Alzheimer's Disease - Why We Need Early Diagnosis

Discovery and characterization of novel pyridone and furan substituted ligands of choline acetyltransferase

The key to the management of two devastating diseases, namely Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS) lies in an early diagnosis, which is difficult due to its multifactorial nature. However, a common hallmark of AD and ALS is degeneration of cholinergic system. Choline acetyltransferase (ChAT) has been proposed as a potential target for development of cholinergic-specific biomarker. However, lack of selective, potent, brain permeable molecular probes of ChAT hinder development of ChAT biomarkers. In this study, we have successfully utilised structure-based virtual screening approach and identified two ChAT inhibitors from a database of 1.4 million compounds. The compounds were then subjected to rigorous in vitro characterization. Compound V6 showed Ki value of 11 μM and IC50 value of 21.73 μM, while V15 showed Ki and IC50 values of 4.5 and 9.42 μM, respectively for ChAT enzyme. V6 and V15 showed good solubility of 0.21 mg/mL and 0.17 mg/mL respectively and cytotoxicity analysis indicated no toxicity. We also performed a 200 ns molecular dynamics simulation, which revealed the intricate interaction dynamics for V6 and V15 with ChAT binding pocket. Moreover, the Tanimoto similarity analysis indicated the novelty and structural diversity of the hits. In conclusion, these validated hits provide a platform to develop potent, selective, blood-brain barrier permeable small molecules as chemical probes of ChAT or as Positron Emission Tomography tracer for early diagnosis and/or in vivo monitoring of the effect of new therapeutic candidates in spectrum of neurodegenerative disorders, in which cholinergic deficit is one of the hallmarks.

Anticipated Responses to Genetic Testing for Alzheimer's Disease Susceptibility among Latinos in Northern Manhattan

Alzheimer's disease (AD) is a debilitating neurodegenerative illness that has become a growing concern for older adults. As such, apolipoprotein E (APOE) genetic testing has become more commonly used to identify individuals' susceptibility to AD. An underrepresented population in AD research, Latinos will be disproportionately affected by AD in the coming decades. To better aid efforts in education and genetic risk counseling for Latino populations, we must first understand the anticipated psychological and behavioral consequences of APOE genetic risk counseling. We conducted semi-structured interviews with 216 Latinos between the ages of 40 and 64 (average age = 53 years) in northern Manhattan to ascertain their hypothetical reactions to learning that they had a higher risk of developing AD compared to other Latinos within their community. Responses were categorized as emotional, practical, and mixed responses. Among our sample, women were more likely to anticipate an emotional response to hearing that they had a higher risk of AD, and participants above the age of 60 were more likely to anticipate disclosing their risk information to immediate family members. Findings support the tailoring of genetic risk counseling sessions across different ethnic groups, genders and age groups. Future work may include the development of psychological and practical support tools for Latinos seeking APOE genetic testing and counseling.

Characterizing the Journey of Early Alzheimer's Disease in Patients Initiating Lecanemab Treatment in the United States: A Real-World Evidence Study

INTRODUCTION

With the advent of disease-modifying therapies for early Alzheimer's disease (AD), a comprehensive characterization of patients initiating lecanemab in the USA is needed to understand its use in real-world settings.

METHODS

This retrospective observational study used administrative claims from the Komodo Research Database (1/1/2023-6/30/2024). Eligible patients had ≥ 1 lecanemab administration (first claim defined the index date) and ≥ 12 months of clinical activity/insurance eligibility before the index date. Patient characteristics, diagnostic process, and AD-related medications were evaluated within 12 months before the index date (baseline), whereas lecanemab treatment patterns and concomitant medications were evaluated on or after the index date (follow-up). Outcomes were reported using descriptive statistics and persistence to lecanemab was evaluated using Kaplan-Meier analysis.

RESULTS

Of 3155 patients included in the study, mean age was 75.0 years, 55.8% were female, 44.2% were male, and most (93.3%) received their index lecanemab administration in an urban setting. Diagnoses of AD (83.8%) and mild cognitive impairment (60.8%) were common at baseline, and 67.6% of patients used AD symptomatic medications. Average time from earliest diagnosis to first lecanemab administration was 4.9 months among patients with a diagnosis in January 2023 (accelerated approval date) or onwards. Over a mean follow-up of 138.8 days, the monthly mean number of administrations of lecanemab was 1.9, with an average of 16.5 days between consecutive administrations and 47.4 days to the first follow-up head magnetic resonance imaging. Persistence to lecanemab was 87.6% at 4 months after treatment initiation.

CONCLUSION

Lecanemab was utilized in appropriate patient populations according to the prescribing information approved by the US Food and Drug Administration. Findings from our study provide first insights into the real-world use of lecanemab in the USA and shed light on the need for increased and timely lecanemab initiation for the long-term management of early AD.

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification. To overcome these challenges, we have designed an end-to-end fairness-aware approach for label-imbalanced classification, tailored specifically for neuroimaging data. This method, built on the recently developed FACIMS framework, integrates into STREAMLINE, an automated ML environment. We evaluated our approach against nine other ML algorithms and found that it achieves comparable balanced accuracy to other methods while prioritizing fairness in classifications with five different sensitive attributes. This analysis contributes to the development of equitable and reliable ML diagnostics for MCI detection.

Red flags for remote cognitive diagnostic assessment: A Delphi expert consensus study by the Canadian Consortium on Neurodegeneration in Aging

Despite the potential benefits of remote cognitive assessment for dementia, it is not appropriate for all clinical encounters. Our aim was to develop guidance on determining a patient's suitability for comprehensive remote cognitive diagnostic assessment for dementia. A multidisciplinary expert workgroup was convened under the auspices of the Canadian Consortium on Neurodegeneration in Aging. We applied the Delphi method to determine 'red flags' for remote cognitive assessment of dementia. This resulted in 14 red flags that met the predetermined consensus criteria. We then developed a novel clinical decision-making infographic that integrated these findings to support multidisciplinary clinicians in determining a patient's readiness to undergo comprehensive remote cognitive assessment.

AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases

BACKGROUND

Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative, non-invasive tools for the early detection of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). Since early diagnosis is crucial for slowing disease progression and improving patient outcomes, leveraging AI-assisted ophthalmic imaging retinal imaging can enhance detection accuracy and clinical decision-making.

METHODS

This review examines clinical applications of AI in identifying retinal biomarkers associated with neurodegenerative diseases. Relevant data was gathered through a comprehensive literature review using PubMed, ScienceDirect, and Google Scholar to evaluate studies utilizing AI algorithms for retinal imaging analysis, focusing on diagnostic performance, sensitivity, specificity, and clinical relevance.

RESULTS

AI-assisted ophthalmic imaging retinal imaging enhances the early identification of neurodegenerative diseases by detecting microscopic structural and vascular changes in the retina. Studies have demonstrated that AI models analyzing Optical Coherence Tomography (OCT) and fundus images achieve high diagnostic accuracy. Studies have reported an area under the curve (AUC) of up to 0.918 in PD detection, with sensitivity ranging from 80 to 100% and specificity up to 85%. Similarly, AI-assisted OCT angiography (OCT-A) analysis has successfully identified retinal vascular alterations in AD patients, correlating with cognitive decline and an AUC of 0.73-0.91. These findings highlight AI's potential to detect preclinical disease stages before significant neurological symptoms manifest.

DISCUSSION

The integration of AI technologies into ophthalmic imaging holds the potential to improve early diagnosis and transform patient outcomes. However, challenges such as model interpretability, dataset biases, and ethical considerations must be addressed to ensure the responsible integration of AI into clinical practice. Future research should focus on refining AI algorithms, integrating multimodal imaging techniques, and developing predictive biomarkers to optimize early intervention strategies for neurodegenerative diseases.

CLINICAL TRIAL NUMBER

Not applicable.

Development of a model of help-seeking for dementia diagnosis by the person experiencing changes and family supporters

OBJECTIVES

This paper aimed to develop a model to describe help-seeking for dementia diagnosis. The practical model is intended to guide public health interventions to increase help-seeking.

METHOD

The model was developed by our multidisciplinary team based on qualitative semi-structured interviews in English (n = 33) and Chinese (n = 8) with older people, people with dementia and carers. The model was also informed by systematic reviews on help-seeking for dementia diagnosis, theories of help-seeking and further iterated based on feedback from a co-design group (n = 10).

RESULTS

The model starts with changes which might be symptoms of dementia being observed by the person or family/friends and ends in dementia assessment. Model steps are (1) The person deciding that the changes represent a health problem; (2) obtaining support or confirmation from family/friends that the changes are a health problem; (3) deciding to seek medical help; and (4) persuading the GP to facilitate dementia assessment. The model applies to English and Chinese-speaking Australians, though there were additional barriers for Chinese speakers. There are personal, family, community and health system barriers at each step.

CONCLUSION

Interventions to improve diagnosis of dementia might target public knowledge of dementia symptoms and benefits of a diagnosis, and general practice.

Mini-SEA: Validity and Normative Data for the French-Quebec Population Aged 50 Years and Above

OBJECTIVE

The mini Social cognition & Emotional Assessment (mini-SEA) is a social cognition battery which assesses theory of mind and emotion recognition. Currently, no psychometrically validated measure of social cognition with adapted normative data exists for the middle-aged and elderly French-Quebec population. This project aims to determine the known-group discriminant validity of a cultural and linguistic adaptation of the mini-SEA between cognitively healthy people, those with mild cognitive impairment (MCI) or living with Alzheimer's Disease (AD). This study also aims to examine the stability of mini-SEA's performance over a 3-4-month time period, as well as to produce normative data for French-Quebec people aged 50 years. Normative data are derived for the full and an abbreviated version of the Faux Pas subtest.

METHOD

The sample included 211 French-speaking participants from Quebec (Canada) aged 50 to 89 years. Mini-SEA's performance between a sub-sample of cognitively healthy people (n = 20), those with MCI (n = 20) or with AD (n = 20) was compared. A sub-sample of cognitively healthy people (n = 30) performed the task twice to estimate test-retest reliability. Socio-demographic variables' effects on scores were examined to produce normative data in the form of regression equations or percentile ranks.

RESULTS

Significant differences emerged between cognitively healthy people and those with MCI or AD. Moreover, scores were relatively stable over a period of 3 to 4 months. Finally, for the normative data, age, gender, and education were associated with performance on the mini-SEA or its subtests.

CONCLUSIONS

This study improves and standardizes social cognition's assessment among French-Quebec individuals, which will help characterize their cognitive profile.

Burden of disease of Alzheimer disease in Italy: a real-world data analysis

BACKGROUND

Alzheimer Disease (AD) represents a growing global health concern with profound socioeconomic implications, with predictions indicating a potential 50% increase in AD cases in Italy over the next 30 years. Timely diagnosis remains challenging due to the slow progression of symptoms and limited accessibility to advanced diagnostic tools, yet it remains one of the few tools available to prevent and alter the clinical course of the disease. The aim of this study is to build a cost-of-illness model to estimate the number of AD patients managed by the National Health Service, analyzing their use of hospital care, and estimating the social costs through real-world data.

METHODS

The analysis encompassed a multifaceted approach, combining real-world data analysis from different sources for the period 2014-2019. Health direct costs related to AD in Italy were estimated thanks to the Italian database of all hospital discharges and a Local Health Unit database (400,000 residents) collecting all information on resource consumption related to AD. The National Social Security System database was used to estimate social security costs (disability compensations) related to Attendance Allowance (AA) recognitions.

RESULTS

In Italy a prevalence of 413,715 AD patients was estimated, with annual health direct costs per patient equal to €3,779. Annual social security costs related to AA recognitions amounted to 240 million euros. Overall, the analysis estimated an annual total cost exceeding 1.8 billion euros.

CONCLUSIONS

This study provides a comprehensive exploration of the multifaceted burden of AD in Italy, shedding light on its economic dimensions. The results underscore the urgency of prioritizing AD on political agendas, especially in the face of the projected global surge in AD cases. The study advocates for proactive policy interventions and informed healthcare decision-making to address the complex challenges posed by AD.

Knowledge and attitudes about dementia and dementia genetics in a cohort of geriatric clinic attendees in Nigeria

BackgroundPopulation ageing in Africa will increase the burden of Alzheimer's disease and related dementias within the next few decades. Despite the potential for discovery of novel genetic risks in African populations, there is still a paucity of dementia genetic research among indigenous Africans.ObjectiveWe aimed to investigate the knowledge and attitudes of elderly population in Nigeria about dementia and dementia genetics.MethodsOne hundred clinic attendees (aged ≥60 years) recruited at the University College Hospital, Ibadan, Nigeria were surveyed using an interviewer-administered questionnaire consisting of the Dementia Knowledge Assessment Scale (DKAS) and other items assessing knowledge and attitudes about dementia genetics.ResultsThe mean age (±SD) of participants was 71.0 (±7.1) years, and the mean (±SD) DKAS score was 8.87 (±10.84). Only 10% were considered to have good knowledge of dementia (i.e., DKAS score ≥26). Attempts by participants to translate "dementia" in their local languages revealed misleading themes in their perception of the condition. Of the 42 participants who claimed to know what dementia is, 32 (76.2%) of them had poor knowledge (i.e., DKAS <26). Twenty-one participants were aware of the existence of genetic risk factors for dementia, but none could name a dementia risk gene. Seventy participants expressed willingness to undergo genetic testing to assess their risk of dementia.ConclusionsThere is a poor level of knowledge about dementia and dementia genetics among the elderly population in Nigeria. Public health education and community engagement is important for maximizing the impact of dementia genetic studies in Africa.

Knowledge, attitude, and practice related to dementia and cognitive impairment among medical specialists with expertise unrelated to dementia

To explore the knowledge, attitude, and practice (KAP) on dementia and cognitive impairment among medical specialists with expertise unrelated to dementia. This study enrolled medical specialists with expertise unrelated to dementia from 318 medical institutions in China, between March and April 2023. A self-designed questionnaire was used for data collection and KAP assessment. A total of 1288 valid questionnaires were collected, 62.58% from female participants. The KAP scores were 11.02 ± 2.11 (range: 0-13), 22.16 ± 3.40 (range: 0-24), and 29.48 ± 6.92 (range: 0-32), respectively. The structural equation model showed that knowledge was positively associated with attitude (path coefficient = 0.503, P < 0.001), while both knowledge (path coefficient = 0.713, P < 0.001) and attitude (path coefficient = 0.797, P < 0.001) were positively associated with practice. Type of institution (path coefficient = 0.184, P = 0.035) and professional title (path coefficient = 0.133, P = 0.026) were positively associated with knowledge score. The mediation analysis revealed the significant total effects for professional title on knowledge, knowledge and professional title on attitude, and knowledge, attitude, and education on practice. Medical specialists in China with expertise unrelated to dementia might have good knowledge, a positive attitude, and proactive practice toward dementia and cognitive impairment. Tailored educational interventions should be specifically designed for individuals with lower professional titles and those working outside public tertiary hospitals.

Dementia classification using two-channel electroencephalography features

This study aimed to develop a novel classification model using wearable two-channel electroencephalography (EEG) data to differentiate between patients with dementia and normal controls (NCs). We employed an extreme gradient boosting (Xgboost) model combined with recursive feature elimination with cross-validation (RFECV) to classify patients and NCs. The study included 54 NCs and 29 patients with dementia. Resting-state EEG was recorded, and Mini-Mental Status Exam (MMSE) and Clinical Dementia Rating (CDR) assessments were conducted. Significant differences were observed in peak frequency (PF), alpha (A), theta (T), the ratio of alpha to theta (A/T), the ratio of alpha to low-beta (A/BL), and coherence (CH) between patients and NCs. Patients with dementia exhibited decreases in PF, CH_A/T, CH_A/BL, A/T, and A/BL, while an increase in T was noted. The primary finding was that the Xgboost model, a tree ensemble classification, achieved a balanced accuracy of 97.05% with the RFECV-selected feature, which was PF. This study suggests that the novel Xgboost with RFECV classification model using two-channel EEG data could be a valuable tool for diagnosing dementia.

The significance of a dementia diagnosis from the perspective of the family caregivers: a qualitative study

BACKGROUND

With the anticipated increase in dementia prevalence over the coming decade, understanding the experience of receiving a dementia diagnosis for people living with cognitive impairment remains limited. This study aims to explore the implications of a family member with cognitive impairment receiving a dementia diagnosis or not from the perspective of their next of kin.

METHODS

A qualitative descriptive design was applied using individual interviews for data collection. Participants were recruited based on the cognitive function level of their family members, which was compatible with dementia as assessed with the Montreal Cognitive Assessment Scale (MoCA). The sample consisted of eight participants, comprising family members of five individuals with confirmed dementia diagnoses and three undiagnosed. The analysis was performed using four steps of systematic text condensation to discern codes, categories, and the overarching theme.

RESULTS

Three main categories were created: (1) Impact of observed cognitive decline, (2) Impact of diagnosis on service engagement, and (3) Support and follow-up for family caregivers. The findings show that next of kin who have received a dementia diagnosis for their family members are more proactive in seeking help and services, are better informed about available resources, and are more concerned about future challenges. On the other hand, next of kin to family members without a diagnosis are more inclined to handle the situation on their own, have less access to information and services, and generally express less concern about future problems.

CONCLUSION

The study reveals the benefits of receiving a timely dementia diagnosis in shaping more effective support systems and policies. This ensures that the next of kin and the person with cognitive impairment can navigate the complexities of dementia with greater confidence and preparedness, thereby enhancing their quality of life.

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease

The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.

Quantification of perforant path fibers for early detection of Alzheimer's disease

INTRODUCTION

The entorhinal cortex (ERC) and perforant path (PP) fibers are critical structures in the pathology of Alzheimer's disease (AD). This study aims to explore these regions using high-field magnetic resonance imaging (MRI), with the goal of identifying reliable biomarkers based on histopathological observations.

METHODS

Twenty post mortem brain specimens were scanned with 11.7T MRI, including diffusion tensor imaging and tractography, and were cut for subsequent histological examinations. The entorhinal cortical thickness and number of PP fibers derived from MRI were compared across neuropathological and premortem clinical diagnoses of AD.

RESULTS

The entorhinal cortical thickness and number of PP fibers decreased along with severities of neurofibrillary tangles in the ERC. Meanwhile, a reduction in the number of PP fibers, but not the entorhinal cortical thickness, was observed during the preclinical stage of AD.

CONCLUSIONS

Degeneration of PP fibers was observed in early AD and progressed along with neuropathological changes.

HIGHLIGHTS

Twenty post mortem brain tissues were scanned with 11.7T MRI. Degeneration of PP fibers was observed at 250 µm isotropic resolution. PP fiber indices were linked with severities of NFTs. The number of PP fibers was decreased in preclinical AD.

Cognitive assessment in hearing aid clinics: Is it feasible to implement in a National Health Service (NHS) setting?

BackgroundCognitive impairment is common in older adults and negatively affects hearing aid use. Audiologists hold the opportunity to identify signs of undiagnosed cognitive impairment and tailor care to optimise hearing aid use.ObjectiveTo assess the feasibility of introducing a brief cognitive assessment in hearing aid appointments for older adults.MethodsProspective feasibility cohort study incorporating quantitative and observational data. Participants were patients aged ≥65 years, new or existing hearing aid users, attending an NHS community hospital hearing aid clinic. Clinical audiologists were trained to conduct the Ascertain Dementia 8 (AD8) and visually-adapted shortened version of the Montreal Cognitive Assessment (mini-MoCA). A research audiologist took informed consent, observed appointments recording outcomes and followed up participants at 3 months. Feasibility was assessed using the following outcome measures: practicality of implementation in a clinical setting and resource requirements; acceptability in terms of recruitment/completion rates; onward care; experiences through standardised intensity scoring of observed emotions and analysis of free-text observations of participant reactions, participants' comments and informal conversations with clinical audiologists.ResultsTwenty patients were recruited, average age 78.6 years, 14 (70%) attended alone. All completed cognitive assessment, average duration was 14 minutes. AD8 and mini-MoCA average scores were 2.4 (range: 0-7) and 12.8 (range: 8-15), respectively. Ten (50%) participants had AD8 scores and one (5%) a Mini-MoCA score indicating potential cognitive impairment. Four of those (40%) contacted their GP, three were referred for further cognitive evaluation, one was diagnosed with dementia, two were awaiting appointments.ConclusionsIntroducing cognitive assessment in hearing aid clinics seems feasible and may provide an opportunity for identifying cognitive impairment in older adults, though further research is needed to establish its clinical utility and impact on care pathways. There are considerable resource implications, highlighting the importance of involving professional organisations, healthcare funders and policy makers early in this discussion.

Memory screening in the community: Facilitating earlier dementia diagnosis and care-Preliminary data

BACKGROUND

This program evaluation was conducted to assess the effectiveness of a community memory screening initiative across 25 Aging and Disability Resource Centers, spanning 39 counties and 5 tribal communities in the state of Wisconsin.

METHODS

We evaluated the screened individuals' characteristics and reasons for screening, the screen results and topics addressed during screening, the rate of sending positive screens to primary care providers, and the incidence of subsequent dementia diagnosis as well as health behavior changes.

RESULTS

Program evaluation results showed 791 completed surveys from individuals, indicating the program's accessibility and potential to reach populations in both urban and rural counties across Wisconsin. Evaluation results also showed that brain health was the most frequently discussed topic during memory screens (discussed during 689 screens, 87.1%), along with other topics such as potential causes of dementia symptoms (670 screens, 84.5%), dementia warning signs (656, 83%), the importance of early detection (605 screens, 76.5%), and caregiver support (106 screens, 13.4%). Of all 791, a total of 273 (34.5%) individuals had screen results sent to a primary care provider. Follow-up surveys completed with a subset of individuals (n = 49) who had their results sent to a primary care provider indicated that 10 (20%) received a diagnosis of dementia and over half made a health behavior change to improve brain health.

CONCLUSIONS

The evaluation results presented herein highlight the program's success in addressing the critical need for accessible dementia-related services. Overall, our evaluation results underscore the importance of community-based initiatives in promoting early dementia detection and intervention, which are crucial for disease management.

Blood phosphorylated Tau217 distinguishes amyloid-positive from amyloid-negative subjects in the Alzheimer's disease continuum. A systematic review and meta-analysis

BACKGROUND

Alzheimer's disease (AD) is the leading cause of dementia worldwide, and cost-effective tools to detect amyloid pathology are urgently needed. Blood-based Tau phosphorylated at threonine 217 (pTau217) seems promising, but its reliability as a proxy for cerebrospinal fluid (CSF) status and ability to identify patients within the AD spectrum remain unclear.

METHODS

We performed a systematic review and meta-analysis on the potential of blood pTau217 to differentiate amyloid-positive (A+) and amyloid-negative (A-) subjects. We included original studies reporting quantitative data on pTau217 concentrations in both blood and CSF in the AD continuum. The single-group meta-analysis computed the pooled pTau217 levels in blood and in CSF, separately in the A+ and A- groups, while the head-to-head meta-analysis compared the mean pTau217 concentrations in the A+ versus A- subjects, both in blood and CSF, stratifying by assessment method in both cases.

RESULTS

Ten studies (819 A+; 1055 A-) were included. The mean pTau217 levels resulted higher in CSF than in blood and, crucially, in A+ individuals than in A- ones, regardless of the laboratory method employed. Most importantly, all laboratory techniques reliably distinguished A+ from A- subjects, whether applied to CSF or blood samples.

CONCLUSIONS

These results confirm that blood-based pTau217 is a reliable marker of amyloid pathology with significant implications for clinical practice in the AD continuum. Indeed, pTau217 might be a non-invasive, scalable biomarker for early AD detection, reducing the reliance on more invasive, expansive, and less accessible methods.

CLINICAL TRIAL REGISTRATION

Prospero CRD42024565187.

Accountable care organizations and Medicare payments for residents with ADRD in disadvantaged neighborhoods

INTRODUCTION

Accountable care organizations (ACOs) are well positioned to promote care coordination. However, robust evidence of ACOs' impact on Medicare payments for residents with Alzheimer's disease and related dementias (ADRD) in disadvantaged neighborhoods remains limited.

METHODS

Using a 2016 to 2020 longitudinal dataset, we examined the effects of ACO enrollment on Medicare payments for people newly diagnosed with ADRD, focusing on the neighborhood Social Vulnerability Index (SVI) and its subcategories. Multivariable generalized estimating equation (GEE) models were applied.

RESULTS

ACO enrollment was associated with significantly reduced total payments across all SVI subcategories. The highest cost savings were observed among ADRD patients living in neighborhoods with high proportions of racial and ethnic minorities. Results also showed that higher quality ACOs were associated with lower total payments.

DISCUSSION

ACOs have a great potential to save health-care costs for beneficiaries with ADRD living in socially vulnerable neighborhoods, particularly for those residing in areas with higher proportions of racial and ethnic minority populations.

HIGHLIGHTS

Accountable care organizations (ACOs) reduced Medicare payments for Alzheimer's disease and related dementias across neighborhood disadvantage levels. The cost reductions varied by specific indicators of social vulnerability. Highest cost savings were found among residents living with high proportion of racial/ethnic minorities. Cost savings were the greatest among the highest quality ACOs.

Mixture Disease Progression Model to Predict and Cluster the Long-Term Trajectory of Cognitive Decline in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease for which many clinical trials failed to detect treatment effects, possibly due to the heterogeneity of disease progression among the patients. Predicting and clustering a long-term trajectory of cognitive decline from the short-term cognition data of individual patients would help develop therapeutic interventions for AD.

METHODS

This study developed mixture disease progression model to predict and cluster the long-term trajectory of cognitive decline in the population. We predicted the 30-year long-term trajectories of the three cognitive scales and categorized the individuals into rapid and slow cognitive decliners by applying the method, which was based on the two-component normal mixture nonlinear mixed-effects model, to the short-term follow-up data of the Mini-Mental State Examination, the 13-item Alzheimer's Disease Assessment Scale-Cognitive, and the Clinical Dementia Rating Scale-sum of boxes collected in patients with mild cognitive impairment and AD in the Alzheimer's Disease Neuroimaging Initiative.

RESULTS

For each cognitive scale, the models identified two distinct subpopulations, including a population of comprising approximately 10-20% of individuals experiencing rapid cognitive decline, wherein the posterior means of the differences in cognitive decline speed between the two groups ranged from 2 to 3 years. We also identified baseline background factors associated with rapid decliners for three cognitive scales.

CONCLUSION

Identifying the risk factors associated with rapid decline of cognition by the proposed method aids in planning eligibility criteria and allocation strategy for accounting for the varying disease progression speeds among the patients enrolled in clinical trials for AD.

A Lightweight Deep Convolutional Neural Network Extracting Local and Global Contextual Features for the Classification of Alzheimer's Disease Using Structural MRI

Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural networks (CNNs) using neuroimaging biomarkers. However, most of the existing CNN-based methods often disregard the local features of the brain data, which leads to a loss of subtle fine-grained features in the brain imaging data. Moreover, the existing CNN architectures, which mainly rely on global features, do not pay much attention to the discriminability of the extracted features for the task of classification of Alzheimer's disease. Moreover, the existing architectures often end up using a large number of parameters to enhance the richness of the extracted features. This paper proposes a novel lightweight deep CNN, which extracts local and global contextual features from the sagittal slices of structural MRI data and uses both of these two types of features for the classification of the disease. The main idea used in designing the proposed network is to process separately the local and global features by using modules that pay a special attention to extract local and global contextual features. The fused local and global contextual features are then used for the classification of Alzheimer's disease. The proposed network is tested for the binary and multiclass classifications of the disease using the MR images taken from the ADNI database. The proposed network is shown to provide a performance that is significantly higher than that provided by other existing state-of-the-art networks, yet using a number of parameters that is a small fraction of that used by the other schemes.

Preclinical Alzheimer's disease shows alterations in circulating neuronal-derived extracellular vesicle microRNAs in a multiethnic cohort

INTRODUCTION

Alzheimer's disease (AD) is the leading cause of dementia, affecting around 50 million individuals worldwide. Brain-derived extracellular vesicles (EVs) can cross the blood-brain barrier carrying neuron-specific molecules, such as microRNAs (miRNAs), which have potential as biomarkers of neurodegeneration.

METHODS

We explored the association between neuronal-derived EV miRNAs from serum and AD clinical status by performing a transcriptome-wide association study involving 46 participants with clinical AD, 14 participants with preclinical AD, and 60 neurologically healthy controls.

RESULTS

We identified 14 miRNAs associated with AD risk, with more pronounced transcriptional alterations in preclinical individuals compared to clinical AD individuals. Functional analysis revealed enrichment of miRNA-target genes in neurodegenerative pathways, highlighting synuclein alpha (SNCA), cytochrome c, somatic (CYCS), and microtubule associated protein tau (MAPT) as key targets.

DISCUSSION

Our results highlight the potential role of neuronal-derived EVs in neurodegeneration and suggest avenues for further research into brain-derived biomarkers.

HIGHLIGHTS

Neuronal-derived extracellular vesicles (NDEVs) carry potential brain biomarkers. We tested the association between NDEV microRNAs (miRNAs) and Alzheimer's disease (AD). Fourteen NDEV miRNAs were associated with AD. Preclinical AD displayed more pronounced transcriptional changes than clinical AD. miRNA-target genes were enriched in pathways associated with neurodegeneration.

Beliefs About the Causes of Alzheimer's Disease Among Latinos in New York City

Latinos face health disparities in Alzheimer's disease (AD), with high disease prevalence relative to non-Latino whites and barriers to healthcare access. Several studies have found misconceptions about AD among Latinos that were linked to reduced preventative or help-seeking behavior. To improve understanding of illness perceptions among Latinos, we examined beliefs about the causes of AD, one of the five dimensions of illness representations from Leventhal's Self-Regulation Theory, among a sample of N = 216 Latinos. We conducted in-depth, semi-structured interviews with participants aged 40 to 64 (average age 53 years) living in northern Manhattan. Seven distinct causes of AD were identified, though participants demonstrated a general understanding of AD as a multifactorial disease. Genetics was found to be the most endorsed cause of AD, followed by unhealthy lifestyle factors. Most Latinos who believed psychosocial factors played a critical role in AD development were first-generation immigrants. No participants attributed AD to a normal process of aging, and few ascribed the disease to brain damage from stroke or head injuries. Several participants expressed the belief that environmental contaminants can cause AD, which has received little mention in prior studies. Though only a small number thought AD could occur by chance, most participants remained uncertain about the exact causes of the disease and used lay knowledge to explain their beliefs. Our findings help identify areas where educational interventions would be beneficial in improving community knowledge and offer perspectives that can foster cultural competency in healthcare.

Relationship Between Cognitive Abilities and Lower-Limb Movements: Can Analyzing Gait Parameters and Movements Help Detect Dementia? A Systematic Review

Identifying and diagnosing cognitive impairment remains challenging. Some diagnostic procedures are invasive, expensive, and not always accurate. Meanwhile, evidence suggests that cognitive impairment is associated with changes in gait parameters. Certain gait parameters manifesting differences between people with and without cognitive impairment are more pronounced when adding a secondary task (dual-task scenario). In this systematic review, the capability of gait analysis to identify cognitive impairment is investigated. Twenty-three studies published between 2014 and 2024 met the inclusion criteria. A significantly lower gait speed and cadence as well as higher gait variability were found in people with mild cognitive impairment (MCI) and/or dementia, compared with the group with no cognitive impairment. While dual tasks appeared to amplify the differences between the two populations, the type of secondary tasks (e.g., calculations and recalling phone numbers) had an effect on gait changes. The activity and volume of different brain regions were also different between the two populations during walking. In conclusion, while this systematic review supported the potential of using gait analysis to identify cognitive impairment, there are a number of parameters researchers need to consider such as gait variables to be studied, types of dual tasks, and analysis of brain changes while performing the movement tasks.

Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning

Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (MCI) is frequently viewed as a crucial stage before the onset of AD, making it the ideal period for therapeutic intervention. AD is marked by the buildup of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles (NFTs), which are believed to cause neuronal loss and cognitive decline. Both Aβ plaques and NFTs accumulate for many years before the clinical symptoms become apparent in AD. As a result, in this study, CerebroSpinal Fluid (CSF) biomarker information is combined with hippocampal volumes to differentiate between MCI and AD. For this, a novel two-stage hybrid learning model that leverages 3D CNN and the notion of a Fuzzy and Machine learning model is proposed. A 3D-CNN architecture is employed to segment the hippocampus from the structural brain 3D-MR images and quantify the hippocampus volume. In stage 1, the hippocampus volume is passed through thirteen machine learning models and fuzzy clustering for classifying symptomatic AD and healthy brain (Normal Control - NC). The CSF data is fuzzified to capture the inherent uncertainty and overlap in clinical data. The identified symptomatic AD data in the stage1 are further classified into MCI and AD with the aid of a fuzzified CSF biomarker in stage 2. The experimental work presented in this study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The proposed hybrid model achieved an average accuracy of 93.6% for distinguishing between NC and symptomatic AD and 93.7% for discriminating between MCI and AD. This approach enhances diagnostic accuracy and provides a more comprehensive assessment, allowing for earlier and more targeted therapeutic interventions.

Development of an Evidence-Based Cognitive Training Application for Elderly Individuals with Cognitive Dysfunction

BACKGROUND

Early cognitive training is important to prevent cognitive decline in patients with mild cognitive impairment (MCI) or dementia. Therefore, developing an application that can provide evidence-based cognitive training is necessary for patients with MCI or dementia.

METHOD

This study aimed to develop and evaluate Smart Brain, an evidence-based application that provides comprehensive cognitive training tailored to this population. The application was developed using an ADDIE (analysis, design, development, implementation, and evaluation) model. A systematic review of databases, including Ovid-MEDLINE, Ovid-EMBASE, Cochrane Library, and CINAHL, was conducted up to April 15, 2021, to identify key content areas. Additionally, a survey of 100 participants highlighted the need for features such as cognitive games, health notes, social networking services, and goal achievement.

RESULT

The application was developed with distinct user and administrator interfaces to support engagement and monitoring. Usability testing involved 7 experts and 11 elderly individuals with MCI or dementia from a daycare center. Based on usability feedback, features such as the time limits for cognitive games were refined. The final application integrates cognitive games, physical exercises, emotional support, and health management tools to address user needs comprehensively.

CONCLUSION

Smart Brain holds significant potential to improve the quality of life and cognitive health of elderly individuals with MCI or dementia. Its usability and functionality make it a promising tool for community-based interventions.

Serum β-secretase 1 (sBACE1) activity in subjective cognitive decline: an exploratory study

β-Secretase-1 (BACE1) plays a key role in the regulation of cerebral amyloid-β homeostasis, being involved in amyloidogenic and, as recently found, amyloidolytic pathways. Growing evidence indicates that increased serum BACE1 (sBACE1) activity might represent an early biomarker for Alzheimer's disease. Here, we tested the hypothesis that an increase in sBACE1 activity may already occur in individuals with subjective cognitive decline (SCD). We found that sBACE1 activity was significantly higher in individuals with SCD (n 118) compared to cognitively normal subjects (controls, n 137) (p < 0.001). Moreover, compared with SCD, sBACE1 activity was even higher in patients affected by amnestic (n 179) or non-amnestic mild cognitive impairment (MCI) (n 99) (p < 0.001 and p 0.02, respectively). In all cases, the respective increase in sBACE1 activity was significant after adjustment for possible confounders including age, sex, and comorbidities. We also found a significant sexual dimorphism, with women affected by either type of MCI, but not by SCD, having higher levels of serum BACE1 activity compared to men. These results provide evidence supporting the potential use of sBACE1 activity as tool for blood-based screening of cognitively healthy individuals at clinical risk of MCI and dementia.

MiRNA-501-3p and MiRNA-502-3p: A Promising Biomarker Panel for Alzheimer's Disease

INTRODUCTION

Alzheimer's disease (AD) lacks a less invasive and early detectable biomarker. Here, we investigated the biomarker potential of miR-501-3p and miR-502-3p using different AD sources.

METHODS

MiR-501-3p and miR-502-3p expressions were evaluated in AD CSF exosomes, serum exosomes, familial and sporadic AD fibroblasts and B-lymphocytes by qRT-PCR analysis. Further, miR-501-3p and miR-502-3p expressions were analyzed in APP, Tau cells and media exosomes.

RESULTS

MiR-501-3p and miR-502-3p expressions were significantly upregulated in AD CSF exosomes relative to controls. MiRNA levels were high in accordance with amyloid plaque and NFT density in multiple brain regions. Similarly, both miRNAs were elevated in AD and MCI serum exosomes compared to controls. MiR-502-3p expression was high in fAD and sAD B-lymphocytes. Finally, miR-501-3p and miR-502-3p expression were elevated intracellularly and secreted extracellularly in response to APP and Tau pathology.

DISCUSSION

These results suggest that miR-501-3p and miR-502-3p could be promising biomarkers for AD.

Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies

OBJECTIVE

Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are common neurodegenerative diseases with distinct but overlapping pathogenic mechanisms. The clinical similarities between these diseases often result in high misdiagnosis rates, leading to serious consequences. Peripheral blood mononuclear cells (PBMCs) are easy to collect and can accurately reflect the immune characteristics of both DLB and AD.

METHODS

We utilized time-of-flight mass cytometry (CyTOF) with single-cell resolution to quantitatively analyze peripheral PBMCs, identifying 1228 immune characteristics. Based on the top-selected immune features, we constructed immunological elastic net (iEN) models.

RESULTS

These models demonstrated high diagnostic efficacy in distinguishing diseased samples from healthy donors as well as distinguishing AD and DLB cases. The selected features reveal that the primary peripheral immune characteristic of AD is a decrease in total T cells, while DLB is characterized by low expression of I-kappa-B-alpha (IKBα) in the classical monocyte subset.

CONCLUSIONS

These findings suggest that peripheral immune characteristics could serve as potential biomarkers, facilitating the diagnosis of neurodegenerative diseases.

A thematic review on therapeutic toys and games for the elderly with Alzheimer's disease

PURPOSE

Alzheimer's disease (AD) is a common and devastating neurological ailment that affects millions of the elderly worldwide. Therapeutic toys and games have emerged as potential non-pharmacological interventions for AD. However, despite a growing number of documents on the subject, research on the future direction of therapeutic toys and games for AD remains scarce. To address this gap, this study aims to (1) map the future trends of therapeutic toys and games for AD and (2) identify the categories and design characteristics.

MATERIALS AND METHODS

Using a thematic review framework, a systematic literature search was conducted in two electronic databases (Scopus and WoS) using established criteria. Thematic analysis was done using ATLAS.ti 23 to identify prominent themes, patterns and trends.

RESULTS

A total of 180 documents were found. Twenty-five articles met the inclusion criteria. A thematic review of these 25 articles identified 13 initial codes, which were been clustered into four themes: detection and evaluation; intervention; toy/game category; and design characteristics. The word "Cognitive" appears most frequently in documents according to word cloud.

CONCLUSIONS

Therapeutic toys and games are used to detect and as an intervention for AD. Most of the current studies focused on specific cognitive functions. More research is needed about play therapy for neuropsychiatric symptoms. This thematic review also proposed a conceptual framework for designing toys and games tailored to the needs of the elderly with AD, offering valuable insights to future researchers focusing on this domain.

A Modality-Flexible Framework for Alzheimer's Disease Diagnosis Following Clinical Routine

Dementia has high incidence among the elderly, and Alzheimer's disease (AD) is the most common dementia. The procedure of AD diagnosis in clinics usually follows a standard routine consisting of different phases, from acquiring non-imaging tabular data in the screening phase to MR imaging and ultimately to PET imaging. Most of the existing AD diagnosis studies are dedicated to a specific phase using either single or multi-modal data. In this paper, we introduce a modality-flexible classification framework, which is applicable for different AD diagnosis phases following the clinical routine. Specifically, our framework consists of three branches corresponding to three diagnosis phases: 1) a tabular branch using only tabular data for screening phase, 2) an MRI branch using both MRI and tabular data for uncertain cases in screening phase, and 3) ultimately a PET branch for the challenging cases using all the modalities including PET, MRI, and tabular data. To achieve effective fusion of imaging and non-imaging modalities, we introduce an image-tabular transformer block to adaptively scale and shift the image and tabular features according to modality importance determined by the network. The proposed framework is extensively validated on four cohorts containing 6495 subjects. Experiments demonstrate that our framework achieves superior diagnostic performance than the other representative methods across various AD diagnosis tasks, and shows promising performance for all the diagnosis phases, which exhibits great potential for clinical application.

Heritability of Alzheimer's disease plasma biomarkers: A nuclear twin family design

INTRODUCTION

Alzheimer's disease (AD) is a highly heritable disease (60%-80%). Amyloid beta (Aβ) 42/40, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) are plasma biomarkers for AD. Clinical biomarker research would be served by an understanding of the sources of variance in these markers.

METHODS

Blood concentrations of Aβ42/40, NfL, and GFAP of twins and their families (monozygotic twins: 1574, dizygotic twins: 1266, other: 3657) were analyzed on the Simoa HD-X. Twin-family models were used to estimate proportional genetic contributions to the variance in biomarker levels.

RESULTS

Heritability estimates were 16% for Aβ42/40, 42% for NfL, and 60% for GFAP. NfL and GFAP were significantly correlated with each other (0.37) but not with Aβ42/40.

DISCUSSION

The heritability of Aβ42/40 (16%) is lower than the heritability of AD, suggesting strong environmental influences on this biomarker. The lack of correlation between NfL/GFAP and Aβ42/40 indicates these markers may be on different biological pathways.

HIGHLIGHTS

Heritability is found for glial fibrillary acidic protein (60%), neurofilament light chain (42%), and amyloid beta (Aβ) 42/40 (16%) plasma levels. Aβ42/40 plasma levels are sensitive to person-specific environmental influences.

Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease

Background

Late-onset Alzheimer's disease (LOAD) has been associated with alterations in the morphology of multiple brain structures, and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.

Objective

To determine causal pathways from genetic risk variants of LOAD via brain morphology to LOAD.

Methods

Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer's Disease Neuroimaging Initiative.

Results

Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala and inferior lateral ventricle mediated the effect of APOE ε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the inferior lateral ventricles, increased the risk of LOAD as well as vice versa.

Conclusions

Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.

Novel techniques for early diagnosis and monitoring of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is the most common neurodegenerative disorder, which is characterized by a progressive loss of cognitive functions. The high prevalence, chronicity, and multimorbidity are very common in AD, which significantly impair the quality of life and functioning of patients. Early detection and accurate diagnosis of Alzheimer's disease (AD) can stop the illness from progressing thereby postponing its symptoms. Therefore, for the early diagnosis and monitoring of AD, more sensitive, noninvasive, straightforward, and affordable screening tools are needed.

AREAS COVERED

This review summarizes the importance of early detection methods and novel techniques for Alzheimer's disease diagnosis that can be used by healthcare professionals.

EXPERT OPINION

Early diagnosis assists the patient and caregivers to understand the problem establishing reasonable goals and making future plans together. Early diagnosis techniques not only help in monitoring disease progression but also provide crucial information for the development of novel therapeutic targets. Researchers can plan to potentially alleviate symptoms or slow down the progression of Alzheimer's disease by identifying early molecular changes and targeting altered pathways.

Writing the Signs: An Explainable Machine Learning Approach for Alzheimer's Disease Classification from Handwriting

Alzheimer's disease is a global health challenge, emphasizing the need for early detection to enable timely intervention and improve outcomes. This study analyzes handwriting data from individuals with and without Alzheimer's to identify predictive features across copying, graphic and memory-based tasks. Machine learning models, including Random Forest, Bootstrap Aggregating (Bagging), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost) and Gradient Boosting, were applied to classify patients, with SHapley Additive exPlanations (SHAP) enhancing model interpretability. Time-related features were crucial in copying and graphic tasks, reflecting cognitive processing speed, while pressure-related features were significant in memory tasks, indicating recall confidence. Simpler graphic tasks showed strong discriminatory power, aiding early detection. Performance metrics demonstrated model effectiveness: For memory tasks, Random Forest achieved the highest accuracy (0.840 ± 0.038 ), while Bagged SVC was the lowest (0.617 ± 0.046 ). Copying tasks recorded a peak accuracy of0.804 ± 0.075 with Gradient Boost and a low of0.566 ± 0.032 for Bagged SVC. Graphic tasks reached0.799 ± 0.041 with Gradient Boost and 0.643 ± 0.071 with AdaBoost. For all tasks combined, Random Forest excelled (0.854 ± 0.033 ), while Gradient Boost performed worst (0.598 ± 0.151 ). These results highlight handwriting analysis's potential in Alzheimer's detection.

Retinal thickness predicts the risk of cognitive decline over five years

BACKGROUND

Dementia poses a significant burden on healthcare systems. Early identification of individuals at risk for cognitive decline is crucial. The retina, an extension of the central nervous system, reflects neurodegenerative changes. Optical coherence tomography (OCT) is a non-invasive tool for assessing retinal health and has shown promise in predicting cognitive decline. However, prior studies produced mixed results.

METHODS

This study investigated a large cohort (n = 490) of Asian individuals attending memory clinics. Participants underwent comprehensive neuropsychological testing annually for five years. Retinal thickness was measured by OCT at baseline. We assessed the association between baseline retinal thickness and subsequent cognitive decline.

RESULTS

Participants with a significantly thinner macular ganglion cell-inner plexiform layer (GCIPL) at baseline (≤ 79 μm) had a 38% greater risk of cognitive decline compared to those who did not (≥ 88 μm; p = 0.037). In a multivariable model accounting for age, education, cerebrovascular disease status, hypertension, hyperlipidemia, diabetes and smoking, thinner GCIPL was associated with an increased risk of cognitive decline (hazard ratio = 1.14, 95% CI = 1.01-1.30, p = 0.035). Retinal nerve fiber layer (RNFL) thickness was not associated with cognitive decline.

CONCLUSIONS

This study suggests that OCT-derived macular GCIPL thickness may be a valuable biomarker for identifying individuals at risk of cognitive decline. Our findings highlight GCIPL as a potentially more sensitive marker compared to RNFL thickness for detecting early neurodegenerative changes.

TRIAL REGISTRATION NUMBER AND NAME OF THE TRIAL REGISTRY

National Healthcare Group Domain-Specific Review Board (NHG DSRB) reference numbers DSRB Ref: 2018/01368. Name of the trial: Harmonisation project.

Multilingual Prediction of Cognitive Impairment with Large Language Models and Speech Analysis

BACKGROUND

Cognitive impairment poses a significant global health challenge, emphasizing the critical need for early detection and intervention. Traditional diagnostics like neuroimaging and clinical evaluations are often subjective, costly, and inaccessible, especially in resource-poor settings. Previous research has focused on speech analysis primarily conducted using English data, leaving multilingual settings unexplored.

METHODS

In this study, we present our results from the INTERSPEECH 2024 TAUKADIAL Challenge, where we aimed to automatically detect mild cognitive impairment (MCI) and predict cognitive scores for English and Chinese speakers (169 in total). Our approach leverages Whisper, a speech foundation model, to extract language-agnostic speech embeddings. We then utilize ensemble models to incorporate task-specific information.

RESULTS

Our model achieved unweighted average recall of 81.83% in an MCI classification task, and root mean squared error of 1.196 in cognitive score prediction task, which placed the model at the second and the first position, respectively, in the ranking for each task. Comparison between language-agnostic and language-specific models reveals the importance of capturing language-specific nuances for accurate cognitive impairment prediction.

CONCLUSIONS

This study demonstrates the effectiveness of language-specific ensemble modeling with Whisper embeddings in enabling scalable, non-invasive cognitive health assessments of Alzheimer's disease, achieving state-of-the-art results in multilingual settings.

Alzheimer's disease: a comprehensive review of epidemiology, risk factors, symptoms diagnosis, management, caregiving, advanced treatments and associated challenges

Background

Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired reasoning. It is the leading cause of dementia in older adults, marked by the pathological accumulation of amyloid-beta plaques and neurofibrillary tangles. These pathological changes lead to widespread neuronal damage, significantly impacting daily functioning and quality of life.

Objective

This comprehensive review aims to explore various aspects of Alzheimer's disease, including its epidemiology, risk factors, clinical presentation, diagnostic advancements, management strategies, caregiving challenges, and emerging therapeutic interventions.

Methods

A systematic literature review was conducted across multiple electronic databases, including PubMed, MEDLINE, Cochrane Library, and Scopus, from their inception to May 2024. The search strategy incorporated a combination of keywords and Medical Subject Headings (MeSH) terms such as "Alzheimer's disease," "epidemiology," "risk factors," "symptoms," "diagnosis," "management," "caregiving," "treatment," and "novel therapies." Boolean operators (AND, OR) were used to refine the search, ensuring a comprehensive analysis of the existing literature on Alzheimer's disease.

Results

AD is significantly influenced by genetic predispositions, such as the apolipoprotein E (APOE) ε4 allele, along with modifiable environmental factors like diet, physical activity, and cognitive engagement. Diagnostic approaches have evolved with advances in neuroimaging techniques (MRI, PET), and biomarker analysis, allowing for earlier detection and intervention. The National Institute on Aging and the Alzheimer's Association have updated diagnostic criteria to include biomarker data, enhancing early diagnosis.

Conclusion

The management of AD includes pharmacological treatments, such as cholinesterase inhibitors and NMDA receptor antagonists, which provide symptomatic relief but do not slow disease progression. Emerging therapies, including amyloid-beta and tau-targeting treatments, gene therapy, and immunotherapy, offer potential for disease modification. The critical role of caregivers is underscored, as they face considerable emotional, physical, and financial burdens. Support programs, communication strategies, and educational interventions are essential for improving caregiving outcomes. While significant advancements have been made in understanding and managing AD, ongoing research is necessary to identify new therapeutic targets and enhance diagnostic and treatment strategies. A holistic approach, integrating clinical, genetic, and environmental factors, is essential for addressing the multifaceted challenges of Alzheimer's disease and improving outcomes for both patients and caregivers.

Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer

Objectives

Colorectal cancer (CRC) is a prevalent disease characterized by significant dysregulation of gene expression. Non-invasive tests that utilize microRNAs (miRNAs) have shown promise for early CRC detection. This study aims to determine the association between miRNAs and key genes in CRC.

Methods

Two datasets (GSE106817 and GSE23878) were extracted from the NCBI Gene Expression Omnibus database. Penalized logistic regression (PLR) and artificial neural networks (ANN) were used to identify relevant miRNAs and evaluate the classification accuracy of the selected miRNAs. The findings were validated through bipartite miRNA-mRNA interactions.

Results

Our analysis identified 3 miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a total accuracy of over 90%. Based on the results of the mRNA-miRNA interaction network, CDK1 and MAD2L1 were identified as target genes of miR-6787-5p.

Conclusions

Our results suggest that the identified miRNAs and target genes could serve as non-invasive biomarkers for diagnosing colorectal cancer, pending laboratory confirmation.

Exploring the Neuroprotective Effects of Rufinamide in a Streptozotocin-Induced Dementia Model

Due to the complex pathophysiology of AD (Alzheimer's Disease), there are currently no effective clinical treatments available, except for acetylcholinesterase inhibitors. However, CREB (cyclic AMP-responsive element binding protein) has been identified as the critical factor for the transcription in memory formation. Understanding the effect of potential drugs on the CREB pathway could lead to the development of new therapeutic molecules. Rufinamide has shown promise in improving memory in animal models, and these effects may be associated with modulation of the CREB pathway, however, this has not been previously reported. Thus, the present study aimed to determine the involvement of the CREB pathway in the cognitive improvement effects of rufinamide in STZ (streptozotocin) induced mouse model of dementia. Administration of STZ [3 mg/kg, i.c.v. (intracerebroventricular) bilaterally] significantly impaired cognitive performance in step-down passive avoidance and Morris water maze tests in animals, reduced brain endogenous antioxidant levels (GSH, superoxide dismutase, and catalase), and increased marker of brain oxidative stress [TBARS (thiobarbituric acid reactive substances)] and inflammation [IL-1β (Interleukin-1 beta), IL-6 (Interleukin-6), TNF-α (Tumor necrosis factor alpha) and NF-κB (Nuclear factor kappa B)], along with neurodegeneration. These effects were markedly reversed by rufinamide (50 and 100 mg/kg) when administered to STZ animals. However, the pre-treatment with the CREB inhibitor (666-15) in STZ and rufinamide-administered animals neutralized the beneficial influence of rufinamide. Our data suggest that rufinamide, acting via CREB signaling, reduced oxidative stress and inflammatory markers while elevating anti-oxidant levels. Our study has established that rufinamide may act through CREB signaling in an investigational AD model, which could be crucial for developing new treatments beneficial in progressive neurological disorders.

Using cerebrospinal fluid biomarkers to diagnose Alzheimer's disease: an Australian perspective

Cerebrospinal fluid (CSF) biomarkers are currently the only clinically validated biofluid diagnostic test for Alzheimer's Disease (AD) available in Australia. Testing of CSF biomarkers via lumbar puncture (LP), including quantification of amyloid-β peptide, total tau protein, and phosphorylated tau, can give insight into underlying pathophysiological changes and provide greater certainty in confirming or excluding the presence of Alzheimer's disease changes compared to standard clinical and radiological assessments. Despite CSF analysis being a safe and cost-effective diagnostic method, the use of CSF biomarkers in the evaluation of potential AD remains limited in Australian clinical practice due to a variety of factors, including regional access challenges, concerns over the perceived invasiveness of LP and a lack of confidence among clinicians in interpreting the results. The advent of disease-modifying therapies as a potential new treatment strategy to reduce the rate of progression in people with AD will drive the demand for early diagnosis of AD. This perspective argues for broader adoption of CSF biomarker testing by providing evidence-based, clinically informed expert guidance on when and why to consider CSF biomarker testing.

Dementia Care Specialists Perspectives of Diagnosis and Early Psychosocial Care: A Qualitative Analysis of Focus Groups in Two Large Academic Medical Centers

Background and Objective

Alzheimer's disease and related dementias (ADRDs) are progressive conditions that substantially impact individuals and families. Timely diagnosis and early support are critical for long-term adjustment. However, current dementia care models do not meet needs of patients and families. Dementia care specialists treating individuals with dementia offer unique insight into care needs of diverse groups of patients, families, and healthcare systems that can be used to identify opportunities to improve care.To understand dementia care specialists' impressions of factors impacting ADRD diagnosis and post-diagnosis support. We aimed to identify factors that impact: (1) timely and accurate diagnosis, (2) diagnostic disclosure and provision of post-diagnosis support, and (3) patient and care-partner adjustment after diagnosis.

Research Design and Methods

We recruited dementia care specialists treating persons living with dementia (n=19) from two academic medical centers. Participants completed 60-minute qualitative focus groups or individual interviews. Data were analyzed using a hybrid inductive-deductive approach to thematic analysis.

Results

We identified subthemes within three overarching a-priori determined themes. Participants highlighted the presence of delays in referrals, time constraints, specialist discomfort, and lack of training as factors impacting the timeliness and accuracy of diagnosis. They also highlighted information needed in disclosure visits, ways of coordinating care, and identifying early support needs. Finally, participants highlighted factors impacting adjustment including families' insight and acceptance, distress, and available resources.

Discussion and Implications

Our study highlights the challenges dementia care specialist specialists face in delivering early support for individuals and families impacted by ADRDs and suggests avenues for revising existing care models.

Stepwise identification of prodromal dementia: Testing a practical model for primary care

BACKGROUND

Prodromal dementia is largely underdiagnosed in primary care.

OBJECTIVE

To develop a clinical model for detecting prodromal dementia within the operative boundaries of primary care practice.

METHODS

The study employed the Functional Activities Questionnaire (FAQ) and Montreal Cognitive Assessment (MoCA) to evaluate a "functional-cognitive" step-down screening model, in which the MoCA is administered subsequent to reported symptoms on the FAQ. It classified participants from the Alzheimer's Disease Imaging Initiative to three diagnostic categories: (1) healthy cognition (n = 396), (2) mild cognitive impairment without conversion (n = 430), and (3) prodromal dementia assessed 24 months before diagnosis (n = 164).

RESULTS

Analyses indicated that the step-down model (Model 1) performed significantly better than an alternative model that applied the FAQ as a single measure (Model 2) and compared well with another model that administered both screening measures to all participants (Model 3). Gradient Boosting Trees classifications yielded the following estimations for Model 1/Model 2/ Model 3, respectively: Sensitivity = 0.87/0.77/0.89, Specificity = 0.68/0.47/0.70, PPV = 0.73/0.40/0.75, NVP = 0.84/0.81/0.87, F1 Score = 0.79/0.52/0.81, AUC = 0.78/0.67/0.79.

CONCLUSIONS

These analyses support the proposed model. The study offers algorithms for validated measures, which were developed from a well characterized clinical sample. Their accuracy will likely improve further with new data from diverse clinical settings. These results can serve primary care in a timely manner in light of the recent advances in pharmacological treatment of dementia and the expected increase in demand for screening.

Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm

Cognitive impairment and dementia-related diseases develop several years before moderate or severe deterioration in cognitive function occurs. Nevertheless, most dementia cases, especially in low- and middle-income countries, remain undiagnosed because of limited access to affordable diagnostic tools. Additionally, the development of accessible tools for diagnosing and predicting cognitive impairment has not been extensively discussed in the literature. The objective of this study is to develop a cost-effective and highly accessible machine learning model to predict the risk of cognitive impairment for up to five years before clinical insight. We utilized easily accessible data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to train and evaluate various machine learning and deep learning models. A novel algorithm was developed to facilitate the selection of cost-effective models that offer high performance while minimizing development and operational costs. We conducted various assessments, including feature selection, time-series analyses, and external validation of the selected model. Our findings indicated that the Support Vector Machine (SVM) model was preferred over other high-performing neural network models because of its computational efficiency, achieving F2-scores of 0.828 in cross-validation and 0.750 in a generalizability test. Additionally, we found that demographic and historical health data are valuable for early prediction of cognitive impairment. This study demonstrates the potential of developing accessible solutions to predict cognitive impairment early using accurate and efficient machine learning models. Future interventions should consider creating cost-effective assessment tools to support global action plans and reduce the risk of cognitive impairment.

Elevated plasma p-tau231 is associated with reduced generalization and medial temporal lobe dynamic network flexibility among healthy older African Americans

BACKGROUND

Phosphorylated tau (p-tau) and amyloid beta (Aβ) in human plasma may provide an affordable and minimally invasive method to evaluate Alzheimer's disease (AD) pathophysiology. The medial temporal lobe (MTL) is susceptible to changes in structural integrity that are indicative of the disease progression. Among healthy adults, higher dynamic network flexibility within the MTL was shown to mediate better generalization of prior learning, a measure which has been demonstrated to predict cognitive decline and neural changes in preclinical AD longitudinally. Recent developments in cognitive, neural, and blood-based biomarkers of AD risk that may correspond with MTL changes. However, there is no comprehensive study on how these generalization biomarkers, long-term memory, MTL dynamic network flexibility, and plasma biomarkers are interrelated. This study investigated (1) the relationship between long-term memory, generalization performance, and MTL dynamic network flexibility and (2) how plasma p-tau231, p-tau181, and Aβ42/Aβ40 influence generalization, long-term memory, and MTL dynamics in cognitively unimpaired older African Americans.

METHODS

148 participants (Meanage: 70.88,SDage: 6.05) were drawn from the ongoing longitudinal study, Pathways to Healthy Aging in African Americans conducted at Rutgers University-Newark. Cognition was evaluated with the Rutgers Acquired Equivalence Task (generalization task) and Rey Auditory Learning Test (RAVLT) delayed recall. MTL dynamic network connectivity was measured from functional Magnetic Resonance Imaging data. Plasma p-tau231, p-tau181, and Aβ42/Aβ40 were measured from blood samples.

RESULTS

There was a significant positive correlation between generalization performance and MTL Dynamic Network Flexibility (t = 3.372, β = 0.280, p < 0.001). There were significant negative correlations between generalization performance and plasma p-tau231 (t = -3.324, β = -0.265, p = 0.001) and p-tau181 (t = -2.408, β = -0.192, p = 0.017). A significant negative correlation was found between plasma p-tau231 and MTL Dynamic Network Flexibility (t = -2.825, β = -0.232, p = 0.005).

CONCLUSIONS

Increased levels of p-tau231 are associated with impaired generalization abilities and reduced dynamic network flexibility within the MTL. Plasma p-tau231 may serve as a potential biomarker for assessing cognitive decline and neural changes in cognitively unimpaired older African Americans.

Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models

Accurate Alzheimer's Disease (AD) progression prediction is essential for early intervention. The TADPOLE challenge, involving 92 algorithms, used multimodal biomarkers to predict future clinical diagnosis, cognition, and ventricular volume. The winning algorithm, FROG, utilized a Longitudinal-to-Cross-sectional (L2C) transformation to convert variable longitudinal histories into fixed-length feature vectors, which contrasted with most existing approaches that fitted models to entire longitudinal histories, e.g., AD Course Map (AD-Map) and minimal recurrent neural networks (MinimalRNN). The TADPOLE challenge only utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To evaluate FROG's generalizability, we trained it on the ADNI dataset and tested it on three external datasets covering 2,312 participants and 13,200 timepoints. We also introduced two FROG variants. One variant, L2C feedforward neural network (L2C-FNN), unified all XGBoost models used by the original FROG with an FNN. Across external datasets, L2C-FNN and AD-Map were the best for predicting cognition and ventricular volume. For clinical diagnosis prediction, L2C-FNN was the best, while AD-Map was the worst. L2C-FNN compared favorably with other approaches regardless of the number of observed timepoints, and when predicting from 0 to 6 years into the future, underscoring its potential for long-term dementia progression prediction. Pretrained ADNI models are publicly available: GITHUB_LINK.

Introduction and preliminary psychometric evaluation of the assessment of functional capacity interview for older adults

Measures of complex functional decision-making capacity can greatly aid in assessing mild cognitive impairment (MCI) and facilitating early intervention in dementia care. We examined the ability of the Assessment of Functional Capacity Interview (AFCI) to detect functional differences among older adults who were cognitively unimpaired (CU), or who presented with subjective cognitive decline (SCD) or MCI. A sample of 97 older adults (CU; n = 30, Mage = 74.64 ± 7.42 years; SCD; n = 34, Mage = 72.56 ± 6.43 years; MCI; n = 33, Mage = 78.28 ± 7.55 years) underwent neuropsychological testing and responded to the Financial Capacity Instrument (FCI-SF). Informants completed the Assessment of Functional Capacity (AFCI), an instrument of functional decision-making capacity, and responded to the Social Vulnerability Scale (SVS15) and Amsterdam Instrumental Activity of Daily Living (A-IADL-Q-SV), a measure of functional status, for comparison. According to informant-reported responses, the CU group had significantly lower AFCI total (and domain) scores, H(2) = 27.59, p<.001, relative to MCI. Additionally, the CU group had significantly lower AFCI scores in the Home and Personal Safety domain relative to the SCD group, H(2) = 14.06, p<.05. In the overall sample, AFCI total scores were associated with FCI-SF, SVS15, and A-IADL-Q-SV scores and cognitive measures. Our results demonstrate that the AFCI is sensitive to impairment in safety, social, financial, and medical functioning in MCI and is associated with measures of cognitive functioning and social vulnerability in older adults. Incorporating this instrument as a supplement to cognitive screening instruments may aid in the prevention of hazardous decision-making in older adults.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Adaptation and Validation of the Psychological Consequences of Screening Questionnaire (PCQ) for Cognitive Screening in Primary Care

BACKGROUND

Context-specific measures with adequate external validity are needed to appropriately determine psychosocial effects related to screening for cognitive impairment.

METHODS

Two-hundred adults aged ≥65 y recently completing routine, standardized cognitive screening as part of their Medicare annual wellness visit were administered an adapted version of the Psychological Consequences of Screening Questionnaire (PCQ), composed of negative (PCQ-Neg) and positive (PCQ-Pos) scales. Measure distribution, acceptability, internal consistency, factor structure, and external validity (construct, discriminative, criterion) were analyzed.

RESULTS

Participants had a mean age of 73.3 y and were primarily female and socioeconomically advantaged. Most had a normal cognitive screening result (99.5%, n = 199). Overall PCQ scores were low (PCQ-Neg: x ¯ = 1.27, possible range 0-36; PCQ-Pos: x ¯ = 7.63, possible range 0-30). Both scales demonstrated floor effects. Acceptability was satisfactory, although the PCQ-Pos had slightly more item missingness. Both scales had Cronbach alphas >0.80 and a single-factor structure. Spearman correlations between the PCQ-Neg with general measures of psychological distress (Impacts of Events Scale-Revised, Perceived Stress Scale, Kessler Distress Scale) ranged from 0.26 to 0.37 (P's < 0.001); the correlation with the World Health Organization-Five Well-Being Index was -0.19 (P < 0.01). The PCQ-Neg discriminated between those with and without a self-reported subjective cognitive complaint (x ¯ = 2.73 v. 0.89, P < 0.001) and was associated with medical visit satisfaction (r = -0.24, P < 0.001) on the Patient Satisfaction Questionnaire. The PCQ-Pos predicted self-reported willingness to engage in future screening (x ¯ = 8.00 v. 3.00, P = 0.03).

CONCLUSIONS

The adapted PCQ-Neg is an overall valid measure of negative psychological consequences of cognitive screening; findings for the PCQ-Pos were more variable. Future studies should address measure performance among diverse samples and those with abnormal screening results.

HIGHLIGHTS

The PCQ scale is an overall valid measure of psychological dysfunction related to cognitive screening in older adults receiving normal screen results.PCQ scale performance should be further validated in diverse populations and those with abnormal cognitive screening results.The adapted PCQ may be useful to both health research and policy stakeholders seeking improved assessment of psychological impacts of cognitive screening.