Metabolomics of subclinical and clinical cardiovascular disease

亚临床和临床心血管疾病的代谢组学

• 批准号: 8286494
• 负责人: Dean Paul Jones
• 金额: $ 69.07万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8286494
• 关键词: Acute; Address; Adverse event; Angiography; Apolipoprotein E; Atherosclerosis; Biochemical Pathway; Bioinformatics; Biological Markers; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Characteristics; Chemicals; Chromatography; Chromosome Mapping; Clinical; Complex; Coronary Arteriosclerosis; Data; Development; Diagnosis; Diet; Disease; Disease Management; Disease Progression; Disease susceptibility; Drug Metabolic Detoxication; Environment; Epigenetic Process; Event; Food; Fourier Transform; Functional disorder; Future; Gene Expression Profiling; Genetic; Genetic Models; Genomics; Goals; Health; Health behavior; Human; Inflammation; Knock-out; Knowledge; Ligation; Maps; Mass Spectrum Analysis; Measures; Medicine; Metabolic; Metabolic Pathway; Methods; Modeling; Mus; Myocardial Infarction; Oligonucleotides; Oxidative Stress; Pathway interactions; Patients; Pattern; Peripheral arterial disease; Pharmaceutical Preparations; Phenotype; Physicians; Physiological; Plasma; Population; Preclinical Testing; Process; Proteins; Recording of previous events; Research; Resolution; Risk; Risk Factors; Sampling; Scientist; Severities; Stratification; System; Testing; Therapeutic; Thick; Validation; Variant; Vascular Diseases; abstracting; adverse outcome; arterial stiffness; base; cardiovascular disorder risk; cohort; disease phenotype; disorder prevention; disorder risk; environmental chemical; epigenomics; experience; genetic profiling; innovation; intima media; life history; metabolomics; microbiome; model development; mouse model; novel; outcome forecast; preclinical evaluation; predictive modeling; programs; public health relevance; repaired; response; small molecule; tool

DESCRIPTION (provided by applicant): Cardiovascular disease (CVD) is a complex process involving genetic, epigenetic and life history variations in diet, environment and health behaviors. We have assembled an integrated team of physician scientists, experimental cardiologists, physiological biochemists and computational biologists to address this problem. We will apply a new high-resolution metabolic platform to extensively phenotyped subclinical and clinically diagnosed CVD cohorts to discover new metabolic risk factors. The metabolomics platform measures 7000 chemicals in human plasma, including chemicals in most known biochemical pathways. The ongoing predictive health cohort has been extensively phenotyped for subclinical vascular disease (endothelial dysfunction, carotid intima-media thickness, arterial stiffness, and microvascular dysfunction) and a wide variety of biomarkers of oxidative stress and inflammation. The ongoing CVD cohort has clinical CVD (measured as presence/absence of coronary artery disease at angiography and its severity, and previous history of myocardial infarction), and will be followed clinically throughout this project. Bioinformatic tools will be ued to develop functional metabolic and genetic maps of the metabolic risk factors. These functional maps will guide mechanistic studies in mouse models of CVD (partial carotid ligation and apoE-/-) employing novel chemical and protein delivery tools to test the metabolites and perturbed metabolic pathways in CVD development. The discovered metabolic, genetic and pathway profiles will be used with existing knowledge to develop a new vascular disease risk model, which will be validated in independent cohorts with data on 4-year progression of subclinical vascular disease, patients with CVD developing adverse cardiovascular events, and patients with peripheral artery disease being followed for adverse CVD events. The interacting components will create a system to classify CVD risk phenotype in terms of metabolic patterns and predictive models that integrate the metabolic patterns, pathways, and functional networks. The long-term goal is an affordable approach that can be used for predicting disease susceptibility, diagnosis, risk stratification, response to therapy and prognosis.

描述（由申请人提供）：心血管疾病（CVD）是一个复杂的过程，涉及饮食、环境和健康行为的遗传、表观遗传和生活史变异。我们已经组建了一个由医生科学家，实验心脏病学家，生理生物化学家和计算生物学家组成的综合团队来解决这个问题。我们将应用一个新的高分辨率代谢平台，以广泛的表型亚临床和临床诊断的CVD队列，以发现新的代谢危险因素。代谢组学平台测量人体血浆中的7000种化学物质，包括大多数已知生化途径中的化学物质。正在进行的预测性健康队列已被广泛用于亚临床血管疾病（内皮功能障碍，颈动脉内膜中层厚度，动脉僵硬度和微血管功能障碍）和各种氧化应激和炎症生物标志物的表型。正在进行的CVD队列具有临床CVD（测量为血管造影时是否存在冠状动脉疾病及其严重程度，以及既往心肌梗死史），并将在整个项目中进行临床随访。生物信息学工具将被用于开发代谢危险因素的功能代谢和遗传图谱。这些功能图谱将指导CVD小鼠模型（部分颈动脉结扎和apoE-/-）中的机制研究，采用新型化学和蛋白质递送工具来测试CVD发展中的代谢物和扰动的代谢途径。所发现的代谢、遗传和途径特征将与现有知识一起用于开发新的血管疾病风险模型，该模型将在独立队列中进行验证，这些队列的数据包括亚临床血管疾病的4年进展、发生不良心血管事件的CVD患者以及正在随访不良CVD事件的外周动脉疾病患者。相互作用的组件将创建一个系统，根据代谢模式和预测模型对心血管疾病风险表型进行分类，该模型集成了代谢模式、途径和功能网络。长期目标是一种负担得起的方法，可用于预测疾病易感性、诊断、风险分层、治疗反应和预后。