bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single cell transcriptomic heterogeneity between sporadic early and late‐onset of Alzheimer’s disease
Authors: Millon Maria Beatriz Bistue , Bruno Martin Alejandro 
Published: December 2023

AbstractBackgroundRegulatory T‐cells (Tregs) are key modulators of immune responses, and may play a key role in the pathophysiology of various neurodegenerative disorders, including Alzheimer’s disease (AD). However, the impact of Tregs modulation of immune responses during disease progression is poorly understood and still controversial. Thus, the main objective of our ongoing study is to depict a high‐resolution transcriptomic landscape of blood immune cells during spontaneous disease progression, which will facilitate a better understanding of the protective and/or pathogenic immune responses at different stages of the disease1‐3.MethodIn 2019 a Multi‐partner consortium to expand dementia research in Latin America (ReDLat) was created. The Consortium aims to combine genomic, neuroimaging and behavioral (clinical, cognitive, socioeconomic status –SES‐) data to improve dementia characterization and identify novel inroads to treat neurodegeneration in diverse populations. ReDLat initiative supports a basic platform anchored in Argentina, Brazil, Colombia, Peru, Mexico, Chile and USA. allowing us to expand our research and combine genomic, neuroimaging and behavioral (clinical, cognitive, socioeconomic status) data to improve dementia characterization.To investigate changes in individual T cell subsets across progressive clinical conditions we included 4 healthy controls (CTLs), 4 early‐onset sporadic AD (EOAD), 4 late onset AD (LOAD) and 4 Parkinson disease patients (PD) from ReDLat. Peripheral blood mononuclear cells (PBMCs) were isolated and a 5' single‐cell transcriptome and immune repertoire sequencing was performed.ResultsWe identified transcriptionally distinct subpopulations, heterogeneity of the immune system, T cell repertoire diversity and antigen specificity among groups. Interestingly, CD4+CD25+Foxp3+ T‐regs gene expression heterogeneity was observed between sporadic EOAD and LOAD, mainly related to cytotoxicity and inflammation. Our next step is to perform multiple association studies incorporating imaging, sex, cognition and socio‐economics status (SES).ConclusionAlthough full analysis is still in progress, we identified significant changes in individual T‐cell subsets among individuals across the four proposed groups, providing a unique opportunity to identify new risk factors and therapeutics targets for AD.1‐ Fu J (2021) Front. Aging Neurosci.2‐ Cira Dansokho (2016). Brain.3‐Baek H (2016) Oncotarget.
Impact of White Matter Hyperintensity Changes on Cognition One Year After Mild Ischemic Stroke
Authors: Jochems Angela CC , Maniega Susana Muñoz , Clancy Una , Arteaga Carmen , Garcia Daniela Jaime , Hewins Will , Locherty Rachel , Wiseman Stewart , Hernandez Maria del C. Valdes , Chappell Francesca M , Stringer Michael S , Thrippleton Michael J , Doubal Fergus N , Wardlaw Joanna M 
Published: December 2023

AbstractBackgroundWhite matter hyperintensities (WMH) are a main MRI feature of cerebral small vessel disease, and are associated with increased risk of stroke and dementia. Progression can lead to cognitive decline, but some evidence suggests that WMH can also regress. Regression might relate to better clinical outcomes, but this is not well examined yet. We explored if net WMH volume change accounts for changes in cognition 1‐year after mild ischemic stroke. We also explore tissue signatures in areas of white matter change using brain diffusion imaging.MethodIn a prospective study, we assessed vascular risk factors (VRF), structural and diffusion‐tensor brain MRI and cognition (Montreal Cognitive Assessment; MoCA) at baseline (≤3 months post‐stroke) and at 1‐year. We assessed change in cognition over 1‐year using a linear mixed model. Predictors were age, sex, stroke severity (National Institutes of Health Stroke Scale; NIHSS), combined VRFs, disability (modified Rankin Score), occurrence of incidental infarct in‐between visits and WMH volume (%intracranial volume). We studied tissue areas of stable normal‐appearing white matter (NAWM), stable WMH, regressing and progressing WMH (Figure1) and measured diffusion‐tensor fractional anisotropy (FA) and mean diffusivity (MD), to assess microstructure in each areaResultAt baseline, mean age = 67.5 (SD = 11.1), 66% male (N = 229), mean MoCA score = 25.0 (SD = 3.5). Between baseline and 1‐year post‐stroke, MoCA score decreased with older age (standardized B[95%CI]: ‐0.271[‐0.383; ‐0.159]) and increasing NIHSS (‐0.193[‐0.287; ‐0.099]). Areas of NAWM that progress to WMH already have lower FA and higher MD at baseline than NAWM that stays NAWM (N = 191; Figure2). WMH that regress to NAWM have higher FA and lower MD than stable WMH. At 1‐year, MD values seem higher and FA values seem lower in progressing WMH than at baseline.ConclusionCognition deteriorates 1 year post‐stroke with older age and higher stroke severity, but not with WMH changes, when considering net WMH volume. Areas of WMH changes exist within individuals, these areas show different underlying tissue structures before becoming apparent on conventional MRI, indicating that WMH regression is real. Separate areas of WMH progression and regression might be more sensitive to cognitive changes and potentially be intervention targets.
Effects of β‐amyloid deposition, dopaminergic depletion, and perfusion on clinical features in cognitively impaired patients
Authors: Lee Young‐gun , Jeon Seun , Kang Sungwoo , Baik Kyoungwon , Ye Byoung Seok 
Published: December 2023

AbstractBackgroundAlthough mixed pathologies are common in Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB), effects of β‐amyloid and dopaminergic depletion on brain perfusion and clinical features have not been elucidated.MethodIn 99 patients with cognitive impairment due to AD and/or DLB and 32 control participants, 18F‐Florbetaben (FBB) PET, dual‐phase dopamine transporter (DAT) PET, and brain MRI were performed to measure β‐amyloid standardized uptake value ratio (SUVR), dopaminergic depletion in the ventral striatum, caudate, and putamen, and regional brain perfusion. Cognitive score, visual hallucination (VH), cognitive fluctuation (CF), and rapid eye movement sleep behavior disorder (RBD) were evaluated. Relationship among global FBB‐SUVR, striatal DAT uptakes, regional brain perfusion, clinical symptoms, and cognitive scores were evaluated using general linear or logistic regression models as appropriate.ResultGlobal and mean lobar FBB SUVRs negatively correlated with DAT uptakes in the ventral striatum and caudate but not with putamen DAT uptake. Lower ventral striatal DAT uptake was associated with right temporo‐parietal hypoperfusion and hyper‐perfusion in the anterior cingulate cortex, vermis, motor cortex, and hippocampus, while higher FBB SUVR was associated with hypoperfusion in the left entorhinal cortex and temporo‐parietal cortex. Lower ventral striatal DAT uptake increased the risks of VH and RBD directly and indirectly through the mediation of hippocampal hyper‐perfusion, while hippocampal hyper‐perfusion fully mediated the effect of lower ventral striatal DAT uptake on the risk of CF. Higher FBB SUVR and lower DAT uptake were associated with language, memory, and executive dysfunction directly and indirectly through the mediation of regional perfusion, while regional perfusion fully mediated their effects on visuospatial dysfunction.Conclusionβ‐amyloid deposition and striatal dopaminergic depletion reflecting AD‐ and DLB‐related neurodegenerations are inter‐correlated but differently contribute to regional perfusion changes and cognitive dysfunction.
Virtual brain model‐guided optimization of transcranial direct current stimulation in Alzheimer’s disease
Authors: Luppi Janne , Stam Cornelis J , Scheltens Philip , de Haan Willem 
Published: December 2023

AbstractBackgroundNon‐invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) can potentially counteract disrupted local and distributed brain network activity in Alzheimer’s disease (AD) with a beneficial effect on cognition. However, results of tDCS studies in AD up till now are inconsistent, probably partly due to different methodological choices such as electrode placement. We therefore explored tDCS optimization using a virtual brain network model of AD. Our aim was to compare a large, representative set of virtual tDCS intervention setups, to identify and potentially explain the optimal theoretical tDCS electrode positions for reducing or restoring functional network disruption.MethodWe simulated 20 tDCS setups in a computational macroscopic dynamic network model of 78 neural masses, coupled according to human structural topology. AD network damage was simulated using an activity‐dependent degeneration algorithm, which produces typical gradual oscillatory slowing and loss of connectivity. Current flow modeling was used to estimate tDCS‐targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density in the lower (8‐10 Hz) and upper (8‐13 Hz) alpha bands, total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC).ResultThe performance of different tDCS strategies varied considerably, with best setups temporarily improving all spectral and connectivity outcome measures, while other setups had little to no effect, or even caused network deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of a region (e.g. hub) and its involvement in successful tDCS was not observed.ConclusionModeling tDCS produces specific, falsifiable predictions on how to counter disrupted brain dynamics in AD. In follow‐up studies, our general predictions will be compared to those of personalized virtual models, and then validated with tDCS‐magnetoencephalography (MEG) in a clinical AD patient cohort. This modeling‐informed approach can guide and perhaps accelerate tDCS therapy development, as well as enhance our understanding of tDCS effects.
Relationships between polymorphisms in keratin genes and Alzheimer’s disease phenotypes.
Authors: Wong Yuen Yan , Xiong Lisa Y. , Mori‐Fegan Daniel K , Noor Shiropa , Chenoweth Meghan J. , Mirza Saira S. , Masellis Mario , Black Sandra E. , Swardfager Walter ,   Alzheimer’s Disease Neuroimaging Initiative
Published: December 2023

AbstractBackgroundKeratins are genetically polymorphic and are the largest subset of intermediate filaments. Keratins are also implicated in signaling pathways, inflammation, and disease states. Keratin 9 (KRT9) protein has been identified to have high diagnostic accuracy for Alzheimer’s disease (AD) (PMID:22045497); it was detected in cerebrospinal fluid (CSF) of AD patients but not healthy controls (PMID:24959311). Plasma keratin 9 concentrations also positively correlated with AD‐associated proteins (e.g., apolipoprotein E and tau) in AD patients (PMID:26973255). Post‐translational modification of keratin 83 (KRT83) protein has been associated with serum asymmetric dimethylarginine levels (PMID:34181092), a vascular risk factor implicated in AD. However, our understanding of the role of genetic variation in AD risk is incomplete. Here, we investigate associations between single nucleotide polymorphisms (SNPs) in KRT9, KRT83, and KRT2 (Keratin 2, couples with keratin 9) and AD biomarker phenotypes.MethodIndividuals with mild cognitive impairment (MCI) or AD, and clinically normal controls were selected among ADNI participants. Participants underwent genome‐wide genotyping. Keratin gene variants were identified from the genome‐wide data using dbSNP variant IDs and genotypes were coded additively. Linkage‐disequilibrium‐based clumping on minor allele frequency (MAF) was applied to prioritize more common SNPs. CSF‐Aβ42, ‐tau, and phosphorylated‐tau (p‐tau) levels were quantified via Roche Elecsys immunoassays. Brain‐Aβ deposition was evaluated via normalized PET‐18F‐AV‐45 cortical summary measures (SUVR). White matter hyperintensities (WMH) were obtained from MRI 3D‐T1 and FLAIR sequences via an automated atlas‐based segmentation pipeline. Linear regression models controlling for age, sex, Mini‐Mental State Exam, APOE‐ε4 status, and head size were used to assess association and interaction effects between SNPs and the AD phenotypes in R.ResultAmong included participants (n = 907, 73±7.2 years, 44% female), SNPs in KRT2 (e.g. 3’UTR‐variant rs117041267‐G/A; MAF = 1.4%) and KRT83 (e.g. upstream‐variant KRT83‐rs17119838‐C/T; MAF = 15.6%) were associated with higher CSF‐Aβ42 concentrations (F(2,905) = 4.26,p = 0.014; and F(2,905) = 6.61,p = 0.0014, respectively). Upstream‐variant KRT83‐rs182354391‐C/G (MAF = 1%) showed an association with lower CSF‐tau (F(1,904) = 5.44,p = 0.020). Multiple KRT9 and KRT2 SNPs also showed association with WMH, while KRT2 and KRT83 intron variants showed associations with PET‐Aβ.ConclusionThese candidate gene analyses suggest involvement of multiple keratin family genes in AD, as indicated by differences in amyloid and tau biomarkers, and white matter disease.
Self‐ and interviewer‐reported cognitive problems in relation to subsequent cognitive decline and dementia: results from two prospective studies
Authors: Huang Yuhui , Chen Hui , Pang Ting , Lv Xiaozhen , Gao Mengyan , Rong Shuang , Xu Xin , Yuan Changzheng 
Published: December 2023

AbstractBackgroundLittle is known regarding the overall association of self‐reported cognitive problems (SCP) and interviewer‐reported cognitive problems (ICP) with age‐related cognitive decline in later life.MethodWe included 10976 Chinese (age: 57.7 ± 8.7) and 40499 European (age: 64.6 ± 9.4) adults without dementia at baseline from CHARLS and SHARE, respectively. Self‐rated current memory (5‐point scale) and interviewer‐rated frequencies of asking for clarification (6‐point scale) were used to define SCP and ICP (dichotomized). Outcomes included objective cognitive test scores (z‐transformed) and incident dementia. We performed generalized estimating equation models to evaluate mean differences in objective cognitive decline (z‐score) across groups. Logistic regression and cox proportional hazards models were used to estimate relative risk of dementia.ResultDuring an average of 5.4 and 5.7 years of follow‐up, rates of cognitive decline were ‐0.039 (‐0.044, ‐0.034) z‐score/year for Chinese and ‐0.027 (‐0.029, ‐0.025) z‐score/year for Europeans. Compared to those without SCP and ICP, participants with coexistence of SCP and ICP were associated with faster cognitive decline (βCHARLS: ‐0.051 [‐0.080, ‐0.021]; βSHARE: ‐0.024 [‐0.043, ‐0.004]; Pooled β: ‐0.035 [‐0.061, ‐0.009] z‐score per year). In addition, individuals with both SCP and ICP had higher risk of developing dementia (ORCHARLS: 1.77 [1.42, 2.20]; HRSHARE: 2.94 [2.42, 3.59]; Pooled relative risk: 2.29 [1.38, 3.77]).ConclusionCombination of SCP and ICP could be utilized as strong early indicators to identify individuals at high risk of cognitive decline and dementia.
Validation of a Computable Phenotype for Successful Cognitive Aging
Authors: Smith Glenn E. , Miller Amber H. , Marra David E , Wu Yonghui , Bian Jiang , Maraganore Demetrius M , Anton Stephen 
Published: December 2023

AbstractBackgroundStudying successful cognitive aging presents an opportunity to identify factors that may mitigate risk for Alzheimer’s disease and related disorders to inform public health initiatives, community interventions, and policy. Yet, identifying these cohorts is challenging, especially at advanced ages. Commutable phenotype algorithms using electronic health records may serve as an aid to identify cohorts of advanced age adults with intact cognitive function. The purpose of this study was to 1) establish, 2) apply, and 3) validate a machine learning‐based computing algorithm for recruitment of successful aging individuals for clinical cohort study.MethodWe identified EHR‐available variables representing successful aging among individuals 85 years and older (85+) by interviewing a panel comprised of 10 aging experts. Based on the identified variables, we developed a rule‐based computable phenotype algorithm composed of 17 inclusion or exclusion criteria. We applied the computable phenotype algorithm to screen all living persons age 85+ at UF Health and identified those meeting successful aging criteria and endeavored to directly evaluate them.There were 52,841 living 85+ individuals identified at UF Health. Of these 24,024 were identified as successful aged by the computable phenotype algorithm. This sample frame was 58% female, 59% White, and 69% non‐Hispanic. A priori permission to be contacted for research had been obtained on 11,898 of these persons, of whom 470 responded to study announcements and 333 consented to evaluation.The main outcomes measures were individuals identified by the computational algorithm subsequently being validated by virtue of having a modified Telephone Interview for Cognitive Status score >27 and Geriatric Depression Scale <6.ResultsForty‐five percent of all living persons 85+ in the UF Health EHR database were identified by the computable phenotype as successfully aged. Approximately 4% of these persons responded to study announcements and 333 were consented. Of these 333, 218 (65%) were ‘validated’ as meeting successful cognitive aging criteria via direct evaluation (See Figure).ConclusionsThe study validated a computable phenotype algorithm for recruitment of individuals for a successful aging study using large‐scale EHRs. Our study provides proof‐of‐concept of using big data and informatics as aids for recruitment of individuals for prospective cohort studies.
Predictive ability of the IQCODE items for detection of cognitive decline and dementia in aging adults with Down syndrome
Authors: Mattar Guilherme Prado , Gonçalves Aline Souza , da Silva Maria de Fátima Rebouças , Leduc Vinicius Ribeiro , Ribeiro Octávio Gonçalves , Martinez Luana Dongue , de Oliveira Alexandra Martini , Sant´Ana Livea Carla Fidalgo , Fonseca Luciana Mascarenhas , Forlenza Orestes Vicente 
Published: December 2023

AbstractBackgroundThere is evidence of a genetic association between Down Syndrome (DS) and early progression of Alzheimer’s disease (AD). Few brief instruments have been validated for the screening of dementia in this population. Previously, we showed that the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) may be useful for cognitive screening in adults with DS1. Nonetheless, the adaptation of certain items to account for the presence of intellectual deficiency may increase diagnostic accuracy. The aim of the present study is to evaluate the predictive ability of IQCODE items for adults with DS.MethodUnivariate and multivariate logistic regressions were used to assess the effects of the 26 IQCODE items on the diagnosis of cognitive decline (prodromal dementia or dementia versus stable cognition) according to the Cambridge Examination for Mental Disorders of Older People with DS and Others with Intellectual Disabilities, CAMDEX‐DS. The study comprised 92 participants with DS aged ≥35 years. The accuracy of the final multivariate model was evaluated using receiver operator characteristics (ROC) curve.ResultAssociations were found between all IQCODE items and diagnosis, except for two items (handling money for shopping and financial matters). Multivariate logistic regression using the CAMDEX‐DS diagnosis as dependent and the items of IQCODE as predictors, showed that four items remained in the model (remembering: members of family and friends, day and month, things they learned when they were younger, and adjusting to change in routine). Participants who presented more difficult with these items were, respectively, 84.1, 86.1, 10.1 and 23.6 times more likely to have a diagnosis of prodromal dementia or dementia. Using the estimated probability of these 4 items, we found an area under the ROC curve of 0.961.ConclusionThe IQCODE items showed good ability to discriminate subjects with cognitive decline as indicated by the CAMDEX‐DS. The use of 4 of the IQCODE items proved to suffice to discriminate between diagnostic groups. We will next perform an adaptation and validation study to warrant the “IQCODE‐DS” as a reliable, brief test for the cognitive screening in this population.
The diagnostic performance of plasma amyloid β, total tau and neurofilament light chain levels in mild cognitive impairment and Alzheimer’s disease dementia: the Health & Aging Brain among Latino Elders study
Authors: Wang Shanshan , Hall James , Petersen Melissa , O'Bryant Sid E. , Nandy Rajesh 
Published: December 2023

AbstractBackgroundThe current diagnostic criteria for Alzheimer’s disease (AD) rely on neuroimaging and cerebrospinal fluid, which are expensive and invasive. Plasma‐based ATN biomarkers, including amyloid β40 (Aβ40), amyloid β42 (Aβ42), total tau, and neurofilament light chain (NfL), are more accessible and cost‐effective. However, the diagnostic performance of plasma‐based ATN biomarkers remains to be determined. This study aims to assess the diagnostic performances of plasma‐based ATN biomarkers for AD dementia and mild cognitive impairment (MCI), and assess whether the diagnostic performances are different between Mexican Americans and non‐Hispanic Whites.MethodWe used data from the Health & Aging Brain among Latino Elders (HABLE) study. A total of 1616 participants (1260 cognitively normal, 247 MCI, and 109 AD dementia) were included in the analyses. Diagnoses of AD dementia and MCI were adjudicated by consensus review. Plasma was analyzed using the single molecule array (Simoa) assays. The overall diagnostic performances of plasma‐based ATN biomarkers were assessed by the area under the curve (AUC) analyses. The DeLong test was used to compare the AUCs.ResultPlasma NfL had better differentiation performances than plasma Aβ42/Aβ40 ratio, total tau and the combination of plasma ATN biomarkers. When age and sex were combined to the diagnostic model, the AUCs did not change significantly. The performances of plasma ATN biomarkers were not significantly different between Mexican Americans and non‐Hispanic Whites (P values>0.05). However, when age and sex were incorporated to the diagnostic model, the performances to distinguish AD dementia and MCI vs. CN, AD dementia vs. MCI, and MCI vs. CN were significantly different between Mexican Americans and non‐Hispanic Whites (P values<0.05).ConclusionThe current study provided more evidence on the potential capacity of plasma‐based ATN biomarkers for diagnosing AD among Mexican Americans and non‐Hispanic Whites. Overall, the diagnostic capacities of plasma ATN biomarkers were not ideal. More research is needed to explore the utilization of plasma ATN biomarkers.