The Journal of Prevention of Alzheimer's Disease

BackgroundOne of the biggest challenges in the field of Alzheimers disease and related dementias (ADRD) is the severe inequitable inclusion of Black and Latino adults in clinical research studies. Despite consistent and persistent efforts, rates of participation among diverse older adults remain critically low. ObjectiveThe purpose of this paper is to set forth The NGAGE Model, one developed at the Rush Alzheimers Disease Research Center (Rush ADRC) to facilitate community engagement and research participation among diverse older adults. MethodsThe NGAGE Model consists of five steps that are conceptually distinct but overlapping in practice: 1) Networking, 2) Give first, 3) Advocate for research, 4) Give back, and 5) Evaluate. We define and describe each step. For steps 1 through 4, we calculated the number of events, number of attendees for each event, and percentages of attendees by racial and ethnic categorizations annually from July 1, 2011, through June 30, 2023, resulting in data for 12 distinct years, as provided in annual progress reports to the National Institute on Aging. For Step 5, we counted the number of persons and computed percentages of people by racial and ethnic groups who consented to our Data and Specimen Repository and enrolled in a research study. ResultsOver 12 years, the Rush ADRC conducted 5,362 events with 265,794 attendees. Give First activities represented the NGAGE step with both the highest number of events (n=2,247) and the most attendees (n=124,403). Among Black adults, the highest attendee percentage existed for Advocate for Research events (47%), while the highest for Latinos occurred for Give First activities (26%). Furthermore, 2,135 persons consented to the Data and Specimen Repository and 5,905 enrolled in a research study across 12 years. Higher percentages of both Black (37%) and Latino (10%) adults enrolled in research studies compared to the Repository with 21% and 7%, respectively. ConclusionsThe NGAGE Model facilitated community engagement and research inclusion among Black and Latino adults, particularly via Give First and Advocate for Research activities. We discuss the impacts of study milestones, staff resources, and the COVID-19 pandemic on The NGAGE Model activities and outcomes.

INTRODUCTIONReduced somatostatin (SST) and SST-expressing GABAergic neurons are well-replicated findings in Alzheimers disease (AD) and are associated with cognitive deficits. SST cells inhibit pyramidal cell dendrites through 5-GABA-A receptors (5-GABAA-R). 5-GABAAR positive allosteric modulation (5-PAM) has procognitive and neurotrophic effects in stress and aging models. METHODSWe tested whether 5-PAM (GL-II-73) could reverse cognitive deficits and neuronal spine loss in early and late stages of {beta}-amyloid deposition in the 5xFAD model (N=48/study; 50% female). RESULTSAcute or chronic administration of GL-II-73 reversed spatial working memory in 5xFAD mice at 2 and 5 months of age. Chronic GL-II-73 treatment reversed 5xFAD-induced loss of spine density, spine count and dendritic length at both time points, despite {beta}-amyloid accumulation. DISCUSSIONThese results demonstrate procognitive and neurotrophic effects of GL-II-73 in early and late stages of Alzheimer-related {beta}-amyloid deposition. This suggests 5-PAM as a novel {beta}-amyloid-independent symptomatic therapeutic approach.

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.

INTRODUCTIONHispanic/Latino (H/L) adults are more likely than non-Hispanic white adults to have Alzheimers disease (AD), but fewer than 1 in 5 H/L adults has APOE {varepsilon}4. H/L adults are underrepresented in AD research studies and trials, which use genetic data to stratify participants. Successful research programs representative of the entire U.S. population, including over 16 million Spanish speakers, require culturally appropriate educational materials about AD and genetic testing. We sought to learn the culturally salient words Spanish-preferring H/L adults use to talk about AD and genetic testing. METHODSParticipants were community-residing and self-identified as Spanish preferring H/L adults. Fourteen individuals completed freelisting interviews, which yielded lists featuring all the words that came to participants minds about AD-related domains. We performed inductive thematic analysis and calculated theme frequency. RESULTSParticipants were aware of AD as a memory disorder due to advancing age and genes, but were unfamiliar with AD genetic testing. Participants suggested genetic testing was more useful for diagnosis than future risk prediction. They also suggested genetic testing of individuals with intact cognition and no AD family history had limited value. DISCUSSIONFindings suggested individuals are motivated by a technological imperative to participate in AD research, reflecting a responsibility to use genetic testing despite having limited knowledge about it. Interest among H/L adults in AD research could be leveraged to develop educational materials co-created by community members and researchers. Content about primary and secondary findings and the use of AD genetic results to inform a future-oriented disposition to health comprises a useful framework for AD outreach serving diverse populations.

INTRODUCTIONPatient and public involvement and engagement (PPIE) is essential for improving research but is often limited in scale. This study explored the potential for large-scale involvement using a web-based approach. METHODSWe created an online portal to collect feedback on dementia research and sought views on the forthcoming UK-based adaptive platform trial testing repurposed drugs for Alzheimers disease (AD-SMART). Participants completed a survey with a ranking task for four anonymized drugs prioritised for inclusion and discrete choice experiments (DCEs) on treatment attributes trade-offs. RESULTSResponses from over 3,250 people across 27 countries and 6 continents strongly supported for the trial. Metformin was the most preferred, followed by Atomoxetine, Isosorbide Mononitrate, and Levetiracetam. Safety ranked highest, followed by evidence of efficacy and convenience. Analyses were stratified by sex, age, and dementia experience. DISCUSSIONWeb-based PPIE can effectively inform dementia research at scale and offers a transferable model for other studies.

BackgroundCognitively unimpaired (CU) adults vary substantially in their risk of developing mild cognitive impairment (MCI), yet most subtyping approaches focus on downstream neurobiological or cognitive markers rather than upstream, modifiable risk factors. We aimed to identify clinically meaningful subgroups of CU adults defined by integrated comorbid, behavioral, and social risk profiles, and to evaluate heterogeneity in both incident MCI risk and cardiometabolic treatment effects. MethodsWe conducted a prospective cohort study of 121,322 CU adults aged [&ge;]50 years from the All of Us Research Program. Baseline comorbidities, lifestyle behaviors, and social determinants of health were jointly modeled using the Bayesian Mixed Integrative Data Subtyping framework, which integrates binary and continuous modalities via modality-specific likelihoods and shared latent constructs. Subtype-specific risk of incident MCI was assessed using multivariable Cox proportional hazards models adjusting for demographics and baseline medication use. A double/debiased machine learning interactive regression model with inverse probability of censoring weights to mitigate bias from informative censoring was implemented to estimate the average treatment effects of antihypertensive agents, Glucagon-Like Peptide (GLP) receptor agonists, and non-GLP antidiabetic medications on time to MCI. ResultsFour distinct subtypes were identified: I low-risk healthy aging, II behavioral/social vulnerability, III cardiometabolic-depressive multimorbidity, and IV mixed social-medical vulnerability profiles. Compared with Subtype I, Subtype III demonstrated the highest risk of incident MCI (HR: 3.69, 95% CI: 3.14-4.33), followed by Subtype IV and Subtype II. In treatment effect analyses, antihypertensive use was associated with a modest prolongation of MCI-free survival overall (time ratio:1.04, 95% CI: 1.03-1.06), with the largest benefit observed in Subtype III (time ratio: 1.14, 95% CI: 1.09-1.19). Non-GLP antidiabetic therapies were similarly associated with modest overall delay, with significant benefits in Subtypes I and III. GLP-class therapies were not associated with overall delay but showed a significant association in Subtype III. ConclusionsIntegrative subtyping based on comorbid, behavioral, and social risk factors reveals clinically meaningful heterogeneity in both cognitive risk and treatment response. Aligning dementia prevention strategies with dominant vulnerability pathways may enhance the effectiveness and equity of population-level precision prevention.

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.

INTRODUCTIONSouth Asian (SA) and East Asian (EA) older adults represent the fastest growing group of Americans at risk for dementia, but their participation in aging and dementia research has been limited. While recruiting healthy SA older adults into a brain health study, we encountered unexpected hesitancy towards structural brain MRI analysis along with some stigmatizing attitudes related to internal locus of control (LoC) for future dementia risks. We hypothesized that support for MRI-related research was influenced by these attitudes as well as ones own MRI experience, perceived MRI safety, and concerns for ones own risks for future dementia/stroke. METHODSWe developed a brief cross-sectional survey to assess older adults MRI experiences and perceptions, desire to learn of six incidental findings of increasing health implications, and attitudes related to dementia as well as research participation. We recruited a convenience sample of 256 respondents (74% reporting as 50+) from the New Jersey/New York City area to complete the survey, and modeled the proportional odds (P.O.) for pro-research attitudes. RESULTS77 SA and 84 EA respondents were analyzed with 95 non-Asian adults. White (P.O.=2.54, p=0.013) and EA (P.O.=2.14, p=0.019) respondents were both more likely than SA respondents to endorse healthy volunteers participation in research, and the difference between White and SA respondents was mediated by the latters greater internal LoC for dementia risks. EA respondents had more worries for future dementia/stroke than SA respondents (p=0.006), but still shared SA respondents low desire to learn of incidental MRI findings. DISCUSSIONSA and EA older adults had different attitudes towards future dementia/stroke risks, but shared a low desire to learn of incidental MRI findings. A culturally-appropriate protocol to disclose incidental MRI findings may improve SA and EA participation in brain health research. Color printingPlease have figure one and two be in color; figure three is in black and white

BackgroundRemote, smartphone-based cognitive testing may improve access to cognitive assessments for Alzheimers disease and related dementias. We evaluated the feasibility, reliability, and validity of unsupervised smartphone-based cognitive tests in a registry-based cohort. MethodsAdults without a record of cognitive impairment (N=1,815; ages 18-92) were recruited from the UCSF Brain Health Registry to complete unsupervised ALLFTD-mApp cognitive tasks three times over two weeks. Reliability was assessed with correlations between sessions. Linear regression models tested associations of ALLFTD-mApp tasks with demographics, self- and informant-rated cognitive concerns (Everyday Cognition Surveys; ECog), and web-based cognitive testing (CogState Brief Battery; CBB). ResultsAdherence was high (82.2%) and usability favorable. Test-retest reliability was moderate to strong ({rho}s = 0.61-0.85, all ps < .001). Lower ALLFTD-mApp scores were associated with older age, lower education, cognitive concerns, and worse CBB performance. ConclusionFindings support the feasibility, reliability, and validity of the ALLFTD-mApp in adults without a record of cognitive impairment.

BackgroundAlzheimers disease (AD) disproportionately affects racial and ethnic minoritized populations in the United States, yet these groups remain markedly underrepresented in clinical research. Phase III clinical trials are critical for informing regulatory decision and treatment guidelines, but the extent to which they report and include racial and ethnic diverse participants in the US context has not been systematically assessed. MethodsWe conducted a comprehensive retrospective review of all US-based Phase III AD clinical trials from 1997 to 2023 using the Trialtrove database, cross-referenced with PubMed, ClinicalTrials.gov, and other public sources. We analyzed long-term trends in the reporting and representation of racial and ethnic groups across the longest observation period to date. ResultsOf 88 identified trials, 71 (80.7%) had published data. Nearly half (49.3%) did not report any race or ethnicity information. Among those that did, most focused on White patients, with limited and inconsistent reporting for racial and ethnic minoritized groups. Median enrollment was 0.9% for Asian or Pacific Islander, 4.5% for Black (ethnicity unspecified), 7.2% for Black (non-Hispanic), 5.2% for Hispanic, and 0.4% for Native American participants, compared to nearly 90% for White participants. Only 4.2% of trials conducted subgroup analysis by race or ethnicity, and none reported detailed outcome differences. Terminology varied widely and no trials acknowledged underrepresentation or proposed corrective strategies. Notably, these patterns showed little to no improvement over time. Conclusions and ImplicationsRacial and ethnic minoritized populations remain consistently underreported and underrepresented in Phase III AD trials in the US, limiting the generalizability of findings and risking the exacerbation of health inequities. Improving equity in AD research will require standardized reporting, inclusive recruitment practices, and intentional efforts to engage underrepresented communities.

INTRODUCTIONFor many patients and caregivers, a major goal of disease-modifying treatments (DMT) for Alzheimer disease (AD) dementia is to extend independence in instrumental and basic activities of daily living (IADLs and BADLs). The goal of this study was to estimate the effect of treatments on the time remaining independent in IADLs and BADLs. METHODSParticipants at the Knight Alzheimer Disease Research Center were selected who were potentially eligible for recent DMT trials: age [&ge;] 60 years at baseline, clinical diagnosis of very mild or mild AD dementia (global Clinical Dementia Rating(R) (CDR(R)) score 0.5 or 1), biomarker confirmation of amyloid pathology, and at least one follow-up CDR assessment within 5 years. For IADLs, a subset of the Functional Assessment Questionnaire (FAQ) was examined that rated the degree of independence in the following: paying bills, driving, remembering medications and appointments, and preparing meals. For BADLs, the Personal Care domain of the CDR was used. Mixed-effects logistic and ordinal regression models were used to examine the relationship between CDR Sum Boxes (CDR-SB) and the individual functional outcomes and their components. The change in CDR-SB over time was estimated with linear mixed effects models. RESULTS282 participants were followed for an average of 2.9 years (SD 1.3 years). For 50% of individuals, loss of independence in IADLs occurred at CDR-SB>4.5 and in BADLs at CDR-SB>11.5. For individuals with a baseline CDR-SB=2, treatment with lecanemab would extend independence in IADLs for 10 months (95% CI 4-18 months) and treatment with donanemab in the low/medium tau group would extend independence in IADLs by 13 months (95% CI 6-24 months). DISCUSSIONIndependence in ADLs can be related to CDR-SB and used to demonstrate the effect of AD treatments in extending the time of independent function, a meaningful outcome for patients and their families.

INTRODUCTIONPredicting the early onset of dementia due to Alzheimers Disease (AD) has major implications for timely clinical management and outcomes. Current diagnostic methods, reliant on invasive and costly procedures, underscore the need for scalable and innovative approaches. To date, considerable effort has been dedicated to developing machine learning (ML) based approaches using different combinations of medical, demographic, cognitive, and clinical data, achieving varying levels of accuracy. However, they often lack the scalability required for large-scale screening and fail to identify underlying risk factors for AD progression. Polygenic risk scores (PRS) have shown promise in predicting disease risk from genetic data. Here, we aim to leverage ML techniques to develop a multi-PRS model that captures both genetic and non-genetic risk factors to diagnose and predict the progression of AD in different stages in older adults. METHODSWe trained and tested ML-based multi-PRS models, integrating genetically predicted clinical, behavioral, psychiatric, and lifestyle risk factors to predict the diagnosis of AD as well as the progression between different cognitive stages. We developed an automatic feature selection pipeline that identifies the relevant traits that predict AD. We also analyzed the interpretability of our pro-posed ML models and the selected features. Leveraging data from the Alzheimers Disease Neuroimaging Initiative (ADNI), Religious Orders Study and Memory and Aging Project (ROSMAP), and the IEU OpenGWAS Project, our study presents the first known end-to-end ML-based multi-PRS model for AD. RESULTSRelevant features were selected from an initial set of 53 polygenic risk scores computed for 1567 patients in the ADNI and 1642 patients in the ROSMAP dataset. The proposed multi-PRS ML method produced AUROC scores of 77% on ADNI and 72% on ROSMAP for predicting the diagnosis of AD, substantially surpassing the performance of the uni-variate PRS models. Our models also showed promise in predicting transitions between various cognitive stages (65%-75% AUROC scores). Moreover, the features identified by our automated feature selection pipeline are closely aligned with the widely recognized potentially modifiable risk factors for AD. DISCUSSIONMulti-PRS-based machine learning models can identify risk factors and construct predictive models for early Alzheimers disease (AD) diagnosis. This approach offers an automated mechanism to harness genetic data for AD diagnosis and prognosis, enhancing our understanding of the role of various traits in AD development and progression. It will facilitate the implementation of preventive measures at an early stage, thereby contributing to more effective interventions and improved patient outcomes.

About 1 in 9 older adults over 65 has Alzheimers disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations--higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

INTRODUCTIONSouth Asian and Chinese individuals are the largest and fastest-growing ethnoracial groups in Canada, yet they remain underrepresented in dementia research. To address this gap, we established the CAnadian Multi-Ethnic Research on Aging (CAMERA) study. METHODSCAMERA is a longitudinal observational study conducted in Toronto, Canada, enrolling 300 adults aged 55-85 who self-identify as South Asian, Chinese, or non-Hispanic White (NHW). Participants complete in-person visits at baseline, Year 3, and Year 5, which include clinical and cognitive assessments, brain MRI, and blood biomarkers. Annual remote questionnaires track health and lifestyle. RESULTSAmong 200 participants, vascular and metabolic profiles differed across groups. South Asian and Chinese participants reported greater cognitive concerns than NHW participants and had lower MoCA scores, driven primarily by language-heavy and culturally dependent items. Eye-tracking measures did not differ across groups. DISCUSSIONCAMERA provides a deep phenotyping framework to investigate dementia risk and resilience in Asian Canadians.

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.

ObjectiveSedentary behavior significantly increases the risk for chronic diseases and cognitive decline in aging, underscoring the need for effective interventions. Older adults exhibit a positivity effect, whereby processing of positive information is prioritized over negative information. In addition, self-affirmation was shown to reduce sedentary behavior in younger adults, but its effects in older adults remain unknown. This study tested a novel, technology-based intervention combining daily self-affirmation and gain-framed health messages to reduce sedentary behavior in older adults. MethodsIn a 6-week randomized controlled trial (NCT0431536), 48 cognitively unimpaired, sedentary older adults were randomized into two groups: The intervention group (mean age=70.0{+/-}5.4years) completed daily self-affirmation based on their highest-ranked value, followed by gain-framed health messages. The active control group (mean age=68.4{+/-}5.0years) performed self-affirmation on their lowest-ranked value, followed by loss-framed messages. This was a single-blinded clinical trial that incorporated a hybrid efficacy and implementation design. Thus, information about intervention feasibility was examined. In addition, baseline motivational traits, including reward sensitivity, were assessed as moderators of behavior change. The neural basis of self-affirmation and gain-framed health messaging was examined at baseline using a task-based, event-related fMRI paradigm across groups, after randomization at the outset of the intervention. ResultsThe intervention showed high adherence (0.92{+/-}0.08) and positive ease-of-use ratings. While the intervention did not significantly reduce sedentary behavior compared to the active control condition, increased reward sensitivity predicted reduced sedentary behavior across all participants. FMRI results showed increased ventral striatal activation in the intervention group, compared to the active control group during reading of gain-framed compared to neutral messages. ConclusionsThis study supports the feasibility of technology-based sedentary beahvior reduction interventions for older adults. While self-affirmation combined with gain-framed messaging did not significantly reduce sedentary behavior, gain-framed messages engaged the reward network, and reward sensitivity predicted future reduction in sedentary behavior.

IntroductionThe eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks perpetuating health disparities due to the tests known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR) - a rapid, FDA-listed digital cognitive assessment - to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage. MethodsWe conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 study (NCT04733989, N=945). Participants were clinically classified as cognitively unimpaired, mild cognitive Impairment, or probable Alzheimers dementia. We trained a Poisson elastic net regression model using age and multimodal digital features derived from the DCR (including drawing kinematics and voice acoustics) to predict MMSE scores. The model was tested for generalizability using an independent external validation cohort from the Apheleia study (NCT05364307, N=238). ResultsThe machine learning model predicted MMSE scores with a root mean squared error (RMSE) of 2.31 in the training cohort. This error margin falls within the established test-retest reliability range of the manual MMSE itself (2-4 points), suggesting the prediction is statistically non-inferior to human administration. External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62). Crucially, the model exhibited demographic fairness, maintaining consistent accuracy across Race (White RMSE = 2.34; Non-White RMSE = 2.14) and Ethnicity (Hispanic RMSE = 2.26; Non-Hispanic RMSE = 2.31). DiscussionMachine learning can leverage multimodal features from the DCR to accurately and equitably crosswalk to MMSE scores in support of current guidelines, transforming a time-intensive manual test into a rapid, automated assessment. By deploying this "digital triage" engine, where traditional assessments are still used for DMT eligibility, healthcare systems can streamline the identification of DMT-eligible patients, reduce specialist referral bottlenecks, and ensure that access to life-altering therapies is determined by pathology rather than demography.

BACKGROUNDEffective treatments for neurodegenerative diseases remain elusive, underscoring the importance of preventive strategies. Probucol, a cholesterol lowering and antioxidant drug with established cardiovascular use, has shown neuroprotective effects in preclinical models of dementia by modulating peripheral lipoprotein amyloid metabolism and preserving capillary integrity. However, no large-scale human studies have examined its association with dementia risk. OBJECTIVETo examine the association between probucol use and incident dementia in older adults. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study used the Japan Medical Data Centre claims database from 2014 to 2023. Adults aged 50 years or older prescribed probucol or statins were included, excluding those with prior dementia or recent drug exposure. Participants were categorized as probucol monotherapy users, statin monotherapy users, or combination users ([&ge;]2 prescriptions). Propensity score matching was used to balance baseline comorbidities. EXPOSURESProbucol or statin therapy. MAIN OUTCOMES AND MEASURESThe primary outcome was incident all cause dementia. Secondary outcomes included Alzheimers disease and mixed Alzheimers and vascular dementia. Odds ratios (ORs) with 95% CIs were calculated using logistic regression adjusted for age and sex. RESULTSAmong 57 231 individuals (52.6% female) followed for up to 10 years (median, 3 years), 7 387 (12.9%) developed dementia. The cohort included 2 896 probucol users (5.1%) and 54 335 statin users (94.9%). Dementia incidence was higher among females (14.7%) than males (10.9%). Dementia incidence was lower in probucol users (5.6%, 162/2 896) than in statin users overall (13.2%, 8 248/62 519), with individual statins ranging from 11.4% (fluvastatin) to 16.7% (pravastatin). Probucol use was associated with a 62% lower adjusted risk of dementia compared with all statin users combined (adjusted OR, 0.38; 95% CI, 0.37-0.38). Protective associations were consistent across individual statin comparisons, with adjusted ORs ranging from 0.30 (pitavastatin) to 0.57 (fluvastatin). CONCLUSIONS AND RELEVANCEIn this large national cohort of Japanese adults, probucol use was associated with a substantially lower risk of incident dementia compared with statins. These findings provide the first large-scale human evidence linking probucol exposure with reduced dementia risk, supporting its further evaluation as a preventive therapy in prospective clinical trials. Key PointsO_ST_ABSQuestionC_ST_ABSIs use of the lipid-lowering and antioxidant agent probucol associated with a reduced risk of incident dementia compared with statin therapy in older adults? FindingsIn this nationwide cohort study of 57 231 Japanese adults aged 50 years and older, dementia incidence was nearly halved in probucol users (5.6%) compared with statin users overall (13.2%), corresponding to 62% lower adjusted odds of dementia. Protective associations were consistent across dementia subtypes and individual statins. MeaningThese findings suggest that probucol, a long-standing and well-tolerated cardiovascular drug, may offer a mechanistically distinct and potentially scalable strategy for dementia prevention, warranting confirmation in prospective intervention trials.

AD pathology is accompanied by increased senescence and reduced levels of autophagy in the brain. We investigated whether pharmacologically inducing autophagy could alter the senescent phenotype and help ameliorate AD pathology. We discovered that Bisdemethoxycurcumin (BDMC), a natural compound found in Curcuma longa, stimulates autophagy in primary astrocytes. We found that autophagy and senescence exhibit an inverse relationship in aging astrocytes, with increased expression of senescent proteins and downregulation of autophagic proteins. However, treatment of aged astrocytes with BDMC reversed the senescent phenotype by ameliorating the impaired autophagy. Interestingly, the senescent phenotype persisted when autophagy was downregulated by knockdown of AMPK. Additionally, BDMC-induced autophagy aided in the removal of amyloid beta that was administered externally to the astrocytes. Further, to validate these results in a mouse model of AD, we confirmed that BDMC can significantly penetrate the blood-brain barrier (BBB) in mice. Therefore, we administered 50 and 100 mg/kg b.w. of BDMC to transgenic 3xTg-AD mice for two months. In their hippocampus, the Control 3xTg-AD animals showed more senescent cells and lower autophagy levels. In contrast, autophagic proteins were significantly upregulated while senescence indicators, such as senescence-associated secretory phenotype (SASP) proteins, were sharply downregulated in the brain of treated animals. Additionally, we discovered that the treated mices hippocampus had a significantly lower amyloid beta load. These molecular changes in the brain were ultimately reflected in the improved working memory and neuromuscular coordination behavior of mice treated with BDMC. This study warrants further evaluation of BDMC for the management of AD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/654834v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1176bbforg.highwire.dtl.DTLVardef@a2e2cdorg.highwire.dtl.DTLVardef@1d824f5org.highwire.dtl.DTLVardef@1628a30_HPS_FORMAT_FIGEXP M_FIG This illustration was created by using biorender.com C_FIG