Can the observed association between serum perfluoroalkyl substances and delayed menarche be explained on the basis of puberty-related changes in physiology and pharmacokinetics? - Wu, Huali; Yoon, Miyoung; Verner, Marc-André; Xue, Jianping; Luo, Man; Andersen, Melvin E.; Longnecker, Matthew P.; Clewell, Harvey J. (Environment international, 2015)
An association between serum levels of two perfluoroalkyl substances (PFAS) and delayed age at menarche was reported in a cross-sectional study of adolescents. Because perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have half-lives of years, growth dilution and the development of a new route of excretion (menstruation) could account for some or all of the reported association. To assess how much of the epidemiologic association between PFAS and delayed menarche can be explained by the correlation of growth and maturation with PFAS body burden. We developed a Monte Carlo (MC) physiologically-based pharmacokinetic (PBPK) model of PFAS to simulate plasma PFAS levels in a hypothetical female population aged 2 to 20years old. Realistic distributions of physiological parameters as well as timing of growth spurts and menarche were incorporated in the model. The association between PFAS level and delayed menarche in the simulated data was compared with the reported association. The prevalence of menarche, distributions of age-dependent physiological parameters, and quartiles of serum PFAS concentrations in the simulated subjects were comparable to those reported in the epidemiologic study. The delay of menarche in days per natural log increase in PFAS concentrations in the simulated data were about one third as large as the observed values. The reported relationship between PFAS and age at menarche appears to be at least partly explained by pharmacokinetics rather than a toxic effect of these substances. •Ability to simulate longitudinal trends in blood biomarkers in early life•Kinetic variation contributed to the association between PFAS and age at menarche.•MC-PBPK modeling can enhance interpretation of epidemiological associations.