Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease
(2023) Molecular Neurodegeneration, 18 (1), art. no. 98, .
Millar, P.R.a , Gordon, B.A.b , Wisch, J.K.a , Schultz, S.A.c d , Benzinger, T.L.b , Cruchaga, C.e , Hassenstab, J.J.a , Ibanez, L.a e f , Karch, C.e , Llibre-Guerra, J.J.a , Morris, J.C.a , Perrin, R.J.a g , Supnet-Bell, C.a , Xiong, C.h , Allegri, R.F.i , Berman, S.B.j , Chhatwal, J.P.c d , Chrem Mendez, P.A.i , Day, G.S.k , Hofmann, A.l m , Ikeuchi, T.n , Jucker, M.l m , Lee, J.-H.o , Levin, J.p q r , Lopera, F.s , Niimi, Y.t , Sánchez-González, V.J.u , Schofield, P.R.v w , Sosa-Ortiz, A.L.x , Vöglein, J.p q , Bateman, R.J.a , Ances, B.M.a b , McDade, E.M.a
a Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
b Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States
c Department of Neurology, Harvard Medical School, Boston, MA, United States
d Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
e Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
f NeuroGenomics & Informatics Center, Washington University in St. Louis, St. Louis, MO, United States
g Department of Pathology & Immunology, Washington University in St. Louis, St. Louis, MO, United States
h Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, United States
i Instituto Neurológico Fleni, Buenos Aires, Argentina
j Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
k Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
l German Center for Neurodegenerative Diseases (DZNE), Tübingen, 72076, Germany
m Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, 72076, Germany
n Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
o Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
p Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
q German Center for Neurodegenerative Diseases, Munich, Germany
r Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
s Universidad de Antioquia, Medellín, Colombia
t Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Bunkyo-Ku, Tokyo, Japan
u Departamento de Clínicas, CUALTOS, Universidad de Guadalajara, Tepatitlán de Morelos, Jalisco, Mexico
v Neuroscience Research Australia, Sydney, NSW, Australia
w School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
x Instituto Nacional de Neurologia y Neurocirugía MVS, CDMX, Ciudad de México, Mexico
Abstract
Background: “Brain-predicted age” estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. Methods: We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE ε4 carrier status, sex, and education. Results: Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. Conclusions: We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI. © 2023, The Author(s).
Author Keywords
Alzheimer disease; Brain aging; Machine learning; Structural MRI
Funding details
AARFD-21-851415, K01AG073526, SG-20-690363
National Institutes of HealthNIHP01 AG00399139, P01AG026276, P30 AG06644403, R01 AG05255005, R01 AG05326704, R01 AG05456705, R01 AG05777705, R01 AG05867604, R01 AG07490901, R01 NS07532111, R01 NS09779906, U19 AG024904-16, U19 AG03243811, U19 AG06970102
National Institute on AgingNIA
Alzheimer’s AssociationAASG-20-690363-DIAN
RocheK23AG064029, U01AG057195, U01NS120901, U19AG032438
BrightFocus FoundationBFFA2022014F
Foundation for Barnes-Jewish HospitalFBJH
Fondation Brain Canada
Japan Agency for Medical Research and DevelopmentAMEDAMED JP23dk0207066
Canadian Institutes of Health ResearchIRSC
Fonds de Recherche du Québec – SantéFRQS
Consejo Nacional de Investigaciones Científicas y TécnicasCONICET
Korea Health Industry Development InstituteKHIDI
Instituto de Salud Carlos IIIISCIII
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Fleni
Document Type: Article
Publication Stage: Final
Source: Scopus
Plasma and cerebrospinal fluid proteomic signatures of acutely sleep-deprived humans: an exploratory study
(2023) SLEEP Advances, 4 (1), art. no. zpad047, .
Vaquer-Alicea, A.a , Yu, J.b , Liu, H.a , Lucey, B.P.a
a Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
b Department of Genetics, Washington University School of Medicine, St Louis, MO, United States
Abstract
Study Objectives: Acute sleep deprivation affects both central and peripheral biological processes. Prior research has mainly focused on specific proteins or biological pathways that are dysregulated in the setting of sustained wakefulness. This exploratory study aimed to provide a comprehensive view of the biological processes and proteins impacted by acute sleep deprivation in both plasma and cerebrospinal fluid (CSF). Methods: We collected plasma and CSF from human participants during one night of sleep deprivation and controlled normal sleep conditions. One thousand and three hundred proteins were measured at hour 0 and hour 24 using a high-scale aptamer-based proteomics platform (SOMAscan) and a systematic biological database tool (Metascape) was used to reveal altered biological pathways. Results: Acute sleep deprivation decreased the number of upregulated and downregulated biological pathways and proteins in plasma but increased upregulated and downregulated biological pathways and proteins in CSF. Predominantly affected proteins and pathways were associated with immune response, inflammation, phosphorylation, membrane signaling, cell-cell adhesion, and extracellular matrix organization. Conclusions: The identified modifications across biofluids add to evidence that acute sleep deprivation has important impacts on biological pathways and proteins that can negatively affect human health. As a hypothesis-driving study, these findings may help with the exploration of novel mechanisms that mediate sleep loss and associated conditions, drive the discovery of new sleep loss biomarkers, and ultimately aid in the identification of new targets for intervention to human diseases. © The Author(s) 2023.
Author Keywords
mRNA and protein expression; sleep; sleep deprivation; the brain
Funding details
National Institutes of HealthNIHK76 AG054863
Document Type: Article
Publication Stage: Final
Source: Scopus
A network approach to the investigation of childhood irritability: probing frustration using social stimuli
(2023) Journal of Child Psychology and Psychiatry and Allied Disciplines, .
Thompson, K.I.a , Schneider, C.J.a , Lopez-Roque, J.A.a , Perlman, S.B.a , Karim, H.T.b , Wakschlag, L.S.c
a Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, United States
b Department of Psychiatry, University of Pittsburg School of Medicine, Pittsburgh, PA, United States
c Department of Medical Social Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
Abstract
Background: Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli. Methods: Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives. Results: ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p <.05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p <.05, FDR-corrected). Conclusions: Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability. © 2023 Association for Child and Adolescent Mental Health.
Author Keywords
frustration; GLM; ICA; Irritability; socioemotional development
Funding details
National Institutes of HealthNIHR01 MH107540, R01 MH124266
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Relationships of Cognitive Measures with Cerebrospinal Fluid but Not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals
(2023) Annals of Neurology, .
Bonomi, S.a , Lu, R.b , Schindler, S.E.a c , Bui, Q.b , Lah, J.J.d e , Wolk, D.f , Gleason, C.E.g h i , Sperling, R.j , Roberson, E.D.k , Levey, A.I.d e , Shaw, L.f l , Van Hulle, C.g h , Benzinger, T.c m , Adams, M.f , Manzanares, C.d e , Qiu, D.e , Hassenstab, J.a c , Moulder, K.L.a c , Balls-Berry, J.E.a c , Johnson, K.n , Johnson, S.C.g h i , Murchison, C.F.k , Luo, J.b , Gremminger, E.a , Agboola, F.b c , Grant, E.A.b c , Hornbeck, R.m , Massoumzadeh, P.m , Keefe, S.m , Dierker, D.m , Gray, J.c , Henson, R.L.a c , Streitz, M.a c , Mechanic-Hamilton, D.f , Morris, J.C.a c , Xiong, C.b c
a Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
b Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
c Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
d Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
e Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, United States
f Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
g Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
h Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
i Geriatric Research, Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, United States
j Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
k Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
l Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
m Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
n Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
Abstract
Objective: Biomarkers of Alzheimer disease vary between groups of self-identified Black and White individuals in some studies. This study examined whether the relationships between biomarkers or between biomarkers and cognitive measures varied by racialized groups. Methods: Cerebrospinal fluid (CSF), amyloid positron emission tomography (PET), and magnetic resonance imaging measures were harmonized across four studies of memory and aging. Spearman correlations between biomarkers and between biomarkers and cognitive measures were calculated within each racialized group, then compared between groups by standard normal tests after Fisher’s Z-transformations. Results: The harmonized dataset included at least one biomarker measurement from 495 Black and 2,600 White participants. The mean age was similar between racialized groups. However, Black participants were less likely to have cognitive impairment (28% vs 36%) and had less abnormality of some CSF biomarkers including CSF Aβ42/40, total tau, p-tau181, and neurofilament light. CSF Aβ42/40 was negatively correlated with total tau and p-tau181 in both groups, but at a smaller magnitude in Black individuals. CSF Aβ42/40, total tau, and p-tau181 had weaker correlations with cognitive measures, especially episodic memory, in Black than White participants. Correlations of amyloid measures between CSF (Aβ42/40, Aβ42) and PET imaging were also weaker in Black than White participants. Importantly, no differences based on race were found in correlations between different imaging biomarkers, or in correlations between imaging biomarkers and cognitive measures. Interpretation: Relationships between CSF biomarkers but not imaging biomarkers varied by racialized groups. Imaging biomarkers performed more consistently across racialized groups in associations with cognitive measures. ANN NEUROL 2023. © 2023 American Neurological Association.
Funding details
National Institutes of HealthNIH067505
National Institute on AgingNIAP01 AG026276, P01 AG036694, P01 AG03991, P20 AG068024, P30 AG066444, P30 AG066511, P30 AG072979, R01 AG053550, R01 AG054059, R01 AG067505, R24 AG074915‐02
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
A Developmentally-Informative Genome-wide Association Study of Alcohol Use Frequency
(2023) Behavior Genetics, .
Thomas, N.S.a , Gillespie, N.A.b , Chan, G.c d , Edenberg, H.J.e f , Kamarajan, C.g , Kuo, S.I.-C.h , Miller, A.P.i , Nurnberger, J.I., Jr.j , Tischfield, J.k , Dick, D.M.h , Salvatore, J.E.h
a Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA 23284-2018, United States
b Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, United States
c Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, United States
d Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, United States
e Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
f Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
g Department of Psychiatry & Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
h Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, United States
i Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
j Department of Psychiatry, Indiana University, Bloomington, IN, United States
k Department of Genetics, Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, United States
Abstract
Contemporary genome-wide association study (GWAS) methods typically do not account for variability in genetic effects throughout development. We applied genomic structural equation modeling to combine developmentally-informative phenotype data and GWAS to create polygenic scores (PGS) for alcohol use frequency that are specific to developmental stage. Longitudinal cohort studies targeted for gene-identification analyses include the Collaborative Study on the Genetics of Alcoholism (adolescence n = 1,118, early adulthood n = 2,762, adulthood n = 5,255), the National Longitudinal Study of Adolescent to Adult Health (adolescence n = 3,089, early adulthood n = 3,993, adulthood n = 5,149), and the Avon Longitudinal Study of Parents and Children (ALSPAC; adolescence n = 5,382, early adulthood n = 3,613). PGS validation analyses were conducted in the COGA sample using an alternate version of the discovery analysis with COGA removed. Results suggest that genetic liability for alcohol use frequency in adolescence may be distinct from genetic liability for alcohol use frequency later in developmental periods. The age-specific PGS predicts an increase of 4 drinking days per year per PGS standard deviation when modeled separately from the common factor PGS in adulthood. The current work was underpowered at all steps of the analysis plan. Though small sample sizes and low statistical power limit the substantive conclusions that can be drawn regarding these research questions, this work provides a foundation for future genetic studies of developmental variability in the genetic underpinnings of alcohol use behaviors and genetically-informed, age-matched phenotype prediction. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Author Keywords
Add health; Alcohol use frequency; ALSPAC; COGA; Development; Genomic structural equation modeling; Polygenic scores
Funding details
National Institutes of HealthNIHAA018333, F31AA029620, G0800612/86812, U10AA008401
National Institute on Drug AbuseNIDA
National Institute on Alcohol Abuse and AlcoholismNIAAAT32DA015035
National Institute on AgingNIAU01 AG071448, U01AG071450
University of North Carolina WilmingtonUNCWP01 HD31921
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD
Wellcome TrustWT076467/Z/05/, 217065/Z/19/Z, 5R01AA018333-05, MRC 092731
Medical Research CouncilMRC
University of Bristol
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
The Cousa objective: a long-working distance air objective for multiphoton imaging in vivo
(2023) Nature Methods, .
Yu, C.-H.a , Yu, Y.a , Adsit, L.M.b , Chang, J.T.c , Barchini, J.c , Moberly, A.H.d , Benisty, H.d , Kim, J.e , Young, B.K.f , Heng, K.f g , Farinella, D.M.h , Leikvoll, A.h , Pavan, R.i , Vistein, R.j , Nanfito, B.R.k , Hildebrand, D.G.C.l , Otero-Coronel, S.l m n , Vaziri, A.m n , Goldberg, J.L.f , Ricci, A.J.o p , Fitzpatrick, D.c , Cardin, J.A.d , Higley, M.J.d , Smith, G.B.h , Kara, P.h , Nielsen, K.J.k , Smith, I.T.b q r , Smith, S.L.V.a q
a Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, United States
b Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, United States
c Max Planck Florida Institute for Neuroscience, Jupiter, FL, United States
d Department of Neuroscience, Yale University, New Haven, CT, United States
e Department of Otolaryngology, Washington University School of Medicine, St. Louis, MO, United States
f Spencer Center for Vision Research, Byers Eye Institute, School of Medicine, Stanford University, Palo Alto, CA, United States
g Neurosciences Interdepartmental Program, Stanford University, Stanford, CA, United States
h Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
i Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
j Department of Molecular and Comparative Pathobiology, and Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, United States
k Solomon H. Snyder Department of Neuroscience, and Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, United States
l Laboratory of Neural Systems, The Rockefeller University, New York, NY, United States
m Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY, United States
n Kavli Neural Systems Institute, The Rockefeller University, New York, NY, United States
o Department of Otolaryngology, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
p Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
q Department of Psychology and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, United States
r Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, United States
Abstract
Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue and has transformed neural imaging, but applying this technique in vivo can be limited by the mechanical and optical constraints of conventional objectives. Short working distance objectives can collide with compact surgical windows or other instrumentation and preclude imaging. Here we present an ultra-long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a more than 4 mm2 field of view with submicrometer lateral resolution and is compatible with commonly used multiphoton imaging systems. A novel mechanical design, wider than typical microscope objectives, enabled this combination of specifications. We share the full optical prescription, and report performance including in vivo two-photon and three-photon imaging in an array of species and preparations, including nonhuman primates. The Cousa objective can enable a range of experiments in neuroscience and beyond. © 2023, The Author(s).
Funding details
18-02-OpticNerve-0005
National Science FoundationNSF1707287, 1934288, 2011542, BRAIN EAGER 1450824
National Institutes of HealthNIH
National Eye InstituteNEI5T32OD011089, DP1EY033975, EY006821, EY011488, EY031133, P30EY026877, R01DC003896, R01EY022951, R01EY024294, R01EY030893, R01EY032416, R01MH099045, R01NS128079, R21DC020325, U01NS103488, U01NS115530, U01NS115585, U01NS126057
National Institute of Neurological Disorders and StrokeNINDSR01NS091335
Brain and Behavior Research FoundationBBRF
Brain Research FoundationBRFBRFSG-2019-05
Johns Hopkins UniversityJHU
Max-Planck-GesellschaftMPG
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer’s disease
(2023) Brain Communications, 5 (6), art. no. fcad280, .
Llibre-Guerra, J.J.a , Iaccarino, L.b , Coble, D.c , Edwards, L.b , Li, Y.c , Mcdade, E.a , Strom, A.b , Gordon, B.d , Mundada, N.b , Schindler, S.E.a , Tsoy, E.b , Ma, Y.c , Lu, R.c , Fagan, A.M.a , Benzinger, T.L.S.d , Soleimani-Meigooni, D.b , Aschenbrenner, A.J.a , Miller, Z.b , Wang, G.c , Kramer, J.H.b , Hassenstab, J.a , Rosen, H.J.b , Morris, J.C.a , Miller, B.L.b , Xiong, C.c , Perrin, R.J.a e , Allegri, R.f , Chrem, P.f , Surace, E.f , Berman, S.B.g , Chhatwal, J.h , Masters, C.L.i , Farlow, M.R.j , Jucker, M.k l , Levin, J.m n o , Fox, N.C.p , Day, G.q , Gorno-Tempini, M.L.b , Boxer, A.L.b , La Joie, R.b , Rabinovici, G.D.b r , Bateman, R.a
a Department of Neurology, Washington University in St Louis, St Louis, MO 63108, United States
b Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, United States
c Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, United States
d Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, United States
e Department of Pathology and Immunology, Washington University in St Louis, St. Louis, MO 63108, United States
f Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
g Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, United States
h Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
i Florey Institute, The University of Melbourne, Melbourne, 3052, Australia
j Neuroscience Center, Indiana University School of Medicine, Indianapolis, IN 46202, United States
k DZNE-German Center for Neurodegenerative Diseases, Tübingen, 72076, Germany
l Hertie-Institute for Clinical Brain Research, University of Tübingen, 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 Dementia Research Centre, Department of Neurodegenerative Disease, University College London, Institute of Neurology, London, WC1N 3BG, United Kingdom
q Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, United States
r Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, United States
Abstract
Approximately 5% of Alzheimer’s disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer’s disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer’s disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer’s disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer’s disease enrolled at the University of California San Francisco Alzheimer’s Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer’s disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ϵ4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer’s disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer’s disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design. © 2023 The Author(s). Published by Oxford University Press on behalf of the Guarantors of Brain.
Author Keywords
dominantly inherited; early-onset Alzheimer’s disease; sporadic
Funding details
K01AG073526
K99AG065501, P01-AG019724, P30 AG062422, R01-AG045611, R35-AG072362
National Institutes of HealthNIH
National Institute on AgingNIAU19AG032438
National Institute of Neurological Disorders and StrokeNINDSR01-NS050915
Michael J. Fox Foundation for Parkinson’s ResearchMJFFMJFF-020770
Alzheimer’s AssociationAAAARFD-21-851415, SG-20-690363, SG-20-690363-DIAN, ZEN-21-848216
Foundation for Barnes-Jewish HospitalFBJH
Fondation Brain Canada
Japan Agency for Medical Research and DevelopmentAMED
Avid RadiopharmaceuticalsP01AG003991, P01AG026276, P30 AG066444, U19 AG024904, U19 AG032438
Canadian Institutes of Health ResearchIRSC
Fonds de Recherche du Québec – SantéFRQS
Korea Health Industry Development InstituteKHIDI
Instituto de Salud Carlos IIIISCIII
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Fleni
Document Type: Article
Publication Stage: Final
Source: Scopus
Disease Course and Response to Immunotherapy in Children With Childhood Disintegrative Disorder: A Retrospective Case Series
(2023) Journal of Child Neurology, .
Sullivan, M.I.a , Gupta, M.J.b , Taylor, K.A.c , Van Mater, H.A.d , Pizoli, C.E.e
a Duke University School of Medicine, Durham, NC, United States
b Department of Neurology, Washington University in St Louis, St Louis, MO, United States
c Division of Child Neurology, Medical University of South Carolina, Charleston, SC, United States
d Division of Rheumatology, Duke University, Durham, NC, United States
e Division of Child Neurology, Duke University School of Medicine, Durham, NC, United States
Abstract
Childhood disintegrative disorder is a poorly understood neurobehavioral disorder of early childhood characterized by acute to subacute profound regression in previously developed language, social behavior, and adaptive functions. The etiology of childhood disintegrative disorder remains unknown and treatment is focused on symptomatic management. Interest in neuroinflammatory mechanisms has grown with the increased recognition of autoimmune brain diseases and similarities between the presenting symptoms of childhood disintegrative disorder and pediatric autoimmune encephalitis. Importantly, a diagnosis of pediatric autoimmune encephalitis requires evidence of inflammation on paraclinical testing, which is absent in childhood disintegrative disorder. Here we report 5 children with childhood disintegrative disorder who were initially diagnosed with possible autoimmune encephalitis and treated with immunotherapy. Two children had provocative improvements, whereas 3 did not change significantly on immunotherapy. Additionally, a sixth patient with childhood disintegrative disorder evaluated in our Autoimmune Brain Disease Clinic showed spontaneous improvement and is included to highlight the variable natural history of childhood disintegrative disorder that may mimic treatment responsiveness. © The Author(s) 2023.
Author Keywords
autism; autoimmune; encephalitis; neurodevelopment; neuroimmunology
Document Type: Article
Publication Stage: Article in Press
Source: Scopus