Nguyen, VK; Colacino, JA; Arnot, JA; Kvasnicka, J; Jolliet, O
BACKGROUND: Chemical biomarker concentrations are driven by complex interactions between chemical use patterns, exposure pathways, and toxicokinetic parameters such as biological half-lives. Criteria to differentiate legacy from current exposures are helpful for interpreting variation in age-based and time trends of chemical exposure and identifying chemicals to which children are highly exposed. A systematic approach is needed to study temporal trends for a wide range of chemicals in the US population.
OBJECTIVES: Using National Health and Nutrition Examination Survey (NHANES) data on measured biomarker concentrations for 141 chemicals from 1999 to 2014, we aim to 1) understand the influence of temporal determinants, in particular time trends, biological half-lives, and restriction dates on age-based trends, 2) systematically define an age-based pattern to identify chemicals with ongoing and high exposure in children, and 3) characterize how age-based trends for six Per- and Polyfluoroalkyl Substances (PFASs) are changing over time.
METHODS: We performed an integrated analysis of biological half-lives and restriction dates, compared distributions of chemical biomarker concentrations by age group, and then applied a series of regression models to evaluate the linear (βage) and nonlinear (βage2) relationships between age and chemical biomarker levels.
RESULTS: For restricted chemicals, a minimum persistence of 1 year in the human body is needed to observe substantial differences between the less exposed young population and historically exposed adults. We define a metric ( [Formula: see text] ) that identifies several phthalates, brominated flame retardants, pesticides, and metals such as lead and tungsten as elevated and ongoing exposures in children. While a substantial reduction in children's exposures was reflected in PFOS and PFOA, levels of PFNA and PFHxS in children were higher in 2013-2014 compared to those in 1999-2000.
CONCLUSIONS: Integrating a series of regression models with systemized stratified analyses by age group enabled us to define an age-based pattern to identify chemicals that are of higher levels in children.
