Alzheimer's Research & Therapy最新文献

Background: Microglia play a crucial role in brain homeostasis through phagocytosis of amyloid-β (Aβ) fibrils, a hallmark of Alzheimer disease (AD) pathology. The balance between Aβ production and clearance is critical for AD pathogenesis, with impaired clearance mechanisms potentially contributing to disease progression. G-protein coupled receptor 34 (GPR34), a microglia-enriched Gi/o-coupled receptor, is highly expressed in homeostatic microglia and may regulate phagocytic functions, yet its role in Aβ clearance remains poorly understood.

Methods: Using flow cytometry-based assays, we investigated the effect of a selective GPR34 agonist (M1) on Aβ uptake in mouse primary microglia and human induced pluripotent stem cell-derived microglia. We evaluated uptake specificity across different Aβ species and phagocytic substrates, and measured intracellular cyclic adenosine monophosphate (cAMP) levels to determine the signaling mechanism. We performed in vivo studies using human amyloid precursor protein knock-in mice with intrahippocampal M1 injections. Additionally, we analyzed GPR34 expression in Japanese AD patient brain samples using single-nucleus RNA sequencing and examined age-dependent expression changes across multiple datasets.

Results: M1 specifically enhanced uptake of Aβ fibrils through reduction of intracellular cAMP levels, without affecting monomeric or oligomeric Aβ internalization. Gpr34 knockdown experiments confirmed GPR34 as the molecular target of M1. An intrahippocampal injection of M1 significantly increased microglial Aβ uptake in vivo, an effect that required functional TREM2 signaling. GPR34 expression was significantly reduced in microglia from AD patients and showed age-dependent decline in both humans and mice.

Conclusions: Our findings identify GPR34 as a promising therapeutic target for enhancing microglial Aβ clearance and highlight the potential of GPR34 agonists for AD treatment. The age-dependent decline in GPR34 expression may contribute to reduced Aβ clearance efficiency in aging brains, exacerbating amyloid accumulation. Pharmacological activation of GPR34 represents a novel strategy to counteract impaired Aβ clearance in both aging and AD brains, potentially modifying disease progression through enhancement of microglial phagocytic function.

Background: Alzheimer's disease (AD) is an escalating global challenge, with more than 40 million people affected, and this number is projected to increase to more than 100 million by 2050. While amyloid-targeting antibody treatments (lecanemab and donanemab) are a significant step forward, the benefits of these therapies remain limited. This highlights the necessity for safe and effective compounds that offer greater therapeutic benefits to the majority of individuals with or at risk of AD. Drug repurposing allows for a cost-effective, time-efficient strategy to accelerate the availability of treatments, owing to the availability of safety information.

Method: This study focuses on the third iteration of the Delphi consensus programme aimed at identifying new high-priority drug candidates for repurposing in AD. An international expert panel comprising academics, clinicians and industry representatives was convened. Through a combination of anonymized drug nominations, systemic evidence reviews, iterative consensus rankings, and lay advisory inputs, drug candidates were evaluated and ranked based on rational, non-clinical, and clinical evidence and overall safety profiles.

Results: Among the 80 candidates that were nominated by the expert panel, seven underwent review, with only three candidates meeting the following consensus criteria of relevant mechanisms for targeting neurodegenerative pathways, non-clinical efficacy, and tolerability in older individuals. The three agents were: [1] the live attenuated herpes zoster (HZ) vaccine (Zostavax) [2], sildenafil, a phosphodiesterase-5 (PDE-5) inhibitor, and [3] riluzole, a glutamate antagonist. The HZ vaccine additionally offers potential for population-level dementia risk reduction.

Conclusion: This Delphi consensus identified three high-priority drug repurposing candidates for AD with favourable safety profiles and mechanistic plausibility, which are considered suitable for pragmatic clinical trials, including remote or hybrid designs. The PROTECT platform, which supports international cohorts in the UK, Norway, and Canada, offers a well-established means to conduct such trials effectively, thus helping to accelerate the evaluation and potential deployment of these drug candidates to benefit individuals with or at risk for AD.

Background: Cognitive impairment (CI) poses a major global health challenge. In China, neuropsychological scales, regarded as the gold standard for cognitive diagnosis, are largely inaccessible in resource-limited communities. The Mobile Eye-Tracking Application (m-ETA), which captures and quantifies eye movement features, has emerged as a promising tool for CI screening.

Methods: We developed a tablet-based m-ETA using a two-step approach. First, a logistic regression (LR) model was trained to discriminate dementia based on six oculometric features in a hospital cohort (N = 204), and regression analyses were conducted to validate the biological relevance of these features with Alzheimer's Disease-related phenotypes in an exploratory dataset (N = 101). Second, the generalizability and accuracy of the LR model were externally validated in a community-based cohort (N = 433) and further evaluated in two real-world community populations (N = 2,685). Model performance was assessed using sensitivity, specificity, negative predictive value (NPV), and area under the ROC curve (AUC).

Results: m-ETA achieved high diagnostic accuracy for dementia (AUC = 0.99). Regression analyses confirmed that the m-ETA-derived oculometric features were significantly associated with cognitive performance, brain atrophy, and tau deposition in the exploratory dataset (all P < 0.05). m-ETA accurately detected CI (AUC = 0.80), with excellent negative predictive value for ruling out CI, and identified individuals with lower cognition performance across diverse communities.

Conclusions: m-ETA offers a low-cost, non-invasive, and efficient tool for large-scale CI screening, particularly suited to underserved and low-literacy communities in China.

Background: Alzheimer's Disease Spectrum (ADS) progresses from preclinical stages to dementia, with dynamic functional connectivity (dFC) changes emerging early. This study aimed to investigate the dynamic changes in brain networks across different stages of ADS and their underlying molecular mechanisms.

Methods: This cross-sectional study included 239 participants: 69 Healthy Controls (HC), 83 with Subjective Cognitive Decline (SCD), 56 with Mild Cognitive Impairment (MCI), and 31 with Alzheimer's disease (AD). All participants underwent neuropsychological testing and resting-state functional magnetic resonance imaging (rs-fMRI). Leading Eigenvector Dynamics Analysis (LEiDA), a data-driven method that captures time-resolved whole-brain dFC, was applied to identify transient brain states and calculated their occupancy rate, dwell time, and transition probabilities. Group differences in these dynamic metrics were assessed using a General Linear Model (GLM), and their correlations with cognitive performance were examined. To explore the molecular basis of significant dFC alterations, we performed gene-category enrichment analysis. This analysis integrated the spatial maps of altered brain states with regional gene expression data from the Allen Human Brain Atlas (AHBA), using spin permutations to ensure statistical robustness.

Results: We identified ten recurring brain states and characterized how their transition patterns, stability, and frequency differed as a function of disease severity. Specifically, early disruptions appeared as altered transition probabilities between states, while later stages showed pronounced changes in the dwell time and occurrence rates of specific states, closely associated with cognitive decline. Notably, one brain state marked by synchronized activity in attention, salience, and default mode networks emerged as a critical hub linked to both cognitive deterioration and excitatory-inhibitory imbalance. Genes associated with this state were enriched in glycine-mediated synaptic pathways and expressed in both excitatory and inhibitory neurons, showing spatial and temporal patterns that extended from early development into late disease stages.

Conclusions: Our study uncovered the stage-dependent dFC changes and their molecular underpinnings of brain network dysfunction across the ADS.

Background: Global reductions in cerebral blood flow (CBF) are among the earliest and most consistent abnormalities observed in Alzheimer's disease (AD), preceding both cortical plaque formation and cognitive decline. While the pial arterial network-a critical supplier of intracortical perfusion-has been overlooked in this context, it may play a pivotal role in early vascular pathology. Here, we report extensive cerebral amyloid angiopathy (CAA) within the pial artery and arteriole network in the J20 (PDGF-APPSw, Ind) mouse model of AD.

Methods: Using premortem delivery of Methoxy-XO4 to label Aβ, and arterial vascular labeling, we assessed Aβ burden on the pial artery/arteriole network and cerebral blood flow in aged male and female WT and J20 AD mice.

Results: We show that 12-month-old J20 mice exhibit significant Aβ deposition across major leptomeningeal arteries (ACA, MCA) and pial collaterals, with ~ 40% vessel coverage in males and ~ 20% in females-substantially exceeding Aβ levels in cortical or hippocampal vessels. This vascular Aβ burden was accompanied by compensatory enlargement and increased tortuosity of pial collateral vessels. Yet, despite this apparent remodeling, CBF was reduced by ~ 15% in J20 mice, and this decline was significantly associated with leptomeningeal CAA burden.

Conclusions: This is the first study to comprehensively characterize meningeal arterial Aβ accumulation in a preclinical model of vascular AD, mirroring recent observations in early-stage human disease. Our findings implicate meningeal CAA as a potential driver of early CBF disruption and suggest that pial collateral remodeling may reflect a compensatory response to vascular insufficiency. Moreover, we identify robust sex differences in CAA burden, paralleling sex-specific patterns of parenchymal Aβ pathology in humans. These results highlight the leptomeningeal vasculature as a novel and understudied locus for early AD pathology and a potential therapeutic target to preserve cerebrovascular integrity.

Background: South Korea is among the fastest-aging countries globally, with a rapidly rising prevalence of dementia. Early identification of individuals at risk is critical for effective prevention, as dementia is influenced by both non-modifiable factors, such as age, sex, and baseline cognitive status, and modifiable factors, including socioeconomic conditions, health behaviors, and psychosocial characteristics. This study aimed to identify multidimensional determinants of dementia using machine learning applied to nationally representative longitudinal data, examining how these factors interact across demographic and cognitive subgroups to inform targeted, evidence-based prevention strategies.

Methods: We analyzed data from the Korean Longitudinal Study of Aging (KLoSA; 2014-2020), including 4,958 participants aged 45 years and older without baseline dementia. Participants were stratified by baseline cognitive status (cognitively normal vs. mild cognitive impairment (MCI)), with further subgroup comparisons by age (< 65 vs. ≥ 65) and sex for cognitively normal individuals. Predictors spanning sociodemographic, health, behavioral, and contextual domains were examined. Four regression algorithms-linear regression, random forests, XGBoost, and CatBoost-were applied, and model performance was evaluated via RMSE, MAE, and R². Predictor importance was assessed using a multi-method approach integrating model-based metrics and SHAP values, with top predictors identified for each subgroup.

Results: Predictive performance was comparable across algorithms, with R² ranging from 0.201 to 0.361, highest in the MCI_All dataset. Age and education were consistently the most influential non-modifiable factors. Key modifiable contributors included oral health, depression, household income, quality of life, and IADL performance. Importance patterns varied by cognitive status, age, and sex: socioeconomic and psychosocial factors were more influential in cognitively normal adults, whereas health status and IADL predominated in MCI participants. Age-stratified analyses highlighted oral health, depression change, and social contact in adults < 65, and cumulative factors including IADL decline in adults ≥ 65. Sex-stratified analyses indicated stronger effects of household income and social engagement in men, and depression and oral health in women. SHAP analyses confirmed inverse associations between changes in depression and IADL performance and predicted cognitive scores.

Conclusions: Age and education were the strongest predictors of cognitive function, while modifiable factors-including oral health, depression, social engagement, and IADL performance-played significant roles across subgroups. This interpretable machine learning approach revealed nuanced patterns of predictor importance across cognitive status, age, and sex, underscoring the value of targeted interventions to r

Background: Determining whether MRI brain scans demonstrate atrophy that is beyond "normal for age" is challenging. Automated measurements of structural metrics in individual brain regions have shown promise as biomarkers of neurodegeneration, yet widely available reference standards that aid interpretation at the individual level are lacking. Normative modelling, enabling standardized "brain charts", represents a significant step in addressing this challenge by generating individualized age- and sex- adjusted centile scores derived from large, aggregated datasets for MRI-derived quantitative metrics.

Methods: Using normative data from 56,173 participants across the life course, we have developed regional cortical thickness and amygdala/hippocampal volume brain charts (adjusted for total intracranial volume) that can be applied at the individual level. At the group level, we investigate whether regional centile scores relate to cognitive performance (mini-mental state examination) and discriminate individuals with neuropathological evidence of Alzheimer's disease (n = 351) from propensity-matched controls from the National Alzheimer's Coordinating Center (NACC) dataset. In addition, we explored the relationships between disease stage, cognition, regional tau deposition and regional centile scores in amyloid-β-PET-positive individuals with Alzheimer's disease dementia (n = 39) and mild cognitive impairment (n = 71) from the Alzheimer's Disease Neuroimaging Initiative-3 (ADNI-3). We then extended this approach to phenotypes of frontotemporal lobar degeneration using the Neuroimaging in Frontotemporal Dementia dataset (n = 113).

Results: We demonstrate BrainChart's application to illustrative individual cases. At the group level, we show that in Alzheimer's disease, regional centile scores from brain charting predicted cognitive performance, temporal lobe tau PET tracer uptake and discriminated disease groups from propensity matched cognitively normal controls in independent cohorts. Distinct patterns of age-inappropriate cortical atrophy were also evident in different clinical phenotypes of frontotemporal lobar degeneration from the Neuroimaging in Frontotemporal Dementia dataset.

Conclusions: Regional centile scores derived from an extensive normative dataset represent a generalizable method for objectively identifying atrophy in neurodegenerative diseases and can be applied to determine neurodegenerative atrophy at the individual level.