Journal of Alzheimer's Disease最新文献

BackgroundWith Alzheimer's disease (AD) presenting an ongoing challenge, innovative treatment methods are essential. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising noninvasive intervention, particularly targeting alpha band oscillations associated with AD-related cognitive decline.ObjectiveThis study aimed to investigate the effects of low-intensity rTMS over posterior cortical areas on alpha band oscillations and memory performance in AD patients compared to age-matched healthy controls.MethodsIn a single-blinded, sham-controlled rTMS-EEG study, we examined 14 amyloid-positive AD patients and 14 age-matched healthy controls. Continuous EEG was recorded at rest (eyes closed) before, during, and after stimulation. During stimulation, participants completed an episodic memory task.ResultsWe were able to demonstrate that during rTMS alpha power increased compared to sham, with a notable 25% increase observed in AD patients. However, comparison of memory performance under the sham and stimulation conditions revealed no significant stimulation effect.ConclusionsThese findings support and extend current knowledge of noninvasive brain stimulation mechanisms. Our results suggest that alpha frequency-tuned rTMS over posterior cortical areas can modulate pathological brain activity in AD patients even at low intensities. Given the limited sample size and moderate effect sizes, results should be interpreted with caution. Nevertheless, our results warrant further studies with long-term EEG-rTMS protocols to evaluate the potential therapeutic benefit.

BackgroundDementia diagnosis is challenging and often delayed. Brain imaging techniques such as single-photon emission computed tomography (SPECT) imaging can help identify subtle changes in brain perfusion. Artificial intelligence methods may support results interpretation for early diagnosis.ObjectiveTo develop and validate multivariate models for the early diagnosis of Alzheimer's disease (AD), using brain perfusion SPECT imaging and interpretable artificial intelligence methods in a real-world clinical setting.MethodsTwo logistic regression models were developed using a training dataset of 420 SPECT scans and tested on an independent clinical dataset of 443 scans. Model 1 was designed to identify abnormal perfusion patterns, while Model 2 identified perfusion changes associated with AD. Input features were extracted from anatomical volumes of interest, with feature selection performed using the Minimum Redundancy Maximum Relevance (MRMR) algorithm.ResultsThe models demonstrated good classification performance using real-world clinical data. Model 1 achieved an area under receiver operator characteristic (AUROC) Curve of 0.89 (Sensitivity 76%, Specificity 87%) in identifying abnormal brain perfusion. Model 2 achieved an AUROC of 0.86 (Sensitivity 87%, Specificity 72%) in identifying AD.ConclusionsMultivariate logistic regression models trained on real-world clinical data show promise as clinical decision support tools for the diagnosis of AD from brain perfusion SPECT imaging. The models use features from clinically relevant brain regions, which enhances interpretability. Future research should focus on expanding model applicability to other dementia types and on prospective evaluation of their utility in improving diagnostic accuracy, consistency, and care pathways in diverse clinical environments.

BackgroundMild cognitive impairment (MCI) is a heterogenous condition which places individuals at higher risk for Alzheimer's disease, yet it is not well understood. Studies of primarily prevalent MCI have identified different subtypes characterized by different neuropsychological profiles, while a recent incident MCI study empirically identified four neuropsychological subtypes (amnestic, dysexecutive, dysnomic, and subtle cognitive impairment (SCI) subtypes).ObjectiveWe aimed to identify whether four distinct neuropsychological subtypes could be empirically derived in a sample of a) incident MCI and b) DSM5 mild neurocognitive disorder (mNCD).MethodsWe used data from the Personality and Total Health Through Life study. Participants were aged 72-78, with a diagnosis of incident MCI (n = 117), and/or mNCD (n = 161). We undertook a cross-sectional cluster analysis on neuropsychological data from participants from four domains: executive, memory, language, and visuospatial.ResultsFor incident MCI, cluster analysis derived four subtypes, (dysexecutive, SCI, mixed dysnomic/visuospatial and mixed dysexecutive/visuospatial). For mNCD, the resulting four cluster solution included dysexecutive, SCI-amnestic/dysnomic, SCI-dysexecutive and mixed/global impairment. Discriminant function analysis revealed that 94% and 91% of MCI and mNCD participants respectively were correctly classified based on the cognitive domain scores, and further analysis confirmed the SCI groups showed reduced cognitive performance compared with matched cognitively unimpaired participants.ConclusionsNeuropsychological subtypes were empirically derived in both incident MCI and mild NCD samples, with both SCI and dysexecutive clusters most reliably detected and consistent with previous studies. The early identification of these MCI/mNCD subtypes may help to identify patient groups for targeted early intervention in clinical settings.

BackgroundAlzheimer's disease (AD) is marked by cognitive decline, depressive symptoms, and gut microbial dysbiosis. Yoga may support cognitive and emotional health while modulating gut microbiota, but integrative clinical evidence is limited.ObjectiveTo evaluate the effects of a 12-week yoga intervention on cognition, depressive symptoms, and gut microbial diversity, composition, and function in Indian patients with mild AD.MethodsIn this hospital-based case-control study, 16 AD patients and 17 cognitively healthy controls (HCs) were recruited at AIIMS, New Delhi. AD diagnosis followed NIA-AA criteria, supported by Montreal Cognitive Assessment (MoCA) and Patient Health Questionnaire-9 (PHQ-9) assessments. AD participants underwent 60-min supervised yoga sessions daily for 12 weeks. Cognitive performance, depressive symptoms, and stool microbiota were assessed pre- and post-intervention. Metagenomic sequencing enabled taxonomic and functional profiling, with alpha diversity, beta diversity (Bray-Curtis distance), and differential abundance analyses performed using standard bioinformatics tools.ResultsYoga was associated with improved cognition (MoCA: 22.33 ± 2.34 → 25.44 ± 2.01; p = 0.001) and reduced depressive symptoms (PHQ-9: 5.78 ± 3.11 → 2.22 ± 1.71; p = 0.007). Alpha diversity remained stable, while beta diversity shifted post-yoga AD samples toward the HC cluster. Beneficial taxa (Faecalibacterium prausnitzii, Roseburia intestinalis, Bifidobacterium, Akkermansia) increased, whereas pro-inflammatory taxa (Collinsella aerofaciens, Klebsiella spp.) decreased. Functional analysis showed partial recovery of metabolic and short-chain fatty acid pathways.ConclusionsA 12-week yoga intervention was associated with cognitive and mood improvements and partial normalization of gut microbial function in mild AD. Larger randomized trials with lifestyle monitoring and multi-omics integration are warranted to confirm causal mechanisms.

BackgroundBehavioral and psychological symptoms of dementia (BPSD) in patients with Alzheimer's disease (AD) may seriously impact caregiver burden and, therefore, quality of care.ObjectiveTo clarify the association of BPSD and BPSD subtypes with caregiver burden and quality of life (QOL) among caregivers of patients with AD in Japan in a multidimensional manner.MethodsThis descriptive, cross-sectional, community-based survey involved administering a web-based questionnaire to live-in caregivers of patients with AD registered with Macromill Inc. BPSD prevalence was measured using the Japanese version of the Neuropsychiatric Inventory-Questionnaire Form. The Japanese version of the Zarit Caregiver Burden Scale (J-ZBI), EQ-5D-5L, and Adult Social Care Outcomes Toolkit for Carers (ASCOT-Carer) were used to assess caregiver burden, health-related QOL, and social care-related QOL, respectively.ResultsOf 705 survey responders, 639 (90.6%) and 66 (9.4%) cared for patients with and without BPSD, respectively. Mean caregiver age was 54.6 years, 56.9% were male, and 84.0% cared for parents or in-laws. In the "with BPSD" group, the J-ZBI score was higher (mean difference [95% confidence interval], 6.7 [4.5, 9.0]; p < 0.001), while EQ-5D-5L and ASCOT-Carer scores were lower (-0.076 [-0.134, -0.018; p = 0.010] and -0.101 [-0.168, -0.033]; p = 0.003, respectively) than in the "without BPSD" group.ConclusionsA significant association between increased caregiver burden and BPSD in patients with AD was demonstrated, which may be associated with decreased caregiver healthcare and social care-related QOL.

BackgroundTechnologies such as assistive devices and social robots show promise in supporting people with dementia and their caregivers. However, their long-term cost-effectiveness remains unclear, and existing health-economic models are limited in capturing the relevant outcomes.ObjectiveThis study aims to conceptualize a health-economic model to assess the potential impact of care technologies in dementia care on lifetime quality of life and care use.MethodsWe summarized an impact pathway of three care technologies and conceptualized a health-economic model to estimate the long-term impact on quality of life and care use, drawing on literature and multidisciplinary expert input.ResultsWe conceptualized a cohort-based Markov state-transition model simulating states of dementia severity progression (mild, moderate, severe), care setting transitions (no formal care, home care, nursing home), and mortality. Intervention effects are modeled through surrogate outcomes such as functional status and caregiver burden associated to care transitions and quality of life.ConclusionsThis model offers a framework for early health technology assessment of assistive technologies in dementia, supporting extrapolation of effects beyond limited trial data. Future work should focus on developing and operationalizing this model, applying it to establish the value of dementia care technologies.

BackgroundSoluble tau oligomers (tauO) are early, synaptotoxic drivers of dysfunction in tauopathies. While selective vulnerability is well documented at the cellular level, emerging evidence suggests that synaptic subtypes may differ in their susceptibility to tau pathology. Still, the key factors that shape synaptic vulnerability to toxic tauO, particularly in humans, remain poorly understood.ObjectiveTo define the synaptic compartments and subtypes most vulnerable to tauO and identify molecular correlates underlying this susceptibility.MethodsSynaptosomes were isolated from cognitively normal human autopsy specimens and acutely challenged with preformed recombinant tauO. Flow cytometry with multiplexed immunophenotyping resolved tauO engagement across intact pre- and postsynaptic compartments and excitatory versus inhibitory subtypes. Functional effects were assessed by microtransplanting synaptic membranes into Xenopus laevis oocytes and recording ligand-gated GABAergic and glutamatergic responses. Complementary LC-MS/MS proteomics of brain-derived tau oligomers (BDTO) from PBS-soluble hippocampal lysates of primary age-related tauopathy (PART) cases were analyzed using SynGO enrichment to identify molecular correlates.ResultsTauO preferentially engaged presynaptic compartments and showed elevated association with GABAergic synapses. Functionally, acute tauO exposure selectively enhanced GABA_AR-mediated responses, with no effect on AMPAR-mediated currents. The PART BDTO interactome was enriched for presynaptic vesicle-associated proteins involved in vesicle cycling and neurotransmitter release, consistent with a presynaptic axis of vulnerability.ConclusionsThis integrative analysis identifies a compartment- and subtype-specific vulnerability of human synapses to tauO, highlighting a presynaptic inhibitory bias as a potential driver of synaptic dysfunction and tau propagation in early-stage tauopathies.

BackgroundThe disparities in Alzheimer's disease and other dementias (ADOD) burden across global regions have been further exacerbated following the COVID-19 pandemic, necessitating urgent systematic research into its changing trends and driving factors.ObjectiveTo evaluate trends and driving factors of ADOD burden over the past 32 years and analyze changes during the COVID-19 pandemic.MethodsData were extracted from the Global Burden of Disease 2021. We used the average annual percentage change to assess ADOD burden trends from 1990 to 2021, and quantified the drivers of burden through decomposition analysis. We further utilized frontier analysis to explore the relationship between socio-demographic index (SDI) and disability-adjusted life-years (DALYs), and employed the Bayesian age-period-cohort (BAPC) model to project DALYs trends for ADOD from 2022 to 2050.ResultsFrom 1990 to 2021, global age-standardized rates (ASRs) of prevalence, incidence, years lived with disability, and DALYs for ADOD showed increasing trends, while the ASRs of death remained stable. Females consistently bore a higher burden than males. However, during the COVID-19 pandemic, global ASRs of all these indicators increased significantly. High-middle and middle SDI regions experienced marked increases across all epidemiological metrics. Decomposition analysis revealed population growth as the primary driver of ADOD burden escalation. BAPC predicted that DALYs in each age group showed varying degrees of upward trends, with the fastest increase in 85-89 years old group.ConclusionsThe COVID-19 pandemic triggered a sharp rise in ADOD burden. As global aging and population growth persist, ADOD burden is likely to escalate, necessitating urgent public health interventions.

BackgroundSubjective cognitive decline (SCD) represents the first early symptomatic stage of Alzheimer's disease (AD).ObjectiveWe aimed to investigate the relationships between features in SCD and to assess the importance of these features in the future development of dementia to inform a targeted management protocol.Methods440 SCD patients underwent neurological and neuropsychological assessments, MRI scans, APOE genotyping, and AD biomarker evaluations. Patients were followed for a median of 10 years. Relationships among features were first assessed univariately, focusing on differences across stratified subgroups. To capture multivariate associations, we applied network analysis using a Markov Random Field. Finally, baseline features were related to dementia progression using an XGboost machine learning model.ResultsWomen comprising 68.9% of the cohort, were generally younger at onset, had lower APOE ε4 prevalence, and differed in neuropsychological performance compared to men. Older patients (age >60) exhibited a higher prevalence of APOE ε4 and cerebral small vessel disease. Patients with depressive symptoms demonstrated lower cognitive performance across multiple domains. Network analysis indicated complex interconnections among gender, cognitive reserve, SCD severity, and depressive symptoms. The XGboost model achieved 74% accuracy in predicting progression to dementia, identifying age at onset, mini-mental state examination scores, and APOE genotype as the most predictive factors.ConclusionsThis study highlights the role of age, gender, APOE genotype, and depressive symptoms in the presentation and progression of cognitive decline. By identifying key predictive features, we propose a personalized management protocol aimed at optimizing care for individuals with SCD.Trial registration number: NCT05569083, registration date: 2019-05-30.

BackgroundAlzheimer's disease (AD) is the leading cause of dementia worldwide, yet long-term persistence with acetylcholinesterase inhibitors remains suboptimal in routine practice.ObjectiveTo compare real-world treatment persistence among patients with mild to moderate AD receiving oral donepezil, rivastigmine capsules, or transdermal rivastigmine patches, and to identify factors influencing discontinuation.MethodsIn this retrospective cohort study, 1062 patients aged ≥65 years with newly diagnosed AD were identified from a hospital registry between 2015 and 2019 and followed through 2021. Treatment persistence was evaluated by duration and 1-year continuation rates. Discontinuation was defined as a prescription gap exceeding 90 days. Multivariable Cox proportional hazards models were used to identify predictors of discontinuation.ResultsPatients receiving donepezil had significantly longer mean treatment duration (3.03 years) and higher 1-year continuation rates (66.2%) than those receiving rivastigmine capsules (1.81 years, 39.9%) or patches (1.43 years, 45.5%). Both rivastigmine formulations were independently associated with greater discontinuation risk (adjusted hazard ratio [aHR] 1.44 and 1.76, respectively; p < 0.001). Participation in a national dementia care program was the strongest protective factor, associated with a 69% lower discontinuation risk (aHR 0.31; p < 0.001). Higher baseline CASI scores, younger age, and milder cognitive impairment predicted greater persistence, whereas adverse events markedly increased discontinuation.ConclusionsDonepezil demonstrated superior real-world persistence compared with rivastigmine. Structured dementia care programs substantially enhanced treatment continuity, underscoring the importance of both pharmacologic choice and system-level support in sustaining long-term therapy in AD.