Alzheimer's Research & Therapy最新文献

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.

Background: Alzheimer's disease (AD) exhibits sex differences in prevalence, symptoms and risk factors. Understanding the effect of sex in AD cerebrospinal fluid (CSF) biomarkers and their association with amyloid-beta (Aβ) pathology in preclinical stages have important implications for their use in prevention trials. The objective of this study was to examine sex differences in core AD CSF biomarkers used in early diagnosis and prevention trials, as well as in CSF biomarkers reflecting downstream pathophysiological mechanisms, and in their associations with Aβ pathology as measured by Positron Emission Tomography (PET).

Methods: Cognitively Unimpaired (CU) participants from the ALFA + (N = 400) and the WRAP/WADRC (N = 548) cohorts were included in the study. CSF biomarkers for core AD pathology (Aβ42, Aβ42/40, p-tau181/Aβ42, p-tau181, p-tau217 and p-tau231), neurodegeneration (NfL, t-tau), synaptic dysfunction (neurogranin, GAP-43, SNAP25, synaptotagmin-1, α-synuclein), glial reactivity (GFAP, S100B, sTREM2, YKL-40), neuroinflammation (IL-6, MCP-1), and vascular dysregulation (sICAM-1, sVCAM-1) were measured. Participants underwent Aβ PET at baseline and follow-up visit. We used Analyses of Covariance (ANCOVA) to evaluate sex differences in CSF biomarker levels and performed sex-stratified Receiver-Operating Characteristic (ROC) analyses to test their performance to identify Aβ PET-positive individuals. Additionally, we run linear regression models to study the modifying effect of sex on the association of baseline CSF biomarkers with cross-sectional and longitudinal Aβ PET uptake.

Results: Men had higher CSF NfL, glial reactivity and vascular dysregulation biomarkers (Cohen's d ranging from -0.22 to -0.44, P < 0.05), and lower synaptic biomarkers (Cohen's d ranging from 0.18 to 0.30, P < 0.05) compared to women at baseline. There were no sex differences in the core AD CSF biomarkers' performance to identify Aβ PET-positive individuals (DeLong's test P values > 0.05), with CSF p-tau181/Aβ42 and p-tau217 showing the highest performance in both sexes (Areas Under the Curve (AUCs) ranging from 87.1 to 96.3). However, sex modified the associations of baseline CSF biomarkers with Aβ PET uptake, which were more pronounced in women than in men.

Conclusions: Our results suggest that tailoring core AD CSF biomarkers by sex is not necessary for detecting Aβ PET positivity in CU individuals. However, sex differences in their association with Aβ deposition could influence their prognostic or monitoring applications.

Background: KLOTHO-VS (KL-VS) heterozygosity, a variant of the KLOTHO gene, and its encoded protein, α-Klotho, are associated with brain health and show neuroprotective potential against Alzheimer's disease (AD). We aimed to assess whether KL-VS heterozygosity, cerebrospinal fluid (CSF) and serum soluble α-Klotho (sαKl) levels, would be associated with a lower likelihood of AD and better performance on memory and other cognitive domains in individuals with AD dementia, amnestic mild cognitive impairment (aMCI) due to AD, and cognitively unimpaired controls.

Methods: In this cross-sectional study, we analyzed two partially overlapping subsamples derived from 296 participants from the Czech Brain Aging Study. The first subsample included 196 participants with KL-VS haplotype data: 71 with AD dementia, 84 with aMCI due to AD, and 41 cognitively unimpaired controls. The second subsample included 147 participants with CSF and/or serum sαKl measurements, including 58 with AD dementia, 59 with aMCI due to AD, and 30 cognitively unimpaired controls. Diagnoses of aMCI and AD dementia were confirmed by positive CSF biomarkers and/or amyloid PET imaging. Logistic regression assessed how KL-VS heterozygosity influenced the odds of aMCI or dementia due to AD. Linear regression investigated associations between cognitive performance and either KL-VS heterozygosity or CSF/serum sαKl levels. Analysis of variance and analysis of covariance with post-hoc tests were used to compare sαKl levels across study groups.

Results: KL-VS heterozygosity carriers showed a consistent trend towards lower odds of being classified with aMCI and dementia due to AD, with similar patterns in both Apolipoprotein E ε4 (APOE ε4) allele carriers and non-carriers, although none of the associations reached statistical significance despite moderate (rather than small) effect sizes. Among individuals with aMCI due to AD, KL-VS heterozygotes displayed better memory performance (β = 0.61, p = .008), particularly those who also carried the APOE ε4 allele (β = 0.64, p = .042). Results with other cognitive domains were non-significant. No significant differences in sαKl levels were found between study groups, and soluble α-Klotho levels did not associate with memory performance.

Conclusions: KL-VS heterozygosity may be linked to lower likelihood of classification as aMCI or dementia due to AD, and its association with memory might be specific to the aMCI stage of AD and modulated by APOE ε4 status.

Background: Early detection of amyloid-β (Aβ) pathology is critical for timely intervention in Alzheimer's disease (AD). While Aβ positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers are accurate, their high cost and limited accessibility hinder routine use. We developed a computed tomography (CT)-based, two-stage workflow combining CT-derived atrophy patterns with plasma phosphorylated tau 217 (p-Tau217) to predict Aβ PET positivity.

Methods: In this cohort of 616 participants (521 with mild cognitive impairment (MCI], 95 with early dementia of Alzheimer's type (DAT]; age 60-93 years), CT, p-Tau217 assays, and Aβ PET were performed. A random forest model incorporating CT-derived regional W-scores and apolipoprotein E ε4 (APOE ε4) status stratified individuals into low-, intermediate-, and high-risk groups. p-Tau217 testing was reserved for the intermediate-risk group.

Results: At a 95% sensitivity/specificity threshold, CT-based stratification yielded a low-risk negative predictive value (NPV) of 95.8% (93.0-98.6%) and a high-risk positive predictive value (PPV) of 98.4% (96.8-100.0%), with 28.2% classified as intermediate-risk. Targeted plasma testing of intermediate-risk group improved the overall PPV to 92.8% (88.5-97.1%) and the overall NPV to 88.9% (78.6-99.2%), achieving an overall accuracy of 95.8% (94.2-97.4%). The CT-based workflow's accuracy was non-inferior to our MRI-based method (area under the curve 0.96 vs. 0.95; p = 0.14).

Conclusions: This CT-based, two-stage approach is a cost-effective, scalable alternative to MRI-based strategies, leveraging routine CT and selective p-Tau217 testing to enhance early AD detection and optimize resource utilization in clinical practice.

Background: Previous literature indicate retinal hyperspectral imaging as a non-invasive method with the potential for identifying amyloid-beta (Aβ) protein deposits. Current diagnostic methods, such as cerebrospinal fluid analysis or positron emission tomography, are costly, invasive, and non-scalable. Hyperspectral imaging offers a potentially accessible alternative for early detection of Alzheimer's disease. The aim of this study is to investigate the potential of retinal hyperspectral imaging in identifying Aβ-positive patients within a clinical cohort from a memory clinic.

Methods: A prospective cross-sectional cohort study was conducted between January 2023 and May 2024 at a single memory clinic in Sweden. The study recruited 57 patients (35 Aβ-positive and 22 Aβ-negative) who underwent lumbar puncture as part of their diagnostic workup for cognitive complaints. Retinal hyperspectral images were captured from all participants at the time of their lumbar puncture and again 2-4 weeks later. Data was collected from five anatomical regions of the retina (Superior 1, Superior 2, Inferior 1, Inferior 2, and the center of the Fovea).The main outcome was the Aβ status (Aβ-positive or Aβ-negative). Catboost machine learning models were trained on hyperspectral imaging data to predict Aβ status. A nested cross-validation approach was used to train and evaluate classification models. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.

Results: The best-performing model used the combination of regions Superior 1, Superior 2, and center of the fovea, achieving a mean AUC of 0.77 (0.05), mean accuracy of 0.66 (0.03), and mean sensitivity of 0.73 (0.13) and mean specificity of 0.55 (0.12). Performance was consistent across outer folds. Models using all five regions or less-informative combinations yielded lower and more variable results.

Conclusions: Retinal hyperspectral imaging combined with the Catboost algorithm demonstrated significant potential as a non-invasive biomarker for detecting Alzheimer's disease in a consecutive clinical cohort. Further studies should validate these findings in larger, more diverse populations and explore the integration of hyperspectral imaging with other diagnostic modalities. Limited sample size and imaging constraints highlight the need for validation in diverse clinical settings.

Trial registration: ClinicalTrials.gov, ID: NCT05604183 (registration date: 2022-10-27).