The Journal of Prevention of Alzheimer's Disease

Improving diversity in U.S. Alzheimers disease (AD) research is a pressing need. By 2050, Hispanic and Latino Americans will comprise 30% of the population. Hispanics are 1.5 times more likely and Blacks are twice as likely to develop AD compared to Whites, yet both remain vastly underrepresented in clinical trials research. Aging and AD research mentorship of underrepresented STEM undergraduates is designed to promote entry into related professions by students committed to decreasing disparities in AD research participation and clinical care. The NIA-funded MADURA program recruited 93 students from backgrounds historically underrepresented in STEM majors and/or from NIH-defined disadvantaged backgrounds. Trainees were placed in aging/AD research labs and received weekly training and mentorship from faculty research PIs and other types of supervisors (postdoctoral researchers, graduate students, research assistant staff...) Our study examined student ratings of the program and mentor behaviors, using a program-specific survey and the Mentoring Competency Assessment-21 (MCA-21). Trainees were highly satisfied with both mentoring relationships and the overall program. Student rated MCA-21 competency areas were quite high for both P.I.s and other types of research mentors. However, there were striking differences in associations between competencies and relationship and program satisfaction, by mentor type. For PI mentors, no MCA-21 competencies were associated with relationship satisfaction, but five of six competencies were associated with relationship satisfaction for other mentor types. Similarly, no PI mentor competencies were significantly correlated with overall placement satisfaction, but all six competencies were correlated with overall placement satisfaction for other mentor types. The authors discuss the likelihood of differing student expectations of faculty PI versus other types of research mentors, recommendations for assessing role-specific student expectations (including functions primarily possible only for senior faculty PIs), and utilizing nearer-peer plus PI faculty mentors to comprehensively address the gamut of mentee needs.

BackgroundEarly recognition of dementia-related changes is critical for timely intervention. The AD8 Dementia Screening Interview (AD8) detects subtle cognitive and functional changes, yet its broader associations with health and wellbeing among Chinese-speaking older adults remain underexplored. MethodsA cross-sectional study was conducted with 144 community-dwelling Chinese older adults (mean age 73.1 years; 81.3% female). Participants completed sociodemographic, health, functional, and psychosocial measures, including the AD8 and the Geriatric Depression Scale (GDS-15). Exploratory Factor Analysis (EFA) assessed the dimensionality of the AD8, and binary logistic regression examined associations between AD8 items and demographic, health, functional, and psychosocial outcomes. ResultsChronic disease was prevalent (68.1 percent), and 13.2 percent reported a mental health disorder. EFA identified three domains: memory impairment, executive and interest decline, and functional recall difficulties, explaining 61.7 percent of the variance. Logistic regression showed predictive roles for judgment problems (AD8_1), repetition (AD8_3), financial difficulties (AD8_6), tool-use difficulties (AD8_4), and daily memory problems (AD8_8). Financial and executive difficulties were associated with age and mobility challenges, while repetition predicted psychological disorders and hopelessness. Judgment problems were linked to lower life satisfaction and happiness but greater helplessness. Worthlessness was predicted by financial, tool-use, and memory difficulties, whereas intact temporal recall (AD8_5) was protective. Several outcomes including boredom, low energy, and staying home were not significant. ConclusionDistinct AD8 items predicted vulnerabilities across physical, psychological, and social domains. Findings highlight the multidimensional value of the AD8 as a culturally relevant screening and risk stratification tool for community-based assessments of Chinese older adults. Summary Statement Implications for PracticeO_ST_ABSWhat does this research add to existing knowledge in gerontology?C_ST_ABSThis study shows that specific AD8 items identify early multidimensional vulnerability among community-dwelling Chinese-speaking older adults. Difficulties with judgment, repetition, financial management, tool use, and daily memory were associated with functional limitations and psychosocial distress, extending the AD8 beyond dementia screening alone. The identification of three AD8 domains supports a broader understanding of early cognitive change as involving cognitive, functional, and emotional processes. The findings contribute culturally specific evidence from an under-researched population in gerontological research. What are the implications of this new knowledge for nursing care with older people?For nursing practice, the AD8 provides a brief, feasible tool to support holistic assessment in community and aged care settings. Key AD8 indicators can guide nurses in identifying older people at risk of functional decline and emotional vulnerability, enabling earlier, person-centred interventions. The findings highlight the importance of culturally and linguistically appropriate assessment when caring for diverse ageing populations. How could the findings be used to influence policy or practice or research or education?The results support integrating brief cognitive screening into routine nursing assessments and community-based aged care services to promote early identification and ageing in place. Nursing education should emphasise interpreting cognitive screening within psychosocial and cultural contexts. Longitudinal research is needed to assess intervention effectiveness. Key Points[tpltrtarr] Early cognitive changes matter for older Chinese-speaking adults, because difficulties with judgment, repetition, financial management, and tool use (AD8 domains) were consistently linked to poorer functional and psychosocial outcomes. [tpltrtarr]Beyond dementia screening, the AD8 proved useful for detecting vulnerabilities in wellbeing and daily functioning, extending its role beyond diagnostic sensitivity. [tpltrtarr]A cultural focus is vital, as this study is among the first to examine AD8 associations in older Chinese-speaking adults, underscoring the need for culturally tailored screening. [tpltrtarr]The psychosocial impact of cognitive changes was evident, with strong associations to helplessness, worthlessness, and reduced life satisfaction, reinforcing the overlap between cognitive and emotional health. [tpltrtarr]In practice, integrating AD8 screening into community and primary care could help identify at-risk individuals early and support targeted interventions in culturally and linguistically diverse populations.

Background and ObjectivesMultifactorial interventions have been reported to be effective in improving cognitive function; however, their long-term effectiveness in community settings remains to be sufficiently examined. This study aimed to investigate the effects of a socially implemented multifactorial intervention program on dementia onset, long-term care insurance certification, and post-intervention cognitive and physical functions. MethodsThis retrospective observational study collected data from three municipalities. The study population comprised individuals suspected of having mild cognitive decline based on cognitive function screening tests conducted by March 31, 2024, and who had been invited to participate in a dementia prevention class, but had not applied for long-term care insurance at the time of the invitation. Participants were classified into class participation and non-participation groups for analysis. Most participants attended the class only once (intervention duration: 4 or 6 months). ResultsData from 104, 218, and 256 individuals were collected from the three municipalities, respectively. No significant association was found between class participation and suppression of dementia onset or long-term care insurance certification in any of the municipalities. Regarding pre-post comparisons among class participants, significant improvements in cognitive function and some physical functions were observed in all the three municipalities. ConclusionsThe multifactorial interventions implemented in community settings showed no effect on dementia onset or health outcomes. However, class participation was associated with improvements in cognitive function and some physical functions. These findings suggest that implementing programs based on evidence can achieve effects similar to those observed in studies conducted under ideal conditions.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[->]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort (NCT04380987). Participants aged 25-65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen's kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored.

Background: For early detection of Alzheimer's disease, it is essential to identify individuals showing cognitive performance consistent with the mild cognitive impairment (MCI) range during preliminary screening, ideally using methods that extend beyond conventional cognitive assessments. Non-invasive, easily accessible screening tools applicable in daily life are increasingly needed. Facial expressions, particularly during rest, may offer promising biomarkers for MCI level detection. This study aimed to identify specific facial features associated with MCI level during rest to inform development of facial expression-based screening tools. Methods: Participants were classified into an MCI level group and a healthy control (HC) group based on the Montreal Cognitive Assessment (MoCA) scores. Facial Action Units (AUs) were extracted from video recordings of resting-state facial expressions in 31 individuals with MCI level and 14 HC. Two statistical models were employed: a multilevel zero-inflated beta regression model for intensity of 17 AUs and a multilevel logistic regression model for presence or absence of 18 AUs. Results: In the zero-inflated beta regression, the AU relates to upper lip raiser showed a significant group effect (MCI level vs. HC; p <0.001), remaining significant after multiple comparison correction. The logistic regression revealed significant group differences for the AUs related to lip tightener (p <0.001) and lip suck (p <0.001), both remained significant after multiple comparison correction. Conclusions: Distinctive facial action patterns during rest were observed in individuals with MCI level. These findings highlight the potential of resting-state facial expressions as a basis for novel, unobtrusive screening tools for early MCI level detection.

INTRODUCTION: Alzheimer's disease (AD) and frontotemporal dementia (FTD) have considerable clinical and pathological overlap. While plasma proteomics has advanced in AD, deep comparative analyses with FTD-particularly in diverse, biomarker-confirmed Asian cohorts-remain limited. METHODS: Plasma from 101 individuals with known pTau217 status was profiled using Olink Explore-HT. Differential expression-pathway enrichment, penalized regression-GLMNET, single-cell transcriptomic integration, associations with cognitive measures and, cross-platform validation were performed. RESULTS: Among 5,400-proteins, 1,168 were differentially expressed in AD and 370 in FTD (FDR<0.05). Distinct and overlapping proteomic signatures were identified in AD and FTD, reflecting gliosis, synaptic dysfunction, immune activation, and metabolic pathways. Prioritized proteins correlated with cognitive performance and plasma phosphorylated tau, A{beta}42, and neurofilament light chain, linking circulating proteins to disease severity. Cross platform validation revealed strong concordance with large independent datasets. CONCLUSION: Comprehensive plasma proteomics in Asian cohort supports scalable framework for blood-based biologically informed targets for precision diagnosis and therapeutic stratification.

The apolipoprotein E {varepsilon}4 (APOE4) allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), the most common form of dementia. APOE4 carriers exhibit cerebrovascular and metabolic dysfunction, structural brain alterations, and gut microbiome changes decades before the onset of clinical symptoms. A better understanding of the early manifestation of these physiological changes is critical for the development of timely AD interventions and risk reduction protocols. Multimodal datasets encompassing a wide range of APOE4- and AD-associated biomarkers provide a valuable opportunity to gain insight into the APOE4 phenotype; however, these datasets often present analytical challenges due to small sample sizes and high heterogeneity. Here, we propose a two-stage multimodal AI model (APOEFormer) that integrates blood metabolites, brain vascular and structural MRI, microbiome profiles, and other clinical and demographic data to predict APOE4 allele status. In the first stage, modality-specific encoders are used to generate initial representations of input data modalities, which are aligned in a shared latent space via self-supervised contrastive learning during pretraining. This objective encourages the learning of informative and consistent representations across modalities by leveraging cross-modality relationships. In the second stage, the pretrained representations are used as inputs to a multimodal transformer that integrates information across modalities to predict a key AD risk genetic variant (APOE4). Across 10 independent experimental runs with different train-validation-test splits, APOEFormer predicts whether an individual carries an APOE4 allele with an average accuracy of 75%, demonstrating robust performance under limited sample sizes. Post hoc perturbation analysis of the predictive model revealed valuable insights into the driving components of the APOE4 phenotype, including key blood biomarkers and brain regions strongly associated with APOE4.

Background. Detection of cerebral amyloid pathology currently requires amyloid PET imaging ($5,000-$8,000) or cerebrospinal fluid analysis via lumbar puncture, procedures that are inaccessible for population-level screening. The FDA-cleared Lumipulse G pTau217/Abeta1-42 plasma ratio test (May 2025) represents the first approved blood-based alternative; however, single-ratio approaches cannot distinguish Alzheimer's disease (AD) from non-AD neurodegeneration or provide multi-dimensional disease characterization. Methods. We developed Virtual Spectral Decomposition (VSD), a framework that decomposes plasma biomarker profiles into biologically interpretable diagnostic channels. Four plasma biomarkers - phosphorylated tau-217 (pTau217), amyloid-beta42/40 ratio, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - were measured in 1,139 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Each biomarker was mapped to a VSD channel representing a distinct pathophysiological axis: tau/amyloid phosphorylation, amyloid clearance, neurodegeneration, and astrocytic activation. Channel weights were calibrated via logistic regression, and performance was evaluated against amyloid PET (UC Berkeley) using 10x5-fold repeated cross-validation. Results. VSD 4-channel fusion achieved AUC = 0.900 (+/-0.018), exceeding pTau217 alone (0.888+/-0.022). Optimal sensitivity was 89.7% with 78.1% specificity (NPV = 90.8%). The NfL channel received a negative weight (beta = -1.1), functioning as a disease-exclusion signal: elevated neurodegeneration without amyloid-tau coupling actively reduces the AD probability, distinguishing AD from non-AD neurodegeneration. Complementary CSF proteomics analysis (7,008 proteins, 533 participants) identified 17 amyloid-specific proteins (0.24% of the proteome), revealing a 49:1 tau-to-amyloid asymmetry that explains why blood-based tau markers outperform amyloid markers. Conclusions. Blood-based VSD provides an interpretable, multi-channel framework for amyloid detection that incorporates explicit disease-exclusion logic unavailable to single-biomarker approaches. The architecture extends to multi-disease screening, where the same blood specimen could be routed through disease-specific modules for AD, Parkinson's disease, and cancer.

BackgroundDespite their potential to serve as a reduced-harm alternative to combustible tobacco, e-cigarette take-up remains low among older (45+) adult smokers, especially in the U.S. While social media is a known driver of vaping attitudes and behaviors in younger populations, its influence on older smokers is poorly understood. This paper provides the first focused analysis of e-cigarette-related social media exposure in this population, documenting its prevalence, characteristics, and attitudinal correlates. MethodsData come from an opt-in survey of U.S. adults (N = 974) recruited via Prolific, comprising three groups: (i) non-vaping smokers aged 45+ (N = 484), (ii) former-smoking vapers aged 45+ (N = 149), and (iii) any-vaping-status smokers aged 18-35 (N = 341). Descriptive statistics, weighted to U.S. population benchmarks, characterize self-reported exposure to e-cigarette-related content on social media. Logistic regressions estimate associations between exposure and intentions for future e-cigarette use, e-cigarette harm perceptions, and related attitudes. ResultsOlder smokers (35.3%) reported exposure to e-cigarette-related content on social media less frequently than both older vapers (44.0%) and younger smokers (72.0%). For older smokers, e-cigarette health risks were the most frequently reported topic of content viewed, followed by youth vaping and e-cigarette addiction. Among this group, exposure was positively associated with stated intentions for future e-cigarette use. Exposure was not significantly associated with perceived e-cigarette harms for any group. ConclusionsFindings provide suggestive evidence that social media exposure may promote e-cigarette adoption among older smokers. However, the cross-sectional design limits causal inference, and the observed associations may reflect selection bias or reverse causality. If a causal relationship exists, the patterns observed suggest that exposure influences e-cigarette adoption through mechanisms other than updating beliefs about e-cigarette risks. While these results tentatively support the potential of social media as a channel for older-smoker harm reduction, any policy applications must carefully weigh privacy concerns and risks to youth. Rigorous experimental studies are needed to confirm these findings and clarify how social media might be leveraged to improve public health outcomes among older smokers.

Aims Indoor air pollution resulting from combustion of unclean cooking fuels has been linked to adverse health outcomes, but evidence regarding its association with mental health in low- and middle-income countries remains limited. We investigated the association between household use of unclean cooking fuels, as a proxy for indoor air pollution, and depression symptoms among adults aged 45 years and older in India, and assessed effect modification by age, sex, caste, and rural/urban residence. Methods We conducted a cross-sectional analysis of the first wave (2017-2018) of data from the Longitudinal Aging Study in India (LASI), a nationally representative survey of adults aged [&ge;]45 years. Cooking fuel type was classified as clean or unclean, and depression symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression (CES-D-10) scale. We used logistic regression to estimate odds ratios for depression symptoms, and linear regression to compare mean CES-D-10 scores by cooking fuel type, adjusting for sociodemographic and housing characteristics. Results We included 62,650 respondents. Median age was 57 years (IQR: 50-65), 46.7% were women, 47.6% reported using unclean cooking fuels, and 27.6% screened positive on the CES-D-10. After adjusting for sociodemographic and housing characteristics, use of unclean cooking fuels was associated with higher odds of screening positive on the CES-D-10 (aOR: 1.08; 95% CI: 1.02, 1.15), and higher mean CES-D-10 scores (adjusted mean difference: 0.34; 95% CI: 0.24, 0.44). The association was more pronounced among individuals living in urban areas (aOR: 1.36; 95% CI: 1.21, 1.53). Conclusion Use of unclean cooking fuels was associated with depression symptoms among older adults in India, and especially among those living in urban areas.

Background: Social determinants of health (SDOH) are increasingly recognized as contributors to Alzheimer disease (AD) risk, yet the impact of multidimensional social disadvantage early AD-related pathophysiology remains poorly understood. Methods: We studied 1,466 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) cohort with SDOH assessed in early midlife (mean age 40, SD 3.6 years) and plasma AD biomarkers measured 20 years later. A comprehensive SDOH index was constructed from 12 indicators spanning five domains (economic stability, education, neighborhood and physical environment, community and social context, and health care access). We examined associations between SDOH quartile and log-transformed, standardized plasma phosphorylated tau 217 (p-tau217), neurofilament light chain (NfL), and amyloid-lower case Greek beta42/40 (Alower case Greek beta42/40) using linear regression adjusted for age, sex, race, and estimated glomerular filtration rate. Linear trends across SDOH quartile were also evaluated. Results: Participants in the most disadvantaged SDOH quartile had higher p-tau217, higher NfL and lower Alower case Greek beta42/40 level compared with those in the least disadvantaged quartile (p-tau 217: lower case Greek beta = 0.12, 95% CI 0.03-0.21, p = 0.008; NfL: lower case Greek beta = 0.20, 95% CI 0.05-0.35, p = 0.009; lower case Greek beta42/40: lower case Greek beta = -0.15, 95% CI -0.30-0.00, p=0.05). There was also a significant trend across quartile (p-tau 217: p for trend = 0.012; NfL: p for trend= 0.001). Analyses of individual SDOH domains indicated that lower economic stability, poorer health care access, and lower education were associated with higher NfL, and poorer health care access was associated with higher p-tau217. Conclusions: Greater SDOH disadvantage in early midlife was associated with higher levels of plasma AD biomarkers reflecting AD pathology and neurodegeneration decades later. These findings suggest that social disadvantage during midlife may contribute to early AD-related biological changes and highlight potentially modifiable social factors relevant for dementia prevention.

Background: Cardiovascular health factors are associated with cognitive decline and risk of dementia, including Alzheimer disease (AD); however, this has been mostly studied in late life. We investigated whether vascular and lifestyle factors are associated with AD plasma and imaging biomarkers in midlife. Methods: We investigated 1,406 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study with information on vascular and lifestyle factors framed from the American Heart Association (AHA) life's essential 8 (LE8) guidelines for cardiovascular health at early midlife (mean age 45.0 SD 3.6) and AD biomarkers in late midlife (mean age 60 SD 3.5). LE8 was calculated and categorized into poor (0-49), intermediate (50-79), and ideal (80-100) cardiovascular health, based on 8 components including smoking, diet, body mass index (BMI), sleep, fasting glucose, blood pressure, cholesterol, and physical activity. We assessed the AD plasma biomarkers phosphorylated tau 217 (ptau-217) and amyloid beta 42/40 ratio (A{beta}42/40) and the Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD), an algorithm that characterizes AD-like brain atrophy on brain MRI. We used linear regression to examine the association between LE8 and log-transformed and standardized AD biomarker measures adjusting for age, sex, race, education, and kidney function. Results: Compared to ideal LE8, intermediate (67.9% of participants) and poor (12.6%) LE8 was associated with lower A {beta}42/40 (adjusted mean difference: -2.37, 95% CI: -2.38 to -2.36 and -2.38, 95% CI: -2.40 to -2.36, respectively). There was no association between the LE8 group and ptau-217 level. Moreover, compared to ideal LE8 participants, those with poor LE8 had higher SPARE-AD atrophy pattern (adjusted mean difference: -0.71, 95% CI: -0.81 to -0.62). Conclusion: These findings indicate that poor cardiovascular health in midlife, as defined by the AHA LE8, is linked to less favorable early AD biomarker profiles, particularly reflecting greater amyloid burden and structural brain changes.

BackgroundSystematic reviews are essential for evidence-based decision making in health sciences but require substantial time and resource for manual processes, particularly title and abstract screening. Recent advances in machine learning and large language models (LLMs) have demonstrated promise in accelerating screening with high recall but are often limited by modest gains in efficiency, mostly due to the absence of a generalisable stopping criterion. Here, we introduce and report preliminary findings on the performance of a novel semi-automated active learning system, JARVIS, that integrates LLM-based reasoning using the PICOS framework, neural networks-based classification, and human decision-making to facilitate abstract screening. MethodsDatasets containing author-made inclusion and exclusion decisions from six published systematic reviews were used to pilot the semi-automated screening system. Model performance was evaluated across recall, specificity and area under the curve precision-recall (AUC-PR), using full-text inclusion as the ground truth. Estimated workload and financial savings were calculated by comparing total screening time and reviewer costs across manual and semi-automated scenarios. ResultsAcross the six review datasets, recall ranged between 98.2% and 100%, and specificity ranged between 97.9% and 99.2% at the defined stopping point. Across iterations, AUC-PR values ranged between 83.8% and 100%. Compared with human-only screening, JARVIS delivered workload savings between 71.0% and 93.6%. When a single reviewer read the excluded records, workload savings ranged between 35.6 % and 46.8%. ConclusionThe proposed semi-automated system substantially reduced reviewer workload while maintaining high recall, improving on previously reported approaches. Further validation in larger and more varied reviews, as well as prospective testing, is warranted.

Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with limited treatment options. Masitinib, a tyrosine kinase inhibitor targeting microglial and mast cell activity in ALS pathogenesis, offers potential neuroprotection. This study presents a post-hoc analysis of long-term survivors treated with masitinib at 4.5 mg/kg/day in study AB10015, comparing observed survival to predicted and historical benchmarks. Methods: Study AB10015 was a randomized, double-blind, placebo-controlled trial assessing masitinib with riluzole in ALS patients. Overall survival (OS) was measured from symptom onset to death, encompassing the double-blind period and post-study follow-up, including an optional open-label program. The ENCALS model predicted survival of long-term survivors ([&ge;]5 years). A delay in the need for mechanical assistance, such as permanent ventilation, gastrostomy, tracheostomy, or wheelchair dependence, was used as a surrogate measure for quality of life (QoL). Results: Among 130 patients receiving masitinib 4.5 mg/kg/day, the 5-year survival rate from onset was 42.3%, increasing to 50.0% in patients with an ALSFRS-R progression rate from disease onset of <1.1 points/month (AB10015 primary efficacy population), and 52.9% in a subgroup of patients without complete loss of functionality at baseline. Half of the long-term survivors had satisfactory QoL, defined as no mechanical assistance. The median OS for long-term survivors (n=55) was 121 months versus the ENCALS-predicted 42 months, yielding a 79-month residual median survival gain. Long-term survivors were prevalent across ALS baseline prognostic factors, including slow or moderate disease progression rate ({Delta}FS), severe or moderate functional severity, bulbar or spinal site of onset, respiratory function, and age. Long-term survival was less likely in patients with complete loss of function at baseline or fast progressing disease ({Delta}FS [&ge;]1.1 points/month) at baseline. Conclusions: Masitinib treatment in ALS patients showed substantial survival benefit. Long-term survivors were largely independent of ALS prognostic factors, suggesting a subpopulation driven by microglial/mast cell activity. A recently identified biomarker detecting masitinib effect on pro-inflammatory microglia may help identify responsive patients.

Objectives: Longer lifespans lead to longer time on retirement, despite the efforts to raise the retirement age. Therefore, it is important to study how the retirement years can be spent without diseases. This study examined socioeconomic and sociodemographic differences in healthy years spent on retirement. Methods: We followed a cohort of retired Finnish municipal employees (N=4231, average follow-up 15.4 years) on national administrative registers for major chronic diseases: cancer, coronary heart disease, cerebrovascular disease, diabetes, asthma or chronic obstructive pulmonary disease, dementia, mental disorders, and alcohol-related disorders. Median healthy years on retirement and age at first occurrence of illness (ICD-10 and ATC-based) in each combination of sex, occupational class, and age of retirement were predicted using Royston-Parmar models. Prevalence rates for each diagnostic group were calculated. Results: Most healthy years on retirement were spent by women having worked in semi-professional jobs who retired at age 60-62 (median predicted healthy years 11.6, 95% CI 10.4-12.7). The least healthy years on retirement were spent by men having worked in routine non-manual jobs who retired after age 62 (median predicted healthy years 6.5, 95% CI 4.4-9.5). Diabetes was slightly more common among lower occupational class women, and dementia among manual working women having retired at age 60-62. Discussion: Healthy years on retirement are not enjoyed equally by women and men and those who retire early or later. Policies aiming to increase the retirement age should consider the effects of these gaps on retirees and the equitability of those effects.

STRUCTERED ABSTRACTO_ST_ABSBACKGROUNDC_ST_ABSCoronary artery bypass graft (CABG) is a widely performed procedure for coronary artery disease (CAD), yet its association with Impaired Cognition (IC), i.e., mild-cognitive impairment or all-cause dementia, while accounting for APO ({varepsilon}) genotype, remains unclear. METHODSWe analyzed AllofUS participants with CAD (Age[&ge;]60 yrs) from 2017-2023. We defined CAD as a history of angina/myocardial infarction/chronic ischemic heart disease or having percutaneous coronary intervention/CABG, and IC as mild cognitive impairment or all-cause dementia using ICD/SNOMED codes. We performed logistic regression analyses to assess the association between CABG and IC, adjusting for clinical factors (age, sex, hypertension, diabetes, hyperlipidemia, depression, stroke, smoking, alcohol use, statin/antihypertensive/antidiabetic use), social determinants (self-reported race/ethnicity, income, employment), and APO ({varepsilon}) genotypes. We further performed stratified analyses across APO ({varepsilon}) genotypes ({varepsilon}2/{varepsilon}2, {varepsilon}2/{varepsilon}3 {varepsilon}3/{varepsilon}3, {varepsilon}2/{varepsilon}4, {varepsilon}3/{varepsilon}4, {varepsilon}4/{varepsilon}4). We defined significance at p [&le;] 0.05. RESULTSWe included 22,349 with CAD and identified 908 with IC after CAD till 2023. 40% were females, 70% were White, 12% were Black, and 9% were Hispanic. The proportion of IC was higher (5.1% vs 3.5%, p=1e-08) in CABG (n=8,135) vs non-CABG (n=14,214). After adjusting for clinical factors, social determinants, and APO ({varepsilon}) genotypes, CABG (1.23;1.06-1.41, p = 0.005) was associated with IC. In APO ({varepsilon}) stratified analysis, the association of CABG with IC was strongest in the APO {varepsilon}2/{varepsilon}3 group (1.91;1.21-3.02, p = 0.005). CONCLUSIONIn the AllofUS cohort, we observed an association between CABG and IC in CAD participants, with the strongest association in the APO {varepsilon}2/{varepsilon}3 group. Key MessageO_ST_ABSWhat is already known on this topicC_ST_ABSCoronary artery disease (CAD) and Impaired Cognitive (IC) disease, i.e., mild cognitive impairment and all-cause dementia, share genetic, sociodemographic, and clinical factors, including cardiovascular conditions like coronary artery bypass grafting (CABG) procedure. What this study addsWe observed an association between CABG and IC in CAD participants after adjusting for sociodemographic, clinical factors, and APO ({varepsilon}) effects. Further, when CAD participants were stratified across APO ({varepsilon}) groups, CABG was significantly associated with IC in the APO {varepsilon}2/{varepsilon}3 group. How this study might affect research, practice or policyOur observations highlight the role of APO ({varepsilon}) genotype evaluation in CAD patients for IC risk assessment.

Background/Objective: Common measures of physical activity (PA) based on duration and intensity do not fully capture its complexity. Adding additional PA components of muscle strength, mechanical strain, and turning actions, can provide a more complete view of activity behavior. Furthermore, PA behaviors differ between men and women. Therefore, the goal of this study is to identify and cluster similar long-term PA patterns over time for each PA component, examined separately for men and women. Methods: We used data from 4963 participants (52% women; mean age 66 years, SD = 8.6) of the Longitudinal Aging Study Amsterdam (1992 to 2019). PA component scores were assigned to self-reported activities, and Sequence Analysis with Optimal Matching was used to identify and cluster similar activity patterns over a period of 10 years, separately for each component and stratified by sex. Results: PA components varied by sex and displayed a unique mix of trajectories, including predominately low, medium, or high activity, increasing or decreasing patterns, and trajectories characterized by early or late mortality. Importantly, trajectories remained independent, indicating that changes in one PA component were not linked to changes in others. Conclusion: Older men and women follow distinct and independent long term PA trajectories across components, underscoring that PA behaviour cannot be described by a single dimension. Significance/Implications: The observed independence and heterogeneity of trajectories suggest that muscle strength, mechanical strain, and turning actions capture meaningful and distinct aspects of PA that are not reflected by traditional measures alone. Future PA-strategies could incorporate these dimensions and acknowledge sex-specific patterns to better reflect natural movement. The independence of components suggests that future interventions should target multiple dimensions, as changes in one component may not translate to others. Such an approach may support more tailored and sustainable PA interventions in later life.

Inadequate discharge communication is a well-documented contributor to medication non-adherence, missed follow-ups, and preventable readmissions across healthcare systems worldwide. In resource-limited oncology settings, where patients are often low-literate, speak non-dominant languages, and manage complex multi-drug regimens, this problem is acute and largely unaddressed. We present Aakhyan, a vernacular patient communication platform that addresses the full post-discharge arc: from converting English-language discharge summaries into structured, voice-based vernacular explanations, through medication adherence support, to proactive follow-up management - all delivered via WhatsApp. The architecture is novel in its strict separation of concerns: a vision-language model performs structured JSON extraction from discharge images; all patient-facing content is generated deterministically from clinician-approved templates with community-sensitive vocabulary registers. This design eliminates the hallucination risk inherent in generative AI patient communication (documented at 18-82% in prior studies) while preserving the extraction capability of large language models. The platform supports four language registers, Bengali, Hindi, simplified English for tribal populations, and Assamese, with text-to-speech synthesis across all registers, including a custom grapheme-to-phoneme engine developed for Assamese phonology. Beyond discharge communication, the platform includes scheduled medication adherence nudges, interactive follow-up reminders, and a Daily Availability and Patient Notification System (DAPNS) that notifies patients the evening before their follow-up whether their doctor and required investigations are available, preventing wasted trips by rural patients who travel 2-6 hours to reach the centre. A 100-patient stratified randomised controlled study is planned at Silchar Cancer Centre, with structured teach-back assessment at 48-72 hours post-discharge as the primary comprehension outcome and preliminary clinical efficacy as a secondary objective. This paper describes the clinical rationale, technical architecture, safety framework, and positioning of Aakhyan within the existing literature on mHealth patient communication interventions.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.