Relationship between lung function impairment, clinical characteristics and systemic inflammation based on a large-scale population screening

XIAO JUN MA^1,2Yan Yu³Wenxia Guan²Shuming Guo²GaoZhancheng Gao³Mengtong Jin²Peng Liu²Lianyu Cheng²Chunting Chen²Kaiyu Ma²Yujie Zhou²Ran Li^3*Qi Wu^1*

• ¹Tianjin Medical University General Hospital, Tianjin, China
• ²Linfen Central Hospital, Linfen, China
• ³Peking University People's Hospital Department of Respiratory and Critical Care Medicine, Beijing, China

Background: Lung function impairment, a hallmark of chronic airway diseases like chronic obstructive pulmonary disease (COPD), is often underdiagnosed in China. Preserved Ratio Impaired Spirometry (PRISm) may represent an early, subclinical stage of this process. However, a comprehensive understanding of their clinical phenotypes, effective predictive strategies for early identification in large populations, and the role of systemic inflammation remains underexplored, particularly in the Chinese context. This study aimed to describe the clinical phenotypes of lung function impairment, identify predictive factors using machine learning, and explore associated systemic inflammation in a large-scale population screening. Methods: A prospective cross-sectional study was conducted in Hongtong County, China (2021China ( -2024)). Participants were classified into airflow obstruction, PRISm, and normal groups via portable spirometry. Using demographic, clinical, and laboratory data, we developed and validated several machine learning (ML) models to predict lung function impairment. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). Serum cytokines were measured by ELISA in matched sub-cohorts to assess systemic inflammation.Results: Among 9,284 enrolled adults, 51.0% had airflow obstruction, 6.7% had PRISm, and 42.3% were normal. We identified distinct phenotypes: the PRISm group was predominantly female with lower smoking rates but a higher risk of coronary heart disease. The airflow obstruction group was characterized by classical risk factors (older age, male sex, lower BMI, smoking) and specific renal and cerebrovascular comorbidities. The ML models identified older age, male sex, lower BMI, respiratory symptoms (cough, dyspnea), and higher creatinine and hemoglobin as key predictors, demonstrating modest performance with an AUC of 0.635 in the validation set. Immunologically, individuals with airflow obstruction or PRISm showed significantly lower serum IL-2 and higher IL-5 and IL-17A levels compared to controls.: In a large-scale screening, individuals with airflow obstruction and PRISm present with distinct clinical phenotypes. A predictive model using simple clinical variables can help identify individuals at higher risk for lung function impairment, despite modest performance. Serum IL-2, IL-5, and IL-17A are potential biomarkers for the early recognition and understanding of airflow limitation.