Leveraging the Genetics of carotid stenosis for identifying novel risk factors and therapeutic opportunities

利用颈动脉狭窄的遗传学来识别新的危险因素和治疗机会

基本信息

项目摘要

Current guidelines for primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD), including coronary heart disease (CHD), peripheral artery disease (PAD), and cerebrovascular disease, focus on the uniform application of risk factor modification irrespective of CVD subtype, despite rising evidence that each of these diseases has specific underlying pathobiology. Large scale genetic studies have identified new biology, clarified the role of modifiable risk factors, improved risk prediction, and identified therapeutic targets for CHD and PAD. This work has demonstrated vascular territory specific effects of risk factors and therapies, motivating disease specific approaches to prevention and treatment of atherosclerotic cardiovascular disease. Unfortunately, genetic studies of cerebrovascular disease have lagged. Extant studies have tended to focus on either early-stage subclinical atherosclerosis (carotid intima to media thickness [cIMT]) or late-stage outcomes (ischemic stroke). Studies of actual atherosclerotic cerebrovascular disease, in the form of carotid stenosis, have been limited by small sample size. The VA Million Veteran Program (MVP) was initiated in 2011 to study how genes, lifestyle, and military exposure affect health and disease and offers a unique opportunity to advance the genetics of atherosclerotic cerebrovascular disease through the study of carotid stenosis. To do this we have developed a validated natural language processing (NLP) algorithm to extract quantitative measures of carotid stenosis severity from imaging reports in the VA electronic health record (EHR), facilitating the study of both disease susceptibility and progression. The overarching hypothesis of this proposal is that the development of carotid plaque, progression of stenosis, and resulting ischemic stroke represent distinct pathobiological states, each offering a separate opportunity for therapeutic intervention. To address this hypothesis, we will conduct genome-wide association studies of both disease susceptibility and progression. The results from these analyses will be then used for genetic causal inference experiments to: 1. Quantify the causal impact of traditional risk factors on the susceptibility to carotid stenosis; 2. Determine the causal risk factors for the progression of carotid stenosis; and 3. Identify the shared genetic architecture of carotid stenosis and ischemic stroke using genomic structural equation modeling. Successfully completion of this project will clarify the role of traditional risk factors with carotid stenosis, identify novel opportunities for prevention and treatment, and establish the basis for tailored, precision medicine approaches to the treatment of carotid stenosis and stroke prevention for Veterans.
动脉粥样硬化性心血管疾病(ASCVD)一级和二级预防的现行指南, 包括冠心病(CHD)、外周动脉疾病(PAD)和脑血管疾病, 尽管越来越多的证据表明, 这些疾病中的每一种都具有特定的潜在病理学。大规模的基因研究已经发现了新的 生物学,阐明了可改变的风险因素的作用,改善了风险预测,并确定了治疗靶点, CHD和PAD。这项工作已经证明了危险因素和治疗对血管领域的特定影响, 激发疾病特异性方法来预防和治疗动脉粥样硬化性心血管疾病。 不幸的是,脑血管疾病的遗传学研究已经滞后。现有的研究往往集中在 早期亚临床动脉粥样硬化(颈动脉内膜中层厚度[cIMT])或晚期结局 (缺血性中风)。对颈动脉狭窄形式的动脉粥样硬化性脑血管疾病的研究, 受样本量小的限制。 VA百万退伍军人计划(MVP)于2011年启动,旨在研究基因、生活方式和军事暴露如何影响退伍军人的健康。 影响健康和疾病,并提供了一个独特的机会, 通过对颈动脉狭窄的研究,为此,我们开发了一种经过验证的天然 语言处理(NLP)算法从成像中提取颈动脉狭窄严重程度的定量测量 VA电子健康记录(EHR)中的报告,便于研究疾病易感性和 进展这项建议的首要假设是,颈动脉斑块的发展,进展, 狭窄和由此产生的缺血性卒中代表不同的病理生物学状态,每种状态都提供了单独的 治疗干预的机会。为了解决这一假设,我们将进行全基因组关联 疾病易感性和进展的研究。这些分析的结果将用于 遗传因果推理实验:1。量化传统风险因素对 颈动脉狭窄的易感性; 2.确定颈动脉狭窄进展的因果风险因素;以及 3.利用基因组结构分析鉴定颈动脉狭窄和缺血性卒中的共同遗传结构 方程建模 本项目的成功完成将阐明颈动脉狭窄的传统危险因素的作用, 预防和治疗的新机会,并为量身定制的精准医学奠定基础 治疗颈动脉狭窄和预防退伍军人中风的方法。

项目成果

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Scott Michael Damrauer其他文献

Scott Michael Damrauer的其他文献

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{{ truncateString('Scott Michael Damrauer', 18)}}的其他基金

Impact of PCSK9 inhibition on abdominal aortic aneurysm pathobiology and growth
PCSK9 抑制对腹主动脉瘤病理学和生长的影响
  • 批准号:
    10566800
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Precision Cardio-Metabolic Phenotyping for Genetic Discovery and Risk Prediction
用于基因发现和风险预测的精准心脏代谢表型分析
  • 批准号:
    10295749
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
Precision Cardio-Metabolic Phenotyping for Genetic Discovery and Risk Prediction
用于基因发现和风险预测的精准心脏代谢表型分析
  • 批准号:
    10710159
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
Precision Cardio-Metabolic Phenotyping for Genetic Discovery and Risk Prediction
用于基因发现和风险预测的精准心脏代谢表型分析
  • 批准号:
    10409699
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:

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