Background: Current exposure assessment research does not sufficiently address multi-pollutant exposure and their correlations in human media. Understanding the extent of chemical exposure in reproductive-aged women is of particular concern due to the potential for in utero exposure and fetal susceptibility.
Objectives: The objectives of this study were to characterize concentrations of chemical biomarkers during preconception and examine correlations between and within chemical classes.
Methods: We examined concentrations of 135 biomarkers from 16 chemical classes in blood and urine from 73 women aged 18-40 enrolled in Snart Foraeldre/Milieu, a prospective cohort study of pregnancy planners in Denmark (2011-2014). We compared biomarker concentrations with United States similarly-aged, non-pregnant women who participated in the National Health and Nutrition Environmental Survey (NHANES) and with other international biomonitoring studies. We performed principal component analysis to examine biomarker correlations.
Results: The mean number of biomarkers detected in the population was 92 (range: 60-108). The most commonly detected chemical classes were phthalates, metals, phytoestrogens and polycyclic aromatic hydrocarbons. Except blood mercury, urinary barium and enterolactone, geometric means were higher in women from NHANES. Chemical classes measured in urine generally did not load on a single component, suggesting high between-class correlation among urinary biomarkers, while there is high within-class correlation for biomarkers measured in serum and blood.
Conclusions: We identified ubiquitous exposure to multiple chemical classes in reproductive-aged Danish women, supporting the need for more research on chemical mixtures during preconception and early pregnancy. Inter- and intra-class correlation between measured biomarkers may reflect common exposure sources, specific lifestyle factors or shared metabolism pathways.
Keywords: Biomonitoring; Environmental exposures; NHANES; Preconception; Pregnancy; Principal component analysis.
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