bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma phospho‐tau predicts differences in white matter microstructural complexity and cognition in non‐demented older adults
Authors: Bender Andrew R. , Dubois Kelly N. , Grabinski Tessa , Peltier Scott , Giordani Bruno , Paulson Henry L. , Kanaan Nicholas M , Hampstead Benjamin M. 
Published: December 2022

AbstractBackgroundWhite matter (WM) measures from diffusion magnetic resonance imaging (dMRI) are sensitive to Alzheimer’s disease (AD) pathology and cognitive impairment. Whereas most extant dMRI studies focused on uniformly‐oriented WM fiber tracts, variations in crossing fiber regions also show sensitivity to mild cognitive impairment (MCI) and AD diagnoses. Such crossing fiber alterations may reflect differential disease effects on constituent fibers, suggesting that quantifying crossing fibers may provide unique markers of early WM changes and neurocognitive risk in AD and MCI. Although few methods exist for quantifying crossing fibers, recent findings show older age is associated with increased microstructural complexity (CX) a novel method to characterize in crossing fibers. The present study evaluated CX in relation to variation in plasma AD biomarkers and cognitive performance in older adults without dementia.MethodData from 48 participants included clinical evaluation, neuropsychological assessment, dMRI neuroimaging and venipuncture. SIMOA assays quantified the AD plasma biomarkers Aβ42, Aβ40, and tau phosphorylated at threonine181 (pTau‐181). Participants were clinically characterized as cognitively normal (n=19) or with MCI (amnestic: n=18; non‐amnestic: n=11). Diagnostic groups did not differ with respect to years of age and education, systolic and diastolic blood pressure, proportions of men and women, proportions of APOE ε4 allele carriers, or plasma biomarker levels. Processing of dMRI data followed the MRtrix fixel‐based analysis (FBA) framework to estimate voxelwise CX data for all participants.ResultVoxelwise regression of whole‐brain CX on pTau‐181 levels revealed a significant positive effect in the dorsal cingulum bundle and adjacent corpus callosum. Post hoc regression of mean CX sampled from significant voxels on pTau‐181 showed greater pTau‐181 level strongly predicts higher CX in this region (partial‐R=0.579, p<0.001), even while controlling for age, education, and Montreal Cognitive Assessment (MoCA) score. Path analysis linking pTau‐181 and cognition showed elevated circulating pTau‐181 levels strongly predict increased white matter complexity in mid‐cingulum bundle, which in turn predicts reduced cognitive performance on measures of working memory, list learning, and semantic fluency.ConclusionMicrostructural complexity in crossing fiber WM regions provides a sensitive marker of early WM alterations associated with elevated plasma phospho‐tau and cognitive decrements in non‐demented older adults.
A new LC‐MS/MS assay for the quantification of Aβ40 and Aβ42 in plasma: validation and clinical performance
Authors: Weber Darren M , Kim Jueun C , Goldman Scott , Racke Michael K , Clarke Nigel J 
Published: December 2022

AbstractBackgroundEarly detection of Alzheimer’s disease (AD) represents an unmet clinical need. Cerebrospinal fluid (CSF) biomarkers can potentially fulfil this need, but specimen collection is invasive. Blood‐derived biomarkers are preferable, and several studies have shown an association between the Aβ42/40 ratio and amyloid PET status. However, blood diagnostic tests for AD are challenging due to interlaboratory differences in analytical techniques and preanalytical handling. Here we describe the development and validation of a LC‐MS/MS assay to quantify Aβ40 and Aβ42 from plasma and assess clinical performance.MethodAβ40 and 42 peptides were simultaneously immunoprecipitated prior to protein digestion and LC‐MS/MS analysis. Blood collection studies were conducted to determine the optimal specimen type, tube type, and storage conditions. Clinical validation studies were performed in a cohort of 209 clinically assessed individuals with PET data (67 cognitively normal, 66 with early MCI, 37 with late MCI, and 39 with AD).ResultBoth Aβ40 and 42 showed a linear measurement range of 10‐2500 pg/mL, with R2 values ≥0.99. Assay precision ranged from 2.6‐9.0% for Aβ40, Aβ42, and Aβ42/40. Plasma specimens were stable for up to 5 freeze‐thaw cycles, 6 hours at room temperature, 5 days refrigerated, at least 32 days frozen and 9 months at ultra‐low temperature. Compared to other tube types, blood collected in K2EDTA tubes had an average analyte response that was 1.6‐fold higher for both Aβ40 and 42 (range 1.4‐4.0 fold). Peptide recovery was higher in plasma than in serum (1.2‐1.4 fold higher for Aβ40 and 42). Variation in the internal standard signal, a measure of overall assay variability, was <10% for K2EDTA compared to >20% for other tube types (range 24.6‐33.3%). The test accurately differentiated PET‐positive from PET‐negative individuals (AUROC=0.862 95% CI 0.814‐0.910). At the Aβ42/40 cut‐point of 0.160, the assay has a sensitivity of 71% and specificity of 89% for detecting PET‐positivity, which is suggestive of AD.ConclusionWe optimized the analytical performance of an LC‐MS/MS assay for plasma Aβ40 and Aβ42. Preanalytical factors, including specimen collection tube types, were key for reproducible and accurate quantification. Performance characteristics were similar to those reported in the literature using CSF specimens.
Longitudinal analysis of auditory dysfunction in Alzheimer’s disease mice 5xFAD
Authors: Na Daxiang 
Published: December 2022

AbstractBackgroundThere is no cure for AD at this moment. All available therapies require accurate diagnosis and staging of AD to ensure proper strategy. Current diagnosis methods rely on high‐cost technologies with radioactive agents, and those are not necessarily included in regular health screenings. Thus, a low‐cost, non‐invasive approach of AD analysis is in great need. The comorbidity between Alzheimer’s disease and hearing loss has been shown to be significant. Beyond hearing loss, AD patients also showed auditory processing disorders. However, it remains unclear if auditory deficits can be a good biomarker for AD analysis, especially for early stages.Method5xFAD animals were bred in CBA/B6 hybrid background to compensate the hearing loss deficits in C57/B6 strain and only F1 animals were used in this study. The auditory features were monitored by Auditory Brainstem Response (ABR) test and Distortion Product Otoacoustic Emission (DPOAE) test at 3, 6, 9 and 12‐month‐old, which correlate to young ‐ middle age of human lifespan. Their anti‐masking ability was evaluated by masking ABR and notched noise ABR in 3 and 12‐month‐old animals. Fluorescent immunostaining was performed to evaluate anatomical change in cochlea.ResultAD animals showed accelerated age‐related hearing loss comparing to their WT littermates. The ABR threshold showed significant change at lower frequencies first, then extended to all testing frequencies. DPOAE threshold did not show significant change at any testing frequency. Aged AD animals showed significantly higher threshold increase in masking ABR and notched‐noise ABR. In addition to the increased hearing threshold, ABR waveform abnormalities were also observed in the AD animals. Longitudinal analysis revealed that ABR waveform abnormalities develop along with the progression of AD. Those abnormalities suggest auditory neuropathy and auditory processing deficits in AD animals. Histology analysis showed no significant change in synapse number or efference morphology.ConclusionAuditory dysfunction is a possible outcome of AD, and more likely reflecting the anatomical changes in central nervous system rather than periphery. Auditory dysfunction is a good candidate as a biomarker for AD progression at early stages.
Applying Data‐Independent Acquisition Mass Spectrometry on Cerebrospinal Fluid Samples from a Neurology Clinic for Biomarker Discovery in Alzheimer’s Disease
Authors: de Geus Matthijs B , Leslie Shannon N , Ramirez Christopher E. , Trombetta Bianca A. , Wang Weiwei , Lam Tukiet , Gotti Clarisse , Roux‐Dalvai Florence , Droit Arnaud , Nairn Angus C , Arnold Steven E. , Carlyle Becky C. 
Published: December 2022

AbstractBackgroundDiagnosis of Alzheimer’s disease (AD) primarily relies on cognitive assessments combined with imaging and limited fluid biomarkers. These biomarkers do not correlate with cognitive prognosis. Analysis of larger sets of biomarkers could improve diagnosis and prognosis of AD and differentiate between comorbid pathophysiologies, and measure treatment efficacy. Here, we report analysis of a Data‐independent Acquisition (DIA) mass‐spectrometry method used to simultaneously quantify hundreds of proteins in a large‐scale patient cohort. We aim to define markers that are able to stratify and diagnose AD in this clinically complex cohort.MethodSamples from 408 patients spanning various dementia diagnoses were collected from the Massachusetts General Hospital Lumbar Puncture clinic. The patient cohort consisted of ATN‐verified cognitively‐unimpaired (n = 81), mild‐cognitive impairment from AD (n = 116), mild‐cognitive impairment from other causes (n = 78), dementia from AD (n = 65) and dementia from other causes (n = 31). Samples were analyzed on an Orbitrap Fusion using a DIA method and raw files were searched using Scaffold‐DIA.ResultAnalyses of the method’s technical performance showed a median intrabatch CV of 24.7% which decreased to 17.5% after filtering for peptides expressed in 95% of all samples. The mean interbatch CV was 73.7% which reduced to 28.8% after ComBat batch‐correction. After filtering for missing values and selecting only the peptides with a mean intrabatch CV below 25%, our dataset consists of 1761 unique peptide sequences belonging to 516 proteins.To determine the differential abundance of peptides we fit a linear regression model to the data. 572 peptides were found to be differentially abundant across all diagnoses compared to AD (padj < 0.05). 11 of these peptides belonging to 5 proteins (PKM, ALDOA, GUAD, BASP1 and CH3L1) were differentially abundant between AD and all non‐AD diagnoses, suggesting specificity for AD. PKM and ALDOA, are involved in regulation of balance between glycolysis and oxidative phosphorylation, potentially indicating a shift in brain metabolism in AD.ConclusionTechnically robust unbiased mass‐spectrometry can highlight novel AD specific biomarkers which may reflect pathophysiological processes key to AD progression.
Supporting decision making among people living with dementia: Developing a DCE tool
Authors: Turner Anne M , Miller Lyndsey M. , Asirot Mary Grace , Sharma Rashmi 
Published: December 2022

AbstractBackgroundPersons living with dementia (PLWD) face many decisions about transitioning to more supportive care as their disease progresses, but are often left out of the decision‐making process. The Decision‐Making in Alzheimer’s Research (DMAR) project is developing a tool, informed by stakeholder input, that uses images, story‐telling, and discrete choice experiments (DCEs) to unpack the preferences of PLWD about transitions in supportive care and facilitate their inclusion in the decision‐making process. This study describes the development and design of the DMAR tool.MethodWe conducted in‐depth interviews between September 2020 and December 2021 with 24 PLWD, 37 caregivers, and 18 direct care staff or providers to understand decisions that PLWD encounter as they transition to supportive care, and the related decision‐making factors. We recorded, transcribed, coded, and analyzed interviews for key themes and factors. We then used heat maps to determine the four key factors for making decisions about obtaining more supportive care.ResultDecisions and roles in decision‐making differ depending on the stage of dementia. However, even persons with very early dementia had difficulty discussing plans regarding more supportive care. Early decision‐making centered on obtaining support around instrumental activities of daily living. Decisions to bring more supportive care into the home or seek supportive care outside the home were mentioned primarily by caregivers and staff members. Overall, the four most commonly mentioned factors influencing decision‐making were level of function, quality of life, out of pocket costs, and amount of supportive careConclusionThese four factors will be incorporated into a DCE tool to identify the relative importance of identified factors in making decisions regarding transitions to more supportive care. In this roundtable, we will further discuss plans to develop an electronically administered DCE tool to track the consistency of preferences of PLWD and their caregivers over time. We will also describe challenges encountered when eliciting preferences of PLWD, the role of technology in decision‐making, and creative methods, such as story‐telling and images, to enhance discussions with PLWD regarding supportive care.
Withdrawn: Glycine transporter 1 (GlyT1) is a novel therapeutic target for Alzheimer’s disease
Authors: Yoo Miran , Kim Seong Muk , Lee Juho , Kim Hye‐Ju , Park Seong Jeong 
Published: December 2022

Withdrawal: Miran Yoo, Seong Muk Kim, Juho Lee, Hye‐Ju Kim, Seong Jeong Park “Glycine transporter 1 (GlyT1) is a novel therapeutic target for Alzheimer's disease” Alzheimer's & Dementia, Volume 18, Supplemental Issue 10.The above abstract, published online on 20th December 2022, on Wiley Online Library (https://alz‐journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.064468) has been withdrawn by agreement between the authors, the Senior Director of Scientific Programs and Outreach, Claire Sexton, the Director of Scientific Programs at the Alzheimer's Association, Ozama Ismail, and John Wiley & Sons Inc. The abstract has been withdrawn at the authors’ request because the authors did not have Amyloid Solution's authorization to publish the information contained in it.
Dementia prediction in the general population using clinically accessible variables: a proof‐of‐concept study using machine learning. The AGES‐Reykjavik Study
Authors: Twait Emma L. , Navarro Constanza L. Andaur , Gudnason Vilmundur , Launer Lenore J. , Geerlings Mirjam I. 
Published: December 2022

AbstractBackgroundEarly identification of dementia is crucial for prompt intervention and better outcomes for high‐risk individuals in the general population. The use of machine learning in dementia prediction has allowed for highly accurate models that could aid in early classification; however, they have focused on expensive predictors. Exploring predictors that are more accessible is crucial for the possible widespread use of machine learning models in clinical practice.MethodData from 4,793 individuals without dementia or mild cognitive impairment at baseline were included from the population‐based AGES‐Reykjavik Study. Cognitive, biometric, and MRI assessments (total: 64 variables) were collected at baseline, with follow‐up of incident dementia diagnoses for a maximum of 12 years. Elastic net regression, random forest, support vector machine, Naïve Bayes, logistic regression, and elastic net Cox regression (for time‐to‐event analysis) were explored as possible algorithms. Model 1 was fit using all variables, model 2 after feature selection using the Boruta package, and model 3 without neuroimaging markers (clinically accessible model). Ten‐fold cross‐validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package.ResultDuring training, the Model 2 elastic net regression had the highest AUC [0.78; 95% CI: 0.76‐0.80], sensitivity [72; 95% CI: 68‐75], and specificity [72; 95% CI: 70‐73]. For Model 3, excluding MRI markers, the AUC remained high [AUC 0.75; 95% CI: 0.73‐0.77]. Similar results were found in our test data for Model 3 [AUC 0.74; 95% CI: 0.71‐0.77]; thus, the risk of overfitting was low. Time‐to‐event analysis using elastic net Cox models showed similar discrimination [c‐statistic 0.79] during testing. The most important variables based on Boruta selection included the Activities of Daily Living, presence of APOE e4 allele, memory functioning, and sex.ConclusionSupervised machine learning could be used to identify individuals at high‐risk for dementia in the general population using easily accessible variables. As dementia becomes a leading problem in developing countries, this clinically accessible model could be explored for use in these areas for better identification of individuals at risk in the community. Further external validation is needed.
Plasma glial fibrillary acidic protein, neurofilament light, phosphorylated‐tau‐181 and amyloid β42/40 as prognostic biomarkers for clinical progression to dementia in individuals with subjective cognitive decline and mild cognitive impairment
Authors: Honey Madison I. J. , Verberk Inge M.W. , Gouda Mariam , Hussainali Zulaiga , Kroeze Lior A. , van Leeuwenstijn Mardou , Mank Arenda , Trieu Calvin , Wilson David , Pijnenburg Yolande A.L. , van der Flier Wiesje M. , van Harten Argonde C. , Teunissen Charlotte E. 
Published: December 2022

AbstractBackgroundBlood‐based biomarkers can provide a non‐invasive and accessible way to identify neurodegenerative diseases before the clinical onset of dementia. Our study aimed to examine whether levels of phosphorylated‐tau‐181 (pTau181), amyloid beta1‐42/1‐40 (Aβ42/40), glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) are associated with risk of developing dementia in a memory clinic population of individuals with either subjective cognitive decline (SCD) or mild cognitive impairment (MCI).MethodFrom the Amsterdam Dementia Cohort we included 311 individuals with SCD (age 61±9 years, n=128 (41%) female, MMSE 29±1) and 252 with MCI (age 65±7 years, n=89 (35%) female, MMSE 27±2), who had annual follow‐up visits for re‐evaluation of diagnosis (average follow‐up duration: 2.7±1.7 years.) Baseline plasma biomarkers were measured using Simoa and the associations of the Z‐transformed biomarker concentrations with incident dementia were evaluated using Cox regression models adjusted for age, sex, baseline diagnosis and the interaction between baseline diagnosis (SCD/MCI) and the biomarker concentration. The markers were first evaluated individually and then simultaneously in a combined model. We stratified results for baseline diagnosis when the biomarker*diagnosis interaction was significant.ResultDuring follow‐up, 94 individuals developed dementia (14 with SCD, 80 with MCI at baseline, 86 Alzheimer’s Disease, 8 other forms of dementia; average time to progression: 2.8±1.8 years). We found an interaction between baseline diagnosis and NfL for incident dementia (p=0.03). No other interactions were found. In the total set, both high baseline pTau181 (hazard ratio (HR)=3.4 (95%CI: 1.3–9.0); figure 1A), and high baseline GFAP (HR=5.5 (95%CI: 1.6–18.6); figure 1B) were associated with increased risk of dementia, but the association with Aβ42/40 was not significant (HR=0.4 (95%CI: 0.2–1.1)). NfL had no significant association with increased dementia incident risk in both SCD subset (HR=1.8, (95%CI: 0.9‐3.6)) and the MCI subset (HR= 1.3, (95%CI: 1.0–1.6)). When we simultaneously entered all biomarkers, pTau181 and GFAP remained significantly associated with incident dementia (table 1.)ConclusionOur results suggest that plasma GFAP and pTau181 have prognostic value to predict dementia in individuals presenting at a memory clinic.
Discovery of [18F]ACI‐12589, a Novel and Promising PET‐Tracer for Alpha‐Synuclein
Authors: Capotosti Francesca , Vokali Efthymia , Molette Jerome , Ravache Myriam , Delgado Christophe , Kocher Jacqueline , Pittet Laure , Vallet Coralie , Serra Andreia , Piorkowska Kasia , Dimitrakopoulos Ioannis K , Luthi‐Carter Ruth , Tsika Elpida , Touilloux Tanja , Hliva Valerie , Smith Ruben , Hansson Oskar , Streffer Johannes , Pfeifer Andrea , Kosco‐Vilbois Marie 
Published: December 2022

AbstractBackgroundParkinson’s disease (PD) and other synucleinopathies such as Multiple System Atrophy (MSA) involve a progressive accumulation of a‐synuclein (a‐syn) inclusions that correlates with disease stage and clinical manifestations. The development of a PET agent capable of imaging a‐syn inclusions would significantly aid in differential diagnosis, staging, and monitoring possible benefits of candidate therapeutic agents targeting a‐syn. Due to the generally lower pathologic burden of a‐syn inclusions relative to other brain proteinopathies, a reliable a‐syn PET tracer must display high binding affinity and selectivity for its intended target.MethodUsing our proprietary Morphomer® library of low‐molecular weight and conformation‐specific ligands, compounds with good affinity and target occupancy were identified. We then iteratively performed medicinal chemistry optimization cycles to further improve on‐target binding properties and selectivity based on radiobinding and classical and high‐resolution autoradiography results. Optimized compounds were then assessed further for off‐target activities and evaluated for their brain pharmacokinetic properties in non‐human primates (NHPs). The clinical candidate [18F]ACI‐12589 was subsequently advanced through preclinical assessment prior to evaluation in a clinically diverse set of synucleinopathy subjects and controls.ResultPromising chemical scaffolds with high‐affinity binding to pathological a‐syn in tissue homogenates and sections have been optimized. Particularly, ACI‐12589 demonstrated improved target occupancy on different synucleinopathy samples, including PD and MSA, with a binding pattern aligning closely with the distribution of pathological a‐syn. Moreover, ACI‐12589 demonstrated selectivity for a‐syn versus other potential amyloid pathologies including amyloid‐beta, Tau, and TDP‐43. In NHPs [18F]ACI‐12589 revealed good brain uptake, homogenous distribution and fast washout. Initial results from the ongoing clinical trials confirm favorable pharmacokinetic parameters in patients, including rapid signal equilibration, potentially allowing short scan times. Moreover, initial clinical data in MSA patients indicate that [18F]ACI‐12589 uptake occurs in brain areas affected by the disease process based on clinical manifestations.Conclusion[18F]ACI‐12589 displays preclinical and clinical characteristics deemed necessary to become a reliable PET radiotracer for the imaging of a‐syn inclusions. Interestingly, ex‐vivo data from different synucleinopathy tissues is in accordance with the promising clinical data in MSA that will be presented by R. Smith et al. at this conference.