Urinary metabolomics reveals potential biomarkers for monitoring carbon black exposure-related airway injury.
Carbon black (CB) exposure is a well-established cause of pulmonary injury, yet sensitive and practical biomarkers for early detection remain lacking. This study aims to address this gap. Here, we investigate whether urinary metabolomics can provide noninvasive signatures for the early identification and risk stratification of CB-associated airway injury.
In 2018, we enrolled 45 CB-exposed packing workers from a CB factory in Henan Province and 45 municipal waterworks employees without occupational particulate exposure as controls from the same city. After accounting for environmental confounding, participants completed baseline questionnaires; internal exposure dose, lung function, and airway structure were assessed, and urine was collected concurrently. Urinary metabolomes were quantified by UPLC-Orbitrap-MS, and covariate-adjusted linear regression identified metabolites associated with CB exposure and airway remodeling, which informed development of an exposure-related small-airway injury prediction model.
CB-exposed workers showed significantly higher lung CB burden, impaired pulmonary ventilatory function (reduced FEF25 and FEF25P), and increased 6th-generation airway wall thickness (WA%), with untargeted metabolomics identifying 210 differential metabolites primarily enriched in the purine metabolism pathway. Correlation analysis between differential metabolites and 6th-generation WA% (LB1+2) further identified four key endogenous metabolites, namely creatine, citric acid, Prostaglandin E2, and 3 beta-Hydroxy-5-cholestenoate. A KNN classifier incorporating these four metabolites demonstrated mean AUCs of 0.782 ± 0.06 and 0.631 ± 0.138 in the training and test sets, respectively.
Urinary metabolomics identified four candidate biomarkers for carbon black-related airway injury, and a metabolite-based predictive model may offer a noninvasive, cost-effective approach for early screening in occupationally exposed populations.
In 2018, we enrolled 45 CB-exposed packing workers from a CB factory in Henan Province and 45 municipal waterworks employees without occupational particulate exposure as controls from the same city. After accounting for environmental confounding, participants completed baseline questionnaires; internal exposure dose, lung function, and airway structure were assessed, and urine was collected concurrently. Urinary metabolomes were quantified by UPLC-Orbitrap-MS, and covariate-adjusted linear regression identified metabolites associated with CB exposure and airway remodeling, which informed development of an exposure-related small-airway injury prediction model.
CB-exposed workers showed significantly higher lung CB burden, impaired pulmonary ventilatory function (reduced FEF25 and FEF25P), and increased 6th-generation airway wall thickness (WA%), with untargeted metabolomics identifying 210 differential metabolites primarily enriched in the purine metabolism pathway. Correlation analysis between differential metabolites and 6th-generation WA% (LB1+2) further identified four key endogenous metabolites, namely creatine, citric acid, Prostaglandin E2, and 3 beta-Hydroxy-5-cholestenoate. A KNN classifier incorporating these four metabolites demonstrated mean AUCs of 0.782 ± 0.06 and 0.631 ± 0.138 in the training and test sets, respectively.
Urinary metabolomics identified four candidate biomarkers for carbon black-related airway injury, and a metabolite-based predictive model may offer a noninvasive, cost-effective approach for early screening in occupationally exposed populations.
Authors
Wen Wen, Wu Wu, Mu Mu, Zhang Zhang, Han Han, Wang Wang, Tang Tang, Zheng Zheng, Han Han, Qin Qin
View on Pubmed