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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.

项目概要/摘要新陈代谢失调是美国许多死亡和发病的主要原因，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、新的治疗方法目标和具有病理相关性的生物途径。代谢组学的新兴技术允许对来自不同途径的大量代谢物进行询问。然而，这些方法仍然昂贵且耗时。将代谢组学应用于非常大的个体群体中进行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规模化。这些挑战限制了新型生物标志物与疾病关联的发现。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，这将使我们能够定义测量代谢物的小群体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来估算大量人群中的代谢物浓度，其中没有测量代谢物。这种方法极大地扩大了发现的样本量，并且可以快速确定用于下游验证的新型生物标志物。该提案的主要目标是：1）构建基于单核苷酸多态性 (SNP) 的循环代谢物预测因子，并识别关联患有心脏代谢表型，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) 识别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实现的，使我们能够整合来自心脏代谢疾病的大规模全基因组关联研究 (GWAS) 的数据和电子健康记录关联 DNA 生物库的集合，包含超过 700,000 名受试者。创新的这种方法的特点包括分析的效率和规模、纳入代表性不足和弱势群体和实施可重复使用且可扩展的分析框架，该框架将加速生物标志物的发现和实施。该项目完成后，我们将建设一个代谢物疾病协会的可公开访问的在线资源，可供研究人员作为假设检验和生成的来源。最终，这些研究将推动该领域的发展通过快速推进将代谢物与临床相关疾病联系起来的过程来进行代谢组学。