Relationship between lung function impairment, clinical characteristics and systemic inflammation based on a...

Area of Science:

• Pulmonary Medicine
• Epidemiology
• Biomarkers

Background:

• Chronic airway diseases like COPD are underdiagnosed in China.
• Preserved Ratio Impaired Spirometry (PRISm) may indicate early lung function impairment.
• Understanding PRISm phenotypes and systemic inflammation is crucial for early detection.

Purpose of the Study:

• Describe clinical phenotypes of lung function impairment in a Chinese population.
• Identify predictive factors for lung function impairment using machine learning.
• Explore the role of systemic inflammation in lung function impairment.

Main Methods:

• Prospective cross-sectional study in Hongtong County, China (2021-2024).
• Spirometry classified participants into airflow obstruction, PRISm, and normal groups.
• Machine learning models were developed to predict lung function impairment; serum cytokines were measured.

Main Results:

• 51.0% had airflow obstruction, 6.7% had PRISm, 42.3% were normal among 9,284 adults.
• PRISm group: predominantly female, lower smoking rates, higher coronary heart disease risk.
• Airflow obstruction group: older, male, lower BMI, smokers, with renal/cerebrovascular comorbidities.
• Machine learning identified age, sex, BMI, symptoms, creatinine, and hemoglobin as predictors (AUC=0.635).
• Airflow obstruction and PRISm groups showed lower IL-2 and higher IL-5/IL-17A serum levels.

Conclusions:

• Distinct clinical phenotypes exist for airflow obstruction and PRISm.
• A predictive model using clinical variables aids in identifying individuals at risk for lung function impairment.
• Serum IL-2, IL-5, and IL-17A may serve as biomarkers for early lung function impairment detection.