Genetics of Cardiometabolic Diseases in the VA Population

VA 人群心脏代谢疾病的遗传学

• 批准号: 9033652
• 负责人: Kyong-Mi Chang
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9033652
• 关键词: Affect; African American; Architecture; Arteries; Atherosclerosis; Biological Markers; Biometry; Blood; Body mass index; Cardiovascular system; Cessation of life; Cholesterol; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Coronary Arteriosclerosis; Coronary artery; Current Procedural Terminology; Data; Development; Diabetes Mellitus; Diagnosis; Discipline; Disease; Dyslipidemias; Electronic Health Record; Environmental Risk Factor; Epidemiology; Ethnic group; European; Functional disorder; Genetic; Genetic Determinism; Genetic Polymorphism; Genetic Risk; Genetic Variation; Genetic study; Genotype; Goals; Healthcare Systems; Heart; Heritability; Hispanic Americans; Individual; International; International Classification of Diseases; Knowledge; Lead; Leg; Link; Lipids; Measurement; Measures; Medical; Medical center; Medicine; Metabolic; Metabolic Diseases; Methods; Minor; Minority; Modification; Morbidity - disease rate; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Onset of illness; Operative Surgical Procedures; Outcome; Participant; Pennsylvania; Peripheral arterial disease; Pharmaceutical Preparations; Phenotype; Philadelphia; Plant Roots; Population; Population Genetics; Positioning Attribute; Prediabetes syndrome; Predisposition; Prevention strategy; Primary Prevention; Quality of life; Race; Registries; Research; Research Personnel; Resources; Risk; Risk Factors; Series; Source; Statistical Algorithm; Susceptibility Gene; Therapeutic Agents; Time; Universities; Validation; Variant; Veterans; base; cardiometabolic risk; case control; clinical investigation; clinical risk; cohort; design; disorder control; disorder risk; electronic data; ethnic diversity; experience; gene environment interaction; genetic analysis; genetic association; genome wide association study; genome-wide; human population genetics; improved; interest; member; mortality; novel; novel therapeutics; prediction algorithm; premature; prevent; programs; public health relevance; racial and ethnic; racial diversity; risk variant; trait

 DESCRIPTION (provided by applicant): Obesity, Type 2 diabetes (T2DM), and dyslipidemia are metabolic disorders that promote the development of coronary artery (CAD) and peripheral arterial disease (PAD). Collectively, these cardio metabolic conditions are leading causes of illness and death among Veterans. A substantial proportion of the variation in risk of clinical complications related to these conditios remains unexplained despite an understanding of the root factors involved. The VA Million Veteran Program (MVP) links information from Veterans' electronic heath record (EHR) to biomarker data measured from blood and provides an unparalleled opportunity to further explore the genetic basis of cardio metabolic diseases. We propose to use the genome wide genotyping data from the first 200,000 participants in MVP linked to the EHR to uncover novel associations between genetic variation and risk of cardio metabolic disease. To perform this research, we have assembled a team of investigators with extensive experience in VA based clinical research and population genetics. Many members of our team have not only participated in, but also have led, the most productive international collaborations over the last 10 years that have studied the genetic basis of cardio metabolic diseases. Our consortium includes investigators from 5 VISNs based at Palo Alto, Philadelphia, Phoenix, Bedford, and Albany as well as from Stanford University and the University of Pennsylvania. In Aim 1, we will establish optimal definitions of five cardio metabolic traits: body mass index, blood levels of cholesterol, as well as diagnoses of Type 2 diabetes (T2DM), CAD, PAD, using EHR derived information on medical diagnoses and treatments, physical exam and lab measures, and medication usage. Preliminary results of our queries of VA EHR data using the most liberal definitions of the traits have identified approximately 160,000 participants with lipid measurements, 195,000 participants with measurements of body-mass index, 100,000 participants with T2DM or prediabetes, 46,000 participants with CAD, and 9,000 participants with PAD. For quantitative traits, we will derive and study not only single time point measures but also long term averages for each individual. For outcomes, we will optimize our definitions by assessing the relationship between established risk factors including phenotype specific genetic risk scores and case-control status. In Aim 2, we will perform a series of genome wide association studies to confirm known loci and to identify novel genetic variation associated with our traits of interest. We will also use the comprehensive VA EHR to examine for the presence of gene-environment interactions. Finally, in Aim 3, we will apply novel statistical algorithms that will improve our understanding of the genetic variation that contributes to the risk of cardio metabolic diseases in both the African American and the Hispanic American populations by leveraging similarities in the genetic architecture among different race/ethnic groups. Successful completion of this project will help us to more thoroughly comprehend the underlying causes of cardio metabolic disease and to develop novel therapies that are safe, effective, and personalized. These discoveries will also result in the more reliable identification of individuals at risk for these disorders, allowing for the more optimal delivery of primary prevention strategies within the VA population.

 描述（由申请人提供）： 肥胖、2型糖尿病（T2 DM）和血脂异常是促进冠状动脉（CAD）和外周动脉疾病（PAD）发展的代谢紊乱。总的来说，这些心脏代谢疾病是退伍军人疾病和死亡的主要原因。尽管了解了相关的根本因素，但与这些条件相关的临床并发症风险的很大一部分变化仍然无法解释。VA百万退伍军人计划（MVP）将退伍军人电子健康记录（EHR）中的信息与从血液中测量的生物标志物数据联系起来，并为进一步探索心脏代谢疾病的遗传基础提供了无与伦比的机会。我们建议使用与EHR相关的MVP中前20万名参与者的全基因组基因分型数据，以揭示遗传变异与心脏代谢疾病风险之间的新关联。为了进行这项研究，我们组建了一个在VA临床研究和群体遗传学方面具有丰富经验的研究人员团队。我们团队的许多成员不仅参与，而且领导了过去10年来最富有成效的国际合作， 研究了心脏代谢疾病的遗传基础。我们的联盟包括来自帕洛阿尔托、费城、凤凰城、贝德福德和奥尔巴尼以及斯坦福大学和宾夕法尼亚大学的5个VISN的调查人员。在目标1中，我们将建立五个心脏代谢特征的最佳定义：体重指数，血液胆固醇水平，以及2型糖尿病（T2 DM），CAD，PAD的诊断，使用EHR导出的医疗诊断和治疗信息，体检和实验室测量，以及药物使用。我们使用最自由的特征定义查询VA EHR数据的初步结果确定了大约160，000名参与者的血脂测量结果，195，000名参与者的体重指数测量结果，100，000名参与者患有T2 DM或前驱糖尿病，46，000名参与者患有CAD，9，000名参与者患有PAD。对于数量性状，我们将推导和研究不仅是单个时间点的措施，而且每个人的长期平均值。对于结果，我们将通过评估已建立的风险因素（包括表型特异性遗传风险评分和病例对照状态）之间的关系来优化我们的定义。在目标2中，我们将进行一系列全基因组关联研究，以确认已知的基因座，并确定与我们感兴趣的性状相关的新的遗传变异。我们还将使用全面的VA EHR来检查基因-环境相互作用的存在。最后，在目标3中，我们将应用新的统计算法，这将提高我们对导致心脏代谢疾病风险的遗传变异的理解， 通过利用不同种族/族裔群体之间遗传结构的相似性，对非洲裔美国人和西班牙裔美国人群体进行研究。该项目的成功完成将有助于我们更深入地了解心脏代谢疾病的根本原因，并开发安全，有效和个性化的新疗法。这些发现还将导致更可靠地识别这些疾病的风险个体，从而在VA人群中提供更优化的一级预防策略。