bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative analysis of summary data from GWAS and eQTL studies predicts tissue‐specific gene targets for Alzheimer’s disease
Authors: Shen Li , Yao Xiaohui 
Published: December 2020

AbstractBackgroundGenome‐wide association studies (GWAS) have been successful in identifying risk variants for Alzheimer’s disease (AD). However, it is unclear how these genetic variants affect tissue‐specific transcriptomic alternations in AD. The summary data‐based Mendelian randomization analysis (SMR) is a method designed to address this problem. It integrates summary data from independent GWAS and expression quantitative trait locus (eQTL) studies to identify genes differentially expressed between phenotypic groups.MethodsWe performed SMR to predict gene targets differentially expressed in AD, using summary statistics of brain eQTL studies and a landmark AD GWAS. Significant eQTL findings (FDR p≤0.05) of thirteen brain tissues were downloaded from https://gtexportal.org. GWAS summary statistics were obtained from an AD meta‐GWAS: https://ctg.cncr.nl/software/summary_statistics. The linkage disequilibrium (LD) information, required by SMR, were estimated using the ADNI genotyping data (N=1,536). For each tissue, SMR was performed to identify the pleiotropic associations between gene expression data and the AD phenotype. Bonferroni method was used for multiple comparison correction.ResultsFigure 1 shows 64 significant AD‐gene associations identified by SMR, involving 14 genes and 13 brain tissues. Some findings have been studied as top AD genes (BIN1 and CR1), or located around the AD genes (APOC2, PVRIG, STAG3, GPC2). Two genes (HLA‐DQA2 and SNX32) are new candidates that have no SNPs achieving meta‐GWAS p<5.0E‐8 within 20 kb from the gene. HLA‐DQA2 is located in the region of AD risk gene HLA‐DRB1. SNX32 is a novel AD associated gene predicted by SMR. SNX32 is a family member of SNX proteins, and is a component of retromer which has been widely reported for its role in the AD etiology.ConclusionsTissue‐specific transcriptomic association analysis using GWAS summary statistics and eQTL data identified genes associated with AD due to pleiotropy. Multiple genes were identified as potential AD targets in various brain tissues, given the estimated pleiotropic associations between their expression levels and AD diagnosis. These identified genes warrant further investigation as potential targets for functional validation to help better understanding of molecular mechanisms of AD.
Developing a novel alpha‐synuclein positron emission tomography (PET) tracer for the diagnosis of synucleinopathies
Authors: Capotosti Francesca , Vokali Efthymia , Molette Jerome , Tsika Elpida , Ravache Myriam , Juergens Tanja , Melo dos Santos Antonio , Darmency Vincent , Piorkowska Kasia , Chauhan Mayank , Kroth Heiko , Hliva Valerie , Sol Olivier , Pfeifer Andrea , Kosco‐Vilbois Marie 
Published: December 2020

AbstractBackgroundThe progressive accumulation of aggregated alpha‐synuclein (a‐syn) in the form of Lewy bodies and neurites is the hallmark of e.g. Parkinson’s disease (PD). Due to the low density of a‐syn pathology and the frequency of co‐existing proteinopathies in PD (e.g., beta‐amyloid, tau), an a‐syn PET tracer must display high binding affinity and selectivity to the target, as well as minimal background.Method[18F]‐ACI‐Cpd‐AE was evaluated in a first‐in‐human (FiH) study. [18F]‐ACI‐Cpd‐AE dynamic PET imaging was conducted over 90 min in five PD subjects (four idiopathic PD, 68.7 years and MDS‐UPDRS III 'off‘ score 43.3, and one with SNCA duplication, 49 years and MDS‐UPDRS III 'off‘ score 52) and five healthy volunteers (HV, 58.4 years). Specific retention of [18F]‐ACI‐Cpd‐AE was evaluated by calculating the distribution volume ratio (DVR) from kinetic analysis with an arterial input function and the standardized uptake value ratios (SUVr) from static analysis, using the middle frontal cortex as reference region. In parallel to the FiH evaluation of [18F]‐ACI‐Cpd‐AE, a thorough medicinal chemistry lead to the design of new promising compounds and the throughput of the screening assays was improved.ResultIn the FiH study, [18F]‐ACI‐Cpd‐AE displayed fast brain uptake, very low non‐specific retention, rapid metabolism and high free fraction. In this small cohort, no significant evidence of cross‐sectional difference between healthy controls and PD subjects was observed. Averaged across target regions the SUVr (30‐90min) range was 1.15‐1.22 in PD subjects and 1.14‐1.28 in HV. Specifically, in left substantia nigra, mean SUVr was 1.32±0.15 in PD and 1.19±0.18 in HV. Based on these clinical data, a new chemical series has been identified to retain affinity and selectivity while improving target occupancy.ConclusionIn house medicinal chemistry efforts led to the discovery of ACI‐Cpd‐AE, a potential a‐syn PET tracer that binds selectively and with high affinity to a‐syn aggregates. In the FiH study, ACI‐Cpd‐AE demonstrated fast brain uptake and low non‐specific retention. Further analyses of the FiH data and evaluation in patients with expected higher levels of pathological brain a‐syn aggregates will be performed. In parallel, compounds with improved binding properties have been identified and optimized for further development.
Aerobic capacity level can influence spatial navigation performance in healthy and MCI older adults
Authors: dos Santos Sacramento Plácido Jessica , Ferreira Jose Vinicius , Silva Felipe de Oliveira , Araujo Juliana , Laks Jerson , Deslandes Andrea Camaz 
Published: December 2020

AbstractBackgroundSpatial navigation is a complex and fundamental cognitive function that emerges as one of the first deficits in patients with Mild Cognitive Impairment (MCI). Successful spatial navigation relies on an interaction between physical and cognition ability. However, the mechanisms of real‐world navigation in this population remain unclear, especially the possible influence of physical performance. Therefore, the objective of this study was to investigate the possible influence of physical variables (balance, mobility and aerobic capacity) on spatial navigation performance of healthy and MCI individuals through the Floor Maze Test (FMT). As a secondary objective, we compare the performance of these elderly and determine which cognitive variables are associated with spatial ability performance on the testMethodSixty‐two elderly subjects (>60years) (healthy=39;MCI=23) were evaluated. Spatial navigation was investigated through the FMT, Planning Time (PT), Immediate Maze Time (IMT) and Delayed Maze Time (DMT). Moreover, while physical capacity was assessed through the Sit to Stand, 8 foot up and go and STEP test (Senior Fitness Test Battery), and cognitive functions were examined by MMSE, the clock drawing test, digit span, and Trail (A and B). The relationship and possible association between FMT performance and independent variables were analyzed using regression and correlation models.ResultThe MCI group was significantly slower in all stages of FMT. In addition, FMT performance was mainly associated with spatial orientation and aerobic capacity (R2=0.29 p= <0.001). Logistic regression analysis showed that the elderly with low aerobic capacity is 3 times more likely to have the worst performances at DMT stage even after controlled by age and diagnostic (O.R =3.1 p=0.04).ConclusionHealthy and MCI older adults should be encouraged to keeping a physical exercise routine in order to maintain their cardiorespiratory levels and spatial navigation ability. Also, future studies must investigate the temporal relationship among the decline of aerobic capacity, spatial navigation and MCI diagnosis.
Significant biomarkers for predicting depression in patients with Alzheimer’s disease
Authors: Ashraf‐Ganjouei Amir , Sedghizadeh Mohammad Javad , Aghajan Hamid , Vahabi Zahra 
Published: December 2020

AbstractBackgroundDepression is considered to be one the most common psychiatric co‐morbidities in Alzheimer’s disease (AD). Besides its prevalence, depression is associated with faster cognitive decline, physical aggression toward caregivers, earlier admission to nursing homes and lower quality of life. Therefore, early prediction of depression and selecting patients with higher risks for developing depression in the course of AD can provide an opportunity for earlier intervention and higher quality of life. This study’s primary intent was to explore the ability of feature selection algorithms to indicate baseline features that can predict the development of depression in AD patients.MethodWe have selected 28 AD patients from The Alzheimer’s Disease Neuroimaging Initiative (ADNI) that developed probable depression in 3‐5 years of follow‐up and 56 age‐ and sex‐matched AD patients without probable depression developed in the course of the disease. We implemented statistical analyses to identify the top significant markers offering the highest f values.ResultOur results indicated that P‐tau csf level, tau csf level, APOE4 genotype, ECogPtLang score (representing the interference of language impairment reported by the participant) and The Mini‐Mental State Exam (MMSE) score were the best features for predicting the development of depression in AD patients (f values: 7.38, 6.17, 3.96, 3.03 and 2.37, respectively). The first 3 markers possess p values < 0.05.ConclusionThese results provide primary evidence in favor of a distinguishable baseline profile in AD patients with higher risk for depression in the future. Therefore, caregivers might be able to classify AD patients prone to developing depression based on these features and provide earlier interventions.
Hospital‐associated deconditioning: Brain health issues beyond physical factors
Authors: Chen Yaohua Sophie , Domínguez‐Vivero Clara , Almirall‐Sanchez Arianna , Oz Didem , Romero‐Ortuno Roman 
Published: December 2020

AbstractBackgroundHospital‐associated deconditioning (HAD) or post‐hospital syndrome is a multisystem decline that leads to poor functional performance after acute hospitalization. Although hospitalization is supposed to treat an acute condition and improve the overall outcome, HAD may be significant, especially in older people. Recommendations for the management of HAD still lack, partly due to the poor understanding of the underlying processes. We aim to review existing data on mechanisms, risk factors, natural history, measuring tools, and intervention in HAD, with a specific focus on brain health.MethodWe searched PubMed, sCielo and Cochrane database for relevant articles published between 1 January 2009 and 31 December 2019. Search terms were “post‐hospitalization syndrome” OR “deconditioning” OR “hospital‐associated functional decline” OR “pre‐existing frailty” OR “unrecognized vulnerability”. Three researchers independently selected papers according to title relevance and then re‐checked for abstract relevance.ResultsWe first selected 63 studies based on the title relevance and 14 of them were excluded after reviewing the abstract. Research on this field has been mainly led from a physical perspective, in geriatrics or rehabilitation settings. 25 studies were related to mechanisms; 18 to risk factors and trajectories; 10 to measuring and predictive tools and 5 to interventions. Mechanisms referred to the inevitable change imposed by the hospitalization (e.g. reorganization of sensorimotor cortex, maladaptive cognitive schemas, vascular deconditioning) and to the own frail condition of the population at risk. Risk factors varied from older age to nutritional status, mobility or functional status before admission among others. Cognitive impairment, ability for ADLs and IADLs at admission and depression were also predictors of functional decline. Assessment of those items worked as a useful tool to identify vulnerable patients for HAD. Regarding interventions, all but one was devoted to physical rehabilitation and environmental modifications. Only one study focused on cognitive stimulation.ConclusionsSeveral studies have focused on HAD in the last decade, with some consistent findings. Cognitive impairment, depression and neurological changes may have a role in hospital deconditioning pathophysiology and may be useful to identify patients and risk. Cognitive rehabilitation and neurological interventions should be evaluated as well in deconditioning prevention and treatment.
The potential role of neuroinflammation and synaptic plasticity for neuropsychiatric symptoms
Authors: Blum Hellen , Lausser Ludwig , von Einem Björn , Fissler Patrick , Kolassa Iris‐Tatjana , Otto Markus , Tumani Hayrettin , Kestler Hans , Rosenbrock Holger , von Arnim Christine 
Published: December 2020

AbstractBackgroundNeuropsychiatric symptoms (NPS) show a high prevalence within Alzheimer’s dementia. Evidence from animals and humans suggest a role of inflammatory cytokines and second messengers related to synaptic plasticity such as cGMP in Alzheimer´s disease (AD). In addition, studies show associations of serum interleukins (IL) and cGMP with NPS. However, the role of different ILs and cGMP in the pathogenesis of NPS is still not fully clear and studies on CSF are lacking. This study examines the associations between NPS with different established AD markers, pro‐ and anti‐inflammatory ILs and cGMP in the CSF.MethodCSF and Serum samples of 120 patients with SCI, MCI or dementia and 19 controls who were previously examined at the memory clinic Ulm were analysed for this study. In clinical routine NPS were assessed by the neuropsychiatric inventory (NPI) total score and the Geriatric Depression Scale‐15 (GDS). Cytokines were determined by MSD V‐Plex assay in serum and CSF of patients, cGMP was determined by ELISA in CSF. Linear and binary logistic regression models were used for investigation. Bootstrapping was used if model assumptions were not fulfilled. Analyses were adjusted for ApoE genotype, age, gender and education. Additionally, an exhaustive screening for predictive marker combinations was performed (NPI>9, yes/no).ResultThe NPI total score was not associated with any of the evaluated ILs, but there was a positive association with CSF‐Abeta‐42 (β = .20, p = .038). Higher cGMP was associated with a lower OR for NPI>9 (OR = 0.69, p = .012). GDS was positively associated with IL‐1RA (β = .20, p = .029). IL‐6 was negatively associated with CSF‐Abeta‐42 (β = ‐0.09, p = .035). The predictive models achieved a maximal accuracy of 67.3%.ConclusionThis study is one of the first, examining the relationship between CSF inflammatory cytokines, cGMP, AD pathology and NPS in memory clinic outpatients. The results indicate a role of cGMP in NPS. Furthermore, results indicate a potential role of ILs in depression in older adults (IL‐1RA) and in AD. Because of the risk for alpha‐error inflation due to multiple comparisons, further research is needed to replicate these findings.
Robot‐assisted detection of subclinical dementia: Progress report and preliminary findings
Authors: Håkansson Krister , Beskow Jonas , Kjellström Hedvig , Gustafsson Joakim , Bonnard Alexandre , Rydén Marie , Stormoen Sara , Hagman Göran , Akenine Ulrika , Pérez Kristal Morales , Henter Gustav , Sundell Maria , Kivipelto Miia 
Published: December 2020

AbstractBackgroundEarlier identification of an underlying AD pathology could increase chances that preventive or curative treatment will be more successful. Human limitations in sensory capacity, attention and parallel processing could mean that automatic and simultaneous registration from several information channels in combination with artificial intelligence processing could advance diagnostic precision and accuracy. Here we report the progress of an interdisciplinary project to develop robot‐assisted diagnostics to detect early neurocognitive disorder.MethodBehavior in 100 patients without previous evidence of cognitive disorder is recorded during their first clinical examination visit. We record gaze, pupil dilations, skin temperature changes, speech characteristics, mimics, pulse, heart rate variability, and motor activity (see image 1). We then use machine learning to identify behavior signals and signal patterns that can distinguish between persons with and without an underlying neuropathology. In addition, we perform human analysis of recorded video and audio on each patient. Over 40 behavior categories are carefully checked, among them repetitions, irrelevant deviations, hesitations, degree of problem awareness, restlessness and inadequate word usage. This analysis is performed by a semi‐blinded interdisciplinary expert group of clinically experienced physicians, neurologists, speech therapists, psychologists, nurses, and experts in automatic verbal and non‐verbal behavior analysis.The results from this study will be implemented to further develop a social robot platform (Furhat, see image 2) with adequate sensors and algorithms so that it can interact with patients and assist in the diagnostic process.ResultApproximately 25 patients have so far been recorded and analyzed. Preliminary results are that the diagnostic prediction by the expert group, solely based on observation of patient behavior from audio and video, fully has coincided with the diagnostic outcome from the subsequent clinical evaluation. Another observation has been the amount of clinically relevant behavior revealed through careful human behavior observation that the interviewing physician did not observe during the actual interview.ConclusionArtificial intelligence could represent an untapped potential for improving sensitivity and accuracy in diagnostic procedures to detect early neurocognitive disorders.
Network Failure Quotient (NFQ) performance in ADNI‐2 and ADNI‐3: Robustness across scanner and protocol changes
Authors: Gunter Jeffrey L. , Weigand Stephen D. , Wiste Heather J. , Therneau Terry M. , Botha Hugo , Jack Clifford R. , Jones David T. ,   the Alzheimer's Disease Neuroimaging Initiative
Published: December 2020

AbstractBackgroundThe Network Failure Quotient (NFQ) metric summarizes functional MRI network integrity. We wanted to investigate the stability of the NFQ results across time, hardware, software and protocol changes during the last decade in ADNI Resting State (RS‐) fMRI.MethodIn ADNI‐2 (2009‐2016) RS‐fMRI was acquired on Philips scanners. In ADNI‐3 (2017‐present) RS‐fMRI is acquired on GE, Philips, and Siemens systems with “Basic” and “Advanced” variants based on the availability of simultaneous multi‐slice (SMS, multi‐band) imaging. (Table 1.) Resting state images were preprocessed by performing slice timing correction, trimming volumes from the first 10 seconds of acquired data, despiking and within‐series alignment. Simultaneous bandpass and regression was performed using spatial registration parameters and their first temporal derivatives, mean WM signal, mean CSF signal and global signal. Dual regression via GIFT was applied creating regional time courses. The NFQ is calculated as NFQ=(pDMN_to_vDMN + pDMN_to_adDMN) / (pDMN +vDMN) where pDMN_to_vDMN and pDMN_to_adDMN are correlations between time courses in the posterior DMN, ventral DMN and anterior‐dorsal DMN. pDMN and vDMN are the within‐region correlations. NFQ values from 1447 scans of 739 individual study participants (Table 1) were analyzed using linear mixed effects models to account for age, clinical diagnosis, vendor, and time trends.ResultBox plots of NFQ values by diagnosis within ADNI‐phase and protocol are shown in Figure 2 and suggest important differences among clinical diagnoses and between ADNI‐2 and ADNI‐3. The mixed model analysis indicates that a 10‐year increase in age is associated with a 0.05±0.01 (mean±SD) increase in NFQ; individuals with MCI average roughly 0.05±0.01 higher than unimpaired and individuals with dementia average 0.14±0.02 higher. Differences by vendor for ADNI‐2 and ADNI‐3 are summarized in Figures 2 and 3 with effect sizes on par with these clinical factors. Mean NFQ for Phillips ADNI‐2 were highest (0.54±0.02); somewhat lower were Philips ADNI‐3 (0.51±0.02) and Siemens ADNI‐3 advanced (0.49±0.03); Siemens ADNI‐3 basic (0.40±0.02) and GE ADNI‐3 were the lowest (0.38±0.02).ConclusionNFQ values consistently increase with impairment. Vendor‐specific differences are observed and vendor changes over the life of a study introduce variability on the scale of disease‐related changes in NFQ.
Mixed brain pathologies account for most dementia in the UK’s Brains for Dementia Research cohort
Authors: McAleese Kirsty E. , Colloby Sean J. , Attems Johannes , Thomas Alan J. , Francis Paul T. 
Published: December 2020

AbstractBackgroundUsing the UK's Brains for Dementia Research (BDR) program, we investigated the frequency of neuropathological dementia diagnosis, the proportion of pure, mixed or concomitant pathologies and the pathological substrates for cognitive impairment.MethodAll cases (n = 673) underwent standardised neuropathological assessment outlined in [1]. Cases were classed as ‘pure’, ‘mixed’ or ‘concomitant’. 473 cases had clinical dementia rating (CDR) scores: binary linear regression was employed to estimate the odds of increasing CDR score due to the presence of pathologies and multiple pathologies.Result142 cases (21.1%) were diagnosed as controls and 531 fulfilled the criteria for a neurodegenerative disease: Alzheimer’s disease (n = 221) > Lewy body disease (n = 65) > vascular dementia (n = 50). Only 23.5% were classified as ‘pure’, 63.1% as ‘concomitant’ and 13.4% as ‘mixed’. 147 (28.9%) had no cognitive impairment and 301 had dementia. Higher burdens of hyperphosphorylated‐ τ (Hpτ), amyloid‐β and α‐synuclein burden had a three‐fold increase on the risk of being demented (OR = <3.4; 95% CI 2.1, 5.5). Regarding the conversion from mild cognitive impairment to dementia, Hpτ had a two‐fold increase on risk (OR = 1.75; 95% CI 1.16, 2.6) and the presence of multiple pathologies had a nine‐fold increase on risk (OR = 8.8; 95% CI 3.19, 24.3).ConclusionThe majority of the BDR cohort contains multiple pathologies. Increasing burden of Hpτ and multiple pathologies greatly increases the odds of dementia. (1) Francis et al, (2018) DOI 10.3233/JAD‐180699.