Urinary metabolomics reveals potential biomarkers for monitoring carbon black exposure-related airway injury.

Background: 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. Methods: 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. Results: CB-exposed workers showed significantly higher lung CB burden, impaired pulmonary ventilatory function (reduced FEF Conclusions: 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.