Interpreting variability in population biomonitoring data: role of elimination kinetics

Aylward, Lesa L., Kirman, Chris R., Adgate, John L., McKenzie, Lisa M. and Hays, Sean M. (2012) Interpreting variability in population biomonitoring data: role of elimination kinetics. Journal of Exposure Science and Environmental Epidemiology, 22 4: 398-408. doi:10.1038/jes.2012.35

Author Aylward, Lesa L.
Kirman, Chris R.
Adgate, John L.
McKenzie, Lisa M.
Hays, Sean M.
Title Interpreting variability in population biomonitoring data: role of elimination kinetics
Journal name Journal of Exposure Science and Environmental Epidemiology   Check publisher's open access policy
ISSN 1559-0631
Publication date 2012-01-01
Year available 2012
Sub-type Article (original research)
DOI 10.1038/jes.2012.35
Open Access Status Not yet assessed
Volume 22
Issue 4
Start page 398
End page 408
Total pages 11
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Abstract Biomarker concentrations in spot samples of blood and urine are implicitly interpreted as direct surrogates for long-term exposure magnitude in a variety of contexts including (1) epidemiological studies of potential health outcomes associated with general population chemical exposure, and (2) cross-sectional population biomonitoring studies. However, numerous factors in addition to exposure magnitude influence biomarker concentrations in spot samples, including temporal variation in spot samples because of elimination kinetics. The influence of half-life of elimination relative to exposure interval is examined here using simple first-order pharmacokinetic simulations of urinary concentrations in spot samples collected at random times relative to exposure events. Repeated exposures were modeled for each individual in the simulation with exposure amounts drawn from lognormal distributions with varying geometric standard deviations. Relative variation in predicted spot sample concentrations was greater than the variation in underlying dose distributions when the half-life of elimination was shorter than the interval between exposures, with the degree of relative variation increasing as the ratio of half-life to exposure interval decreased. Results of the modeling agreed well with data from a serial urine collection data set from the Centers for Disease Control. Data from previous studies examining intra-class correlation coefficients for a range of chemicals relying upon repeated sampling support the importance of considering the half-life relative to exposure frequency in design and interpretation of studies using spot samples for exposure classification and exposure estimation. The modeling and data sets presented here provide tools that can assist in interpretation of variability in cross-sectional biomonitoring studies and in design of studies utilizing biomonitoring data as markers for exposure.
Keyword Biomonitoring
Dose estimation
Risk assessment
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Pharmacy Publications
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Citation counts: TR Web of Science Citation Count  Cited 46 times in Thomson Reuters Web of Science Article | Citations
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