The Journal of Prevention of Alzheimer's Disease

Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.

Background: Alzheimer's Disease and Related Dementias (ADRD) is a growing global public health challenge, and caregivers experience high rates of burden, unmet needs, and system failures. These challenges vary by caregiver role and relationship to the care recipient, reflecting the heterogeneous nature of caregiving. Yet prior work has largely studied burden, unmet needs, and system failures as separate domains rather than examining how they co-occur within individual caregivers. Methods: We applied an LLM-based classification framework (Claude 3.5 Sonnet) to 7,198 posts from three ALZConnected caregiver forums (general, spouse/partner, and adult child caregivers), coding each post for burden, unmet needs, and system failures across 9, 12, and 10 categories respectively. We compared expression rates by caregiver role (primary vs. secondary) and relationship to the care recipient (spousal vs. child) and used post-level co-occurrence networks to map how categories cluster within and across domains. Results: Burden was expressed in 89.0% of posts and unmet needs in 93.3%, while system failures appeared in 34.8%. Primary caregivers reported burden more often than secondary caregivers (91.6% vs. 84.7%), while secondary caregivers reported more unmet needs (94.6% vs. 92.5%) and more system failures (37.2% vs. 33.4%). Child caregivers reported higher rates than spousal caregivers across all three domains. Co-occurrence networks showed dense within-domain clustering (density 0.61-0.65) and 84 significant cross-domain connections, with the strongest links between behavioral/safety burden and safety-management needs (21.7% of posts) and between emotional burden and emotional-support needs (20.9%). Conclusion: Burden, unmet needs, and system failures are not independent problems but form interconnected challenge ecosystems that vary by caregiver role and relationship. This suggests caregiver support should be designed around these connected patterns rather than treated as separate, single-domain interventions.

Alzheimer's disease is characterized by progressive cognitive decline and early cerebrovascular dysfunction, including impaired neurovascular coupling (NVC) and reduced cerebral blood flow (CBF). Tau pathology is a major driver of these deficits, yet therapeutic strategies targeting tau-induced neurovascular dysfunction remain limited. The M1 muscarinic acetylcholine receptor (M1 mAChR) is a promising therapeutic target because of its critical role in cognition. We previously demonstrated that pharmacological activation of M1 mAChR improves cognitive function and neuronal survival in amyloid-based Alzheimer's disease mouse models through sex-specific mechanisms. However, whether M1 mAChR activation restores tau-mediated NVC deficits remains unknown. P301S mice were used as a model of tauopathy. Cognitive function was evaluated using the novel object recognition and Morris water maze tests, and NVC was assessed by measuring whisker stimulation-induced changes in CBF using laser speckle contrast imaging. Following baseline measurements, mice received an acute intraperitoneal injection of VU0486846, a selective M1 mAChR positive allosteric modulator (3 mg/kg), and CBF responses were reassessed over time. P301S tau mice exhibited impaired recognition and spatial memory functions, associated with reduced whisker stimulation-induced increase in CBF, indicative of impaired NVC response, while acute treatment with VU0486846 reversed these changes in NVC. This rescuing effect of VU0486846 was observed earlier in female tau mice compared to males, suggesting a sex-biased effect of M1 mAChR modulation. These findings demonstrate that M1 mAChR positive allosteric modulation reverses tau-induced neurovascular dysfunction, supporting M1 mAChR activation as a promising disease-modifying approach for Alzheimer's disease. The earlier improvement observed in females further suggests that therapeutic efficacy is influenced by biological sex.

Accurate prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for prognosis, patient management, and clinical trial enrollment. Cognitive and functional assessments are routinely used in memory clinics, but their relative predictive value remains unclear. We sought to identify which assessments are most predictive of 24-month progression from MCI to AD. We analyzed 2,430 participants with baseline MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who were classified by 24-month progression to AD. Extreme Gradient Boosting (XGBoost) models were trained using repeated stratified 5-fold cross-validation with 10 repetitions. We compared demographic and genetic variables, global cognitive measures (MMSE, ADAS-Cog13, CDR-SB, MoCA), episodic memory, executive function, functional status, and Everyday Cognition (ECog) questionnaires. The baseline clinical model (age, sex, education, APOE {varepsilon}4 status) achieved an area under the receiver operating characteristic curve (AUC) of 0.692. Episodic memory showed the highest predictive performance (AUC = 0.915), followed by the Functional Activities Questionnaire (AUC = 0.913). Combining episodic memory, functional assessment, and executive function achieved the best performance (AUC = 0.943, sensitivity = 0.857, specificity = 0.889). Among individual memory measures, Logical Memory Delayed Recall achieved the highest standalone performance (AUC = 0.896), whereas RAVLT Learning provided minimal incremental value. Episodic memory demonstrated the strongest predictive performance among the individual assessment domains evaluated of 24-month progression from MCI to AD, with functional assessment providing substantial complementary value. Streamlined assessment batteries emphasizing episodic memory and functional status may improve efficient risk stratification in memory clinics and AD clinical trials.

Background: Alzheimer's disease (AD) is characterized by amyloid beta and tau accumulation accompanied by altered inflammatory responses. Sex is an important modifier of AD risk and pathology, however, its influence on relationships between peripheral inflammatory markers and cerebrospinal fluid (CSF) AD biomarkers remains unclear. The aim of this study was to determine whether sex modifies peripheral inflammatory biomarker relationships associated with AD pathology and clinical stage. Materials and Methods: Twelve CSF biomarkers and 41 plasma biomarkers spanning AD pathology, neurodegeneration, and inflammation were measured on the Luminex platform in a cross-sectional cohort of 261 participants from the Cleveland Clinic Lou Ruvo Center for Brain Health Biobank. Associations were evaluated in sex-adjusted, sex-interaction, and sex-stratified models, accounting for age, APOE4 carrier status, and diagnosis. Results: Plasma IL-5 was inversely associated with clinical stage, and 10 plasma inflammatory markers, including Flt-3L, MCP-1, soluble TREM2 (sTREM2), TNF, IL-8, IL-5, IL-12P40, IL-1RA, fractalkine, and G-CSF, were inversely associated with the CSF pTau181/A{beta}42 ratio in pooled models adjusted for sex. Although formal biomarker x sex interactions did not survive FDR correction, significant associations between the CSF pTau181/A{beta}42 ratio and plasma MCP-1 and IL-12P40 were observed in females but not males. Discussion: Our findings identify a group of peripheral inflammatory markers associated with AD pathology and suggest that some of these relationships may vary by sex, warranting larger studies to clarify the role of sex in AD pathobiology.

Multimodal biomarkers have transformed Alzheimer's disease research, but the incremental contribution of individual modalities to predicting progression from mild cognitive impairment (MCI) remains unclear. We systematically evaluated the contribution of demographic, cognitive, genetic, structural imaging, cerebrospinal fluid (CSF), and positron emission tomography (PET) biomarkers using a comprehensive ablation framework. We analyzed 2,430 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative with known 24-month progression status. XGBoost models were trained using combinations of demographic variables, cognitive assessments, apolipoprotein E (APOE) genotype, structural MRI, CSF biomarkers, and PET biomarkers. Performance was evaluated using repeated stratified 5X10 cross-validation, with out-of-fold AUC comparisons and Holm-Bonferroni correction. Sensitivity analyses assessed the effects of missing-data handling. The full multimodal model achieved the highest discrimination (AUC=0.934). Excluding cognitive assessments produced the largest reduction in performance (AUC=0.883, P<0.001). Removing APOE, CSF, or MRI produced only modest reductions (AUC=0.933, 0.931, and 0.932, respectively). PET produced a similarly small reduction in the primary analysis (AUC=0.932), although complete-case analysis indicated that imputation significantly inflated its performance (P=0.005), suggesting that its contribution may be underestimated or obscured by missingness. The baseline clinical model performed near chance (AUC=0.556). These findings establish an evidence-based hierarchy of biomarker contributions and provide a quantitative framework for prioritizing biomarker acquisition and designing cost-effective multimodal prediction models.

Introduction. Neuropsychiatric symptoms (NPS), including aggression, psychosis, anxiety, apathy, and depression, affect up to 85% of individuals with Alzheimer's disease (AD) and are among its most disabling and costly manifestations, accelerating cognitive and functional decline, institutionalization, mortality, and healthcare costs. NPS prevalence has largely been characterized using self-reported race. Whether NPS differs across genetically defined ancestry groups and whether self-reported race obscures these differences remains unknown, limiting accurate risk stratification and treatment development. Methods. Using whole-genome sequencing data from 7,118 ADSP participants, we defined three NPS clusters from the NPI-Q: early psychosis (CDR 0.5-1), late psychosis (CDR 2-3), and affective symptoms. Genetic ancestry was inferred by principal component clustering, identifying six groups (EUR, AFR, EAS, SAS, AMR, ADMIXED), and compared with self-reported race/ethnicity. NPS prevalence was compared across genetic ancestry groups and genetic ancestry and self-reported race using Fisher's exact and regression models. Results. Genetic ancestry assignment differed markedly from self-reported race, affecting NPS prevalence estimates. NPS prevalence also differed across ancestry groups; affective symptoms were highest in EAS (90%) and SAS (77%) and lowest in AFR (66%), while psychosis was highest in EAS (74%) and SAS (70%) and lowest in AMR (55%) and EUR (56%), with similar patterns for early and late psychosis. Discussion. Genetically defined ancestry alters NPS prevalence estimates in AD, suggesting that standard race categories obscure population-level disease burden and compromise risk stratification, screening, and trial design. Ancestry-associated differences suggest partially distinct genetic and environmental drivers, underscoring the need to incorporate genetic ancestry into AD research and care.

ABSTRACT Importance: The biological mechanisms underlying the associations of social isolation and loneliness with dementia risk are not well understood. Objective: To evaluate the relationship of prospectively measured social isolation and loneliness with AD/ADRD blood-based biomarkers. Design: Observational study using the U.S. Health and Retirement Study (2010-2016). Venous blood draws were conducted in 2016 and AD/ADRD biomarkers were released in 2025. We estimated associations of social isolation and loneliness patterns between 2012 and 2014 with continuous biomarkers using linear regressions, accounting for socio-demographic and health covariates. We evaluated effect modification by sex and APOE {varepsilon}4 carrier status. Setting: Population-based Participants: Community-dwelling HRS participants aged 50 years or older (n = 3862). Exposures: Primary exposures were four-category multi-wave variables of persistent, resolving, new-onset, or no social isolation/loneliness across the two exposure waves. Social isolation was classified as "severe" and "moderate-to-severe" based on a 5-item scale including marital status, household size, proximity to children, religious service attendance, and volunteering. Past-week loneliness was measured with a single-item question (yes/no). Main Outcomes and Measures: Neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and the ratio of amyloid beta 42 to amyloid beta 40 (A{beta}42/40), measured in plasma via a Multiplex Simoa Assay and phosphorylated tau (p-tau181), measured in serum via a Simoa Assay. Results: At the analytic baseline, respondents were a mean age of 64 (9.5) years, 59% female, and 25% APOE {varepsilon}4 carriers. Across the two exposure waves, 4% experienced persistent severe social isolation, 16% experienced persistent moderate-to-severe social isolation, and 8% reported persistent loneliness. Multiple patterns of social isolation (vs. no social isolation) were associated with higher NfL, including persistent severe social isolation ({beta}: 0.31), new-onset moderate-to-severe social isolation ({beta}: 0.14), and resolving moderate-to-severe social isolation ({beta}: 0.20). Persistent severe social isolation was associated with lower GFAP ({beta}: -0.31) while persistent loneliness and, for men, new-onset loneliness were associated with higher GFAP ({beta}_persistent: 0.16; {beta}_(new onset_men): 0.25). New-onset severe social isolation was associated with a lower A{beta}42/40 ratio ({beta}: -0.25) while resolving moderate-to-severe social isolation and, for men, persistent severe social isolation were each associated with higher p-tau181 ({beta}_resolving: 0 .11; {beta}_(persistent_men): 0.41). There was some additional variation by APOE {varepsilon}4 carriership, although selective survival is a concern. Conclusions: Social isolation was associated with elevated blood-based biomarkers of neuronal injury, with variation by patterns of exposure over time. Associations between social isolation and loneliness with biomarkers related to astrocyte damage and Alzheimer's disease were less consistent, and varied in sign and magnitude by exposure and sex.

BACKGROUND: Chronological age and plasma p-tau217 each predicts cognitive decline, but whether their prognostic associations interact is unclear. In this study, we examined their joint associations in a memory-clinic cohort. METHODS: We included 3,741 participants from the Pittsburgh ADRC, with up to 28 years of follow-up (3.0 [IQR 2.0-6.0]). The primary outcome was increase in Clinical Dementia Rating global score (CDR-GS). Secondary outcomes included clinical-stage progression and longitudinal change in CDR Sum of Boxes. Plasma p-tau217 cut-off value was derived and externally validated in amyloid-beta-PET and autopsy sub-cohorts, respectively. Cox proportional hazards and linear mixed-effects models tested age-by-p-tau217 interactions while repeated cross-validation evaluated prognostic performance. RESULTS: Age and plasma p-tau217 interacted in their associations with CDR-GS progression ({chi}(1)2 = 23.94; p=9.81x10-7). Comparing the oldest displayed age with the youngest reference age, the adjusted hazard ratio (HR) was 4.80 (95% CI 2.74-8.27) in the lowest vs. 1.05 (95% CI 0.69-1.47) in the highest p-tau217 quartile. Older age was associated with clinical progression at low-p-tau217 (HR=1.97; 95% CI 1.58-2.45) but not at high-p-tau217 (HR=1.10; 95% CI 0.95-1.26) concentrations; adjusted 5-year risk differences were 19.3 and 3.3 percentage points, respectively. Adding plasma p-tau217 improved 5-year discrimination most accurately among participants younger than 60 years (AUC 0.66-0.81). DISCUSSION: Prognostic association between age and clinical progression varies by plasma p-tau217 concentration. Age stratifies risk at low plasma p-tau217 levels, whereas elevated p-tau217 identifies higher risk across age groups and attenuates the age-related gradient. These findings support further evaluation of age-contextualized plasma p-tau217 interpretation for prognosis and trial enrichment.

INTRODUCTION: Characterizing the prognostic utility of blood-based biomarkers for Alzheimer's disease (AD) in predicting longitudinal cognitive trajectories is essential; however, population-based evidence is needed. METHODS: We evaluated plasma phosphorylated tau at threonine 217 (p-tau217) and plasma neurofilament light chain (NfL) in 4,971 dementia-free individuals aged 70 years and older from the population-based Norwegian HUNT study. Predefined cut offs categorized biomarker ranges (p-tau217: low, intermediate, high; NfL: low, high), in addition to continuous biomarker analysis. RESULTS: Adjusted for other risk factors, higher baseline p-tau217 and NfL ranges indicated a significantly increased dementia risk after four years compared to low ranges (intermediate p-tau217: risk ratio [RR] 1.23, 95% CI 1.01-1.50; high p-tau217: RR 2.05, 95% CI 1.73-2.44; high NfL: RR 1.72, 95% CI 1.31-2.27; jointly high p-tau217 and NfL: RR 3.32, 95% CI 2.61-4.23). The estimated cumulative risk of all-cause dementia was 10.6% (95% CI 9.3-12.1) for low p-tau217, 16.9% (95% CI 14.6-19.5) for intermediate p-tau217, 33.0% (95% CI 29.9-36.1) for high p-tau217, 15.7% (95% CI 14.4-17.0) for low NfL, 35.4% (95% CI 30.6-40.5) for high NfL, and 47.8% (95% CI 40.7-55.0) for jointly high p-tau217 and NfL. The association of p-tau217 with incident dementia differed by sex. DISCUSSION: These findings support the use of blood-based biomarkers for population-level dementia risk stratification, underscore the value of combining markers to improve prognostic precision, and can aid clinicians using plasma p-tau217 or NfL in interpreting dementia risk.

INTRODUCTION: Novel capillary-blood collection methods have not yet been evaluated for a wide range of central nervous system (CNS) and neurodegenerative disease-related proteins. Biomarkers of Alzheimer's disease (AD) and related disorders (ADRD) collected from devices like the Tasso+, a minimally invasive upper-arm capillary blood collection device, must be compared to traditional venipuncture to assess for validity. METHODS: Participants underwent blood collection via traditional venipuncture and Tasso+ in a clinical research setting. The Nucleic Acid Linked Immuno-Sandwich Assay (NULISA) CNS panel was used for biomarker quantification in venous and Tasso-derived plasma. RESULTS: Eighty-three participants (age mean{+/-}SD 76.8{+/-}8.2 years, 79.5% cognitively unimpaired) completed blood collection. Little to no correlation was found between venous and Tasso+ plasma for p-tau217, but the correlation was improved by using a brain-derived (BD)-p-tau217/BD-p-tau181 ratio. Extremely strong correlations were found for neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Among the 131 biomarkers measured, 51 (38.9%) had a Pearson R [&ge;] 0.90; 27 (20.6%) had values between 0.70-0.90; 26 (19.9%) had values between 0.30-0.70; and 27 (20.6%) had values [&le;] 0.30. DISCUSSION: The Tasso+ accurately measures NfL and GFAP, but caution is warranted when measuring other AD/ADRD biomarkers, as agreement with venous plasma appears to be protein or ratio dependent. These results highlight that important biomarker-specific differences must be considered when translating capillary blood collection approaches. They also further support foundations for development of these methods, highlighting both the opportunities and remaining challenges for translating the promise of blood-based biomarkers beyond AD/ADRD specialty clinics and research settings.

Background Fluid biomarkers enable the demonstration of the biological effects of novel therapies in Alzheimers disease (AD). However, longitudinal biomarker data are sparse and sample size calculations for fluid biomarkers are often lacking. Here, we provided longitudinal CSF and plasma AD biomarkers measured in samples collected in a placebo arm in a 1.5-year phase 2b trial, allowing us to study natural trajectories, required sample sizes and heterogeneity in early AD clinical trials. Methods We studied individuals from the placebo group (MCI due to AD (n=65) and AD dementia (n=41)) of the T-817MA trial (NCT04191486) with positive CSF AD biomarkers (mean age=69(7) years, Female=63%). Longitudinal biomarker changes in CSF (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, tTau, YKL40, NRGN, ABL1, CHIT1, CLEC5A, ITGB2, MMP10, SDC4, SPON2, THBD) and plasma biomarkers (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, GFAP) were analyzed with linear mixed-effect models. Required sample size estimates for predefined treatment effects were generated. Lastly, we investigated the influence of between person variability in biomarker change by simulating a randomized clinical trial (1:1) 10000 times, and assessed the group differences at 1.5 years. Findings Fourteen biomarkers changed over time, with the largest annual changes observed for plasma pTau217 (+9.8%), CSF MMP10 (+7.1%), and CSF NFL (+6.9%), and CSF A{beta}40 by (-4.0%), CSF pTau217 (-3.0%), and CSF NRGN (-2.5%). To show a 30% change, similar to biomarker effects of approved AD drugs, almost all markers required less than 45 patients per trial arm. To reach normalized levels, established CSF markers required lower sample sizes than plasma markers. The effects of heterogeneity over time were approximately twice as large in plasma compared to CSF. Interpretation These findings offer insights into the biomarker trajectories and power in early AD, supporting more informed endpoint selection and forming a frame of reference for the interpretation of treatment effects in clinical trials.

Background: Plasma phosphorylated tau at threonine 217 (p-Tau217) is a leading blood-based biomarker for Alzheimer's disease (AD). Robust analytical characterization on high-throughput platforms is essential for research use and clinical translation. Objective: To evaluate the analytical performance of an automated plasma p-Tau217 immunoassay and characterize its discrimination of PET-defined amyloid status. Methods: We performed analytical validation of the Access Research Use Only (RUO) plasma p-Tau217 immunoassay on the Beckman Coulter DxI 9000 Access Immunoassay Analyzer and evaluated biomarker discrimination of PET-defined amyloid pathology in a subset of the Bio-Hermes-001 cohort spanning the symptomatic cognitive continuum (mild cognitive impairment or mild AD dementia; cognitively unimpaired participants excluded; n = 449). Analytical precision, sensitivity, linearity, specificity, interference, and sample stability were assessed per Clinical and Laboratory Standards Institute guidelines. Discrimination of PET-defined amyloid status was evaluated using receiver operating characteristic curve and indeterminate zone analyses. Results: The assay demonstrated high precision (within-laboratory CV </=7.1%), excellent sensitivity (limit of detection 0.018-0.021 pg/mL), linearity across the analytical measuring range (R-squared > 0.99), strong epitope specificity (</=1.0% cross-reactivity with other tau phosphoisoforms), and minimal interference from over 60 endogenous and exogenous substances. In 449 research participants plasma p-Tau217 showed strong discrimination between amyloid-positive and amyloid-negative groups (AUC 0.881; 95% CI 0.846-0.915). Application of indeterminate zones systematically improved classification metrics at the cost of fewer definitive classifications. Conclusions: These findings support the Access p-Tau217 (RUO) assay as a robust, high-throughput assay for plasma biomarker-based discrimination of PET-defined amyloid pathology in AD applications.

INTRODUCTION: Neurodegenerative research identifies biomarkers to confirm presence of disease and inform about risk for clinical symptoms. Expert guidance advises caution about disclosing individual research results (IRR), but participant interest remains high even when IRR may not inform individual prognosis. Existing studies of stakeholder attitudes emphasize Alzheimer's disease (AD) biomarkers. We explore participant attitudes toward IRR from the United States CADASIL Consortium (USCC), an observational study of Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), the most heritable form of vascular dementia. METHODS: Since CADASIL research participant attitudes are unstudied and AD-focused guidelines for IRR may not generalize to populations with dominantly inherited conditions, we surveyed USCC participants using three 5-point Likert items and one open-ended question. Descriptive statistics were analyzed for Likert items. The distribution of responses to one item was directly compared to an AD participant survey. Open-ended responses underwent qualitative content analysis. RESULTS: We received 152 responses. The highest-rated reason to return IRR was "learn about my disease and its predicted course". The highest-rated IRR were imaging/MRI scans and cognitive testing. Hypothetical negative outcomes were rated as a little to somewhat concerning. USCC respondents rated reasons to return IRR higher than AD counterparts, with statistically significant differences for seven of eight items. In open-ended responses, the most frequent code was "IRR return will help improve my health and well-being". DISCUSSION: Most respondents expressed support for disclosure upon participant request. These findings could inform IRR guidance for CADASIL and other disorders and investigations of personal utility.

Importance: Patients are increasingly learning Alzheimers disease (AD) genetic and biomarker results through electronic health portals. Evaluation of alternative scalable delivery models for return of AD risk information is needed to best support patient understanding and psychological well-being. Objective: To determine whether a patient-centered digital platform is comparable to clinician-mediated telehealth sessions for returning APOE and plasma pTau-217 results on outcomes of knowledge and psychological well-being. Design: The Evaluation of Self-Mediated Alternatives for Risk Testing Education and Return of Results (eSMARTER) study was a noninferiority trial of a patient-centered digital platform compared to clinician-mediated disclosure of APOE genotype and optional pTau-217 disclosure. Setting: Decentralized, fully remote trial enrolled participants in the contiguous United States (U.S.) between October 2024 and February 2025, with follow-up completed in November 2025. Participants: Eligible participants were aged 60-80 and had previously undergone APOE genotyping (without disclosure) via the GeneMatch program, passed psychological screening, had internet access, and were English-speaking. Interventions: Participants were randomized, 2:1, to the eSMARTER digital platform or clinician-mediated disclosure of APOE genotype. Following the 6-month post-APOE assessment, participants were offered optional pTau-217 disclosure via the same randomized modality. Main Outcomes and Measures: Primary outcomes at 1-7 days following APOE disclosure included changes in anxiety, disease-specific distress, and AD-related knowledge within a priori non-inferiority margins. Results: 674 persons (mean [SD] age 68 [4.7] years; 451 [67%] female; mean [SD] telephone MoCA=19 [2]) were eligible and provided demographic information. 651 participants were randomized to clinician-mediated (n=216) or digital disclosure (n=435) and completed APOE disclosure (66 [10%] APOE4 homozygotes, 377 [58%] heterozygotes, 208 [32%] non-carriers). 604 participants completed the study; 500 completed optional pTau-217 disclosure. Baseline characteristics were balanced across groups. At 1-7 days following APOE disclosure, scores on AD-related knowledge, PROMIS Anxiety, and disease-specific distress measures met non-inferiority. Conclusions and Relevance: Disclosure of APOE genotype by the eSMARTER digital platform is non-inferior to clinician-mediated telehealth disclosure. No significant between group differences were found following disclosure of pTau-217 results. Together, these results suggest that this digital platform may provide an evidence-based scalable approach for returning AD genetic and biomarker results.

Background: Up to 30% of individuals harboring core Alzheimer's disease (AD) neuropathology, amyloid-beta (Abeta; A) and tau (T)-remain cognitively unimpaired. Objective: We examine whether MRI measures of cerebral blood flow (CBF) and white matter hyperintensities (WMH) differentiate cognitively impaired (AD or mild cognitive impairment (MCI);AD+MCI) from non-demented individuals with (NDAN) and without (Controls) core AD neuropathology. Methods: A retrospective cohort study (2018-2023) using data from The Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center studies employed linear models comparing group differences in regional gray matter (GM) CBF and whole brain WMH volume. Participants (N=500) underwent 3T MRI, amyloid- (11C-Pittsburgh Compound B) and tau- (18F-MK6240) PET, and neuropsychological assessments. We categorized participants based on PET and cognitive status as Controls (n=416; cognitively unimpaired, A-T-), NDAN (n=38; cognitively unimpaired, A+T+), or AD+MCI (n=46; cognitively impaired, A+T+). Results: Participants were 67 years old (mean), 68% female, and 37% APOE 4+. NDAN, like Controls, had significantly higher CBF than AD+MCI in GM regions susceptible to neurofibrillary tangle formation in early AD (frontal and temporal cortices, and limbic regions (ps<0.05)). Largest CBF differences (22-34%) were observed in GM regions that accumulate amyloid early but remain largely tangle-free during early AD progression, namely the occipital and parietal cortices. WMH volume differentiated AD+MCI from Controls (p=0.002) but not NDAN (p=0.16). Conclusion: CBF differentiated NDAN from AD+MCI, highlighting vascular contributions to cognitive resilience. Most pronounced CBF preservation in NDAN was observed in parietal and occipital cortices, regions typically free of tau until late stages of AD progression.

Background: Epidemiological studies increasingly associate several adult vaccinations with lower risk of Alzheimer disease (AD) and dementia, but the biological mechanisms underlying these observations remain unclear. We investigated whether adult vaccination history is associated with AD-related and immune-related biomarker profiles in cognitively unimpaired individuals at increased familial risk for AD. Methods: This exploratory study included 192 participants from the PREVENT-AD cohort. Adult vaccination and infection histories were collected using a structured questionnaire and examined in relation to plasma and cerebrospinal fluid (CSF) biomarkers. Adjusted regression models evaluated individual vaccine exposures, vaccination-profile breadth, broader proteomic signatures, CSF biomarkers, viral-history interactions, and psychological symptoms, with false-discovery-rate (FDR) correction applied for multiple testing. Results: Influenza, herpes zoster, pneumococcal, and Td/Tdap vaccination were not associated with FDR-significant differences in the principal plasma amyloid and tau biomarker panel. Hepatitis B vaccination was associated with lower plasma total tau/MAPT, p-tau181, and p-tau231 after correction within the AD biomarker panel, although these findings may reflect residual behavioral or healthcare-related confounding. Greater vaccination-profile breadth was associated with higher plasma NPTX1 (beta = 0.261, p = 0.007, q = 0.049), whereas its nominal association with a lower Amyloid Beta 42/40 ratio did not survive FDR correction. Nominal herpes zoster associations with lower CSF Amyloid Beta 42 and pTau were similarly attenuated after correction. No robust FDR-significant associations emerged from viral-history interaction or psychological symptom analyses. Conclusions: Adult vaccination history was not associated with a broad plasma amyloid or tau signature in this asymptomatic, familial-risk cohort. However, the association between broader vaccination exposure and higher NPTX1 suggests a potentially distinct synaptic-related signal, while the hepatitis B findings identify additional hypothesis-generating tau-related associations. Longitudinal studies incorporating vaccine timing, infection burden, and repeated biomarker measurements are needed to determine whether vaccination influences biological pathways relevant to AD resilience.

Background: Polypharmacy is an escalating global health challenge, yet longitudinal evidence regarding its impact on mortality in Latin American aging populations remains limited. This study evaluated the association between medication burden and all cause mortality among community dwelling older adults in a rapidly aging region of Colombia. Methods: A longitudinal analysis was conducted using a sub-cohort of 4,110 participants (aged 60 years or more) from the SABE Colombia survey (Antioquia, Caldas, Risaralda, and Quindio). Vital status was adjudicated via the National Health System Resources Administrator (ADRES) database over a mean follow-up of 79 months. Polypharmacy was defined as the concurrent use of 5 9 medications and excessive polypharmacy as 10 or more. Extended Cox proportional hazards models were employed to estimate hazard ratios (HR), adjusting for sociodemographic factors, multimorbidity, and functional dependency. Results: At baseline, 20.2% of participants presented polypharmacy and 2.1% excessive polypharmacy. A total of 1,092 deaths (26.6%) were recorded during follow-up. After multivariable adjustment, both moderate polypharmacy (HR 1.17; 95% CI 1.02 - 1.31; p=0.029) and excessive polypharmacy (HR 1.82; 95% CI 1.34 - 2.47; p<0.001) were identified as independent predictors of mortality. Notably, the risk was markedly higher at the 10 or more medication threshold, suggesting a non-linear relationship between pharmacological burden and survival. Conclusions: Polypharmacy is a significant and independent predictor of mortality in Colombian older adults, with the risk nearly doubling in cases of excessive medication use. These findings underscore the urgent need for structured medication review and deprescribing interventions tailored to resource-constrained healthcare systems to mitigate the risks associated with high pharmacological accumulation. Keywords: Polypharmacy, Aged, Mortality, Longitudinal, Colombia.

Background: The brain age gap (BAG), the difference between neuroimaging-predicted and chronological age, captures inter-individual variation in brain aging. Although sensitive to Alzheimer's disease (AD) pathology, its longitudinal patterns across the clinical AD continuum and prognostic relevance remain unclear. Methods: 577 participants from the DELCODE cohort (>2,100 MRI scans) were analysed: healthy controls individuals (HC, N=202), and patients with subjective cognitive decline (SCD, N=248), mild cognitive impairment (N=93), and AD dementia (N=34). All underwent structural MRI, amyloid (Ab42/40) and phosphorylated tau181 assessment, and lifestyle-related dementia risk profiling (LIBRA). BAG was derived using brainageR. Associations with baseline cognition, cognitive decline, and clinical progression (up to eight years) were examined using mixed-effects and Cox models. Mediation analyses tested whether BAG accounted for LIBRA-cognition associations. Biomarker-related and clinical findings were replicated in ADNI (N=461). Findings: BAG showed excellent short-term reliability, increased stepwise across the clinical spectrum and was elevated in amyloid-positive SCD, but not in asymptomatic amyloid-positive HC. Longitudinal BAG increases were strongest in amyloid- and tau-positive participants (Ab+T+). Higher BAG was associated with poorer baseline cognition and predicted cognitive decline, with strongest effects in Ab+T+. All main findings replicated in ADNI. BAG was associated with LIBRA only in biomarker-negative participants and partly mediated associations with cognitive outcomes in DELCODE. Interpretation: BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk. Detectable divergence prior to objective cognitive impairment supports its relevance for early risk stratification and prevention-oriented research. Funding: Helmholtz AI Cooperation Unit (ZT-I-PF-5-163).

Alzheimer's Disease (AD) remains a leading cause of cognitive decline with no known cure, motivating the development of therapies that slow neurodegeneration. Rapamycin, an FDA-approved inhibitor of the mammalian target of rapamycin (mTOR) pathway, has demonstrated promising anti-aging and neuroprotective effects. However, characterizing its treatment effects and identifying the biological factors that contribute to treatment response remain challenging because of complex interactions across multiple biological systems and the limited availability of patient data. In this work, we propose a three-stage multimodal deep learning framework called TreatmentFormer for predicting rapamycin treatment status from heterogeneous biomedical data including both brain imaging data and tabular data (e.g., microbiome profiles, blood-based biomarkers, cerebral blood flow measurements, and clinical variables (e.g., gender, age, and body mass index)). First, a Random Forest-based feature selection module reduces noise in high-dimensional tabular data while preserving representation across modalities. Second, modality-specific encoders map imaging and tabular inputs into a shared latent space via self-supervised contrastive learning, enabling alignment across modalities. Finally, a transformer-based architecture integrates these representations to capture cross-modal interactions and perform treatment classification. Evaluated on a cohort of 23 participants with baseline and post-treatment timepoints, TreatmentFormer achieves an average prediction accuracy of 71.25\% across 10 independent test runs. Despite the challenges of small sample size and heterogeneous data, the model demonstrates stable and consistent performance. Post hoc SHAP-based feature analysis further identifies key biomarkers associated with treatment response, particularly within blood-based and inflammatory modalities. These findings demonstrate that combining feature selection with multimodal representation learning provides a promising and robust approach for modeling treatment effects in small-sample biomedical studies. Importantly, this framework may have significant implications for clinical research and medical applications by identifying the biological features and quantitative measurements that drive individual responses to rapamycin. Such insights could facilitate the development of predictive biomarkers, improve patient stratification, and ultimately inform future approaches to AD diagnosis and therapeutic development.