bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma Aβ biomarker associated with cognitive decline in preclinical Alzheimer’s disease
Authors: Lim Yen Ying , Maruff Paul T. , Kaneko Naoki , Fowler Christopher J. , Villemagne Victor L.L. , Kato Takashi , Rowe Christopher C. , Arahata Yutaka , Iwamoto Shinichi , Ito Kengo , Tanaka Koichi , Yanagisawa Katsuhiko , Masters Colin L. , Nakamura Akinori 
Published: December 2020

AbstractBackgroundUsing immunoprecipitation‐mass spectrometry, we recently developed and validated a plasma composite biomarker for the assessment of Aβ levels. However, as yet, it’s utility to predict clinical and cognitive outcomes remains unclear. This study aimed to examine the relationship between this plasma Aβ composite biomarker and cognitive function in cognitively normal older adults in two independent cohorts.MethodThis is a longitudinal study conducted in two independent cohorts – the Australian imaging, Biomarkers and Lifestyle (AIBL) study and Japan’s National Centre for Geriatrics and Gerontology (NCGG) study. A series of linear mixed models were conducted to determine the relationships between plasma Aβ composite scores and cognitive decline. Cognitively normal (CN) older adults enrolled in the AIBL study (n=156) and the NCGG study (n=57) were included in this analysis. Participants had undergone Aβ neuroimaging using positron emission tomography (PET), cognitive assessments and provided blood samples. We derived a high‐performance plasma Aβ composite biomarker by immunoprecipitation with mass‐spectrometry. The main outcome measures were composites of episodic memory, executive function, language and attention.ResultBoth continuous and categorical measures of the plasma Aβ composite biomarker were significantly related to decline in episodic memory and executive function (Figure 1). The magnitude of effects of the plasma Aβ composite on episodic memory and executive function were comparable to that observed for the effects of PET Aβ levels on these same outcome measures.ConclusionSeveral plasma Aβ biomarkers have been developed, but none have yet been applied to investigate their relationship with cognitive outcomes. Our results have important implications for the use of this biomarker in the detection of at‐risk individuals.
Prevalence of memory impairment 2000‐2015 in Sao Paolo, Brazil
Authors: Ribeiro Fabiana , Duarte Yeda Aparecida de Oliveira , Santos Jair Lício Ferreira , Leist Anja 
Published: December 2020

AbstractBackgroundDecreases in prevalence of memory impairment and dementia over the last two decades have been observed in different countries for cohorts entering older age. We sought to explore the changes in prevalence of cognitive impairment across four waves of the Health, Welfare and Aging survey (SABE) with data collected in Sao Paolo, Brazil, in 2000, 2006, 2010, and 2015.MethodMini Mental State Exam (MMSE) scores and covariate values were available for 5,191 respondents (62.4% female) aged 60 to 99 across four waves. Cognitive impairment was defined as having <=12 points on a 19‐point abbreviated version of the MMSE. Age group, educational attainment (no formal schooling, primary, secondary, post‐secondary), self‐reported heart disease, stroke, diabetes, hypertension, and BMI (<18.5, 18.5‐24.9, 25‐29.9, 30+) were adjusted for.ResultIn more recent waves, prevalence of cognitive impairment was higher for respondents aged 60‐64 years (9.0% in 2015 vs. 4.6% in 2000), 65‐69 years (9.7% in 2015 vs. 5.8% in 2006) and 70‐74 years (13.9% in 2015 vs. 5.8% in 2006). Between 2015 and 2000, respondents were increasingly more likely to report some formal education (90.8% vs. 75.3%), secondary (17.6% vs. 7.3%) or post‐secondary education (11.4% vs. 4.3%). Respondents were increasingly more likely to report hypertension (66.5% vs. 54.2%), diabetes (28.7% vs. 17.8%), and be overweight/obese (74.1% vs. 58.9%). In covariate‐adjusted logistic regressions, respondents in 2015 were more likely to show cognitive impairment compared to 2000 (OR 1.84, CI 1.20‐2.82). In age‐stratified analyses, younger respondents were more likely to show cognitive impairment over time (aged 60‐64 years: OR 3.52, CI 1.98‐6.26; aged 65‐69 years: OR 3.31, CI 1.50‐7.33; aged 70‐74 years: OR 3.11, CI 1.37‐7.09).ConclusionSABE respondents younger than 75 years showed higher prevalence of cognitive impairment in 2015 compared to respondents of the same age in earlier waves after adjusting for chronic disease burden and educational attainment. In contrast to decreases in prevalence of memory impairment in other countries and despite increases in educational attainment of SABE respondents, the findings suggest that the more recently born cohorts residing in Sao Paolo have entered older age in a cognitively less healthy status.
Deficits in learning are greater than memory dysfunction in preclinical Alzheimer’s disease
Authors: Lim Yen Ying , Baker Jenalle E. , Bruns Loren , Mills Andrea , Fowler Christopher J. , Fripp Jurgen , Rainey‐Smith Stephanie R. , Ames David , Masters Colin L. , Maruff Paul T. 
Published: December 2020

AbstractBackgroundReports from longer studies of preclinical Alzheimer’s disease (i.e., Aβ+ cognitively normal older adults) now show that performance on memory tests remains stable across re‐assessments, whereas in matched Aβ‐ controls, performance improves substantially (i.e. a practice effect). Such observations have led to proposals that the absence of expected practice effects may provide a superior cognitive marker of Aβ+ in older adults. However, an alternative explanation may be that disruption to MTL areas in preclinical AD manifest more strongly as deficits in learning than as a progressive decline in memory recall. Using a test designed specifically to measure learning over days, we aimed to determine the extent to which deficits in learning over 6 days are associated with Aβ+ and hippocampal volume in CN adults.MethodEighty CN older adults who had undergone positron emission tomography (PET) neuroimaging to determine Aβ status (n=42 Aβ‐ and 38 Aβ+), magnetic resonance imaging (MRI) to determine hippocampal and ventricular volume and repeated assessment of memory were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Participants completed the Online Repeatable Cognitive Assessment‐Language Learning Test (ORCA‐LLT), which required they learn associations between 50 Chinese characters and their English language equivalents over 6 days. ORCA‐LLT assessments were supervised on the first day, and were completed remotely online for all remaining days.ResultLearning curves in the Aβ+ CNs were significantly worse than those in matched Aβ‐ CNs, with the magnitude of this difference very large (d=2.16, p<.001) (Figure 1), and greater than differences between these groups for memory decline since their enrolment in AIBL (d=0.52, p=.021), or memory impairment at their most recent visit. In Aβ+ CN adults, slower rates of learning were associated with smaller hippocampal, and larger ventricular volumes.ConclusionThese results suggest that in CNs, Aβ+ is associated more strongly with a deficit in learning than any aspect of memory dysfunction. Slower rates of learning in Aβ+ CNs were associated with hippocampal volume loss. Considered together, these data suggest that the primary cognitive consequence of Aβ+ is a failure to benefit from experience when exposed to novel stimuli, even over very short periods.
Genetic risk, education and incidence of dementia
Authors: Ranson Janice M , Lourida Ilianna , Hannon Eilis , Littlejohns Thomas J , Ballard Clive , Langa Kenneth M , Hyppönen Elina , Kuzma Elzbieta , Llewellyn David J 
Published: December 2020

AbstractBackgroundThere is evidence that educational attainment increases cognitive reserve, and may prevent or delay the development of dementia. However, the extent to which education offsets or interacts with genetic risk is unknown. We therefore investigated the relationship between education and genetic risk for dementia.MethodIn this cohort study, we used data from UK Biobank including adults aged 60 years and older of European ancestry without cognitive impairment or dementia at baseline. Participants joined the study between 2006 and 2010 and were followed until 2016 or 2017. Genetic risk was estimated using a polygenic score for dementia incorporating 249,273 single‐nucleotide polymorphisms with low (lowest quintile), intermediate (quintiles 2 to 4), and high (highest quintile) categories. Education was categorized as low (no/other qualifications), intermediate (secondary/vocational qualifications), or high (college/university degree or other professional qualifications). The outcome was incident all‐cause dementia, ascertained through hospital inpatient and death records.Result196,383 individuals (mean [SD] age, 64.1 [2.9] years; 52.7% were women) were followed up for 1,545,433 person‐years (median [interquartile range] follow‐up, 8.0 [7.4‐8.6] years). Overall, 41.5% had low education, 31.0% had intermediate education and 27.5% had high levels of education. 20.0% had high polygenic risk scores, 60.0% had intermediate risk scores, and 20.0% had low risk scores. Of the participants with high genetic risk, 1.2% developed dementia compared with 0.6% of the participants with low genetic risk (adjusted hazard ratio, 1.92 [95% CI, 1.64‐2.24]). Of the participants with low education, 1.3% developed dementia compared with 0.7% of participants with high levels of education (adjusted hazard ratio, 1.51 [95% CI, 1.35‐1.70]). Of the participants with a high genetic risk and low education, 1.5% developed dementia compared with 0.4% of participants with low genetic risk and high education (adjusted hazard ratio, 2.79 [95% CI, 2.12‐3.68]). There was no significant interaction between genetic risk and education (p = 0.108).ConclusionAmong older adults without cognitive impairment or dementia, high education was independently associated with lower dementia risk when accounting for genetic risk. Education may increase cognitive reserve even in those with a high genetic risk of dementia.
Preferential degeneration of the locus coeruleus in tauopathies relative to TDP‐43 proteinopathies
Authors: Ohm Daniel T. , Peterson Claire , Lobrovich Rebecca , Bahena Alejandra , Gibbons Garrett S. , Wolk David A. , Grossman Murray , Lee Eddie B. , Trojanowski John Q. , Irwin David J. 
Published: December 2020

AbstractBackgroundThe locus coeruleus (LC) is the brain’s primary source of norepinephrine and is vulnerable to neurodegeneration in Parkinson’s disease and tauopathies including Alzheimer’s disease (AD). However, detailed comparative studies of LC neurodegeneration between clinically similar tauopathies (FTLD‐tau) and TDP‐43 proteinopathies (FTLD‐TDP) are lacking. Using a large autopsy cohort, we tested the hypothesis that the LC has greater neuropathologic change in FTLD‐tau compared to FTLD‐TDP.MethodThe LC was examined in 333 patients representing autopsy‐confirmed FTLD‐tau (n=126), FTLD‐TDP (n=152), and two reference cohorts: clinical/pathological AD (n=36) and age‐matched healthy controls (HC, n=19). Pons tissue containing the LC was cut into 6µm‐thick sections and stained for hematoxylin and near‐adjacent sections immunostained for phosphorylated TDP‐43 (p409/410), hyperphosphorylated tau (PHF‐1), and AD‐neurofibrillary tau (GT‐38) to detect comorbid AD tau pathology in a FTLD‐tau subgroup (n=69). Blinded to diagnoses, an ordinal rating scale (0‐3) was used to semi‐quantitatively score tau inclusions, TDP‐43 inclusions, and neurodegeneration based on an averaged composite score of neuromelanin depigmentation and extracellular neuromelanin in hematoxylin sections.ResultTau burden in FTLD‐tau (median=2.5, IQR[2,3]) was greater than TDP‐43 burden in FTLD‐TDP (median=0.5, IQR[0,1]; p<0.0001). Similarly, neurodegeneration was greater in FTLD‐tau (median=1.5, IQR[1,2]) compared to FTLD‐TDP (median=1, IQR[1,1]; p<0.0001). Subanalyses of clinical subgroups with either dementia (n=117) or motor syndromes (n=124) found similar results: tau burden in FTLD‐tau was greater than TDP‐43 burden in FTLD‐TDP (dementia syndromes, p<0.0001; motor syndromes, p<0.0001); neurodegeneration was greater in FTLD‐tau compared to FTLD‐TDP (dementia syndromes, p=0.004; motor syndromes, p<0.0001). AD neurodegeneration (median=1.75, IQR[1,2]) was greater than neurodegeneration in FTLD‐tau (p=0.0004), FTLD‐TDP (p<0.0001), and HC (p<0.0001). HC neurodegeneration (median=1, IQR[0.5,1]) was significantly less than neurodegeneration in FTLD‐tau (p=0.002), but similar to the minimal neurodegeneration in FTLD‐TDP (p=0.38). Greater tau burden correlated with greater neurodegeneration (rho=0.35, p<0.0001). Only 39% of the FTLD‐tau subgroup had a mild burden of AD tau co‐pathology suggesting LC neurodegeneration was driven by FTLD‐tauopathies.ConclusionFTLD‐tau showed histological evidence of greater LC neurodegeneration compared to FTLD‐TDP, independent of clinical phenotypes or AD co‐pathology. These findings suggest FTLD‐tau‐mediated noradrenergic neurodegeneration may be a commonality of tauopathies that could help improve antemortem diagnoses and symptomatic treatments.
Development of a massively parallel reporter assay to identify functional regulatory variants in the PICALM Alzheimer disease associated locus
Authors: Nuytemans Karen , Van Booven Derek , Dykxhoorn Derek M. , Vance Jeffery M. , Pericak‐Vance Margaret A. , Griswold Anthony J. 
Published: December 2020

AbstractBackgroundMost significant genome‐wide association markers for Alzheimer disease (AD) are in noncoding portions of the genome making assigning a corresponding gene involved in risk challenging. Coding variants identified in some these GWAS loci thus far do not explain the proportion of risk attributable to the associated markers, indicating a contribution of noncoding variants to AD. We set out to develop a high throughput method investigating the functional role of noncoding variants associated with AD using the PICALM locus on chromosome 14:85,600,000‐85,860,000, which has been associated with AD across multiple ancestries, as a proof of concept region.MethodWe used the gnomAD database of over 15,000 whole genomes to identify all variants located in the PICALM locus across a wide variety of ancestral populations (non‐Hispanic whites, Hispanics, African Americans, and Asians). We developed a pipeline for epigenomic annotation from available databases and the subsequent design of oligos carrying wild‐type and variant alleles for each variant. For unbiased analyses of functional potential to regulate enhancer activity of each of the alleles we established a large vector library for massively parallel reporter assays (MPRA) in AD relevant cell lines.ResultWe identified >23,000 variants for analysis in the 260 kb PICALM locus. More than 4,000 of those had prior evidence for location in transcription factor binding sites, putative promotors/enhancers and eQTLs. 180 bp oligos centered around the variant alleles were synthesized by TWIST Biosciences. 20 bp random barcodes were added to each of the oligos through PCR amplification with a barcoded primer library. Sequencing of the resulting vector library indicated highly efficient incorporation with 95% unique barcodes present in the library. Reporter gene GFP and a minimal promoter were subsequently cloned in between the oligo and the barcode to establish the final MPRA library for transformation in cell lines.ConclusionHere we established a computational framework for in‐silico epigenomic annotation and oligo design for subsequent high‐throughput analyses and developed an efficient protocol to create a final MPRA library with highly unique barcodes at fragment incorporation. Cell line transformations and transcriptome analyses of the PICALM library to identify regulatory variants are ongoing.
ALFA+: A cohort study to understand and model the preclinical stage of Alzheimer’s disease
Authors: Minguillón Carolina , Fauria Karine , Soteras Anna , Menchon Tania , Meléndez Xavier , Freixa Jordi , Sánchez‐Benavides Gonzalo , Gispert Juan Domingo , Molinuevo Jose Luis ,   ALFA Study
Published: December 2020

AbstractBackgroundWith the aim of shedding light into the pathophysiology and pathogenic factors involved in early preclinical Alzheimer’s disease (AD), we established the ALFA program for the prospective follow‐up of cognitively unimpaired, many offspring of AD patients, participants. The nested ALFA+ cohort study incorporates deep phenotyping, including blood and CSF sampling for biomarker determination, FDG, amyloid and tau PET, and extended MRI and cognitive testing protocols.MethodsBaseline visit is split into the following sessions (S) performed within ∼3‐6 months: S1: clinical visit to gather risk factor information and blood sampling, cognitive testing and an MRI scan. S2: lumbar puncture (LP) to obtain CSF. S3: extended neuropsychological and MRI exploration. Finally, amyloid (S4) and FDG (S5) PET are performed. Participants undergo follow‐up visits approximately every 3 years.ResultsBaseline visit resulted in the inclusion of 450 participants (mean age 61.1 [4.7]; 3.3% screening failure rate; 3.8% discontinuation rate). Remarkably, the number of APOE‐ε4 heterozygotes and homozygotes in ALFA+ is significantly higher than in the general population (Figure 1). Most ALFA+ participants have undergone both CSF sampling and [18F]flutemetamol PET (81.9%); the remaining have only CSF sampling (13.3%) or PET (4.8%). The prevalence of the amyloid positivity in the ALFA+ population is of 34%, which is also much higher than reported for the general population of this age range (Figure 2). The rate of amyloid accumulation and incidence of amyloid positivity is currently under investigation.ConclusionsTo our knowledge, the ALFA+ cohort study is the biggest monocentric research platform of middle‐aged cognitively unimpaired individuals, representing a highly valuable cohort of thoroughly characterized middle‐age participants ALFA+ is enriched in genetic risk factors for AD resulting in a higher proportion of participants with altered biomarkers compared with other populations.
Decreases in slow wave sleep associate with a higher risk of incident Alzheimer’s disease dementia in a community sample
Authors: Himali Jayandra J , Beiser Alexa S , Baril Andree‐Ann , Seshadri Sudha , Pase Matthew P 
Published: December 2020

AbstractBackgroundSleep disturbance is common in dementia, although it is unclear whether changes in sleep architecture precede dementia onset. We examined the prospective association of changes in sleep architecture with the risk of incident Alzheimer’s disease (AD) dementia in the community‐based Framingham Heart Study (FHS).MethodsOur sample comprised a subset of 294 FHS Offspring participants who participated in the Sleep Heart Health Study Visit 1 (1995‐1998) and Visit 2 (2001‐2003), were dementia‐free and aged over 60 years at the time of Visit 2. Stages of sleep were quantified using home‐based overnight polysomnography. Annualized change in the sleep metrics were computed by subtracting Visit 1 from Visit 2 measures divided by the time interval (in years) between the two visits. Surveillance for incident AD dementia commenced at visit 2 and continued for up to 17 years (mean follow‐up 11 (SD, 4)). We used a series of proportional hazards models to relate changes in sleep to the risk of incident AD dementia, adjusting for age, sex, APOE ε4 allele status, smoking status, and sleeping medication, antidepressant and anxiolytic use.ResultsParticipant mean age was 69 years (SD, 6, 49% male). Over 17‐years follow‐up, we observed 27 cases of AD dementia. The two sleep assessments were separated by a mean of 5.2 years (SD, 0.4). Levels of wake after sleep onset increased by a mean of 4.8 minutes (SD, 11.7) per year whereas both sleep efficiency and the percentage of slow‐wave sleep declined by 0.4% (SD, 2.3) and 0.6% (SD, 1.5) per year, respectively. Each percentage reduction in slow‐wave sleep associated with a 30% increase in the risk of incident AD dementia (HR, 0.70; 95% CI: 0.54, 0.91, P=0.007). Changes in other stages of sleep (stage 1, 2 and REM), sleep fragmentation (sleep efficiency and wake after sleep onset), and respiratory disturbances (apnea‐hypopnea index) did not associate with dementia risk.ConclusionsBy linking decreases in slow‐wave sleep to the risk of clinical AD up to 17 years later, our results extend observations suggesting that slow‐wave sleep is important for synaptic plasticity underlying memory and for glymphatic clearance of neurotoxins that potentially lead to AD dementia.
Characterising the temporal relationship between Aβ and tau accumulation in AD using in vivo imaging data
Authors: Whittington Alex , Gunn Roger N 
Published: December 2020

AbstractBackgroundIn 2010 Jack et al. proposed sigmoidal curves for Amyloid‐β (Aβ) and tau accumulation in Alzheimer’s Disease (AD) progression with Aβ preceding tau. Our recent work has shown that a logistic growth model consistent with this can describe the accumulation of Aβ as measured by imaging data (Whittington et al. 2018). Here, we use Aβ and Tau imaging data combined with mathematical modelling to characterise the temporal relationship between the two key pathological hallmarks of AD.MethodUnder the assumption that tau accumulation can also be characterised by a logistic growth model, a pair of equations (Figure 1, Eqns 1 &2) for Aβ and tau accumulation can be derived. These equations can be solved by substitution to yield a function for Tau in terms of Aβ (Figure 1, Eqn 3) which has 4 parameters (rAβ & rTau: unconstrained exponential accumulation rates; T50Aβ & T50Tau : time of half maximal concentrations). rAβ and T50Aβ were previously estimated as 0.20 yrs‐1 and 14.9 yrs respectively (Whittington et al. 2018) and were fixed to these values in the current work. AmyloidIQ and TauIQ analysis (Whittington & Gunn, 2018) were performed on 233 subjects’ data downloaded from ADNI where each subject had both an Aβ ([18F]Florbetapir) and Tau ([18F]Flortaucipir) scan to derive estimates of Aβ Load and Global Tau Load. Equation 3 was fitted to these data to estimate the parameters rTau & T50Tau and consequently the temporal relationship between Aβ and tau including the temporal lag parameter TLAG (= T50Tau ‐ T50Aβ).ResultsThe model fit described the data well (Figure 2A: Solid black line) and provided clear in vivo evidence that Tau accumulation follows Aβ (TLAG = 5.5+‐0.9SE yrs). The estimated tau exponential growth rate (rTau) was 0.13 yrs‐1 which was lower than that observed for Aβ (r Ab = 0.2 yrs‐1). These parameters then allowed for the generation of predicted population accumulation curves for Aβ and tau over time (Figure 2B).ConclusionUsing the IQ analytics platform and temporal regression modelling we provide clear in vivo evidence that in AD tau accumulation follows Aβ and characterise this temporal relationship.