Qing Ye

BACKGROUND: Tau neurofibrillary pathology is a hallmark of Alzheimer's disease (AD) and can be quantified in vivo using tau‐selective positron emission tomography (tau PET). Tau PET signal closely correlates with cognitive decline and disease stage, yet the molecular networks underpinning tau accumulation remain incompletely defined. METHODS: We performed multi‐omics integration of proteomics, transcriptomics, and tau PET standardized uptake value ratios (SUVRs), and clinical assessments data from cognitively normal and cognitively impaired individuals. Using Light Gradient Boosting Machine (LightGBM), two‐way orthogonal partial least squares, and network‐based approaches, we explored key tau‐associated proteomic signatures and constructed protein–protein interaction (PPI) modules. Module activities were quantified by gene set variation analysis and related to tau PET and cognition. RESULTS: Among 60 regions, 15 tau PET imaging biomarkers were selected based on group differences, LightGBM importance, and cognitive relevance. Fifty key tau‐associated proteins were identified and organized into four functional modules. PPI modules 1 (metabolic‐cytoskeletal) and 3 (adhesion‐nutrient sensing) exhibited strong associations with elevated tau PET uptake across selected cortical and limbic regions, as well as with cognitive impairment. CONCLUSION: Distinct modules reflected regional tau PET burden and cognitive outcomes in AD, highlighting convergent disruptions in energy metabolism, cytoskeletal stability, and intercellular signaling. Highlights: Integration of proteomics, transcriptomics, tau positron emission tomography (PET) imaging, and cognition in Alzheimer's disease. Fifteen key tau PET imaging biomarkers were prioritized. Fifty key tau‐associated proteins were identified. Four distinct molecular networks contribute to regional tau pathology and cognition. Modules 1 (metabolic‐cytoskeletal) and 3 (adhesion‐nutrient sensing) strongly associated with tau PET burden and cognitive impairment.

Ferroptosis is an iron-dependent cell death form characterized by reactive oxygen species (ROS) overgeneration and lipid peroxidation. Myricetin, a flavonoid that exists in numerous plants, exhibits potent antioxidant capacity. Given that iron accumulation and ROS-provoked dopaminergic neuron death are the two main pathological hallmarks of Parkinson’s disease (PD), we aimed to investigate whether myricetin decreases neuronal death through suppressing ferroptosis. The PD models were established by intraperitoneally injecting 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) into rats and by treating SH-SY5Y cells with 1-methyl-4-phenylpyridinium (MPP⁺), respectively. Ferroptosis was identified by assessing the levels of Fe²⁺, ROS, malondialdehyde (MDA), and glutathione (GSH). The results demonstrated that myricetin treatment effectively mitigated MPTP-triggered motor impairment, dopamine neuronal death, and α-synuclein (α-Syn) accumulation in PD models. Myricetin also alleviated MPTP-induced ferroptosis, as evidenced by decreased levels of Fe²⁺, ROS, and MDA and increased levels of GSH in the substantia nigra (SN) and serum in PD models. All these changes were reversed by erastin, a ferroptosis activator. In vitro, myricetin treatment restored SH-SY5Y cell viability and alleviated MPP⁺-induced SH-SY5Y cell ferroptosis. Mechanistically, myricetin accelerated nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) and subsequent glutathione peroxidase 4 (Gpx4) expression in MPP⁺-treated SH-SY5Y cells, two critical inhibitors of ferroptosis. Collectively, these data demonstrate that myricetin may be a potential agent for decreasing dopaminergic neuron death by inhibiting ferroptosis in PD.

Background: The association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson’s disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients.Methods: In this cross-sectional study, a total of 1,115 PD participants were enrolled from Parkinson’s Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models.Results: We identified 426 (38.2%) RBD. The proportion of underweight, normal, overweight and obese was 2.61, 36.59, 40.36, and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m², 28.10 kg/m², and 28.23 kg/m² derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results.Conclusion: The current study found a significant dose–response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients.