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

基本信息

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
项目类别：
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

项目摘要

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

项目概要/摘要这是授予 Jason P. Glotzbach 医学博士的 K08 指导临床科学家研究职业发展奖。博士。 Glotzbach 是一位有前途的早期职业转化研究临床医生兼科学家。他是一名心脏和主动脉外科医生犹他大学终身教授外科助理教授。他的主要导师提议者是儿科心脏病专家、精准医学和基因组学专家 Martin Tristani-Firouzi 博士（医学博士）心血管疾病。该提案跨越五年，包括三个研究目标和四个职业目标发展目标。二叶式主动脉瓣 (BAV) 是最常见的先天性心血管畸形，与主动脉瓣相关动脉瘤和主动脉夹层，一种定义为 BAV 主动脉病的疾病。尽管 BAV 和 BAV 主动脉病被认为是高度遗传的病症，其致病临床因素和基因组变异与这种疾病的发生和进展仍知之甚少。当前提案的目的是通过三管齐下的方法来填补这一知识空白：1）我们将使用创新的统计方法称为泊松二项式合并症发现来定义相关的临床和人口统计学变量患有 BAV 主动脉病； 2) 我们将使用最新的方法开发 BAV 主动脉病风险的预测模型艺术人工智能方法称为概率图模型； 3）我们将利用详细的对高患病率多代家庭进行谱系驱动的全基因组测序分析 BAV 主动脉病和接受 BAV 主动脉病手术的患者以确定遗传变异与 BAV 主动脉病相关。通过将临床风险模型与对基因组的理解相结合与 BAV 主动脉病相关的变异，我们期望对其发病机制有新的了解具有高度影响的临床状况。这一系列调查产生的信息具有重大前景通过建立允许发展的基础，帮助完善主动脉疾病治疗的临床范例精准医疗工具可预测个体患者的主动脉疾病风险。这一行查询，如果成功，将改善这些复杂和异质患者的临床结果。通过追求本提案的研究目标，格洛茨巴赫博士将发展他的专业知识统计学、预测模型、流行病学、生物信息学、基因组分析等基本技能研究团队的领导能力将使他能够成为一名独立的转化研究者。 1