Exploring the Association Between Per‐ and Polyfluoroalkyl Substances Exposure and the Risk of Stroke: A Systematic Investigation Using NHANES Data Analysis, Network Toxicology and Molecular Docking Approaches
Notice bibliographique
Résumé
BACKGROUND: Epidemiologic evidence regarding the association between per- and polyfluoroalkyl substances (PFAS) exposure and stroke risk remains limited and inconclusive. Consequently, the current study sought to further examine this association and clarify the underlying molecular mechanisms. MATERIALS AND METHODS: This cohort study analyzed data from 8081 participants of the 2003-2012 National Health and Nutrition Examination Survey (NHANES), employing multistage weighted logistic regression, weighted quantile sum (WQS) modeling, and partial least squares discriminant analysis (PLS-DA) to systematically evaluate the association between per- and polyfluoroalkyl substances (PFAS) exposure and stroke. Restricted cubic spline analysis was subsequently used to examine the nonlinear dose-response relationships. To investigate the underlying mechanisms, we integrated data from six databases (e.g., ChEMBL and GeneCards) to identify common molecular targets of PFAS and stroke. A protein-protein interaction (PPI) network was then constructed to identify core genes, while the binding interactions between PFAS and key targets were evaluated through molecular docking and dynamics simulations. Finally, functional enrichment analysis was performed on these core genes using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. RESULTS: After adjusting for potential confounders, six individual PFAS compounds, including perfluorooctane sulfonic acid (PFOS) (Odds Ratio [OR] = 1.59, 95% CI: 1.09-2.31), exhibited a significant positive association with the risk of stroke. The WQS model revealed a significant positive association for the PFAS mixture (OR = 1.027, 95% CI: 1.017-1.036), with PFOS contributing the highest weight (0.379). These findings were corroborated by the PLS-DA model, and the association remained significant in all subgroup analyses. The network toxicology analysis identified 183 common targets between PFOS and stroke, while the subsequent PPI network analysis identified six core genes, including AKT1 and HSP90AA1. GO and KEGG enrichment analyses demonstrated that these targets were markedly enriched in pathways associated with lipid and atherosclerosis metabolism, in addition to the PI3K-Akt and MAPK signaling pathways. Furthermore, molecular docking and molecular dynamics simulations supported potential interactions between PFOS and core targets such as AKT1. This suggests that PFOS may contribute to stroke pathogenesis by disrupting pathways involved in inflammatory regulation and apoptosis. CONCLUSIONS: This study identified a positive association between PFOS exposure and stroke risk, suggesting that the PI3K/AKT signaling pathway, along with its key effector molecule AKT1, may play a crucial role in mediating PFOS-induced stroke, thereby offering a theoretical foundation for the prevention and management of PFOS-associated stroke.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».