Prospective Multicenter Cohort to Discriminate Asthma Versus Chronic Obstructive Pulmonary Disease and Predict Treatment Response Using Quantitative Chest CT and Multi-Omics
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 2
研究概览
简要总结
This study aims to improve the diagnosis and treatment prediction of asthma and chronic obstructive pulmonary disease (COPD) by combining quantitative chest computed tomography (CT) imaging with multi-omics data.
Adults with asthma or COPD will be enrolled and undergo routine clinical evaluations, pulmonary function tests, blood tests, and chest CT scans. Additional samples, such as sputum and microbiome specimens, may also be collected. No experimental drugs or devices will be administered as part of this study.
Researchers will analyze CT imaging features together with clinical, laboratory, and biological data to better distinguish asthma from COPD and to identify factors that may predict treatment response. The findings are expected to contribute to more precise and personalized management of chronic airway diseases.
详细描述
This is a prospective, observational, multi-center cohort study designed to integrate quantitative chest CT imaging with multi-omics data to improve differentiation between asthma and chronic obstructive pulmonary disease (COPD) and to identify biomarkers associated with treatment response.
Eligible participants will include adults diagnosed with asthma or COPD who agree to participate in longitudinal clinical follow-up. At baseline and during follow-up, participants will undergo standard clinical assessments, including symptom questionnaires, pulmonary function testing, blood sampling, and chest CT imaging. Additional biological samples, such as sputum and microbiome specimens, may be collected when clinically feasible.
Quantitative CT metrics (e.g., low attenuation area percentage, parametric response mapping features, airway wall measurements, and mucus plug scores) will be extracted from imaging data. These imaging biomarkers will be integrated with clinical variables, laboratory parameters (including inflammatory markers and immunoglobulin profiles), and microbiome data.
The primary objectives are: (1) to identify imaging and biological signatures that distinguish asthma from COPD, and (2) to determine whether these signatures can predict response to standard clinical treatments. No investigational drugs or medical devices are involved, and all procedures reflect routine clinical care.
Data will be analyzed using advanced statistical and computational methods to explore associations between imaging, biological markers, and clinical outcomes. Results are expected to enhance understanding of disease mechanisms and support the development of personalized treatment strategies for chronic airway diseases.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 19 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥19 years
- •COPD group: post-bronchodilator FEV1/FVC < 0.70
- •Asthma group: clinically confirmed diagnosis of asthma by a physician
- •Able to provide voluntary written informed consent
排除标准
- •Acute exacerbation or active lower respiratory tract infection (e.g., pneumonia) within the past 4 weeks
- •Pregnancy or breastfeeding
- •Inability to undergo chest CT (e.g., poor cooperation or severe medical condition)
- •Refusal to consent to study procedures
研究组 & 干预措施
Prospective Asthma-COPD Cohort
This cohort includes adults with physician-diagnosed asthma or chronic obstructive pulmonary disease (COPD) who are enrolled in a prospective, observational study. Participants undergo routine clinical assessments, pulmonary function testing, blood sampling, and chest computed tomography (CT) imaging as part of standard care and study-related data collection. No investigational drugs or medical devices are administered. Data from clinical evaluations, imaging, and biospecimens (e.g., blood and sputum) will be analyzed to characterize disease features and predict treatment response.
研究者
Kim, Sang Hyuk
Clinical Assistant Professor
Korea University Guro Hospital
