Measurement of Urinary 1-Nitropyrene Metabolites as Biomarkers ofExposure to Diesel Exhaust in Underground Miners
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Exposure to diesel exhaust (DE) is prevalent in both occupational and environmental settings and has been associated with several adverse health outcomes including cancer and respiratory and cardiovascular disease. The ability to accurately quantify DE levels is therefore crucial for understanding and controlling exposures. DE is a complex mixture of particulate matter and gaseous components which complicates exposure measurement. Current methods rely on the use of elemental carbon (EC) to monitor DE exposure, however, EC is formed by multiple other sources in addition to DE which introduces the potential for exposure misclassification. 1-Nitropyrene (1-NP), a chemical component specific to DE, has been proposed as a potential marker for exposure to DE in air and biological samples. In this study the suitability of 1-NP urinary metabolites as biomarkers for monitoring occupational exposure to DE in underground miners was evaluated. The study took place in a large underground metal mine that makes extensive use of diesel engines. Air and urine samples were collected from a cohort of 20 miners who performed a variety of underground and surface jobs within the mine. Four sampling campaigns were conducted, each 2-3 months apart. During each campaign personal air samples, pre- and post-shift urine, and job task/activity surveys were collected for each subject. Air samples (n=103) were analyzed for EC and 1-NP. Urine samples (n=170) were analyzed for 1-NP metabolites using an HPLC-MS/MS assay. The association between 1-NP metabolites in urine and exposure to 1-NP in air was assessed using a regression model to determine if 1-NP urinary metabolites are a suitable biomarker for DE. Additionally, the suitability of survey data as a surrogate estimate for DE exposure was evaluated using a predictive model for 1-NP metabolites based on job and time-activity covariates. A range of EC and 1-NP exposures were observed within this cohort (EC: GM = 8.5 µg/m3, GSD = 2.5 µg/m3; 1-NP: GM = 47 pg/m3, GSD = 2.9 pg/m3). Levels of EC, 1-NP, and urinary metabolites in this cohort were high relative to environmental exposures, but were within the range of reported occupational levels. Underground workers tended to have higher 1-NP and EC exposures than surface workers, however none of the miners were overexposed to DE using the MSHA standard for EC. A predictive mixed effects model was generated to estimate exposure to 1-NP in air on unmonitored work shifts. This model included terms for time spent underground and time spent working around diesel exhaust as well as subject-specific random effects. The out-of-sample R2 (RMSE) was 0.41 (0.80) for this model. Of the measured 1-NP metabolites 6-OHNP and 8-OHNP were detected at the highest levels (6-OHNP: GM = 0.13 pg/mg creatinine, GSD = 2.9 pg/mg creatinine; 8-OHNP: GM = 0.006 pg/mg creatinine, GSD = 2.8 pg/mg creatinine). Very few workers reported off-shift exposure to DE, suggesting that metabolite levels reflect occupational exposures. A significant trend for increasing metabolite levels with day of work week was observed indicating that uptake, elimination, or both of 1-NP is delayed relative to the within-day variability in occupational exposure. To account for this delay an association model was developed that paired urine samples with air exposures lagged by 1 day. This model estimated that for every doubling of exposure to 1-NP on the day before sample collection there would be a 12% increase in geometric mean 6-OHNP concentration and a 10% increase in geometric mean 8-OHNP concentration. A predictive model for estimating post-shift levels for 6-OHNP and 8-OHNP was developed. The models including job location, time exposed to diesel exhaust, respirator use, time since previous void, and day of week provided the most practical approach to estimating metabolite levels. These models had relatively poor out of sample predictive ability but were capable of identifying general trends between metabolite levels and predictors.
- Environmental health