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.

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.

Background: Electronic health record (EHR)-linked biorepositories provide opportunities to advance epidemiological research in Alzheimer's disease (AD) and related dementias. Objective: Evaluate the extraction, curation, and associative validity of Mini Mental State Examination (MMSE) scores from the VA EHR for participants in the VA Million Veteran Program (MVP). Methods: The sample (N = 49,555; 7.4% women) included a multiethnic cohort (European [68.3%], African [20.4%], Hispanic [9.0%]) with EHR-extracted MMSE scores; 30.7% were apolipoprotein E (APOE) {epsilon}4 carriers, and 25.8% had multiple scores. Linear regressions examined cross-sectional associations between {epsilon}4 dosage (0, 1, 2) and first and lowest MMSE scores. MMSE scores were also evaluated against MVP dementia diagnostic algorithms in participants aged [&ge;]65 years. Results: Among participants of European ancestry, there was a significant {epsilon}4 dose-response relationship (ps < .001) with MMSE scores. Homozygote carriers scored lower than heterozygote carriers (Mdiff: first = -0.5; lowest = -0.9), who scored lower than non-carriers (Mdiff: first = -0.4; lowest = -0.6). Among Veterans of African and Hispanic ancestry, no dose-response relationship was observed, although {epsilon}4 carriers had lower scores than non-carriers (ps [&le;] .04). MMSE scores corresponded strongly with dementia case/control status across phenotypes: mild impairment on the MMSE was strongly associated with AD (odds ratio [OR] = 11.48), with more severe MMSE impairment showing stronger associations (moderate OR = 17.95; severe OR = 27.83). Conclusion: This study demonstrated MMSE scores can be systematically extracted and curated from the VA EHR. Findings offer a scalable framework for future studies on risk stratification, highlighting the potential for harnessing MVP to explore genetic and clinical factors contributing to cognitive and dementia outcomes in diverse samples.

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: 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.

INTRODUCTION: The biomarker-based amyloid/ tau/ neurodegeneration (A/T/N) framework has become a popular staging method for Alzheimer's disease (AD) research. Previous studies use the framework either as a rule-based or data-driven approach but typically sacrifice either adaptivity or interpretability. METHODS: We present an interpretable, hybrid method, called Neurosymodal Data Fusion, for predicting incident AD in the ADNI dataset. Specifically, we encode the A/T/N framework as a logic program, where the input biomarker features are extracted by one or more neural networks. RESULTS: Our pipeline predicted four-year incident AD with a sensitivity of up to 0.84. Additionally, our models learned scores for each A/T/N profile, denoting relative importances to model predictions. These scores also indicated that empirically-derived cut-off values for the A and T criteria might be uninformative for the ADNI data. DISCUSSION: Our pipeline provides a novel way to use the A/T/N framework that could potentially improve early AD screening years before clinical manifestations.

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

Background: Plasma brain-derived pTau217(BD-pTau217) may provide a Alzheimers disease-specific plasma tau measure than total pTau217, but its prognostic value is unclear. We compared BD-pTau217 and total plasma pTau217 for predicting clinical and amyloid PET progression in cognitively unimpaired (CU) ADNI participants. Methods: Plasma NULISAseq biomarkers were measured in 1,427 ADNI participants, including 529 CU individuals. Amyloid PET progression was assessed in baseline CU amyloid-negative participants (Centiloid [&ge;] 24.1) with longitudinal PET imaging; clinical progression was assessed in all baseline CU participants. Associations were evaluated using Cox models and time-dependent AUC. Results: BD-pTau217 did not clearly outperform total pTau217 for predicting progression to mild cognitive impairment or dementia. However, among baseline amyloid-negative participants (N=175), BD-pTau217 better predicted amyloid PET positivity at 2.5 years (tdAUC 0.82 vs 0.69; HR=10.54, p=0.00015) and 4 years (tdAUC 0.77 vs 0.64; HR=7.03, p=0.00055). Conclusion: BD-pTau217 improved prediction of near-term amyloid PET progression, with less clear advantage for clinical progression.

Background: Plasma phosphorylated tau-217 (ptau217) has demonstrated accuracy exceeding 90% for Alzheimers disease (AD) diagnosis. While its diagnostic validity has been established, its real-world clinical utility in altering or corroborating clinician diagnoses remains less understood. This study evaluates the impact of plasma ptau217 on diagnostic reclassification in a memory disorders clinic. Methods: We conducted a retrospective chart review of 100 patients evaluated for memory impairment who subsequently underwent plasma biomarker testing. Initial clinical diagnoses were established using the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer Disease and Related Disorders Association criteria, neuropsychological testing, and brain magnetic resonance imaging without knowledge of plasma biomarker results. Follow-up diagnoses were assigned after the availability of plasma ptau217 or Precivity AD2 results. Diagnostic reclassification was evaluated under two p-tau217 classification schemes to determine the AD etiology: a binary cutoff with lower threshold (AD = ptau217 > 0.18) and a three-level cutoff incorporating a higher threshold (AD = ptau217 > 0.325). Reclassification matrices and McNemar's tests were used to assess changes in diagnosis. Subgroup analyses were conducted according to apolipoprotein E (APOE) {epsilon}4 carrier status. Results: The overall diagnosis reclassification rates were 37.8% (chi-square (1) = 10.81, p = 0.001) and 46.9% (chi-square (1) = 29.76; p < 0.001) using lower and higher threshold analyses, respectively. With the higher threshold (ptau217 > 0.325), reclassification among APOE {epsilon}4 carriers occurred in both directions at similar frequencies, without evidence of a net shift toward AD or non-AD (42% from AD to non-AD, 43% from non-AD to AD, chi-square (1) = 2.77 p = 0.096). Among {epsilon}4 non-carriers, reclassification was significantly asymmetric toward non-AD (66% from AD to non-AD, 14% from non-AD to AD, chi-square (1) = 16.41, p < 0.001), suggesting ptau217 may be identifying diagnostically heterogeneous cases in which the clinical presentation resembles AD but instead reflects an alternative or co-morbid etiologies. Conclusion: Plasma ptau217 meaningfully influences diagnostic decisions in a memory clinic setting and may be particularly valuable among APOE {epsilon}4 non-carriers, where biomarker-informed evaluation frequently shifted diagnoses away from AD.

INTRODUCTION: The Clinical Dementia Rating Sum of Boxes (CDR-SB), a primary outcome in anti-amyloid therapy (AAT) trials, integrates information from participants and study partners. CDR-SB scores may vary by study partner characteristics, but their impact on 18-month change interpretation remains unclear. METHODS: Using the NACC Uniform Data Set, we fitted linear mixed-effects calibration models in an Alzheimer's disease (AD)-primary early symptomatic cohort and propagated study partner-associated coefficients through Monte Carlo simulations. We estimated components of 18-month CDR-SB change under observed profile changes, simulated follow-up imbalance in a common female living-with profile, and tipping-point scenarios. Analyses were repeated in amyloid-positive and trial-like cohorts. RESULTS: The AD-primary cohort included 15,061 participants and 7,683 baseline-to-18-month pairs. Observed profile changes generated a negligible cohort-level component (mean 0.0014 points, 95% simulation interval 0.0006 to 0.0022). Simulated follow-up imbalance generated differences of 0.014 to 0.071 points across 10% to 50% reassignment. Under the primary calibration model, generating a 0.45-point difference, equal to the reported Clarity AD CDR-SB group difference, required median net imbalance >100% and was feasible in 48% of iterations. Amyloid-positive and trial-like cohorts had lower median tipping points but wider intervals, reflecting coefficient imprecision. DISCUSSION: In the large AD-primary cohort, observed study partner profile changes and simulated follow-up imbalance generated CDR-SB differences that were small relative to the 0.45-point Clarity AD benchmark. Biomarker-confirmed estimates were less stable because of coefficient imprecision. These findings suggest limited impact under typical AD-primary conditions but support systematic study partner profile collection and sensitivity analyses in observational and external-comparator CDR-SB studies for AAT evaluation.

Background: Plasma phosphorylated tau-217 (pTau-217) measurements for use in Alzheimer disease (AD) identification require thresholds to define positivity and there exist different approaches to operationally defining the boundary. We compared amyloid {beta} (AB) PET-anchored and distribution-based positivity cut-off values and explored how these mapped onto latent biomarker states. Methods: We analysed plasma pTau-217 measured in the Bio-Hermes-001 cohort (N = 990) using an immunoassay (Lilly) and mass spectrometry assay (University of Gothenburg). Gaussian mixture models were used to identify latent classes and thresholds were derived in two ways: achieving 90% specificity for AB PET positivity and exceeding the mean + 2SDs of the lowest latent class. We explore classes in reference to AB PET status and clinical diagnosis, as well as agreement between approaches using Cohen kappa for both assays. Results: In both assays, three latent biomarker classes were identified with monotonic increases in AD clinical diagnosis and AB PET positivity. PET-anchored thresholds showed lower specificity but higher sensitivity to amyloid positivity than distribution-based thresholds. Overall agreement between the approaches was acceptable (k = 0.678 for Lilly and 0.575 for University of Gothenburg), with disagreement concentrated in the intermediate latent class. Classes with the lowest and highest pTau-217 concentrations were classified consistently using both thresholds Discussion: The two thresholding approaches yielded similar classifications at both the negative and positive tail of the observed biomarker distribution, but classify intermediate concentrations differently. The boundary definition influenced pTau-217 positivity more than the analytical platform itself. Thresholding approaches may capture different pTau-217 biomarker states, therefore such methodological decisions should be grounded in the context of the intended application.

Network hyperexcitability is increasingly implicated in prodromal Alzheimer's disease and may be suppressed by antiseizure medications (ASMs). ASMs are widely prescribed to older adults, yet whether their use relates to Alzheimer's-disease biomarkers at the population level is unknown. In 52,537 participants in the National Alzheimer's Coordinating Center (NACC) study, we compared cerebrospinal-fluid biomarkers, amyloid and tau positron emission tomography (PET) between ASM users and non-users using inverse-probability-of-treatment weighting with gradient-boosted propensity scores. ASM users showed directionally lower amyloid across multiple brain regions, amplifying markedly in APOE epsilon 4 carriers (Centiloid beta = -25.7, p = 0.007). All three temporal tau-PET composites were significantly lower in users (META-temporal beta = -0.05, p = 0.01). The amyloid finding replicated independently in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (Centiloid beta = -8.6, p = 0.01), whereas four comparator drug classes showed no amyloid signal. These convergent observational findings provide a quantitative framework for evaluating ASMs as candidate disease-modifying agents in Alzheimer's disease.

Emerging as a significant global health challenge, Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and cognitive decline. Despite the ever-increasing waiting time for a specialist diagnosis, the need for a cost-effective and fast diagnostic technique is evident. This study explores the development of an explainable deep learning model to diagnose AD using only routine and low-cost clinical data, including demographic information, patient history, and results of neuropsychological tests (limited to those that can be automatically acquired). The analysis was carried out using a dataset provided by the National Alzheimer's Coordinating Center, comprising 167,364 observations and 1,024 features. The findings demonstrate diagnostic performance comparable, and slightly superior, to that of clinicians when evaluated under similar informative constraints. This study introduces two classification models to discriminate whether the presumptive etiological cause of cognitive impairment is Alzheimer's disease. The deep neural network achieved an accuracy of 90\% with an area under the receiver operating characteristic curve (ROC-AUC) of 0.96, whereas the Light Gradient Boosting Machine reached the same accuracy with a ROC-AUC of 0.97.

Background: Accelerated biological aging can be assessed with DNA methylation (DNAm)- based epigenetic clocks. Research suggests that greater DNAm is associated with faster cognitive decline and risk of Alzheimer disease (AD) and other dementias. However, most studies have relied on single-time-point measurements of clocks, rather than evaluating dynamic changes over time. We examined the association between 15-year epigenetic aging trajectories and brain health outcomes in midlife. Methods: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with [&ge;]3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within &plusmn1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, A{beta}42/A{beta}40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*{varepsilon}4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes). Results: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower A{beta}42/A{beta}40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group. Conclusion: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.

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.

Background. Scores on neuropsychological assessments are typically corrected for the influences of age, education, and gender (AEG). However, other demographic factors, such as crystallized ability and race/ethnicity, independently affect test performance. As a result, standard scores systematically over- or under-classify impairment in patients whose demographic profile differs from that of the reference population. Methods. We developed a Comprehensive (C-) model scoring algorithm that added vocabulary, age-squared, race/ethnicity, Latino background, a coarse socioeconomic status proxy, computer use, and daily prescription medications to the standard AEG predictor pool. The model was developed using data from 1,914 community-dwelling adults assessed with the California Cognitive Assessment Battery (CCAB; Woods et al., 2024). For each of 118 individual cognitive measures, stability-selection LASSO identified robust predictors in 300 random 80/20 splits retained at >=80% frequency and then estimated mean coefficients and confidence intervals in 1,000 bootstrap OLS samples. Cross-sample frozen-coefficient validation was used to evaluate scoring model generalization in two subgroups: Group 1 (n = 1,033, older, first enrolled cohort) and Group 2 (n = 881, a recently recruited younger cohort). Results. Stability selection retained a mean of 2.81 predictors per measure (range 1-6). Compared to the AEG model, the C-model approximately doubled variance explained (r2 = 0.50 vs 0.25; mean across cognitive domains r2 = 0.32 vs 0.18) and outperformed AEG in 98.8% of individual measures with non-trivial demographic signal. Racial disparities in MCI classification (the bottom-7th-percentile) were substantially reduced: Black-vs-White ratios fell from 5.6 (AEG) to 1.8 (C). Conversely, sensitivity was improved in individuals with elevated premorbid function: MCI classification ratios in low-vs-high vocabulary quartiles fell from 11.3 to 2.1. AIC favored the C-model in 88.1% of measures (mean delta-AIC = -167), ruling out overfitting. Frozen-coefficient validation preserved the C-model's r2 advantage in every cognitive domain. Conclusions. By correcting scores for race, premorbid cognitive functioning (vocabulary), and other demographic predictors, the C-model explains substantially more variance than the AEG model, reduces racial bias, and increases sensitivity to cognitive decline in high-functioning participants. C and AEG models can be used in parallel: model concordance increases diagnostic confidence, while disagreement carries diagnostic information.

Introduction: Neuropsychiatric symptoms in dementia (NPS) are common and among the most troubling aspects of living with dementia, yet their underlying mechanisms remain unclear. Here, we aimed to identify cerebrospinal fluid (CSF) proteins associated with NPS. Methods: Proteomes were profiled from CSF collected at baseline from participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) using mass spectrometry. Here, we included participants having positive AD CSF biomarkers (i.e., pTtau181 / Abeta42 ratio >0.025) and mild cognitive impairment or AD dementia (n=419). Eight NPS domains were assessed longitudinally with the Neuropsychiatric Symptom Inventory Questionnaire. Severity of cognitive impairment was evaluated using the CDR-SB. Proteome-wide differential expression analysis for each NPS domain at baseline was performed. Significant protein-NPS associations underwent mediation analysis to test whether they were mediated by cognitive impairment severity. Cox proportional hazard was modeled for baseline CSF proteins and incident NPS. Additionally, we tested whether candidate NPS causal proteins previously identified in brain are associated with NPS in CSF. Results: We identified 8 CSF proteins associated with apathy at baseline (FDR q<0.05) after adjusting for sex, age, and education - NTNG2, S100A1, FZD1, FSTL5, CDH7, CHODL, FBXO2, and CACNA2D2. Mediation analysis revealed these associations were independent of cognitive impairment severity in four proteins and only partially mediated by cognitive impairment severity in the remaining four proteins. Among the 10 NPS candidate causal proteins previously identified in brain and detected in CSF, the abundance of two proteins (CPD, GRN) in CSF was associated with baseline disinhibition and of two other proteins (PIK3IP1, PCMT1) with both baseline apathy and incident apathy after adjusting for sex, age, and education. Discussion: These findings suggest that proteomic alterations in apathy in MCI/AD encompass synaptic connectivity, calcium regulation, Wnt-signaling, and neuronal proteostasis, highlighting potential CSF biological processes and biomarker candidates for apathy.

Background: Alzheimer's disease (AD) involves complex molecular alterations in the cerebrospinal fluid (CSF) proteome, yet the links between these protein changes and hallmark AD pathology remain incompletely defined. We investigated the relationship between the CSF proteome with CSF biomarkers of Alzheimer's disease (AD). Methods: CSF was collected in 500 individuals of non-Hispanic white, African Americans, and Caribbean Hispanic individuals. CSF biomarkers of AD were measured including P-tau181, A{beta}40, A{beta}42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). CSF was depleted of abundant proteins followed by precipitation, cysteine reduction/alkylation, and proteolytic cleavage by trypsin. Peptides were measured using a Q-Exactive HF mass spectrometer (Thermo Scientific). Association of individual and co-abundant modules of proteins were tested using elevated CSF P-tau181 and reduced A{beta}42/A{beta}40 to confirm the diagnosis of AD. We validated results in CSF from 397 participants in the Accelerated Medicine Partnership-Alzheimer's Disease cohort. Associated proteins were functionally validated in postmortem human brains and zebrafish. Results: We detected 1030 proteins, yielding an overall data completeness value of 97%. CSF levels of 75 (7.3%) proteins were significantly associated with CSF P-tau181 levels after multiple testing correction. Notably phospholipase D3 (PLD3, p=2.41E-09), apoE (p=4.25e-08) and osteopontin (OPN p=1.4E-16) were increased and autotaxin (ATX/ENPP2, p= 8.39E-09) and ceruloplasmin (CP) (p=2.72E-07) were lower among individuals with high P-tau181 levels. These proteins were also associated with CSF A{beta}42/A{beta}40 ratio and total tau levels but not with NfL. OPN was also associated with CSF levels of GFAP (p=1.32e-05). Among proteins associated with P-tau181 levels, pathways related to axon development (p=2.4E-12), axonogenesis (p=1.45E-11) and regulation of axonogenesis (p=5.1E-09) were enriched. Immunostaining on postmortem human and zebrafish brain found that ENPP2 expression, the gene encoding ATX, was significantly reduced in AD brain and in the amyloidosis model in zebrafish. Reduced ENPP2 expression was consistent with reduced lysophosphatidic acid (LPA) levels in the CSF of individuals with AD. LPA administration into zebrafish CSF reduced the pathological changes in synapses and vasculature due to A{beta}42. Conclusion: Unbiased profiling of circulating CSF proteins among individuals with antemortem diagnosis of AD, identified key proteins PLD3, apoE, OPN, ATX, and ceruloplasmin. Validation in postmortem human brains and zebrafish models support potential roles in endosomal sorting and APP processing, inflammation, angiogenesis, lipid transport, and oxidative stress.

Background: It remains unclear why biomarker-defined Alzheimers disease neuropathological change (ADNC) leads to cognitive decline and dementia in some individuals but not others. Multidomain lifestyle profiles may influence both pathology risk and clinical expression. Objectives: To determine whether multidomain lifestyle profiles are associated with (1) the risk of biomarker-defined ADNC and (2) the clinical expression of AD pathology, including longitudinal cognitive test score change and incident dementia. Design/Setting: Retrospective longitudinal population-based cohort study within the Trondelag Health Study (HUNT), Norway, including five waves over a 40-year follow-up period. Participants: The late-life source population comprises 9,956 HUNT4 70+ participants, of whom 5,729 participated in the Ageing in Trondelag (AiT) follow-up. Outcome measurements. ADNC is operationalized primarily using plasma phosphorylated tau at threonine 217 (p-tau217), with plasma neurofilament light (NfL) available as a complementary marker of neurodegeneration. In HUNT4 70+, participants aged 70 and older had a standardized cognitive diagnostic assessment, which was repeated at AiT four years later. Lifestyle Measurements: Multidomain lifestyle will be defined across five domains: nutrition, physical activity and skeletal muscle health, mental and social health, cardiovascular/metabolic status, and cognitive stimulation. Genetic susceptibility, including APOE and genome-wide/polygenic risk measures, and available multi-omics data will be examined as modifiers. Results: Analyses will examine the associations of multidomain lifestyle profiles and domain-specific exposures with both ADNC risk and clinical expression. Conclusions: This study will test whether multidomain lifestyle profiles are associated with both the risk of biomarker-defined ADNC and the clinical expression, informing risk stratification and modifiable prevention in AD.

Background: The urinary sodium-to-potassium (Na/K) ratio is an integrated biomarker associated with cardiovascular risk. However, its association with deep white matter lesions (DWMLs), a manifestation of cerebral small vessel disease (CSVD), remains unclear. We investigated the associations of the urinary Na/K ratio and albuminuria with DWMLs. Methods: We conducted a cross-sectional study of 296 Japanese adults (mean age, 68.7 years). Brain magnetic resonance imaging was used to assess DWMLs using the Fazekas scale, and lesions were classified as absent (grade 0) or present (grades 1?3). The urinary Na/K ratio and albumin excretion were measured using 24-h urine collections. Multivariable logistic regression models examined the associations between urinary biomarkers and DWMLs. Results: DWMLs were present in 119 (40.2%) participants. A higher urinary Na/K ratio was independently associated with DWMLs (odds ratio per 1?standard deviation increase, 1.44; 95% confidence interval, 1.09?1.90; P=0.010). Participants in the highest quartile had greater odds of DWMLs than those in the lowest quartile (odds ratio, 2.48; 95% confidence interval, 1.16?5.29; P=0.019). Urinary potassium excretion was inversely associated with DWMLs, whereas urinary sodium excretion alone showed no significant association. Findings were consistent across sensitivity and subgroup analyses. Conclusions: A higher 24-h urinary Na/K ratio was independently associated with DWMLs in older adults. This association appeared to be driven primarily by lower urinary potassium excretion rather than higher sodium excretion alone. The urinary Na/K ratio may serve as a simple, noninvasive marker of CSVD.