IntroductionEarly detection of Alzheimer’s disease (AD) is critical for timely intervention, particularly during the mild cognitive impairment (MCI) stage. This study aimed to develop and evaluate a multidomain speech analysis framework to support cognitive screening, biomarker prediction within the amyloid, tau and neurodegeneration (ATN) framework, and estimation of cognitive function across the AD continuum.MethodsThis study analyzed speech from 2,320 individuals spanning the cognitive spectrum-including those with subjective cognitive decline (SCD), MCI, and Alzheimer’s disease dementia (ADD)-using three spoken tasks (∼3 min) and extracted multidomain features including acoustic, lexical, syntactic, and semantic features. Machine learning models were trained to classify cognitive status, predict amyloid, tau and neurodegeneration (ATN) biomarker positivity, and estimate scores across six neuropsychological domains.ResultsMultidomain speech models achieved high performance in differentiating cognitive stages, with AUC values of up to 0.94 for SCD vs. ADD and 0.82 for SCD vs. MCI classifications. In biomarker prediction, the models yielded AUCs of 0.71, 0.74, and 0.73 for ATN classification, respectively. Speech-based models also showed strong correlations (up to 0.83) with cognitive function scores. Feature importance analysis revealed that verbal fluency measures were the most predictive. Explainability analyses indicated minimal dependency on age, sex, or education, supporting model fairness.DiscussionThese findings show that multidomain speech features capture clinically and biologically relevant information across the AD continuum, enabling cognitive classification, biomarker prediction, and cognitive estimation. These results underscore the potential of speech analysis as a non-invasive, accessible tool for scalable cognitive screening and early detection of AD. These results underscore the potential of speech analysis as a non-invasive, accessible tool for scalable cognitive screening and early detection of AD.

Schizophrenia (SCZ) and bipolar disorder (BD) are complex psychiatric disorders with partially overlapping genetic architectures and shared features with neurodegenerative diseases. Short tandem repeats (STRs), particularly CAG expansions in HTT , ATXN1 and ATXN2 , are established causes of neurodegenerative disorders, yet their role as genetic modifiers in major mental disorders remains poorly understood. We analysed CAG repeat sizes in a cohort of 1,604 individuals, including 234 SCZ patients, 329 BD patients and 1,041 healthy controls. Repeat lengths were determined by fluorescent PCR and capillary electrophoresis, and associations with disease risk and clinical phenotypes were evaluated using non-parametric tests, multinomial logistic regression and survival analyses. Intermediate HTT alleles were more frequent in BD type I compared with controls (8.8% vs 4.4%, p = 0.033), whereas ATXN1 intermediate alleles were less frequent in SCZ than in BD (4.7% vs 11.2%, p = 0.02). Although overall differences in repeat size were modest, ATXN2 CAG length showed consistent shifts across diagnostic groups, with slightly larger repeats in BD and SCZ than in controls. Multinomial models identified the ATXN2 long allele as associated with disease risk with a non-linear effect. Intermediate ATXN2 alleles were also associated with shorter disease duration in BD (p = 0.00048). Functional enrichment analysis revealed convergence of these genes with SCZ- and BD-related networks involved in synaptic function and RNA metabolism. These findings support a role for CAG repeat variation as a genetic modifier influencing susceptibility and clinical trajectories in psychiatric disorders.

Background Polygenic risk scores for Alzheimer’s disease (AD-PRS) are widely used to estimate genetic susceptibility to AD, but their relationship with the rate of cognitive decline (CD) after clinical onset remains insufficiently characterized. Objectives To examine the association between AD-PRS and longitudinal CD across the AD spectrum and to evaluate the predictive contribution of individual AD-PRS variants. Design Large longitudinal observational study in a single-center cohort, with an external cohort to assess generalizability. Setting Memory clinic cohort from Ace Alzheimer Center Barcelona (Ace) with external cohort using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants The study included 7,233 patients from Ace and 863 from ADNI, with a mean follow-up of 5.4 years in Ace and 3.6 years in ADNI. A biomarker sub-cohort included 1075 participants from Ace and 569 from ADNI. Measurements CD was quantified as the annual change in Mini-Mental State Examination (MMSE) scores estimated using linear mixed-effects models. Associations between AD-PRS and longitudinal MMSE trajectories were tested adjusting for clinical and sociodemographic (CSD) variables and APOE genotype. Machine learning models and SHapley Additive exPlanations (SHAP) were used to evaluate the predictive relevance of individual variants. Results Higher AD-PRS was associated with faster CD in the full clinical cohort and in biomarker subset, independently of APOE genotype. AD-PRS was not associated with baseline MMSE. APOE ε4 was associated with lower baseline MMSE and faster CD only in the full clinical sample. Genetic predictors provided limited improvement beyond CSD variables, and model performance showed limited reproducibility across cohorts. Conclusions AD-PRS is associated with longitudinal CD across the AD spectrum. Although polygenic burden contributes to variability in cognitive trajectories, its added predictive value beyond routinely available clinical variables remains modest.

The cerebrospinal fluid (CSF) proteome offers a direct readout of central nervous system (CNS) biology but its genetic architecture remains incompletely defined. We conducted the largest single-site CSF genome-wide association study (GWAS) to date, analysing 7,092 SomaScan proteins in 1,259 individuals. Using a covariate-adjusted model including proteomic PCs and disease status, we identified 1,971 genome-wide significant pQTLs (954 cis, 971 trans), 1,409 of which replicated in an independent CSF dataset. We discovered 264 previously unreported loci, replicated 511 associations, refined 80 known loci, and 265 proxy-based associations. Using a previously published reproducibility framework, we show that robust discovery concentrates in reliable measurements, underscoring the importance of rigorous quality control. Enrichment analyses revealed immune/complement and extracellular matrix biology. Mendelian randomization prioritised causal proteins: PILRA, TREM2, IL34, CR2, SHARPIN and ERBB1 (Alzheimer's disease); BST1 and GPNMB (Parkinson's disease); STX6 (Creutzfeldt Jacobs disease); and ATXN3 and B4GALNT1 (Amyotrophic lateral sclerosis), providing a scalable framework for orthogonal target validation in neurodegeneration.

Introduction:Ageing is accompanied by gradual biological and cognitive changes that increase vulnerability to chronic diseases and neurodegenerative conditions. As populations age, dementia prevalence continues to rise, highlighting the need for earlier detection and personalised prevention strategies. Against this background, the COMFORTage project, funded by Horizon Europe, brings together a multidisciplinary consortium across 12 countries to advance innovative, scalable solutions for dementia care. By integrating digital platforms, biomarker research, and precision medicine, COMFORTage seeks to develop artificial intelligence (AI)-driven tools that support more precise and adaptive interventions. Central to this effort are the Virtualized AI-Based Healthcare Platform and Patient Digital Twins, which enable personalised monitoring and decision support. Within this framework, Pilot 3 at Ace Alzheimer Center Barcelona focuses on individuals with mild cognitive impairment and mild Alzheimer's disease dementia, evaluating the effects of cognitive and functional stimulation and contributing multimodal data to optimise the AI platform. Methods:Pilot 3 is a randomised, open-label study involving retrospective and prospective datasets. Participants undergo clinical, genetic, neuropsychological, cerebrospinal fluid (CSF) and plasma biomarker assessments, magnetic resonance imaging (MRI), and spontaneous speech analysis. The primary outcomes assess cognitive decline using composite scores from the Neuropsychological Battery used in Ace (NBACE), targeting attention, memory, visuospatial/perceptual functions, executive functions, and language, over a two-year follow-up. Three digital platforms provided by the consortium will be used as cognitive and functional stimulation tools for participants. The intervention's effects on cognitive decline will be evaluated through changes in NBACE composite scores. Secondary objectives include assessing impacts on physical, psychological, social, and functional well-being; examining associations between biological variables and cognitive changes; and analyzing spontaneous speech as a remote, scalable proxy for cognitive status. Discussion:Findings from Pilot 3 will contribute to COMFORTage's broader mission, offering critical insights into the scalability and real-world implementation of AI-powered dementia care solutions. This integrated approach highlights the potential of precision medicine and advanced digital tools to elevate global standards in dementia management. Clinical Trial Registration:identifier NCT07031167.

Alzheimer’s disease (AD) stands as the leading cause of dementia worldwide, and projections estimate over 150 million patients by 2050. AD prevalence is notably higher in women, nearly twice that of men, with discernible sex differences in certain risk factors. To enhance our understanding of how sex influences the characteristics of AD patients and its potential impact on the disease trajectory, we conducted a comprehensive analysis of demographic, clinical, cognitive, and genetic data from a sizable and well-characterized cohort of AD dementia patients at a memory clinic in Barcelona, Spain. The study cohort comprised individuals with probable and possible AD dementia with a Clinical Dementia Rating (CDR) score between 1 and 3 diagnosed at the Memory Unit from Ace Alzheimer Center Barcelona, Spain, between 2008 and 2018. We obtained cognitive baseline data and follow up scores for the Mini-Mental State Examination (MMSE), the CDR scale, and the neuropsychological battery used in our center (NBACE). We employed various statistical techniques to assess the impact of sex on cognitive evolution in these dementia patients, accounting for other sex-related risk factors identified through Machine Learning methods. The study cohort comprised a total of 6108 individuals diagnosed with AD dementia during the study period (28.4

Cerebrospinal fluid (CSF) biomarkers are key sources of insight for research and clinical practice in the neurodegeneration field. Here, we used omics data to characterize a physiological source of variability which has a major impact in the concentration of CSF analytes and is rarely accounted for in these studies. We studied 1,372 samples from the ACE Alzheimer Center Barcelona memory clinic, including cognitively unimpaired subjects and patients with mild cognitive impairment or dementia. We analysed CSF lipidomics (Lipometrix, 386 species), proteomics (SomaScan 7K, 2395 species), and genomics data (TOPMed-imputed Affymetrix Axiom 815K Array). We estimated the overall concentration (mean standardized values) and principal components (PCs) of the proteomics and lipidomics data independently. Genome-wide associations (GWAs) were performed using PLINK v2.00a3 adjusting by age, sex and population microstructure. Enrichment analysis was conducted using WebGestalt. Mean standardized intensities and the PC1 (explaining 40% and 60% of the CSF lipidomics and proteomics variance, respectively) were highly intercorrelated (Figure 1). Despite independent from disease groups and progression, these metrics were strongly associated with CSF p -tau181 and Aβ42 levels (Figure 2). GWAs revealed that GMNC , previously associated with CSF p -tau levels and lateral ventricular volume, and linked to multicilliated cell differentiation in the choroid plexus, was associated ( p  <5·10 -08 ) with these metrics in both experiments. Known alleles increasing ventricular volume were negatively associated with the mean intensity and PC1, downregulating a substantial portion of the proteome (Figure 3). Interestingly, we observed a large overlap in both the down- and upregulated proteomic signature of these variants. Enrichment analysis revealed that proteins diluted by ventricular volume-increasing alleles were related to neuronal function, while increased proteins were associated with immune functions, pinpointing CSF production/clearance rates as likely contributors to this phenomenon. These alleles also caused widespread CSF-specific proteome and metabolome downregulation in an external validation cohort ( https://ontime.wustl.edu/ ). We demonstrate that the main source of variability in the CSF is a non-disease related trait traceable to genetic loci and closely related to ventricular volume. Accounting for this physiological variability is essential for accurate interpretation of CSF biomarkers, with broad implications for neurodegeneration research and clinical practice.

Progressive supranuclear palsy (PSP) is a rare 4-repeat tauopathy that causes behavioural, movement and cognitive abnormalities. We genotyped all available clinical and histopathological PSP cases in Spain and Portugal (N = 522), and conducted the largest PSP GWAS of the Iberian population to date. Genetic burden analysis revealed reduced diagnostic specificity in clinically diagnosed atypical PSP cases—when applying the 2017 MDS criteria—compared to Richardson’s syndrome cases. We independently replicated eight PSP risk variants in seven known loci (MAPT, MOBP, EIF2AK3, STX6, SLCO1A2, DUSP10 and APOE), and identified a novel locus in NFASC/CNTN2 (rs12744678 C: OR[95

Parkinson's disease genetic embraces genetic and non-genetic factors. It has been suggested a link between CAG repeat number in the HTT, ATXN1 and ATXN2 genes and different neurodegenerative diseases. Several genetic factors involved in Parkinson's disease development are indeed associated with cancer pathways. Moreover, several studies found a low prevalence of cancer in neurodegenerative diseases that can be associated with a low CAG repeat size in several genes. This study aimed to investigate the influence of CAG repeat sizes in ATXN1, ATXN2 and HTT genes on the risk for developing cancer and Parkinson's disease in a large cohort of patients with idiopathic Parkinson's disease and healthy controls. The work included 1052 patients with idiopathic Parkinson's disease and 1070 controls of European ancestry. CAG repeat sizes in HTT, ATXN1 and ATXN2 genes were analysed. Dunn's multiple comparison test for quantitative variables and logistic and linear regression were used. The long ATXN1 and HTT alleles and CAG size and both the ATXN2 short and long alleles were predictors for the Parkinson's disease risk. The long CAG ATXN1 allele gene was associated with the risk of cancer. No association was observed between CAG size in the HTT and ATXN2 genes and risk of cancer in patients with Parkinson's disease. We described an association of HTT, ATXN1 and ATXN2 with the risk of Parkinson's disease, which reinforce the hypothesis of the common pathway of neurodegeneration. Besides, ATXN1 could be a predictor of cancer risk among patients with Parkinson's disease, and these results suggest that cancer and neurodegeneration processes can share common pathways. P & eacute;rez-Oliveira et al. reported association between CAG repeat expansion disorders and the risk of Parkinson disease and also investigated the connection between CAG repeats and cancer comorbidity in patients with Parkinson's disease. ATXN1 could be a predictor of cancer risk among patients with Parkinson's disease, suggesting that cancer and neurodegeneration process can share common pathways.

BACKGROUND:Cerebrospinal fluid (CSF) biomarkers are key sources of insight for research and clinical practice in the neurodegeneration field. Here, we used omics data to characterize a physiological source of variability which has a major impact in the concentration of CSF analytes and is rarely accounted for in these studies. METHODS:We studied 1,372 samples from the ACE Alzheimer Center Barcelona memory clinic, including cognitively unimpaired subjects and patients with mild cognitive impairment or dementia. We analysed CSF lipidomics (Lipometrix, 386 species), proteomics (SomaScan 7K, 2395 species), and genomics data (TOPMed-imputed Affymetrix Axiom 815K Array). We estimated the overall concentration (mean standardized values) and principal components (PCs) of the proteomics and lipidomics data independently. Genome-wide associations (GWAs) were performed using PLINK v2.00a3 adjusting by age, sex and population microstructure. Enrichment analysis was conducted using WebGestalt. RESULTS:Mean standardized intensities and the PC1 (explaining 40% and 60% of the CSF lipidomics and proteomics variance, respectively) were highly intercorrelated (Figure 1). Despite independent from disease groups and progression, these metrics were strongly associated with CSF p-tau181 and Aβ42 levels (Figure 2). GWAs revealed that GMNC, previously associated with CSF p-tau levels and lateral ventricular volume, and linked to multicilliated cell differentiation in the choroid plexus, was associated (p <5·10-08) with these metrics in both experiments. Known alleles increasing ventricular volume were negatively associated with the mean intensity and PC1, downregulating a substantial portion of the proteome (Figure 3). Interestingly, we observed a large overlap in both the down- and upregulated proteomic signature of these variants. Enrichment analysis revealed that proteins diluted by ventricular volume-increasing alleles were related to neuronal function, while increased proteins were associated with immune functions, pinpointing CSF production/clearance rates as likely contributors to this phenomenon. These alleles also caused widespread CSF-specific proteome and metabolome downregulation in an external validation cohort (https://ontime.wustl.edu/). CONCLUSIONS:We demonstrate that the main source of variability in the CSF is a non-disease related trait traceable to genetic loci and closely related to ventricular volume. Accounting for this physiological variability is essential for accurate interpretation of CSF biomarkers, with broad implications for neurodegeneration research and clinical practice.

Background: The group intervention aimed at caregivers of persons with dementia is regarded as an effective tool for support and education. However, these groups do not specifically cater to caregivers of individuals with spouses affected by young-onset dementia. Objective: To assess the effectiveness of a support and training group specifically targeted towards spouses of individuals with young onset dementia and its impact on reducing caregiver burden. Participants and Methods: Participants were recruited from a single memory clinic in Catalonia, Spain. The Zarit Burden Interview (ZBI) was utilized to evaluate caregiver burden both before and after participation in the support groups and a combined quantitative and qualitative analysis approach was employed. Results: A total of 77 caregivers were included and assessed, comprising 45.5% females with a mean age of 55 years and 54.5% males with a mean age of 63 years. While the overall caregiver burden, as measured by the ZBI, did not exhibit a significant reduction following participation in the group sessions, a notable decrease in ZBI scores was observed among caregivers with the highest burden at baseline. Three key stages were identified throughout the sessions: (1) discussions pertaining to the type of dementia and its associated changes; (2) the provision of care and attention to spouses along with the exchange of information among participants; and (3) identification of caregivers’ needs. Conclusions: The subgroup of caregivers of persons with young onset dementia who exhibited the highest burden at baseline derived the greatest benefit from the support groups. Various qualitative indicators.

Background:The apolipoprotein E (APOE) gene is a key genetic determinant of Alzheimer's disease (AD) risk, with the ε4 allele significantly increasing susceptibility. While the pathogenic effects of the ε4 allele are well established, the functional impact of distinct haplotype configurations within the broader ε3 and ε4 backgrounds remains poorly understood. This study investigates the role of intragenic sub haplotypes in modulating APOE expression and their potential influence on AD progression. Methods:We utilized Oxford Nanopore Technology (ONT) long-read sequencing to phase variants within a 4-kilobase comprising the APOE locus in a cohort of 1,265 individuals with known APOE genotypes. We evaluated the impact of the identified intragenic haplotypes on APOE protein levels in cerebrospinal fluid (CSF) using the Olink platform, adjusting for demographic and molecular covariates. Statistical modeling was employed to assess the independent effects of these haplotypes alongside traditional APOE genotypes. Additionally, their influence on dementia progression in mild cognitive impairment (MCI) subjects was analyzed using adjusted Cox proportional hazards models. Results:Our analysis identified 48 Single Nucleotide Variants (SNVs) within a 4-kilobase region containing the APOE gene, including nine novel variants. Phasing of variants within the APOE locus revealed 59 unique haplotypes in the Spanish population, which were grouped into five major haplogroups-ε2, ε3A, ε3B, ε4A, and ε4B-including two common haplogroups for each of the ε3 and ε4 isoforms. The ε4A haplogroup was associated with a significant decrease in APOE ε4 protein levels in CSF (p = 0.004), suggesting a regulatory mechanism that may mitigate the toxic gain-of-function effect typically attributed to the ε4 allele. Conversely, the ε3B haplogroup was linked to increased APOE ε3 protein levels in ε3/ε4 carriers (p = 0.025), potentially serving a compensatory role.These effects were independent of overall APOE genotype and remained significant after adjusting for covariates. Both haplogroups (ε4A and ε3B) demonstrated protective effects in the progression from MCI to dementia, underscoring their potential relevance in Alzheimer's disease. Conclusions:This study provides new insights into the intragenic allelic variability of the APOE gene, demonstrating that intragenic APOE haplogroups within the ε3 and ε4 backgrounds can modulate APOE isoform expression in ways that might modulate AD. Our findings highlight the importance of considering haplotype-specific effects when interpreting the functional impact of APOE and in designing targeted therapeutic strategies. Further research is needed to explore the broader regulatory network of the APOE locus and its interaction with neighboring loci in the 19q13 region.

IntroductionIn Alzheimer’s disease (AD) research, clinical, neuroimaging, genetic, and biomarker data are vital for advancing its understanding and treatment. However, privacy concerns and limited datasets complicate data sharing. Federated learning (FL) offers a solution by enabling collaborative research while preserving data privacy.MethodsThis study analyzed data from patients assessed at the Memory Unit of the Ace Alzheimer Center Barcelona who completed a standardized digital speech protocol. Acoustic features extracted from these recordings were used to distinguish between cognitively unimpaired (CU) and cognitively impaired (CI) individuals. The aim was to evaluate how data heterogeneity impacted the FL model performance across three scenarios: (1) equal contributions and class ratios, (2) unequal contributions, and (3) imbalanced class ratios. In each scenario, the performance of local models trained using an MLP feed-forward neural network on institutional data was analyzed and compared to a global model created by aggregating these local models using Federated Averaging (FedAvg) and Iterative Data Aggregation (IDA).ResultsThe cohort included 2,239 participants: 221 CU individuals (mean age 66.8, 64.7% female) and 2,018 CI subjects, comprising 1,219 with mild cognitive impairment (mean age 74.3, 61.9% female) and 799 with mild AD dementia (mean age 80.8, 64.8% female). In scenarios 1 and 3, FL provided modest gains in accuracy and AUC. In scenario 2, FL markedly improved performance for the smaller dataset (balanced accuracy rising from 0.51 to 0.80) while preserving 0.86 accuracy in the larger dataset, highlighting scalability across heterogeneous conditions.ConclusionThese findings demonstrate the potential of FL to enable collaborative modeling of speech-based biomarkers for cognitive impairment detection, even under conditions of data imbalance and institutional disparity. This work highlights FL as a scalable and privacy-preserving approach for advancing digital health research in neurodegenerative diseases.

Alzheimer’s Disease (AD) is a complex disorder and much of its etiopathology is still unknown. Here, we applied dimensionality reduction methods to disentangle cyptic patterns in CSF proteomic and lipidomic data. We studied 1121 CSF samples using targeted lipidomics based on liquid chromatography (LC)-MS/MS (mass spectrometry), generated by Lipometrix (Lueven, Belgium), and proteomic data generated by Somalogic (Boulder, Colorado) using the SOMAscan 7k Assay. We independently computed the principal components for the proteomic and lipidomic datasets using good quality lipids (N=388) and proteins (N=2469). CSF samples were obtained by lumbar punctures at ACE Alzheimer Center (Barcelona, Spain), including patients at different points of the dementia continuum (SCD, MCI and dementia). Principal components were calculated using the princomp() function in R. PC1 explained a substantial fraction of the variance in lipidomic (∼40%) and proteomic (∼60%) datasets and was highly correlated between both omics (R2=0.43; p=2.3·10 -138 , Figure 1). We explored the association profile of the PCs to AD risk factors (age, sex, APOE, PRS, diabetes, hypertension, dyslipidemia, BMI), endophenotypes (abeta, tau), disease progression and total protein in the CSF. PC1, as well as the individual lipid species, were strongly associated with abeta, tau and total CSF protein levels (Figure 2). Finally, we conducted a GWAS of the first 20 lipidomic and proteomic PCs. PC1 displayed a GWS signal at chr3q28 in both omics. This region has been previously linked with CSF tau levels and brain morphology. Subsequent lipidomic PCs were associated with variants in the FADS1/FADS2 locus, while other proteomic PCs were associated with variants in the APOE locus (Figure 3). Our findings revealed a shared major contributor to the variance in CSF between lipidomic and proteomic data, which is related to the tau, abeta and total protein signature, and identified a QTL associated to both the lipidomic and proteomic PC1. Accounting for this major variance contributor may enhance future studies involving CSF biomarkers. Subsequent principal components had specific association profiles to AD risk factors and endophenotypes.

The apolipoprotein E (APOE) ε4 allele remains the strongest genetic risk factor for late-onset Alzheimer’s disease (AD), yet the marked variability in its pathogenicity suggests underlying genetic complexity. Historically, efforts to resolve the intragenic architecture of APOE have been hampered by the limitations of conventional genotyping and short-read sequencing, as well as the presence of homoplasy in common intragenic markers—misleading similarities arising from convergent variants. We leveraged Oxford Nanopore Technology (ONT) to phase intragenic APOE variants, resolve homoplasy, and examine the impact of phased haplotypes on cerebrospinal fluid (CSF) APOE protein levels and AD progression. Using long-read sequencing in a Spanish memory clinic cohort (n = 1,267), we reconstructed full-length 4 kb APOE haplotypes, identifying 59 unique configurations grouped into five major haplogroups. Common intragenic variants defined ancestral ε4 (4A, 4B) and ε3 (3A, 3B) haplogroups. These were analyzed for associations with CSF APOE levels (Olink platform) and progression from mild cognitive impairment (MCI) to dementia using adjusted Cox regression models. ONT sequencing successfully resolved homoplasy between the APOE promoter region—particularly at rs405509—and the canonical protein isoforms, uncovering common but functionally distinct ε3A/B and ε4A/B intragenic sub-haplotypes with independent biological effects. Carriers of the ε4A haplotype exhibited significantly lower CSF APOE protein levels (p = 0.004), whereas the ε3B haplotype was associated with elevated CSF APOE protein levels (p = 0.025). Notably, both haplotypes were linked to a slower progression from MCI to AD, independent of APOE genotype, age, sex and core CSF biomarkers. This study redefines the human APOE ε3 and ε4 alleles as genetically heterogeneous entities. Using ONT long-read sequencing, we achieved high-resolution mapping of intragenic haplotypic structure and regulatory variation previously obscured by conventional approaches. This enabled the identification of ancestral haplotypes with distinct functional profiles and potential relevance to Alzheimer’s disease pathogenesis. These findings highlight the importance of incorporating haplotype-level resolution into Alzheimer’s risk assessment, therapeutic targeting, and precision medicine strategies.

High-throughput proteomic platforms are crucial to identify novel Alzheimer’s disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

INTRODUCTION:Understanding the impact of biomarker-based dementia risk estimation in people with mild cognitive impairment (MCI) and their care partners is critical for patient care. METHODS:MCI patients and study partners were counseled on Alzheimer's disease (AD) biomarker and dementia risk was disclosed. Data on mood, quality of life (QoL), and satisfaction with life (SwL) were obtained 1 week and 3 months after disclosure. RESULTS:Seventy-five dyads were enrolled, and two-thirds of the patients opted for biomarker testing. None of the participants experienced clinically relevant depression or anxiety after disclosure. All dyads reported moderate to high QoL and SwL throughout the study. Patients reported more subthreshold depressive symptoms 1 week and lower QoL and SwL 3 months after disclosure. In patients, depression (odds ratio [OR]: 0.76) and anxiety (OR: 0.81) were significant predictors for the decision against biomarker testing. DISCUSSION:No major psychological harm is to be expected in MCI patients and care partners after dementia risk disclosure. TRIAL REGISTRATION:This study is registered in the German clinical trials register (Deutsches Register Klinischer Studien, DRKS): http://www.drks.de/DRKS00011155, DRKS registration number: DRKS00011155, date of registration: 18.08.2017. HIGHLIGHTS:Patients with mild cognitive impairment (MCI) and study partners were counseled on Alzheimer's disease (AD) biomarker-based dementia risk estimation. About two-thirds of patients opted for biomarker testing and received their dementia risk based on their AD biomarker status. Patients who decided in favor or against CSF biomarker testing differed in psychological features. We did not observe major psychological harm after the dementia risk disclosure. Coping strategies were associated with better subsequent mood and well-being in all participants.

BACKGROUND:The identification of patients with an elevated risk of developing Alzheimer's disease (AD) dementia and eligible for the disease-modifying treatments (DMTs) in the earliest stages is one of the greatest challenges in the clinical practice. Plasma biomarkers has the potential to predict these issues, but further research is still needed to translate them to clinical practice. Here we evaluated the clinical applicability of plasma pTau181 as a predictive marker of AD pathology in a large real-world cohort of a memory clinic. METHODS:Three independent cohorts (modelling [n = 991, 59.7% female], testing [n = 642, 56.2% female] and validation [n = 441, 55.1% female]) of real-world patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD dementia, and other dementias were included. Paired cerebrospinal fluid (CSF) and plasma samples were used to measure AT(N) CSF biomarkers and plasma pTau181. FINDINGS:CSF and plasma pTau181 showed correlation in all phenotypes except in SCD and other dementias. Age significantly influenced the biomarker's performance. The general Aβ(+) vs Aβ(-) ROC curve showed an AUC = 0.77 [0.74-0.80], whereas the specific ROC curve of MCI due to AD vs non-AD MCI showed an AUC = 0.89 [0.85-0.93]. A cut-off value of 1.30 pg/ml of plasma pTau181 exhibited a sensitivity of 93.57% [88.72-96.52], specificity of 72.38% [62.51-79.01], VPP of 77.85% [70.61-83.54], and 8.30% false negatives in the subjects with MCI of the testing cohort. The HR of cox regression showed that patients with MCI up to this cut-off value exhibited a HR = 1.84 [1.05-3.22] higher risk to convert to AD dementia than patients with MCI below the cut-off value. INTERPRETATION:Plasma pTau181 has the potential to be used in the memory clinics as a screening biomarker of AD pathology in subjects with MCI, presenting a valuable prognostic utility in predicting the MCI conversion to AD dementia. In the context of a real-world population, a confirmatory test employing gold-standard procedures is still advisable. FUNDING:This study has been mainly funded by Ace Alzheimer Center Barcelona, Instituto de Salud Carlos III (ISCIII), Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Spanish Ministry of Science and Innovation, Fundación ADEY, Fundación Echevarne and Grífols S.A.

Background: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, sensitive to Alzheimer’s disease, using information and communication technologies, and offered freely worldwide. Objective: To investigate the demographic and clinical variables associated with the total FACEmemory score, and to identify distinct patterns of memory performance on FACEmemory. Methods: Data from the first 3,000 subjects who completed the FACEmemory test were analyzed. Descriptive analyses were applied to demographic, FACEmemory, and medical and family variables; t-test and chi-square analyses were used to compare participants with preserved versus impaired performance on FACEmemory (cut-off = 32); multiple linear regression was used to identify variables that modulate FACEmemory performance; and machine learning techniques were applied to identify different memory patterns. Results: Participants had a mean age of 50.57 years and 13.65 years of schooling; 64.07% were women, and 82.10% reported memory complaints with worries. The group with impaired FACEmemory performance (20.40%) was older, had less schooling, and had a higher prevalence of hypertension, diabetes, dyslipidemia, and family history of neurodegenerative disease than the group with preserved performance. Age, schooling, sex, country, and completion of the medical and family history questionnaire were associated with the FACEmemory score. Finally, machine learning techniques identified four patterns of FACEmemory performance: normal, dysexecutive, storage, and completely impaired. Conclusions: FACEmemory is a promising tool for assessing memory in people with subjective memory complaints and for raising awareness about cognitive decline in the community.

Background Advancement in screening tools accessible to the general population for the early detection of Alzheimer’s disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. Methods Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. Results The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. Conclusions In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.