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Schultz SA, Liu L, Schultz AP, Fitzpatrick CD, Levin R, Bellier JP, Shirzadi Z, Joseph-Mathurin N, Chen CD, Benzinger TLS, Day GS, Farlow MR, Gordon BA, Hassenstab JJ, Jack CR, Jucker M, Karch CM, Lee JH, Levin J, Perrin RJ, Schofield PR, Xiong C, Johnson KA, McDade E, Bateman RJ, Sperling RA, Selkoe DJ, Chhatwal JP. γ-Secretase activity, clinical features, and biomarkers of autosomal dominant Alzheimer's disease: cross-sectional and longitudinal analysis of the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS). Lancet Neurol 2024; 23:913-924. [PMID: 39074479 DOI: 10.1016/s1474-4422(24)00236-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 04/26/2024] [Accepted: 05/28/2024] [Indexed: 07/31/2024]
Abstract
BACKGROUND Genetic variants that cause autosomal dominant Alzheimer's disease are highly penetrant but vary substantially regarding age at symptom onset (AAO), rates of cognitive decline, and biomarker changes. Most pathogenic variants that cause autosomal dominant Alzheimer's disease are in presenilin 1 (PSEN1), which encodes the catalytic core of γ-secretase, an enzyme complex that is crucial in production of amyloid β. We aimed to investigate whether the heterogeneity in AAO and biomarker trajectories in carriers of PSEN1 pathogenic variants could be predicted on the basis of the effects of individual PSEN1 variants on γ-secretase activity and amyloid β production. METHODS For this cross-sectional and longitudinal analysis, we used data from participants enrolled in the Dominantly Inherited Alzheimer Network observational study (DIAN-OBS) via the DIAN-OBS data freeze version 15 (data collected between Feb 29, 2008, and June 30, 2020). The data freeze included data from 20 study sites in research institutions, universities, hospitals, and clinics across Europe, North and South America, Asia, and Oceania. We included individuals with PSEN1 pathogenic variants for whom relevant genetic, clinical, imaging, and CSF data were available. PSEN1 pathogenic variants were characterised via genetically modified PSEN1 and PSEN2 double-knockout human embryonic kidney 293T cells and immunoassays for Aβ37, Aβ38, Aβ40, Aβ42, and Aβ43. A summary measure of γ-secretase activity (γ-secretase composite [GSC]) was calculated for each variant and compared with clinical history-derived AAO using correlation analyses. We used linear mixed-effect models to assess associations between GSC scores and multimodal-biomarker and clinical data from DIAN-OBS. We used separate models to assess associations with Clinical Dementia Rating Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE), and Wechsler Memory Scale-Revised (WMS-R) Logical Memory Delayed Recall, [11C]Pittsburgh compound B (PiB)-PET and brain glucose metabolism using [18F] fluorodeoxyglucose (FDG)-PET, CSF Aβ42-to-Aβ40 ratio (Aβ42/40), CSF log10 (phosphorylated tau 181), CSF log10 (phosphorylated tau 217), and MRI-based hippocampal volume. FINDINGS Data were included from 190 people carrying PSEN1 pathogenic variants, among whom median age was 39·0 years (IQR 32·0 to 48·0) and AAO was 44·5 years (40·6 to 51·4). 109 (57%) of 190 carriers were female and 81 (43%) were male. Lower GSC values (ie, lower γ-secretase activity than wild-type PSEN1) were associated with earlier AAO (r=0·58; p<0·0001). GSC was associated with MMSE (β=0·08, SE 0·03; p=0·0043), CDR-SB (-0·05, 0·02; p=0·0027), and WMS-R Logical Memory Delayed Recall scores (0·09, 0·02; p=0·0006). Lower GSC values were associated with faster increase in PiB-PET signal (p=0·0054), more rapid decreases in hippocampal volume (4·19, 0·77; p<0·0001), MMSE (0·02, 0·01; p=0·0020), and WMS-R Logical Memory Delayed Recall (0·004, 0·001; p=0·0003). INTERPRETATION Our findings suggest that clinical heterogeneity in people with autosomal dominant Alzheimer's disease can be at least partly explained by different effects of PSEN1 variants on γ-secretase activity and amyloid β production. They support targeting γ-secretase as a therapeutic approach and suggest that cell-based models could be used to improve prediction of symptom onset. FUNDING US National Institute on Aging, Alzheimer's Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korea Health Industry Development Institute, South Korean Ministry of Health and Welfare, South Korean Ministry of Science and ICT, and Spanish Institute of Health Carlos III.
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Affiliation(s)
- Stephanie A Schultz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA
| | - Lei Liu
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Ann Romney Center for Neurologic Diseases, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA
| | - Colleen D Fitzpatrick
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA
| | - Raina Levin
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA
| | - Jean-Pierre Bellier
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Ann Romney Center for Neurologic Diseases, Boston, MA, USA
| | - Zahra Shirzadi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Martin R Farlow
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | | | | | - Mathias Jucker
- German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Celeste M Karch
- Department of Psychiatry, Washington University, St Louis, MO, USA
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, South Korea
| | - Johannes Levin
- German Center for Neurodegenerative Diseases, Munich, Germany; Department of Neurology, Ludwig Maximilian University of Munich, Munich, Germany; Munich Cluster for Systems Neurology, Munich, Germany
| | - Richard J Perrin
- Department of Psychiatry, Washington University, St Louis, MO, USA; Department of Pathology and Immunology, Washington University, St Louis, MO, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Randwick, NSW, Australia; School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Chengjie Xiong
- Division of Biostatistics, Washington University, St Louis, MO, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA
| | - Eric McDade
- Department of Neurology, Washington University, St Louis, MO, USA
| | | | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Dennis J Selkoe
- Department of Neurology, Medical School, Harvard University, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Ann Romney Center for Neurologic Diseases, Boston, MA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Medical School, Harvard University, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Ann Romney Center for Neurologic Diseases, Boston, MA, USA.
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Daniels AJ, McDade E, Llibre-Guerra JJ, Xiong C, Perrin RJ, Ibanez L, Supnet-Bell C, Cruchaga C, Goate A, Renton AE, Benzinger TL, Gordon BA, Hassenstab J, Karch C, Popp B, Levey A, Morris J, Buckles V, Allegri RF, Chrem P, Berman SB, Chhatwal JP, Farlow MR, Fox NC, Day GS, Ikeuchi T, Jucker M, Lee JH, Levin J, Lopera F, Takada L, Sosa AL, Martins R, Mori H, Noble JM, Salloway S, Huey E, Rosa-Neto P, Sánchez-Valle R, Schofield PR, Roh JH, Bateman RJ. 15 Years of Longitudinal Genetic, Clinical, Cognitive, Imaging, and Biochemical Measures in DIAN. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.08.24311689. [PMID: 39148846 PMCID: PMC11326320 DOI: 10.1101/2024.08.08.24311689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
This manuscript describes and summarizes the Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs), highlighting the wealth of longitudinal data, samples, and results from this human cohort study of brain aging and a rare monogenic form of Alzheimer's disease (AD). DIAN Obs is an international collaborative longitudinal study initiated in 2008 with support from the National Institute on Aging (NIA), designed to obtain comprehensive and uniform data on brain biology and function in individuals at risk for autosomal dominant AD (ADAD). ADAD gene mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes are deterministic causes of ADAD, with virtually full penetrance, and a predictable age at symptomatic onset. Data and specimens collected are derived from full clinical assessments, including neurologic and physical examinations, extensive cognitive batteries, structural and functional neuro-imaging, amyloid and tau pathological measures using positron emission tomography (PET), flurordeoxyglucose (FDG) PET, cerebrospinal fluid and blood collection (plasma, serum, and whole blood), extensive genetic and multi-omic analyses, and brain donation upon death. This comprehensive evaluation of the human nervous system is performed longitudinally in both mutation carriers and family non-carriers, providing one of the deepest and broadest evaluations of the human brain across decades and through AD progression. These extensive data sets and samples are available for researchers to address scientific questions on the human brain, aging, and AD.
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Affiliation(s)
- Alisha J. Daniels
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Eric McDade
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Chengjie Xiong
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Richard J. Perrin
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Laura Ibanez
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Carlos Cruchaga
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Alison Goate
- Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alan E. Renton
- Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Brian A. Gordon
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Jason Hassenstab
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Celeste Karch
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Brent Popp
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Allan Levey
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, USA
| | - John Morris
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Virginia Buckles
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Patricio Chrem
- Institute of Neurological Research FLENI, Buenos Aires, Argentina
| | | | - Jasmeer P. Chhatwal
- Massachusetts General and Brigham & Women’s Hospitals, Harvard Medical School, Boston MA, USA
| | | | - Nick C. Fox
- UK Dementia Research Institute at University College London, London, United Kingdom
- University College London, London, United Kingdom
| | | | - Takeshi Ikeuchi
- Brain Research Institute, Niigata University, Niigata, Japan
| | - Mathias Jucker
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | | | - Johannes Levin
- DZNE, German Center for Neurodegenerative Diseases, Munich, Germany
- Ludwig-Maximilians-Universität München, Munich, Germany
| | | | | | - Ana Luisa Sosa
- Instituto Nacional de Neurologia y Neurocirugla Innn, Mexico City, Mexico
| | - Ralph Martins
- Edith Cowan University, Western Australia, Australia
| | | | - James M. Noble
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, and GH Sergievsky Center, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Edward Huey
- Brown University, Butler Hospital, Providence, RI, USA
| | - Pedro Rosa-Neto
- Centre de Recherche de L’hopital Douglas and McGill University, Montreal, Quebec
| | - Raquel Sánchez-Valle
- Hospital Clínic de Barcelona. IDIBAPS. University of Barcelona, Barcelona, Spain
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Jee Hoon Roh
- Korea University, Korea University Anam Hospital, Seoul, South Korea
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Wisch JK, McKay NS, Boerwinkle AH, Kennedy J, Flores S, Handen BL, Christian BT, Head E, Mapstone M, Rafii MS, O'Bryant SE, Price JC, Laymon CM, Krinsky-McHale SJ, Lai F, Rosas HD, Hartley SL, Zaman S, Lott IT, Tudorascu D, Zammit M, Brickman AM, Lee JH, Bird TD, Cohen A, Chrem P, Daniels A, Chhatwal JP, Cruchaga C, Ibanez L, Jucker M, Karch CM, Day GS, Lee JH, Levin J, Llibre-Guerra J, Li Y, Lopera F, Roh JH, Ringman JM, Supnet-Bell C, van Dyck CH, Xiong C, Wang G, Morris JC, McDade E, Bateman RJ, Benzinger TLS, Gordon BA, Ances BM. Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study. Lancet Neurol 2024; 23:500-510. [PMID: 38631766 PMCID: PMC11209765 DOI: 10.1016/s1474-4422(24)00084-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/01/2024] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease. METHODS In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (18F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid. FINDINGS We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associated with increases in amyloid for people with Down syndrome compared with autosomal-dominant Alzheimer's disease. INTERPRETATION Although the general progression of amyloid followed by tau is similar for people Down syndrome and people with autosomal-dominant Alzheimer's disease, we found subtle differences in the spatial distribution, timing, and magnitude of the tau burden between these two cohorts. These differences might have important implications; differences in the temporal pattern of tau accumulation might influence the timing of drug administration in clinical trials, whereas differences in the spatial pattern and magnitude of tau burden might affect disease progression. FUNDING None.
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Affiliation(s)
- Julie K Wisch
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA.
| | - Nicole S McKay
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Anna H Boerwinkle
- McGovern Medical School, University of Texas in Houston, Houston, TX, USA
| | - James Kennedy
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Benjamin L Handen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bradley T Christian
- Department of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Elizabeth Head
- Department of Pathology, Gillespie Neuroscience Research Facility, University of California, Irvine, CA, USA
| | - Mark Mapstone
- Department of Neurology, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Michael S Rafii
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Sid E O'Bryant
- Institute for Translational Research Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Julie C Price
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Charles M Laymon
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sharon J Krinsky-McHale
- Department of Psychology, New York State Institute for Basic Research in Developmental Disabilities, New York, NY, USA
| | - Florence Lai
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - H Diana Rosas
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sigan L Hartley
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, University of Cambridge, Cambridge, UK
| | - Ira T Lott
- Department of Pediatrics, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Zammit
- Department of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Adam M Brickman
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Joseph H Lee
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA; Department of Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Thomas D Bird
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Annie Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Patricio Chrem
- Centro de Memoria y Envejecimiento, Buenos Aires, Argentina
| | - Alisha Daniels
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA; Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Mathias Jucker
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Celeste M Karch
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asian Medical Center, Seoul, South Korea
| | - Johannes Levin
- Department of Neurology, LMU University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases, site Munich, Munich, Germany; Munich Cluster for Systems Neurology, Munich, Germany
| | - Jorge Llibre-Guerra
- Hope Center for Neurological Disorders, Washington University in St Louis, St Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jee Hoon Roh
- Departments of Physiology and Neurology, Korea University College of Medicine, Seoul, South Korea
| | - John M Ringman
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | | | | | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Guoqiao Wang
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA; Department of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | | | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
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Liu G, Shen C, Qiu A. Amyloid-β Accumulation in Relation to Functional Connectivity in Aging: a Longitudinal Study. Neuroimage 2023; 275:120146. [PMID: 37127190 DOI: 10.1016/j.neuroimage.2023.120146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/11/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n=300) and explored possible relationships between amyloid beta (Aβ) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aβ accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions. Each participant had at least 2 to 3 follow-up visits. A linear mixed-effect model was used to examine longitudinal changes of Aβ accumulation and FC throughout the whole brain. We found that the limbic and frontoparietal networks had a greater annual Aβ accumulation and a slower decline in FC in aging. Additionally, the amount of the Aβ deposition in the amygdala network at baseline slowed down the decline in its FC in aging. Furthermore, the functional connectivity of the limbic, default mode network (DMN), and frontoparietal networks accelerated the Aβ propagation across their functionally highly connected regions. The functional connectivity of the somatomotor and visual networks accelerated the Aβ propagation across the brain regions in the limbic, frontoparietal, and DMN networks. These findings suggested that the slower decline in the functional connectivity of the functional hubs may compensate for their greater Aβ accumulation in aging. The Aβ propagation from one brain region to the other may depend on their functional connectivity strength.
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Affiliation(s)
- Guodong Liu
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Chenye Shen
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; NUS (Suzhou) Research Institute, National University of Singapore, China; The N.1 Institute for Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore; Department of Biomedical Engineering, the Johns Hopkins University, USA.
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Su Y, Flores S, Hornbeck RC, Speidel B, Vlassenko AG, Gordon BA, Koeppe RA, Klunk WE, Xiong C, Morris JC, Benzinger TLS. Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies. NEUROIMAGE-CLINICAL 2018; 19:406-416. [PMID: 30035025 PMCID: PMC6051499 DOI: 10.1016/j.nicl.2018.04.022] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 04/17/2018] [Accepted: 04/22/2018] [Indexed: 01/18/2023]
Abstract
Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p < 0.05) differences in the variability and group mean values. Because of these differences, even after expression in CL, the 95% specificity amyloid positivity thresholds derived from different analytic approaches varied from 5.7 CL to 11.9 CL, and the reliable worsening threshold varied from −2.0 CL to 11.0 CL. Although this difference is in part due to the dependency of the threshold determination methodology on the statistical characteristics of the measurements. When amyloid measurements obtained from different centers are combined for analysis, one should not expect Centiloid conversion to eliminate all the differences in amyloid burden measurements due to variabilities in underlying acquisition protocols and analysis techniques. The Centiloid approach brings amyloid burden measurements into a common scale. The Centiloid value inherits the characteristics of the underlying method. The Centiloid value derived from different analysis techniques remains different. The amyloid positivity thresholds in Centiloid are sensitive to implementation.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA.
| | - Shaney Flores
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Russ C Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Benjamin Speidel
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Andrei G Vlassenko
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Robert A Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - William E Klunk
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO 63110, USA
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Abstract
Alzheimer's disease (AD) is an irreversible, incurable, progressive neurodegenerative illness, where dementia symptoms gradually worsen over a number of years. The research of validated biomarkers for AD is essential to improve diagnosis and accelerate the development of new therapies. Biochemical markers including neuroimaging could facilitate diagnosis, predict AD progression from a pre-AD state of mild cognitive impairment, and be used to detect the efficacies of disease-modifying therapies. Established biomarkers of AD from cerebrospinal fluid and neuroimaging are highly accurate, but barriers to clinical implementation exist. The focus on blood-based AD biomarkers has grown exponentially during the past few decades. An ideal diagnostic test for AD should be noninvasive and easily applicable. Clinical cost-effectiveness also needs to be established.
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Affiliation(s)
- Martina Zvěřová
- Department of Psychiatry, General University Hospital in Prague, Prague, Czech Republic, .,First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic,
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Vāvere AL, Scott PJH. Clinical Applications of Small-molecule PET Radiotracers: Current Progress and Future Outlook. Semin Nucl Med 2017; 47:429-453. [PMID: 28826519 DOI: 10.1053/j.semnuclmed.2017.05.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiotracers, or radiopharmaceuticals, are bioactive molecules tagged with a radionuclide used for diagnostic imaging or radiotherapy and, when a positron-emitting radionuclide is chosen, the radiotracers are used for PET imaging. The development of novel PET radiotracers in many ways parallels the development of new pharmaceuticals, and small molecules dominate research and development pipelines in both disciplines. The 4 decades since the introduction of [18F]FDG have seen the development of many small molecule PET radiotracers. Ten have been approved by the US Food and Drug Administration as of 2016, whereas hundreds more are being evaluated clinically. These radiotracers are being used in personalized medicine and to support drug discovery programs where they are greatly improving our understanding of and ability to treat diseases across many areas of medicine including neuroscience, cardiovascular medicine, and oncology.
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Affiliation(s)
- Amy L Vāvere
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN
| | - Peter J H Scott
- Department of Radiology, University of Michigan, Ann Arbor, MI.
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