bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tissue‐specific genetically regulated expression in late‐onset Alzheimer’s disease implicates risk genes within known and 30 novel loci
Authors: Chen Hung‐Hsin , Petty Lauren , Lee Bommi , Sha Jin , Zhao Yi , Gamazon Eric , Bush William S. , Naj Adam C. , Below Jennifer 
Published: December 2020

AbstractBackgroundLate‐Onset Alzheimer Disease (LOAD) is the most common neurodegenerative disease. Its heritability is estimated to be 40‐80%, yet the numerous genome‐wide studies (GWAS) conducted to date have identified risk variants that explain only ∼8% phenotypic variance.MethodTo improve understanding of the functional genetic etiology of LOAD, we used 39 GWAS datasets including 58,713 cases and controls from the Alzheimer’s Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). For the 31 ADGC datasets, high quality variants were used to impute gene expression with PrediXcan, an approach that infers the genetically‐regulated portion of gene expression (GReX) from common expression quantitative trait loci. GReX was modeled using models from 50 different tissue types including 48 tissues from Genotype‐Tissue Expression Project, whole blood samples from the Depression Genes and Networks dataset, and dorsolateral prefrontal cortex samples from the CommonMind Consortium. Also, a multi‐tissues model was applied principal components analysis to extract the common expression pattern across all tissues. In addition, the summary GWAS statistics from the 8 IGAP studies were used to evaluate the GReX‐phenotype associations through S‐PrediXcan with the same 50 diverse tissue models.ResultMeta‐analysis across all studies identified 225 unique genes reaching Benjamini‐Hochberg (BH)‐corrected genome‐wide significance (BH‐adjusted p < 0.05) across all tissue‐specific models, including 26 with no previously‐reported GWAS associations with LOAD. Top hits included BLOC1S3 (p = 2.95 × 10−273), CD3EAP (p = 7.19 × 10−239), and RELB (p = 1.52 × 10−167). In a multi‐tissues model, 33 genes had significant effect, including BLOC1S3 (p = 1.21 × 10−64), APOC1 (p = 2.94 × 10−61), and CD3EAP (p = 1.10 × 10−56). Conditional analysis of previously reported loci using prior LOAD GWAS risk variants identified 8 genes reaching genome‐wide significance independent of known signals, including SLC39A13, SDHD, and MTCH2. Further independent validation of identified genes was performed in ∼23,000 individuals in a large, electronic health record‐linked DNA databank. In a phenome‐wide enrichment analysis with a million times permutation, our identified genes were enriched for associations with insomnia (p < 1 × 10−5) and degenerative disease of the spinal cord (p = 1 × 10−5).ConclusionIn conclusion, leveraging genotype‐based GReX in ∼60,000 individuals illustrates the utility of gene regulatory models in the discovery of new loci and function‐based fine mapping of known regions for LOAD and highlights other potential clinical consequences of disrupted regulation at LOAD‐associated genes.
Network diffusion model enhances predictions of future tau‐PET burden in Alzheimer’s patients
Authors: Damasceno Pablo F. , La Joie Renaud , Shcherbinin Sergey , Southekal Sudeepti , Kotari Vikas , Higgins Ixavier A. , Collins Emily C. , Rabinovici Gil D. , Mintun Mark A. , Raj Ashish 
Published: December 2020

AbstractBackgroundPrecisely measuring longitudinal tau PET is important for clinical trial design. Here we use a group of patients diagnosed with AD dementia (N = 28) and mild cognitive impairment (MCI) (N = 60) to investigate whether a network‐based pathological spread model could predict future tau accumulation based on regional standardized uptake value ratios (tau‐SUVR) and amyloid values determined at baseline.MethodBaseline and 18‐month follow‐up flortaucipir and MRI images were analyzed from the Avid A05 phase 2 study (NCT 02016560, (Pontecorvo et al. 2019)). PET images were aligned to their respective Freesurfer‐conformed MRI images. Regional mean SUVR were extracted for 68 cortical regions using a white matter reference region (Southekal et al. 2018). The Network Diffusion Model (NDM) (Raj et al. 2012; 2015) was used to estimate longitudinal tau following a simple regression Δτr = ‐mLτr, where Δτr is a vector of regional change in tau‐SUVR, the difference between tau‐SUVR at month 18 and baseline (Figure 2), L is the Laplacian matrix of the healthy‐based structural connectome, τr is the regional tau‐SUVR vector at baseline (Figure 2), m is a linear model proportional to baseline features (see Figure 1B). For each model m, the parameters were fit via Ordinary Least Regression and the resulting R2 was computed via bootstrapping. When fitting those parameters, predictions of Δτr were made either per region (Figure 3A), averaging over all cortical regions (Figure 3B); or averaging over selected ROIs – estimated via the NDM to have Pareto optimal Δτr (Figure 3C‐3D).ResultFor patients diagnosed with AD dementia, the explained variance increases as the number of regions decreases, independently of the model used for m (columns in Figure 3). The highest explained variance (R2 = 0.69) was found when predicting mean Δτ over four regions: (L/R) Superior Temporal, (L/R) Lateral Occipital, and when m is a linear combination of 3 baseline features: total cortical amyloid, total cortical tau, and ratio between these two quantities.ConclusionStructural brain connectivity may contribute to future tau accumulation, indicating its potential impact in monitoring patient’s progression and the design of future clinical trials. Further validation using independent datasets is warranted.
Frequency of comedication in patients with dementia
Authors: Wurm Raphael , Stamm Tanja , Reichardt Berthold , Parvizi Tandis , Silvaieh Sara , König Theresa , Cetin Hakan , Stögmann Elisabeth 
Published: December 2020

AbstractBackgroundComorbidity, especially cardiovascular disease, is frequent in people with dementia. At the same time, access to healthcare professionals and appropriate treatment might be more difficult to obtain. We used the Austrian prescription claims database to analyse the treatment of common comorbidities in patients with dementia and controls.MethodsInsurance claims data covering 98% of the Austrian population were used. We identified patients treated with one of the four approved antidementives (Rivastigmine, Galantamine, Donepezil, Memantine) in the period of 2005‐16, and extracted age‐ and sex‐matched controls that were not being treated with an antidementive. Total number of medication was recorded and odds ratios (OR) for being prescribed that specific medication were calculated separately for the most common classes.ResultsWe identified 127896 patients treated with an antidementive and matched them to controls in a 1:1 fashion. The median age was 82.3 (IQR 76.7 ‐ 86.6) and 65% were female. Those treated with an antidementive were prescribed significantly fewer drugs in total, including the antidementive drug, compared to controls (median 8 [IQR 5 ‐ 11] vs 9 [5 ‐ 15], p < 0.001, Table 1). The odds of receiving any of the recorded somatic comedication were significantly lower in patients treated with antidementives, with OR ranging from 0.96 for antidiabetics to 0.5 for antihypertensive and 0.34 for anti inflammatory drugs. Conversely, the odds for treatment with psychiatric drugs were significantly higher in this group, with an OR for antidepressants of 1.8 and 4 for antipsychotic drugs (Figure 1).ConclusionPatients treated with antidementives are less likely to receive treatment for common somatic comorbidities and more likely to receive treatment for psychiatric comorbidities than age and sex‐matched controls. Since available data overwhelmingly suggests that patients with dementia suffer from more comorbidities than those without, this data can be interpreted as evidence for undertreatment of somatic comorbidities in this patient group. with dementia and controls. Additionally, usage of antipsychotic was very frequent and exceeded previously published numbers, emphasizing the need for integrated care for patients with dementia.
Evaluating the implementation of a memory clinic using the RE‐AIM model: The experience of the “memory and neuropsychiatry clinic” in Hospital Del Salvador, Chile
Authors: Leon Tomas , Castro Loreto , Mascayano Franco , Lawlor Brian , Slachevsky Andrea 
Published: December 2020

AbstractBackgroundThe prevalence of dementia in Chile is 1.06%, meaning that over 200,000 people are affected. In 2017, the Ministry of Health launched the National Plan of Dementia in Chile which proposed establishing a range of health‐care services from primary care to Memory Units (MU). MUs have emerged as a new health care service composed of multidisciplinary teams with the goal of improving diagnosis and/or management of dementia patients. The creation and implementation of an MU should take into consideration policies and the organization of a particular healthcare system. In this context, the evaluation of a Chilean MU might provide information for the standardization and replication of such a health service nationwide. The objective of this paper was to evaluate the implementation processes of an MU using the RE‐AIM model, a multi‐component model aimed to assist the evaluation of the implementation of ongoing programs.MethodWe analyze the interventions made during the MU lifespan using the RE‐AIM model, a multi‐component model useful in evaluating the implementation of ongoing programsResultFrom March 2018 up to June 2019, a total of 510 patients were referred and assessed at the Hospital del Salvador, Santiago, Chile. Most patients came from primary care (51.9%) and from outpatient services at the Hospital (39.2%), particularly from the Neurology (63.3%) and Psychiatry (16.0%) departments. We estimated that MU assessed 5.39% of dementia patients in the referring area. Of all the patients evaluated by the MU, 11.11% were discharged. Of these, 66.1% were referred to primary health care, 16.94% to other outpatient services, 10.16% to a specialized mental health care centre, and 6.77% % to a daycare centre.ConclusionThis was the first evaluation of the implementation of an MU as part of the Plan. It showed that it is possible to implement an MU in a low‐middle income country and improve access to dementia diagnosis, management, and treatment. Ongoing challenges include continuing to collect clinical data, creating research projects as part of the MU, and developing an MU protocol that can be adopted elsewhere in Chile.
Characteristics of amyloid‐PET‐negative/tau‐PET‐positive individuals
Authors: Yoon Bora ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: December 2020

AbstractBackgroundLittle is known about tauopathy without β‐amyloid (Aβ) (A‐T+). Since the introduction of the term primary age‐related tauopathy (PART) several clinicopathological studies have revealed the characteristics of this syndrome. We performed this study of people characterized as A‐T+ with PET biomarkers to describe these individuals, and to determine how such individuals did or did not reflect characteristics of AD.MethodWe included 506 nondemented participants from the ADNI dataset. We used Aβ (18F florbetapir or florbetaben) and tau (18F‐AV1451) PET. We subdivided them into 3 groups (A‐T‐ 324 vs A‐T+ 55 vs A+T+ 127) using predefined thresholds for Aβ (A) and tau (T). We compared these groups by demographics, brain amyloid, brain tau level and distribution, hippocampal volumes, and cognition.ResultThe A‐T+ group was the oldest among the 3 groups and showed a prevalence of the apolipoprotein E (APOE) ɛ4 genotype similar to A‐T‐ and lower than A+T+. Both the proportion of individuals who were diagnosed with mild cognitive impairment (MCI) and the severity of tau deposition in the A‐T+ group was intermediate between A‐T‐ and A+T+. There was no evidence of even subtly elevated Aβ in the A‐T+ group. A‐T+ showed no asymmetry of tau deposition, and the brain region with the highest tau deposition was the amygdala in both A‐T+ and A+T+ groups (Figure 1). However, the A+T+ group showed a more widespread distribution of tau including temporal, parietal, and occipital areas compared to A‐T+ (Figure 1). Hippocampal volumes (Figure 2) and cognition (Figure 3) were also intermediate to A‐T‐ and A+T+ after adjustment by age, sex, and education. In the A‐T+ group, tau deposition showed a negative association with hippocampal volume and cognition.ConclusionThere is no clear evidence that the A‐T+ group is on the AD continuum. Brain Aβ shows no evidence of elevation and APOE genotypes do not show a high prevalence of ɛ4. These individuals are intermediate between A‐T‐ and A+T+ on most characteristics. Such individuals could reflect PART, although longitudinal follow‐up will be necessary to confirm whether any proportion develops overt Aβ‐positive dementia consistent with AD.
Predicting Alzheimer's disease progression: Results from the TADPOLE Challenge
Authors: Marinescu Razvan V. , Bron Esther E. , Oxtoby Neil P. , Young Alexandra L. , Toga Arthur W. , Weiner Michael W. , Barkhof Frederik , Fox Nick C. , Golland Polina , Klein Stefan , Alexander Daniel C. 
Published: December 2020

AbstractBackgroundAccurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials, at the right time. However, an unbiased comparison of state‐of‐the‐art algorithms for predicting disease onset and progression is currently lacking.MethodWe present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms (Fig. 1) from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease (demographics in Fig. 2). Challenge participants were required to make predictions, for each month of a 5‐year future time period, of three key outcomes: clinical diagnosis, ADAS‐Cog13, and total volume of the ventricles.ResultsNo single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperformed simple baselines (Fig. 3). However, for ADAS‐Cog13 no single submitted prediction method was significantly better than random guessing. On a limited cross‐sectional dataset emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (+3% error) compared to the full longitudinal dataset (Fig. 4). Two consensus methods — mean and median over all predictions — obtained top scores on almost all tasks (Fig. 3‐4). Better‐than‐average performance at predicting clinical progression was associated with the additional inclusion of features from cerebrospinal fluid (CSF) and diffusion tensor imaging (DTI) (Fig. 5). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient‐specific biomarker trends (Fig. 5). Full results can be found on the website https://tadpole.grand‐challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole‐share.github.io/.ConclusionOur work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease.
Alzheimer’s disease biomarker roadmap 2020: Time for tau
Authors: Dodich Alessandra , Boccardi Marina , Ashton Nicholas J , Barthel Henryk , Bischof Gerard N. , Carrillo Maria C. , Chiotis Konstantinos , Corre Julie , Démonet Jean‐François , Drzezga Alexander , Gietl Anton F. , Hansson Oskar , Johnson Keith A. , Leuzy Antoine , Lorenzi Marco , Nordberg Agneta K. , Ossenkoppele Rik , Rabinovici Gil D. , Ratib Osman , Sabri Osama , Treyer Valerie , Unschuld Paul G. , Villemagne Victor L.L. , Winblad Bengt , Wolters Emma E. , Frisoni Giovanni B. , Garibotto Valentina 
Published: December 2020

AbstractBackgroundIn the last decade, the research community has focused on defining reliable biomarkers for the early detection of the pathological hallmarks of Alzheimer’s Disease (AD). In 2017, the Geneva AD Biomarker Roadmap Initiative adapted the framework for the systematic validation of oncological diagnostic biomarkers (Pepe et al., 2001) to AD, with the aim to accelerate their development and implementation in clinical practice (Frisoni et al., 2017). With this work we assess the maturity of tau biomarkers and we define the research priorities.MethodsIn a two‐day workshop (Geneva, November 2019), we convened a panel of experts in AD biomarkers. The level of maturity for first and second generation tau PET tracers, CSF, and blood biomarkers was assessed based on the Biomarker Roadmap (Frisoni et al., 2017). Biomarker maturity and research priorities were processed by thematic subgroups before the meeting, and presented and discussed during the workshop.ResultsTau PET biomarkers showed partial evidence of analytical and clinical validity, particularly in their efficiency to distinguish AD dementia patients from healthy controls. First‐generation and second‐generation tracers showed different properties in terms of specific binding and accuracy in distinguishing AD from other neurodegenerative diseases. Plasma biomarkers showed promising achievements in analytical validity for p‐tau and Aβ42, while total tau was judged as unsuccessful by the experts. Compared to the recent introduction of tau PET and plasma, cerebrospinal fluid (CSF) biomarkers have been explored for decades in AD, and consistently they showed the highest level of maturity according to the strategic roadmap, with significant improvements compared to 2017, the development of fully automated assays being the most valuable. Research priorities consequently mainly concern analytical and clinical validity aims, with a main focus on covering assay definition and validation versus post‐mortem specimens. In view of studies on clinical utility, the definition of appropriate patient‐ and clinically‐relevant outcomes emerged as an urgent priority.ConclusionsDespite the recent development of PET‐ and plasma‐based tau biomarkers, both so far provide partial evidence for clinical validity. However, completion of studies on analytical validity is a key step required to properly investigate their clinical utility.
Intermediate outcomes of a randomized trial of Tele‐Savvy: An online psychoeducation program
Authors: Hepburn Kenneth , Griffiths Patricia , Nocera Joe R , Higgins Melinda , Lindauer Allison , Morhardt Darby , Shah Raj C , Epps Fayron , Brewster Glenna S , Bonds Kalisha , Nash Rachel 
Published: December 2020

AbstractBackgroundThis presentation provides an intermediate report on the outcomes of the first twenty cohorts of family caregivers (N=220) engaged in a NIA‐supported randomized, three‐arm trial of Tele‐Savvy, a fully online version of the Savvy Caregiver, an evidence‐based in‐person psychoeducation program. Tele‐Savvy is specifically aimed at enhancing caregivers’ capacity to manage the day‐to‐day challenges of caregiving by promoting caregiving mastery; the intended outcome is the reduction of caregiving distress. Tele‐Savvy engages groups of 5‐8 caregivers in 7 weekly synchronous online groups led by a trained facilitator; in between, caregivers are provided with 36 daily video lessons they can view whenever and as often as they wish. Caregivers were recruited in cohorts of 15 from 34 states and 2 Canadian provinces and randomly assigned (in a 2:2:1 ratio) to immediate participation in Tele‐Savvy, an attention control condition (Healthy Living), or usual care.MethodData reported here were gathered on key outcomes at baseline and then 3 and 6 months. Multilevel mixed models were used to test for group, time, and group‐by‐time effects for each of the outcomes.ResultWe found a significant group‐by‐time effect on CES‐D‐assessed caregiver depression (p<.001) with the active group showing significant decreases over time (p<.001). Of note, 35% of the active group reported clinically significant depression at baseline; this reduced to 22% by six months. In contrast, 25.5% those in the waitlist group scored as clinically depressed, a figure that increased to 33% by six months. We found a significant group‐by‐time effect on caregiver perceived strain (p<.001) with the active group demonstrating significant reductions over time (p<.001) and the waitlist control group demonstrating significant increases (p=.010). Likewise, there was a significant group‐by‐time effect (p=.001) on caregivers’ reactions to care recipient behaviors (RMBPC) with significant reductions over time among the active caregivers (p=.001). Caregiver competence demonstrated a significant group‐by‐time effect (p‐.009) with the active group demonstrating significant increases over time (p<.001). Findings regarding caregivers’ management of the situation were comparable: a significant group‐by‐time effect (p=.003) with the active group demonstrating significant increases over time (p<.001).ConclusionAt this point, Tele‐Savvy appears to be achieving its aims of enhancing mastery and decreasing distress.
Effect of metabolic syndrome risk factors on processing speed in three racial groups
Authors: Bouges Shenikqua , Norton Derek L , Wyman Mary F , Lambrou Nickolas H , Zuelsdorff Megan , Van Hulle Carol A , Ennis Gilda E , James Taryn T , Johnson Adrienne L , Clark Lindsay R , Carlsson Cynthia M , Gleason Carey E 
Published: December 2020

AbstractBackgroundMetabolic syndrome (MetS) in mid‐life is a known risk factor for Alzheimer’s disease & related dementias (ADRD). Racial and ethnic minorities represent under‐represented groups (URGs) in ADRD research. Targeted outreach, recruitment and retention initiatives broaden the reach of Alzheimer Disease Research Centers (ADRC) within prioritized communities. Understanding the association between MetS risk factors and cognition during preclinical ADRD in URGs may elucidate avenues for ameliorating ADRD risk in URGs prior to dementia onset. Generalizability of findings vary by the population represented within an analytic sample. We describe MetS‐cognition relationships within URGs and referral source, a key determinant of our sample composition.MethodsCognitively unimpaired adults from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer’s Disease Prevention (WRAP) completed blood‐work and neuropsychological testing. Sample sizes varied based on the outcome analyzed and ranged from 714 to 1088 subjects. The 6 cognitive outcomes were: Trails A, Trails B, Animal Fluency, Digit Symbol, a Simple Speed composite, and Speed/Executive composite. Linear mixed effect models with an Age‐by‐Race‐by‐MetS interaction tested whether MetS count moderated age‐related cognitive trajectories differently in White (N=990), African American (AA; N=85), and Native American (NA; N=15) participants. In secondary analyses, referral source was examined for WRAP and ADRC cohorts to assess the potential for selection‐based effects in our data.ResultsOnly Trails A had a significant 3‐way interaction (p=0.004). Graphing this interaction revealed that the cognitive trajectory in Whites remained stable as MetS count increased, while the NA trajectory appeared to worsen with increasing MetS count. AA and Whites appeared to have similar longitudinal trajectories despite a faster processing speed in White group. A small NA sample limits the precision of our results. Examinations of referral source data (Figure 5) suggest that enrollment differed between WRAP and the Wisconsin ADRC participants.ConclusionResults indicate disparities for level and decline in cognitive function for racial minority participants. Affected cognitive domain varies by race, but overall findings highlight the importance of prioritizing recruitment and retention of URG participants. Sample size, social & enrollment factors are all possible contributors to our findings and need to be examined in the future.