bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impaired neuroinflammatory response of ApoE4 in Alzheimer’s disease patients
Authors: Kloske Courtney M. , Dugan Adam , Woolums Abigail E. , Lee Tiffany , Anderson Sonya , Patel Ela , Abner Erin L. , Nelson Peter T , Fardo David W. , Wilcock Donna M 
Published: December 2020

AbstractBackgroundAmyloid plaques and neurofibrillary tangles distinguish Alzheimer’s disease (AD) from other dementias. Additionally, there is a neuroinflammatory response to these hallmarks which induces the activation and proliferation of microglia. While this initially helps clear AD pathology, it can also lead to detrimental consequences depending on length of activation. In addition to the pathology, there are genetic risk factors for AD. One of the most influential is Apolipoprotein E (ApoE), a cholesterol and lipid transporter in the brain. There are three common isoforms: ApoE3 is believed to be the “control” phenotype, ApoE2 is believed to protect against AD, and ApoE4 to confer an increased risk for AD. Studies have shown that the ApoE isoforms have differing effects on many aspects of AD including, amyloid‐beta clearance and microglial activation.MethodWe used the Human Neuroinflammation NanoString panel to assess the neuroinflammatory profile in the superior medial temporal gyrus (SMTG) and cerebellar regions of age and sex matched patients with the following genotypes and pathology: ApoE3/3‐AD (Braak V/VI, Thal 5) (N=9); ApoE4/4‐AD (Braak V/VI, Thal 5) (N=10); and ApoE3/3‐control (Braak I/II, Thal 0) (N=5). RNA was extracted from frozen SMTG, and cerebellum. Nanostring results were analyzed with NanoStringDiff followed by additional statistical analysis. qPCR was performed to verify results.ResultWe first examined the inflammatory gene expression changes between ApoE3/3‐AD and ApoE3/3‐control patients to investigate differences that may be due to AD. Here we found significant gene changes specifically in pathways involved in microglial activation and apoptosis. Further, we examined differences between ApoE4/4‐AD and ApoE3/3‐control patients to determine the AD‐associated effects with ApoE4. These results showed no significant differences between the groups, suggesting that while ApoE4/4‐AD patients have both AD pathology and an impaired inflammatory response to the pathology. Next, we investigated ApoE3/3‐AD vs ApoE4/4‐AD to look at ApoE isoform‐associated effects with AD pathology. We found significant differences; however, the differences paralleled those between ApoE3/3‐AD and ApoE3/3‐control, once again suggesting ApoE4/4‐AD patients have an impaired inflammatory response to the pathology.ConclusionThese results suggest differences in the neuroinflammation between ApoE3/3 and ApoE4/4 patients with AD and may be implicated in disease progression.
Type 2 diabetes and cognitive status: A causal inference approach to estimate effects
Authors: Katsumata Yuriko , Karanth Shama D. , Nelson Peter T. , Kryscio Richard J. , Schmitt Frederick A. , Fardo David W. , Abner Erin L. 
Published: December 2020

AbstractBackgroundAlzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two of the most common causes of death in the US. Multiple large epidemiological studies suggest that T2DM is strongly associated with dementia. However, the association between T2DM and dementia is still not well understood. In this study, we investigated the association of T2DM with cognitive performance using causal inference approaches.MethodParticipant data were drawn from the National Alzheimer’s Coordinating Center (NACC). Diabetes status was determined based on any reported diabetes diagnosis or diabetic treatment during annual assessments. Cognitive data obtained at the last clinical visit included CDR and MMSE for global function, verbal fluency (Animal and Vegetable Naming) and Boston Naming Test for language/fluency, WMS‐R Logical Memory – immediate and delayed for memory, WMS‐R Digit Span Forward and Backward trials, and Trail Making A for attention, and Trail Making B for executive function. We generated a score for each cognitive domain using principal component analysis (PCA). Analysis of covariance (ANCOVA) with either inverse‐variance (IV) or inverse probability (IP) weights was used to examine the associations between diabetes status and the cognitive domain scores represented by the first PC, controlling for gender, age at the last visit, years of education, APOE ε4 dosage, hypertension status, and study center.ResultAfter excluding participants who reported any other type of diabetes, whose age at the last visit were less than 65 years, and who had rare neuropathological diseases, a total of 33,756 NACC participants were included in this study. 4,885 cases were determined to have positive diabetes status. Participants with positive diabetes status had significantly worse domain scores in language/fluency (p = 3.0 × 10‐5), attention (p = 5.3 × 10‐22), and executive function (p = 6.7 × 10‐16) when IV weights were used. Analysis with IP weights revealed that positive diabetes status was additionally associated with memory (p = 0.0075).ConclusionThere was a clear deficit in language/fluency, attention, and executive domain functions for participants with diabetes. After taking into account potential selection bias, diabetes was also associated with tests assessing the memory domain.
APOE‐ε4 carriers have superior recall on the ‘What was where?’ visual short‐term memory binding test at age 70, despite a detrimental effect of β‐amyloid
Authors: Lu Kirsty , Nicholas Jennifer M. , Pertzov Yoni , Grogan John , Husain Masud , Pavisic Ivanna M. , James Sarah‐Naomi , Parker Thomas D. , Lane Christopher A , Keshavan Ashvini , Keuss Sarah E. , Buchanan Sarah M. , Murray‐Smith Heidi , Cash David M. , Malone Ian B. , Coath William , Wong Andrew , Henley Susie M.D. , Crutch Sebastian J. , Fox Nick C. , Richards Marcus , Schott Jonathan M. 
Published: December 2020

AbstractBackgroundWhile APOE‐ε4 carriers are at higher risk of Alzheimer’s disease (AD), there is evidence that APOE‐ε4 may have some beneficial effects across the life‐span, including on cognition. It is unclear how such effects may relate to subtle memory decline during the preclinical phase of AD. Two previous studies reported that APOE‐ε4 carriers recalled object locations more accurately than non‐carriers on the “What was where?” visual short‐term memory binding test (10.1016/j.cortex.2016.12.016; 10.1016/j.neurobiolaging.2018.09.017), but these studies did not account for preclinical AD pathology.MethodThe “What was where?” task (Figure 1) was administered to participants in Insight 46 – a sub‐study of the British 1946 birth cohort – who were all born during the same week (aged 69‐71 at assessment (Table 1)). Outcomes included object identification and a sensitive analogue measure of localisation error (the distance between the location reported by the participant and the true location). Two‐dimensional mixture models (10.31234/osf.io/q57fm) were used to isolate three sources of localisation error: imprecision, guessing, and misbinding (swapping an object’s location with that of a different object). β‐Amyloid status (positive / negative) was determined from 18F‐Florbetapir‐PET. Multivariable regression models were used to investigate differential effects of APOE genotype (ε4‐carrier / non‐carrier) and β‐amyloid status on performance in 398 cognitively‐normal participants, adjusting for confounders including a prospectively‐collected measure of childhood cognitive ability.Result
APOE‐ε4 and β‐amyloid had opposing effects on object identification, with APOE‐ε4 predicting better recall and β‐amyloid‐positivity predicting poorer recall. APOE‐ε4 carriers also recalled object locations more precisely, but a subtle detrimental effect of β‐amyloid on localisation was seen only among non‐carriers (Table 2, Figure 2). Childhood cognitive ability also predicted performance over 60 years later (Table 2).ConclusionIn this large population‐based sample of cognitively‐normal ∼70‐year‐olds, a positive association between APOE‐ε4 and short‐term visual memory was observed. For the localization measure, this appeared to be protective against a subtle deficit associated with β‐amyloid pathology. This is consistent with the antagonistic pleiotropy hypothesis – whereby a gene controls both beneficial and detrimental traits – and provides novel evidence that these effects persist into older age, even among individuals who may be in the preclinical stages of AD.
Calibration of a simplified ARIA‐E MRI severity scale suitable for clinical practice
Authors: Klein Gregory , Bracoud Luc , Purcell Derk D. , Delmar Paul , Bullain Szofia S. , Abi‐Saab Danielle , Barakos Jerome , Baudler Monika , Fontoura Paulo , Doody Rachelle , Kerchner Geoffrey A. 
Published: December 2020

AbstractBackgroundVasogenic edema and sulcal effusions detected with magnetic resonance imaging (MRI) are known and manageable possible side effects of many anti‐amyloid immunotherapies. Periodic MRI scans and severity assessment of these imaging findings, known as amyloid‐related imaging abnormalities – edema (ARIA‐E) are currently mandated in clinical trials evaluating these therapies. Previous studies of gantenerumab have used a 60‐point severity scale (the Barkhof Grand Total Scale [BGTS]),1 which is effective for research purposes with good inter‐rater reliability (ICC = 0.78, 95%CI[0.36‐0.94]) between trained neuroradiologists. Other anti‐amyloid studies have used a simpler 3‐point severity scale, which has excellent inter‐rater reliability (ICC = 0.93, 95%CI[0.89‐0.96])2 and is likely to be of more practical use in clinical practice. This work compares the BGTS with the 3‐point scale, and with a more refined, 5‐point scale.2
MethodT2‐FLAIR and T2*‐GRE MRI scans (5 mm slices, no gap, 256 × 256 matrix) from 100 participants (87 with previously detected ARIA‐E, 13 without ARIA from original BGTS assessment) in the Marguerite RoAD gantenerumab study (NCT02051608) were selected. After excluding 3 scans due to missing data or unacceptable quality, all valid scans were re‐read using all three severity scales by one neuroradiologist with extensive experience (>500 reads) on all three scales. The reader was presented with pairs of baseline/follow‐up scans, blinded to prior read results. Assessment of ARIA‐H was also completed. Tables 1 and 2 summarize the scale definitions.ResultMedian(inter‐quartile range) BGTS scores for the 3‐point scale were 0 = 0(0‐0), 1 = 1(1‐3), 2 = 8(5‐14), and 3 = 20(20‐20), and for the 5‐point scale were 0 = 0(0‐0), 1 = 1(1‐3), 2 = 6(4‐9), 3 = NA, 4 = 15(10‐20), and 5 = 20(20‐20) (Figure 1). Spearman’s rank correlation between BGTS and the 3‐point scale was 0.85 and 0.91 for the 5‐point scale. Regression analysis of ARIA‐E severity scale to ARIA‐H count showed moderate positive association with Pearson’s r of 0.37, 0.40, 0.50 for the 3‐point, 5‐point, and BGTS scales, respectively.ConclusionThe simplified 3‐ and 5‐point scales are well correlated to the 60‐point BGTS ARIA‐E severity scale; the 5‐point scale had better correlation than the 3‐point scale. Further study is warranted to assess reliability for reads performed by non‐specialist radiologists in real‐world applications. References: (1) AJNR 2013. (2) Alz&Dementia 2017.
Beta‐amyloid deposition and Apolipoprotein E4 carrier status predict cerebral blood flow velocity response to exercise
Authors: Kaufman Carolyn S. , Ward Jaimie L. , Vidoni Eric D. , Morris Jill K. , Burns Jeffrey M. , Billinger Sandra A. 
Published: December 2020

AbstractBackgroundCerebral blood flow (CBF) provides crucial oxygen and nutrients and clears harmful metabolic waste products from the brain. The CBF decline observed in healthy aging is accelerated in Alzheimer’s disease (AD). We previously reported a lower CBF response during exercise in cognitively‐normal older adults with beta‐amyloid deposition. Recent studies suggest the Apolipoprotein E4 (ApoE4) gene may act mechanistically through vascular pathways to cause AD. Thus, we expanded our model to include ApoE4 carrier status. We hypothesized that beta‐amyloid deposition and positive ApoE4 carrier status would be associated with an attenuated CBF velocity response to exercise.MethodsWe conducted a secondary analysis on sedentary, cognitively‐normal adults (N = 39). For the parent study, individuals were recruited from the University of Kansas Alzheimer’s Disease Center. Genotyping was performed by LCG Genomics using KASP proprietary genotyping technology. Beta‐amyloid load was obtained from Positron Emission Tomography scans after [18F] Florbetapir administration. We recorded middle cerebral artery velocity (MCAv) using transcranial Doppler ultrasound at rest and during 8 minutes of moderate‐intensity exercise. We used these data to assess the CBF velocity response to exercise, defined as the percent change in MCAv from rest to exercise (%ΔMCAv = exercising MCAv – resting MCAv / resting MCAv). We ran a multiple regression to predict %ΔMCAv from age, gender, exercising carbon dioxide, exercising mean arterial pressure, beta‐amyloid load, and ApoE4 carrier status.ResultsOf the 39 participants (65‐86 years old), fourteen (36%) were ApoE4 carriers. The multiple regression model significantly predicted %ΔMCAv, F(6, 32) = 2.760, p = 0.028, adjusted R2 = 0.217. Regression coefficients and standard errors are found in Table 1. The standardized regression coefficients were β = ‐0.406 for beta‐amyloid load (p = 0.014) and β = 0.348 for ApoE4 carrier status (p = 0.025).ConclusionsHigher beta‐amyloid load was associated with a smaller %ΔMCAv, as hypothesized. However, contrary to our hypothesis, positive ApoE4 carrier status was associated with a larger %ΔMCAv from rest to exercise. This may reflect a compensatory CBF response in these adults who have maintained normal cognition despite having an elevated genetic risk of AD.
Deep learning detection of informative features in [18F] flortaucipir PET for Alzheimer’s disease classification
Authors: Jo Taeho , Nho Kwangsik , Risacher Shannon L. , Saykin Andrew J. 
Published: December 2020

AbstractBackgroundDeep learning has been applied to disease classification using neuroimaging data. To date, most classification of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) by deep learning has focused on MRI, FDG PET, and amyloid PET. Here we developed a deep learning classification model using tau PET measured with [18F]flortaucipir to classify AD or MCI against cognitively normal older adults (CN). We also visualized the process of the deep learning classification to examine whether it could detect informative features or patterns that would potentially improve diagnostic classification.MethodWe downloaded 458 tau PET images (196 CN, 196 MCI, and 66 AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and included only one scan per individual. SPM12 was used to process the tau PET data using standard techniques. We used a 3D convolution neural network (CNN) method for the classification, and applied a layer‐wise relevance propagation (LRP) algorithm to identify informative features and to visualize the classification results (Fig. 1). Five‐fold cross validation was applied, where 70% of the entire data set was used for model training, 20% for model testing, and 10% for independent validation.ResultOur deep learning‐based classification model distinguished AD from CN with an average accuracy of 96.2% (sensitivity: 95.4%; specificity: 96.9%) in an independent validation set, whereas MCI vs. CN showed 64.2% accuracy (sensitivity: 48.6%; specificity: 82.4%) (Table1). Visualization of deep learning decisions using the LRP algorithm identified patterns similar to the regions showing significant diagnostic group differences by conventional SPM analysis. Fig. 2 compared SPM contrasts to relevance heatmaps of 3D‐CNN classification.ConclusionOur results showed that tau PET‐based classification yielded higher accuracies than other imaging modalities and the LRP approach detected informative features in tau PET for AD classification. MCI classification was much less accurate reflecting the known heterogeneity within this group. The LRP‐based deep learning strategy can help visualize patterns of tau deposition in AD, MCI, or CN to assist with identification of features and patterns. Future directions include multi‐modality analysis and prognostic prediction.
Predicting cognitive resiliency in older women using machine learning
Authors: Casanova Ramon , Gaussoin Sarah A , Wallace Robert B , Baker Laura D , Sachs Bonnie C , Justice Jamie , Chen Jiu‐Chiuan , Manson JoAnn E , Henderson Victor , Whitsel Eric A , Hayden Kathleen M , Rapp Stephen R 
Published: December 2020

AbstractBackgroundIdentification of factors that help preserve cognitive function in late life could facilitate interventions that can improve the lives of millions of people as they age. However, the large number of potential factors associated with cognitive resiliency pose an analytical challenge. Machine learning can be used to discover complex functional relationships in high dimensional data. We used data from the Women’s Health Initiative Memory Study (WHIMS) and Random Forests (RF) methods to investigate predictors of cognitive resiliency in 80+ year old women from a set of demographic, biological, behavioral, social, and psychological variables.MethodWe included WHIMS women who were at least 80 years old as of 01/01/18, and had at least one cognitive assessment following their 80th birthday. We created two groups of participants: a cognitively resilient group and an impaired group of participants. The cognitively resilient group (N=205) included participants: (a) not previously adjudicated to have mild cognitive impairment (MCI) or probable dementia; (b) had a score >39 on the most recent administration of the TICSm and (c) the average of scores in previous visits>=39. The cognitively impaired group (N=176) included participants: (a) adjudicated with probable dementia within the first 14 years of follow‐up or; (b) who had at least two classifications of MCI within the first 14 years of follow‐up and (c) whose last TICS score was<31. RF classification was used to identify discriminative predictors of these two groups based on 67 baseline variables available in WHIMS. Analyses used the randomForestSRC R library. The minimal depth index (MDI) was used to determine predictors’ relevance.ResultDiscrimination between the resilient and impaired groups based on area under the curve was 0.80(95% CI‐0.76‐0.85). Figure 1 shows that according to the MDI values the most relevant predictors were age, self‐reported forgetfulness, physical function, optimism, hemoglobin, glucose, sleep disturbances, education, systolic pressure and depression.ConclusionWe investigated discrimination of cognitively resilient older women from women who develop cognitive impairment using machine learning applied to a large set of predictors. The potential contributors to cognitive resilience include demographic, psychological, physical, metabolic and vascular factors suggesting multiple influences contributing to cognitive resiliency.
The API Generation program: Biomarker phenotyping of cognitively unimpaired participants screened in Generation study 1 and Generation study 2
Authors: Rouzade‐Dominguez Marie‐Laure , Riviere Marie‐Emmanuelle , Borowsky Beth , Liu Fonda , Cazorla Pilar , Quinn Matt , Seneca Nicholas , Neumann Ulf , Eichenlaub Udo , Chien Yuchen , Viglietta Vissia , Reiman Eric M. , Sui Yihan , Caputo Angelika , Graf Ana 
Published: December 2020

AbstractBackgroundAPI Generation Program evaluated the effectiveness of the BACE1 inhibitor umibecestat and the active immunotherapy CAD106 in delaying the onset of AD symptoms in APOE4 carriers. Data from imaging (vMRI and PET scans), and fluid biomarkers (blood and CSF) were collected during the 12‐week Screening phase. In this presentation, we will summarize results across the two studies and investigate the relationships between the various biomarkers and key baseline characteristics.MethodAmyloid levels were assessed via CSF and/or Amyloid PET scan in both studies for the APOE4 carriers. Generation Study 2 enrolled heterozygotes based on the following elevated amyloid inclusion criteria: Roche Elecsys® CSF p‐Tau/Abeta42 ratio > 0.024 and/or PET scans with any of the 3 amyloid F18 tracers (florbetapir, flutemetamol or florbetaben). If the centralized visual read of PET scan was negative, the SUVr was calculated (cortical composite in reference to whole cerebellum) for the three tracers in order to assess borderline cases. Thresholds for amyloid positivity equivalent to SUVR of 1.1 with florbetapir were used for the final decision on inclusion.ResultAcross both studies, over 500 lumbar punctures, 2700 amyloid PET scans and about 150 tau PET scans (conducted using flortaucipir) were performed. In Generation Study 1, over 200 FDG PET scans were also performed. Participants underwent amyloid testing during screening, blood sampling (plasma and serum) and underwent MRI scans for safety and to measure brain volumes. All baseline biomarker data will be presented, including available concentrations from CSF Tau/p‐Tau/Aβ42/Aβ40 along with the eligibility ratio for pTau/ab42. In about 150 participants, concordance between elevated/non elevated amyloid status by CSF and PET (using centiloids) will be shown. Baseline brain volume (whole brain and hippocampus) will be summarized for the different co‐variates (age, sex, cognition, genotype and amyloid status). Correlations among the various fluid biomarkers and imaging will be discussed.ConclusionThese data describe the biomarker signature of the world’s largest cohort of cognitively unimpaired APOE4 carriers with and without elevated amyloid. The anonymized study data, biomarker samples as well as images collected will be shared with the scientific community after study completion and reporting. ELECSYS is a registered trademark of Roche.