Functional role of myosin-binding protein H in thick filaments of developing vertebrate fast-twitch skeletal muscle
(2024) The Journal of General Physiology, 156 (12), .
Mead, A.F.a b , Wood, N.B.a , Nelson, S.R.a b , Palmer, B.M.a b , Yang, L.c , Previs, S.B.a b , Ploysangngam, A.a , Kennedy, G.G.a , McAdow, J.F.d , Tremble, S.M.e , Zimmermann, M.A.a b , Cipolla, M.J.e f , Ebert, A.M.g , Johnson, A.N.h , Gurnett, C.A.h , Previs, M.J.a b , Warshaw, D.M.a b
a Department of Molecular Physiology and Biophysics, Larner College of Medicine, University of Vermont, Burlington, VT, United States
b Cardiovascular Research Institute, University of Vermont, Burlington, VT, United States
c National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, United States
d Department of Neurlogical Sciences, Larner College of Medicine, University of Vermont, Burlington, VT, United States
e Department of Electrical and Biomedical Engineering, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, United States
f Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
g Department of Biology, College of Arts and Sciences, University of Vermont, Burlington, VT, United States
h Department of Developmental Biology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
Abstract
Myosin-binding protein H (MyBP-H) is a component of the vertebrate skeletal muscle sarcomere with sequence and domain homology to myosin-binding protein C (MyBP-C). Whereas skeletal muscle isoforms of MyBP-C (fMyBP-C, sMyBP-C) modulate muscle contractility via interactions with actin thin filaments and myosin motors within the muscle sarcomere “C-zone,” MyBP-H has no known function. This is in part due to MyBP-H having limited expression in adult fast-twitch muscle and no known involvement in muscle disease. Quantitative proteomics reported here reveal that MyBP-H is highly expressed in prenatal rat fast-twitch muscles and larval zebrafish, suggesting a conserved role in muscle development and prompting studies to define its function. We take advantage of the genetic control of the zebrafish model and a combination of structural, functional, and biophysical techniques to interrogate the role of MyBP-H. Transgenic, FLAG-tagged MyBP-H or fMyBP-C both localize to the C-zones in larval myofibers, whereas genetic depletion of endogenous MyBP-H or fMyBP-C leads to increased accumulation of the other, suggesting competition for C-zone binding sites. Does MyBP-H modulate contractility in the C-zone? Globular domains critical to MyBP-C’s modulatory functions are absent from MyBP-H, suggesting that MyBP-H may be functionally silent. However, our results suggest an active role. In vitro motility experiments indicate MyBP-H shares MyBP-C’s capacity as a molecular “brake.” These results provide new insights and raise questions about the role of the C-zone during muscle development. © 2024 Mead et al.
Document Type: Article
Publication Stage: Final
Source: Scopus
The Multiple Sclerosis Prodrome in a Retrospective Pediatric Cohort
(2024) Pediatric Neurology, 161, pp. 144-148.
Barter, K., Sharayah, S., Mange, U., Gaudioso, C.M., Schanzer, N., Keuchel, C., Zolno, R., Mar, S.
Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, United States
Abstract
Background: Evidence suggests a prodromal phase in multiple sclerosis (MS), with increased health care use preceding the first demyelinating event (FDE). Although prior studies have explored this in adults, limited data exist for pediatric cases. We aimed to analyze health care utilization and prodromal symptoms in the two years before FDE in patients with pediatric-onset MS (POMS). Methods: From 122 patients at the Pediatric Multiple Sclerosis & Demyelinating Diseases Center at Washington University School of Medicine from 2011 to 2021, 37 POMS cases were identified. Of these, 21 with at least two years of records preceding FDE were included. Retrospective analysis covered symptoms and health care utilization in the two-year period before FDE, including ambulatory visits, hospital admissions, and office calls. Results: Patients showed increased health care utilization in the year preceding FDE (period B; 96 interactions) compared with the prior year (period A; 77 interactions) and an increase in the percentage of neurology-related encounters (P < 0.001). There was an increase in all office calls from period A to period B (P = 0.01). Neurological complaints included headaches (28.6%), visual (19.0%), sensory (14.3%), and balance (14.3%) in the two years before FDE, and 28.6% of patients presented for psychiatric complaints. Common non-neurological complaints included infection, dermatologic, and musculoskeletal issues and injury. Conclusions: Our POMS cohort showed increased health care use before FDE, consistent with population-based data. This study emphasizes diverse symptoms in prodromal POMS, with headaches being the most common neurological symptom in the two-year period before FDE. © 2024 Elsevier Inc.
Author Keywords
First demyelinating event; Multiple sclerosis; Neuroimmunology; Pediatric-onset multiple sclerosis; Prodromal multiple sclerosis
Document Type: Article
Publication Stage: Final
Source: Scopus
25-hydroxycholesterol promotes brain cytokine production and leukocyte infiltration in a mouse model of lipopolysaccharide-induced neuroinflammation
(2024) Journal of Neuroinflammation, 21 (1), art. no. 251, .
Romero, J.a , Toral-Rios, D.a , Yu, J.b , Paul, S.M.a c d , Cashikar, A.G.a c d
a Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, United States
b Department of Genetics & amp; Genome Technology Access Center, Washington University School of Medicine, St Louis, MO 63110, United States
c Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO 63110, United States
d Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, 425 S Euclid Ave, Campus Box 8134, St Louis, MO 63110, United States
Abstract
Neuroinflammation has been implicated in the pathogenesis of several neurologic and psychiatric disorders. Microglia are key drivers of neuroinflammation and, in response to different inflammatory stimuli, overexpress a proinflammatory signature of genes. Among these, Ch25h is a gene overexpressed in brain tissue from Alzheimer’s disease as well as various mouse models of neuroinflammation. Ch25h encodes cholesterol 25-hydroxylase, an enzyme upregulated in activated microglia under conditions of neuroinflammation, that hydroxylates cholesterol to form 25-hydroxycholesterol (25HC). 25HC can be further metabolized to 7α,25-dihydroxycholesterol, which is a potent chemoattractant of leukocytes. We have previously shown that 25HC increases the production and secretion of the proinflammatory cytokine, IL-1β, by primary mouse microglia treated with lipopolysaccharide (LPS). In the present study, wildtype (WT) and Ch25h-knockout (KO) mice were peripherally administered LPS to induce an inflammatory state in the brain. In LPS-treated WT mice, Ch25h expression and 25HC levels increased in the brain relative to vehicle-treated WT mice. Among LPS-treated WT mice, females produced significantly higher levels of 25HC and showed transcriptomic changes reflecting higher levels of cytokine production and leukocyte migration than WT male mice. However, females were similar to males among LPS-treated KO mice. Ch25h-deficiency coincided with decreased microglial activation in response to systemic LPS. Proinflammatory cytokine production and intra-parenchymal infiltration of leukocytes were significantly lower in KO compared to WT mice. Amounts of IL-1β and IL-6 in the brain strongly correlated with 25HC levels. Our results suggest a proinflammatory role for 25HC in the brain following peripheral administration of LPS. © The Author(s) 2024.
Author Keywords
25-hydroxycholesterol; Alzheimer’s disease; Cholesterol-25-hydroxylase; Cytokines; Lipopolysaccharide; Microglial activation; Neuroinflammation; Neutrophil infiltration; Toll-like receptor-4
Document Type: Article
Publication Stage: Final
Source: Scopus
Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum
(2024) Translational Psychiatry, 14 (1), art. no. 420, .
Wen, J.a , Yang, Z.b , Nasrallah, I.M.b , Cui, Y.b , Erus, G.b , Srinivasan, D.b , Abdulkadir, A.b c , Mamourian, E.b , Hwang, G.b , Singh, A.b , Bergman, M.b , Bao, J.d , Varol, E.e , Zhou, Z.b , Boquet-Pujadas, A.a , Chen, J.b , Toga, A.W.f , Saykin, A.J.g , Hohman, T.J.h , Thompson, P.M.i , Villeneuve, S.j , Gollub, R.k , Sotiras, A.l , Wittfeld, K.m , Grabe, H.J.m , Tosun, D.n , Bilgel, M.o , An, Y.o , Marcus, D.S.p , LaMontagne, P.p , Benzinger, T.L.p , Heckbert, S.R.q , Austin, T.R.q , Launer, L.J.r , Espeland, M.s , Masters, C.L.t , Maruff, P.t , Fripp, J.u , Johnson, S.C.v , Morris, J.C.w , Albert, M.S.x , Bryan, R.N.y , Resnick, S.M.o , Ferrucci, L.z , Fan, Y.b , Habes, M.aa , Wolk, D.b ab , Shen, L.d , Shou, H.b ac , Davatzikos, C.b
a Laboratory of AI and Biomedical Science (LABS), University of Southern California, Los Angeles, CA, United States
b Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
c Research Lab in Neuroimaging of the Department of Clinical Neurosciences at Lausanne University Hospital, Lausanne, Switzerland
d Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
e Department of Statistics, Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY, United States
f Laboratory of NeuroImaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
g Radiology and Imaging Sciences, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
h Vanderbilt Memory and Alzheimer’s Center, Vanderbilt Genetics Institute, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
i Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del ReyCA, United States
j Douglas Mental Health University Institute, McGill University, Montréal, QC, Canada
k Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
l Department of Radiology and Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States
m Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
n Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States
o Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD, United States
p Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
q Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, United States
r Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, United States
s Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, NC, United States
t Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
u CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
v Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
w Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, United States
x Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
y Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
z Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD 21225, United States
aa Glenn Biggs Institute for Alzheimer’s & amp; Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
ab Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, United States
ac Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
Abstract
Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOEε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE—which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/. © The Author(s) 2024.
Document Type: Article
Publication Stage: Final
Source: Scopus
Size and Topography of the Brain’s Functional Networks with Psychotic Experiences, Schizophrenia, and Bipolar Disorder
(2024) Biological Psychiatry Global Open Science, 4 (6), art. no. 100386, .
Mamah, D.a , Chen, S.S.a , Gordon, E.b , Kandala, S.a , Barch, D.M.a c , Harms, M.P.a
a Department of Psychiatry, Washington University Medical School, St LouisMissouri, United States
b Department of Radiology, Washington University Medical School, St LouisMissouri, United States
c Department of Psychological and Brain Sciences, Washington University Medical School, St LouisMissouri, United States
Abstract
Background: Existing functional connectivity studies of psychosis use population-averaged functional network maps, despite highly variable topographies of these networks across the brain surface. We aimed to define the functional network areas and topographies in the general population and the changes associated with psychotic experiences (PEs) and disorders. Methods: Maps of 8 functional networks were generated using an individual-specific template-matching procedure for each participant from the Human Connectome Project Young Adult cohort (n = 1003) and from a matched case cohort (schizophrenia [SCZ], n = 27; bipolar disorder, n = 35) scanned identically with the same Connectom scanner. In the Human Connectome Project Young Adult cohort, PEs were estimated based on scores from the Achenbach Self-Report Scale. The relationship of symptoms to the probability of network representation at each cortical vertex was assessed using logistic regression. Results: In Human Connectome Project Young Adult participants, PE severity on the Achenbach thought problems scale was predicted by increased language network (LAN) and dorsal attention network (DAN) areas and decreased cingulo-opercular network area (r < 0.12). Significant effects were found in SCZ, with a larger DAN and LAN and a smaller frontoparietal network. Network pattern analysis in SCZ showed an increased probability of LAN in the posterior region of the left superior temporal gyrus and of the visual network in the left insula. Regression analyses in SCZ found that mood dysregulation was related to increased DAN surface area. Conclusions: Those with PEs and SCZ showed abnormal functional network cortical topographies, particularly involving DAN and LAN. Network findings may predict psychosis progression and guide earlier intervention. © 2024 The Authors
Author Keywords
Bipolar disorder; Functional connectivity; Human Connectome Project; Psychotic experiences; Schizophrenia
Document Type: Article
Publication Stage: Final
Source: Scopus
The association of dementia risk symptoms and functional activity in adults with Down syndrome
(2024) Alzheimer’s and Dementia: Translational Research and Clinical Interventions, 10 (4), art. no. e70007, .
Washington, S.E.a , Bodde, A.E.b , Helsel, B.C.c , Bollinger, R.M.d , Smith, N.a , Ptomey, L.T.b , Ances, B.e , Stark, S.L.d
a Department of Occupational Science and Occupational Therapy, Saint Louis University, St. Louis, MO, United States
b Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
c Department of Neurology, University of Kansas Alzheimer’s Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States
d Program in Occupational Therapy, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
e Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
Abstract
INTRODUCTION: Adults with Down syndrome (DS) have an increased risk of Alzheimer’s disease (AD) dementia, often showing neuropathological indicators by age 40. Physical function and activities of daily living (ADLs) are understudied areas of function that may inform dementia risk. We investigated associations among age, physical function (gait/balance, grip strength, and lower extremity strength), ADLs, and dementia risk symptoms in adults with DS. We hypothesized that compromised physical function and lower independence with ADLs would be associated with an informant/caregiver-reported measure of dementia risk symptoms. METHODS: A secondary analysis for this cross-sectional study was completed using data from two academic research centers with 43 adults with DS (age 30 ± 12 years). We examined the association of dementia risk symptoms, captured through the Dementia Screening Questionnaire for Individuals with Intellectual Disabilities (DSQIID), with physical function (timed up and go [TUG], sit-to-stand [STS], grip strength) and ADLs (Waisman Activities of Daily Living Scale). A linear regression model for the continuous dementia risk measure in the DSQIID used a log transformation of (1 + log(Y + 1)) to account for a high zero count. Wilcoxon rank-sum tests were used to assess differences in the physical function measures, DSQIID questionnaire, and Waisman ADL by dividing mean age categories. RESULTS: Higher DSQIID scores were associated with lower independence with ADLs (β = −0.103, p = 0.008), slower gait times (TUG; β = 0.112, p = 0.034), and impaired lower extremity strength (STS; β = 0.112, p = 0.017) and grip strength (β = −0.039, p = 0.034). DSQIID scores differed significantly between the ≥30 and <30 age groups. Participants ≥30 years of age scored 5 points higher on the DSQIID than participants <30, suggesting that age was associated with greater dementia risk. DISCUSSION: Greater dementia risk symptoms were associated with age, lower physical function scores, and independence with ADLs, suggesting that declines in physical function and ADLs may be early indicators of subsequent dementia risk in adults with DS. Highlights: We explored the association of physical function and activities of daily living (ADLs) in aging adults with DS and their relationship with informant/caregiver report of dementia risk symptoms. Our findings demonstrated a significant relationship between a higher number of dementia risk symptoms observed and lower independence with ADLs, and impaired gait/balance, grip strength, and lower extremity strength. Further research with larger longitudinal studies is necessary to investigate any causative relationships among physical function, ADL function, and dementia risk symptoms. © 2024 The Author(s). Alzheimer’s & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.
Author Keywords
activities of daily living; cognition; dementia; Down syndrome; physical function
Funding details
National Institute on AgingNIAR01AG057680‐05S1
National Institute on AgingNIA
K01AG083130-01
R01AG063909-03
Document Type: Article
Publication Stage: Final
Source: Scopus
Head-to-head comparison of leading blood tests for Alzheimer’s disease pathology
(2024) Alzheimer’s and Dementia, .
Schindler, S.E.a , Petersen, K.K.a , Saef, B.a , Tosun, D.b , Shaw, L.M.c , Zetterberg, H.d e f g h i , Dage, J.L.j k , Ferber, K.l , Triana-Baltzer, G.m , Du-Cuny, L.n , Li, Y.a , Coomaraswamy, J.o , Baratta, M.o , Mordashova, Y.n , Saad, Z.S.m , Raunig, D.L.o , Ashton, N.J.d p q , Meyers, E.A.r , Rubel, C.E.l , Rosenbaugh, E.G.s , Bannon, A.W.t , Potter, W.Z.u , Alzheimer’s Disease Neuroimaging Initiative (ADNI) Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium Plasma Abeta and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer’s Disease Project Teamv
a Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
b Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
c Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
d Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
e Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
f UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL, London, United Kingdom
g Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
h Hong Kong Center for Neurodegenerative Diseases, Hong Kong
i Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
j Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States
k Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
l Biogen, Biomarkers Group, Cambridge, MA, United States
m Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, CA, United States
n AbbVie, Rheinland-Pfalz, Ludwigshafen am Rhein, Germany
o Takeda Pharmaceutical Company Ltd., Cambridge, MA, United States
p Banner Alzheimer’s Institute, Phoenix, AZ, United States
q Banner Sun Health Research Institute, Sun City, AZ, United States
r Alzheimer’s Association, Chicago, IL, United States
s The Foundation for the National Institutes of Health, North Bethesda, MD, United States
t AbbVie, North Chicago, IL, United States
u Highly qualified expert, Philadelphia, PA, United States
Abstract
INTRODUCTION: Blood tests have the potential to improve the accuracy of Alzheimer’s disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. METHODS: Plasma samples from the Alzheimer’s Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. RESULTS: Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. DISCUSSION: This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes. Highlights: Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low. © 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.
Author Keywords
A/T/N; amyloid; biomarkers; blood; glial fibrillary acidic protein; neurofilament light; p-tau; plasma; tau
Funding details
National Institute on AgingNIA
University of Pennsylvania
Document Type: Article
Publication Stage: Article in Press
Source: Scopus