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.

Alzheimer's disease (AD) patients suffer from consequential diagnostic delay due to the lack of accessible biomarkers. They also show different responses to treatments due to disease heterogeneity and progression. Here, we developed a novel framework to identify disease progression and subtypes by using geometric brain signatures derived from multiple neuroimaging modalities, including [18F]-Florbetapir (AV45) Positron Emission Tomography (PET), [18F]-Fludeoxyglucose (FDG) PET, and structural Magnetic Resonance Imaging (MRI). These signatures were derived by decomposing corresponding maps of amyloid-beta levels, metabolic activity, and cortical thickness in terms of the fundamental, resonant modes-eigenmodes-of cortical geometry, each tied to a specific spatial resolution scale. Our results showed that geometric eigenmode-based features identified trajectories of disease progression, quantified as pseudotime, in distinct subtypes. The disease progression trajectories and subtypes are identified with high stability and are highly related to biological and cognitive measures. These performances are superior to those obtained using conventional localised features and remain robust across datasets, indicating that geometric signatures of brain structure and function can be used to uncover new markers of AD diagnosis and prognosis that are missed by conventional localisation approaches.

Innovative therapeutic approaches providing clinically effective medication for Alzheimers disease (AD) patients are urgently needed. In recent years, monoclonal antibodies against Amyloid-{beta} (A{beta}) oligomers were approved as the first disease modifying AD therapies. However, their effects on cognitive decline of AD patients are still limited - most likely because neuroinflammatory processes downstream of A{beta} plaques remain activated, even when plaques are depleted. Accordingly, anti-inflammatory drugs are currently considered as valuable (co-)treatments to target A{beta} associated neuro-inflammation. The anti-inflammatory sphingosine-1-phosphate receptor (S1PR) modulator fingolimod (FTY720) has been shown to alleviate synaptic and cognitive deficits in numerous mouse models of AD. Whether other S1PR modulators exert similar beneficial effects is largely unknown. Here we used a transgenic APP/PS1 AD mouse model to investigate whether the S1PR modulator Ozanimod (RPC1063) can rescue AD pathology and synaptic dysfunctions even when treatment is initiated at 16-17 months of age, which is 10 months after onset of cognitive deficits. We performed quantitative dendritic spine analysis in hippocampal CA1 pyramidal neurons and immunohistochemical labelling of Iba1-positive activated microglia, GFAP-positive reactive astrocytes, and A{beta} plaques in hippocampus and neocortex. Our results reveal that 6 weeks of Ozanimod treatment via drinking water rescues synaptic spine deficits, counteracts A{beta} pathology and reduces neuro-inflammation in the hippocampus and neocortex of APP/PS1 mice. Therefore, it might hold promise to examine a potential disease-modifying effect of S1PR modulators in clinical trials with AD patients.

BackgroundThe current biomarker framework for the diagnosis and staging of Alzheimers disease (AD) relies mainly on neuropathological features; thus, its performance for diagnosis is limited prior to the initiation of neurodegeneration. Here, we leveraged transcriptomic data to develop a new framework for omic-informed blood-based diagnostic biomarkers for AD from early-stage. MethodsMicroglial gene expression from single-nucleus (sn)RNA-seq data was analyzed via 6 statistical methods to identify candidate panels of genes predictive of AD. A total of 78 gene panels, 30-2000 genes in size, were selected and evaluated for their ability to distinguish AD patients from controls. Three top-ranked panels of 300, 50 and 30 genes were transferred to blood (monocyte) transcriptomic data obtained from living subjects via a graph-based mapping approach based on optimal transport statistics. ResultsThe 300-panel method resulted in an AUC of 0.7 and moderate accuracy (75%) in classifying AD; however, the accuracy in predicting cognitively normal patients was lower (53%). While the 300 genes provided high accuracy, inspection of the distribution of p values for the gene set revealed that the panel could be greatly reduced in size to capture the most significant differences between AD patients and cognitively normal individuals. The accuracy and specificity of the 50 and 30 panels demonstrated similar AUC values but improved the balance between the prediction of AD patients and normal controls. Specifically, the 50-gene panel resulted in an AUC of 0.7, with 65% AD accuracy and 71% normal accuracy. ConclusionsIntegrating multiomics datasets into the AD biomarker discovery pipeline offers a powerful modality to increase precision and comprehensiveness in AD research and clinical applications.

Alzheimers disease (AD) is a neurodegenerative disorder affecting more than 55 million people worldwide, with a diagnosis that remains predominantly clinical and frequently delayed. The electroretinogram (ERG) offers a non-invasive electrophysiological method for detecting retinal dysfunction associated with neurodegeneration; however, it remains unclear whether robust and reliable candidate biomarkers can be extracted from ERG signals beyond conventional amplitude- and latency-based parameters. Here we present a pilot study of a multi-domain signal processing framework applied to ERGs recorded from 46 participants (20 AD patients, 26 controls) with a handheld device (RETeval, LKC Technologies) using sinusoidal (1-50 Hz) and photopic ISCEV protocols. Five complementary techniques were implemented: (i) multiscale fuzzy entropy (MSFuzzyEn); (ii) FFT harmonic analysis; (iii) stimulus-response wavelet time-frequency coherence (WTC); (iv) a novel inter-cycle lag variant of sample entropy (SampEnT), introduced to isolate cycle-to-cycle retinal response consistency independently of stimulus periodicity; and (v) discrete wavelet transform (DWT) for energetic extraction of oscillatory potentials (OPs). Univariate comparisons (Mann-Whitney, Cliffs{delta} , Benjamini-Hochberg FDR) identified seven significant candidate biomarkers (q < 0.05), five with large effect size: AUCfast (|{delta}| = 0.546, q = 0.009), Slopevery-slow (|{delta}| = 0.554, q = 0.007), R14f (|{delta}| = 0.515, q = 0.031), SampEnT (|{delta}| = 0.504, q = 0.019) and WTCR,mean (|{delta}| = 0.531, q = 0.023); and two with medium effect size (OP_amp_sum, band_snr). A logistic regression classifier combining three candidate biomarkers, validated by leave-one-out cross-validation, achieved ROC-AUC = 0.858, sensitivity = 70.0% and specificity = 88.5% (n = 46). These proof-of-concept results demonstrate that multi-domain ERG analysis captures retinal temporal dysfunction signatures in AD that are inaccessible to standard clinical analysis, supporting further investigation of portable ERG devices as a source of non-invasive candidate biomarkers for early AD detection.

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.

PURPOSE: Alzheimer disease (AD) is associated with cognitive impairment, brain atrophy, and elevated amyloid-beta and tau. The study aimed to characterize regional atrophy associated with elevated amyloid-beta and tau, as measured by [18F]florbetapir (FBP) and [18F]flortaucipir (FTP) positron emission tomography (PET), respectively, and determine whether combining PET and atrophy data improves the prediction of cognitive impairment. METHODS: Alzheimer Disease Neuroimaging Initiative data (n = 381) were retrospectively analyzed. PET results were correlated with cortical thickness, gray matter (GM) volumes, Mini-Mental State Examination, and Montreal Cognitive Assessment. Linear/logistic regression and area under the curve (AUC) were used to evaluate for significant correlations and compare performances in distinguishing cognitive impairment, respectively. RESULTS: Incremental loss of cortical thickness and GM volume was observed from FBP-/FTP- (n = 205) to single PET-positive (FBP+/FTP-, n = 133; FBP-/FTP+, n = 5) and FBP+/FTP+ (n = 38) groups, particularly in the temporal and parietal lobes. FBP+/FTP+ showed the most severe cortical thickness loss in the entorhinal cortex, temporal lobe GM atrophy, and cognitive impairment. Adding brain atrophy as the third variable resulted in higher odds ratios and improved AUCs for cognitive impairment, with FBP+/FTP+/temporal GM or entorhinal cortical atrophy+ demonstrating the strongest associations with cognitive impairment. CONCLUSION: A multimodal approach combining PET and MRI may help improve the assessment of cognitive impairment in AD.

Digital health technologies (DHT) offer a promising solution to the timely identification of early Alzheimer's disease (eAD) to enable early treatment. This study evaluated the feasibility, acceptability, adherence, reliability, and preliminary clinical and content validity of the novel AD Digital Assessment Suite (AD-DAS). 123 individuals (32 healthy controls (HC), 31 amyloid-PET negative (SCDn), 30 amyloid-PET positive (SCDp) with subjective cognitive decline, and 30 early AD (eAD)) participated. AD-DAS was remotely deployed for 28 days. Remote testing was feasible (97.6% completers), acceptable (>85% ''good''), and associated with high adherence (96%). Metrics showed moderate to excellent test-retest reliability (ICC 0.53-0.91), associations with clinical comparators (adjusted R2 0.01-0.24), differentiated eAD from other known groups (absolute log odds differences 0.6-3.28), and correlated with brain atrophy in expected regions. Episodic and working memory AD-DAS metrics differentiated SCDp from SCDn participants. These preliminary findings suggest that AD-DAS may be a promising tool for detecting cognitive impairments in early AD stages.

Diabetes mellitus (DM) is a major risk factor contributing to the development of Alzheimers disease-related dementias (ADRD). While one of the early symptoms of both Alzheimers disease (AD) and DM-related ADRD is a reduction in cerebral blood flow, the underlying biological mechanisms driving this decline remain to be fully elucidated. Genome-wide association studies have linked AD/ADRD to single-nucleotide polymorphisms in the gene encoding soluble epoxide hydrolase (sEH), an enzyme we previously reported to be upregulated in the brains of an AD rat model. Our previous work also demonstrated that chronic inhibition of sEH with 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea (TPPU) preserves hippocampal-dependent spatial learning and memory and improves cerebral hemodynamics in both AD and DM-ADRD models. In the present study, we found that chronic TPPU treatment (1 mg/kg/day for 9 weeks) reduced brain sEH expression, improved cortical-based long-term non-spatial recognition memory involving both cortical and hippocampal networks, and reduced anxiety in DM-ADRD rats. TPPU improved brain perfusion and normalized impaired whisker-evoked functional hyperemia, an effect linked to upregulation of Kir2.1 expression in cerebral capillaries. Furthermore, TPPU restored tight junction proteins (ZO-1 and OCLN), mitigated capillary rarefaction, and suppressed astrocyte and microglial activation. At the cellular level, TPPU attenuated hippocampal neurodegeneration, restored the expression of synaptic proteins (PSD95 and SY38), and reduced levels of key pro-inflammatory chemokines, including MCP-1, RANTES, and MIP-1, in DM-ADRD. In conclusion, TPPU preserves cognitive function in DM-ADRD by mitigating cerebrovascular dysfunction, neuroinflammation, and gliosis while protecting synaptic integrity and neuronal survival, representing a promising therapeutic strategy for DM-ADRD.

BackgroundCardiovascular 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. MethodsWe 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) "lifes 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. ResultsCompared 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). ConclusionThese 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.

The TREM2 R47H variant increases the risk of Alzheimers disease (AD), yet its functional impact in aged mouse models remains incompletely understood. We generated a humanized Trem2 R47H knock-in (KI) line on the AppNL-F background and compared it with a Trem2 knockout (KO) line to assess the degree of TREM2 functional impairment. Accumulation of amyloid {beta} 42 and formation of dystrophic neurites were increased in Trem2 KO mice but not in Trem2 R47H KI mice at 18 or 24 months. qPCR and transcriptomic analyses revealed Trem2 KO mice showed deficits in upregulation of microglial genes while Trem2 R47H KI mice showed a response similar to control mice. Differential gene expression analysis identified altered expressions of genes responsible for ER stress/unfolded protein response and intracellular signalling in Trem2 R47H KI mice. Among the differentially expressed genes, Pmel and Gpnmb were or tended to be downregulated in Trem2 R47H KI as well as in Trem2 KO mice indicating their involvement in AD pathogenesis. These results clearly indicate that the TREM2 R47H variant confers a mild, rather than null, effect on microglial alterations during AD development and that Trem2 R47H KI mice should be used to understand pathological mechanism elicited by TREM2. Further identification and characterization of genes differentially expressed in Trem2 R47H KI mice will provide important insights into how the TREM2 risk variant modulates Alzheimers disease-related pathology. HighlightsO_LIExon2-humanized Trem2 R47H knock-in mice are established, which will serve as a platform to study the role of TREM2 in Alzheimers disease development. C_LIO_LITrem2 knockout mice exhibit deficits in clearance of highly aggregated A{beta}42, suppression of dystrophic neurites and regulation of microglial genes in AppNL-F mice, whereas Trem2 R47H knock-in mice do not. C_LIO_LIRNA-seq reveals transcriptional profiles of Trem2 R47H knock-in mice C_LIO_LIqPCR confirms that Gpnmb and Pmel are or tended to be downregulated in Trem2 R47H knock-in mice. C_LIO_LIFindings demonstrate that TREM2 R47H is hypomorphic rather than loss of function. C_LI

Temporal dynamics in functional connectomes provide a physiologically grounded signature of 'hidden' pathologies during preclinical stages of Alzheimer's disease (AD). We evaluated the effect of beta-amyloid (A{beta}) on dynamic functional connectomes in transgenic mice and human subjects. Functional magnetic resonance images (fMRI) were collected in two strains of A{beta} mice. fMRI-derived connectomes were segmented into discrete states using a hidden Markov model, and network strength, efficiency, and transitivity were analyzed per state. Human fMRI-derived connectome measures were analyzed across 3 states. Static network measures were significantly different between A{beta} mice and controls, the former having high values for strength, efficiency and clustering coefficient in anterior cingulate, hippocampus, and retrosplenium. Dynamic network measures were stable within-states in A{beta} mice. Similarly, human subjects with high A{beta} had high node strength in precuneus and temporoparietal areas compared to low A{beta}. Conversely, high A{beta} was associated with high switch rates, high fractional occupancy, and state dwell times. Also, global strength, efficiency, and transitivity were less stable within states in the high A{beta} group. Our results indicate that static, but not dynamic, connectome strength, efficiency, and network integration are increased in A{beta} mice, while dynamic network states appear less stable in human functional connectomes. This data supports a dissociable, species-specific impact of A{beta}, with dynamic network alterations present in humans but not in A{beta} mouse models, suggesting additional non-A{beta}-driven influences on dynamic functional connectivity in preclinical AD.

ObjectivesStage-sensitive markers can aid in early diagnosis of Alzheimers disease (AD) and can improve sensitivity, performance and interpretability. In this study, causal markers from longitudinal imaging data were extracted and integrated with risk factors to improve diagnostic models. Data DescriptionOASIS-3, a longitudinal dataset consisting of 613 controls and 214 cases with very mild to moderate Alzheimers disease is used for this study. A meta model was built using a predisposition model built from risk factors, a stage-sensitization model built from MRI markers at various stages of atrophy and a confirmatory model built using PET markers. The meta model achieved good diagnostic performance (accuracy = 93%, sensitivity = 80%, specificity = 95%). Exclusion of PET data achieved comparable performance (accuracy = 91%, sensitivity = 85%, specificity = 92%). The results demonstrate that integrating causal pathological markers with risk factors improves diagnosis and aids in elucidating stage-specific patterns of AD.

BackgroundEpidemiological studies consistently report inverse associations between caffeinated coffee consumption and dementia risk. However, the molecular mechanisms linking coffee-derived phytochemicals to neuroprotection remain only partially understood. ObjectiveTo evaluate, through integrated in silico pharmacology, the relative contribution of adenosine receptor modulation versus direct amyloidogenic enzyme and kinase inhibition in mediating the putative neuroprotective effects of major coffee constituents. MethodsMolecular docking analyses were conducted for caffeine, paraxanthine, chlorogenic acid, trigonelline, cafestol, and kahweol against adenosine A2A and A1 receptors (A2AR, A1R), {beta}-secretase 1 (BACE1), glycogen synthase kinase-3{beta} (GSK-3{beta}), and NLRP3 inflammasome components. Docking was performed using the CB-Dock2 platform. Binding affinities, interaction patterns, and ligand efficiency metrics were assessed. Blood-brain barrier permeability and ADMET properties were predicted using pkCSM. ResultsCaffeine and paraxanthine demonstrated structurally coherent binding within the orthosteric pockets of A2AR and A1R, supported by favorable predicted blood-brain barrier penetration and high unbound fractions. Ligand efficiency analysis identified adenosine receptors as the most pharmacologically plausible targets for small xanthine derivatives. Although larger phytochemicals exhibited stronger absolute docking scores at BACE1, GSK-3{beta}, and NLRP3, predicted pharmacokinetic constraints suggest a small biological effect due to a limited central exposure. ConclusionsThese findings support an adenosine receptor-centered mechanism as the dominant molecular axis linking caffeinated coffee consumption to reduced dementia risk, favoring neuroinflammatory and signaling modulation over direct enzymatic inhibition. Experimental validation is warranted to confirm translational relevance. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/723029v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1a02629org.highwire.dtl.DTLVardef@129890dorg.highwire.dtl.DTLVardef@1e4c05corg.highwire.dtl.DTLVardef@110ec7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Background: A recent Lancet Commission estimated that up to 45% of Alzheimers Disease and Related Dementias (ADRD) cases could be prevented by addressing modifiable lifestyle risk factors. Meanwhile, genome wide association studies (GWAS) have shown that common genetic variants also account for substantial ADRD risk. Whether a favorable lifestyle can offset risk in genetically predisposed individuals remains unclear. Methods: We conducted a retrospective cohort study of 105,886 participants from the All of Us Research Program enrolled between 2018 and 2023. Participants were over age 49, assigned male or female at birth, of European ancestry, and without ADRD at baseline. ADRD diagnoses were identified via electronic health records (EHR). Fourteen potentially modifiable risk factors for ADRD were assessed using surveys, EHR records, and wearable data. Genetic risk was quantified as a polygenic risk score (PRS) based on 81 independent GWAS loci and APOE E4 genotype. Results: Overall, 967 incident ADRD events occurred over a median follow-up of 3.7 years. Ten out of 13 modifiable risk factors were significantly associated with ADRD. When grouped into risk factor profiles, intermediate and unfavorable modifiable risk factor scores were associated with substantially higher ADRD risk (HR 3.07, 95% CI 2.47, 3.83; HR 8.01, 95% CI 6.39, 10.05, respectively) compared to a favorable lifestyle; APOE E4 dosage and polygenic risk score were also independently associated with ADRD risk. Among individuals in the highest polygenic risk group, a favorable lifestyle reduced ADRD risk from HR 18.63 (95% CI 10.25, 33.86) to 1.90 (95% CI 0.94, 3.81), whereas APOE E4 homozygotes remained at elevated risk even with a favorable lifestyle (HR 6.52, 95% CI 2.97, 14.33). Conclusions: Our data suggest ADRD risk is driven more by modifiable risk factors and APOE genotype than polygenic risk score. Future genomic informed risk assessments for ADRD should calibrate their findings to accurately identify high risk individuals.

INTRODUCTIONThe slow, age-related development of Alzheimers disease (AD) and inaccessibility of early-stage brain tissue necessitates model studies to understand its origins and progression. Non-human primate models can provide a platform for linking molecular changes to translatable phenotypes. Here, we assess fibroblast lines derived from marmosets with engineered variants in the PSEN1 gene. METHODSFibroblast cultures were obtained from 10 animals and assayed using a NanoString AD gene expression panel and label-free proteomics. We compared mutant expression changes to human AD signatures in human iPSC-derived neurons and postmortem brains to assess disease relevance. RESULTSGene products involved in amyloid-beta interaction and regulation were differentially expressed, providing evidence for the functional relevance of the engineered fibroblasts. Both gene and protein expression changes in the undifferentiated fibroblasts correlated with human iPSCs from AD donors reprogrammed into neuronal lineages, as well as postmortem brains derived from case-control cohorts. Altered expression profiles were noted based on marmoset donor sex and mutation status, highlighting underlying sex-specific biology relevant to Alzheimers disease. DISCUSSIONThese findings demonstrate that disease-relevant pathways and processes are altered in fibroblasts from mutant marmosets, emphasize the complementarity of transcriptomic and proteomic profiling in AD, and provide a roadmap for more advanced molecular studies of AD in aging marmosets and marmoset-derived cell models.

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.