Exhaled-Breath Volatile Fingerprints Detect Early-Stage COPD and Stratify Disease Severity

Exhaled-Breath Volatile Fingerprints Detect Early-Stage COPD and Stratify Disease Severity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Exhaled-Breath Volatile Fingerprints Detect Early-Stage COPD and Stratify Disease Severity Xinming Wang, Jianlin Cheng, Wei Chen, Yuanhang Jia, Wei Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8293452/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide, yet most cases remain undiagnosed until substantial, irreversible airflow limitation has occurred because case-finding depends on spirometry. Here we show that rapid, non-invasive profiling of exhaled volatile organic compounds (VOCs) can detect COPD, including early-stage disease, and stratify severity. In a prospective, two-centre, observational case-control study of 825 breath samples, we used proton-transfer-reaction time-of-flight mass spectrometry to derive a compact, interpretable 16-compound VOC “fingerprint” and trained eight classifiers. A k-nearest neighbours model distinguished COPD from controls with an area under the receiver operating characteristic curve (AUC) of 0.955 in an internal validation set and 0.851 in an external cohort. The model detected early-stage COPD (GOLD I–II) with AUCs ≥ 0.940 internally and 0.861 externally. A separate 11-compound model graded GOLD stages with a micro-averaged AUC of 0.849. Shapley additive explanations provided patient-level attribution and chemically plausible features, supporting biological interpretability. These findings support breathomics as a scalable complement to spirometry for COPD case-finding and severity grading, and motivate prospective trials of breath-guided early intervention. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Respiratory tract diseases/Respiratory distress syndrome chronic obstructive pulmonary disease (COPD) exhaled breath analysis interpretable machine learning early diagnosis severity grading Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8293452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":564198274,"identity":"3f86c022-8ce4-4c9c-afc4-04d10f394223","order_by":0,"name":"Xinming 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