Alzheimer's & Dementia: Translational Research & Clinical Interventions

Background: Three San Francisco health system emergency departments have developed Geriatric Emergency Department (GED) models of care programs supporting and providing care for emergency department (ED) patients at risk for or living with dementia. Each system recognized: 1) the high proportion of older adult ED patients and those at risk for dementia, 2) the need to identify cognitive impairment in older adult ED patients, 3) the importance of developing approaches to connect older adult ED patients and their care partners with resources and diagnostic specialty services. Methods: We describe how each hospital adopted and implemented pragmatic GED models of care to support and improve care for ED patients at risk or living with dementia. We also report the proportion of ED encounters made by patients with dementia histories and the number of these reached by GED programs. Results: Three San Francisco hospitals (a tertiary care, critical access, and large integrated health system-community ED) independently implemented GED programs to support and enhance emergency care for patients living with dementia. Each uses screening and assessment tools to identify patients at risk for cognitive impairment. Each captures screening and assessment data to facilitate care and resources for post-discharge care, ensuring coordinated transitions and support for older adults. Programs varied by target patient population age and staff and resource allocation to support program goals. Site-specific pathways differed by location, patient populations, and support from geriatrics, emergency medicine, palliative medicine, neurology, psychiatry, pharmacy, referral processes, and/or pastoral care. Conclusions: Developing GED care interventions that facilitate care for patients at risk of or living with dementia is possible and sustainable when the pathway aligns with health system leadership goals through persistent value demonstration, communication, and promotion. Ultimately, developing and disseminating models of GED care is designed to address geriatric syndromes inclusive of dementia care through continuous quality improvement.

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: Predictive risk stratification tools are widely used to support proactive care for older adults, yet head-to-head external validation within local English health systems remains limited. Eclipse-PRISM implemented in UK primary care settings, while the Johns Hopkins Adjusted Clinical Groups (ACG) system provides risk scores derived from diagnosis groupings and healthcare utilisation data. Aim: To externally validate and compare PRISM and Johns Hopkins ACG scores for predicting emergency hospital admission among older adults in an English integrated care system. Methods: We conducted a retrospective cohort study in the Norfolk and Waveney Integrated Care System. Individuals aged 75 years and over at the index date with valid PRISM and ACG emergency admission risk scores and linkage to hospital activity data were included. The primary outcome was 1 or more emergency hospital admission within 1 month. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC), with paired AUCs compared using DeLongs test. Calibration was evaluated using calibration plots and quantified using calibration intercept and slope from logistic recalibration models. Overall accuracy was summarised using the Brier score. Clinical utility was assessed using decision curve analysis (DCA). Results: The cohort included 114,407 patients aged 75 years and over; 2,136 (1.87%) had 1 or more emergency admission within 1 month. ePRISM showed higher discrimination than Johns Hopkins ACG (AUC 0.860 [95% CI 0.852 to 0.867] vs 0.739 [95% CI 0.728 to 0.749]; difference-in-AUC 0.121 [95% CI 0.111 to 0.130]; DeLong p<0.0001), with consistent differences across age and sex subgroups. Calibration differed materially: ePRISM showed closer agreement between predicted and observed risks, whereas Johns Hopkins ACG systematically overpredicted risk across much of the range. Brier scores favoured ePRISM (0.017 [95% CI 0.017 to 0.018] vs 0.051 [95% CI 0.050 to 0.051]). In DCA, ePRISM provided higher net benefit across clinically plausible thresholds, while Johns Hopkins ACG showed lower or negative net benefit across much of the threshold range. Conclusions: In this English older population, ePRISM demonstrated higher discrimination and more favourable apparent calibration, overall accuracy and decision-analytic performance for predicting 1-month emergency admission than Johns Hopkins ACG. Model selection for short-term risk stratification should therefore consider calibration and clinical utility alongside discrimination, with local validation and recalibration where appropriate before implementation.

Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of these changes is important for improving disease monitoring, identifying progression patterns, and supporting early risk stratification. This study developed an explainable longitudinal machine-learning framework for dementia progression, using cognitive and Magnetic Resonance Imaging (MRI)-derived biomarkers from the Open Access Series of Imaging Studies (OASIS-2) longitudinal dataset. The dataset included 150 subjects and 373 repeated observations classified as Non-demented, Demented, or Converted. Current-visit features, previous-visit features, and slope-based temporal features were constructed from Mini-Mental State Examination, Clinical Dementia Rating, normalized whole-brain volume, estimated total intracranial volume, atlas scaling factor, Age, and MRI delay. Baseline models were compared with a longitudinal gradient-boosted model, using patient-level splitting to reduce data leakage across repeated visits. The proposed longiGradient Gradient boosting model achieved the best held-out test performance, with an accuracy of 88.16%, a macro F1-score of 0.776, and a weighted F1-score of 0.860. The model showed strong classification performance for Demented and Non-demented individuals, while converted cases remained more difficult to identify. A regularized gradient boosting model was also evaluated as an overfitting sensitivity analysis; although it reduced the perfect training fit, it did not improve held-out test performance. Feature importance, permutation importance, and SHapley Additive exPlanations identified Clinical Dementia Rating as the dominant predictor, with slope-based Clinical Dementia Rating providing additional longitudinal information. These findings suggest that combining cognitive measures, MRI-derived biomarkers, and temporal feature engineering can improve dementia progression modeling, although external validation in larger longitudinal cohorts is needed.

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.

INTRODUCTION Psychosis affects 40% of individuals with Alzheimer's disease (AD) and is associated with accelerated cognitive decline. Blood-based biomarkers, particularly plasma phosphorylated tau (ptau), have demonstrated utility in predicting cognitive decline in AD, with ptau217 showing superior performance in many studies. However, whether these biomarkers predict differential cognitive trajectories in AD with psychosis (ADP) remains unknown. METHODS Two independent cohorts were analyzed: Alzheimer's Disease Neuroimaging Initiative (ADNI; n=659: 172 cognitively unimpaired [CU], 406 AD, 81 ADP) and Litwin-Zucker Research Center (LZ; n=142: 68 CU, 57 AD, 17 ADP) with 6-year follow-up. Psychosis was defined by non-zero Neuropsychiatric Inventory delusions or hallucinations scores. In ADNI, plasma ptau181, ptau217, ptau231, amyloid-{beta}42/40, GFAP, and NfL were quantified using NULISA. In LZ, ptau181, ptau205, ptau212, ptau217, amyloid-{beta}42/40, GFAP, and NfL were quantified using Simoa. Linear mixed-effects models assessed prediction of cognitive decline across memory, language, visuospatial, and executive function domains. RESULTS In ADNI, baseline ptau181 predicted differential ADP decline in language (p<0.05), visuospatial (p<0.05), and executive function (p<0.05); ptau217 predicted language (p<0.05) and visuospatial (p<0.05) decline; GFAP predicted language (p<0.05) and visuospatial (p<0.05) decline; and NfL visuospatial decline (p=0.01). In LZ, ptau181 predicted decline in memory (p<0.05), language (p<0.0001), visuospatial (p<0.05), and executive function (p<0.05); ptau217 predicted memory (p<0.05) and visuospatial (p<0.05) decline; and GFAP predicted language decline (p<0.05). Johnson-Neyman analyses revealed ADP-AD divergence at low ptau181 thresholds in ADNI, while LZ showed crossover patterns with steeper ADP decline at low biomarker levels that attenuated at high levels where AD decline was steeper. DISCUSSION ADP exhibited accelerated cognitive decline across domains driven by a distinct biomarker landscape compared to non-psychotic AD. Plasma ptau181 demonstrated broader domain-specific associations with decline in ADP than other blood-based biomarkers and associated exclusively with executive function impairment, indicating its unique utility for predicting cognitive trajectories in this pathophysiological subtype.

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

Purpose: People with type 2 diabetes mellitus (DM2) have an increased risk of Alzheimer's Disease (AD). GLP1 receptor agonists (GLP1RAs) and SGLT2 inhibitors (SGLT2i) have emerged as potential interventions to prevent AD and dementia and may provide insights into disease mechanisms. Methods: PubMed, Embase, and Cochrane were searched for randomized controlled trials (RCTs), cohort, and case control studies between January 1, 2006, and April 15, 2025, comparing GLP1RAs and/or SGLT2i in adults with DM2 without cognitive impairment at baseline. A total of 12 studies met the inclusion criteria (1 RCT and 11 cohort studies). The primary outcome was incident AD and dementia, assessed primarily through adjusted hazard ratio (HR). Secondary outcomes were HbA1C levels and their relation to cognitive protection. A regression analysis was performed to assess the variables that could explain the result obtained. Results: GLP1RAs and SGLT2i consistently demonstrated a reduced risk of AD (HR = 0.72; 95% CI, 0.59 to 0.87) and dementia (HR = 0.84; 95% CI, 0.76 to 0.92) compared to other glucose-lowering therapies, with moderate/high heterogeneity and moderate certainty. Also, SGLT2i showed a modest benefit over GLP1RAs in protecting against AD (HR: = 1.07; 95% CI, 1.00 to 1.14; p = 0.0496), and baseline HbA1c was associated with AD risk estimates among SGLT2i users but not among GLP-1RAs users. Conclusion: These findings reinforce the potential of GLP1RAs and SGLT2i as therapeutic options to slow cognitive decline in DM2, possibly depending on glycemic control and central modulation.

Objectives The cost-effectiveness of new amyloid-targeting therapies (ATTs) for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease (AD) is influenced by assumptions about treatment effectiveness beyond the trial durations. To assess the cost-effectiveness of ATTs over a lifetime horizon, an AD microsimulation model was applied. Methods The AD microsimulation model is based on statistical joint models, which link cognitive decline (Mini-Mental State Examination [MMSE]) and states of functional independence (MCI, dementia, institutionalisation), fitted to the Amsterdam Dementia Cohort. The time from MCI to death was simulated under care-as-usual (CAU) and two ATT scenarios, assuming an ATT duration of eighteen months and treatment effect waning of 0% (no-waning) or 20% per year. The main outcome was the incremental cost-effectiveness ratio (ICER), defined as incremental costs per quality-adjusted life year (QALY) gained. A societal perspective was taken for costs and effects. Results The ATT scenario without waning resulted in 0.72 additional QALYs and {euro}28,502 additional costs per person compared to CAU. At a list price of {euro}22,600/year for the ATT, the ICER was {euro}39,745/QALY for the no-waning and {euro}151,016/QALY for the 20%-waning scenario. At a willingness-to-pay threshold of 20,000 EUR/QALY, the corresponding threshold prices were 10,500 EUR (95% CI: <{euro}0 to {euro} 23,000; no-waning) and <{euro}0 (95% CI: <{euro}0 EUR to {euro} 2,250; 20%-waning), respectively. Conclusions The cost-effectiveness of ATTs is strongly influenced by the waning of the treatment effect. A favourable cost-effectiveness profile was only achieved when the treatment effect did not wane after the eighteen-month treatment period.

Detection and characterisation of dementia and Mild Cognitive Impairment (MCI) is essential for diagnosis and to support recruitment of patients into trials of disease-targeted therapies. Computerised systems offer a means of improving detection in community and primary care settings in a scalable way. This study presents further validation of the self-test PROTECT Cognitive Test System of eight assessments of memory, attention and executive function in 36,941 participants (35,822 healthy, 1046 MCI, 73 dementia). PROTECT shows robust separation of dementia and non-dementia participants (p < 0.001), and good discriminative ability for dementia (Area Under the Curve = 0.966) with 90.90% sensitivity and 87.80% specificity. An optimised detection algorithm robustly identified MCI (P<0.001) and predicted 24-month decline across cognitive domains in both amnestic MCI and non-amnestic MCI phenotypes compared with healthy controls. PROTECT cognitive data also correlated strongly with the plasma biomarkers p-tau217 and Neurofilament Light (n = 46). The system provides a means of improving dementia and MCI detection using self-testing, offering a scalable triage and monitoring tool for clinical pathways and trials.

Dementia is a progressive condition that impairs cognitive processes such as memory, decision making, and the ability to manage daily activities. Recent estimates suggest that more than half of all dementia cases could be preventable by addressing their risk factors, including disease comorbidities such as diabetes and vision loss. Yet, we lack a comprehensive molecular map of dementia comorbidities. In this work, we analyzed Austrian nationwide hospital claims data, comprising 13 million hospital stays from 2015 to 2019, to systematically assess dementia-related risk across disease comorbidity patterns, covering both their molecular relationships and their epidemiological overrepresentation. We identified disease trajectories occurring before and at the time of dementia diagnosis, revealing both sex-specific and shared comorbidity patterns. Overall, we identified 51 potential risk factors, with a prominent contribution from endocrine and metabolic disorders. While Parkinson's disease emerged as a strong molecularly related driver of dementia, we also identified emerging and previously under chracterized risk factors, including vitamin D deficiency. This integrative framework provides a comprehensive view of dementia associated disease networks and identifies novel, potentially modifiable risk factors. These results offer new opportunities for targeted prevention strategies and advance our understanding of the complex interplay between comorbidities and dementia development.

ABSTRACT INTRODUCTION: Early identification of individuals with mild cognitive impairment (MCI) at high risk of conversion to Alzheimer's disease (AD) is essential for timely intervention. We evaluated whether routinely obtainable clinical assessments can accurately predict 24-month MC to AD conversion. METHODS: Data from 2,430 participants with MCI in the Alzheimer's Disease Neuroimaging Initiative were analyzed. XGBoost, Random Forest, and Logistic Regression models were evaluated. SHAP-based feature selection and feature ablation analyses assessed the incremental value of APOE4 genotype. RESULTS: A six-feature model incorporating age, sex, education, RAVLT Immediate Recall, MMSE, and EcogSPTotal achieved an AUC of 0.922 (95% CI, 0.911~0.933). APOE4 provided negligible additional predictive value once cognitive measures were included. The XGBoost model outperformed Clinical Dementia Rating Sum of Boxes classification. DISCUSSION: Routine cognitive assessments accurately predict 24-month MCI-to-AD progression without biomarkers, neuroimaging, or genetic testing, offering a practical, low-cost tool for clinical risk stratification.

Introduction: Multimorbidity clusters have been associated with increased dementia risk. While lifestyle factors may modify dementia risk, their role in multimorbidity clusters remains unclear. Method: Data from UK Biobank was used to identify clusters of chronic conditions using latent class analysis, assess their associations with dementia risk using Cox regression, and potential moderating effects of lifestyle factors. Results: We included 465,175 participants (mean age (SD) = 56.52 (8.01), 53.87 % female). Five clusters were identified and significantly associated with increased dementia risk, with the cardiometabolic (HR = 2.14, p < 0.001) and mental health cluster (HR =1.99, p < 0.001) exhibiting the highest risk. Only moderate physical activity lowered dementia risk in the pain-dominated multimorbidity cluster (HR = 0.77, p = 0.039). Discussion: Lifestyle factors including physical activity may protect against dementia in specific multimorbidity clusters. Future research involving objective and multiple lifestyle measures is needed.

The Dementia Trials Accelerator (DTA) is a UK-wide programme designed to improve the feasibility, efficiency, and inclusiveness of recruitment into clinical trials of dementia and related brain-health conditions. Dementia trials are frequently constrained by the difficulty and cost of identifying eligible participants, which often requires cognitive assessments and measurement of blood-based biomarkers. The DTA addresses these barriers with two linked services. First, the DTA provides a federated platform to improve findability of potential participants across existing UK-based cohorts with consent to recontact. A single point of contact across multiple cohorts would allow increased efficiency of search for participants for studies. Second, the DTA provides a community-centred pre-screening service with information relevant to trial eligibility and a linked plasma and DNA tissue bank, with participant consent for recontact. Participants aged 65-75 years are approached via existing cohorts and registries. Consenting participants complete an online questionnaire and digital cognitive assessment, attend an in-person assessment for physical measures and face-to-face cognitive testing, and provide venous blood samples which are processed to plasma and whole blood for long-term storage and biomarker measurement. The initial programme target is to recruit at least 10,000 participants into the DTA pre-screening service. The DTA is designed to support approved academic and industry studies by enabling the DTA team to approach eligible participants for specific studies, without transferring identifiable information without consent. In parallel with its immediate trial-readiness purpose, the DTA is positioned to interoperate with emerging national approaches to biomarker-led recruitment by generating high-quality, recontactable cohorts with standardised cognitive characterisation and scalable biosampling suitable for blood-based biomarkers.

Vascular dementia (VaD) is a complex clinical syndrome arising from cerebrovascular disease, characterized by cognitive decline and functional impairment, and is projected to double in prevalence over the next three decades. Deficiencies in one-carbon (1C) metabolism are linked to the onset of VaD. Our previous work using mouse models has demonstrated that reduced dietary folic acid intake or genetic disruptions in 1C metabolism exacerbate outcomes in a model of VaD. However, the impact of VaD on one-carbon metabolism remains poorly understood. This study aims to provide a detailed molecular portrait of 1C metabolism within the context of VaD, shedding light on potential molecular mechanisms. In post-mortem medial prefrontal cortex tissue from VaD female and male patients and controls we measured protein expression of the folate receptor (FR) and 1C enzymes including methylenetetrahydrofolate reductase (MTHFR), thymidylate synthase (TS), choline acetyltransferase (ChAT), acetylcholinesterase (AChE), cystathionine {beta}-synthase (CBS) co-localized with NeuN. There was an interaction between VaD and gender for levels of FR. Both male and female VaD had increased levels of ChAT. Female VaD patients had higher levels of MTHFR and CBS when compared to males. VaD is a complex disease; the results of this study demonstrate that VaD impacts neuronal levels of 1C enzymes. Future studies should assess 1C in other cell types of the brain, as well as measure enzyme activity levels.

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.

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.

Background: ACE variants are genetic risk factors for Alzheimer's disease (AD), potentially through reduced enzymatic activity and impaired amyloid {beta} hydrolysis. Objectives: To create a publicly available database of ACE variants relevant to ACE deficiency and AD, and to estimate the population frequency of damaging ACE variants and their impact on blood ACE levels. Methods: ACE variants were compiled from literature, public databases (VarSome, dbSNP, ClinVar, gnomAD), and sequencing data (WES/WGS) from 5147 Russian individuals. Variants were classified using a consensus in silico score (AlphaMissense, MetaRNN, EVE). Blood ACE levels were measured in 330 carriers of 64 different ACE mutations. Results: We identified 1682 unique ACE variants. Of these, 608 (36.2%) were classified as functionally damaging, including 17 signal peptide, 210 loss of function, and 381 missense variants. The estimated carrier frequency of damaging ACE variants was 2 % (1/50). Notably, 24 variants associated with experimentally confirmed reductions in blood ACE levels had a combined estimated carrier frequency of 3.9 % in the general population, calculated from cumulative gnomAD v4.1.0 allele frequencies under a rare-variant independence model. An open-access browser is available at https://ace-browser.com/. Conclusions: Variants associated with reduced blood ACE levels were estimated to be carried by approximately 1 in 25 individuals in the general population. This frequency is of the same order of magnitude as the 13.2% prevalence of Alzheimer's dementia in individuals aged 75-84 years (Alzheimer's Association, 2025), consistent with the hypothesis that ACE deficiency may represent an underrecognized contributor to late-onset AD susceptibility. The ACE mutations-AD browser and integrated genotype-phenotype data presented here provide a novel resource for future basic, translational, and clinical research on ACE-dependent AD.

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.

Persistent racial disparities in dementia raise concerns regarding the validity and generalizability of existing prognostic models across diverse populations. We evaluated the utility of the 2024 Lancet Commission risk factors and resting heart rate (RHR) for race-specific dementia risk prediction using data from 55,004 participants in the National Alzheimer's Coordinating Center cohort. Cox proportional hazards and Random Survival Forest (RSF) models were developed separately for Black, White, Asian, and American Indian participants to predict 1-, 3-, and 5-year time to dementia. RSF consistently outperformed Cox models across all racial groups and prediction horizons, achieving 5-year AUCs of 0.88-0.91 compared with 0.69-0.81 for Cox models. Inclusion of RHR modestly and consistently improved predictive performance across racial groups. Predictor importance varied between racial groups, suggesting heterogeneity in dementia risk profiles and disease presentation. These findings support the potential utility of RHR as a complementary prognostic biomarker and highlight the importance of equitable, personalized dementia risk prediction across diverse populations.