The Journal of Prevention of Alzheimer's Disease

INTRODUCTIONThere is need to detect and intervene in pre-clinical phases of Alzheimers disease (AD). Electronic health records (EHRs) may help predict AD using machine learning methods. METHODSWe identified EHRs for 19,473 cases with AD and 111,922 controls. Records spanned 10 or more years prior to AD diagnosis. We trained a random forest model (employing 5-fold cross-validation with 2,499 features) to predict AD 10 years prior to its onset using a 75/25% train/test split and then computed permuted feature importance. RESULTSWe achieved an area under the ROC curve of 0.80. Feature importance identified factors associated with AD, including age, sex, race, ethnicity, BMI, cardiovascular diseases, inflammation, pain, sleep and mood disorders, trauma, other neurodegenerative disorders, diuretics, colon-related disorders and procedures, seizures, and vitamin B12. DISCUSSIONThis is the first EHR-based model to predict AD 10 years prior to onset, which could help predict AD and inform prevention/early intervention.

Alzheimers disease (AD) patients have varying responses to AD drugs and there may be no single treatment for all AD patients. Trial after trial shows that identifying non-responsive and responsive subgroups and their corresponding moderators will provide better insights into subject selection and interpretation in future clinical trials. We aim to extensively investigate pre-treatment features that moderate treatment effect of Galantamine, Bapineuzumab, and Semagacestat from completed trial data. We obtained individual-level patient data from ten randomized clinical trials. Six Galantamine trials and two Bapineuzumab trials were from Yale University Open Data Access Project and two Semagacestat trials were from the Center for Global Clinical Research Data. We included a total of 10,948 subjects. The trials were conducted worldwide from 2001 to 2012. We estimated treatment effect using causal forest modeling on each trial. Finally, we identified important pre-treatment features that determine treatment efficacy and identified responsive or nonresponsive subgroups. As a result, patients pre-treatment conditions that determined the treatment efficacy of Galantamine differed by dementia stages, but we consistently observed that non-responders in Galantamine trials had lower BMI (25 vs 28, P < .001) and increased ages (74 vs 68, P < .001). Responders in Bapineuzumab and Semagacestat trials had lower A{beta}42levels (6.41 vs 6.53 pg/ml, P < .001) and smaller whole brain volumes (983.13 vs 1052.78 ml, P < .001). 6 positive treatment trials had subsets of patients who had, in fact, not responded. 4 "negative" treatment trials had subsets of patients who had, in fact, responded. This study suggests that analyzing heterogeneity in treatment effects in "positive" or "negative" trials may be a very powerful tool for identifying distinct subgroups that are responsive to treatments, which may significantly benefit future clinical trial design and interpretation.

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.

ObjectivesTo characterize contemporary pharmacologic treatment patterns for Alzheimers disease and related dementias (ADRD) among U.S. long-term care residents and to examine facility- and resident-level factors associated with treatment. DesignRetrospective, observational study. Setting and ParticipantsElectronic health record data from 1,675,873 long-term care residents in the PointClickCare Life Sciences database included 359,801 with a documented ADRD diagnosis in skilled nursing facilities in the U.S. (January-April 2025). MethodsResidents were classified as treated/untreated based on receipt of guideline-directed ADRD therapy, consistent with Alzheimers Association guidelines. Analyses incorporated demographics, comorbidities, medication burden, and facility characteristics. Multivariate logistic regression estimated odds of receiving guideline-concordant therapy. ResultsOverall, 72.5% of residents with ADRD received [&ge;]1 pharmacologic treatment recommended for ADRD. Treatment was most common among residents with Lewy body dementia (83.9%) and early-onset Alzheimers disease (82.3%) and least frequent among residents aged [&ge;]90 years (65.1%), Black/African American residents (66.8%), and those with cerebral degeneration (66.8%). Treated residents exhibited higher medication burden (mean 4.4 vs 3.3). Diagnoses for other chronic conditions as well as specific ADRD subtypes strongly impacted probability of treatment; diabetes and hyperlipidemia were associated with lower odds of treatment, whereas ADRD subtypes strongly predicted treatment. Conclusions and ImplicationsMore than one-quarter of residents with ADRD remain untreated with guideline-recommended pharmacotherapy, and treatment varied significantly by non-clinical predictors. These findings underscore the need to investigate and understand possible treatment disparities, optimize polypharmacy management, and discover new ADRD treatments, as current options are often ineffective with many side effects. Brief SummaryThis study used real-world data from electronic health records (EHR) to understand treatment patterns of those with Alzheimers disease and related dementias (ADRD) in U.S. long-term care facilities. International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10) codes were used to identify ADRD diagnoses and medication orders were used to identify treatment. From January to April 2025, there were 359,801 with a documented ADRD diagnosis in skilled nursing facilities. Over 25% of those with ADRD did not have a medication order for a guideline-recommended pharmacological treatment. Comorbidities of diabetes and hyperlipidemia were associated with lower odds of receiving ADRD treatment, suggesting concerns related to adverse drug reactions and competing clinical priorities. The use of cognitive and disease-modifying therapies was low compared to behavioral/psychiatric medications; this finding suggests a need for more effective and safe drugs that target the root causes of ADRD opposed to the behavioral and psychiatric complications. Taken together, the results of this study call for targeted interventions to address disparities in treatment, enhanced clinical decision-making support regarding polypharmacy, and improved pharmacological options for those with ADRD.

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.

IntroductionThe study aimed to identify strategies to increase older Black adults participation in Alzheimers disease (AD) biomarker research studies. Methods399 community-dwelling Black and White older adults (age [&ge;] 55) who had never participated in AD research completed a survey about their perceptions of AD research involving blood draw, MRI, and PET. ResultsAlthough most participants expressed interest in AD biomarker research (Black participants: 63.0%, White participants: 80.6%), Black participants were significantly more hesitant than White participants (28.9% vs 15.1%), were more concerned about study risks, (30.8% vs. 11.1%) and perceived multiple barriers to participating in brain scans. Lack of information was perceived as a barrier to participation across groups (45.8%) and return of study results was perceived as a participation incentive (78.9-85.7%) (Ps < .05). DiscussionStrategies to increase Black older adult participation in AD research may include disseminating additional study information and return of results. Declaration of InterestNone of the investigators have a conflict of interest. JE receives support from VA IK HX002283, NIA P30AG072976, and NIA P30AG010133. AJP receives support from NIA (NIA U01 AG057195) and Alzheimers Association (LDRFP-21-818464). SW receives support from multiple NIA grants (K23AG062555, P30AG072976, P30AG010133, and R21AG074179) and the VA for clinical services. She receives book royalties from APPI and DSMB consultant fees (total less than $2000/year). AJS receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, and U01 AG068057 and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions USA, Inc. (Dementia Advisory Board); Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging and Behavior).

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSIf mild cognitive impairment (MCI) is diagnosed after dementia, it suggests either the dementia diagnosis was premature, or the MCI diagnosis is incorrect. We investigated the prevalence and predictors of such "diagnostic reversion"--MCI diagnosis following dementia diagnosis--in a large academic health system. METHODSAmong 5,965 patients aged 50+ with incident dementia in UCSF Health electronic health records, we identified "reverters" with a subsequent MCI diagnosis. We used Group LASSO-regularized logistic regression and random forest models to identify predictors. RESULTSReversion occurred in 13.7% of patients. Lower odds were observed among older adults (OR=0.95/year; 95% CI: 0.92-0.98), while higher odds were found among Spanish speakers (OR=2.26; 95% CI: 1.28-4.00), those with greater cardiovascular risk (OR=1.16; 95% CI: 1.01-1.33), and higher Charlson comorbidity burden (OR=1.09; 95% CI: 1.05-1.14). DISCUSSIONDiagnostic reversion is common and socially patterned, suggesting contributions from misdiagnosis, clinical uncertainty, or variability in clinical presentation and care setting.

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.

BackgroundThere are many myths regarding Alzheimers disease (AD) that have been circulated on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models such as ChatGPT, may be a useful tool to help assess these myths for veracity and inaccuracy. However, they can induce misinformation as well. The objective of this study is to assess ChatGPTs ability to identify and address AD myths with reliable information. MethodsWe conducted a cross-sectional study of clinicians evaluation of ChatGPT (GPT 4.0)s responses to 20 selected AD myths. We prompted ChatGPT to express its opinion on each myth and then requested it to rephrase its explanation using a simplified language that could be more readily understood by individuals with a middle school education. We implemented a survey using Redcap to determine the degree to which clinicians agreed with the accuracy of each ChatGPTs explanation and the degree to which the simplified rewriting was readable and retained the message of the original. We also collected their explanation on any disagreement with ChatGPTs responses. We used five Likert-type scale with a score ranging from -2 to 2 to quantify clinicians agreement in each aspect of the evaluation. ResultsThe clinicians (n=11) were generally satisfied with ChatGPTs explanations, with a mean (SD) score of 1.0({+/-}0.3) across the 20 myths. While ChatGPT correctly identified that all the 20 myths were inaccurate, some clinicians disagreed with its explanations on 7 of the myths. Overall, 9 of the 11 professionals either agreed or strongly agreed that ChatGPT has the potential to provide meaningful explanations of certain myths. ConclusionsThe majority of surveyed healthcare professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation. However, the need for more refined and detailed explanations of the diseases mechanisms and treatments was highlighted. Impact StatementThere are many statements regarding Alzheimers disease (AD) diagnosis, management, and treatment circulating on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models are a popular topic currently, and many patients and caregivers may turn to LLMs such as ChatGPT to learn more about the disease. This study aims to assess ChatGPTs ability to identify and address AD myths with reliable information. We certify that this work is novel. Key Points- Geriatricians acknowledged the potential value of ChatGPT in mitigating misinformation in Alzheimers Disease - There remain nuanced cases where ChatGPT explanations are not as refined or appropriate. - Why does this matter? Large language models such as ChatGPT are very popular nowadays and patients and caregivers often may use them to learn about their disease. The paper seeks to determine whether ChatGPT does an appropriate job in moderating understanding of Alzheimers Disease myths.

INTRODUCTIONThere is a lack of cognitive tools to predict disease progression in mild cognitive impairment (MCI) and Alzheimers disease (AD). METHODSWe assessed patients with MCI, AD, and cognitively healthy controls (cHC) using NIH toolbox assessments for attention/concentration and executive functioning and overall cognitive decline by the Alzheimers Disease Assessment Scale-Cognitive (ADAS-Cog). RESULTSAmong 183 participants over a median follow-up of 540 days, both between- and within-subjects variance in NIH toolbox and ADAS-Cog assessments increased from cHC to MCI to AD patients. Among patients with AD, pattern comparison processing speed (PCPS) and dimensional change card sort tests (DCCS) declined at 3 and 6 months prior to global cognitive decline (p=0.008 & 0.0012). A 5-point decrease in either PCPS or DCCS increased risk of global cognitive decline (HR 1.32 (1.08-1.60) and 1.62 (1.16-2.26)). DISCUSSIONTesting for cognitive domains of attention/concentration and executive functioning may predict subsequent global cognitive, and functional decline.

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

BACKGROUNDAlzheimers Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls in an aging global population. Real World Data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD. METHODSWe conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using a novel transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of models using Gradient Boosting Machine (GBM) to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts. RESULTSIn a group of 4,985 patients, we identified 219 sequences of medication-diagnosis records for constructing the best classification models. The models with the sequential features improved AD classification by a magnitude of up to 16 percent (over the use of AD diagnosis codes). Six groups of sequences, which we refer to as temporal digital markers, were identified for characterizing the AD cohorts, including sequences that involved (1) a symptom or (2) a risk factor in the past, followed by an AD diagnosis, (3) AD medications, (4) indirect risk factors, symptom management, and potential side effects, (5) comorbidities with possible shared roots or side effects, and (6) plural encounters with of AD diagnosis codes. Discussions of how the identified sequential patterns can be interpreted are provided. CONCLUSIONSWe present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimers Disease. Classification algorithms developed on the sequential patterns can replace standard features from EHRs to enrich phenotype modeling.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackground and PurposeC_ST_ABSAlzheimers Disease (AD) is a complex neurodegenerative disease that has been becoming increasingly prevalent in recent decades. Efforts to identify predictive biomarkers of the disease have proven difficult. Advances in the collection of multi-omic data and deep learning algorithms have opened the possibility of integrating these various data together to identify robust biomarkers for predicting the onset of the disease prior to the onset of symptoms. This study performs a systematic review of recent methods used to predict AD using multi-omic and multi-modal data. MethodsWe systematically reviewed studies from Google Scholar, Pubmed, and Semantic Scholar published after 2018 in relation to predicting AD using multi-omic data. Three reviewers independently identified eligible articles and came to a consensus of papers to review. The Quality in Prognosis Studies (QUIP) tool was used for the risk of bias assessment. Results22 studies which use multi-omic data to either predict AD or develop AD biomarkers were identified. Those studies which aimed to directly classify AD or predict the progression of AD achieved area under the receiver operating characteristic curve (AUC) between .70 - .98 using varying types of patient data, most commonly extracted from blood. Hundreds of new genes, single nucleotide polymorphisms (SNPs), RNA molecules, DNA methylation sites, proteins, metabolites, lipids, imaging features, and clinical data have been identified as successful biomarkers of AD. The most successful techniques to predict AD have integrated multi-omic data together in a single analysis. ConclusionThis review has identified many successful biomarkers and biosignatures that are less invasive than cerebral spinal fluid. Together with the appropriate prediction models, highly accurate classifications and prognostications can be made for those who are at risk of developing AD. These early detection of risk factors may help prevent the further development of cognitive impairment and improve patient outcomes.

INTRODUCTIONDementia is a disabling condition that progressively impairs daily function. Timely identification of older adults at high risk for dementia or cognitive impairment (a potential precursor) is critical to maximizing opportunities for intervention. METHODSUtilizing structured electronic health record data from 122,633 patients aged 55-80 years, we leveraged demographics, encounter diagnoses, and patient problem lists to develop and prospectively validate a ML model. RESULTSThe ML model achieved a C-statistic of 0.811 (95% confidence interval: 0.810, 0.812) with adequate calibration overall and in subgroups based on race and sex. Recommending screening for patients with 3-year predicted risk > 5%, the ML model obtained satisfactory fairness across race and sex subgroups, with a net benefit of 18 true positive MCI/dementia diagnoses per 1,000 patients. DISCUSSIONThe ML model developed in this study can effectively identify individuals at high risk for a future diagnosis of MCI/dementia, potentially facilitating earlier screening and intervention to reduce the burden of cognition-related disability.

INTRODUCTIONDelaying cognitive decline in Alzheimers disease can significantly impact both function and quality of life. METHODSLongitudinal analysis of National Alzheimers Coordinating Center (NACC) dataset of 7,653 mild dementia CDR-SB AD participants at baseline with prescriptions for diabetes (DBMD), lipid-lowering (LIPL), anti-hypertensive (AHTN), and non-steroidal anti-inflammatory (NSD) medications over 10 years was evaluated for change in cognitive function relative to non-treated stratified by sex and APOE genotype. RESULTSCombination therapy of DBMD+LIPL+AHTN+NSD resulted in a 44% / 35% (MMSE/CDR-SB) delay in cognitive decline at 5 years and 47% / 35% (MMSE/CDR-SB) delay at 10 years. Females and APOE4 carriers exhibited greatest cognitive benefit of combination therapy. DISCUSSIONCombination therapies significantly delayed cognitive decline in NACC AD participants at a magnitude comparable to or greater than beta-amyloid immunomodulator interventions. These data support combination precision medicine targeting AD risk factors to alter the course of the disease that persists for a decade.

ImportanceInterpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimers disease (AD) and promote early intervention at non-specialist settings. ObjectiveTo develop a risk score to predict the likelihood of AD with interpretable machine learning using variables that are obtainable at integrated primary care settings. DesignA secondary data analysis including cohort studies from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the National Alzheimers Coordinating Center (NACC) extracted in August 2023 and March 2024. SettingThe ADNI and NACC are multi-site cohort studies in North America. ParticipantsParticipants with normal cognition or mild cognitive impairment at baseline visit were identified. Participants with the same diagnosis overtime were assigned to the stable group, and those converted to AD were placed in the progressive group. Main Outcome(s) and Measure(s)Cognitive tests and daily functioning measured with Functional Assessment Questionnaire (FAQ) at baseline visit. ResultsA total of 676 participants from ADNI and 4592 participants from NACC were identified. After removing incomplete data, 665 ADNI (mean age [SD]: 73.44 [6.90]; 293 [44.1%] female; 374 stable and 291 progressive) and 3657 NACC participants (mean age [SD]: 70.96 [10.03]; 2405 [65.8%] female; 2445 stable and 1212 progressive) remained. Combinations of 4 measures were selected to generate 10 scorecards using FasterRisk algorithm, showing strong performance (area under the curve [AUC] = 0.868-0.892) in ADNI and remaining robust when validated in NACC (AUC = 0.795). The features were Category Animal [&le;] 20 (2 points), Trail Making Test B [&le;] 143 (-3 points), Logical Memory Delayed [&le;] 3 (4 points), Logical Memory Delayed [&le;] 8 (3 points), and FAQ [&le;] 2 (-5 points). The probable AD risk corresponded to total points: 7.4% (-8), 25.3% (-4), 50% (-1), 74.7% (2), and > 90% ([&ge;] 6). We refer to this model as the (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or FLAME scorecard. Conclusions and RelevanceOur findings highlight the potential to predict AD development using obtainable information, allowing for applicability at integrated primary care. While our scope centers on AD, this foundation paves the way for other dementia types Key PointsO_ST_ABSQuestionC_ST_ABSCan accessible information, such as demographics, cognitive tests, and functioning questionnaire, yield in reliable results for predicting Alzheimers disease development using interpretable machine learning? FindingsThe results of 665 participants from the Alzheimers Disease Neuroimaging Initiative demonstrated robust performance of determining Alzheimers disease development using four separate measures of (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or the FLAME scorecard. It remains reliable when externally validated with a separate dataset of 3657 participants from the National Alzheimers Coordinating Center. MeaningThe FLAME scorecard shows potential to be implemented in integrated primary care settings to promote early detection and intervention of cognitive decline due to Alzheimers disease.

ObjectivesTo test the psychometric properties of several dementia care-related scales among Latinos in the US. DesignWe leveraged secondary baseline data from a one-arm mHealth trial on dementia caregiver support. We included 100 responses for caregiver-focused scales and 88 responses for care recipient-focused scales. Scales included the Neuropsychiatric Inventory Questionnaire Severity and Distress scales, six-item Zarit Burden Inventory, Ten-item Center for Epidemiologic Studies Depression Scale, Geriatric Depression Inventory, Quality of Life in Alzheimers Disease, and Single-item Satisfaction With Life Scale. We calculated concurrent validity using Pearson and Spearman correlations and expected correlations amongst all variables in line with the Stress Process Framework. We calculated internal consistency reliability using Cronbachs alpha. ResultsAll concurrent validity correlations followed the expected directionality, with 19/21 inter-scale correlations in the total sample reaching statistical significance (p<0.05), and 17/21 reaching at least a low correlation (0.3). Cronbachs alpha ranged from 0.832 to 0.879 in all scales in the total sample. ConclusionThe English and Spanish caregiver-administered scales tested in this manuscript have good psychometric properties. Clinical ImplicationsThe dementia care-related scales are now appropriately available for use among US Latinos in research and clinical contexts.

This study challenges the assumption that undiagnosed cognitive impairment (CI) is driven primarily by patient-level barriers like poor awareness. In a population-weighted cohort of 1,856 older Singaporeans, CI prevalence was 24.7% (95%CI 18.8-31.8); yet the undiagnosed rate was high (81.4%, 95%CI 65.6-90.9), especially for mild CI (97.9%, 95%CI 94.1-99.3). This diagnostic gap persisted despite high symptom awareness (81.3%, 95%CI 63.6-91.5) and help-seeking intent (63.3%, 95%CI 47.5-76.7), with informants becoming key as CI worsened. Findings suggest successful public health campaigns have shifted the bottleneck from community awareness to healthcare system capacity, creating an opportunity for a policy shift to meet rising demand for diagnosis--by empowering primary care with efficient case-finding tools, formalizing integrated diagnostic pathways, and establishing channels for family informants involvement. From these findings, we conceptualized a paradox of success model, providing a framework for other health systems to adapt policy as public engagement grows.

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.

STRUCTURED ABSTRACTO_ST_ABSINTRODUCTIONC_ST_ABSRecent advances allow blood tests to detect key proteins linked to Alzheimers disease (AD). METHODSIn this before-and-after study across three Australian Memory and Cognition Clinics, we evaluated the impact on clinicians diagnostic confidence and management following disclosure of routine patients AD probability, using predefined plasma pTau181 thresholds set at 90% sensitivity and 90% specificity for amyloid-{beta} (A{beta}) PET positivity. RESULTS113 participants (age:71.2{+/-}8.4; MMSE:27.7{+/-}2.5) with dementia (n=17, 15.0%), mild cognitive impairment (n=48, 42.5%) and subjective cognitive decline (n=48, 42.5%) were enrolled. Blood test results were probably negative, n=81, 71.7%; indeterminate, n=24, 21.2%; probably positive, n=8, 7.1%. In 12 cases (10.6%), pTau181 changed clinician diagnosis and increased mean diagnostic confidence from low-to-moderate (61%) to moderate-to-high (80%). A{beta}-PET in 40 participants showed plasma pTau181 improved diagnostic accuracy by 5%. DISCUSSIONThis study demonstrates the benefits of plasma pTau181 in real-world clinical practice particularly when diagnostic confidence is only low-to-moderate.