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Co-variate adjusted associations between serum concentrations of selected perfluoroalkyl substances and urinary concentrations of selected arsenic species.

Ram Baboo Jain
Published in: Environmental science and pollution research international (2022)
Data from National Health and Nutrition Examination Survey for 2011-2012 were used to estimate associations of the serum concentrations of perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluoroundecanoic acid (PFUnDA), and 2-(N-methyl-perfluorooctane sulfonamido) acetic acid (Me-PFOSA) with urinary concentrations of total arsenic (UAS), inorganic arsenic (IAS), arsenobetaine (UAB), and dimethyl arsinic acid (UDMA) among US adults aged >  = 20 years. Concentrations of PFNA were positively associated with all four arsenic variables but statistical significance was observed for IAS only (β = 0.33364, P = 0.04). Concentrations of PFDA were positively associated with UAS (β = 0.20688, P = 0.01), IAS (β = 0.23712, P = 0.02), and UAB (β = 0.26049, P = 0.02). Concentrations of PFUnDA were positively associated with UAS (β = 0.49946, P < 0.01), IAS (β = 0.51782, P < 0.01), UAB (β = 0.62924, P < 0.01), and UDMA (β = 0.26375, P < 0.01). Concentrations of Me-PFOSA with PFAS were inversely associated with every PFAS but statistical significance was observed for UDMA only (β =  - 0.05613, P = 0.03). PFOA, PFHxS, and PFOS were, in general, negatively associated with concentrations of all four arsenic variables but without reaching statistical significance. Positive associations of PFDA, PFNA, and PFUnDA with arsenic necessitate investigation about impact of the co-exposure of these PFAS with arsenic and their impact on health. Fluorinated carbon chain length > 8 as opposed to ≤ 8 may have a role in defining associations of PFAS with arsenic.
Keyphrases
  • drinking water
  • heavy metals
  • healthcare
  • climate change
  • electronic health record
  • artificial intelligence
  • deep learning