ImportanceAlzheimer disease biomarkers in cognitively unimpaired older adults are associated with later cognitive and clinical decline, yet substantial heterogeneity in the timing and rate of decline remains insufficiently characterized.ObjectiveTo identify subgroups of cognitive decline among biomarker-defined cognitively unimpaired adults and determine baseline predictors of heterogeneity in preclinical Alzheimer disease progression.Design, Setting, and ParticipantsLongitudinal data were drawn from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Study, which enrolled amyloid-positive participants, and the parallel LEARN Study, which enrolled amyloid-negative individuals meeting all other A4 criteria. Participants completed baseline amyloid PET, plasma P-tau217, structural MRI, and serial cognitive assessments. Latent Class Mixed-Effects Models (LCMMs) were used to identify distinct cognitive trajectory classes. Associations between class membership and demographic, clinical, and biomarker characteristics were evaluated.Main Outcomes and MeasuresThe primary outcome was longitudinal change in the Preclinical Alzheimer’s Cognitive Composite (PACC).ResultsThree cognitive trajectory classes were identified: stable, slow decliners, and fast decliners. Higher plasma P-tau217, smaller hippocampal volume, and elevated tau PET were associated with greater odds of belonging to declining classes. Among amyloid-positive individuals, approximately 70% were classified as stable over the observed follow-up interval.Conclusions and RelevanceLatent class modeling reveals marked heterogeneity in preclinical cognitive trajectories, even among individuals with biomarker evidence of Alzheimer pathology. The high proportion of stable individuals is consistent with the long presymptomatic interval. Identifying subgroups of decline may improve prognostic modeling and guide enrichment strategies for precision secondary prevention trials.

Alzheimer's disease (AD), the most common form of dementia, is typically diagnosed using the AT(N) framework, which assesses amyloid, tau, and neurodegeneration biomarkers. However, this framework has limitations, particularly in predicting cognitive decline among amyloid-positive individuals. This study explores the synergistic potential of combining small RNA biomarkers with established protein markers to improve the early detection of AD. Using data from 1,913 participants aged 50+ from the European Prevention of Alzheimer's Dementia (EPAD) clinical trial, all free of dementia at enrollment, we performed ultra-deep small RNA sequencing on whole blood samples. A refined sequencing protocol reduced erythroid RNA interference, enabling the identification of rare biomarker RNAs. We define high and low amyloid groups based on a cutoff on the p-tau181/Ab1-42 ratio as determined from cerebrospinal fluid. Analysis revealed small RNAs that predicted early cognitive decline (Clinical Dementia Rating of 0.5) with an AUC of ∼0.7, which increased to 0.77 within the high-amyloid subgroup. Functional evaluation linked these RNAs to dementia-relevant pathways, including neuronal, cardiovascular, and inflammatory activities. Our findings highlight that combining small RNA and amyloid protein markers significantly outperforms either biomarker type alone, offering a more robust and precise approach to early AD detection. This integrated strategy addresses current gaps in the AT(N) framework, enhancing stratification and enabling more effective interventions. Small nucleolar RNAs and microRNAs emerge as promising candidates for further exploration as part of this synergistic diagnostic model.

Clinical detection of amyloid-positive individuals is generally not possible without expensive biomarkers. This results in delays in diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment due to AD (MCI-AD) reducing the window for treatment with amyloid-lowering therapies and missed opportunities for enrollment into clinical trials. 887 individuals in the Bio-Hermes Study (Global Alzheimer's Platform Foundation) completed Cognivue Clarity, amyloid PET, and pTau217. The 4-level Cognivue Amyloid Risk Measure (CARM) was derived using a machine learning paradigm. We developed a rapid screening paradigm for MCI-AD and AD. The cohort had a mean age of 71.8±6.7y, 15.5±2.7y of education, 56.4% female, 37.3% ApoE e4 carriers, and was 78.8% non-Hispanic White. The clinical-pathological diagnoses were 297 True Controls, 91 Preclinical AD, 111 MCI-AD, 171 MCI-non-AD, 130 AD dementia, and 87 non-AD dementia. Amyloid PET SUVR (p<.001) and pTau217 (p<.001) levels were significantly different by Cognivue Clarity thresholds. Amyloid positivity increased across the 4 CARM thresholds (p<.001). Combining Cognivue Clarity global scores and CARM created a 2x2 paradigm of Impaired/Not Impaired, and Low/High risk of amyloid with significant differences in Amyloid PET SUVR (p<.001) and pTau217 (p<.001). The majority of MCI-AD, and AD dementia individuals were in the Cognivue Impaired, CARM 3/4 category. Most True Controls were in the Cognivue Not-Impaired, CARM 1/2 category. Non-AD cases were scattered across all 4 categories. Preclinical AD individuals had lower Cognivue Clarity global scores than True Controls (p<.001) and were largely CARM 3/4. Cognivue Clarity, a 10-minute computerized battery, can detect individuals with cognitive impairment and with the CARM can identify individuals likely to have amyloid positivity. To further increase the efficiency and cost-effectiveness of cognitive screening, a staged screening approach likely makes the most sense. Cognivue Clarity global scores establish whether there is cognitive impairment, and in the same sitting CARM predicts the likelihood of amyloid. This could be followed by measuring a readily accessible AD biomarker such as plasma pTau217. Such a strategy would increase the likelihood of identifying early AD for treatment or trial enrollment, avoiding the cost of expensive PET scans in a time- and cost-effective fashion.

Blood-based biomarkers (BBMs) demonstrate high accuracy in detecting Alzheimer disease (AD) pathological changes in symptomatic individuals. In autosomal dominant AD and in individuals with Down syndrome, both populations with near-universal development of AD pathology, elevations in BBMs are detectable years before clinical onset, supporting their utility for identifying preclinical disease in these cases. Among BBMs, plasma phosphorylated tau 217 (p-tau217) exhibits strong concordance with established in vivo markers of AD pathology. However, its ability to identify older adults without cognitive impairment who are amyloid-positive remains variable across studies and settings. To assess the standardized effect size of mean differences and classification accuracy of p-tau217 for published studies that compared amyloid-positive and amyloid-negative older adults without cognitive impairment. PubMed, Embase, and EBSCOhost databases from inception to September 1, 2025. Observational studies or randomized clinical trials with baseline data on individuals without cognitive impairment who were classified as either amyloid positive or amyloid negative and reported numeric data for p-tau217 levels. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) reporting guideline was used for this study. Two authors independently carried out literature searches to identify studies with older adults without cognitive impairment who were classified as either amyloid positive or amyloid negative where p-tau217 was quantified. The standardized mean difference (Hedges g) was used to characterize differences in mean p-tau217 levels. A pooled area under the curve (AUC) value was used to summarize the diagnostic accuracy of p-tau217 in identifying amyloid-positive individuals. Between-study heterogeneity was investigated using subgroup and sensitivity analyses. Publication bias was assessed using Egger tests. Data for 7834 participants (2533 amyloid positive, 5301 amyloid negative) from 18 publications were analyzed. A large effect size was observed for p-tau217 (Hedges g = 1.50; 95% CI, 1.33-1.68). Values for p-tau217 also demonstrated high accuracy for identifying amyloid-positive individuals without cognitive impairment (AUC = 0.87; 95% CI, 0.85-0.90). These findings demonstrate that plasma p-tau217 can reliably detect AD pathology in the preclinical stage. These findings support the clinical utility of plasma p-tau217 as a scalable, minimally invasive tool for early identification of AD, particularly in settings where timely intervention with disease-modifying therapies may offer the greatest benefit in slowing or preventing disease progression.

There are 14 modifiable factors that are associated with a significantly lower risk of dementia. We tested the interactive effect of modifiable factors, genetic determinants, and initial pathologic burden on the spatial progression and local amplification of tau pathology. Methods: In total, 162 amyloid-positive individuals were included, for whom longitudinal [18F]AV-1451 PET scans, baseline information on global amyloid burden, ApoE4 status, body mass index (BMI), hypertension, education, neuropsychiatric symptom severity, and demographic information were available in the Alzheimer Disease Neuroimaging Initiative. All [18F]AV-1451 scans were intensity-standardized (reference: inferior cerebellum), z-transformed (control sample: 147 amyloid-negative subjects), thresholded (z score, >1.96), and converted to volume maps. Longitudinal tau changes were then assessed in terms of tau spatial extent (i.e., newly affected volume at follow-up) and tau level rise (i.e., tau increase in previously affected volume). These 2 measures were entered as dependent variables in linear mixed-effects models, including baseline modifiable risk factors (BMI, education, hypertension, neuropsychiatric symptom severity), global amyloid, tau volume or tau burden, ApoE4 status, clinical stage, sex, and age as predictors. Next, we tested the interactive effects between baseline amyloid or tau burden with the 4 modifiable factors on tau extent or tau level rise, respectively. Results: Greater tau extent was linked to higher BMI (β = 0.002; 95% CI, 0.0003-0.003), ApoE4 status (β = 0.024; 95% CI, 0.001-0.046), and baseline tau volume (β = 0.207; 95% CI, 0.107-0.308) across groups. In terms of tau level rise, we observed that absence of hypertension (β = 0.295; 95% CI, -0.477 to -0.114), dementia group (β = 0.305; 95% CI, 0.088-0.522), and BMI (β = 0.011; 95% CI, 0.00004-0.022) were linked to increased tau burden. A load-dependent effect of baseline amyloid and tau volume/burden was found for both tau extent (β = -0.005; 95% CI, -0.008 to -0.002) and tau level rise (β = -0.003; 95% CI, -0.005 to -0.001). Higher amyloid and BMI (β = 0.001; 95% CI, 0.0004-0.001) and lower education and higher tau burden (β = -0.035; 95% CI, -0.064 to -0.006) were linked to greater tau level rise. Conclusion: Education, BMI, and hypertension differentially influence tau's spatial extent and increase by its interaction with initial pathologic burden. Timely modification of these factors may overall slow tau progression.

Background and ObjectivesInsulin resistance is emerging as a modifiable risk factor for Alzheimer’s, though its impact on grey matter volume across clinical stages remains poorly understood. The objective of the research is to investigate how insulin resistance affects grey matter integrity across the Alzheimer’s disease continuum using structural MRI.MethodsImaging, clinical, and metabolic data were extracted from 374 non-diabetic participants within the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Participants were classified as cognitively impaired (CI: n=186; 137 mild cognitive impairment, 49 early-to-moderate dementia; all AD biomarker positive) or cognitively unimpaired (CU: n=188; 122 amyloid-negative, 66 amyloid-positive). Insulin resistance was assessed at the time of MRI and clinical evaluation using the dichotomized triglyceride-glucose index (TyG). The Interactions between TyG and diagnostic group on grey matter volume were investigated using both voxel-wise and region-of-interest (ROI) based analyses, adjusted for age, sex, education, vascular risk factors, and global cognitive performance across the AD continuum.ResultsInsulin resistance significantly impacted gray matter volume across the AD continuum, demonstrating stage-dependent effects. In early AD disease stages, insulin resistance was associated with lower grey matter volume in fronto-parietal regions, a finding that extended to several cortical areas in CI individuals. Temporal and fronto-limbic regions were particularly highlighted by the IR-diagnosis interaction. In amyloid-positive CU individuals, IR was linked to bilateral temporal atrophy, in contrast to amyloid-negative CU participants.DiscussionThis study underscores the impact of insulin resistance on brain structure across the AD continuum, particularly within key vulnerability areas characteristic of AD pathology. These findings highlight the need for future research into potential therapeutic strategies targeting insulin signaling to mitigate neurodegeneration in AD.

White matter hyperintensities contribute to early cortical thinning in addition to tau in aging.

Alzheimer disease and its clinical variants have characteristic spatial and temporal progression patterns of amyloid and tau driving symptomatology, but the distribution of microglia density, as measured by 18kDa translocator protein (TSPO) PET, is unknown. Baseline TSPO, amyloid, and tau PET as well as T1 MRI from the longitudinal imaging of microglial activation in different clinical variants of Alzheimer disease study were adjusted for age, sex, body mass index, APOE4 status, TSPO genotype, and intracranial total volume. Imaging outcomes were standardized against controls, visualized across the brain, and placed along a pseudo-longitudinal timeline using disease duration. Microglia density follows the spatial distribution of tau in amyloid-positive individuals and that of neurodegeneration in amyloid-negative individuals. The magnitude, location, and timing of elevated microglia density relative to amyloid, tau, and neurodegeneration is specific to different clinical subtypes of Alzheimer disease.

To determine how 18kDa Translocator (TSPO) PET, which measures inflammatory alterations, is associated with domain-specific cognition in the presence and absence of elevated amyloid. This cross-sectional study uses data collected on 46 adults aged 50 and over who underwent TSPO PET (11C-ER176 SUVR) and amyloid PET (18F-Florbetaben SUVR) as well as medical, neurological, and neuropsychological assessments. Controls (n=21) were amyloid-negative and cognitively unimpaired while individuals with Alzheimer disease or related dementias (ADRD; n=25) were cognitively impaired, regardless of amyloid positivity. Cognition was assessed using the Mini-Mental State Examination (MMSE) and domain-specific tests included in the NACC UDSv3. Greater TSPO is associated with lower performance in episodic memory, attention/processing speed, executive function, language, and visuospatial ability in amyloid-positive individuals, but restricted to episodic memory in amyloid-negative individuals. Targeting microglia as an additional or alternative strategy for cognitive impairment in Alzheimer disease and related dementias may provide further benefits, even in amyloid-negative individuals.

While tau pathology is closely associated with neurodegeneration in Alzheimer's disease (AD), our prior work using multi-modality imaging revealed that mismatch between tau (T) and neurodegeneration (N) may reflect contributions from non-AD processes. The medial temporal lobe (MTL), an early site of AD pathology, is also a common target of co-pathologies such as limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), often following an anterior-posterior atrophy gradient. Given the susceptibility of MTL to co-pathologies, here we explored T-N mismatch specifically within MTL using plasma ptau 217 and MTL morphometry for identifying vulnerabilities and resilience in cognitively impaired or unimpaired AD patients. We parcellated the MTL into 100 spatially contiguous segments and calculated their T-N mismatch using plasma ptau 217 as a measure for T and thickness as a marker of N. Based on these mismatch profiles, we clustered 447 amyloid-positive individuals from ADNI cohort into data-driven T-N phenotypes. We characterized the T-N phenotypes by examining their cross-sectional and longitudinal atrophy both within the MTL and across the whole brain, as well as cognitive trajectories. This framework was replicated in an independent cohort and finally translated to a real-world clinical sample of 50 patients undergoing anti-amyloid therapy. Clustering identified three T-N phenotypes with different MTL T-N mismatch profiles, atrophy patterns, and cognitive outcomes, despite comparable AD severity. The "canonical" group, characterized by low T-N residuals (N ∼ T), showed AD-like neurodegeneration patterns. The "vulnerable" group, characterized by disproportionately greater neurodegeneration than tau (N > T), showed atrophy primarily in the anterior MTL that extended into temporal-limbic regions, both in cross-sectional and longitudinal analyses. This group also exhibited neurodegeneration that preceded estimated tau onset and experienced faster cognitive decline across multiple domains, aligning with the typical characteristics of mixed LATE-NC with AD. In contrast, the "resilient" group (N < T) showed minimal atrophy and preserved cognitive function. These phenotypes were reproducible in an independent research cohort. Importantly, in a feasibility study applying the model developed from ADNI to a clinical cohort of patients receiving lecanemab, we identified vulnerable individuals with LATE-like atrophy patterns. This highlights its potential utility for identifying individuals with co-pathology in clinical settings. Our findings demonstrate that T-N mismatch within MTL using MRI and plasma biomarkers can reveal AD groups with varying vulnerability/resilience, with the vulnerable group displaying structural and cognitive outcomes suggestive of LATE-NC. This approach offers a cost-effective strategy for clinical trial stratification and precision medicine for AD therapeutics.

Association of microplastics in human cerebrospinal fluid with Alzheimer's disease-related changes.

Alzheimer's disease (AD) progression varies widely among individuals. Identifying factors influencing timing of pathology and clinical progression is crucial for optimizing early intervention trials. To investigate how estimated age at amyloid and tau PET positivity, and the time interval between amyloid and tau PET positivity ("amyloid-tau interval"), relate to symptom onset and clinical progression, and whether APOE- ε4 status and sex modify these associations. Longitudinal observational study. Multicenter study using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI participants with at least one positive amyloid PET scan (n=792) or at least one positive tau PET scan (n=212) were included. Both cognitively unimpaired and impaired individuals were included and all had information on sex, APOE- ε4 status, and longitudinal cognitive assessments. 18 F-florbetapir or 18 F-florbetaben amyloid PET, 18 F-flortaucipir tau PET, CDR global, CDR-SB. We examined the influence of APOE -ε4 status, sex, and their interaction on the age at biomarker positivity and the amyloid-tau interval. Accelerated Failure Time (AFT) models were used to predict time to symptom onset based on biomarker positivity age and the amyloid-tau interval. Linear mixed-effects (LME) models evaluated differences in the rate of cognitive decline over five years following symptom onset, by biomarker positivity age and amyloid-tau interval duration. Additional models included interaction terms with sex or APOE -ε4 status. APOE- ε,4 carriers and women had earlier amyloid and tau PET positivity ages. APOE- ε,4 carriers, women, and those with older age at amyloid PET positivity had a shorter amyloid-tau interval. An older age at amyloid or tau PET positivity and a shorter amyloid-tau interval predicted earlier symptom onset. An older age at amyloid or tau PET positivity was also associated with slower rate of cognitive decline after symptom onset. The amyloid-tau interval did not influence time until symptom onset after tau PET positivity or rate of cognitive decline. APOE- ε4 and sex influenced the timing of amyloid and tau PET positivity and the amyloid-tau interval, which in turn affected symptom onset and clinical progression. These factors should guide the identification of amyloid-positive individuals at highest risk of rapid AD progression, enabling more efficient selection of participants for clinical trials.

To evaluate the predictive utility of baseline plasma biomarkers and neuropsychological measures in identifying cognitively unimpaired older adults at risk of cognitive and functional decline over five years. The clinical and biological heterogeneity observed in Alzheimer's disease (AD) complicates design of trials and the identification of appropriate participants. Identifying practical tools to define more homogenous subgroups could enhance clinical trial enrichment and improve early detection. We analyzed data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) trial and its companion Evaluation of Amyloid Risk and Neurodegeneration (LEARN) observational study. The sample included 866 cognitively unimpaired, amyloid-positive individuals from the A4 trial (comprising participants randomized to receive Solanezumab or placebo) and 343 cognitively unimpaired, amyloid-negative individuals from LEARN. Cognitive/functional decline was defined as an increase of ≥0.5 in Clinical Dementia Rating-Global Score (CDR-GS) during a 240-week period. Using multiple logistic regression models, we evaluated the predictive value of demographic variables, APOE4 status, amyloid PET SUVR, plasma P-tau217, and Alzheimer's Disease Cooperative Study-Preclinical Alzheimer's Cognitive Composite (ADCS-PACC) in three groups: A4-Solanezumab, A4-placebo and LEARN. In a sub-study including 656 participants with available data, we assessed the incremental value of additional plasma biomarkers (Aβ42/Aβ40 ratio, GFAP, and NfL). Both plasma P-tau217 and ADCS-PACC significantly improved predictive performance over a base model with demographics and APOE4. The full model combining all predictor variables yielded the highest AUCs across A4 Solanezumab (0.80 ± 0.06), A4 Placebo (0.80 ± 0.06), and LEARN (0.78 ± 0.08). Adding other plasma biomarkers yielded small but consistent improvements in AUC (1-3%). Plasma P-tau217 and ADCS-PACC, individually and in combination, improved prediction of cognitive/functional decline in asymptomatic older adults. Predictive models incorporating these scalable and non-invasive measures improved clinical trial enrichment and earlier identification of at-risk individuals preclinical AD.

This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural information available in multi-shell diffusion MRI (dMRI) data, using a vision transformer-based deep learning framework. We present a classification pipeline that employs the Swin Transformer, a hierarchical vision transformer model, on multi-shell dMRI data for the classification of Alzheimer's disease and amyloid presence. Key metrics from DTI and NODDI were extracted and projected onto 2D planes to enable transfer learning with ImageNet-pretrained models. To efficiently adapt the transformer to limited labeled neuroimaging data, we integrated Low-Rank Adaptation. We assessed the framework on diagnostic group prediction (cognitively normal, mild cognitive impairment, Alzheimer's disease dementia) and amyloid status classification. The framework achieved competitive classification results within the scope of multi-shell dMRI-based features, with the best balanced accuracy of 95.2% for distinguishing cognitively normal individuals from those with Alzheimer's disease dementia using NODDI metrics. For amyloid detection, it reached 77.2% balanced accuracy in distinguishing amyloid-positive mild cognitive impairment/Alzheimer's disease dementia subjects from amyloid-negative cognitively normal subjects, and 67.9% for identifying amyloid-positive individuals among cognitively normal subjects. Grad-CAM-based explainability analysis identified clinically relevant brain regions, including the parahippocampal gyrus and hippocampus, as key contributors to model predictions. This study demonstrates the promise of diffusion MRI and transformer-based architectures for early detection of Alzheimer's disease and amyloid pathology, supporting biomarker-driven diagnostics in data-limited biomedical settings.

Cognitive resilience refers to maintaining cognitive function despite Alzheimer's disease (AD) pathophysiology. We analyzed amyloid-positive individuals across clinical stages of AD in two cohorts: the Amsterdam Dementia Cohort (ADC, N=1036) and Alzheimer's Disease Neuroimaging Initiative (ADNI, N=685). Cognitive resilience was conceptualized from a canonical correlation analysis of magnetic resonance imaging and neuropsychological data in each cohort separately. Model validation involved education as a resilience proxy and key genetic factors (apolipoprotein E [APOE] ε4 and APOE ε2) of AD. We explored associations between 83 AD risk loci and cognitive resilience. Resilience was correlated with education (ADC: β=0.144, p<0.001; ADNI: β=0.149, p<0.001) and APOE ε4 (βmeta-analysis=-0.052, p=0.014). Exploratory single nucleotide polymorphism meta-analysis identified potential involvement of genetic variants around genes UNC5CL, USP6NL, and TPCN1 in lower, and genes COX7C and MINDY2 in higher resilience. Our novel resilience approach showed conceptual validity and potential for future discovery of resilience-related genetic variants. ·We define a novel approach to resilience using canonical correlation analysis (CCA). ·Apolipoprotein E ε4 is linked to lower resilience, suggesting increased vulnerability. ·Genetic loci around COX7C and MINDY2 are potentially involved in higher resilience. ·This novel approach may be used for multi-cohort studies such as genome-wide association studies in the future.

The independent contributions of baseline and longitudinal tau positron emission tomography (PET) and magnetic resonance imaging (MRI) to cognitive decline remain unclear. We included n=761 amyloid-positive individuals from the Swedish BioFINDER-2 study with [18F]RO948-tau-PET, 3-Tesla structural-MRI, and cognition (n=322 with longitudinal imaging data). Linear-mixed-models with random-intercepts and -slopes or linear-regressions were adjusted for age, sex, education, diagnosis, and other-imaging-modality. Tau-PET showed stronger associations with cognitive decline than MRI, showing the strongest associations in a neocortical-composite-region with a cognitive composite (β=-0.25±0.02, p<0.001) for baseline and longitudinal tau-PET (β=-0.62±0.05, p<0.001). Baseline tau-PET explained the largest proportion of cognitive decline (54.0%-94.0%), with modest mediation effects for longitudinal tau-PET or MRI pathways (2.0%-15.0%). Simulated reductions of tau-PET-slopes (up to 100%) were associated with marginally altered cognitive trajectories. The strong associations between baseline tau-PET and longitudinal cognition, with marginal contributions of longitudinal tau-PET and MRI, emphasize the importance of baseline tau aggregates for prognostics and treatments in Alzheimer's disease (AD). Baseline and longitudinal regional tau-PET uptake were more closely associated than structural MRI with longitudinal cognitive decline. Baseline tau-PET was a stronger determinant of longitudinal cognitive decline than longitudinal tau-PET. Simulated reductions of tau-PET accumulation showed limited alterations of cognitive trajectories, with potential implications for tau-targeting therapies.

18F-PI-2620 is a newer tau-PET tracer with minimal off-target binding in thechoroid plexus. Defining tau cut-points to differentiate between cognitively unimpaired andimpaired individuals is crucial for both biomarker validation and clinical use, but researchusing18F-PI-2620 is limited. In 675 participants (mean age: 64, 64% Female, 186Hispanic, 209 non-Hispanic Black, and 280 non-Hispanic White) from the Health and Aging BrainStudy-Health Disparities, we used the area under the receiver operating characteristic curve toidentify a region of interest and corresponding cut-point at which18F-PI-2620standardized uptake value ratio best distinguished between amyloid negative cognitivelyunimpaired and amyloid-positive cognitively-impaired individuals. The regional or compositestandardized uptake value ratio that maximized sensitivity and specificity (measured by theYouden index) was selected as the best-performing region of interest and was further evaluatedusing Gaussian mixture modeling. We evaluated the performance of the chosen region of interestand cut-point in the three ethnoracial groups. The best-performing region of interest was themedial temporal composite, with a cut-point of 1.26. This region performed well in Hispanic andnon-Hispanic White subgroups, but not in the non-Hispanic Black subgroup. The data show theutility of this region to identify clinically relevant levels of tau. Future work shouldexplore the relationship of tau to comorbid conditions across ethnic and racial groups.

Depressive/anxiety symptoms are common in subjective cognitive decline (SCD) and may relate to Alzheimer's pathology, potentially modulated by personality characteristics. Depressive/anxiety symptoms were assessed over 4 ± 2 years in 329 SCD (88 amyloid-positive/241 amyloid-negative) using Geriatric Depression Scale-15 (GDS), Center for Epidemiological Studies-Depression (CES-D), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Mixed-effects models assessed associations between amyloid status and these symptoms, with neuroticism and somatization as effect-modifiers. Amyloid status was not directly associated with GDS, CES-D or HADS-A. However, neuroticism modified the association between amyloid status and GDS (p<0.05). In lower neuroticism, amyloid positivity was associated with GDS increase (β:0.10 ± 0.08), but not in higher neuroticism (β:-0.04 ± 0.12). Somatization modified the association between amyloid status and CES-D (p<0.05). In lower somatization, amyloid positivity was associated with CES-D increase (β:0.65 ± 0.23), but not in higher somatization (β:-0.12 ± 0.29). Amyloid-positive individuals with lower neuroticism/somatization increased more in depressive symptoms over time, suggesting a preclinical AD-related depressive phenotype.

Background and ObjectivesThe amyloid cascade hypothesis posits that Alzheimer disease (AD) progresses from amyloid deposition to tau deposition, neurodegeneration, and eventually cognitive impairment and is the foundation of the revised criteria of Alzheimer's Association Workgroup 2024 (AA-2024). To account for copathologies and cognitive resilience that affect the penetrance of the AD cascade, AA-2024 introduced a 2-dimensional biological-clinical staging framework. We aimed to estimate the proportion of persons along the AD continuum whose biological and clinical trajectories align with the amyloid cascade.MethodsCross-sectional data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were tested in the 4 × 4 biological/clinical staging matrix adapted from the AA-2024 criteria. Biological stages were defined by amyloid and tau-PET burden: stage A (amyloid positivity, A+), stage B (medial temporal tau, A+/T2MTL+), stage C (moderate neocortical tau, A+/T2MOD+), and stage D (high neocortical tau, A+/T2HIGH+). Clinical stages were cognitively unimpaired (stage 1), subtle cognitive impairment (stage 2), mild cognitive impairment (stage 3), and dementia (stages 4–6). Tau-PET cutoffs were defined through the implementation of 5 distinct methods. Participants were categorized into (1) compliant with the amyloid cascade (matrix diagonal), (2) resilient (advanced biological stage—early clinical stage), and (3) copathologic (early biological stage—advanced clinical stage). Observed distributions were compared with hypothetical scenarios with zero and high amyloid cascade penetrance using the χ2 test, and differences among the 5 methods were tested using the Cochran Q test.ResultsTwo-hundred and fifty-six amyloid-positive individuals (mean age: 72.7 years; 51% female) from the ADNI cohort were considered. The proportion of participants compliant with the amyloid cascade was between 31% (95% CI 25%–37%) and 36% (95% CI 30%–42%) depending on the tau-PET cutoff method. The observed number of individuals compliant with the amyloid cascade was higher than in the zero-penetrance scenario but lower than in the high-penetrance distribution (p < 0.01). The proportion of copathologic (17%–63%) and resilient (6%–52%) individuals varied widely by tau-PET cutoff (p < 0.001).DiscussionOnly approximately one-third of persons with an AA-2024 diagnosis of AD complied with the predictions of the amyloid cascade hypothesis. These results suggest the heterogeneity in how clinical symptoms and pathology are coupled along the AD continuum, which has significant implications for interpreting completed antiamyloid clinical trials and designing future studies.

Females have an increased risk of developing Alzheimer's disease (AD). The innate immune system plays a key role in AD pathology, and sex differences in innate immune responses may contribute to differences in disease risk and progression. This study investigated sex differences in innate immune responses among participants without cerebrospinal fluid (CSF) determined amyloid pathology [A-; cognitively normal (CN), n = 83] and those with amyloid pathology (A+, n = 202), further stratified into preclinical (CN with A+, n = 72) and mild cognitive impairment (MCI with A+, n = 130). Participants were drawn from the Norwegian Dementia Disease Initiation cohort (n = 285). We measured plasma glial fibrillary acidic protein (GFAP) and CSF concentrations of nine innate immune markers: soluble triggering receptor expressed on myeloid cells 2 (sTREM2), monocyte chemoattractant protein 1 (MCP-1), fractalkine, chitinase 3-like 1 (YKL-40), clusterin, interferon gamma (IFN-γ), interleukin-6 (IL-6), IL-10, and IL-18. Linear regression was used, adjusted for multiple comparisons using the false discovery rate. In A+ cases (n = 202, females = 105), females had lower MCP-1 (P < 0.01), IL-6 and IL-18 (both P < 0.05) than males, while no sex differences were observed in A- cases (n = 83, females = 39). Among A+ participants, no sex differences were observed in CN cases (n = 72, females = 37), but females (n = 68) with MCI had lower MCP-1 and IL-6 (both P < 0.05) than males (n = 62) with MCI. Moreover, A+ females exhibited stronger positive associations between sTREM2 and clusterin with CSF total tau (P < 0.001; P < 0.05) and Neurofilament light chain (P < 0.01; P < 0.01) than males. These findings suggest sex-specific differences in innate immune responses, which may contribute to disease progression in amyloid-positive individuals.