ArticleJournal of exposure science & environmental epidemiology2012
Interpreting variability in population biomonitoring data: role of elimination kinetics.
Article in Journal of exposure science & environmental epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 91 citations in OpenAlex.
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- Modelling the Renal Excretion of the Mycotoxin Deoxynivalenol in Humans in an Everyday Situation.Toxins · 2021Article
- Quality assurance and harmonization for targeted biomonitoring measurements of environmental organic chemicals across the Children's Health Exposure Analysis Resource laboratory network.International journal of hygiene and environmental health · 2021Article
- Transient Multimedia Model for Investigating the Influence of Indoor Human Activities on Exposure to SVOCs.Environmental science & technology · 2020Article
- Identification of pathognomonic purine synthesis biomarkers by metabolomic profiling of adolescents with obesity and type 2 diabetes.PloS one · 2020Observational
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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