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

项目摘要/摘要这是Jason P. Glotzbach，医学博士的K08指导的临床科学家研究职业发展奖。博士格洛茨巴赫（Glotzbach）是一个有前途的早期职业转化研究临床医生 - 科学家。他是心脏和主动脉外科医生以及犹他大学任职期间的手术助理教授。他的主要导师提案是医学博士Martin Tristani-Firouzi博士，儿科心脏病专家兼精密医学和基因组学专家心血管疾病。该建议跨越了五年，包括三个研究目标和四个职业发展目的。双质主动脉瓣（BAV）是最常见的先天性心血管异常，与主动脉有关动脉瘤和主动脉夹层，一种定义为BAV主动脉病。虽然BAV和BAV主动脉病被认为是高度可遗传的条件，与该疾病的发展和进展仍然知之甚少。当前建议的目的是通过三管齐下的方法填补此知识差距：1）我们将使用创新的统计方法被称为泊松二项合并症发现，以定义相关的临床和人口统计学变量患有BAV主动脉疾病； 2）我们将使用最先进的艺术人工智能方法称为概率图形模型； 3）我们将使用详细的多代家族的谱系驱动的整个基因组测序分析 BAV主动脉疾病和接受BAV主动脉疾病手术的患者定义遗传变异与BAV主动脉病有关。通过将临床风险模型与对基因组的了解与BAV主动瘤相关的变体，我们期望对此发病有新的理解高度影响力的临床状况。这一调查导致的信息具有巨大的希望通过建立基础来帮助完善临床范例来治疗主动脉疾病精确医学工具可预测个别患者水平的主动脉疾病风险。这条询问线，如果成功的，将导致这些复杂和异质患者的临床结果改善。通过追求该提案的研究目的，Glotzbach博士将在统计学，预测建模，流行病学，生物信息学，基因组分析和研究团队的领导才能使他能够建立独立翻译调查员的职业。 1