Distribution, variability, and predictors of urinary bisphenol A levels in 50 North Carolina adults over a six-week monitoring period

Bisphenol A (BPA) is commonly manufactured to make polycarbonate plastics and epoxy resins for use in consumer products and packaged goods. BPA has been found in several different types of environmental media (e.g., food, dust, and air). Many cross-sectional studies have frequently detected BPA concentrations in adult urine samples. However, limited data are available on the temporal variability and important predictors of urinary BPA concentrations in adults. In this work, the major objectives were to: 1) quantify BPA levels in duplicate-diet solid food, drinking water, hard floor surface wipe, and urine samples (first-morning void [FMV], bedtime, and 24-h) collected from adults over a six-week monitoring period; 2) determine the temporal variability of urinary BPA levels using concentration, specific gravity (SG) adjusted, creatinine (CR) adjusted, and excretion rate values, and; 3) examine associations between available study factors and urinary BPA concentrations. In 2009–2011, a convenience sample of 50 adults was recruited from residential settings in North Carolina. The participants completed diaries and collected samples during weeks 1, 2, and/or 6 of a six-week monitoring period. BPA was detected in 38%, 4%, and 99% of the solid food (n=775), drinking water (n=50), and surface wipe samples (n=138), respectively. Total BPA (free plus conjugated) was detected in 98% of the 2477 urine samples. Median urinary BPA levels were 2.07ng/mL, 2.20ng/mL-SG, 2.29ng/mg, and 2.31ng/min for concentration, SG-adjusted, CR-adjusted, and excretion rate values, respectively. The intraclass correlation coefficient (ICC) estimates for BPA showed poor reproducibility (≤0.35) for all urine sample types and methods over a day, week, and six weeks. CR-adjusted bedtime voids collected over six-weeks required the fewest, realistic number of samples (n=11) to obtain a reliable biomarker estimate (ICC=0.80). Results of linear mixed-effects models showed that sex, race, season, and CR-level were all significant predictors (p