Bayesian inference of nonylphenol exposure for assessing human dietary risk

Abstract

Nonylphenols (NPs) are endocrine-disrupting compounds commonly found in the environment and a number of food products. In this study, we constructed a probabilistic risk framework incorporating a Bayesian inference of exposure level in foodstuffs in conjunction with effect analysis of reproduction and renal disease. Our objective was to contrast the risk of dietary exposure to NPs among individuals in various age groups, with a particular focus on fertile females. In this study, seafood presented relatively high NP concentrations; however, seafood accounts for only a small proportion of the total food intake of most individuals. Rice was shown to make the largest contribution to NP daily intake among males and females in most age groups. Chicken made the largest contribution in the 12–16 and 16–18 year age groups. The mean average daily dose of NPs tended to decrease with age, regardless of gender. The estimated distribution of hazard quotients of <1 in all groups means that the risk of reproductive or renal abnormalities due to dietary exposure to NPs is negligible within most of the Taiwanese population. Nonetheless, preschoolers (3–6-year-olds) appear to be more vulnerable to NPs than do individuals in other age groups. There has been growing concern among researchers concerning the neurotoxic effects of NPs on offspring via maternal exposure. We recommend conducting a comprehensive assessment of exposure to NPs via multiple exposure routes, particularly among fertile women and preschoolers.