Alzheimer's & Dementia: Translational Research & Clinical Interventions

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.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSINTRODUCTIONC_ST_ABSTreatment response in Alzheimers disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODSWe developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, and policy learning. We applied it to cholinesterase inhibitors (ChEIs) in MCI due to AD to patients from the NACC and ADNI cohorts. RESULTSAnalysing 4,049 patients with 12-month and 2,223 with 36-month follow-up, all estimators indicated null or negative long-term ChEI effects on cognitive and functional outcomes, notably on functional measures. ChEIs showed slightly more deleterious effects among men than women. DISCUSSIONThis framework provides a methodology for estimating HTE from observational data. It revealed no beneficial responder subgroups, highlighting the challenge of detecting treatment heterogeneity in moderately sized cohorts. This approach can inform treatment selection for other AD therapies including memantine, anti-amyloid agents, and emerging treatments.

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

INTRODUCTIONThe 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.

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

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

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.

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.

BackgroundCognitively unimpaired (CU) adults vary substantially in their risk of developing mild cognitive impairment (MCI), yet most subtyping approaches focus on downstream neurobiological or cognitive markers rather than upstream, modifiable risk factors. We aimed to identify clinically meaningful subgroups of CU adults defined by integrated comorbid, behavioral, and social risk profiles, and to evaluate heterogeneity in both incident MCI risk and cardiometabolic treatment effects. MethodsWe conducted a prospective cohort study of 121,322 CU adults aged [&ge;]50 years from the All of Us Research Program. Baseline comorbidities, lifestyle behaviors, and social determinants of health were jointly modeled using the Bayesian Mixed Integrative Data Subtyping framework, which integrates binary and continuous modalities via modality-specific likelihoods and shared latent constructs. Subtype-specific risk of incident MCI was assessed using multivariable Cox proportional hazards models adjusting for demographics and baseline medication use. A double/debiased machine learning interactive regression model with inverse probability of censoring weights to mitigate bias from informative censoring was implemented to estimate the average treatment effects of antihypertensive agents, Glucagon-Like Peptide (GLP) receptor agonists, and non-GLP antidiabetic medications on time to MCI. ResultsFour distinct subtypes were identified: I low-risk healthy aging, II behavioral/social vulnerability, III cardiometabolic-depressive multimorbidity, and IV mixed social-medical vulnerability profiles. Compared with Subtype I, Subtype III demonstrated the highest risk of incident MCI (HR: 3.69, 95% CI: 3.14-4.33), followed by Subtype IV and Subtype II. In treatment effect analyses, antihypertensive use was associated with a modest prolongation of MCI-free survival overall (time ratio:1.04, 95% CI: 1.03-1.06), with the largest benefit observed in Subtype III (time ratio: 1.14, 95% CI: 1.09-1.19). Non-GLP antidiabetic therapies were similarly associated with modest overall delay, with significant benefits in Subtypes I and III. GLP-class therapies were not associated with overall delay but showed a significant association in Subtype III. ConclusionsIntegrative subtyping based on comorbid, behavioral, and social risk factors reveals clinically meaningful heterogeneity in both cognitive risk and treatment response. Aligning dementia prevention strategies with dominant vulnerability pathways may enhance the effectiveness and equity of population-level precision prevention.

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.

INTRODUCTIONDementia reflects vascular and neurodegenerative processes in late life, yet studies often examine risks and outcomes individually. This study tested whether the cumulative burden of risks relates to structural brain pathology and cognition, and whether brain markers mediate these associations. METHODSCross-sectional data were drawn from 38,414 older adults in the National Alzheimers Coordinating Center database. A composite score summed ten binary risk factors: hypertension, diabetes, hypercholesterolemia, alcohol misuse, smoking, depression, obesity, hearing loss, vision loss, and low education. Outcomes included white matter hyperintensities (WMH), infarcts, hippocampal atrophy, global cognition, cognitive status, delayed recall, and semantic fluency. RESULTSHigher burden was associated with poorer global cognition, greater clinical severity, worse memory and fluency, and higher odds of WMHs, infarcts, and hippocampal atrophy. Structural equation models identified hippocampal atrophy as the primary mediator, with smaller effects for WMHs and infarcts. DISCUSSIONFindings support multidomain prevention strategies targeting clustered modifiable risks.

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.

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.

ObjectiveTo evaluate risk of dementia after cancer diagnosis among Medicaid beneficiaries with HIV. DesignLongitudinal observational study of Medicaid enrollment, inpatient, and outpatient claims data from 14 states, 2001-2015. MethodsBeneficiaries aged 18-64 with HIV and [&ge;]6 months of enrollment were matched 1:1 on cancer status by age, sex, race, year, and state. We estimated the weighted cumulative incidence functions (CIFs) of dementia at 1, 2, and 5 years after cancer diagnosis using the Aalen-Johansen estimator to account for the competing risk of death and cluster stratified analyses to account for matching. We calculated the corresponding risk differences (RD) and 95% confidence intervals (CI) using nonparametric bootstrap. ResultsAt 5 years, the CIF of dementia was 9.6% (95%CI: 8.2, 11.6) and 4.7% (95%CI: 3.7, 6.1) among those with and without AIDS-defining cancer, respectively (RD: 4.9%; 95%CI: 2.9, 7.0). At 5 years, the CIF of dementia was 7.1% (95%CI: 5.9, 7.8) and 5.3% (95%CI: 4.2, 6.2) among those with and without non-AIDS-defining cancer, respectively (RD: 1.8%; 95%CI: 0.34, 2.9). Dementia incidence appeared higher among beneficiaries with lung cancer (2yr RD: 1.9%; 95%CI: 0.01, 5.2) and beneficiaries [&le;]50 with colon cancer (2yr RD: 4%; 95%CI: 0.3, 10.5), but lower among beneficiaries [&le;]50 with prostate cancer (2yr RD: -1.9%; 95%CI: -2.3, -1.6). Dementia incidence did not differ among beneficiaries with and without breast cancer. ConclusionsDementia risk may be increased among people with HIV with certain cancers, including AIDS-defining cancers. Dementia risk appears to vary by cancer type and age at diagnosis.

BackgroundScalable, non-invasive markers for cognitive-decline risk are limited. Olfactory dysfunction is predictive, and oral dysbiosis is mechanistically linked to neurocognitive pathways. Hence, we tested whether pairing smell and global cognition with salivary microbiome profiling yields a targeted, clinically useful signal. MethodsWe enrolled 113 Memory Center attendees and community controls. Same-day MMSE, UPSIT, and saliva were obtained for 16S rRNA gene sequencing and cytokine measurement. Unsupervised k-means clustering on standardized MMSE-UPSIT defined two groups of participants: CNN (cognitively normal, normosmia) and CIH (cognitively impaired, hyposmia). Ordination and elastic-net models adjusted for age, sex, BMI, and sequencing depth. Functions were inferred with PICRUSt2 and were integrated with taxa via DIABLO. ResultsOverall, the 16S-based microbial community structure was similar between groups, indicating minor compositional shifts. CIH showed enrichment of periodontal anaerobes (Porphyromonas, Treponema and Prevotella), whereas CNN retained nitrate-reducing commensals (e.g. Neisseria subflava, Aggregatibacter aphrophilus). Functional shifts showed mixed consistency with literature, aligning for outer membrane usher proteins and alkyldihydroxy phosphate synthase, but diverging for thiaminase, alpha-glucuronidase, and chemotaxis protein CheX. Most salivary cytokines levels did not differ between groups. ConclusionsThis integrated smell, cognition, and saliva workflow delineates an olfactory- cognitive phenotype linked to a targeted, potentially modifiable salivary dysbiosis, periodontal anaerobes vs nitrate-reducers, rather than diffuse salivary inflammatory elevation. This approach may support non-invasive triage and monitoring along the oral- brain axis, pending independent, longitudinal validation.

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.

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.

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.

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.

Until now, most genetic risk for vascular dementia (VD) remains unknown. Here, we firstly performed the largest cross-ancestry genome-wide association study meta-analysis comprising 5,886 VD and 1,027,883 controls of European, East Asian, South Asian, African, and Admixed American ancestry. We identified 37 genome-wide significant loci including CLU and APOE tagged by common variants and 35 loci tagged by rare variants, and demonstrated enrichment of VD heritability in lung and genetic association between VD and lung function traits. We further conducted a cross-trait of VD and Alzheimers disease, and identified 13 genome-wide significant loci including CR1, BIN1, GRM7, HLA-DRA, TREM2, CLU, ECHDC3, AGBL2, MS4A4E, PICALM, SLC24A4, ABCA7, and APOE. A multi-omics integrative analysis identified 619 genes. 241 genes were significantly differentially expressed in VD cells and 21 exhibited strong evidence of interaction with FDA-approved drugs. Collectively, our findings provide valuable insights into the potential underlying mechanisms of VD.