Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study

作者：Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou · 发表于：Respiratory Research · 年份：2025 · DOI：10.1186/s12931-025-03242-5 · 被引用次数：19 · 研究领域：Advanced Chemical Sensor Technologies、Respiratory and Cough-Related Research、Biochemical Analysis and Sensing Techniques

BACKGROUND: Globally, chronic respiratory diseases have become the third leading cause of death, including chronic obstructive pulmonary disease (COPD) and asthma, and have been threatening human life for a long time. To alleviate the disease burden, it is crucial to develop rapid and convenient screening methods for COPD, preserved ratio impaired spirometry (PRISm), and asthma. Volatile organic compounds (VOCs) in breath can reflect the pathophysiological processes of disease, thereby having the potential to serve as a promising approach for diagnosing respiratory diseases. Can we identify VOC markers in breath with the potential to serve as classification indicators, and further establish learning models for the early detection of COPD, asthma, or PRISm patients? METHODS: This is a cross-sectional study in which exhaled breath samples were collected from 184 patients with COPD, 66 patients with asthma, 72 PRISm individuals, and 45 healthy individuals. From August 2023 to June 2024, the breath samples were analyzed using portable micro gas chromatography (CXBA-Alpha, ChromX Health Co., Ltd.). Potential VOC markers for classification were identified by univariate and multivariate analyses. Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. The sensitivity, specificity, and accuracy of these models were calculated to assess their overall discriminatory performance. RESULTS: A tota...