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Sun P, He Z, Li A, Yang J, Zhu Y, Cai Y, Ma T, Ma S, Guo T. Spatial and temporal patterns of cortical mean diffusivity in Alzheimer's disease and suspected non-Alzheimer's disease pathophysiology. Alzheimers Dement 2024. [PMID: 39132849 DOI: 10.1002/alz.14176] [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: 04/12/2024] [Revised: 07/12/2024] [Accepted: 07/16/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION The spatial and temporal patterns of cortical mean diffusivity (cMD), as well as its association with Alzheimer's disease (AD) and suspected non-Alzheimer's pathophysiology (SNAP), are not yet fully understood. METHODS We compared baseline (n = 617) and longitudinal changes (n = 421) of cMD, cortical thickness, and gray matter volume and their relations to vascular risk factors, amyloid beta (Aβ), and tau positron emission tomography (PET), and longitudinal cognitive decline in Aβ PET negative and positive older adults. RESULTS cMD increases were more sensitive to detecting brain structural alterations than cortical thinning and gray matter atrophy. Tau-related cMD increases partially mediated Aβ-related cognitive decline in AD, whereas vascular disease-related increased cMD levels substantially mediated age-related cognitive decline in SNAP. DISCUSSION These findings revealed the dynamic changes of microstructural and macrostructural indicators and their associations with AD and SNAP, providing novel insights into understanding upstream and downstream events of cMD in neurodegenerative disease. HIGHLIGHTS Cortical mean diffusivity (cMD) was more sensitive to detecting structural changes than macrostructural factors. Tau-related cMD increases partially mediated amyloid beta-related cognitive decline in Alzheimer's disease (AD). White matter hyperintensity-related higher cMD mainly explained the age-related cognitive decline in suspected non-Alzheimer's pathophysiology (SNAP). cMD may assist in tracking earlier neurodegenerative signs in AD and SNAP.
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Affiliation(s)
- Pan Sun
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Zhengbo He
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jie Yang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Yalin Zhu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Ting Ma
- School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Shaohua Ma
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
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Guo T, Li A, Sun P, He Z, Cai Y, Lan G, Liu L, Li J, Yang J, Zhu Y, Zhao R, Chen X, Shi D, Liu Z, Wang Q, Xu L, Zhou L, Ran P, Wang X, Sun K, Lu J, Han Y. Astrocyte reactivity is associated with tau tangle load and cortical thinning in Alzheimer's disease. Mol Neurodegener 2024; 19:58. [PMID: 39080744 PMCID: PMC11290175 DOI: 10.1186/s13024-024-00750-8] [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: 01/15/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND It is not fully established whether plasma β-amyloid(Aβ)42/Aβ40 and phosphorylated Tau181 (p-Tau181) can effectively detect Alzheimer's disease (AD) pathophysiology in older Chinese adults and how these biomarkers correlate with astrocyte reactivity, Aβ plaque deposition, tau tangle aggregation, and neurodegeneration. METHODS We recruited 470 older adults and analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. Among them, 301, 195, and 70 underwent magnetic resonance imaging, Aβ and tau positron emission tomography imaging. The plasma Aβ42/Aβ40 and p-Tau181 thresholds were defined as ≤0.0609 and ≥2.418 based on the receiver operating characteristic curve analysis using the Youden index by comparing Aβ-PET negative cognitively unimpaired individuals and Aβ-PET positive cognitively impaired patients. To evaluate the feasibility of using plasma Aβ42/Aβ40 (A) and p-Tau181 (T) to detect AD and understand how astrocyte reactivity affects this process, we compared plasma GFAP, Aβ plaque, tau tangle, plasma NfL, hippocampal volume, and temporal-metaROI cortical thickness between different plasma A/T profiles and explored their relations with each other using general linear models, including age, sex, APOE-ε4, and diagnosis as covariates. RESULTS Plasma A+/T + individuals showed the highest levels of astrocyte reactivity, Aβ plaque, tau tangle, and axonal degeneration, and the lowest hippocampal volume and temporal-metaROI cortical thickness. Lower plasma Aβ42/Aβ40 and higher plasma p-Tau181 were independently and synergistically correlated with higher plasma GFAP and Aβ plaque. Elevated plasma p-Tau181 and GFAP concentrations were directly and interactively associated with more tau tangle formation. Regarding neurodegeneration, higher plasma p-Tau181 and GFAP concentrations strongly correlated with more axonal degeneration, as measured by plasma NfL, and lower plasma Aβ42/Aβ40 and higher plasma p-Tau181 were related to greater hippocampal atrophy. Higher plasma GFAP levels were associated with thinner cortical thickness and significantly interacted with lower plasma Aβ42/Aβ40 and higher plasma p-Tau181 in predicting more temporal-metaROI cortical thinning. Voxel-wise imaging analysis confirmed these findings. DISCUSSION This study provides a valuable reference for using plasma biomarkers to detect AD in the Chinese community population and offers novel insights into how astrocyte reactivity contributes to AD progression, highlighting the importance of targeting reactive astrogliosis to prevent AD.
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Affiliation(s)
- Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China.
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Pan Sun
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Zhengbo He
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Guoyu Lan
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Lin Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Jieyin Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Jie Yang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, #45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Yalin Zhu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Ruiyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Xuhui Chen
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518000, China
| | - Dai Shi
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Zhen Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Qingyong Wang
- Department of Neurology, Shenzhen Guangming District People's Hospital, Shenzhen, 518107, China
| | - Linsen Xu
- Department of Medical Imaging, Shenzhen Guangming District People's Hospital, Shenzhen, 518106, China
| | - Liemin Zhou
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Pengcheng Ran
- Department of Nuclear Medicine, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Xinlu Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Jie Lu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, #45 Changchun Street, Xicheng District, Beijing, 100053, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchun Street, Xicheng District, Beijing, 100053, China.
| | - Ying Han
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China.
- Department of Neurology, Xuanwu Hospital of Capital Medical University, #45 Changchun Street, Xicheng District, Beijing, 100053, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
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Shao K, Hu X, Kleineidam L, Stark M, Altenstein S, Amthauer H, Boecker H, Buchert R, Buerger K, Butryn M, Cai Y, Cai Y, Cosma NC, Chen G, Chen Z, Daamen M, Drzezga A, Düzel E, Essler M, Ewers M, Fliessbach K, Gaertner FC, Glanz W, Guo T, Hansen N, He B, Janowitz D, Kilimann I, Krause BJ, Lan G, Lange C, Laske C, Li Y, Li R, Liu L, Lu J, Meng F, Munk MH, Peters O, Perneczky R, Priller J, Ramirez A, Rauchmann BS, Reimold M, Rominger A, Rostamzadeh A, Roy-Kluth N, Schneider A, Spottke A, Spruth EJ, Sun P, Teipel S, Wang X, Wei M, Wei Y, Wiltfang J, Yan S, Yang J, Yu X, Zhang M, Zhang L, Wagner M, Jessen F, Han Y, Kuhn E. Amyloid and SCD jointly predict cognitive decline across Chinese and German cohorts. Alzheimers Dement 2024. [PMID: 39072956 DOI: 10.1002/alz.14119] [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: 11/17/2023] [Revised: 04/10/2024] [Accepted: 05/11/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Subjective cognitive decline (SCD) in amyloid-positive (Aβ+) individuals was proposed as a clinical indicator of Stage 2 in the Alzheimer's disease (AD) continuum, but this requires further validation across cultures, measures, and recruitment strategies. METHODS Eight hundred twenty-one participants from SILCODE and DELCODE cohorts, including normal controls (NC) and individuals with SCD recruited from the community or from memory clinics, underwent neuropsychological assessments over up to 6 years. Amyloid positivity was derived from positron emission tomography or plasma biomarkers. Global cognitive change was analyzed using linear mixed-effects models. RESULTS In the combined and stratified cohorts, Aβ+ participants with SCD showed steeper cognitive decline or diminished practice effects compared with NC or Aβ- participants with SCD. These findings were confirmed using different operationalizations of SCD and amyloid positivity, and across different SCD recruitment settings. DISCUSSION Aβ+ individuals with SCD in German and Chinese populations showed greater global cognitive decline and could be targeted for interventional trials. HIGHLIGHTS SCD in amyloid-positive (Aβ+) participants predicts a steeper cognitive decline. This finding does not rely on specific SCD or amyloid operationalization. This finding is not specific to SCD patients recruited from memory clinics. This finding is valid in both German and Chinese populations. Aβ+ older adults with SCD could be a target population for interventional trials.
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Affiliation(s)
- Kai Shao
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Xiaochen Hu
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
| | - Melina Stark
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Holger Amthauer
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henning Boecker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Yanning Cai
- Department of clinical biobank, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Nicoleta Carmen Cosma
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Guanqun Chen
- Department of Neurology, Beijing ChaoYang Hospital of Capital Medical University, Beijing, China
| | - Zhigeng Chen
- Department of Radiology and Nuclear Medicine, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Marcel Daamen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Alexander Drzezga
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Jülich, 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
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
| | | | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Niels Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | - Beiqi He
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Bernd J Krause
- Department of Nuclear Medicine, Rostock University Medical Centre, Rostock, Germany
| | - Guoyu Lan
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Humboldt-Universität zu Berlin, Berlin, 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
| | - Yuxia Li
- Department of Neurology, Tangshan Central Hospital, Tanshan, China
| | - Ruixian Li
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Lin Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Fansheng Meng
- Medical Imaging Department of Hainan Cancer Hospital, Haikou, China
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Köln, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Köln, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, Texas, USA
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Department of Neuroradiology, University Hospital LMU, Munich, Germany
| | - Matthias Reimold
- Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard-Karls-University, Tuebingen, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Ludwig-Maximilian-University Munich, Munich, Germany
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ayda Rostamzadeh
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Nina Roy-Kluth
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Pan Sun
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Xiao Wang
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Yongzhe Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Shaozhen Yan
- Department of Radiology and Nuclear Medicine, XuanWu Hospital of Capital Medical University, Beijing, China
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jie Yang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Xianfeng Yu
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Mingkai Zhang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Liang Zhang
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Köln, Germany
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- School of Biomedical Engineering, Hainan University, Haikou, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
- The Central Hospital of Karamay, Xinjiang, China
| | - Elizabeth Kuhn
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn Medical Center, Bonn, Germany
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Liu Z, Shi D, Cai Y, Li A, Lan G, Sun P, Liu L, Zhu Y, Yang J, Zhou Y, Guo L, Zhang L, Deng S, Chen S, Yu X, Chen X, Zhao R, Wang Q, Ran P, Xu L, Zhou L, Sun K, Wang X, Peng Q, Han Y, Guo T. Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS). Alzheimers Res Ther 2024; 16:84. [PMID: 38627753 PMCID: PMC11020808 DOI: 10.1186/s13195-024-01458-z] [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: 10/28/2023] [Accepted: 04/12/2024] [Indexed: 04/19/2024]
Abstract
INTRODUCTION The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. METHODS Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups. RESULTS The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. DISCUSSION The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.
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Affiliation(s)
- Zhen Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Dai Shi
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Guoyu Lan
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Pan Sun
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Lin Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Yalin Zhu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Jie Yang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Yajing Zhou
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Lizhi Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Laihong Zhang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Shuqing Deng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Shuda Chen
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Xuhui Chen
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518000, China
| | - Ruiyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Qingyong Wang
- Department of Neurology, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518107, China
| | - Pengcheng Ran
- Department of Nuclear Medicine, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Linsen Xu
- Department of Medical Imaging, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518106, China
| | - Liemin Zhou
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Xinlu Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Qiyu Peng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Ying Han
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
- National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China.
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
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5
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Cai Y, Shi D, Lan G, Chen L, Jiang Y, Zhou L, Guo T. Association of β-Amyloid, Microglial Activation, Cortical Thickness, and Metabolism in Older Adults Without Dementia. Neurology 2024; 102:e209205. [PMID: 38489560 DOI: 10.1212/wnl.0000000000209205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/13/2023] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Plasma β-amyloid42 (Aβ42)/Aβ40 levels have shown promise in identifying Aβ-PET positive individuals. This study explored the concordance and discordance of plasma Aβ42/Aβ40 positivity (Plasma±) with CSF Aβ42/Aβ40 positivity (CSF±) and Aβ-PET positivity (PET±) in older adults without dementia. Associations of Aβ deposition, cortical thickness, glucose metabolism, and microglial activation were also investigated. METHODS We selected participants without dementia who had concurrent plasma Aβ42/Aβ40 and Aβ-PET scans from the Alzheimer's Disease Neuroimaging Initiative cohort. Participants were categorized into Plasma±/PET± based on thresholds of composite 18F-florbetapir (FBP) standardized uptake value ratio (SUVR) ≥1.11 and plasma Aβ42/Aβ40 ≤0.1218. Aβ-PET-negative individuals were further divided into Plasma±/CSF± (CSF Aβ42/Aβ40 ≤0.138), and the concordance and discordance of Aβ42/Aβ40 in the plasma and CSF were investigated. Baseline and slopes of regional FBP SUVR were compared among Plasma±/PET± groups, and associations of regional FBP SUVR, FDG SUVR, cortical thickness, and CSF soluble Triggering Receptor Expressed on Myeloid Cell 2 (sTREM2) levels were analyzed. RESULTS One hundred eighty participants (mean age 72.7 years, 51.4% female, 96 cognitively unimpaired, and 84 with mild cognitive impairment) were included. We found that the proportion of Plasma+/PET- individuals was 6.14 times higher (odds ratio (OR) = 6.143, 95% confidence interval (CI) 2.740-16.185, p < 0.001) than that of Plasma-/PET+ individuals, and Plasma+/CSF- individuals showed 8.5 times larger percentage (OR = 8.5, 95% CI: 3.031-32.974, p < 0.001) than Plasma-/CSF+ individuals in Aβ-PET-negative individuals. Besides, Plasma+/PET- individuals exhibited faster (p < 0.05) Aβ accumulation predominantly in bilateral banks of superior temporal sulcus (BANKSSTS) and supramarginal, and superior parietal cortices compared with Plasma-/PET- individuals, despite no difference in baseline FBP SUVRs. In Plasma+/PET+ individuals, higher CSF sTREM2 levels correlated with slower BANKSSTS Aβ accumulation (standardized β (βstd) = -0.418, 95% CI -0.681 to -0.154, p = 0.002). Conversely, thicker cortical thickness and higher glucose metabolism in supramarginal and superior parietal cortices were associated with faster (p < 0.05) CSF sTREM2 increase in Plasma+/PET- individuals rather than in Plasma+/PET+ individuals. DISCUSSION These findings suggest that plasma Aβ42/Aβ40 abnormalities may predate CSF Aβ42/Aβ40 and Aβ-PET abnormalities. Higher sTREM2-related microglial activation is linked to thicker cortical thickness and higher metabolism in early amyloidosis stages but tends to mitigate Aβ accumulation primarily at relatively advanced stages.
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Affiliation(s)
- Yue Cai
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Dai Shi
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Guoyu Lan
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Linting Chen
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Yanni Jiang
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Liemin Zhou
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
| | - Tengfei Guo
- From the Institute of Biomedical Engineering (Y.C., G.L., L.C., T.G.), Shenzhen Bay Laboratory; Neurology Medicine Center (D.S., L.Z.), The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Psychology (Y.J.), University of Texas at Austin; and Institute of Biomedical Engineering (T.G.), Peking University Shenzhen Graduate School, China
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6
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Yuyama K, Sun H, Fujii R, Hemmi I, Ueda K, Igeta Y. Extracellular vesicle proteome unveils cathepsin B connection to Alzheimer's disease pathogenesis. Brain 2024; 147:627-636. [PMID: 38071653 PMCID: PMC10834236 DOI: 10.1093/brain/awad361] [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: 04/26/2023] [Revised: 09/27/2023] [Accepted: 10/10/2023] [Indexed: 02/03/2024] Open
Abstract
Extracellular vesicles (EVs) are membrane vesicles that are released extracellularly and considered to be implicated in the pathogenesis of neurodegenerative diseases including Alzheimer's disease. Here, CSF EVs of 16 ATN-classified cases were subjected to quantitative proteome analysis. In these CSF EVs, levels of 11 proteins were significantly altered during the ATN stage transitions (P < 0.05 and fold-change > 2.0). These proteins were thought to be associated with Alzheimer's disease pathogenesis and represent candidate biomarkers for pathogenic stage classification. Enzyme-linked immunosorbent assay analysis of CSF and plasma EVs revealed altered levels of cathepsin B (CatB) during the ATN transition (seven ATN groups in validation set, n = 136). The CSF and plasma EV CatB levels showed a negative correlation with CSF amyloid-β42 concentrations. This proteomic landscape of CSF EVs in ATN classifications can depict the molecular framework of Alzheimer's disease progression, and CatB may be considered a promising candidate biomarker and therapeutic target in Alzheimer's disease amyloid pathology.
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Affiliation(s)
- Kohei Yuyama
- Lipid Biofunction Section, Faculty of Advanced Life Science, Hokkaido University, Sapporo 001-0021, Japan
| | - Hui Sun
- Lipid Biofunction Section, Faculty of Advanced Life Science, Hokkaido University, Sapporo 001-0021, Japan
| | - Risa Fujii
- Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo 035-8550, Japan
| | - Isao Hemmi
- Department of Nursing, Japanese Red Cross College of Nursing, Tokyo 150-0012, Japan
| | - Koji Ueda
- Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo 035-8550, Japan
| | - Yukifusa Igeta
- Department of Dementia, Dementia Center, Toranomon Hospital, Tokyo 105-8470, Japan
- Division of Dementia Research, Okinaka Memorial Institute for Medical Research, Tokyo 105-8470, Japan
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Hale MR, Langhough R, Du L, Hermann BP, Van Hulle CA, Carboni M, Kollmorgen G, Basche KE, Bruno D, Sanson-Miles L, Jonaitis EM, Chin NA, Okonkwo OC, Bendlin BB, Carlsson CM, Zetterberg H, Blennow K, Betthauser TJ, Johnson SC, Mueller KD. Associations between recall of proper names in story recall and CSF amyloid and tau in adults without cognitive impairment. Neurobiol Aging 2024; 133:87-98. [PMID: 37925995 PMCID: PMC10842469 DOI: 10.1016/j.neurobiolaging.2023.09.018] [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: 01/18/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 11/07/2023]
Abstract
Neuropsychological measures sensitive to decline in the preclinical phase of Alzheimer's disease are needed. We previously demonstrated that higher amyloid-beta (Aβ) assessed by positron emission tomography in adults without cognitive impairment was associated with recall of fewer proper names in Logical Memory story recall. The current study investigated the association between proper names and cerebrospinal fluid biomarkers (Aβ42/40, phosphorylated tau181 [pTau181], neurofilament light) in 223 participants from the Wisconsin Registry for Alzheimer's Prevention. We assessed associations between biomarkers and delayed Logical Memory total score and proper names using binary logistic regressions. Sensitivity analyses used multinomial logistic regression and stratified biomarker groups. Lower Logical Memory total score and proper names scores from the most recent visit were associated with biomarker positivity. Relatedly, there was a 27% decreased risk of being classified Aβ42/40+/pTau181+ for each additional proper name recalled. A linear mixed effects model found that longitudinal change in proper names recall was predicted by biomarker status. These results demonstrate a novel relationship between proper names and Alzheimer's disease-cerebrospinal fluid pathology.
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Affiliation(s)
- Madeline R Hale
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA
| | - Rebecca Langhough
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Lianlian Du
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Bruce P Hermann
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Carol A Van Hulle
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | | | | | - Kristin E Basche
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Davide Bruno
- School of Psychology, Liverpool John Moores University, Liverpool, UK
| | - Leah Sanson-Miles
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA
| | - Erin M Jonaitis
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Nathaniel A Chin
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA; VA Geriatric Research, Education and Clinical Center (GRECC), William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Ozioma C Okonkwo
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Barbara B Bendlin
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA; VA Geriatric Research, Education and Clinical Center (GRECC), William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Cynthia M Carlsson
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Henrik Zetterberg
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; UK Dementia Research Institute at UCL, London, UK; Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Tobey J Betthauser
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA; VA Geriatric Research, Education and Clinical Center (GRECC), William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Kimberly D Mueller
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA; Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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Yang Z, Sreenivasan K, Toledano Strom EN, Osse AML, Pasia LG, Cosme CG, Mugosa MRN, Chevalier EL, Ritter A, Miller JB, Cordes D, Cummings JL, Kinney JW. Clinical and biological relevance of glial fibrillary acidic protein in Alzheimer's disease. Alzheimers Res Ther 2023; 15:190. [PMID: 37924152 PMCID: PMC10623866 DOI: 10.1186/s13195-023-01340-4] [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: 05/06/2023] [Accepted: 10/24/2023] [Indexed: 11/06/2023]
Abstract
INTRODUCTION There is a tremendous need for identifying reliable blood-based biomarkers for Alzheimer's disease (AD) that are tied to the biological ATN (amyloid, tau and neurodegeneration) framework as well as clinical assessment and progression. METHODS One hundred forty-four elderly participants underwent 18F-AV45 positron emission tomography (PET) scan, structural magnetic resonance imaging (MRI) scan, and blood sample collection. The composite standardized uptake value ratio (SUVR) was derived from 18F-AV45 PET to assess brain amyloid burden, and the hippocampal volume was determined from structural MRI scans. Plasma glial fibrillary acidic protein (GFAP), phosphorylated tau-181 (ptau-181), and neurofilament light (NfL) measured by single molecular array (SIMOA) technology were assessed with respect to ATN framework, genetic risk factor, age, clinical assessment, and future functional decline among the participants. RESULTS Among the three plasma markers, GFAP best discriminated participants stratified by clinical diagnosis and brain amyloid status. Age was strongly associated with NfL, followed by GFAP and ptau-181 at much weaker extent. Brain amyloid was strongly associated with plasma GFAP and ptau-181 and to a lesser extent with plasma NfL. Moderate association was observed between plasma markers. Hippocampal volume was weakly associated with all three markers. Elevated GFAP and ptau-181 were associated with worse cognition, and plasma GFAP was the most predictive of future functional decline. Combining GFAP and ptau-181 together was the best model to predict brain amyloid status across all participants (AUC = 0.86) or within cognitively impaired participants (AUC = 0.93); adding NfL as an additional predictor only had a marginal improvement. CONCLUSION Our findings indicate that GFAP is of potential clinical utility in screening amyloid pathology and predicting future cognitive decline. GFAP, NfL, and ptau-181 were moderately associated with each other, with discrepant relevance to age, sex, and AD genetic risk, suggesting their relevant but differential roles for AD assessment. The combination of GFAP with ptau-181 provides an accurate model to predict brain amyloid status, with the superior performance of GFAP over ptau-181 when the prediction is limited to cognitively impaired participants.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA.
| | - Karthik Sreenivasan
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | | | | | | | - Celica Glenn Cosme
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Maya Rae N Mugosa
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Emma Léa Chevalier
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Aaron Ritter
- Hoag's Pickup Family Neurosciences Institute, Newport Beach, CA, USA
| | - Justin B Miller
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, 80309, USA
| | - Jeffrey L Cummings
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Jefferson W Kinney
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, USA
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10
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Sung YJ, Yang C, Norton J, Johnson M, Fagan A, Bateman RJ, Perrin RJ, Morris JC, Farlow MR, Chhatwal JP, Schofield PR, Chui H, Wang F, Novotny B, Eteleeb A, Karch C, Schindler SE, Rhinn H, Johnson EC, Se-Hwee Oh H, Rutledge JE, Dammer EB, Seyfried NT, Wyss-Coray T, Harari O, Cruchaga C. Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease. Sci Transl Med 2023; 15:eabq5923. [PMID: 37406134 PMCID: PMC10803068 DOI: 10.1126/scitranslmed.abq5923] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/13/2023] [Indexed: 07/07/2023]
Abstract
Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.
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Affiliation(s)
- Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Joanne Norton
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Matt Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Anne Fagan
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Randall J. Bateman
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Richard J. Perrin
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - John C. Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jasmeer P. Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Peter R. Schofield
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Helena Chui
- Department of Neurology, University of Southern California, Los Angeles, CA 90089, USA
| | - Fengxian Wang
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Brenna Novotny
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Abdallah Eteleeb
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Suzanne E. Schindler
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Herve Rhinn
- Department of Bioinformatics. Alector, Inc. 151 Oyster Point Blvd. #300 South San Francisco CA 94080, USA
| | - Erik C.B. Johnson
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Hamilton Se-Hwee Oh
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Jarod Evert Rutledge
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Eric B Dammer
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Nicholas T. Seyfried
- Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA 30329, USA
- Department of Biochemistry, Emory School of Medicine, Atlanta, GA 30329, USA
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Oscar Harari
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63108, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63108, USA
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
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11
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Dong Y, Li Y, Liu K, Han X, Liu R, Ren Y, Cong L, Zhang Q, Hou T, Song L, Tang S, Shi L, Luo Y, Kalpouzos G, Laukka EJ, Winblad B, Wang Y, Du Y, Qiu C. Anosmia, mild cognitive impairment, and biomarkers of brain aging in older adults. Alzheimers Dement 2023; 19:589-601. [PMID: 36341691 DOI: 10.1002/alz.12777] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/14/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022]
Abstract
Olfactory impairment is a potential marker for prodromal dementia, but the underlying mechanisms are poorly understood. This population-based study included 4214 dementia-free participants (age ≥65 years). Olfaction was assessed using the 16-item Sniffin' Sticks identification test. In the subsamples, we measured plasma amyloid beta (Aβ)40, Aβ42, total tau, and neurofilament light chain (NfL; n = 1054); and quantified hippocampal, entorhinal cortex, and white matter hyperintensity (WMH) volumes, and Alzheimer's disease (AD)-signature cortical thickness (n = 917). Data were analyzed with logistic and linear regression models. In the total sample, mild cognitive impairment (MCI) was diagnosed in 1102 persons (26.2%; amnestic MCI, n = 931; non-amnestic MCI, n = 171). Olfactory impairment was significantly associated with increased likelihoods of MCI, amnestic MCI, and non-amnestic MCI. In the subsamples, anosmia was significantly associated with higher plasma total tau and NfL concentrations, smaller hippocampal and entorhinal cortex volumes, and greater WMH volume, and marginally with lower AD-signature cortical thickness. These results suggest that cerebral neurodegenerative and microvascular lesions are common neuropathologies linking anosmia with MCI in older adults.
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Affiliation(s)
- Yi Dong
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Yuanjing Li
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Keke Liu
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Xiaolei Han
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Rui Liu
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Yifei Ren
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Lin Cong
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Qinghua Zhang
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Tingting Hou
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Lin Song
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Shi Tang
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Grégoria Kalpouzos
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Erika J Laukka
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Bengt Winblad
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Yongxiang Wang
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Yifeng Du
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Chengxuan Qiu
- Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China.,Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
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12
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Öhrfelt A, Benedet AL, Ashton NJ, Kvartsberg H, Vandijck M, Weiner MW, Trojanowski JQ, Shaw LM, Zetterberg H, Blennow K. Association of CSF GAP-43 With the Rate of Cognitive Decline and Progression to Dementia in Amyloid-Positive Individuals. Neurology 2023; 100:e275-e285. [PMID: 36192174 PMCID: PMC9869758 DOI: 10.1212/wnl.0000000000201417] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/31/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES To test the associations between the presynaptic growth-associated protein 43 (GAP-43), quantified in CSF, and biomarkers of Alzheimer disease (AD) pathophysiology, cross-sectionally and longitudinally. METHODS In this retrospective study, GAP-43 was measured in participants from the AD Neuroimaging Initiative (ADNI) cohort using an in-house ELISA method, and levels were compared between groups, both cross-sectionally and longitudinally. Linear regression models tested the associations between biomarkers of AD (amyloid beta [Aβ] and tau pathologies, neurodegeneration, and cognition) adjusted by age, sex, and diagnosis. Linear mixed-effect models evaluated how baseline GAP-43 predicts brain hypometabolism, atrophy, and cognitive decline over time. Cox proportional hazard regression models tested how GAP-43 levels and Aβ status, at baseline, increased the risk of progression to AD dementia over time. RESULTS This study included 786 participants from the ADNI cohort, which were further classified in cognitively unimpaired (CU) Aβ-negative (nCU- = 197); CU Aβ-positive (nCU+ = 55), mild cognitively impaired (MCI) Aβ-negative (nMCI- = 228), MCI Aβ-positive (nMCI+ = 193), and AD dementia Aβ-positive (nAD = 113). CSF GAP-43 levels were increased in Aβ-positive compared with Aβ-negative participants, independent of the cognitive status. In Aβ-positive participants, high baseline GAP-43 levels led to worse brain metabolic decline (p = 0.01), worse brain atrophy (p = 8.8 × 10-27), and worse MMSE scores (p = 0.03) over time, as compared with those with low GAP-43 levels. Similarly, Aβ-positive participants with high baseline GAP-43 had the highest risk to convert to AD dementia (hazard ratio [HR = 8.56, 95% CI 4.94-14.80, p = 1.5 × 10-14]). Despite the significant association with Aβ pathology (η2 Aβ PET = 0.09, P Aβ PET < 0.001), CSF total tau (tTau) and phosphorylated tau (pTau) had a larger effect size on GAP43 than Aβ PET (η2 pTau-181 = 0.53, P pTau-181 < 0.001; η2 tTau = 0.59, P tTau < 0.001). DISCUSSION High baseline levels of CSF GAP-43 are associated with progression in Aβ-positive individuals, with a more aggressive neurodegenerative process, faster rate of cognitive decline, and increased risk of converting to dementia.
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Affiliation(s)
- Annika Öhrfelt
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China.
| | - Andréa L Benedet
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Nicholas J Ashton
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Hlin Kvartsberg
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Manu Vandijck
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Michael W Weiner
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - John Q Trojanowski
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Leslie M Shaw
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Henrik Zetterberg
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Kaj Blennow
- From the Department of Psychiatry and Neurochemistry (A.Ö., A.L.B., N.J.A., H.K., H.Z., K.B.), Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal; Wallenberg Centre for Molecular and Translational Medicine (N.J.A.), University of Gothenburg, Sweden; Department of Old Age Psychiatry (N.J.A.), Institute of Psychiatry, Psychology and Neuroscience, King's College London; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation (N.J.A., H.Z.), London, United Kingdom; Clinical Neurochemistry Laboratory (H.K., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Fujirebio Europe NV (M.V.), Ghent, Belgium; Department of Veterans Affairs Medical Center (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco, CA; Departments of Radiology (M.W.W.), Medicine (M.W.W.), Psychiatry (M.W.W.) and Neurology (M.W.W.), University of California, San Francisco; Department of Pathology and Laboratory Medicine (J.Q.T., L.M.S.), Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute (H.Z.), London; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
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13
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Abildgaard A, Parkner T, Knudsen CS, Gottrup H, Klit H. Diagnostic Cut-offs for CSF β-amyloid and tau proteins in a Danish dementia clinic. Clin Chim Acta 2023; 539:244-249. [PMID: 36572135 DOI: 10.1016/j.cca.2022.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Analysis of beta-amyloid 1-42 (Aβ42), total tau (t-tau) and phosphorylated-tau 181 (p-tau) in the cerebrospinal fluid (CSF) is often performed as a part of the diagnostic work-up in case of suspected Alzheimer's dementia (AD). Unfortunately, studies on optimal CSF biomarker cut-offs in a real-world clinical setting are scarce. METHODS We retrospectively evaluated the biomarker levels of 264 consecutive patients referred to our dementia clinic. The biomarkers were analysed with the Elecsys(R) assays. Diagnoses were based on all available clinical information, including FDG-PET scans. RESULTS In total, we identified 233 patients diagnosed with dementia. The median MMSE score was 22 (IQR 18-25). AD pathophysiology was suspected in 156 patients, and the corresponding cut-offs based on the Youden index were: Aβ42: 903 ng/L (ROC-AUC 0.78); t-tau: 272 ng/L (ROC-AUC 0.78); p-tau: 24 ng/L (ROC-AUC 0.85); t-tau/Aβ42 ratio: 0.34 (ROC-AUC 0.91); p-tau/Aβ42 ratio: 0.029 (ROC-AUC 0.92). CONCLUSIONS We found the tau/Aβ42 ratios to possess the best diagnostic performance, but our estimated cut-off values for the ratios were somewhat higher than previously reported. Consequently, if the CSF analyses are used to support a diagnosis of AD in a heterogeneous high-prevalence cohort, adjustment of the cut-offs may be warranted.
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Affiliation(s)
- Anders Abildgaard
- Department of Clinical Biochemistry, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, Denmark.
| | - Tina Parkner
- Department of Clinical Biochemistry, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, Denmark
| | - Cindy Soendersoe Knudsen
- Department of Clinical Biochemistry, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, Denmark
| | - Hanne Gottrup
- Department of Neurology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, Denmark
| | - Henriette Klit
- Department of Neurology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, Denmark
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14
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Li A, Du J, Cai Y, Chen X, Sun K, Guo T. Body Mass Index Decrease Has a Distinct Association with Alzheimer's Disease Pathophysiology in APOE ɛ4 Carriers and Non-Carriers. J Alzheimers Dis 2023; 96:643-655. [PMID: 37840490 DOI: 10.3233/jad-230446] [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] [Indexed: 10/17/2023]
Abstract
BACKGROUND Body mass index (BMI) changes may be related to Alzheimer's disease (AD) alterations, but it is unclear how the apolipoprotein E ɛ4 (APOE ɛ4) allele affects their association. OBJECTIVE To explore the association of BMI changes with AD pathologies in APOE ɛ4 carriers and non-carriers. METHODS In 862 non-demented ADNI participants with≥2 BMI measurements, we investigated the relationships between BMI slopes and longitudinal changes in amyloid-β (Aβ) accumulation, neurodegeneration and cognition, and follow-up tau deposition in different Aβ and APOE ɛ4 statuses. RESULTS In Aβ+ APOE ɛ4 non-carriers, faster BMI declines were associated with faster rates of Aβ accumulation (standardized β (βstd) = -0.29, p = 0.001), AD meta regions of interest (metaROI) hypometabolism (βstd = 0.23, p = 0.026), memory declines (βstd = 0.17, p = 0.029), executive function declines (βstd = 0.19, p = 0.011), and marginally faster Temporal-metaROI cortical thinning (βstd = 0.15, p = 0.067) and higher follow-up Temporal-metaROI tau deposition (βstd = -0.17, p = 0.059). Among Aβ- individuals, faster BMI decreases were related to faster Aβ accumulation (βstd = -0.25, p = 0.023) in APOE ɛ4 carriers, whereas predicted faster declines in memory and executive function in both APOE ɛ4 carriers (βstd = 0.25, p = 0.008; βstd = 0.32, p = 0.001) and APOE ɛ4 non-carriers (βstd = 0.11, p = 0.030; βstd = 0.12, p = 0.026). CONCLUSIONS This study highlights the significance of tracking BMI data in older adults by providing novel insights into how body weight fluctuations and APOE ɛ4 interact with AD pathology and cognitive decline.
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Affiliation(s)
- Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jing Du
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Xuhui Chen
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China
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15
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Lan G, Cai Y, Li A, Liu Z, Ma S, Guo T. Association of Presynaptic Loss with Alzheimer's Disease and Cognitive Decline. Ann Neurol 2022; 92:1001-1015. [PMID: 36056679 DOI: 10.1002/ana.26492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Increased presynaptic dysfunction measured by cerebrospinal fluid (CSF) growth-associated protein-43 (GAP43) may be observed in Alzheimer's disease (AD), but how CSF GAP43 increases relate to AD-core pathologies, neurodegeneration, and cognitive decline in AD requires further investigation. METHODS We analyzed 731 older adults with baseline β-amyloid (Aβ) positron emission tomography (PET), CSF GAP43, CSF phosphorylated tau181 (p-Tau181 ), and 18 F-fluorodeoxyglucose PET, and longitudinal residual hippocampal volume and cognitive assessments. Among them, 377 individuals had longitudinal 18 F-fluorodeoxyglucose PET, and 326 individuals had simultaneous longitudinal CSF GAP43, Aβ PET, and CSF p-Tau181 data. We compared baseline and slopes of CSF GAP43 among different stages of AD, as well as their associations with Aβ PET, CSF p-Tau181 , residual hippocampal volume, 18 F-fluorodeoxyglucose PET, and cognition cross-sectionally and longitudinally. RESULTS Regardless of Aβ positivity and clinical diagnosis, CSF p-Tau181 -positive individuals showed higher CSF GAP43 concentrations (p < 0.001) and faster rates of CSF GAP43 increases (p < 0.001) compared with the CSF p-Tau181 -negative individuals. Moreover, higher CSF GAP43 concentrations and faster rates of CSF GAP43 increases were strongly related to CSF p-Tau181 independent of Aβ PET. They were related to more rapid hippocampal atrophy, hypometabolism, and cognitive decline (p < 0.001), and predicted the progression from MCI to dementia (area under the curve for baseline 0.704; area under the curve for slope 0.717) over a median 4 years of follow up. INTERPRETATION Tau aggregations rather than Aβ plaques primarily drive presynaptic dysfunction measured by CSF GAP43, which may lead to sequential neurodegeneration and cognitive impairment in AD or neurodegenerative diseases. ANN NEUROL 2022;92:1001-1015.
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Affiliation(s)
- Guoyu Lan
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.,Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.,Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Zhen Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Shaohua Ma
- Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.,Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
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16
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Discordant Amyloid Status Diagnosis in Alzheimer’s Disease. Biomedicines 2022; 10:biomedicines10112880. [DOI: 10.3390/biomedicines10112880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
Introduction: Early and accurate Alzheimer’s disease (AD) diagnosis has evolved in recent years by the use of specific methods for detecting its histopathological features in concrete cases. Currently, biomarkers in cerebrospinal fluid (CSF) and imaging techniques (amyloid PET) are the most used specific methods. However, some results between both methods are discrepant. Therefore, an evaluation of these discrepant cases is required. Objective: The aim of this work is to analyze the characteristics of cases showing discrepancies between methods for detecting amyloid pathology. Methodology: Patients from the Neurology Department of La Fe Hospital (n = 82) were diagnosed using both methods (CSF biomarkers and amyloid-PET). Statistical analyses were performed using logistic regression, and sex and age were included as covariables. Additionally, results of standard neuropsychological evaluations were taken into account in our analyses. Results: The comparison between CSF biomarker (Aβ42) and amyloid PET results showed that around 18% of cases were discrepant—mainly CFS-negative and PET-positive cases had CSF levels close to the cut-off point. In addition, a correlation between the episodic memory test and CSF biomarkers levels was observed. However, the same results were not obtained for other neuropsychological domains. In general, CSF- and PET-discrepant cases showed altered episodic memory in around 66% of cases, while 33% showed normal performance. Conclusions: In common clinical practice at tertiary memory centers, result discrepancies between tests of amyloid status are far more common than expected. However, episodic memory tests remain an important support method for AD diagnosis, especially in cases with discrepant results between amyloid PET and CSF biomarkers.
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17
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Fristed E, Skirrow C, Meszaros M, Lenain R, Meepegama U, Papp KV, Ropacki M, Weston J. Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity. Brain Commun 2022; 4:fcac231. [PMID: 36381988 PMCID: PMC9639797 DOI: 10.1093/braincomms/fcac231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/30/2022] [Accepted: 09/13/2022] [Indexed: 08/27/2023] Open
Abstract
Early detection of Alzheimer's disease is required to identify patients suitable for disease-modifying medications and to improve access to non-pharmacological preventative interventions. Prior research shows detectable changes in speech in Alzheimer's dementia and its clinical precursors. The current study assesses whether a fully automated speech-based artificial intelligence system can detect cognitive impairment and amyloid beta positivity, which characterize early stages of Alzheimer's disease. Two hundred participants (age 54-85, mean 70.6; 114 female, 86 male) from sister studies in the UK (NCT04828122) and the USA (NCT04928976), completed the same assessments and were combined in the current analyses. Participants were recruited from prior clinical trials where amyloid beta status (97 amyloid positive, 103 amyloid negative, as established via PET or CSF test) and clinical diagnostic status was known (94 cognitively unimpaired, 106 with mild cognitive impairment or mild Alzheimer's disease). The automatic story recall task was administered during supervised in-person or telemedicine assessments, where participants were asked to recall stories immediately and after a brief delay. An artificial intelligence text-pair evaluation model produced vector-based outputs from the original story text and recorded and transcribed participant recalls, quantifying differences between them. Vector-based representations were fed into logistic regression models, trained with tournament leave-pair-out cross-validation analysis to predict amyloid beta status (primary endpoint), mild cognitive impairment and amyloid beta status in diagnostic subgroups (secondary endpoints). Predictions were assessed by the area under the receiver operating characteristic curve for the test result in comparison with reference standards (diagnostic and amyloid status). Simulation analysis evaluated two potential benefits of speech-based screening: (i) mild cognitive impairment screening in primary care compared with the Mini-Mental State Exam, and (ii) pre-screening prior to PET scanning when identifying an amyloid positive sample. Speech-based screening predicted amyloid beta positivity (area under the curve = 0.77) and mild cognitive impairment or mild Alzheimer's disease (area under the curve = 0.83) in the full sample, and predicted amyloid beta in subsamples (mild cognitive impairment or mild Alzheimer's disease: area under the curve = 0.82; cognitively unimpaired: area under the curve = 0.71). Simulation analyses indicated that in primary care, speech-based screening could modestly improve detection of mild cognitive impairment (+8.5%), while reducing false positives (-59.1%). Furthermore, speech-based amyloid pre-screening was estimated to reduce the number of PET scans required by 35.3% and 35.5% in individuals with mild cognitive impairment and cognitively unimpaired individuals, respectively. Speech-based assessment offers accessible and scalable screening for mild cognitive impairment and amyloid beta positivity.
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Affiliation(s)
| | | | | | | | | | - Kathryn V Papp
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Michael Ropacki
- Strategic Global Research & Development, Temecula, California, 94019, USA
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18
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Ebenau JL, Visser D, Kroeze LA, van Leeuwenstijn MSSA, van Harten AC, Windhorst AD, Golla SVS, Boellaard R, Scheltens P, Barkhof F, van Berckel BNM, van der Flier WM. Longitudinal change in ATN biomarkers in cognitively normal individuals. Alzheimers Res Ther 2022; 14:124. [PMID: 36057616 PMCID: PMC9440493 DOI: 10.1186/s13195-022-01069-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/23/2022] [Indexed: 04/14/2023]
Abstract
BACKGROUND Biomarkers for amyloid, tau, and neurodegeneration (ATN) have predictive value for clinical progression, but it is not clear how individuals move through these stages. We examined changes in ATN profiles over time, and investigated determinants of change in A status, in a sample of cognitively normal individuals presenting with subjective cognitive decline (SCD). METHODS We included 92 individuals with SCD from the SCIENCe project with [18F]florbetapir PET (A) available at two time points (65 ± 8y, 42% female, MMSE 29 ± 1, follow-up 2.5 ± 0.7y). We additionally used [18F]flortaucipir PET for T and medial temporal atrophy score on MRI for N. Thirty-nine individuals had complete biomarker data at baseline and follow-up, enabling the construction of ATN profiles at two time points. All underwent extensive neuropsychological assessments (follow-up time 4.9 ± 2.8y, median number of visits n = 4). We investigated changes in biomarker status and ATN profiles over time. We assessed which factors predisposed for a change from A- to A+ using logistic regression. We additionally used linear mixed models to assess change from A- to A+, compared to the group that remained A- at follow-up, as predictor for cognitive decline. RESULTS At baseline, 62% had normal AD biomarkers (A-T-N- n = 24), 5% had non-AD pathologic change (A-T-N+ n = 2,) and 33% fell within the Alzheimer's continuum (A+T-N- n = 9, A+T+N- n = 3, A+T+N+ n = 1). Seventeen subjects (44%) changed to another ATN profile over time. Only 6/17 followed the Alzheimer's disease sequence of A → T → N, while 11/17 followed a different order (e.g., reverted back to negative biomarker status). APOE ε4 carriership inferred an increased risk of changing from A- to A+ (OR 5.2 (95% CI 1.2-22.8)). Individuals who changed from A- to A+, showed subtly steeper decline on Stroop I (β - 0.03 (SE 0.01)) and Stroop III (- 0.03 (0.01)), compared to individuals who remained A-. CONCLUSION We observed considerable variability in the order of ATN biomarkers becoming abnormal. Individuals who became A+ at follow-up showed subtle decline on tests for attention and executive functioning, confirming clinical relevance of amyloid positivity.
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Affiliation(s)
- Jarith L Ebenau
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
| | - Denise Visser
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Lior A Kroeze
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Mardou S S A van Leeuwenstijn
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Sandeep V S Golla
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Bart N M van Berckel
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology & Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
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19
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Chen YH, Lin RR, Huang HF, Xue YY, Tao QQ. Microglial Activation, Tau Pathology, and Neurodegeneration Biomarkers Predict Longitudinal Cognitive Decline in Alzheimer's Disease Continuum. Front Aging Neurosci 2022; 14:848180. [PMID: 35847667 PMCID: PMC9280990 DOI: 10.3389/fnagi.2022.848180] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/12/2022] [Indexed: 01/02/2023] Open
Abstract
Purpose Biomarkers used for predicting longitudinal cognitive change in Alzheimer's disease (AD) continuum are still elusive. Tau pathology, neuroinflammation, and neurodegeneration are the leading candidate predictors. We aimed to determine these three aspects of biomarkers in cerebrospinal fluid (CSF) and plasma to predict longitudinal cognition status using Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Patients and Methods A total of 430 subjects including, 96 cognitive normal (CN) with amyloid β (Aβ)-negative, 54 CN with Aβ-positive, 195 mild cognitive impairment (MCI) with Aβ-positive, and 85 AD with amyloid-positive (Aβ-positive are identified by CSF Aβ42/Aβ40 < 0.138). Aβ burden was evaluated by CSF and plasma Aβ42/Aβ40 ratio; tau pathology was evaluated by CSF and plasma phosphorylated-tau (p-tau181); microglial activation was measured by CSF soluble TREM2 (sTREM2) and progranulin (PGRN); neurodegeneration was measured by CSF and plasma t-tau and structural magnetic resonance imaging (MRI); cognition was examined annually over the subsequent 8 years using the Alzheimer's Disease Assessment Scale Cognition 13-item scale (ADAS13) and Mini-Mental State Exam (MMSE). Linear mixed-effects models (LME) were applied to assess the correlation between biomarkers and longitudinal cognition decline, as well as their effect size on the prediction of longitudinal cognitive decline. Results Baseline CSF Aβ42/Aβ40 ratio was decreased in MCI and AD compared to CN, while CSF p-tau181 and t-tau increased. Baseline CSF sTREM2 and PGRN did not show any differences in MCI and AD compared to CN. Baseline brain volumes (including the hippocampal, entorhinal, middle temporal lobe, and whole-brain) decreased in MCI and AD groups. For the longitudinal study, there were significant interaction effects of CSF p-tau181 × time, plasma p-tau181 × time, CSF sTREM2 × time, and brain volumes × time, indicating CSF, and plasma p-tau181, CSF sTREM2, and brain volumes could predict longitudinal cognition deterioration rate. CSF sTREM2, CSF, and plasma p-tau181 had similar medium prediction effects, while brain volumes showed stronger effects in predicting cognition decline. Conclusion Our study reported that baseline CSF sTREM2, CSF, and plasma p-tau181, as well as structural MRI, could predict longitudinal cognitive decline in subjects with positive AD pathology. Plasma p-tau181 can be used as a relatively noninvasive reliable biomarker for AD longitudinal cognition decline prediction.
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Affiliation(s)
- Yi-He Chen
- Department of Neurology and Research Center of Neurology in Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Rong-Rong Lin
- Department of Neurology and Research Center of Neurology in Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Hui-Feng Huang
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, China
| | - Yan-Yan Xue
- Department of Neurology and Research Center of Neurology in Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Qing-Qing Tao
- Department of Neurology and Research Center of Neurology in Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
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20
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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21
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Smirnov DS, Ashton NJ, Blennow K, Zetterberg H, Simrén J, Lantero-Rodriguez J, Karikari TK, Hiniker A, Rissman RA, Salmon DP, Galasko D. Plasma biomarkers for Alzheimer's Disease in relation to neuropathology and cognitive change. Acta Neuropathol 2022; 143:487-503. [PMID: 35195758 PMCID: PMC8960664 DOI: 10.1007/s00401-022-02408-5] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 12/14/2022]
Abstract
Plasma biomarkers related to amyloid, tau, and neurodegeneration (ATN) show great promise for identifying these pathological features of Alzheimer's Disease (AD) as shown by recent clinical studies and selected autopsy studies. We have evaluated ATN plasma biomarkers in a series of 312 well-characterized longitudinally followed research subjects with plasma available within 5 years or less before autopsy and examined these biomarkers in relation to a spectrum of AD and related pathologies. Plasma Aβ42, Aβ40, total Tau, P-tau181, P-tau231 and neurofilament light (NfL) were measured using Single molecule array (Simoa) assays. Neuropathological findings were assessed using standard research protocols. Comparing plasma biomarkers with pathology diagnoses and ratings, we found that P-tau181 (AUC = 0.856) and P-tau231 (AUC = 0.773) showed the strongest overall sensitivity and specificity for AD neuropathological change (ADNC). Plasma P-tau231 showed increases at earlier ADNC stages than other biomarkers. Plasma Aβ42/40 was decreased in relation to amyloid and AD pathology, with modest diagnostic accuracy (AUC = 0.601). NfL was increased in non-AD cases and in a subset of those with ADNC. Plasma biomarkers did not show changes in Lewy body disease (LBD), hippocampal sclerosis of aging (HS) or limbic-predominant age-related TDP-43 encephalopathy (LATE) unless ADNC was present. Higher levels of P-tau181, 231 and NfL predicted faster cognitive decline, as early as 10 years prior to autopsy, even among people with normal cognition or mild cognitive impairment. These results support plasma P-tau181 and 231 as diagnostic biomarkers related to ADNC that also can help to predict future cognitive decline, even in predementia stages. Although NfL was not consistently increased in plasma in AD and shows increases in several neurological disorders, it had utility to predict cognitive decline. Plasma Aβ42/40 as measured in this study was a relatively weak predictor of amyloid pathology, and different assay methods may be needed to improve on this. Additional plasma biomarkers are needed to detect the presence and impact of LBD and LATE pathology.
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Affiliation(s)
- Denis S Smirnov
- University of California, San Diego and Shiley-Marcos Alzheimer's Disease Research Center, La Jolla, CA, USA
| | - Nicholas J Ashton
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, 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
- Wallenberg Centre for Molecular and Translational Medicine, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Joel Simrén
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Juan Lantero-Rodriguez
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Thomas K Karikari
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Annie Hiniker
- University of California, San Diego and Shiley-Marcos Alzheimer's Disease Research Center, La Jolla, CA, USA
| | - Robert A Rissman
- University of California, San Diego and Shiley-Marcos Alzheimer's Disease Research Center, La Jolla, CA, USA
| | - David P Salmon
- University of California, San Diego and Shiley-Marcos Alzheimer's Disease Research Center, La Jolla, CA, USA
| | - Douglas Galasko
- University of California, San Diego and Shiley-Marcos Alzheimer's Disease Research Center, La Jolla, CA, USA.
- Department of Neurosciences, UC San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0624, USA.
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22
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Casanova R, Hsu FC, Barnard RT, Anderson AM, Talluri R, Whitlow CT, Hughes TM, Griswold M, Hayden KM, Gottesman RF, Wagenknecht LE. Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study. Alzheimers Dement 2022; 18:561-571. [PMID: 34310039 PMCID: PMC8789939 DOI: 10.1002/alz.12427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 01/10/2023]
Abstract
INTRODUCTION A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Fang-Chi Hsu
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Ryan T. Barnard
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Andrea M. Anderson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem
| | - Rajesh Talluri
- University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Timothy M. Hughes
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Kathleen M. Hayden
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem
| | | | - Lynne E. Wagenknecht
- Divison of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
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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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24
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Morar U, Izquierdo W, Martin H, Forouzannezhad P, Zarafshan E, Unger E, Bursac Z, Cabrerizo M, Barreto A, Vaillancourt DE, DeKosky ST, Loewenstein D, Duara R, Adjouadi M. A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non-converter Alzheimer's disease subjects with consideration for their amyloid beta status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12258. [PMID: 35229014 PMCID: PMC8865744 DOI: 10.1002/dad2.12258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION This study aims to determine whether newly introduced biomarkers Visinin-like protein-1 (VILIP-1), chitinase-3-like protein 1 (YKL-40), synaptosomal-associated protein 25 (SNAP-25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology. METHODS We examined baseline and longitudinal changes over a 7-year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We stratified all CSF (140) and MRI (525) cohort participants into five diagnostic groups (including converters) further dichotomized by CSF amyloid beta (Aβ) status. Linear mixed models were used to compare within-person rates of change across diagnostic groups and to evaluate the association of CSF biomarkers as predictors of magnetic resonance imaging (MRI) biomarkers. CSF biomarkers and disease-prone MRI regions are assessed for CSF proteins levels and brain structural changes. RESULTS VILIP-1 and SNAP-25 displayed within-person increments in early symptomatic, amyloid-positive groups. CSF amyloid-positive (Aβ+) subjects showed elevated baseline levels of total tau (tTau), phospho-tau181 (pTau), VILIP-1, and NG. YKL-40, SNAP-25, and NG are positively intercorrelated. Aβ+ subjects showed negative MRI biomarker changes. YKL-40, tTau, pTau, and VILIP-1 are longitudinally associated with MRI biomarkers atrophy. DISCUSSION Converters (CNc, MCIc) highlight the evolution of biomarkers during the disease progression. Results show that underlying amyloid pathology is associated with accelerated cognitive impairment. CSF levels of Aβ42, pTau, tTau, VILIP-1, and SNAP-25 show utility to discriminate between mild cognitive impairment (MCI) converter and control subjects (CN). Higher levels of YKL-40 in the Aβ+ group were longitudinally associated with declines in temporal pole and entorhinal thickness. Increased levels of tTau, pTau, and VILIP-1 in the Aβ+ groups were longitudinally associated with declines in hippocampal volume. These CSF biomarkers should be used in assessing the characterization of the AD progression.
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Affiliation(s)
- Ulyana Morar
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Walter Izquierdo
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Harold Martin
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Parisa Forouzannezhad
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Elaheh Zarafshan
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Elona Unger
- College of PharmacyFlorida A&M UniversityTallahasseeFloridaUSA
| | - Zoran Bursac
- Department of BiostatisticsRobert Stempel College of Public HealthFlorida International UniversityMiami
| | - Mercedes Cabrerizo
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - Armando Barreto
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
| | - David E. Vaillancourt
- Department of Neurology and McKnight Brain InstituteCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
- Department of Applied Physiology and KinesiologyUniversity of FloridaGainesvilleFloridaUSA
- Florida Alzheimer's Disease Research Center (ADRC)University of FloridaGainesvilleFloridaUSA
| | - Steven T. DeKosky
- Department of Neurology and McKnight Brain InstituteCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
- Florida Alzheimer's Disease Research Center (ADRC)University of FloridaGainesvilleFloridaUSA
| | - David Loewenstein
- Florida Alzheimer's Disease Research Center (ADRC)University of FloridaGainesvilleFloridaUSA
- Department of Psychiatry and Behavioral SciencesMiller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Ranjan Duara
- Florida Alzheimer's Disease Research Center (ADRC)University of FloridaGainesvilleFloridaUSA
- Wien Center for Alzheimer's Disease and Memory DisordersMount Sinai Medical CenterMiamiFloridaUSA
| | - Malek Adjouadi
- Center for Advanced Technology and EducationDepartment of Electrical and Computer EngineeringFlorida International UniversityMiamiFloridaUSA
- Florida Alzheimer's Disease Research Center (ADRC)University of FloridaGainesvilleFloridaUSA
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Canal-Garcia A, Gómez-Ruiz E, Mijalkov M, Chang YW, Volpe G, Pereira JB. Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data. Cereb Cortex 2022; 32:3501-3515. [PMID: 35059722 PMCID: PMC9376877 DOI: 10.1093/cercor/bhab429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022] Open
Abstract
The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.
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Affiliation(s)
- Anna Canal-Garcia
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
| | | | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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26
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Jansson D, Wang M, Thomas RG, Erickson MA, Peskind ER, Li G, Iliff J. Markers of Cerebrovascular Injury, Inflammation, and Plasma Lipids Are Associated with Alzheimer's Disease Cerebrospinal Fluid Biomarkers in Cognitively Normal Persons. J Alzheimers Dis 2022; 86:813-826. [PMID: 35124650 PMCID: PMC10010435 DOI: 10.3233/jad-215400] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a multifactorial process that takes years to manifest clinically. We propose that brain-derived indicators of cerebrovascular dysfunction and inflammation would inform on AD-related pathological processes early in, and perhaps prior to neurodegenerative disease development. OBJECTIVE Define the relationship between cerebrospinal fluid (CSF) markers of cerebrovascular dysfunction and neuroinflammation with AD CSF biomarkers in cognitively normal individuals. METHODS Analytes were measured from CSF and plasma collected at baseline from two randomized control trials. We performed Pearson correlation analysis (adjusting for age, sex, APOE haplotype, and education) between markers of central nervous system (CNS) barrier disruption, cerebrovascular dysfunction, CSF inflammatory cytokines and chemokines, and plasma lipid levels. We then developed a statistical prediction model using machine learning to test the ability of measured CSF analytes and blood lipid profiles to predict CSF AD biomarkers (total tau, phospho-tau (181), Aβ42) in this clinical population. RESULTS Our analysis revealed a significant association between markers of CNS barrier dysfunction and markers of cerebrovascular dysfunction, acute inflammatory responses, and CSF inflammatory cytokines. There was a significant association of blood lipid profiles with cerebrovascular injury markers, and CSF inflammatory cytokine levels. Using machine learning, we show that combinations of blood lipid profiles, CSF markers of CNS barrier disruption, cerebrovascular dysfunction and CSF inflammatory cytokines predict CSF total tau, p-tau, and, to a lesser extent, Aβ42 in cognitively normal subjects. CONCLUSION This suggests that these parallel pathological processes may contribute to the development of AD-related neuropathology in the absence of clinical manifestations.
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Affiliation(s)
- Deidre Jansson
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Marie Wang
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Ronald G Thomas
- Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, USA
| | - Michelle A Erickson
- Geriatrics Research Education and Clinical Center (GRECC), VA Puget Sound Healthcare System, Seattle, WA, USA.,Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Elaine R Peskind
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA
| | - Ge Li
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA.,Geriatrics Research Education and Clinical Center (GRECC), VA Puget Sound Healthcare System, Seattle, WA, USA
| | - Jeffrey Iliff
- VA Northwest Mental Illness Research, Education, and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA.,Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, USA.,Department of Neurology, University of Washington School of Medicine, Seattle, WA, USA
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27
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Jiang C, Wang Q, Xie S, Chen Z, Fu L, Peng Q, Liang Y, Guo H, Guo T. OUP accepted manuscript. Brain Commun 2022; 4:fcac084. [PMID: 35441134 PMCID: PMC9014538 DOI: 10.1093/braincomms/fcac084] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/21/2021] [Accepted: 03/29/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Chenyang Jiang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Qingyong Wang
- Department of Neurology, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen 518107, China
| | - Siwei Xie
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Zhicheng Chen
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Liping Fu
- Department of Nuclear Medicine, China-Japan Friendship Hospital, 2 Yinghuayuan Dongjie, Beijing 100029, China
| | - Qiyu Peng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Ying Liang
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Hongbo Guo
- Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Correspondence to: Tengfei Guo, PhD Institute of Biomedical Engineering Shenzhen Bay Laboratory, No.5 Kelian Road Shenzhen 518132, China E-mail:
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28
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Fristed E, Skirrow C, Meszaros M, Lenain R, Meepegama U, Cappa S, Aarsland D, Weston J. A remote speech‐based AI system to screen for early Alzheimer's disease via smartphones. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12366. [DOI: 10.1002/dad2.12366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/16/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Affiliation(s)
| | | | | | | | | | - Stefano Cappa
- IUSS Cognitive Neuroscience (ICoN) Center University School for Advanced Studies Pavia Italy
- IRCCS Mondino Foundation Pavia Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry Institute of Psychiatry Psychology & Neuroscience King's College London London UK
- Centre for Age‐Related Diseases Stavanger University Hospital Stavanger Norway
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29
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Willemse EAJ, Tijms BM, van Berckel BNM, Le Bastard N, van der Flier WM, Scheltens P, Teunissen CE. Comparing CSF amyloid-beta biomarker ratios for two automated immunoassays, Elecsys and Lumipulse, with amyloid PET status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12182. [PMID: 33969174 PMCID: PMC8088096 DOI: 10.1002/dad2.12182] [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: 12/11/2020] [Revised: 03/07/2021] [Accepted: 03/15/2021] [Indexed: 12/13/2022]
Abstract
INTRODUCTION We evaluated for two novel automated biomarker assays how cerebrospinal fluid (CSF) amyloid beta (Aβ)1- 42-ratios improved the concordance with amyloid positron emission tomography (PET) positivity compared to Aβ1- 42 alone. METHODS We selected 288 individuals from the Amsterdam Dementia Cohort across the Alzheimer's disease clinical spectrum when they had both CSF and amyloid PET visual read available, regardless of diagnosis. CSF Aβ1- 42, phosphorylated tau (p-tau), and total tau (t-tau) were measured with Elecsys and Lumipulse assays, and Aβ1-40 with Lumipulse. CSF cut-points were defined using receiver operating characteristic (ROC) for amyloid PET positivity. RESULTS For both Elecsys and Lumipulse the p-tau/Aβ1- 42, Aβ1- 42/Aβ1- 40, and t-tau/Aβ1- 42 ratios showed similarly good concordance with amyloid PET (Elecsys: 93,90,90%; Lumipulse: 94,92,90%) and were higher than Aβ1- 42 alone (Elecsys 85%; Lumipulse 84%). DISCUSSION Biomarker ratios p-tau/Aβ1- 42, Aβ1- 42/Aβ1- 40, t-tau/Aβ1- 42 on two automated platforms show similar optimal concordance with amyloid PET in a memory clinic cohort.
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Affiliation(s)
- Eline A. J. Willemse
- Department of Clinical ChemistryNeurochemistry LaboratoryAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Betty M. Tijms
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Bart N. M. van Berckel
- Department of Radiology & Nuclear MedicineAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | | | - Wiesje M. van der Flier
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Philip Scheltens
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Charlotte E. Teunissen
- Department of Clinical ChemistryNeurochemistry LaboratoryAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
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30
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Yang J, Jia L, Li Y, Qiu Q, Quan M, Jia J. Fluid Biomarkers in Clinical Trials for Alzheimer's Disease: Current and Future Application. J Alzheimers Dis 2021; 81:19-32. [PMID: 33749646 DOI: 10.3233/jad-201068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Alzheimer's disease (AD) research is entering a unique moment in which enormous information about the molecular basis of this disease is being translated into therapeutics. However, almost all drug candidates have failed in clinical trials over the past 30 years. These many trial failures have highlighted a need for the incorporation of biomarkers in clinical trials to help improve the trial design. Fluid biomarkers measured in cerebrospinal fluid and circulating blood, which can reflect the pathophysiological process in the brain, are becoming increasingly important in AD clinical trials. In this review, we first succinctly outline a panel of fluid biomarkers for neuropathological changes in AD. Then, we provide a comprehensive overview of current and future application of fluid biomarkers in clinical trials for AD. We also summarize the many challenges that have been encountered in efforts to integrate fluid biomarkers in clinical trials, and the barriers that have begun to be overcome. Ongoing research efforts in the field of fluid biomarkers will be critical to make significant progress in ultimately unveiling disease-modifying therapies in AD.
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Affiliation(s)
- Jianwei Yang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China
| | - Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China.,National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China.,Clinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, People's Republic of China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
| | - Yan Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China
| | - Qiongqiong Qiu
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China
| | - Meina Quan
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China.,National Clinical Research Center for Geriatric Diseases, Beijing, People's Republic of China.,Clinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, People's Republic of China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, People's Republic of China
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31
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Abstract
Imaging has made an immense contribution toward supporting the diagnosis of dementias, detecting preclinical and prodromal pathology, and allowing disease progression to be objectively tracked. This has led to consensus guidelines for the use of imaging in dementias to be published and a future task will be to validate these guidelines. Additionally, there needs to be standardised approaches over the use of binary thresholds when assigning an abnormality status. Other medical unmet needs include the need for specific imaging markers of (1) linear tau tangles, TDP-43 and alpha synuclein aggregates; (2) microglial phenotypes that throw light on the activity of these inflammatory cells; (3) activity of intracellular processes which normally act to clear misfolded proteins; (4) epigenetic activity which regulates gene expression. Future imaging studies are predicted to be active in all these areas. Finally, as safer and more effective immunotherapy and other protective strategies against the pathologies of dementias are developed and trialed, imaging will play a major future role in determining the efficacy of neuroprotective treatments and their mechanism of action to be examined.
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Affiliation(s)
- David J Brooks
- Translational and Clinical Research Institute, University of Newcastle upon Tyne, UK; Department of Nuclear Medicine, PET Centre, Aarhus University, Denmark; Department of Brain Sciences, Imperial College London, UK.
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32
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Guo T, Korman D, La Joie R, Shaw LM, Trojanowski JQ, Jagust WJ, Landau SM. Normalization of CSF pTau measurement by Aβ 40 improves its performance as a biomarker of Alzheimer's disease. Alzheimers Res Ther 2020; 12:97. [PMID: 32799929 PMCID: PMC7429887 DOI: 10.1186/s13195-020-00665-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/04/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Alzheimer's disease (AD)-related tauopathy can be measured with CSF phosphorylated tau (pTau) and tau PET. We aim to investigate the associations between these measurements and their relative ability to predict subsequent disease progression. METHODS In 219 cognitively unimpaired and 122 impaired Alzheimer's Disease Neuroimaging Initiative participants with concurrent amyloid-β (Aβ) PET (18F-florbetapir or 18F-florbetaben), 18F-flortaucipir (FTP) PET, CSF measurements, structural MRI, and cognition, we examined inter-relationships between these biomarkers and their predictions of subsequent FTP and cognition changes. RESULTS The use of a CSF pTau/Aβ40 ratio eliminated positive associations we observed between CSF pTau alone and CSF Aβ42 in the normal Aβ range likely reflecting individual differences in CSF production rather than pathology. Use of the CSF pTau/Aβ40 ratio also increased expected associations with Aβ PET, FTP PET, hippocampal volume, and cognitive decline compared to pTau alone. In Aβ+ individuals, abnormal CSF pTau/Aβ40 only individuals (26.7%) were 4 times more prevalent (p < 0.001) than abnormal FTP only individuals (6.8%). Furthermore, among individuals on the AD pathway, CSF pTau/Aβ40 mediates the association between Aβ PET and FTP PET accumulation, but FTP PET is more closely linked to subsequent cognitive decline than CSF pTau/Aβ40. CONCLUSIONS Together, these findings suggest that CSF pTau/Aβ40 may be a superior measure of tauopathy compared to CSF pTau alone, and CSF pTau/Aβ40 enables detection of tau accumulation at an earlier stage than FTP among Aβ+ individuals.
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Affiliation(s)
- Tengfei Guo
- Helen Wills Neuroscience Institute, University of California, 132 Barker Hall, Berkeley, CA, 94720, USA.
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Deniz Korman
- Helen Wills Neuroscience Institute, University of California, 132 Barker Hall, Berkeley, CA, 94720, USA
| | - Renaud La Joie
- Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, 132 Barker Hall, Berkeley, CA, 94720, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, 132 Barker Hall, Berkeley, CA, 94720, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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