Alzheimer's Research & Therapy最新文献

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.

Background: Studying the spontaneous emergence of biological anomalies within the animal kingdom can provide insights into the causes of diseases. It is often assumed that Alzheimer's Disease (AD), like other neurodegenerative diseases, is specific to humans. However, the age-related occurrence of AD neuropathological changes (ADNC) in non-human primates (NHPs) and their comparison with humans has not been formally studied. Moreover, a conceptual framework for interpreting the spontaneous occurrence of ADNC in NHPs has yet to be established.

Methods: We conducted a systematic review of the available data describing spontaneous ADNC in various NHP species. To study the biological scaling of ADNC, we used logistic regression models to compare NHP and human findings, based on both chronological age and age standardized to each species' maximum longevity.

Results: Amyloid plaques appear in all primate species according to the same temporal dynamics once the theoretical maximum age is considered, and are significantly more frequent in NHPs than in humans. In contrast, tau neurofibrillary tangles are rare in NHPs and only appear at the limit of their life expectancy.

Conclusion: The biological scaling of amyloid plaque development follows an isometric model (proportional to lifespan), whereas tau tangles emerge at a similar temporal horizon across primate species, regardless of lifespan (a chronometric model). This temporal decoupling challenges the amyloid cascade hypothesis as a universal, cross-species biological mechanism in late-onset sporadic AD. The occurrence of full-blown ADNC may depend on the phylogenetic temporal coupling of these two biological processes.

Background: The accumulation of amyloid-β_1-42 (Aβ_1-42) peptides and phosphorylated-Tau₁₈₁ (p-Tau₁₈₁) tangles from the preclinical stages of Alzheimer's disease (AD) has led to a biological definition of the disease. However, among Aβ_1-42-positive individuals, cognitive decline onset varies, and some never develop symptoms. Genetic influences on molecular pathways and their interactions with proteinopathy may underlie this heterogeneity. Leveraging data from a large sample of cognitively intact older adults in the European Prevention of Alzheimer Dementia (EPAD) cohort, we examined how AD-related pathophysiological changes (i.e., Aβ_1-42 and p-Tau₁₈₁), polygenic pathways and their interaction are associated with WM micro- and macrostructural properties.

Methods: We selected 803 individuals (mean age = 64.7 ± 7.3 years, 458 [57.0%] females, 275 [34.2%] APOE-ε4 carriers) with CSF-Aβ_1-42 and p-Tau₁₈₁ measurements available, full genotyping, and structural and diffusion MRI. Polygenic risk scores (PRSs) were computed using 85 AD-related genetic variants. These were mapped to their corresponding genes and, after excluding those belonging to the APOE locus, clustered by function into six pathway-specific PRSs (i.e., immune activation, signal transduction, inflammation, lipid, amyloid, and clearance pathways). Diffusion MRIs were processed through the fixel-based analysis framework to derive fiber density (FD) and fiber cross-section (FC) metrics, which were averaged within WM tracts. Linear models assessed the effects of AD-related pathophysiological changes, global and pathway-specific PRSs, and their interactions on FD and FC at both the tract and fixel levels. Models were corrected for multiple comparisons.

Results: P-Tau₁₈₁ was primarily associated with greater FD. The lipid pathway was associated with greater FD and FC, with these effects predominantly occurring in the left hemisphere, consistent with evidence of hemispheric dominance. The clearance pathway moderated the effect of Aβ_1-42 on FD, with a positive slope in A + compared to A- individuals. The immune activation pathway moderated the effect of p-Tau₁₈₁ on FD, with a negative slope in T + compared to T- individuals.

Conclusions: Pathway-specific genetic vulnerability to AD is associated with alterations in WM tracts both directly and by moderating the effects of AD-related pathophysiological changes. AD-associated genetic risk should be integrated into the AD diagnostic framework to enable targeted screening and intervention for future preclinical trials aimed at specific biological pathways.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that presents challenges for early detection and intervention. Mild cognitive impairment (MCI), a critical precursor of AD, progresses to dementia in a substantial proportion of individuals annually. Genetic factors, particularly single nucleotide polymorphisms (SNPs), play a key role in the pathogenesis of AD, as identified by genome-wide association studies (GWAS). Therefore, we aimed to develop and evaluate predictive models for classifying patients with MCI into high- and low-risk groups for dementia using SNP chip data and machine learning (ML) algorithms.

Methods: Using data from the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer's Disease Study, we conducted a GWAS to identify dementia-associated SNPs in a Korean population cohort. The SNPs identified were used to train six ML algorithms-random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), XGBoost, and LightGBM to predict dementia risk. Three predictive models were developed using different SNP subsets: Model 1 (54 SNPs, subjective cognitive decline [SCD] vs. AD + Vascular dementia [VD]), Model 2 (60 SNPs, SCD vs. AD), and Model 3 (76 SNPs, union set of SNPs from the AD vs. SCD and AD + VD vs. SCD). Performance was evaluated primarily using AUC and PR-AUC, which summarize discrimination independent of threshold choice. Thresholds were pre-specified within training folds using Youden's J (balanced sensitivity/specificity) and F1-max (converter-sensitive) criteria, and then applied unchanged to the temporally separated follow-up cohort.

Results: In repeated cross-validation, boosting models achieved the strongest performance (e.g., Model 3, XGBoost AUC = 0.881 ± 0.074, PR-AUC = 0.924 ± 0.055). Probabilistic outputs were well-calibrated (Brier scores 0.116-0.183), and calibration plots confirmed good agreement between predicted and observed risks. In a temporally separated follow-up cohort (n = 61, 14 converters), discrimination was modest (AUROC approximately 0.45-0.55), reflecting limited power but showing consistent enrichment of events in predicted high-risk groups. Under F1-max thresholds, sensitivity was high (approximately 0.86-0.93) with NPV approximately 0.80-0.92, whereas specificity was modest (approximately 0.19-0.30) and PPV approximately 0.20-0.27, highlighting the trade-off between capturing converters and limiting false positives.

Conclusions: Our study highlights the potential of integrating genetic data with ML-based approaches for personalized dementia risk assessment. Although performance was modest in temporal validation, these findings support the feasibility of SNP-based ML stratification in Korean MCI populations.

Background: Cognitive impairment leads to poor daily social and occupational functions and sleep disturbances. Approximately two-thirds of all individuals with mild cognitive impairment (MCI) experience sleep problems that further reduce cognitive function. Melatonin, a hormone secreted by the pineal gland, has proven effective in mitigating sleep problems and cognitive function in individuals with MCI. The current review investigated the efficacy of melatonin in improving cognitive function in adults with cognitive impairment.

Methods: Seven databases were systematically searched for relevant randomized controlled trials published (in English or Chinese) until April 2025. Two reviewers independently selected studies, assessed quality (using the Physiotherapy Evidence Database scale), and extracted data.

Results: In total, 394 potentially eligible articles were identified. Finally, 8 studies (518 participants) were included. Five, one, and two studies had good, excellent, and low quality, respectively. Pooled results indicated that melatonin significantly improved cognitive function in adults with cognitive impairment (mean difference [MD]: 1.08; p < 0.0001). Subgroup analyses by treatment duration, administration time, and cognitive impairment level revealed that the effects of melatonin were significant when it was administered for 13-24 weeks (MD: 2.04; p < 0.00001), between the times of 20:30 and 21:00 (MD: 2.2; p < 0.00001), and to individuals with MCI (MD: 2.63; p < 0.000001).

Conclusions: Our findings suggest that melatonin is relatively safe for individuals with cognitive impairment. Thus, we recommend it for adults with MCI. It should be administered between 20:30 and 21:00 for 13-24 weeks.

Background: In Alzheimer's disease (AD), limbic non-AD co-pathologies are common and contribute to memory impairment and accelerated clinical progression. To date, in vivo biomarkers of these co-pathologies are lacking. Here, we examined whether specific regional gray matter (GM) atrophy patterns on magnetic resonance imaging (MRI) allow distinguishing between patients with 'pure' AD pathology and those with AD pathology and limbic non-AD co-pathologies (AD⁺).

Methods: Atrophy patterns based on ante-mortem MRI scans of histopathologically confirmed 'pure' AD (n = 36) and AD⁺, i.e., AD pathology with concomitant limbic TDP-43 pathology and argyrophilic grain disease (n = 39), were applied to classify an independent cohort of clinically diagnosed patients with mild cognitive impairment (MCI, n = 224) and dementia (n = 221). Furthermore, we examined the degree to which an MRI marker of cortical degeneration reflecting tau pathology could improve the estimation of clinical progression.

Results: Histopathologically confirmed AD⁺ pathology was associated with more substantial hippocampal atrophy but less cortical degeneration in intermediate Braak stage regions than 'pure' AD pathology. Clinically diagnosed AD patients with an AD⁺-classified ratio of cortical-to-hippocampal GM exhibited significantly more memory impairment. At the stage of MCI, AD⁺-classified atrophy was also associated with speeded clinical decline. Furthermore, tau-associated cortical degeneration emerged as the primary predictor of clinical progression across groups and disease stages.

Conclusions: The data suggest that in MCI due to AD, additional non-AD limbic co-pathologies are associated with greater hippocampal but less cortical atrophy and more rapid clinical decline. In contrast, in mild dementia due to AD, hippocampal GM was not associated with prognosis. Instead, cortical degeneration appeared to drive clinical progression.