The Journal of Prevention of Alzheimer's Disease

BackgroundCognitively unimpaired (CU) adults vary substantially in their risk of developing mild cognitive impairment (MCI), yet most subtyping approaches focus on downstream neurobiological or cognitive markers rather than upstream, modifiable risk factors. We aimed to identify clinically meaningful subgroups of CU adults defined by integrated comorbid, behavioral, and social risk profiles, and to evaluate heterogeneity in both incident MCI risk and cardiometabolic treatment effects. MethodsWe conducted a prospective cohort study of 121,322 CU adults aged [≥]50 years from the All of Us Research Program. Baseline comorbidities, lifestyle behaviors, and social determinants of health were jointly modeled using the Bayesian Mixed Integrative Data Subtyping framework, which integrates binary and continuous modalities via modality-specific likelihoods and shared latent constructs. Subtype-specific risk of incident MCI was assessed using multivariable Cox proportional hazards models adjusting for demographics and baseline medication use. A double/debiased machine learning interactive regression model with inverse probability of censoring weights to mitigate bias from informative censoring was implemented to estimate the average treatment effects of antihypertensive agents, Glucagon-Like Peptide (GLP) receptor agonists, and non-GLP antidiabetic medications on time to MCI. ResultsFour distinct subtypes were identified: I low-risk healthy aging, II behavioral/social vulnerability, III cardiometabolic-depressive multimorbidity, and IV mixed social-medical vulnerability profiles. Compared with Subtype I, Subtype III demonstrated the highest risk of incident MCI (HR: 3.69, 95% CI: 3.14-4.33), followed by Subtype IV and Subtype II. In treatment effect analyses, antihypertensive use was associated with a modest prolongation of MCI-free survival overall (time ratio:1.04, 95% CI: 1.03-1.06), with the largest benefit observed in Subtype III (time ratio: 1.14, 95% CI: 1.09-1.19). Non-GLP antidiabetic therapies were similarly associated with modest overall delay, with significant benefits in Subtypes I and III. GLP-class therapies were not associated with overall delay but showed a significant association in Subtype III. ConclusionsIntegrative subtyping based on comorbid, behavioral, and social risk factors reveals clinically meaningful heterogeneity in both cognitive risk and treatment response. Aligning dementia prevention strategies with dominant vulnerability pathways may enhance the effectiveness and equity of population-level precision prevention.

Importance: Little is known about the impact of returning Alzheimer disease (AD) biomarkers to cognitively unimpaired (CU) research participants. Objective: Does return of research results (RoRR) negatively impact longitudinal symptoms of depression and cognition. Design: Randomized, noninferiority, delayed-start clinical trial, 2021-2025 Setting: AD biomarker research results offered to CU participants in a longitudinal study of aging Participants: CU participants age 65+ were offered research AD biomarker results (APOE genotype and either plasma AB42/40 or amyloid PET and MRI hippocampal volume) with an estimated 5-year risk of symptomatic AD. Intervention(s) (for clinical trials) or Exposure(s) (for observational studies): 147 participants were randomized to receive results either soon after consent (RoRR arm, N=73) or one year later (delayed-start arm, N=74). Main Outcome(s) and Measure(s): Longitudinal change in Geriatric Depression Scale (GDS), Clinical Dementia Rating sum of boxes (CDR-SB), and global cognitive composite. Outcomes were measured at annual assessments for a longitudinal study of aging. Results: 187 participants received results: 70 in RoRR arm (average age 75, 60% female), 66 in delayed-start arm (average age 73, 53% female). The observed changes in annual measures did not differ between arms in both those with elevated amyloid (AB+) and in those without elevated amyloid (AB-) for GDS (AB+ difference 0.7, 95% CI 0.0-1.3; AB- difference -0.1, 95% CI -0.7-0.5; clinically significant decline >4.0), CDR-SB (AB+ difference 0.0, 95% CI -0.1-0.1; AB difference 0.0, 95% CI 0.0-0.1; clinically significant decline >0.5), and cognitive composite (AB+ difference -0.10, 95% CI -0.25-0.06; AB- difference -0.05, 95% CI -0.17-0.07; clinically significant decline < -0.26). Secondary analyses found no evidence of association between RoRR and proximity to follow-up testing. Conclusions and Relevance: In the first randomized, delayed-start clinical trial of returning AD research results to CU older-adult participants, no effect was seen on longitudinal changes in symptoms of depression or cognition. This supports evidence that there are no harms to returning AD research results, although the results may not apply to more diverse populations not included in this study. Trial Registration: NCT04699786

Background: Alzheimers disease (AD) is a multifactorial neurodegenerative disorder in which copper dyshomeostasis, mitochondrial stress, oxidative injury and immune dysregulation may contribute to pathogenesis. Cuproptosis, a copper-triggered regulated cell death pathway, has emerged as a potential mechanistic link to AD, but its therapeutic and biomarker implications remain incompletely defined. Methods: We integrated transcriptomic, machine learning, immune infiltration, QSFR, molecular docking, docking validation and ADME analyses using GEO blood- and brain-based AD cohorts. Differentially expressed genes were intersected with curated cuproptosis-related genes, followed by pathway enrichment, construction and validation of a hybrid ensemble classifier, CIBERSORT-based immune correlation analysis, QSFR-driven target prioritization, ligand docking, consensus docking validation and SwissADME profiling. Results: The transcriptomic analyses revealed reproducible AD associated signatures enriched in neurodegenerative, oxidative stress, mitochondrial and inflammatory pathways. Across multiple machine learning models, FDX1, PDHB, PDHA1, DLAT and DLD consistently emerged as the most important cuproptosis-related genes, with the hybrid ensemble achieving the best diagnostic performance. Immune profiling suggested that these genes are linked to distinct immune infiltration patterns. QSFR and docking prioritized FDX1 as a key target and Clioquinol, PBT2 and Ebselen showed the strongest and most consistent binding behavior. Docking validation confirmed reliable pose reproduction and enrichment over decoys, while ADME analysis supported Clioquinol, PBT2 and Ebselen as the most balanced candidates for further consideration. Conclusion: This integrated workflow identifies a cuproptosis-centered mitochondrial gene module as a robust AD signature and highlights Clioquinol, PBT2 and Ebselen as promising repurposing candidates. The findings provide a prioritized computational framework for future experimental validation of copper-linked therapeutic strategies in AD.

Background The majority of family dementia caregivers in the United States (U.S.) are now young and middleaged adults. However, little research has been conducted to understand how caregiver needs and preferences for support differ depending on their phase of adulthood. This study evaluated differences in mental health, caregiving readiness, desired supports, and intervention preferences among early (<46 years), middle (46 to 60 years), and late (>60 years) adulthood dementia caregivers. Methods A cross sectional survey was conducted with 202 family dementia caregivers aged 22 to 88. Caregivers completed validated measures of burden, anxiety, depression, well being, time pressure, dementia knowledge, caregiving preparedness, and positive aspects of caregiving. Desired supports and preferences for intervention format, program type, and frequency were assessed. Analyses examined both categorical adulthood phase and continuous age associations with caregiver outcomes, with alpha thresholds of p<.05. Results Early adulthood caregivers self reported higher anxiety symptoms (relative to late adulthood caregivers) and perceived time pressure (relative to middle and late adulthood caregivers). Relative to late adulthood caregivers only, early adulthood caregivers more frequently endorsed desired support for supplemental care and safety tools for the person with dementia, as well as willingness to engage in individual counseling and automated, digital supports. Relative to both middle adulthood and late adulthood caregivers, they also more frequently expressed desired support for their own mental health. Conclusions Dementia caregiving in early adulthood is associated with distinct psychological and practical support needs, suggesting life course informed interventions may enhance relevance and engagement.

IntroductionThe eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks perpetuating health disparities due to the tests known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR) - a rapid, FDA-listed digital cognitive assessment - to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage. MethodsWe conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 study (NCT04733989, N=945). Participants were clinically classified as cognitively unimpaired, mild cognitive Impairment, or probable Alzheimers dementia. We trained a Poisson elastic net regression model using age and multimodal digital features derived from the DCR (including drawing kinematics and voice acoustics) to predict MMSE scores. The model was tested for generalizability using an independent external validation cohort from the Apheleia study (NCT05364307, N=238). ResultsThe machine learning model predicted MMSE scores with a root mean squared error (RMSE) of 2.31 in the training cohort. This error margin falls within the established test-retest reliability range of the manual MMSE itself (2-4 points), suggesting the prediction is statistically non-inferior to human administration. External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62). Crucially, the model exhibited demographic fairness, maintaining consistent accuracy across Race (White RMSE = 2.34; Non-White RMSE = 2.14) and Ethnicity (Hispanic RMSE = 2.26; Non-Hispanic RMSE = 2.31). DiscussionMachine learning can leverage multimodal features from the DCR to accurately and equitably crosswalk to MMSE scores in support of current guidelines, transforming a time-intensive manual test into a rapid, automated assessment. By deploying this "digital triage" engine, where traditional assessments are still used for DMT eligibility, healthcare systems can streamline the identification of DMT-eligible patients, reduce specialist referral bottlenecks, and ensure that access to life-altering therapies is determined by pathology rather than demography.

BackgroundMultiple studies suggest bidirectional links between delirium and Alzheimers Disease and Related Dementias (ADRD). Although they establish a strong association between delirium and subsequent ADRD, it has not been explored using statistical causal inference which makes the best use of observational data to minimize biases. MethodsWe conducted an emulated clinical trial to estimate the effect of experiencing delirium during hospitalization between April 2017 and September 2019 on the cumulative incidence of ADRD over two years following hospital admission in patients 65 and older. The emulated trial used observational data from individuals in the Mass General Brigham Electronic Medical Record (EMR). We carried out statistical causal survival analysis using methods that adjust for confounding, censoring, competing risks, and immortal-time bias, including inverse propensity weighting (IPW) and g-formula approaches. ResultsOf the 6029 patients hospitalized in this time frame who were 65 or older with evidence of a PCP in the EMR, 5901 were included in the analysis based on no history of dementia diagnosis or medications 12 months prior to admission. At two years post-admission, the adjusted cumulative incidence of ADRD in individuals who did not experience delirium was 7.6% (95% Confidence Interval [CI] 4.0-12.1%) while it was 20.2% (95% CI 13.2-27.9%) for those who did experience delirium when calculated using the IPW method. ConclusionsOur emulated trial results argue for a strong association between delirium during hospitalization and the risk of developing ADRD in the two years following hospital admission in individuals 65 and older. Key PointsO_ST_ABSQuestionC_ST_ABSWe sought to answer whether statistical causal inference would show the same association between delirium and the onset of dementia in the two years following hospitalization. FindingsOur emulated trial results argue for a strong association between delirium during hospitalization and the risk of developing ADRD in the two years following hospital admission in individuals 65 and older. MeaningThe implications of demonstrating this relationship underscore the importance of delirium-mitigating interventions for long-term cognitive outcomes.

IntroductionStudies of the risk and timing of symptomatic Alzheimers disease (AD) in cognitively unimpaired individuals are challenging due to the relatively small number of clinical progressors and limited clinical follow-up, which can lead to design-related associations. Clock models can be used to anchor the timing of events to biological events such as biomarker positivity. We hypothesized that estimated age at plasma %p-tau217 positivity based on clock models is less affected by design-related associations as compared to baseline age. MethodsData from the Knight Alzheimer Disease Research Center (Knight ADRC) and Alzheimers Disease Neuroimaging Initiative (ADNI) were analyzed. Age at %p-tau217 positivity was estimated using two clock model approaches, TIRA and SILA. The C-index of estimated age at plasma %p-tau217 positivity and age at the baseline plasma sample (baseline age) for ranking age of AD symptom onset was evaluated in initially cognitively unimpaired individuals, including progressors and non-progressors. In progressor sub-cohorts, baseline age and time from %p-tau217 positivity to baseline were associated with time from baseline until symptom onset; baseline age and estimated age at %p-tau217 positivity were associated with age at symptom onset. Commonality analyses partitioned the variance unique to each predictor and shared between predictors. Randomization analyses evaluated whether observed associations exceeded those expected by chance. ResultsEstimated age at %p-tau217 positivity enabled analyses of a greater number of progressors in the research cohorts, which did not have plasma %p-tau217 data from every clinical assessment. The estimated age at %p-tau217 positivity had a higher C-index than baseline age for ordering the likelihood of AD symptom onset when all follow-up was considered; when follow-up was truncated, the C-index for estimated age at %p-tau217 positivity remained stable while the C-index for baseline age became inflated. In progressors, estimated age at %p-tau217 positivity contributed unique variance beyond baseline age in associations with age at symptom onset. Randomization analyses in the larger Knight ADRC found that associations between clock-derived measures and time from baseline until symptom onset and age at symptom onset exceeded the permuted null distribution, with some mixed results in the smaller ADNI cohort. ConclusionsCompared to baseline age, the biologically-anchored estimated age at %p-tau217 positivity is less susceptible to design-related associations and incrementally improves prediction of age at symptom onset in analyses conditional on progression.

Age-related cognitive decline (ARCD) is driven by conserved biological mechanisms of aging, yet no gerotherapeutic directly targets these processes in the brain. Glycyl-L-histidyl-L-lysine complexed with copper (GHK-Cu) is an endogenous peptide with regenerative and anti-inflammatory properties that declines with age. Whether its effects on cognitive aging depend on delivery route or exposure duration remains unclear. Aged C57BL/6J mice (20-21 months) received GHK-Cu (15 mg/kg) via short-term intraperitoneal (IP; 5 days) or longer-term intranasal (IN; 8 weeks) administration. Hippocampal-dependent escape learning was assessed using a spatial navigation task. Molecular effects were evaluated using hippocampal immunohistochemistry and bulk RNA sequencing. Differential gene expression was analyzed using DESeq2 with false discovery rate (FDR) correction, and pathway-level changes were assessed via gene set enrichment analysis (GSEA). IN GHK-Cu improved escape latency across Trials 2-4 in both sexes (P < 0.05), whereas IP dosing produced a transient improvement in males during Trial 2 (P < 0.05) without sustained effects or improvement in females. IN treatment increased synaptophysin in females (P < 0.001) and decreased GFAP in both sexes (P < 0.01), while IP treatment reduced TGF-{beta}, GFAP, and MCP-1 in males (P < 0.05) and decreased p21 in females (P < 0.0001). Transcriptomic analysis revealed distinct molecular programs. IN GHK-Cu induced coordinated suppression of oxidative phosphorylation (male NES -5.44, female NES -4.20; FDR < 0.0001) and MYC target pathways (female NES -4.31, FDR < 0.0001), with additional attenuation of PI3K-AKT-mTOR signaling in females (NES -3.15, FDR = 0.062). In contrast, IP treatment activated oxidative phosphorylation (female NES 4.97, FDR < 0.001), DNA repair (NES 5.58, FDR < 0.001), and MYC targets (NES 4.34, FDR = 0.002), indicating engagement of acute stress-response and repair pathways. GHK-Cu improves hippocampal-dependent learning in aged mice through distinct biological modes: IP exposure activates repair and stress-response pathways, whereas IN delivery induces sustained suppression of growth and mitochondrial metabolic signaling associated with aging biology. These findings demonstrate that functional cognitive improvement can arise from divergent molecular states and identify administrative route and exposure duration as key determinants of gerotherapeutic response.

Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (ADRD). A systematic representation of their interplay is essential for advancing prevention and targeted interventions. However, BSF-related knowledge is scattered across heterogeneous sources, limiting scalable evidence synthesis and computational analysis. To address this, we created a Behavioral Social Data and Knowledge Ontology for ADRD (BSOAD) to represent and integrate BSFs with respect to ADRD. Material and Methods: BSOAD was developed following established ontology design principles, prioritizing reuse of existing ontology elements to ensure semantic interoperability. It was built upon the Social Determinants of Health Ontology (SDoHO) and the Drug-Repurposing Oriented Alzheimer Disease Ontology (DROADO). BSF-related classes were enriched with ICD 10 CM Z55 Z65 codes and ADRD related classes with AD Onto. Relationships between BSFs and ADRD were derived through literature mining. Ontology quality was evaluated through Hootation based expert review and an LLM assisted framework assessing structural coverage and semantic coherence. Results: BSO AD contains 2275 classes, 153 object properties, and 49 data properties. Expert review demonstrated strong rational agreement (0.95), with disagreements resolved through discussion. LLM-based evaluation showed high category coverage rates ([&ge;] 0.97) and robust semantic alignment with the relevant literature (average completeness = 0.79; conciseness = 0.94). Discussion and Conclusion: BSOAD is, to our knowledge, the first ontology to systematically represent BSFs and hierarchically model their interrelationships in ADRD. It establishes a semantic backbone for computational analysis and knowledge integration. The LLM assisted evaluation framework demonstrates the feasibility of scalable, automated ontology assessment.

Digital health technologies (DHT) offer a promising solution to the timely identification of early Alzheimer's disease (eAD) to enable early treatment. This study evaluated the feasibility, acceptability, adherence, reliability, and preliminary clinical and content validity of the novel AD Digital Assessment Suite (AD-DAS). 123 individuals (32 healthy controls (HC), 31 amyloid-PET negative (SCDn), 30 amyloid-PET positive (SCDp) with subjective cognitive decline, and 30 early AD (eAD)) participated. AD-DAS was remotely deployed for 28 days. Remote testing was feasible (97.6% completers), acceptable (>85% ''good''), and associated with high adherence (96%). Metrics showed moderate to excellent test-retest reliability (ICC 0.53-0.91), associations with clinical comparators (adjusted R2 0.01-0.24), differentiated eAD from other known groups (absolute log odds differences 0.6-3.28), and correlated with brain atrophy in expected regions. Episodic and working memory AD-DAS metrics differentiated SCDp from SCDn participants. These preliminary findings suggest that AD-DAS may be a promising tool for detecting cognitive impairments in early AD stages.

INTRODUCTIONWhether Alzheimers disease (AD) blood biomarker-cognition associations differ across cognitive domains, analytic context, and biomarker modeling strategy in population-based cohorts is unclear. METHODSIn 1,170 older adults from the Health and Retirement Study Harmonized Cognitive Assessment Protocol, we examined cross-sectional (2016) and prospective (2016-2022) associations of blood p-tau181, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and amyloid-{beta}42/40 with memory, executive function, language, visuospatial ability, and global cognition using individual biomarker, principal components analysis-derived composite, and multibiomarker panel models. RESULTSCross-sectionally, NfL and GFAP showed the broadest associations. Prospectively, p-tau181 was independently associated with memory and global cognition, whereas GFAP was associated with executive function, memory, and global cognition. P-tau181 also showed relative memory-versus-executive selectivity. The comparatively best-fitting modeling approach differed by cognitive domain and analytic context. DISCUSSIONAD blood biomarker-cognition associations in community-dwelling older adults are domain-differentiated and context-dependent, supporting domain-specific outcomes and flexible biomarker modeling strategies.

Improving diversity in U.S. Alzheimers disease (AD) research is a pressing need. By 2050, Hispanic and Latino Americans will comprise 30% of the population. Hispanics are 1.5 times more likely and Blacks are twice as likely to develop AD compared to Whites, yet both remain vastly underrepresented in clinical trials research. Aging and AD research mentorship of underrepresented STEM undergraduates is designed to promote entry into related professions by students committed to decreasing disparities in AD research participation and clinical care. The NIA-funded MADURA program recruited 93 students from backgrounds historically underrepresented in STEM majors and/or from NIH-defined disadvantaged backgrounds. Trainees were placed in aging/AD research labs and received weekly training and mentorship from faculty research PIs and other types of supervisors (postdoctoral researchers, graduate students, research assistant staff...) Our study examined student ratings of the program and mentor behaviors, using a program-specific survey and the Mentoring Competency Assessment-21 (MCA-21). Trainees were highly satisfied with both mentoring relationships and the overall program. Student rated MCA-21 competency areas were quite high for both P.I.s and other types of research mentors. However, there were striking differences in associations between competencies and relationship and program satisfaction, by mentor type. For PI mentors, no MCA-21 competencies were associated with relationship satisfaction, but five of six competencies were associated with relationship satisfaction for other mentor types. Similarly, no PI mentor competencies were significantly correlated with overall placement satisfaction, but all six competencies were correlated with overall placement satisfaction for other mentor types. The authors discuss the likelihood of differing student expectations of faculty PI versus other types of research mentors, recommendations for assessing role-specific student expectations (including functions primarily possible only for senior faculty PIs), and utilizing nearer-peer plus PI faculty mentors to comprehensively address the gamut of mentee needs.

BackgroundEpidemiological studies consistently report inverse associations between caffeinated coffee consumption and dementia risk. However, the molecular mechanisms linking coffee-derived phytochemicals to neuroprotection remain only partially understood. ObjectiveTo evaluate, through integrated in silico pharmacology, the relative contribution of adenosine receptor modulation versus direct amyloidogenic enzyme and kinase inhibition in mediating the putative neuroprotective effects of major coffee constituents. MethodsMolecular docking analyses were conducted for caffeine, paraxanthine, chlorogenic acid, trigonelline, cafestol, and kahweol against adenosine A2A and A1 receptors (A2AR, A1R), {beta}-secretase 1 (BACE1), glycogen synthase kinase-3{beta} (GSK-3{beta}), and NLRP3 inflammasome components. Docking was performed using the CB-Dock2 platform. Binding affinities, interaction patterns, and ligand efficiency metrics were assessed. Blood-brain barrier permeability and ADMET properties were predicted using pkCSM. ResultsCaffeine and paraxanthine demonstrated structurally coherent binding within the orthosteric pockets of A2AR and A1R, supported by favorable predicted blood-brain barrier penetration and high unbound fractions. Ligand efficiency analysis identified adenosine receptors as the most pharmacologically plausible targets for small xanthine derivatives. Although larger phytochemicals exhibited stronger absolute docking scores at BACE1, GSK-3{beta}, and NLRP3, predicted pharmacokinetic constraints suggest a small biological effect due to a limited central exposure. ConclusionsThese findings support an adenosine receptor-centered mechanism as the dominant molecular axis linking caffeinated coffee consumption to reduced dementia risk, favoring neuroinflammatory and signaling modulation over direct enzymatic inhibition. Experimental validation is warranted to confirm translational relevance. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/723029v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1a02629org.highwire.dtl.DTLVardef@129890dorg.highwire.dtl.DTLVardef@1e4c05corg.highwire.dtl.DTLVardef@110ec7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Alzheimer's disease is a progressive neurodegenerative disorder that poses a growing global public health challenge. Early and accurate diagnosis is critical for effective treatment, clinical trial participation, and disease management. This systematic review and meta-analysis evaluates the diagnostic performance of machine learning (ML) and deep learning (DL) algorithms for detecting Alzheimer's disease (AD) and mild cognitive impairment (MCI) using neuroimaging and clinical data. Relevant studies were identified from PubMed, IEEE Xplore, and arXiv (2015 to 2025). Random-effects models were applied to estimate pooled performance metrics (AUC, sensitivity, specificity, and F1-score), and subgroup analyses compared results by model type, imaging modality, and validation strategy. Thirty studies met inclusion criteria. The pooled AUC was 0.962, indicating high overall discriminative accuracy. However, studies relying solely on internal validation or with smaller datasets often reported inflated metrics, suggesting potential overfitting and optimism bias. ML and DL methods demonstrate strong potential for early AD detection, but standardized evaluation protocols and external validation are necessary for clinical translation.

Summary Background Although CAIDE (Cardiovascular Risk Factors, Aging, and Dementia) score estimates 20 year dementia risk, prior studies have largely focused on global or composite measures. Only a few studies investigated on cognitive functions and structural neuroimaging markers, and the available structural neuroimaging evidence has largely been derived from subsamples or highly selected small cohorts rather than full population based cohorts. We therefore not only investigated associations between CAIDE score and cognitive performance but also explored structural neuroimaging markers in middle to older aged population. Methods Of 2,864 participants who were available for structural magnetic resonance imaging (MRI) data at baseline, we excluded 230 participants who have neurological and cardiovascular disease at baseline. We also further excluded 209 participants without having exposure, covariates, and cognitive assessments data, including 2,425 participants for the final analysis. The main exposure is CAIDE score (0 to 15) were calculated from age, sex, education, systolic blood pressure, body mass index, total cholesterol, and physical activity and categorized as low risk (<6), moderate risk (6 to 7), and high risk (7<) at baseline. The main outcomes were neuropsychological assessment battery included Story recall, Visual reproductions, Verbal fluency, Trail making, Digit symbol coding, and Stroop tests. Findings Of 2,425 healthy participants (mean age of 58.5 [6.5]; men 1,189 [49.0]), higher CAIDE risk groups were associated with poorer cognitive performance. Compared with low risk group, the high risk group showed significantly lower performance across all 12 cognitive assessments (all p <.001). The moderate risk group also showed lower performance in visual reproduction (immediate and delayed recall), digit symbol oding, and Stroop (word and color) reading tests. Interpretation This large based population study showed the highest risk group were independently associated with lower cognitive performance across all domains compare to the lowest risk group, suggesting the potential importance of managing these features for preserving neurological health in middle and older aged adults.

INTRODUCTION: Knowledge about how Alzheimer's disease (AD) and AD-related dementia (AD/ADRD) plasma biomarkers relate to global and domain-specific cognitive functioning across diagnostic groups remains limited, particularly in heterogeneous, community-dwelling populations with multiple comorbidities. METHODS: We evaluated associations between baseline plasma biomarker levels (A{beta}42/40, p-tau181, p-tau217, NfL, GFAP) and cognitive performance at baseline and longitudinally (up to 7 years). Participants (n=590) enrolled in the Wake Forest Alzheimer's Disease Research Center Clinical Core (314 cognitively unimpaired [CU]; 206 mild cognitive impairment [MCI]; and 70 dementia) completed annual cognitive assessments including the Uniform Data Set (UDSv3; NACC). Domain-specific cognitive composites including memory, executive function, attention, language, visuospatial ability, and phonemic fluency, as well as a modified Preclinical Alzheimer's Cognitive Composite (PACC5), were evaluated. General linear and mixed-effects models were adjusted for demographics (age, sex, race, education), APOE-{epsilon}4 status, comorbidities (estimated glomerular filtration rate; BMI), and cardiometabolic health factors (hypertension, diabetes). Effect modification by cognitive diagnosis was evaluated. RESULTS: Baseline plasma biomarkers, particularly p-tau217, were associated with poorer baseline cognitive performance and greater longitudinal decline on the PACC5 and all cognitive domains assessed, except phonemic fluency (strongest for memory). Post-hoc analyses indicated associations between plasma biomarker levels and cognition were generally more pronounced in MCI compared with CU participants. Effect modification by baseline cognitive status was limited and attenuated when all biomarkers were modeled simultaneously. Comorbidities and cardiometabolic factors modified select associations. DISCUSSION: Plasma AD/ADRD biomarkers, particularly p-tau217, were associated with cognitive impairment and decline in a heterogenous community cohort.

Background and Aims: Plasma phosphorylated tau 217 (p-tau217), including %p-tau217, has emerged as a robust biomarker of Alzheimer's disease (AD) pathology, with increasing interest in its longitudinal behavior. In "Predicting onset of symptomatic Alzheimer's disease with plasma p-tau217 clocks," Petersen et al. applied disease clock models, Sampled Iterative Local Approximation (SILA) and Temporal Integration of Rate Accumulation (TIRA), to estimate age at plasma %p-tau217 positivity and reported that this measure predicts age at onset of symptomatic AD. We aimed to determine whether this apparent predictive performance reflects biomarker information or arises from structural artifacts in the analysis. Methods: We analyzed digitized data from published figures and decomposed the clock-derived predictor into baseline age and estimated time from %p-tau217 positivity. We quantified shared and unique explained variance between baseline age and the clock-derived predictor using commonality analysis. To further disentangle structural and biomarker contributions, we evaluated a null scenario in which the biomarker-derived timing component was replaced with randomly generated values drawn over the observed range, preserving the predictor distribution while removing biomarker information. Results: The reported predictive performance was largely driven by structural artifacts arising from bounded follow up and constraints among the variables. Restriction to individuals who progressed during limited follow up, together with constraints on the allowable timing of events, induced a strong association between baseline age and age at symptom onset. In ADNI, baseline age alone explained substantially more variance in age at onset than the clock-derived predictors (R2=0.78 vs. 0.337 and 0.470 for TIRA and SILA). The estimated time from %p-tau217 positivity contributed minimal additional information, and randomized predictors yielded comparable performance to baseline age alone (R2=0.79). Conclusion: The apparent predictive ability of plasma %p-tau217 disease clocks is driven largely by structural age relationships rather than independent biomarker signal. The plasma %p-tau217 timing component provided minimal predictive value, and its combination with age obscured these structural dependencies. These findings underscore the need for careful evaluation of constructed predictors and outcomes in longitudinal analyses of disease progression.

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.

Objective: To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage biological annotations for risk prediction, using brain amyloid-beta positivity as a case study. Methods: We systematically benchmarked approaches for integrating pathway information into PRS construction to predict brain A{beta} positivity. Using two cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 969) and Australian Imaging, Biomarkers and Lifestyle (AIBL, n = 251), we compared Apolipoprotein E (APOE) genetic risk score (GRS), clumping and thresholding (C+T) PRS, pathway-guided single nucleotide polymorphism (SNP) selection PRS, and pathway-specific PRSs ensembled via machine learning. Pathways were derived from manually curated literature or from pathway databases via Functional Mapping and Annotation (FUMA). Results: In cross-validation on the ADNI cohort, pathway-informed PRS using a narrow-set of pathways to guide SNP selection (PathPRS-SNPLit without APOE locus) significantly outperformed the standard PRS model (median AUC = 0.742, p = 0.006) and the APOE locus model (median AUC = 0.736, p = 5.1 x 10-5) based on the Mann-Whitney U test, achieving a median AUC of 0.763. This model showed enhanced ability to identify subgroups within the 10% lowest- and highest-risk groups compared to the current standard of APOE locus alone (odds ratio = 0.67, 95% CI: 0.56-0.81; and OR = 13.23, 95% CI: 10.23-17.11), highlighting its clinical potential. Using a focused set of literature-curated pathways outperformed using a broader set of database-derived pathways across configurations. When contrasting strategies for aggregating information across pathways, we observed that using pathways to guide selection of SNPs and then building a single PRS performed comparably to building PRS for each pathway and using machine learning (ML) to aggregate these, though the latter enabled pathway-level interpretability. Similar trends were observed in the external AIBL validation dataset. Interpretation: Pathway-informed PRS can meaningfully improve genetic risk enrichment for A{beta} positivity beyond APOE and standard C+T approaches, provided pathway definitions are carefully curated. The choice of pathway source has the strongest impact on predictive performance, with aggregation strategies or ML model choice having far less impact. Our findings highlight the utility of literature-curated, pathway-informed PRSs for A{beta} prediction and offer practical guidance for pathway-informed PRS construction in other polygenic traits.

Background: Improvements in health technology offer opportunities for remote disease screening, diagnosis and monitoring. The Withings Sleep Analyzer (WSA), an under mattress ballistocardiograph sensor able to detect body movement, breathing, and cardiac ejection is a promising technology for the non-invasive detection and monitoring of neurodegenerative diseases. InSleep46 aims to evaluate whether the WSA is able to detect preclinical Alzheimer's disease in members of the 1946 British Birth cohort, now in their late 70s. Objectives: To assess feasibility of deployment of a remote sleep, circadian and physiological monitoring device in a population of older adults. Participants: 356 participants from the Insight 46 neuroimaging sub-study (1946 British Birth Cohort), all born in one week in March 1946. Methods: We describe remote recruitment, device installation, and troubleshooting protocols. Feasibility analysis examined participant characteristics associated with recruitment and successful device set-up using logistic regression. Troubleshooting events for device installation and maintenance were recorded over a mean 14-month follow-up period. Results: During the feasibility analysis period, 263 (74%) participants, mean (SD) age 77 years (0.47) agreed to take part, of whom 245 (93%) successfully set up the WSA. Recruitment and successful set up of the WSA were not dependent on cognitive ability, socioeconomic position, or educational attainment. 162 (62%) of recruited individuals required [&ge;]1 troubleshooting call (mean 2.3 per participant, range 0-16). 603 calls were required in total. Conclusion: Deployment of a remote sleep and physiological monitoring device in an older adult population is feasible. Most participants required individualised assistance to set up the device. For the technology to be widely implemented, the set up must be accessible, with dedicated support available.