Alzheimer's & Dementia

INTRODUCTION: Alzheimer's disease (AD) and frontotemporal dementia (FTD) have considerable clinical and pathological overlap. While plasma proteomics has advanced in AD, deep comparative analyses with FTD-particularly in diverse, biomarker-confirmed Asian cohorts-remain limited. METHODS: Plasma from 101 individuals with known pTau217 status was profiled using Olink Explore-HT. Differential expression-pathway enrichment, penalized regression-GLMNET, single-cell transcriptomic integration, associations with cognitive measures and, cross-platform validation were performed. RESULTS: Among 5,400-proteins, 1,168 were differentially expressed in AD and 370 in FTD (FDR<0.05). Distinct and overlapping proteomic signatures were identified in AD and FTD, reflecting gliosis, synaptic dysfunction, immune activation, and metabolic pathways. Prioritized proteins correlated with cognitive performance and plasma phosphorylated tau, A{beta}42, and neurofilament light chain, linking circulating proteins to disease severity. Cross platform validation revealed strong concordance with large independent datasets. CONCLUSION: Comprehensive plasma proteomics in Asian cohort supports scalable framework for blood-based biologically informed targets for precision diagnosis and therapeutic stratification.

INTRODUCTION: Herpes simplex virus-1 (HSV-1) has been implicated in Alzheimers disease (AD). METHODS: Reads from Alzheimers Disease Sequencing Project whole-genome sequencing data collected from brain (2,203 AD; 616 controls) and blood (8,908 AD; 15,768 controls) were aligned to viral genomes. Generalized linear mixed-models tested for the effect of HSV-1 DNA on AD, and we performed GWAS on HSV-1 presence and SNPxHSV-1 interaction effects on AD, adjusting for age, sex, tissue, library preparation, relatedness, and ancestry principal components. RESULTS: Across ancestry groups, HSV-1 DNA was consistently less frequent in AD cases; reads predominantly mapped to regions containing the latency-associated transcript region. DNA prevalence was lower in APOE-{epsilon}4 carriers; HSV-1 was associated with reduced AD risk in {epsilon}4 non-carriers but increased risk in carriers. GWAS identified host genetic influences on HSV-1 detection and interaction loci affecting AD risk. DISCUSSION: HSV-1 DNA showed an inverse association with AD and is affected by genetics.

Background Autosomal dominant Alzheimer's disease (ADAD) serves as a model for presymptomatic biomarker discovery. Characterising the temporal profile of plasma biomarker levels in presymptomatic individuals may enhance understanding of disease pathogenesis, inform future clinical trials, and guide clinical interpretation. Methods We evaluated 124 proteins using a NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) panel in 270 plasma samples from a longitudinal cohort study of ADAD, comprising 113 individuals (73 mutation carriers and 40 non-carriers). We determined the plasma proteomic changes that distinguished mutation carriers from non-carriers. We then used predicted age at symptom onset to determine the approximate timing of presymptomatic divergence in biomarker levels in carriers relative to non-carriers. Results Nine proteins (A{beta}42, BACE1, GFAP, pTau181, pTau231, pTau217, MAPT, NfL, and AChE) robustly differed between carriers and non-carriers, cross-sectionally. Longitudinal analyses showed A{beta}42 levels were elevated in carriers at least 26 years before expected symptom onset. Carriers diverged from non-carriers in phosphorylated tau markers at 21-24 years before expected symptoms, total-tau at 19 years, GFAP and BACE1 at 14 years, and NfL at 6 years. Differences in AChE were seen in symptomatic individuals, likely reflecting cholinesterase inhibitor use. Conclusion Multiple plasma proteins are elevated in presymptomatic and symptomatic autosomal dominant AD mutation carriers relative to non-carriers. Changes in eight biomarkers occur sequentially from 26 to 6 years prior to symptom onset. Combining biomarkers may help in staging presymptomatic AD and optimise clinical trial inclusion. Further work is needed to assess how these findings generalise to non-monogenic AD.

Background: The apolipoprotein E (APOE) locus is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). Variation in APOE isoforms is known to have diverse pleiotropic effects on circulating lipids and other metabolites, but effects on the circulating proteome across the life course are not well characterised. We investigated the specific effects of APOE {epsilon}4 and APOE {epsilon}2 carriage on the circulating proteome in middle-age and later life. Methods: In primary modelling, we analysed associations of APOE {epsilon}4 and {epsilon}2 carriage (reference {epsilon}3/{epsilon}3) with circulating proteins in UK Biobank participants (N = 42,642; age = 39.1 to 70.9 years). Using multivariable linear regression, we conducted ancestry-specific analyses of 2,922 assayed plasma proteins across individuals of European (EUR), African (AFR), and South Asian (SAS) ancestry. To identify age-dependent effects, stratified analyses were performed with the sample split into age groups. We then performed replication analyses of APOE-associated proteins in age-matched groups, using data from two independent UK-based cohorts. Results: We identified 351 proteins associated with {epsilon}2 carriage and 480 with {epsilon}4 carriage among individuals of European ancestry (n = 40,092); 130 of these were associated with both {epsilon}2 and {epsilon}4 carriage (with either consistent or inverse association directions). These included established biomarkers of neurodegeneration (GFAP and NEFL) and other proteins implicated by AD genetic risk loci (e.g., TREM2, CTSB, IDUA, SORT1, GRN). Many of these proteins are linked to other neurodegenerative diseases besides AD. In multiple age groups, {epsilon}4 carriage was strongly associated with consistent differences in circulating APOE, MENT, and PLA2G7 levels across ancestries and cohorts. Conclusion: APOE {epsilon}4 and {epsilon}2 exert broad, often age-dependent effects on the plasma proteome, detectable decades before typical ages of AD diagnoses, highlighting a potential early window for monitoring and intervention.

INTRODUCTIONAlpha-synuclein (Syn) is the most common co-pathology in Alzheimers disease (AD), yet its role within the amyloid-tau-neurodegeneration (ATN) cascade is unknown. METHODSWe analyzed 636 ADNI participants with CSF Syn seed amplification assay, amyloid PET, regional tau PET (Braak I-VI), structural MRI, and cognitive composites. Interaction models tested whether Syn modifies the amyloid-tau and tau-cognition associations. RESULTSSyn positivity (19.0%) amplified the amyloid-tau association across all Braak stages (meta-temporal interaction {beta} = 0.258, 95% CI 0.104-0.411, p = 0.001), with strongest effects in Braak III-IV. Syn did not modify tau-cognition associations in any domain (all interaction p > 0.18). DISCUSSIONSyn co-pathology selectively amplifies amyloid-driven tau propagation without modifying downstream tau-cognition relationships, identifying a node-specific effect within the ATN cascade with implications for patient stratification. Research in ContextO_ST_ABSSystematic reviewC_ST_ABSWe searched PubMed for studies combining -synuclein seed amplification assays with amyloid and tau PET in Alzheimers disease. One recent study (Franzmeier et al., 2025) demonstrated that -synuclein co-pathology accelerates amyloid-driven tau accumulation. No study has examined whether -synuclein modifies the downstream tau-cognition relationship or assessed regional tau specificity across all Braak stages. InterpretationIn 636 ADNI participants, -synuclein co-pathology amplified the amyloid-tau association across all Braak stages but did not modify tau-cognition relationships. This dissociation identifies -synuclein as a node-specific modifier of the ATN cascade, acting at the amyloid-to-tau transition. Future directionsLongitudinal studies with serial tau PET and -synuclein SAA are needed to establish temporality. Clinical trials should evaluate whether -synuclein stratification improves prediction of anti-amyloid treatment response.

Background. Detection of cerebral amyloid pathology currently requires amyloid PET imaging ($5,000-$8,000) or cerebrospinal fluid analysis via lumbar puncture, procedures that are inaccessible for population-level screening. The FDA-cleared Lumipulse G pTau217/Abeta1-42 plasma ratio test (May 2025) represents the first approved blood-based alternative; however, single-ratio approaches cannot distinguish Alzheimer's disease (AD) from non-AD neurodegeneration or provide multi-dimensional disease characterization. Methods. We developed Virtual Spectral Decomposition (VSD), a framework that decomposes plasma biomarker profiles into biologically interpretable diagnostic channels. Four plasma biomarkers - phosphorylated tau-217 (pTau217), amyloid-beta42/40 ratio, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - were measured in 1,139 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Each biomarker was mapped to a VSD channel representing a distinct pathophysiological axis: tau/amyloid phosphorylation, amyloid clearance, neurodegeneration, and astrocytic activation. Channel weights were calibrated via logistic regression, and performance was evaluated against amyloid PET (UC Berkeley) using 10x5-fold repeated cross-validation. Results. VSD 4-channel fusion achieved AUC = 0.900 (+/-0.018), exceeding pTau217 alone (0.888+/-0.022). Optimal sensitivity was 89.7% with 78.1% specificity (NPV = 90.8%). The NfL channel received a negative weight (beta = -1.1), functioning as a disease-exclusion signal: elevated neurodegeneration without amyloid-tau coupling actively reduces the AD probability, distinguishing AD from non-AD neurodegeneration. Complementary CSF proteomics analysis (7,008 proteins, 533 participants) identified 17 amyloid-specific proteins (0.24% of the proteome), revealing a 49:1 tau-to-amyloid asymmetry that explains why blood-based tau markers outperform amyloid markers. Conclusions. Blood-based VSD provides an interpretable, multi-channel framework for amyloid detection that incorporates explicit disease-exclusion logic unavailable to single-biomarker approaches. The architecture extends to multi-disease screening, where the same blood specimen could be routed through disease-specific modules for AD, Parkinson's disease, and cancer.

BackgroundTraumatic brain injury with loss of consciousness (TBI-LOC) is an established risk factor for dementia, yet the pathways linking remote TBI to Alzheimers disease (AD) biology remain incompletely defined. APOE {varepsilon}4 is the strongest genetic predictor of amyloid accumulation in late-onset AD, it may moderate the long-term consequences of head injury. This study investigates whether history TBI-LOC independently contributes or synergistically interacts with APOE {varepsilon}4 to amplify late-life amyloid and tau burden. Methods429 participants completed the Ohio State University TBI screening tool and an amyloid PET scan (centiloids). A subcohort (n=352) also underwent tau PET. TBI history was classified by recency (<10 vs >10 years) and severity (no TBI, dazing/confusion [TBI-DZ], TBI-LOC). Analyses were stratified by degree of clinical impairment as assessed by Clinical Dementia Rating (CDR=0 vs CDR>0). Logistic and linear regression models examined associations between TBI and amyloid, adjusting for age, sex, education, and APOE {varepsilon}4, including an APOE*TBI-LOC status interaction term, while Fishers exact tests evaluated TBI recency and biomarker positivity. ResultsIn CDR=0 participants (n=365), 119 reported a history of TBI, comprising 56 TBI-DZ and 63 TBI-LOC. TBI-LOC but not TBI-DZ, correlated with elevated amyloid PET levels (p<0.001; [4.6-17]). Furthermore, an interaction between APOE {varepsilon}4 and TBI-LOC indicated that TBI-LOC augmented the amyloid-related risk associated with the APOE {varepsilon}4 allele (p=0.003; [4.3-21]). The interaction persisted when stratified by TBI recency with only remote TBI-LOC (occurring more than 10 years prior) associated with increased amyloid PET (p=0.003 [5.2-25]). No association between TBI and tau was identified in a subset with tau PET, and no TBI-amyloid correlations were observed among symptomatic participants (CDR>0; n=64) suggesting a ceiling effect of pathology once clinical dementia is present. ConclusionsHistory of remote TBI-LOC is linked to elevated amyloid PET levels in later life, particularly among APOE {varepsilon}4 carriers with a CDR=0. The robust findings for amyloid, contrasted with null tau results and the reduced association in symptomatic cases underscore the importance of considering TBI history when screening for preclinical AD and assessing early-stage risk.

BackgroundAlzheimers disease (AD) can be diagnosed using cerebrospinal fluid (CSF) biomarkers reflecting amyloid and tau pathology. However, it provides no information about functional network status. We aimed to determine whether CSF biomarkers (A{beta}42, p-Tau, t-Tau, and A{beta}42/p-Tau ratio) are associated with altered stimulus differentiation in vergence and pupil responses during an oddball task, and to evaluate oculomotor metrics as predictors of CSF core AD biomarkers in patients at mild cognitive impairment (MCI) stage. MethodsThirty-eight participants with abnormal CSF core AD biomarkers at MCI stage completed a visual oddball task while oculomotor responses were recorded. Linear mixed-effects models examined condition x biomarker interactions, controlling for sex, age, and MMSE. Temporal and magnitude features were tested as predictors using linear regression. ResultsHigher p-Tau levels were negatively associated with target-distractor differentiation in cognitive vergence ({beta} = -0.035, p < 0.001) and pupil responses ({beta} = - 0.060, p < 0.001). Higher A{beta}42 and A{beta}42/p-Tau showed positive associations with vergence differentiation but opposite effects on pupil responses. Oculomotor features predicted p-Tau levels (R2 = 0.20-0.21). ConclusionOculomotor differentiation metrics capture functional signatures of tau-related network dysfunction, positioning them as accessible biomarkers complementing CSF measures for detecting network disruption at MCI stage.

Background: Alzheimer's disease (AD) exhibits marked sex differences. While sex hormone levels across the lifespan likely contribute to this, little remains known about their causal impact and their relation to sex-biased genetic risk for AD. We therefore sought to identify potential shared genetic architectures, as well as causal genes and relationships, between sex hormone-related traits and AD risk. Methods: Large-scale AD sex-stratified genome-wide association study (GWAS) results were available from case-control, proxy-based, and population-based cohorts, including the Alzheimer's Disease Genetics Consortium, Alzheimer's Disease Sequencing Project, UK Biobank, and FinnGen. Sex hormone-related trait GWAS were available for age at menarche, menopause, and voice breaking, as well as testosterone, sex hormone-binding globulin (SHBG), progesterone, follicle stimulating hormone, luteinizing hormone, and estradiol levels. Cross-trait conjunctional analyses were conducted to identify pleiotropic overlap between sex-hormone traits and AD, followed by prioritization of candidate causal sex-biased AD genes through quantitative trait locus genetic colocalization analyses. The potential regulatory impact of sex hormones on these genes was assessed through transcription factor motif analyses. Finally, sex-stratified mendelian randomization analyses were used to infer causal effects of sex hormones on AD risk. Results: Genome-wide pleiotropy analyses demonstrated enrichment of AD with testosterone, SHBG, and age-at-menarche traits in women. We identified 12 high-confidence pleiotropic loci, 9 of which showed stronger AD effect sizes in women (3 in men) and 8 that were novel. Genes at these loci were often causally implicated in brain tissues and enriched for promoter-associated androgen receptor transcription factor binding motifs. Mendelian randomization indicated higher bioavailable testosterone in women (OR:0.88; 95%-CI:0.82-0.96) and higher SHBG levels in men (OR:0.86; 95%-CI:0.77-0.96) were associated with lower AD risk. Conclusions: Our findings reveal sex-specific shared genetic architectures between AD and sex hormone-related traits and nominate related genes that may drive sex-biases in AD risk. Several of the implicated female-biased genes are relevant to phosphatidylinositol and lipid metabolism, including Fatty Acid Desaturase 2 (FADS2). While we observed no causal effect of estradiol-related traits on AD risk, the protective effects of bioavailable testosterone in women and SHBG in men provide targets for sex-informed AD risk stratification and prevention strategies.

BackgroundIndividual clinical cognitive assessments (CCA) for Alzheimers disease (AD) provide broad disease stratification but are limited in sensitivity and specificity, requiring integration of multiple CCA for optimal disease staging. Recent work from our lab suggests that neuro-metabolic and vascular dysregulation (MVD) occurs early in AD, prior to clinical symptoms, and may provide higher sensitivity and specificity than CCA alone. In this study, we combined three widely accepted CCA with MVD readouts and developed a multimodal ensemble machine learning approach across the AD spectrum to predict disease stage and grade. MethodsAD subjects (N=372) across the disease spectrum with imaging (PET:18F-FDG, MRI:T1w, T2 FLAIR, ASL) and CCAs (ADAS-Cog, CDR, MoCA) data were analyzed from ADNI. Imaging data were registered to MNI152+, z-scored relative to cognitively normal controls, and processed for MVD. A clinical-set-enrichment analysis (CSEA) was developed to link regional brain changes with CCA scores, map changes to functional categories, project them into a 3D Cartesian space, and model trajectories, thus revealing at-risk and resilient regions. In addition, an ensemble machine-learning approach was utilized for disease stage classification, and a disease grading scheme across the AD spectrum was developed to further stratify within disease stages. FindingsRegional data followed an MVD pattern across AD stages stratified by CSEA scores. Females showed greater stage separation along the CCA axis within each region, indicating faster disease progression. Moreover, progression in at-risk brain regions (e.g., mid- and inf-temporal gyri, amygdala) was associated with longer disease path lengths, whereas progression in resilient brain regions (supramarginal gyrus) was not. Moreover, our classification and grading approach can predict AD stage and grade independent of amyloid-beta and tau with high precision and accuracy. InterpretationA framework was developed to evaluate MVD and CCA variations across the AD spectrum, thereby distinguishing at-risk and resilient brain regions. Distinct disease trajectories were identified, and a new data-driven grading scheme was proposed to highlight the potential for precision medicine and therapeutic evaluation. FundingNIH T32AG071444

BackgroundDepression is associated with risk for late-onset Alzheimers disease (LOAD), but its underlying pathogenesis in at-risk individuals remains unclear. We examined multimodal imaging correlates of depressive symptoms in cognitively normal middle-aged offspring of patients with LOAD (O-LOAD) compared with control individuals without LOAD history up to a 4th degree of kinship (HC). MethodsParticipants (n=58; 52{+/-}3 years; 74% female) underwent assessment with the Beck Depression Inventory-II (BDI), structural MRI, resting-state fMRI, FDG-PET, and PiB-PET. Resting-state fMRI data were available for 28 O-LOAD and 24 HC; PET data for 24 O-LOAD and 22 HC. General linear models tested associations between imaging measures and BDI, including group interactions. ResultsIn O-LOAD, higher BDI scores were associated with reduced cortical thickness in the left postcentral gyrus. Resting-state fMRI revealed significant group-by-BDI interactions involving cingulate and orbitofrontal networks. In O-LOAD, greater depressive symptom severity was associated with reduced cingulate connectivity across distributed corticolimbic, prefrontal, insular, occipital, and cerebellar regions ({beta} range -0.10 to -0.18). In HC, depressive symptoms were associated with reduced right orbitofrontal and somatosensory-medial orbitofrontal connectivity ({beta}=-0.13), with divergent patterns of cingulate connectivity. FDG-PET showed no significant associations with depressive symptoms. PiB-PET demonstrated regionally specific associations between amyloid signal and BDI in HC, involving an inverse pattern in anterior and posterior insular cortices. ConclusionsDepressive symptoms in middle-aged individuals at familial risk for LOAD are associated with distinct structural and functional alterations, involving circuitry subserving salience and reward, and suggesting early network-level mechanisms linking affective symptoms with vulnerability to neurodegeneration.

BackgroundGenome-wide association studies have identified numerous variants associated with neuropsychiatric disorders. Although some significant loci can carry substantial risk, as in Alzheimers Disease, the remaining genetic variance is distributed across many small-effect loci. Polygenic risk scores (PRS) aggregate this risk but do not capture epistatic interactions, and offer limited biological interpretability and predictive accuracy. Computing gene level risk scores and integrating known or statistically validated gene-gene associations has the potential to increase interpretability and/or accuracy. Graph Neural Networks (GNNs) can leverage graph structured genetic data that models potential epistatic interactions to achieve these goals. MethodsWe developed a three-stage Graph Attention Network (GAT) classifier using individual-level GWAS data from 7,358 participants across seven Alzheimers Disease Center cohorts. Nodes were defined as genes, with risk scores from AD and 11 genetically correlated phenotypes serving as features. We evaluated two graph construction strategies: gene co-expression networks derived from hippocampal transcriptomic data and curated pathway-based graphs. Additionally, a bilinear context module was incorporated to capture global gene-gene interactions beyond the graph topology. In Stage 1, a GNN encoder was trained on the graphs; Stage 2 injected PRS for non-coding SNPs after the encoder to better capture genetic risk via transfer learning, and Stage 3 applied adversarial training with gradient reversal for ancestry debiasing. GNN predictions were ensembled with whole-genome PRS using elastic net regression. ResultsThe best-performing GNN model -- a GAT with bilinear context operating on the pathway graph -- achieved an AUROC of 0.78 (95% CI: 0.75-0.80). Ensemble models combining Stage 2 or 3 GNN logits with whole-genome PRS achieved an AUROC of 0.82 (0.79-0.84), outperforming PRS alone (0.80). GxI attribution and additional explainability analyses revealed stage-specific biological signals, some of which re-capitulated known gene-phenotype associations and others which may reflect potential new areas of inquiry. ConclusionA multi-stage GAT framework captures complementary, non-additive genetic signal that, when ensembled with PRS, improves the accuracy of AD classification. Post-hoc explainability analyses yield biologically interpretable gene networks, supporting the utility of graph-based deep learning for dissecting complex genetic architectures.

Background: The role of biological age acceleration (BioAgeAccel) in the dynamic progression from single cardiovascular-kidney-metabolic disease (CKMD) to multimorbidity, and subsequently to dementia and mortality remains elusive. Methods: We conducted a longitudinal study with data of 433,911 UK Biobank participants. Cardiovascular-kidney-metabolic multimorbidity (CKMM) was defined as the coexistence of two or more CKMDs, including cardiovascular disease (CVD), stroke, type 2 diabetes (T2D), and chronic kidney disease. Biological aging was measured via PhenoAge and KDM-BA. Multistate models examined the association between BioAgeAccel and disease transitions, ranging from healthy to the first occurrence of CKMD (FCKMD), then progression to CKMM, dementia, and mortality. Restricted mean survival time estimated the disease transition time or life expectancy between states. Results: BioAgeAccel was significantly associated with increased risks across all disease transitions. Specifically, during CKMM progression, the hazard ratios (HRs) of the transition from healthy to FCKMD were 1.24 [95%CI 1.23-1.25] for PhenoAgeAccel and 1.16 [1.15-1.17] for KDM-BA-Accel. For subsequent transition to CKMM, the HRs were 1.20 [1.18-1.22] and 1.19 [1.17-1.21], respectively. In dementia-related transitions, PhenoAgeAccel showed the higher risk for CKMM to dementia (HR=1.13 [1.04-1.22]) than for the transition from healthy or from FCKMD to dementia. These associations were further moderated by age, physical activity, educational, and lifestyle factors. BioAgeAccel also accelerated disease progression and reduced life expectancy; for example, during CKMM progression, BioAgeAccel shortened the time between disease transitions by about 1.09 years from healthy to FCKMD, and an additional 1.75 years to CKMM. Regarding life expectancy, individuals with CKMM experienced an average reduction of about 1.36 years under PhenoAge, while those with dementia showed a decrease of about 0.77 years. Among individuals with CVD or T2D as the initial diagnosis, the impact of BioAgeAccel on progression to CKMM or dementia was stronger. Conclusions: BioAgeAccel exerts significant promotive role in the onset of CKMD and their subsequent progression to CKMM, dementia, and mortality, helping identify high-risk individuals. Implementing biological age assessments and health lifestyle interventions in middle-aged populations serves as an effective strategy for alleviating the burden of CKMDs and dementia.

BACKGROUND: Increasing evidence suggests that accurate prediction of Alzheimer disease (AD) symptom onset requires more than amyloid- and tau-centric biomarkers such as cerebrospinal fluid (CSF) A{beta}42/40, total tau and p-tau181 and plasma p-tau217. Autosomal dominant AD (ADAD), caused by pathogenic PSEN1, PSEN2 and APP mutations with predictable age at symptom onset, presents a unique opportunity to characterize the chronological changes in proteins beyond amyloid and tau and clarify them as early biomarkers of disease onset or as biomarkers related to disease staging and progression monitoring. METHODS: We measured 972 CSF samples corresponding to 484 participants of the Dominantly Inherited Alzheimer Disease Network (DIAN) using the NULISASeq 120 CNS Disease Panel. We first benchmarked the technology against gold-standard measurements followed by the identification of proteins that were differentially abundant in relation to mutation status and symptomatology. Next, we determined the chronological emergence of protein changes in relation to the estimated years to onset (EYO). Finally, we assessed whether specific protein measures improved the prediction of EYO in the ADAD. FINDINGS: NULISA measurements were comparable to those previously published. We demonstrated that known early alterations in CSF amyloid and tau were followed by inflammatory and neurodegenerative responses suggesting that clinical manifestation of AD happens before the inflammatory processes is fully developed. Finally, we found a multi-protein composite approach for predicting EYO that outperformed single biomarker values. INTERPRETATION: Our results suggest that the main CSF proteomic landscape changes in ADAD are due to the presence of a pathogenic mutation and occur prior to symptom onset. Improved performance of multi-protein composite to predict EYO compared to single biomarker values highlights the added value of multiplex proteomic signatures for biomarker panel development. FUNDING: National Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea, Spanish Institute of Health Carlos III.

Alzheimer s disease (AD) and other tauopathies are characterized by the hyperphosphorylation of tau (pTau), leading to its aggregation in the brain, a process strongly predictive of neurodegeneration and future cognitive decline. Currently, tau positron emission tomography (PET) is the only validated method for detecting tau aggregates in vivo. However, its high cost, invasiveness, and limited accessibility restrict its use in clinical settings and preclude large-scale screening. Moreover, existing plasma biomarkers that quantify the level of pTau at specific sites (e.g., pTau217) have limited specificity for confirming AD-related tau aggregation, partly due to the heterogeneous and irregular phosphorylation patterns of pTau. Besides, the concentration of pTau is frequently elevated in the context of isolated amyloid-{beta} pathology, which is less strongly associated with cognitive decline in the absence of aggregated tau. There is therefore an urgent need for a reliable and scalable blood-based biomarker of tau pathology. A key mechanism underlying AD tau pathology is the ability of pathologically active pTau (PA pTau) to bind to and seed normal tau, facilitating prion-like propagation of insoluble tau aggregates. Here, we assessed the diagnostic performance of the VeraBIND Tau assay, the first functional assay to detect PA pTau seeding activity in plasma. Seventy-nine cognitively unimpaired (CU) and 66 cognitively impaired older adults underwent blood sampling, cognitive assessment, amyloid-PET or cerebrospinal fluid (CSF) analysis, and [18F]-MK6240 tau-PET imaging. Plasma pTau217 concentrations were quantified using the Lumipulse platform (Fujirebio). The VeraBIND Tau assay isolated PA pTau from plasma and evaluated its ability to bind recombinant normal tau using a tagged-tau chemiluminescent readout. VeraBIND Tau demonstrated 94.2% sensitivity and 96.1% specificity for predicting tau-PET positivity (AUC=0.97). It outperformed plasma pTau217 in CU individuals (PPV=85.9%), regardless of the pTau217 threshold used (maximal PPV of 57.5% using the 0.256pg/mL pTau217 threshold). This higher VeraBIND Tau diagnostic accuracy was driven by early tau-PET stages (Braak-like tau-PET stages 1-3; AUC=0.96 vs. 0.74 for pTau217, p=0.003). Moreover, both cross-sectional values and annual changes in VeraBIND Tau were significantly correlated with cognitive performance and entorhinal tau-PET signal (all absolute Spearman r[&ge;]0.23, p<0.05). These findings highlight the strong potential of VeraBIND Tau as a scalable and accurate screening tool to detect AD tau pathology in the general population. The assay may also help enrich clinical trials with tau-PET positive CU individuals, enhance clinical diagnostic workflows and support monitoring of tau-targeted therapies. Future work should evaluate its utility in optimizing triage and early-intervention strategies.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[-&gt;]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

BackgroundThe AT(N) biological framework classifies Alzheimers disease (AD) pathology using CSF biomarkers, with the A+T+ profile defining biological AD and the A-T+ profile representing a biologically distinct entity consistent with suspected non-Alzheimers pathophysiology, such as primary age-related tauopathy. Functional assessment capable of differentiating these profiles non-invasively remains limited. This study investigates whether cognitive vergence and pupillary temporal dynamics during a visual oddball task can distinguish A-T+ from A+T+ biological profiles in individuals with mild cognitive impairment (MCI). MethodsThirty-eight participants with MCI (12 A-T+, 26 A+T+) classified by CSF biomarkers completed a visual oddball task (80% distractors, 20% targets) under continuous eye-tracking. Linear mixed-effects models examined profile x condition interactions on full time series and six trial-level temporal features. Participant-level differentiation was assessed using binomial logistic regression, adjusting for age, sex, and MMSE. ResultsBoth profiles showed comparable overall oculomotor response magnitudes but diverged markedly in temporal organization. Significant profile x condition interactions emerged for cognitive vergence global slope, time to peak, and pupillary time to peak. Logistic regression confirmed that timing features discriminated biological profiles at the participant level, with differentiation reversing direction between distractor and target conditions. A-T+ participants also maintained superior target detection accuracy (89.3% vs. 82.4%, p = 0.001). ConclusionCognitive Vergence and pupillary temporal dynamics during an oddball task provide condition-dependent functional oculomotor signatures that systematically differentiate AT(N) biological profiles in MCI, suggesting that oculomotor assessment may offer an accessible, non-invasive complement to CSF-based profile characterization.

Objectives: To evaluate the diagnostic performance of the -synuclein seed amplification assay (SAA) and characterize the impact of -synuclein co-pathology on cognitive and biological profiles in routine clinical practice. Methods: We included 398 patients from the prospective multicenter ALZAN cohort recruited from memory clinics in Montpellier, Nimes, and Perpignan. All participants underwent CSF and blood sampling with measurement of CSF biomarkers (A{beta}42/40, tau, ptau181) and plasma biomarkers (A{beta}42/40, ptau181, ptau217, GFAP, NfL). Cognitive assessment was performed using the Mini-Mental State Examination (MMSE). Clinical diagnoses were independently confirmed by two senior neurologists. Syn status was determined by SAA (RT-QuIC). Results: Of 398 patients, 19 out of 20 patients with Lewy body dementia (LBD) (95.0%) and 32 out of 203 patients with AD (15.8%) were SAA+. SAA-positivity presented a sensitivity of 95% and a specificity of 93.5% for distinguishing LBD from patients without LBD or AD. In the entire cohort, SAA+ patients showed lower MMSE scores (p<0.01), lower CSF A{beta}42/40 ratio (p<0.01), and elevated plasma GFAP (p<0.05). Within the AD group, no significant differences in CSF or blood biomarkers were observed between SAA+ and SAA- patients. Within the AD subgroup, no significant differences in CSF or blood biomarkers were observed between SAA+ and SAA- patients, except for a lower CSF A{beta}42/40 ratio in SAA+ patients (p<0.01). Interpretation: SAA demonstrates good diagnostic capabilities for detecting LBD and confirms notable Syn co-pathology in AD. This study highlights the limitations of routine CSF and emerging blood biomarkers in capturing Syn pathology and the value of integrating SAA into routine neurodegenerative disease assessment.

Frailty is a multisystem clinical syndrome closely linked to cognitive aging, yet its cerebral underpinnings and co-contribution to adverse outcomes remain poorly understood. In 63,509 dementia-free UK Biobank participants (aged 65.0{+/-}7.7), higher frailty index (FI) was associated with multiple neuroimaging markers, including reduced hippocampal volume, decreased cortical thickness, greater white matter hyperintensities burden, and impaired brain diffusion metrics. FI and neuroimaging markers additively increased the risks of incident dementia and mortality. An extreme gradient boosting with accelerated failure time framework highlighted FI and key regional neuroimaging features in dementia risk prediction (nested C-index=0.825, iAUC=0.759). Integrating the top 10 predictors into a novel point-based cerebral frailty risk score (CFRS) showed strong performance in predicting dementia onset (optimism-corrected C-index=0.838, iAUC=0.778), and was robust to the competing risk of mortality. These findings highlight the potential utility of a CFRS framework that integrates cumulative systemic and cerebral vulnerabilities for dementia risk stratification.

INTRODUCTION: Bilingualism may confer resilience via enhanced neural integrity. However, evidence for bilingualism's neuroprotective effect is mixed, and studies across Alzheimer's disease (AD) variants are scarce. This study examined gray matter volume (GMV) differences between bilinguals and monolinguals with amnestic AD and logopenic variant primary progressive aphasia (lvPPA). METHODS: In 136 amnestic AD and 88 lvPPA participants with neuropsychological assessments and structural MRI, we analyzed differences between monolinguals and bilinguals within each variant, controlling for demographic covariates. RESULTS: Amnestic AD bilinguals exhibited less GMV in hippocampal, fusiform, and occipital regions compared to monolinguals. LvPPA bilinguals had less temporal and occipital volumes, but they had greater volumes in inferior parietal regions, which are considered a disease epicenter in lvPPA. Cognitive performance in monolinguals and bilinguals was comparable within variants. DISCUSSION: Bilingualism may support cognitive reserve (preserved cognition despite reduced GMV) in both AD variants, with additional brain reserve in lvPPA.