The Journal of Prevention of Alzheimer's Disease最新文献

Background: The cognitive consequences of cerebral microbleeds (CMBs) in memory clinic population with a diverse cognitive spectrum remain unclear.

Objectives: This study aimed to investigate how CMBs at different locations are associated with cognitive performance in a memory clinic population and whether these associations are independent of related small vessel disease (SVD) markers.

Design: A cross-sectional study.

Setting: A university hospital memory clinic. Data were collected from December 2004 to September 2014 and analyzed in June 2024.

Participants: A total of 910 participants, composed of 64 individuals with subjective cognitive decline, 399 with mild cognitive impairment (MCI), 339 with Alzheimer disease dementia (AD), 58 with vascular dementia and mixed dementia, and 50 with other types of dementia, were included.

Main outcomes and measures: Summary scores for global cognition, memory, language, visuospatial function, and executive function measured by the Consortium to Establish a Registry for Alzheimer's disease (CERAD) neuropsychological battery.

Results: In the overall population, the presence of deep/infratentorial CMBs was significantly associated with executive dysfunction; however, this association was attenuated after adjusting for related SVD markers. When stratified by diagnostic subgroup, strictly lobar CMBs were significantly associated with impairments in global cognition and memory in the MCI group, independent of related SVD markers. In contrast, no significant associations between CMBs and cognitive domains were observed in the AD group.

Conclusions: These findings, based on memory clinic population with a broad cognitive spectrum, suggest that CMBs, depending on their location, may have different implications for cognitive function.

Alzheimer's disease remains one of the most complex and contested domains in biomedicine, characterized by fragmented findings, competing hypotheses, and limited translational success. We propose that AI can offer not just technical acceleration but a deeper epistemic contribution: reconciliation. Rather than optimizing predictive performance or replicating existing assumptions, the goal is to align disparate data, methods, and mechanistic insights into coherent models that explain how the disease emerges, progresses, and can be treated. This approach centers on digital twins, not as monolithic models, but as flexible, testable architectures grounded in homeostasis, destabilization, and multiscale coherence. Through an iterative, interoperable AI architecture, digital twins integrate evidence, resolve contradictions, and highlight where critical gaps remain. This framework moves beyond incremental progress within the prevailing model to catalyzing a paradigm shift in how Alzheimer's is understood. Reconciliation, in this sense, is not a method but a guiding principle for transforming both the science and its applications.

Breakthroughs in biomarkers for amyloid (A), tau (T), and neurodegeneration (N) have advanced the prospects of accurate Alzheimer's disease (AD) diagnosis. However, presence of pathology does not always translate into clinical expression and there are still clear knowledge gaps as to whether someone with AD biological indicators will lead to clinically apparent disease necessary to warrant drug treatments that carry toxicity risk. Reliance on decades-old assessment tools inhibits detection and monitoring at preclinical and early disease stages when new treatments could prove most effective. Evidence has accumulated that digital measures provide accurate detection of disease at early stages. We call for a re-evaluation of the A/T/N diagnostic framework, with digital evaluation measures complementing non-AD specific neurodegeneration markers, and even potentially replacing those non-specific to AD, to provide a clinically relevant feature critical to clinical trial advances and treatment decisions. Achieving this will only be possible if further research into novel digital evaluation tools is pursued with the same support and consideration as amyloid and tau.

There is considerable opportunity to fast-track novel treatments for Alzheimer's Disease (AD) through repurposing of existing licensed medications as a way of complementing ongoing drug discovery efforts. Given the complex interplay between AD neuropathological mechanisms, there is also a strong rationale that treatment benefits may be enhanced by examining combinations of treatments to identify potential synergies that would address multiple disease-modifying mechanisms. A Delphi consensus programme combined with a pragmatic analysis of primary care data has identified a series of individual and combined therapies that warrant further investigation in pre-clinical and clinical trials. These include treatments which target well-established neurodegeneration pathways and more explorative agents, including hormonal and anti-infective agents, which align to emerging hypotheses relating to endocrine and immune pathways in AD. Whilst caution is critical when considering combined therapy due to the risks of interaction and polypharmacy, this study provides valuable indications of potential synergistic drug pairs that warrant further investigation.

Background: Cholinergic basal forebrain (cBF) atrophy is a critical early marker of neurodegeneration in Alzheimer's disease (AD). While cBF degeneration is linked to cognitive decline, the role of cortical thinning in this process, especially during the preclinical phase of AD, remains underexplored. Additionally, the impact of amyloid-β (Aβ) pathology on these relationships warrants further examination.

Methods: We analyzed longitudinal structural MRI and PIB-PET data from 230 cognitively normal older adults enrolled in the Harvard Aging Brain Study, with a mean follow-up of six years. cBF volume and cortical thickness were quantified using FreeSurfer. Cognitive performance was assessed with the Preclinical Alzheimer Cognitive Composite-5 (PACC5). Linear mixed-effects models were used to investigate the longitudinal associations between cBF atrophy, cortical thinning, and cognitive decline. Mediation analyses explored whether cortical thinning mediated the relationship between cBF degeneration and cognitive decline, and the moderating role of Aβ burden was examined.

Results: Progressive cortical thinning in multiple cognition-related regions was significantly associated with cBF atrophy. Mediation analysis revealed that cortical thinning accounted for approximately 44 % of the relationship between cBF degeneration and cognitive decline. These associations were more pronounced in individuals with elevated Aβ, suggesting a synergistic interaction between amyloid pathology and cholinergic system degeneration.

Conclusions: Our findings suggest that cBF atrophy accelerates cognitive decline through its impact on cortical thinning, with Aβ pathology further exacerbating these effects. These results highlight the potential of cBF and cortical thinning as early biomarkers for preclinical AD and underscore the importance of targeting cholinergic dysfunction in early intervention strategies.

Alzheimer's disease (AD) clinical trials continue to face major hurdles in patient identification, resulting in delayed timelines, underpowered studies, and escalating costs. This perspective explores these challenges through the lens of a memory clinic, where hundreds of cases often translate into only a handful of enrollments. We highlight the potential of artificial intelligence (AI) to address this gap by powering chatbots for awareness and pre-screening, decision support tools for case identification, and algorithms for matching patients to trial-specific criteria, automating and streamlining the recruitment process. We also examine critical considerations in developing such AI-driven tools, including data standardization, privacy protections, and ethical safeguards. With thoughtful implementation, these innovations could accelerate more inclusive and efficient AD trials, ultimately bringing therapies to patients faster.

Artificial intelligence (AI), often seen as a harbinger of future innovation, also presents a dilemma: it can perpetuate existing human biases. However, this issue is not novel or unique to AI. Humans have long been the progenitors of biases, and AI, as a product of human creation, often mirrors these inherent tendencies. Here, we present a perspective on the development and use of AI, recognizing it as a tool influenced by human input and societal norms, rather than an autonomous entity. Modern efforts to technologically enabled data collection approaches and model development, particularly in the context of Alzheimer's disease and related dementias, can potentially reduce bias in AI. We also highlight the importance of data sharing from existing legacy cohorts to help accelerate ongoing AI model development efforts for greater scientific good and clinical care.

Background: The relationship between alcohol consumption and cerebral small vessel disease (CSVD) remains uncertain, particularly regarding drinking patterns and beverage types. We investigated how total alcohol intake, drinking frequency, and beverage-specific consumption are associated with CSVD burden using cross-sectional data.

Methods: We included 27,326 UK Biobank (UKB) participants with MRI data, among whom 21,130 were current drinkers with full alcohol intake data. Alcohol consumption (frequency and beverage type) was self-reported. CSVD burden was measured via normalized white matter hyperintensity volume (WMHV) on T2-FLAIR MRI. Multivariable linear regression models adjusted for demographics, lifestyle, and vascular risk factors were used to examine associations.

Results: Compared with non-drinkers, alcohol consumers had greater CSVD burden (Beta = 0.07; 95 % CI, 0.00-0.15). Among them, higher drinking frequency (≥5 times/week) was associated with increased CSVD burden (Beta = 0.10; 95 % CI, 0.07-0.13). High consumption of red wine, white wine/champagne, and spirits (≥7 servings/week) correlated positively with CSVD burden. In contrast, low-to-moderate beer/cider intake (≤3 servings/week) was inversely associated with burden. A dose-response relationship between total ethanol intake and CSVD burden was observed, with minimal intake (<1.97 g/day) showing a mild negative association, and higher levels increasing risk.

Conclusion: Greater frequency and volume of alcohol intake, especially from wine and spirits, are linked with higher CSVD burden. Conversely, low beer/cider consumption may be inversely associated with CSVD burden. These findings underscore the importance of moderating alcohol consumption to maintain cerebrovascular health.