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Early identification of Alzheimer’s disease (AD) pathology is essential for timely intervention, particularly in primary care. We evaluated the diagnostic performance of a scalable, multimodal framework in a real-world, population-based cohort. A total of 277 community-dwelling individuals aged ≥ 60 years from the STOP-ALZHEIMER DEBA study (Basque Country, Spain) underwent brief cognitive screening (MMSE, M@T, Fototest, AD8) with optimized cut-offs, along with clinical risk assessment. Among them, 181 participants also completed structural MRI, plasma biomarker profiling (p-tau181, Aβ42/40, GFAP, NfL), and cerebrospinal fluid (CSF) analysis. We assessed performance for detecting cognitive impairment, CSF amyloid positivity (A+), and combined amyloid–tau positivity (A + T+). Optimized cognitive tests showed moderate accuracy (AUC 0.66–0.77), with the Fototest performing best. For biological outcomes, GFAP and p-tau181 had the highest predictive value (AUCs: 0.813 and 0.755 for A+; 0.852 and 0.710 for A + T+), and their combination further improved accuracy (AUC = 0.842). Fully adjusted models incorporating optimized cognitive scores, plasma biomarkers, APOE genotype, MRI, and demographics achieved high diagnostic performance (AUC = 0.886 for A+; 0.893 for A + T+). Results were consistent across sex and age strata. These findings support a stepwise diagnostic strategy combining brief, minimally invasive tools to enhance early AD detection in community settings.

The aging global population underscores the need to understand brain aging and its links to neurodegenerative diseases. While most brain aging studies use cognitive impairment as exclusion criteria, preclinical biomarkers may influence results, potentially masking early pathological effects. This study evaluates how preclinical AD and vascular biomarkers impact brain aging models in cognitively normal subjects. Using baseline data from the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS), we analyzed 1,380 cognitively unimpaired participants (50+ years) stratified into five groups based on cerebrospinal fluid biomarkers (Aβ42, t-tau, p-tau) and vascular pathology (Fazekas scale, microbleeds). Structural MRI volumes of cortical/subcortical regions were normalized and compared using Nadaraya-Watson kernel regression. Bootstrapping and Bonferroni-corrected statistical tests assessed differences in the relationship between age and brain volume between groups. Significant differences emerged in the relationship between age and brain volume in biomarker-negative and biomarker-positive groups, particularly in the entorhinal cortex, amygdalas, and basal forebrain ( < 0.01). The AD and mixed AD/vascular groups showed the largest deviations. Gender-specific analyses revealed stronger effects in males. Vascular pathology alone affected distinct regions (e.g., left entorhinal cortex) without amygdala involvement, suggesting disease-specific atrophy patterns. Preclinical AD and vascular biomarkers significantly alter brain aging in cognitively normal individuals. These findings highlight the importance of biomarker stratification in brain age studies to avoid biased estimates. Entorhinal cortex and amygdala volumes may serve as sensitive early indicators of neurodegeneration, supporting their use in targeted interventions and personalized monitoring.

Background: Aging is a well-established independent risk factor for both cognitive impairment and sleep disorders, including obstructive sleep apnea (OSA), a modifiable yet underrecognized condition. OSA has been implicated in biological mechanisms contributing to Alzheimer’s disease, including amyloid-β accumulation, tau phosphorylation, and neuroinflammation. This underscores the need to optimize OSA diagnosis in individuals with an increased risk of dementia. Methods: This cross-sectional observational study enrolled adults aged 60–85 years with a CAIDE dementia risk score ≥6. Subjective sleep was evaluated using validated questionnaires (Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and the Oviedo Sleep Questionnaire), while objective sleep data were obtained through a single-night peripheral arterial tonometry (PAT)-based wearable device, complemented by a 7-day sleep diary. Participants also completed the STOP-BANG and Berlin questionnaires, with clinically relevant findings communicated to participants. Results: Among 322 participants (48.8% women; mean age 71.4 ± 6.4 years), moderate-to-severe OSA (apnea–hypopnea index [AHI] ≥ 15) was identified in 48.49%, despite the absence of prior diagnoses. Subjective screening tools frequently underestimated OSA severity compared to objective assessments. While no significant sex-based differences were noted, higher AHI values correlated strongly with increased body mass index and elevated dementia risk scores. Conclusions: A marked discrepancy between subjective and objective sleep measurements complicates the accurate diagnosis and management of most sleep disorders, including OSA. Sleep disorders remain significantly underdiagnosed in individuals at increased risk for dementia. Integrating wearable technologies and structured tools such as sleep diaries into routine assessments can enhance diagnostic precision, enabling timely interventions for these modifiable risk factors of dementia.

Growing research suggests that dementia is a complex disorder with multiple risk factors and causes. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) demonstrated that lifestyle interventions could confer cognitive benefits. Inspired by this, the GOIZ-ZAINDU (GZ) feasibility study adapted the FINGER approach to the Basque context. Building upon the GZ study, the CITA GO-ON trial aims to enhance and expand the evidence supporting dementia prevention through a multidomain intervention of risk factor management and resilience promotion. It is a two-year, population-based, randomized controlled trial to prevent cognitive decline in adults aged 60-85 years with Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score ≥6, no dementia, and below-than-expected performance on at least one of three cognitive screening tests. Participants are randomized (1:1) to receive either Regular Health Advice (RHA) or a Multidomain Intervention (MD-Int) that encompasses cognitive training, socio-emotional skills, multicomponent physical exercise, nutritional and culinary intervention, and monitoring for cardiovascular risks, pharmacological drug mismanagement, and comorbidities. The primary outcome is the efficacy of the intervention to reduce the risk of cognitive decline measured by the global composite -score of the modified Neuropsychological Test Battery over two years. The secondary outcomes measure cost-effectiveness, quality of life, and functional abilities. Blood samples and brain imaging will also be collected to evaluate the effects of the intervention on brain structure and plasma biomarkers. Recruitment has been completed with 1051 participants selected (mean age (standard deviation, SD) of 69.65 (6.36), 58,50 % female, and mean CAIDE (SD) of 7.62 (1.427). The final participant is expected to complete the last study visit by the autumn of 2026. The CITA GO-ON Study, as a part of the World-Wide FINGERS network, is designed to validate the efficacy of a multidomain lifestyle intervention for dementia prevention and contribute valuable data to inform public health strategies fostering healthy, active aging.

Dislipidemia is a risk factor for cognitive impairment. We studied the association between interindividual variability of plasma lipids and white matter (WM) microstructure, using diffusion tensor imaging (DTI) in 273 healthy adults. Special focus was placed on 7 regions of interest (ROI) which are structural components of cognitive neurocircuitry. We also investigated the effect of plasma lipids on cerebrospinal fluid (CSF) neurofilament light chain (NfL), an axonal degeneration marker. Low density lipoprotein (LDL) and triglyceride (TG) levels showed a negative association with axial diffusivity (AxD) in multiple regions. High density lipoproteins (HDL) showed a positive correlation. The association was independent of Apolipoprotein E (APOE) genotype, blood pressure or use of statins. LDL moderated the relation between NfL and AxD in the body of the corpus callosum ( p  = 0.041), right cingulum gyrus (p = 0.041), right fornix/stria terminalis ( p  = 0.025) and right superior longitudinal fasciculus ( p  = 0.020) and TG in the right inferior longitudinal fasciculus ( p  = 0.004) and left fornix/stria terminalis ( p  = 0.001). We conclude that plasma lipids are associated to WM microstructural changes and axonal degeneration and might represent a risk factor in the transition from healthy aging to disease.

Background: Chronological age is an imprecise proxy for cognitive aging. The Cognitive Age Delta (CAD)—the difference between predicted cognitive age and chronological age—offers a scalable, individualized marker of functional brain aging. We examined determinants of CAD in cognitively unimpaired (CU) adults stratified by Alzheimer’s disease (AD) and vascular biomarkers. Methods: We analyzed 177 CU participants from the Gipuzkoa Alzheimer Project (Basque Country, Northern Spain) classified as amyloid-negative/vascular-negative (CUA−V−, n = 140), amyloid-positive (CUA+, n = 23), or vascular-positive (CUV+, n = 14) using CSF and MRI criteria; vascular burden was defined as Fazekas ≥ 2 on T2-FLAIR or ≥4 microbleeds on SWI, excluding non-traumatic superficial siderosis and established ischemic lesions. MRI was used solely for vascular classification. Associations with demographic, genetic, lifestyle, and reserve measures were tested with General Linear Models. Results: CAD did not differ across biomarker groups (Kruskal–Wallis H(2) = 0.17, p = 0.91). Median (IQR) CAD values were 0.28 (−4.13, 4.69) for CUA−V−, −0.14 (−3.15, 2.87) for CUA+, and 0.77 (−2.22, 3.76) for CUV+, indicating comparable distributions. Higher vocabulary scores (proxy of cognitive reserve) related to a younger cognitive age in CUA−V− (β = −1.39, p < 0.001) and CUA+ (β = −2.08, p = 0.054). In CUA+, greater sedentary time—particularly computer-based sitting—was also associated with lower CAD (daily sitting β = −2.13, p = 0.009; workday computer sitting β = −2.32, p = 0.015). CAD showed no associations with CSF Aβ42, p-tau or t-tau, APOE ε4 load, or vascular risk factors (all p > 0.05). Conclusions: CAD captures interindividual resilience-related variability beyond classical AD biomarkers. Vocabulary, a marker of lifelong enrichment, emerged as a robust determinant of a younger cognitive age, while amyloid and vascular pathology exerted limited influence at preclinical stages. These findings support CAD as a sensitive, scalable endpoint for identifying protective factors and guiding personalized prevention in early Aging.

Accelerated long‐term forgetting (ALF) refers to a rapid loss of information over days or weeks despite normal acquisition/encoding. Notwithstanding its potential relevance as a presymptomatic marker of cognitive dysfunction, no study has addressed the relationship between ALF and Alzheimer’s disease (AD) biomarkers. We examined ALF in APOE ɛ4 carriers versus noncarriers, and its relationships with AD cerebrospinal fluid (CSF) biomarkers. We found ALF over three months in APOE ɛ4 carriers (F(1,19) = 5.60; P < 0.05; Cohen’s d = 1.08), and this performance was associated with abnormal levels of the CSF Aβ42/ptau ratio (r = −.614; P < 0.01). Our findings indicate that ALF is detectable in at‐risk individuals, and that there is a relationship between ALF and the pathophysiological processes underlying AD.

Cognitive aging describes changes in thinking, learning, and memory abilities as people age. While chronological age measures time lived, cognitive age reflects mental function, influenced by health, education, lifestyle, and social factors. The aims of this study were: to estimate individual's cognitive age based on neuropsychological test results; to determine the cognitive age delta (CAD, difference between an individual's predicted and actual chronological age) as a measure of cognitive aging; to examine the association between cognitive aging and lifestyle factors to identify potential contributors to cognitive maintenance with aging. Cross-sectional study. Population-based recruitment from cohort with extensive clinical-biological phenotyping (Gipuzkoa-Alzheimer-Project -PGA-) in the Basque Country. CADs were computed using a Multiple Linear Regression model on neuropsychological test results. Deltas were compared between cognitively unimpaired (CU) and mildly cognitively impaired (MCI) participants using t-tests, and analyzed correlations with factors including APOE4 genotype, bilingualism, years of education, vocabulary WAIS-III as an estimate of intellectual level, cognitive reserve questionnaire (CRQ), and leisure and productive activity participation, perceived stress, and physical activity. Analyses used AgeML, an open-source Python package. Sample of 411 participants: 62 MCI individuals (average age 58±8 years, 52% female) and 349 CU individuals (average age 57±7 years, 56% female; see Table-1 for participant characteristics). MCI subjects showed higher Cognitive Age (mean difference +3.82 years). Correlated factors with aging in CU and MCI participants are in Table-2. In the CU group, CRQ score, vocabulary WAIS-III score participation in leisure and productive activities, years of education and the Hollingshead Social position index were significantly correlated with CAD. In the MCI group, the differences for lifestyle factors were not statistically significant after Bonferroni and FDR correction, though APOE4 carrier status had a detrimental effect in the sample. We found factors of interest related to intellectual and social activities to target in the design of lifestyle interventions to maintain cognitive health and slow cognitive aging, with a particular focus on CU individuals. These findings are consistent with previous studies and reinforce the need to target the identified lifestyle factors.

Background Cognitive aging describes changes in thinking, learning, and memory abilities as people age. While chronological age measures time lived, cognitive age reflects mental function, influenced by health, education, lifestyle, and social factors. The aims of this study were: to estimate individual's cognitive age based on neuropsychological test results; to determine the cognitive age delta (CAD, difference between an individual's predicted and actual chronological age) as a measure of cognitive aging; to examine the association between cognitive aging and lifestyle factors to identify potential contributors to cognitive maintenance with aging. Method Cross‐sectional study. Population‐based recruitment from cohort with extensive clinical‐biological phenotyping (Gipuzkoa‐Alzheimer‐Project ‐PGA‐) in the Basque Country. CADs were computed using a Multiple Linear Regression model on neuropsychological test results. Deltas were compared between cognitively unimpaired (CU) and mildly cognitively impaired (MCI) participants using t‐tests, and analyzed correlations with factors including APOE4 genotype, bilingualism, years of education, vocabulary WAIS‐III as an estimate of intellectual level, cognitive reserve questionnaire (CRQ), and leisure and productive activity participation, perceived stress, and physical activity. Analyses used AgeML, an open‐source Python package. Result Sample of 411 participants: 62 MCI individuals (average age 58±8 years, 52% female) and 349 CU individuals (average age 57±7 years, 56% female; see Table‐1 for participant characteristics). MCI subjects showed higher Cognitive Age (mean difference +3.82 years). Correlated factors with aging in CU and MCI participants are in Table‐2. In the CU group, CRQ score, vocabulary WAIS‐III score participation in leisure and productive activities, years of education and the Hollingshead Social position index were significantly correlated with CAD. In the MCI group, the differences for lifestyle factors were not statistically significant after Bonferroni and FDR correction, though APOE4 carrier status had a detrimental effect in the sample. Conclusion We found factors of interest related to intellectual and social activities to target in the design of lifestyle interventions to maintain cognitive health and slow cognitive aging, with a particular focus on CU individuals. These findings are consistent with previous studies and reinforce the need to target the identified lifestyle factors.