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Kuhn E, Klinger HM, Amariglio RE, Wagner M, Jessen F, Düzel E, Heneka MT, Chételat G, Rentz DM, Sperling RA, Ebenau JL, Butterbrod E, Van Der Flier WM, Sikkes SAM, Teunnissen CE, Van Harten AC, Van De Giessen EM, Rami L, Tort A, Sánchez Benavides G, Gifford KA, Van Hulle C, Buckley RF. SCD-plus features and AD biomarkers in cognitively unimpaired samples: A meta-analytic approach for nine cohort studies. Alzheimers Dement 2025. [PMID: 39985404 DOI: 10.1002/alz.14307] [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: 05/29/2024] [Accepted: 09/10/2024] [Indexed: 02/24/2025]
Abstract
INTRODUCTION Specific features of subjective cognitive decline (SCD-plus) have been proposed to indicate an increased risk of preclinical Alzheimer's disease (AD). However, few studies have examined how these features relate to AD biomarkers in cognitively unimpaired (CU) older adults. METHODS Meta-analyses were performed using cross-sectional data from nine cohorts (n = 7219, mean age (SD): 71.17 (5.9), 56.5% female) to determine associations of SCD-plus features with positron emission tomography (PET)- or cerebrospinal fluid (CSF)-derived amyloid beta (Aβ) and tau biomarkers. RESULTS Participants with preclinical AD (community-based only) were more likely to fulfill SCD-plus features. The presence of self-reported memory decline, associated concern/worry, and a higher number of fulfilled features were all associated with high Aβ levels. Only the latter was associated with abnormal tau. DISCUSSION Simultaneous endorsement of multiple SCD-plus features is a robust indicator of abnormal AD biomarkers in CU older adults, whereas isolated SCD features seem only sensitive to elevated Aβ, supporting their value as early behavioral markers of preclinical AD. HIGHLIGHTS About two-tenths of our sample had abnormal amyloid beta (Aβ) levels with evidence of subjective cognitive decline (SCD). Preclinical AD subsamples (community-based) had a higher percentage of participants meeting SCD-plus features. Self-reported memory decline and concern/worry were the sole features associated with high Aβ, but not tau, burden. A higher number of fulfilled SCD-plus features are linked to high Aβ and tau burden. Use of multiple SCD-plus features may help identify early stages of biological AD.
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Grants
- BN012 Deutsches Zentrum für Neurodegenerative Erkrankungen
- W81XWH-12-2-0012 U.S. Department of Defense
- DP2AG082342 NIA NIH HHS
- University Caen Normandy
- INSERM
- Fondation Philippe Chatrier
- ZT-I-PF-5-163 Helmholtz Artificial Intelligence Cooperation Unit
- R00-AG061238 National Institutes of Health/National Institute on Aging (NIH/NIA)
- DP2AG082342 National Institutes of Health/National Institute on Aging (NIH/NIA)
- R01-AG079142 National Institutes of Health/National Institute on Aging (NIH/NIA)
- K23-AG045966 National Institutes of Health/National Institute on Aging (NIH/NIA)
- R01-AG062826 National Institutes of Health/National Institute on Aging (NIH/NIA)
- U01AG024904 National Institutes of Health/National Institute on Aging (NIH/NIA)
- R01-AG063689 National Institutes of Health/National Institute on Aging (NIH/NIA)
- U19AG010483 National Institutes of Health/National Institute on Aging (NIH/NIA)
- U24AG057437 National Institutes of Health/National Institute on Aging (NIH/NIA)
- P01AG036694 National Institutes of Health/National Institute on Aging (NIH/NIA)
- R01-AG027161 National Institutes of Health/National Institute on Aging (NIH/NIA)
- UL1TR000427 NIH-NCATS
- CP23/00039 Instituto de Salud Carlos III
- European Union, FSE+
- NIBIB NIH HHS
- AbbVie
- IIRG-08-88733 Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd and Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- Johnson & Johnson Pharmaceutical Research & Development LLC
- Lumosity
- Lundbeck
- Merck & Co., Inc.
- Meso Scale Diagnostics, LLC
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- CIHR
- Foundation for the National Institutes of Health
- Northern California Institute for Research and Education
- Alzheimer's Therapeutic Research Institute, University of Southern California
- Laboratory for Neuro Imaging, University of Southern California
- Austin Health
- Commonwealth Scientific and Industrial Research Organisation (CSIRO)
- Edith Cowan University
- Florey Institute, The University of Melbourne
- National Ageing Research Institute
- GHR Foundation
- Davis Alzheimer Prevention Program
- Brigham and Women's Hospital
- Albert Einstein College of Medicine
- Foundation for Neurologic Disease
- 2011-A01493-38 PHRCN
- 2012-12-006-0347 PHRCN
- Agence Nationale de la Recherche (ANR LONGVIE 2007)
- Fondation Plan Alzheimer (Alzheimer Plan 2008-2012)
- Association France Alzheimer et maladies apparentées (AAP 2013)
- Région Basse Normandie
- Dioraphte and the Noaber Foundation
- AVID
- Pasman Chair
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Affiliation(s)
- Elizabeth Kuhn
- German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Hannah M Klinger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rebecca E Amariglio
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Center for Alzheimer Research and Treatment (CART), Brigham & Women's Hospital, Boston, Massachusetts, USA
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany
| | - Michael T Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Esch-sur-Alzette, Luxembourg
| | - Gael Chételat
- Normandie Univ, UNICAEN, INSERM, U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), Neuropresage Team, Cyceron, Caen cedex, France
| | - Dorene M Rentz
- Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Center for Alzheimer Research and Treatment (CART), Brigham & Women's Hospital, Boston, Massachusetts, USA
| | - Jarith L Ebenau
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elke Butterbrod
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, 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 and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Sietske A M Sikkes
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Charlotte E Teunnissen
- Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, 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
| | - Elsmarieke M Van De Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lorena Rami
- Hospital Clinic. Fundació Clinic, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Adria Tort
- Hospital Clinic. Fundació Clinic, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | | | - Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Carol Van Hulle
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Center for Alzheimer Research and Treatment (CART), Brigham & Women's Hospital, Boston, Massachusetts, USA
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
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Puig-Pijoan A, García-Escobar G, Fernández-Lebrero A, Manero-Borràs RM, Sánchez-Benavides G, Navalpotro-Gómez I, Cascales Lahoz D, Suárez-Calvet M, Grau-Rivera O, Boltes Alandí A, Pont-Sunyer MC, Ortiz-Gil J, Carrillo-Molina S, López-Villegas D, Abellán-Vidal MT, Martínez-Casamitjana MI, Hernández-Sánchez JJ, Peña-Casanova J, Roquer J, Padrós Fluvià A, Puente-Périz V. The CORCOBIA study: Cut-off points of Alzheimer's disease CSF biomarkers in a clinical cohort. Neurologia 2024; 39:756-765. [PMID: 35961506 DOI: 10.1016/j.nrleng.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/24/2022] [Indexed: 10/15/2022] Open
Abstract
INTRODUCTION The analysis of the core biomarkers of Alzheimer's Disease (AD) in the cerebrospinal fluid (CSF) is recommended in the clinical units where it is available. Because of the absence of universal validated values, the determination of specific cut-off points for each center and its population is recommended. The main objective of the CORCOBIA study was to determine the cut-off points of core AD CSF biomarkers for several centers (Parc de Salut Mar, Barcelona and Hospital General de Granollers), which work with the same reference laboratory (Laboratori de Referència de Catalunya). METHODS Prospective study including cognitively unimpaired individuals (CU, n = 42), subjects with amnestic mild cognitive impairment (aMCI, n = 35) and patients with dementia due to Alzheimer's Disease (AD, n = 48), in whom clinical and neuropsychological assessment, neuroimaging, APOE genotyping and lumbar puncture to analyse amyloid beta peptides (Aβ42, Aβ40), total tau (tTau) and phosphorylated Tau (pTau181) using the Lumipulse G600II (Fujirebio) was performed. The values of sensitivity (SE), specificity (SP), predictive values and area under the curve (AUC) were calculated, determining the cut-off point according to the Youden index by comparing the CU and AD groups. RESULTS The resulting cut-offs and their AUC were the following: Aβ42 750 pg/mL (AUC 0.809); Aβ42/Aβ40 0.062 (AUC 0.78); pTau181 69.85 pg/mL (AUC 0.81); tTau 522.0 pg/mL (AUC 0.79); Aβ42/tTau 1.76 (AUC 0.86); Aβ42/pTau181 10.25 (AUC 0.86). CONCLUSIONS The determination of cut-off points of core AD CSF biomarkers for the participating centers allows a better diagnostic accuracy. The ratio CSF Aβ42/pTau181 shows the highest AUC and better balance between sensitivity and specificity.
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Affiliation(s)
- A Puig-Pijoan
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.
| | - G García-Escobar
- Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - A Fernández-Lebrero
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain
| | - R M Manero-Borràs
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - G Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain
| | - I Navalpotro-Gómez
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain
| | - D Cascales Lahoz
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - M Suárez-Calvet
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - O Grau-Rivera
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - A Boltes Alandí
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - M C Pont-Sunyer
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - J Ortiz-Gil
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; Unitat de Psicologia, Hospital General de Granollers, Granollers, Barcelona, Spain; Fundación para la Investigación y Docencia Maria Angustias Gimenez (FIDMAG), Sant Boi de Llobregat, Barcelona, Spain
| | - S Carrillo-Molina
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain; Unitat de Psicologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - D López-Villegas
- Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | - M T Abellán-Vidal
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | - M I Martínez-Casamitjana
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | | | - J Peña-Casanova
- Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - J Roquer
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain
| | - A Padrós Fluvià
- Laboratori de Referència de Catalunya, Sant Boi de Llobregat, Barcelona, Spain
| | - V Puente-Périz
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.
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Abdul Manap AS, Almadodi R, Sultana S, Sebastian MG, Kavani KS, Lyenouq VE, Shankar A. Alzheimer's disease: a review on the current trends of the effective diagnosis and therapeutics. Front Aging Neurosci 2024; 16:1429211. [PMID: 39185459 PMCID: PMC11341404 DOI: 10.3389/fnagi.2024.1429211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 07/25/2024] [Indexed: 08/27/2024] Open
Abstract
The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance in-depth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed.
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Affiliation(s)
- Aimi Syamima Abdul Manap
- Department of Biomedical Science, College of Veterinary Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Reema Almadodi
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Shirin Sultana
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | | | | | - Vanessa Elle Lyenouq
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
| | - Aravind Shankar
- Faculty of Pharmacy and Biomedical Sciences, MAHSA University, Selangor, Malaysia
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Bougea A, Gourzis P. Biomarker-Based Precision Therapy for Alzheimer's Disease: Multidimensional Evidence Leading a New Breakthrough in Personalized Medicine. J Clin Med 2024; 13:4661. [PMID: 39200803 PMCID: PMC11355840 DOI: 10.3390/jcm13164661] [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: 06/22/2024] [Revised: 07/25/2024] [Accepted: 08/05/2024] [Indexed: 09/02/2024] Open
Abstract
(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD.
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Affiliation(s)
- Anastasia Bougea
- 1st Department of Neurology, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Philippos Gourzis
- 1st Department of Psychiatry, University of Patras, 26504 Rio, Greece;
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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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Vos SJB, Delvenne A, Jack CR, Thal DR, Visser PJ. The clinical importance of suspected non-Alzheimer disease pathophysiology. Nat Rev Neurol 2024; 20:337-346. [PMID: 38724589 DOI: 10.1038/s41582-024-00962-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 06/06/2024]
Abstract
The development of biomarkers for Alzheimer disease (AD) has led to the origin of suspected non-AD pathophysiology (SNAP) - a heterogeneous biomarker-based concept that describes individuals with normal amyloid and abnormal tau and/or neurodegeneration biomarker status. In this Review, we describe the origins of the SNAP construct, along with its prevalence, diagnostic and prognostic implications, and underlying neuropathology. As we discuss, SNAP can be operationalized using different biomarker modalities, which could affect prevalence estimates and reported characteristics of SNAP in ways that are not yet fully understood. Moreover, the underlying aetiologies that lead to a SNAP biomarker profile, and whether SNAP is the same in people with and without cognitive impairment, remains unclear. Improved insight into the clinical characteristics and pathophysiology of SNAP is of major importance for research and clinical practice, as well as for trial design to optimize care and treatment of individuals with SNAP.
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Affiliation(s)
- Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Aurore Delvenne
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Dietmar R Thal
- Laboratory for Neuropathology, Department of Imaging and Pathology and Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Pathology, University Hospital Leuven, Leuven, Belgium
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
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7
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Canu E, Rugarli G, Coraglia F, Basaia S, Cecchetti G, Calloni SF, Vezzulli PQ, Spinelli EG, Santangelo R, Caso F, Falini A, Magnani G, Filippi M, Agosta F. Real-word application of the AT(N) classification and disease-modifying treatment eligibility in a hospital-based cohort. J Neurol 2024; 271:2716-2729. [PMID: 38381175 DOI: 10.1007/s00415-024-12221-7] [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: 12/06/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND AND OBJECTIVES The AT(N) classification system stratifies patients based on biomarker profiles, including amyloid-beta deposition (A), tau pathology (T), and neurodegeneration (N). This study aims to apply the AT(N) classification to a hospital-based cohort of patients with cognitive decline and/or dementia, within and outside the Alzheimer's disease (AD) continuum, to enhance our understanding of the multidimensional aspects of AD and related disorders. Furthermore, we wish to investigate how many cases from our cohort would be eligible for the available disease modifying treatments, such as aducanemab and lecanemab. METHODS We conducted a retrospective evaluation of 429 patients referred to the Memory Center of IRCCS San Raffaele Hospital in Milan. Patients underwent clinical/neuropsychological assessments, lumbar puncture, structural brain imaging, and positron emission tomography (FDG-PET). Patients were stratified according to AT(N) classification, group comparisons were performed and the number of eligible cases for anti-β amyloid monoclonal antibodies was calculated. RESULTS Sociodemographic and clinical features were similar across groups. The most represented group was A + T + N + accounting for 38% of cases, followed by A + T - N + (21%) and A - T - N + (20%). Although the clinical presentation was similar, the A + T + N + group showed more severe cognitive impairment in memory, language, attention, executive, and visuospatial functions compared to other AT(N) groups. Notably, T + patients demonstrated greater memory complaints compared to T - cases. FDG-PET outperformed MRI and CT in distinguishing A + from A - patients. Although 61% of the observed cases were A + , only 17% of them were eligible for amyloid-targeting treatments. DISCUSSION The AT(N) classification is applicable in a real-world clinical setting. The classification system provided insights into clinical management and treatment strategies. Low cognitive performance and specific regional FDG-PET hypometabolism at diagnosis are highly suggestive for A + T + or A - T + profiles. This work provides also a realistic picture of the proportion of AD patients eligible for disease modifying treatments emphasizing the need for early detection.
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Affiliation(s)
- Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Rugarli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Federico Coraglia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giordano Cecchetti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sonia Francesca Calloni
- Neuroradiology Unit and High Field MRI Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Edoardo Gioele Spinelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Santangelo
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesca Caso
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milan, Italy
- Neuroradiology Unit and High Field MRI Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giuseppe Magnani
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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8
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Xiong X, He H, Ye Q, Qian S, Zhou S, Feng F, Fang EF, Xie C. Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework. CNS Neurosci Ther 2024; 30:e14357. [PMID: 37438991 PMCID: PMC10848089 DOI: 10.1111/cns.14357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/21/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023] Open
Abstract
OBJECTIVES The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline. METHODS Based on the availability of plasma ATN biomarkers (plasma-derived Aβ42/40 , p-tau181, NFL, respectively), CSF ATN biomarkers (CSF-derived Aβ42 /Aβ40 , p-tau181, NFL), and neuroimaging ATN biomarkers (18F-florbetapir (FBP) amyloid-PET, 18F-flortaucipir (FTP) tau-PET, and fluorodeoxyglucose (FDG)-PET), a total of 2340 participants were selected from ADNI. RESULTS Our data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N. CONCLUSIONS Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.
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Affiliation(s)
- Xi Xiong
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Haijun He
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Qianqian Ye
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Shuangjie Qian
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Shuoting Zhou
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Feifei Feng
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Evandro F. Fang
- Department of Clinical Molecular BiologyAkershus University Hospital, University of OsloLørenskogNorway
- The Norwegian Centre on Healthy Ageing (NO‐Age)OsloNorway
| | - Chenglong Xie
- Department of NeurologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Key Laboratory Of Alzheimer's Disease Of Zhejiang ProvinceWenzhouChina
- Institute of AgingWenzhou Medical UniversityWenzhouChina
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang ProvinceWenzhouChina
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Rodriguez-Hernandez MA, Alemany I, Olofsson JK, Diaz-Galvan P, Nemy M, Westman E, Barroso J, Ferreira D, Cedres N. Degeneration of the cholinergic system in individuals with subjective cognitive decline: A systematic review. Neurosci Biobehav Rev 2024; 157:105534. [PMID: 38220033 DOI: 10.1016/j.neubiorev.2024.105534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND Subjective cognitive decline (SCD) is a risk factor for future cognitive impairment and dementia. It is uncertain whether the neurodegeneration of the cholinergic system is already present in SCD individuals. We aimed to review the current evidence about the association between SCD and biomarkers of degeneration in the cholinergic system. METHOD Original articles were extracted from three databases: Pubmed, Web of Sciences, and Scopus, in January 2023. Two researchers screened the studies independently. RESULTS A total of 11 research articles were selected. SCD was mostly based on amnestic cognitive complaints. Cholinergic system biomarkers included neuroimaging markers of basal forebrain volume, functional connectivity, transcranial magnetic stimulation, or biofluid. The evidence showed associations between basal forebrain atrophy, poorer connectivity of the cholinergic system, and SCD CONCLUSIONS: Degenerative changes in the cholinergic system can be present in SCD. Subjective complaints may help when identifying individuals with brain changes that are associated with cognitive impairment. These findings may have important implications in targeting individuals that may benefit from cholinergic-target treatments at very early stages of neurodegenerative diseases.
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Affiliation(s)
- Marta A Rodriguez-Hernandez
- Department of Psychology, Faculty of Health Sciences, University Fernando Pessoa-Canarias, Santa María de Guia, Spain
| | - Iris Alemany
- Department of Psychology, Faculty of Health Sciences, University Fernando Pessoa-Canarias, Santa María de Guia, Spain
| | - Jonas K Olofsson
- Department of Psychology, Sensory Cognitive Interaction Laboratory (SCI-lab), Stockholm University, Stockholm, Sweden
| | | | - Milan Nemy
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic; Department of Biomedical Engineering and Assistive Technology, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Center for Alzheimer Research, Stockholm, Sweden; Division of Clinical Geriatrics, Care Sciences and Society. Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Center for Alzheimer Research, Stockholm, Sweden; Division of Clinical Geriatrics, Care Sciences and Society. Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jose Barroso
- Department of Psychology, Faculty of Health Sciences, University Fernando Pessoa-Canarias, Santa María de Guia, Spain
| | - Daniel Ferreira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Center for Alzheimer Research, Stockholm, Sweden; Division of Clinical Geriatrics, Care Sciences and Society. Karolinska Institutet, Stockholm, Sweden
| | - Nira Cedres
- Department of Psychology, Faculty of Health Sciences, University Fernando Pessoa-Canarias, Santa María de Guia, Spain; Department of Psychology, Sensory Cognitive Interaction Laboratory (SCI-lab), Stockholm University, Stockholm, Sweden; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Center for Alzheimer Research, Stockholm, Sweden; Division of Clinical Geriatrics, Care Sciences and Society. Karolinska Institutet, Stockholm, Sweden.
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Hernández‐Lorenzo L, Gil‐Moreno MJ, Ortega‐Madueño I, Cárdenas MC, Diez‐Cirarda M, Delgado‐Álvarez A, Palacios‐Sarmiento M, Matias‐Guiu J, Corrochano S, Ayala JL, Matias‐Guiu JA. A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers. CNS Neurosci Ther 2024; 30:e14382. [PMID: 37501389 PMCID: PMC10848077 DOI: 10.1111/cns.14382] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/29/2023] Open
Abstract
AIMS The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau. RESULTS The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.
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Affiliation(s)
- Laura Hernández‐Lorenzo
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
- Department of Computer Architecture and Automation, Computer Science FacultyComplutense University of MadridMadridSpain
| | - Maria José Gil‐Moreno
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - Isabel Ortega‐Madueño
- Department of Clinical Analysis, Institute of Laboratory MedicineIdSSC, Hospital Clínico San CarlosMadridSpain
| | - Maria Cruz Cárdenas
- Department of Clinical Analysis, Institute of Laboratory MedicineIdSSC, Hospital Clínico San CarlosMadridSpain
| | - Maria Diez‐Cirarda
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - Alfonso Delgado‐Álvarez
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - Marta Palacios‐Sarmiento
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - Jorge Matias‐Guiu
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - Silvia Corrochano
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
| | - José L. Ayala
- Department of Computer Architecture and Automation, Computer Science FacultyComplutense University of MadridMadridSpain
| | - Jordi A. Matias‐Guiu
- Department of NeurologySan Carlos Research Institute (IdSSC), Hospital Clínico San CarlosMadridSpain
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11
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Cho SH, Kim S, Choi SM, Kim BC. ATN Classification and Clinical Progression of the Amyloid-Negative Group in Alzheimer's Disease Neuroimaging Initiative Participants. Chonnam Med J 2024; 60:51-58. [PMID: 38304128 PMCID: PMC10828081 DOI: 10.4068/cmj.2024.60.1.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 02/03/2024] Open
Abstract
Alzheimer's disease has recently been classified using three biological markers (amyloid [A], tau [T], and neurodegeneration [N]) to help elucidate its progression. We aimed to investigate whether there were differences between cognitive function and the clinical dementia symptoms over time relative to the ATN classification in the amyloid-negative group. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, 310 participants who underwent all the tests required for ATN classification were enrolled. The cognitive function score differences (Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 [ADAS-Cog 13], Clinical Dementia Rating Sum of Boxes [CDR-SOB], and Mini-Mental State Examination [MMSE]) between the groups were analyzed using the analysis of covariance and score changes over time with a linear mixed-effects model. In the cross-sectional analysis, ADAS-Cog 13 scores were higher for A-T-N+ and A-T+N+ than for A-T-N- (p<0.001) and A-T+N- (p<0.001). In the longitudinal analysis, CDR-SOB scores for A-T+N+ deteriorated faster than A-T-N- (p<0.001), A-T+N- (p<0.001) and A-T-N+ (p<0.001). Hippocampal atrophy progressed faster in A-T-N+ (p<0.001) and A-T+N+ (p=0.02) than in A-T-N-. Through this study, we discovered that even in individuals classified as amyloid negative, neurodegeneration with tau deposition exacerbates cognitive decline and worsens clinical symptoms, underscoring the need for continuous monitoring and observation.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Shina Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Byeong Chae Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
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12
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Waziry R, Williams OA. Alzheimer Disease: A Life-Limiting Disease With a Burden of a Terminal Illness. Neurol Clin Pract 2023; 13:e200208. [PMID: 37854176 PMCID: PMC10581074 DOI: 10.1212/cpj.0000000000200208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 09/15/2023] [Indexed: 10/20/2023]
Abstract
Purpose of Review An estimated 6.5 million Americans live with Alzheimer dementia. Better understanding of advanced stages of Alzheimer disease (AD) and timely monitoring of its preventable complications would translate to improved survival and quality of life in this population. Recent Findings In this perspective review, we proposed shifting the narrative to recognizing AD as a chronic life-limiting illness instead of a terminal disease. In support of this view, we discussed the following: (1) the biochemical, cellular (preclinical), and clinical phases of AD; (2) survival after AD; (3) AD therapeutics and potential implications for the population with AD in the advanced stages. Summary On the bases of the prolonged preclinical phase in AD, promising advances in AD therapeutics and the varying survival after AD, we proposed a new classification for AD and more broadly neurodegenerative disorders to be recognized as chronic life-limiting illnesses rather than terminal diseases with important implications for patients with AD in the advanced stages given the challenges that are specific to this population.
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Affiliation(s)
- Reem Waziry
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, Columbia University, NY
| | - Olajide A Williams
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, Columbia University, NY
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13
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Yosypyshyn D, Kučikienė D, Ramakers I, Schulz JB, Reetz K, Costa AS. Clinical characteristics of patients with suspected Alzheimer's disease within a CSF Aß-ratio grey zone. Neurol Res Pract 2023; 5:40. [PMID: 37533121 PMCID: PMC10398972 DOI: 10.1186/s42466-023-00262-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/28/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND The AT(N) research framework for Alzheimer's disease (AD) remains unclear on how to best deal with borderline cases. Our aim was to characterise patients with suspected AD with a borderline Aß1-42/Aß1-40 ratio in cerebrospinal fluid. METHODS We analysed retrospective data from two cohorts (memory clinic cohort and ADNI) of patients (n = 63) with an Aß1-42/Aß1-40 ratio within a predefined borderline area-Q1 above the validated cut-off value(grey zone). We compared demographic, clinical, neuropsychological and neuroimaging features between grey zone patients and patients with low Aß1-42 (normal Aß ratio but pathological Aß1-42, n = 42) and patients with AD (pathological Aß, P-Tau, und T-Tau, n = 80). RESULTS Patients had mild cognitive impairment or mild dementia and a median age of 72 years. Demographic and general clinical characteristics did not differ between the groups. Patients in the grey zone group were the least impaired in cognition. However, they overlapped with the low Aß1-42 group in verbal episodic memory performance, especially in delayed recall and recognition. The grey zone group had less severe medial temporal atrophy, but mild posterior atrophy and mild white matter hyperintensities, similar to the low Aß1-42 group. CONCLUSIONS Patients in the Aß ratio grey zone were less impaired, but showed clinical overlap with patients on the AD continuum. These borderline patients may be at an earlier disease stage. Assuming an increased risk of AD and progressive cognitive decline, careful consideration of clinical follow-up is recommended when using dichotomous approaches to classify Aß status.
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Affiliation(s)
- Dariia Yosypyshyn
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Domantė Kučikienė
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Inez Ramakers
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Jörg B Schulz
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
- JARA Institute Molecular Neuroscience and Neuroimaging, RWTH Aachen & Forschungszentrum Jülich, Aachen, Germany
| | - Kathrin Reetz
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany.
- JARA Institute Molecular Neuroscience and Neuroimaging, RWTH Aachen & Forschungszentrum Jülich, Aachen, Germany.
| | - Ana Sofia Costa
- Department of Neurology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
- JARA Institute Molecular Neuroscience and Neuroimaging, RWTH Aachen & Forschungszentrum Jülich, Aachen, Germany
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14
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Peretti DE, Ribaldi F, Scheffler M, Mu L, Treyer V, Gietl AF, Hock C, Frisoni GB, Garibotto V. ATN profile classification across two independent prospective cohorts. Front Med (Lausanne) 2023; 10:1168470. [PMID: 37559930 PMCID: PMC10407659 DOI: 10.3389/fmed.2023.1168470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/10/2023] [Indexed: 08/11/2023] Open
Abstract
Background The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer's disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts. Methods A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests. Results Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A-T-N-) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers' statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts. Conclusion Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria.
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Affiliation(s)
- Débora E. Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Linjing Mu
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute of Pharmaceutical Sciences, Zurich, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Anton F. Gietl
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Christoph Hock
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging, University of Geneva, Geneva, Switzerland
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15
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Heinzinger N, Maass A, Berron D, Yakupov R, Peters O, Fiebach J, Villringer K, Preis L, Priller J, Spruth EJ, Altenstein S, Schneider A, Fliessbach K, Wiltfang J, Bartels C, Jessen F, Maier F, Glanz W, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Killimann I, Göerß D, Laske C, Munk MH, Spottke A, Roy N, Heneka MT, Brosseron F, Dobisch L, Ewers M, Dechent P, Haynes JD, Scheffler K, Wolfsgruber S, Kleineidam L, Schmid M, Berger M, Düzel E, Ziegler G. Exploring the ATN classification system using brain morphology. Alzheimers Res Ther 2023; 15:50. [PMID: 36915139 PMCID: PMC10009950 DOI: 10.1186/s13195-023-01185-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. METHODS We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups. RESULTS The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. CONCLUSION Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. TRIAL REGISTRATION DRKS00007966, 04/05/2015, retrospectively registered.
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Affiliation(s)
- Nils Heinzinger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jochen Fiebach
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.,University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jacob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany.,Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, 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, Cologne, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Killimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Doreen Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- 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
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, Göttingen, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Berlin, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
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16
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García-Escobar G, Puig-Pijoan A, Puente-Periz V, Fernández-Lebrero A, María Manero R, Navalpotro-Gómez I, Suárez-Calvet M, Grau-Rivera O, Contador-Muñana J, Cascales-Lahoz D, Duran-Jordà X, Boltes N, Pont-Sunyer MC, Ortiz-Gil J, Carrillo-Molina S, López-Villegas MD, Abellán-Vidal MT, Martínez-Casamitjana MI, Hernández-Sánchez JJ, Padrós-Fluvià A, Peña-Casanova J, Sánchez-Benavides G. NEURONORMA Cognitive Battery Associations with Cerebrospinal Fluid Amyloid-β and Tau Levels in the Continuum of Alzheimer's Disease. J Alzheimers Dis 2023; 92:1303-1321. [PMID: 37038810 DOI: 10.3233/jad-220930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
BACKGROUND Neuropsychological assessments are essential to define the cognitive profile and contribute to the diagnosis of Alzheimer's disease (AD). The progress in knowledge about the pathophysiological process of the disease has allowed conceptualizing AD through biomarkers as a biological continuum that encompasses different clinical stages. OBJECTIVE To explore the association between cerebrospinal fluid (CSF) biomarkers of AD and cognition using the NEURONORMA battery, in a sample of cognitively unimpaired (CU), mild cognitive impaired (MCI), and mild dementia of the Alzheimer type (DAT) subjects, and to characterize the cognitive profiles in MCI subjects classified by A/T/N system. METHODS 42 CU, 35 MCI, and 35 mild DAT were assessed using the NEURONORMA battery. Core AD biomarkers [amyloid-β42 (Aβ42) peptide, total tau (t-tau), and phosphorylated tau 181 (p-tau181)] proteins were measured in CSF. Correlation coefficients, multivariate regression, and effect sizes were calculated. We explored the age- and education-adjusted cognitive profiles by A/T/N variants within the MCI group. RESULTS Cognitive outcomes were directly associated with CSF Aβ42 and inversely with CSF tau measures. We found differences in both biomarkers and cognitive outcomes comparing all pairs except for CSF measures between cognitively impaired groups. The highest effect size was in memory tasks and biomarkers ratios. Lower performances were in memory and executive domains in MCI subjects with AD pathology (A+T+N±) compared to those with normal levels of AD biomarkers (A- T- N). CONCLUSION This study provides further evidence of the validity of Spanish NEURONORMA cognitive battery to characterize cognitive impairment in the AD pathological continuum.
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Affiliation(s)
- Greta García-Escobar
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Albert Puig-Pijoan
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - Víctor Puente-Periz
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - Aida Fernández-Lebrero
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Rosa María Manero
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - Irene Navalpotro-Gómez
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Marc Suárez-Calvet
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Oriol Grau-Rivera
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - José Contador-Muñana
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Diego Cascales-Lahoz
- Cognitive Impairment and Movement Disorders Unit, Neurology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | | | - Núncia Boltes
- Neurology Department, Hospital General de Granollers, Granollers, Spain
| | | | - Jordi Ortiz-Gil
- Neurology Department, Hospital General de Granollers, Granollers, Spain
- Psychology Unit, Hospital General de Granollers, Granollers, Spain
- Maria Angustias Gimenez Research Foundation (FIDMAG), Sant Boi del Llobregat, Spain
| | - Sara Carrillo-Molina
- Neurology Department, Hospital General de Granollers, Granollers, Spain
- Psychology Unit, Hospital General de Granollers, Granollers, Spain
| | - María Dolores López-Villegas
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Spain
| | - María Teresa Abellán-Vidal
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Spain
| | | | | | | | - Jordi Peña-Casanova
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
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17
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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: 19] [Impact Index Per Article: 6.3] [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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18
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Nosheny RL, Amariglio R, Sikkes SA, Van Hulle C, Bicalho MAC, Dowling NM, Brucki SMD, Ismail Z, Kasuga K, Kuhn E, Numbers K, Aaronson A, Moretti DV, Pereiro AX, Sánchez‐Benavides G, Sellek Rodríguez AF, Urwyler P, Zawaly K. The role of dyadic cognitive report and subjective cognitive decline in early ADRD clinical research and trials: Current knowledge, gaps, and recommendations. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12357. [PMID: 36226046 PMCID: PMC9530696 DOI: 10.1002/trc2.12357] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 11/05/2022]
Abstract
Efficient identification of cognitive decline and Alzheimer's disease (AD) risk in early stages of the AD disease continuum is a critical unmet need. Subjective cognitive decline is increasingly recognized as an early symptomatic stage of AD. Dyadic cognitive report, including subjective cognitive complaints (SCC) from a participant and an informant/study partner who knows the participant well, represents an accurate, reliable, and efficient source of data for assessing risk. However, the separate and combined contributions of self- and study partner report, and the dynamic relationship between the two, remains unclear. The Subjective Cognitive Decline Professional Interest Area within the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment convened a working group focused on dyadic patterns of subjective report. Group members identified aspects of dyadic-report information important to the AD research field, gaps in knowledge, and recommendations. By reviewing existing data on this topic, we found evidence that dyadic measures are associated with objective measures of cognition and provide unique information in preclinical and prodromal AD about disease stage and progression and AD biomarker status. External factors including dyad (participant-study partner pair) relationship and sociocultural factors contribute to these associations. We recommend greater dyad report use in research settings to identify AD risk. Priority areas for future research include (1) elucidation of the contributions of demographic and sociocultural factors, dyad type, and dyad relationship to dyad report; (2) exploration of agreement and discordance between self- and study partner report across the AD syndromic and disease continuum; (3) identification of domains (e.g., memory, executive function, neuropsychiatric) that predict AD risk outcomes and differentiate cognitive impairment due to AD from other impairment; (4) development of best practices for study partner engagement; (5) exploration of study partner report as AD clinical trial endpoints; (6) continued development, validation, and optimization, of study partner report instruments tailored to the goals of the research and population.
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Affiliation(s)
- Rachel L. Nosheny
- University of California San FranciscoDepartment of PsychiatrySan FranciscoCaliforniaUSA
- Veteran's Administration Advanced Research CenterSan FranciscoCaliforniaUSA
| | - Rebecca Amariglio
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalDepartment of Neurology Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Sietske A.M. Sikkes
- Amsterdam University Medical CentersDepartment of NeurologyAlzheimer Center AmsterdamNorth Hollandthe Netherlands/VU UniversityDepartment of ClinicalNeuro & Development PsychologyNorth Hollandthe Netherlands
| | - Carol Van Hulle
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Maria Aparecida Camargos Bicalho
- UFMG: Federal University of Minas GeraisDepartment of Clinical MedicineJenny de Andrade Faria – Center for Geriatrics and Gerontology of UFMGBelo HorizonteBrazil
| | - N. Maritza Dowling
- George Washington UniversityDepartment of Acute & Chronic CareSchool of NursingDepartment of Epidemiology & BiostatisticsMilken Institute School of Public HealthWashingtonDistrict of ColumbiaUSA
| | | | - Zahinoor Ismail
- Hotchkiss Brain Institute and O'Brien Institute for Public HealthCumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Kensaku Kasuga
- Department of Molecular GeneticsBrain Research InstituteNiigata UniversityNiigataJapan
| | - Elizabeth Kuhn
- UNICAEN, INSERM, PhIND “Physiopathology and Imaging of Neurological Disorders,”Institut Blood and Brain @ Caen‐NormandieNormandie UniversityCaenFrance
| | - Katya Numbers
- Centre for Healthy Brain Ageing (CHeBA)Department of PsychiatryUniversity of New South WalesSydneyNew South WalesAustralia
| | - Anna Aaronson
- Veteran's Administration Advanced Research CenterSan FranciscoCaliforniaUSA
| | - Davide Vito Moretti
- IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliAlzheimer Rehabilitation Operative UnitBresciaItaly
| | - Arturo X. Pereiro
- Faculty of PsychologyDepartment of Developmental PsychologyUniversity of Santiago de CompostelaGaliciaSpain
| | | | - Allis F. Sellek Rodríguez
- Costa Rican Foundation for the Care of Older Adults with Alzheimer's and Other Dementias (FundAlzheimer Costa Rica)CartagoCosta Rica
| | - Prabitha Urwyler
- ARTORG Center for Biomedical EngineeringUniversity of BernUniversity Neurorehabilitation UnitDepartment of NeurologyInselspitalBernSwitzerland
| | - Kristina Zawaly
- University of AucklandDepartment of General Practice and Primary Health CareSchool of Population HealthFaculty of Medical and Health SciencesAucklandNew Zealand
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19
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Vipin A, Koh CL, Wong BYX, Zailan FZ, Tan JY, Soo SA, Satish V, Kumar D, Wang BZ, Ng ASL, Chiew HJ, Ng KP, Kandiah N. Amyloid-Tau-Neurodegeneration Profiles and Longitudinal Cognition in Sporadic Young-Onset Dementia. J Alzheimers Dis 2022; 90:543-551. [DOI: 10.3233/jad-220448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We examined amyloid-tau-neurodegeneration biomarker effects on cognition in a Southeast-Asian cohort of 84 sporadic young-onset dementia (YOD; age-at-onset <65 years) patients. They were stratified into A+N+, A– N+, and A– N– profiles via cerebrospinal fluid amyloid-β1–42 (A), phosphorylated-tau (T), MRI medial temporal atrophy (neurodegeneration– N), and confluent white matter hyperintensities cerebrovascular disease (CVD). A, T, and CVD effects on longitudinal Mini-Mental State Examination (MMSE) were evaluated. A+N+ patients demonstrated steeper MMSE decline than A– N+ (β = 1.53; p = 0.036; CI 0.15:2.92) and A– N– (β = 4.68; p = 0.001; CI 1.98:7.38) over a mean follow-up of 1.24 years. Within A– N+, T– CVD+ patients showed greater MMSE decline compared to T+CVD– patients (β = – 2.37; p = 0.030; CI – 4.41:– 0.39). A+ results in significant cognitive decline, while CVD influences longitudinal cognition in the A– sub-group.
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Affiliation(s)
- Ashwati Vipin
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
| | - Chen Ling Koh
- National Neuroscience Institute, Singapore, Singapore
| | | | - Fatin Zahra Zailan
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
| | - Jayne Yi Tan
- National Neuroscience Institute, Singapore, Singapore
| | - See Ann Soo
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
| | - Vaynii Satish
- National Neuroscience Institute, Singapore, Singapore
| | - Dilip Kumar
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
| | | | - Adeline Su Lyn Ng
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Hui Jin Chiew
- National Neuroscience Institute, Singapore, Singapore
| | - Kok Pin Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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20
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Baldeiras I, Silva-Spínola A, Lima M, Leitão MJ, Durães J, Vieira D, Tbuas-Pereira M, Cruz VT, Rocha R, Alves L, Machado Á, Milheiro M, Santiago B, Santana I. Alzheimer’s Disease Diagnosis Based on the Amyloid, Tau, and Neurodegeneration Scheme (ATN) in a Real-Life Multicenter Cohort of General Neurological Centers. J Alzheimers Dis 2022; 90:419-432. [DOI: 10.3233/jad-220587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The ATN scheme was proposed as an unbiased biological characterization of the Alzheimer’s disease (AD) spectrum, grouping biomarkers into three categories: brain Amyloidosis-A, Tauopathy-T, Neurodegeneration-N. Although this scheme was mainly recommended for research, it is relevant for diagnosis. Objective: To evaluate the ATN scheme performance in real-life cohorts reflecting the inflow of patients with cognitive complaints and different underlying disorders in general neurological centers. Methods: We included patients (n = 1,128) from six centers with their core cerebrospinal fluid-AD biomarkers analyzed centrally. A was assessed through Aβ 42/Aβ 40, T through pTau-181, and N through tTau. Association between demographic features, clinical diagnosis at baseline/follow-up and ATN profiles was assessed. Results: The prevalence of ATN categories was: A-T-N-: 28.3% ; AD continuum (A + T-/+N-/+): 47.8% ; non-AD (A- plus T or/and N+): 23.9% . ATN profiles prevalence was strongly influenced by age, showing differences according to gender, APOE genotype, and cognitive status. At baseline, 74.6% of patients classified as AD fell in the AD continuum, decreasing to 47.4% in mild cognitive impairment and 42.3% in other neurodegenerative conditions. At follow-up, 41% of patients changed diagnosis, and 92% of patients that changed to AD were classified within the AD continuum. A + was the best individual marker for predicting a final AD diagnosis, and the combinations A + T+(irrespective of N) and A + T+N+had the highest overall accuracy (83%). Conclusion: The ATN scheme is useful to guide AD diagnosis real-life neurological centers settings. However, it shows a lack of accuracy for patients with other types of dementia. In such cases, the inclusion of other markers specific for non-AD proteinopathies could be an important aid to the differential diagnosis.
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Affiliation(s)
- Inês Baldeiras
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Anuschka Silva-Spínola
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Marisa Lima
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
- Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Maria João Leitão
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - João Durães
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Daniela Vieira
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Miguel Tbuas-Pereira
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | | | - Raquel Rocha
- ULSM Unidade Local de Sáude de Matosinhos, Matosinhos, Portugal
| | - Luisa Alves
- Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal
| | | | | | | | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculdade de Medicina, Universidade de Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology; Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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21
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Caprioglio C, Garibotto V, Jessen F, Frölich L, Allali G, Assal F, Frisoni GB, Altomare D. The Clinical Use of Alzheimer's Disease Biomarkers in Patients with Mild Cognitive Impairment: A European Alzheimer's Disease Consortium Survey. J Alzheimers Dis 2022; 89:535-551. [PMID: 35912743 PMCID: PMC9535580 DOI: 10.3233/jad-220333] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Recent advances occurred in the field of Alzheimer's disease (AD) biomarkers and the introduction of a research framework grounded on a biomarker-based definition of AD might have fostered an increased clinical use of AD biomarkers. For this reason, an up-to-date depiction of the clinical use of AD biomarkers is needed. OBJECTIVE To investigate the clinical use of the main AD biomarkers in patients with mild cognitive impairment (MCI) by examining the beliefs and preferences of professionals (clinicians and biomarker experts) of the European Alzheimer's Disease Consortium (EADC). METHODS 150 professionals filled in an online survey from May to September 2020. The investigated biomarkers were medial temporal lobe atrophy score (MTA) on structural MRI, typical AD (i.e., temporoparietal and posterior cingulate) hypometabolism on FDG-PET, CSF (Aβ42, p-tau, t-tau), amyloid-PET and tau-PET. RESULTS The frequency of responders reporting a frequent-to-constant use of MTA (77%) is higher than that of those reporting a frequent-to-constant use of the other AD biomarkers (i.e. , CSF 45%, p = 0.014; FDG-PET: 32%, p < 0.001; amyloid-PET: 8%, p < 0.001; and tau-PET: 2%, p < 0.001). CSF is considered the most valuable biomarker in terms of additional diagnostic value, followed by amyloid-PET, tau-PET, and typical AD hypometabolism on FDG-PET. CONCLUSION AD biomarkers are widely used across European memory clinics with a clinical research background for the diagnosis of MCI. Overall, we observed that CSF is currently considered as the most useful biomarker, followed by amyloid-PET.
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Affiliation(s)
- Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland,Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Frank Jessen
- Department of Psychiatry, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute for Mental Health, University of Heidelberg, Mannheim, Germany
| | - Gilles Allali
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland,Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA
| | - Frédéric Assal
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland,Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland,Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland,Correspondence to: Daniele Altomare, PhD, Memory Clinic, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 6, 1205 Geneva, Switzerland. Tel.: +41 22 372 58 00; E-mail:
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22
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Puig-Pijoan A, García-Escobar G, Fernández-Lebrero A, Manero Borràs R, Sánchez-Benavides G, Navalpotro-Gómez I, Cascales Lahoz D, Suárez-Calvet M, Grau-Rivera O, Boltes Alandí A, Pont-Sunyer M, Ortiz-Gil J, Carrillo-Molina S, López-Villegas D, Abellán-Vidal M, Martínez-Casamitjana M, Hernández-Sánchez J, Peña-Casanova J, Roquer J, Padrós Fluvià A, Puente-Périz V. Estudio CORCOBIA: determinación de puntos de corte de biomarcadores de enfermedad de Alzheimer en LCR en una cohorte clínica. Neurologia 2022. [DOI: 10.1016/j.nrl.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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23
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Kikuchi M, Kobayashi K, Itoh S, Kasuga K, Miyashita A, Ikeuchi T, Yumoto E, Kosaka Y, Fushimi Y, Takeda T, Manabe S, Hattori S, Disease Neuroimaging Initiative A, Nakaya A, Kamijo K, Matsumura Y. Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer’s disease using multimodal data. Comput Struct Biotechnol J 2022; 20:5296-5308. [PMID: 36212530 PMCID: PMC9513733 DOI: 10.1016/j.csbj.2022.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/27/2022] Open
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24
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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25
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Ebenau JL, Pelkmans W, Verberk IMW, Verfaillie SCJ, van den Bosch KA, van Leeuwenstijn M, Collij LE, Scheltens P, Prins ND, Barkhof F, van Berckel BNM, Teunissen CE, van der Flier WM. Association of CSF, Plasma, and Imaging Markers of Neurodegeneration With Clinical Progression in People With Subjective Cognitive Decline. Neurology 2022; 98:e1315-e1326. [PMID: 35110378 PMCID: PMC8967429 DOI: 10.1212/wnl.0000000000200035] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 01/03/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Multiple biomarkers have been suggested to measure neurodegeneration (N) in the AT(N) framework, leading to inconsistencies between studies. We investigated the association of 5 N biomarkers with clinical progression and cognitive decline in individuals with subjective cognitive decline (SCD). METHODS We included individuals with SCD from the Amsterdam Dementia Cohort and SCIENCe project, a longitudinal cohort study (follow-up 4±3 years). We used the following N biomarkers: CSF total tau (t-tau), medial temporal atrophy visual rating on MRI, hippocampal volume (HV), serum neurofilament light (NfL), and serum glial fibrillary acidic protein (GFAP). We determined correlations between biomarkers. We assessed associations between N biomarkers and clinical progression to mild cognitive impairment or dementia (Cox regression) and Mini-Mental State Examination (MMSE) over time (linear mixed models). Models included age, sex, CSF β-amyloid (Aβ) (A), and CSF p-tau (T) as covariates, in addition to the N biomarker. RESULT We included 401 individuals (61±9 years, 42% female, MMSE 28 ± 2, vascular comorbidities 8%-19%). N biomarkers were modestly to moderately correlated (range r -0.28 - 0.58). Serum NfL and GFAP correlated most strongly (r 0.58, p < 0.01). T-tau was strongly correlated with p-tau (r 0.89, p < 0.01), although these biomarkers supposedly represent separate biomarker groups. All N biomarkers individually predicted clinical progression, but only HV, NfL, and GFAP added predictive value beyond Aβ and p-tau (hazard ratio 1.52 [95% CI 1.11-2.09]; 1.51 [1.05-2.17]; 1.50 [1.04-2.15]). T-tau, HV, and GFAP individually predicted MMSE slope (range β -0.17 to -0.11, p < 0.05), but only HV remained associated beyond Aβ and p-tau (β -0.13 [SE 0.04]; p < 0.05). DISCUSSION In cognitively unimpaired older adults, correlations between different N biomarkers were only moderate, indicating they reflect different aspects of neurodegeneration and should not be used interchangeably. T-tau was strongly associated with p-tau (T), which makes it less desirable to use as a measure for N. HV, NfL, and GFAP predicted clinical progression beyond A and T. Our results do not allow to choose one most suitable biomarker for N, but illustrate the added prognostic value of N beyond A and T. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that HV, NfL, and GFAP predicted clinical progression beyond A and T in individuals with SCD.
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Affiliation(s)
- Jarith L Ebenau
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK.
| | - Wiesje Pelkmans
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Inge M W Verberk
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Sander C J Verfaillie
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Karlijn A van den Bosch
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Mardou van Leeuwenstijn
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Lyduine E Collij
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Philip Scheltens
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Niels D Prins
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Frederik Barkhof
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Bart N M van Berckel
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Charlotte E Teunissen
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Wiesje M van der Flier
- From the Alzheimer Center, Departments of Neurology (J.L.E., W.P., I.M.W.V., K.A.v.d.B., M.v.L., P.S., N.D.P., B.N.M.v.B., W.M.V.d.F.) and Radiology & Nuclear Medicine (S.C.J.V., L.E.C., F.B., B.N.M.v.B.), Amsterdam Neuroscience, and Neurochemistry Laboratory, Department of Clinical Chemistry (I.M.W.V., C.E.T.), and Department of Epidemiology & Biostatistics (W.M.v.d.F.), Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
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Huang S, Wang YJ, Guo J. Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives. Neurosci Bull 2022; 38:677-691. [PMID: 35306613 PMCID: PMC9206048 DOI: 10.1007/s12264-022-00836-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/25/2021] [Indexed: 12/19/2022] Open
Abstract
Since the establishment of the biomarker-based A-T-N (Amyloid/Tau/Neurodegeneration) framework in Alzheimer’s disease (AD), the diagnosis of AD has become more precise, and cerebrospinal fluid tests and positron emission tomography examinations based on this framework have become widely accepted. However, the A-T-N framework does not encompass the whole spectrum of AD pathologies, and problems with invasiveness and high cost limit the application of the above diagnostic methods aimed at the central nervous system. Therefore, we suggest the addition of an “X” to the A-T-N framework and a focus on peripheral biomarkers in the diagnosis of AD. In this review, we retrospectively describe the recent progress in biomarkers based on the A-T-N-X framework, analyze the problems, and present our perspectives on the diagnosis of AD.
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27
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Florean I, Penolazzi B, Menichelli A, Pastore M, Cattaruzza T, Mazzon G, Manganotti P. Using the ATN system as a guide for the neuropsychological assessment of Alzheimer's disease. J Clin Exp Neuropsychol 2022; 43:926-943. [PMID: 35166171 DOI: 10.1080/13803395.2022.2036327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Many studies have attempted to determine whether Alzheimer's disease (AD) in-vivo biomarkers can predict neuropsychological performance since pathophysiological changes precede cognitive changes by several years. Nonetheless, neuropsychological measures can also detect cognitive deterioration in cognitively normal individuals with AD-positive biomarkers. Recent studies have investigated whether cognitive measures can be used as a proxy for biomarkers. This is a crucial issue since biomarker analysis is expensive, invasive, and not yet widespread in clinical practice. However, these studies have so far considered only one or two classes of AD biomarkers. Here, we aim at preliminarily evaluating whether and which neuropsychological measures can discriminate individuals that have been classified according to the full scheme of biomarkers known as ATN system. This scheme groups biomarkers as a function of the three main AD-related pathologic processes they measure (i.e., β-amyloidosis, tauopathy, and neurodegeneration) to provide an unbiased and descriptive definition of the Alzheimer's continuum. METHOD Biomarkers and neuropsychological data from 78 patients (70.01 ± 9.15 years; 38 females) with suspected cognitive decline were extracted from a medical database. Participants' biomarker profiles were classified into the following ATN categories: normal AD biomarkers; Alzheimer's continuum; non-AD pathologic change. Data were analyzed using a Bayesian approach, to guarantee reliable result interpretation of data stemming from small samples. RESULTS The discrimination ability of each neuropsychological measure varied depending on the pairs of ATN categories compared. The best-discriminating predictor in the Alzheimer's continuum vs. normal biomarkers comparison was the figure naming ability. In contrast, in the Alzheimer's continuum vs. non-AD pathologic change comparison the best predictor was the wordlist forgetting rate. CONCLUSIONS Although the study was exploratory in nature, the proposed methodological approach may have the potential to identify the best neuropsychological measures for estimating AD neuropathological changes, leading to a more biologically informed use of neuropsychological assessment.
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Affiliation(s)
- Irene Florean
- Department of Life Sciences, University of Trieste, Trieste, Italy
| | | | - Alina Menichelli
- Rehabilitation Unit, Department of Medicine, Surgery and Health Sciences, Maggiore City Hospital Asugi, Trieste, Italy
| | - Massimiliano Pastore
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
| | - Tatiana Cattaruzza
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital Asugi, University of Trieste, Trieste, Italy
| | - Giulia Mazzon
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital Asugi, University of Trieste, Trieste, Italy
| | - Paolo Manganotti
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital Asugi, University of Trieste, Trieste, Italy
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28
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Frisoni GB, Altomare D, Thal DR, Ribaldi F, van der Kant R, Ossenkoppele R, Blennow K, Cummings J, van Duijn C, Nilsson PM, Dietrich PY, Scheltens P, Dubois B. The probabilistic model of Alzheimer disease: the amyloid hypothesis revised. Nat Rev Neurosci 2022; 23:53-66. [PMID: 34815562 PMCID: PMC8840505 DOI: 10.1038/s41583-021-00533-w] [Citation(s) in RCA: 242] [Impact Index Per Article: 80.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2021] [Indexed: 01/03/2023]
Abstract
The current conceptualization of Alzheimer disease (AD) is driven by the amyloid hypothesis, in which a deterministic chain of events leads from amyloid deposition and then tau deposition to neurodegeneration and progressive cognitive impairment. This model fits autosomal dominant AD but is less applicable to sporadic AD. Owing to emerging information regarding the complex biology of AD and the challenges of developing amyloid-targeting drugs, the amyloid hypothesis needs to be reconsidered. Here we propose a probabilistic model of AD in which three variants of AD (autosomal dominant AD, APOE ε4-related sporadic AD and APOE ε4-unrelated sporadic AD) feature decreasing penetrance and decreasing weight of the amyloid pathophysiological cascade, and increasing weight of stochastic factors (environmental exposures and lower-risk genes). Together, these variants account for a large share of the neuropathological and clinical variability observed in people with AD. The implementation of this model in research might lead to a better understanding of disease pathophysiology, a revision of the current clinical taxonomy and accelerated development of strategies to prevent and treat AD.
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Affiliation(s)
- Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland.
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Dietmar Rudolf Thal
- Laboratory for Neuropathology, Department of Imaging and Pathology, and Leuven Brain Institute, University of Leuven, Leuven, Belgium
- Department of Pathology, University Hospital Leuven, Leuven, Belgium
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Rik van der Kant
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Kaj Blennow
- Cinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences; University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Peter M Nilsson
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | | | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Life Science Partners, Amsterdam, Netherlands
| | - Bruno Dubois
- Institut de la Mémoire et de la Maladie d'Alzheimer, IM2A, Groupe Hospitalier Pitié-Salpêtrière, Sorbonne Université, Paris, France
- Institut du Cerveau et de la Moelle Épinière, UMR-S975, INSERM, Paris, France
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29
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Song R, Wu X, Liu H, Guo D, Tang L, Zhang W, Feng J, Li C. Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study. Korean J Radiol 2022; 23:89-100. [PMID: 34983097 PMCID: PMC8743156 DOI: 10.3348/kjr.2021.0323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). MATERIALS AND METHODS A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. RESULTS The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. CONCLUSION We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.
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Affiliation(s)
- Rao Song
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Tang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junbang Feng
- Department of Radiology, Chongqing Emergency Medical Center, Chongqing, China
| | - Chuanming Li
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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30
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Guo T, Landau SM, Jagust WJ. Age, vascular disease, and Alzheimer's disease pathologies in amyloid negative elderly adults. ALZHEIMERS RESEARCH & THERAPY 2021; 13:174. [PMID: 34654465 PMCID: PMC8520216 DOI: 10.1186/s13195-021-00913-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/03/2021] [Indexed: 02/04/2023]
Abstract
Background We recently reported that CSF phosphorylated tau (p-Tau181) relative to Aβ40 (CSF p-Tau/Aβ40 ratio) was less noisy and increased associations with Alzheimer’s disease (AD) biomarkers compared to CSF p-Tau181 alone. While elevations of CSF p-Tau/Aβ40 can occur in amyloid-β (Aβ) negative (Aβ-) individuals, the factors associated with these elevations and their role in neurodegeneration and cognitive decline are unknown. We aim to explore factors associated with elevated tau in CSF, and how these elevated tau are related to neurodegeneration and cognitive decline in the absence of Aβ positivity. Methods We examined relationships between CSF p-Tau/Aβ40, and CSF Aβ42/Aβ40, Aβ PET, and white matter hyperintensities (WMH) as well as vascular risk factors in 149 cognitively unimpaired and 52 impaired individuals who were presumably not on the Alzheimer’s disease (AD) pathway due to negative Aβ status on both CSF and PET. Subgroups had 18F-fluorodeoxyglucose (FDG) PET and adjusted hippocampal volume (aHCV), and longitudinal measures of CSF, aHCV, FDG PET, and cognition data, so we examined CSF p-Tau/Aβ40 associations with these measures as well. Results Elevated CSF p-Tau/Aβ40 was associated with older age, male sex, greater WMH, and hypertension as well as a pattern of hippocampal atrophy and temporoparietal hypometabolism characteristic of AD. Lower CSF Aβ42/Aβ40, higher WMH, and hypertension but not age, sex, Aβ PET, APOE-ε4 status, body mass index, smoking, and hyperlipidemia at baseline predicted CSF p-Tau/Aβ40 increases over approximately 5 years of follow-up. The relationship between CSF p-Tau/Aβ40 and subsequent cognitive decline was partially or fully explained by neurodegenerative measurements. Conclusions These data provide surprising clues as to the etiology and significance of tau pathology in the absence of Aβ. It seems likely that, in addition to age, both cerebrovascular disease and subthreshold levels of Aβ are related to this tau accumulation. Crucially, this phenotype of CSF tau elevation in amyloid-negative individuals share features with AD such as a pattern of metabolic decline and regional brain atrophy. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00913-5.
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Affiliation(s)
- Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China.
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA.,Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA.,Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
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31
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Yoon B, Guo T, Provost K, Korman D, Ward TJ, Landau SM, Jagust WJ. Abnormal tau in amyloid PET negative individuals. Neurobiol Aging 2021; 109:125-134. [PMID: 34715443 DOI: 10.1016/j.neurobiolaging.2021.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 09/03/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
We examined the characteristics of individuals with biomarker evidence of tauopathy but without β-amyloid (Aβ) (A-T+) in relation to individuals with (A+T+) and without (A-T-) evidence of Alzheimer's disease (AD). We included 561 participants with Aβ and tau PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared A-T- (n = 316), A-T+ (n = 63), and A+T+ (n = 182) individuals on demographics, amyloid, tau, hippocampal volumes, and cognition. A-T+ individuals were low on apolipoprotein E ɛ4 prevalence (17%) and had no evidence of subtly elevated brain Aβ within the negative range. The severity of tau deposition, hippocampal atrophy, and cognitive dysfunction in the A-T+ group was intermediate between A-T- and A+T+ (all p < 0.001). Tau uptake patterns in A-T+ individuals were heterogeneous, but approximately 29% showed tau deposition in the medial temporal lobe only, consistent with primary age-related tauopathy and an additional 32% showed a pattern consistent with AD. A-T+ individuals also share other features that are characteristic of AD such as cognitive impairment and neurodegeneration, but this group is heterogeneous and likely reflects more than one disorder.
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Affiliation(s)
- Bora Yoon
- Department of Neurology, Konyang University Hospital, Konyang University, College of Medicine, Daejeon, Korea.
| | - Tengfei Guo
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Karine Provost
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Deniz Korman
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Tyler J Ward
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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32
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Barker W, Quinonez C, Greig MT, Behar R, Chirinos C, Rodriguez RA, Rosselli M, Rodriguez MJ, Cid RC, Rundek T, McFarland K, Hanson K, Smith G, DeKosky S, Vaillancourt D, Adjouadi M, Marsiske M, Ertekin-Taner N, Golde T, Loewenstein DA, Duara R. Utility of Plasma Neurofilament Light in the 1Florida Alzheimer's Disease Research Center (ADRC). J Alzheimers Dis 2021; 79:59-70. [PMID: 33216030 PMCID: PMC7902971 DOI: 10.3233/jad-200901] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Plasma NfL (pNfL) levels are elevated in many neurological disorders. However, the utility of pNfL in a clinical setting has not been established. Objective: In a cohort of diverse older participants, we examined: 1) the association of pNfL to age, sex, Hispanic ethnicity, diagnosis, and structural and amyloid imaging biomarkers; and 2) its association to baseline and longitudinal cognitive and functional performance. Methods: 309 subjects were classified at baseline as cognitively normal (CN) or with cognitive impairment. Most subjects had structural MRI and amyloid PET scans. The most frequent etiological diagnosis was Alzheimer’s disease (AD), but other neurological and neuropsychiatric disorders were also represented. We assessed the relationship of pNfL to cognitive and functional status, primary etiology, imaging biomarkers, and to cognitive and functional decline. Results: pNfL increased with age, degree of hippocampal atrophy, and amyloid load, and was higher in females among CN subjects, but was not associated with Hispanic ethnicity. Compared to CN subjects, pNfL was elevated among those with AD or FTLD, but not those with neuropsychiatric or other disorders. Hippocampal atrophy, amyloid positivity and higher pNfL levels each added unique variance in predicting greater functional impairment on the CDR-SB at baseline. Higher baseline pNfL levels also predicted greater cognitive and functional decline after accounting for hippocampal atrophy and memory scores at baseline. Conclusion: pNfL may have a complementary and supportive role to brain imaging and cognitive testing in a memory disorder evaluation, although its diagnostic sensitivity and specificity as a stand-alone measure is modest. In the absence of expensive neuroimaging tests, pNfL could be used for differentiating neurodegenerative disease from neuropsychiatric disorders.
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Affiliation(s)
- Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Carlos Quinonez
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Maria T Greig
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Raquel Behar
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Cesar Chirinos
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Rosemarie A Rodriguez
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
| | - Monica Rosselli
- Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science, Davie, FL, USA
| | | | - Rosie Curiel Cid
- Department of Psychiatry and Behavioral Sciences and Neurology, Miller School of Medicine, University of Miami, FL, USA
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | | | - Kevin Hanson
- Florida ADRC, University of Florida, Gainesville, FL, USA
| | - Glenn Smith
- Florida ADRC, University of Florida, Gainesville, FL, USA
| | - Steven DeKosky
- Florida ADRC, University of Florida, Gainesville, FL, USA
| | | | - Malek Adjouadi
- College of Engineering and Computing, Florida International University, Miami, Florida, USA
| | | | - Nilufer Ertekin-Taner
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL, USA.,Mayo Clinic Florida, Department of Neurology, Jacksonville, FL, USA
| | - Todd Golde
- Florida ADRC, University of Florida, Gainesville, FL, USA
| | - David A Loewenstein
- Department of Psychiatry and Behavioral Sciences and Neurology, Miller School of Medicine, University of Miami, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorder, Mount Sinai Medical Center, Miami Beach, FL, USA
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33
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Dartora CM, Borelli WV, Koole M, Marques da Silva AM. Cognitive Decline Assessment: A Review From Medical Imaging Perspective. Front Aging Neurosci 2021; 13:704661. [PMID: 34489675 PMCID: PMC8416532 DOI: 10.3389/fnagi.2021.704661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.
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Affiliation(s)
- Caroline Machado Dartora
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
| | - Wyllians Vendramini Borelli
- Neurology Department, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Brain Institute of Rio Grande do Sul, BraIns, Porto Alegre, Brazil
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Ana Maria Marques da Silva
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil.,Brain Institute of Rio Grande do Sul, BraIns, Porto Alegre, Brazil.,Medical Image Computing Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, PUCRS, Porto Alegre, Brazil
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34
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Developing the ATX(N) classification for use across the Alzheimer disease continuum. Nat Rev Neurol 2021; 17:580-589. [PMID: 34239130 DOI: 10.1038/s41582-021-00520-w] [Citation(s) in RCA: 190] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2021] [Indexed: 02/06/2023]
Abstract
Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical-biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-β (Aβ) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood-brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.
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Thijssen EH, La Joie R, Strom A, Fonseca C, Iaccarino L, Wolf A, Spina S, Allen IE, Cobigo Y, Heuer H, VandeVrede L, Proctor NK, Lago AL, Baker S, Sivasankaran R, Kieloch A, Kinhikar A, Yu L, Valentin MA, Jeromin A, Zetterberg H, Hansson O, Mattsson-Carlgren N, Graham D, Blennow K, Kramer JH, Grinberg LT, Seeley WW, Rosen H, Boeve BF, Miller BL, Teunissen CE, Rabinovici GD, Rojas JC, Dage JL, Boxer AL. Plasma phosphorylated tau 217 and phosphorylated tau 181 as biomarkers in Alzheimer's disease and frontotemporal lobar degeneration: a retrospective diagnostic performance study. Lancet Neurol 2021; 20:739-752. [PMID: 34418401 PMCID: PMC8711249 DOI: 10.1016/s1474-4422(21)00214-3] [Citation(s) in RCA: 294] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 06/02/2021] [Accepted: 06/29/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Plasma tau phosphorylated at threonine 217 (p-tau217) and plasma tau phosphorylated at threonine 181 (p-tau181) are associated with Alzheimer's disease tau pathology. We compared the diagnostic value of both biomarkers in cognitively unimpaired participants and patients with a clinical diagnosis of mild cognitive impairment, Alzheimer's disease syndromes, or frontotemporal lobar degeneration (FTLD) syndromes. METHODS In this retrospective multicohort diagnostic performance study, we analysed plasma samples, obtained from patients aged 18-99 years old who had been diagnosed with Alzheimer's disease syndromes (Alzheimer's disease dementia, logopenic variant primary progressive aphasia, or posterior cortical atrophy), FTLD syndromes (corticobasal syndrome, progressive supranuclear palsy, behavioural variant frontotemporal dementia, non-fluent variant primary progressive aphasia, or semantic variant primary progressive aphasia), or mild cognitive impairment; the participants were from the University of California San Francisco (UCSF) Memory and Aging Center, San Francisco, CA, USA, and the Advancing Research and Treatment for Frontotemporal Lobar Degeneration Consortium (ARTFL; 17 sites in the USA and two in Canada). Participants from both cohorts were carefully characterised, including assessments of CSF p-tau181, amyloid-PET or tau-PET (or both), and clinical and cognitive evaluations. Plasma p-tau181 and p-tau217 were measured using electrochemiluminescence-based assays, which differed only in the biotinylated antibody epitope specificity. Receiver operating characteristic analyses were used to determine diagnostic accuracy of both plasma markers using clinical diagnosis, neuropathological findings, and amyloid-PET and tau-PET measures as gold standards. Difference between two area under the curve (AUC) analyses were tested with the Delong test. FINDINGS Data were collected from 593 participants (443 from UCSF and 150 from ARTFL, mean age 64 years [SD 13], 294 [50%] women) between July 1 and Nov 30, 2020. Plasma p-tau217 and p-tau181 were correlated (r=0·90, p<0·0001). Both p-tau217 and p-tau181 concentrations were increased in people with Alzheimer's disease syndromes (n=75, mean age 65 years [SD 10]) relative to cognitively unimpaired controls (n=118, mean age 61 years [SD 18]; AUC=0·98 [95% CI 0·95-1·00] for p-tau217, AUC=0·97 [0·94-0·99] for p-tau181; pdiff=0·31) and in pathology-confirmed Alzheimer's disease (n=15, mean age 73 years [SD 12]) versus pathologically confirmed FTLD (n=68, mean age 67 years [SD 8]; AUC=0·96 [0·92-1·00] for p-tau217, AUC=0·91 [0·82-1·00] for p-tau181; pdiff=0·22). P-tau217 outperformed p-tau181 in differentiating patients with Alzheimer's disease syndromes (n=75) from those with FTLD syndromes (n=274, mean age 67 years [SD 9]; AUC=0·93 [0·91-0·96] for p-tau217, AUC=0·91 [0·88-0·94] for p-tau181; pdiff=0·01). P-tau217 was a stronger indicator of amyloid-PET positivity (n=146, AUC=0·91 [0·88-0·94]) than was p-tau181 (n=214, AUC=0·89 [0·86-0·93]; pdiff=0·049). Tau-PET binding in the temporal cortex was more strongly associated with p-tau217 than p-tau181 (r=0·80 vs r=0·72; pdiff<0·0001, n=230). INTERPRETATION Both p-tau217 and p-tau181 had excellent diagnostic performance for differentiating patients with Alzheimer's disease syndromes from other neurodegenerative disorders. There was some evidence in favour of p-tau217 compared with p-tau181 for differential diagnosis of Alzheimer's disease syndromes versus FTLD syndromes, as an indication of amyloid-PET-positivity, and for stronger correlations with tau-PET signal. Pending replication in independent, diverse, and older cohorts, plasma p-tau217 and p-tau181 could be useful screening tools to identify individuals with underlying amyloid and Alzheimer's disease tau pathology. FUNDING US National Institutes of Health, State of California Department of Health Services, Rainwater Charitable Foundation, Michael J Fox foundation, Association for Frontotemporal Degeneration, Alzheimer's Association.
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Affiliation(s)
- Elisabeth H Thijssen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Corrina Fonseca
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Amy Wolf
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Salvatore Spina
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Isabel E Allen
- Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Yann Cobigo
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Hilary Heuer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Lawren VandeVrede
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | | | - Argentina Lario Lago
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Suzanne Baker
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | | | | | - Lili Yu
- Novartis, Cambridge, MA, USA
| | | | | | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Lund University, Lund, Sweden; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden; Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | | | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Lea T Grinberg
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Pathology, University of California, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Pathology, University of California, San Francisco, CA, USA
| | - Howard Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | | | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Julio C Rojas
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | | | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
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Joseph S, Patterson R, Wang W, Blumberger DM, Rajji T, Kumar S. Quantitative Assessment of Cortical Excitability in Alzheimer's Dementia and Its Association with Clinical Symptoms: A Systematic Review and Meta-Analyses. J Alzheimers Dis 2021; 88:867-891. [PMID: 34219724 DOI: 10.3233/jad-210311] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is characterized by cognitive and neuropsychiatric symptoms (NPS) due to underlying neurodegenerative pathology. Some studies using electroencephalography (EEG) have shown increased epileptiform and epileptic activity in AD. OBJECTIVE This review and meta-analyses aims to synthesize the existing evidence for quantitative abnormalities of cortical excitability in AD and their relationship with clinical symptoms. METHODS We systematically searched and reviewed publications that quantitatively assessed cortical excitability, using transcranial magnetic stimulation (TMS) resting motor threshold (rMT), active motor threshold (aMT), motor evoked potential (MEP) or directly from the cortex using TMS-EEG via TMS-evoked potential (TEP). We meta-analyzed studies that assessed rMT and aMT using random effects model. RESULTS We identified 895 publications out of which 37 were included in the qualitative review and 30 studies using rMT or aMT were included in the meta-analyses. The AD group had reduced rMT (Hedges' g = -0.99, 95%CI [-1.29, -0.68], p < 0.00001) and aMT (Hedges' g = -0.87, 95%CI [-1.50, -0.24], p < 0.00001) as compared with control groups, indicative of higher cortical excitability. Qualitative review found some evidence of increased MEP amplitude, whereas findings related to TEP were inconsistent. There was some evidence supporting an inverse association between cortical excitability and global cognition. No publications reported on the relationship between cortical excitability and NPS. CONCLUSION There is strong evidence of increased motor cortex excitability in AD and some evidence of an inverse association between excitability and cognition. Future studies should assess cortical excitability from non-motor areas using TMS-EEG and examine its relationship with cognition and NPS.
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Affiliation(s)
- Shaylyn Joseph
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Rachel Patterson
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Tarek Rajji
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada.,Toronto Dementia Research Alliance, Toronto, Canada
| | - Sanjeev Kumar
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
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Ingala S, De Boer C, Masselink LA, Vergari I, Lorenzini L, Blennow K, Chételat G, Di Perri C, Ewers M, van der Flier WM, Fox NC, Gispert JD, Haller S, Molinuevo JL, Muniz‐Terrera G, Mutsaerts HJMM, Ritchie CW, Ritchie K, Schmidt M, Schwarz AJ, Vermunt L, Waldman AD, Wardlaw J, Wink AM, Wolz R, Wottschel V, Scheltens P, Visser PJ, Barkhof F. Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort. Alzheimers Dement 2021; 17:1189-1204. [PMID: 33811742 PMCID: PMC8359976 DOI: 10.1002/alz.12292] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/11/2020] [Accepted: 12/22/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND We classified non-demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles. MATERIALS AND METHODS From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut-offs of 1000 pg/mL for amyloid beta (Aß)1-42 and 27 pg/mL for p-tau181 were validated using Gaussian mixture models. Given strong correlation of p-tau and t-tau (R2 = 0.98, P < 0.001), neurodegeneration was defined by age-adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages. RESULTS Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001). DISCUSSION In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Casper De Boer
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Larissa A Masselink
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Ilaria Vergari
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Kaj Blennow
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Gaël Chételat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders,”Institut Blood and Brain @ Caen‐NormandieCyceronCaenFrance
| | - Carol Di Perri
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Michael Ewers
- Institute for Stroke and Dementia ResearchKlinikum der Universitat MünchenLudwig‐Maximilians‐Universitat LMUMunichGermany
| | - Wiesje M van der Flier
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Nick C Fox
- Dementia Research CentreDepartment of Neurodegenerative Disease & UK Dementia Research InstituteInstitute of NeurologyUniversity College LondonLondonUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Sven Haller
- CIRD Centre d'Imagerie Rive DroiteGenevaSwitzerland
| | - José Luís Molinuevo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Hopsital Clínic‐IDIBAPSAlzheimer's Disease & Other Cognitive Disorders UnitBarcelonaSpain
| | - Graciela Muniz‐Terrera
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
| | - Henri JMM Mutsaerts
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI)Ghent UniversityGhentBelgium
| | - Craig W Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Karen Ritchie
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Adam J Schwarz
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
| | - Lisa Vermunt
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Adam D Waldman
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Joanna Wardlaw
- Centre for Dementia PreventionEdinburgh Imaging, UK Dementia Research Institute at The University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Alle Meije Wink
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | | | - Viktor Wottschel
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Philip Scheltens
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Department of Psychiatry & NeuropsychologySchool for Mental Health and NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Institutes of Neurology and Healthcare EngineeringUniversity College LondonLondonUK
| | - the EPAD consortium
- Department of Radiology and Nuclear MedicineAmsterdam UMC Location VUmcVrije Universiteit Amsterdam, Amsterdam NeuroscienceAmsterdamthe Netherlands
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Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet 2021; 397:1577-1590. [PMID: 33667416 PMCID: PMC8354300 DOI: 10.1016/s0140-6736(20)32205-4] [Citation(s) in RCA: 2385] [Impact Index Per Article: 596.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/21/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies.
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Affiliation(s)
- Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Neurology, Amsterdam University Medical Centers, Amsterdam, Netherlands; Life Science Partners, Amsterdam, Netherlands.
| | - Bart De Strooper
- VIB Center for Brain and Disease Research, Leuven, Belgium; KU Leuven Department for Neurology, Leuven, Belgium; Dementia Research Institute, University College London, London, UK
| | - Miia Kivipelto
- Division of Clinical Geriatrics and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska University Hospital, Stockholm, Sweden; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Ageing and Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Henne Holstege
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Clinical Genetics, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gael Chételat
- Normandie Université, Université de Caen, Institut National de la Santé et de la Recherche Médicale, Groupement d'Intérêt Public Cyceron, Caen, France
| | - Charlotte E Teunissen
- Department of Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, University of Nevada, Las Vegas, NV, USA; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Wiesje M van der Flier
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Epidemiology and Datascience, Amsterdam University Medical Centers, Amsterdam, Netherlands
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Delmotte K, Schaeverbeke J, Poesen K, Vandenberghe R. Prognostic value of amyloid/tau/neurodegeneration (ATN) classification based on diagnostic cerebrospinal fluid samples for Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:84. [PMID: 33879243 PMCID: PMC8059197 DOI: 10.1186/s13195-021-00817-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 03/23/2021] [Indexed: 11/12/2022]
Abstract
Objective The primary study objective of this retrospective academic memory clinic-based observational longitudinal study was to investigate the prognostic value of a cerebrospinal fluid (CSF)-based ATN classification for subsequent cognitive decline during the 3 years following lumbar puncture in a clinical, real-life setting. The secondary objective was to investigate the prognostic value of CSF biomarkers as continuous variables. Methods Data from 228 patients (median age 67 (47–85) years), who presented at the Neurology Memory Clinic UZ/KU Leuven between September 2011 and December 2016, were included with a follow-up period of up to 36 months. Patients underwent a CSF AD biomarker test for amyloid-beta 1–42 (Aβ42), hyperphosphorylated tau (p181-tau) and total tau (t-tau) in the clinical work-up for diagnostic reasons. Patients were divided into ATN classes based on CSF biomarkers: Aβ42 for amyloid (A), p181-tau for tau (T), and t-tau as a measure for neurodegeneration (N). Based on retrospective data analysis, cognitive performance was evaluated by Mini Mental State Examination (MMSE) scores every 6 months over a period up to 36 months following the lumbar puncture. The statistical analysis was based on linear mixed-effects modeling (LME). Results The distribution in the current clinical sample was as follows: A−/T−/N− 32.02%, A+/T−/N− 33.33%, A+/T+/N+ 17.11%, A+/T−/N+ 11.84%, A−/T−/N+ 4.39%, A−/T+/N+ 1.32% (3 cases), with no cases in the A−/T+/N− and A+/T+/N− class. Hence, the latter 3 classes were excluded from further analyses. The change of MMSE relative to A−/T−/N− over a 36-month period was significant in all four ATN classes: A+/T+/N+ = − 4.78 points on the MMSE; A−/T−/N+ = − 4.76; A+/T−/N+ = − 2.83; A+/T−/N− = − 1.96. The earliest significant difference was seen in the A+/T+/N+ class at 12 months after baseline. The effect of ATN class on future cognitive decline was confirmed for a different set of CSF thresholds. All individual baseline CSF biomarkers including the Aβ42/t-tau ratio showed a significant correlation with subsequent cognitive decline, with the highest correlation seen for Aβ42/t-tau. Conclusion ATN classification based on CSF biomarkers has a statistically significant and clinically relevant prognostic value for the course of cognitive decline in a 3-year period in a clinical practice setting. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00817-4.
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Affiliation(s)
- Koen Delmotte
- Department of Neurology, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium. .,Department of Neurology, Jessa Hospital, Hasselt, Belgium.
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, Belgium.,Laboratory of Neuropathology, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Koen Poesen
- Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium.,Laboratory for Molecular Neurobiomarker Research, KU Leuven, Leuven, Belgium
| | - Rik Vandenberghe
- Department of Neurology, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium.,Laboratory for Cognitive Neurology, Leuven Brain Institute, KU Leuven, Leuven, Belgium
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40
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Grøntvedt GR, Lauridsen C, Berge G, White LR, Salvesen Ø, Bråthen G, Sando SB. The Amyloid, Tau, and Neurodegeneration (A/T/N) Classification Applied to a Clinical Research Cohort with Long-Term Follow-Up. J Alzheimers Dis 2021; 74:829-837. [PMID: 32116257 PMCID: PMC7242836 DOI: 10.3233/jad-191227] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The unbiased amyloid, tau, and neurodegeneration (A/T/N) classification is designed to characterize individuals in the Alzheimer continuum and is currently little explored in clinical cohorts. Objective: A retrospective comparison of the A/T/N classification system with the results of a two-year clinical study, with extended follow-up up to 10 years after inclusion. Methods: Patients (n = 102) clinically diagnosed as Alzheimer’s disease (AD) with dementia or amnestic mild cognitive impairment (MCI), and 61 cognitively healthy control individuals were included. Baseline cerebrospinal fluid core biomarkers for AD (Aβ42, phosphorylated tau, and total tau) were applied to the A/T/N classification using the final clinical diagnosis at extended follow-up as the gold standard. Results: A + T + N+ was a strong predictor for AD dementia, even among cognitively healthy individuals. Amnestic MCI was heterogenous, considering both clinical outcome and distribution within A/T/N. Some individuals with amnestic MCI progressed to clinical AD dementia within all four major A/T/N groups. The highest proportion of progression was among triple positive cases, but progression was also common in individuals with suspected non-Alzheimer pathophysiology (A-T + N+), and those with triple negative status. A-T-N- individuals who were cognitively healthy overwhelmingly remained cognitively intact over time, but in amnestic MCI the clinical outcome was heterogenous, including AD dementia, other dementias, and recovery. Conclusion: The A/T/N framework accentuates biomarkers over clinical status. However, when selecting individuals for research, a combination of the two may be necessary since the prognostic value of the A/T/N framework depends on clinical status.
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Affiliation(s)
- Gøril Rolfseng Grøntvedt
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Camilla Lauridsen
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Guro Berge
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Linda R White
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Øyvind Salvesen
- Unit for Applied Clinical Research, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Geir Bråthen
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sigrid Botne Sando
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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Eckerström C, Svensson J, Kettunen P, Jonsson M, Eckerström M. Evaluation of the ATN model in a longitudinal memory clinic sample with different underlying disorders. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12031. [PMID: 33816750 PMCID: PMC8015813 DOI: 10.1002/dad2.12031] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/06/2020] [Accepted: 03/06/2020] [Indexed: 11/10/2022]
Abstract
INTRODUCTION To evaluate the usefulness of the 2018 NIA-AA (National Institute on Aging and Alzheimer's Association) research framework in a longitudinal memory clinic study with different clinical outcomes and underlying disorders. METHODS We included 420 patients with mild cognitive impairment or subjective cognitive impairment. During the follow up, 27% of the patients converted to dementia, with the majority converting to Alzheimer's disease (AD) or mixed dementia. Based on the baseline values of the cerebrospinal fluid biomarkers, the patients were classified into one of the eight possible ATN groups (amyloid beta [Aβ] aggregation [A], tau aggregation reflecting neurofibrillary tangles [T], and neurodegeneration [N]). RESULTS The majority of the patients converting to AD and mixed dementia were in ATN groups positive for A (71%). The A+T+N+ group was highly overrepresented among converters to AD and mixed dementia. Patients converting to dementias other than AD or mixed dementia were evenly distributed across the ATN groups. DISCUSSION Our findings provide support for the usefulness of the ATN system to detect incipient AD or mixed dementia.
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Affiliation(s)
- C. Eckerström
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
- Department of Immunology and Transfusion MedicineRegion Västra GötalandSahlgrenska University HospitalSweden
| | - J. Svensson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska AcademyUniversity of GothenburgSweden
| | - P. Kettunen
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
| | - M. Jonsson
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
| | - M. Eckerström
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
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Rosenberg A, Solomon A, Soininen H, Visser PJ, Blennow K, Hartmann T, Kivipelto M. Research diagnostic criteria for Alzheimer's disease: findings from the LipiDiDiet randomized controlled trial. ALZHEIMERS RESEARCH & THERAPY 2021; 13:64. [PMID: 33766132 PMCID: PMC7995792 DOI: 10.1186/s13195-021-00799-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/23/2021] [Indexed: 12/18/2022]
Abstract
Background To explore the utility of the International Working Group (IWG)-1 criteria in recruitment for Alzheimer’s disease (AD) clinical trials, we applied the more recently proposed research diagnostic criteria to individuals enrolled in a randomized controlled prevention trial (RCT) and assessed their disease progression. Methods The multinational LipiDiDiet RCT targeted 311 individuals with IWG-1 defined prodromal AD. Based on centrally analyzed baseline biomarkers, participants were classified according to the IWG-2 and National Institute on Aging–Alzheimer’s Association (NIA-AA) 2011 and 2018 criteria. Linear mixed models were used to investigate the 2-year change in cognitive and functional performance (Neuropsychological Test Battery NTB Z scores, Clinical Dementia Rating-Sum of Boxes CDR-SB) (criteria × time interactions; baseline score, randomization group, sex, Mini-Mental State Examination (MMSE), and age also included in the models). Cox models adjusted for randomization group, MMSE, sex, age, and study site were used to investigate the risk of progression to dementia over 2 years. Results In total, 88%, 86%, and 69% of participants had abnormal cerebrospinal fluid (CSF) β-amyloid, total tau, and phosphorylated tau, respectively; 64% had an A+T+N+ profile (CSF available for N = 107). Cognitive-functional decline appeared to be more pronounced in the IWG-2 prodromal AD, NIA-AA 2011 high and intermediate AD likelihood, and NIA-AA 2018 AD groups, but few significant differences were observed between the groups within each set of criteria. Hazard ratio (95% CI) for dementia was 4.6 (1.6–13.7) for IWG-2 prodromal AD (reference group no prodromal AD), 7.4 (1.0–54.7) for NIA-AA 2011 high AD likelihood (reference group suspected non-AD pathology SNAP), and 9.4 (1.2–72.7) for NIA-AA 2018 AD (reference group non-Alzheimer’s pathologic change). Compared with the NIA-AA 2011 high AD likelihood group (abnormal β-amyloid and neuronal injury markers), disease progression was similar in the intermediate AD likelihood group (medial temporal lobe atrophy; no CSF available). Conclusions Despite being less restrictive than the other criteria, the IWG-1 criteria reliably identified individuals with AD pathology. More pragmatic and easily applicable selection criteria might be preferred due to feasibility in certain situations, e.g., in multidomain prevention trials that do not specifically target β-amyloid/tau pathologies. Trial registration Netherlands Trial Register, NL1620. Registered on 9 March 2009
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Affiliation(s)
- Anna Rosenberg
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
| | - Alina Solomon
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Neurocenter, Department of Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centre Limburg, University of Maastricht, Maastricht, Netherlands.,Department of Neurology, Alzheimer Centre, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Tobias Hartmann
- Deutsches Institut für Demenz Prävention (DIDP), Medical Faculty, and Department of Experimental Neurology, Saarland University, Homburg, Germany
| | - Miia Kivipelto
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Theme Aging, Karolinska University Hospital, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
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Tosun D, Veitch D, Aisen P, Jack CR, Jagust WJ, Petersen RC, Saykin AJ, Bollinger J, Ovod V, Mawuenyega KG, Bateman RJ, Shaw LM, Trojanowski JQ, Blennow K, Zetterberg H, Weiner MW. Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers. Brain Commun 2021; 3:fcab008. [PMID: 33842885 PMCID: PMC8023542 DOI: 10.1093/braincomms/fcab008] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 01/18/2023] Open
Abstract
In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.
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Affiliation(s)
- Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Dallas Veitch
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA
| | | | - William J Jagust
- School of Public Health and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Ronald C Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - James Bollinger
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Kwasi G Mawuenyega
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, 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
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, 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
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Lee S, Cho EJ, Kwak HB. Personalized Healthcare for Dementia. Healthcare (Basel) 2021; 9:healthcare9020128. [PMID: 33525656 PMCID: PMC7910906 DOI: 10.3390/healthcare9020128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 01/07/2023] Open
Abstract
Dementia is one of the most common health problems affecting older adults, and the population with dementia is growing. Dementia refers to a comprehensive syndrome rather than a specific disease and is characterized by the loss of cognitive abilities. Many factors are related to dementia, such as aging, genetic profile, systemic vascular disease, unhealthy diet, and physical inactivity. As the causes and types of dementia are diverse, personalized healthcare is required. In this review, we first summarize various diagnostic approaches associated with dementia. Particularly, clinical diagnosis methods, biomarkers, neuroimaging, and digital biomarkers based on advances in data science and wearable devices are comprehensively reviewed. We then discuss three effective approaches to treating dementia, including engineering design, exercise, and diet. In the engineering design section, recent advances in monitoring and drug delivery systems for dementia are introduced. Additionally, we describe the effects of exercise on the treatment of dementia, especially focusing on the effects of aerobic and resistance training on cognitive function, and the effects of diets such as the Mediterranean diet and ketogenic diet on dementia.
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Affiliation(s)
- Seunghyeon Lee
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
- Department of Chemical Engineering, Inha University, Incheon 22212, Korea
| | - Eun-Jeong Cho
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
| | - Hyo-Bum Kwak
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (E.-J.C.)
- Correspondence: ; Tel.: +82-32-860-8183
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Villemagne VL, Barkhof F, Garibotto V, Landau SM, Nordberg A, van Berckel BNM. Molecular Imaging Approaches in Dementia. Radiology 2021; 298:517-530. [PMID: 33464184 DOI: 10.1148/radiol.2020200028] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The increasing prevalence of dementia worldwide places a high demand on healthcare providers to perform a diagnostic work-up in relatively early stages of the disease, given that the pathologic process usually begins decades before symptoms are evident. Structural imaging is recommended to rule out other disorders and can only provide diagnosis in a late stage with limited specificity. Where PET imaging previously focused on the spatial pattern of hypometabolism, the past decade has seen the development of novel tracers to demonstrate characteristic protein abnormalities. Molecular imaging using PET/SPECT is able to show amyloid and tau deposition in Alzheimer disease and dopamine depletion in parkinsonian disorders starting decades before symptom onset. Novel tracers for neuroinflammation and synaptic density are being developed to further unravel the molecular pathologic characteristics of dementia disorders. In this article, the authors review the current status of established and emerging PET tracers in a diagnostic setting and also their value as prognostic markers in research studies and outcome measures for clinical trials in Alzheimer disease.
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Affiliation(s)
- Victor L Villemagne
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Frederik Barkhof
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Valentina Garibotto
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Susan M Landau
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Agneta Nordberg
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
| | - Bart N M van Berckel
- From the Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (V.L.V.); Department of Medicine, the University of Melbourne, Melbourne, Australia (V.L.V.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, the Netherlands (F.B., B.N.M.v.B.); UCL institutes of Neurology and Healthcare Engineering, London, England (F.B.); Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and Laboratory of Neuroimaging and Innovative Molecular Tracers, Geneva University, Geneva, Switzerland (V.G.); Helen Wills Neuroscience Institute, University of California, Berkeley, Calif (S.M.L.); Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, Calif (S.M.L.); Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden (A.N.); and Theme Aging, Karolinska University Hospital, Stockholm, Sweden (A.N.)
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Cousins KAQ, Phillips JS, Irwin DJ, Lee EB, Wolk DA, Shaw LM, Zetterberg H, Blennow K, Burke SE, Kinney NG, Gibbons GS, McMillan CT, Trojanowski JQ, Grossman M. ATN incorporating cerebrospinal fluid neurofilament light chain detects frontotemporal lobar degeneration. Alzheimers Dement 2020; 17:822-830. [PMID: 33226735 DOI: 10.1002/alz.12233] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The ATN framework provides an in vivo diagnosis of Alzheimer's disease (AD) using cerebrospinal fluid (CSF) biomarkers of pathologic amyloid plaques (A), tangles (T), and neurodegeneration (N). ATN is rarely evaluated in pathologically confirmed patients and its poor sensitivity to suspected non-Alzheimer's pathophysiologies (SNAP), including frontotemporal lobar degeneration (FTLD), leads to misdiagnoses. We compared accuracy of ATN (ATNTAU ) using CSF total tau (t-tau) to a modified strategy (ATNNfL ) using CSF neurofilament light chain (NfL) in an autopsy cohort. METHODS ATNTAU and ATNNfL were trained in an independent sample and validated in autopsy-confirmed AD (n = 67) and FTLD (n = 27). RESULTS ATNNfL more accurately identified FTLD as SNAP (sensitivity = 0.93, specificity = 0.94) than ATNTAU (sensitivity = 0.44, specificity = 0.97), even in cases with co-occurring AD and FTLD. ATNNfL misclassified fewer AD and FTLD as "Normal" (2%) than ATNTAU (14%). DISCUSSION ATNNfL is a promising diagnostic strategy that may accurately identify both AD and FTLD, even when pathologies co-occur.
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Affiliation(s)
- Katheryn A Q Cousins
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jeffrey S Phillips
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, Institute of Neurology, University College London, London, UK.,UK Dementia Research Institute, University College London, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sarah E Burke
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nikolas G Kinney
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Garrett S Gibbons
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Corey T McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Li Z, Li K, Luo X, Zeng Q, Zhao S, Zhang B, Zhang M, Chen Y. Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer's Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging. Front Aging Neurosci 2020; 12:550664. [PMID: 33328953 PMCID: PMC7719833 DOI: 10.3389/fnagi.2020.550664] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 10/14/2020] [Indexed: 12/24/2022] Open
Abstract
Background: Suspected non-Alzheimer disease pathophysiology (SNAP) refers to the subjects who feature negative β-amyloid (Aβ) but positive tau or neurodegeneration biomarkers. It accounts for a quarter of the elderly population and is associated with cognitive decline. However, the underlying pathophysiology is still unclear. Methods: We included 111 non-demented subjects, then classified them into three groups using cerebrospinal fluid (CSF) Aβ 1-42 (A), phosphorylated tau 181 (T), and total tau (N). Specifically, we identified the normal control (NC; subjects with normal biomarkers, A-T-N-), SNAP (subjects with normal amyloid but abnormal tau, A-T+), and predementia Alzheimer's disease (AD; subjects with abnormal amyloid and tau, A+T+). Then, we used the static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance to reflect the intrinsic functional network strength and stability, respectively. Further, we performed a correlation analysis to explore the possible relationship between intrinsic brain activity changes and cognition. Results: SNAP showed decreased sALFF in left superior frontal gyrus (SFG) while increased sALFF in left insula as compared to NC. Regarding the dynamic metric, SNAP showed a similarly decreased dALFF in the left SFG and left paracentral lobule as compared to NC. By contrast, when compared to NC, predementia AD showed decreased sALFF in left inferior parietal gyrus (IPG) and right precuneus, while increased sALFF in the left insula, with more widely distributed decreased dALFF variance across the frontal, parietal and occipital lobe. When directly compared to SNAP, predementia AD showed decreased sALFF in left middle occipital gyrus and IPG, while showing decreased dALFF variance in the left temporal pole. Further correlation analysis showed that increased sALFF in the insula had a negative correlation with the general cognition in the SNAP group. Besides, sALFF and dALFF variance in the right precuneus negatively correlated with attention in the predementia AD group. Conclusion: SNAP and predementia AD show distinct functional impairment patterns. Specifically, SNAP has functional impairments that are confined to the frontal region, which is usually spared in early-stage AD, while predementia AD exhibits widely distributed functional damage involving the frontal, parietal and occipital cortex.
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Affiliation(s)
- Zheyu Li
- Department of Neurology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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48
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Clinical Utility of the Pathogenesis-Related Proteins in Alzheimer's Disease. Int J Mol Sci 2020; 21:ijms21228661. [PMID: 33212853 PMCID: PMC7698353 DOI: 10.3390/ijms21228661] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/05/2020] [Accepted: 11/13/2020] [Indexed: 12/16/2022] Open
Abstract
Research on the Aβ cascade and alternations of biomarkers in neuro-inflammation, synaptic dysfunction, and neuronal injury followed by Aβ have progressed. But the question is how to use the biomarkers. Here, we examine the evidence and pathogenic implications of protein interactions and the time order of alternation. After the deposition of Aβ, the change of tau, neurofilament light chain (NFL), and neurogranin (Ng) is the main alternation and connection to others. Neuro-inflammation, synaptic dysfunction, and neuronal injury function is exhibited prior to the structural and metabolic changes in the brain following Aβ deposition. The time order of such biomarkers compared to the tau protein is not clear. Despite the close relationship between biomarkers and plaque Aβ deposition, several factors favor one or the other. There is an interaction between some proteins that can predict the brain amyloid burden. The Aβ cascade hypothesis could be the pathway, but not all subjects suffer from Alzheimer's disease (AD) within a long follow-up, even with very elevated Aβ. The interaction of biomarkers and the time order of change require further research to identify the right subjects and right molecular target for precision medicine therapies.
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49
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Lee J, Jang H, Kang SH, Kim J, Kim JS, Kim JP, Kim HJ, Seo SW, Na DL. Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum. J Korean Med Sci 2020; 35:e361. [PMID: 33200589 PMCID: PMC7669457 DOI: 10.3346/jkms.2020.35.e361] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/27/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Cerebrospinal fluid (CSF) biomarkers are increasingly used in clinical practice for the diagnosis of Alzheimer's disease (AD). We aimed to 1) determine cutoff values of CSF biomarkers for AD, 2) investigate their clinical utility by estimating a concordance with amyloid positron emission tomography (PET), and 3) apply ATN (amyloid/tau/neurodegeneration) classification based on CSF results. METHODS We performed CSF analysis in 51 normal controls (NC), 23 mild cognitive impairment (MCI) and 65 AD dementia (ADD) patients at the Samsung Medical Center in Korea. We attempted to develop cutoff of CSF biomarkers for differentiating ADD from NC using receiver operating characteristic analysis. We also investigated a concordance between CSF and amyloid PET results and applied ATN classification scheme based on CSF biomarker abnormalities to characterize our participants. RESULTS CSF Aβ42, total tau (t-tau) and phosphorylated tau (p-tau) significantly differed across the three groups. The area under curve for the differentiation between NC and ADD was highest in t-tau/Aβ42 (0.994) followed by p-tau/Aβ42 (0.963), Aβ42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aβ42 and amyloid PET results was 92%. Finally, ATN classification based on CSF biomarker abnormalities led to a majority of NC categorized into A-T-N-(73%), MCI as A+T-N-(30%)/A+T+N+(26%), and ADD as A+T+N+(57%). CONCLUSION CSF biomarkers had high sensitivity and specificity in differentiating ADD from NC and were as accurate as amyloid PET. The ATN subtypes based on CSF biomarkers may further serve to predict the prognosis.
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Affiliation(s)
- Jongmin Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer's Research Center, Samsung Medical Center, Seoul, Korea
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer's Research Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer's Research Center, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer's Research Center, Samsung Medical Center, Seoul, Korea
- Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
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50
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Wang J, Zheng B, Yang S, Hu M, Wang JH. Differential Circulating Levels of Naturally Occurring Antibody to α-Synuclein in Parkinson's Disease Dementia, Alzheimer's Disease, and Vascular Dementia. Front Aging Neurosci 2020; 12:571437. [PMID: 33088272 PMCID: PMC7544955 DOI: 10.3389/fnagi.2020.571437] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/17/2020] [Indexed: 01/10/2023] Open
Abstract
Background: Aggregation of alpha-synuclein (α-Syn) is considered to be a significant pathological hallmark and a driving force of Parkinson’s disease (PD). PD dementia (PDD) occurs in a substantial number of PD patients. Naturally occurring antibody against α-Syn (NAb-α-Syn) exists ubiquitously in human blood and is reported to be altered in PD. However, it is not clear yet whether PDD had similar changes of circulating NAb-α-Syn. Methods: In this study, we recruited 61 PDD patients, 52 patients with Alzheimer’s disease (AD), 51 patients with vascular dementia (VaD), and 50 normal controls (NCs). ELISA was used to examine NAb-α-Syn levels in serum. Results: In comparison with NCs, serum levels of NAb-α-Syn were significantly lower in patients with PDD. However, serum levels of NAb-α-Syn were comparable among AD, VaD, and NC groups. Serum levels of NAb-α-Syn were positively correlated with the cognitive function, as reflected by Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Serum levels of NAb-α-Syn were negatively correlated with the severity of PD [as reflected by the Unified Parkinson Disease Rating Scale (UPDRS)] and the duration of PD and PDD. Serum NAb-α-Syn can differentiate PDD patients from AD and VaD patients. Conclusion: These results suggest that circulating NAb-α-Syn might be a potential biomarker of PDD.
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Affiliation(s)
- Jian Wang
- Department of Neurology, Yaan People's Hospital, Yaan, China
| | - Bo Zheng
- Department of Neurology, Yaan People's Hospital, Yaan, China
| | - Shu Yang
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Mei Hu
- Department of Imaging, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian-Hong Wang
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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