bluuebunny/crossref_metadata_2025_split huggingface dataset:
Serum glial fibrillary acidic protein and neurofilament light as prognostic biomarkers for clinical progression in subjective cognitive decline: The SCIENCe project
Authors: Verberk Inge M.W. , Laarhuis Malika B , van den Bosch Karlijn A. , Ebenau Jarith L. , Leeuwenstijn Mardou , Wesselman Linda M.P. , Prins Niels D. , Scheltens Philip , Teunissen Charlotte E. , van Der Flier Wiesje 
Published: December 2020

AbstractBackgroundBlood‐based biomarkers might be easy‐to‐use tools for predicting future decline to dementia in cognitively normal individuals presenting at memory clinics (i.e. subjective cognitive decline; SCD). We previously found that glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) relate to neurodegenerative disease processes (Verberk, AAIC 2019). In this study we aimed to assess the prognostic value, by investigating the relation between baseline serum GFAP and NfL and cognitive decline in individuals with SCD.MethodWe included 303 individuals with SCD from the Amsterdam Dementia Cohort and SCIENCe project (age 61±9 years, 42% females, MMSE 28±2), who were followed annually for re‐evaluation of diagnosis and cognitive performance on a standardized neuropsychological test battery covering global cognition and the main cognitive domains (average follow‐up: 3.5 ± 2.6 years, median number visits: 3 (range 2‐12), total evaluations: 1014). Baseline serum NfL and GFAP were measured using Simoa, and Z‐transformed levels were associated with incident dementia in age and sex‐adjusted COX regression models, and with longitudinal neuropsychological test performance in age, sex and education‐adjusted linear mixed models.ResultUpon follow‐up, 27(9%) individuals developed dementia (18 Alzheimer’s, 9 non‐Alzheimer’s). High baseline GFAP was associated with increased risk of progression to dementia (HR=3.5 (95%CI: 2.2–5.7); Figure 1A), as was high baseline NfL (HR=1.8 (95%CI: 1.2–2.8); Figure 1B). When simultaneously entering both serum markers, only GFAP remained independently associated with risk of dementia (HR=3.2 (1.9–5.4); NfL: HR=1.3 (0.8–2.0)). Additionally entering plasma amyloid showed that both GFAP (HR=2.6 (1.4–4.8) and amyloid (HR=2.4 (1.2–4.5)) independently associated with risk of dementia. Investigating trajectories of cognitive decline, we found that higher baseline GFAP was associated with a steeper rate of decline on tests for global cognition, memory, attention and executive functioning (range standardized betas: ‐0.03–‐0.17, all: p<0.05FDR), but not language, whereas for NfL, none of the associations were significant after multiple testing correction (Table 1).ConclusionOur results suggest that especially GFAP is a putative prognostic blood‐based biomarker for identifying individuals with SCD at‐risk for future clinical disease progression. Serum NfL proves to be less useful as a prognostic marker in these earliest disease stages.
Converging evidence for a “gray‐zone” of amyloid burden and its relevance
Authors: Bullich Santiago , Salvadó Gemma , Alves Isadora Lopes , Marquié Marta , Stephens Andrew W , Gispert Juan Domingo , Molinuevo Jose Luis , Buckley Christopher , Boada Mercè , Barkhof Frederik 
Published: December 2020

AbstractBackgroundTraditional quantitative cut‐offs for amyloid PET positivity have been defined to discriminate Alzheimer’s dementia (AD) subjects from elderly Aβ‐negative non‐demented controls. Currently, observational and interventional studies focus on earlier stages of Aβ deposition, where established cut‐offs might not be appropriate. Here, we review recent developments on early pathology identification, and provide supporting evidence from three cohorts for the establishment of a “gray‐zone” of amyloid burden.MethodData from three cohorts of cognitively unimpaired individuals were included, namely ALFA+ (n=357), FACEHBI (n=228) and the AMYPAD Prognostic Study (n=122). PET scans ([18F]flutemetamol and [18F]florbetaben) were analyzed in Centiloid (CL) units. Gaussian Mixture Modelling was used to fit two/three Gaussians to the global CL values, and the cut‐off for early pathology detection was defined as two standard deviations above the mean of the left Gaussian. Then, the data was merged to derived a joint cut‐off. Finally, these data‐driven cut‐offs were compared to previously established thresholds in the context defining a range of CL values where pathology is emerging.ResultGMM identified a cut‐off of CL=11 for FACEHBI, CL=14 for ALFA+, CL=17 for AMYPAD and CL=11 for the cohorts combined (Figure 1). Similarly, Salvadó et al identified two cut‐offs based on a direct comparison with established CSFAβ42 thresholds: CL=12 to rule out‐amyloid pathology and CL=29 to denote established pathology. With a different approach, the FACEHBI working group finds CL=13 as an early threshold defined based on young healthy controls, and CL=36 as an established cut‐off to separate healthy elderly from Aβ‐positive AD‐dementia subjects. In AMYPAD, where a more comprehensive sampling of the AD continuum is present, fitting 3 Gaussians through the data allows the identification of two data‐driven cut‐offs: CL=13 and CL=29.ConclusionA number of recent and current reports converge to the utility of two cut‐offs for amyloid PET abnormality, an early cut‐off around CL=11‐17 where pathology may be emerging, and a second around CL=29‐36 where amyloid burden levels greatly correspond to neuropathology findings. Together, these create a gray‐zone of CL values pre‐AD dementia levels of amyloid burden, which can improve the detection of emerging pathology in observational and secondary prevention trials.
Default mode network dedifferentiation predicts cognitive performance in Alzheimer disease
Authors: Meeker Karin L. , Ances Beau M. , Gordon Brian A. , Morris John C. , Benzinger Tammie L.S. , Waring Jill 
Published: December 2020

AbstractBackgroundDuring aging, brain networks become less functionally specialized, known as dedifferentiation. When dedifferentiation occurs, brain networks become less adaptive, leading to failures in cognitive processing. Although global dedifferentiation occurs with age, it is unknown how global and local dedifferentiation change as a function of cognitive impairment (measured by the Clinical Dementia Rating Scale [CDR]). We assessed whether global and/or local dedifferentiation differ as a function of CDR status and predict cognitive performance in cognitively normal individuals (i.e., CDR=0) and those with mild symptomatic Alzheimer disease (AD; CDR>0).MethodNeuroimaging and clinical data from 432 participants (CDR0=356; CDR0.5=63; CDR1=13) were analyzed. Graph theory analytic approaches were applied to RS‐FC data for global dedifferentiation and 7 individual association networks. In graph theory, dedifferentiation is measured by computing segregation, which quantifies the presence of specialized and densely interconnected brain regions. Segregation was computed as (within‐network mean minus between‐network mean)/within‐network mean. Cognitive performance was assessed using Preclinical Alzheimer Cognitive Composite (PACC). The association between age and global dedifferentiation and network specific dedifferentiation was assessed using Pearson’s r correlations. Changes in global and association network dedifferentiation as a function of CDR were evaluated using a multivariate analysis of variance (MANOVA). Individual regression models determined if global and/or network specific dedifferentiation predicted cognitive performance. Age was accounted for in all models.ResultGlobal dedifferentiation increased with age (i.e., segregation decreased: r= ‐0.15, p<0.01; see Figure 1), as did dedifferentiation of the default mode network (DMN; r= ‐0.14, p<0.01) and ventral attention network (VAN; r= ‐0.11, p<0.05). No other networks were associated with age (ps>0.05). Dedifferentiation of DMN differed as a function of CDR with dedifferentiation higher (i.e., lower segregation) in cognitively impaired individuals compared to cognitively normal individuals (ps£0.01; see Figure 2). Greater DMN dedifferentiation also predicted poorer cognitive performance, i.e., lower PACC score; p=0.001. No other measure of dedifferentiation (i.e., global or network specific) differed by CDR or predicted cognitive performance.ConclusionResults suggest that global and focal dedifferentiation occurs with aging, but only focal dedifferentiation occurs in AD. Furthermore, dedifferentiation of the DMN is sensitive to decreases in cognitive performance.
Air pollution and biomarkers of Alzheimer’s disease in cognitively unimpaired individuals
Authors: Crous‐Bou Marta , Alemany Silvia , Vilor‐Tejedor Natalia , Milà‐Alomà Marta , Suárez‐Calvet Marc , Salvadó Gemma , Cirach Marta , Arenaza‐Urquijo Eider M , Sánchez‐Benavides Gonzalo , Grau‐Rivera Oriol , Minguillón Carolina , Fauria Karine , Gispert Juan Domingo , Nieuwenhuijsen Mark , Zetterberg Henrik , Blennow Kaj , Kollmorgen Gwendlyn , Eichenlaub Udo , Sunyer Jordi , Molinuevo Jose Luis ,   ALFA Study
Published: December 2020

AbstractBackgroundAir quality might contribute to incidence of dementia‐related disorders, including Alzheimer’s disease (AD). The aim of our study was to examine whether air pollution may be associated with abnormal concentrations of AD cerebrospinal fluid (CSF) and neuroimaging biomarkers in cognitively unimpaired individuals at increased risk of AD. Additionally, potential effect modification by amyloid‐β(Aβ) and APOE‐ɛ4 status was investigated.MethodsThe ALFA (ALzheimer and FAmilies) study is a prospective cohort of cognitively unimpaired, middle‐aged subjects, many AD patients’ offspring. Land Use Regression models were used to estimate residential exposure to air pollutants including nitrogen oxides (NO2, NOx) and particulate matter (PM2.5, PM2.5 abs, PM10, PM coarse). For a subset, we measured CSF levels of Aβ42, Aβ40, p‐tau, t‐tau and neurofilament light (NfL) using NeuroToolKit and Elecsys® immunoassays (Roche Diagnostics). They also underwent a [18F]flutemetamol amyloid PET scan to assess cerebral amyloid load (Centiloid). A total of 148 participants (all residents of Barcelona) with data available on exposure to air pollutants and core AD biomarkers were included in this cross‐sectional analysis. Multiple linear regression models were used to test the association between exposure to air pollutants and biomarkers. Age, sex, years of education and APOE‐ɛ4 status were included as potential confounders. To test for effect modification, Aβ positivity was defined by an Aβ42/Aβ40 ratio<0.071. Stratified analyses by APOE genotype were also performed.ResultsGreater exposure to air pollutants was associated with increased levels of CSF p‐tau, t‐tau and NfL, and reduced Aβ42/40 ratio. Additionally, higher exposure to nitrogen oxides was significantly associated with increased Aβ deposition in the brain (Figure1;*p<0.05). Such associations were driven by Aβ‐negative individuals (p‐interaction<0.05) (Figure 2;*p<0.05). Furthermore, the association between increased exposure to air pollutants and Ab deposition was only seen in APOE‐ɛ4 non‐carriers, while the positive association between exposure to air pollutants and CSF p‐tau, t‐tau and NfL was only observed among APOE‐ɛ4 carriers (Figure 3;*p<0.05).ConclusionIn a population of cognitively unimpaired adults with increased risk of AD, exposure to air pollution is associated with abnormal core AD biomarker results. Further research is granted to elucidate the mechanisms involved in such associations.
Impact of putative cilia targeting sequences on APP trafficking
Authors: Swaminathan Swathi 
Published: December 2020

AbstractBackgroundPrimary cilia are non‐motile, sensory antennae protruding from most eukaryotic cells. These cilia are enveloped in plasma membrane that is adorned with an array of receptor proteins and are essential in regulating fundamental neurophysiological activities, including neurogenesis and memory encoding. In the mature brain, both neurons and astrocytes exhibit a single primary cilium. Defects in the primary cilia lead to a myriad of diseases, one such being impaired neuronal development and cognitive domains, also compromised in Alzheimer’s disease (AD). AD is primarily characterized by the accumulation of amyloid β plaques (Aβ) on the brain. Aβ plaques result from the proteolytic cleavage of the Aβ peptide, a product of the amyloid precursor protein (APP). In our lab, we have previously shown that increased Aβ disrupts primary cilia structure and APP co‐localization at the primary cilium, indicating a possible functional role of APP in AD pathogenesis. At the base of all primary cilia is a protein complex responsible for maintaining a distinct protein and lipid profile from the cytoplasm. Therefore, only those proteins intended for the primary cilia are trafficked from the Golgi to the base of the primary cilia, which is believed to be mediated by specific cilia targeting sequences.Examples of cilia targeting sequences include VxPx, RVxP, AxxxQ amino acid sequences.MethodIn order to characterize these putative cilia targeting sequences, we utilized site directed mutagenesis to create and compare mutants of all the putative sequences and confocal microscopy in multiple cell lines like MDCKs, astrocytes and neurons. These experiments were conducted to investigate the consequences of disrupting the cilia targeting sequences in APP 695.ResultWe have identified a total of 7 putative cilia targeting sequences in the human APP 695 isoform and also found that only one of the seven targeting sequences is located in the C‐terminal ectodomain of the isoform.ConclusionSince most motor proteins identified at the cytoplasmic C‐terminus are responsible for trafficking APP to its destination, we believe that this cilium targeting sequence may be responsible for APP localization at the primary cilia,thereby rendering them as a potential therapeutic target for AD.
Cumulative effects of sleep quality on risk for incident mild cognitive impairment (MCI): Results from the Einstein Aging Study (EAS) cohort
Authors: Derby Carol A , Wang Cuiling , Katz Mindy J , Lipton Richard B , Buxton Orfeu M 
Published: December 2020

AbstractBackgroundSleep deficiency and sleep disorders are common in older adults and have been linked to cognitive decline and dementia risk. Better understanding the association of sleep patterns over time with onset of early cognitive changes may facilitate interventions to prevent AD and dementia. Few studies have reported on the association of cumulative longitudinal measures of sleep quality with onset of mild cognitive impairment (MCI). The Einstein Aging Study (EAS) longitudinal cohort provided the opportunity to examine whether cumulative exposure to poor sleep quality predicts incident MCI in a community‐based cohort.MethodAnalyses include 639 EAS participants from Bronx, NY, age > 70 years (mean 78.6± 5.3, 62% female, 65% White/28% African American), without MCI or dementia at initial assessment and with at least one annual follow‐up. Sleep was assessed annually using the Medical Outcomes Study of Sleep (MOS‐S) questionnaire. Factor analysis of the 12 MOS‐S items yielded three factor scores based on the sum of items with loading ≥ 0.5, such that higher scores indicate better sleep quality. Factors included were: Daytime somnolence, Disturbed sleep and Perceived sleep adequacy. Trajectories of these indices over annual follow‐up were examined using linear mixed effects models. The associations of concurrent and cumulative sleep quality indices on MCI risk were estimated from Cox models with time dependent exposure, adjusting for age, sex, education and race/ethnicity.ResultOver a mean of 4.4 annual assessments, 162 cases of incident MCI occurred. During follow‐up, sleep adequacy increased (p < 0.0001), daytime somnolence decreased (p = 0.025) and sleep disturbance showed no significant pattern of change (p > 0.05. Cox models showed no association between concurrent indices of sleep quality and incident MCI (all p > 0.05). However, cumulative effects of sleep quality on MCI risk were observed. Over time, less daytime somnolence, less frequent disturbed sleep and better sleep adequacy were associated with reduced risk of incident MCI (HR’s: Somnolence = 0.97, Disturbed = 0.98), Adequacy = 0.97; all p< 0.0001).ConclusionAdverse effects of poor sleep quality on cognition may be cumulative suggesting that improving sleep in older adults may prevent or delay MCI onset.
Distinct patterns of time spent together in older adult couples: The role of cognitive function, illness, and activity engagement
Authors: Miller Lyndsey M , Au‐Yeung Wan‐Tai M , Reynolds Christina L , Beattie Zachary , Kaye Jeffrey 
Published: December 2020

AbstractBackgroundPrevious research has indicated that the way in which older adults spend their time (i.e. together with a partner or outdoors) is indicative of cognitive function, overall health, and well‐being, but few studies have assessed time spent together objectively. Accordingly, we sought to characterize distinct patterns in the amount of time that couples spend together and out of home using data from unobtrusive in‐home sensors, and identify factors that differentiated couples in distinct patterns.MethodContinuously‐assessed in‐home sensor data was obtained from 152 older adult participants with varying levels of cognitive function living independently as couples. An algorithm was created to divide the data into segments by day, occupancy time by room, and home exits. The data on time together, apart, and out of home was then used in a latent profile analysis.ResultTwo distinct patterns were identified (p = 0.05; entropy = 0.85; posterior probabilities > 0.90). Couples observed in the “together” pattern (n=60 homes) were together within the same room 55% of the time, out of the home 31% of the time, and were apart within different rooms 14% of the time. In the second “apart” pattern (n=16 homes), couples spent less time together, less time out of the home, and more time apart, when compared with the “together” pattern. Multivariable logistic regression indicated that couples were more likely to be observed in the ‘apart’ pattern when men exhibited higher cognitive function (OR = 1.23, 95% CI = 1.01‐1.53), had more health problems (OR = 1.25, 95% CI = 1.03‐1.54), and were more engaged in life activities such as hobbies and computer use (OR = 1.19, 95% CI = 1.02‐1.40). None of the variables derived from female participants significantly differentiated the patterns, nor did couples’ age, education, depressive symptoms, physical activity, or sleep duration.ConclusionMen may have a more influential role on time spent together within older adult couples. Since couples observed in the “together” pattern were differentiated by men with lower cognitive function and less activity engagement, future research will examine whether this pattern may be an objective indicator of waning independence and increased strain on their partners.
Cognitive status, brain amyloid pathology, and neurodegeneration are associated with altered white matter microstructure
Authors: Okonmah‐Obazee Stephanie , Craft Suzanne , Bateman James R. , Williams Benjamin J. , Cleveland MaryJo , Yang Mia , Haq Ihtsham , Rogers Samantha D. , Sachs Bonnie C. , Fischer Eric , Sai Kiran Solingapuram , Williamson Jeff D. , Whitlow Christopher T. , Lockhart Samuel N. 
Published: December 2020

AbstractBackgroundBrain amyloid (AB) and neurodegeneration (assessed using PET and MRI) are key AD pathology biomarkers, particularly during transitions from cognitively unimpaired (CU) to cognitively impaired (CI) states. Importantly, the role of brain white matter microstructural measures in these changes, such as those derived from neurite orientation dispersion and density imaging (NODDI), are poorly understood. We explored roles of cognitive status, brain amyloid pathology, and neurodegeneration on NODDI Free Water (FW), a potential neuroinflammation biomarker.MethodParticipants enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort (N=74, Table 1) underwent neuropsychological assessment, T1/NODDI MRI, and PiB PET. Participants were adjudicated as cognitively impaired (MCI or AD) or unimpaired using NIA‐AA criteria. T1 MRI were processed (FreeSurfer v5.3) to generate regions of interest (ROIs). An “AD‐signature” cortical thickness value was calculated, and thresholded to classify participants as neurodegeneration‐positive (N+) or ‐negative (N‐). A set of AD‐sensitive cortical ROIs was used to extract mean PET signal, which was thresholded to adjudicate AB positivity (AB‐/AB+). NODDI was processed to yield parameter maps using AMICO; mean isotropic volume fraction (FW) was calculated over a set of white matter tract ROIs. A two‐way ANOVA/ANCOVA (covariates: age, sex, education) assessed effects of neurodegeneration and AB on FW (N+/N‐ x AB+/AB‐), and a two‐way ANOVA/ANCOVA assessed effects of cognitive impairment and AB on FW (CU/CI x AB+/AB‐).ResultWe first examined effects of neurodegeneration and AB on NODDI FW. We found a significant main effect of neurodegeneration (p=.042), such that N+ had higher FW; this was marginal after adjustment (p=.07). We also observed a significant main effect of AB (p<.001; adjusted p=.012), with AB+ having higher FW. There was no significant interaction. We next explored how cognitive status and AB interacted on FW. We found a significant main effect of cognitive impairment, with CI having higher FW (p=.018; adjusted p=.013). We also found an interaction between diagnosis and AB (p=.037; adjusted p=.044) such that among AB+ individuals, presence of CI related to increased FW (Figure 1).ConclusionNODDI FW increases were significantly affected by the presence of neurodegeneration, AB positivity, and cognitive impairment.