Multiple system atrophy (MSA) is a synucleinopathy characterized by regionally and cellularly heterogeneous α-synuclein (αsyn) pathology. Although MSA-derived brain lysates can robustly seed αsyn pathology in experimental models, the extent to which the anatomical origin of pathogenic seeds influences pathological induction and propagation remains unclear. Previous studies suggest that the cellular environment in which αsyn aggregates form may influence their seeding properties. Here, Triton X-100-insoluble brain lysates derived from the cerebellum and pons of two neuropathologically confirmed MSA donors were stereotaxically inoculated into the hippocampus of TgM20 mice expressing human wild-type αsyn to compare their seeding properties in vivo. Cerebellar white matter lysates, which are predominantly enriched in αsyn oligodendroglial glial cytoplasmic inclusion (GCI) pathology, induced broader and more robust early αsyn pathology than pontine lysates, which contained both αsyn neuronal and oligodendroglial inclusion pathology. These differences were most apparent at 4 months post-inoculation and were particularly evident with antibodies recognizing C-terminally truncated αsyn species. By 8 months post-inoculation, the overall anatomical distributions of induced αsyn pathology were more comparable between lysate groups; however, cerebellar lysates continued to produce greater spinal cord pathology, and temporal analyses identified lysate-dependent differences in progression in selected antibody and regional readouts. Oligodendroglial αsyn inclusions were also observed within Olig2-positive cells in the corpus callosum following inoculation with both lysate types. Collectively, these findings demonstrate that the anatomical origin of MSA-derived brain lysates influences the induction and propagation of αsyn pathology in vivo. Although the regional differences are consistent with a potential contribution of the cellular composition of the donor pathology, the present experiments do not directly isolate GCI- from αsyn neuronal cytoplasmic inclusion-derived seeds, thus the involvement of other molecular factors cannot be excluded.
BackgroundMemory and mobility may deteriorate together or follow different courses in later life. We examined joint trajectories and their associated factors in older Chinese and US adults.MethodsWe analyzed four waves of the China Health and Retirement Longitudinal Study (2011–2018) and the US Health and Retirement Study (2012–2018). Participants were aged ≥60 years at baseline and had at least three valid measurements of both episodic memory and self-reported mobility difficulties. Cohort-specific multivariate latent class mixed models identified joint trajectories. Multinomial logistic regression examined 13 covariates. Six classification algorithms distinguished two unfavorable trajectories from a higher-memory/low-difficulty group using stratified training/test splits. Shapley additive explanations characterized contributions to XGBoost predictions.ResultsTrajectory analyses included 4,360 Chinese and 8,407 US participants; regression analyses included 4,357 and 8,352, respectively. Four trajectories were retained in each cohort. Dual decline accounted for 12.9% and 14.9%, and memory decline with high mobility difficulty for 16.0% and 15.0%, respectively. Memory decline with low mobility difficulty occurred in 42.8% of Chinese participants, whereas decreasing self-reported mobility difficulty with memory decline occurred in 17.7% of US participants. Depressive symptoms were associated with dual decline per cohort-specific standard deviation increase: adjusted odds ratios were 1.56 (95% confidence interval 1.38–1.76) and 1.51 (1.39–1.63), respectively. Corresponding estimates for arthritis were 1.53 (1.22–1.93) and 2.40 (2.07–2.77). Across algorithms, test areas under the receiver operating characteristic curve ranged from 0.783 to 0.807 and 0.692 to 0.735 for dual decline, and 0.863 to 0.890 and 0.859 to 0.881 for high mobility difficulty. Logistic regression performed best in both US comparisons.ConclusionEpisodic memory and mobility difficulties followed both parallel and divergent courses in CHARLS and HRS. Depressive symptoms and arthritis were associated with unfavorable trajectories in both cohorts. Joint assessment characterizes distinct patterns of functional aging beyond describing either domain alone.
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β deposition, tau hyperphosphorylation, chronic neuroinflammation, and progressive cognitive decline. Increasing evidence suggests that, beyond classical neuropathology, alterations in the oral and gut microbiota contribute to AD through interconnected inflammatory, metabolic, immune, and epigenetic mechanisms. This review synthesizes recent findings on the oral–gut–brain axis, highlighting how oral dysbiosis may influence gut microbial ecology and how gut microbial metabolites mediate communication between the gastrointestinal tract and the CNS. Particular emphasis is placed on microbial metabolites, including short-chain fatty acids (SCFAs), lipopolysaccharide, trimethylamine N-oxide, bile acids, tryptophan-derived metabolites, vitamins, and neurotransmitter-related compounds, which regulate blood–brain barrier integrity, immune cell activation, microglial function, and neuronal survival. We further discuss how these metabolites modify host epigenetic machinery through histone acetylation, DNA methylation, and non-coding RNA regulation, thereby providing a mechanistic link between microbial dysbiosis and AD pathogenesis. The review also examines the reciprocal interactions between dietary phytochemicals and the gut microbiota, demonstrating that microbial biotransformation is often required to generate bioactive metabolites with improved bioavailability, blood–brain barrier penetration, and neuroprotective efficacy. Consequently, individual differences in microbial metabolic capacity may underlie the heterogeneous responses observed in clinical studies of nutraceutical interventions. Finally, we propose that integrating microbiome modulation, microbial metabolite profiling, epigenetic regulation, and microbiome-dependent phytochemical bioactivation offers a promising framework for developing personalized therapeutic strategies. Future research combining longitudinal human cohorts with multi-omics approaches and mechanistic studies will be essential to establish causality and translate microbiome-informed interventions into effective disease-modifying therapies for Alzheimer's disease.
BackgroundWhite matter hyperintensities (WMHs) are associated with cognitive impairment, but their diffusion, astroglial and network correlates remain incompletely characterized. We examined glymphatic-related diffusion captured by diffusion tensor image analysis along the perivascular space (DTI-ALPS) across WMH severity. We also tested its relationships with structural network topology, circulating glial fibrillary acidic protein (GFAP) and cognition.MethodsWe enrolled 102 participants and classified them as normal controls or as having mild, moderate or severe WMH according to Fazekas scores. DTI-ALPS assessed glymphatic-related diffusion, while multidomain neuropsychological testing and circulating GFAP characterized cognition and astroglial injury. Structural networks were constructed using the AAL-90 atlas, deterministic tractography and graph theory. Ninety-nine participants passed imaging quality control and entered the final analyses. Linear models were adjusted for age, sex and education, and false-discovery-rate correction was applied within prespecified analysis families. A bootstrap model tested the indirect association of left DTI-ALPS between global efficiency and global cognition.ResultsAfter covariate adjustment and false-discovery-rate correction, greater WMH severity was associated with lower bilateral DTI-ALPS indices and higher circulating GFAP (all q < 0.001). It was also associated with lower Mini-Mental State Examination scores and longer Shape Trail Test Part B completion times (both q = 0.044). Higher GFAP was associated with a lower right DTI-ALPS index (β = −0.34, 95% CI −0.56 to −0.12; q = 0.004). Right superior parietal and right precuneus nodal efficiencies were positively associated with the right DTI-ALPS index after correction across 90 regions. The indirect effect of left DTI-ALPS between global efficiency and Mini-Mental State Examination score was not supported (1.45, 95% bootstrap CI −2.06 to 7.17).ConclusionGreater WMH severity was associated with lower bilateral DTI-ALPS indices, higher circulating GFAP and poorer performance on selected cognitive measures. Regional nodal efficiency was associated with DTI-ALPS, but neither global network effects nor a robust indirect pathway through left DTI-ALPS was supported. These cross-sectional findings identify related diffusion, astroglial and regional network features without establishing a causal glymphatic-network-cognition pathway.
Background Alzheimer's disease (AD) exhibits substantial heterogeneity in brain atrophy patterns and progression rates. Data-driven subtyping based on the Subtype and Stage Inference (SuStaIn) model has identified distinct MRI-derived subtypes, but the longitudinal dynamics of these subtypes within the AT(N) framework remain unknown.Methods This study included 1,576 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with at least two visits. SuStaIn was applied to volumetric measurements of 11 brain regions to identify imaging-based subtypes. Within each subtype, we fitted a Bayesian latent time longitudinal joint mixed-effects model incorporating six biomarkers: amyloid PET (A), CSF p-tau (T), hippocampal volume, cingulate volume, thalamic volume (N), and ADAS-Cog13 (X), with APOE epsilon 4 as a covariate.Results The three subtypes exhibited distinctly different longitudinal patterns. Subtype 1 showed early elevation of amyloid, followed by a sharp decline in hippocampal volume, which was tightly coupled with cognitive deterioration. Subtype 2 featured early involvement of cortical regions such as the cingulate cortex, with hippocampal changes occurring at a similar timing to Subtype 1 but accompanied by earlier cortical and thalamic involvement. Subtype 3 exhibited early rapid hippocampal atrophy that later plateaued, and demonstrated the strongest hippocampus-cognition coupling. The association of APOE epsilon 4 with biomarker trajectories was subtype-dependent: its effect estimates were largest for amyloid and p-tau in Subtype 1, whereas in Subtype 3 the effect estimates were significant for nearly all biomarkers, with the largest estimate for cognition.Conclusion This study demonstrates that AD subtypes defined by spatial atrophy patterns also differ fundamentally in temporal biomarker dynamics and APOE epsilon 4 association, supporting subtype-stratified modeling of disease progression.
