Asthma (Diagnosis), COPD (Chronic Obstructive Pulmonary Disease)
Conditions
Keywords
Quantitative chest CT, Airway disease, Asthma, Chronic Obstructive Pulmonary Disease, Treatment response
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Korea University Guro Hospital
The Korean Academy of Tuberculosis and Respiratory Diseases
Seoul National University Boramae Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Imaging and multi-omic signatures that differentiate asthma from COPD and predict treatment response | From baseline to last follow-up visit (anticipated up to 12 months after enrollment) | Composite signatures derived from quantitative chest CT metrics (e.g., low attenuation area percentage, parametric response mapping features, airway measurements, and mucus plug score) integrated with clinical variables, pulmonary function indices, blood-based inflammatory markers, and sputum/microbiome profiles. These integrated features will be evaluated for their ability to (1) distinguish asthma from COPD and (2) predict clinical treatment response. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in Lung Function (FEV1) | Baseline to 12 months | Change in pre-bronchodilator and/or post-bronchodilator FEV1 (mL) from baseline to last follow-up visit. |
| Frequency of acute exacerbations | Up to 12 months after enrollment | Number of moderate or severe exacerbations during follow-up. |
| Changes in Quantitative Chest CT Imaging Biomarkers (LAA-950, PRMfSAD, Pi10, BV5/TBV) | Baseline to last follow-up visit (up to 12 months) | Changes in chest CT-derived quantitative imaging biomarkers including parametric response mapping of functional low attenuation area at -950 HU (LAA-950), small airway disease (PRMfSAD), airway wall thickness (Pi10), and small vessel fraction (BV5/TBV) from baseline to last follow-up visit. |
Countries
South Korea
Contacts
CONTACTSang Hyuk Kim, MD
CONTACTClinical Research Office Korea University Guro Hospital
PRINCIPAL_INVESTIGATORSang Hyuk Kim, MD
Korea University Guro Hospital
Outcome results
None listed