GENETIC EPIDEMIOLOGY OF COPD (COPD GENE) TASK A: STUDY VISIT 4, COLLECTION OF COPDGENE STUDY DATA ANDBIOSPECIMENS AND OVERSIGHT OF THE COPDGENE STUDY

COPD 的遗传流行病学（COPD 基因） 任务 A：研究访问 4、收集 COPDGENE 研究数据和生物样本以及 COPDGENE 研究的监督

• 批准号: 10974166
• 负责人: James D Crapo
• 金额: $ 819.35万
• 依托单位: NATIONAL JEWISH HEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2024-08-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10974166
• 关键词: GENETIC; EPIDEMIOLOGY; COPD; GENE; TASK

Chronic obstructive pulmonary disease (COPD) is a syndrome commonly defined by a single physiological feature–chronic airflow obstruction. To improve diagnosis and treatment of COPD, it is essential to divide this complex syndrome into biologically meaningful subgroups of COPD patients and to predict disease progression accurately within those subtypes. Our primary approaches to assess COPD progression will use physiology (i.e., FEV1 decline) and imaging (i.e., increased emphysema); however, we will also assess clinical characteristics (e.g., increased exacerbations) and epidemiology (e.g., increased mortality). We will apply imaging, physiology, epidemiology, transcriptomics, proteomics, metabolomics, epigenetics, and genetics to a 15-year longitudinal cohort of smokers to provide new insights into COPD progression. Longitudinal analysis of physiological and imaging data will provide valuable information regarding progression (or stability) of airflow obstruction, CT measurements of emphysema and airway disease, and clinical characteristics. Longitudinal analysis of Omics data will identify biomarkers of COPD progression within disease subtypes. Machine learning and network analysis of multiple Omics data will implicate pathobiological mechanisms for COPD progression. Genetic association analysis using existing whole genome sequencing (WGS) data will identify both rare and common genetic determinants of COPD progression. Integrating WGS with RNA-Seq, DNA methylation, metabolomics, and proteomics will also facilitate identification of functional variants within regions of genetic association.

慢性阻塞性肺疾病(COPD)是一种通常由单一生理特征--慢性气流阻塞--定义的综合征。为了改善COPD的诊断和治疗，将这种复杂的综合征分为具有生物学意义的COPD患者亚组并在这些亚型中准确地预测疾病进展是至关重要的。我们评估COPD进展的主要方法将使用生理学(即FEV1下降)和成像(即肺气肿增加)；然而，我们也将评估临床特征(例如加重)和流行病学(例如死亡率增加)。我们将把影像学、生理学、流行病学、转录组学、蛋白质组学、代谢组学、表观遗传学和遗传学应用到15年的纵向吸烟者队列中，为COPD的进展提供新的见解。对生理和影像数据的纵向分析将提供有关气流阻塞进展(或稳定性)、肺气肿和呼吸道疾病的CT测量以及临床特征的有价值的信息。OMICS数据的纵向分析将确定疾病亚型中COPD进展的生物标记物。机器学习和对多个OMICS数据的网络分析将涉及COPD进展的病理生物学机制。利用现有的全基因组测序(WGS)数据进行的遗传关联分析将识别COPD进展的罕见和常见的遗传决定因素。将WGS与RNA-Seq、DNA甲基化、代谢组学和蛋白质组学相结合，也将有助于识别基因关联区域内的功能变异。