The Journal of Prevention of Alzheimer's Disease

Background: International Classification of Diseases (ICD) codes are often used in epidemiological studies to track disease rates over time. Objective: This evaluation of ICD-code-based algorithms for electronic medical record (EMR) studies of Alzheimers disease (AD) and related dementias (ADRD) examines the impact of incorporating Centers for Medicare and Medicaid (CMS) data as an additional source of diagnostic and treatment information in Department of Veterans Affairs (VA) EMR studies. Methods: We performed a chart review of 100 VA Million Veteran Program (MVP) participants to evaluate algorithm performance. We also assessed genetic associations across algorithms in a large MVP cohort (n=396k). Results: Adding CMS data increased the number of detected cases, sensitivity, and positive predictive value, but decreased specificity and negative predictive value. Genetic analyses showed that broader (ADRD/dementia) algorithms with just VA data performed similarly to narrow (AD-focused) algorithms incorporating both VA and CMS ICD codes. Additionally, narrow AD algorithms based solely on VA data yielded the highest ORs, indicating the largest proportion of late-onset AD cases. Conclusions: We recommend using a broad (ADRD) algorithm without CMS or medication data, particularly for epidemiological studies or a strict AD algorithm including CMS and medication cases for genetic discovery of late-onset AD associations in VA EMR, and a strict AD algorithm without CMS data for applications focused solely on AD and sensitive to misspecification. Careful evaluation of algorithm performance is warranted in different EMR systems, as ICD coding practices vary by institution, as demonstrated by this comparison of VA EMR and CMS data.

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.

Background: The heart-brain axis links cardiovascular and neurodegenerative disease through shared vascular and inflammatory mechanisms. Although low-density lipoprotein cholesterol (LDL-C) is an established causal factor in atherosclerotic cardiovascular disease (ASCVD), its relationship with dementia remains uncertain, with midlife elevations associated with increased risk but late-life associations often appearing null or inverse. To address this cholesterol paradox, we integrated mendelian randomization (MR) with an active-comparator new-user target trial emulation. Methods: We applied a triangulated causal inference framework integrating two-sample MR with observational target trial emulation. Genetic variants associated with LDL-C were used as instrumental variables to evaluate Alzheimer disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and any dementia (AnyDem), with causal estimates derived using inverse-variance weighted models and sensitivity analyses for heterogeneity and pleiotropy. In parallel, an active-comparator new-user design compared statin versus ezetimibe initiation among adults aged 60 years or older using propensity score (PS) overlap weighting and Cox proportional hazards models to evaluate cardiovascular and dementia outcomes. Results: Genetically predicted LDL-C was associated with increased risk of DLB (OR 1.65, 95% CI 1.30-2.10; p<0.001), but not AD or AnyDem; FTD estimates were inconsistent. Sensitivity analyses suggested heterogeneity and possible pleiotropy for DLB. In the observational analysis (n=6,977), statin initiation was associated with higher risks of ASCVD (HR 1.26, 95% CI 1.11-1.45) and AnyDem (HR 1.66, 95% CI 1.16-2.38), although estimates attenuated after lipid adjustment and lagged analyses, suggesting residual confounding, treatment selection, and reverse causation in late-life observational associations. Conclusions: These findings suggest that LDL-C reflects accumulated vascular and metabolic risk rather than a direct causal driver of AD or overall dementia, although a subtype-specific association was observed for DLB. Late-life associations appeared influenced by timing, reverse causation, and treatment selection, warranting cautious interpretation. Keywords: Heart-brain axis, dementia, cardiovascular disease, low-density lipoprotein cholesterol, causal inference

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.

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.

Background: Anti-amyloid antibody therapies have changed the clinical pathway for early Alzheimer disease (AD). Lecanemab and donanemab are now clinically available in many countries, including Japan, and their use requires biomarker confirmation, repeated magnetic resonance imaging monitoring, management of amyloid-related imaging abnormalities, infusion capacity, staff resources, and shared decision-making. In Japan, these treatments are provided under the universal public health insurance system and are regulated by the optimal use guidelines. Therefore, it is important to understand not only the number of treated patients but also how specialists perceive clinical, logistical, and policy challenges in routine practice. Objective: This paper describes the protocol for a nationwide anonymous online survey of dementia specialists in Japan. The survey aims to evaluate the real-world implementation of anti-amyloid antibody therapies, including current clinical practice, facility readiness, perceived barriers, possible policy solutions, and physician preferences assessed using a discrete choice experiment and best-worst scaling. Methods: This is a prospective, cross-sectional, anonymous online survey using Google Forms. The survey targets board-certified specialists of the Japanese Society for Dementia Research and the Japanese Psychogeriatric Society, with a main focus on physicians who have completed the official training course required for anti-amyloid antibody therapy. The questionnaire includes items on respondent and facility characteristics, perceived value of treatment, treatment experience, diagnostic and eligibility assessment, amyloid and APOE testing, MRI monitoring, infusion capacity, continued-administration facilities, blood-based biomarkers, preclinical AD, a discrete choice experiment, and best-worst scaling. Among respondents routed to the DCE section, the discrete choice experiment asks respondents to choose between hypothetical anti-amyloid antibody treatment profiles for early AD, defined by expected efficacy, risk of amyloid-related imaging abnormalities requiring treatment interruption or discontinuation, treatment duration, visit frequency, waiting time, and monthly out-of-pocket cost. Best-worst scaling evaluates the relative importance of policy and system-level solutions. Results: Data collection started on June 3, 2026, and is planned to close on June 30, 2026. This protocol was prepared before data lock and before any outcome analyses. The main results will be reported after data cleaning and analysis according to the prespecified analysis plan. Conclusions: This protocol describes a nationwide survey designed to clarify clinical, logistical, and policy challenges in the implementation of anti-amyloid antibody therapies in Japan. By publishing the survey design and analysis plan before data lock, this study aims to improve transparency and interpretability. The findings will help identify where Japanese dementia specialists perceive bottlenecks in diagnosis, biomarker testing, safety monitoring, infusion delivery, continued administration, and reimbursement. They may also inform policy discussions on APOE testing, blood-based biomarkers, regional care coordination, and service reimbursement for anti-amyloid antibody therapy.

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: 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: 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.

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.

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.

Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus angiotensin converting enzyme inhibitors (ACEIs) on dementia risk remains uncertain. Objective: To compare the risk of dementia and dementia-free death of ARB versus ACEI initiation among US Veterans with incident hypertension. Methods: We conducted a retrospective target trial emulation using a new-user, active-comparator design among Veterans with incident hypertension. We analyzed longitudinal electronic health records from 2,577,000 individuals who initiated ARBs or ACEIs between 1/1/2000-12/31/2017, with up to five years of follow-up. The exposure was initiation of an ARB-based versus ACEI-based antihypertensive regimen. Co-primary outcomes were dementia, identified using natural language processing of clinical notes, and dementia-free death. We used inverse probability of treatment weights based on 66 pretreatment covariates to estimate the cumulative incidence of the outcomes for each treatment group. Weighted risk ratios and absolute risk differences through five years were computed with bootstrapped 95% CIs. Secondary outcomes included all-cause death and a composite of dementia or death, evaluated using a weighted Kaplan-Meier approach. Results: Among 2,577,000 Veterans (mean age, 63 years; 4.5% female; 65% White; 15% Black), 10% initiated ARBs and 90% initiated ACEIs. Over five years of follow up, 6% developed dementia, 12% died without dementia, and 13% died overall. ARB initiation yielded consistently lower risk of dementia (risk ratio, 0.88; 95% CI, 0.83-0.93 at 6 months to 0.92; 95% CI, 0.90-0.94 at 5 years) and dementia-free death (risk ratio, 0.90; 95% CI, 0.86-0.96 at 6 months to 1.00; 95% CI, 0.98-1.01 at 5 years) than ACEI initiation. Effects on secondary outcomes were similar to those for primary outcomes. Greater protective dementia effects were observed in older and male Veterans and non-statin users, with similar effects on dementia-free death. Discussion: Among US Veterans with incident treated hypertension, initiation of ARB versus ACEI antihypertensive regimen conveyed a modestly lower risk of dementia. Given the high prevalence of hypertension, these modest effects may confer meaningful population-level benefits on brain health. Future research estimating per-protocol effects using a more generalizable population is needed to confirm our findings. Key words: antihypertensive medication, dementia, natural language processing, target trial emulation, Veteran

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.

INTRODUCTION: Dementia, particularly Alzheimer disease (AD), is a major global health challenge, with prevalence projected to reach 150 million cases by 2050. AD is characterized by progressive cognitive decline linked to neuroinflammation and neurodegeneration. Non-steroidal anti-inflammatory drugs (NSAIDs) have been explored as potential neuroprotective agents, particularly diclofenac, which has been proposed to modulate microglial inflammasome signaling. However, prior studies investigating NSAIDs in AD have yielded inconsistent findings. We therefore reexamined the relationship between selected NSAIDs and dementia outcomes in a large longitudinal cohort from the National Alzheimer Coordinating Center (NACC). METHODS: We analyzed cross-sectional and longitudinal data from the NACC database collected between 2005 and 2022. Associations between NSAID exposure and dementia, AD, and cognitive trajectories were examined. Propensity score matching was performed to compare NSAID users with matched non-users while adjusting for demographic and clinical confounders. Longitudinal mixed-effects models were used to assess cognitive decline based on Montreal Cognitive Assessment (MoCA) scores. RESULTS: Among 47,165 participants, diclofenac and naproxen use were associated with a lower prevalence of dementia and AD compared with matched non-users, whereas etodolac showed no significant associations. Diclofenac users demonstrated reduced odds of dementia and AD. Naproxen showed similar cross-sectional associations. In longitudinal modeling, diclofenac users had a significantly slower rate of cognitive decline than non-users. DISCUSSION: These findings suggest a compound-specific association between NSAID use and AD, with diclofenac potentially modulating disease progression through anti-inflammatory mechanisms. The observed modulation of longitudinal cognitive decline supports further investigation of inflammatory pathways, including microglial and inflammasome signaling, as therapeutic targets in biomarker-defined AD populations.

Background: Multidomain lifestyle interventions targeting multiple risk factors have been proposed to reduce cognitive impairment and dementia risk. However, mixed findings hamper their application. More evidence is needed to optimise approaches in different settings. The increasing number of clinical trials being conducted warrants an up-to-date synthesis. Methods: We conducted a systematic review and meta-analysis of randomised controlled trials (RCTs) testing multidomain interventions (three or more components) on cognition or dementia incidence. Maximum-likelihood random-effect models were applied. Sensitivity analyses were conducted to explore source of heterogeneity Meta-regression analyses were conducted, including by intervention duration and intensity. Risk of bias on cognition was assessed using the revised Cochrane risk-of-bias tool for RCTs (RoB-2) Heterogeneity was estimated using Chi2 test, I2 statistics, and 95% prediction intervals GRADE was used for evidence certainty assessment. Results: After screening 4759 and full-text reading 128 publications, 43 RCTs were eligible and 41 included in the meta-analysis (N=23209). Risk of bias was generally low, with most concerns in older studies Small but statistically significant intervention benefits were found for global cognition (composite score of validated neuropsychological tests; SMD=0,28; 95% CI: 0,10 to 0,45), and most of the other cognitive measures. High heterogeneity was observed for global cognition composite scores and could be only partially explained by three smaller RCTs. Intervention effect-size was significantly associated with shorter duration (P-value=0,009) and higher observed intensity (P-value=0,008). Interpretation: Multidomain interventions have small but consistent beneficial effects on cognitive measures, suggesting the potential to reduce cognitive impairment and dementia risk. High heterogeneity across RCTs can hinder data pooling. More evidence on longer-term effect is needed. Future research should prioritise harmonisation of methodologies and reporting, long-term extended follow-up data, clinically relevant dementia-risk surrogate outcomes, and evidence from more diverse cultural, geographical, and socio-economic contexts.

Depression and dementia frequently co-occur, yet sex-specific population-level associations remain unclear. This ecological study examined cross-national relationships between sex-specific depressive disorder incidence and dementia incidence using Institute for Health Metrics and Evaluation data. Analyses included sex-stratified scatterplots, Pearson and Spearman correlations, principal component analysis, partial correlations adjusting for macro-structural factors, and sex-specific multiple linear regression. Visual analyses suggested positive associations in both sexes, but patterns were stronger and more structured among females. Female depressive disorder incidence correlated with dementia incidence in females and males, clustered with structural-development indicators, and remained associated after adjustment. Male depressive disorder incidence showed no significant associations. Overall, depressive disorder incidence was independently associated with dementia incidence among females but not males, supporting a sex-differentiated population level mental health dementia relationship with implications for global womens health and ageing. These findings inform sex-sensitive surveillance, prevention, and policy frameworks in rapidly ageing societies worldwide, particularly within resource-constrained transitional contexts.

Background: Blood-based biomarkers are increasingly used to identify Alzheimer's disease (AD)-related pathology, but differences in p217tau assay methodology, training cohorts, and model-development context can substantially influence machine-learning (ML) predictions. Whether emerging biomarker platforms preserve biologically meaningful AD-related information across independently developed ML frameworks remains incompletely understood. Objective: To evaluate the biological coherence and translational consistency of plasma biomarker measurements generated using the 5ADCSI platform by applying multiple externally trained ML frameworks and developing a consensus-risk approach that integrates framework predictions while quantifying prediction uncertainty. Methods: Plasma biomarker measurements from 472 participants in the Louisville Twins Study were analyzed using three independently trained ML frameworks: an A4-derived model using the Lilly p217tau MSD assay and two ADNI-derived models using Quanterix Simoa p217tau measured with either the AlzPath or Janssen antibody. Framework-specific predictions of amyloid positivity probability and predicted centiloid burden were integrated into consensus amyloid risk, consensus centiloid burden, and composite consensus AD-risk scores. Prediction uncertainty and rank instability were used to characterize framework agreement and participant-level classification stability. Results: All three frameworks recognized biologically coherent AD-related signal despite differences in training cohort and assay methodology. Agreement was strongest between the A4-MSD and ADNI-AlzPath frameworks, whereas agreement involving the ADNI-Jan framework was weaker. Consensus-risk modeling identified a reproducibly high-risk subgroup characterized by elevated consensus-risk scores, low prediction uncertainty, and low rank instability. Participants prioritized by the consensus framework were enriched for APOE {varepsilon}4 burden, p-tau217, p-tau217/A{beta}42, and GFAP, while discordant high-risk participants exhibited substantially greater framework disagreement. Conclusions: Plasma biomarker measurements generated using the 5ADCSI platform preserve biologically meaningful AD-related information that is consistently recognized across multiple independent ML frameworks. Consensus-risk modeling provides a practical strategy for integrating complementary information from external biological reference models while explicitly characterizing prediction uncertainty, thereby supporting evaluation of emerging blood-based biomarker platforms when direct pathological validation is unavailable.