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Associations between concentrations of selected perfluoroalkyl acids and concentrations of blood cadmium, lead, and total mercury.

Ram Baboo Jain
Published in: Environmental science and pollution research international (2021)
Data (N = 2552) from National Health and Nutrition Examination Survey for US adults aged ≥ 20 years for 2011-2016 were analyzed to estimate the associations between the concentrations of blood cadmium, lead, and total mercury and the concentrations of seven perfluoroalkyl acids (PFAA), namely, 2-(N-Methyl-perfluorooctane sulfonamido) acetic acid (MPAH), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), perfluorohexane sulfonic acid (PFHxS), and perfluorooctane sulfonic acid (PFOS). Concentrations of blood cadmium were negatively associated with the concentrations of PFHxS (β = - 0.05428, p < 0.01) and PFOS (β = - 0.0212, p = 0.02). Concentrations of blood lead were positively associated with the concentrations of MPAH (β = 0.03301, p < 0.01), PFOA (β = 0.04783, p = 0.01), PFNA (β = 0.11761, p < 0.01), PFDA (β = 0.08007, p < 0.01), PFUA (β = 0.11382, p < 0.01), and PFOS (β = 0.04996, p = 0.02). Percent increases in the concentration of blood lead were 0.32%, 0.46%, 1.13%, 0.77%, 1.09%, and 0.48% for 10% increases in the concentrations of MPAH, PFOA, PFNA, PFDA, PFUA, and PFOS, respectively. Concentrations of blood total mercury were positively associated with the concentrations of PFNA (β = 0.37105, p < 0.01), PFDA (β = 0.46875, p < 0.01), PFUA (β = 0.56934, p < 0.01), and PFOS (β = 0.17557, p < 0.01). Percent increases in the concentration of blood total mercury were 3.6%, 4.57%, 5.58%, and 1.69% for 10% increases in the concentrations of PFNA, PFDA, PFUA, and PFOS, respectively. Associations between the concentrations of PFAAs with blood total mercury were substantially stronger than the concentrations with blood lead. Higher the carbon chain length for PFAAs, stronger were the associations between PFAAs with lead and mercury.
Keyphrases
  • machine learning
  • big data
  • risk assessment