bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unified Parkinson’s Disease Rating Scale (UPDRS) motor score subscale trajectories in Dementia with Lewy Bodies and Parkinson Disease Dementia in a community autopsy cohort
Authors: Choudhury Parichita , Zhang Nan , Adler Charles , Chen Kewei , Shill Holly , Shprecher David , Mehta Shyamal , Driver‐Dunckley Erika , Choi Alexander , Belden Christine , Goldfarb Danielle , Serrano Geidy E , Beach Thomas G , Atri Alireza 
Published: December 2022

AbstractBackgroundWe previously reported that in our autopsy‐confirmed cohort of Dementia with Lewy Bodies (DLB), Parkinson’s Disease Dementia (PDD) and Alzheimer’s Disease (AD), DLB with parkinsonism (DLB+Park) predicted faster progression of UPDRS‐III scores compared to PDD, DLB without parkinsonism (DLB‐Park), and AD. This study examined annualized rate of change of UPDRS‐III subscales in our cohort.MethodsData included were from participants in Arizona Study of Aging and Neurodegenerative Diseases (AZSAND) with clinicopathologic diagnosis of DLB (n = 48), PDD (n = 98) or pure AD (n = 48), and longitudinal movement exams. Mixed effects model (accounting for age, education, and sex) was used to determine predicted mean UPDRS‐III and subscale scores (0‐4) for all groups. Individual UPDRS‐III subscale scores were derived for: Body Bradykinesia, Limb Bradykinesia, Gait, Postural Instability, Limb Rest Tremor, Neck Rigidity and Limb Rigidity. Where applicable, scores for multiple limbs involved were averaged.ResultsLongitudinal data was analyzed for over 1430‐participant years. Parkinsonism was present in 65.6% of DLB participants. PDD participants were more likely to be treated with dopaminergic agents (87.8%) compared to (p<0.001) DLB+Park (57.6%), DLB‐park (0%) and AD (2.1%). Frequency (29.4% vs. 29.0%) and severity (mean±SE = 0.4±0.07 and 0.4±0.12) of Limb Rest Tremor was similar for PDD and DLB+park. Limb Bradykinesia frequency and severity were PDD: 100%, 2.4±0.11, DLB+Park: 96.8%, 2.1±0.18, DLB‐Park: 20%, 0.4±0.23, and AD: 62%, 1.1±0.16. Frequency of gait abnormality was 98.5% in PDD, 90.3% in DLB+park, 60% in DLB‐Park and 57.8% in AD. Rigidity was most frequent in the PDD group (88.2%), followed by DLB+Park (71%), AD (19.6%), and none DLB‐park (0%). Over 8 years trajectories for limb bradykinesia and gait were greater for DLB+park than PDD (Figure 1, Cohen’s d range 0.89‐2.18 and 0.63‐1.99 over 8 years, p<0.001).ConclusionOur results suggest that DLB+Park group have faster motor progression predominantly related to gait abnormalities and limb bradykinesia. Future analyses will explore more convergent data, integrated effects of various measures and their differential contributions in this cohort.
Differential responsiveness of outcome measures according to biomarker inclusion criteria: implications for trial design
Authors: Ottenhoff Lois , Vijverberg Everard G.B. , Prins Niels D. , Kroeze Lior A. , van Harten Argonde C. , van Berckel Bart NM , van der Flier Wiesje M. , Sikkes Sietske A.M. , Hendriksen Heleen M.A. 
Published: December 2022

AbstractBackgroundAccurate selection of outcome measures, sensitive to the earliest cognitive changes, is essential to demonstrate treatment efficacy in secondary prevention trials in preclinical Alzheimer’s disease(AD). These outcome measures may differ according to biomarker inclusion criteria. We aimed to determine sensitivity to change over time, of commonly used cognitive measures.MethodParticipants with longitudinal neuropsychological assessments with available baseline biomarkers were included, from the SCIENCe project. We created different (overlapping) target groups based on inclusion criteria in pre‐clinical AD trials; (1)amyloid PET or CSF (A+, n = 114), (2)CSF p‐tau (T+, n = 124), (3)AT(N)+ (n = 41), (4)APoE4 carriers(E4+, n = 195) and (5)APoE4 carriers with amyloid(E4+A+,n = 65). As a control population, we included 150 controls with normal biomarkers. Linear mixed models, adjusting for age, sex and education, were used to investigate the sensitivity to change of 14 individual (standardized) neuropsychological tests and two composites with time as the variable of interest. Composites were based on Preclinical Alzheimer Cognitive Composite; PACC(1) and the Cognitive‐Functional Composite; CFC(2).ResultA total of 556 participants where included(Age = 61.7±8.7, 44%F, follow‐up 3.1y ±2.7y)(Table 1). Linear mixed models in controls showed improvement over time in several tests, perhaps reflecting a learning effect: Stroop II/III, Wordlist Immediate Recall, Wordlist Delayed Recall and Letter Fluency. In contrast, all target groups showed significant decline in MMSE(Beta[‐0.09–0.26]), Digit‐Span‐Backwords (Beta[‐0.04‐ ‐0.06]), Trailmaking‐A (Beta[‐0.06–0.19]),‐B (Beta[‐0.15–0.29]), Stroop‐I (Beta[‐0.04–0.09]), Word‐List‐Recognition (Beta[‐0.09–0.16]) and Category‐Fluency (Beta[‐0.06–0.12])(Table 2). Linear mixed models showed a steeper decline on tests in AT(N)+ group. The composite measures showed significant decline in A+, AT(N)+ and E4+A+, (Beta[‐0.08–0.26]). With a sensitivity analyses we limited the follow up time to 2 years, leading to fewer neuropsychological tests showing decline and no decline in the composite measures.ConclusionWe demonstrated that in preclinical AD, commonly‐used neuropsychological tests capture cognitive decline dependent on biomarker profiles. Tests for memory, attention and executive functioning were sensitive to decline, as were composite measures. When follow‐up time was limited to 2 years, fewer tests were sensitive to change including the composite measures. Therefore, careful selecting cognitive outcome measures and an appropriate period of time is important when defining the target population for the clinical trial.
Applying the German Version of the TOMMORROW Neuropsychological Assessment Battery for the diagnosis of Mild Cognitive Impairment in Clinical and Research Settings
Authors: Kunze Ursi , Welsh‐Bohmer Kathleen A. , Berres Manfred , Aebi Chantal , Monsch Andreas U. 
Published: December 2022

AbstractBackgroundAmong cognitively healthy participants, the TOMMORROW study (NCT01931566) aimed to identify those who went on to develop MCI due to AD over time (see Burns et al., 2021). Thus, based on the criteria of DSM‐5 and Albert et al. (2011) a battery of neuropsychological tests, i.e., TOMMORROW Neuropsychological Assessment battery (TOMM‐NAB) was compiled to assess cognition: memory (California Verbal Learning Test (CVLT‐II) long‐delay free recall, CVLT‐II short‐delay free recall, Brief Visuospatial Memory Test–Revised (BVMT‐R) delayed recall); executive function (Trail Making Test (TMT) B, digit span backward); language (semantic fluency (animals), lexical fluency (S‐, D, F‐words), Multilingual Naming Test (MiNT)); attention (TMT A, digit span forward) and visuospatial function (Clock Drawing Test (CDT), BVMT‐R copy). For the study subjects at the German‐speaking TOMMORROW study site, normative data were collected from 198 cognitively healthy individuals (see Romero et al., 2018). The goal of this study was to make the normative data of this excellent NAB available to clinicians and researchers in German‐speaking Europe.MethodIn order to provide clinicians and researchers with demographically‐corrected standard scores for all TOMM‐NAB variables, raw scores of the TOMM‐NAB were analyzed using the predicted sums of square (PRESS) statistics for variable selection and optimal nonlinear transformation to achieve normally distributed residuals. The following type of models were analyzed for each TOMM‐NAB variable: standard score = ((transformed) raw score ‐ predicted value) divided by residual standard error. The selection of terms was based on the PRESS ststistic (see Berres et al.2008 for details)ResultThe Figure provides an impression of the resulting TOMM‐NAB neurocognitive profile. Bars illustrate deviations from normal zero in terms of standard deviations.ConclusionThe German version of the TOMM‐NAB can now be made available to clinicians and researchers and will allow for the best possible assessment with a set of sophisticated neuropsychological paper‐pencil tests.ReferenceBurns et al. Lancet Neurol 2021;20(7):537‐47. Albert et al. Alzheimers Dement 2011;7:270‐9. Romero et al. Alzheimers Dement 2018;4:314‐23. Berres et al. Statistics in Medicine 2008;27(10):1777‐90.
The eye as a biomarker for Alzheimer’s disease: oculomotor behaviours yield a novel digital biomarker for preclinical risk detection
Authors: Fernandez Gerardo , Mendez Luis , Lopera Francisco , Parra‐Rodriguez Mario A 
Published: December 2022

AbstractBackgroundThey eyes seem to hold a key to unveil early manifestations of Alzheimer’s disease (AD) pathology beyond retinal analysis. We have recently confirmed that eye‐tracking (ET) metrics recorded during performance on memory tasks can identify early and subtle changes in individuals at risk of or with AD dementia. There is a need for more robust and automatic classification methods that can provide affordable digital biomarker solutions for the preclinical detection of AD. This was the aim of this study.MethodsA sample of 16 asymptomatic carriers (AC) of the mutation E280A‐PSEN1 from the widely investigated cohort of Antioquia, Colombia, and 55 healthy controls (HC) entered the study. Both groups were assessed with the novel Visual Short‐Term Memory Binding Task (VSTMBT) coupled with an ET device. The VSTMBT assesses the ability to temporarily hold bicoloured objects whose colours had to be remembered either as individual features (baseline) or integrated within unified representations (binding). Oculomotor behaviours (i.e., fixations, saccades, pupillometry) recorded using ET were subjected to Artificial Intelligence technology. This relied on Machine Learning classification using Lasso and Elastic‐Net Regularized Generalized Linear Models ("glmnet").ResultsAC and HC could not be distinguished based on traditional neuropsychological assessment (e.g., MMSE: HC = 29.7(0.4), AC = 29.5(0.8), p= 0.09). ML accuracy for classifying subjects into correct groups (HC and AC) was 98%. For a cut‐off of 0.72, 55 out of 55 HC and 14 out of 16 AC were correctly classified (see Fig 1).ConclusionRelative to behavioural measures (i.e., percentage correct recognition) drawn from the VSTMBT, AI‐powered technology can significantly enhance the detection of at risk individuals. By better characterising abnormal oculomotor behaviours, phenotypic features of the preclinical stages of AD can be unveiled. The facts that such a phenotypic expression characterised the majority of AC warrants further investigation in populations who hold less‐well known risk for AD.
Socioeconomic Deprivation, Genetics and Risk of Dementia
Authors: Klee Matthias , Leist Anja K , Veldsman Michele , Ranson Janice M , Llewellyn David J 
Published: December 2022

AbstractBackgroundThe risk of Alzheimer’s disease and other subtypes of dementia is determined by multiple pathways including genetic, environmental and lifestyle factors. With higher risk in socioeconomically deprived individuals, it remains unclear whether associations of genetic risk with dementia incidence may be modified by individual‐ or area‐level socioeconomic deprivation.MethodParticipants of the UK Biobank study attended baseline assessments at 22 assessment centers between 2006 and 2010 and were followed‐up until 2016 or 2017. Cox proportional‐hazards regressions with area‐level socioeconomic deprivation based on the Townsend Deprivation Index and individual‐level socioeconomic deprivation based on car and home ownership, housing type, and income were run. A comprehensive polygenic risk score was employed and potential mediation through lifestyle and depressive symptoms was assessed. In an imaging substudy, associations of socioeconomic deprivation with six imaging‐derived phenotypes including white matter hyperintensities were examined.Result196,368 participants (mean [standard deviation] age, 64.1 [2.9] years, 52.7% female) aged 60 years and older, of European ancestry, without dementia diagnosis or cognitive impairment at baseline were followed‐up for 1,545,316 person‐years (median [interquartile range] follow‐up, 8.0 [7.4‐8.6] years). In high genetic risk and high area‐level deprivation 1.71% (95% confidence interval, 1.44% to 2.01%) developed dementia compared to 0.56% (95% confidence interval, 0.48% to 0.65%) in low genetic risk and low‐to‐moderate area‐level deprivation (hazard ratio, 2.31; 95% confidence interval, 1.84 to 2.91). In high genetic risk and high individual‐level deprivation 1.78% (95% confidence interval, 1.50% to 2.09%) developed dementia compared to 0.31% (95% confidence interval, 0.20% to 0.45%) in low genetic risk and low individual‐level deprivation (hazard ratio, 4.06; 95% confidence interval, 2.63 to 6.26). There was no significant interaction between genetic risk and area‐level (P =.77) or individual‐level (P =.07) deprivation. An imaging substudy including 11,083 participants found greater burden of white matter hyperintensities associated with higher socioeconomic deprivation.ConclusionIn older adults without dementia area‐level and individual‐level socioeconomic deprivation and genetic risk were significantly and independently associated with a higher risk of dementia. Dementia prevention interventions may be particularly effective if targeted to people living in deprived households and areas, regardless of genetic vulnerability.
Using 7T MRI to Evaluate COVID‐19 and Brain
Authors: Hosseini Akram A. , Bowtell Richard , Mougin Olivier , Penny Gowland , Katshu Mohammad Zia , Mukaetova‐Ladinska Elizabeta , Ibrahim Tamer , Girard Timothy D. , Vahidy Farhaan S. , Jacobs Heidi I.L. , Snyder Heather M. , de Erausquin Gabriel A. , Seshadri Sudha 
Published: December 2022

AbstractBackgroundSARS‐CoV‐2 causes neurological, psychiatric and neurocognitive deficits in a large proportion of patients via direct or indirect viral invasion, systemic or intracranial inflammation, micro‐ or macrovascular pathologies, and hypoxic or systemic metabolic complications. The insult to brain function during the acute phase of COVID‐19 may accelerate neurodegenerative process and potentially increase the risk of Alzheimer’s Disease or Related Dementias. Ultrahigh Field (7T) MRI has an enhanced sensitivity and spatial resolution over and above clinical 3T MRI, allowing detection of small changes in brain sub‐structures and integrity.MethodsThe 7T COVID Consortium is an international collaboration across 5 sites which aims to study Ultrahigh Field neuroimaging correlates of clinical phenotypes, neuroimmunology, viral and host genetics, and neurodegenerative markers in a diverse, multi‐ethnic cohort of adults following SARS‐CoV2 infection. Multiple population cohorts include matched individuals following a clinically similar non‐COVID19 illness, and healthy controls including Framingham cohort and further comparative data from an independently‐funded genetically inherited Alzheimer’s disease. Synergized imaging sequences across the sites with Ultrahigh Field (7T) facilities, standardized bio‐sampling protocols, and adaptation of centralised REDCAP with WHO protocols and other studies within the COVID consortia1permit harmonised data analyses.ResultsPilot data from the UK site, in Nottingham, included individuals who presented with neurological disorders associated with SARS‐CoV2 Alpha variant of concern (B.1.1.7). Clinical phenotypes and biochemical measures of patients during the acute phase of COVID‐19 were documented2and a prospective follow up for neurocognitive assessments and 7T MRI were arranged. Compared with healthy controls, hospitalised patients showed neuroimaging features of cerebral white matter hyperintensities suggestive of neurovascular ischaemia or neuroinflammation. These radiological cerebral white matter hyperintensities could progress 7 months after the acute illness despite clinical silence. Long COVID appeared to have increased susceptibility, that is suggestive of iron accumulation or inflammation, within the basal ganglia structures.ConclusionPilot data suggest persistent or emergent neuroimaging abnormalities within a year after SARS‐CoV2 infection. Serial follow up may clarify long‐term impact of COVID‐19.Funding: NIH/NIA, USA (R56AG074467), Nottingham Biomedical Research Centre (NIHR), Medical Research Council, UK (MR/T005580/1).References:1.de Erausquin, et al. http://doi.org/10.1002/alz.122552.Dhillon, et al. https://doi.org/10.3389/fneur.2021.640017
A Multimodal Genomic Analysis Framework Identifies Transposable Element (TE) Activation in Alzheimer's Disease
Authors: Feng Yayan , Hou Yuan , Pieper Andrew A. , Leverenz James B. , Cummings Jeffrey , Cheng Feixiong 
Published: December 2022

AbstractBackgroundTransposable elements (TEs), known colloquially as “jumping genes” or “viral elements”, constitute approximately 45% of the human genome. Recent evidences showed that TEs are activated in AD by age‐associated epigenetic alterations or retroviral infection; yet, mechanisms of AD‐risk TE activation remain elusive.MethodWe investigated TE activation underlying AD pathobiology and therapeutic discovery using three large RNA‐sequencing datasets from the AD knowledge portal: (1) 284 AD patients and 150 healthy controls from Mount Sinai Brain Bank (MSBB), (2) 161 AD patients and 195 healthy controls from Mayo Clinic (Mayo), and (3) 106 AD patients and 44 healthy controls from Religious Orders Study or the Rush Memory and Aging Project (ROSMAP). We developed a multi‐modal genomic analysis framework that leverages TE differential expression, genome‐wide association study (GWAS), and X‐quantitative trait loci (X‐QTLs).ResultWe identified 6, 13, and 4 differentially expressed TEs (i.e., L1MC4, HERVK22‐int, and HERV1_LTRa) across MSBB, Mayo, and ROSMAP cohorts, respectively, at the family level. We further identified 442, 857, and 806 differentially expressed TEs (i.e., L1ME2) at locus levels across MSBB, Mayo, and ROSMAP cohorts, including 18, 5, and 2 locus‐based TEs in Mayo cohorts that specifically activated in neurons, microglia, and endothelial cells, respectively. Using integrative analysis of differentially expressed TEs with AD GWAS loci, and X‐QTLs, we identified the likely AD‐risk genes (including CEACAM19, CR1, and TOMM40, and cg04406254) involved in TE activation of AD. Using a drug repurposing approach, we showed that valacyclovir (an approved antiviral drug) was significantly associated with reduced incidence of AD in two large patient databases: (a) Hazard ratio (HR) = 0.55 (p < 0.001) in MarketScan with 7.32 individuals; and (b) HR = 0.72 (p < 0.001) in Optum with 5.62 million older individuals (> 65 years old). In addition, valacyclovir alters LINE‐1 expression in AD patient‐induced pluripotent stem cells (iPSC)‐derived neurons, supporting mechanistically potential beneficial effect of suppressing TE activation in AD.ConclusionThis study characterized the genome regulatory control of AD‐risk TE activation and established the likely causal relationship between TE activation and AD pathobiology. The TE‐targeted therapeutic approaches (i.e., valacyclovir) will require randomized controlled trials.
Trajectories of Neurological Recovery 12 Months after Hospitalization for COVID‐19: A Prospective Longitudinal Study
Authors: Frontera Jennifer 
Published: December 2022

AbstractBackgroundLittle is known about trajectories of recovery 12‐months after hospitalization for severe COVID.MethodWe conducted a prospective, longitudinal cohort study of patients with and without neurological complications during index hospitalization for COVID‐19 from March 10, 2020‐May 20, 2020. Phone follow‐up batteries were performed at 6‐ and 12‐months post‐COVID symptom onset. The primary 12‐month outcome was the modified Rankin Scale (mRS) comparing patients with or without neurological complications using multivariable ordinal analysis. Secondary outcomes included: activities of daily living (Barthel Index), telephone Montreal Cognitive Assessment (t‐MoCA) and Neuro‐QoL batteries for anxiety, depression, fatigue and sleep. Changes in outcome scores from 6 to 12‐months were compared using non‐parametric paired‐samples sign test.ResultTwelve‐month follow‐up was completed in N = 242 patients (median age 65, 64% male, 34% intubated during hospitalization) and N = 174 completed both 6‐ and 12‐month follow‐up. At 12‐months 197/227 (87%) had ≥1 abnormal metric: mRS>0 (75%), Barthel<100 (64%), t‐MoCA≤18 (50%), high anxiety (7%), depression (4%), fatigue (9%) and poor sleep (10%). 12‐month mRS scores did not differ significantly among those with (N = 113) or without (N = 129) neurological complications during hospitalization after adjusting for age, sex, race, pre‐COVID mRS and intubation status (adjusted OR 1.4, 95% CI0.8‐2.5), though those with neurological complications had higher fatigue scores (T‐score 47 vs 44, P = 0.037). Significant improvements in outcome trajectories from 6‐ to 12‐months were observed in t‐MoCA scores (56% improved, median difference 1 point, P = 0.002), and Neuro‐QoL anxiety scores (45% improved, P = 0.003). Non‐significant improvements occurred in fatigue, sleep and depression scores in 48%, 48% and 38% of patients, respectively. Barthel and mRS scores remained unchanged between 6 and 12‐months in >50% of patients.ConclusionAt 12‐months post‐hospitalization for severe COVID, 87% of patients had ongoing abnormalities in functional, cognitive or Neuro‐QoL metrics and abnormal cognition persisted in 50% of patients without a prior history of dementia/cognitive abnormality. Only fatigue severity differed significantly between patients with or without neurological complications during index hospitalization. However, significant improvements in cognitive (t‐MoCA) and anxiety (Neuro‐QoL) scores occurred in 56% and 45% of patients, respectively, between 6‐ to 12‐months. These results may not be generalizable to those with mild/moderate COVID.