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ORIGINAL RESEARCH article

Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Volume 11 - 2024 | doi: 10.3389/fmolb.2024.1436135
This article is part of the Research Topic Recent Advances in Breath Analysis: Exploring Exhaled Breath Biomarkers for Disease Diagnostics View all articles

Breathomics for diagnosing tuberculosis in diabetes mellitus patients

Provisionally accepted
Rong Xu Rong Xu 1Ying Zhang Ying Zhang 2Zhaodong Li Zhaodong Li 3Mingjie He Mingjie He 1Hailin Lu Hailin Lu 1Guizhen Liu Guizhen Liu 1,2Min Yang Min Yang 2,4Liang Fu Liang Fu 2,4Xinchun Chen Xinchun Chen 3Guofang Deng Guofang Deng 2,4*Wenfei Wang Wenfei Wang 4*
  • 1 Gannan Medical University, Ganzhou, Jiangxi Province, China
  • 2 Shenzhen Third People’s Hospital, Shenzhen, Guangdong Province, China
  • 3 Shenzhen University, Shenzhen, Guangdong Province, China
  • 4 National Clinical Research Center for Infectious Disease, Shenzhen, China

The final, formatted version of the article will be published soon.

    Individuals with diabetes mellitus (DM) are at an increased risk of Mycobacterium tuberculosis (Mtb) infection and progressing from latent tuberculosis infection to active tuberculosis (TB) disease. TB in the DM population is more likely to go undiagnosed due to smear-negative results. Exhaled breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry. An XGBoost model was utilized for breathomics analysis and TB detection, achieving a sensitivity of 88.5%, specificity of 100%, accuracy of 90.2%, and an AUC of 98.8%. The most significant feature across the entire set was m106, which demonstrated a sensitivity of 93%, specificity of 100%, and an AUC of 99.7%. The breathomics-based TB detection method utilizing m106 exhibited high sensitivity and specificity, potentially beneficial for clinical TB screening and diagnosis in individuals with diabetes.

    Keywords: breathomics, Tuberculosis, Diabetes Mellitus, Volatile Organic Compounds, XGBoost model

    Received: 21 May 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Xu, Zhang, Li, He, Lu, Liu, Yang, Fu, Chen, Deng and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Guofang Deng, National Clinical Research Center for Infectious Disease, Shenzhen, China
    Wenfei Wang, National Clinical Research Center for Infectious Disease, Shenzhen, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.