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BACKGROUND: The relationship between serum uric acid (SUA) levels and brain-related health remains uncertain. OBJECTIVES: This study aimed to investigate the relationship between SUA levels and some neurodegenerative disorders and brain structure. DESIGN: A longitudinal study. SETTING AND PARTICIPANTS: 384,517 participants who did not have stroke, dementia, and Parkinsonism, with complete urate testes and covariates were included. MEASUREMENTS: Cox proportional hazards models, competing risk models, and restricted cubic spine models were applied. RESULTS: During the median follow-up time of 12.7 years (interquartile range [IQR]:12.0, 13.5), 7821 (2.0%) participants developed stroke, 5103 (1.3%) participants developed dementia, and 2341 (0.6%) participants developed Parkinsonism. Nonlinear relationships were identified between SUA levels and stroke (J-shaped), dementia, and Parkinsonism (U-shaped). SUA levels of 4.2 mg/dl, 6.4 mg/dl, and 6.6 mg/dl yielded the lowest risk of stroke, dementia, and Parkinsonism, respectively. Besides, we found high SUA levels reduced the volumes of total brain, grey matter, white matter, grey matter in the hippocampus, and hippocampus, but increased lateral-ventricle volume. Inflammation accounted for 9.1% and 10.0% in the association of SUA with stroke and lateral-ventricle volume. CONCLUSIONS: Lower SUA levels increased the risk of Parkinsonism, while both lower and higher SUA levels were positively associated with increased risk of stroke and dementia. Moreover, high SUA levels reduced brain structure volumes. Our findings suggest the association between SUA levels and brain-related disorders and highlight the importance of SUA management.
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Introduction: Hyperuricemia (HUA) is a metabolic disorder caused by purine metabolism dysfunction in which the increasing purine levels can be partially attributed to seafood consumption. Perillae Folium (PF), a widely used plant in functional food, has been historically used to mitigate seafood-induced diseases. However, its efficacy against HUA and the underlying mechanism remain unclear. Methods: A network pharmacology analysis was performed to identify candidate targets and potential mechanisms involved in PF treating HUA. The candidate targets were determined based on TCMSP, SwissTargetPrediction, Open Targets Platform, GeneCards, Comparative Toxicogenomics Database, and DrugBank. The potential mechanisms were predicted via Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analyses. Molecular docking in AutoDock Vina and PyRx were performed to predict the binding affinity and pose between herbal compounds and HUA-related targets. A chemical structure analysis of PF compounds was performed using OSIRIS DataWarrior and ClassyFire. We then conducted virtual pharmacokinetic and toxicity screening to filter potential inhibitors. We further performed verifications of these inhibitors' roles in HUA through molecular dynamics (MD) simulations, text-mining, and untargeted metabolomics analysis. Results: We obtained 8200 predicted binding results between 328 herbal compounds and 25 potential targets, and xanthine dehydrogenase (XDH) exhibited the highest average binding affinity. We screened out five promising ligands (scutellarein, benzyl alpha-D-mannopyranoside, elemol, diisobutyl phthalate, and (3R)-hydroxy-beta-ionone) and performed MD simulations up to 50 ns for XDH complexed to them. The scutellarein-XDH complex exhibited the most satisfactory stability. Furthermore, the text-mining study provided laboratory evidence of scutellarein's function. The metabolomics approach identified 543 compounds and confirmed the presence of scutellarein. Extending MD simulations to 200 ns further indicated the sustained impact of scutellarein on XDH structure. Conclusion: Our study provides a computational and biomedical basis for PF treating HUA and fully elucidates scutellarein's great potential as an XDH inhibitor at the molecular level, holding promise for future drug design and development.