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Associations of a Prenatal Serum Per- and Polyfluoroalkyl Substance Mixture with the Cord Serum Metabolome in the HOME Study.

Amber M HallElvira FleuryGeorge D PapandonatosJessie P BuckleyKim M CecilAimin ChenBruce P LanphearKimberly YoltonDouglas I WalkerKurt D PennellJoseph M BraunKatherine E Manz
Published in: Environmental science & technology (2023)
Per- and polyfluoroalkyl substances (PFAS) are ubiquitous and persistent chemicals associated with multiple adverse health outcomes; however, the biological pathways affected by these chemicals are unknown. To address this knowledge gap, we used data from 264 mother-infant dyads in the Health Outcomes and Measures of the Environment (HOME) Study and employed quantile-based g-computation to estimate covariate-adjusted associations between a prenatal (∼16 weeks' gestation) serum PFAS mixture [perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid (PFHxS), and perfluorononanoic acid (PFNA)] and 14,402 features measured in cord serum. The PFAS mixture was associated with four features: PFOS, PFHxS, a putatively identified metabolite (3-monoiodo-l-thyronine 4- O -sulfate), and an unidentified feature (590.0020 m / z and 441.4 s retention time; false discovery rate <0.20). Using pathway enrichment analysis coupled with quantile-based g-computation, the PFAS mixture was associated with 49 metabolic pathways, most notably amino acid, carbohydrate, lipid and cofactor and vitamin metabolism, as well as glycan biosynthesis and metabolism (P(Gamma) <0.05). Future studies should assess if these pathways mediate associations of prenatal PFAS exposure with infant or child health outcomes, such as birthweight or vaccine response.
Keyphrases
  • pregnant women
  • healthcare
  • amino acid
  • small molecule
  • emergency department
  • machine learning
  • mental health
  • deep learning
  • current status
  • case control
  • artificial intelligence
  • adverse drug
  • cell surface