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

ObjectiveTo assess intra-individual cognitive variability (IICV) in relation to Alzheimers Disease (AD) biomarkers. MethodsThe sample included 879 adults from the National Alzheimers Coordinating Center, aged 50 and above with a complete neuropsychological evaluation and AD biomarker data available (64% cognitively intact; 36% cognitively impaired). We conducted a series of moderated regression models where AD biomarkers, neurocognitive status, and their interaction effects predicted IICV. IICV measures included demographically adjusted normed scores for the intraindividual standard deviation (iSD) and coefficient of variance (CoV). AD biomarkers included cerebrospinal fluid (CSF) measures of A{beta}1-42, phosphorylated tau 181 (p-Tau181), and total tau (t-Tau), as well as amyloid positron emission tomography (PET; with both continuous centiloid values and a dichotomous variable). ResultsIncreased AD biomarker burden was associated with increased IICV among cognitively impaired individuals (correlational strength ranging from .206 to .391 for iSD and from .149 to .460 for CoV) but not among the cognitively intact group (correlational strength ranging from .008 to .085 for iSD and from .016 to .085 for CoV). The pattern of results held even after controlling for demographic factors and was comparable in magnitude to the association between AD biomarkers and mean cognitive performance. ConclusionsIncreases in measures of amyloid, soluble tau, and neurodegeneration are associated with increased IICV among cognitively impaired older adults. The findings underscore the potential of IICV as a sensitive outcome measure in the AD clinical disease phase. Future studies should replicate findings longitudinally and in more diverse samples.

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.

ABSTRACT BACKGROUND: Recent studies consisting primarily of white participants have found lowered plasmalogen levels to be associated with lower cognitive function. We explore the association of blood plasmalogen levels with global cognition and brain imaging metrics in older African Americans. METHODS: Included in these cross-sectional analyses were participants in the Minority Aging Research Study (MARS) and the Rush Clinical Core without dementia, available serum lipid levels, and a concurrent cognitive function assessment. A plasmalogen biosynthesis value (PBV) was calculated for each participant utilizing five ratios of four key glycerophospholipids. A linear regression model of global cognition was constructed with PBV, adjusted for sex, age, education, total cholesterol, and body mass index. In participants with 3T MRI brain imaging, the association between PBV and white matter hyperintensities (WMH) was explored. RESULTS: Of the 298 participants, the mean age was 74.6 years, mean education was 15.6 years, and 84% were women. The median PBV was 0.42 (interquartile range: 0.22 to 1.14). A unit higher in PBV was suggestively associated with a 0.17 {beta}-unit higher cognitive z-score (SE =0.09, p = 0.06). In 254 participants with MRI data, an increase in log10 SD of PBV suggested the less white matter hyperintensities (estimate = -0.20, SE = 0.12, p = 0.08). DISCUSSION: In older African Americans, higher PBV was associated with higher level of global cognition, and potentially lower levels of brain white matter hyperintensities. Larger studies are needed in additional cohorts to determine if PBV is associated with annual rate of change in cognitive function.

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.

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.

BACKGROUNDEpisodic memory declines early with aging, reflecting reduced neural resources and diminished memory specificity. However, few studies have created a cognitive challenge with multiple levels of task demand to investigate this early subtle decline. Furthermore, it is unclear whether the genetic Alzheimers disease risk factor APOE4 and early Alzheimers pathology constrain the neural resources required to cope with increasing task demands. METHODSIn this preregistered behavioral study named EMCOMP (Episodic Memory & COMPensation), we conducted a semantic episodic memory retrieval task using a sentence-based task demand manipulation with five demand levels at recognition (novel foils, "old" target sentences, and three lure levels) and two demand levels at free and cued recall (gist and details). We collected data from 100 cognitively unimpaired adults (37 young (mean age 24 years), 63 older (mean age 72 years)) with additional neuropsychological testing and blood-based measures available for the older group. First, we investigated differences in EMCOMP episodic memory retrieval accuracy and confidence between young and older adults in linear mixed-effects models. Second, we investigated differences associated with the APOE4 genotype and plasma-derived Alzheimers pathology. Third, we assessed correlations between EMCOMP recognition and recall and established cognitive tests. RESULTSYoung adults showed higher recognition accuracy and confidence as well as a higher recall score and a lower recall error rate compared to older adults. As recognition-task demand increased there was a steeper decline in recognition accuracy and a steeper increase in high-confidence errors in older compared to younger adults and in older APOE4 carriers compared to older non-carriers. However, we found no associations with Alzheimers pathology. EMCOMP performance was positively correlated with established cognitive tests. CONCLUSIONOur study demonstrates age- and APOE4-related differences in episodic memory retrieval under increasing task demands - differences that may not be detectable in paradigms with only one or two levels of task demand. By manipulating semantic retrieval specificity, we provide a novel approach to detect subtle cognitive deficits and potential functional compensation in cognitively unimpaired older adults with risk factors or early Alzheimers pathology. Future research should extend this work to more diverse samples and combine behavioral assessment with fMRI to examine underlying brain activation patterns and functional connectivity.

ObjectiveBrain branks preserve extensive material relevant to neurodegenerative disease research. As these collections age, tissue becomes archival, raising the question of whether long-term fixed and stored human brain tissue remains suitable for contemporary immunohistochemical analyses. Materials and MethodsForty-one autopsy brains collected between 1946 to 1980 were examined. For each case, midbrain and hippocampus were available both as original paraffin-embedded blocks and as tissue stored long term in fixative. New paraffin blocks were prepared from the long-term fixated tissue. Sections from original and newly prepared blocks were immunohistochemically stained for -synuclein, hyperphosphorylated tau and amyloid-{beta}. Immunoreactivity was assessed using semi-quantitative scoring. ResultsOriginal blocks consistently showed good staining intensity and morphological preservation for each protein pathology. Newly prepared blocks showed slightly lower semi-quantitative scores for Lewy-related pathology, without statistically significant differences, except for astrocytic -synuclein in the substantia nigra in cases from the 1960s. Tau pathology displayed modestly reduced labelling, particularly of the neuropil threads and neurofibrillary tangles, most evident in cases from the 1950s. Amyloid-{beta}-positive senile plaques showed similar or slightly higher scores in newly prepared blocks, with no significant differences across regions. ConclusionHuman brain tissue preserved as paraffin-embedded blocks or stored in fixative for up to 78 years remains suitable for immunohistochemical analyses. Adequate-to-good detection of aggregated of -synuclein, hyperphosphorylated tau and amyloid-{beta} is achievable, indicating preserved pathological hallmarks of Lewy Body Disease and Alzheimers Disease in archival tissue.

BackgroundThe AT(N) biological framework classifies Alzheimers disease (AD) pathology using CSF biomarkers, with the A+T+ profile defining biological AD and the A-T+ profile representing a biologically distinct entity consistent with suspected non-Alzheimers pathophysiology, such as primary age-related tauopathy. Functional assessment capable of differentiating these profiles non-invasively remains limited. This study investigates whether cognitive vergence and pupillary temporal dynamics during a visual oddball task can distinguish A-T+ from A+T+ biological profiles in individuals with mild cognitive impairment (MCI). MethodsThirty-eight participants with MCI (12 A-T+, 26 A+T+) classified by CSF biomarkers completed a visual oddball task (80% distractors, 20% targets) under continuous eye-tracking. Linear mixed-effects models examined profile x condition interactions on full time series and six trial-level temporal features. Participant-level differentiation was assessed using binomial logistic regression, adjusting for age, sex, and MMSE. ResultsBoth profiles showed comparable overall oculomotor response magnitudes but diverged markedly in temporal organization. Significant profile x condition interactions emerged for cognitive vergence global slope, time to peak, and pupillary time to peak. Logistic regression confirmed that timing features discriminated biological profiles at the participant level, with differentiation reversing direction between distractor and target conditions. A-T+ participants also maintained superior target detection accuracy (89.3% vs. 82.4%, p = 0.001). ConclusionCognitive Vergence and pupillary temporal dynamics during an oddball task provide condition-dependent functional oculomotor signatures that systematically differentiate AT(N) biological profiles in MCI, suggesting that oculomotor assessment may offer an accessible, non-invasive complement to CSF-based profile characterization.

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 [≥]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.

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.

INTRODUCTIONHealthful dietary patterns may attenuate dementia risk by preserving cerebrovascular health. Prior work has focused on systemic arterial stiffness, but cerebrovascular measures may be more sensitive to neuroprotective effects of diet. We examined associations between Mediterranean diet adherence, prefrontal cortex (PFC) arterial elasticity, and cognition in older adults. METHODSParticipants were 198 older adults (58% female; mean age 65.6 years) from the Newcastle ACTIVate cohort. Mediterranean Diet (MedDiet) scores were derived from the Australian Eating Survey food frequency questionnaire. Pulse Relaxation Function (PReFx), an index of PFC arterial elasticity, was measured using pulse Diffuse Optical Tomography. Cognition was assessed with CANTAB and a cued task-switching paradigm. RESULTSHigher MedDiet was associated with higher PFC arterial elasticity. MedDiet was not associated with cognition, and PReFx did not mediate diet-cognition associations. DISCUSSIONGreater Mediterranean diet alignment was cross-sectionally associated with PFC arterial elasticity, suggesting a pathway through which diet may influence brain health in ageing.

Background and ObjectivesCarotid plaque reflects systemic atherosclerosis and may serve as an early marker of cognitive decline, but its longitudinal association with cognitive trajectories remains unclear. We investigated whether carotid plaque status (small-to-medium size) was associated with cognitive performance over time in a population-based cohort. MethodsThis prospective cohort study followed-up neuropsychological assessment battery quadrennially in two cycles (2015-2018 and 2019-2022) from the baseline (2011-2014). Of 2,819 participants, 2,176 participants who were free of dementia and cerebrovascular disease at baseline with cognitive function testing at least two time points over the follow-up time were analyzed. Carotid plaque assessed by B-mode ultrasound sonography five segments were scanned in both left and right sides. The plaques were graded based on vessel thickness and the diameter of the lumen (none, small-to-medium, and large). We categorized our participants into without (none) and with the plaques (small-to-medium, and large combined) at baseline. The main outcomes were multivariable adjusted mean differences of cognitive test performances by baseline plaque status over time. The neuropsychological assessment battery included story recall, visual reproductions, verbal fluency, trail making, digit symbol - coding, and Stroop tests. ResultsOf the total, 291 (13.4%) participants had carotid plaque at baseline. There were no differences at baseline and 4-year. At 8-year follow-up, participants with carotid plaque performed significantly worse than participants without carotid plaque in visual reproduction delayed recall [mean difference -0.525 (95% CI: -0.915 to -0.135), p=.008], Stroop word reading [mean difference -2.732 (95% CI: -5.164 to -0.300), p=.028] and color reading [mean difference -3.573 (95% CI: -5.199 to -1.948), p<.001]. Additionally, participants with carotid plaque performed lower than those without carotid plaque on logical memory delayed recall [mean difference -1.577 (95% CI: -2.843 to -0.311), p=.015] at 8-year follow-up period. DiscussionIn this large cohort study, carotid plaque status at baseline was independently associated with in cognitive function decline, especially in non-verbal memory and executive functioning over 8-year follow-up period in the general population. Therefore, it may be important for earlier intervention on carotid plaque to preserve neurological health in middle-aged to older population.

BackgroundOdor identification impairment is an early marker of Alzheimers disease (AD) that predicts memory decline, yet its underlying microstructural basis remains unclear. We hypothesized that mild cognitive impairment (MCI) involves early myelin and lipid disruption within olfactory-limbic circuits, detectable using a synthetic MRI-derived contrast that provides complementary sensitivity to myelin volume fraction (MVF). MethodsThirty-three older adults (healthy controls [HC], n = 16; mild cognitive impairment [MCI], n = 17) completed olfactory and cognitive testing and underwent 3T brain MRI using a QALAS sequence. An MVF map and synthetic FLAIR and DIR images were generated, and a FLAIR-DIR-derived metric (FD) was computed as FD = (FLAIR - DIR) / FLAIR. We investigated ROI-based group differences in olfactory-limbic gray-matter regions and associated white-matter tracts, voxel-wise regressions investigating FD-odor identification associations, and ROI-based MCI vs HC classification using cross-validated logistic regression models. ResultsCompared with HC, MCI showed significantly lower FD across olfactory-limbic gray-matter regions and white-matter pathways--including hippocampus, amygdala, orbitofrontal cortex, thalamus, and corpus callosum--whereas MVF differences were more limited. FD achieved moderate discrimination, with baseline performance comparable to MVF. Voxel-wise analyses revealed that better odor identification was associated with higher FD in the hippocampus/parahippocampal and insula; the association persisted after adjusting for voxel-wise MVF. MVF also showed significant positive voxel-wise associations with odor identification in the insula and genu of the corpus callosum. ConclusionFD is a practical, myelin- and lipid-sensitive contrast derived from routinely acquired synthetic FLAIR & DIR images that complement quantitative MVF. It captures behaviorally relevant variance beyond local myelin content and may improve detection of early olfactory-limbic microstructural changes in MCI. These findings support FD as a scalable candidate marker linking early network disruption to olfactory symptoms across the AD continuum.

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.

The Montreal Cognitive Assessment (MoCA) is widely used to screen for cognitive impairment, yet commonly applied cutoff scores have been found to perform poorly among US Latinos. Prior studies relied on small samples, combining persons with mild cognitive impairment (MCI) and dementia into a single group, or failing to account for multiple intersecting demographic factors. We identified optimal MoCA cutoffs for MCI and dementia among US Latinos while addressing these limitations. We analyzed cross-sectional data from the National Alzheimers Coordinating Center Unified Data Set. Participants included English- and Spanish-speaking Latinos who completed testing in their primary language. Research diagnostic groups consisted of cognitively unimpaired (CU), MCI, and dementia. Groups were further stratified by testing language, age, and level of education. Diagnostic accuracy and receiver operating characteristic (ROC) analyses were performed. The Youden Index was used to determine the optimal cutoff score. Of the 1,673 participants in the total sample, 46% completed the MoCA in Spanish and 54% in English, 54% were CU, and 28% had MCI and 19% had dementia. Area under the curve (AUC) values for CU vs. MCI were 0.70 for Spanish-tested participants and 0.79 for English-tested participants, while values for MCI vs. dementia were 0.85 and 0.89 for the Spanish and English tested participants, respectively. Overall AUC values were 0.76 for CU vs. MCI and 0.86 for mild cognitive impairment vs. dementia. Optimal cutoffs were consistently found to be lower among participants tested in Spanish, those older than age 75, and participants with the fewest years of education, with some optimal cutoffs shown to be substantially lower than the traditionally used standard cutoff. These findings provide cutoffs that better reflect differences amongst language and demographic groups. We also provide a scoring calculator for clinical and research use.

BackgroundVitamins are important modulators of intestinal health and may affect the gut-brain axis through microbial metabolites such as short-chain fatty acids (SCFAs). However, the neurocognitive effects of colon-delivered vitamins in older adults remain unexplored - a critical gap given the gut-brain axiss emerging role in cognitive aging. ObjectiveWe investigated the effect of a colon-delivered multivitamin (CDMV) supplement on intestinal health and neurocognitive outcomes in older adults at risk of cognitive decline. MethodsWithin the double-blind randomized placebo-controlled trial COMBI (ClinicalTrials.govID: NCT05675007), we included 75 older adults (60-75 years) at risk of cognitive decline based on lifestyle-related factors. Participants consumed a colon-delivered capsule with vitamins B2, B3, B6, B9, C and D3, or a placebo, daily for six weeks. Pre- and post-intervention, we employed neuroimaging, feces- and blood collection. Primary outcomes were fecal SCFA concentrations, working memory (WM)-related fMRI responses, and WM performance measured with the n-back task. ResultsAfter adjusting for baseline values, we found no significant between-group differences in total fecal SCFA levels (p=0.30) and WM performance (p=0.50). Post-intervention WM-related fMRI responses in the hippocampus (p=0.01; p{superscript 2}=0.09) and dorsolateral prefrontal cortex (dlPFC) (p=0.06; p{superscript 2}=0.04), driven by the right dlPFC (p=0.02), were higher in the CDMV group compared to placebo. Independent of intervention group, post-pre increases in fecal SCFA levels were significantly correlated to increases in dlPFC fMRI responses ({rho}=0.31; p=0.02) and WM performance ({rho}=0.43; p=0.001). ConclusionsOur findings suggest that CDMV supplementation increases WM-related responses of the dlPFC and hippocampus in older adults, but this effect was not accompanied by changes in fecal SCFA levels or WM performance. The positive correlation of within-subject changes in fecal SCFAs with changes in WM dlPFC responses and performance across intervention groups provides human evidence for gut-brain communication in cognitive aging beyond cross-sectional associations.

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.

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[-&gt;]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.

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.

BackgroundQuantitative 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. MethodsWe 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 Alzheimers 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. ResultsBoth GWAS and GIFT identified genome-wide significant associations (pvalue<10-6) 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. ConclusionsGIFT 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.