bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex differences in the relation between subjective memory complaints, impairments in instrumental activities of daily living, and risk of dementia
Authors: Abdulrahman Hanna , Richard Edo , van Gool Willem A , van Charante Eric P Moll , van Dalen Jan Willem 
Published: December 2021

AbstractBackgroundOlder people with symptoms of subjective memory complaints (SMC) and impairments in instrumental activities of daily living (IADL) have an increased risk of developing dementia. The predictive value of SMC and IADL‐impairments may be modified by sex. This could have consequences for personalized risk prediction and prognosis.MethodsFrom the Prevention of Dementia by Intensive Vascular care study (preDIVA), 3495 community‐dwelling older people, aged 70‐78 years (mean=74.3±2.5) without dementia, were investigated over a median follow‐up of 6.7 years. SMC at baseline were assessed using the memory question from the 15‐item Geriatric Depression Scale. IADL‐impairments were assessed using the Academic Medical Center Linear Disability Score (ALDS). Potential sex‐differences in the predictive value of SMC and IADL‐impairments for incident dementia were assessed using Cox regression models with an interaction term for sex*ALDS_SMC.ResultsWomen with isolated SMC (HR=2.10,95%CI=0.94‐4.69) and SMC combined with IADL‐impairments (HR=2.96,95%CI=1.66‐5.28) were at greater risk of developing dementia than men (HR=1.51,95%CI=0.83‐2.75; HR=1.24,95%CI=0.59‐2.61 respectively), but these differences were not significant (p‐interaction=0.67 and 0.13). Isolated IADL‐impairments were associated with a significantly higher risk of mortality (HR=1.36,95%CI=1.09‐1.68), and a lower risk of dementia (HR=0.70,95%CI=0.48‐1.00). These associations were particularly prominent in men, but were not significantly different compared to women (p‐interaction=0.25 and 0.83).ConclusionSMC increase dementia risk, especially when occurring together with IADL‐impairments. This relation may be somewhat stronger in women. Isolated IADL‐impairments do not increase dementia risk. Competing mortality risk may strongly influence these associations, especially in men, suggesting it is essential to assess when comparing sex‐differences in dementia risk.
Slow waves contribute to memory consolidation only in older adults without sleep‐disordered breathing
Authors: Champetier Pierre , André Claire , Rehel Stéphane , Ourry Valentin , Landeau Brigitte , Mézenge Florence , de Flores Robin , De la Sayette Vincent , Chetelat Gaël , Rauchs Géraldine ,   Medit‐Ageing Research Group
Published: December 2021

AbstractBackgroundSleep‐dependent memory consolidation, which is thought to rely on the dialogue between the hippocampus and the medial prefrontal cortex (mPFC) during NREM sleep, is mediated by slow waves (SW) and sleep spindles. Some studies indicate that this process is impaired in ageing but also in sleep‐disordered breathing (SDB), a very common sleep disorder in ageing. In this study, we investigated sleep‐dependent memory consolidation in older adults with or without SDB and explored the underlying mechanisms.MethodBaseline data of 57 cognitively unimpaired older adults (mean age ± SD: 68.6 ± 3.3 years) from the Age‐Well cohort were analysed. Participants underwent a structural MRI scan to obtain mPFC and hippocampal grey matter volumes, and performed a visuospatial memory task until reaching a learning criterion of 66.6% of correct answers. Post‐learning sleep was monitored using polysomnography, and delayed recall was probed the next morning. An overnight change in memory performance (OCMP) was computed as follows: (recall performance – learning performance) / learning performance. SW, slow and fast spindles during N2 and N3 sleep were automatically detected using the SleepTrip toolbox. Based on the standard apnea‐hypopnea index (AHI) cutoff of 15 events/h, participants were classified as having SDB (SDB+, n=47) or not (SDB‐, n=10).ResultThere was no between‐group difference in OCMP, as well as in amplitude, duration and frequency of SW, slow and fast spindles (p>0.05). However, we found a significant group by SW density interaction after controlling for age, sex, education, total sleep time and trait‐anxiety (p=0.03). Thus, a positive association was only found between SW density and OCMP in the SDB‐ group (p=0.02; Fig 1). No correlation was found between slow or fast spindle density and OCMP in either group. Finally, mPFC and hippocampal volumes did not differ between SDB+ and SDB‐ participants (p>0.05), but SW density positively correlated with the volume of the mPFC (p=0.01; Fig 2).ConclusionOur findings suggest that the contribution of SW to sleep‐dependent memory consolidation is impaired in participants with SDB and incriminate the mPFC. We plan to conduct connectivity and SW‐spindle coupling analyses to unravel the functional substrates of this deficit.
Antihypertensive drug classes and incident dementia: Findings from the PreDIVA observational extension study
Authors: Schroevers Jakob L , Eggink Esmé , Hoevenaar‐Blom Marieke P , Richard Edo , van Dalen Jan Willem , van Middelaar Tessa , van Gool Willem A , van Charante Eric P Moll 
Published: December 2021

AbstractBackgroundMidlife hypertension is a risk factor for all‐cause dementia. Treatment of hypertension with antihypertensive medication (AHM) may therefore be a promising strategy to delay or prevent dementia. Recent reports have suggested that AHM are associated with differential effects on dementia risk, due to class‐specific mechanisms. In this study, we assess the association between different AHM‐classes and incident dementia, using data from the preDIVA observational extension (POE) study.MethodAll participants who reported use of AHM at baseline were included in the analyses. We distinguished angiotensin‐converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), beta‐blockers, calcium channel blockers (CCBs), and diuretics. Data on incident dementia was collected during the randomised phase at 2, 4, 6‐8 and, after discontinuation of the intervention, 10‐12 years after baseline. Cox proportional hazards regression was used to study the association between use of different AHM‐classes, compared with use of any other AHM class, and incident dementia. A post‐hoc analysis was performed, dividing follow‐up time in two periods (preDIVA median follow‐up acting as cut‐off point) to account for the effect of time between exposure and dementia.Result1953 participants who reported AHM use at baseline were included, with a mean age of 74.5 (±2.5) and a mean systolic blood pressure of 156.4 mmHg (±21.5). After a median follow‐up of 10.4 years (IQR 6.7‐11.0), 228 (11.7%) participants had developed dementia. Adjusted hazard ratios (HR) for dementia were 1.07 (95%CI 0.80‐1.42) for ACE inhibitors, 0.75 (95%CI 0.53‐1.06) for ARBs, 1.00 (95%CI 0.77‐1.31) for beta‐blockers, 0.92 (95%CI 0.68‐1.24) for CCBs, and 1.02 (95%CI 0.78‐1.33) for diuretics. ARBs and CCBs were inversely associated with dementia during the first time period (HR 0.46, 95%CI 0.25‐0.84 and HR 0.62, 95%CI 0.38‐0.99 respectively), which was not apparent anymore during the second one.ConclusionDespite short‐term beneficial associations of CCBs and ARBs and a promising HR for ARBs also after longer follow‐up, associations between any AHM‐class and incident dementia were not convincing after complete follow‐up. These findings are possibly best explained by the long follow‐up in older participants with high mortality‐ and dementia rates, possibly attenuating AHM‐class effects on dementia incidence.
Dual pathogenic mutations in SQSTM1 and C9orf72 as a cause of frontotemporal dementia
Authors: Benotmane Hanya 
Published: December 2021

AbstractBackgroundHexanucleotide repeat expansions in the C9orf72 gene have been found to be the leading genetic cause of both familial frontotemporal dementia (FTD) and motor neuron disease (MND). However there are a number of other genetic causes of both FTD and MND including, less commonly, mutations in sequestosome 1 (SQSTM1) which codes for the cargo protein p62. Occasionally, people with FTD or MND have been described with dual pathogenic mutations, although this is rare.MethodWe describe the clinical and imaging features of a woman who presented with FTD and motor neurone involvement who was found to carry both a pathogenic C9orf72 expansion and a deleterious c.1185dup, p.Glu396* heterozygous SQSTM1 mutation.ResultSymptoms started at the age of 60 with change in personality and impaired behaviour followed at the age of 63 by prosopagnosia and semantic impairment. Brain magnetic resonance imaging showed right‐sided predominant temporal more than frontal lobe atrophy, and she was initially diagnosed with behavioural variant FTD. However at the age of 66 she developed asymmetrical limb weakness and stiffness, with upper motor neurone features only on examination, suggestive of primary lateral sclerosis (PLS). She deteriorated rapidly over the next couple of years, although never developed lower motor neurone features when subsequently examined. She died at the age of 69.ConclusionThe co‐presence of both a SQSTM1 mutation and C9orf72 expansion is rare. It remains unclear which mutation accounts for which of the features seen. Focal right temporal lobe atrophy is more common with SQSTM1 mutations, but PLS is uncommon with both mutations. It may be therefore that it is the dual mutations together that explain the altered phenotype. It is paramount to better understand the roles which SQSTM1 and C9orf72 mutations have on cellular pathways and how their combination results in rare phenotypes.
Artificial neural network visualization methods reveal diagnostically relevant brain regions to detect Alzheimer’s disease: The first step towards comprehensive artificial intelligence
Authors: Dyrba Martin , Hanzig Moritz , Buerger Katharina , Cantré Daniel , Düzel Emrah , Heneka Michael T. , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Schneider Anja , Spottke Annika , Wagner Michael , Weber Marc‐André , Wiltfang Jens , Jessen Frank , Teipel Stefan J. ,   DELCODE
Published: December 2021

AbstractBackgroundAlthough deep learning approaches achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) based on MRI scans, they are rarely applied in clinical research due to a lack of suitable methods for model comprehensibility and interpretability. Recent advances in convolutional neural networks (CNN) visualization algorithms may help to overcome these problems.MethodWe implemented a CNN model structure, trained it on 662 T1‐weighted MRI scans obtained from ADNI in a twentyfold cross‐validation procedure, and validated it on 1655 cases from three independent samples. Various CNN visualization algorithms were compared, which generated relevance maps indicating the contribution of individual image areas for detecting AD. We developed an interactive web application to display the 3D relevance maps and interactively change various visualization parameters. We assessed the clinical utility of relevance maps by comparing hippocampus relevance scores with hippocampus volume.ResultAcross the three independent validation datasets (Fig. 1), group separation showed high accuracy for AD dementia versus controls (AUC≥0.92) and moderate accuracy for amnestic mild cognitive impairment (MCI) versus controls (AUC≥0.73). Relevance maps obtained from Layer‐wise Relevance Propagation (LRP) provided a high spatial resolution and strongest focus (Fig. 2 & Fig. 3). LRP maps indicated that hippocampal atrophy was the most informative factor for AD detection (Fig. 4), with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (median: r=‐0.81, Fig. 5). The relevance maps of individual patients revealed additional clusters in the frontal and occipital lobe, which may be an indicator of model overfitting or potential bias of the training sample. When comparing the twenty cross‐validation models, stronger focus of the models on irrelevant areas was associated with lower accuracy to detect MCI.ConclusionThe relevance maps highlighted atrophy in regions that we had hypothesized a priori, which strengthens the overall comprehensibility and validity of the CNN models.
Detecting amyloid positivity in early Alzheimer disease using plasma biomarkers
Authors: Janelidze Shorena , Palmqvist Sebastian , Leuzy Antoine , Stomrud Erik , Verberk Inge M.W. , Zetterberg Henrik , Ashton Nicholas J. , Mattsson‐Carlgren Niklas , Pesini Pedro , Sarasa Leticia , Allué Jose A. , Teunissen Charlotte E. , Dage Jeffrey L. , Blennow Kaj , Hansson Oskar 
Published: December 2021

AbstractBackgroundRecent data suggest that blood biomarkers of amyloid‐β (Aβ) pathology (Aβ42/Aβ40), tau pathology (phosphorylated tau [P‐tau]) and neurodegeneration (neurofilament light [NfL]) in Alzheimer disease (AD) become abnormal very early in the disease course, albeit with different dynamics; Aβ42/Aβ40 starts to change first followed next by P‐tau and then by NfL.MethodWe studied the usefulness of plasma AD biomarkers measured using mass spectrometry (Aβ42/Aβ40) and immunoassays (P‐tau217 and NfL) to identify abnormal brain Aβ status (according to CSF Aβ42/Aβ40 or Aβ‐PET) in a total of 895 participants with early disease (cognitively unimpaired individuals [CU] and patients with mild cognitive impairment [MCI]) from two independent cohorts (BioFINDER‐1 and BioFINDER‐2). CSF Aβ42/Aβ40 measures were available in all study participants; 159 (26.9%) CU and 175 (57.6%) MCI were CSF Aβ42/Aβ40‐positive. Out of 858 subjects who underwent Aβ‐PET imaging, 142 (24.0%) CU and 169 (55.6%) MCI were Aβ‐PET positive.ResultIn CU, a combination of plasma Aβ42/Aβ40 and plasma P‐tau217 accurately detected abnormal CSF Aβ42/Aβ40 status (BioFINDER‐2, area under the curve [AUC]=0.86; BioFINDER‐1, AUC=0.83) with no significant difference in the accuracy compared with the model including all 3 plasma biomarkers (BioFINDER‐2, AUC=0.87, p=0.62; BioFINDER‐1, AUC=0.84, p=0.57). In MCI patients, plasma P‐tau217 alone showed high discriminative accuracy (BioFINDER‐2, AUC=0.88; BioFINDER‐1, AUC=0.86) which was not significantly different from the discriminative accuracy of the model combining all 3 plasma biomarkers (BioFINDER‐2, AUC=0.88, p=0.94; BioFINDER‐1, AUC=0.88, p=0.29). Adding APOE ε4 status improved model fit but did not significantly change the AUCs of the models. The results were similar when using Aβ‐PET as the outcome.ConclusionThese findings suggest that different plasma biomarkers are not equally useful along the AD continuum. In the preclinical phase, plasma Aβ42/Aβ40 and plasma P‐tau217 would be needed to accurately detect abnormal brain Aβ status whereas in prodromal AD plasma P‐tau217 might alone be sufficient.
Clustering biomarkers in dominantly inherited Alzheimer’s disease
Authors: Luckett Patrick H. , Chen Charles D. , Gordon Brian A. , McDade Eric , Benzinger Tammie L.S. , Bateman Randall J. , Ances Beau M. 
Published: December 2021

AbstractBackgroundNumerous biomarkers exist for potentially studying autosomal dominant Alzheimer disease (ADAD) pathology. Understanding the relationships between biomarkers and disease progression can inform patient care and clinical trials. We utilized machine learning‐based clustering and feature selection to identify an optimal subset of biomarkers reflective of disease stage and pathology.MethodCross sectional clinical and neuropsychological evaluation (Clinical Dementia Rating (CDR), Geriatric Depression Scale (GDS), Mini‐Mental State Exam (MMSE)), structural magnetic resonance imaging (AD cortical signature), amyloid measures (amyloid positron emission tomography (PET) and CSF Aβ42 and 40), tau measures (CSF total tau and phosphorylated tau isoforms pT181, pT205, pT202, and pT217), and neurodegeneration measures (fluorodeoxyglucose (FDG) PET, serum NFL, CSF neurogranin, SNAP‐25, VILLIP‐1) and neuroinflammation (CSF YKL‐40) were acquired in 130 mutation carriers (MC) and 79 non‐carriers (NC) from the Dominantly Inherited Alzheimer Network observational study (Table 1). Hierarchical clustering was used to group biomarkers. Decision tree‐based feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years to symptom onset (EYO).ResultFigure 1 shows the absolute value correlation matrix used for hierarchical clustering. Figure 2 shows the dendrogram of the clustering results. FDG, GDS, and Aβ40 were the least similar to other biomarkers. Aβ42 and 42/40 ratio, PET amyloid, cortical signature, CDR, MMSE, and NFL were similar. With the exception of YKL‐40, the “emerging” biomarkers (neurogranin, VILLIP‐1, SNAP‐25) clustered together. Total tau and tau isoforms were grouped together. Feature selection identified YKL‐40, pT205, and pT217 as the strongest predictors of EYO in MCs (Figure 3). YKL‐40 had the highest correlation with EYO (.59,p‐value<.001), suggesting a possible role of persistent increasing neuroinflimation. With regard to mutation status, the strongest predictors were measures related to amyloid, including Aβ42/40 ratio, amyloid PET PIB, and pT217 (Figure 4).ConclusionUnderstanding the relationships biomarkers have with each other, as well as understanding which biomarkers are most reflective of disease processes, is vital for patient care and clinical trials. In this study, we have identified groups of biomarkers that are most similar to each other, as well as identified which biomarkers are most reflective of EYO and mutation status in ADAD.
Subclinical but high level of glycemia and platelet activity are associated with decreased structural and functional integrity in Alzheimer’s disease‐sensitive brain regions
Authors: Palix Cassandre , Felisatti Francesca , Kuhn Elizabeth , Mézenge Florence , Landeau Brigitte , Gonneaud Julie , Quillard Anne , Chocat Anne , De la Sayette Vincent , Vivien Denis , Martinaud Olivier , Chetelat Gaël , Poisnel Géraldine 
Published: December 2021

AbstractBackgroundThere is an increasing research focus on type 2 diabetes (T2D) as a risk factor for Alzheimer’s disease (AD) (Lee, 2018). T2D is a disease characterized notably by hyperglycemia and platelet hyper‐reactivity (Schneider, 2009). However, relatively little is known about subclinical but high levels of glycemia and platelet reactivity in the context of ageing and AD. The aim of this study was to investigate the association between glycemia and platelet level, size and activity (platelet count and mean platelet volume (MPV)) and neuroimaging markers of ageing and AD.Method107 cognitively unimpaired adults (18‐84 years old) from the IMAP+ study (Caen) were included, comprising 23 amyloid‐negative older adults (Aβ‐ controls) and 48 amyloid‐positive patients with mild cognitive impairment or dementia (Aβ+ patients). Participants underwent blood tests to dose glycemic and platelet indices, structural MRI, FDG‐PET and amyloid‐PET assessments. Multiple regressions were performed between blood indices and neuroimaging data and inter‐group comparisons of blood indices were assessed between Aβ+ patients and Aβ‐ controls.ResultCompared to Aβ‐ controls, Aβ+ patients showed no difference in glycemia levels but had lower platelet count and higher MPV. Increased MPV was associated with entorhinal and perirhinal cortex atrophy in Aβ+ patients (fig.B) while platelet count was not associated with any neuroimaging data. High glycemia levels were associated with both atrophy of the right hippocampus and posterior cingulate cortex (PCC) and hypometabolism in the precuneus, cuneus, PCC, angular cortex and medial temporal gyrus in cognitively unimpaired adults (fig.A). None of the blood indices were associated with amyloid‐PET.ConclusionBoth high glycemia and MPV levels were associated with atrophy and/or hypometabolism in AD‐sensitive brain regions, notably the entorhinal cortex, hippocampus and posterior cingulate cortex. These results suggest that subclinical but high levels of glycemia and MPV could be associated with more vulnerability in brain regions sensitive to AD but not at the same stage, while amyloid deposition does not appear to be involved at these stages. Our findings highlight the importance of monitoring these blood indices as risk factors early in the context of AD prevention.
Analysing behavioural changes in people with dementia using in‐home monitoring technologies
Authors: Rezvani Roonak , Kouchaki Samaneh , Nilforooshan Ramin , Sharp David J , Barnaghi Payam 
Published: December 2021

AbstractBackgroundBehavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in‐home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in‐home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated.MethodWe have used connected sensory devices to collect environmental markers, including Passive Infra‐Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self‐supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo‐labels using the samples from the unlabelled data. We use these pseudo‐labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block.ResultsComparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision‐recall curve with at most 40% improvement. The recall measure using the 10‐fold cross‐validation technique is 61%.ConclusionThis method can support early interventions and help develop new pathways to support people affected by dementia. A limitation in our current study is that the environmental and movement data is at the home level and not personalised.