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.

INTRODUCTION: Multimodal lifestyle interventions can delay or slow Alzheimer's disease (AD) in at-risk older adults, and plasma pTau217, a blood-based biomarker, now enables scalable AD risk stratification. Population-level screening integrating genetic, biomarker, and lifestyle risk factors, alongside attitudes toward risk disclosure, remains understudied. This pilot assessed dementia-risk attitudes before and after disclosure and tested a personalized, data-driven multimodal coaching intervention in a high-risk Alabama population. METHODS: Cognitively normal adults aged 65-75 with a first-degree relative affected by AD/dementia were recruited in north Alabama and screened using the Montreal Cognitive Assessment, family history, APOE genotyping, non-APOE AD polygenic risk score, and plasma pTau217. A composite rubric classified high-risk status (score >5 and/or pTau217 >0.18 pg/mL); high-risk participants received results disclosure and a 6-month personalized intervention (dietitian-led coaching, cognitive training, activity tracking). Pre- and post-disclosure attitudes, distress (REVEAL IGT-AD scale), and biomarker/cognitive measures were assessed. RESULTS: Of 138 screened participants, 52 (37.7%) were high-risk, most (69.2%) by pTau217 alone. Before disclosure, subjective dementia-risk perception correlated with objective risk score. Scores consistent with post-disclosure clinically significant distress occurred in 28% of survey respondents in at least one timepoint but was often (50%) observed with pre-existing anxiety/depression. Over 6 months, MoCA scores and lifestyle risk indices were unchanged, pTau217 continued rising but at a slowed rate compared to between screening and the beginning of the lifestyle intervention, and self-reported worry and perceived dementia risk decreased significantly. DISCUSSION: A genomics- and biomarker-informed screening and coaching model is feasible in a high-risk cohort in the deep south, with manageable distress and improved attitudes despite limited short-term biological change. Findings support larger, longer, and more diverse trials validating this approach for AD screening.

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.

Objective To examine the association between patient-reported shared decision-making (SDM) and documented advance care planning (ACP) among adults with Alzheimer disease and related dementias (ADRD), and to assess whether this association differed between adults with and without ADRD. Design Retrospective observational study using linked survey and electronic health record (EHR) data. Setting and Participants Participants aged [≥]50 years in the All of Us Research Program who completed a patient-reported SDM measure and had linked EHR records. The primary analytic cohort included 3,635 adults with ADRD; 123,681 adults without ADRD comprised a secondary comparison cohort. Methods SDM was measured by how often clinicians asked participants about their opinions or preferences regarding medical care or treatment and was categorized as high (always/most of the time) or low (some of the time/ None of the time). Documented ACP was identified within 3 years before or after the SDM assessment. Modified Poisson regression with robust variance estimated prevalence ratios (PRs) and adjusted PRs (aPRs). Results Among adults with ADRD, 60.6% reported high SDM. Documented ACP prevalence was 5.1% among participants with high SDM and 3.6% among those with low SDM (P=0.027). High SDM was associated with greater ACP prevalence in the unadjusted model (PR, 1.44; 95% CI, 1.04-1.99); the association remained positive but was attenuated after full adjustment (aPR, 1.28; 95% CI, 0.93-1.77). Among adults without ADRD, high SDM was associated with documented ACP (aPR, 1.17; 95% CI, 1.03-1.33). Conclusions and Implications Among adults with ADRD, high SDM was associated with greater ACP prevalence in unadjusted analyses, although the fully adjusted association was not statistically significant. In contrast, higher SDM remained significantly associated with ACP among those without ADRD. The low prevalence of ACP highlights an important opportunity to expand discussions of current and future care preferences as a routine component of patient-centered care.

Background: Blood-based biomarkers (BBMs) may facilitate early detection of Alzheimers disease (AD), yet data in Latino populations remain limited. We examined associations of plasma biomarkers with cognition and depressive symptoms in Spanish-speaking Latino older adults enrolled in the Boston Latino Aging Study (BLAST). Methods: Baseline data from 122 older adults without dementia were analyzed. Plasma AB42/40, brain-derived phosphorylated tau (p-tau) 217, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were measured using NULISA. Cognition was assessed with the Latin American Spanish version of the Preclinical Alzheimers Cognitive Composite (LAS-PACC5) and depressive symptoms with the Geriatric Depression Scale (GDS). Regression models were adjusted for age, sex, and education. Results: Cognitively impaired participants had higher p-tau217 and GFAP than cognitively normal participants. Higher p-tau217 and GFAP were associated with poorer LAS-PACC5 performance, while higher NfL was associated with greater depressive symptoms. AB42/40 showed no significant associations. Conclusions: Distinct biomarker associations with cognitive and emotional functioning support further longitudinal evaluation of BBMs for risk characterization in Latino older adults and potential disease monitoring applications.

INTRODUCTION: This study examined the extent to which educational attainment, Alzheimer's disease (AD) knowledge, and research attitudes are associated with willingness to participate in AD research, and whether AD knowledge and research attitudes mediate associations between educational attainment and willingness to participate. METHODS: 201 older adults completed self-report measures of AD knowledge, research attitudes, prior research participation, and willingness to participate in diagnostic procedures (neuropsychological assessment, blood draw, brain MRI) and non-pharmacological intervention research (tracking physical activity, aerobic exercise). RESULTS: Favorable research attitudes were consistently associated with greater willingness to participate across all proposed research activities, and research attitudes mediated the relationship between educational attainment and willingness to participate in research. In contrast, AD knowledge did not mediate the association between educational attainment and willingness to participate. DISCUSSION: Research attitudes may be a key, modifiable determinant of willingness to participate in AD research and a promising focus for broadening AD research participation.

INTRODUCTION: Accurately identifying risk of Alzheimers Dementia (AD) is essential for supporting people living with symptoms in clinical settings, as well as recruiting adults in prospective medical research. Various algorithms have been created to calculate AD risk based on evidenced risk factors which are weighted toward a total score. As daily life conditions determining risk change at scale, it remains unclear how effective gold standard algorithms remain in modern cohorts. METHODS: In the Bio-Hermes-001 diverse cohort, we assessed algorithm discrimination and calibration in six outcomes: classifying AB PET binary outcome (negative N = 603, positive N = 342); phosphorylated tau-217 binary outcome (pTau-217 negative N = 166, positive N = 469); participants with Healthy Cognition (N = 417) from probable AD (N = 272); HC from Mild Cognitive Impairment (N = 312), HC from pooled MCI or AD; and MCI from AD. Approximately a third of the cohort are individuals from populations typically underrepresented in dementia research (HC: 19%; MCI: 24%; AD: 33%). RESULTS: Hosmer-Lemeshow tests and Brier score demonstrate acceptable calibration of all algorithms except the oldest algorithm. However, Receiver Operating Characteristic (ROC) curves and the associated area under the curve (AUC) estimates evidenced that in this cohort only the BDSI exceeded conventional thresholds for good discrimination (.8 AUC in HC-AD classification, with AUC approximately .7 in the other clinical, AB PET, and pTau-217 comparisons). When the functional item is removed from the BDSI score, it remains acceptably calibrated, but DeLong tests reflect statistically significant reduction in discriminatory performance for all group comparisons. The two earliest published algorithms were only chance-level accurate. DISCUSSION: In a contemporary, diverse cohort, most established dementia risk algorithms had limited power in discriminating amyloid positivity, pTau-217 positivity, and current cognitive status despite acceptable calibration. Including a functional measure markedly improved discrimination across both clinical and biomarker-defined outcomes, suggesting that proximal indicators of cognitive vulnerability are critical for identifying individuals with underlying AD-related pathology.

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.

INTRODUCTION: Cognitive characterization is increasingly important for Alzheimer's disease treatment decisions, highlighting the need for scalable cognitive assessment. We evaluated the diagnostic accuracy of a remote self-administered cognitive screening battery, Mayo Test Drive (MTD), across clinically and biologically defined groups. METHODS: In this prospectively designed study, 2,131 cognitively unimpaired (CU) and 118 cognitively impaired participants, including 96 with mild cognitive impairment and 22 with mild dementia (MCI/DEM), were recruited from observational cohort studies and completed MTD remotely (mean age=70 years, SD=12; 48% male; mean education=16 years, SD=2). Clinical diagnoses were determined by multidisciplinary consensus independently of MTD. Amyloid- and tau-PET were available for 1,224 participants. Logistic regression estimated associations between MTD and CU versus MCI/DEM, CU A- versus MCI/DEM A+, and A-T- versus A+T+. MTD diagnostic accuracy was compared with person-administered cognitive measures. Sensitivity and specificity across score types and comparison groups were examined. RESULTS: MTD showed excellent discrimination of CU versus MCI/DEM (AUC=.89; 95% CI=.87-.92), and discriminability remained high for CU versus MCI (AUC=.88; 95% CI=.85-.92). Discriminability was highest when comparing CU A- and MCI/DEM A+ groups (AUC=.95; 95% CI=.92-.97). MTD outperformed estimated MMSE (p=.02) and was comparable to Mayo-PACC (p=.71) for A-T- versus A+T+ discrimination (AUC=.76; 95% CI=.71-.81). Combining both raw score and normative score cutoffs helped balance sensitivity and specificity relative to use of either cutoff in isolation. A two-stage interpretation framework further improved sensitivity while maintaining clinically useful specificity and informed development of an interpretation guide. DISCUSSION: A remote self-administered digital cognitive assessment showed strong diagnostic accuracy for detecting MCI and mild dementia in a large, primarily population-based cohort. The remote, self-administered MTD composite also showed comparable performance to established in-person cognitive composites for differentiating amyloid and tau-defined biomarker groups. Remote self-administered digital cognitive assessment may complement existing cognitive evaluation approaches and support scalable cognitive characterization.

INTRODUCTION: Observational studies suggest that angiotensin receptor blockers (ARBs) are associated with lower dementia risk than angiotensin-converting enzyme inhibitors (ACEIs). Whether dementia risk differs across individual ARBs remains uncertain and was investigated in this study. METHODS: This retrospective cohort study emulated target trials using electronic health record and claims data from 44 US health care organizations in the TriNetX Linked Research Network. ACEI/ARB-naive adults aged 55 to 80 years initiating an ARB or lisinopril were eligible. Separate 1:1 propensity-score matching was performed for each comparison. Individual and pooled ARBs were compared with lisinopril (between-class), and non-losartan ARBs with losartan (within-class). The primary outcome was incident all-cause dementia; secondary outcomes included Alzheimer disease, vascular dementia, cerebral infarction, death, and dementia or death. Cox models estimated hazard ratios (HRs) and 95% CIs. Sensitivity analyses included lagged and treatment-persistence analyses, control outcomes, and E-values. RESULTS: Of 22,025,599 individuals screened, 2,051,275 were eligible; matched cohorts ranged from 10,113 to 857,773, with median follow-up of 4.7 to 7.2 years. Pooled ARBs were associated with lower dementia risk than lisinopril (HR, 0.86; 95% CI, 0.85 to 0.87), with substantial variation across individual agents (HR range, 0.66 to 0.93). Within-class comparisons showed similar heterogeneity (HR range, 0.68 to 0.94) and reproduced between-class agent ordering by HR estimates. Considerable agent-level variation was also observed across secondary outcomes. Losartan, the most commonly prescribed ARB, had the least favorable dementia associations. Negative-control associations were near null within class, and sensitivity analyses supported robustness. DISCUSSION: Individual ARBs were associated with differing dementia risks, suggesting clinically relevant within-class heterogeneity pending confirmation in randomized trials.

Introduction: Remote cognitive assessments offer an accessible, scalable way to support clinical characterization and longitudinal monitoring of cognition. This study evaluates the feasibility of implementing Mayo Test Drive (MTD), a remote, self-administered digital cognitive assessment, within a specialized clinic focused on eligibility assessments for anti-amyloid therapies for Alzheimer's disease (AD). Method: Two-hundred and nine (N=209) patients (Mage=71.2, 53% female) were evaluated through the Mayo Clinic Rochester Alzheimer's Disease Treatment Clinic (ADTC) and had the opportunity to complete MTD, either as a remote, patient-initiated session through a patient-facing healthcare portal or during an in-clinic neuropsychological evaluation. Results: Clinical diagnoses included 55.2% mild cognitive impairment (MCI), 31.0% mild dementia, and 11.6% moderate dementia, in addition to 2.2% cognitively unimpaired (CU), as determined by neurology consensus at a multidisciplinary case conference. Among those who initiated MTD (n=197/209), 97.5% completed a session (70.6% remotely, 26.9% in clinic). Completion rates were high across diagnostic groups and did not significantly differ by clinical severity (p=.12): CU (100%), MCI (99.1%), mild dementia (97.0%), and moderate dementia (89.5%). Remote completion rates overall were high (97.9%). Among completers, administration setting (remote vs. in-clinic) did not significantly differ by clinical stage, {chi}2(3)=2.37, p=.49. Reasons for non-initiation included not receiving or opening portal messages and other/unknown factors. Discussion: Remote digital cognitive assessment is feasible within the context of clinic eligibility evaluations for AD therapies. The high completion rates of MTD across clinical impairment levels in this setting support development of paths for broader use in cognitive characterization in AD care.

Importance. Hypertension is among the most important modifiable risk factors for Alzheimers disease and related dementias (ADRD), but whether different antihypertensive drug classes confer different cognitive risk, particularly through differential modulation of angiotensin II receptor signaling, remains uncertain. Prior pharmacoepidemiologic evidence is based largely on a single Medicare cohort, and external validation across distinct healthcare data environments is needed. Objective. To externally replicate and extend prior findings across two structurally independent healthcare data sources, evaluating whether predominant use of antihypertensive medications that stimulate versus inhibit angiotensin II type 2 and type 4 receptor signaling is associated with risk of incident ADRD. Design, Setting, and Participants. Retrospective new user, active comparator cohort study with propensity score matching, conducted in parallel across two independent data sources: the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD; deidentified academic medical center electronic health record through February 2025) and the Merative MarketScan Medicare Database (2015 to 2023). Adults aged 65 years or older with hypertension and at least 365 cumulative days of antihypertensive exposure were included after a 365-day blanking period. Exposures. Predominant use ([&ge;]80% of cumulative exposure days) of receptor-stimulating antihypertensive medications (angiotensin II receptor blockers, dihydropyridine calcium channel blockers, thiazide diuretics) vs receptor-inhibiting antihypertensive medications (angiotensin-converting enzyme inhibitors, {beta}blockers, nondihydropyridine calcium channel blockers); a third group of nonusers comprised participants without predominant use of either class. Main Outcomes and Measures. Time to first incident ADRD diagnosis was identified using ICD9 and ICD10 codes consistent with the CMS Chronic Conditions Data Warehouse definition. Adjusted hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards models with time-dependent covariates. Results. The Medicare cohort included 1,596,034 beneficiaries (mean [SD] age, 75.0 [8.0] years; 55% female), and the VUMC SD cohort included 80,267 patients (mean [SD] age, 72.3 [5.8] years; 57% female). Over approximately 7 years of follow-up, ADRD incidence was lower among predominant users of stimulating vs. inhibiting medications in both cohorts (Medicare: 6.7% vs. 8.2%; VUMC SD: 4.9% vs. 5.7%). After adjustment, predominant use of stimulating medications was associated with lower ADRD risk versus inhibiting medications in both cohorts (Medicare: HR, 0.92; 95% CI, 0.90,0.93; P < .001; VUMC SD: HR, 0.92; 95% CI, 0.85,0.99; P = .03). Established cardiovascular and neuropsychiatric risk factors (atrial fibrillation, heart failure, chronic kidney disease, depression) showed expected associations with ADRD risk in both cohorts, supporting internal validity. Conclusions and Relevance. Across two structurally independent data sources, the predominant use of antihypertensive medications that stimulate angiotensin II type 2 and type 4 receptor signaling was associated with a modestly lower risk of incident ADRD compared with predominantly inhibiting agents. By replicating in two distinct data environments, these findings extend prior single-source pharmacoepidemiologic evidence and support evaluation of receptor targeted antihypertensive strategies for cognitive outcomes in randomized comparative-effectiveness trials.

Background: Early diagnosis and etiological classification of dementia remain challenging, as clinicians typically lack tools to integrate cognitive, neuroimaging, and genetic data quantitatively. We developed and validated multimodal risk models to support early diagnosis of dementia and differential diagnosis of Alzheimer's disease (AD) versus non-AD dementias in real-world clinical settings and translated model outputs into individualized risk reports. Methods: Utilizing real-world clinical cohorts (n = 1,100 for early diagnosis of dementia, using clinical diagnoses up to three years after clinical assessment; n = 788 for AD differential diagnosis) from Norwegian Memory Clinics, we trained and validated the Multimodal Hazard Score for Real-World Data (MHS-RWD) model integrating demographics (age, sex), cognitive assessments (MMSE-NR3 or CERAD 10-word delayed recall), the MRI-derived Imaging Hazard Score, and the Polygenic Hazard Score. Discrimination performance was examined using the area under the receiver operating characteristic curve (AUC). Results: In real-world clinical data, the MHS-RWD consistently outperformed any single predictor used alone. For early diagnosis of dementia, the full model achieved an AUC of 0.89 in females and 0.84 in males. For the differential diagnosis of AD from other dementias, the multimodal model yielded an AUC of 0.91 in females and 0.83 in males. A patient-level risk report was designed to present individualized risk estimates. Conclusions: Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis. The tool relies on data obtainable in clinical care, and genetic information that is becoming increasingly available in routine practice. Delivered through intuitive patient-level risk reports, it could support etiologically informed dementia decisions in real-world settings, with potential utility in primary care.

Background: Anti-amyloid-{beta} (A{beta}) monoclonal antibodies are disease-modifying treatments for early Alzheimer's disease but are associated with amyloid-related imaging abnormalities (ARIA) and intracranial hemorrhage (ICH). However, the safety of concomitant antithrombotic use remains unclear. Objective: To evaluate potential drug-drug interaction (DDI) signals between anti-A{beta} antibodies and antithrombotic drugs for ARIA and ICH using the FDA Adverse Event Reporting System (FAERS; JAPIC AERS). Methods: FAERS reports from 1997 to 2024 were analyzed. The anti-A{beta} antibodies evaluated were aducanumab, lecanemab, and donanemab. Antithrombotics were aspirin, P2Y12 inhibitors, direct oral anticoagulants, warfarin, and tissue-type plasminogen activators (tPAs). Outcomes were any ARIA, ARIA with edema or effusion (ARIA-E), ARIA with hemosiderin deposition (ARIA-H), and ICH. Individual-drug signals were assessed using reporting odds ratios and information components; DDIs were evaluated using four complementary models. Results: All three antibodies showed positive signals for all outcomes. When the three antibodies were analyzed together, aspirin showed positive DDI signals for any ARIA, ARIA-E, and ARIA-H in three models. Lecanemab-aspirin also showed signals for any ARIA and ARIA-H in multiple models. For ICH, tPAs showed signals in all four models with lecanemab and in the analysis combining all anti-A{beta} antibodies. Both analyses were based on the same five lecanemab reports. Conclusions: DDI signals were detected for anti-A{beta} antibody-aspirin combinations for ARIA and anti-A{beta} antibody-tPA combinations for ICH, particularly with lecanemab. These findings may warrant careful review of aspirin indications and baseline hemorrhagic risk and caution with thrombolysis during anti-A{beta} antibody treatment, although prospective validation is needed.

Identifying progressive dementia during its prodromal stage is a challenging yet critical task for healthcare providers. Traditional clinical assessments are often not sensitive enough to detect warning signs of cognitive decline. Machine learning (ML) methods have shown promise in modeling clinical trajectories associated with neurodegenerative disorders such as Alzheimer's disease (AD). Validation of these models' forecasting abilities, however, is widely lacking in the literature. To address this gap, we established a longitudinal cohort from the National Alzheimer's Coordinating Center (n = 15,727) comprising individuals who were cognitively normal (CN) or had mild cognitive impairment (MCI) at baseline. Cognitive status and clinical observations collected over up to 10 visits were used to train several ML classifiers. We employed the gradient-boosted tree ensemble method XGBoost with a custom feature-engineering pipeline. On non-imputed data, our models achieved an AUC-ROC of 0.9447 [0.9417-0.9476] and PR-AUC of 0.8554 [0.8497-0.8611] for the CN to MCI task, and an AUC-ROC of 0.9059 [0.8983-0.9134] and PR-AUC of 0.9214 [0.9156-0.9272] for the MCI to AD task. In a lead-time forecasting analysis spanning mean follow-up periods of 56.1 months (range, 9.1-214.1) for CN to MCI and 42.7 months (range, 9.1-126.1) for MCI to AD, the non-imputed models detected future progression before diagnosis in 29.58% [28.29%-30.87%] of CN to MCI progressors and 22.41% [21.36%-23.46%] of MCI to AD progressors. Among these individuals, the mean lead time was 33.04 months [32.52-33.56] and 17.95 months [17.71-18.19], respectively. False alarm rates among non-progressors were 12.6% [11.5%-13.7%] and 20.0% [18.4%-21.6%], respectively. These findings suggest that longitudinal machine learning models can identify future cognitive decline before diagnosis, indicating clinical utility for early dementia risk stratification.

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.

Alzheimers Disease and Related Dementias (ADRD) impacts 7 million people in the US over the age of 65, costing $360B in reimbursable care and $347B in unpaid care annually. ADRD is a salient health issue, being the most feared medical condition in the US and surpassing a fear of cancer. Despite this concern, there is almost no data about how Americans view their future lives if they receive an ADRD diagnosis. To investigate US preferences for end-of-life if given an ADRD diagnosis, 1,015 participants reviewed four vignettes of people in different stages of Alzheimer's disease, each of whom eventually experiences a fatal heart attack. Participants were then asked: if they were given an ADRD diagnosis then which person would they hope to be? We found that 75% of participants would choose for their life to end in the early stages of ADRD. Factors associated with a hope of later ADRD stages were race (OR = 1.9; 95CI = [1.1, 3.3]; p = 0.02; Black vs White, Non-hispanic) and education level (OR = 2.7; 95CI = [1.2, 5.7]; p = 0.01; Less than high school vs College). Of the majority who would choose for their life to end in the early stages of ADRD, we used Latent Dirichlet Allocation to find that the most representative rationales given were to prevent burdening their family and loved ones with their care as well as emphasizing their own quality of life (rather than longevity alone) as important. Current medical practice focuses on patient longevity as an important marker of success and progress in treatments of many diseases. However, our work here shows that for ADRD the focus of medical practice and the wishes of patients may not be aligned. For people with a diagnosis of ADRD, longevity may not be what they are hoping for.

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.

Identifying individuals with mild cognitive impairment (MCI) likely to progress to Alzheimer's disease (AD) is important for patient management and clinical trial enrollment. Although cognitive assessments, genetics, neuroimaging, and fluid biomarkers are each associated with disease progression, their predictive value has not been systematically compared using an identical cohort and evaluation framework. This study compared the predictive discrimination of clinical, cognitive, genetic, imaging, and cerebrospinal fluid (CSF) biomarkers, individually and combined, for 24-month progression from MCI to AD. A retrospective analysis used data from 2,430 participants with MCI enrolled in ADNI, including 547 who progressed to AD within 24 months and 1,883 who remained stable. Seven models were evaluated using identical preprocessing and modeling procedures: a clinical baseline (age and sex), the baseline plus a single modality out of cognitive assessment, APOE {varepsilon}4 genotype, structural MRI, CSF biomarkers, or PET biomarkers and a multimodal model combining all five. Performance was assessed using repeated 5 x 10 stratified cross-validation. Out-of-fold predictions from a separate 5-fold split were used to estimate confidence intervals and compare AUCs via DeLong's test with Holm/Bonferroni correction. Discrimination increased progressively across modalities. The clinical baseline achieved an AUC of 0.556; adding APOE e4 genotype increased performance to 0.692, CSF biomarkers to 0.729, PET biomarkers to 0.783, structural MRI to 0.836, and cognitive assessment to 0.918. Cognitive assessment significantly outperformed all other individual modalities, including MRI (difference in AUC = 0.079, P < 0.001). The multimodal model achieved the highest overall discrimination (AUC = 0.933), significantly outperforming cognitive assessment alone (difference in AUC = 0.016, P < 0.001), though it required complete data from only 20.5% of participants, versus 99.3% for cognitive assessment. Within a common evaluation framework, cognitive assessment demonstrated the greatest predictive discrimination among individual modalities for 24-month progression from MCI to AD, followed by structural MRI and PET. A multimodal model achieved the highest overall discrimination but required complete data from only one-fifth of the cohort. These findings suggest that routinely collected cognitive assessments capture substantial prognostic information, while full multimodal integration offers only modest incremental value relative to its reduced applicability.