bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between ophthalmic conditions and risk of dementia in older adults
Authors: Hwang Phillip H , Longstreth WT , Thielke Stephen , Francis Courtney E , Carone Marco , Fitzpatrick Annette L 
Published: December 2020

AbstractBackgroundIt remains unclear whether common ophthalmic conditions in older adults, such as age‐related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, and cataracts, are related to dementia by causing vision loss or serving as markers of underlying neurodegenerative processes, such as Alzheimer’s disease (AD). We examined associations between these ophthalmic conditions and risk of dementia, including AD and vascular dementia (VaD).MethodWe identified 3,236 participants without dementia from the Cardiovascular Health Study (CHS) Cognition Study who were aged ≥65 years and underwent cranial MRI. Presence of AMD, DR, glaucoma, and cataracts were based on ICD‐9 and CPT codes from Medicare Part B and CHS medical records. Dementia classification was completed by consensus committee using data from neuropsychiatric tests or by other data for deceased participants or those unable to come into the clinic. Cranial MRIs were used for classification of dementia subtype. AD was determined using the National Institute of Neurological and Communication Disorders and Stroke – Alzheimer’s Disease and Related Disorders Association criteria. VaD was determined using the Alzheimer’s Disease Diagnostic and Treatment Centers criteria. Cox models with time‐varying exposure and adjustment for demographics, health behaviors and conditions, and ApoE genotype were used to evaluate associations between these four ophthalmic conditions and risk of all‐cause dementia, AD, and VaD.ResultIncreased risk of all‐cause dementia and VaD was significantly associated with AMD, DR, and cataracts, while increased risk of AD was significantly associated with AMD and cataracts (Table 1). When accounting for treatment status, glaucoma was significantly associated with increased risk of all‐cause dementia (HR: 1.53; 95% CI: 1.09 – 2.16; p = 0.015), but all other results remained similar. ApoE genotype was a significant effect modifier as only those without an ɛ4 allele were at increased risk for dementia associated with DR (HR: 5.14; 95% CI: 2.63 – 10.05; p<0.0001) and glaucoma (HR: 1.64; 95% CI: 1.13 – 2.39; p = 0.01).ConclusionAMD, DR, glaucoma, and cataracts were associated with increased risk of dementia, though not all by the same pathways as some were more strongly related to AD, while others were more strongly related to VaD.
Distinct relationships of self‐reported subjective memory decline to neurodegeneration across the Alzheimer's clinical continuum
Authors: Kuhn Elizabeth , Perrotin Audrey , Tomadesso Clémence , Andre Claire , Sherif Siya , Bejanin Alexandre , Touron Edelweiss , Landeau Brigitte , Mézenge Florence , La Sayette Vincent De , Vivien Denis , Chetelat Gael 
Published: December 2020

AbstractBackgroundSelf‐reported memory decline (SMD) is associated with increased risk of cognitive decline and neurodegeneration in patients with no or mild cognitive deficits, while diverging findings are reported in later clinical stages of the Alzheimer’s clinical continuum. Our aim is to provide a comprehensive overview of the relationships of SMD to brain volume and glucose metabolism across the entire cognitive spectrum, from normal cognition to dementia.MethodWe used data from two independent cohorts, including 180 participants from IMAP+ (64 controls, 30 subjective cognitive decline (SCD), 50 mild cognitive impairment (MCI) and 36 AD‐dementia patients) and 731 participants from ADNI (147 controls, 84 SCD, 369 MCI and 121 AD‐dementia). We assessed the voxelwise links between SMD and neuroimaging measures of atrophy (T1‐MRI GM volume) and hypometabolism (partial volume effect (PVE)‐corrected FDG‐PET data) within each cohort, and tested for an interactive effect of clinical stage or global cognitive impairment, to assess whether these links differed across cognitive/clinical stages. All analyses were adjusted for age, gender and education (p<0.005, cluster‐level corrected for multiple comparisons).ResultsWe found only negative interactive effects of the cognitive/clinical stage, such that SMD and neuroimaging measures were negatively associated (more neurodegeneration with higher SMD) in early stages, while they were positively associated (more neurodegeneration with lower SMD) in late stages. This effect was particularly clear‐cut and similar in both cohorts for glucose metabolism and when splitting participants by cognitive stages (tercile of MMSE score; Figure 1), rather than by clinical groups (Figure 2), likely as the sampling of SCD and MCI patients differ between cohorts. Findings were recovered when using an episodic memory score instead of the MMSE to grade cognitive deficits’ severity, using PVE‐uncorrected FDG‐PET data, and restricting the analyses to the amyloid‐positive participants.ConclusionOur findings reveal a reverse relationship of SMD to neurodegeneration, indicating that SMD should be interpreted differently in early versus late stages of the Alzheimer’s clinical continuum. At early (e.g. SCD) stage, greater SMD is indicative of greater neurodegeneration, while at later stages (late MCI or dementia), lower SMD is indicative of greater neurodegeneration, probably as a reflect of anosognosia.
In vivo amyloid progression in healthy middle‐aged to older people at risk of Alzheimer’s disease
Authors: Levin Fedor , Jelistratova Irina , Betthauser Tobey J. , Johnson Sterling C. , Teipel Stefan J. , Grothe Michel J. 
Published: December 2020

AbstractBackgroundPrevious studies applied in‐vivo amyloid staging to amyloid‐sensitive PET scans of healthy older people as well as patients with Alzheimer’s disease (AD) dementia or mild cognitive impairment to characterize stages of amyloid deposition. We adapted this approach to a sample of healthy middle‐aged to older participants at elevated risk for AD due to parental history of AD dementia.MethodWe analyzed 11C‐PiB‐PET scans of 220 participants enrolled in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) (mean age = 61.4y, range: 46‐76y). Distribution volume ratio (DVR) maps were calculated from the dynamic PIB‐PET acquisitions and corrected for partial volume effects. DVR values were extracted from 52 cortical and subcortical regions defined in the Harvard‐Oxford atlas and were used together with global mean DVR values in two‐dimensional Gaussian Mixture Modeling to establish regional thresholds for amyloid‐positivity. Resampled regional frequencies of amyloid‐positivity across participants were used to estimate a four‐stage model of regional amyloid accumulation. We staged individual DVR maps, and assessed the associations between amyloid stage and episodic memory (delayed wordlist recall) and executive function performance (TMT‐B) with Spearman correlation.ResultAccording to the amyloid progression model, stage I involved the anterior and posterior cingulate as well as lateral temporal cortex; stage II had more extensive frontal and temporo‐parietal involvement; stage III proceeded into occipito‐parietal cortical regions and the hippocampus; stage IV extended to the occipital pole, pre‐ and postcentral gyri, and subcortical structures (Figure 1). Out of 220 participants, 36 (16%) were assigned to one of the four amyloid stages, and two cases (1%) deviated from the hierarchical pattern of amyloid accumulation. Higher amyloid stages (I‐IV) correlated with lower executive function (rho = 0.35, p = 0.043), but not with lower episodic memory performance (rho = 0.13, p = 0.48).ConclusionThe estimated 11C‐PiB‐PET‐based amyloid staging model is largely consistent with recently reported amyloid staging models for [F18]‐based amyloid‐PET scans. In this relatively young sample of healthy participants at elevated risk of AD, advanced amyloid stages were rare and not consistently linked to decreased cognitive performance. In future work we will assess whether the derived amyloid stages translate into a stage‐proportional risk of longitudinal cognitive decline in this population.
MEG detects abnormal hippocampal activity in amyloid‐positive MCI
Authors: Luppi Janne , Schoonhoven Deborah N , van Nifterick Anne M , Arjan Hillebrand , Gouw Alida A , Scheltens Philip , Stam Cornelis J , de Haan Willem 
Published: December 2020

AbstractBackgroundEarly Alzheimer’s disease (AD) is characterized by pathological hippocampal hyperactivity1,2. Non‐invasive detection of hippocampal hyperactivity is relevant for diagnostic and therapeutic purposes, but no reliable method is available. We tested magneto‐encephalographical (MEG) detection of hippocampal activity, hypothesizing that in amyloid‐positive MCI patients resting‐state hippocampal activity would be higher than in both controls and AD patients.MethodWe used resting‐state MEG data from 18 mild/moderate AD patients, 18 amyloid‐positive (CSF) amnestic MCI patients, and 18 persons with subjective cognitive decline (SCD). MEG source space signals were reconstructed for 80 AAL regions using beamforming3. We obtained 20 non‐selected, eyes‐closed, 13‐second epochs per person. Global spectral power group differences were identified for common frequency bands using ANOVA and t‐tests. Classification was tested with ROC curves.ResultPeak frequency was similar in SCD and MCI groups, but lower in AD patients (P<0.001). In MCI and AD, hippocampal total power increased, with a significant rise in the right hippocampus in MCI (p<0.01). In MCI, this was mainly driven by high relative lower alpha (8‐10 Hz) power, significant only in the right hippocampus (P<0.01). Relative gamma (30‐45 Hz) power was lowest in the MCI patient group, hippocampal as well as overall (p<0.01). Classification between MCI and SCD was most accurate using right hippocampal gamma power (Area under the curve = 0.821).ConclusionResting‐state MEG detects abnormal hippocampal activity in preclinical AD patients. This may be a macroscale 'signature' of underlying neuronal hyperactivity1,4. Relevant findings in these subcortical regions and in the gamma frequency band show the added value of MEG compared to EEG and fMRI. The present approach may facilitate diagnostic and therapeutic strategies. References: (1) Canter ea. (2016). The road to restoring neural circuits for the treatment of Alzheimer's disease. Nature, 539(7628), 187; (2) Palop & Mucke (2016). Network abnormalities and interneuron dysfunction in Alzheimer disease. Nature Reviews Neuroscience, 17(12), 777; (3) López, ea. (2017). MEG beamformer‐based reconstructions of functional networks in mild cognitive impairment. Frontiers in aging neuroscience, 9, 107; (4) Busche & Konnerth (2016). Impairments of neural circuit function in Alzheimer's disease. Phil Trans Royal Soc B: Biological Sciences, 371(1700), 20150429.
Does an advanced statistical technique perform better than a simple technique for dementia prediction?
Authors: Goerdten Jantje , Carriere Isabelle , Terrera Graciela Muniz 
Published: December 2020

AbstractBackgroundCox regression models (CM) employ two strong assumptions: the proportional hazards (PH) and the log‐linearity (LL) of covariates. These assumptions might not hold for all predictor variables, which in turn can lead to biased results. (Abrahamowicz et al., 1997; Ritchie et al., 2016). Generalised Cox regression models (GCM) enable to independently test and relax the assumptions of CM (Abrahamowicz & MacKenzie, 2007).MethodData is used from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes around 140,000 individuals aged 50 or older followed over seven waves. Data from The Aging, Demographics, and Memory Study (ADAMS) is used for external validation. CM and GCM are used to estimate dementia risk.ResultNone of the predictor included in the analyses fulfils the assumptions of Cox regression. The pseudo R2 is 0.06 (95% CI: 0.047, 0.062; CM) and 0.493 (95% CI: 0.460, 0.506; GCM). The likelihood ratio test results in a p‐value of <0.001. Both models predict dementia moderately well (10‐year risk: 0.737; 95% CI: 0.695, 0.783; CM and 0.746; 95% CI: 0.711, 0.790; GCM).ConclusionThe GCM performs significantly better than the CM in modelling dementia risk. However, this approach has also disadvantages: larger sample needed, only visual examination of effects, long computational time possible. Yet, the generalised Cox regression is an interesting extension of the Cox regression. The option to relax and independently test the assumptions is especially appealing when including continuous variables. References: (1) Abrahamowicz, M., du Berger, R., & Grover, S. A. (1997). Flexible modeling of the effects of serum cholesterol on coronary heart disease mortality. Am J Epidemiol, 145(8), 714‐729. (2) Abrahamowicz, M., & MacKenzie, T. A. (2007). Joint estimation of time‐dependent and non‐linear effects of continuous covariates on survival. Stat Med, 26(2), 392‐408. doi:10.1002/sim.2519. (3) Ritchie, K., Carrière, I., Berr, C., Amieva, H., Dartigues, J.‐F., Ancelin, M.‐L., & Ritchie, C. W. (2016). The clinical picture of Alzheimer's disease in the decade before diagnosis: clinical and biomarker trajectories. The Journal of clinical psychiatry, 77(3), e305‐311. doi:10.4088/jcp.15m09989.
Apathy and white matter integrity in amnestic mild cognitive impairment: A whole brain analysis with tract‐based spatial statistics
Authors: Setiadi Tania M , Opmeer Esther M , Martens Sander , Marsman Jan‐Bernard C , Tumati Shankar , Reesink Fransje E , De Deyn Peter Paul , Aleman André , Ćurčić‐Blake Branislava 
Published: December 2020

AbstractBackgroundApathy is one of the most prevalent neuropsychiatric symptoms in amnestic mild cognitive impairment (aMCI) and is associated with an increased risk for progression to Alzheimer’s Disease (AD). Previous diffusion tensor imaging (DTI) studies in AD have shown that apathy is associated with changes in the cingulum, corpus callosum and uncinate fasciculus (Hahn et al., 2013; Kim et al., 2011). However, the underlying white matter (WM) correlates of apathy in aMCI are still unclear. Therefore, we aimed to investigate the association between the severity of apathy and white matter integrity in aMCI using diffusion tensor imaging (DTI) and tract‐based spatial statistics (TBSS).MethodTwenty‐nine aMCI patients and 20 cognitively healthy controls were included. Apathy severity was assessed with the Apathy Evaluation Scale Clinician version (AES‐C). Depressive symptoms were assessed using the Geriatric Depression Scale (GDS). We calculated a sub‐score of the GDS (i.e. GDS non‐apathy) which excluded apathy‐related items (Adams et al., 2004). We applied whole‐brain TBSS analyses to all DTI parameters: i.e. fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), investigating which WM areas were associated with apathy severity. Age, gender, MMSE, and GDS non‐apathy were used as covariates. Significance was set to p< .05, corrected for multiple comparisons using Threshold‐Free Cluster Enhancement (TFCE).ResultThere was no statistical difference between groups on age, gender, education level, MMSE, and AES‐C scores. Group comparison TBSS analyses showed that the aMCI group did not differ in any of the DTI parameters compared to the control group. Within the aMCI group, significant inverse associations were observed between AES‐C scores and FA values in the bilateral genu and body of the corpus callosum, anterior and superior corona radiata, anterior thalamic radiation, anterior part of inferior frontooccipital fasciculus, and the right forceps minor, superior longitudinal fasciculus/arcuate fasciculus anterior segment, corticospinal tract/internal capsule (pTFCE< .05). A similar pattern was observed in the combined group of aMCI and controls (pTFCE< .025).ConclusionThere was no significant WM integrity difference between aMCI and control groups. Our findings point to reduced integrity in widely distributed WM pathways being related to apathy severity, regardless of aMCI diagnosis.
Comparison of the different approaches of drawing test for the detection of dementia and mild cognitive impairment: A systematic review and meta‐analysis
Authors: Chan Joyce YC , Chan Tak Kit , Tsoi Kelvin KF 
Published: December 2020

AbstractBackgroundDrawing tests are commonly used to detect dementia and mild cognitive impairment (MCI). It is because drawing tests aim to assess visuo‐spatial ability and executive functions, and the deterioration of visuospatial abilities and executive functions is one of the common cognitive symptoms in patients with dementia and MCI. However, there are various types and administrative approaches of the drawing tests, and their diagnostic performance may vary. Thus, this study aims to evaluate the diagnostic performance of different approaches of the drawing tests for the detection dementia and MCI.MethodsThis study is a systematic review and meta‐analysis. Literature searches on each of the drawing tests were performed in OVID databases including MEDLINE, EMBASE, and PsycINFO from the earliest available dates of individual databases to September 1, 2019. Studies that were used to assess the diagnostic performance of the drawing tests to detect dementia or MCI were included. Each test’s performance was compared with the standard diagnostic criteria. Bivariate random‐effects models were used to summarize the test performance as a point estimate for sensitivity and specificity.ResultsA total of 64 studies with 15,567 participants were included to compare different approaches of the drawing tests. Most studies used clock drawing with command approach test (87%), 6 studies used clock drawing with copy approach (10%). In the detection of dementia and MCI, the sensitivity and specificity of command approach were 0.75 and 0.70, respectively, and copy approach were 0.52 and 0.85, respectively. The digital clock drawing tests showed good diagnostic accuracy, the sensitivity was 0.87 and the specificity was 0.89. The digital and traditional pentagon drawing tests were with good sensitivity but fair specificity (Table 1).ConclusionsThis study showed that the digital clock drawing tests have better diagnostic performance than the traditional clock drawing tests. In the comparison between different approaches of traditional clock drawing tests, the command approach have better diagnostic performance than the copy approach. Digital drawing tests have higher sensitivity than traditional approaches, so the development of digital drawing tests are important and useful in the aging population.
The development of a microsimulation model to predict the future burden of dementia and effects of public health interventions
Authors: Brück Chiara , Wolters Frank J , Ikram M Arfan , Kok Inge MCM de 
Published: December 2020

AbstractBackgroundMicrosimulation models can synthesise complex information, evaluate uncertainties, estimate long‐term and population wide effects of (hypothetical) interventions, and take time trends in risk factors into account. No microsimulation models have been developed so far that include all stages from cognitively healthy to severe dementia and evaluate risk factor changes and the effects of (hypothetical) interventions. We developed a microsimulation model that predicts how dementia incidence and mortality will develop over the coming decades in The Netherlands, taking into account different risk factor scenarios and interventions.MethodBased on the well‐known Microsimulation Screening Analysis (MISCAN) model from cancer research, a dementia microsimulation model was developed. It synthesises dementia incidence data and survival probabilities from the large population‐based Rotterdam Study cohort (Ikram et al., 2017) with dementia severity stage duration estimates from the literature (Vermunt et al., 2019). The model simulates the life histories of a large number of individuals, each of whom can develop mild cognitive impairment (MCI) which can then progress to mild dementia and moderate/severe dementia and finally death. Based on these life histories, the number of dementia cases, dementia deaths, (quality adjusted) life years of both patients and caregivers, and costs can be evaluated by age and gender. Finally, the model can estimate the effect of different interventions on the life histories and subsequently the outcome measures.ResultWe were able to develop the MISCAN‐Dementia model, as depicted in Figure 1. MISCAN‐Dementia consists of three modules: a demography module, a natural history module, and a screening module. Given this structure, the model can be used to evaluate long‐term benefits and harms of important interventions at different disease stages: 1) changes in risk factors (i.e. onset of cognitive decline); 2) earlier detection and treatment by population screening (i.e. early, pre‐dementia stage); and 3) care and cure (i.e. dementia stage).ConclusionThe dementia microsimulation model developed in this study predicts how the burden of dementia will develop over the coming decades, including incidence, mortality, quality of life, and costs. In addition, modelling the effect of interventions on a population level will provide essential information for researchers and policy makers.
Identifying patient‐relevant endpoints in the progression of Alzheimer’s disease
Authors: Mank Arenda , van Maurik Ingrid S. , Bakker Els D. , van de Glind Esther M.M. , van Der Flier Wiesje , Visser Leonie N.C. 
Published: December 2020

AbstractBackgroundAlzheimer’s disease (AD) is characterized by a long predementia stage and high symptom variability. Prediction of disease progression is important in clinical decision‐making and care planning. Prognostic studies mainly attempted to predict progression to dementia, but it is questionable whether from a patient’s perspective, this is most relevant. We aimed to identify endpoints of relevance to patients and caregivers in AD progression, as part of the European ADDITION project.MethodWe used a two‐step approach. First, we conducted four focus groups with in total 12 patients and 14 care partners between July and October 2019 (Table 1). Participants were recruited through two Dutch memory clinics, and purposefully selected to compose heterogeneous groups in terms of gender, age, and disease stage and setting. Transcripts of the audiotaped focus groups were independently coded by two researchers using thematic content analysis. The resulting list of endpoints then served as input for an online survey. Patients and caregivers were asked to potentially add endpoints, rate their importance, and rank them in order of relevance.ResultEight major categories of endpoints were: 1) Cognition (e.g. How long do I still recognize others?); 2) Functioning and dependency (e.g. How long can I still perform hobbies?); 3) Behavioral and neuropsychiatric (e.g. When can I expect behavioral problems?); 4) Social environment (e.g. When should I tell others?); 5) Psychical health (e.g. Which physical symptoms can I expect?); 6) Patient quality of life; 7) Time to death; 8) Caregiver‐oriented outcomes (e.g. How is caregiver quality of life impacted?). In the preliminary analysis of the survey (data collection ongoing; available now N=34 patients; N=36 caregivers), we found that both patients and caregivers considered the cognition‐related endpoints as most important (Table 2). We are currently extending data collection to other European countries for cross‐cultural validation.ConclusionWe identified patient and caregiver‐relevant outcomes, and will incorporate these in a large‐scale, online data‐collection. The ultimate goal is to predict these endpoints in individualized prediction modeling, that will help to provide patients and care partners with personalized information on the expected disease course.