Clinical significance and genetic determinants of novel imaging measures of emphysema distribution in 9,743 smokers

9,743 名吸烟者肺气肿分布的新型影像学测量的临床意义和遗传决定因素

基本信息

批准号：
9975215
负责人：
Adel El Boueiz
金额：
$ 17.06万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975215
关键词：
ACVR1B gene Accounting Advisory Committees Affect Airway Disease Area Automobile Driving Bioinformatics Biological Process Chest Chronic Obstructive Airway Disease Clinical Clinical Trials Clinical/Radiologic Cohort Studies Collection Complex Computing Methodologies Critical Care Data Detection Development Diagnostic radiologic examination Disease Disease Progression Dyspnea Environment Evaluation Exhibits Funding Generations Genes Genetic Genetic Determinism Genetic Polymorphism Genomic approach Genomics Goals Heterogeneity Hospitals Image Investigation Lobar Lobe Lung Lung Volume Reductions Lung diseases Machine Learning Measures Medical Medical Genetics Medical Research Medicine Mentors Mentorship Methods Modeling Morbidity - disease rate Nature Outcome Pathologic Patients Pattern Phenotype Physicians Physiological Positioning Attribute Predictive Analytics Procedures Public Health Pulmonary Emphysema Pulmonary Function Test/Forced Expiratory Volume 1 Quantitative Trait Loci Radiology Specialty Records Research Research Personnel Resources Respiratory physiology Science Scientist Smoker Structure of parenchyma of lung Subgroup Technology Testing Texture Therapeutic Therapeutic Intervention Tissues Translations United States Variant Vascular remodeling Visual Walking Woman Work X-Ray Computed Tomography attenuation base biomedical informatics career career development clinical practice clinically relevant clinically significant cohort comorbidity data mining disease heterogeneity disease phenotype disorder risk disorder subtype effective therapy genetic architecture genetic association genetic epidemiology genetic predictors genetic variant genome sequencing genome-wide genomic predictors imaging biomarker imaging genetics improved instructor interest machine learning method medical schools mortality multidisciplinary novel outcome forecast personalized care predicting response predictive modeling prognostic quantitative imaging rare variant research and development respiratory whole genome

项目摘要

PROJECT SUMMARY/ABSTRACT Candidate: Dr. Adel El Boueiz is a pulmonary and critical care physician-scientist completing a period of T32- funded support at the Channing Division of Network Medicine (CDNM) and Harvard Medical School (HMS). He received a Master's of Medical Science in Biomedical Informatics from HMS in May 2016. He will be promoted to Instructor of Medicine at the CDNM and HMS on July 1, 2017. His principal research interests are the genetic epidemiology of chronic obstructive pulmonary disease (COPD) and the translation of genomic discoveries into clinical practice and public health. His long-term goal is to be an independent investigator with expertise in imaging phenotyping, genomics, and predictive analytics of the regional heterogeneity of the various aspects of COPD (emphysema, airway disease, and pulmonary vascular remodeling). Environment: Dr. El Boueiz will continue to pursue his research and career development in the rich and multidisciplinary environment of the CDNM and the Brigham and Women's Hospital Applied Chest Imaging Lab (ACIL). He will be mentored by Drs. Edwin K. Silverman, Peter J. Castaldi, and Raúl San José Estépar, leaders in the field of COPD quantitative imaging, genetic epidemiology, and predictive analytics with excellent track records of mentoring young investigators towards independent research careers. His career development will also be overseen by an advisory committee with expertise related to key areas of his proposal. Research: COPD is a major cause of morbidity and mortality that is of increasing public health importance. COPD is a heterogeneous disease and this heterogeneity complicates the identification of the predictors of disease progression and consequently, the development of effective therapies. Emphysema distribution is an important COPD-related phenotype that emerged as a strong predictor of the response to lung volume reduction procedures. Despite the availability of advanced texture-based CT quantification methods, global threshold-based quantitative metrics have to date been the cornerstone for the radiological characterization of emphysema distribution with inability to differentiate centrilobular, panlobular, and paraseptal emphysema patterns. In this project, we will apply a texture-based CT quantification method to discover novel imaging biomarkers of the regional heterogeneity of centrilobular, panlobular, and paraseptal emphysema in a large cohort of well-characterized smokers and identify their genetic determinants using whole genome sequencing and integrative genomics analyses. The results will be considered for inclusion along with other rich phenotypic and imaging data in COPD disease progression machine learning predictive models. Relevance: Through improved radiographic phenotyping of emphysema distribution, better understanding of disease pathobiology, and more accurate prediction of disease progression, the proposed work will open new avenues of investigation for the development of personalized and improved COPD therapeutic strategies.

项目概要/摘要候选人：Adel El Boueiz 博士是一名肺部和重症监护医师科学家，完成了 T32- 阶段钱宁网络医学部 (CDNM) 和哈佛医学院 (HMS) 的资助支持。 2016 年 5 月获得 HMS 生物医学信息学医学硕士学位。他将晋升 2017 年 7 月 1 日成为 CDNM 和 HMS 的医学讲师。他的主要研究兴趣是慢性阻塞性肺疾病（COPD）的遗传流行病学和基因组翻译他的长期目标是成为一名独立研究者，并致力于临床实践和公共卫生领域的发现。成像表型、基因组学和区域异质性预测分析方面的专业知识 COPD 的各个方面（肺气肿、气道疾病和肺血管重塑）。环境：El Boueiz 博士将继续在富裕和富裕国家从事研究和职业发展。 CDNM 和布莱根妇女医院应用胸部影像实验室的多学科环境 (ACIL)，他将受到 Edwin K. Silverman 博士、Peter J. Castaldi 和 Raúl San José Estépar 博士的指导。慢性阻塞性肺病定量成像、遗传流行病学和预测分析领域的领导者，具有出色的指导年轻研究人员走向独立研究生涯的记录。还将受到一个咨询委员会的监督，该委员会拥有与其提案关键领域相关的专业知识。研究：慢性阻塞性肺病是发病和死亡的主要原因，对公共卫生的重要性日益增加。 COPD 是一种异质性疾病，这种异质性使预测因素的识别变得复杂疾病进展以及有效治疗方法的开发是肺气肿分布的一个重要问题。重要的慢性阻塞性肺病相关表型，成为肺容量反应的有力预测因子尽管有先进的基于纹理的 CT 量化方法，但全球范围内仍然存在这种情况。迄今为止，基于阈值的定量指标已成为放射学特征的基石肺气肿分布无法区分小叶中心型、全小叶型和间隔旁型肺气肿在这个项目中，我们将应用基于纹理的 CT 量化方法来发现新颖的成像。大范围小叶中心、全小叶和间隔旁气肿区域异质性的生物标志物一组特征明确的吸烟者，并使用全基因组测序确定其遗传决定因素和综合基因组学分析的结果将被考虑与其他丰富的表型一起纳入。以及 COPD 疾病进展机器学习预测模型中的成像数据。相关性：通过改进肺气肿分布的放射照相表型，更好地了解疾病病理生物学，以及更准确地预测疾病进展，所提出的工作将开辟新的领域开发个性化和改进的慢性阻塞性肺病治疗策略的研究途径。