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: 10414765
• 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/10414765
• 关键词: 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; 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; Sample Size; Sampling; Single Nucleotide Polymorphism; Source; Stroke; Sum; Target Populations; Testing; Therapeutic; Time; Urate; Validation; Vulnerable Populations; base; biobank; biomarker discovery; burden of illness; cardiometabolism; case control; circulating biomarkers; clinical diagnosis; clinically relevant; clinically significant; cohort; cost; 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; phenome; pleiotropism; predictive marker; risk stratification; 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 非调节的新陈代谢是美国许多主要原因和发病率的主要原因的基础 心脏代谢性疾病。代谢组学研究可以鉴定新型疾病生物标志物，新型疗法 靶标和具有病理相关性的生物途径。代谢组学领域的新兴技术允许 从各种途径中询问大量代谢物。但是，这些方法仍然存在 昂贵且耗时。将代谢组学应用于非常大的人群进行 由于实施这些测定的实际挑战和成本，流行病学研究是不可行的 大规模。这些挑战的发现有限，对新型生物标志物 - 疾病协会的发现。我们建议 通过基于遗传学的“虚拟”代谢物研究设计来解决这些限制，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， 然后使用这些遗传预测因子将代谢物浓度归为代谢物，在大量人群中 未测量代谢物。这种方法大大放大了发现的样本量，并且可以迅速 确定新型生物标志物以进行下游验证。该提案的主要目的是：1）构建 基于循环代谢物的基于核苷酸多态性（SNP）的预测指标，并识别关联 具有心脏代谢表型，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现象。这些分析是通过遗传方法来实现的，使我们 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 电子健康记录的收集链接的DNA生物库完成了70万多名受试者。创新的 这种方法的特征包括分析的效率和规模，包括代表性不足和 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。该项目完成后，我们将构建一个 代谢物 - 疾病协会的公开访问的在线资源，研究人员将作为一个 假设检验和产生的来源。最终，这些研究将推动 代谢组学通过快速推进将代谢产物与临床相关疾病联系起来的过程。