Alzheimer's Disease Biomarkers Research Articles

Alzheimer's Disease-Related Cerebrospinal Fluid Biomarkers in Progressive Supranuclear Palsy.

Progressive Supranuclear Palsy (PSP) is the most common four-repeat tauopathy. PSP cases are typically characterized by vertical gaze palsy and postural instability; however, various phenotypes have been reported, making antemortem diagnosis based on clinical symptoms challenging. The development of biomarkers reflecting brain pathology and the ability to diagnose patients based on these biomarkers are essential for developing future intervention strategies, including disease-modifying therapies. However, despite many dedicated efforts, no highly specific fluid biomarker for PSP has yet been established. Conversely, several cerebrospinal fluid (CSF) biomarkers of Alzheimer's Disease (AD) have been established, and an AT(N) classification system has been proposed. Typically, among patients with AD, CSF amyloid β42 (Aβ42), but not Aβ40, is decreased, resulting in a reduction in the Aβ42/Aβ40 ratio, while tau phosphorylated at threonine 181 (p-tau181) and total tau (t-tau) are increased. Interestingly, the core CSF AD biomarkers show unique patterns in patients with PSP. Furthermore, reports have indicated that the CSF levels of both Aβ42 and Aβ40 are decreased independently of Aβ accumulation in PSP. Therefore, the Aβ42/Aβ40 ratio could potentially be used to differentiate PSP from AD. Additionally, studies have reported that CSF p-tau and t-tau are reduced in PSP, and that the neurofilament light chain is remarkably increased compared to healthy controls and patients with AD, even though PSP is a neurodegenerative disease associated with tau accumulation. These PSP-specific changes in AD-related core biomarkers may reflect the pathology of PSP and contribute to its diagnosis. As such, elucidating the mechanisms underlying the observed decreases in Aβ and tau levels could facilitate a better understanding of the pathogenesis of PSP.

A survey study of Alzheimer’s stigma among Black adults: intersectionality of Black identity and biomarker diagnosis

ABSTRACT Objective We urgently need to understand Alzheimer’s disease (AD) stigma among Black adults. Black communities bear a disproportionate burden of AD, and recent advances in early diagnosis using AD biomarkers may affect stigma associated with AD. The goal of our study is to characterize AD stigma within our cohort of self-identified Black participants and test how AD biomarker test results may affect this stigma. Design We surveyed a sample of 1,150 self-identified Black adults who were randomized to read a vignette describing a fictional person, who was described as either having a positive or negative biomarker test result. After reading the vignette, participants completed the modified Family Stigma in Alzheimer’s Disease Scale (FS-ADS). We compared FS-ADS scores between groups defined by age, gender, and United States Census region. We examined interactions between these groupings and AD biomarker test result. Results Participants over age 65 had lower scores (lower stigma) on all 7 FS-ADS domains compared to those under 65: structural discrimination, negative severity attributions, negative aesthetic attributions, antipathy, support, pity, and social distance. In the biomarker positive condition, worries about structural discrimination were greater than in the biomarker negative condition and statistically similar in the two age groups (DOR, 0.39 [95%CI, 0.22–0.69]). This pattern of results was similar for negative symptom attributions (DOR, 0.51 [95%CI, 0.28–0.90]). Conclusion While older adults reported less AD stigma than younger adults, AD biomarker testing caused similarly high concerns about structural discrimination and negative severity attributions. Thus, use of AD biomarker diagnosis may increase AD stigma and exacerbate healthcare disparities known to effect AD diagnosis in some Black adults. Advances in AD diagnosis may interact with social and structural factors to differentially affect groups of Black adults.

Integrated analyses of 5 mC, 5hmC methylation and gene expression reveal pathology-associated AKT3 gene and potential biomarkers for Alzheimer's disease

Aims5 mC methylation and hydroxymethylation (5hmC) are associated with Alzheimer's disease (AD). However, previous studies were limited by the absence of a 5hmC calculation. This study aims to find AD associated predictors and potential therapeutic chemicals using bioinformatics approach integrating 5 mC, 5hmC, and expression changes, and an AD mouse model. MethodsGene expression microarray and 5 mC and 5hmC sequencing datasets were downloaded from GEO repository. 142 AD and 52 normal entorhinal cortex specimens were enrolled. Data from oxidative bisulfite sequencing (oxBS)-treated samples, which represent only 5 mC, were used to calculate 5hmC level. Functional analyses, random forest supervised classification and methylation validation were applied. Potential chemicals were predicted by CMap. Morris water maze, Y maze and novel object recognition behavior tests were performed using FAD4T AD mice model. Cortex and hippocampus tissues were isolated for immunohistochemical staining. ResultsC1QTNF5, UBD, ZFP106, NEDD1, AKT3, and MBP genes involving 13 promoter CpG sites with 5mc, 5hmC methylation and expression difference were identified. AKT3 and MBP were down-regulated in both patients and mouse model. Three CpG sites in AKT3 and MBP showed significant methylation difference on validation. FAD4T AD mice showed recession in brain functions and lower AKT3 expression in both cortex and hippocampus. Ten chemicals were predicted as potential treatments for AD. ConclusionsAKT3 and MBP may be associated with AD pathology and could serve as biomarkers. The ten predicted chemicals might offer new therapeutic approaches. Our findings could contribute to identifying novel markers and advancing the understanding of AD mechanisms.

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Association of polygenic risk scores with Alzheimer's disease and plasma biomarkers among Chinese older adults: A community-based study.

We examined the associations of polygenic risk score (PRS) with Alzheimer's disease (AD) and plasma biomarkers in the Chinese population. This population-based study used baseline data from MIND-China (2018; n=4873) and follow-up data from dementia-free individuals (2014-2018; n=2117). We measured AD-related plasma biomarkers in a subsample (n=1256). Data were analyzed using logistic and Cox regression models. We developed PRS with (PRSAPOE) and without (PRSnon- APOE) apolipoprotein E (APOE) gene. In the longitudinal analysis, PRSAPOE was associated with a multivariable-adjusted hazards ratio of 1.91 (95% CI=1.13-3.23) for AD. PRSAPOE in combination with demographics yielded discriminative (area under the curve [AUC]) and predictive(C-statistic) accuracy of 0.80 (95% confidence interval [CI]=0.77-0.84) and 0.80 (0.77-0.82), respectively. PRSnon- APOE showed an association with AD risk similar to PRSAPOE. PRSAPOE, but not PRSnon- APOE, was associated with reduced plasma Aβ42/Aβ40 ratio and increased Neurofilament light chain (NfL) (p<0.05). The PRS with and without APOE gene, in combination with demographics, shows good discriminative and predictive ability for AD. The AD-related pathologies underlie AD risk associated with PRSAPOE. The PRSAPOE and PRSnon- APOE were associated with AD risk in the Chinese population. The PRSAPOE and PRSnon- APOE, in combination with demographics, showed good discriminative and predictive ability for AD. The AD-related pathologies underlie the AD risk associated with PRSAPOE but not PRSnon- APOE.

SpectroCVT-Net: A convolutional vision transformer architecture and channel attention for classifying Alzheimer’s disease using spectrograms

Dementia arises from various brain-affecting diseases and injuries, with Alzheimer’s disease being the most prevalent, impacting around 55 million people globally. Current clinical diagnosis often relies on biomarkers indicative of Alzheimer’s distinctive features. Electroencephalography (EEG) serves as a cost-effective, user-friendly, and safe biomarker for early Alzheimer’s detection. This study utilizes EEG signals processed with Short-Time Fourier Transform (STFT) to generate spectrograms, facilitating visualization of EEG signal properties. Leveraging the Brainlat database, we propose SpectroCVT-Net, a novel convolutional vision transformer architecture incorporating channel attention mechanisms. SpectroCVT-Net integrates convolutional and attention mechanisms to capture local and global dependencies within spectrograms. Comprising feature extraction and classification stages, the model enhances Alzheimer’s disease classification accuracy compared to transfer learning methods, achieving 92.59 ± 2.3% accuracy across Alzheimer’s, healthy controls, and behavioral variant frontotemporal dementia (bvFTD). This article introduces a new architecture and evaluates its efficacy with unconventional data for Alzheimer’s diagnosis, contributing: SpectroCVT-Net, tailored for EEG spectrogram classification without reliance on transfer learning; a convolutional vision transformer (CVT) module in the classification stage, integrating local feature extraction with attention heads for global context analysis; Grad-CAM analysis for network decision insight, identifying critical layers, frequencies, and electrodes influencing classification; and enhanced interpretability through spectrograms, illuminating key brain wave contributions to Alzheimer’s, frontotemporal dementia, and healthy control classifications, potentially aiding clinical diagnosis and management.

Interval-specific likelihood ratios and probability-based models for interpreting combined CSF biomarkers for Alzheimer’s disease

BackgroundIn Alzheimer’s disease (AD) diagnosis, a cerebrospinal fluid (CSF) biomarker panel is commonly interpreted with binary cutoff values. However, these values are not generic and do not reflect the disease continuum. We explored the use of interval-specific likelihood ratios (LRs) and probability-based models for AD using a CSF biomarker panel. MethodsCSF biomarker (Aβ1-42, tTau and pTau181) data for both a clinical discovery cohort of 241 patients (measured with INNOTEST) and a clinical validation cohort of 129 patients (measured with EUROIMMUN), both including AD and non-AD dementia/cognitive complaints were retrospectively retrieved in a single-center study. Interval-specific LRs for AD were calculated and validated for univariate and combined (Aβ1-42/tTau and pTau181) biomarkers, and a continuous bivariate probability-based model for AD, plotting Aβ1-42/tTau versus pTau181 was constructed and validated. ResultsLR for AD increased as individual CSF biomarker values deviated from normal. Interval-specific LRs of a combined biomarker model showed that once one biomarker became abnormal, LRs increased even further when another biomarker largely deviated from normal, as replicated in the validation cohort. A bivariate probability-based model predicted AD with a validated accuracy of 88% on a continuous scale. ConclusionsInterval-specific LRs in a combined biomarker model and prediction of AD using a continuous bivariate biomarker probability-based model, offer a more meaningful interpretation of CSF AD biomarkers on a (semi-)continuous scale with respect to the post-test probability of AD across different assays and cohorts.

The Alzheimer's Association Global Biomarker Standardization Consortium (GBSC) plasma phospho-tau Round Robin study.

Phosphorylated tau (p-tau) is a specific blood biomarker for Alzheimer's disease (AD) pathology. Multiple p-tau biomarkers on several analytical platforms are poised for clinical use. The Alzheimer's Association Global Biomarker Standardisation Consortium plasma phospho-tau Round Robin study engaged assay developers in a blinded case-control study on plasma p-tau, aiming to learn which assays provide the largest fold-changes in AD compared to non-AD, have the strongest relationship between plasma and cerebrospinal fluid (CSF), and show the most consistent relationships between methods (commutability) in measuring both patient samples and candidate reference materials (CRM). Thirty-three different p-tau biomarker assays, built on eight different analytical platforms, were used to quantify paired plasma and CSF samples from 40 participants. AD biomarker status was categorised as "AD pathology" (n=25) and "non-AD pathology" (n=15) by CSF Aβ42/Aβ40 (US-FDA; CE-IVDR) and p-tau181 (CE-IVDR) methods. The commutability of four CRM, at three concentrations, was assessed across assays. Plasma p-tau217 consistently demonstrated higher fold-changes between AD and non-AD pathology groups, compared to other p-tau epitopes. Fujirebio LUMIPULSE G, UGOT IPMS, and Lilly MSD p-tau217 assays provided the highest median fold-changes. In CSF, p-tau217 assays also performed best, and exhibited substantially larger fold-changes than their plasma counterparts, despite similar diagnostic performance. P-tau217 showed the strongest correlations between plasma assays (rho=0.81 to 0.97). Plasma p-tau levels were weakly-to-moderately correlated with CSF p-tau, and correlations were non-significant within the AD group alone. The evaluated CRM were not commutable across assays. Plasma p-tau217 measures had larger fold-changes and discriminative accuracies for detecting AD pathology, and better agreement across platforms than other plasma p-tau variants. Plasma and CSF markers of p-tau, measured by immunoassays, are not substantially correlated, questioning the interchangeability of their continuous relationship. Further work is warranted to understand the pathophysiology underlying this dissociation, and to develop suitable reference materials facilitating cross-assay standardisation. Alzheimer's Association (#ADSF-24-1284328-C).

A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer’s Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA’s potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.

The role of GZMA as a target of cysteine and biomarker in Alzheimer's disease, pelvic organ prolapse, and tumor progression.

Objective: This study aims to investigate how changes in peripheral blood metabolites in Alzheimer's Disease (AD) patients affect the development of Pelvic Organ Prolapse (POP) using a multi-omics approach. We specifically explore the interactions of signaling pathways, gene expression, and protein-metabolite interactions, with a focus on GZMA and cysteine in age-related diseases. Methods: This study utilized multi-omics analysis, including metabolomics and transcriptomics, to evaluate the perturbations in peripheral blood metabolites and their effect on POP in AD patients. Additionally, a comprehensive pan-cancer and immune infiltration analysis was performed on the core targets of AD combined with POP, exploring their potential roles in tumor progression and elucidating their pharmacological relevance to solid tumors. Results: We identified 47 differential metabolites linked to 9 significant signaling pathways, such as unsaturated fatty acid biosynthesis and amino acid metabolism. A thorough gene expression analysis revealed numerous differentially expressed genes (DEGs), with Gene Set Enrichment Analysis (GSEA) showing significant changes in gene profiles of AD and POP. Network topology analysis highlighted central nodes in the AD-POP co-expressed genes network. Functional analyses indicated involvement in critical biological processes and pathways. Molecular docking studies showed strong interactions between cysteine and proteins PTGS2 and GZMA, and molecular dynamics simulations confirmed the stability of these complexes. In vitro validation demonstrated that cysteine reduced ROS levels and protected cell viability. GZMA was widely expressed in various cancers, associated with immune cells, and correlated with patient survival prognosis. Conclusion: Multi-omics analysis revealed the role of peripheral blood metabolites in the molecular dynamics of AD and their interactions with POP. This study identified potential biomarkers and therapeutic targets, emphasizing the effectiveness of integrative approaches in treating AD and POP concurrently. The findings highlight the need for in-depth research on novel targets and biomarkers to advance therapeutic strategies.

Proteomics of plasma-derived extracellular vesicles reveals S100A8 as a novel biomarker for Alzheimer's disease: A preliminary study

Extracellular vesicles (EVs) act as mediators for intercellular transfer of Aβ and tau proteins, promoting the propagation of these pathological misfolded proteins throughout the brain in Alzheimer's disease (AD). Levels of blood exosomal Aβ42, total Tau (t-Tau) and phosphorylated Tau (p-Tau) had a high correlation with their concentrations in cerebrospinal fluid (CSF), demonstrating that exosomal biomarkers have equal contribution as those in CSF for the diagnosis of AD. We aimed to comprehensively characterize the proteome of plasma-derived EVs to identify differentially expressed proteins (DEPs) and pathways in AD. Tandem mass tag (TMT) labeled quantitative proteomics was applied to analyze plasma-derived EV proteins in 9 AD patients and 9 healthy controls. 335 proteins were quantified, and 12 DEPs were identified including seven upregulated proteins and five down-regulated proteins. Oligomerized Aβ1–42 induced SH-SY5Y cell damage model was built to mimic the pathological changes of AD, and small interfering RNA (siRNA) against S100A8 was used to knock down S100A8 expression. Results displayed S100A8 was down regulated in plasma-derived EVs from AD patients, while enriched in EVs derived from Aβ1–42-induced SH-SY5Y cells. Furthermore, Aβ1–42-induced SH-SY5Y cells treated with S100A8 siRNA showed decreased Aβ levels in cell lysate and EVs, especially in EVs. SignificanceThe investigation aimed to comprehensively characterize the proteome of plasma-derived EVs to identify DEPs and potential biomarker of AD. S100A8 was found down regulated in plasma-derived EVs from AD patients using TMT labeled quantitative proteomics. The diagnostic value of S100A8 was also confirmed using receiver operating characteristic curve (ROC) analysis. Furthermore, Aβ1–42-induced SH-SY5Y cells treated with S100A8 siRNA showed decreased Aβ levels in cell lysate and EVs, especially in EVs. The preliminary findings suggest that suppression of S100A8 expression inhibits Aβ aggregation both in cell lysate and EVs from Aβ1–42-induced SH-SY5Y cells, and S100A8 more likely regulates Aβ aggregation via EVs. Therefore, plasma-derived EV S100A8 might be a potential biomarker of AD. Manipulation of S100A8 expression may be a novel therapeutic strategy in the treatment of AD.

CSF markers of neurodegeneration Alzheimer’s and Lewy body pathology in isolated REM sleep behavior disorder

We investigated the biomarker profile of neurodegeneration, Alzheimer’s and Lewy body pathology in the CSF of 148 polysomnography-confirmed patients with isolated REM sleep behavior disorder (IRBD), a condition that precedes Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We assessed misfolded α-synuclein (AS) by RT-QuIC assay, amyloid-beta peptides (Aβ42 and Aβ40), phosphorylated tau (p-tau), and total tau (t-tau) by CLEIA and neurofilament light chain (NfL) by ELISA. We detected AS in 75.3% of patients, pathologically decreased Aβ42/Aβ40 ratio in 22.5%, increased p-tau in 15.5%, increased t-tau in 14.9%, and elevated NfL in 14.7%. After a mean follow-up of 2.48 ± 2.75 years, 47 (38.1%) patients developed PD (n = 24) or DLB (n = 23). At CSF collection, AS positivity [HR 4.05 (1.26–12.99), p = 0.019], mild cognitive impairment [3.86 (1.96–7.61), p < 0.001], and abnormal DAT-SPECT [2.31 (1.09–4.91), p < 0.030] were independent predictors of conversion to PD and DLB. Among the other CSF markers, only elevated p-tau/Aβ42 was predictive of conversion, although only to DLB and not as an independent variable. In IRBD, CSF AS assessment by RT-QuIC provides an added value in defining the risk of short-term conversion to PD and DLB independent of clinical and instrumental investigations. Positive Alzheimer's disease (AD) pathology markers and elevated NfL occur in a subgroup of patients, but p-tau/Aβ42 is the only marker that predicts short-term conversion to DLB. Longer follow-up is needed to assess if AD biomarkers predict the later development of PD and DLB in IRBD.

Blood-brain barrier-associated biomarker correlated with cerebral small vessel disease and shunt outcome in normal pressure hydrocephalus: a prospective cohort study.

Blood-brain barrier (BBB) breakdown is associated with neurodegeneration and cognitive impairment. Cerebral small vessel disease (CSVD) is also common in idiopathic normal pressure hydrocephalus (iNPH). Biomarkers in the cerebrospinal fluid (CSF) may reflect the severity of neuropathological damage and indicate a relationship between BBB integrity and iNPH and its surgical outcome. We investigated the association of CSVD and comorbidity-related CSF biomarkers with shunt outcomes in iNPH. This prospective cohort study recruited 53 patients with iNPH, who were subgrouped by CSVD severity. CSF proteins were analyzed, including soluble platelet-derived growth factor receptor-β (sPDGFR-β), Alzheimer's disease biomarkers, neurofilament light chain (NfL), and triggering receptor expressed on myeloid cells 2 (Trem2). We assessed symptom improvement, investigated its association with biomarkes levels, calculated protein cutoffs for surgical outcomes using receiver operating characteristic (ROC) curves, and compared model predictions using different proteins using hierarchical regression analysis. Among patients with iNPH, 74% had comorbid CSVD. Patients with severe CSVD exhibited significantly higher sPDGFR-β levels (P=0.019) and better postoperative performance (β=0.332, t=2.174, P=0.039; r=0.573, P=0.001). Analysis of the predictive potential of the biomarkers showed that sPDGFR-β was predictive of surgical outcomes (area under curve=0.82, sensitivity=66.8%, specificity=94.7%). A Comparison of the models revealed a greater effect of sPDGFR-β (Adjusted R2=0.247, ∆R2=0.160, ∆F(1, 37)=8.238, P=0.007) on cognitive improvement. This study highlighted the relevance of CSF biomarkers in assessing CSVD severity and predicting iNPH surgical outcome. CSF shunt surgery may provide an alternative treatment for neurodegenerative diseases with BBB breakdown and dysfunctional CSF clearance.

Neuroimaging correlates of Alzheimer's disease biomarker concentrations in a racially diverse high-risk cohort of middle-aged adults.

In this study, we investigated biomarkers in a midlife, racially diverse, at-risk cohort to facilitate early identification and intervention. We examined neuroimaging measures, including resting state functional magnetic resonance imaging (fMRI), white matter hyperintensity vo (WMH), and hippocampal volumes, alongside cerebrospinal fluid (CSF) markers. Our data set included 76 cognitively unimpaired, middle-aged, Black Americans (N=29, F/M=17/12) and Non-Hispanic White (N=47, F/M=27/20) individuals. We compared cerebrospinal fluid phosphorylated tau141 and amyloid beta (Aβ)42 to fMRI default mode network (DMN) subnetwork connectivity, WMH volumes, and hippocampal volumes. Results revealed a significant race × Aβ42 interaction in Black Americans: lower Aβ42 was associated with reduced DMN connectivity and increased WMH volumes regions but not in non-Hispanic White individuals. Our findings suggest that precuneus DMN connectivity and temporal WMHs may be linked to Alzheimer's disease risk pathology during middle age, particularly in Black Americans. Cerebrospinal fluid (CSF) amyloid beta (Aβ)42 relates to precuneus functional connectivity in Black, but not White, Americans. Higher white matter hyperintensity volume relates to lower CSF Aβ42 in Black Americans. Precuneus may be a hub for early Alzheimer's disease pathology changes detected by functional connectivity.

Novel β-amyloid PET Imaging Study of [18F]92 in Patients with Cognitive Decline.

[18F]-4-((E)-(((E)-4-(2-(2-(2-Fluoroethoxy)ethoxy)ethoxy)benzylidene)-hydrazono)methyl)-N-methylaniline ([18F]92) is a novel positron emission tomography (PET) tracer previously reported to exhibit high binding affinity to aggregated β-amyloid (Aβ). This study aims to report a fully automated radiosynthesis procedure for [18F]92, explore its radioactive distribution in the brains of healthy subjects, and investigate its potential application value in the early diagnosis of Alzheimer's disease (AD). The fully automated radiosynthesis of [18F]92 was performed on the AllinOne module. Thirty one participants were recruited for this study. Dynamic [18F]92 PET imaging was conducted over 0-90 min period to assess time-activity curves (TAC) and standardized uptake value ratio (SUVR) curves in cognitively normal (CN) subjects. All participants were visually classified as either positive (+) or negative (-). Semiquantitative analyses of [18F]92 were performed by calculating SUVRs in different regions of interest. Furthermore, the study analyzed the relationships between global SUVR and plasma AD biomarkers, including Aβ42, Aβ40, P-tau181, and T-tau. The automated radiosynthesis of [18F]92 was completed within 50 min, yielding a radiochemical purity of greater than 95% and a radiochemical yield of 36 ± 3% (nondecay-corrected). Among the participants, 15 were estimated as Aβ (-) and 16 as Aβ (+). TACs indicated that [18F]92 rapidly crossed the blood-brain barrier within 10 min, followed by a rapid decrease, which then slowed down in the last 50-90 min. SUVR curves revealed that SUVR values stabilized around 60-70 min after injection and reached an equilibrium between 70 and 90 min, primarily in the cerebral cortex. SUVRs of Aβ (+) participants were significantly higher than those of Aβ (-) individuals within the cerebral cortex. In addition, Aβ42 and the Aβ42/Aβ40 ratio exhibited negative correlations with global SUVR, while plasma P-tau181 and the P-tau181/T-tau ratio displayed positive correlations with global SUVR. [18F]92 exhibits excellent pharmacokinetic properties in the human brain and can be synthesized automatically on a large scale. [18F]92 is a promising and reliable radiotracer for estimating Aβ pathology accumulation, providing valuable assistance in AD diagnosis and guiding clinical trials of therapeutic drugs.

Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia.

Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE ε4 dose, CSF β-amyloid (Aβ) 1-42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1-4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1-42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1-42, age, APOE ε4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1-42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4-6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder afflicting the elderly population worldwide. The identification of potential gene candidates for AD holds promises for diagnostic biomarkers and therapeutic targets. Employing a comprehensive strategy, this study integrated transcriptomic data from diverse data sources, including microarray and single-cell datasets from blood and tissue samples, enabling a detailed exploration of gene expression dynamics. Through this thorough investigation, 19 notable candidate genes were found with consistent expression changes across both blood and tissue datasets, suggesting their potential as biomarkers for AD. In addition, single cell sequencing analysis further highlighted their specific expression in excitatory and inhibitory neurons, the primary functional units in the brain, underscoring their relevance to AD pathology. Moreover, the functional enrichment analysis revealed that three of the candidate genes were downregulated in synaptic signaling pathway. Further validation experiments significantly showed reduced levels of rabphilin-3A (RPH3A) in 3xTg-AD model mice, implying its role in disease pathogenesis. Given its role in neurotransmitter exocytosis and synaptic function, further investigation into RPH3A and its interactions with neurotrophic proteins may provide valuable insights into the complex molecular mechanisms underlying synaptic dysfunction in AD.

Insights from a 7-Year Dementia Cohort (VALCODIS): ApoE Genotype Evaluation.

Background: The VALCODIS (Valencian Cognitive Diseases Study) cohort was designed and studied at the Hospital Universitari i Politècnic La Fe (Valencia, Spain) for the research of cognitive diseases, especially in the search for new biomarkers of Alzheimer's disease (AD). Methods: Participants in the VALCODIS cohort had cerebrospinal fluid (CSF) and blood samples, neuroimaging, and neuropsychological tests. The ApoE genotype was evaluated to identify its relationship with CSF biomarkers and neuropsychological tests in AD and non-AD participants. Results: A total of 1249 participants were included. They were mainly AD patients (n = 547) but also patients with other dementias (frontotemporal lobar dementia (n = 61), Lewy body dementia without AD CSF signature (n = 10), vascular dementia (n = 24) and other specific causes of cognitive impairment (n = 442), and patients with subjective memory complaints (n = 165)). In the ApoE genotype evaluation, significant differences were found for Aβ42 levels between genotypes in both AD and non-AD patients, as well as a negative correlation between tau values and a cognitive test in non-carriers and ε4 heterozygous. Conclusions: The VALCODIS cohort provides biologically diagnosed patients with demographical, clinical and biochemical data, and biological samples for further studies on early AD diagnosis. Also, the ApoE genotype evaluation showed correlations between CSF biomarkers and neuropsychological tests.

Proteomic analyses reveal plasma EFEMP1 and CXCL12 as biomarkers and determinants of neurodegeneration.

Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes. We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n=815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk. Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization. Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration. Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

Psychosocial implications of learning amyloid PET results in an observational cohort.

Information on the psychosocial impact of Alzheimer's disease (AD) biomarker testing in adults at risk of AD is needed to inform best practices for communicating biomarker results. Ninety-nine cognitively unimpaired older adults learned amyloid positron emission tomography (PET) results (mean age=72.0±4.8, 95% White, 28% elevated amyloid). Linear mixed-effects regression models were used to test the main effects and interaction of PET result × time on psychosocial outcomes up to 6 months after learning results. A significant interaction of PET result × time was observed for concern about AD (β=0.28, p=0.02) and intrusive thoughts and avoidance (β=-0.82, p<0.001). A main effect of PET result was observed for AD test-related distress (β=12.09, p<0.001). Cognitively unimpaired adults learning elevated-amyloid PET results reported mildly intrusive thoughts/avoidance initially following disclosure, but these symptoms decreased over time. Concern about AD dementia and AD biomarker test-related distress remained higher in elevated-amyloid compared to non-elevated-amyloid participants. Longitudinal assessment of psychosocial reactions after amyloid PET disclosure was conducted. Transient highly intrusive thoughts or avoidance after learning elevated amyloid results. Persistent test result-related distress after receiving elevated-amyloid results. There is increased concern about AD dementia after receiving elevated-amyloid results. Happiness and relief are experienced after receiving non-elevated-amyloid results.