ERJ open research · 2026
Exhaled breath profiling for early silicosis detection in workers at risk: a pilot study.
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Van Loon Nele, Ruttens David, Verbrugghe Mathieu, Zwijsen Kathleen, Van Damme Pierre, Lapperre Thérèse, Lamote Kevin
Аннотация
BACKGROUND: Silicosis remains a major occupational health concern in respirable crystalline silica (RCS)-exposed workers. Exhaled breath analysis using volatile organic compound (VOC) profiling has emerged as a promising noninvasive approach for early disease detection. This feasibility study evaluated multicapillary column/ion mobility spectrometry (MCC/IMS)-based VOC profiling for differentiating silicosis from other respiratory conditions and healthy controls in an occupational health setting.
METHODS: We conducted a multicentre, cross-sectional observational study employing MCC/IMS to identify VOC patterns. Breath data were reduced to principal components and used in logistic lasso regression to develop predictive models. Internal and external validation assessed model performance, and subgroup analyses explored the impact of comorbidities and spirometry abnormalities.
RESULTS: A total of 185 participants were enrolled, including 24 COPD patients, 26 with benign asbestos-related diseases (BARD) and 32 with confirmed silicosis, alongside 25 healthy controls and 78 RCS-exposed controls. Silicosis was distinguished from BARD and COPD with 100% and 87.5% accuracy (area under the receiver operating characteristic curve (AUCROC) 1.00 and 0.89), respectively. Comparison with healthy RCS-exposed controls yielded 87.8% accuracy with 98.5% sensitivity (AUCROC 0.58), improving to 82.8% accuracy and AUCROC 0.90 when considering spirometry abnormalities. Eight VOCs, notably P13 and P14, were identified as key discriminators.
CONCLUSION: MCC/IMS-based breath analysis shows promise as a rapid, portable and noninvasive technique for early detection of silicosis in RCS-exposed workers. Future research should validate these findings in larger cohorts, integrate cross-validation of candidate biomarkers across analytical platforms and establish standardised protocols to enable routine implementation in occupational health surveillance.
METHODS: We conducted a multicentre, cross-sectional observational study employing MCC/IMS to identify VOC patterns. Breath data were reduced to principal components and used in logistic lasso regression to develop predictive models. Internal and external validation assessed model performance, and subgroup analyses explored the impact of comorbidities and spirometry abnormalities.
RESULTS: A total of 185 participants were enrolled, including 24 COPD patients, 26 with benign asbestos-related diseases (BARD) and 32 with confirmed silicosis, alongside 25 healthy controls and 78 RCS-exposed controls. Silicosis was distinguished from BARD and COPD with 100% and 87.5% accuracy (area under the receiver operating characteristic curve (AUCROC) 1.00 and 0.89), respectively. Comparison with healthy RCS-exposed controls yielded 87.8% accuracy with 98.5% sensitivity (AUCROC 0.58), improving to 82.8% accuracy and AUCROC 0.90 when considering spirometry abnormalities. Eight VOCs, notably P13 and P14, were identified as key discriminators.
CONCLUSION: MCC/IMS-based breath analysis shows promise as a rapid, portable and noninvasive technique for early detection of silicosis in RCS-exposed workers. Future research should validate these findings in larger cohorts, integrate cross-validation of candidate biomarkers across analytical platforms and establish standardised protocols to enable routine implementation in occupational health surveillance.