Early Alzheimer Research Articles

Amyloid-Related Imaging Abnormalities in Clinical Trials of Gantenerumab in Early Alzheimer Disease

ImportanceData from 2 phase 3 studies of gantenerumab, GRADUATE I/II, and their open-label extensions represent a resource to further characterize amyloid-related imaging abnormalities (ARIA), including long-term sequelae.ObjectivesTo describe the characteristics of ARIA and risk factors and clinical consequences of ARIA-edema (ARIA-E).Design, Setting, and ParticipantsSecondary data collection from the GRADUATE I/II phase 3 randomized, double-blind, placebo-controlled, 116-week parallel-group studies and their open-label extensions, including PostGraduate, with up to 210 (mean, 125) weeks of total gantenerumab treatment were conducted between 2018 and 2023. The study included multicenter trials at 288 sites across 30 countries. GRADUATE I/II enrolled 985 and 980 participants, respectively, with early symptomatic Alzheimer disease (AD) and amyloid-beta (Aβ) pathology who were aged 50 to 90 years. PostGraduate enrolled 1382 participants (671 previously randomized to gantenerumab). Data were analyzed from November 2, 2022, to October 10, 2023.InterventionsGRADUATE I/II participants were randomized 1:1 to gantenerumab or placebo. Nine-month uptitration was used to mitigate ARIA risk.Main outcomes and measuresPostbaseline safety monitoring, including brain magnetic resonance imaging (MRI) findings, and adverse events and cognitive assessments.ResultsThe safety-evaluable MRI population of GRADUATE I/II comprised 1939 participants (mean age, 71.7 years; 1105 female [57.0%]). Severity of AD–related Aβ neuropathology (lower cerebrospinal fluid [CSF] Aβ42, hazard ratio [HR] for CSF Aβ42: 0.4; 95% CI, 0.2-0.7) and comorbid cerebrovascular pathology (Fazekas score: HR, 1.6; 95% CI, 1.3-2.0; total superficial siderosis count: HR, 1.9; 95% CI, 1.3-2.6; total microhemorrhage count: HR, 1.3; 95% CI, 1.0-1.5) may be important baseline risk factors for ARIA-E, in addition to apolipoprotein E (APOE) ε4 status (APOE ε4 heterozygous carrier: HR, 2.0; 95% CI, 1.4-2.8 and APOE ε4 homozygous carrier: HR, 4.7; 95% CI, 3.2-6.7). At the group level, ARIA-E did not impact long-term cognitive and functional performance (relative difference in adjusted means for Clinical Dementia Rating–Sum of Boxes was −9% in pooled GRADUATE analysis at week 116 and when censored at first ARIA-E). While taking gantenerumab, ARIA-E and ARIA-hemosiderin occurred in 24.9% (247 of 993) and 22.9% (227 of 993) participants, respectively; first ARIA-E occurred by week 64 in 86.2% (213 of 247) of participants with ARIA-E. Narratives are provided for all serious symptomatic ARIA-E cases.Conclusions and RelevanceThese results show that in addition to APOE ε4 allele count, severity of Aβ neuropathology and comorbid cerebrovascular pathology may be relevant for clinicians prescribing anti-Aβ monoclonal antibodies for early AD and developing individualized safety monitoring plans. Evaluation of these risk factors in other anti-Aβ monoclonal antibodies is recommended.Trial registrationsClinicalTrials.gov Identifiers: NCT03444870, NCT03443973, NCT04374253.

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study

Background Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. Objective This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. Methods The study included 28 participants from the Boston University Alzheimer’s Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants’ experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform. Results Word count analysis revealed a generally positive sentiment toward the digital platform, with “like,” “well,” and “good” being the most frequently mentioned positive terms. However, terms such as “problem” and “hard” indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from –1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort. Conclusions The study highlights the importance of understanding older adults’ attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.

Discovery of Urinary Biomarker: Urine Amyloid Beta as a Non-Invasive Biomarker for Early Alzheimer’s Disease and Cognitive Impairment - A Study of 24 Patients with Cognitive Decline

Discovery of Urinary Biomarker: Urine Amyloid Beta as a Non-Invasive Biomarker for Early Alzheimer’s Disease and Cognitive Impairment - A Study of 24 Patients with Cognitive Decline

Functional connectivity and cognitive decline: a review of rs-fMRI, EEG, MEG, and graph theory approaches in aging and dementia

Age-related changes in the brain cause cognitive decline and dementia. In recent year’s researchers’ extensively studied the relationship between age related changes in functional connectivity (FC) in dementia. Those studies explore the alterations in FC patterns observed in aging and neurodegenerative disorders using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI), electroencephalography (EEG) coherence analysis, and graph theory approaches. The current review summarizes the findings, which highlight the impact of FC changes on cognitive decline and neurodegenerative disease progression using these techniques and emphasize the importance of understanding neural alterations for early detection and intervention. The findings underscore the complexity of cognitive aging and the need for further research to differentiate normal aging from pathological conditions. rs-fMRI is essential for studying brain changes associated with aging and pathology by capturing coherent fluctuations in brain activity during rest, providing insights into FC without task-related confounds. Key networks such as the default mode network and front parietal control network are crucial in revealing age-related connectivity changes. Despite challenges like neurovascular uncoupling and data complexity, ongoing advancements promise improved clinical applications of rs-fMRI in understanding cognitive decline across the lifespan. EEG and magnetoencephalography (MEG) are cost-effective techniques with high temporal resolution, allowing detailed study of brain rhythms and FC. Recent studies highlight EEG/MEG’s potential in early Alzheimer’s disease detection by identifying changes in brain connectivity patterns. Integration of machine learning techniques enhances diagnostic accuracy, although further validation and research are necessary. Graph theory offers a quantitative framework to analyze cognitive networks, identifying distinct topological differences between healthy aging and pathological conditions. Future research should expand exploration into diverse neurodegenerative disorders beyond mild cognitive impairment, integrating neuroimaging techniques to refine diagnostic precision and deepen insights into brain function and connectivity.

Mild cognitive impairment and early Alzheimer's disease eligibility for disease modification therapies in a tertiary centre for cognitive disorders: A simultaneous real-word study on aducanumab and lecanemab.

The Food and Drug Administration approved two disease-modifying treatments (DMTs) for Alzheimer's disease (AD), aducanumab and lecanemab, with limited clinical impact but significant biomarker changes. Identifying suitable candidates for these DMTs outside randomized clinical trials (RCTs) remains uncertain. This cross-sectional study, conducted in an Italian tertiary centre for cognitive disorders, aimed to evaluate how the RCT eligibility criteria for DMT treatments applies to participants with early AD. The broader Cummings etal. (Journal of Prevention of Alzheimer's Disease, 2021, 2023) criteria and the clinical differences between DMT candidates were also assessed. The study involved 408 participants (mean age 71.1 ± 8.5 years, 48% male) with a clinical diagnosis of mild cognitive impairment (161/408, 39.5%) or mild dementia (247/408, 60.5%). Amongst them, 169 individuals (41%) showed positive AD pathology biomarkers. Eligibility RCT assessment revealed 14 patients eligible for aducanumab (3.43% of 408) and 28 for lecanemab (6.86% of 408). Following Cummings' real-world criteria, aducanumab eligibility increased to 9.56%, whereas lecanemab eligibility rose to 8.33%. Applying selection criteria to only the amyloid positive (169 out of 408), the selection for DMTs was 8.3% for aducanumab and 16.5% for lecanemab. Amongst subjects diagnosed with mild AD and mild cognitive impairment in a tertiary centre for cognitive disorders, only a small percentage of patients using RCT diagnostic criteria are eligible for DMT. The application of Cummings criteria strongly increased the DMT candidates. Nevertheless, the majority of patients with cognitive disorders have been excluded from DMTs approved so far.

Lecanemab for Alzheimer's Disease

Lecanemab is a novel drug developed by Sisai and Biogen aimed at treating Alzheimer’s Disease. Alzheimer’s Disease is characterized by the misfolding and accumulation of neurotoxic amyloid-beta plaques in the brain, leading to neuronal cell death and brain shrinkage. Lecanemab contains monoclonal antibodies that have a high affinity for soluble and insoluble forms of amyloid-beta peptides. These antibodies bind, neutralize, and eliminate amyloid-beta aggregates to slow Alzheimer’s Disease progression.2 Phase II dose-finding trial for Lecanemab conducted with 854 participants with early Alzheimer’s Disease demonstrated no significant difference between Lecanemab and placebo groups at the 12-month mark. However, a 27% reduction in clinical decline in the Lecanemab group was seen after 18 months compared to the placebo group. Adverse effects of Lecanemab include transient fever and asymptomatic fluid formation in the brain. The drug was approved by the Food and Drug Administration and launched in the United States on January 18, 2023. Manufacturing and marketing applications for Lecanemabwere also recently submitted in Europe, Japan, and Canada1 With FDA approval, the drug is currently priced at 26,500 USD per year, with Medicare pledging to cover 80% of the cost.3

Costs of Care in Relation to Alzheimer's Disease Severity in Sweden: A National Registry-Based Cohort Study.

The advancement of diagnostic and therapeutic interventions in early Alzheimer's disease (AD) has demanded the economic evaluation of such interventions. Resource utilization and cost estimates in early AD and, more specifically, the amyloid-positive population are still lacking. We aimed to provide cost estimates in AD in relation to disease severity and compare these with the control population. We also aimed to provide cost estimates for a subset of the AD population with both clinical diagnosis and amyloid-positive confirmation. This was a retrospective longitudinal analysis of resource utilization using data from national registries. A cohort from the national Swedish registry for cognitive/dementia disorders (SveDem) includes all clinically diagnosed AD between 2013 and 2020. The study population included 31,951 people with AD and 63,902 age- and sex-matched controls (1:2). The population was followed until death, the end of December 2020, or 2years from the last clinic visit. Direct medical and social costs were estimated from other national registries. Direct medical costs include costs for medications and inpatient and outpatient clinical visits. Direct social costs include costs for institutionalization, home care, short-term care, support for daytime activities, and housing support. Mean annual costs and 95% confidence intervals were obtained by bootstrapping, presented in 2021 Swedish Krona (SEK) (1 SEK=0.117 USD, 1 SEK=0.0985 EUR in 2021), and disaggregated by AD severity, cost component, sex, age group, and care setting. Mean annual costs for individuals with clinically diagnosed AD were SEK 99,906, SEK 290,972, SEK 479,524, and SEK 795,617 in mild cognitive impairment (MCI), mild, moderate, and severe AD. The mean annual costs for the population with both clinical diagnosis and amyloid-positive AD confirmation (N=5610) were SEK 57,625, SEK 179,153, SEK 333,095, and SEK 668,073 in MCI, mild, moderate, and severe AD, respectively. The mean annual costs were higher in institutionalized than non-institutionalized patients, females than males, and older than younger age groups. Inpatient and drug costs were similar in all AD severity stages, but outpatient costs decreased with AD severity. Costs for institutionalization, home care, support for daytime activities, and short-term care increased with AD severity, whereas the cost of housing support decreased with AD severity. This is the first study estimating annual costs in people with AD from MCI to severe AD, including those for the amyloid-positive population. The study provides cost estimates by AD severity, cost components, care settings, sex, and age groups, allowing health economic modelers to apply the costs based on different model structures and populations.

Early Alzheimer's disease diagnosis via handwriting with self-attention mechanisms.

The neurodegenerative diseases like Alzheimer's disease (AD) can result in progressive decline in both cognitive functions and motor skills, which have critical need for accurate early diagnosis. However, current diagnosis approaches primarily rely on timely clinical magnetic resonance imaging (MRI) scans, which impede widely application for potential patients. Leveraging handwriting as a diagnostic tool offers significant potential for identifying AD in its early stages. This study aims to develop an efficient, rapid, and accurate method for early diagnosis of AD by utilizing handwriting analysis, a promising avenue due to its association with compromised motor skills in neurodegenerative diseases. We propose a novel methodology that leverages self-attention mechanisms for the early diagnosis of AD. Our approach integrates data from 25 distinct handwriting tasks available in the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset. The Self-Attention model achieved an accuracy of 94.3% and an F1-score of 94.5%, outperforming other state-of-the-art models, including traditional machine learning and deep learning approaches. Specially, the Self-Attention model surpassed the previous best model, the convolutional neural networks, by approximately 4% in both accuracy and F1-score. Additionally, the model demonstrated superior precision (94.7%), sensitivity (94.5%), and specificity (94.1%), indicating high reliability and excellent identification of true positive and true negative cases, which is crucial in medical diagnostics. Handwriting analysis, powered by self-attention mechanisms, offers significant potential as a diagnostic tool for identifying AD in its early stages, providing an effective alternative to traditional MRI-based diagnosis.

Exploring EEG-Based Biomarkers for Improved Early Alzheimer's Disease Detection: A Feature-Based Approach Utilizing Machine Learning

This paper presents a comprehensive investigation into Electroencephalogram (EEG) signal processing and analysis techniques aimed at enhancing early diagnosis methods for Alzheimer's Disease (AD). Leveraging a dataset that has EEG data of individuals diagnosed with Mild Cognitive Impairment (MCI), AD, Healthy Controls, and the study explores Preprocessing Methods and Feature Extraction Techniques, with machine learning model notably Support Vector Machines (SVM).In the preprocessing phase, a combination of high pass, lowpass, Savitzky–Golay, and median filters are applied, informed by a comprehensive review of filter comparison literature. Feature extraction encompasses three primary categories: ‘Statistical, ‘Frequency Domain’ and ‘Time Domain’. The scope of this work is to explore features in all these three domains and build SVM based model for efficient classification. In our investigation, we achieved a categorization accuracy of 92% through the utilization of statistical features. Employing time domain features resulted in an accuracy of 87%, while frequency domain features also yielded an 87% accuracy rate in our study. The primary objective of this study is that it aims to enhance early AD diagnosis through advanced EEG signal processing and machine learning techniques, focusing on preprocessing methods, feature extraction, and classification accuracy.

News Brief: Blood biomarkers can diagnose early Alzheimer disease in symptomatic patients.

News Brief: Blood biomarkers can diagnose early Alzheimer disease in symptomatic patients.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer’s disease neuropathology

Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD). Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n=291), resilient in both (n=260) or advanced in one/resilient in the other (n=163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

REAL AD-Validation of a realistic screening approach for early Alzheimer's disease.

Early diagnosis is crucial to treatment success. This is especially relevant for Alzheimer's disease (AD), with its protracted preclinical phase. Most health care systems do not have the resources to conduct large-scale AD screenings in middle-aged individuals in need of novel AD treatment options and early, accurate diagnosis. Recent developments in blood-based biomarkers and remote cognitive testing offer novel, cost-effective, and scalable methods to detect cognitive and biomarker changes that may indicate early AD. In research cohorts, promising results have been reported, but these modalities have not been validated in population-based settings. The validation of a realistic screening approach for early Alzheimer's disease (REAL AD) study aims to validate the diagnostic and prognostic performance of the combined use of blood-based biomarkers and remote cognitive testing as a screening approach for early AD employing an existing health care infrastructure (the Swedish Västra Götaland Region Primary Healthcare). REAL AD aims to provide a concrete, individualized diagnostic framework, which could significantly improve AD prognosis. HIGHLIGHTS: In Sweden, most Alzheimer's disease (AD) diagnoses are made in primary care, where access to AD biomarkers is almost non-existent. Most health care systems have limited resources for the screening of middle-aged adults for early evidence of AD pathology. Blood-based biomarkers and remote cognitive testing offer novel, cost-effective, and scalable methods for detecting cognitive and biomarker changes that may indicate early AD. The REAL AD study aims to validate the diagnostic and prognostic performance of blood-based biomarkers and remote cognitive testing as a screening approach for early AD in an existing primary health care infrastructure in the Västra Götaland Region in Sweden. Studies such as REAL AD will play a vital role in helping to move the field toward concrete implementation of biomarkers in AD diagnostic workup at all care levels, eventually providing more comprehensive treatments options for the large and growing AD population, and for those at risk.

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

Plasma phosphorylated tau181 outperforms [18F] fluorodeoxyglucose positron emission tomography in the identification of early Alzheimer disease.

This study was undertaken to compare the performance of plasma p-tau181 with that of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in the identification of early biological Alzheimer disease (AD). We included 533 cognitively impaired participants from the Alzheimer's Disease Neuroimaging Initiative. Participants underwent PET scans, biofluid collection, and cognitive tests. Receiver operating characteristic analyses were used to determine the diagnostic accuracy of plasma p-tau181 and [18F]FDG-PET using clinical diagnosis and core AD biomarkers ([18F]florbetapir-PET and cerebrospinal fluid [CSF] p-tau181) as reference standards. Differences in the diagnostic accuracy between plasma p-tau181 and [18F]FDG-PET were determined by bootstrap-based tests. Correlations of [18F]FDG-PET and plasma p-tau181 with CSF p-tau181, amyloid β (Aβ) PET, and cognitive performance were evaluated to compare associations between measurements. We observed that both plasma p-tau181 and [18F]FDG-PET identified individuals with positive AD biomarkers in CSF or on Aβ-PET. In the MCI group, plasma p-tau181 outperformed [18F]FDG-PET in identifying AD measured by CSF (p = 0.0007) and by Aβ-PET (p = 0.001). We also observed that both plasma p-tau181 and [18F]FDG-PET metabolism were associated with core AD biomarkers. However, [18F]FDG-PET uptake was more closely associated with cognitive outcomes (Montreal Cognitive Assessment, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and logical memory delayed recall, p < 0.001) than plasma p-tau181. Overall, although both plasma p-tau181 and [18F]FDG-PET were associated with core AD biomarkers, plasma p-tau181 outperformed [18F]FDG-PET in identifying individuals with early AD pathophysiology. Taken together, our study suggests that plasma p-tau181 may aid in detecting individuals with underlying early AD.

Analytic Challenges in Clinical Trials of Early Alzheimer’s Disease

Background: The heavily right-skewed data seen in recently reported Alzheimer’s disease (AD) clinical trials influenced treatment contrasts when data were analyzed via the typical mixed-effects model for repeated measures (MMRM). Methods: An MMRM analysis similar to what is commonly used in AD clinical trials was compared versus robust regression (RR) and the non-parametric Hodges–Lehman estimator (HL). Results: Results in simulated data patterned after AD trials showed that imbalance across treatment arms in the number of patients in the extreme right tail (those with rapid disease progression) frequently occurred. Each analysis method controlled Type I error at or below the nominal level. The RR analysis yielded smaller standard errors and more power than MMRM and HL. In data sets with appreciable imbalance in the number of rapid progressing patients, MMRM results favored the treatment arm with fewer rapid progressors. Results from HL showed the same trend but to a lesser degree. Robust regression yielded similar results regardless of the ratio of rapid progressors. Conclusions: Although more research is needed over a wider range of scenarios, it should not be assumed that MMRM is the optimal approach for trials in early Alzheimer’s disease.

Varoglutamstat: Inhibiting Glutaminyl Cyclase as a Novel Target of Therapy in Early Alzheimer's Disease.

Varoglutamstat is a first-in-class, small molecule being investigated as a treatment for early Alzheimer's disease (AD). It is an inhibitor of glutaminyl cyclase (QC), the enzyme that post-translationally modifies amyloid-β (Aβ) peptides into a toxic form of pyroglutamate Aβ (pGlu-Aβ) and iso-QC which post-translationally modifies cytokine monocyte chemoattractant protein-1 (CCL2) into neuroinflammatory pGlu-CCL2. Early phase clinical trials identified dose margins for safety and tolerability of varoglutamstat and biomarker data supporting its potential for clinical efficacy in early AD. Present the scientific rationale of varoglutamstat in the treatment of early AD and the methodology of the VIVA-MIND (NCT03919162) trial, which uses a seamless phase 2A-2B design. Our review also includes other pharmacologic approaches to pGlu-Aβ. Phase 2A of the VIVA-MIND trial will determine the highest dose of varoglutamstat that is safe and well tolerated with sufficient plasma exposure and a calculated target occupancy. Continuous safety evaluation using a pre-defined safety stopping boundary will help determine the highest tolerated dose that will carry forward into phase 2B. An interim futility analysis of cognitive function and electroencephalogram changes will be conducted to inform the decision of whether to proceed with phase 2B. Phase 2B will assess the efficacy and longer-term safety of the optimal selected phase 2A dose through 72 weeks of treatment. Varoglutamstat provides a unique dual mechanism of action addressing multiple pathogenic contributors to the disease cascade. VIVA-MIND provides a novel and efficient trial design to establish its optimal dosing, safety, tolerability, and efficacy in early AD.

A Randomized-Controlled Trial Targeting Cognition in Early Alzheimer's Disease by Improving Sleep with Trazodone (REST).

Alzheimer's disease (AD) is a leading cause of mortality and morbidity among aging populations worldwide. Despite arduous research efforts, treatment options for this devastating neurodegenerative disease are limited. Sleep disturbances, through their link to changes in neural excitability and impaired clearance of interstitial abnormal protein aggregates, are a key risk factor for the development of AD. Research also suggests that the neuroprotective effects of sleep are particularly active during slow wave sleep. Given the strong link between sleep disturbance and AD, targeting sleep in the prodromal stages of AD, such as in mild cognitive impairment (MCI), represents a promising avenue for slowing the onset of AD-related cognitive decline. In efforts to improve sleep in older individuals, several pharmacologic approaches have been employed, but many pose safety risks, concern for worsening cognitive function, and fail to effectively target slow wave sleep. Trazodone, a safe and widely used drug in the older adult population, has shown promise in inducing slow wave sleep in older adults, but requires more rigorous research to understand its effects on sleep and cognition in the prodromal stages of AD. In this review, we present the rationale and study design for our randomized, double-bind, placebo-controlled, crossover trial (NCT05282550) investigating the effects of trazodone on sleep and cognition in 100 older adults with amnestic MCI and sleep complaints.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau positron emission tomography (PET) in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16), logopenic variant Primary Progressive Aphasia (n = 15), and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent magnetic resonance imaging (MRI), tau PET, and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = .30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = .10), tau in other functional networks (R2 = .11-.26), and the magnitude of cortical atrophy (R2 = .20) and amyloid burden (R2 = .09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients one year later. This simple measure could aid the development of a personalized prognostic, monitoring, and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Microglial responses partially mediate the effect of Aβ on cognition in Alzheimer's disease.

Microglial responses are an integral part of Alzheimer's disease (AD) pathology and are associated with amyloid beta (Aβ) deposition. This study aimed to investigate the effects of Aβ and microglial responses on global cognitive impairment. In this longitudinal study, 28 patients with mild cognitive impairment and 11 healthy controls underwent 11C-PK11195 and 11C-Pittsburgh compound B positron emission tomography (PET), structural magnetic resonance imaging scans, and global cognitive ratings at baseline and 2-year follow-up. Correlations between PET uptake and global cognition were assessed. Additionally, the mediation effect of the microglial response on the association between Aβ load and global cognition was assessed. Aβ load and the microglial response were both independently detrimental to global cognitive performance at baseline; however, at 2-year follow-up the association between Aβ load and global cognitive ratings was partially mediated by the microglial response. As AD progresses, the associated microglial response partially mediates the detrimental effect of aggregated Aβ on cognition. This was a longitudinal study of amyloid beta (Aβ), microglial responses, and global cognitive performance. Aβ and microglial responses both affect cognition in early Alzheimer's disease. Microglial response partially mediates the effect of Aβ on cognition in later stages.

Alzheimer's dementia in people with Down syndrome : Results of guideline-assisted expert interviews on healthcare deficits in the diagnostics and treatment as well as solution approaches

People with Down syndrome have agenetically increased risk of developing early onset Alzheimer's dementia. An interview study with healthcare providers, patient representatives and employees in residential and work facilities was conducted to identify deficits in the healthcare process and approaches to overcoming them. In this study 14 semi-structured interviews were conducted and analyzed using qualitative content analysis. Alack of knowledge and experience on the part of medical service providers in dealing with and providing medical care for people with Down syndrome was identified as akey challenge. In addition, the diagnosis of dementia in people with Down syndrome is difficult for various reasons (including lack of appropriate diagnostic tools in standard care and lack of time or financial resources). Doubts were expressed about the efficacy of antidementia medications and the reasons for the increased use of sedatives were discussed. Attentive observation of behavior and involvement of caregivers, regular review and reduction of polypharmacy and the use of alternative behavior modification techniques were mentioned as possible solutions. The identified deficits in the medical care of the target population and the approaches to solving them will be incorporated into the development of health policy recommendations in order to optimize the care situation of those affected in the long term.