bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modifiable factors, cardiorespiratory fitness and cardiovascular risk are associated with cognitive and structural brain health in midlife: Results from the BBHI
Authors: Morris TP , Gomes‐Osman Joyce , España‐Irla Goretti , Cattaneo Gabriele , Albu Sergiu , Solana Javier , Macià Dídac , Redondo‐Camós María , Delgado‐Gallén Selma , Alviarez‐Schulze Vanessa , Pachón‐García Catherine , Tormos‐Muñoz Josep M , Bartres‐Faz David , Pascual‐Leone Alvaro 
Published: December 2020

AbstractBackgroundNeurodegenerative brain changes that lead to cognitive decline generally begin decades prior to the onset of symptoms. On the other hand, brain health relates to overall, and specifically cardio‐vascular health, and vice‐versa. The objective of this investigation was to assess the relationships between two important domains of cardiovascular health (cardiorespiratory fitness, CRF, and cardiovascular risk, CVR) and two important markers of brain health (cognitive performance and volumetric measures of brain structure) in late middle age.MethodIn a relatively large sample (n=327) of middle‐aged adults in the Barcelona Brain Health Initiative (Cattaneo et al., 2018), we assessed the associations between CRF (measured by VO2 max), CVR (measured by Framingham risk score), domain‐specific cognitive performance (measured by performance in a neuropsychological test battery) and brain volumes (measured by high‐resolution brain MRI). All regression models were corrected for age and education.ResultWe found differential associations between CRF and CVR and cognitive performance. CRF was significantly associated with visuo‐spatial abilities and frontal loading abstract problem solving (p=.003), whereas CVR was associated with visuo‐spatial abilities (p=.023), flexibility (p=.005), processing speed (p<.001) and memory (p<.001). Moreover we found that CRF was associated with frontal (p=.042), temporal (p=.016) and sensorimotor (p=.013) cortical volume, whereas CVR was significantly associated with all cortical volume measures (frontal, temporal, parietal, occipital and sensorimotor; ps<0.05).ConclusionWe found that two modifiable factors in mid‐life (CRF and CVR) are significantly associated with a number of metrics of brain health in a cross‐sectional analysis. Future longitudinal analyses of this cohort will show whether these associations have predictive value for the maintenance of brain health across the lifespan. Finally, our findings suggest that lifestyle habits that promote CRF and reduce CVR, e.g. sufficient physical activity and appropriate nutrition, might help sustain brain health across the lifespan and prevent cognitive decline.
Hearing loss and cognition: A protocol for ensuring speech understanding in neuropsychological assessment
Authors: Goman Adele M , Deal Jennifer A , Arnold Michelle L , Chisolm Theresa , Coresh Josef , Hayden Kathleen M. , Owens Tiffany A , Pankow James , Reed Nicholas S , Sanchez Victoria A , Lin Frank R , Mosley Thomas H ,   ACHIEVE Study Group
Published: December 2020

AbstractBackgroundMany neuropsychological tests involve auditory components including test stimuli and instructions. Hearing loss may adversely affect neuropsychological performance through participants not being able to adequately understand auditory stimuli or verbal instructions, thus hampering identification and accurate classification of cognitive impairment. Formal measures to quickly assess speech understanding are lacking. Herein, we describe a protocol developed to determine adequate speech understanding and report on the relationship between hearing loss and speech understanding performance.MethodParticipants were 2864 older adults (mean age: 80.8 years; SD: 4.6) in the Atherosclerosis Risk in Communities Study visit 7 who completed a test to ensure speech understanding, had hearing data, and had covariate data. To ensure speech understanding, a psychometrist verbally presented 5 short sentences (each containing 3 key words) articulating clearly with normal inflections at a volume and pace commensurate with normal cognitive testing methods. After each sentence, the participant reported back what they heard with performance scored as the number of “key” words accurately repeated. A passing score (i.e., adequate speech understanding) was defined as ≥ 13. The protocol included steps on how to proceed with neurocognitive testing if speech understanding was not established. Better‐ear (0.5,1,2,4kHz pure‐tone‐average) hearing was categorized according to World Health Organization Criteria as normal (<25dBHL), or hearing loss (>25dBHL; mild: ≥25dBHL & <40dBHL, moderate or greater: >40dB HL). Logistic regression analyses adjusting for demographic factors (age, sex, race‐center, and education) assessed the association between hearing loss and performance on the ensuring speech understanding test.ResultTest failure was higher with increasing hearing loss severity (normal: 0.23%; mild: 0.52%, moderate or greater: 6.61%). Adjusting for demographic characteristics, hearing loss was associated with reduced odds of passing the ensuring speech understanding test (OR: 0.14, 95% CI: 0.02, 0.44).ConclusionHearing loss is associated with reduced speech understanding in environments commensurate with normal neurocognitive testing. Given the high prevalence of hearing loss among older adults, it is important to ensure neuropsychological performance is accurately captured and not adversely biased by reduced hearing. The protocol described here provides a brief measure to quickly assess speech understanding in the research setting.
The role of microglial HIF‐1α in Aβ pathology
Authors: Steinbrecher Lisa , Wendeln Ann‐Christin , Brösamle Desirée , Liu Ping , Wild Katleen , Haesler Lisa M. , Al‐Shaana Rawaa , Neher Jonas J. 
Published: December 2020

AbstractBackgroundMicroglial cells play an important role in the pathogenesis of Alzheimer’s disease. We have previously shown that microglia activate the Hypoxia inducible factor‐1α (HIF‐1α) signalling pathway in response to cerebral β‐amyloidosis in a mouse model of Alzheimer’s pathology (Wendeln et al., Nature, 2018). Importantly, we also found that activation of microglial HIF‐1α signalling correlated with disease severity and that HIF‐1α levels were particularly elevated in plaque‐associated microglia.MethodIn the experiments presented here, we analyzed the role of microglial HIF‐1α in promoting cerebral β‐amyloidosis by characterizing pathological hallmarks and microglial activation states in two APP transgenic mouse models with an induced HIF‐1α knockout (KO) in microglia cells.ResultOur results indicate that the lack of HIF‐1α in microglia increases their clustering around amyloid‐β plaques and alters the microglial phenotype and brain inflammatory state. While plaque load was indistinguishable in mice with/out microglial HIF‐1α, our data indicate that the increase in plaque‐associated microglia limits plaque‐induced neuronal damage.ConclusionOur data indicate that HIF‐1α inhibition alters the microglial phenotype, leading to an increased number of microglia around amyloid‐β plaques, thereby protecting neurons.
Stage vs subtype hypothesis: A longitudinal MRI study investigating the heterogeneity in AD
Authors: Poulakis Konstantinos , Ferreira Daniel , Pereira Joana B. , Westman Eric 
Published: December 2020

AbstractBackgroundLongitudinal measurements of brain atrophy have revolutionized our understanding of how Alzheimer’s disease (AD) pathophysiology evolves during the disease course. The distribution of neurofibrillary tangles (NFTs) in AD, varies between patients and correlates in vivo atrophy measures and specific profiles of cognitive decline. Longitudinal progression of atrophy markers in AD will help understanding the course of the disease. We wanted to investigate if atrophy differences between patients with AD dementia already exist at the time of the clinical onset and estimate their atrophy patterns over time.MethodOur dataset consist of Freesurfer preprocessed T1‐weighted structural MRIs(2 to 5 longitudinal MRI visits) for 322 amyloid positive AD diagnosed patients and 304 amyloid negative cognitively unimpaired (CU) subjects. We applied an in‐house longitudinal clustering pipeline on the AD sample after controlling for normal ageing effect (CU sample), to explore the heterogeneous patterns of AD over time, after the AD clinical onset.ResultThe model revealed heterogeneous patterns of atrophy in the AD subjects, predominantly in the baseline data, but also in their trajectories over time. Three distinct patterns of regional brain atrophy at AD onset and decline over time were identified. Approximately 55% of the AD patients presented the typical AD atrophy pattern, including some subjects who initially showed atrophy mainly in the temporal lobes but progressed similarly to typical AD. Atypical patterns of atrophy were also found in our dataset: i) Minimal atrophy AD with very few atrophy at baseline and subtle atrophy over time (10%), ii) hippocampal sparing early onset (HSEO) with posterior cortical atrophy (15%) and hippocampal sparing late onset (HSLO) with milder posterior atrophy and later AD onset (10%). The HSEO cluster has steeper atrophy trajectories and faster decline in all cognitive domains compared to HSLO. A 10% of the sample had no clear classification in any of the groups. Regional atrophy between subtypes was associated with longitudinal cognitive performance.ConclusionOur results show not only that AD is heterogeneous which is known from cross sectional data, but also that atrophy subtypes exist most probably due to different diseases and not due to different stages of the disease.
Using polygenic risk scores to assess the importance of microglia in Alzheimer’s disease
Authors: Baker Emily Ann , Leonenko Ganna , Williams Julie , Hill Matt , Escott‐Price Valentina 
Published: December 2020

AbstractBackgroundAlzheimer’s disease (AD) is a highly polygenic disorder; this has been demonstrated by the identification of a large number of risk loci from genome‐wide association studies (GWAS), and the ability of polygenic risk scores (PRS) to differentiate between AD cases and controls with high prediction accuracy (AUC = 78.2%) [Escott‐Price et al. 2015]. Many AD risk alleles have been shown to be located at open chromatin sites in immune cells, specifically microglia [Tansey et al. 2018].MethodA whole genome PRS was produced for individuals in the Genetic and Environmental Risk for AD (GERAD) data (N = 5,113) [Harold et al. 2009]. The score was weighted using effect sizes from the Kunkle GWAS [Kunkle et al. 2019], excluding GERAD individuals, and only single nucleotide polymorphisms (SNPs) with a p‐value less than 0.5 are included in the score. Linkage disequilibrium between SNPs was removed using standard pruning methods. In addition to a whole genome PRS, which uses all available SNPs, a microglia‐specific PRS was also produced, using SNPs which reside within the microglia regions defined by Gosselin et al. 2017. We also produced microglia‐specific PRSs with microglia genes defined from alternative sources for comparison.ResultWe compare the association between the whole genome and microglia‐specific PRSs, which show a moderate correlation (r = 0.24, p = 7 × 10−10). The prediction accuracy of both PRSs was determined; preliminary results show that prediction accuracy does not decrease dramatically when comparing whole genome and microglia PRS (AUC decreases by 3% and R2 drops to 0.04 from 0.08). We also determine the proportion of prediction accuracy and SNP‐based heritability explained by the microglial component of the polygenic risk score.ConclusionAD research has been directed towards microglia as it has been shown to be an important cell in neurodegenerative disorders [Young et al. 2019]. The decrease in prediction accuracy observed is due to the reduced number of SNPs; the goal to improving prediction accuracy is reducing SNP numbers whilst retaining heritability [Wray et al. 2019]. Microglial PRS show improved signal‐to‐noise ratio by reducing SNP numbers and are therefore a valuable approach to experimental design and cell line selection.
Plasma phospho‐tau in familial Alzheimer’s disease
Authors: O'Connor Antoinette , Karikari Thomas K. , Poole Teresa , Khatun Ayesha , Swift Imogen , Heslegrave Amanda J. , Abel Emily , Chung Elisha , Weston Philip S.J. , Polke James , Pavisic Ivanna M. , Rossor Martin N. , Ryan Natalie S. , Blennow Kaj , Zetterberg Henrik , Fox Nick C. 
Published: December 2020

AbstractBackgroundBlood biomarkers have the potential to advance clinical care and accelerate the development of disease‐modifying treatments. P‐tau181 is a promising blood biomarker, with levels increasing in Alzheimer’s disease (AD) dementia (doi:10.1016/j.jalz.2018.02.013). However, a better understanding of the timing and trajectory of plasma p‐tau181 changes is needed. Therefore, we conducted a longitudinal study in familial AD (FAD).MethodsUsing an in house Single molecule array method, P‐tau181 was measured in 153 plasma samples from 70 individuals from families with PSEN1 or APP mutations (mean ± SD = 2.2 ±1.3 samples/participant;median [IQR] duration of follow up =1.0 (0, 3.7) years). We compared plasma p‐tau181 between symptomatic mutation carriers, presymptomatic carriers, and noncarriers, adjusting for age and sex. We also examined the relationship between plasma p‐tau181 and estimated years to/from symptom onset (EYO), as well as years to/from actual symptom onset (AAO) in a symptomatic subgroup. In addition, we studied associations between plasma p‐tau181 and clinical severity, as well testing for differences in concentration between genetic subgroups (PSEN1 vs APP carriers, PSEN1 pre‐codon 200 vs PSEN1 post‐codon 200 carriers).Results24 of the asymptomatic participants were mutation carriers (mean baseline EYO ‐9.6 years); 27 were noncarriers. Compared with noncarriers, plasma p‐tau181 concentration was higher in symptomatic (p<0.001) and presymptomatic mutation carriers (p<0.001) (Figure 1). Plasma p‐tau181 discriminated symptomatic (AUC 0·93[95% CI 0·84−0·98]) and presymptomatic (AUC 0.86 [95% CI 0·73−0·95]) carriers from noncarriers. Plasma p‐tau181 concentration increased in mutation carriers from 16 years prior to estimated symptom onset (p=0.049) (Figure 2). Plasma p‐tau181 in symptomatic mutation carriers, modelled using AAO, appeared eventually to plateau. Longitudinal p‐tau181 measures demonstrated significant inter‐ and intra‐individual variability, with some participants exhibiting large changes over relatively short time intervals. We did not find a difference in plasma p‐tau181 concentration between APP and PSEN1 carriers, but there was weak evidence (p=0.053) that symptomatic PSEN1 post‐codon 200 carriers had a 54% higher p‐tau181 concentration (95% CI:1% lower, 138% higher) than pre‐codon 200 carriers.ConclusionOur finding that plasma p‐tau181 concentration is increased in presymptomatic and symptomatic FAD suggests its potential utility as an easily accessible biomarker of AD pathology.
Description of amyloid beta influence on synaptic plasticity and tau hyperphosphorylation by mechanistic quantitative systems pharmacology model
Authors: Lerner Stepan , Karelina Tatiana 
Published: December 2020

AbstractBackgroundLong‐term potentiation (LTP) is a dependent increase in synaptic plasticity that may be an appropriate experimental and theoretical model for learning and memory. In animal models of Alzheimer’s disease (AD), amyloid beta (Aβ) impacts the LTP decline and tau hyperphosphorylation. Increase of the intracellular calcium level, mediated by NMDA and nACh receptors, is responsible for appearance of LTP, which depends on increased in AMPA phosphorylation. Quantitative systems pharmacology (QSP) model of synaptic plasticity was developed to investigate influence of Aβ on impairment of LTP and tau hyperphosphorylation.MethodThe model describes a calcium influx in postsynapse through acetylcholine and glutamate receptors, activation of efflux from intracellular stores, activation of kinase/phosphatase cascade, tau phosphorylation and level of phosphorylation of AMPA receptors, which is associated with LTP. AD pathology is modeled by Aβ impact on receptors (inhibits nACh) and glial glutamate uptake. Model is developed step by step from simpler variants by consecutive addition of new mechanistic details, using in vitro data. In vitro data from the literature on synaptic plasticity in presence of different effectors, are used for verification.ResultThe model qualitatively describes impairment of LTP and tau hyperphosphorylation by Aβ influence and effect of receptor blockers, kinase and phosphatase inhibitors. It demonstrates that: 1) cholinergic and glutamate stimulation leads to LTP, depending on the timing of cholinergic input relative to glutamate input; 2) impaired LTP may recover due to extrasynaptic NMDA receptor blockers (e.g. memantine) or agonists of nACh receptors (e.g. SSR180711); 3) inhibitors of calcineurin decreased tau phosphorylation and recover LTPConclusionThe QSP model describes an influence Aβ on different postsynaptic mechanisms (receptors activation and kinase/phosphatase cascade, phosphorylation and dephosphorylation of AMPA receptors), which seems to underlie LTP and tau hyperphosphorylation.
Low doses of lithium and gene ontology analysis: Different action of dose response in neurons
Authors: De‐Paula Vanessa V.J. , Forlenza Orestes Vicente , Brentani Helena Paula 
Published: December 2020

AbstractBackgroundThe pharmacological mechanisms of lithium are not completely understood, but current evidence suggests the direct involvement of classic pharmacological targets affecting neurotransmission and signal transduction. These include the modulation of cell‐surface receptors, the release of second‐messengers and downstream signaling molecules, and the subsequent effect on the activity of important regulatory systems, with an impact on the release of transcription factors and gene expression. The aim of this work was to analyze the gene ontology of sub therapeutic and therapeutic doses of lithium in the hippocampus and correlate with lithium response and response to the GSK3B pathway.MethodWe made a gene block network, called “GSKome” and a “LITHIome”, based on protein‐protein interaction data, to identify genes with direct interaction. For this we used the program STRING (v 10.5) and PathCards (v 4.6). All selected proteins for Rattus norvegicus (microarray from primary culture of hippocampal neurons treated with different lithium doses: 0.02mM, 0.2mM and 2mM) had their nomenclature modified in r v3.6.1 (Bioconductor). In the Gene Ontology (GO) analysis we listed the top 5 FDR (false dicover ratio) of GSKome and LITHIome. ClueGO package (Cytoscape) were used for comparative analysis of the gene ontology (GO) between lithium doses with GSKOma and LITIOma.ResultGSKoma and LITIOma lists were compared with 0.02mM, 0.2mM and 2mM neurons samples. The 0.2mM lithium doses compared with LITIOma presents Biological processes presented the highest number of pathways, 27 in total. The Cell Component presented only the caveoline pathway and in the Molecular Functions four altered pathways. The GO analysis of 0.02mM lithium treated samples compared to GSKOma showed no statistical difference in Biological Processes, Cellular Component and Molecular Functions. The 0.2mM dose of lithium‐treated neurons compared with GSKOma were statistically significant in two of the three GO categories. Biological processes presented three pathways: caveolin (40%), axons (40%) and transcription factor (20%) and Molecular Functions, also with three altered pathways: protein kinase (53.64%) DNA transcription (27.27%) and DNA regulation (19.09%).ConclusionThe lithioma block was related to the ontological pathways of protection in therapeutic and subtherapeutic doses of lithium.
Biomarker profiles for staging of pre‐dementia patients
Authors: Kuehnel Line , Bouteloup Vincent , Lespinasse Jérémie , Chêne Geneviève , Dufouil Carole , Raket Lars Lau 
Published: December 2020

AbstractBackgroundPatient cohorts in clinical trials for Alzheimer’s disease are often highly inhomogeneous affecting statistical power to detect treatment effects. Objective methods that enable better staging of patients can be used to design inclusion criteria that result in less variability in progression and hereby increase statistical power. Furthermore, better prediction of disease stage is a key issue to determine when an intervention may have highest chance of success.MethodContinuous time‐progression of cognitive decline was estimated via a nonlinear mixed‐effects disease‐progression model. The model simultaneously estimated evolution of MMSE scores over the course of the disease and predicted patient disease stage. To investigate if combinations of baseline CSF and imaging biomarkers were predictive of disease stage, AIC‐based forward selection was used to include biomarker effects on disease stage in the disease‐progression model. In addition to biomarkers, effects of education, sex and age were also investigated. The included biomarkers were MRI cortical thickness signature, FDG PET, and core CSF biomarkers (total tau, phosphorylated tau, aβ42, aβ40, and aβ42/aβ40 ratio). To investigate the utility of biomarker profile on the model’s ability to predict disease stage, its performance in predicting future progression on MMSE was compared to a model that did not include biomarkers. The model was fitted to individuals with subjective or mild cognitive impairment (SCI/MCI) from the French MEMENTO study. The results of the model were validated in individuals with either significant memory complaints or MCI in ADNI.ResultThere were 312 individuals with SCI/MCI and complete biomarkers available at baseline. The optimal model included effects of total tau, MRI cortical thickness, and aβ42 on patients’ disease stage measured via MMSE scores. A model with the same terms was fitted to data from 609 individuals from ADNI to validate the findings. Inclusion of selected biomarkers was shown to improve staging of patients and to have similar directional contributions as in MEMENTO.ConclusionWe demonstrated that biomarker profiles are predictive of disease stage in pre‐dementia. We found that a combination of MRI cortical thickness, CSF total tau and CSF aβ42 was predictive of disease stage in the French MEMENTO cohort and validated this finding in ADNI.