiometabolism circulating biomarkers clinical diagnosis clinically relevant clinically significant cohort cost data integration diagnostic strategy disease phenotype disorder risk epidemiology study genetic approach genetic predictors genome wide association study improved innovation large scale data metabolomics mortality multi-ethnic new therapeutic target novel novel marker online resource optimal treatments personalized strategies phenome pleiotropism predictive marker risk stratification tool treatment strategy virtual web portal

项目摘要

PROJECT SUMMARY / ABSTRACT Dysregulated metabolism underlies many of the leading causes of mortality and morbidity in the US including cardiometabolic diseases. Metabolomics studies can identify novel disease biomarkers, novel therapeutic targets, and biological pathways with pathological relevance. Emerging technologies in metabolomics allow the interrogation of large numbers of metabolites from diverse pathways. However, these approaches remain expensive and time-consuming. Applying metabolomics to very large cohorts of individuals to conduct epidemiological studies is not feasible, due to the practical challenges and costs of implementing these assays at scale. These challenges have limited discovery of novel biomarker-disease associations. We propose to address these limitations with a genetics-based “virtual” metabolite study design that will allow us to define genetic predictors of metabolite concentrations in a small population in whom the metabolite was measured, and then use these genetic predictors to impute metabolite concentrations in a large population in whom the metabolite was not measured. This approach vastly amplifies the sample size for discovery, and can rapidly identify novel biomarkers for downstream validation. The primary aims of this proposal are to: 1) construct single nucleotide polymorphism (SNP)-based predictors of circulating metabolites, and identify associations with cardiometabolic phenotypes, including type 2 diabetes and coronary artery disease; 2) validate the associations with direct metabolite measurements; 3) identify pleiotropic associations between metabolite genetic predictors and the clinical phenome. These analyses are enabled by genetic approaches that allow us to integrate data from large scale genome-wide association studies (GWAS) of cardiometabolic diseases and a collection of electronic health record linked-DNA biobanks comprising over 700,000 subjects. Innovative features of this approach include the efficiency and scale of the analysis, inclusion of under-represented and vulnerable populations and implementation of a re-usable and scalable analytical framework that will accelerate biomarker discovery and implementation. Upon completion of this project, we will construct a publicly accessible online resource of metabolite-disease associations that will be available to researchers as a source for both hypothesis testing and generation. Ultimately, these studies will advance the field of metabolomics by rapidly advancing the process of linking metabolites to clinically-relevant diseases.

项目摘要 /摘要在美国的许多主要原因导致死亡和病态的许多主要原因下心脏代谢性疾病。具有病理相关性的靶标和生物学途径。从不同途径中审问大量代谢物。昂贵且耗时。由于实施本文的实际挑战和成本，流行病学研究是不可行的在规模上，这些挑战的发现有限。通过基于遗传学的“虚拟”代谢物研究设计来解决限制，该设计将使我们能够定义在测量代谢物的少数人群中代谢物浓度的遗传预测因子，以及使用该预测因子将代谢产物浓度归为大量人群未测量代谢物。确定新的生物标志物以进行下游验证。单核苷酸多态性（SNP）的循环代谢物预测指标，并鉴定关联具有心脏代谢表型，包括2型糖尿病和冠状动脉疾病；与直接代谢物测量的关联； 3）确定代谢物之间的多效性遗传预测因素和临床现象。整合来自大规模基因组关联研究（GWAS）的数据，心脏代谢性疾病和A 收集电子记录Linkord-DNA生物库，其中包括700,000多个受试者这种方法的特征包括分析的效率和规模，包括不受欢迎以及脆弱的人群和可重复使用和可扩展的分析框架的实施加速生物标志物发现和实施。代谢物饮食协会协会的公开访问在线资源将为研究人员提供作为一个假设测试和产生的来源。代谢组学通过快速推进将代谢物与临床上相关的探测联系起来的过程。