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Morcillo-Nieto AO, Zsadanyi SE, Arriola-Infante JE, Carmona-Iragui M, Montal V, Pegueroles J, Aranha MR, Vaqué-Alcázar L, Padilla C, Benejam B, Videla L, Barroeta I, Fernandez S, Altuna M, Giménez S, González-Ortiz S, Bargalló N, Ribas L, Arranz J, Torres S, Iulita MF, Belbin O, Camacho V, Alcolea D, Lleó A, Fortea J, Bejanin A. Characterization of white matter hyperintensities in Down syndrome. Alzheimers Dement 2024. [PMID: 39087352 DOI: 10.1002/alz.14146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/20/2024] [Accepted: 07/02/2024] [Indexed: 08/02/2024]
Abstract
INTRODUCTION In Down syndrome (DS), white matter hyperintensities (WMHs) are highly prevalent, yet their topography and association with sociodemographic data and Alzheimer's disease (AD) biomarkers remain largely unexplored. METHODS In 261 DS adults and 131 euploid controls, fluid-attenuated inversion recovery magnetic resonance imaging scans were segmented and WMHs were extracted in concentric white matter layers and lobar regions. We tested associations with AD clinical stages, sociodemographic data, cerebrospinal fluid (CSF) AD biomarkers, and gray matter (GM) volume. RESULTS In DS, total WMHs arose at age 43 and showed stronger associations with age than in controls. WMH volume increased along the AD continuum, particularly in periventricular regions, and frontal, parietal, and occipital lobes. Associations were found with CSF biomarkers and temporo-parietal GM volumes. DISCUSSION WMHs increase 10 years before AD symptom onset in DS and are closely linked with AD biomarkers and neurodegeneration. This suggests a direct connection to AD pathophysiology, independent of vascular risks. HIGHLIGHTS White matter hyperintensities (WMHs) increased 10 years before Alzheimer's disease symptom onset in Down syndrome (DS). WMHs were strongly associated in DS with the neurofilament light chain biomarker. WMHs were more associated in DS with gray matter volume in parieto-temporal areas.
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Affiliation(s)
- Alejandra O Morcillo-Nieto
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Sara E Zsadanyi
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jose E Arriola-Infante
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria Carmona-Iragui
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Victor Montal
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Jordi Pegueroles
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Mateus Rozalem Aranha
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Lídia Vaqué-Alcázar
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Concepción Padilla
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Estudis de Ciències de la Salut, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Bessy Benejam
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Laura Videla
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Isabel Barroeta
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Susana Fernandez
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Miren Altuna
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Fundación CITA-Alzheimer Fundazia, Donostia, Spain
| | - Sandra Giménez
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Multidisciplinary Sleep Unit, Respiratory Department, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
| | - Sofía González-Ortiz
- Neuroradiology Section, Radiology Department, Diagnostic Image Center, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain
| | - Núria Bargalló
- Neuroradiology Section, Radiology Department, Diagnostic Image Center, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Laia Ribas
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Javier Arranz
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Soraya Torres
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Maria Florencia Iulita
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Olivia Belbin
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Valle Camacho
- Nuclear Medicine Department, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Alexandre Bejanin
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
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Li K, Wang S, Luo X, Zeng Q, Liu X, Hong L, Li J, Hong H, Xu X, Zhang Y, Jiaerken Y, Zhang R, Xie L, Xu S, Zhang X, Chen Y, Liu Z, Zhang M, Huang P. Associations of Alzheimer's Disease Pathology and Small Vessel Disease With Cerebral White Matter Degeneration: A Tract-Based MR Diffusion Imaging Study. J Magn Reson Imaging 2024; 60:268-278. [PMID: 37737474 DOI: 10.1002/jmri.29022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND White matter (WM) degeneration is a key feature of Alzheimer's disease (AD). However, the underlying mechanism remains unclear. PURPOSE To investigate how amyloid-β (Aβ), tau, and small vascular disease (SVD) jointly affect WM degeneration in subjects along AD continuum. STUDY TYPE Retrospective. SUBJECTS 152 non-demented participants (age: 55.8-91.6, male/female: 66/86) from the ADNI database were included, classified into three groups using the A (Aβ)/T (tau)/N pathological scheme (Group 1: A-T-; Group 2: A+T-; Group 3: A+T+) based on positron emission tomography data. FIELD STRENGTH/SEQUENCE 3T; T1-weighted images, T2-weighted fluid-attenuated inversion recovery images, T2*-weighted images, diffusion-weighted spin-echo echo-planar imaging sequence (54 diffusion directions). ASSESSMENT Free-water diffusion model (generated parameters: free water, FW; tissue fractional anisotropy, FAt; tissue mean diffusivity, MDt); SVD total score; Neuropsychological tests. STATISTICAL TESTS Linear regression analysis was performed to investigate the independent contribution of AD (Aβ and tau) and SVD pathologies to diffusion parameters in each fiber tract, first in the entire population and then in each subgroup. We also investigated associations between diffusion parameters and cognitive functions. The level of statistical significance was set at p < 0.05 (false discovery rate corrected). RESULTS In the entire population, we found that: 1) Increased FW was significantly associated with SVD and tau, while FAt and MDt were significantly associated with Aβ and tau; 2) The spatial pattern of fiber tracts related to a certain pathological marker is consistent with the known distribution of that pathology; 3) Subgroup analysis showed that Group 2 and 3 had more alterations of FAt and MDt associated with Aβ and tau; 4) Diffusion imaging indices showed significant associations with cognitive score in all domains except memory. DATA CONCLUSION WM microstructural injury was associated with both AD and SVD pathologies, showing compartment-specific, tract-specific, and stage-specific WM patterns. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Luwei Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiting Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Linyun Xie
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shan Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Pappas C, Bauer CE, Zachariou V, Maillard P, Caprihan A, Shao X, Wang DJ, Gold BT. MRI free water mediates the association between water exchange rate across the blood brain barrier and executive function among older adults. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-15. [PMID: 38947942 PMCID: PMC11211995 DOI: 10.1162/imag_a_00183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/27/2024] [Accepted: 05/03/2024] [Indexed: 07/02/2024]
Abstract
Vascular risk factors contribute to cognitive aging, with one such risk factor being dysfunction of the blood brain barrier (BBB). Studies using non-invasive magnetic resonance imaging (MRI) techniques, such as diffusion prepared arterial spin labeling (DP-ASL), can estimate BBB function by measuring water exchange rate (kw). DP-ASL kw has been associated with cognition, but the directionality and strength of the relationship is still under investigation. An additional variable that measures water in extracellular space and impacts cognition, MRI free water (FW), may help explain prior findings. A total of 94 older adults without dementia (Mean age = 74.17 years, 59.6% female) underwent MRI (DP-ASL, diffusion weighted imaging (DWI)) and cognitive assessment. Mean kw was computed across the whole brain (WB), and mean white matter FW was computed across all white matter. The relationship between kw and three cognitive domains (executive function, processing speed, memory) was tested using multiple linear regression. FW was tested as a mediator of the kw-cognitive relationship using the PROCESS macro. A positive association was found between WB kw and executive function [F(4,85) = 7.81, p < .001, R2= 0.269; β = .245, p = .014]. Further, this effect was qualified by subsequent results showing that FW was a mediator of the WB kw-executive function relationship (indirect effect results: standardized effect = .060, bootstrap confidence interval = .0006 to .1411). Results suggest that lower water exchange rate (kw) may contribute to greater total white matter (WM) FW which, in turn, may disrupt executive function. Taken together, proper fluid clearance at the BBB contributes to higher-order cognitive abilities.
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Affiliation(s)
- Colleen Pappas
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Christopher E. Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, United States
- Center for Neurosciences, University of California at Davis, Davis, CA, United States
| | | | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Brian T. Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
- Department of Radiology, College of Medicine, University of Kentucky, Lexington, KY, United States
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY, United States
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, United States
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Tranfa M, Lorenzini L, Collij LE, Vállez García D, Ingala S, Pontillo G, Pieperhoff L, Maranzano A, Wolz R, Haller S, Blennow K, Frisoni G, Sudre CH, Chételat G, Ewers M, Payoux P, Waldman A, Martinez‐Lage P, Schwarz AJ, Ritchie CW, Wardlaw JM, Gispert JD, Brunetti A, Mutsaerts HJMM, Wink AM, Barkhof F. Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure. Ann Clin Transl Neurol 2024; 11:1541-1556. [PMID: 38757392 PMCID: PMC11187968 DOI: 10.1002/acn3.52071] [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: 03/18/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults. METHODS Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β1-42 and p-Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract. RESULTS AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect. INTERPRETATION Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches.
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Affiliation(s)
- Mario Tranfa
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Clinical Memory Research Unit, Department of Clinical SciencesLund UniversityMalmöSweden
| | - David Vállez García
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Department of RadiologyCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Cerebriu A/SCopenhagenDenmark
| | - Giuseppe Pontillo
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
| | - Leonard Pieperhoff
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Alessio Maranzano
- Department of Neurology and Laboratory of NeuroscienceIRCCS Istituto Auxologico ItalianoMilanItaly
| | | | - Sven Haller
- CIMC ‐ Centre d'Imagerie Médicale de CornavinGenevaSwitzerland
- Department of Surgical Sciences, RadiologyUppsala UniversityUppsalaSweden
- Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Giovanni Frisoni
- Laboratory Alzheimer's Neuroimaging & EpidemiologyIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- University Hospitals and University of GenevaGenevaSwitzerland
| | - Carole H. Sudre
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC)University College London (UCL)LondonUK
- MRC Unit for Lifelong Health & Ageing at UCLUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gael Chételat
- Normandie Univ, Unicaen, Inserm, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders”, institut Blood‐and‐Brain @ Caen‐Normandie, CyceronUniversité de NormandieCaenFrance
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
| | - Pierre Payoux
- Department of Nuclear MedicineToulouse University HospitalToulouseFrance
- ToNIC, Toulouse NeuroImaging CenterUniversity of Toulouse, Inserm, UPSToulouseFrance
| | - Adam Waldman
- Centre for Clinical Brain SciencesThe University of EdinburghEdinburghUK
- Department of MedicineImperial College LondonLondonUK
| | - Pablo Martinez‐Lage
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA‐Alzheimer FoundationSan SebastiánSpain
| | - Adam J. Schwarz
- Takeda Pharmaceuticals, Ltd.CambridgeMassachusettsUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Craig W. Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General HospitalUniversity of EdinburghEdinburghUK
- Brain Health ScotlandEdinburghUK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesThe University of EdinburghEdinburghUK
- UK Dementia Research Institute Centre at the University of EdinburghEdinburghUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER‐BBN)MadridSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Arturo Brunetti
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Henk J. M. M. Mutsaerts
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI)Ghent UniversityGhentBelgium
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Amsterdam Neuroscience, Brain ImagingAmsterdamThe Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentreVrije UniversiteitAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
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da Silva SP, de Castro CCM, Rabelo LN, Engelberth RC, Fernández-Calvo B, Fiuza FP. Neuropathological and sociodemographic factors associated with the cortical amyloid load in aging and Alzheimer's disease. GeroScience 2024; 46:621-643. [PMID: 37870702 PMCID: PMC10828279 DOI: 10.1007/s11357-023-00982-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia and is characterized by a progressive decline in cognitive abilities. A pathological hallmark of AD is a region-specific accumulation of the amyloid-beta protein (Aβ). Here, we explored the association between regional Aβ deposition, sociodemographic, and local biochemical factors. We quantified the Aβ burden in postmortem cortical samples from parietal (PCx) and temporal (TCx) regions of 27 cognitively unimpaired (CU) and 15 AD donors, aged 78-100 + years. Histological images of Aβ immunohistochemistry and local concentrations of pathological and inflammatory proteins were obtained at the "Aging, Dementia and TBI Study" open database. We used the area fraction fractionator stereological methodology to quantify the Aβ burden in the gray and white matter within each cortical region. We found higher Aβ burdens in the TCx of AD octogenarians compared to CU ones. We also found higher Aβ loads in the PCx of AD nonagenarians than in AD octogenarians. Moreover, AD women exhibited increased Aβ deposition compared to CU women. Interestingly, we observed a negative correlation between education years and Aβ burden in the white matter of both cortices in CU samples. In AD brains, the Aβ40, Aβ42, and pTau181 isoforms of Aβ and Tau proteins were positively correlated with the Aβ burden. Additionally, in the TCx of AD donors, the proinflammatory cytokine TNFα showed a positive correlation with the Aβ load. These novel findings contribute to understanding the interplay between sociodemographic characteristics, local inflammatory signaling, and the development of AD-related pathology in the cerebral cortex.
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Affiliation(s)
- Sayonara P da Silva
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
| | - Carla C M de Castro
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
| | - Lívia N Rabelo
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Biosciences Center, Federal University of Rio Grande Do Norte, Natal, Brazil
| | - Rovena C Engelberth
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Biosciences Center, Federal University of Rio Grande Do Norte, Natal, Brazil
| | - Bernardino Fernández-Calvo
- Department of Psychology, University of Córdoba, Córdoba, Spain
- Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Reina Sofia University Hospital, Córdoba, Spain
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
| | - Felipe P Fiuza
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Macaíba, RN, 59280-000, Brazil.
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Jiang J, Pan H, Shen F, Tan Y, Chen S. Ketogenic diet alleviates cognitive dysfunction and neuroinflammation in APP/PS1 mice via the Nrf2/HO-1 and NF-κB signaling pathways. Neural Regen Res 2023; 18:2767-2772. [PMID: 37449643 DOI: 10.4103/1673-5374.373715] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023] Open
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by cognitive decline and chronic inflammation within the brain. The ketogenic diet, a widely recognized therapeutic intervention for refractory epilepsy, has recently been proposed as a potential treatment for a variety of neurological diseases, including Alzheimer's disease. However, the efficacy of ketogenic diet in treating Alzheimer's disease and the underlying mechanism remains unclear. The current investigation aimed to explore the effect of ketogenic diet on cognitive function and the underlying biological mechanisms in a mouse model of Alzheimer's disease. Male amyloid precursor protein/presenilin 1 (APP/PS1) mice were randomly assigned to either a ketogenic diet or control diet group, and received their respective diets for a duration of 3 months. The findings show that ketogenic diet administration enhanced cognitive function, attenuated amyloid plaque formation and proinflammatory cytokine levels in APP/PS1 mice, and augmented the nuclear factor-erythroid 2-p45 derived factor 2/heme oxygenase-1 signaling pathway while suppressing the nuclear factor-kappa B pathway. Collectively, these data suggest that ketogenic diet may have a therapeutic potential in treating Alzheimer's disease by ameliorating the neurotoxicity associated with Aβ-induced inflammation. This study highlights the urgent need for further research into the use of ketogenic diet as a potential therapy for Alzheimer's disease.
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Affiliation(s)
- Jingwen Jiang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Pan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fanxia Shen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuyan Tan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengdi Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; Lab of Translational Research of Neurodegenerative Diseases, Institute of Immunochemistry, ShanghaiTech University, Shanghai, China
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7
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Liu J, Zuo X, Huang M, Fang J, Li W, Shi Q, Wang Q, Liang Y. Multifunctional Gomisin B enhances cognitive function in APP/PS1 transgenic mice by regulating Aβ clearance and neuronal apoptosis. Biomed Pharmacother 2023; 166:115423. [PMID: 37673021 DOI: 10.1016/j.biopha.2023.115423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/08/2023] Open
Abstract
This study aimed to investigate the potential effects of Gomisin B, a natural compound known for its inhibition of CYP3A4, on cognitive dysfunction in APP/PS1 transgenic mice with Alzheimer's disease (AD). Additionally, the study explored the combined effects of Gomisin B and Osthole (OST). The research involved male wild-type (WT) mice and 7-month-old APP/PS1 transgenic AD mice. The assessment of behavioral changes included the use of the open field test (OFT) and the Morris water maze (MWM). OST levels in brain tissue were quantified using LC-MS/MS, while levels of oxidative stress were measured through an assay kit. Neuronal apoptosis was studied using Nissl staining, RT-qPCR, and immunofluorescence. Amyloid plaque clearance was assessed using thioflavine-S (Th-S) staining, RT-qPCR, and ELISA. The results of the study revealed that Gomisin B led to a significant improvement in cognitive dysfunction in APP/PS1 mice. Moreover, the simultaneous administration of OST and Gomisin B demonstrated enhanced therapeutic effects. These effects were attributed to the inhibition of β-site APP-Cleaving Enzyme 1 (BACE1) and oxidative stress by Gomisin B, along with its anti-apoptotic properties. The combined use of OST and Gomisin B exhibited a synergistic impact, resulting in more pronounced anti-oxidant and anti-apoptotic effects. In summary, this study pioneers the exploration of Gomisin B's multifunctional anti-AD properties in APP/PS1 mice. The findings provide a solid groundwork for the development of anti-Alzheimer's drugs based on natural active ingredients.
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Affiliation(s)
- Jinman Liu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Affiliated Jiangmen TCM Hospital of Ji'nan University, Jiangmen 529099, China
| | - Xue Zuo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Mingjun Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Weirong Li
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Qing Shi
- Affiliated Jiangmen TCM Hospital of Ji'nan University, Jiangmen 529099, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
| | - Yong Liang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China; Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
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8
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Badji A, Youwakim J, Cooper A, Westman E, Marseglia A. Vascular cognitive impairment - Past, present, and future challenges. Ageing Res Rev 2023; 90:102042. [PMID: 37634888 DOI: 10.1016/j.arr.2023.102042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 08/29/2023]
Abstract
Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.
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Affiliation(s)
- Atef Badji
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Jessica Youwakim
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada; Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Montreal, QC, Canada; Groupe de Recherche sur la Signalisation Neuronal et la Circuiterie (SNC), Montreal, QC, Canada
| | - Alexandra Cooper
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Anna Marseglia
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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9
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Wang L, Kolobaric A, Aizenstein H, Lopresti B, Tudorascu D, Snitz B, Klunk W, Wu M. Identifying sex-specific risk architectures for predicting amyloid deposition using neural networks. Neuroimage 2023; 275:120147. [PMID: 37156449 PMCID: PMC10905666 DOI: 10.1016/j.neuroimage.2023.120147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/08/2023] [Accepted: 04/28/2023] [Indexed: 05/10/2023] Open
Abstract
In older adults without dementia, White Matter Hyperintensities (WMH) in MRI have been shown to be highly associated with cerebral amyloid deposition, measured by the Pittsburgh compound B (PiB) PET. However, the relation to age, sex, and education in explaining this association is not well understood. We use the voxel counts of regional WMH, age, one-hot encoded sex, and education to predict the regional PiB using a multilayer perceptron with only rectilinear activations using mean squared error. We then develop a novel, robust metric to understand the relevance of each input variable for prediction. Our observations indicate that sex is the most relevant predictor of PiB and that WMH is not relevant for prediction. These results indicate that there is a sex-specific risk architecture for Aβ deposition.
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Affiliation(s)
- Linghai Wang
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
| | | | - Howard Aizenstein
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Brian Lopresti
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Dana Tudorascu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Beth Snitz
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - William Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Minjie Wu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
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10
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Wang W, Shi L, Ma H, Zhu S, Ge Y, Xu K. Comparison of the clinical value of MRI and plasma markers for cognitive impairment in patients aged ≥75 years: a retrospective study. PeerJ 2023; 11:e15581. [PMID: 37366421 PMCID: PMC10290829 DOI: 10.7717/peerj.15581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background Dementia has become the main cause of disability in older adults aged ≥75 years. Cerebral small vessel disease (CSVD) is involved in cognitive impairment (CI) and dementia and is a cause of vascular CI (VCI), which is manageable and its onset and progression can be delayed. Simple and effective markers will be beneficial to the early detection and intervention of CI. The aim of this study is to investigate the clinical application value of plasma amyloid β1-42 (Aβ42), phosphorylated tau 181 (p-tau181) and conventional structural magnetic resonance imaging (MRI) parameters for cognitive impairment (CI) in patients aged ≥75 years. Methods We retrospectively selected patients who visited the Affiliated Hospital of Xuzhou Medical University and were clinically diagnosed with or without cognitive dysfunction between May 2018 and November 2021. Plasma indicators (Aβ42 and p-tau181) and conventional structural MRI parameters were collected and analyzed. Multivariate logistic regression and receiver operator characteristic (ROC) curve were used to evaluate the diagnostic value. Results One hundred and eighty-four subjects were included, including 54 cases in CI group and 130 cases in noncognitive impairment (NCI) groups, respectively. Univariate logistic regression analysis revealed that the percentages of Aβ42+, P-tau 181+, and Aβ42+/P-tau181+ showed no significant difference between the groups of CI and NCI (all P > 0.05). Multivariate logistic regression analysis showed that moderate/severe periventricular WMH (PVWMH) (OR 2.857, (1.365-5.983), P = 0.005), lateral ventricle body index (LVBI) (OR 0.413, (0.243-0.700), P = 0.001), and cortical atrophy (OR 1.304, (1.079-1.575), P = 0.006) were factors associated with CI. The combined model including PVWMH, LVBI, and cortical atrophy to detect CI and NCI showed an area under the ROC curve (AUROC) is 0.782, with the sensitivity and specificity 68.5% and 78.5%, respectively. Conclusion For individuals ≥75 years, plasma Aβ42 and P-tau181 might not be associated with cognitive impairment, and MRI parameters, including PVWMH, LVBI and cortical atrophy, are related to CI. The cognitive statuses of people over 75 years old were used as the endpoint event in this study. Therefore, it can be considered that these MRI markers might have more important clinical significance for early assessment and dynamic observation, but more studies are still needed to verify this hypothesis.
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Affiliation(s)
- Wei Wang
- Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Lin Shi
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shangdong, China
| | - Hong Ma
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shiguang Zhu
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yaqiong Ge
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Kai Xu
- Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Institute of Medical Imaging and Digital Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
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11
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 90.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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12
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Bao H, Shen Y. Unmasking BACE1 in aging and age-related diseases. Trends Mol Med 2023; 29:99-111. [PMID: 36509631 DOI: 10.1016/j.molmed.2022.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/24/2022] [Indexed: 12/13/2022]
Abstract
The beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) has long been considered a conventional target for Alzheimer's disease (AD). Unfortunately, AD clinical trials of most BACE1 inhibitors were discontinued due to ineffective cognitive improvement or safety challenges. Recent studies investigating the involvement of BACE1 in metabolic, vascular, and immune functions have indicated a role in aging, diabetes, hypertension, and cancer. These novel BACE1 functions have helped to identify new 'druggable' targets for BACE1 against aging comorbidities. In this review, we discuss BACE1 regulation during aging, and then provide recent insights into its enzymatic and nonenzymatic involvement in aging and age-related diseases. Our study not only proposes the perspective of BACE1's actions in various systems, but also provides new directions for using BACE1 inhibitors and modulators to delay aging and to treat age-related diseases.
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Affiliation(s)
- Hong Bao
- Institute on Aging and Brain Disorders, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China; Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yong Shen
- Institute on Aging and Brain Disorders, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China; Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Anhui Provincial Key Laboratory of Biomedical Aging Research, University of Science and Technology of China, Hefei, 230026, China; CAS Key Laboratory of Brain Function and Disease, Division of Biological and Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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13
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Su C, Yang X, Wei S, Zhao R. Association of Cerebral Small Vessel Disease With Gait and Balance Disorders. Front Aging Neurosci 2022; 14:834496. [PMID: 35875801 PMCID: PMC9305071 DOI: 10.3389/fnagi.2022.834496] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Cerebral small vessel disease (CSVD) is a common cerebrovascular disease and an important cause of gait and balance disorders. Gait and balance disorders can further lead to an increased risk of falls and a decreased quality of life. CSVD can damage gait and balance function by affecting cognitive function or directly disrupting motor pathways, and different CSVD imaging features have different characteristics of gait and balance impairment. In this article, the correlation between different imaging features of sporadic CSVD and gait and balance disorders has been reviewed as follows, which can provide beneficial help for standardized management of CSVD.
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Affiliation(s)
| | | | | | - Renliang Zhao
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
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14
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Lee D, Kim HV, Kim HY, Kim Y. Chemical-Driven Outflow of Dissociated Amyloid Burden from Brain to Blood. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104542. [PMID: 35106958 PMCID: PMC9036038 DOI: 10.1002/advs.202104542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/11/2022] [Indexed: 06/14/2023]
Abstract
Amyloid-β (Aβ) deposition in the brain is a primary biomarker of Alzheimer's disease (AD) and Aβ measurement for AD diagnosis mostly depends on brain imaging and cerebrospinal fluid analyses. Blood Aβ can become a reliable surrogate biomarker if issues of low concentration for conventional laboratory instruments and uncertain correlation with brain Aβ are solved. Here, brain-to-blood efflux of Aβ is stimulated in AD transgenic mice by orally administrating a chemical that dissociates amyloid plaques and observing the subsequent increase of blood Aβ concentration. 5XFAD transgenic and wild-type mice of varying ages and genders are prepared, and blood samples of each mouse are collected six times for 12 weeks; three weeks of no treatment and additional nine weeks of daily oral administration, ad libitum, of Aβ plaque-dissociating chemical agent. By the dissociation of Aβ aggregates, the altered levels of plasma Aβ distinguish between transgenic and wild-type mice, displaying potential as an amyloid burden marker of AD brains.
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Affiliation(s)
- Donghee Lee
- Department of PharmacyCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
- Yonsei Institute of Pharmaceutical SciencesCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
| | - Hyunjin Vincent Kim
- Korea Institute of Science and Technology (KIST)University of Science and Technology (UST)5 Hwarang‐ro 14‐gilSeongbuk‐guSeoul02792South Korea
| | - Hye Yun Kim
- Department of PharmacyCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
- Yonsei Institute of Pharmaceutical SciencesCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
| | - YoungSoo Kim
- Department of PharmacyCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
- Yonsei Institute of Pharmaceutical SciencesCollege of PharmacyYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
- Department of Integrative Biotechnology and Translational MedicineYonsei University85 Songdogwahak‐roYeonsu‐guIncheon21983South Korea
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15
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APOE-ε4 modulates the association among plasma Aβ 42/Aβ 40, vascular diseases, neurodegeneration and cognitive decline in non-demented elderly adults. Transl Psychiatry 2022; 12:128. [PMID: 35351867 PMCID: PMC8964707 DOI: 10.1038/s41398-022-01899-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 01/18/2023] Open
Abstract
Including apolipoprotein E-ε4 (APOE-ε4) status and older age into consideration may increase the accuracy of plasma Aβ42/Aβ40 detecting Aβ+ individuals, but the rationale behind this remains to be fully understood. Besides, both Aβ pathology and vascular diseases are related to neurodegeneration and cognitive decline, but it is still not fully understood how APOE-ε4 modulates these relationships. In this study, we examined 241 non-demented Alzheimer's Disease Neuroimaging Initiative participants to investigate the associations among age, white matter hyperintensities (WMH), hypertension, hyperlipidemia, body mass index (BMI), plasma Aβ42/Aβ40 measured by liquid chromatography tandem mass spectrometry, and 18F-florbetapir Aβ PET as well as their prediction of longitudinal adjusted hippocampal volume (aHCV) and cognition in APOE-ε4 carriers and non-carriers. We found older age predicted faster WMH increase (p = 0.024) and cortical Aβ accumulation (p = 0.043) in APOE-ε4 non-carriers only, whereas lower plasma Aβ42/Aβ40 predicted faster cortical Aβ accumulation (p < 0.018) regardless of APOE-ε4 status. While larger WMH and underweight predicted (p < 0.05) faster decreases in aHCV and cognition in APOE-ε4 non-carriers, lower plasma Aβ42/Aβ40 predicted (p < 0.031) faster decreases in aHCV and cognition in APOE-ε4 carriers. Higher Aβ PET also predicted faster rates of aHCV (p = 0.010) in APOE-ε4 carriers only, but was related to faster rates of cognitive decline (p < 0.022) regardless of APOE-ε4 status. These findings may provide novel insights into understanding different mechanisms underlie neurodegeneration and cognitive decline in non-demented elderly adults with and without APOE-ε4 allele, which may help the design of anti-Alzheimer's clinical trials.
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16
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Raghavan S, Przybelski SA, Reid RI, Lesnick TG, Ramanan VK, Botha H, Matchett BJ, Murray ME, Reichard RR, Knopman DS, Graff-Radford J, Jones DT, Lowe VJ, Mielke MM, Machulda MM, Petersen RC, Kantarci K, Whitwell JL, Josephs KA, Jack CR, Vemuri P. White matter damage due to vascular, tau, and TDP-43 pathologies and its relevance to cognition. Acta Neuropathol Commun 2022; 10:16. [PMID: 35123591 PMCID: PMC8817561 DOI: 10.1186/s40478-022-01319-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/27/2022] Open
Abstract
Multi-compartment modelling of white matter microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on white matter health through neurite density index and free water measures. We hypothesized that cerebrovascular disease, Alzheimer's disease, and TDP-43 proteinopathy would be associated with distinct NODDI readouts of white matter damage which would be informative for identifying the substrate for cognitive impairment. We identified two independent cohorts with multi-shell diffusion MRI, amyloid and tau PET, and cognitive assessments: specifically, a population-based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota, population and a clinical research-based cohort of 61 amyloid positive Alzheimer's dementia participants. We observed an increase in free water and decrease in neurite density using NODDI measures in the genu of the corpus callosum associated with vascular risk factors, which we refer to as the vascular white matter component. Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal white matter where we measured parahippocampal cingulum and inferior temporal white matter bundles. Worsening temporal white matter neurite density was associated with (antemortem confirmed) FDG TDP-43 signature. Post-mortem neuropathologic data on a small subset of this sample lend support to our findings. In the community-dwelling cohort where vascular disease was more prevalent, the NODDI vascular white matter component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory). In the AD dementia cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non-trivial contribution of the temporal white matter component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition. White matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether cerebrovascular disease versus neurodegenerative disease caused by tau deposition or TDP-43 pathology) for cognitive impairment in older adults.
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Affiliation(s)
| | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | - Robert I. Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905 USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Jennifer L. Whitwell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | | | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
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Prescott JW, Doraiswamy PM, Gamberger D, Benzinger T, Petrella JR. Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort. Radiology 2022; 302:143-150. [PMID: 34636637 PMCID: PMC9127824 DOI: 10.1148/radiol.2021210383] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.
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Affiliation(s)
- Jeffrey W. Prescott
- Department of Radiology, The MetroHealth System, 2500 MetroHealth Dr, Cleveland, OH 44109,Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
| | - P. Murali Doraiswamy
- Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
| | | | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo
| | - Jeffrey R. Petrella
- Departments of Radiology and Psychiatry, Duke University Medical Center, Durham, NC
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18
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Lee KH, Kang KM. Association between Cerebral Small Vessel and Alzheimer’s Disease. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:486-507. [PMID: 36238505 PMCID: PMC9514514 DOI: 10.3348/jksr.2022.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 11/15/2022]
Abstract
뇌소혈관질환은 뇌 자기공명영상에서 흔히 관찰되는 혈관성 변화로 뇌백질 고신호강도, 뇌미세출혈, 열공성 경색, 혈관주위공간 등을 포함한다. 이러한 혈관성 변화가 알츠하이머병(Alzheimer’s disease; 이하 AD)의 발병 및 진행과 관련되어 있고, 대표 병리인 베타 아밀로이드 및 타우 단백의 침착과도 연관되어 있다는 증거들이 축적되고 있다. 혈관성 변화는 생활 습관 개선이나 약물 치료를 통해 예방과 개선이 가능하기 때문에 뇌소혈관질환과 AD 및 AD 생체지표의 관련성을 연구하는 것이 중요하다. 본 종설에서는 AD와 AD 생체지표에 대해 간략히 소개하고, AD와 혈관성 변화의 관련성에 대해 축적된 증거들을 제시한 다음, 뇌소혈관질환의 병태 생리와 MR 영상 소견을 설명하고자 한다. 또 뇌소혈관질환과 AD 진단의 위험도 및 AD 생체지표와의 관련성에 대한 기존 연구 결과들을 정리하고자 한다.
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Affiliation(s)
- Kyung Hoon Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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19
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Bazydlo AM, Zammit MD, Wu M, Lao PJ, Dean DC, Johnson SC, Tudorascu DL, Cohen A, Cody KA, Ances B, Laymon CM, Klunk WE, Zaman S, Handen BL, Hartley SL, Alexander AL, Christian BT. White matter microstructure associations to amyloid burden in adults with Down syndrome. Neuroimage Clin 2021; 33:102908. [PMID: 34902714 PMCID: PMC8672096 DOI: 10.1016/j.nicl.2021.102908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 10/25/2021] [Accepted: 12/03/2021] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Individuals with Down syndrome (DS) are at an increased risk of developing Alzheimer's Disease (AD). One of the early underlying mechanisms in AD pathology is the accumulation of amyloid protein plaques, which are deposited in extracellular gray matter and signify the first stage in the cascade of neurodegenerative events. AD-related neurodegeneration is also evidenced as microstructural changes in white matter. In this work, we explored the correlation of white matter microstructure with amyloid load to assess amyloid-related neurodegeneration in a cohort of adults with DS. METHODS In this study of 96 adults with DS, the relation of white matter microstructure using diffusion tensor imaging (DTI) and amyloid plaque burden using [11C]PiB PET were examined. The amyloid load (AβL) derived from [11C]PiB was used as a global measure of amyloid burden. AβL and DTI measures were compared using tract-based spatial statistics (TBSS) and corrected for imaging site and chronological age. RESULTS TBSS of the DTI maps showed widespread age-by-amyloid interaction with both fractional anisotropy (FA) and mean diffusivity (MD). Further, diffuse negative association of FA and positive association of MD with amyloid were observed. DISCUSSION These findings are consistent with the white matter microstructural changes associated with AD disease progression in late onset AD in non-DS populations.
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Affiliation(s)
- Austin M Bazydlo
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
| | - Matthew D Zammit
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Minjie Wu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Patrick J Lao
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Douglas C Dean
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sterling C Johnson
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Dana L Tudorascu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ann Cohen
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Karly A Cody
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Beau Ances
- Washington University, St. Louis, MO, USA
| | - Charles M Laymon
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William E Klunk
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, University of Cambridge, Cambridge, United Kingdom
| | | | - Sigan L Hartley
- Waisman Center, Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew L Alexander
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Bradley T Christian
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
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20
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Zang C, Liu H, Shang J, Yang H, Wang L, Sheng C, Zhang Z, Bao X, Yu Y, Yao X, Zhang D. Gardenia jasminoides J.Ellis extract GJ-4 alleviated cognitive deficits of APP/PS1 transgenic mice. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 93:153780. [PMID: 34607163 DOI: 10.1016/j.phymed.2021.153780] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/08/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Accumulating evidence demonstrates that traditional Chinese medicines that act on multiple targets could effectively treat various multi-etiological diseases, including cerebrovascular diseases, Alzheimer's disease (AD), Parkinson's disease (PD) and so on. Previous studies have shown that crocin richments (GJ-4), Gardenia jasminoides J.Ellis extract, provide neuroprotective effects on cognitive impairments in AD mouse models. However, the mechanism how GJ-4 improves cognition remains still unclear. PURPOSE The aim of this study was to uncover the protective effects and underlying mechanism of GJ-4 on PrP-hAβPPswe/PS1ΔE9 (APP/PS1) transgenic mice. METHODS APP/PS1 mice were given GJ-4 (10, 20, and 50 mg/kg), donepezil (5 mg/kg) and memantine (5 mg/kg) orally at eight months of age for 12 consecutive weeks. Morris water maze and novel object recognition were conducted to assess the cognitive ability of mice. The release of inflammatory cytokines was determined by RT-PCR assay, and the pathological features of neurons and microglia were assayed by immunohistochemistry and immunofluorescence assay. The expression of Aβ-related proteins and signaling pathways were determined by Western blot. RESULTS The behavioral results revealed that GJ-4 ameliorated the cognitive deficits of APP/PS1 mice measured by Morris water maze and novel object recognition tests. Mechanism studies indicated that GJ-4 significantly decreased β-amyloid (Aβ) level through reducing Aβ production and promoting Aβ degradation. It has been reported that Aβ plaques trigger the hyper-phosphorylation of tau protein in APP/PS1 mice. Consistent with previous studies, hyper-phosphorylation of tau was also occurred in APP/PS1 mice in the present study, and GJ-4 inhibited Tau phosphorylation at different sites. Overwhelming evidence indicates that neuroinflammation stimulated by Aβ and hyperphosphorylated tau is involved in the pathological progression of AD. We found that GJ-4 suppressed neuroinflammatory responses in the brain through regulating phosphatidylinositide 3-kinase/AKT (PI3K/AKT) signaling pathway activation, and subsequent expression of inflammatory proteins and release of inflammatory cytokines. CONCLUSION Altogether, GJ-4 ameliorated cognition of APP/PS1 transgenic mice through multiple targets, including Aβ, tau and neuroinflammation. This study provides a solid research basis for further development of GJ-4 as a potential candidate for the treatment of AD.
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Affiliation(s)
- Caixia Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Hui Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Junmei Shang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Hanyu Yang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Lu Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Chanjuan Sheng
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Zihong Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Xiuqi Bao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Yang Yu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Xinsheng Yao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China
| | - Dan Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, PR China.
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21
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18F-florbetapir PET as a marker of myelin integrity across the Alzheimer's disease spectrum. Eur J Nucl Med Mol Imaging 2021; 49:1242-1253. [PMID: 34581847 PMCID: PMC8921113 DOI: 10.1007/s00259-021-05493-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 07/08/2021] [Indexed: 01/23/2023]
Abstract
Purpose Recent evidence suggests that PET imaging with amyloid-β (Aβ) tracers can be used to assess myelin integrity in cerebral white matter (WM). Alzheimer’s disease (AD) is characterized by myelin changes that are believed to occur early in the disease course. Nevertheless, the extent to which demyelination, as measured with Aβ PET, contributes to AD progression remains unexplored. Methods Participants with concurrent 18F-florbetapir (FBP) PET, MRI, and cerebrospinal fluid (CSF) examinations were included (241 cognitively normal, 347 Aβ-positive cognitively impaired, and 207 Aβ-negative cognitively impaired subjects). A subset of these participants had also available diffusion tensor imaging (DTI) images (n = 195). We investigated cross-sectional associations of FBP retention in the white matter (WM) with MRI-based markers of WM degeneration, AD clinical progression, and fluid biomarkers. In longitudinal analyses, we used linear mixed models to assess whether FBP retention in normal-appearing WM (NAWM) predicted progression of WM hyperintensity (WMH) burden and clinical decline. Results In AD-continuum individuals, FBP retention in NAWM was (1) higher compared with WMH regions, (2) associated with DTI-based measures of WM integrity, and (3) associated with longitudinal progression of WMH burden. FBP uptake in WM decreased across the AD continuum and with increasingly abnormal CSF biomarkers of AD. Furthermore, FBP retention in the WM was associated with large-calibre axon degeneration as reflected by abnormal plasma neurofilament light chain levels. Low FBP uptake in NAWM predicted clinical decline in preclinical and prodromal AD, independent of demographics, global cortical Aβ, and WMH burden. Most of these associations were also observed in Aβ-negative cognitively impaired individuals. Conclusion These results support the hypothesis that FBP retention in the WM is myelin-related. Demyelination levels progressed across the AD continuum and were associated with clinical progression at early stages, suggesting that this pathologic process might be a relevant degenerative feature in the disease course. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05493-y.
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22
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Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Graff-Radford J, Schwarz CG, Knopman DS, Mielke MM, Machulda MM, Petersen RC, Jack CR, Vemuri P. Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition. Brain Commun 2021; 3:fcab106. [PMID: 34136811 PMCID: PMC8202149 DOI: 10.1093/braincomms/fcab106] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/24/2021] [Accepted: 04/12/2021] [Indexed: 01/20/2023] Open
Abstract
White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment and 7 dementia with a mean age of 68.3 ± 13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.
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Affiliation(s)
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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23
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Chen X, Farrell ME, Rundle MM, Chan MY, Moore W, Wig GS, Park DC. The relationship of functional hippocampal activity, amyloid deposition, and longitudinal memory decline to memory complaints in cognitively healthy older adults. Neurobiol Aging 2021; 105:318-326. [PMID: 34147860 DOI: 10.1016/j.neurobiolaging.2021.04.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 03/16/2021] [Accepted: 04/21/2021] [Indexed: 12/22/2022]
Abstract
We evaluated whether self-reports of worse cognition in older adults with normal cognitive function reflected actual memory decline, amyloid pathology, and subtle vulnerabilities in hippocampal function. We measured subjective cognitive decline (SCD) in 156 older participants from the Dallas Lifespan Brain Study. Functional hippocampal activation during encoding, measured with fMRI, and longitudinal memory change that was measured in the four years preceding the SCD measures were used to predict the magnitude of SCD. A subsample (N=105) also underwent 18F-Florbetapir PET imaging that measured amyloid burden. Results showed that increased SCD were associated with greater prior memory decline and amyloid deposition. Importantly, decreased hippocampal activation during encoding was a significant predictor of SCD, particularly in young-old adults below 69 years old, above and beyond prior memory change and amyloid deposition. These results indicate that multiple measures of neural and cognitive dysfunction are simultaneously associated with SCD. Moreover, SCD in younger seniors appears to reflect deficient hippocampal activity that increases their reports of poorer memory, independent of amyloid. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEU-ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Xi Chen
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA; Present affiliation: Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Present affiliation: Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
| | - Michelle E Farrell
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA; Present affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Melissa M Rundle
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA.
| | - Micaela Y Chan
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA.
| | - William Moore
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Gagan S Wig
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Denise C Park
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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24
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Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Rodriguez JL, Snellman A, Suárez-Calvet M, Zetterberg H, Blennow K, Schöll M. Time course of phosphorylated-tau181 in blood across the Alzheimer's disease spectrum. Brain 2021; 144:325-339. [PMID: 33257949 PMCID: PMC7880671 DOI: 10.1093/brain/awaa399] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/15/2020] [Accepted: 09/20/2020] [Indexed: 12/31/2022] Open
Abstract
Tau phosphorylated at threonine 181 (p-tau181) measured in blood plasma has recently been proposed as an accessible, scalable, and highly specific biomarker for Alzheimer’s disease. Longitudinal studies, however, investigating the temporal dynamics of this novel biomarker are lacking. It is therefore unclear when in the disease process plasma p-tau181 increases above physiological levels and how it relates to the spatiotemporal progression of Alzheimer’s disease characteristic pathologies. We aimed to establish the natural time course of plasma p-tau181 across the sporadic Alzheimer’s disease spectrum in comparison to those of established imaging and fluid-derived biomarkers of Alzheimer’s disease. We examined longitudinal data from a large prospective cohort of elderly individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 1067) covering a wide clinical spectrum from normal cognition to dementia, and with measures of plasma p-tau181 and an 18F-florbetapir amyloid-β PET scan at baseline. A subset of participants (n = 864) also had measures of amyloid-β1–42 and p-tau181 levels in CSF, and another subset (n = 298) had undergone an 18F-flortaucipir tau PET scan 6 years later. We performed brain-wide analyses to investigate the associations of plasma p-tau181 baseline levels and longitudinal change with progression of regional amyloid-β pathology and tau burden 6 years later, and estimated the time course of changes in plasma p-tau181 and other Alzheimer’s disease biomarkers using a previously developed method for the construction of long-term biomarker temporal trajectories using shorter-term longitudinal data. Smoothing splines demonstrated that earliest plasma p-tau181 changes occurred even before amyloid-β markers reached abnormal levels, with greater rates of change correlating with increased amyloid-β pathology. Voxel-wise PET analyses yielded relatively weak, yet significant, associations of plasma p-tau181 with amyloid-β pathology in early accumulating brain regions in cognitively healthy individuals, while the strongest associations with amyloid-β were observed in late accumulating regions in patients with mild cognitive impairment. Cross-sectional and particularly longitudinal measures of plasma p-tau181 were associated with widespread cortical tau aggregation 6 years later, covering temporoparietal regions typical for neurofibrillary tangle distribution in Alzheimer’s disease. Finally, we estimated that plasma p-tau181 reaches abnormal levels ∼6.5 and 5.7 years after CSF and PET measures of amyloid-β, respectively, following similar dynamics as CSF p-tau181. Our findings suggest that plasma p-tau181 increases are associated with the presence of widespread cortical amyloid-β pathology and with prospective Alzheimer’s disease typical tau aggregation, providing clear implications for the use of this novel blood biomarker as a diagnostic and screening tool for Alzheimer’s disease.
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Affiliation(s)
- Alexis Moscoso
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden
| | - Michel J Grothe
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden.,Unidad de Trastornos del Movimiento, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden.,King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK.,NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Juan Lantero Rodriguez
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Anniina Snellman
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Turku PET Centre, University of Turku, FI-20520 Turku, Finland
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Servei de Neurologia, Hospital del Mar, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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25
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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26
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Collij LE, Ingala S, Top H, Wottschel V, Stickney KE, Tomassen J, Konijnenberg E, ten Kate M, Sudre C, Lopes Alves I, Yaqub MM, Wink AM, Van ‘t Ent D, Scheltens P, van Berckel BN, Visser PJ, Barkhof F, Braber AD. White matter microstructure disruption in early stage amyloid pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12124. [PMID: 33816751 PMCID: PMC8015832 DOI: 10.1002/dad2.12124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. METHODS We included 179 individuals from the European Medical Information Framework for AD (EMIF-AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract-level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. RESULTS Regression analyses showed a non-linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. DISCUSSION Early amyloid deposition is associated with changes in WM microstructure. The non-linear relationship might reflect multiple stages of axonal damage.
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Affiliation(s)
- Lyduine E. Collij
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Silvia Ingala
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Herwin Top
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Viktor Wottschel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Jori Tomassen
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | | | - Mara ten Kate
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Carole Sudre
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Isadora Lopes Alves
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Maqsood M. Yaqub
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Alle Meije Wink
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Dennis Van ‘t Ent
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
| | - Philip Scheltens
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Bart N.M. van Berckel
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Pieter Jelle Visser
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience (MHeNS), Alzheimer Centrum LimburgMaastricht UniversityMaastrichtThe Netherlands
- Department of NeurobiologyCare Sciences Division of NeurogeriatricsKarolinska InstitutetStockholmSweden
| | - Frederik Barkhof
- Dept. of Radiology and Nuclear MedicineAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
- Institute of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - Anouk Den Braber
- Dept. of Biological PsychologyVU University AmsterdamAmsterdamThe Netherlands
- Alzheimer CenterAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
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27
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Lower cerebral perfusion is associated with tau-PET in the entorhinal cortex across the Alzheimer's continuum. Neurobiol Aging 2021; 102:111-118. [PMID: 33765424 DOI: 10.1016/j.neurobiolaging.2021.02.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 02/01/2021] [Accepted: 02/04/2021] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is associated with reduced temporo-parietal cerebral blood flow (CBF). However, a substantial variability in CBF across the clinical spectrum of AD has been reported, possibly due to differences in primary AD pathologies. Here, we assessed CBF (ASL-MRI), tau (AV1451-PET) and amyloid (AV45/FBB-PET) in 156 subjects across the AD continuum. Using mixed-effect regression analyses, we assessed the local associations between amyloid-PET, tau-PET and CBF in a hypothesis-driven way focusing on each pathology's predilection areas. The contribution of Apolipoprotein E (APOE) genotype, and MRI markers of small vessel disease (SVD) to alterations in CBF were assessed as well. Tau-PET was associated with lower CBF in the entorhinal cortex, independent of Aβ. Amyloid-PET was associated with lower CBF in temporo-parietal regions. No associations between MRI markers of SVD and CBF were observed. These results provide evidence that in addition to Aβ, pathologic tau is a major correlate of CBF in early Braak stages, independent of Aβ, APOE genotype and SVD markers.
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28
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Reduced [ 18F]flortaucipir retention in white matter hyperintensities compared to normal-appearing white matter. Eur J Nucl Med Mol Imaging 2021; 48:2283-2294. [PMID: 33475761 DOI: 10.1007/s00259-021-05195-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/04/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE Recent research has suggested the use of white matter (WM) reference regions for longitudinal tau-PET imaging. However, tau tracers display affinity for the β-sheet structure formed by myelin, and thus WM lesions might influence tracer retention. Here, we explored whether the tau-sensitive tracer [18F]flortaucipir shows reduced retention in WM hyperintensities (WMH) and how this retention changes over time. METHODS We included 707 participants from the Alzheimer's Disease Neuroimaging Initiative with available [18F]flortaucipir-PET and structural and FLAIR MRI scans. WM segments and WMH were automatically delineated in the structural MRI and FLAIR scans, respectively. [18F]flortaucipir standardized uptake value ratios (SUVR) of WMH and normal-appearing WM (NAWM) were calculated using the inferior cerebellar grey matter as reference region, and a 3-mm erosion was applied to the combined NAWM and WMH masks to avoid partial volume effects. Longitudinal [18F]flortaucipir SUVR changes in NAWM and WMH were estimated using linear mixed models. The percent variance of WM-referenced cortical [18F]flortaucipir SUVRs explained by longitudinal changes in the WM reference region was estimated with the R2 coefficient. RESULTS Compared to NAWM, WMH areas displayed significantly reduced [18F]flortaucipir SUVR, independent of cognitive impairment or Aβ status (mean difference = 0.14 SUVR, p < 0.001). Older age was associated with lower [18F]flortaucipir SUVR in both NAWM (- 0.002 SUVR/year, p = 0.005) and WMH (- 0.004 SUVR/year, p < 0.001). Longitudinally, [18F]flortaucipir SUVR decreased in NAWM (- 0.008 SUVR/year, p = 0.03) and even more so in WMH (- 0.02 SUVR/year, p < 0.001). Between 17% and 66% of the variance of longitudinal changes in cortical WM-referenced [18F]flortaucipir SUVRs were explained by longitudinal changes in the reference region. CONCLUSIONS [18F]flortaucipir retention in the WM decreases over time and is influenced by the presence of WMH, supporting the hypothesis that [18F]flortaucipir retention in the WM is partially myelin-dependent. These findings have implications for the use of WM reference regions for [18F]flortaucipir-PET imaging.
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29
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Gaubert M, Lange C, Garnier-Crussard A, Köbe T, Bougacha S, Gonneaud J, de Flores R, Tomadesso C, Mézenge F, Landeau B, de la Sayette V, Chételat G, Wirth M. Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:29. [PMID: 33461618 PMCID: PMC7814451 DOI: 10.1186/s13195-020-00759-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/23/2020] [Indexed: 12/26/2022]
Abstract
Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.
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Affiliation(s)
- Malo Gaubert
- German Center for Neurodegenerative Diseases, Dresden, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, LMU University Hospital Munich, Ludwig-Maximilians-Universität, Munich, Germany
| | - Catharina Lange
- German Center for Neurodegenerative Diseases, Dresden, Germany. .,Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.
| | - Antoine Garnier-Crussard
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France.,Clinical and Research Memory Center of Lyon, Lyon Institute for Elderly, Hospices Civils de Lyon, Lyon, France
| | - Theresa Köbe
- German Center for Neurodegenerative Diseases, Dresden, Germany
| | - Salma Bougacha
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Julie Gonneaud
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Robin de Flores
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Clémence Tomadesso
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Florence Mézenge
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Brigitte Landeau
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Vincent de la Sayette
- Normandy University, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU of Caen, Neuropsychology and Imaging of Human Memory, Caen, France
| | - Gaël Chételat
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases, Dresden, Germany.
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30
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Finsterwalder S, Vlegels N, Gesierich B, Caballero MÁA, Weaver NA, Franzmeier N, Georgakis MK, Konieczny MJ, Koek HL, Karch CM, Graff-Radford NR, Salloway S, Oh H, Allegri RF, Chhatwal JP, Jessen F, Düzel E, Dobisch L, Metzger C, Peters O, Incesoy EI, Priller J, Spruth EJ, Schneider A, Fließbach K, Buerger K, Janowitz D, Teipel SJ, Kilimann I, Laske C, Buchmann M, Heneka MT, Brosseron F, Spottke A, Roy N, Ertl-Wagner B, Scheffler K, Seo SW, Kim Y, Na DL, Kim HJ, Jang H, Ewers M, Levin J, Schmidt R, Pasternak O, Dichgans M, Biessels GJ, Duering M. Small vessel disease more than Alzheimer's disease determines diffusion MRI alterations in memory clinic patients. Alzheimers Dement 2020; 16:1504-1514. [PMID: 32808747 PMCID: PMC8102202 DOI: 10.1002/alz.12150] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/25/2020] [Accepted: 06/25/2020] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Microstructural alterations as assessed by diffusion tensor imaging (DTI) are key findings in both Alzheimer's disease (AD) and small vessel disease (SVD). We determined the contribution of each of these conditions to diffusion alterations. METHODS We studied six samples (N = 365 participants) covering the spectrum of AD and SVD, including genetically defined samples. We calculated diffusion measures from DTI and free water imaging. Simple linear, multivariable random forest, and voxel-based regressions were used to evaluate associations between AD biomarkers (amyloid beta, tau), SVD imaging markers, and diffusion measures. RESULTS SVD markers were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analysis across all memory clinic samples. Voxel-wise analyses between tau and diffusion measures were not significant. DISCUSSION In memory clinic patients, the effect of SVD on diffusion alterations largely exceeds the effect of AD, supporting the value of diffusion measures as markers of SVD.
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Affiliation(s)
- Sofia Finsterwalder
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Miguel Á. Araque Caballero
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nick A. Weaver
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Celeste M. Karch
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | | | | | - Hwamee Oh
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Ricardo F. Allegri
- Department of Cognitive Neurology, FLENI Institute for Neurological Research, Buenos Aires, Argentina
| | | | | | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Coraline Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Enise I. Incesoy
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Eike J. Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Klaus Fließbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Martina Buchmann
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Michael T. Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Birgit Ertl-Wagner
- Institute of Clinical Radiology, University Hospital, LMU Munich, Munich, Germany
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | | | | | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
- Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine Chuncheon, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Ofer Pasternak
- Department of Psychiatry and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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Salvadores N, Gerónimo-Olvera C, Court FA. Axonal Degeneration in AD: The Contribution of Aβ and Tau. Front Aging Neurosci 2020; 12:581767. [PMID: 33192476 PMCID: PMC7593241 DOI: 10.3389/fnagi.2020.581767] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/09/2020] [Indexed: 12/25/2022] Open
Abstract
Alzheimer's disease (AD) represents the most common age-related neurodegenerative disorder, affecting around 35 million people worldwide. Despite enormous efforts dedicated to AD research over decades, there is still no cure for the disease. Misfolding and accumulation of Aβ and tau proteins in the brain constitute a defining signature of AD neuropathology, and mounting evidence has documented a link between aggregation of these proteins and neuronal dysfunction. In this context, progressive axonal degeneration has been associated with early stages of AD and linked to Aβ and tau accumulation. As the axonal degeneration mechanism has been starting to be unveiled, it constitutes a promising target for neuroprotection in AD. A comprehensive understanding of the mechanism of axonal destruction in neurodegenerative conditions is therefore critical for the development of new therapies aimed to prevent axonal loss before irreversible neuronal death occurs in AD. Here, we review current evidence of the involvement of Aβ and tau pathologies in the activation of signaling cascades that can promote axonal demise.
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Affiliation(s)
- Natalia Salvadores
- Center for Integrative Biology, Faculty of Sciences, Universidad Mayor, Santiago, Chile.,Fondap Geroscience Center for Brain Health and Metabolism, Santiago, Chile
| | - Cristian Gerónimo-Olvera
- Center for Integrative Biology, Faculty of Sciences, Universidad Mayor, Santiago, Chile.,Fondap Geroscience Center for Brain Health and Metabolism, Santiago, Chile
| | - Felipe A Court
- Center for Integrative Biology, Faculty of Sciences, Universidad Mayor, Santiago, Chile.,Fondap Geroscience Center for Brain Health and Metabolism, Santiago, Chile.,Buck Institute for Research on Aging, Novato, CA, United States
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Kim HW, Hong J, Jeon JC. Cerebral Small Vessel Disease and Alzheimer's Disease: A Review. Front Neurol 2020; 11:927. [PMID: 32982937 PMCID: PMC7477392 DOI: 10.3389/fneur.2020.00927] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 07/17/2020] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Despite this, clear pathophysiology for AD has not been confirmed, and effective treatments are still not available. As AD results in a complex disease process for cognitive decline, various theories have been suggested as the cause of AD. Recently, cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of AD, as well as contributing to vascular dementia. Cerebral SVD refers to a varied group of diseases that affect cerebral small arteries and microvessels. These can be seen as white matter hyperintensities, cerebral microbleeds, and lacunes on magnetic resonance imaging. Data from epidemiological and clinical-pathological studies have found evidence of the relationship between cerebral SVD and AD. This review aims to discuss the complex relationship between cerebral SVD and AD. Recent reports that evaluate the association between these diseases will be reviewed.
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Affiliation(s)
- Hae Won Kim
- Department of Nuclear Medicine, Keimyung University Dongsan Medical Center, Daegu, South Korea
| | - Jeongho Hong
- Department of Neurology, Keimyung University Dongsan Medical Center, Daegu, South Korea
| | - Jae Cheon Jeon
- Institute for Medical Science, Keimyung University School of Medicine, Daegu, South Korea
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