Alzheimer's Research & Therapy

Understanding tau pathology progression across the Alzheimers disease (AD) continuum is critical for diagnosis and stratification. This study examined how age of onset and disease stage influence regional tau deposition using [{superscript 1}F]RO948-PET, and its relationship with plasma biomarkers, cognition, and cortical atrophy. In total, 57 participants underwent tau-PET, MRI, blood sampling, and neuropsychological testing: 39 patients with MCI (A{beta}-/A{beta}+) or AD, and 18 cognitively normal controls. The MCI A{beta}+ and AD groups were further divided into early-onset (EOAD, <65y) and late-onset (LOAD, >65y) subgroups. MCI A{beta}+ patients showed early tau accumulation in medial-temporal regions, extending to inferior-temporal cortex. MCI-EOAD exhibited more advanced neocortical tau binding, while MCI-LOAD showed intermediate lateral temporal involvement. In AD, EOAD patients had higher parietal tau burden than LOAD. Plasma biomarkers (p-tau181, p-tau217, p-tau231, GFAP, NFL) were elevated in MCI A{beta}+ and AD. Plasma p-tau217 showed strong correlations with tau-PET in medial and inferior temporal regions, with weaker correlations in neocortical areas. EOAD showed non-linear tau-PET/p-tau217 associations, contrasting with LOADs linear pattern. Tau-PET was negatively correlated with global cognition and executive function, while p-tau217 better reflected early episodic memory decline. Both tau measures correlated with cortical thinning, especially in the entorhinal cortex. These findings highlight [{superscript 1}F]RO948-PETs sensitivity in detecting early tau pathology and superiority in capturing individual differences in tau burden, particularly in advanced stages where plasma biomarkers plateaued. Tau-PET demonstrated superior resolution of disease progression and individual variability, reinforcing its value as a prognostic biomarker and a critical tool for patient stratification in clinical trials. One Sentence Summary[{superscript 1}F]RO948 tau-PET detects early tau pathology and onset-related patterns, outperforming plasma biomarkers in tracking Alzheimers progression.

ImportanceSome individuals are capable of tolerating Alzheimer disease neuropathological changes (ADNC) without manifesting clinical symptoms. Elucidating the neuropathological, and molecular mediators may facilitate the identification of more accurate in vivo biomarkers and inform the development of targeted therapeutic strategies. ObjectiveTo investigate cellular distribution of tau pathology, microglial responses, and gene expression profiles associated with divergent clinical outcomes (dementia vs. no dementia) in individuals exhibiting comparable ADNC. Design, Setting and ParticipantsWe analyzed postmortem brain tissue from 97 participants from the ROSMAP study:49 with high likelihood of AD (25 demented [demented AD] and 24 cognitively normal [resilient]), and 48 with low likelihood (22 demented [impaired-other (IMP-O)] and 26 cognitively normal [control]). Cases were matched for age, gender, and co-pathologies. Main outcomes and measuresAmyloid-{beta} plaques, phospho-tau pathology (tangles and tau-positive neurites), tau oligomers in synaptic-fractions, tau seeding activity, neurons, synapse density, and astrocyte and microglia activation were quantified. The relationships with bulk and microglia-specific RNA-sequencing were also examined. Statistical analyses employed ANOVA with Tukeys HSD/Bonferroni corrections for pathological and clinical variables, and the Wald test for differential gene expression ResultsDemented AD and resilient brains exhibited comparable tau tangle and amyloid-{beta} plaques; however, demented AD showed higher total pTau burden (tangles and tau-positive neurites) (Mean[SD], 27.39%[21.89%] vs. 10.60%[14.71%]; p= 0.0004) and elevated pTau oligomer levels in synaptic-enriched fractions (0.48[0.52] vs. 0.16[0.19]; p=0.0010). Both demented AD (0.21x107[0.14x107]; p<0.0001) and IMP-O (0.62x107 [0.42x107]; p<0.0001) showed greater synaptic loss than resilient (1.54x107[0.55x107]; p=0.0008) and controls (2.92x107[1.13x107]). CD68+ microglia burden was increased in demented (1.14%[0.27%]; p<0.0001) and IMP-O (0.97%[0.36%]; p<0.0001) but not in resilient (0.72% [0.17%]; p=0.2835) compared to controls (0.59%[0.17%]). Synaptic pTau oligomers and CD68+ microglia were the strongest correlates with antemortem cognition. Resilient brains exhibited downregulation of neuroinflammation-related genes and possessed a distinct microglial subpopulation supporting resilience, characterized by overexpression of CD83, DUSP1, and NAMPT. Conclusion and relevanceOur findings suggest that aberrant accumulation of tau in neurites and synapses, rather than tangles within neuronal soma, may trigger a microglial pro-inflammatory activation linked to synaptic loss and impaired cognition. Cell-specific transcriptomic analysis identified a distinct microglial cell subpopulation associated with resilience to ADNC.

BackgroundMild cognitive impairment (MCI) precedes Alzheimers disease (AD) in [~]40% of cases, with early language deficits distinguishing converters. This study develops a DTI radiomics model from language network gray matter to predict MCI to AD conversion and identify preclinical biomarkers. MethodsThis retrospective case-control study analyzed diffusion tensor imaging (DTI) data from 97 individuals with MCI (29 converters, 68 non-converters) from the Alzheimers Disease Neuroimaging Initiative (ADNI). Ethical approval and participant consent were obtained by ADNI. Radiomic features were extracted from fractional anisotropy (FA) and mean diffusivity (MD) maps within language network gray matter. A logistic regression model using eleven selected features performed classification. Performance was evaluated using area under the receiver operating characteristic curve (AUC). Radiomic-cognitive associations were analyzed using Pearson correlations; group differences were assessed with Fishers r-to-z transformation. ResultsThe model achieved cross-validation AUC = 0.84 and test AUC = 0.83. SHAP analysis identified two top predictors: lower right temporal pole original_glcm_Correlation_FA and higher right frontal orbital cortex original_glszm_SmallAreaHighGrayLevelEmphasis_FA. Right frontal orbital cortex original_glszm_SmallAreaHighGrayLevelEmphasis_FA correlated positively with ADAS-Q4 in non-converters (r = 0.27, p < 0.001) but negatively in converters (r = -0.48, p < 0.001). ConclusionsA DTI radiomics model achieved AUC = 0.83 for predicting MCI to AD conversion, with bilateral language network microstructural features showing group-specific cognitive associations, supporting their potential as early Alzheimers risk biomarkers. Key pointsO_LINew method identifies Alzheimers risk before significant cognitive decline occurs C_LIO_LIBrain language regions show detectable changes in future converters C_LIO_LITexture analysis reveals early disease signatures in brain tissue C_LI

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.

Background and ObjectivesBlood-based biomarkers (BBMs), especially tau phosphorylated at threonine 217 (p-tau217), offer a minimally invasive approach to diagnose Alzheimers disease (AD) with strong potential for clinical implementation. However, the robustness and transferability of diagnostic thresholds across sites remain uncertain. This study aimed to evaluate BBMs ability to predict amyloid and tau status in a deeply phenotyped AD research cohort and to test p-tau217 cutoffs across diverse clinical populations and analytical instruments. MethodsWe included four Western European cohorts (total n=411): an exploration cohort from Brussels with amyloid and tau-positron emission tomography (PET) data (n=215), and clinical validation cohorts from Paris (memory clinic, n=117, and monogenic FTLD, n=43) and Bordeaux (early-onset neurodegenerative disorders, n=36). BBMs concentrations (p-tau217, p-tau181, NfL, BD-tau, A{beta}42, A{beta}40) were measured. Of these, p-tau217 was measured at each site using three Lumipulse analyzers (G600II in Brussels and Bordeaux; G1200 in Paris). Amyloid status was determined by PET or CSF A{beta}42, and tau status by tau-PET or CSF p-tau181. ResultsPlasma p-tau217 outperformed other BBMs, and their related ratios. In the exploration cohort, p-tau217 closely related to amyloid and tau PET, clearly separating A{beta}+ from A{beta}- individuals (AUC=0.96; optimal cutoff 0.193 pg/mL with [~]92% sensitivity/specificity) and tracking tau-PET Braak stages (AUC=0.93-0.98). Using amyloid-derived thresholds, p-tau217 detected medial temporal and neocortical tauopathy with >90% sensitivity and specificity at stricter dual 95-97.5% sensitivity/specificity cutpoints (0.142/0.256 pg/mL and 0.110/0.319 pg/mL). In validation cohorts, the 0.193 pg/mL cutoff accurately discriminated clinical-biological AD from non-AD in routine care (AUC=0.98; [~]93% sensitivity/specificity), in early-onset dementia (AUC=0.94; >92% sensitivity/specificity), and in the monogenic FTLD cohort (89% specificity). In routine care, gray zones (8-25%) were largely resolved with second-line CSF testing (>75%). Elevated p-tau217 was rare in non-AD, mainly in FTLD-ALS and some older individuals with possible AD copathology. p-tau217/A{beta}42 and p-tau217/A{beta}42/A{beta}40 ratios added no diagnostic value. Discussionp-tau217 thresholds maintained high diagnostic accuracy (>90%) across independent sites, analytical platforms, and various clinical situations, supporting their robustness and transferability. These observations support implementing p-tau217 as a reliable, scalable test for detecting AD pathology, for diagnostic work-ups of patients with objective cognitive impairment, not for general population screening.

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.

INTRODUCTIONSleep disturbances are prevalent in patients with Alzheimers disease (AD) and may emerge before overt clinical symptoms. We characterized sleep alterations in cognitively unimpaired older adults with cerebral amyloid deposition, and assessed their associations with regional amyloid deposition, cognitive and psychoaffective outcomes. METHODSSeventy-six older adults (69.1 {+/-} 3.4 years, 63.2% female) underwent a multi-night (4.5 {+/-} 0.8 nights) objective sleep assessment using the Somno-Art(R) wearable device, Florbetapir-PET scanning, and an extensive neuropsychological and psychoaffective evaluation. RESULTSAmyloid {beta} (A{beta})-positive individuals had a shorter total sleep time (TST) and greater night-to-night variability in rapid eye movement (REM) sleep duration than A{beta}-negative individuals. Across the whole sample, these sleep characteristics were associated with increased A{beta} deposition in widespread brain regions, but not with cognitive or psychoaffective measures. DISCUSSIONShorter sleep duration and greater REM sleep variability may index early AD-related brain changes, warranting longitudinal studies to establish their prognostic significance. Age-Well randomized clinical trial of the Medit-Ageing European Project. Trial registration number: EudraCT:2016-002,441-36; IDRCB:2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819

Early-onset Alzheimers disease (EOAD) and Late-onset AD (LOAD) differ in clinical presentations and rates of progression. We aimed to compare baseline and longitudinal tau PET burden, and their relationship with clinical variables in amyloid-PET positive, cognitively impaired participants from the Longitudinal Early-Onset Alzheimers Disease Study (EOAD; n=390) and Alzheimers Disease Neuroimaging Initiative (LOAD; n=211). Patients with EOAD showed higher baseline tau PET retention, broader neuroanatomical involvement and faster accumulation rates over time compared to LOAD, after adjusting for amyloid load and clinical stage. Tau PET showed stronger correlations with baseline amyloid burden and clinical measures of global cognition and function in EOAD than LOAD. We conclude that earlier age of onset in AD is linked to a more aggressive tauopathy, which in turn is a primary driver of clinical decline. These findings suggest that optimal therapeutic targets and strategies may differ between EOAD and LOAD. One Sentence SummaryYounger patients with Alzheimers disease show more aggressive tau spread, suggesting age of onset defines distinct disease pathways with key clinical implications.

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.

INTRODUCTIONGrowing evidence supports a critical role of sleep slow waves (SW) in Alzheimers disease (AD). However, wake SW (sleep-like SW potentially reflecting local intrusions of sleep) remain unexplored in AD. METHODS274 older adults with subjective cognitive decline (INSIGHT-preAD cohort, 76.6 {+/-} 3.5 years) underwent i) PET scans for amyloid (A) and neurodegeneration (N), ii) high-density resting-state EEG recordings to detect wake SW, and iii) cognitive assessments. Biomarkers were reassessed two years later. We examined wake SW associations with 1) current A/N status, 2) cognition, and 3) amyloid conversion. RESULTSA+N-, A-N+ and A+N+ individuals exhibited lower delta wake SW density than A-N- participants. Wake SW amplitude 1) was higher in A+N+ than A-N- individuals, 2) correlated with poorer cognition, and 3) predicted A- to A+ conversion. DISCUSSIONWake SW represent promising early EEG biomarkers for AD pathology and amyloid conversion, facilitating risk stratification before cognitive decline onset.

INTRODUCTIONDementia clinical risk scores (CRS) provide accessible tools for identifying individuals at risk for Alzheimers disease (AD), yet their performance across diverse populations and relationships to AD endophenotypes remains unclear. METHODSWe evaluated four CRS, mCAIDE, WHICAP, LIBRA, and CogDRisk, in relation to cognitive impairment diagnoses and endophenotypes for AD, including plasma biomarkers, neuroimaging measures, and cognitive composite scores. Logistic and linear regression models stratified by self-reported race/ethnicity were used to assess the associations of CRS with diagnosis and their predictive performance, and associations with endophenotypes. RESULTSHigher CRS were consistently associated with increased odds of dementia across all races/ethnicities. CogDRisk showed the strongest and most consistent performance across diagnostic and endophenotypic outcomes. The other three CRS performed similarly, with mCAIDE performing the worst and lacking associations with plasma biomarkers. CONCLUSIONSCRS capture AD-related risk across diverse populations and modestly reflect underlying biological endophenotypes, supporting their utility in community-based risk assessment.

IntroductionPhosphorylated tau-217 (p-tau 217) is widely used as a plasma-based biomarker for Alzheimers Disease (AD) detection, demonstrating superior accuracy for detecting brain amyloid pathology. However, 30-50% of patients fall within an intermediate diagnostic "gray zone" where biomarker results are indeterminate, often decreasing physician confidence and requiring subsequent diagnostic workup. To address this, we developed a two-stage machine learning algorithm GRAD: Gatekeeper & Reflex for Alzheimers Disease to increase clinical confidence and reduce the AD health economic burden. MethodsWe initially analyzed 320 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI) with plasma biomarkers and amyloid PET. We then built a two-stage machine learning classifier mimicking real clinical workflow where the stage 1 Gatekeeper used the gold-standard marker: p-tau217 with respective 25%/75% probability thresholds. The stage 2 Reflex step applied Random Forest multi-marker classification (p-tau 217, AB42/40, NFL, GFAP) for difficult-to-diagnose gray zone cases. To ensure statistical robustness, leave-one-out cross-validation with bootstrap confidence intervals was used. We externally validated the GRAD architecture on 1,644 A4 Study participants, with MRI enhancement analysis in 1,044 gray zone cases. To measure cost-effectiveness we compared our GRAD-staged testing to universal PET. ResultsThe models Gatekeeper resolved 55.6% of ADNI cases with 88.8% accuracy (NPV 91.8%, PPV 85.0%). The complete pipeline achieved AUC 0.867 (95% CI: 0.825-0.904), with 80.6% sensitivity, 80.0% specificity, LR+ 4.03, LR-0.24. For the difficult-to-diagnose gray zone cases, the Reflex machine learning model achieved AUC 0.755. In our A4 validation, the predictions correlated strongly with centiloid (r= 0.693). Expanding beyond plasma biomarkers, MRI integration improved gray zone classification from AUC 0.829 to 0.853 (p=0.014). The cost modeling analysis projected a 67% reduction in spending versus the current standard of universal PET. DiscussionOur clinically-staged diagnostic algorithm, GRAD, provides actionable classifications for the majority of patients while routing uncertain cases for additional workup. The GRAD framework offers a practical, cost-effective approach for implementing plasma biomarkers in clinical practice. Future iterations of this framework, with integration of novel biomarkers like MTBR-tau243 present a significant opportunity to alleviate the AD health-economic burden and eliminate expensive but unnecessary diagnostic measures. HighlightsO_LIGRAD: Two-stage "Gatekeeper + Reflex for Alzheimers Disease" algorithm resolves indeterminate plasma p-tau217 or gray zone patients with AUC of 0.755. C_LIO_LIOverall AUC of 0.867 (95% CI: 0.825-0.904) validated via leave-one-out cross-validation C_LIO_LIExternal validation in A4 Study demonstrates strong correlation with amyloid burden (r=0.693) C_LIO_LIMRI volumetric integration provides significant incremental value ({Delta}AUC=+0.025, p=0.014) C_LIO_LIProjected 67-71% cost reduction compared to universal PET screening C_LI Research in ContextO_ST_ABSLiterature ReviewC_ST_ABSWe searched PubMed, Google Scholar, and medRxiv databases for studies up to December 2025 that examined plasma p-tau217 diagnostic accuracy as well as "gray zone" management of patients. While several studies demonstrate area-under-the-curve (AUC) of >0.90, these studies largely compare cognitively normal individuals to those with established AD dementia with maximal biomarker separation [1-6]. The gray zone problem, affecting 30-50% of tested individuals, remains unaddressed in the vast majority of clinical implementation frameworks [7,8]. More recent work has established probability-based interpretation [9], but more cohesive algorithms for gray zone resolution through multi-marker integration remain rare if present. Furthermore, the health economic impacts of such resolution have not been fully established. InterpretationOur two-stage algorithm provides a workflow with clinical implementation potential, analogous to established laboratory medicine (i.e TSH with reflex free T4 testing). By first identifying high-confidence cases through univariate p-tau217 (55.6% resolution at 88.8% accuracy), and then applying multi-marker classification only to uncertain cases, we are able to achieve optimal resource utilization while simultaneously maintaining diagnostic accuracy. The finding that MRI usage provides statistically significant improvement ({Delta}AUC=+0.025) has practical implications given the fact that there is a reasonable level of MRI availability in clinical settings. Future DirectionsWhile this work accomplishes several key priorities, future work is required to validate them in diverse clinical populations. In addition, integration of other plasma markers (ex. MTBR-tau243), development of clinical decision support tools, reimbursement mechanisms, and longitudinal validation for treatment monitoring will be necessary to ensure the appropriate infrastructure exists to support providers and patients. Preliminary evidence suggests that %p-tau217 (the ratio of phosphorylated to total tau-217) and MTBR-tau243, a mass spectrometry-based marker of tau tangle pathology, may substantially improve gray zone classification by capturing complementary aspects of tau biology not reflected in absolute p-tau217 concentrations alone, which is a direction that future technical work should examine further.

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.

BackgroundWhile blood-based biomarkers for Alzheimers Disease (AD) such as p-Tau and NfL characterize established pathology, the systemic biological cascade triggering these events remains incompletely mapped. We hypothesized that proteins exhibiting a rising trajectory in the prodromal phase might reveal novel mechanisms of disease progression. MethodsUsing data from the UK Biobank Pharma Proteomics Project (N = 4,519 incident AD cases), we performed a blind trajectory scan of [~]3,000 plasma proteins. We utilized an elimination strategy, systematically excluding known AD markers (e.g., APOE, NEFL) and verified biological responses (e.g., MMP3, GLRX) to isolate novel signals. ResultsAfter excluding established markers, VSIG10L--a V-set and immunoglobulin domain-containing protein--emerged as the most significant novel marker (beta = - 0.037, P = 0.0019), exhibiting a progressive rise as patients approached diagnosis. Crucially, VSIG10L was accompanied by a cluster of co-regulated proteins involved in embryonic development and cell cycle regulation, including NACC1 (stem cell pluripotency), VASN (vasculogenesis), and ZBTB17 (cell cycle checkpoint). ConclusionThe emergence of VSIG10L and its associated developmental cohort suggests that prodromal AD is characterized by a retrogenesis phenomenon, the unsilencing of developmental programs in a failed attempt at neural repair. These proteins offer a new window into the brains response to neurodegeneration and represent potential therapeutic targets.

Structured AbstractO_ST_ABSBACKGROUNDC_ST_ABSPositron emission tomography (PET), cerebrospinal fluid (CSF), and plasma assessments are used to measure amyloid abnormality in Alzheimers disease (AD). However, it remains unclear if these three measures are similarly associated with brain structure and cognitive measures. METHODSLinear regressions examined the relationship between amyloid levels measured by PET, CSF, and plasma and brain volumes, white matter hyperintensities (WMHs), and cognitive measures. RESULTSModerate correlations were found between PET and CSF amyloid measurements and PET and plasma measurements, while weak correlations were found between CSF and plasma. PET, CSF, and plasma amyloid measurements differed in their associations with brain volume, WMHs, and cognition. DISCUSSIONUsing different measurement methods, amyloid was not consistently associated with volumetric or cognitive measures. Our findings also suggest that plasma markers may not be associated with cognitive and brain changes in the same manner as CSF and PET.

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.

ObjectiveTo evaluate associations between an in vivo (MRI) marker of arteriolosclerosis (ARTS) and multimodal neuroimaging and plasma ATN(V) biomarkers. MethodsAmong 238 participants with both amyloid and tau PET scans within one year of MRI, we examined multivariable adjusted models relating ARTS with structural MRI (cortical thickness/volume, white matter hyperintensities [WMH]), diffusion MRI (fractional anisotropy [FA], mean diffusivity [MD], NODDI free water [FW]), cerebral blood flow, plasma biomarkers (p-tau217, A{beta}42/40, neurofilament light, glial fibrillary acidic protein [GFAP]), and PET imaging. ResultsAs expected, ARTS was most strongly linked to age and greater WMH burden and diffusion-based indices of microstructural disruption (FA, MD, FW). ARTS was elevated in ATN biomarker-positive groups (highest in A+T+N+) and was associated with greater neurodegeneration and higher plasma biomarker levels, GFAP in particular. ConclusionsARTS relates to other markers of vascular brain injury, neurodegeneration, amyloid and tau pathology within the ATN(V) framework, and inflammation. HighlightsO_LIARTS scores are elevated in ATN-positive individuals, most prominently in A+T+N+ C_LIO_LIARTS may exert stage-specific effects on neurodegeneration rather than track A/T burden. C_LIO_LIStrong ARTS-GFAP association suggests role of astroglial activation in vascular-related neurodegeneration C_LIO_LIFindings underscore the importance of vascular contributions to AD/ADRD pathophysiology, especially in high-cardiometabolic-risk populations C_LI

BackgroundAPOE-{rho}.4 is the strongest genetic risk factor for Alzheimers disease (AD), and plasma phosphorylated tau217 (P-tau217) is a highly sensitive and specific biomarker for AD pathology. Their combined utility to predict cognitive decline before onset of AD has not been systematically evaluated. MethodsUsing longitudinal data from multiple cohorts, we evaluated plasma P-tau217 as a predictor of when cognitive impairment occurs in AD. P-tau217 concentrations were analyzed as continuous and binary variables using cohort-specific biomarker positivity thresholds. Association of plasma P-tau217 with prevalent and incident cognitive impairment were assessed using logistic regression and Cox models, stratified by APOE genotype. Adjusted survival curves and restricted mean survival time characterized when the onset of cognitive impairment occurred. Cohort-specific estimates were pooled using random-effects meta-analyses, and analyzed by discrimination performance with AUC, incremental R{superscript 2}, and Harrells C-index. ResultsElevated P-tau217 levels were significantly associated with the onset of cognitive impairment. Among APOE-{varepsilon}4 allele carriers, increased P-tau217 levels anticipated subsequent cognitive impairment. While P-tau217 levels reached clinically significant levels up to four years before onset of cognitive impairment independent of APOE, the symptom-free interval was briefest for APOE-{varepsilon}4 carriers with elevated P-tau217. ConclusionsPlasma P-tau217 levels and the presence APOE genotype can be used to estimate the interval before the onset of overt cognitive impairment and the diagnosis of AD. The findings here support the use of commercially available APOE genotyping and plasma P-tau217 to determine optimal timing for therapeutic intervention, particularly during the preclinical phase of the disease.

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.