bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability of plasma amyloid‐β 1‐40, amyloid‐β 1‐42 and total Tau protein over repeated freeze/thaw cycles
Authors: Yang Shieh‐Yueh , Liu H.C. , Lin Chin‐Hsien , Chiu Ming‐Jang 
Published: December 2021

AbstractBackgroundBlood biomarkers of Alzheimer’s disease (AD) attract much attention of researchers recently. In clinical studies, repeated freeze/thaw cycles often occur and may influence the stability of biomarkers. This study is to investigate the stability of amyloid‐β 1‐40 (Aβ1‐40), amyloid‐β 1‐42 (Aβ1‐42) and total Tau protein (T‐Tau) in plasma over freeze/thaw cycles.MethodPlasma samples from healthy control (HC, n = 3), AD patients (AD, n = 3) and Parkinson’s disease (PD, n = 3) were collected by standardized procedure, immediately frozen at ‐80 °C. Samples underwent 5 freeze/thaw (‐80°C/room temperature) cycles. The concentrations of Aβ1‐40, Aβ1‐42 and T‐Tau were monitored during the freeze/thaw tests by using immunomagnetic reduction (IMR) assay. The relative percentage (%) of concentrations after every freeze/thaw cycle was calculated for each biomarker.ResultA tendency of decrease in the averaged relative percentages over samples through the freeze and thaw cycles for Aβ1‐40 (100 to 97.37%), Aβ1‐42 (100 to 95.56%) and T‐Tau (100 to 96.42%) was found. However, the decreases are less than 5%. For all three biomarkers, no statistical significance was found between the levels of fresh plasma and that of the plasma experiencing five freeze/thaw cycles (p > 0.1).ConclusionPlasma Aβ1‐40, Aβ1‐42 and T‐Tau are stable through five freeze/thaw cycles measured with IMR.
Identification and validation of plasma proteome signatures associated with MRI measurements in healthy individuals
Authors: Shi Liu , Hillary Robert Francis , Marioni Riccardo E. , Campbell Archie , Hayward Caroline , Stolicyn Aleks , Whalley Heather C. , Buckley Noel , Nevado‐Holgado Alejo J 
Published: December 2021

AbstractBackgroundPeripheral biomarkers reflecting early neurodegeneration change are critical to the development of treatments for Alzheimer’s disease (AD). The most widely used indicator of AD neurodegeneration in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with neuroimaging in prodromal disease stage.MethodTo identify blood‐based proteins relating to neurodegeneration (“N”) in preclinical or prodromal disease stage, we used SOMAscan assay to measure 5033 proteins in two groups of healthy individuals (discovery group = 564, validation group = 497) selected from Generation Scotland cohort. We firstly performed both linear regression and protein co‐expression network to identify differentially expressed proteins and co‐expressed protein modules associated with “N” in the discovery group. We then validated such associations in the validation group.ResultUsing linear regression, we identified different proteins that were significantly associated with whole brain volume, cortical volume and hippocampus volume after multiple correction, indicating that the associations between proteins and “N” were dependent on brain regions. From protein co‐expression network analysis, we identified seven modules (from 20 to 2088 proteins) in the discovery group, all of which were significantly preserved in the validation group. Furthermore, several modules were significantly associated with “N” and such associations were affected by sex and APOE genotype. Moreover, we replicated the associations between differentially expressed proteins and modules with “N” in the validation group.ConclusionOur study is the largest we are aware of to report a plasma biomarker indicative of neurodegeneration in prodromal disease stage both in terms of the number of proteins assayed and in sample size. We identified and validated a groups of proteins in two large independent groups of healthy individuals. These proteins provide tractable targets for further mechanistic studies of neurodegeneration pathology, particularly in preclinical stages of Alzheimer’s.
The association of KIBRA rs17070145 polymorphism with dementia and cognitive function among older adults in China: A population‐based cross‐sectional study
Authors: Wang Chaoqun , Han Xiaolei , Wang Yongxiang , Qiu Chengxuan , Du Yifeng 
Published: December 2021

AbstractBackgroundKIBRA, as a ligand protein, is mainly expressed in kidney and brain. A genome‐wide association study in 2006 revealed that KIBRA rs17070145 was associated with impairment in episodic memory. Since then, several studies that investigate the association of this SNP and cognitive function have yielded mixed results. This study aims to explore the association of KIBRA rs17070145 with dementia and cognitive function among rural‐dwelling older adults in China.MethodThis population‐based cross‐sectional study included 4700 participants from the Multidomain Interventions to Delay Dementia and Disability in Rural China (MIND‐CHINA), who were aged ≥65 years (mean 71.7 years; 57.2% women). Data on demographic features and cognitive function were collected through face‐to‐face interviews, clinical examinations, and laboratory tests. SNPs were detected by multiplex PCR assay. Global cognitive function was assessed with the Mini‐Mental State Examination (MMSE). Diagnoses of dementia Alzheimer’s disease (AD), and vascular dementia (VaD) were reached according to the DSM‐IV, NIA‐AA, and NINDS‐AIREN criteria, respectively. Data were analyzed using multiple logistic regression and general linear regression models.ResultOf all participants, 2689(57.2%) carried the KIBRA rs17070145 TT polymorphism in intron 9, 1719(36.6%) carried CT polymorphism, and 292(6.2%) were homozygous CC carriers. The multi‐adjusted odds ratio (95% CI) of dementia associated with C allele and genotype CC was 1.27 (1.02‐1.58) and 1.78 (1.07‐2.97), respectively. The association of KIBRA genotype CC with VaD was significantly, but not with AD. However, the association of genotype CC with VaD was reduced and became non‐significant when further adjusting for cerebrovascular diseases.ConclusionKIBRA rs17070145 genotype CC is associated with an increased likelihood of dementia among rural‐dwelling Chinese older adults, driven largely by the association with VaD. Cerebrovascular disease may partly explain the association of genotype CC with VaD.
Impairment of hippocampal synaptic plasticity induced by pathological microglial activation
Authors: Piccioni Gaia , Mango Dalila , Nisticò Robert 
Published: December 2021

AbstractBackgroundNeuroinflammation has demonstrated a key role in the pathogenesis of Alzheimer disease (AD). Among the innate immune cells, microglia are the primary players in neuroinflammation. When microglia is activated under pathological inflammatory conditions, it can lead to synaptic alterations thereby influencing the progression of AD. Among other mechanisms, an increased activation of microglial colony stimulating factor 1 receptor (CSF1R) can lead to pathological microglia activation in experimental models of AD (Olmos‐Alonso et al., 2016). In this work, using electrophysiological techniques, we investigated hippocampal synaptic transmission and plasticity in experimental models of neuroinflammation. We then tested whether the pharmacological manipulation of microglia activation was able to restore the neuroplasticity alterations associated with these models.MethodThe experiments were conducted using C57BL6/J mice of both sexes aged between 30‐ and 40‐days. Using electrophysiological approach (field and whole cell patch clamp), we performed recordings in area CA1 in response to stimulation of the Schaffer collaterals. We evaluated Long‐Term Potentiation (LTP) induced by theta‐burst stimulation. Responses were recorded for 1 hour after tetanization and measured as field Excitatory Post‐Synaptic Potential (fEPSP). Two in vitro inflammatory models were obtained applying Lipopolysaccharide (LPS) (10 μg/ml) or Aβ 1‐42 oligomers (200 nM). As a control, we tested the effect of Aβ 1‐42 with a scrambled sequence applied in the same condition of Aβ 1‐42. To inhibit the activation of microglia we used Pexidartinib (PLX‐3397) (10 μM), a specific inhibitor of activated microglia, and Minocycline (500 nM) a no‐specific blocker.ResultSlices treated with Aβ 1‐42 and LPS showed impairment of LTP. Indeed, LTP was fully suppressed after exposition to oligomeric Aβ while no change of fEPSP was observed after incubation of slices with Aβ scrambled. Similarly, a selective inhibition of LTP was observed in the presence of LPS. Minocycline application reversed synaptic dysfunction in slices treated with Aβ 1‐42 and LPS. Preliminary data also indicate that PLX‐3397 rescues LTP impairment mediated by oligomeric Aβ.ConclusionOur data show a functional role of activated microglia in the synaptic plasticity alteration induced by neuroinflammation and suggest CSF1R as an emerging target in the treatment of AD.
Evidence for the role of semantic cueing and executive functioning tools in early and differential diagnosis of neurocognitive disorder (NCD): A neuropsychological approach
Authors: Abbatantuono Chiara , Taurisano Paolo , Pepe Ilaria , Clemente Livio , Alfeo Federica , Ricciardi Elisabetta , Calia Clara , Lancioni Giulio , De Caro Maria Fara 
Published: December 2021

AbstractBackgroundRecent advances in the diagnosis of Mild Cognitive Impairment (MCI) and Neurocognitive Disorders (NCDs) build on neuropsychological models (Dodich et al., 2018; Possin et al., 2018). Despite the lack of harmonized protocols (Costa et al., 2017), tools such as the Free and Cued Selective Reminding Test (FCSRT) yield specific scores (e.g., ISC for cue sensitivity) for the early detection of hippocampal syndromes involved in Alzheimer’s Disease (AD), and for the prediction of MCI‐to‐AD conversion (Brugnolo et al., 2021). Accordingly, the study aims to investigate the correlation between neuropsychological profiles and cognitive patterns through an extensive assessment procedure accounting for the heterogeneity of MCI and NCDs.MethodsN=150 outpatients (mean age=72.29±5.42 SD) were administered a battery meeting both MCI (Petersen et al., 2014) and NCD criteria (APA, 2013) including: two screening tools; ten domain‐specific cognitive tests; psychodiagnostic‐functional assessment. Analyses through paired‐sample tests and multivariate models focused on: (1) episodic memory and executive deficits; (2) predictive indices for MCI/NCD diagnosis.Results70.7% of the clinical sample obtained scores consistent with MCI or mildNCD profiles, and 75% showed episodic memory impairment on cut‐off basis (FCSRT‐ISC<0.9). Wilcoxon signed‐rank test revealed poorer performance in FCSRT‐ISC compared to Babcock Story‐IR (p‐value<0,001<α=0,05), notably in MCI/mildNCDs but not in majorNCDs. The full sample and MCI/mildNCD subset also obtained lower scores in Digit Span Backward (DS‐B) than in the Clock Drawing Test (CDT), whereas majorNCD performed worse in CDT. Logistic regression showed three predictive indices for majorNCD: the lower the scores on FCSRT‐IFR, DS‐B and CDT, the greater the chances of fitting majorNCD profile.ConclusionDriven by the need for early and differential diagnosis of NCDs, our findings showed episodic memory impairment within the total sample and the MCI/mildNCD subsets, indicating: (1) a high prevalence of AD‐type MCI/mildNCDs; (2) the possible key role of FCSRT‐ISC in detecting early or mild conditions of impairment; (3) the good predictive values (86%) of some memory (i.e. FCSRT‐IFR) and executive tasks (i.e. DS‐B and CDT) contributing to a major NCD diagnosis, potentially enhancing NCD nosography and differential diagnosis.
Tau and synaptic biomarkers but not amyloid‐β are associated with cerebral perfusion in the Alzheimer’s disease spectrum
Authors: Ahmadi Khazar , Pereira Joana B. , Berron David , Vogel Jacob W. , Ingala Silvia , Strandberg Olof , Janelidze Shorena , Barkhof Frederik , Knutsson Linda , van Westen Danielle , Mutsaerts Henri J.M.M. , Palmqvist Sebastian , Hansson Oskar 
Published: December 2021

AbstractBackgroundA large body of evidence has shown decreased cerebral blood flow (CBF) in Alzheimer’s disease (AD). However, the link between CBF and the primary AD pathologies as well as synaptic integrity remains unclear.MethodBaseline CBF was measured using arterial spin labeling (ASL) in a 3T MRI scanner in 137 cognitively unimpaired individuals with and without amyloid‐beta (Aβ) pathology, in addition to 119 cognitively impaired participants with mild cognitive impairment or AD dementia who all had Aβ pathology from the Swedish BioFINDER‐2 study. Aβ and tau pathology were measured using cerebrospinal fluid (CSF) levels of Aβ42/40 and amyloid‐PET, as well as CSF P‐tau217 levels and tau‐PET, respectively. Synaptic integrity was estimated using CSF neuronal pentraxin 2 (NPTX2)/total‐tau (T‐tau) ratio. Voxel‐wise and linear regression analyses in a set of a priori defined regions of interest (ROIs) were used to assess between‐group differences in CBF, and the relationship between CBF and the PET and CSF measures.ResultCBF was decreased in the lateral temporal, lateral and medial parietal and superior lateral occipital cortex in the cognitively impaired compared to cognitively unimpaired individuals, independent of Aβ pathology. In contrast, no differences were found in CBF between the cognitively unimpaired groups with and without Aβ pathology. Further, in cognitively unimpaired individuals, CBF was not associated with any of the biomarkers of Aβ, tau or synaptic integrity. However, in individuals on the AD spectrum i.e., those with Aβ pathology regardless of their cognitive status, an inverse association was observed between CBF and tau pathology (tau‐PET and CSF P‐tau217), but not Aβ pathology (CSF Aβ42/40 and amyloid‐PET), predominantly in the occipito‐temporo‐parietal regions. Moreover, lower NPTX2/T‐tau was associated with hypoperfusion in similar regions.ConclusionThese findings suggest that tau‐tangle pathology and synaptic degeneration are more closely associated with overt changes in CBF than Aβ pathology. This supports the notion that blood flow changes, at least those measured by ASL, are not an early event associated with the build‐up of Aβ pathology during the preclinical phases of AD.
Pilot trial of dronabinol adjunctive treatment of agitation in Alzheimer's disease (THC‐AD)
Authors: Rosenberg Paul B. , Outen John D. , Amjad Halima , Burhanullah Haroon , Vandrey Ryan , Agronin Marc , Castaneda Ricardo , Isesalaya Maria , Walsh Patricia , Ash Eleanor T. , Cohen Leah , Wilkins James M , Harper David G. , Forester Brent P. 
Published: December 2021

AbstractBackgroundAlthough agitation in Alzheimer’s Disease (Agit‐AD) is a common and troubling neuropsychiatric syndrome, behavioral interventions lack consistent efficacy and there are no FDA‐approved medications. Neurobiological mechanisms that contribute to Agit‐AD include brain atrophy, degradation of neurotransmission, neuroinflammation, disrupted circadian rhythms, comorbidities and frailty. Agit‐AD is a major source of disease progression, patient disability, financial burden, and caregiver stress. Dronabinol is synthetic tetrahydrocannabinol (THC, one of the predominant biochemical constituents of cannabis). Cannabinoids may improve Agit‐AD by providing protection against neuroinflammation and excitotoxicity, regulating neurotransmitters, improving comorbidities, stabilizing circadian rhythms, and increasing cerebral blood flow.MethodTHC‐AD is a three‐week placebo‐controlled, double‐blind, RCT of dronabinol (10 mg QD) in 80 patients with severe Agit‐AD. Twice daily administration maximizes daytime coverage for agitation and minimizes sundowning. Inclusionary criteria include a diagnosis of AD, severe agitation, and being 60‐95 years old, while exclusionary criteria include serious or unstable medical illness, seizure disorder, delirium, current use of lithium, and inability to swallow a pill. Primary outcomes include a change in the Pittsburgh Agitation Scale and NPI‐C Agitation/Aggression subscales.ResultWe have enrolled 37 out of 80 participants (Table 1: mean age 78.2 years, 78.4% female, 83.8% Caucasian, mean education 13.2 years, 48.6% family history). Study participants are significantly cognitively impaired (Table 2: mean baseline MMSE of 7.1), agitated (mean NPI‐C Agitation 14.8, mean NPI‐C Aggression 6.4) and in reasonable overall health (Figure 1: General Medical Health Rating, 10.8% “excellent,” 48.6% "good" and 40.5% "fair”). Recorded AEs have been tolerable (Figure 2). Due to the COVID‐19 pandemic, we expanded our inpatient trial to include outpatient enrollments and implemented hybrid visits with telemedicine to limit in‐person interactions. To bolster our recruitment, we are collaborating with additional clinical sites, increasing dementia bed capacity, and deploying recruitment strategies for outpatients, including referrals from providers and other research trials, social media ads, and virtual community outreach. Updated results will be presented at AAIC (estimated 6‐10 additional participants).ConclusionSafe and effective interventions for severe agitation are greatly needed. This pilot trial will help to examine the safety and efficacy of dronabinol for Agit‐AD.
Identifying veterans at risk for dementia using claims‐based electronic health record data and machine learning
Authors: Castellanos Alejandro , Ysea‐Hill Otoniel , Lin Chaohao , Hasan Shafiul , Bai Ou , Ruiz Jorge G 
Published: December 2021

AbstractBackgroundEarly identification of individuals at risk for dementia represents an important challenge for the design and implementation of prevention strategies. Machine learning (ML) algorithms might help to identify dementia risk from claims‐based electronic health records (EHR). The study aim was to develop and validate a new method based on ML to identify Veterans at risk for dementia using EHR claims‐based data.MethodWe propose a ML–based probabilistic method to evaluate dementia risk within 10 years, based on information from claims‐based EHR data as part of a retrospective cohort study. We identified veterans without baseline dementia from January 2000 to December 2009 and followed them until December 2019 for dementia onset (ICD codes). A ML model was established using veteran’s EHR data to predict dementia occurrence during follow‐up. The features for the modeling included 72 EHR associated with dementia in previous studies including socio‐demographic, medical and mental morbidities, vital signs, medications, hospitalizations and laboratory data. ML algorithms including Linear Discrimination, Random Forest Classifier, Ensemble Bootstrapping, and Multilayer Perception (MLP) neural network. MLP provided more robust prediction outcomes and was employed for the final dementia modeling and prediction.ResultData comes from 7202 veterans, mean age 54.89 (SD=6.64, range 40‐65) years, 90.3% male, 49.3% Caucasian, and 72.1% Non‐Hispanic. Over a median follow up of 9.82 (IQR=3.44) years, 786 (10.9%) of these veterans developed dementia. The training and testing sets at a ratio of 9:1 were pseudo‐randomly extracted from the original EHR database. The performance was determined on the testing dataset using the neural network model learned from the training dataset. The results from three experiments of modeling and testing are (Overall Accuracy/Sensitivity/Specificity): (1) 84.2%/12.8%/92.9%, (2) 85.7%/13.9%/94.5%, and (3) 87.5%/17.7%/96.1%. The modeling and prediction were implemented by missing data for some variables. The experimental results are: (1) 82.4%/9.84%/89.1%, (2) 82.9%/12.2%/91.1%, (3) 83.1%/16.5%/92.0%.ConclusionThough the model prediction specificity is acceptable (above 90%), the sensitivity of the prediction is low (less than 20%). Exploring better modeling algorithms and treatment methods for unbalanced samples may improve prediction. Future research could benefit from ML techniques that evaluate the optimal combination of variables that best predict dementia.
Can MR imaging biomarkers readily available in rural and underserved areas aid in the accurate clinical diagnosis of Alzheimer’s disease?
Authors: Gustavsen Tanner , He Yao , Duff Kevin M. , Anzai Yoshimi , Hoffman John M. 
Published: December 2021

AbstractBackgroundHippocampal volume (HV) measured with brain magnetic resonance imaging (MRI), a NIA‐AA biomarker of Alzheimer’s disease (AD), could be widely available in rural and underserved areas in the United States. Amyloid positron imaging tomography (aPET) identifies the presence of amyloid AD pathology but is available only to cognitive specialty centers in predominantly large metropolitan areas. We wished to determine whether rural primary care providers could benefit from commercially available automated measurements of HV to improve the accuracy of their diagnosis of early ADMethodWe used NeuroQuant ®□ (NQ) to derive operator‐independent volumetric measurements of HV relative to total intracranial volume (rHV) in 70 patients (27 men, 43 women; mean age 73 ± 8, range 45‐89) found to have cognitive impairment during a clinical evaluation. We compared the frequency of abnormal rHV using a recommended 5th percentile cutoff value from NQ’s extensive normative database in patients with visually interpreted positive (n=57) or negative (n=13) aPET with flutemetamol. We assessed the validity of rHV to measure underlying AD pathology with a 2x2 factorial analysis to calculate sensitivity, specificity, positive and negative predictive value (PPV, NPV). We measured the rHV in MCI (n=39) versus dementia (n=31). We contrasted the age and severity at time of referral of rural (n=16) and non‐rural (n=54) patients.Result12/14 (86%) patients with an abnormal rHV had a positive aPET, however most patients with a positive aPET did not have an abnormal rHV (45/57). The ability of small rHV to predict underlying AD pathology had a specificity=0.84, PPV=0.85, sensitivity=0.21, and NPV=0.19. rHV was smaller in dementia than MCI (0.37,0.42; p=.003). Rural patients were younger (68 ± 11 vs. 75 ± 7; p=.02) and more likely to have dementia when referred (56% vs. 40%; p=.03).ConclusionAn abnormal rHV is highly predictive of cortical amyloid AD pathology in the right setting, but is insensitive at the time of initial cognitive evaluation. Commercially available automated rHV decreases as the severity of cognitive impairment increases. Imaging biomarkers could aid rural primary care providers to validate the clinical significance of a memory complaint and justify earlier evaluation, referral, and treatment.