Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic.

BACKGROUND: Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups. METHODS: The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aß-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments. RESULTS: We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aß- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding. CONCLUSIONS: We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection. TRIAL REGISTRATION: The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23-09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

BACKGROUND: The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. OBJECTIVE: A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. METHODS: A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. RESULTS: A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. CONCLUSION: Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

ANTECEDENTES: O diagnóstico precoce da doença de Alzheimer (DA) e do comprometimento cognitivo leve (CCL) continua sendo um desafio significativo na neurologia, com métodos convencionais frequentemente limitados pela subjetividade e variabilidade na interpretação. A integração da aprendizagem profunda com a inteligência artificial (IA) na análise de imagens de ressonância magnética surge como uma abordagem transformadora, oferecendo o potencial para insights diagnósticos imparciais e altamente precisos. OBJETIVO: Uma metanálise foi projetada para analisar a precisão diagnóstica do aprendizado profundo de imagens de ressonância magnética em modelos de DA e CCL. MéTODOS: Uma metanálise foi realizada nos bancos de dados das bibliotecas PubMed, Embase e Cochrane seguindo as diretrizes Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), com foco na precisão diagnóstica do aprendizado profundo. Posteriormente, a qualidade metodológica foi avaliada por meio do checklist QUADAS-2. Medidas diagnósticas, incluindo sensibilidade, especificidade, razões de verossimilhança, razão de chances diagnósticas e área sob a curva característica de operação do receptor (area under the receiver operating characteristic curve [AUROC]) foram analisadas, juntamente com análises de subgrupo para ressonância magnética ponderada em T1 e não ponderada em T1. RESULTADOS: Um total de 18 estudos elegíveis foram identificados. O coeficiente de correlação de Spearman foi de -0,6506. A metanálise mostrou que a sensibilidade e a especificidade combinadas, a razão de verossimilhança positiva, a razão de verossimilhança negativa e a razão de chances de diagnóstico foram 0,84, 0,86, 6,0, 0,19 e 32, respectivamente. A AUROC foi de 0,92. O ponto quiescente do resumo hierárquico da característica de operação do receptor (hierarchical summary of receiver operating characteristic [HSROC]) foi 3,463. Notavelmente, as imagens de 12 estudos foram adquiridas apenas por ressonância magnética ponderada em T1, e as dos outros 6 foram obtidas apenas por ressonância magnética não ponderada em T1. CONCLUSãO: Em geral, a aprendizagem profunda da ressonância magnética para o diagnóstico de DA e CCL mostrou boa sensibilidade e especificidade e contribuiu para melhorar a precisão diagnóstica.

Automatic speech analysis for detecting cognitive decline of older adults.

Background: Speech analysis has been expected to help as a screening tool for early detection of Alzheimer's disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China. Objective: Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers. Methods: A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared. Results: The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features. Conclusion: Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data.

Factor Structure and Internal Consistency of the National Alzheimer Coordinating Center's Uniform Data Set Version 3 Neuropsychological Test Battery (UDSNB 3.0): The Nigeria Sample.

BACKGROUND: Construct validation of cognitive batteries across Africa is imperative to understanding dementia in the region. We examined construct validity and internal consistency of the neuropsychological battery of Uniform Data Set version 3 (UDSNB 3.0) of the Alzheimer Coordinating Center in Nigeria older adults. METHOD: Three hundred forty-nine (220 females; age: 65 to 85) community dwellers were recruited. UDSNB 3.0 with 12 subscales were used to measure cognition. Two sets of data were collected. First was for exploratory factor analysis (EFA) and second was confirmatory factor analysis (CFA). Four models were specified for CFA. RESULT: EFA principal axis factor with varimax rotation yielded 4 factors: Executive function, memory, visual-spatial ability, and processing speed. Four CFA were performed based on 4 specified models, with only model 3 showing good model fit: CMIN/DF=2.13; confirmatory fit index=0.94; root mean square error of approximation=0.07. Model 3 had 5 latent variables: working memory, language, verbal memory, visual-spatial ability, and processing speed. UDSNB 3.0 had an overall Cronbach alpha of 0.73, suggesting strong internal reliability with ANOVA model F134,1619​​​=183.65 significant at P<0.001 level of testing. CONCLUSIONS: Our study showed that UDSNB 3.0 has construct validity and good internal consistency in our older adult population.

Asian Cohort for Alzheimer Disease (ACAD) Pilot Study: Vietnamese Americans.

INTRODUCTION: The objective of this pilot study was to establish the feasibility of recruiting older Vietnamese Americans for research addressing genetic and nongenetic risk factors for Alzheimer disease (AD). METHODS: Twenty-six Vietnamese Americans were recruited from communities in San Diego. A Community Advisory Board provided cultural and linguistic advice. Bilingual/bicultural staff measured neuropsychological, neuropsychiatric, lifestyle, and medical/neurological functioning remotely. Saliva samples allowed DNA extraction. A consensus team reviewed clinical data to determine a diagnosis of normal control (NC), mild cognitive impairment (MCI), or dementia. Exploratory analyses addressed AD risk by measuring subjective cognitive complaints (SCC), depression, and vascular risk factors (VRFs). RESULTS: Twenty-five participants completed the study (mean age=73.8 y). Eighty percent chose to communicate in Vietnamese. Referrals came primarily from word of mouth within Vietnamese communities. Diagnoses included 18 NC, 3 MCI, and 4 dementia. Participants reporting SCC acknowledged more depressive symptoms and had greater objective cognitive difficulty than those without SCC. Eighty-eight percent of participants reported at least 1 VRF. DISCUSSION: This pilot study supports the feasibility of conducting community-based research in older Vietnamese Americans. Challenges included developing linguistically and culturally appropriate cognitive and neuropsychiatric assessment tools. Exploratory analyses addressing nongenetic AD risk factors suggest topics for future study.

Serial Cerebrospinal Fluid Sampling Reveals Trajectories of Potential Synaptic Biomarkers in Early Stages of Alzheimer's Disease.

Background: Synaptic dysfunction is closely associated with cognitive function in Alzheimer's disease (AD), and is present already in an early stage of the disease. Objective: Using serial cerebrospinal fluid (CSF) sampling, we aimed to investigate slopes of CSF synaptic proteins, and their relation with cognition along the AD continuum. Methods: We included subjects with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) (n = 50 amyloid-ß+ [A +], n = 50 A-) and 50 patients with AD dementia from the Amsterdam dementia cohort, with CSF at two time points (median[IQR] 2.1[1.4-2.7] years). We analyzed 17 synaptic proteins and neurofilament light (NfL). Using linear mixed models we assessed trajectories of protein levels, and associations with cognitive decline (repeated Mini-Mental State Examination). We used Cox regression models to assess predictive value of protein levels for progression to AD dementia. Results: At baseline most proteins showed increased levels in AD dementia compared to the other groups. In contrast NPTX2 levels were lower in AD dementia. Higher baseline levels of SNAP25, ß-syn, and 14-3-3 proteins were associated with faster cognitive decline (St.B[SE] -0.27[0.12] to -0.61[0.12]). Longitudinal analyses showed that SYT1 and NPTX levels decreased over time in AD dementia (st.B[SE] -0.10[0.04] to -0.15[0.05]) and SCD/MCI-A+ (St.B[SE] -0.07[0.03] to -0.12[0.03]), but not in SCD/MCI-A- (pinteraction < 0.05). Increase over time in NfL levels was associated with faster cognitive decline in AD dementia (St.B[SE] -1.75[0.58]), but not in the other groups (pinteraction < 0.05). Conclusions: CSF synaptic proteins showed different slopes over time, suggesting complex synaptic dynamics. High levels of especially SNAP-25 may have value for prediction of cognitive decline in early AD stages, while increase in NfL over time correlates better with cognitive decline in later stages.

Development of SERS Active Nanoprobe for Selective Adsorption and Detection of Alzheimer's Disease Biomarkers Based on Molecular Docking.

Purpose: Development of SERS-based Raman nanoprobes can detect the misfolding of Amyloid beta (Aß) 42 peptides, making them a viable diagnostic technique for Alzheimer's disease (AD). The detection and imaging of amyloid peptides and fibrils are expected to help in the early identification of AD. Methods: Here, we propose a fast, easy-to-use, and simple scheme based on the selective adsorption of Aß42 molecules on SERS active gold nanoprobe (RB-AuNPs) of diameter 29 ± 3 nm for Detection of Alzheimer's Disease Biomarkers. Binding with the peptides results in a spectrum shift, which correlates with the target peptide. We also demonstrated the possibility of using silver nanoparticles (AgNPs) as precursors for the preparation of a SERS active nanoprobe with carbocyanine (CC) dye and AgNPs known as silver nanoprobe (CC-AgNPs) of diameter 25 ± 4 nm. Results: RB-AuNPs probe binding with the peptides results in a spectrum shift, which correlates with the target peptide. Arginine peak appears after the conjugation confirms the binding of Aß 42 with the nanoprobe. Tyrosine peaks appear after conjugated Aß42 with CC-AgNPs providing binding of the peptide with the probe. The nanoprobe produced a strong, stable SERS signal. Further molecular docking was utilized to analyse the interaction and propose a structural hypothesis for the process of binding the nanoprobe to Aß42 and Tau protein. Conclusion: This peptide-probe interaction provides a general enhancement factor and the molecular structure of the misfolded peptides. Secondary structural information may be obtained at the molecular level for specific residues owing to isotope shifts in the Raman spectra. Conjugation of the nanoprobe with Aß42 selectively detected AD in bodily fluids. The proposed nanoprobes can be easily applied to the detection of Aß plaques in blood, saliva, and sweat samples.

Screening for dysphagia in older people with dementia: Evidence of validity based on internal structure and reliability of the Caregiver Questionnaire - RaDID-QC.

OBJECTIVE: To identify internal structure validity evidence of a dysphagia screening questionnaire for caregivers of older adults with Alzheimer's disease dementia and/or vascular dementia. METHODS: The 24-question Dysphagia Screening in Older Adults with Dementia - Caregiver Questionnaire (RaDID-QC) was administered by interviewing 170 caregivers of older people with dementia, selected by convenience at the Outpatient Reference Center for Older People. Exploratory Factor Analysis (EFA) was used to assess the internal structure validity of the questionnaire, and Cronbach's alpha was used to analyze reliability. Questions with factor loadings lower than 0.45 in magnitude were removed from the final questionnaire. Multivariate multiple linear regression was used to assess the percentage of variance explained by the remaining questions. RESULTS: Kayser-Meyer-Olkin (KMO) and Bartlett's tests suggested that the questionnaire was adequate for EFA. Principal Component Analysis (PCA) suggested that 12 components captured at least 75 % of the total variance. The corresponding 12-factor EFA model showed a statistically significant fit, and 15 out of the 24 questions had factor loadings greater than 0.45. Cronbach's alpha was 0.74 for the 15 questions, which explained 71 % of the total variance in the complete dataset. The questionnaire has adequate internal structure validity and good reliability. Based on EFA, RaDID-QC decreased from 24 to 15 questions. Other internal validity and reliability parameters will be obtained by administering the questionnaire to larger target populations. CONCLUSION: The RaDID-QC applied to caregivers of older adults with dementia due to Alzheimer's disease and/or vascular dementia produced valid and reliable responses to screen dysphagia signs and symptoms.

Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts.

Background: Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer's disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient. Objective: This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD. Methods: AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology. Results: We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction. Conclusions: We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease.

OBJECTIVE: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI. METHODS: A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort. RESULTS: Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341). CONCLUSION: The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

Performance of plasma p-tau217 for the detection of amyloid-ß positivity in a memory clinic cohort using an electrochemiluminescence immunoassay.

BACKGROUND: Plasma p-tau217 has emerged as the most promising blood-based marker (BBM) for the detection of Alzheimer Disease (AD) pathology, yet few studies have evaluated plasma p-tau217 performance in memory clinic settings. We examined the performance of plasma p-tau217 for the detection of AD using a high-sensitivity immunoassay in individuals undergoing diagnostic lumbar puncture (LP). METHODS: Paired plasma and cerebrospinal fluid (CSF) samples were analysed from the TIMC-BRAiN cohort. Amyloid (Aß) and Tau (T) pathology were classified based on established cut-offs for CSF Aß42 and CSF p-tau181 respectively. High-sensitivity electrochemiluminescence (ECL) immunoassays were performed on paired plasma/CSF samples for p-tau217, p-tau181, Glial Fibrillary Acidic Protein (GFAP), Neurofilament Light (NfL) and total tau (t-tau). Biomarker performance was evaluated using Receiver-Operating Curve (ROC) and Area-Under-the-Curve (AUC) analysis. RESULTS: Of 108 participants (age: 69 ± 6.5 years; 54.6% female) with paired samples obtained at time of LP, 64.8% (n = 70/108) had Aß pathology detected (35 with Mild Cognitive Impairment and 35 with mild dementia). Plasma p-tau217 was over three-fold higher in Aß + (12.4 pg/mL; 7.3-19.2 pg/mL) vs. Aß- participants (3.7 pg/mL; 2.8-4.1 pg/mL; Mann-Whitney U = 230, p < 0.001). Plasma p-tau217 exhibited excellent performance for the detection of Aß pathology (AUC: 0.91; 95% Confidence Interval [95% CI]: 0.86-0.97)-greater than for T pathology (AUC: 0.83; 95% CI: 0.75-0.90; z = 1.75, p = 0.04). Plasma p-tau217 outperformed plasma p-tau181 for the detection of Aß pathology (z = 3.24, p < 0.001). Of the other BBMs, only plasma GFAP significantly differed by Aß status which significantly correlated with plasma p-tau217 in Aß + (but not in Aß-) individuals. Application of a two-point threshold at 95% and 97.5% sensitivities & specificities may have enabled avoidance of LP in 58-68% of cases. CONCLUSIONS: Plasma p-tau217 measured using a high-sensitivity ECL immunoassay demonstrated excellent performance for detection of Aß pathology in a real-world memory clinic cohort. Moving forward, clinical use of plasma p-tau217 to detect AD pathology may substantially reduce need for confirmatory diagnostic testing for AD pathology with diagnostic LP in specialist memory services.

Influence of Different Diagnostic Criteria on Alzheimer Disease Clinical Research.

BACKGROUND AND OBJECTIVES: Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals. METHODS: We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria. RESULTS: A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only ß-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115). DISCUSSION: Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.

EEG and ERP biosignatures of mild cognitive impairment for longitudinal monitoring of early cognitive decline in Alzheimer's disease.

Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention. We compared individuals with MCI (n = 38) to age-matched healthy controls HC (n = 44). In resting state EEG, the MCI group exhibited higher power in Theta (3-7Hz) and lower power in Beta (13-20Hz) frequency bands. In both ERP tasks, the MCI group exhibited reduced ERP late positive potential (LPP), delayed ERP early component latency, slower reaction time, and decreased response accuracy. Cluster-based permutation analysis revealed significant clusters of difference between the MCI and HC groups in the frequency-channel and time-channel spaces. Cluster-based measures and performance measures (12 biosignatures in total) were selected as predictors of MCI. We trained a support vector machine (SVM) classifier achieving AUC = 0.89, accuracy = 77% in cross-validation using all data. Split-data validation resulted in (AUC = 0.87, accuracy = 76%) and (AUC = 0.75, accuracy = 70%) on testing data at baseline and follow-up visits, respectively. Classification scores at baseline and follow-up visits were correlated (r = 0.72, p<0.001, ICC = 0.84), supporting test-retest reliability of EEG biosignature. These results support the utility of EEG/ERP for prognostic testing, repeated assessments, and tracking potential treatment outcomes in the limited duration of clinical trials.

Serum Beta-Secretase 1 Activity Is a Potential Marker for the Differential Diagnosis between Alzheimer's Disease and Frontotemporal Dementia: A Pilot Study.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two major neurodegenerative diseases causing dementia. Due to similar clinical phenotypes, differential diagnosis is challenging without specific biomarkers. Beta-site Amyloid Precursor Protein cleaving enzyme 1 (BACE1) is a ß-secretase pivotal in AD pathogenesis. In AD and mild cognitive impairment subjects, BACE1 activity is increased in brain/cerebrospinal fluid, and plasma levels appear to reflect those in the brain. In this study, we aim to evaluate serum BACE1 activity in FTD, since, to date, there is no evidence about its role. The serum of 30 FTD patients and 30 controls was analyzed to evaluate (i) BACE1 activity, using a fluorescent assay, and (ii) Glial Fibrillary Acid Protein (GFAP) and Neurofilament Light chain (NfL) levels, using a Simoa kit. As expected, a significant increase in GFAP and NfL levels was observed in FTD patients compared to controls. Serum BACE1 activity was not altered in FTD patients. A significant increase in serum BACE1 activity was shown in AD vs. FTD and controls. Our results support the hypothesis that serum BACE1 activity is a potential biomarker for the differential diagnosis between AD and FTD.

Digital remote assessment of speech acoustics in cognitively unimpaired adults: feasibility, reliability and associations with amyloid pathology.

BACKGROUND: Digital speech assessment has potential relevance in the earliest, preclinical stages of Alzheimer's disease (AD). We evaluated the feasibility, test-retest reliability, and association with AD-related amyloid-beta (Aß) pathology of speech acoustics measured over multiple assessments in a remote setting. METHODS: Fifty cognitively unimpaired adults (Age 68 ± 6.2 years, 58% female, 46% Aß-positive) completed remote, tablet-based speech assessments (i.e., picture description, journal-prompt storytelling, verbal fluency tasks) for five days. The testing paradigm was repeated after 2-3 weeks. Acoustic speech features were automatically extracted from the voice recordings, and mean scores were calculated over the 5-day period. We assessed feasibility by adherence rates and usability ratings on the System Usability Scale (SUS) questionnaire. Test-retest reliability was examined with intraclass correlation coefficients (ICCs). We investigated the associations between acoustic features and Aß-pathology, using linear regression models, adjusted for age, sex and education. RESULTS: The speech assessment was feasible, indicated by 91.6% adherence and usability scores of 86.0 ± 9.9. High reliability (ICC ≥ 0.75) was found across averaged speech samples. Aß-positive individuals displayed a higher pause-to-word ratio in picture description (B = -0.05, p = 0.040) and journal-prompt storytelling (B = -0.07, p = 0.032) than Aß-negative individuals, although this effect lost significance after correction for multiple testing. CONCLUSION: Our findings support the feasibility and reliability of multi-day remote assessment of speech acoustics in cognitively unimpaired individuals with and without Aß-pathology, which lays the foundation for the use of speech biomarkers in the context of early AD.

Neutrophil-Lymphocytes Ratio as Potential Early Marker for Alzheimer's Disease.

Background: Neutrophil-lymphocyte ratio (NLR) is a noninvasive, inexpensive, and easily applicable marker of inflammation. Since immune dysregulation leading to inflammation is regarded as a hallmark of dementia, in particular Alzheimer's disease (AD), we decided to investigate the potentials of NLR as a diagnostic and predictive biomarker in this clinical setting. Materials and Methods: NLR was measured in the blood of patients with AD (n = 103), amnestic type mild cognitive impairment (aMCI, n = 212), vascular dementia (VAD, n = 34), and cognitively healthy Controls (n = 61). One hundred twelve MCI patients underwent a regular clinical follow-up. Over a 36-months median follow-up, 80 remained stable, while 32 progressed to overt dementia. Results: NLR was higher in patients with aMCI or dementia compared to Controls; however, the difference was statistically significant only for aMCI (+13%, p=0.04) and AD (+20%, p=0.03). These results were confirmed by multivariate logistic analysis, which showed that high NLR was associated with an increase in the likelihood of receiving a diagnosis of aMCI (odd ratio (OR): 2.58, 95% confidence interval (CI): 1.36-4.89) or AD (OR: 3.13, 95%CI: 1.47-6.70), but not of VAD. NLR did not differ when comparing stable vs. progressing aMCI. Conclusions: This is the first report showing that NLR is significantly increased in MCI and AD but not in VAD. We also found that NLR was unable to predict the conversion from aMCI to AD. Further research on larger cohorts is warranted to definitely ascertain the application of NLR as a possible marker for aMCI and AD.

Analysis of eligibility criteria in Alzheimer's and related dementias clinical trials.

Overly restrictive clinical trial eligibility criteria can reduce generalizability, slow enrollment, and disproportionately exclude historically underrepresented populations. The eligibility criteria for 196 Alzheimer's Disease and Related Dementias (AD/ADRD) trials funded by the National Institute on Aging were analyzed to identify common criteria and their potential to disproportionately exclude participants by race/ethnicity. The trials were categorized by type (48 Phase I/II pharmacological, 7 Phase III/IV pharmacological, 128 non-pharmacological, 7 diagnostic, and 6 neuropsychiatric) and target population (51 AD/ADRD, 58 Mild Cognitive Impairment, 25 at-risk, and 62 cognitively normal). Eligibility criteria were coded into the following categories: Medical, Neurologic, Psychiatric, and Procedural. A literature search was conducted to describe the prevalence of disparities for eligibility criteria for African Americans/Black (AA/B), Hispanic/Latino (H/L), American Indian/Alaska Native (AI/AN) and Native Hawaiian/Pacific Islander (NH/PI) populations. The trials had a median of 15 criteria. The most frequent criterion were age cutoffs (87% of trials), specified neurologic (65%), and psychiatric disorders (61%). Underrepresented groups could be disproportionately excluded by 16 eligibility categories; 42% of trials specified English-speakers only in their criteria. Most trials (82%) contain poorly operationalized criteria (i.e., criteria not well defined that can have multiple interpretations/means of implementation) and criteria that may reduce racial/ethnic enrollment diversity.