Inflammation and metabolic abnormalities in pollutant-exposed children

项目总结

Project Summary Metabolic diseases, including obesity and type 2 diabetes, are major public health problems of multifactorial etiology, in which early-life exposures to environmental pollutants, such as the perfluoroalkyl substances (PFASs), are suspected to play a role. PFASs have been in use for over 60 years, but omnipresent human exposures were discovered less than 20 years ago, and general population studies of these high-priority pollutants were initiated only recently. We recently discovered that PFAS exposure is associated with depressed antibody response to certain childhood vaccinations. We therefore hypothesize that PFAS-induced inflammation may be involved in the suspected obesogenic effects of PFAS exposure that contribute to the pathogenesis of obesity and metabolic disease. Current evidence, including our own studies, suggests that developmental PFAS exposure may affect metabolic processes in childhood. With the aim to determine the possible link between PFAS exposures, immune dysfunction, and metabolic abnormalities, we will examine at age 8 years an already established birth cohort from the Faroe Islands (N = 490) to explore the associations between age-related PFAS exposure profiles and metabolic abnormalities (using markers of adiposity, dyslipidemia, glycemia and insulin resistance). We will also determine the possible role of metabolic and inflammatory serum markers related to adiposity and insulin action (adipocytokines) and immune dysfunction (vaccine antibody concentrations). Exposure data for PFAS and other environmental pollutants are already available from analyses of maternal pregnancy serum and from clinical examinations at ages 18 months and 5 years. Prospective data and banked serum for potential biomarker analyses are also available from a previous Faroese cohort (N= 656) examined neonatally and at ages 5 and 7.5 years, thus allowing replication studies of new findings. The data analysis will take into account important covariates, such as sex, time of the day for blood sampling, and maternal prepregnancy body mass index and gestational weight gain. Multiple regression analyses will be complemented by structural equation models and other advanced statistical methods, including benchmark dose calculations. The proposed project will provide new insight into the role of high- priority environmental pollutants on metabolic and immune dysfunction, the potential underlying mechanisms and potential new strategies for early prevention of metabolic disease.