Alzheimer''s and Dementia: Translational Research and Clinical Interventions最新文献

Abstract: Recent clinical trials on slowing dementia progression have led to renewed focus on finding safer, more effective treatments. One approach to identify plausible candidates is to assess whether existing medications for other conditions may affect dementia risk. We conducted a systematic review to identify studies adopting a data-driven approach to investigate the association between a wide range of prescribed medications and dementia risk. We included 14 studies using administrative or medical records data for more than 130 million individuals and 1 million dementia cases. Despite inconsistencies in identifying specific drugs that may modify Alzheimer's or dementia risk, some themes emerged for drug classes with biological plausibility. Antimicrobials, vaccinations, and anti-inflammatories were associated with reduced risk, while diabetes drugs, vitamins and supplements, and antipsychotics were associated with increased risk. We found conflicting evidence for antihypertensives and antidepressants. Drug repurposing for use in dementia is an urgent priority. Our findings offer a basis for prioritizing candidates and exploring underlying mechanisms.

Highlights: ·We present a systematic review of studies reporting association between drugs prescribed for other conditions and risk of dementia including 139 million people and 1 million cases of dementia.·Our work supports some previously reported associations, for example, showing decreased risk of dementia with drugs to treat inflammatory disease and increased risk with antipsychotic treatment.·Antimicrobial treatment was perhaps more surprisingly associated with decreased risk, supportive of recent increased interest in this potential therapeutic avenue.·Our work should help prioritize drugs for entry into adaptive platform trials in Alzheimer's disease and will serve as a useful resource for those investigating drugs or classes of drugs and risk of dementia.

Introduction: Observational studies have revealed a close relationship between reduced bone mineral density (BMD) and Alzheimer's disease (AD) risk. The receptor activator of nuclear factor kappa-B ligand (RANKL) and osteoprotegerin (OPG) system, pivotal in regulating bone metabolism, has been implicated in brain function, but the causal impact on AD risk remains unclear.

Methods: We employed bi-directional Mendelian randomization (MR) and multivariable MR (MVMR) approaches to elucidate the effect of blood soluble RANKL (sRANKL) and OPG levels on AD, assessing whether this influence was independent of BMD and inflammation. Three distinct AD genome-wide association study (GWAS) data sets from the International Genomics of Alzheimer's Project (IGAP), UK Biobank (UKB), and FinnGen were utilized. Summary-level data on blood sRANKL and OPG were sourced from deCODE Genetics.

Results: Genetically predicted per standard deviation (SD) increase in blood sRANKL levels was significantly associated with a reduced risk of AD across all three AD GWAS data sets (IGAP: odds ratio [OR] = 0.82, 95% confidence interval [CI] = 0.72-0.94, p = 0.004; UKB: OR = 0.85, 95% CI = 0.78-0.91, p < 0.001; FinnGen: OR = 0.83, 95% CI = 0.73-0.94, p = 0.004). No significant causal relationship was observed between OPG levels and AD. In addition, there was no causal impact of AD on the blood levels of sRANKL and OPG. MVMR results showed that the inverse association between sRANKL and AD risk persisted after adjusting for BMD and interleukin-1α and chemoattractant protein-1.

Discussion: Our study provides evidence that elevated sRANKL levels are causally linked to a reduced risk of AD, independent of BMD and inflammation. These findings enhance our understanding of the complex interactions between bone metabolism and AD.

Highlights: Blood soluble receptor activator of nuclear factor kappa-B ligand (sRANKL) levels are linked to a reduced risk of Alzheimer's disease (AD).The association between sRANKL levels and AD is independent of bone mineral density (BMD) and inflammation.No causal link exists between blood osteoprotegerin levels and AD.AD does not affect blood levels of sRANKL or osteoprotegerin.

Introduction: Forty-five percent of Alzheimer's disease (AD) cases may have been preventable through protective factors. Reserve, resilience, and resistance share common neurocognitive adaptive processes, acting through protective mechanisms. In this article we propose the development and validation of a new scale, called dynamic Neurocognitive Adaptation, developed in this direction.

Methods: We included 815 participants (50% women; 65+ years inclusive of age), divided into two subsamples for exploratory and confirmatory factor analysis. Our initial scale was composed of 30 items, investigating seven dimensions, explored by a 5-point Likert scale reflecting the frequency of activities, for seven time windows.

Results: Our final scale had 20 items divided among four dimensions: physical, cognitive, creative, and social. There were no issues related to multi-collinearity or non-collinearity. Kaiser-Meyer-Olkin (KMO) = 0.80 and Bartlett's test of sphericity indicated all values ≤0.01; Cronbach's alpha = 0.83.

Discussion: We have validated a reliable, novel, easy to complete, and comprehensive scale to assess lifetime behaviors, which can be applied in research on AD risk reduction, mild cognitive impairment, and in clinical practice.

Highlights: Reserve, resistance, and resilience share similar adaptive mechanisms.Dynamic Neurocognitive Adaptation is a new scale to assess lifetime protective factors.Dynamic Neurocognitive Adaptation is a reliable, novel, and easy to complete scale.This approach can characterize specific life stages that are ripe for risk-reduction interventions.Our scale can be used to personalize health recommendations in aging.

Despite some skepticism regarding the amyloid hypothesis, there is growing evidence that clearing amyloid by targeting specific species of amyloid (plaque, oligomers, fibrils, and protofibrils) for removal has therapeutic benefits. Specifically, there is growing evidence that, in mild cognitive impairment and mild dementia due to Alzheimer's disease (AD), robust and aggressive removal of amyloid can slow cognitive decline as measured by global instruments, composite measures, and cognitive testing. Furthermore, clinical efficacy signals coupled with clear biomarker changes provide the first evidence of disease modification. This effect seems to be in addition to symptomatic treatments and opens speculation that the effect of anti-amyloid monoclonal antibodies might be clinically meaningful through symptomatic amelioration that is a result of disease modification.

Highlights: Clearance of brain amyloid plaques may lead to a clinical benefit in patients with early AD.Aggregated Aβ may play a role in both disease expression and progression.Anti-amyloid monoclonal antibodies might be clinically meaningful through symptomatic amelioration resulting from disease modification.

Introduction: A lack of national consensus on the roles and responsibilities of Australian memory and cognition clinics contributes to the large variability seen across services. The introduction of guidelines and a quality assessment framework could facilitate greater harmonization and quality improvements.

Methods: We used a modified Delphi process to develop the guidelines. Pilot clinics completed a self-assessment, case-note audit, and review meeting to evaluate their service against the guidelines.

Results: The final guidelines included 160 standards on 14 different topics. Standards around maximum waiting times for an assessment and minimum post-diagnostic care responsibilities were particularly controversial. Seven clinics participated in the pilot. On average, clinics achieved 56% of standards (range of 18% to 87%).

Discussion: The Memory and Cognition Clinic Guidelines form the first step toward greater harmonization and quality improvements. Key learnings from the clinics' feedback included reducing the number of secondary standards and streamlining data collection with the national dementia clinical quality registry.

Highlights: We developed and implemented the first national consensus-based best-practice guidelines for memory and cognition clinics in Australia.The guidelines are based on consultation with 125 Australian health professionals and 89 Australians living with dementia and care partners.First-time national agreement on standards around maximum waiting times for an assessment and minimum post-diagnostic care requirements is presented in the guidelines.The guidelines were implemented in seven memory and cognition clinics from five different states.Clinicians' feedback included: reducing the number of secondary standards to increase conciseness and practicability should be considered for future iterations.

Dementia affects 55 million people globally, with the number projected to triple by 2050. Statins, widely prescribed for cardiovascular benefits, may also have neuroprotective effects, although studies on their impact on dementia risk have shown contradictory results. In this systematic review and meta-analysis, we searched PubMed, Embase, and Cochrane following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We assessed the risk of dementia, Alzheimer's disease (AD), and vascular dementia (VaD), with subgroup analyses by gender, statin type, and diabetes status. Fifty-five observational studies including over 7 million patients were analyzed. Statin use significantly reduced the risk of dementia compared to nonusers (hazard ratio [HR] 0.86; 95% confidence interval [CI]: 0.82 to 0.91; p < 0.001). It was also associated with reduced risks of AD (HR 0.82; 95% CI: 0.74 to 0.90; p < 0.001) and VaD (HR 0.89; 95% CI: 0.77 to 1.02; p = 0.093). Subgroup analyses revealed significant dementia risk reductions among patients with type 2 diabetes mellitus (HR 0.87; 95% CI: 0.85 to 0.89; p < 0.001), those with exposure to statins for more than 3 years (HR 0.37; 95% CI: 0.30 to 0.46; p < 0.001), and populations from Asia, where the greatest protective effect was observed (HR 0.84; 95% CI: 0.80 to 0.88). Additionally, rosuvastatin demonstrated the most pronounced protective effect for all-cause dementia among specific statins (HR 0.72; 95% CI: 0.60 to 0.88). Our findings underscore the neuroprotective potential of statins in dementia prevention. Despite the inherent limitations of observational studies, the large dataset and detailed subgroup analyses enhance the reliability of our results. Future randomized clinical trials are necessary to confirm these findings and enlighten clinical guidelines.

Highlights: Largest meta-analysis to date on statins and dementia risk, including 55 studies and more than 7 million patients.Statin use linked to lower risks of all-dementia, AD, and VaD.Numerous significant subgroup results highlight statins' diverse neuroprotective effects.Findings support statins as a public health tool, especially in low-income countries.Future research should explore the impact of statins across diverse patient populations.

Introduction: Understanding how a research sample compares to the population from which it is drawn can help inform future recruitment planning. We compared the Wisconsin Alzheimer's Disease Research Center (WADRC) participant sample to the Wisconsin state population (WI-pop) on key demographic, social exposome, and vascular risk measures.

Methods: The WADRC sample included 930 participants. Population statistics were estimated using several national and state data sources. We compared WADRC to WI-pop for two age groups, 45-64 years and ≥65 years, separately.

Results: Compared to WI-pop, WADRC participants were older and included more women, more Black and American Indian individuals, and fewer Hispanic and Asian individuals. WADRC participants had higher levels of educational attainment, consisted of smaller proportions living in rural areas and disadvantaged neighborhoods, and showed lower vascular risks. Greater differences between WADRC and WI-pop were found for most metrics in the ≥65 group compared to the 45-64 group.

Discussion: The findings revealed opportunities to increase enrollment from the Hispanic/Latino and Asian American populations, to include participants from a broader range of educational backgrounds, and to enroll more residents from rural areas and disadvantaged neighborhoods, which may lead to a broader distribution of cardiovascular risk factors. Expanding sociodemographic and health profiles represented in the participant candidate pool for study selection and including those who are underrepresented in research may potentially reduce selection bias but not eliminate it. Statistical approaches can be applied to address bias and generalize findings from a study sample to its target population by adjusting for their differences in the joint distribution of covariates. Although research centers have different regional populations and specific recruitment focuses for scientific reasons, evaluating their participant characteristics may help plan engagement efforts to improve the inclusion of underrepresented groups and collaboratively support generalizable research nationwide.

Highlights: We compared the characteristics of Wisconsin Alzheimer's Disease Research Center (WADRC) participants with the Wisconsin population.Metrics of comparison included demographics, social exposomes, and vascular risks.WADRC participants are different from the Wisconsin population.We explored the implications and causes of the differences.We discussed strategies for engaging and recruiting underrepresented groups.

Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

Methods: The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non-Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD).

Results: 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog.

Discussion: The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies.

Highlights: Growing genomic datasets require advanced tools to identify genetic loci in sequencing.Deep-Block, a novel AI framework, was used to process large-scale ADSP WGS data.Deep-Block identified both known and novel AD-associated genetic loci.rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant.The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.