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

• 批准号: 9883038
• 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
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883038
• 关键词: Acute; Address; African; Asians; Automobile Driving; Biological; Biological Assay; Biological Markers; Biology; Blood; Blood specimen; Cardiac; Cardiovascular Diseases; Clinical; Collection; Communities; Consumption; Coronary Arteriosclerosis; Cost Savings; DNA; Diabetes Mellitus; Diagnostic; Disease; Disease Progression; Electronic Health Record; Electronic Medical Records and Genomics Network; Emerging Technologies; Epidemiology; European; Event; Generations; Genetic; Genotype; Gout; Heritability; High Density Lipoprotein Cholesterol; Individual; LDL Cholesterol Lipoproteins; Link; Logistics; 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; Risk stratification; Sample Size; Sampling; Single Nucleotide Polymorphism; Source; Stroke; Sum; Target Populations; Testing; Therapeutic; Time; Underrepresented Populations; Urate; Validation; Vulnerable Populations; base; biobank; biomarker discovery; burden of illness; cardiometabolism; case control; circulating biomarkers; clinical Diagnosis; clinically relevant; clinically significant; cohort; cost; disease phenotype; disorder risk; epidemiology study; genetic approach; genetic predictors; genome wide association study; improved; innovation; large scale data; metabolomics; mortality; new therapeutic target; novel; novel marker; online resource; optimal treatments; phenome; pleiotropism; predictive marker; tool; treatment strategy; virtual; web portal; web-accessible

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，代谢失调是导致死亡和发病的许多主要原因，包括 心脏代谢疾病。代谢组学研究可以识别新的疾病生物标志物，新的治疗方法 靶点，以及与病理相关的生物途径。新陈代谢组学的新兴技术使 对来自不同途径的大量代谢物的询问。然而，这些方法仍然存在 既昂贵又耗时。将代谢组学应用于非常大的个体队列 由于实施这些检测的实际挑战和成本，流行病学研究是不可行的。 在规模上。这些挑战限制了新的生物标记物与疾病关联的发现。我们建议 用一种基于遗传学的“虚拟”代谢物研究设计来解决这些限制，这将使我们能够定义 代谢物浓度在测量代谢物的小群体中的遗传预测因子， 然后使用这些遗传预测因子来推算一大群人的代谢物浓度 未检测到代谢物。这种方法极大地扩大了发现的样本大小，并可以迅速 确定用于下游验证的新生物标记物。这项提议的主要目的是：1)建立 基于单核苷酸多态性(SNP)的循环代谢物预测因子，并确定关联 心脏代谢表型，包括2型糖尿病和冠状动脉疾病；2)验证 与直接代谢物测量的关联；3)确定代谢物之间的多效性关联 遗传预测因子和临床表型。这些分析是由基因方法实现的，这些方法允许我们 为了整合来自心脏代谢性疾病的大规模全基因组关联研究(GWAS)的数据和一个 收集电子健康记录链接的DNA生物库，包括超过700,000名受试者。创新型 这种方法的特点包括分析的效率和规模，包括未得到充分说明的情况和 弱势群体和实施可重复使用和可扩展的分析框架 加快生物标志物发现和实施。这个项目完成后，我们将建造一个 可公开访问的代谢物-疾病关联的在线资源，将作为 假设检验和生成的来源。最终，这些研究将推动 代谢组学通过快速推进代谢物与临床相关疾病的联系的过程。