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：
8463610
负责人：
Dean Paul Jones
金额：
$ 102.81万
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/8463610
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 Markers 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

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。我们组建了一个由医师科学家、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。我们将把一个新的高分辨率代谢平台应用于广泛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。该代谢组学平台可测量人血浆中的 7000 种化学物质，包括大多数已知生化途径中的化学物质。正在进行的预测健康队列已对亚临床血管疾病（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）以及氧化应激和炎症的多种生物标志物进行了广泛的表型分析。正在进行的 CVD 队列具有临床 CVD（通过血管造影测量是否存在冠状动脉疾病及其严重程度，以及既往心肌梗塞病史），并将在整个项目中进行临床跟踪。生物信息学工具将用于开发代谢风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-/-）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、遗传和通路谱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通过亚临床血管疾病 4 年进展、CVD 患者发生不良心血管事件的数据进行验证，患有外周动脉疾病的患者正在接受不良心血管事件的随访。相互作用的组件将创建一个系统，根据代谢模式和整合代谢模式、途径和功能网络的预测模型对 CVD 风险表型进行分类。长期目标是一种经济实惠的方法，可用于预测疾病易感性、诊断、风险分层、治疗反应和预后。