Metabolomics of subclinical and clinical cardiovascular disease

亚临床和临床心血管疾病的代谢组学

基本信息

批准号：
8625332
负责人：
Dean Paul Jones
金额：
$ 101.37万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
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/8625332
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 Future Gene Expression Profiling Genetic Genetic Markers Genetic Models Genomics Goals Health Health behavior Human Inflammation Knock-out Knowledge Ligation Maps Mass Spectrum Analysis Measures Medicine Metabolic Metabolic Pathway Methods Microvascular Dysfunction 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 endothelial dysfunction 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

项目摘要

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化。我们已经组建了一个由医师科学家，实验性心脏病学家，生理生物化学家和计算生物学家组成的综合团队，以解决这个问题。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队列发现新的代谢风险因素。代谢组学平台在人血浆中测量7000种化学物质，包括大多数知名的生化途径中的化学物质。持续的预测健康队列已被广泛表型，用于亚临床血管疾病（内皮功能障碍，颈动脉内膜膜厚度，动脉僵硬和微血管功能障碍）和各种氧化应激和炎症的生物标志物。正在进行的CVD队列具有临床CVD（在血管造影及其严重程度以及先前的心肌梗塞史上以冠状动脉疾病的存在/不存在衡量），并且在整个项目中都会在临床上遵循。生物信息学的工具将被用来开发代谢风险因素的功能代谢和遗传图。这些功能图将指导使用新型的化学和蛋白质递送工具来测试CVD开发中的代谢物和扰动代谢途径的CVD小鼠模型（部分颈动脉连接和APOE - / - ）的机械研究。发现的代谢，遗传和途径剖面将与现有知识一起使用，以开发一种新的血管疾病风险模型，该模型将在独立的队列中进行验证，并具有有关亚临床血管疾病4年进展的数据，CVD患有患有不良心血管事件的患者，患有外周疗法的患者患有外周疾病的患者发生不良CVD事件。相互作用的组件将创建一个系统，以代谢模式和整合代谢模式，途径和功能网络的预测模型来对CVD风险表型进行分类。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，对治疗和预后的反应。