You are here:
Biomarker Variance Component Estimation for Exposure Surrogate Selection and Toxicokinetic Inference
Citation:
SOBUS, J., J. D. PLEIL, M. D. McClean, R. F. Herrick, AND S. M. Rappaport. Biomarker Variance Component Estimation for Exposure Surrogate Selection and Toxicokinetic Inference. TOXICOLOGY LETTERS. Elsevier Science Ltd, New York, NY, 199(3):247-253, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Biomarkers are useful exposure surrogates given their ability to integrate exposures through all routes and to reflect interindividual differences in toxicokinetic processes. Also, biomarker concentrations tend to vary less than corresponding environmental measurements, making them less-biasing surrogates for exposure. In this article, urinary PAH biomarkers (namely, urinary naphthalene [U-Nap]; urinary phenanthrene [U-Phe]; 1-hydroxypyrene [1-OH-Pyr]; and 1-, (2+3)-, 4-, and 9-hydroxyphenanthrene [1-, (2+3)-, 4-, and 9-OH-Phe]) were evaluated as surrogates for exposure to hot asphalt emissions using data from 20 road-paving workers. Linear mixed-effects models were used to estimate the within- and between-person components of variance for each urinary biomarker. The ratio of within- to between person variance was then used to estimate the biasing effects of each biomarker on a theoretical exposure–response relationship. Mixed models were also used to estimate the amounts of variation in Phe metabolism to individual OH-Phe isomers that could be attributed to Phe exposure (as represented by U-Phe concentrations) and covariates representing time, hydration level, smoking status, age, and body mass index. Results showed that 1-OH-Phe, (2+3)-OH-Phe, and 1-OH-Pyr were the least-biasing surrogates for exposure to hot asphalt emissions, and that effects of hydration level and sample collection time substantially inflated bias estimates for the urinary biomarkers. Mixed-model results for the individual OH-Phe isomers showed that between 63% and 82% of the observed biomarker variance was collectively explained by Phe exposure, the time and day of sample collection, and the hydration level, smoking status, body mass index, and age of each worker. By difference, the model results also showed that, depending on the OH-Phe isomer, a maximum of 6–23% of the total biomarker variance was attributable to differences in unobserved toxicokinetic processes between the workers. Therefore, toxicokinetic processes are probably less influential on urinary biomarker variance than are exposures and observable covariate effects. The methods described in this analysis should be considered for the selection and interpretation of biomarkers as exposure surrogates in future exposure investigations.
URLs/Downloads:
Biomarker Variance Component Estimation for Exposure Surrogate Selection and Toxicokinetic Inference (PDF, NA pp, 237 KB, about PDF)Toxicology Letters
