Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

This record corresponds to a peer-reviewed journal article published in Advanced Science (Volume 8, Issue 15, 2021), a Q1 top-tier Journal Citation Reports (JCR) indexed journal.

This work presents a novel, non-invasive diagnostic approach for the detection of active pulmonary tuberculosis based on the analysis of disease-specific volatile organic compounds (VOCs) emitted from the skin. The study introduces, for the first time, a comprehensive framework combining skin headspace sampling, gas chromatography–mass spectrometry (GC–MS), nanomaterial-based sensor arrays, and machine learning algorithms to achieve rapid and accurate tuberculosis discrimination without the need for sputum samples.

The proposed methodology was validated in a large, multicentric clinical study involving more than 600 participants across two geographically distinct regions (India and South Africa), including confirmed active tuberculosis patients, non-tuberculosis patients, and healthy controls. GC–MS analysis enabled the identification and quantification of tuberculosis-associated VOC profiles, while cross-reactive nanomaterial-based sensor arrays coupled with discriminant analysis achieved sensitivities above 90% and specificities exceeding the thresholds defined by the World Health Organization for triage tests.

Beyond laboratory validation, the work demonstrates the feasibility of real-time, point-of-care diagnosis through the development and testing of a wearable electronic sensing device applied directly to the skin. The system exhibits robust performance across relevant confounding factors such as HIV status, smoking habits, and geographic variability, highlighting its translational potential for deployment in resource-limited settings.

This study establishes a new diagnostic paradigm for tuberculosis detection, integrating volatolomics, advanced materials, and artificial intelligence. The results position the proposed technology as a scalable, cost-effective, and clinically relevant solution aligned with global health priorities, with significant implications for early diagnosis, disease monitoring, and the reduction of undetected tuberculosis cases worldwide.

This Zenodo record is provided for dissemination and accessibility purposes. The version of record for evaluation and citation is the article published in Advanced Science, available at https://doi.org/10.1002/adv…