ObjectivesTo examine associations between immigration-related factors and mild cognitive impairment (MCI) or dementia among Hispanics; and associations between immigration-related factors and cognitive performance among cognitively unimpaired Hispanics.MethodsData from the Health & Aging Brain Study-Health Disparities study were used. 1231 Hispanic participants were included. Six indicators, including time living in the US, nativity, age of migration, primary language, bilingualism, and acculturation level, were considered immigration-related factors. Both the three-category (dementia, MCI, and cognitively unimpaired) and binary (cognitively impaired and cognitively unimpaired) outcomes were used. The cognitive performance was evaluated by the Mini-Mental State Examination (MMSE). Multiple logistic, multinomial logistic, and linear regression models adjusted for age, sex, and education were applied.ResultsThe likelihood of cognitive impairment did not differ significantly between native-born and foreign-born Hispanics, whereas foreign-born cognitively unimpaired Hispanics had significantly lower global cognition than their native-born counterparts. When stratifying by age of migration, a significant association between time living in the US and cognitive impairment was observed among early-life immigrants. A higher acculturation level was associated with lower odds of both dementia and MCI among native-born Hispanics, but was insignificant in foreign-born Hispanics. Bilingualism was significantly associated with dementia or MCI in foreign-born Hispanics. Monolingual foreign-born cognitively unimpaired Hispanics had significantly lower MMSE scores than bilingual foreign-born Hispanics.ConclusionsForeign-born Hispanics might be more vulnerable to cognitive aging. Bilingualism may play a more important role in cognitive health in foreign-born Hispanics than in native-born Hispanics. Acculturation may have distinct effects on foreign-born and native-born Hispanics.

BackgroundNeighborhood disadvantage has been associated with reduced cognitive reserve, increased risk for cognitive impairment, and greater Alzheimer's disease (AD) neuropathology, with particularly pronounced effects among Black and Hispanic/Latino older adults. Blood-based AD biomarkers offer a scalable approach to population-level study of AD risk; however, whether neighborhood-level social determinants influence these biomarkers across diverse populations remains unknown.ObjectiveTo characterize associations between neighborhood disadvantage and AD blood biomarkers in a racially and ethnically diverse community sample of older adults with and without cognitive impairment, and to examine whether these associations differ by race, ethnicity, and cognitive status.MethodsRegression models predicting AD biomarkers (amyloid-β 42/40 ratio, phosphorylated tau-181, total tau, and neurofilament light chain) from demographics and the Area Deprivation Index (ADI) were fit for 1179 Non-Hispanic White, 1264 Hispanic/Latino, and 724 Black adults. Models were stratified by cognitive impairment status and fit separately by race and ethnicity.ResultsAmyloid markers were associated with ADI, but only in cognitively impaired individuals living in highly disadvantaged areas. Total tau was elevated in those from disadvantaged neighborhoods, regardless of cognitive status; however, pTau-181 was not associated with ADI for any group. Significant associations were primarily evident among Black and Hispanic/Latino older adults.ConclusionsThe social exposome is an important factor in AD research, and findings show associations between neighborhood disadvantage and AD blood biomarkers; however, associations are mainly evident among ethnic/racial minority older adults living in moderately to severely disadvantaged neighborhoods. More work is needed to understand these associations.

INTRODUCTION:The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS:This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography [PET] scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS:DeepSUVR increased correlation between tracers, and reduced variability in the Aß-negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION:DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. HIGHLIGHTS:Novel artificial intelligence (AI)-method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross-sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR-derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three- to five-fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

Blood-based biomarkers (BBMs) are transforming the diagnostic landscape of Alzheimer's disease by enabling scalable, less invasive, and potentially earlier biological characterization. However, most evidence supporting their performance, interpretation, and clinical integration derives from highly selected cohorts in high-income settings, raising concerns about external validity, threshold transportability, and equitable implementation across diverse populations. In this Opinion, we argue that advancing BBMs from analytical validity to real-world use requires a shift from biomarker-centric accuracy toward context-aware interpretation frameworks that explicitly account for social, environmental, and health system determinants. Using the amyloid, tau, and neurodegeneration (AT(N)) system as a conceptual anchor, we discuss how BBMs should be positioned according to clearly defined contexts of use, including triage, diagnostic support, prognosis, and clinical trial readiness, rather than treated as universal diagnostic substitutes. We examine how social determinants of health, life-course exposures, and the cumulative exposome interact with comorbidity burden, systemic physiological stress, and health system readiness to shape biomarker distributions, trajectories, and clinical meaning. Evidence from Latin America and other underrepresented settings illustrates how cardiometabolic, vascular, and inflammatory load can modify baseline biomarker levels, challenging the uncritical transfer of cutoffs, reference ranges, and predictive models developed in high-income settings. We conclude that BBMs hold substantial potential to expand access to biological characterization of Alzheimer's disease, but their responsible adoption depends on aligning biological signals with clinical context, social and environmental conditions, and system capacity. Without this alignment, large-scale deployment risks misclassification, inequitable access to care, biased trial enrollment, and distorted estimates of disease burden.

Abstract Introduction The approval of anti‐amyloid monoclonal antibodies (mAbs), including lecanemab and donanemab, represents a significant advance in disease‐modifying therapies (DMTs) for early Alzheimer's disease (AD). While appropriate use recommendations (AURs) have been established to guide clinical decision‐making, the proportion of individuals with cognitive impairment in real‐world, multiethnic populations meeting eligibility criteria remains unknown, as do potential differences in treatment‐related risks across ethnic groups. Methods We included 513 cognitively impaired individuals from the Health and Aging Brain Study–Health Disparities study, a multiethnic community‐based cohort. Eligibility for lecanemab and donanemab was determined using published AUR criteria. Counts of amyloid‐related imaging abnormalities were estimated based on apolipoprotein E (APOE) ε4 genotype and ethnicity using published incidence rates. Results Only 15% of participants met eligibility criteria for lecanemab or donanemab. Black individuals had a numerically higher estimated ARIA burden, though differences were not statistically significant. Discussion Few individuals in this community‐based, multiethnic cohort met eligibility for anti‐amyloid therapy, highlighting limited real‐world applicability of current AURs. Highlights Only 15% of community‐based individuals with MCI or dementia met eligibility criteria for lecanemab and donanemab. Black participants had numerically higher estimated ARIA cases, though not statistically significant. Current AUR criteria have limited real‐world applicability across multiethnic populations. Broader inclusion criteria and real‐world safety data are needed to ensure equitable, safe implementation.

OBJECTIVE:Multimorbidity, the coexistence of 2 or more chronic conditions, has been linked to cognitive aging and Alzheimer's disease (AD) and AD-related dementias, yet the mechanisms remain unclear. We aimed to examine the associations of multimorbidity with cognition and biomarkers across multiple mechanistic pathways. METHODS:We cross-sectionally analyzed 3,808 dementia-free participants (mean age 64.9 ± 8.5 years, 62% female) from the Health and Aging Brain Study: Health Disparities. Multimorbidity burden was assessed using a latent construct derived from chronic conditions identified through objective measures, medical history, and self-report. A latent factor score for cognition was estimated using confirmatory factor analysis and neuropsychological tests. Using linear and logistic regression, we examined the associations of multimorbidity burden with biomarkers of AD (positron emission tomography [PET] amyloid, plasma β-amyloid 42/40, and phosphorylated tau [p-tau] measures), neurodegeneration (cortical thickness, hippocampal volume, and plasma neurofilament light and total tau), and cerebral small vessel disease (SVD) (magnetic resonance imaging white matter hyperintensities, cerebral microbleeds, and lacunes). RESULTS:Greater multimorbidity burden was associated with worse cognition and biomarkers of AD (PET amyloid standardized uptake value ratios and positivity, p-tau181, and p-tau217), neurodegeneration (neurofilament light, total tau, cortical thickness, and hippocampal volume), and SVD (white matter hyperintensity volume and presence of lacune and cerebral microbleeds). INTERPRETATION:Among dementia-free individuals, higher multimorbidity burden was associated with biomarkers for greater AD pathology, neurodegeneration, and SVD. These findings support a more holistic approach to managing chronic disease burden, which has the potential to reduce overall pathophysiological burden and delay cognitive decline. ANN NEUROL 2026;100:370-379.

Alzheimer's disease and related dementias (ADRDs) presents significant biological heterogeneity, which influences clinical outcomes and treatment responses. However, current ADRDs research has been predominately conducted in non-Hispanic white cohorts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), limiting the understanding of ADRDs progression in diverse populations. The Health and Aging Brain Study – Health Disparities (HABS-HD), which includes Black/African American (AA), Hispanic (HIS), and non-Hispanic white (NHW) participants, offers a broader sociodemographic perspective. This study aims to test the generalizability of ADRDs subtypes and progression patterns identified in ADNI to the more diverse HABS-HD cohort. Structural MRI data from HABS-HD (AA n  = 588, HIS n  = 1005, NHW n  = 1009) and ADNI ( n  = 864) cohorts were processed using FreeSurfer to extract brain cortical thickness and hippocampal volume. The Subtype and Stage Inference (SuStaIn) algorithm was then applied to both datasets to identify spatial atrophy subtypes and disease stages. To compare subtypes between cohorts, Positional Variance Diagrams (PVDs) in Figure 1. were used to visualize the variability and uncertainty in disease progression patterns derived by the model. SuStaIn identified three spatial atrophy subtypes and progression stages in HABS-HD and ADNI. HABS-HD's PVD patterns aligned with ADNI's, revealing distinct progression patterns: Subtype 1 was characterized by initial degeneration of the hippocampus, followed by posterior and then anterior spread; Subtype 2 began with occipital atrophy, progressing to parietal regions; Subtype 3 was characterized by initial frontal degeneration, with subsequent spread to posterior regions. The table 1. Shows significant differences in HABS-HD's distinct demographic and clinical features ( p  < 0.05), where Subtype 3 exhibits the most severe cognitive impairment and represents the oldest age group. Similarity of disease progression patterns between HABS-HD and ADNI supports generalizability of AD subtypes to diverse populations, highlighting the subtypes' potential to advance clinical trials and precision medicine for neurodegenerative disorders.

Depression is associated with a higher risk for developing Alzheimer's Disease (AD) [1], but the mechanisms that underlie the complex relationship between depression and AD remain largely elusive. The hippocampus is a region of the brain that is commonly affected by both AD and depression and serves as a focal point for our study. We aim to understand the relationship of 1) depression and antidepressant medications to hippocampal subfield volume during normal aging, and 2) whether AD risk, as measured by amyloid and tau pathology and APOE4 status, impacts these relationships. We studied 2009 ethno-racially diverse cognitively unimpaired older adults aged 50 to 90 years who either had depression (n = 630) or were not depressed (n = 1379). Participants with depression were further stratified by antidepressant medication usage. High-resolution MRI scans were used to calculate hippocampal subfield volumes that included the CA1, the subiculum, and a composite region that included the CA2, CA3, and the dentate gyrus (CA23DG). Having depression was associated with a smaller CA23DG, independent of amyloid and tau pathology in the brain. Within the subgroup of participants with depression, those who used antidepressant medications had smaller CA1 and CA23DG volumes than those who did not use these medications.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based AD classifiers have been developed in Non-Hispanic White (NHW) cohorts, limiting generalizability.ObjectiveAssess ethnic differences in AD prediction using classification performance and feature importance derived from multimodal neuroimaging biomarkers across African American (AA), Hispanic, and NHW participants.MethodsSupport vector machine classifiers were applied to multimodal neuroimaging data from a multi-ethnic cohort, incorporating structural magnetic resonance imaging measures, diffusion tensor imaging metrics, and positron emission tomography-based amyloid and tau measures. Models classified cognitively unimpaired (CU) versus cognitively impaired (CI) individuals and mild cognitive impairment (MCI) versus AD dementia, with and without adjustment for age, sex, and education.ResultsClassification performance varied by ethnicity and disease stage. NHW participants showed the strongest overall performance, particularly for CU versus CI, while Hispanic participants demonstrated high sensitivity and balanced performance for MCI versus AD. AA participants exhibited lower AUC and accuracy across tasks but maintained high negative predictive value. Demographic adjustment improved performance primarily for AA and NHW participants. Feature importance analyses revealed shared and population-specific patterns: tau positron emission tomography (PET) measures, especially posterior cingulate and lateral parietal standardized uptake value ratios, consistently ranked highest for CU versus CI across groups, whereas MCI versus AD classification diverged, with amyloid PET predominating in AA participants, tau PET in NHW participants, and mixed medial temporal atrophy and white matter signatures in Hispanics.ConclusionsShared early AD neuroimaging signatures exist across ethnic groups, but biomarker importance diverges at later disease stages, underscoring the need for ethnicity-aware ML models to improve prediction and equitable clinical translation.

INTRODUCTION:Down syndrome (DS) exhibits a genetic form of Alzheimer's disease (AD). We used a blood-based proteomic algorithm to predict cognitive status, treatment responders, and change to vitamin E in DS adults from a completed clinical trial, "Vitamin E in Aged Persons with Down Syndrome," which originally showed no significant cognitive benefit using the primary endpoint cognition (Brief Praxis Test [BPT]). METHODS:Plasma and extracellular vesicle (EV; astrocytic and neuronal) biomarkers were assayed at baseline and 36 months (n = 138 each). Cognitive response was measured using combined scores from the BPT, vocabulary, and behavior and function DS tests. Support vector machine (SVM) analyses predicted diagnostic and treatment responders and change accuracy. RESULTS:SVM classified demented versus non-demented with up to 99% accuracy and predicted treatment response and changes with up to 100% accuracy in plasma and EV. DISCUSSION:Our study supports blood-based screening and precision diagnostics for AD therapy in DS.

INTRODUCTION:Dementia prevalence is rising with population aging, disproportionately affecting females and ethnically diverse groups. Physical activity (PA) may mitigate age-related cognitive and brain decline; sex- and ethnicity-specific associations remain poorly understood. METHODS:Data from the Health and Aging Brain Study-Health Disparities were analyzed from 3585 participants (63% female). PA was categorized using the Rapid Assessment of Physical Activity (RAPA) questionnaire as no/little (< 4) or moderate/high (≥ 4). Outcomes included cognitive composites, plasma biomarkers, and magnetic resonance imaging-derived hippocampal subregions and dorsolateral prefrontal cortex volumes. RESULTS:Higher PA was associated with higher global cognition and executive functions in all females and in Hispanic males (ps < 0.05), higher episodic memory and larger hippocampal volumes across participants (ps < 0.05), and lower amyloid beta 40, total tau, and larger hippocampal subregion volumes in females (ps < 0.05). CONCLUSIONS:Findings support PA as a modifiable factor associated with healthy brain aging, emphasizing the importance of incorporating sex and ethnicity into future research.

BACKGROUND:Older adults undergoing surgery frequently experience postoperative delirium and postoperative neurocognitive disorders (NCD), but the neuropathophysiology of these conditions remains obscure. A postoperative inflammatory cascade with subsequent neuronal injury is one theory that requires further investigation. We report the incidence of delirium and postoperative NCD using updated nomenclature and investigate changes in inflammatory and neuronal injury biomarkers associated with these conditions. METHODS:We performed a prospective, longitudinal, cohort study with older adults aged 60 years or more undergoing major elective noncardiac surgery. Patients completed cognitive assessments preoperatively and at 3 and 28 months postoperatively, alongside twice-daily delirium assessments during hospital admission. Plasma was obtained before surgical incision and at 30 minutes, 6, 24, and 48 hours postoperatively and assayed for cytokines (interleukin [IL]-6, IL-10, IL-18, tumor necrosis factor [TNF]α) and neuronal injury markers (neurofilament light [NfL], total Tau, and pTau181). RESULTS:We enrolled 79 patients (mean age: 69 [standard deviation {SD}: 6.5]; female: 45.6%). Thirteen patients (15.2%) experienced postoperative delirium. At 3 months, 18 of 63 (28.6%) had postoperative NCD, and at long-term follow-up, 17 of 46 (37.0%) had NCD. IL-6, IL-10, total Tau, p-Tau181, and NfL levels were 10.3, 1.9, 1.3, 1.2, and 1.6 times higher at 48 hours postsurgery compared to baseline (all P <.05). IL-18 levels were 1.1 times lower. TNFα remained unchanged. Linear mixed-effects models revealed that greater elevations in IL-6 levels were associated with increased delirium risk (β = 22.23, standard error {SE} = 9.48, P =.02) and long-term NCD (β = 12.46, SE = 6.02, P =.04). CONCLUSIONS:Using updated nomenclature, postoperative delirium and postoperative NCD affect a third of surgical patients and inflammatory and neuronal injury markers are elevated after surgical intervention. Increases in IL-6 and pTau181 at the time of surgery are associated with postoperative delirium; increases in IL-6 are also associated with long-term NCD. Specific biomarkers follow unique time courses after anesthesia and surgery.

Dementia frequently goes undetected in community settings, particularly among socially disadvantaged populations. Here, we estimated the prevalence of underdiagnosed dementia across diverse sociodemographic determinants of health in the Health and Aging Brain Study-Health Disparities (HABS-HD), a community-based cohort of adults recruited through community outreach in Fort Worth, Texas. We estimated age-specific probabilities of underdiagnosis using Poisson regression models with a log link, including age and sex as covariates. Robust (sandwich) variance estimators were used to obtain standard errors and 95% confidence intervals (CI). Group differences or trends for continuous measures were assessed using robust variance estimates. The prevalence of underdiagnosed dementia was higher among individuals without physician access (98.1% vs. 78.1%, p<.0001), non-English speakers (97.9% vs. 76.8%, p<.0001), and the uninsured (91.5% vs. 79.5%, p=.03). Black and Hispanic participants also showed higher prevalence (85.8% and 90.9%) compared to non-Hispanic White participants (64.9%; p=.02 and p=.002, respectively). Each additional year of education was associated with a 2.5% lower risk of underdiagnosis (p<.0001). No differences were observed by sex, marital status, income or social support. Our results highlight that several sociodemographic factors contribute to the likelihood of living with undiagnosed dementia.

Importance:Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective:To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources:PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection:Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis:Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures:Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results:This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I2 = 80%). Conclusions and Relevance:In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Alzheimer's disease (AD) is defined by its characteristic neuropathological changes, which allow for diagnosis and assessment of severity. Recently, the Alzheimer's Association proposed a framework to stage AD biologically based on tau-PET. Furthermore, the framework hypothesizes a degree of alignment between biological AD severity and clinical symptom severity. We aimed to investigate the concordance between clinical and biological stages of AD and explore factors contributing to discordance using in vivo and post-mortem neuropathological data. Data from 768 amyloid-β-positive individuals were drawn from four observational cross-sectional in vivo cohorts (TRIAD, ADNI, HABS-HD and SCAN) in addition to a post-mortem autopsy dataset from the National Alzheimer's Coordinating Center (NACC; n = 3188). All in vivo participants had tau-PET imaging, clinical diagnosis and neurobehavioural assessments. Participants were assigned a biological AD stage based on their tau-PET scan according to the Alzheimer's Association revised criteria stages. The autopsy dataset included individuals with moderate-to-frequent neuritic plaques (CERAD scores 2-3), along with pre-mortem clinical and neurobehavioural data. Clinical-biological concordance was quantified using squared-weighted Cohen's κ. Ordinal and linear regression models assessed associations between biological stage and clinical severity (Clinical Dementia Rating-Sum of Boxes, Mini-Mental State Examination), adjusting for age, sex and cohort. Post-mortem analyses evaluated the impact of comorbid neuropathologies on clinical-biological discordance using adjusted odds ratios and ordinal regression. Overall concordance between clinical and biological AD staging was moderate (Cohen's κ = 0.52, P < 0.001). Approximately 70% of individuals classified as cognitively unimpaired or with dementia exhibited biological stages consistent with their clinical diagnoses. In contrast, transitional decline and mild cognitive impairment groups were more heterogeneous. Notably, 25% of amyloid-β-positive individuals with mild cognitive impairment demonstrated no detectable tau-PET abnormality. Nonetheless, advanced tau-PET stage was reliably associated with clinical impairment. In the NACC autopsy dataset, nearly all individuals with a more severe clinical stage than their proposed biological stage exhibited comorbid neuropathologies, including frontotemporal lobar degeneration (FTLD)-TDP-43, FTLD-tau, Lewy bodies, limbic-predominant age-related TDP-43 encephalopathy (LATE) and cerebrovascular disease. The number of comorbid pathologies was strongly associated with increased odds of clinical dementia (t = 8.45, P < 0.001). Although there is moderate agreement between clinical and biological stages of AD across the entire disease spectrum, strong agreement is found in clinically unimpaired and dementia stages. Comparison of clinical and biological AD stages provides a framework for understanding the large contributions of non-AD neurodegenerative diseases to dementia in amyloid-β-positive individuals. Our results have important implications for clinical trial recruitment strategies and highlight the urgent need for biomarkers for non-AD pathological processes.

Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self-reported sex, educational attainment and race/ethnicity on amyloid-positivity, and tau-PET-positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Logistic generalized estimating equations were used to estimate frequency of amyloid-positivity (using cohort-specific thresholds for amyloid-PET [84%] or CSF) and tau-PET-positivity (cohort-specific thresholds of 2SD above mean temporal uptake in amyloid-negative controls). We assessed: i) sex and APOEε4 ( N  = 10,098) associations, to complement earlier findings of a higher frequency of tau-positivity in females, ii) effects of lower/higher education ( N  = 10,970; cohort-specific median-split), and iii) effects of race/ethnicity (non-Hispanic White [hereafter: White], N  = 4880; Asian, N  = 116; Black or African-American [hereafter: Black], N  = 353; Hispanic, N  = 356, only from Northern-American cohorts). Outcomes were frequency of amyloid-positivity in CU individuals only, and tau-PET-positivity in both amyloid-positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau-positivity were assessed and only retained in the models when significant. Female sex was associated with an APOEε4 -independent increased frequency of amyloid-positivity (β=0.51[0.22], p  = 0.02) in CU and increase of tau-positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p  <0.01). Remarkably, tau-positivity frequencies of female APOEε4 non-carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid-positivity frequency (β=-0.12[0.05], p  = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau-positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p  <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non-White groups compared to White (Hispanic:β interaction =-0.05[0.01], p  <0.01; Black:-0.04[0.01], p  <0.01; Asian:-0.02[0.01], p  = 0.04). This suggests that the impact of age on amyloid-positivity was less pronounced in non-White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau-positivity frequency than White (β=0.51[0.22], p  = 0.02; Figure 3). In this multi-center initiative comprised of clinical and community-based cohorts, we observed that self-reported sex, educational attainment and race/ethnicity were related to positivity-frequencies of Alzheimer's disease pathology.

INTRODUCTION:It is unknown if neurodegeneration trajectories differ between Down syndrome (DS) and autosomal dominant Alzheimer's disease (ADAD), both of which are genetic forms of Alzheimer's disease (AD). METHODS:We compared brain volumes in DS, ADAD, and unaffected family members serving as controls. Participants underwent magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET), deriving volumetric and amyloid burden, respectively. Nonlinear associations between regional volumes and estimated years to clinical symptom onset (EYO) were evaluated using generalized additive mixed-models. RESULTS:Longitudinal data from 267 controls, 341 participants with DS, and 358 participants with ADAD were included, totaling 1908 scans. DS volumes were lower than ADAD and controls initially and dropped linearly. ADAD had similar volumes to controls until diverging, beginning at EYO -7. Amyloid was negatively associated with volume, with similar slopes in DS and ADAD. DISCUSSION:ADAD and DS demonstrate distinct patterns of brain volume decline prior to symptom onset despite being similarly affected by amyloid.

Homozygous APOE- ɛ4 carriers have exhibited heightened white matter hyperintensity (WMH) burden based on severity ratings (Rojas et al., 2018). A higher high-density lipoprotein cholesterol (HDL-c) to low-density lipoprotein cholesterol (LDL-c) ratio is associated with less severe WMHs (Wei et al., 2023). Additionally, higher concentrations of APOE protein are linked to higher cholesterol efflux in Alzheimer's Disease cohorts (Yassine et al., 2016). Despite the understanding of APOE as a lipid carrier (F. Yin, 2021), its mechanistic role in modulating dementia risk is still evolving. Here, we examine whether APOE- ɛ4 positivity modifies the relationship between blood cholesterol levels and WMH volume in a multi-ethnoracial cohort. We examined 1645 cognitively unimpaired (CU) individuals from the Health and Aging Brain Study-Health Disparities cohort (65.96% female, 25.6% APOE- ɛ4+, aged 50-90) (Table 1). Participants underwent a MR scanning (Siemens 3T Skyra or Vida), which included a T2 FLAIR image. We calculated WMH volume using SPM's lesion growth algorithm (LGA) and ran robust linear regressions to test for an interaction between log-transformed blood cholesterol levels and APOE- ɛ4 status on log-transformed WMH volume. Associations between cholesterol markers and WMH volume also were separately examined in APOE- ɛ4 carriers and non-carriers. Covariates included age, sex, years of education, intracranial volume, MRI scanner, body mass index, diabetes, and hypertension. All continuous variables were standardized. We corrected for three comparisons (HDL-c, LDL-c, triglycerides) using the false discovery rate method. There was a significant HDL-c× APOE- ɛ4 interaction on WMH volume (β= -0.10, p -corrected= 0.03) in the fully-corrected model driven mainly by a non-significant association between higher HDL-c and lower WMH volume in APOE- ɛ4 carriers (β= -0.10, p -corrected= 0.17) only. Interactions between APOE 4 carrier status and LDL-c and triglycerides were not significant. APOE- ɛ4 moderated the relationship between HDL-c and WMH volume such that greater HDL-c levels were associated with lower WMH burden in ɛ4 carriers only. APOE- ɛ4 did not interact with LDL-c or triglycerides on WMH volume. This investigation provides support for investigating HDL-c further in the context of brain health in ɛ4 carriers.

INTRODUCTION:Individuals with Down syndrome (DS) face high risk for Alzheimer's disease (AD), yet presymptomatic detection of cognitive decline is hindered by lifelong intellectual disability. METHODS:Using data from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS), blood samples from 246 participants were analyzed, yielding 404 longitudinal observations (45 Converters, 359 Stable) collected at 0, 16, and 32 months were analyzed. A Support Vector Machine was trained on 25 plasma biomarkers spanning neurodegeneration, inflammation, and vascular health, along with demographic factors (age, sex, ethnicity, karyotype, apolipoprotein E [APOE ε4]). Batch-effect correction and feature selection were applied, resulting in 13 key markers. RESULTS:The refined model achieved 92.4% sensitivity, 59.9% specificity, and an area under the curve (AUC) of 77.9%, accurately identifying individuals at risk of cognitive decline up to 16 months before clinical progression. DISCUSSION:This multi-domain, blood-based machine learning approach demonstrates that plasma biomarkers are valuable non-invasive tools for early detection and risk stratification of cognitive decline in DS.