Probabilistic modeling of dietary arsenic exposure and dose and evaluation with 2003-2004 NHANES data

Jianping Xue, Valerie Zartarian, Sheng Wei Wang, Shi V. Liu, Panos Georgopoulos

Research output: Contribution to journalArticle

118 Scopus citations

Abstract

BACKGROUND: Dietary exposure from food to toxic inorganic arsenic (iAs) in the general U.S. population has not been well studied. OBJECTIVES: The goal of this research was to quantify dietary As exposure and analyze the major contributors to total As (tAs) and iAs. Another objective was to compare model predictions with observed data. METHODS: Probabilistic exposure modeling for dietary As was conducted with the Stochastic Human Exposure and Dose Simulation-Dietary (SHEDS-Dietary) model, based on data from the National Health and Nutrition Examination Survey. The dose modeling was conducted by combining the SHEDS-Dietary model with the MENTOR-3P (Modeling ENvironment for TOtal Risk with Physiologically Based Pharmacokinetic Modeling for Populations) system. Model evaluation was conducted via comparing exposure and dose-modeling predictions against duplicate diet data and biomarker measurements, respectively, for the same individuals. RESULTS: The mean modeled tAs exposure from food is 0.38 μg/kg/day, which is approximately 14 times higher than the mean As exposures from the drinking water. The mean iAs exposure from food is 0.05 μg/kg/day (1.96 μg/day), which is approximately two times higher than the mean iAs exposures from the drinking water. The modeled exposure and dose estimates matched well with the duplicate diet data and measured As biomarkers. The major food contributors to iAs exposure were the following: vegetables (24%); fruit juices and fruits (18%); rice (17%); beer and wine (12%); and flour, corn, and wheat (11%). Approximately 10% of tAs exposure from foods is the toxic iAs form. CONCLUSIONS: The general U.S. population may be exposed to tAs and iAs more from eating some foods than from drinking water. In addition, this model evaluation effort provides more confidence in the exposure assessment tools used.

Original languageEnglish (US)
Pages (from-to)345-350
Number of pages6
JournalEnvironmental health perspectives
Volume118
Issue number3
DOIs
StatePublished - Mar 1 2010

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All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

Keywords

  • Arsenic
  • Dietary
  • Drinking water
  • Exposure
  • MENTOR
  • Model
  • Probabilistic
  • SHEDS

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