Sex-specific association of exposure to a mixture of phenols, parabens, and phthalates with thyroid hormone and antibody levels in US adolescents and adults

Abstract

Individuals are exposed to multiple phenols, parabens, and phthalates simultaneously since they are important endocrine-disrupting compounds (EDCs) and share common exposure pathways. It is necessary to assess the effects of the co-exposure of these EDCs on thyroid hormones (THs). In this study, data included 704 adolescents and 2911 adults from the 2007-2012 National Health and Nutrition Examination Survey (NHANES). Serum THs measured total triiodothyronine (T3), total thyroxine (T4), free forms of T3 (FT3) and T4 (FT4), thyroid-stimulating hormone (TSH), thyroglobulin (Tg), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TgAb). And 16 EDCs (3 phenols, 2 parabens, and 11 phthalates) were measured from urine. The relationship between single EDCs and single THs was analyzed using generalized linear regression. And results showed that several EDCs were positively associated with serum T3 and FT3 levels in boys but negatively associated with serum T4 and FT4 levels in girls. And in adults, five EDCs were negatively associated with T3, T4, or FT4. The effects of co-exposure to 16 EDCs on THs were calculated using Bayesian kernel machine regression and quantile-based g-computational modeling, confirmed that co-exposure was related to the increase of T3 in adolescents and the decrease of T4 in both adolescents and adults. Besides, nonlinear and linear relationships were identified between co-exposure and the risk of positive TPOAb and TgAb in girls and adult females, respectively. In conclusion, phenols, parabens, and phthalates as a mixture might interfere the concentrations of THs and thyroid autoantibodies, and the interfering effect varies significantly by sex as well as by age. Further prospective research is warranted to investigate the causal effects and underlying mechanisms of co-exposure on thyroid dysfunction.

Keywords: Bayesian kernel machine regression; Endocrine-disrupting compounds; Quantile-based g-computation; Thyroid function.