{
  "abstract": "Background Prenatal exposure to per- and polyfluoroalkyl substances (PFAS) may adversely affect neurodevelopment, but comparative risks and metabolic disruptions remain unclear.Objective To integrate various levels of evidence and illustrate the relationship between prenatal PFAS exposure and neurodevelopmental trajectories in children.Methods In the Shanghai-Minhang birth cohort, maternal PFAS were measured from plasma collected at 12–16 weeks’ gestation. Neurodevelopment was assessed at 6, 12 and 48 months using the Ages and Stages Questionnaire-3 and group-based trajectory modelling was performed. Associations between PFAS (co-)exposure and neurodevelopment were evaluated using logistic regression and mixture model effects. Metabolomic perturbations of PFAS with potential neurodevelopmental effects were analysed in PC12 neuronal cells.Findings Among 412 mother-child pairs, higher PFAS exposure was linked to suboptimal development in communication (highest vs lowest tertile: OR PFHxS=2.96, 95% CI 1.05 to 8.35, ptrend=0.064; ORPFOS=3.45, 95% CI 1.27 to 9.43, ptrend=0.012), personal-social (highest vs lowest tertile: ORPFNA=2.41, 95% CI 1.05 to 5.56, ptrend=0.036), and total score (highest vs lowest tertile: ORPFOS=2.32, 95% CI 1.09 to 4.91, ptrend=0.028) across early childhood. Metabolomic analysis revealed disrupted excitatory/inhibitory (E/I) neurotransmission. Integrated toxicological assessment, including exposure risk information, toxicogenomic signatures, bioassay-inferred toxicity and in vitro neurotoxicity profiles, ranked PFOS as the most neurotoxic, followed by perfluorooctanoic acid, perfluorohexanoic acid, perfluorohexanesulfonic acid and 6:2 chlorinated polyfluorinated ether sulfonate. An E/I imbalance-based screening identified 8.3% of children as high-risk for PFAS-related neurodevelopmental deficits.Conclusions Prenatal PFAS exposure is associated with suboptimal neurodevelopment, particularly in communication and social interaction.Clinical implications Maternal exposure profiles during early gestation that are associated with disrupted E/I neurotransmission may serve as early indicators of increased risk for suboptimal neurodevelopment, thereby informing timely preventive interventions.",
  "authors": [
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China",
        "Department of Pediatrics and Child Health, Suzhou Hospital of Anhui Medical University, Suzhou, China"
      ],
      "name": "Chang Gao"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Ruonan Li"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Hongyan Wu"
    },
    {
      "affiliations": [
        "Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China"
      ],
      "name": "Zhenzhen Xie"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Weitian Tang"
    },
    {
      "affiliations": [
        "Department of Pediatrics and Child Health, Suzhou Hospital of Anhui Medical University, Suzhou, China"
      ],
      "name": "Yuanyuan Hou"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Lin Tao"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Shengmei Zhang"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Jia Lv"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Tianrui Gao"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "Xiu-Hong Meng"
    },
    {
      "affiliations": [
        "Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China"
      ],
      "name": "Maohua Miao"
    },
    {
      "affiliations": [
        "Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China"
      ],
      "name": "Wei Yuan"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China"
      ],
      "name": "De-Xiang Xu"
    },
    {
      "affiliations": [
        "Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China"
      ],
      "name": "Hong Liang"
    },
    {
      "affiliations": [
        "School of Public Health and Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China",
        "Department of Pediatrics and Child Health, Suzhou Hospital of Anhui Medical University, Suzhou, China"
      ],
      "name": "Yichao Huang"
    }
  ],
  "title": "Prenatal exposure to per- and polyfluoroalkyl substances and neurodevelopment trajectory: towards early warning via cohort- and metabolomics-based risk ranking",
  "uid": "e92e34a0-7619-55a8-b16d-b2dd85ad45f5"
}