Towards Precision Medicine for Thoracic Aortic Disease: Defining the Clinical and Genomic Drivers of Bicuspid Aortopathy

迈向胸主动脉疾病的精准医学：定义二尖瓣主动脉病的临床和基因组驱动因素

• 批准号: 10664513
• 负责人: Jason Paul Glotzbach
• 金额: $ 17.12万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664513
• 关键词: Acute; Aneurysm; Aorta; Aortic Aneurysm; Aortic Diseases; Artificial Intelligence; Artificial Intelligence platform; Bicuspid; Bioinformatics; Birth; Cardiac; Cardiovascular Diseases; Cardiovascular system; Clinical; Clinical Medicine; Collaborations; Complex; Congenital Cardiovascular Abnormality; Data; Data Science; Demographic Factors; Dependence; Development; Diabetes Mellitus; Diagnosis; Diameter; Disease; Disease Outcome; Disease Progression; Dissection; Electronic Health Record; Epidemiology; Face; Family; Foundations; Frustration; Funding; General Population; Generations; Genetic Predisposition to Disease; Genome; Genomic medicine; Genomics; Goals; Guidelines; Health system; Heritability; High Prevalence; Hospital Mortality; Hypertension; Individual; Investigation; K-Series Research Career Programs; Knowledge; Leadership; Logistic Regressions; Mentors; Methodology; Methods; Modeling; Morbidity - disease rate; Operative Surgical Procedures; Outcome; Pathogenesis; Patient Care; Patient risk; Patients; Persons; Phenotype; Population; Population Database; Population Study; Positioning Attribute; Prevalence; Prevention; Preventive treatment; Principal Investigator; Procedures; Regression Analysis; Research; Research Personnel; Risk; Science; Scientist; Statistical Methods; Surgeon; Testing; Thoracic aorta; Training; Translational Research; Universities; Unnecessary Surgery; Utah; Variant; aortic valve; artificial intelligence method; bicuspid aortic valve; career; career development; clinical risk; cohort; comorbidity; disorder risk; experience; genetic linkage analysis; genetic pedigree; genetic variant; genome sequencing; high risk; improved; improved outcome; individual patient; innovation; mortality; multidisciplinary; novel; pediatric cardiologist; population based; precision medicine; predictive modeling; prevent; professor; programs; prospective; risk prediction model; risk stratification; sex; skills; statistics; surgical risk; tenure track; tool; translational scientist; whole genome

PROJECT SUMMARY/ABSTRACT This is a K08 Mentored Clinical Scientist Research Career Development Award for Jason P. Glotzbach, MD. Dr. Glotzbach is a promising early career translational research clinician-scientist. He is a cardiac and aortic surgeon and Assistant Professor of Surgery on the tenure track at the University of Utah. His primary mentor for this proposal is Dr. Martin Tristani-Firouzi, MD, a pediatric cardiologist and expert in precision medicine and genomics of cardiovascular disease. This proposal spans five years and includes three Research Aims and four Career Development Aims. Bicuspid aortic valve (BAV) is the most common congenital cardiovascular anomaly and is associated with aortic aneurysm and aortic dissection, a condition defined as BAV aortopathy. Although both BAV and BAV aortopathy are thought to be highly heritable conditions, the causative clinical factors and genomic variants associated with development and progression of this disease remain poorly understood. The aim of the current proposal is to fill this knowledge gap through a three-pronged approach: 1) we will use an innovative statistical method called Poisson binomial comorbidity discovery to define clinical and demographic variables associated with BAV aortopathy; 2) we will develop a predictive model for BAV aortopathy risk using a state-of-the- art artificial intelligence method called probabilistic graphical models; and 3) we will utilize detailed pedigree-driven whole genome sequencing analysis of multigenerational families with a high prevalence of BAV aortopathy and patients undergoing surgery for BAV aortopathy to define genetic variants associated with BAV aortopathy. By combining a clinical risk model with an understanding of the genomic variants associated with BAV aortopathy, we expect to gain novel understanding of the pathogenesis of this highly impactful clinical condition. The information produced by this line of investigation has significant promise to help refine the clinical paradigms for treatment of aortic disease by building a foundation to allow development of precision medicine tools to predict aortic disease risk at the individual patient level. This line of inquiry, if successful, will lead to improved clinical outcomes in these complex and heterogenous patients. Through pursuit of the Research Aims of this proposal, Dr. Glotzbach will develop his expertise with the fundamental skills of statistics, predictive modeling, epidemiology, bioinformatics, genomic analysis, and research team leadership that will enable him to build a career as an independent translational investigator. 1

项目总结/摘要 这是K 08指导临床科学家研究职业发展奖Jason P. Glotzbach，MD。博士 Glotzbach是一位有前途的早期职业转化研究临床科学家。他是心脏和主动脉外科医生 犹他州大学外科学助理教授。他的主要导师 一位儿科心脏病专家、精准医学和基因组学专家Martin Tristani-Firouzi博士 心血管疾病该计划为期五年，包括三个研究目标和四个职业生涯 发展目标。 二叶式主动脉瓣（BAV）是最常见的先天性心血管畸形， 动脉瘤和主动脉夹层，一种被定义为BAV动脉瘤病的疾病。尽管BAV和BAV动脉病变 被认为是高度遗传性的条件，致病的临床因素和基因组变异与 这种疾病的发生和发展仍然知之甚少。本提案的目的是 我们将从三方面填补这一知识空白：1）我们将使用创新的统计方法 称为泊松二项共病发现，以定义相关的临床和人口统计学变量 与BAV动脉粥样硬化病; 2）我们将开发一个预测模型，BAV动脉粥样硬化病的风险使用的状态- 一种称为概率图形模型的人工智能方法; 3）我们将利用详细的 高患病率多代家庭的家系驱动的全基因组测序分析 BAV脊椎病患者和因BAV脊椎病接受手术的患者，以确定遗传变异 与BAV脊髓病相关。通过将临床风险模型与对基因组的理解相结合， 变异与BAV脊椎病，我们希望获得新的理解，这一发病机制， 非常有影响力的临床状况。这条调查线所产生的信息具有重大的前景 通过建立一个基础来帮助完善治疗主动脉疾病的临床范例， 精确的医学工具来预测个体患者水平的主动脉疾病风险。这条调查线，如果 成功，将导致这些复杂和异质性患者的临床结局改善。 通过追求这项提案的研究目标，Glotzbach博士将发展他的专业知识， 统计学，预测建模，流行病学，生物信息学，基因组分析的基本技能， 研究团队的领导，这将使他能够建立一个独立的翻译研究者的职业生涯。 1