Solutes unmask differences in clustering versus phase separation of FET proteins
(2024) Nature Communications, 15 (1), art. no. 4408, .
Kar, M.a , Vogel, L.T.b , Chauhan, G.c , Felekyan, S.b , Ausserwöger, H.d , Welsh, T.J.d , Dar, F.c , Kamath, A.R.a , Knowles, T.P.J.d , Hyman, A.A.a , Seidel, C.A.M.b , Pappu, R.V.c
a Max Planck Institute of Cell Biology and Genetics, Dresden, 01307, Germany
b Department of Molecular Physical Chemistry, Heinrich Heine University, Düsseldorf, 40225, Germany
c Department of Biomedical Engineering and Center for Biomolecular Condensates, Washington University in St. Louis, St. Louis, MO 63130, United States
d Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, United Kingdom
Abstract
Phase separation and percolation contribute to phase transitions of multivalent macromolecules. Contributions of percolation are evident through the viscoelasticity of condensates and through the formation of heterogeneous distributions of nano- and mesoscale pre-percolation clusters in sub-saturated solutions. Here, we show that clusters formed in sub-saturated solutions of FET (FUS-EWSR1-TAF15) proteins are affected differently by glutamate versus chloride. These differences on the nanoscale, gleaned using a suite of methods deployed across a wide range of protein concentrations, are prevalent and can be unmasked even though the driving forces for phase separation remain unchanged in glutamate versus chloride. Strikingly, differences in anion-mediated interactions that drive clustering saturate on the micron-scale. Beyond this length scale the system separates into coexisting phases. Overall, we find that sequence-encoded interactions, mediated by solution components, make synergistic and distinct contributions to the formation of pre-percolation clusters in sub-saturated solutions, and to the driving forces for phase separation. © The Author(s) 2024.
Funding details
Max-Planck-GesellschaftMPG
St. Jude Children’s Research Hospital
Technische Universität DresdenTUD
NOMIS Stiftung
Deutsche ForschungsgemeinschaftDFGEXC-2068—390729961
National Science FoundationNSFMCB-2227268
National Institutes of HealthNIHR01NS121114
Wellcome TrustWT209194/Z/17/Z, SPP2191
European Research CouncilERC337969
Document Type: Article
Publication Stage: Final
Source: Scopus
Predicting Driving Cessation Among Cognitively Normal Older Drivers: The Role of Alzheimer Disease Biomarkers and Clinical Assessments
(2024) Neurology, 102 (12), p. e209426.
Babulal, G.M., Chen, L., Murphy, S.A., Carr, D.B., Morris, J.C.
From the Department of Neurology (G.M.B., S.A.M., Division of Biostatistics (L.C.), Department of Medicine (D.B.C.), Washington University School of Medicine, St. Louis, MO, United States
Abstract
BACKGROUND AND OBJECTIVES: With the aging US population and increasing incidence of Alzheimer disease (AD), understanding factors contributing to driving cessation among older adults is crucial for clinicians. Driving is integral for maintaining independence and functional mobility, but the risk factors for driving cessation, particularly in the context of normal aging and preclinical AD, are not well understood. We studied a well-characterized community cohort to examine factors associated with driving cessation. METHODS: This prospective, longitudinal observation study enrolled participants from the Knight Alzheimer Disease Research Center and The DRIVES Project. Participants were enrolled if they were aged 65 years or older, drove weekly, and were cognitively normal (Clinical Dementia Rating [CDR] = 0) at baseline. Participants underwent annual clinical, neurologic, and neuropsychological assessments, including β-amyloid PET imaging and CSF (Aβ42, total tau [t-Tau], and phosphorylated tau [p-Tau]) collection every 2-3 years. The primary outcome was time from baseline visit to driving cessation, accounting for death as a competing risk. The cumulative incidence function of driving cessation was estimated for each biomarker. The Fine and Gray subdistribution hazard model was used to examine the association between time to driving cessation and biomarkers adjusting for clinical and demographic covariates. RESULTS: Among the 283 participants included in this study, there was a mean follow-up of 5.62 years. Driving cessation (8%) was associated with older age, female sex, progression to symptomatic AD (CDR ≥0.5), and poorer performance on a preclinical Alzheimer cognitive composite (PACC) score. Aβ PET imaging did not independently predict driving cessation, whereas CSF biomarkers, specifically t-Tau/Aβ42 (hazard ratio [HR] 2.82, 95% CI 1.23-6.44, p = 0.014) and p-Tau/Aβ42 (HR 2.91, 95% CI 1.28-6.59, p = 0.012) ratios, were independent predictors in the simple model adjusting for age, education, and sex. However, in the full model, progression to cognitive impairment based on the CDR and PACC score across each model was associated with a higher risk of driving cessation, whereas AD biomarkers were not statistically significant. DISCUSSION: Female sex, CDR progression, and neuropsychological measures of cognitive functioning obtained in the clinic were strongly associated with future driving cessation. The results emphasize the need for early planning and conversations about driving retirement in the context of cognitive decline and the immense value of clinical measures in determining functional outcomes.
Document Type: Article
Publication Stage: Final
Source: Scopus
Defining cis-regulatory elements and transcription factors that control human cortical interneuron development
(2024) iScience, 27 (6), art. no. 109967, .
Chapman, G., Determan, J., Jetter, H., Kaushik, K., Prakasam, R., Kroll, K.L.
Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, United States
Abstract
Although human cortical interneurons (cINs) are a minority population in the cerebral cortex, disruption of interneuron development is a frequent contributor to neurodevelopmental disorders. Here, we utilized a model for deriving cINs from human embryonic stem cells to profile chromatin state changes and generate an atlas of cis-regulatory elements (CREs) controlling human cIN development. We used these data to define candidate transcription factors (TFs) that may bind these CREs to regulate interneuron progenitor specification. Among these were RFX3 and RFX4, risk genes for autism spectrum disorder (ASD) with uncharacterized roles in human neuronal development. Using RFX3 and RFX4 knockdown models, we demonstrated new requirements for both genes in interneuron progenitor specification, with RFX3 deficiency causing precocious neuronal differentiation while RFX4 deficiency instead resulted in cessation of progenitor cell proliferation. Together, this work both defined central features of cis-regulatory control and identified new TF requirements for human interneuron development. © 2024 The Author(s)
Author Keywords
Biological sciences; Developmental neuroscience; Epigenetics; Neuroscience
Funding details
West Virginia Clinical and Translational Science InstituteWVCTSI
Intellectual and Developmental Disabilities Research Center, School of Medicine, Washington University in St. LouisIDDRC
Children’s Discovery InstituteCDI
Simons FoundationSF
National Institutes of HealthNIHR01NS114551, R01MH124808, R01HD110556, P50HD103525
National Institutes of HealthNIH
Document Type: Article
Publication Stage: Final
Source: Scopus
Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes
(2024) eLife, 12, .
Yan, D.a , Hu, B.b , Darst, B.F.c , Mukherjee, S.d , Kunkle, B.W.e , Deming, Y.c , Dumitrescu, L.f , Wang, Y.a , Naj, A.g , Kuzma, A.g , Zhao, Y.g , Kang, H.b , Johnson, S.C.h i j , Carlos, C.k , Hohman, T.J.f , Crane, P.K.d , Engelman, C.D.c h j , Lu, Q.b l , Alzheimer’s Disease Genetics Consortium (ADGC)m
a University of Wisconsin-Madison, Madison, United States
b Department of Statistics, University of Wisconsin-Madison, Madison, United States
c Department of Population Health Sciences, University of Wisconsin-Madison, Madison, United States
d Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, United States
e University of Miami Miller School of Medicine, Miami, United States
f Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, United States
g School of Medicine, University of Pennsylvania, Philadelphia, United States
h Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, United States
i Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, United States
j Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, United States
k Department of Psychiatry, Washington University in St. Louis, St. Louis, United States
l Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, United States
Abstract
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer’s disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD. © 2023, Yan, Hu et al.
Author Keywords
Alzheimer’s disease; genetics; genomics; GWAS; human; neuroscience; UK-biobank
Document Type: Article
Publication Stage: Final
Source: Scopus
Genetic Complexities of Cerebral Small Vessel Disease, Blood Pressure, and Dementia
(2024) JAMA Network Open, 7 (5), p. e2412824.
Sargurupremraj, M.a b , Soumaré, A.a , Bis, J.C.c , Surakka, I.d , Jürgenson, T.e , Joly, P.a , Knol, M.J.f , Wang, R.g h , Yang, Q.g h , Satizabal, C.L.b g h , Gudjonsson, A.i , Mishra, A.a , Bouteloup, V.a , Phuah, C.-L.j k , van Duijn, C.M.l , Cruchaga, C.k m n , Dufouil, C.a , Chêne, G.a o , Lopez, O.L.p q , Psaty, B.M.c r s , Tzourio, C.a o , Amouyel, P.t u , Adams, H.H.v w , Jacqmin-Gadda, H.a , Ikram, M.A.f , Gudnason, V.i x , Milani, L.e , Winsvold, B.S.y z aa , Hveem, K.z ab , Matthews, P.M.ac ad ae , Longstreth, W.T.r af , Seshadri, S.b g h , Launer, L.J.ag , Debette, S.a g ah
a Bordeaux Population Health Research Center, University of Bordeaux, Inserm, Bordeaux, France
b Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, Mexico
c Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, United States
d Department of Internal Medicine, University of Michigan, Ann Arbor, United States
e Estonian Genome Centre, Institute of Genomics, University of TartuTartu, Estonia
f Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
g School of Public Health, Boston University and the National Heart, Lung, Blood Institute Framingham Heart Study, Boston, MA, United States
h Department of Neurology, Boston University School of Medicine, Boston, MA, United States
i Icelandic Heart Association, Kopavogur, Iceland
j Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, United States
k NeuroGenomics and Informatics Center, Washington University in St Louis, St Louis, MO, United States
l Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
m Department of Psychiatry, Washington University School of Medicine, St Louis, MO, United States
n Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO, United States
o Department of Public Health, CHU de Bordeaux, Bordeaux, France
p Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
q Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
r Department of Epidemiology, University of Washington, Seattle, United States
s Department of Health Systems and Population Health, University of Washington, Seattle, United States
t INSERM U1167, University of Lille, Institut Pasteur de Lille, Lille, France
u Department of Epidemiology and Public Health, CHRU de Lille, Lille, France
v Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
w Latin American Brain Health (BrainLat), Universidad Adolfo IbáñezSantiago, Chile
x Faculty of Medicine, University of Iceland, Reykjavik, Iceland
y Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University HospitalOslo, Norway
z K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
aa Department of Neurology, Oslo University HospitalOslo, Norway
ab HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
ac Department of Brain Sciences, Imperial College London, London, United Kingdom
ad UK Dementia Research Institute, Imperial College London, London, United Kingdom
ae Data Science Institute, Imperial College London, London, United Kingdom
af Department of Neurology, University of Washington, Seattle, United States
ag Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, Liberia
ah Institute for Neurodegenerative Diseases, Department of Neurology, Bordeaux University Hospital, Bordeaux, France
Abstract
Importance: Vascular disease is a treatable contributor to dementia risk, but the role of specific markers remains unclear, making prevention strategies uncertain. Objective: To investigate the causal association between white matter hyperintensity (WMH) burden, clinical stroke, blood pressure (BP), and dementia risk, while accounting for potential epidemiologic biases. Design, Setting, and Participants: This study first examined the association of genetically determined WMH burden, stroke, and BP levels with Alzheimer disease (AD) in a 2-sample mendelian randomization (2SMR) framework. Second, using population-based studies (1979-2018) with prospective dementia surveillance, the genetic association of WMH, stroke, and BP with incident all-cause dementia was examined. Data analysis was performed from July 26, 2020, through July 24, 2022. Exposures: Genetically determined WMH burden and BP levels, as well as genetic liability to stroke derived from genome-wide association studies (GWASs) in European ancestry populations. Main Outcomes and Measures: The association of genetic instruments for WMH, stroke, and BP with dementia was studied using GWASs of AD (defined clinically and additionally meta-analyzed including both clinically diagnosed AD and AD defined based on parental history [AD-meta]) for 2SMR and incident all-cause dementia for longitudinal analyses. Results: In 2SMR (summary statistics-based) analyses using AD GWASs with up to 75 024 AD cases (mean [SD] age at AD onset, 75.5 [4.4] years; 56.9% women), larger WMH burden showed evidence for a causal association with increased risk of AD (odds ratio [OR], 1.43; 95% CI, 1.10-1.86; P = .007, per unit increase in WMH risk alleles) and AD-meta (OR, 1.19; 95% CI, 1.06-1.34; P = .008), after accounting for pulse pressure for the former. Blood pressure traits showed evidence for a protective association with AD, with evidence for confounding by shared genetic instruments. In the longitudinal (individual-level data) analyses involving 10 699 incident all-cause dementia cases (mean [SD] age at dementia diagnosis, 74.4 [9.1] years; 55.4% women), no significant association was observed between larger WMH burden and incident all-cause dementia (hazard ratio [HR], 1.02; 95% CI, 1.00-1.04; P = .07). Although all exposures were associated with mortality, with the strongest association observed for systolic BP (HR, 1.04; 95% CI, 1.03-1.06; P = 1.9 × 10-14), there was no evidence for selective survival bias during follow-up using illness-death models. In secondary analyses using polygenic scores, the association of genetic liability to stroke, but not genetically determined WMH, with dementia outcomes was attenuated after adjusting for interim stroke. Conclusions: These findings suggest that WMH is a primary vascular factor associated with dementia risk, emphasizing its significance in preventive strategies for dementia. Future studies are warranted to examine whether this finding can be generalized to non-European populations.
Document Type: Article
Publication Stage: Final
Source: Scopus
Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning
(2024) Journal of Neuro-Oncology, .
Luckett, P.H.a , Olufawo, M.O.a , Park, K.Y.a , Lamichhane, B.a b , Dierker, D.c , Verastegui, G.T.a , Lee, J.J.c , Yang, P.a , Kim, A.a , Butt, O.H.d , Chheda, M.G.d , Snyder, A.Z.c d , Shimony, J.S.c , Leuthardt, E.C.a e f g h i j
a Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
b Center for Health Sciences, Oklahoma State University, Tulsa, OK, United States
c Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
d Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
e Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, United States
f Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
g Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, United States
h Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, United States
i Brain Laser Center, Washington University School of Medicine, St. Louis, MO, United States
j National Center for Adaptive Neurotechnologies, Albany, NY, United States
Abstract
Purpose: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care. Methods: Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70). Results: The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor. Conclusion: The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor’s location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients. © The Author(s) 2024.
Author Keywords
Brain tumor; Functional MRI; High-grade glioma; Machine learning
Funding details
University of WashingtonUW
National Cancer InstituteNCIR01CA203861
National Cancer InstituteNCI
National Institute of Neurological Disorders and StrokeNINDSU24NS109103
National Institute of Neurological Disorders and StrokeNINDS
National Institute of Biomedical Imaging and BioengineeringNIBIBP41EB018783, R01EB026439
National Institute of Biomedical Imaging and BioengineeringNIBIB
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer’s disease” (2024) Brain Communications
Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer’s disease
(2024) Brain Communications, 6 (3), art. no. fcae159, .
Iaccarino, L.a , Llibre-Guerra, J.J.b c , Mcdade, E.b c , Edwards, L.a , Gordon, B.d , Benzinger, T.d , Hassenstab, J.b c , Kramer, J.H.a , Li, Y.e , Miller, B.L.a , Miller, Z.a , Morris, J.C.b c , Mundada, N.a , Perrin, R.J.f , Rosen, H.J.a , Soleimani-Meigooni, D.a , Strom, A.a , Tsoy, E.a , Wang, G.e , Xiong, C.e , Allegri, R.g , Chrem, P.g , Vazquez, S.g , Berman, S.B.h , Chhatwal, J.i , Masters, C.L.j , Farlow, M.R.k , Jucker, M.l , Levin, J.m n o , Salloway, S.p , Fox, N.C.q , Day, G.S.r , Gorno-Tempini, M.L.a , Boxer, A.L.a , La Joie, R.a , Bateman, R.b c , Rabinovici, G.D.a s
a Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, Memory and Aging Center, San Francisco, CA 94158, United States
b Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, United States
c Department of Neurology, Washington University in St Louis, St Louis, MO 63108, United States
d Department of Radiology, Washington University in St Louis, St Louis, MO 63110, United States
e Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, United States
f Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO 63110, United States
g Institute for Neurological Research Fleni, Department of Cognitive Neurology, Buenos Aires, 1428, Argentina
h Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, United States
i Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, United States
j Department of Neuroscience, Florey Institute, University of Melbourne, Melbourne, 3052, Australia
k Neuroscience Center, Indiana University School of Medicine, Indianapolis, IN 46202, United States
l DZNE-German Center for Neurodegenerative Diseases, Tübingen, 72076, Germany
m Department of Neurology, Ludwig-Maximilians-University, Munich, 80539, Germany
n German Center for Neurodegenerative Diseases, Munich, 81377, Germany
o Munich Cluster for Systems Neurology (SyNergy),, Munich, 81377, Germany
p Memory and Aging Program, Butler Hospital, Brown University, Providence, RI 02906, United States
q Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London, WC1N 3BG, United Kingdom
r Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, United States
s Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, United States
Abstract
Approximately 5% of Alzheimer’s disease patients develop symptoms before age 65 (early-onset Alzheimer’s disease), with either sporadic (sporadic early-onset Alzheimer’s disease) or dominantly inherited (dominantly inherited Alzheimer’s disease) presentations. Both sporadic early-onset Alzheimer’s disease and dominantly inherited Alzheimer’s disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer’s disease and dominantly inherited Alzheimer’s disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer’s disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer’s Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer’s disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ϵ4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer’s disease, sporadic early-onset Alzheimer’s disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen’s d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ϵ4 carriers (54.6% ϵ4 versus 28.1%, Cramer’s V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer’s disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer’s disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer’s disease versus sporadic early-onset Alzheimer’s disease. Sporadic early-onset Alzheimer’s disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer’s disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer’s disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer’s disease. © The Author(s) 2024.
Author Keywords
Alzheimer’s disease; amyloid-PET; brain glucose metabolism; FDG-PET
Funding details
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Instituto de Salud Carlos IIIISCIII
Fleni
Fonds de Recherche du Québec – SantéFRQS
Fondation Brain Canada
Japan Agency for Medical Research and DevelopmentAMED
Canadian Institutes of Health ResearchCIHR
Korea Health Industry Development InstituteKHIDI
Alzheimer’s AssociationAASG-20-690363, ZEN-21-848216, SG-20-690363-DIAN
Alzheimer’s AssociationAA
University of CaliforniaUCP30 AG062422, R35-AG072362, P01-AG019724, K99AG065501, R01-AG045611
University of CaliforniaUC
National Institute of Neurological Disorders and StrokeNINDSR01-NS050915
National Institute of Neurological Disorders and StrokeNINDS
National Institute on AgingNIAU19AG032438
National Institute on AgingNIA
Document Type: Article
Publication Stage: Final
Source: Scopus
Sex Differences in Dystonia
(2024) Movement Disorders Clinical Practice, .
Kilic-Berkmen, G.a , Scorr, L.M.a , McKay, L.a b c , Thayani, M.a , Donsante, Y.d , Perlmutter, J.S.e , Norris, S.A.f , Wright, L.g , Klein, C.h , Feuerstein, J.S.i , Mahajan, A.j , Wagle-Shukla, A.k , Malaty, I.k , LeDoux, M.S.l m , Pirio-Richardson, S.n , Pantelyat, A.o , Moukheiber, E.o , Frank, S.p , Ondo, W.q , Saunders-Pullman, R.r , Lohman, K.h , Hess, E.J.a d , Jinnah, H.A.a s
a Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
b Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States
c Department of Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
d Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA, United States
e Department of Neurology, Radiology, Neuroscience, Physical Therapy and Occupational Therapy, Washington University School of Medicine, St Louis, MO, United States
f Department of Neurology and Radiology, Washington University School of Medicine, St Louis, MO, United States
g Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
h Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
i Department of Neurology, University of Colorado, Aurora, CO, United States
j Department of Neurological Sciences, Rush Parkinson’s Disease and Movement Disorders Program, Chicago, IL, United States
k Fixel Institute for Neurological Disease, University of Florida Department of Neurology, University of Florida, Gainesville, FL, United States
l Department of Psychology, University of Memphis, Memphis, TN, United States
m Veracity Neuroscience LLC, Memphis, TN, United States
n Department of Neurology, University of New Mexico/New Mexico VA Healthcare System, Albuquerque, NM, United States
o Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
p Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
q Movement Disorders Clinic, Methodist Neurological Institute, Houston, TX, United States
r Department of Neurology, Icahn School of Medicine at Mount Sinai, and Mount Sinai Beth Israel, New York, NY, United States
s Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, United States
Abstract
Background: Prior studies have indicated that female individuals outnumber male individuals for certain types of dystonia. Few studies have addressed factors impacting these sex differences or their potential biological mechanisms. Objectives: To evaluate factors underlying sex differences in the dystonias and explore potential mechanisms for these differences. Methods: Data from individuals with various types of dystonia were analyzed in relation to sex. Data came from two different sources. One source was the Dystonia Coalition database, which contains predominantly idiopathic adult-onset focal and segmental dystonias. The second source was the MDSGene database, which contains predominantly early-onset monogenic dystonias. Results: The 3222 individuals from the Dystonia Coalition included 71% female participants and 29% male participants for an overall female-to-male ratio (F:M) of 2.4. This ratio varied according to body region affected and whether dystonia was task-specific. The female predominance was age-dependent. Sex did not have a significant impact on co-existing tremor, geste antagoniste, depression or anxiety. In the 1377 individuals from the MDSGene database, female participants outnumbered male participants for some genes (GNAL, GCH1, and ANO3) but not for other genes (THAP1, TH, and TOR1A). Conclusions: These results are in keeping with prior studies that have indicated female individuals outnumber male individuals for both adult-onset idiopathic and early onset monogenic dystonias. These results extend prior observations by revealing that sex ratios depend on the type of dystonia, age, and underlying genetics. © 2024 International Parkinson and Movement Disorder Society.
Author Keywords
blepharospasm; dystonia; sex differences; spasmodic dysphonia; writer’s cramp
Funding details
Rare Diseases Clinical Research NetworkRDCRN
National Institute of Neurological Disorders and StrokeNINDS
National Institutes of HealthNIHNS124764
National Institutes of HealthNIH
Dystonia CoalitionDCU54 NS065701, U54 TR001456, U54 NS116025
Dystonia CoalitionDC
Document Type: Article
Publication Stage: Article in Press
Source: Scopus