Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

BackgroundMild behavioural impairment (MBI), characterized by later-life emergence of persistent neuropsychiatric symptoms (NPS), is an early clinical indicator of dementia risk. MBI as a global construct has been associated with Alzheimer disease (AD) pathology; studies have also explored MBI domains. Prior work has linked MBI-apathy to cerebrospinal fluid (CSF) biomarkers of AD, but whether similar associations are detectable using plasma-based biomarkers such as phosphorylated tau (p-tau) is unknown. Establishing such relationships is critical, as plasma biomarkers are more accessible than CSF. ObjectiveTo explore cross-sectional and longitudinal associations between MBI-apathy and plasma p-tau181 using Alzheimers Disease Neuroimaging Initiative data. MethodsOlder adults with normal cognition or mild cognitive impairment were categorized as MBI-apathy (n=69), non-MBI NPS (n=112), and no-NPS (n=215) based on Neuropsychiatric Inventory scores and symptom persistence over one year. Linear regression modelled cross-sectional associations between NPS group and plasma p-tau181 levels, adjusting for age, sex, education, apolipoprotein E4 status, and Mini-Mental State Examination score. Hierarchical linear mixed-effects modelling assessed associations over two and three years, including time-by-NPS group interactions. ResultsMBI-apathy was associated with significantly higher plasma p-tau181 levels at baseline (24.05% [6.06-45.08%]; adjusted p=0.014), and over two (26.46% [7.24-49.12%]; adjusted p=0.012) and three years (29.28% [10.17-51.72%]; adjusted p=0.004) compared to no-NPS. No significant associations were observed for non-MBI NPS. ConclusionsMBI-apathy is associated with elevated plasma p-tau181 cross-sectionally and longitudinally. These findings support MBI-apathy as a potential proxy marker of tau pathology for early AD detection.

Mild Behavioural Impairment (MBI) is defined by later-life onset of persistent behavioural changes and is recognized as a risk marker for cognitive decline and dementia. Apathy, a core MBI domain characterized by diminished interest, initiative, and emotional reactivity, can emerge before dementia and is hypothesized to be associated with structural brain changes. While previous studies have explored Alzheimer disease (AD)-related neuroanatomical substrates of apathy in the dementia clinical stage, few have investigated these associations in cognitively normal (CN) or mild cognitive impairment (MCI) individuals with persistent apathy consistent with MBI. Thus, this study explores structural brain differences between individuals with MBI-apathy and those without neuropsychiatric symptoms (no-NPS). Participants (n = 446; mean age = 69.6 years; 79.8% CN; 62.8% female) were drawn from the National Alzheimers Coordinating Center and categorized into MBI-apathy (n = 59) and no-NPS (n = 387) groups. Linear regressions were used to model associations between NPS group and regional brain measures, with adjustments for age, sex, years of education, apolipoprotein E4 carrier status, intracranial volume, and Mini-Mental State Examination score, with false discovery rate (FDR) correction for multiple comparisons. Primary outcomes included two predefined AD meta-regions-of-interest (ROIs): 1) thickness: a composite measure of mean cortical thickness across the entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, inferior parietal lobule, fusiform gyrus, and precuneus; and 2) volume: a composite measure of mean cortical and subcortical grey matter volume across the hippocampus, entorhinal cortex, amygdala, middle temporal gyrus, inferior parietal lobule, and precuneus. Primary outcomes also included cortical thickness and grey matter volume among individual ROIs including the ventral striatum (VS), anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), ventrolateral prefrontal cortex (vlPFC), and dorsolateral prefrontal cortex (dlPFC). MBI-apathy status was associated with significantly lower AD-meta-ROI cortical thickness (Z-score difference [95% CI]; FDR-corrected p-value, -0.43 [-0.73 - [-0.12]]; 0.025) and lower AD meta-ROI grey matter volume (-0.50 [-0.71 - [-0.30]]; <0.001). MBI-apathy was also associated with significantly lower dlPFC thickness (-0.40, [-0.70 - [-0.09]]; 0.02) and volume (-0.28 [-0.50- [-0.06]]; 0.026) and lower OFC volume (-0.32, [-0.57 - [-0.07]]; 0.026) compared to the no-NPS group. Within a non-dementia sample, MBI-apathy was more strongly associated with established AD-vulnerable regions than with regions that have been traditionally implicated in apathy in dementia. Results suggests that during CN and MCI stages, MBI-apathy may reflect early AD-related neurodegeneration, with conventional apathy-related structural changes becoming more prominent as disease progresses.

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.

INTRODUCTIONAgitation is a common and burdensome neuropsychiatric symptom in dementia that fluctuates from day to day, but objective tools for short-term risk stratification are limited. We examined whether nocturnal physiological signals from unobtrusive under-mattress sensors predict next-day daytime agitation and whether associations differ for agitation occurrence versus severity. METHODSWe extracted cardiorespiratory, movement, and sleep-proxy features from two long-term care cohorts (N=55; 333 nights) and one external home-monitoring cohort (N=18; 803 nights). A two-part mixed-effects framework was used to model next-day agitation episodes. RESULTSLower nocturnal respiratory rate and greater activity instability independently predicted higher odds of next-day agitation occurrence. Associations were stronger for motor than verbal agitation. Respiration-related predictors were validated externally. Conversely, no nocturnal features significantly predicted agitation severity. DISCUSSIONPassive sleep monitoring identified reproducible, physiologically interpretable markers of next-day agitation occurrence, supporting the potential of under-mattress sensing for short-term risk stratification and more proactive dementia care.

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.

Structured AbstractO_ST_ABSBACKGROUNDC_ST_ABSPatient reports are the standard when examining subjective cognitive decline (SCD). Recent research suggests that informant and clinician reports may also be associated with cognition. This study examined differences between patient, informant, and clinician definitions of SCD and their relationship to cognition. METHODSData from 4290 older adults (n=1690 normal controls, NC; n=840 mild cognitive impairment, MCI; n=1760 Alzheimers disease, AD) were examined from the National Alzheimers Coordinating Center. Linear models examined the relationships between SCD status using the three definitions and cognition at baseline and over time. RESULTSIn NC, informant and clinician SCD were associated with worse cognition at baseline, with patient and clinician SCD associated with worse cognition over time. All definitions were associated with worse cognition at baseline and over time in MCI and AD. DISCUSSIONOur findings suggest the importance of examining different SCD definitions, especially the inclusion of clinician SCD.

Background and ObjectivesProgression from mild cognitive impairment (MCI) to Alzheimers Disease and Related Dementias (AD/ADRD) varies widely across individuals, yet the mechanisms underlying this heterogeneity remain unclear. Identifying clinical and social determinants influencing this transition could enable earlier intervention. While cardiovascular and social risk factors are established contributors to dementia incidence, their role in progression from MCI to dementia may differ. Few studies using real world clinical data have evaluated these potential determinants of MCI progression. MethodsUsing electronic health records (EHR) from patients with incident MCI at UCSF Health (2010-2024), we evaluated cardiovascular (blood pressure [BP], body mass index [BMI], and type II diabetes) and social (marital status, language preference, race/ethnicity, and neighborhood disadvantage) risk factors for rate of progression from MCI to AD/ADRD. Covariate-adjusted Cox proportional hazards models estimated hazard ratios for incident AD/ADRD, with evaluation of interactions by sex. ResultsAmong 6,529 patients, higher systolic BP was associated with AD/ADRD incidence (HR per 10 mmHg: 1.09, 95% CI: 1.05-1.14). BMI was inversely associated with incidence in both males (HR: 0.94; 95% CI: 0.92-0.97) and females (HR:0.98; 95% CI: 0.96-0.99). Compared to married individuals, widowed patients had a higher hazard of progression (HR: 1.15; 95% CI: 1.00-1.32). Spanish-speaking (HR: 1.38; 95% CI: 1.04-1.81), Chinese-speaking (HR: 1.19; 95% CI: 1.00-1.42), and "Other non-English" speaking patients (HR:1.24; 95% CI: 1.03-1.51) had a higher hazard of progression compared to English speakers. Latinx (HR:1.22; 95% CI: 1.01-1.48) and Asian patients (HR:1.14, 95% CI: 1.00-1.30; p=0.04) also had higher hazards of progression compared to White patients. Neighborhood disadvantage was not significantly associated with disease progression. DiscussionCardiovascular and social factors independently influence dementia progression, with some sex-specific patterns. Integrating clinical and social indicators highlights the potential of EHR data to identify high-risk patients earlier in the care continuum and support equitable dementia prevention.

ObjectiveDemographic corrections (e.g., sex, education, race, ethnicity) are often applied when assessing cognition in adults; however, these corrections have significant limitations (e.g., using years of education does not capture the quality of, or access to, education). It is therefore critical to develop novel assessment options that are less susceptible to demographic factors. This study compared demographic effects on a verbal memory test and a performance-based test of cognition and daily functioning in older adults. Based on prior work, we hypothesized the performance-based tests would be less susceptible to demographic factors than paired associates learning. MethodData from 1326 participants (mean{+/-}SD age=61.9{+/-}10.9 yrs; Female = 1066, 80%) were collected through the MindCrowd electronic cohort, with 79 (6%) non-White, 109 (8.2%) identifying as Hispanic/Latino ethnicity, and 327 (25%) reporting education as less than a college degree. Paired associates learning is a well-established measure of medial temporal lobe-dependent learning and memory through recall of word-pairs, scored as the number of correct word pairs entered out of 36 possible. The performance-based test involved functional upper-extremity movement, specifically transporting beans to target cups in a repeating sequence (a task also shown to be dependent on the medial temporal lobe), scored as the intraindividual variability (standard deviation) in trial time across four consecutive trials. ResultsAs hypothesized, linear regression analysis showed that PAL was significantly affected by sex, education, race (particularly Black/African American), and ethnicity, whereas the performance-based test was affected only by sex and with a much smaller effect size than that of PAL. ConclusionsPerformance-based assessments may be an equitable approach to evaluating cognition without requiring score corrections, particularly for diverse populations.

There is a predicted increase in older adults presenting with mild to severe cognitive impairment. Screening tools with high sensitivity are the first frontier in identifying a cognitive pathology; however, to ensure that they are measuring the intended concept or criterion, thorough psychometric procedures should be followed. In this study, convergent criterion validity of Riga Cognitive Screening Task was measured, using cortical thickness of regions of interest as the criterion. 106 older adults (Mage = 70.49, SD =8.08, 35.8% male) with varying levels of cognitive functioning were involved in the study. All participants underwent cognitive assessment with the screening task and a 3T MRI. Cortical thickness of selected temporal and parietal regions was used as a brain measure. Behavioural Partial Least Squares Correlation was conducted and one latent variable was extracted. The results confirmed that Riga Cognitive Screening Task shows good criterion validity, suggesting successful use for screening.

BackgroundNatural language processing (NLP) systems integrated into clinical workflows show promise for detecting early cognitive impairment, yet causal evidence from real-world implementation remains limited. Observational studies comparing outcomes between hospitals with and without NLP-enhanced clinical decision support (CDS) systems face significant confounding from systematic differences in patient populations and institutional characteristics. ObjectiveTo estimate the causal effect of NLP-enhanced CDS implementation on time-to-diagnosis of mild cognitive impairment (MCI) and early dementia using propensity score analysis with inverse probability of treatment weighting (IPTW). MethodsWe conducted a retrospective cohort study across 12 hospitals in Singapore (n=8,247 patients aged [&ge;]60 years presenting with memory complaints, 2022-2025). Six hospitals implemented NLP-based cognitive screening systems analyzing clinical notes; six used standard care protocols. Propensity scores were estimated using gradient-boosted models incorporating 42 patient-level and 18 institutional covariates. IPTW-adjusted Cox proportional hazards models estimated treatment effects on time-to-diagnosis. Sensitivity analyses included trimming, augmented IPTW (AIPTW), and E-value calculations. ResultsAfter IPTW adjustment, the standardized mean differences for all covariates were <0.10, indicating adequate balance. NLP-enhanced CDS was associated with significantly earlier cognitive impairment diagnosis (IPTW-adjusted HR=1.58; 95% CI: 1.41-1.77; p<0.001), corresponding to a median reduction of 4.2 months in diagnostic delay. The average treatment effect on the treated (ATT) was 3.8 months earlier diagnosis (95% CI: 2.9-4.7 months). Results were robust across sensitivity analyses with E-values of 2.47 for point estimate and 1.98 for confidence interval bound. ConclusionsUsing rigorous causal inference methods, we demonstrate that NLP-enhanced clinical decision support systems significantly accelerate cognitive impairment detection in routine clinical practice. These findings support broader implementation of AI-enhanced screening tools to facilitate earlier therapeutic intervention.

Subtle alterations in awareness may emerge in the preclinical stage of Alzheimers disease (AD), yet their clinical significance and translational relevance remain unclear. This study aimed to evaluate associations of distinct awareness trajectories with clinical and multimodal AD biomarker measurements in cognitively unimpaired (CU) older adults. This prospective study analyzed data from the Anti-Amyloid Treatment in Asymptomatic Alzheimers (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) cohorts ([~]4.5-year follow-up). Awareness trajectories were defined using a mixed-effects regression model estimating normative longitudinal changes in the Cognitive Function Index (Participant--Study Partner Discrepancy). Based on individual-specific time slopes, participants were classified into three trajectories: stable awareness, heightened awareness (hypernosognosia), and decreased awareness (anosognosia). Study outcomes included the Preclinical Alzheimers Cognitive Composite (PACC), Alzheimers Disease Cooperative Study (ADCS) Activities of Daily Living Prevention Instrument (ADL-PI), Clinical Dementia Rating (CDR), plasma phosphorylated-tau at threonine 217 (p-tau217), A{beta}-PET ([18F]-florbetapir), tau-PET ([18F]-flortaucipir), and gray matter volume (GMv) via structural magnetic resonance imaging. The associations of awareness trajectories with clinical and multimodal biomarker measurements were evaluated using the general linear model framework, primarily implemented as mixed-effects, including voxel-wise and Braak-stage regional approaches for neuroimaging data. Sequential--longitudinal multimodal neuroimaging mediation analyses evaluated whether regional tau-PET propagation contributed to the emergence of distinct awareness trajectories through downstream GMv loss. In the full sample (n= 1,643) the mean age was 71.49[{+/-}4.72] years, [~]60% female sex, mean education of 16.63[{+/-}2.74] years, [~]69% A{beta}-PET positive, and [~]27% showing clinical progression on CDR-Global (>0). Compared to stable awareness trajectory (n= 1,325[[~]80%]; [~]67% A{beta}-PET positive; [~]18% clinical progression), hypernosognosia trajectory (n= 157[[~]10%]; [~]68% A{beta}-PET positive; [~]36% clinical progression) showed modest clinical implications and limited biomarker associations, including plasma p-tau217, medial temporal tau-PET, and brain structure. In contrast, anosognosia trajectory (n= 161[[~]10%]; [~]89% A{beta}-PET positive; [~]90% clinical progression) was associated with more adverse outcomes, including steeper cognitive and functional decline, higher risk of progression, greater plasma p-tau217, neocortical tau-PET, and widespread neurodegeneration. Associations between regional tau-PET and awareness trajectories were partially mediated by GMv loss, with sequential Braak-stage II tau-PET effects in hypernosognosia and generalized tau-PET propagation effects extending across Braak-stages II-IV in anosognosia. These findings suggest that distinct awareness trajectories emerge from stage-specific pathological processes, alongside downstream neurodegenerative mechanisms, reflecting separate clinical consequences. This study identifies anosognosia as a high-risk trajectory across the early stages of the AD continuum, while suggesting that hypernosognosia may reflect both age-related and early AD-related processes.

BackgroundCommunity-based clinical-pathologic studies have been instrumental to examine the association of Alzheimers disease and related disorders (AD/ADRD) with age and dementia in very-old non-Latino Whites. Here, we show the age distribution of four AD and three additional common neuropathologies across the adult lifespan and examine their relation to dementia and cognitive impairment in old and young Brazilian adults. MethodsWe examined 5,376 brains from decedents age 18 years or older (52.5% male, 39.8% Black), from the Pathology, Alzheimers and Related Dementias Study (PARDoS), collected between July 2021 and September 2025. Clinical diagnoses were rendered by a clinician who reviewed the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), informant-based Clinical Dementia Rating (CDR) Scale, and other selected data. Four indices of AD including {beta}-amyloid deposits (Thal stage), PHF-tau tangles (Braak stage), neocortical phosphorylated plaques and AD neuropathologic change (ADNC), and three other common neuropathologies, i.e., Lewy-body disease (LBD), chronic gross infarcts, and cerebral amyloid angiopathy (CAA) were assessed. Logistic regression was used for associations of pathologies with clinical diagnoses, adjusting for demographics. ResultsIntermediate to high ADNC were first found as early as the fourth decade. Chronic gross infarcts were found in one-fifth of the brains of young adults. Intermediate to high ADNC, limbic and neocortical LBD, chronic gross infarct and moderate to severe CAA were associated with dementia and cognitive impairment (CI) in older adults with mixed pathologies being the most common. Intermediate to high ADNC was associated with CI but not dementia in young adults, whereas, chronic gross infarcts were associated with both CI and dementia in young adults; overall, mixed pathologies were a small minority. ConclusionIn a community-based, clinical-pathologic study including 5300+ brains from diverse Brazilians, we show that AD and other common pathologies frequently begin in young adulthood. In older adults, mixed pathologies are most commonly associated with dementia, whereas in young adults a single pathology, most commonly chronic gross infarcts rather than ADNC is related to dementia.

INTRODUCTIONBilingualism is among several lifestyle factors associated with protection against cognitive decline, yet the biological mechanisms through which it exerts these effects remain poorly understood. METHODSWe compared neuropsychological functioning and biofluid markers of brain health between active (n = 280) and passive (n = 287) Spanish-Catalan bilinguals with biomarker-confirmed Alzheimers disease (AD). RESULTSActive bilinguals outperformed passive bilinguals on tests assessing attention/executive functions, language, and visuospatial/visuomotor functioning, demonstrating resilience given the same AD biological stage across participants. Active bilinguals also exhibited significant differences in cerebrospinal fluid and plasma biomarkers of amyloid burden and neuroinflammation, suggesting both resilience and resistance to AD pathophysiologic mechanisms. DISCUSSIONThe protective effects of bilingual experience may engage both resilience and resistance to AD pathophysiology mechanisms. These results underscore the importance of capturing bilingualism in aging cohorts and the study of how lifestyle and sociocultural factors shape the biological expression of neurodegenerative disease.

INTRODUCTIONAlzheimers disease (AD) diagnostic guidelines emphasize subjective cognitive decline (SCD) preceding mild cognitive impairment (MCI), implicitly assuming awareness of cognitive decline (ACD) is preserved in preclinical AD. This study aimed to evaluate associations of decreased ACD with multimodal core AD biomarkers in cognitively unimpaired (CU) individuals. METHODSWe analyzed data from CU individuals with baseline CSF biomarkers and 3-year longitudinal neuropsychological assessment (ALFA+ cohort). Decreased ACD was defined by concurrent decline in episodic memory and awareness using robust longitudinal references (Free and Cued Selective Reminding Test, Memory Binding Test, Wechsler Memory Scale, and Subjective Cognitive Decline Questionnaire). Biomarker outcomes included plasma and CSF p-tau181, p-tau181/A{beta}42, p-tau217; A{beta} ([{superscript 1}F]flutemetamol) and tau PET ([{superscript 1}F]RO948). Associations of ACD with AD biomarkers were evaluated using linear regression models. Sensitivity analyses were restricted to individuals with memory decline. RESULTS350 CU individuals were included (mean age 61 years; 60% female; mean education 14 years; 35% CSF A{beta}-positive). Episodic memory decline was identified in 61 (17%) individuals, of whom 25 (41%) also exhibited awareness decline; meeting criteria for decreased ACD. This group demonstrated greater levels of AD pathology compared to the remaining sample. Among fluid biomarkers, CSF p-tau217 showed the strongest association. Neuroimaging revealed elevated frontoparietal A{beta} PET, alongside temporal, insular, and frontal tau PET deposition. Sensitivity analyses showed that, at the same threshold of memory decline, decreased ACD reflects greater AD pathology. DISCUSSIONStandardized assessment of cognitive awareness, integrating objective neuropsychological performance with subjective reports, may provide a crucial extension of current clinical frameworks.

Biomarkers from diverse methodological domains are increasingly important in the detection, diagnosis and tracking of neurological diseases and brain health, yet they are often evaluated in isolation. Statistical integration approaches, such as factor analysis, provide a means to combine complementary biomarkers and capture higher-order domains of brain health. Exploratory factor analysis has previously been employed to identify latent brain health constructs using multimodal MRI, fluid biomarkers and cardiovascular risk factors in a non-clinical older population. The current study aimed to validate this integrative framework using confirmatory factor analysis in an independent cohort and test construct associations with cognition and diagnosis of mild cognitive impairment (MCI) or Alzheimers disease (AD) dementia. Data were analysed from 197 participants in the Prospective Imaging Study of Ageing, including 157 cognitively normal controls (CN), 18 participants with MCI and 22 participants with early AD dementia. MRI, cardiovascular, and plasma biomarker processing closely replicated previous methods. Confirmatory factor analysis was conducted in CNs to validate the previously reported latent constructs. Weighted factor composites were then compared between each diagnostic group and tested for associations with cognitive performance (verbal reasoning, verbal memory, visual memory and language) and sensitivity to MCI and AD diagnosis. Three factors were reproducible across cohorts: 1) Brain & Vascular Health (hippocampal and ventricular volumes, cerebral blood flow); 2) White Matter (WM) Fluid Dysregulation (Free Water, WM enlarged perivascular spaces); and 3) Blood Biomarkers (GFAP, NfL, pTau181). Compared to the CN group, both MCI ({beta}=-1.25, SE=0.19, p<.001) and AD dementia ({beta}=-1.52, SE=0.16, p<.001) participants had lower Brain & Vascular Health composite scores. MCI ({beta}=0.80, SE=0.20, p<.001) and AD dementia ({beta}=1.85, SE=0.17, p<.001) participants also had higher Blood Biomarkers composite scores than CNs, but there was no difference in WM Fluid Dysregulation scores across groups (F(2,192)= 0.89, p=.411). The Brain & Vascular Health composite had the strongest association with MCI/AD dementia among all individual measures and composites. Across all participants, Brain & Vascular Health and Blood Biomarkers composite scores were associated with tests of cognition (p<.0125), while WM Fluid Dysregulation did not show any significant associations. These findings demonstrate that reproducible, multimodal composites can index distinct yet complementary dimensions of brain health relevant to cognition and AD dementia. Importantly, this work highlights the value of an adaptable, integrative framework for combining imaging and plasma biomarkers to characterise system-level brain health and support early detection and mechanistic investigation of cognitive decline and neurodegenerative disease.

Background: Quantitative genome wide association studies (GWAS) primarily rely on additive linear models that compare average phenotypic differences between genotype groups. While effective for detecting common variants of moderate effect in large sample sizes, such approaches inherently reduce high resolution phenotypic data to summary statistics (group averages), potentially limiting the detection of subtle genotype phenotype relationships. Genomic Informational Field Theory (GIFT) is a recently developed methodology that preserves the fine-grained informational structure of quantitative traits by analysing ranked phenotypic configurations rather than relying solely on mean differences. Methods: We applied GIFT to genetic and neuropathological data from the Brains for Dementia Research cohort, a well characterised dataset of 563 individuals, and compared its performance with conventional GWAS. Principal component analysis (PCA) derived matrix was used to derive independent quantitative traits linked to from Alzheimer disease (AD) neuropathology measures (CERAD, Thal, Braak staging), with and without inclusion of age at death. Principal component analyses were performed using GWAS and GIFT frameworks on the same filtered genotype dataset. Results: Both GWAS and GIFT identified genome-wide significant associations (pvalue<0.000001) within the APOE locus (NECTIN2/TOMM40/APOE/APOC1), demonstrating concordance with established AD genetic variants. However, GIFT detected additional significant 19 SNPs beyond those identified by GWAS. Variants associated with AD pathology implicated genes involved in amyloid processing, neuronal apoptosis, synaptic function, neuroinflammation, and metabolic regulation. Notably, GIFT identified 29 loci associated with age at death related variation that were not detected by GWAS, highlighting genes linked to lipophagy, mitochondrial quality control, sphingolipid metabolism, frailty, and aging-related processes. Conclusions: GIFT recapitulates canonical GWAS findings while uncovering additional biologically relevant associations. By preserving the fine-grained structure of phenotypic data distributions and detecting non random genotype segregation across ranked trait values, GIFT enables the identification of associations that remained undetected by traditional average based GWAS approaches. These results demonstrate that rethinking analytical representation, rather than solely increasing sample size, can expand discovery potential of genetic association studies, offering a transparent and complementary framework for quantitative genomics in deeply phenotyped datasets.

Lifestyle and environmental factors such as diet, physical activity, residential greenspace exposure, alcohol consumption, and sleep are increasingly promoted as modifiable targets for maintaining cognitive health and mitigating age-related decline. Yet, it remains unclear how well they predict cognitive functioning and, importantly, to what extent their associations with cognition are reflected in brain and bodily health. Here, we applied machine learning to multimodal data from over 10,000 UK Biobank participants to evaluate the predictive value of twelve lifestyle and environment domains, spanning physical activity, diet, smoking and alcohol consumption, sleep, sexual behavior, electronic device use, and environmental exposures, for cognitive functioning - both individually and in combination - and performed commonality analysis to quantify the extent to which these associations are captured by body and brain markers. A model integrating all lifestyle and environment domains explained 23% of the variance in cognition at an out-of-sample r=0.48, comparable to models based on body and brain measures. Physical activity, together with diet, alcohol consumption, sun exposure, and local environmental characteristics, emerged as the strongest predictors of cognitive functioning. A composite brain marker integrating three neuroimaging modalities accounted for 57.7% of the lifestyle-cognition association, while a composite body marker spanning nine physiological systems accounted for 47.8%. Jointly, lifestyle, environment, body, and brain captured nearly all age-related variation in cognition (92.6%). Collectively, these results indicate that integrating lifestyle and environmental factors enables robust prediction of cognitive functioning and that a substantial portion of this association is reflected in brain and body health.

IntroductionAlzheimers disease (AD) disproportionately affects women, with accumulating evidence suggestion a contributary role of hormones in this disparity. Given the known influence of hormones on brain health and cognition, characterizing specific profiles in dementia is crucial. In addition, sex-stratified hormonal alterations in AD and other dementias remain poorly understood. MethodsWe quantified nine steroid hormones: 11-deoxycortisol, 17-hydroxyprogesterone, aldosterone, cortisol, dihydrotestosterone, estrone, progesterone, testosterone and estradiol. The hormones were quantified in cerebrospinal fluid (CSF) and plasma from 204 participants across five cognitive categories: no cognitive impairment (n=32), mild cognitive impairment (MCI) non-AD (n=38), MCI due to AD (n=21), AD dementia (n=81), and vascular dementia (VaD) (n=32). Participants were recruited at the Danish Dementia Research Centre, Copenhagen University Hospital, Copenhagen, Denmark. Hormone levels were measured using liquid chromatography-tandem mass spectrometry. Sex-stratified generalized linear models were adjusted for age. Overall, 50.5% of participants were women with a mean age of 69 (SD = 9.8) compared to men with a mean age of 70 (SD = 9.1). ResultsIn women with AD, CSF cortisol and 11-deoxycortisol were significantly elevated compared to women with no cognitive impairment (Fold Change (FC) (95% CI) = 1.13 (1.01-1.27), p-value = 0.04 and (FC (95% CI) = 1.01, (1.00-1.01), p-value = 0.03, respectively). Plasma progesterone was decreased (FC (95% CI) = 0.90 (0.81, 0.99), p-value = 0.04). Women with VaD exhibited reduced CSF estradiol (FC (95% CI) = 0.86 (0.74, 0.98), p-value = 0.03). In men with AD, plasma aldosterone was elevated (FC (95% CI) = 1.19 (1.06, 1.33), p-value = 2.81e-03). Correlation analyses revealed that CSF cortisol in women was significantly correlated with CSF AD pathology markers in amyloid-beta 42 (r = -0.29, p-value = 3.02e-03) and phosphorylated tau (r = 0.2, p-value = 0.04). The increase of cortisol was validated in an external cohort where t-test showed significant difference in cortisol between people with AD and cognitively healthy controls (CN), this difference was larger in women (mean AD = 0.26 vs mean CN = 0.21, p-value = 1.79e-06) than men (mean AD = 0.23 vs mean CN 0.21, p-value = 0.04) ConclusionOur findings demonstrate sex-dependent dysregulation of steroid hormone in dementia. Specifically, cortisol and aldosterone are highlighted, which are potential modifiable targets.

BackgroundDementia affects over 57 million people worldwide. UK and international policy position personalised, conversation-based care planning as central to post-diagnostic support. However, delivery in primary care is inconsistent, and many practitioners lack dementia-specific communication training. Existing evidence focuses on single roles or settings, leaving a gap in understanding how communication operates across the primary care workforce. AimsTo identify what helps and hinders effective communication for integrated dementia care planning and determine the support and training needs of the wider primary care workforce. MethodsO_LISemi-structured interviews - 11 people with dementia, 13 family carers, and 19 primary care practitioners from diverse roles, exploring experiences of care planning conversations C_LIO_LIReflexive thematic analysis C_LI ResultsThree themes were developed, progressing from micro-level communication practices (Theme 1: Beyond the tick-box), through triadic dynamics (Theme 2: Balancing voices in the conversation), to organisational influences (Theme 3: From silos to meaningful shared care planning). Time and Conversation as intervention cut across all themes, shaping trust and disclosure. Participants reported reliance on tick box approaches, inconsistent preparation, and uncertainty about care plan purpose and ownership. Non-clinical roles were commonly viewed as well placed to support meaningful conversations, but were often described as constrained by unclear remit and weak integration. ConclusionsA persistent gap remains between policy ambitions and everyday practice. Time-pressured, checklist-driven encounters and fragmented systems undermine shared decision-making. The expanded primary care workforce offers untapped potential to address these gaps, but this requires clearer roles, formal integration, and targeted investment in communicative skills.

aStructured abstractO_ST_ABSBACKGROUNDC_ST_ABSOver the past couple of decades, the role of infections, as well as the involvement of the immune system, have been highlighted in the development of dementia. METHODData from the Wisconsin Registry for Alzheimers Prevention cohort were utilized for the analysis. A history of medical conditions was searched across the cohort, and known infections and autoimmune conditions were recorded for each participant. These conditions were then compared with the diagnosis and cognitive performances of each participant. Furthermore, plasma markers were analyzed using two different protein quantification methods. RESULTSOur analysis revealed poorer cognitive performances among participants with listed medical conditions. In plasma samples, Ab42/ICAM1 was identified as a protein ratio with significant variation across condition statuses. DISCUSSIONOur study confirmed that infections and autoimmune conditions contribute to cognitive decline. Ab42/ICAM1 was identified as a relevant marker.