bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex/Gender, Relationship Status, and Alzheimer’s and Related Dementia Diagnoses after Probable Dementia
Authors: Hanes Douglas William , Clouston Sean A.P. 
Published: December 2022

AbstractBackgroundDifferences by relationship status have been found in both cognitive‐assessment scores and prevalence and incidence rates for Alzheimer’s disease and related dementias (ADRD), but little is known about delayed and missed ADRD diagnoses after cognitive impairment emerges. Existing research has reported underdiagnosis for people of color, men, and people with less education.MethodUsing data from the Health and Retirement Study (2000‐2018), we analyzed associations between relationship status and time to ADRD diagnosis following probable dementia (PD). We examined risk of ADRD diagnosis by sex/gender and relationship status (married/partnered or single at the time of the first PD‐scoring assessment), adjusting for race/ethnicity, age, and education. Cox proportional hazards modeling was used to account for right‐censoring, with the risk period beginning at the time of the incident PD. Failure occurred when ADRD diagnosis was self‐reported. We also examined time until diagnosis among all participants with a recorded case of incident PD (N = 3,216 subjects: 9,540 observations). Restricted mean times to diagnosis and multivariable‐adjusted hazard ratios (aHR) with 95% Confidence Intervals (95% C.I.) were reported.ResultTable 1 shows summary statistics; Table 2 provides hazard ratios; Table 3 shows mean times to diagnosis. Married/partnered women had the highest likelihood of receiving a diagnosis during the study period (HR: 1.197; 95% CI: 1.020‐1.360; p = <.001); single woman also had higher risk of diagnosis (HR: 1.178; 95% CI: 1.025–1.400; p = <.001). Mean time to diagnosis for men was 8.69 (±0.20); for women, 7.38 (±0.13). Singles received diagnoses sooner (7.32±0.135 years) than married/partnered people (8.39±0.169). Categorizing by sex/gender and relationship status, married/partnered women have the lowest mean times (7.00±0.148); single and married/partnered men’s mean times to diagnosis do not differ.ConclusionRelationship status is associated with women’s likelihood of reporting ADRD diagnosis and time to diagnosis, but not men’s. Further investigation is required to analyze potential sources of this disparity, including interactions with education, income, and insurance status, and additional gendered relationship, social, and familial factors.
Identifying delirium in advanced dementia in the ECT‐AD clinical trial
Authors: Lapid Maria I. , Forester Brent P. , Patrick Regan E. , Fong Tamara G. , Green Morgan E. , Harper David G. , Heintz Hannah L. , Hermida Adriana P. , McManus Kaitlin R. , Mueller Martina , Nykamp Louis J. , Petrides Georgios , Inouye Sharon K. 
Published: December 2022

AbstractBackgroundDelirium and dementia are common causes of cognitive impairment in older adults and frequently co‐exist. Delirium is associated with poor clinical outcomes when comorbid with dementia. Identifying delirium superimposed on dementia (DSD) is clinically challenging given the overlap in symptoms, including inattention, cognitive impairment, and altered level of consciousness, especially in advanced stages of dementia. Recognition of delirium is also critical in ADRD (Alzheimer’s Disease and Related Dementias) clinical trials for both validity and safety reasons. The ECT‐AD study is one such trial investigating the safety and efficacy of electroconvulsive therapy (ECT) for treatment‐refractory agitation and aggression in individuals with advanced ADRD. Herein we review the Confusion Assessment Method (CAM) for identifying delirium in the ECT‐AD study.MethodInclusion criteria for ECT‐AD are hospitalized individuals diagnosed with ADRD (ages 55–89, MMSE ≤ 15) and refractory agitation/aggression, as measured by the Cohen‐Mansfield Agitation Inventory (CMAI). Procedures for detecting baseline and treatment‐emergent delirium involve a 3‐step approach conducted by raters trained in the CAM, which assesses inattention, disorganized thinking, altered levels of consciousness, and fluctuating mental status. Step 1 involves gathering information from nurse informants regarding the patient’s mental status over the past 24 hours. Step 2 involves collecting observational data through informal patient interaction (e.g., brief conversation). Step 3 is an objective assessment of cognitive status using the Severe Impairment Battery – 8 Item (SIB‐8), while also making qualitative observations about delirium features (e.g., level of consciousness). The rater then codes the CAM and determines delirium status based on these 3 steps.ResultOf our 6 consented subjects, 1 experienced delirium detected by the CAM during the Randomization Phase. The subject received three positive CAMs on consecutive treatment days, citing change in mental status, inability to arouse, restlessness, psychomotor agitation, and swearing. ECT/S‐ECT was held each treatment day and the subject was withdrawn from the Randomization Phase.ConclusionIdentifying DSD is challenging due to overlapping symptoms and the fluctuating nature of delirium. The CAM may be useful in this regard by using a multi‐step approach that incorporates observations from reliable informants, direct patient interactions, and structured cognitive assessments.
Identifying risk genes and therapeutics for Alzheimer’s disease through integration multi‐omics and drug‐perturbation signatures
Authors: Zhong Xue 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is a severe and irreversible neurodegenerative disease with no effective treatments currently availableMethodTo better understand AD mechanisms and identify therapeutics, we collected AD GWAS loci from the largest meta‐analysis and incorporated multiple lines of genomic evidence, including human brain chromatin interaction map in microglia, brain cell‐type specific expression and gene‐gene interaction network, to infer high‐confidence risk genes (HRGs) at each GWAS locus. We then used the HRGs and network propagation to construct a genetic perturbation signature of AD, and queried a database of drug‐induced transcriptomic signatures (LINCS 1000) to evaluate compounds that are able to reverse the genetic perturbation signature.ResultThis resulted in 72 HRGs, which were further validated in multiple independent datasets of human microglia expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs), protein quantitative trait loci (pQTLs) in human brain, cerebrospinal fluid (CSF) and plasma, and genes of protein targets in brain and CSF associated with asymptomatic AD. Pathway analysis implicates the involvement of endocytosis and amyloid pathways, and highlights cellular compartments such as gamma‐secretase complex, NF‐kappaB complex, neurofibrillary tangle and endosome. Query of the genetic perturbation signature of AD against LINCS 1000 revealed several drugs that can reverse the genetic perturbation signature.ConclusionIn conclusion, by incorporating multiple lines of evidence and resources, our study offers new insights into the pathological processes underlying AD and potential therapeutic drugs.
Tau Pathology in the Entorhinal‐Hippocampal Circuit
Authors: Ash Alyssa , Baek Sue Rim , Singh Sara , Snyder Jason 
Published: December 2022

AbstractBackgroundThe lateral entorhinal cortex (LEC) is the site of tau accumulation in early stages of Alzheimer’s disease and has projections downstream to the dentate gyrus (DG) of the hippocampus. The pathological form of tau has the ability to propagate across synapses, spreading to connected brain regions and correlating with memory deficits and synaptic loss. Downstream to the LEC, the hippocampal DG is the site of ongoing adult neurogenesis, with new granule neurons added showing enhanced plasticity and increased survival compared to older neurons. We are interested in examining the role of neuron age for vulnerability to tau pathology in the LEC‐DG circuit.MethodWe use a transgenic mouse model to label neurons born at different ages in the DG (development vs. adulthood) with a fluorescent reporter and inject a human Tau‐expressing virus, or control virus, into the LEC directly to mimic early, localized tau pathology. Following a 4‐month incubation, animals are tested for memory deficits specific to the LEC and hippocampus with Novel Object Recognition (NOR) and Novel Place Recognition (NPR) and tissue is immunohistochemically processed to analyze tau levels and cellular morphology in fluorescent‐labelled DG neurons. We assess synaptic changes in DG neurons via measuring dendritic spines, dendritic complexity and mossy fibre boutons.ResultOur findings demonstrate that tau animals perform worse on NOR compared to healthy controls, while no difference is found for NPR. Morphological results show adult‐born neurons have an increase in thin dendritic spines and decrease in mushroom spines in tau animals relative to controls, as well as reduced mossy fibre bouton filopodia length.ConclusionOur initial results demonstrate the effect of tau on the LEC‐DG circuit, with altered synaptic structures and impaired memory performance under tau pathology.
The use of neuropsychiatric symptoms as biomarkers for a prodromal state among an electronic medical record cohort of neurodegenerative disorders
Authors: Clos Siara , Chabrashvili Tinatin 
Published: December 2022

AbstractBackgroundNeuropsychiatric symptoms including anxiety, depression, psychosis, hallucinations, delusions, agitation, as well as sleep disturbances are associated with the development of dementia. We aim to show how they may be utilized as biomarkers of a prodromal state of neurodegenerative disorders.MethodA cohort of Alzheimer’s disease (AD) and Dementia with Lewy body (DLB) patients was created using EPIC electronic medical record software. Data was compiled and analyzed using Microsoft Excel program.ResultIn our cohort of 5,784 AD patients, 1,721 (29.8%) held a diagnosis of anxiety, 2,727 (47.2%) depression, 2,231 (38.6%) psychosis, 165 (2.9%) hallucinations, 144 (2.5%) delusions, 376 (6.5%) agitation, and 1,242 (21.5%) sleep disturbance. Of 355 DLB patients, 110 (31.0%) had anxiety, 165 (46.5%) depression, 183 (51.6%) psychosis, 81 (22.8%) hallucinations, 18 (5.1%) delusions, 34 (9.6%) agitation, and 95 (26.8%) sleep disturbance. Of 81 patients with diagnoses of both AD and DLB, 36 (44.4%) had anxiety, 45 (55.6%) depression, 57 (70.4%) psychosis, 18 (22.2%) hallucinations, 7 (8.6%) delusions, 7 (8.6%) agitation, and 27 (33.3%) sleep disturbance. Many patients in the cohort held more than one neuropsychiatric diagnosis.ConclusionNeuropsychiatric symptoms have been shown to precede the onset of neurodegenerative disorders, including AD and DLB, by years to decades. Our study aims to demonstrate the use of neuropsychiatric symptoms as biomarkers for the future development of neurodegenerative disease.
Clinically normal elderly adults at genetic risk for sporadic Alzheimer’s disease have a path integration deficit
Authors: Colmant Lise , Bierbrauer Anne , Kunz Lukas , Axmacher Nikolai , Lefèvre Philippe , Hanseeuw Bernard J 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is associated with cognitive impairment, including spatial disorientation. Disorientation may be an early indicator of AD because spatial navigation relies on entorhinal cortex, the first cortical region affected by AD pathology. To test the hypothesis that spatial navigation declines in preclinical AD, we investigated age and genotype effects on a virtual path integration task, the "Apple Game" (Bierbrauer et al. 2020).MethodParticipants navigated in a virtual field and had to drop a basket to their initial location. Difficulty varied according to two parameters: (1) the number of interim locations (distractor) participants had to visit (0, 1, or 2); and (2) the presence or absence of a landmark. We recruited 145 clinically normal participants (MMSE>24/30, median age= 64). We genotyped the apolipoproteinE (APOE) gene, the ε4 allele of which is a risk factor for AD. We classified participants as E4 (n=41) if they had at least one ε4 allele and E3 (n=104) otherwise (table 1). We used linear mixed models to predict the drop error, i.e., the Euclidean distance between the correct and the actual drop location. The two components of the drop error, the angle and distance errors, were analyzed.ResultsThe drop error increased with age. In trials with no distractor and no landmark, we observed higher drop error for E4 than E3 participants older than 70 years (fig.1), suggesting a deficit in pure path integration for oldest E4 carriers. This deficit was due to higher angle error (fig.2) rather than distance error (fig.3). In trials with distractors, performance dropped to chance level for all participants above 50 years (fig.4), suggesting the task was too difficult in general. In trials with landmark, the error was not influenced by the APOE genotype.ConclusionThis spatial navigation task detected a deficit in pure path integration in elderly E4 carriers, when the task was not too difficult. The deficit may reflect an impairment in grid cells localized in the entorhinal cortex. Indeed, this group has probably the most AD neuropathology brain deposit. By contrast, landmark‐based navigation was impaired with age but not related to AD risk.
Tau protein spreads through functionally connected neurons in Alzheimer’s disease
Authors: Schoonhoven Deborah N , Coomans Emma M , Millan Ana P , van Nifterick Anne M , Visser Denise , Ossenkoppele Rik , Tuncel Hayel , van der Flier Wiesje M. , Golla Sandeep SV , Scheltens Philip , Hillebrand Arjan , van Berckel Bart NM , Stam Cornelis J , Gouw Alida A 
Published: December 2022

AbstractBackgroundRecent studies in Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functionally connected); through the pattern of anatomical connections (structural connectivity); or simple diffusion to spatially adjacent regions (Euclidean distance (EC)). We investigated this by modelling the tau‐spreading process on these different networks, and compared the modelled tau‐depositions with tau‐depositions at several stages of the AD continuum as measured with in‐vivo 18F‐flortaucipir PET.MethodsWe collected 10‐minutes eyes‐closed, whole‐head magnetoencephalography (MEG) recordings from 82 subjects (Table‐1) with and without Aβ‐pathology (CSF‐Aβ42 and/or amyloid‐PET) from the Amsterdam Dementia Cohort. Subjects were classed as controls (subjective cognitive decline (SCD) Aβ‐,n=25), preclinical AD (SCD Aβ+,n=16), mild cognitive impairment (MCI) due to AD (n=16) and probable AD dementia (n=25). Tau propagation was modelled as an epidemic process (susceptible‐infected (SI) model) on structural networks (Exponential Distance Rule (EDR) or EC), or functional networks (corrected‐amplitude envelope correlation (AEC‐c) in alpha (8‐13Hz) and beta (13‐30Hz) bands) derived from source‐reconstructed MEG data. The group‐level control network was used as backbone for the SI‐model to predict tau spread in the next stages of the AD continuum; parameters were tuned to produce an optimum correlation to the group‐specific tau propagation patterns as measured with dynamic 130‐minutes 18F‐flortaucipir PET, according to 50 regions‐of‐interest (ROIs) in an adjusted version of the probabilistic Hammers atlas. Tau propagation was modelled to start from one seed region (ROI14), which was selected based on its reliability to accurately measure tau deposition.ResultThe functional network predicted tau‐spread with highest accuracy in the preclinical AD stage (AEC‐c alpha r=0.59; AEC‐c beta r=0.58), followed by the structural network (r=0.45) and simple diffusion (r=0.44), Figure‐1, Figure‐2, Figure‐3. Prediction accuracy declined for the MCI and AD dementia stages (AEC‐c alpha r=0.50 and r=0.33; AEC‐c beta r=0.40 and r=0.37; structural r=0.29 and r=0.25; diffusion r=0.29 and r=0.25, respectively).ConclusionUsing a relatively simple SI‐model we predicted tau propagation in preclinical AD with an accuracy of approximately 60%. Our results suggest that tau spreads through functional connections, rather than structural connections or simple diffusion.
The joint association of arteriosclerosis and plasma amyloid with cognitive function: the population‐based Rotterdam Study
Authors: Frentz Ingeborg , van Arendonk Joyce , Leeuwis Anna E , Vernooij Meike W. , van der Flier Wiesje M. , Bos Daniel , De Deyn Peter Paul , Wolters Frank J. , Ikram M. Arfan 
Published: December 2022

AbstractBackgroundAlzheimer's disease and vascular brain injury are the most common causes of dementia, and are present jointly in 40% of individuals at time of death. Yet, the interaction between Alzheimer's and vascular pathology on cognition is largely undetermined. We studied the interaction between arteriosclerosis and plasma biomarkers of Alzheimer's disease on cognition in a general population without dementia.MethodOn stored plasma samples, obtained between 2002‐2005, we measured plasma levels of amyloid‐β40 and amyloid‐β42 by Simoa assay in 2252 participants of the population‐based Rotterdam Study (mean age: 68.9 years, 52% women). Arteriosclerosis was assessed using arterial calcification volumes that were quantified (semi‐)automatically on unenhanced computed tomography (CT). We captured the systemic burden of calcification, using a principal component analysis of calcification volumes in 4 different arterial vessel beds (intracranial and extracranial carotid arteries, coronary arteries and aortic arch). Cognitive assessment included the verbal fluency test, the letter‐digit substitution task, a 15‐word learning test, the Stroop test, and Purdue pegboard task. Global cognition (g‐factor) was calculated using a principal component analysis of these tests. We then determined the interaction between plasma amyloid‐β and calcification on cognitive performance, using multivariable linear regression models.ResultPlasma levels of amyloid‐β and systemic arteriosclerosis were both associated with poorer cognitive performance. After mutual adjustment in multivariable models only calcification remained significantly associated (beta [95%CI] for g‐factor per 1‐SD increase in amyloid‐β40: ‐0.02 [‐0.07; 0.03]; amyloid‐β42: 0.03 [‐0.01; 0.08]; and calcification burden: ‐0.06 [‐0.09; ‐0.04]). The association between amyloid‐β and cognition was stronger in individuals with higher burden of calcification (Figure 1), particularly for amyloid‐β40 (interaction amyloid‐β40×calcification: p = 0.04; amyloid‐β42×calcification: p = 0.001). Results were broadly similar across cognitive tests. The observed interaction between amyloid‐β40 and calcification volume was most profound for calcification burden in the intracranial carotid artery and aortic arch.ConclusionArteriosclerosis and amyloid‐β display synergistic effects on cognition in the general population. This interaction was more profound for amyloid‐β40 than for amyloid‐β42, suggesting our observations may relate particularly to amyloid angiopathy.
Genetic Data Identify Dual‐enrolled Participants in Alzheimer's Disease and Related Dementia (ADRD) Studies
Authors: Nudelman Kelly N. H. , Franklin Erin E. , Wyss Catharine M. , Jackson Trever , Bernhardt Haley , Case K. Rose , Lacy Kaci , Faber Kelley M. , Kukull Walter A. , Wang Li‐San , Saykin Andrew J. , Perrin Richard J. , Morris John C. , Weiner Michael W. , Foroud Tatiana M. 
Published: December 2022

AbstractBackgroundIt is not unusual for Alzheimer’s Disease (AD) Research Center (ADRC) participants to enroll in additional studies such as the AD Neuroimaging Initiative (ADNI). In such cases, shared access to neuropathologic biospecimens is important to both studies, but dual‐enrollment status is not always known to acting ADRC personnel, and currently there is no centralized method to track dual enrollment. For this study, we aimed to identify individuals enrolled in ADNI and an ADRC, and identify those with autopsy data available through an ADRC but not ADNI.MethodGWAS data were obtained for ADRC participants (N=22,935) through the AD Genetics Consortium (ADGC) and for ADNI participants (N=2,048) through the Laboratory of NeuroImaging (LONI); data were compared in PLINK v1.9 by identity‐by‐descent (IBD). ADRC/ADNI matches were filtered for pi‐hat > 0.8. Genotype data from a custom‐96 SNP microarray for ADRC and ADNI samples banked at the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) were also compared to identify additional matches. Year of birth, sex, race, and ethnicity were compared within matches. Participant IDs from matches were reviewed for autopsy data available through the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) and from ADNI, to identify participants with autopsy data available through NACC but not ADNI. We generated counts for matches that had year of death, and among those, how many had neuropathology variables.ResultWe identified 364 pairs with matched genetic (pi‐hat>0.99) and available demographic data from GWAS data, and another 52 from NCRAD genotype data. Of these, 143 matched ADRC participants had a year of death. 98 of the 143 had autopsy variables in the NACC UDS. 44 of these 98 were included in the ADNI autopsy data set (2018 version) or pending integration by the ADNI Neuropathology Core (NPC). Thus, 54 ADRC brain donors could be reported to the ADRCs as dually‐enrolled in ADNI, providing the involved ADRCs the opportunity to share these biospecimens with the ADNI NPC.ConclusionThis case study illustrates how identification of dually‐enrolled participants represents a unique opportunity to leverage data collected through multiple studies to enhance AD research.