Epigenomic and Metabolomics Determinants of Subclinical and Clinical Vascular and Myocardial Disease

亚临床和临床血管和心肌疾病的表观基因组学和代谢组学决定因素

• 批准号: 10333818
• 负责人: Yan Sun
• 金额: $ 27.24万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10333818
• 关键词: 20 year old; Age; Algorithms; Asia; Atherosclerosis; Biological Process; Cardiomyopathies; Cardiovascular Diseases; Cardiovascular system; Caring; Carotid Artery Plaques; Cessation of life; Clinical; Collection; Complex; Coronary Arteriosclerosis; Detection; Development; Diabetes Mellitus; Disease; Disease Management; Disease Outcome; Dyslipidemias; EFRAC; Echocardiography; Environment; Environmental Exposure; Environmental Risk Factor; Epigenetic Process; Evaluation; Feasibility Studies; Functional disorder; Funding; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Genomics; Goals; Heart failure; High Density Lipoprotein Cholesterol; High Prevalence; Hypertriglyceridemia; India; Individual; Investigation; Joints; Lead; Left; Link; Low Prevalence; Measurement; Measures; Mediating; Mendelian randomization; Methods; Modification; Molecular; Myocardial Infarction; National Heart, Lung, and Blood Institute; Natural History; Obesity; Participant; Pathway interactions; Phenotype; Physiological; Play; Population; Population Study; Prevention; Program Research Project Grants; Risk; Risk Assessment; Risk Factors; Risk Reduction; Role; Sampling; South Asian; Testing; The Sun; Time; Vascular Diseases; Ventricular; Visit; acquired factor; ambient air pollution; arterial stiffness; base; cardiometabolic risk; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular health; carotid intima-media thickness; clinical phenotype; cohort; coronary calcium scoring; design; disease phenotype; disorder risk; epigenome; epigenomics; follow-up; genetic risk factor; genome wide association study; genomic locus; human disease; improved; inter-individual variation; machine learning algorithm; metabolome; metabolomics; multiple omics; novel; personalized diagnostics; population based; precision medicine; predictive marker; predictive modeling; preservation; response; risk prediction; small molecule; synergism

PROJECT SUMMARY / ABSTRACT: Project 3 To transform conventional imprecise cardiovascular disease (CVD) risk assessments towards more precise risk prediction and early-stage detection, Project 3 will conduct an untargeted multi-omic study to investigate molecular factors associated with subclinical and clinical CVD phenotypes along atherosclerotic CVD (ASCVD) and heart failure (HF) pathways including HF with preserved (HFpEF) and reduced ejection fraction (HFrEF). Both genetic and environmental risk factors contribute to the development of CVD. Identified genetic variations only account for a small proportion of ASCVD and HF risk in the population. Effect of environmental contributions to CVD, however, remain largely unexplored at the molecular level. Biomolecules, such as epigenetic and metabolomic markers, can capture complementary molecular effectors in response to environmental exposures, and alter biological functions at multiple levels. Therefore, studying multiple molecular layers (e.g., epigenome and metabolome) may help discover novel pathways and mechanisms through which environmental exposures influence CVD development and provide new targets for prevention and management of CVD. This is particularly important for populations, such as South Asians, who suffer a high burden of CVD at young ages and have unique cardiometabolic risk profile (e.g., very high prevalence of diabetes and dyslipidemia despite relatively low prevalence of obesity), but are underrepresented in current population studies of epigenomics, metabolomics and genomics. Examining the changes in multi-omic markers in relation to the natural history of CVD phenotypes within the same individuals provides a controlled and unbiased evaluation of these relationships. To identify epigenomic and metabolomic factors associated with subclinical (Aim 1, atherosclerosis and left ventricular systolic/diastolic dysfunction) and clinical CVD (Aim 2, coronary artery disease, HFpEF, ischemic and non-ischemic HFrEF), we will conduct epigenome-wide and metabolome-wide association analyses of 3,000 participants informatively selected from the Precision Cardiovascular Phenotyping and Pathophysiological Pathways in the CARRS cohort (Precision-CARRS), measured at baseline and the first follow-up visit, and replicate the findings in external cohorts. To determine the joint impact of genomic, epigenomic and metabolomic profiles on subclinical and clinical CVD and build prediction models (Aim 3), we will apply advanced multi-omic approaches, machine learning algorithms and causal inference methods. Lastly, we will assess whether epigenomic and metabolomic profiles are associated with, and mediate ambient air pollution (from Project 2) in relation to subclinical and clinical CVD (Aim 4). Through comprehensive multi-omic measurements and integrative analyses, Project 3 will improve the risk assessment, molecular understanding and precision medicine of CVD in South Asians.

项目摘要/摘要：项目三