Alzheimer's Research & Therapy

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.

BackgroundAlzheimers disease (AD) is a neurodegenerative disorder marked by cognitive decline, memory impairment, and functional deterioration. Its complex pathogenesis involves factors such as amyloid plaques, tau tangles, neuroinflammation, and synaptic dysfunction, but the precise mechanisms remain unclear, hindering effective treatment. Genetic, environmental, and lifestyle factors contribute to AD risk, yet their interactions are poorly understood. Recent advances in transcriptomics and metabolomics have shed light on the molecular underpinnings of AD, with gene expression alterations and metabolic disruptions implicated in disease progression. These multi-omics disruptions highlight the need for integrative analytical approaches to better characterize AD-relevant biology and advance biomarker discovery. ObjectivesTo integrate genetically imputed whole blood transcriptomics and plasma metabolomics to predict cognitive performance (PACC3) and to identify risk genes and metabolites contributing to prediction, thereby characterizing molecular signatures associated with cognitive performance in AD. MethodsThis study applies a machine learning algorithm to integrate genetically imputed whole blood transcriptomics and measured plasma metabolomics data to predict cognitive performance, as measured by PACC3 score, using data from the Wisconsin Registry for Alzheimers Prevention (WRAP) cohort (N = 1,046). After training a machine learning model on WRAP, the predictive performance was evaluated using an independent dataset from the Wisconsin Alzheimers Disease Research Center (ADRC) cohort (N = 85). Feature importance was assessed to identify genes and metabolites that may play a role as potential risk factors in AD. ResultsThe machine learning model achieved a normalized root mean squared error (NRMSE) of 0.743 {+/-} 0.037 and an R{superscript 2} of 0.311 {+/-} 0.016 across 5-fold holdout test folds in WRAP (p = 5.93 x 10-30), and an NRMSE of 0.915 and an R{superscript 2} of 0.061 when applied to the Wisconsin ADRC cohort. Feature importance revealed transcriptomic biomarkers such as RIPK1, IL6ST, and BIN1 whose higher imputed expression levels were associated with poorer cognitive performance whereas other potential biomarkers including UGP2, NDUFB5, and TMOD2 were associated with better cognitive performance, reflecting mitochondrial energy metabolism and molecular processes associated with cognitive resilience. Several predictive metabolites including benzoate, 3-phenylpropionate, and imidazolelactate also mapped to AD vulnerability signatures, while acyl-carnitine species such as hexanoylcarnitine (C6) and propionate-related metabolites aligned with metabolic resilience. ConclusionIntegrated analysis of transcriptomics and metabolomics demonstrated potential utility for identifying candidate biomarkers associated with cognition in AD. Genes and metabolites reflecting inflammatory signaling, mitochondrial dysregulation, and lipid metabolism emerged consistently among the most influential contributors. These findings align with well-established AD vulnerability pathways and highlight convergent biology across two omics layers. Collectively, this supports the value of multi-omics integration for improving molecular characterization of AD and advancing biomarker prioritization for future mechanistic and translational studies.

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.

Background and ObjectivesAssociations of common infections with Alzheimer disease (AD) risk have been reported. A hypothesized mechanism to explain these is cerebral amyloid-beta (A{beta}) aggregation as a defence in response to infection, with subsequent tau accumulation. However, few studies have assessed associations of infections with tau and A{beta} pathology. We investigated associations of serological measures of several common infections with plasma p-tau217 and A{beta} status measured by neuroimaging in the 1946 British birth cohort. MethodsCirculating antibodies against 14 pathogens, measured at age 60-64 years, were modelled as pathogen serostatus (indicating lifetime exposure to an agent), pathogen burden indices (measuring cumulative exposure to 2+ pathogens), and seroreactivity tertiles (indicating recent immunological activity against pathogens). Associations of these were tested with plasma p-tau217 (primary outcome) and A{beta} status measured by positron emission tomography imaging (A{beta}-PET; secondary outcome), measured approximately 7 years after serology measurements. Modelling used multivariable quantile and logistic regression, respectively. Model 1 adjusted for sex and ages at serology and outcome assessment, models 2 and 3 additionally adjusted for APOE {varepsilon}4 carriage and education, respectively. We also tested for interactions in associations with APOE {varepsilon}4 carriage and education, and for interactions between herpes simplex virus 1 (HSV1) exposure with both cytomegalovirus (CMV) and varicella zoster virus (VZV) exposure. Results1356 and 424 individuals had complete data for p-tau217 and A{beta}-PET analyses, respectively. Mean age at p-tau217 was 69.9 years (SD 0.7) and 51.3% of participants were female. No notable associations were observed for either outcome in main models, with the exception being an unexpected relationship between seropositivity for herpes simplex virus 2 and lower p-tau217 at the 75th quantile. There was also some evidence for potential interactions in p-tau217 associations by APOE {varepsilon}4 carriage (for Helicobacter pylori and CMV) and by educational attainment (for Helicobacter pylori serostatus). DiscussionThese findings are not supportive of associations between exposures to many common infections and aggregation of core AD neuropathology measures. The possibility that some pathogens might interact with APOE {varepsilon}4 carriage and education in relation to AD neuropathology warrants further study.

Background and ObjectivesAlzheimers disease (AD) is characterized by early disruptions in brain connectivity. However, how genetic and biological markers of AD risk relate to dynamic functional connectivity (dFC) remains unclear. This study examined whether AD-related pathology, genetic risk, and blood-based biomarkers (BBMs) of neurodegeneration are associated with local and distant resting-state dFC patterns, and whether these relate to cognitive performance in cognitively normal older adults. Research Design and MethodsWe analyzed baseline data from 86 cognitively normal older adults (71.6 {+/-} 3.9 years; 60.5% female) enrolled in the AGUEDA trial (NCT05186090). Participants underwent A{beta}-PET imaging, APOE4 genotyping, and plasma quantification of BBMs (A{beta}42/40, BD-tau, GFAP, NfL, p-tau181, p-tau217). Resting-state fMRI was used to compute voxel-wise local and distant dFC using a stepwise connectivity framework. General linear models tested associations between AD pathology, APOE4 status, and BBMs with dFC, adjusting for age, sex, and education. Additional models examined links between dFC and six cognitive domains ResultsA{beta}-positive individuals and APOE4 carriers showed lower local connectivity in frontal regions, while APOE4 carriers exhibited higher distant connectivity in the superior motor area, inferior frontal gyrus, and anterior insula. Among BBMs, only neurofilament light chain (NfL) was associated with both lower local (insula, cingulate) and higher distant (precuneus, putamen, thalamus, supramarginal, superior motor area) connectivity. Regions showing higher distant connectivity related to APOE4 or NfL were associated with poorer cognitive performance. Discussion and ImplicationsDynamic functional connectivity reveals early network alterations in AD risk, characterized by reduced local and elevated distant connectivity--patterns linked to poorer cognition and potential early neurofunctional vulnerability in aging.

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.

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.

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.

Individuals who carry two copies of the apolipoprotein E {varepsilon}4 (APOE{varepsilon}4) allele are at high risk of developing Alzheimers disease (AD), yet the effects of APOE {varepsilon}4 homozygosity on biological pathways related to AD over the lifespan are unknown. Here we analyzed the plasma proteomes of APOE {varepsilon}4/{varepsilon}4 individuals with and without AD-related cognitive impairment (n=413) and compared them to the proteomes of cognitively unimpaired individuals with APOE {varepsilon}3/{varepsilon}3 genotype (n=2764) from ages 20 to 90. Multiple biological pathways were altered in young adulthood in {varepsilon}4 homozygotes including metabolism and glucagon-like peptide 1/insulin growth factor (GLP-1/IGF), mitochondrial, microtubule, proteostasis, and synaptic pathways. Semaglutide--a GLP-1 receptor agonist--demonstrated reversal effects on metabolic and synaptic pathway alterations in {varepsilon}4 homozygotes at preclinical and clinical AD stages. Targeting metabolic and other pathways for therapeutic intervention in {varepsilon}4/{varepsilon}4 individuals by at least age 50 will likely be the most effective approach to decrease risk for AD in this special population.

BackgroundNeurodegenerative diseases, including Alzheimers disease (AD), exhibit substantial clinical and molecular heterogeneity, complicating accurate diagnosis and development of effective therapies. Although multi-omics profiling provides unprecedented molecular resolution, systematic integration of high-dimensional, imbalanced data modalities with disease-relevant biological networks remains a methodological challenge. MethodsWe developed a network-informed multi-omics integration framework that combines data-driven molecular networks with brain transcriptomic, proteomic, and metabolomic data from 356 participants in the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP). Utilizing 25 functional, data-driven multi-omics groups (DAD-MUGs) derived by graph embedding from the AD Atlas, co-expression-guided feature extraction and systematic two-phase feature balancing were applied to derive representative molecular features, which were subsequently learned using DAD-MUG-specific autoencoders to generate compact multi-omics expression scores. These were then used to identify molecular subgroups via hierarchical clustering. Subgroup robustness was assessed in an independent ROS/MAP cohort (n=327) using a two-round nested classification strategy. ResultsSubgroup identification based on DAD-MUG-derived expression scores resulted in five molecular subgroups exhibiting significant differences in cognitive performance and core neuropathological measures. Cross-validated nested classification using transcriptomic and proteomic data demonstrated reliable discrimination of subgroups. Applying these classifiers to the replication cohort, subgroup-trait association patterns showed strong agreement with discovery findings (Spearman {rho} = 0.65). Differential expression analysis further revealed stage-dependent biological patterns of brain pathologies, ranging from early synaptic and immune activation to mitochondrial bioenergetic dysfunction at disease transition and proteostatic impairment in advanced stages. ConclusionUsing a balanced, network-informed multi-omics integration framework, we identified five molecular subgroups of brain aging, including a reference control subgroup and a distinct mixed subgroup characterized by amyloid, vascular pathology, and early-life adversity. Three additional subgroups formed a structured spectrum comprising molecularly Alzheimers-like but cognitively and neuropathologically unimpaired At-risk controls, an intermediate stage, and typical Alzheimers disease, with tau pathology differentiating advanced disease, underscoring the value of molecular subgroup identification beyond clinical diagnosis.

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.

INTRODUCTIONThe pathophysiology and risk factors for Alzheimers disease (AD) and dementia are insufficiently known. We studied the connections between gut microbiome, overall dementia and AD in a prospective, population-based cohort. METHODSWe followed a population based random sample of 4,055 individuals (FINRISK 2022) for 16 years, with 330 cases of incident dementia and 280 AD cases. Gut microbiome community diversity and composition were assessed against future dementia and AD risk. Competing mortality risks were accounted for using Fine-Gray models. RESULTSCommunity diversity was not associated with dementia or AD. However, a supervised ordination with dbRDA suggested a possible compositional link between gut microbiome and dementia. One putative bacterial genus, Dorea, was associated with a decreased dementia risk. APOE {varepsilon}4 genotype associated with several taxa; of these, phylum Verrucomicrobiota and species Nocardia carnea were associated with incident dementia. DISCUSSIONThe gut-brain axis has a modest association on future dementia or AD risk. Microbial composition, rather diversities, may contribute to dementia risk.

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.

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.

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.

We examined whether a 24-week resistance training program influenced brain amyloid-{beta} (A{beta}) and Alzheimers Disease (AD)-related blood-based biomarkers. Ninety cognitively normal, physically inactive older adults aged 65-80 years were randomly allocated to a 24-week resistance training program (three [~]60-min supervised sessions/week) or a wait-list control group. Primary analyses assessed exercise-induced changes in brain A{beta} (Centiloid values) and plasma ptau217/A{beta}1-42 IPMS ratio. Secondary analyses examined ptau217/A{beta}42 SIMOA ratio, ptau217, ptau181 and A{beta}42/40, as well as potential interactions with sex, age, education, apolipoprotein {varepsilon}4 (APOE4) status, amyloid PET-positivity, and comorbidities. The intervention produced no significant differences on brain A{beta} or AD-related blood-based biomarkers (p>0.05) compared to the control group. However, the ptau217/A{beta}1-42 IPMS ratio showed a small, non-significant increase in the control group (SMD = 0.162; 95% CI: -0.159 to 0.483) while remaining stable in the exercise group (SMD = 0.01; 95% CI: -0.291 to 0.310) with a similar trend for ptau217/A{beta}42 SIMOA. Moderator analyses indicated differential responses by amyloid PET-positivity and APOE4 status on brain A{beta} (p for interaction<0.05), with increases observed in APOE4 carriers and amyloid PET-positive individuals in the control group, whereas those allocated to the exercise intervention reduced their levels. The specificity observed within our subgroups suggests that resistance exercise may serve as a targeted intervention to modulate AD pathophysiology, raising new questions regarding its broader role in the delay of the disease in vulnerable populations.

BackgroundGenome-wide association studies (GWAS), with independent replication in large European consortia, have identified a common nonsense variant in IL-34 (Y213X) as a genetic risk factor for late-onset Alzheimers disease (AD). However, the biological consequences of this IL-34 mutation in humans, its prevalence in the population, and the mechanisms by which IL-34-Y213X alters microglial homeostasis, cerebrospinal fluid (CSF) proteomic networks, and amyloid pathology remain poorly understood. MethodsWe combined human genetics, cerebrospinal fluid (CSF) and serum proteomics, large-scale phenome-wide association analyses, and preclinical experimental models to define the impact of human IL-34 deficiency. IL-34 concentrations were first quantified in CSF and serum from deeply phenotyped AD cohorts stratified by the common IL-34-Y213X nonsense variant. IL-34 levels and IL-34-Y213X status were then integrated with unbiased CSF proteomic networks and AD biomarkers. Using complementary mouse models of IL-34 loss in an APP/PS1 transgenic background, we examined the effects of IL-34 deficiency on microglial survival, tiling, and plaque encapsulation. Finally, we performed postmortem analyses of temporal cortex from AD patients carrying IL-34-Y213X to assess microglial density, spatial organization, and plaque-associated responses. FindingsIL-34-Y213X was a strong, dose-dependent loss-of-function allele that reduced IL-34 levels by up to 2.5 standard deviations in CSF and serum and was common in multiple populations. IL-34 deficiency reshaped CSF proteomic networks, downregulating axon guidance and microglial support modules while upregulating inflammatory and extracellular matrix signatures, and showed pleiotropic associations with neurological, inflammatory, and metabolic traits. In APP/PS1 mice, genetic IL-34 deletion selectively depleted homeostatic gray-matter microglia, disrupted microglial tiling, and impaired plaque encapsulation, resulting in altered amyloid structure and enhancing neuritic injury. Concordantly, AD patients homozygous for IL-34-Y213X displayed markedly reduced cortical microglial density and increased microglial spatial dispersion, indicating a breakdown of the microglial network organization in the human brain. InterpretationA common human IL-34 loss-of-function variant creates a naturally occurring model of IL-34 deficiency that links microglial survival, CSF network signatures, and amyloid pathology in both mice and humans. These findings position IL-34/CSF1R signaling as a critical determinant of microglial resilience in AD and highlight IL-34-dependent pathways as potential targets for disease modification.

Blood-based biomarkers have emerged as a promising tool for the detection and monitoring of neurodegenerative diseases such as Alzheimers disease (AD), yet broad implementation of ultrasensitive protein quantification remains constrained by reliance on specialized instrumentation and centralized laboratory infrastructure. Here we present SPLASH (Solid Phase Ligation Assay with Single wasH), an ultrasensitive proximity ligation assay platform that achieves sub-pg/mL sensitivity using only standard benchtop qPCR equipment. We developed five assays targeting Alzheimers disease biomarkers - pTau-217, A{beta}1-40, A{beta}1-42, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - with limits of detection ranging from 0.0005 to 0.119 pg/mL. Direct comparison with Simoa demonstrated high concordance (R2 = 0.95) for plasma pTau-217 quantification across AD-positive and AD-negative samples. We further established compatibility with dried plasma spot samples, enabling decentralized collection and quantitation without cold-chain storage. A multiplexed five-analyte panel was applied to 69 plasma samples, revealing heterogeneous biomarker profiles consistent with AD-associated patterns. By eliminating dependencies on proprietary instrumentation, SPLASH facilitates broad implementation of ultrasensitive protein quantification for neurodegenerative disease research and diagnostics.

IntroductionThis clinical trial investigates the efficacy and safety of a personalized 15-day accelerated intermittent theta-burst stimulation (aiTBS) protocol, targeted at either the default mode network (DMN) or the fronto-parietal network (FPN), in individuals with mild Alzheimers disease (AD). Methods45 patients with mild AD were randomized 1:1:1 to receive 15 consecutive days of high-dose aiTBS (7200 pulses/day) targeting the DMN or FPN, or sham. The primary outcome was the change in ADAS-Cog after 15 days of treatment. ResultsBoth active aiTBS groups demonstrated significantly greater ADAS-Cog improvement than sham at the primary endpoint. Response rates for a clinically meaningful improvement ([&ge;]3-points on ADAS-Cog) were significantly higher in the active groups (DMN: 38%; FPN: 47%) than in the sham group (0%). The improvement in active groups was sustained at 3-month follow-up. DiscussionPersonalized aiTBS targeting the DMN or FPN produced clinically meaningful cognitive benefits in mild AD and was safe.