Detecting latent tuberculosis infection with a breath test using mass spectrometer: A pilot cross-sectional study

Fu, Liang; Feng, Yong; Ren, Tantan; Yang, Min; Yang, Qianting; Lin, Yi Wen; Zeng, Hui; Zhang, Jiaohong; Liu, Lei; Li, Qingyun; He, Mengqi; Zhang, Peize; Chen, Haibin; Deng, Guofang

doi:10.5582/bst.2022.01476

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…While a small cohort size, the study provides insight as TB is a leading cause of childhood mortality, and adults and children have different breath profiles. Another study (n = 435) utilized breath analysis to distinguish latent tuberculosis infection (LTBI) from controls, the latter comprising active TB (ATB) and healthy controls (Fu et al, 2023 ). The model showed that breath VOCs could distinguish LTBI from controls with 80.0% sensitivity and 80.8% specificity.…”

Section: Infectious Diseasesmentioning

confidence: 99%

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Chou,

Godbeer,

Allsworth

et al. 2024

Metabolomics

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Background The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review. Aim of review This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research. Key scientific concepts of review VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

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Section: Infectious Diseasesmentioning

confidence: 99%

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Chou,

Godbeer,

Allsworth

et al. 2024

Metabolomics

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show abstract

“…In this study, for the first time, we investigated the potential of breath analysis to differentiate among PTB, NTM-PD, OPD and healthy controls (HC), and identified 22 potential VOC biomarkers associated with PTB and NTM-PD, extending our previous PTB breath detection research [17] [18]. Moreover, by employing our self-developed on-line mass spectrometer for human breath-the highpressure photon ionisation time-of-flight mass spectrometry (HPPI-TOF-MS) [20], we built a rapid and simple online breath analysis and modeling method for MPD screening and diagnosis.…”

Section: Introductionmentioning

confidence: 97%

“…Recently, Beccaria et al used GC×GC-MS to analyse exhaled VOCs of PTB patients and suspected PTB patients / other controls in South Africa / Haiti, achieving high sensitivity and specificity based on 23 / 22 feature VOCs [14] [15], respectively. Other recent studies have focused on pediatric [16] and adult PTB diagnosis [17] and TB infection (TBI) screening [18]. In addition to the above TB VOC-identified studies, sensor-based breath analyses have also shown promise in detecting PTB without biomarkers revealed [7].…”

Section: Introductionmentioning

confidence: 99%

Detection of mycobacterial pulmonary diseases via breath analysis in clinical practice

Feng²,

Chen³

et al. 2023

Preprint

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Background Current clinical tests for mycobacterial pulmonary diseases (MPD), such as pulmonary tuberculosis (PTB) and non-tuberculous mycobacteria pulmonary diseases (NTM-PD), are inaccurate, time-consuming, sputum-dependent, and/or costly. We aimed to develop a simple, rapid and accurate breath test for screening and differential diagnosis of MPD patients in clinical settings. Methods Exhaled breath samples were collected from 142 PTB, 68 NTM-PD and 9 PTB&NTM-PD patients, 93 patients with other pulmonary diseases (OPD) and 181 healthy controls (HC), and tested using the online high-pressure photon ionisation time-of-flight mass spectrometer (HPPI-TOF-MS). Machine learning models were trained and blindly tested for the detection of MPD, PTB, NTM-PD, and the discrimination between PTB and NTM-PD, respectively. Diagnostic performance was evaluated by metrics of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). Results The breath PTB detection model achieved a sensitivity of 81.8%, a specificity of 94.3%, an accuracy of 90.7%, and an AUC of 0.957 in the blinded test set (n=150). The corresponding metrics for the NTM-PD detection model were 95.5%, 86.7%, 88.0% and 0.947, respectively. For distinguishing PTB from NTM-PD, the model also achieved good performance with sensitivity, specificity, accuracy, and AUC of 95.5%, 90.9%, 93.9% and 0.974, respectively. 24 potential breath biomarkers associated with MPD were putatively identified and discussed, which included 2-picoline, ethanol, 1-Pentene, etc. Conclusions The developed breathomics-based MPD detection method was demonstrated for the first time with good performance for potential screening and diagnosis of PTB and NTM-PD using a refined operating procedure on the HPPI-TOF-MS platform.

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“…A standardized workflow has been established using HPPI-TOF-MS for the collection, detection, and data processing of exhaled VOCs, and has validated the feasibility of this technology in detecting diseases such as breast cancer [ 24 ], lung cancer [ 25 ], esophageal cancer [ 26 ], tuberculosis [ 27 ], and Covid-19 [ 28 ]. We aimed to integrate breath-omics, ultrasound radiomics, and clinical features to construct a multi-omics machine learning model, to develop a more accurate, objective, and automated non-invasive diagnostic tool for breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information

Yang,

Long,

Feng

et al. 2024

Heliyon

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Detecting latent tuberculosis infection with a breath test using mass spectrometer: A pilot cross-sectional study

Cited by 5 publications

References 18 publications

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Detection of mycobacterial pulmonary diseases via breath analysis in clinical practice

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information

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