Scopus list of publications for August 20, 2023
Parenchymal border macrophages regulate tau pathology and tau-mediated neurodegeneration
(2023) Life Science Alliance, 6 (11), .
Drieu, A.a b , Du, S.a b , Kipnis, M.c d e , Bosch, M.E.c d e , Herz, J.a b , Lee, C.c d e , Jiang, H.c d e , Manis, M.c d e , Ulrich, J.D.c d e , Kipnis, J.a b , Holtzman, D.M.c d e , Gratuze, M.c d e f
a https://ror.org/04cf69335 Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
b https://ror.org/04cf69335 Center for Brain Immunology and Glia, Washington University School of Medicine, St. Louis, MO, United States
c https://ror.org/04cf69335 Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
d https://ror.org/04cf69335 Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
e https://ror.org/04cf69335 Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
f Aix-Marseille University, Marseille, France
Abstract
Parenchymal border macrophages (PBMs) reside close to the central nervous system parenchyma and regulate CSF flow dynamics. We recently demonstrated that PBMs provide a clearance pathway for amyloid-β peptide, which accumulates in the brain in Alzheimer’s disease (AD). Given the emerging role for PBMs in AD, we explored how tau pathology affects the CSF flow and the PBM populations in the PS19 mouse model of tau pathology. We demonstrated a reduction of CSF flow, and an increase in an MHCII+PBM subpopulation in PS19 mice compared with WT littermates. Consequently, we asked whether PBM dysfunction could exacerbate tau pathology and tau-mediated neurodegeneration. Pharmacological depletion of PBMs in PS19 mice led to an increase in tau pathology and tau-dependent neurodegeneration, which was independent of gliosis or aquaporin-4 depolarization, essential for the CSF-ISF exchange. Together, our results identify PBMs as novel cellular regulators of tau pathology and tau-mediated neurodegeneration. © 2023 Drieu et al.
Document Type: Article
Publication Stage: Final
Source: Scopus
Effect of Direction and Frequency of Skull Motion on Mechanical Vulnerability of the Human Brain
(2023) Journal of Biomechanical Engineering, 145 (11), .
Okamoto, R.J.a , Escarcega, J.D.b , Alshareef, A.c , Carass, A.d , Prince, J.L.d , Johnson, C.L.e , Bayly, P.V.b
a Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, MSC 1185-208-125, One Brookings Drive, St. Louis, MO 63130, United States
b Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, United States
c Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, United States
d Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
e Department of Biomedical Engineering, University of Delaware, Newark, DE 19713
Abstract
Strain energy and kinetic energy in the human brain were estimated by magnetic resonance elastography (MRE) during harmonic excitation of the head, and compared to characterize the effect of loading direction and frequency on brain deformation. In brain MRE, shear waves are induced by external vibration of the skull and imaged by a modified MR imaging sequence; the resulting harmonic displacement fields are typically “inverted” to estimate mechanical properties, like stiffness or damping. However, measurements of tissue motion from MRE also illuminate key features of the response of the brain to skull loading. In this study, harmonic excitation was applied in two different directions and at five different frequencies from 20 to 90 Hz. Lateral loading induced primarily left-right head motion and rotation in the axial plane; occipital loading induced anterior-posterior head motion and rotation in the sagittal plane. The ratio of strain energy to kinetic energy (SE/KE) depended strongly on both direction and frequency. The ratio of SE/KE was approximately four times larger for lateral excitation than for occipital excitation and was largest at the lowest excitation frequencies studied. These results are consistent with clinical observations that suggest lateral impacts are more likely to cause injury than occipital or frontal impacts, and also with observations that the brain has low-frequency (∼10 Hz) natural modes of oscillation. The SE/KE ratio from brain MRE is potentially a simple and powerful dimensionless metric of brain vulnerability to deformation and injury. Copyright © 2023 by ASME.
Document Type: Article
Publication Stage: Final
Source: Scopus
Entry receptor LDLRAD3 is required for Venezuelan equine encephalitis virus peripheral infection and neurotropism leading to pathogenesis in mice
(2023) Cell Reports, 42 (8), art. no. 112946, .
Kafai, N.M.a b , Janova, H.a , Cain, M.D.a , Alippe, Y.a , Muraro, S.a , Sariol, A.a , Elam-Noll, M.a , Klein, R.S.a b c , Diamond, M.S.a b d e
a Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
b Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, United States
c Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States
d Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, United States
e The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, MO 63110, United States
Abstract
Venezuelan equine encephalitis virus (VEEV) is an encephalitic alphavirus responsible for epidemics of neurological disease across the Americas. Low-density lipoprotein receptor class A domain-containing 3 (LDLRAD3) is a recently reported entry receptor for VEEV. Here, using wild-type and Ldlrad3-deficient mice, we define a critical role for LDLRAD3 in controlling steps in VEEV infection, pathogenesis, and neurotropism. Our analysis shows that LDLRAD3 is required for efficient VEEV infection and pathogenesis prior to and after central nervous system invasion. Ldlrad3-deficient mice survive intranasal and intracranial VEEV inoculation and show reduced infection of neurons in different brain regions. As LDLRAD3 is a determinant of pathogenesis and an entry receptor required for VEEV infection of neurons of the brain, receptor-targeted therapies may hold promise as countermeasures. © 2023 The Author(s)
Author Keywords
alphavirus, pathogenesis, receptor, tropism, brain, mice, animal model, infection, neuron; CP: Microbiology
Funding details
National Institutes of HealthNIHF30AI164842, R01AI14367, R01AI164653, T32 AI007172
Defense Threat Reduction AgencyDTRAW15QKN1691002
University of WashingtonUW
Vir Biotechnology
Document Type: Article
Publication Stage: Final
Source: Scopus
Sleep Behaviors, Genetic Predispositions, and Risk of Esophageal Cancer
(2023) Cancer Epidemiology Biomarkers and Prevention, 32 (8), pp. OF1-OF8.
Wang, X.a b , Tian, R.a b , Zong, X.a , Jeon, M.S.a c , Luo, J.a c , Colditz, G.A.a c , Wang, J.S.d , Tsilidis, K.K.e f , Ju, Y.-E.S.g h i , Govindan, R.c j , Puri, V.k , Cao, Y.a c d g
a Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
b Brown School, Washington University in St. Louis, St. Louis, MO, United States
c Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, United States
d Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
e Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
f Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
g Center on Biological Rhythms and Sleep (COBRAS), Washington University School of Medicine, St. Louis, MO, United States
h Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
i Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
j Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
k Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Risk factors contributing to more than 10-fold increase in esophageal cancer in the last 50 years remain underexplored. We aim to examine the associations of sleep behaviors with esophageal adenocarcinoma (EAC) and squamous cell carcinoma (ESCC). Methods: We prospectively assessed the associations between sleep behaviors (chronotype, duration, daytime napping, daytime sleepiness, snoring, and insomnia) and EAC and ESCC risk in 393,114 participants in the UK Biobank (2006-2016). Participants with 0, 1, and ≥2 unhealthy behaviors, including sleep <6 or >9 h/d, daytime napping, and usual daytime sleepiness were classified as having a good, intermediate, and poor sleep. For EAC, we also examined interactions with polygenic risk score (PRS). Cox models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI). Results: We documented 294 incident EAC and 95 ESCC. Sleep >9 h/d (HR, 2.05; 95% CI, 1.18-3.57) and sometimes daytime napping (HR, 1.36; 95% CI, 1.06-1.75) were individually associated with increased EAC risk. Compared with individuals with good sleep, those with intermediate sleep had a 47% (HR, 1.47; 95% CI, 1.13-1.91) increased EAC risk, and those with poor sleep showed an 87% (HR, 1.87; 95% CI, 1.24-2.82) higher risk (Ptrend < 0.001). The elevated risks for EAC were similar within strata of PRS (Pinteraction = 0.884). Evening chronotype was associated with elevated risk of ESCC diagnosed after 2 years of enrollment (HR, 2.79; 95% CI, 1.32-5.88). Conclusions: Unhealthy sleep behaviors were associated with an increased risk of EAC, independent of genetic risk. Impact: Sleep behaviors may serve as modifiable factors for the prevention of EAC. © 2023 American Association for Cancer Research.
Funding details
National Institutes of HealthNIHP30CA091842
Alvin J. Siteman Cancer Center
Document Type: Article
Publication Stage: Final
Source: Scopus
White matter hyperintensity longitudinal morphometric analysis in association with Alzheimer disease
(2023) Alzheimer’s and Dementia, .
Strain, J.F.a , Phuah, C.-L.a b , Adeyemo, B.a , Cheng, K.a c , Womack, K.B.a , McCarthy, J.d , Goyal, M.e , Chen, Y.a , Sotiras, A.e f , An, H.e , Xiong, C.g , Scharf, A.h , Newsom-Stewart, C.i , Morris, J.C.a j , Benzinger, T.L.S.e j , Lee, J.-M.a c e , Ances, B.M.a c e j
a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
b NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
c Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
d Department of Mathematics, Washington University School of Medicine, St. Louis, MO, United States
e Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
f Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States
g Division of Biostatics, Washington University School of Medicine, St. Louis, MO, United States
h Department of Biological Sciences, Missouri University for Science and Technology, Rolla, MO, United States
i Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States
j Knight Alzheimer Disease Research Center, St. Louis, MO, United States
Abstract
INTRODUCTION: Vascular damage in Alzheimer’s disease (AD) has shown conflicting findings particularly when analyzing longitudinal data. We introduce white matter hyperintensity (WMH) longitudinal morphometric analysis (WLMA) that quantifies WMH expansion as the distance from lesion voxels to a region of interest boundary. METHODS: WMH segmentation maps were derived from 270 longitudinal fluid-attenuated inversion recovery (FLAIR) ADNI images. WLMA was performed on five data-driven WMH patterns with distinct spatial distributions. Amyloid accumulation was evaluated with WMH expansion across the five WMH patterns. RESULTS: The preclinical group had significantly greater expansion in the posterior ventricular WM compared to controls. Amyloid significantly associated with frontal WMH expansion primarily within AD individuals. WLMA outperformed WMH volume changes for classifying AD from controls primarily in periventricular and posterior WMH. DISCUSSION: These data support the concept that localized WMH expansion continues to proliferate with amyloid accumulation throughout the entirety of the disease in distinct spatial locations. © 2023 the Alzheimer’s Association.
Author Keywords
AD; longitudinal; preclinical; WLMA; WMH
Funding details
K23 NS110927, R01AG052550, R01AG057680, R01AG067103
National Institutes of HealthNIHU01 AG024904
U.S. Department of DefenseDODW81XWH‐12‐2‐0012
National Institute on AgingNIA
National Institute of Biomedical Imaging and BioengineeringNIBIB
Alzheimer’s AssociationAA
American Heart AssociationAHA19CDA34620004
Alzheimer’s Drug Discovery FoundationADDF
BiogenR01 AG053550
AbbVie
Alzheimer’s Disease Neuroimaging InitiativeADNI
BioClinica
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease
(2023) Nature Medicine, .
Johnson, E.C.B.a b , Bian, S.c , Haque, R.U.a , Carter, E.K.a d , Watson, C.M.a b , Gordon, B.A.e , Ping, L.a b d , Duong, D.M.a d , Epstein, M.P.f , McDade, E.g , Barthélemy, N.R.g , Karch, C.M.h , Xiong, C.g i , Cruchaga, C.h , Perrin, R.J.g j , Wingo, A.P.a k l , Wingo, T.S.a b f , Chhatwal, J.P.m , Day, G.S.n , Noble, J.M.o , Berman, S.B.p , Martins, R.q , Graff-Radford, N.R.n , Schofield, P.R.r s , Ikeuchi, T.t , Mori, H.u , Levin, J.v , Farlow, M.w , Lah, J.J.a b , Haass, C.x y z , Jucker, M.aa ab , Morris, J.C.g , Benzinger, T.L.S.e , Roberts, B.R.a d , Bateman, R.J.g , Fagan, A.M.g , Seyfried, N.T.a b d , Levey, A.I.a b , Noble, J.M.o , Day, G.S.n , Graff-Radford, N.R.n , Voglein, J.v x , Allegri, R.ac , Mendez, P.C.ac , Surace, E.ad , Berman, S.B.ae af , Ikonomovic, S.ae , Nadkarni, N.ae ag , Lopera, F.ah , Ramirez, L.ah , Aguillon, D.ah , Leon, Y.ah , Ramos, C.ah , Alzate, D.ah , Baena, A.ah , Londono, N.ah , Moreno, S.ah , Laske, C.x ai aj , Kuder-Buletta, E.x , Graber-Sultan, S.x , Preische, O.x ai aj , Hofmann, A.x ai , Ikeuchi, T.t , Kasuga, K.ak , Niimi, Y.al , Ishii, K.am , Senda, M.an , Sanchez-Valle, R.ao , Rosa-Neto, P.ap , Fox, N.aq ar , Cash, D.aq ar , Lee, J.-H.as , Roh, J.H.as , Riddle, M.at , Menard, W.at , Bodge, C.at , Surti, M.at , Takada, L.T.au , Chhatwal, J.P.m , Sanchez-Gonzalez, V.J.av , Orozco-Barajas, M.av , Goate, A.aw , Renton, A.aw ax ay , Esposito, B.aw ay , Karch, C.M.h , Marsh, J.h , Cruchaga, C.h az , Fernandez, V.h az , Gordon, B.A.e , Fagan, A.M.g , Jerome, G.g , Herries, E.g , Llibre-Guerra, J.g , Johnson, E.C.B.a b , Seyfried, N.T.a b d , Schofield, P.R.r s , Brooks, W.r ba , Bechara, J.r , Bateman, R.J.g , Hassenstab, J.g bb , Perrin, R.J.g j , Franklin, E.j , Benzinger, T.L.S.e , Chen, A.e , Chen, C.e , Flores, S.e , Friedrichsen, N.e , Hantler, N.e , Hornbeck, R.e , Jarman, S.e , Keefe, S.e , Koudelis, D.e , Massoumzadeh, P.e , McCullough, A.e , McKay, N.e , Nicklaus, J.e , Pulizos, C.e , Wang, Q.e , Mishall, S.e , Sabaredzovic, E.e , Deng, E.e , Candela, M.e , Smith, H.e , Hobbs, D.e , Scott, J.e , Levin, J.v , Xiong, C.g i , Wang, P.i , Xu, X.i , Li, Y.i , Gremminger, E.i , Ma, Y.i , Bui, R.i , Lu, R.i , Ortiz, A.L.S.bc , Daniels, A.bd , Courtney, L.bd , Supnet-Bell, C.g , Xu, J.be , Ringman, J.bf , the Dominantly Inherited Alzheimer Networkbg
a Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA, United States
b Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
c Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States
d Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, United States
e Mallinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO, United States
f Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, United States
g Department of Neurology, Washington University in St Louis, St Louis, MO, United States
h Department of Psychiatry, Washington University in St Louis, St Louis, MO, United States
i Division of Biostatistics, Washington University in St Louis, St Louis, MO, United States
j Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO, United States
k Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, United States
l Division of Mental Health, Atlanta VA Medical Center, Atlanta, GA, United States
m Massachusetts General and Brigham & Women’s Hospitals, Harvard Medical School, Boston, MA, United States
n Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
o Department of Neurology, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, and GH Sergievsky Center, Columbia University Irving Medical Center, New York, NY, United States
p Departments of Neurology and Clinical and Translational Science, Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA, United States
q Edith Cowan University, Perth, WA, Australia
r Neuroscience Research Australia, Sydney, NSW, Australia
s School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
t Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
u Osaka Metropolitan University Medical School, Nagaoka Sutoku University, Nagaoka, Japan
v Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
w Indiana University, Bloomington, IN, United States
x German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
y Metabolic Biochemistry, Biomedical Center (BMC), Ludwig-Maximilians University, Munich, Germany
z Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
aa Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
ab German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
ac Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
ad Department of Molecular Biology and Neuropathology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
ae Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
af Clinical and Translational Science, Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA, United States
ag Department of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, United States
ah Grupo de Neurociencias de Antioquia (GNA), Universidad de Antioquia, Medellín, Colombia
ai Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
aj Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
ak Brain Research Institute, Niigata University, Niigata, Japan
al The University of Tokyo Hospital Unit for Early and Exploratory Clinical Development, Tokyo, Japan
am Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
an Kobe City Medical Center General Hospital, Tokyo, Japan
ao Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic de Barcelona, Barcelona, Spain
ap McGill University, Montreal, QC, Canada
aq Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
ar UK Dementia Research Institute at UCL, London, United Kingdom
as Korea University College of Medicine, Seoul, South Korea
at Butler Hospital, Warren Alpert School of Medicine, Brown University, Providence, RI, United States
au Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
av Doctorado en Biociencias & Departamento de Clinicas, Centro Universitario de Los Altos, UDG, Tepatitlán de Morelos, Mexico
aw Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
ax Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
ay Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States
az NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, United States
ba School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
bb Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, United States
bc Mexico City, Mexico
bd Washington University School of Medicine in St Louis, St Louis, MO, United States
be Department of Radiology, Washington University in St Louis, St Louis, MO, United States
bf Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
Abstract
Alzheimer’s disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes—aggregation of the amyloid-β (Aβ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)—are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aβ plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aβ plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aβ and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aβ and tau. © 2023, The Author(s).
Funding details
National Institute on AgingNIA
Alzheimer’s AssociationAASG-20-690363-DIAN
Alzheimer’s Disease Research Center, Emory UniversityADRCP30AG066511, U01AG061357
Fondation Brain Canada
Japan Agency for Medical Research and DevelopmentAMEDAMED 17929884, JP22dk0207049, P01AG003991, P01AG026276, P30 AG066444, U19 AG024904
Canadian Institutes of Health ResearchIRSC
Fonds de Recherche du Québec – SantéFRQS
Deutsche ForschungsgemeinschaftDFG390857198, HA1737/16-1, U19AG032438
Korea Health Industry Development InstituteKHIDI
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Exploring hand and upper limb function in patients with inclusion body myositis (IBM)
(2023) Neuromuscular Disorders, .
Hunn, S.a , Alfano, L.b c , Seiffert, M.a , Weihl, C.C.a
a Washington University in St. Louis, 660 South Euclid Ave Campus Box 8111, St. Louis, MO 63110, United States
b Nationwide Children’s Hospital, 700 Children’s Dr., AB7036, Columbus, OH 43205, United States
c The Department of Pediatrics, The Ohio State University College of Medicine, 700 Children’s Dr, Columbus, OH 43205, United States
Abstract
Inclusion body myositis (IBM) is an inflammatory myopathy characterized by progressive weakness of knee extensors and finger flexors. Many patients lose independence with fine motor tasks; however, a gap remains as to how these deficits correlate with performance on functional outcome measures. We describe functional hand impairments as measured by performance-based outcome measures in a cross-sectional sample of 74 patients with IBM. Subjects completed a series of outcome measures (Functional Dexterity Test (FDT), Performance of the Upper Limb (PUL), and Sollerman Hand Function Test (SHFT)) alongside a collection of patient reported outcomes (PROs). Assessments were compared to standard IBM measurements, including grip strength and IBM Functional Rating Scale (IBMFRS). FDT and SHFT demonstrated significant correlations to grip (p<0.001; Spearman correlations r=0.48–0.70). Significant correlation was found between all functional outcome measures and IBMFRS (p<0.001; Spearman correlations r=0.51–0.77), as well as PRO Upper Extremity Scale for IBM (IBM-PRO) (p<0.05; Spearman correlations r=0.55–0.73). Non-ambulatory patients demonstrated significantly weaker grip (p<0.001), resulting in lower PUL scores and increased FDT completion times (p<0.001). Collectively, these assessments may provide insight to understanding functional limitations of the hands and potentially allow for more inclusive clinical trials with future validation of hand assessments in IBM. © 2023 The Author(s)
Author Keywords
Functional dexterity test; Grip strength; Hand weakness; Inclusion body myositis; Outcome measures; Patient reported outcome
Funding details
National Institutes of HealthNIHK24AR073317
Myositis AssociationTMA
Document Type: Article
Publication Stage: Article in Press
Source: Scopus