Alzheimer's Research & Therapy

BackgroundP-glycoprotein (P-gp/ABCB1) is a key efflux transporter that maintains barrier integrity by clearing xenobiotics and toxic metabolites. At the feto-maternal interface, trophoblast-derived extracellular vesicles (CTC-EVs) naturally and transiently transfer functional P-gp to maternal decidual cells, restoring lost and or reduced P-gp function (exofection) to sustain pregnancy homeostasis. A similar loss of P-gp at the blood brain barrier (BBB) contributes to impaired amyloid-{beta} (A{beta}) clearance and neuroinflammation in Alzheimers disease. We investigated whether CTC-EV-mediated exofection could restore P-gp function in human brain endothelial cells (hBECs) and enhance A{beta} clearance under inflammatory and neurodegenerative conditions. MethodsCTC-EVs were isolated and characterized by nanoparticle tracking analysis and western blotting for P-gp and EV markers. Transcriptomic profiling of CTC-EVs identified enrichment of transporter-related genes, including solute carriers and ABC transporters, along with inflammatory mediators. Network analysis revealed coordinated modules linking EV cargo to transporter regulation, endocytosis/trafficking pathways, and inflammatory remodeling processes converging on BBB efflux activity. hBECs were exposed to LPS (500 ng/mL, 48 h) with or without CTC-EVs. P-gp expression was assessed by immunofluorescence (mean fluorescence intensity, MFI) and western blotting, while functional efflux was measured using Calcein-AM assays. A{beta} oligomer transport was evaluated using a transwell hBEC model. In vivo, 3xTg-AD mice received intravenous CTC-EVs (1x10L/day for 5 days), followed by assessment of P-gp expression, A{beta} burden, and neuroinflammatory markers. Pharmacokinetic studies in P-gp knockout mice were conducted to confirm functional transporter recovery. ResultsLPS exposure significantly reduced P-gp expression in hBECs (41.3% decrease in MFI, p=0.0084), which was restored by CTC-EVs (46.7% increase vs. LPS, p=0.0121). Exofection increased P-gp by a 2.1-fold following EV treatment as determined by western blot. Functional assays demonstrated enhanced efflux, with a 38.5% reduction in intracellular Calcein fluorescence (p<0.001). Network-informed mechanisms supported coordinated regulation of transporter and trafficking pathways. CTC-EVs improved A{beta} transport across inflamed hBEC monolayers. In vivo, EV-treated 3xTg-AD mice exhibited increased P-gp expression in the frontal cortex (38.6%) and hippocampus (42.1%), reduced A{beta} plaque burden (27.9%), and decreased inflammatory markers (IL-1{beta} and TNF-, p<0.05). In P-gp knockout mice, EVs reduced brain drug accumulation by 22.4% (p=0.032), confirming restoration of transporter function. ConclusionCTC derived EVs are natural carriers of functional transporter proteins and restore efflux capacity in compromised endothelial barriers. Integration of transcriptomic and network analyses highlights coordinated regulation of transporter, trafficking, and inflammatory pathways underlying exofection. This reproductive biology inspired strategy offers a promising therapeutic approach for enhancing A{beta} clearance and mitigating neuroinflammation in Alzheimers disease.

Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglial immune receptor genetically and functionally linked to Alzheimers disease (AD). VG-3927, the first clinical-stage small-molecule TREM2 agonist, has been proposed to function as a transmembrane molecular glue and positive allosteric modulator (PAM). Whether it directly engages the extracellular ligand-recognition surface of TREM2 remains unknown. Here, we used a deep learning-based blind docking algorithm to map potential VG-3927 binding sites across TREM2 and identified a binding site within the ectodomain hydrophobic groove, a ligand-recognition surface previously implicated in A{beta} and apoE binding. Microscale thermophoresis (MST) confirmed direct interaction of VG-3927 with TREM2 under optimized PEG-400 buffer conditions and independently demonstrated binding of A{beta}1-42 to the receptor. Co-incubation with A{beta} reduced the VG-3927 thermophoretic response, consistent with interference at an overlapping ectodomain binding surface. Consistently, A{beta} induced a rightward shift in the VG-3927 dose-response curve in a Jurkat TREM2-DAP12 NFAT reporter assay and attenuated VG-3927-induced phospho-SYK signaling. Together, these findings support the presence of a previously unrecognized ectodomain interaction mode for VG-3927 and suggest that amyloid-associated ligand occupancy may modulate TREM2 agonist activity within the AD microenvironment.

Alzheimers disease (AD) is a brain disease characterized by deposition of insoluble amyloid-{beta} plaque, intraneuronal neurofibrillary tangles, and cognitive dysfunction. AD can be characterized as early-onset or late-onset based on age and genetic factors. For early-onset, these genetic factors can include amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). For late-onset, these can include apolipoprotein E e4 (APOE4), and the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2). Mouse models incorporating these risk factors provide critical knowledge for studying AD pathology and preclinical studies for drug development. However, these transgenic mice depend on early-onset genetic mutations and are deficient in certain AD features that are present in late-onset. Here, we developed innovative non-linear and feature selection procedures for our cross-species translation framework, Translatable Components Regression (TransComp-R), to identify transcriptomic features in mouse models predictive of human late-onset AD pathobiology. We used the cross-species computational translatability links of TransComp-R to perform computational high-throughput drug screening and identified multiple repurposable drugs for AD treatment that targeted the sleep-wake cycle. We tested these predictions in an orthogonal, prospective cohort of human subjects treated with an orexin receptor antagonist, suvorexant. We correlated conserved protein-level biomarkers from our cross-species transcriptomics model with significant reductions in phosphorylated tau in cerebrospinal fluid collected from humans treated with suvorexant. This study demonstrates the power of computational methods like TransComp-R to enhance the utility of murine disease models for discovering new therapeutic approaches for AD. One Sentence SummaryCross-species translation modeling across different mouse models reveals sleep-relevant drug mechanisms as potentially therapeutic for Alzheimers disease.

Understanding the time course of Alzheimer's disease biomarkers of amyloid and tau pathology and their temporal relation to clinical symptoms is key to identifying optimal windows for disease intervention and planning future drug trials. The goal of this work was to determine the extent to which Sampled Iterative Local Approximation (SILA), an algorithm extensively validated for amyloid PET, is capable of modeling longitudinal tau (T) PET trajectories and estimating person-level tau positivity onset ages in two commonly analyzed brain regions and two tracers from two different cohorts. Methods: 385 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; mean (SD) age = 73.4 (7.3) years) with longitudinal flortaucipir tau PET and 288 participants from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center (collectively referred to as WISC; mean (SD) age = 67.4 (6.7) years) with longitudinal MK-6240 tau PET were included in the study. Standard uptake value ratios (SUVRs) in the entorhinal cortex and a meta-temporal ROI were modeled with SILA separately, for each cohort and region. Forward and backward SUVR and T+/- prediction were characterized with ten-fold cross-validation and in-sample validation techniques. Accuracy of estimated T+ onset ages (ETOA) was characterized in T- to T+ converters. Differences in ETOA were tested between APOE-e4 carriers and non-carriers, as well as differences in time T+ between levels of cognitive impairment. Results: SILA was able to accurately estimate retrospective change in tau SUVR in the meta-temporal region regardless of age, sex, APOE-e4 carriage, tau SUVR, and dementia (p >0.05) whereas dementia was associated with model residuals in entorhinal cortex (p [&le;] 0.05; ADNI). In subsets of observed T- to T+ converters, the difference between "observed" and estimated meta-temporal T+ onset age [95% CI] was 0.12 [-0.27, 0.52] years for ADNI and -0.09 [0.93, 0.74] years for WISC. ETOA was significantly earlier, and odds of SILA-estimated T+ status were higher amongst APOE-e4 carriers (p <0.05) and those with dementia (p <0.05). Conclusions: Our results suggest SILA can be used to accurately model longitudinal tau PET trajectories and retrospectively estimate individual T+ onset ages in the meta-temporal region. The accuracy of SILA time estimates in entorhinal cortex worsened amongst those with dementia in ADNI suggesting entorhinal cortex may only be suitable for studying the temporal progression of tau during the preclinical time frame.

BackgroundQuantitative 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. MethodsWe 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 Alzheimers 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. ResultsBoth GWAS and GIFT identified genome-wide significant associations (pvalue<10-6) 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. ConclusionsGIFT 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.

Anti-A{beta} immunotherapies for Alzheimers Disease (AD) have high rates of amyloid-related imaging abnormalities (ARIA), an adverse side effect with markedly higher rates in APOE4 carriers. We developed a mouse model of ARIA centered on human APOE3 and APOE4 genotypes with amyloidosis (5xFAD transgene) and microglia tagged with green fluorescent protein (from the CX3CR1 promoter). We measured acute changes following a single intravenous treatment with 3D6 anti-A{beta} immunotherapy. Across 82 mice, APOE4 mice showed stepwise reductions in the number of plaques from one to ten days, with significant reductions in the subiculum (48%) and thalamus (40%) at ten days. There was no significant reduction in APOE3 mice. There was a concomitant significant increase in deposition of cerebral amyloid angiopathy (CAA) in APOE4 mice at one (76%) and three (51%) days in leptomeningeal vessels. The increased CAA correlated with a significant 189% increase in A{beta} within microglia of APOE4 (but not APOE3) mice at one day. Smooth muscle actin staining showed significant 58% reduction near CAA. MRI analysis revealed a significant 32% increase in microhemorrhages ten days following treatment. These data demonstrate an APOE4-specific redistribution of parenchymal amyloid to CAA by 3D6 within days, leading to increased vascular damages associated with ARIA.

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.

Amyloid-{beta} (A{beta}) PET imaging is a core biomarker and is considered sufficient for the biological diagnosis of Alzheimers disease (AD). However, it is typically reduced to a binary A{beta}/A{beta}+ classification. In this study, we aimed to identify subgroups along the continuum of A{beta} accumulation including subgroups within A{beta}- and A{beta}+. We used a total of 3,110 of A{beta} PET scans from Alzheimers Disease Neuroimaging Initiative (ADNI) and Anti-Amyloid Treatment in Asymptomatic Alzheimers Disease (A4) datasets to develop petVAE, a 2D variational autoencoder model. The model accurately reconstructed A{beta} PET scans without prior labeling or pre-selection based on scanner type or region of interest. Latent representations of scans extracted from the petVAE (11,648 latent features per scan) were used to visualize, analyze, and cluster the AD continuum. We identified the latent features most representative of the continuum, and clustering of PET scans using these features produced four clusters. Post-hoc characterization revealed that two clusters (A{beta}-, A{beta}-+) were predominantly A{beta} negative and two (A{beta}+, A{beta}++) were predominantly A{beta} positive. All clusters differed significantly in standardized uptake value ratio (p < 1.64x10-8) and cerebrospinal fluid (CSF) A{beta} (p < 0.02), demonstrating petVAEs ability to assign scans along the A{beta} continuum. The clusters at the extremes of the continuum (A{beta}-, A{beta}++) resembled to the conventional A{beta} negative and A{beta} positive groups and differed significantly in cognitive performance, Apolipoprotein E (APOE) {varepsilon}4 prevalence, and A{beta}, tau and phosphorylated tau CSF biomarkers (p < 3x10-6). The two intermediate clusters (A{beta}-+, A{beta}+) showed significantly higher odds of carrying at least one APOE {varepsilon}4 allele compared with the A{beta}-cluster (p < 0.026). Participants in A{beta}+ or A{beta}++ clusters exhibited a significantly faster rate of progression to AD compared to A{beta}-group (Hazard ratio = 2.42 and 9.43 for groups A{beta}+ and A{beta}++, respectively, p < 1.17x10-7). Thus, petVAE was capable of reconstructing PET scans while also extracting latent features that effectively represented the AD continuum and defined biologically meaningful clusters. By capturing subtle A{beta}-related changes in brain PET scans, petVAE-based classification enables the detection of preclinical AD stages and offers a new data-driven framework for studying disease progression.

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.

Alzheimers disease (AD) is characterized by the mis-aggregation of amyloid {beta} (A{beta}) and tau, which is proposed to be driven by impaired A{beta} and tau clearance. While sleep-active glymphatic transport contributes to the clearance of A{beta} and tau in humans, studies have yet to demonstrate that it is possible to enhance glymphatic transport in humans and that augmenting glymphatic transport improves the clearance of A{beta} and tau from the human brain. In two cross-over clinical trials in healthy older adults, we demonstrated that a fixed-dose combination therapy of intravenous dexmedetomidine (0.7 g/kg/h) and 10 mg oral midodrine (ACX-02), that suppressed central noradrenergic tone while maintaining systemic arterial pressure, increased EEG slow waves, enhanced cerebrovascular pulsatility, and reduced parenchymal resistance to perivascular fluid flow, that have shown to be key determinants of glymphatic transport. Dynamic shifts in plasma mass balance indices of clearance within the brain demonstrated that pharmacological enhancement of glymphatic transport increased A{beta} and tau clearance by approximately 9%-10% during a single 4h 15min sleep opportunity. Bayesian mediation analysis demonstrated that increasing EEG slow waves and declining parenchymal resistance were key mediators, and cerebrovascular compliance was a moderator, of the effect of ACX-02 on plasma AD biomarker dynamics. These findings demonstrate that pharmacologic enhancement of glymphatic transport increased brain-to-blood clearance of A{beta} and tau in human participants. This suggests that enhancement of A{beta} and tau clearance may serve as a complementary approach to existing disease-modifying therapies, and as a therapeutic approach in AD and AD-related proteinopathies.

BackgroundGamma entrainment shows promise for Alzheimers disease (AD) treatment in preclinical models, but human trials have yielded heterogeneous results. We hypothesized that the clinical efficacy of gamma entrainment depends on individual neurophysiological receptivity, specifically the capacity for neural circuit plasticity. MethodsIn this open-label pilot study, we screened 37 individuals and enrolled 16 participants with early AD (CDR 0.5-1.0, amyloid-positive) who completed 12 weeks of home-based flickering light stimulation at individually optimized gamma frequencies (32-40 Hz). Pre- and post-intervention assessments included 64-channel EEG recordings and MMSE. ResultsParticipants demonstrated dichotomous neurophysiological responses: 43.8% showed CF increase (ICF+) while 56.3% showed no change/decrease (ICF-). CF restoration was significantly associated with cognitive preservation (r=0.52, p=0.039). Notably, future responders exhibited distinct baseline signatures of "neural reserve," characterized by higher temporal gamma power (Cohens d=0.70-0.92) and stronger frontotemporal connectivity (Cohens d=1.11-1.47). Almost 30% of screened candidates failed to show baseline entrainment, highlighting a distinct "non-responsive" biological subtype. DiscussionCF restoration following personalized gamma entrainment identifies a neurophysiological subtype capable of meaningful plasticity. Rather than a universal remedy, gamma entrainment appears to act on specific neural substrates preserved in a subset of patients. These findings suggest that baseline electrophysiological profiling could unlock gamma entrainments therapeutic potential by stratifying likely responders for precision neuromodulation.

Background: Cardiovascular health factors are associated with cognitive decline and risk of dementia, including Alzheimer disease (AD); however, this has been mostly studied in late life. We investigated whether vascular and lifestyle factors are associated with AD plasma and imaging biomarkers in midlife. Methods: We investigated 1,406 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study with information on vascular and lifestyle factors framed from the American Heart Association (AHA) life's essential 8 (LE8) guidelines for cardiovascular health at early midlife (mean age 45.0 SD 3.6) and AD biomarkers in late midlife (mean age 60 SD 3.5). LE8 was calculated and categorized into poor (0-49), intermediate (50-79), and ideal (80-100) cardiovascular health, based on 8 components including smoking, diet, body mass index (BMI), sleep, fasting glucose, blood pressure, cholesterol, and physical activity. We assessed the AD plasma biomarkers phosphorylated tau 217 (ptau-217) and amyloid beta 42/40 ratio (A{beta}42/40) and the Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD), an algorithm that characterizes AD-like brain atrophy on brain MRI. We used linear regression to examine the association between LE8 and log-transformed and standardized AD biomarker measures adjusting for age, sex, race, education, and kidney function. Results: Compared to ideal LE8, intermediate (67.9% of participants) and poor (12.6%) LE8 was associated with lower A {beta}42/40 (adjusted mean difference: -2.37, 95% CI: -2.38 to -2.36 and -2.38, 95% CI: -2.40 to -2.36, respectively). There was no association between the LE8 group and ptau-217 level. Moreover, compared to ideal LE8 participants, those with poor LE8 had higher SPARE-AD atrophy pattern (adjusted mean difference: -0.71, 95% CI: -0.81 to -0.62). Conclusion: These findings indicate that poor cardiovascular health in midlife, as defined by the AHA LE8, is linked to less favorable early AD biomarker profiles, particularly reflecting greater amyloid burden and structural brain changes.

Despite a growing body of evidence implicating genetic variants and proteins encoded by them with risk and pathogenesis of Alzheimers disease (AD), this knowledge has not been successfully translated into effective AD treatments. We integrated current genomic, transcriptomic and proteomic profiles of AD into a network pharmacology framework that leverages comprehensive gene-gene and drug-target interactions. This approach allowed us to screen 2,413 drugs for repurposing opportunities in AD. Computational validation and drug prioritization was followed by experimental validation in 33 cell culture-based phenotypic assays combined with Bayesian hypothesis testing. Our network-based screen rediscovered drugs in clinical trials for AD, providing computational validation. Besides many cancer drugs, the screen identified three drugs previously implicated in AD-related endophenotypes: the primary bile acid chenodiol, arundine (3,3-diindolylmethane), and cysteamine. In analysis of results from culture-based phenotypic assays, large Bayes factors supported the hypothesized benefits of arundine and the chenodiol derivative, tauroursodeoxycholic acid (TUDCA), in amyloid-{beta} clearance and release and neuroinflammation. Follow-up network analyses mechanistically implicated Regulator of G protein signaling 4 (RGS4) in the plausible therapeutic actions of arundine and TUDCA. A network pharmacology approach identified TUDCA and arundine as promising repurposing candidates in AD that rescue disease-relevant molecular phenotypes by acting on AD-associated genes through regulation of G protein signaling.

Alzheimer s disease (AD) and other tauopathies are characterized by the hyperphosphorylation of tau (pTau), leading to its aggregation in the brain, a process strongly predictive of neurodegeneration and future cognitive decline. Currently, tau positron emission tomography (PET) is the only validated method for detecting tau aggregates in vivo. However, its high cost, invasiveness, and limited accessibility restrict its use in clinical settings and preclude large-scale screening. Moreover, existing plasma biomarkers that quantify the level of pTau at specific sites (e.g., pTau217) have limited specificity for confirming AD-related tau aggregation, partly due to the heterogeneous and irregular phosphorylation patterns of pTau. Besides, the concentration of pTau is frequently elevated in the context of isolated amyloid-{beta} pathology, which is less strongly associated with cognitive decline in the absence of aggregated tau. There is therefore an urgent need for a reliable and scalable blood-based biomarker of tau pathology. A key mechanism underlying AD tau pathology is the ability of pathologically active pTau (PA pTau) to bind to and seed normal tau, facilitating prion-like propagation of insoluble tau aggregates. Here, we assessed the diagnostic performance of the VeraBIND Tau assay, the first functional assay to detect PA pTau seeding activity in plasma. Seventy-nine cognitively unimpaired (CU) and 66 cognitively impaired older adults underwent blood sampling, cognitive assessment, amyloid-PET or cerebrospinal fluid (CSF) analysis, and [18F]-MK6240 tau-PET imaging. Plasma pTau217 concentrations were quantified using the Lumipulse platform (Fujirebio). The VeraBIND Tau assay isolated PA pTau from plasma and evaluated its ability to bind recombinant normal tau using a tagged-tau chemiluminescent readout. VeraBIND Tau demonstrated 94.2% sensitivity and 96.1% specificity for predicting tau-PET positivity (AUC=0.97). It outperformed plasma pTau217 in CU individuals (PPV=85.9%), regardless of the pTau217 threshold used (maximal PPV of 57.5% using the 0.256pg/mL pTau217 threshold). This higher VeraBIND Tau diagnostic accuracy was driven by early tau-PET stages (Braak-like tau-PET stages 1-3; AUC=0.96 vs. 0.74 for pTau217, p=0.003). Moreover, both cross-sectional values and annual changes in VeraBIND Tau were significantly correlated with cognitive performance and entorhinal tau-PET signal (all absolute Spearman r[&ge;]0.23, p<0.05). These findings highlight the strong potential of VeraBIND Tau as a scalable and accurate screening tool to detect AD tau pathology in the general population. The assay may also help enrich clinical trials with tau-PET positive CU individuals, enhance clinical diagnostic workflows and support monitoring of tau-targeted therapies. Future work should evaluate its utility in optimizing triage and early-intervention strategies.

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.

Early detection of disease progression using clinically-relevant biomarkers in animal models is important for mechanistic studies and for developing therapeutics in neurodegenerative diseases including Alzheimers disease (AD). The preclinical stage of AD, when amyloid-{beta} (A{beta}) starts to accumulate before cognitive decline, provides a critical window for disease modification. In humans, decreases in cerebrospinal fluid (CSF) A{beta}42 and the A{beta}42/A{beta}40 ratio in preclinical AD are considered to reflect the preferential sequestration of aggregation-prone A{beta}42 into {beta}-sheet-rich deposition in the brain, with corresponding changes being detectable in plasma. However, the extent to which these biomarker-pathology relationships are recapitulated in AD model mice remains incompletely defined. Here we show that CSF and plasma A{beta}42 and the A{beta}42/A{beta}40 ratio decline with age in parallel with the progression of {beta}-sheet-rich A{beta} deposition in preclinical 5XFAD mice, one of the most widely used AD mouse models, as assessed through monthly profiling of these biomarkers. Notably, the CSF A{beta}42/A{beta}40 ratio showed a negative correlation with {beta}-sheet-rich A{beta} deposition in the brain, whereas CSF A{beta}40 did not show a comparable association. In addition, the plasma A{beta}42/A{beta}40 ratio showed a positive correlation with the CSF A{beta}42/A{beta}40 ratio, suggesting that the plasma A{beta}42/A{beta}40 ratio may also reflect brain A{beta} deposition in this model. The strength of these correlations differed by sex, suggesting that sex-dependent differences in the A{beta} kinetics in this model may influence how closely fluid biomarkers reflect pathological progression. These findings support the potential utility of fluid A{beta} as a pathology-linked, translatable biomarker in preclinical 5XFAD mice. Highlights- Fluid A{beta} biomarkers are associated with early A{beta} deposition in preclinical 5XFAD mice. - The CSF A{beta}42/A{beta}40 ratio negatively correlates with {beta}-sheet-rich brain A{beta} deposition. - The plasma A{beta}42/A{beta}40 ratio positively correlates with the CSF A{beta}42/A{beta}40 ratio. - Monthly profiling defines fluid A{beta} biomarker dynamics in preclinical 5XFAD mice. - Sex differences may affect biomarker-pathology relationships in these mice.

Background and Aims: Plasma phosphorylated tau 217 (p-tau217), including %p-tau217, has emerged as a robust biomarker of Alzheimer's disease (AD) pathology, with increasing interest in its longitudinal behavior. In "Predicting onset of symptomatic Alzheimer's disease with plasma p-tau217 clocks," Petersen et al. applied disease clock models, Sampled Iterative Local Approximation (SILA) and Temporal Integration of Rate Accumulation (TIRA), to estimate age at plasma %p-tau217 positivity and reported that this measure predicts age at onset of symptomatic AD. We aimed to determine whether this apparent predictive performance reflects biomarker information or arises from structural artifacts in the analysis. Methods: We analyzed digitized data from published figures and decomposed the clock-derived predictor into baseline age and estimated time from %p-tau217 positivity. We quantified shared and unique explained variance between baseline age and the clock-derived predictor using commonality analysis. To further disentangle structural and biomarker contributions, we evaluated a null scenario in which the biomarker-derived timing component was replaced with randomly generated values drawn over the observed range, preserving the predictor distribution while removing biomarker information. Results: The reported predictive performance was largely driven by structural artifacts arising from bounded follow up and constraints among the variables. Restriction to individuals who progressed during limited follow up, together with constraints on the allowable timing of events, induced a strong association between baseline age and age at symptom onset. In ADNI, baseline age alone explained substantially more variance in age at onset than the clock-derived predictors (R2=0.78 vs. 0.337 and 0.470 for TIRA and SILA). The estimated time from %p-tau217 positivity contributed minimal additional information, and randomized predictors yielded comparable performance to baseline age alone (R2=0.79). Conclusion: The apparent predictive ability of plasma %p-tau217 disease clocks is driven largely by structural age relationships rather than independent biomarker signal. The plasma %p-tau217 timing component provided minimal predictive value, and its combination with age obscured these structural dependencies. These findings underscore the need for careful evaluation of constructed predictors and outcomes in longitudinal analyses of disease progression.

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.

Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critical role in clinical trial design. Recently, researchers have leveraged positron emission tomography (PET) to inform data-driven staging models of brain pathology related to AD. However, most approaches have focused on staging either amyloid or tau progressions separately, while both pathologies constitute defining factors of AD. Here, we aimed to derive a data-driven staging model which encompasses the spatial spread of both amyloid and tau. We assembled a large sample (n=3,293) of individuals with both amyloid and tau PET imaging stemming from 8 neuroimaging studies of AD and aging. We applied unsupervised machine learning to estimate brain areas which showed coordinated pathological accumulation across our sample, and we used these regions to inform a data-driven model for staging amyloid and tau. The resulting six stage model showed two stages of amyloid progression followed by four stages of tau spread, which were associated with cross-sectional and longitudinal assessments of cognitive decline. Comparison of our biological staging model with clinical disease stages recommended by the Alzheimer's Association showed evidence of heterogenous symptom profiles. Replication of results in holdout data demonstrated the generalizability and prognostic value of our staging model. Together, these findings establish a comprehensive and rigorously validated biological staging model that jointly characterizes amyloid and tau progression, advances beyond global or anatomically predefined summaries, and provides a scalable framework for studying disease heterogeneity and progression in AD.

Functional neuroimaging studies suggested a biphasic trajectory of neuronal activity in Alzheimers disease (AD), with early hyperactivity followed by later hypoactivity. However, the underlying neurochemical mechanisms in humans remain unclear. Animal studies suggested that amyloid-beta (A{beta}) causes intrasynaptic glutamate increases through impaired astrocytic clearance. This study aimed to build a mechanistic bridge between findings from human neuroimaging studies and preclinical models by providing in vivo measurements of glutamate, GABA, and glutamine across the AD continuum using 7T magnetic resonance spectroscopy (MRS). We acquired longitudinal data from cognitively normal (CN) individuals with and without A{beta} pathology (CN A{beta}-n=43, CN A{beta}+ n=17), individuals with mild cognitive impairment (MCI A{beta}+, n=20) or AD dementia (AD A{beta}+, n=24). Over the course of up to 5 years, glutamate levels increased in the CN A{beta}+ and MCI A{beta}+ groups but decreased in the AD A{beta}+ group. Consistent with a transient homeostatic response to glutamatergic hyperexcitation, GABA levels showed modest increases in both CN A{beta}+ and MCI A{beta}+ groups. Elevated plasma GFAP was associated with reduced glutamine, suggesting astrocytic dysfunction and impaired glutamate-glutamine cycling as contributors to early hyperexcitability. Together, our results provide the first direct demonstration in patients with AD that glutamate alterations underlie the biphasic trajectory of neuronal activity and that astrocytic glutamate regulation might be a potential therapeutic target.