Limiting to directly measured exposures, incorporating the full NHANES design in the primary regression, and triangulating across complementary mixture frameworks provide a more rigorous platform than prior single-pollutant or design-naive approaches.
Abstract
Human per- and polyfluoroalkyl substances (PFAS) and metal exposures occur as mixtures, but most studies evaluate them independently. Using data from adults in the 2017–2018 National Health and Nutrition Examination Survey (NHANES) with directly measured PFAS and blood metals (N = 1648), we assessed joint associations of perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), lead, cadmium, and mercury with prevalent diabetes via survey-weighted logistic regression, Weighted Quantile Sum (WQS) regression, quantile g-computation, and design-aware and naïve Bayesian Kernel Machine Regression (BKMR). Survey-weighted diabetes prevalence was 11.4%. Lead and PFOS emerged as the dominant contributors to the exposure mixture in the BKMR model, with posterior inclusion probabilities of 1.00 and 0.997, respectively. In the WQS model, PFOS and PFOA were the primary drivers of the positive mixture index, consistent with their proposed roles in endocrine disruption and insulin resistance. BKMR exposure-response functions were non-monotonic, indicating that lead and cadmium associations with diabetes are nonlinear rather than uniformly directional across the exposure range, a structure that conventional logistic regression cannot capture. Overall, restricting to directly measured exposures, incorporating the full NHANES design in the primary regression, and triangulating across complementary mixture frameworks provide a more rigorous platform than prior single-pollutant or design-naive approaches.
It is suggested that higher plasma PFAS concentrations, particularly HFPO-DA, may be associated with more advanced TNM stage in colorectal cancer and warrant confirmation in larger prospective studies.
Ning Kang, Yang Zhao, Zhi Huang et al.· Frontiers in Toxicology· 1 citation
It is demonstrated that adherence to a healthy lifestyle was associated with substantially weaker associations between 6:2 Cl-PFESA and PFDA exposures and hyperlipidemia, highlighting lifestyle modification may serve as a potential public health strategy to reduce environmental metabolic hazards.
Peiwen Li, Lianlong Yu, Xiao Zhang et al.· International Journal of Hyg...· 0 citations
Prenatal serum concentrations of 13 PFAS, including PFBS, are measured among 500 participants in the New York University Children’s Health and Environment Study between 2016 and 2019 to investigate associations of PFAS exposures with demographic characteristics, including location of residence, and an exploratory factor analysis to investigate common sources of exposure.
Eleanor A. Medley, Duong Q. Nguyen, A. Nigra et al.· Environmental Research· 0 citations
It is suggested that mixed PFAS exposure, particularly to PFOA and PFHpA, may induce impaired renal function, with increased blood lipids playing an important mediating role.
Dandan Xu, Z. Mo, Jie Xiang et al.· Environmental Pollution· 0 citations
BACKGROUND
The association between organochlorine pesticides (OCPs)/synthetic pyrethroids (SPs) and thyroid cancer (TC) remains poorly understood, with metabolic mechanisms unexplored.
METHODS
We conducted a 1:1 age- and sex-matched case-control study (n = 668). Serum levels of 27 target analytes (19 OCPs and 8 SPs) were quantified; subsequent analyses were restricted to 13 compounds (10 OCPs and 3 SPs) with detection frequencies ≥85%. Eight machine learning (ML) algorithms with Shapley Additive Explanations (SHAP) were used to identify key pollutants in the 334 case-control pairs. Untargeted metabolomics was performed in a subset of 50 age- and sex-matched case-control pairs. Mixture effects were assessed by Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression. Furthermore, the Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) model was employed to integrate exposure and metabolic data, enabling the identification of TC patient subgroups and the exploration of underlying metabolic mechanisms.
RESULTS
Participants (mean age 45.2 years, 82.3% female) had serum OCPs at 0.007-0.333 ng/mL and SPs at 0.046-0.095 ng/mL. ML algorithms identified fenpropathrin, β-BHC, cyhalothrin, α-BHC, and p,p'-DDD as the top five contributors to TC. Elevated OCPs/SPs exposure was significantly associated with increased TC risk (WQS: adjusted OR = 1.45, 95%CI = 1.34-2.24, P = 0.019; LUCID: OR = 9.33). Fenpropathrin was the primary contributor (BKMR posterior inclusion probability = 1.00; WQS weight = 67.6%). A total of 45 significant differential metabolites (DMs) were identified (VIP ≥1, P < 0.05, and qualitative level 1). LUCID revealed a distinct TC cluster characterized by upregulated S-sulfo-L-cysteine/adenosine and downregulated 2-hydroxycaprylic acid.
CONCLUSION
OCPs/SPs mixtures, driven by fenpropathrin, disrupt amino acid/nucleotide metabolism while suppressing organic acid metabolism, representing a potential TC-associated metabolic signature.
Fei Wang, Chunxiang Li, Linfang Zou et al.· Environment International· 0 citations
