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Noda K, Kageyama I, Kobayashi Y, Lim Y, Sengoku S, Kodama K. Leveraging mHealth wearables for managing patients with Alzheimer's disease: a scoping review. Drug Discov Today 2025; 30:104363. [PMID: 40250750 DOI: 10.1016/j.drudis.2025.104363] [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: 10/30/2024] [Revised: 04/04/2025] [Accepted: 04/14/2025] [Indexed: 04/20/2025]
Abstract
In this scoping review, we examine the role of wearable devices in diagnosing, treating, and monitoring Alzheimer's disease (AD) and mild cognitive impairment (MCI). It identifies various devices, including fitness trackers, smartwatches, electroencephalographic equipment, and sensors, which are used for monitoring physical activity, sleep patterns, and cognitive functions. Our review highlights the potential of these devices for early diagnosis and treatment, improving patient autonomy and quality of life. However, challenges, such as data privacy, device adherence, and technical limitations, remain. Future research should focus on integrating wearable devices with advanced diagnostic tools and validating their effectiveness across diverse populations.
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Affiliation(s)
- Kenta Noda
- Graduate School of Design and Architecture, Nagoya City University, Nagoya 464-0083, Japan; School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan
| | - Itsuki Kageyama
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan
| | - Yoshiyuki Kobayashi
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan
| | | | - Shintaro Sengoku
- School of Environment and Society, Institute of Science Tokyo, Tokyo 108-0023, Japan
| | - Kota Kodama
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan; Ritsumeikan University, Osaka 567-8570, Japan; Center for Research and Education on Drug Discovery, The Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan.
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2
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Malchesky PS. The journey. Artif Organs 2025; 49:171-178. [PMID: 39578937 DOI: 10.1111/aor.14904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 11/01/2024] [Indexed: 11/24/2024]
Affiliation(s)
- Paul S Malchesky
- International Center for Artificial Organs & Transplantation (ICAOT), Painesville, Ohio, USA
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Zeller CJ, Wunderlin M, Wicki K, Teunissen CE, Nissen C, Züst MA, Klöppel S. Multi-night acoustic stimulation is associated with better sleep, amyloid dynamics, and memory in older adults with cognitive impairment. GeroScience 2024; 46:6157-6172. [PMID: 38744792 PMCID: PMC11493878 DOI: 10.1007/s11357-024-01195-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024] Open
Abstract
Sleep is a potential early, modifiable risk factor for cognitive decline and dementia. Impaired slow wave sleep (SWS) is pronounced in individuals with cognitive impairment (CI). Cognitive decline and impairments of SWS are bi-directionally linked in a vicious cycle. SWS can be enhanced non-invasively using phase-locked acoustic stimulation (PLAS), potentially breaking this vicious cycle. Eighteen healthy older adults (HC, agemean±sd, 68.3 ± 5.1) and 16 older adults (agemean±sd, 71.9 ± 3.9) with CI (Montreal Cognitive Assessment ≤ 25) underwent one baseline (sham-PLAS) night and three consecutive stimulation nights (real-PLAS). EEG responses and blood-plasma amyloid beta Aβ42/Aβ40 ratio were measured pre- and post-intervention, as was episodic memory. The latter was again evaluated 1 week and 3 months after the intervention. In both groups, PLAS induced a significant electrophysiological response in both voltage- and time-frequency analyses, and memory performance improved in association with the magnitude of this response. In the CI group, both electrophysiological and associated memory effects were delayed compared to the healthy group. After 3 intervention nights, electrophysiological response to PLAS was no longer different between CI and HC groups. Only in the CI sample, stronger electrophysiological responses were significantly associated with improving post-intervention Aβ42/Aβ40 ratios. PLAS seems to improve SWS electrophysiology, memory, and amyloid dynamics in older adults with CI. However, effects on memory require more time to unfold compared to healthy older adults. This indicates that PLAS may become a potential tool to ameliorate cognitive decline, but longer interventions are necessary to compensate for declining brain integrity. This study was pre-registered (clinicaltrials.gov: NCT04277104).
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Affiliation(s)
- Céline J Zeller
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000, Bern 60, Switzerland
- Graduate School for Health Sciences, University of Bern, 3012, Bern, Switzerland
| | - Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000, Bern 60, Switzerland
| | - Korian Wicki
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000, Bern 60, Switzerland
- Graduate School for Health Sciences, University of Bern, 3012, Bern, Switzerland
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, Netherlands
| | - Christoph Nissen
- Division of Psychiatric Specialties, Department of Psychiatry, Geneva University Hospitals (HUG), 1201, Geneva, Switzerland
- Department of Psychiatry, University of Geneva, 1201, Geneva, Switzerland
| | - Marc A Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000, Bern 60, Switzerland.
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000, Bern 60, Switzerland
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Brill E, Holfelder A, Falkner M, Krebs C, Brem AK, Klöppel S. Behavioural and neuronal substrates of serious game-based computerised cognitive training in cognitive decline: randomised controlled trial. BJPsych Open 2024; 10:e200. [PMID: 39501844 PMCID: PMC11698156 DOI: 10.1192/bjo.2024.797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 01/06/2025] Open
Abstract
BACKGROUND Investigations of computerised cognitive training (CCT) show heterogeneous results in slowing age-related cognitive decline. AIMS To comprehensively evaluate the effectiveness of serious games-based CCT, integrating control conditions, neurophysiological and blood-based biomarkers, and subjective measures. METHOD In this bi-centric randomised controlled trial with parallel groups, 160 participants (mean age 71.3 years) with cognitive impairment ranging from subjective decline to mild cognitive impairment, were pseudo-randomised to three arms: an intervention group receiving CCT immediately, an active control (watching documentaries) and a waitlist condition, which both started the CCT intervention after the control period. Both active arms entailed a 3-month intervention period comprising a total of 60 at-home sessions (five per week) and weekly on-site group meetings. In the intervention group, this was followed by additional 6 months of CCT, with monthly booster sessions to assess long-term training effects. Behavioural and subjective changes were assessed in 3-month intervals. Biological effects were measured by amyloid blood markers and magnetic resonance imaging obtained before and after training. RESULTS Adherence to the training protocol was consistently high across groups and time points (4.87 sessions per week). Domain-specific cognitive scores showed no significant interaction between groups and time points. Significant cognitive and subjective improvements were observed after long-term training. Voxel-based morphometry revealed no significant changes in grey matter volume following CCT, nor did amyloid levels moderate its effectiveness. CONCLUSIONS Our study demonstrates no benefits of 3 months of CCT on cognitive or biological outcomes. However, positive effects were observed subjectively and after long-term CCT, warranting the inclusion of CCT in multicomponent interventions.
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Affiliation(s)
- Esther Brill
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland; and Swiss Institute for Translational and Entrepreneurial Medicine (SITEM), University of Bern, Switzerland
| | - Alexa Holfelder
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland; and Swiss Institute for Translational and Entrepreneurial Medicine (SITEM), University of Bern, Switzerland
| | - Michael Falkner
- ARTORG Centre for Biomedical Engineering Research, University of Bern, Switzerland
| | - Christine Krebs
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Anna-Katharine Brem
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland; and Centre for Healthy Brain Ageing, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
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Karikari T, Chen Y, Zeng X, Olvera-Rojas M, Sehrawat A, Lafferty T, Pascoal T, Villemagne V, Solis-Urra P, Triviño-Ibañez E, Gómez-Rí M, Cohen A, Ikonomovic M, Esteban-Cornejo I, Erickson K, Lopez O, Yates N. A streamlined, resource-efficient immunoprecipitation-mass spectrometry method for quantifying plasma amyloid-β biomarkers in Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-4947448. [PMID: 39281858 PMCID: PMC11398558 DOI: 10.21203/rs.3.rs-4947448/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
High-performance, resource-efficient methods for plasma amyloid-β (Aβ) quantification in Alzheimer's disease are lacking; existing mass spectrometry-based assays are resource- and time-intensive. We developed a streamlined mass spectrometry method with a single immunoprecipitation step, an optimized buffer system, and ≤75% less antibody requirement. Analytical and clinical performances were compared with an in-house reproduced version of a well-known two-step assay. The streamlined assay showed high dilution linearity (r2>0.99) and precision (< 10% coefficient of variation), low quantification limits (Aβ1-40: 12.5 pg/ml; Aβ1-42: 3.125 pg/ml), and high signal correlation (r2~0.7) with the two-step immunoprecipitation assay. The novel single-step assay showed more efficient recovery of Aβ peptides via fewer immunoprecipitation steps, with significantly higher signal-to-noise ratios, even at plasma sample volumes down to 50 pl. Both assays had equivalent performances in distinguishing non-elevated vs. elevated brain Aβ-PET individuals. The new method enables simplified yet robust evaluation of plasma Aβ biomarkers in Alzheimer's disease.
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Twait EL, Kamarioti M, Verberk IMW, Teunissen CE, Nooyens ACJ, Monique Verschuren WM, Visser PJ, Huisman M, Kok AAL, Eline Slagboom P, Beekman M, Vojinovic D, Lakenberg N, Arfan Ikram M, Schuurmans IK, Wolters FJ, Moonen JEF, Gerritsen L, van der Flier WM, Geerlings MI. Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies. Am J Geriatr Psychiatry 2024; 32:1141-1153. [PMID: 38553327 DOI: 10.1016/j.jagp.2024.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/22/2024] [Accepted: 03/06/2024] [Indexed: 08/11/2024]
Abstract
BACKGROUND Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aβ) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia. METHODS A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aβ42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis. RESULTS Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (β 0.11; 95% CI: 0.05-0.17). CONCLUSIONS Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.
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Affiliation(s)
- Emma L Twait
- Julius Center for Health Sciences and Primary Care (ELT, MK, WMMV, MIG), University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands; Amsterdam UMC, Location Vrije Universiteit (ELT), Department of General Practice, Amsterdam Public Health, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Maria Kamarioti
- Julius Center for Health Sciences and Primary Care (ELT, MK, WMMV, MIG), University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Inge M W Verberk
- Neurochemistry Laboratory (IMWV, CET), Department of Laboratory Medicine, Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory (IMWV, CET), Department of Laboratory Medicine, Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Astrid C J Nooyens
- National Institute for Public Health and the Environment (ACJN, WMMV), Bilthoven, The Netherlands
| | - W M Monique Verschuren
- Julius Center for Health Sciences and Primary Care (ELT, MK, WMMV, MIG), University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands; National Institute for Public Health and the Environment (ACJN, WMMV), Bilthoven, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam (PJV, JEFM, WMF), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands; Department of Psychiatry and Neuropsychology (PJV), School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Martijn Huisman
- Amsterdam UMC Location Vrije Universiteit Amsterdam (MH, AALK, WMF), Epidemiology and Data Science, Amsterdam, The Netherlands; Department of Sociology, Faculty of Social Sciences (MH), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health (MH, AALK), Ageing and Later Life, Amsterdam, The Netherlands
| | - Almar A L Kok
- Amsterdam UMC Location Vrije Universiteit Amsterdam (MH, AALK, WMF), Epidemiology and Data Science, Amsterdam, The Netherlands; Amsterdam Public Health (MH, AALK), Ageing and Later Life, Amsterdam, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology (PES, MB, DV, NL), Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology (PES, MB, DV, NL), Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Dina Vojinovic
- Molecular Epidemiology (PES, MB, DV, NL), Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands; Department of Epidemiology (DV, MAI, IKS, FJW), Erasmus University Medical Center, Rotterdam, Netherlands
| | - Nico Lakenberg
- Molecular Epidemiology (PES, MB, DV, NL), Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology (DV, MAI, IKS, FJW), Erasmus University Medical Center, Rotterdam, Netherlands; Harvard T.H. Chan School of Public Health (MAI), Boston, MA
| | - Isabel K Schuurmans
- Department of Epidemiology (DV, MAI, IKS, FJW), Erasmus University Medical Center, Rotterdam, Netherlands
| | - Frank J Wolters
- Department of Epidemiology (DV, MAI, IKS, FJW), Erasmus University Medical Center, Rotterdam, Netherlands; Department of Radiology & Nuclear Medicine (FJW), Erasmus MC, Rotterdam The Netherlands
| | - Justine E F Moonen
- Alzheimer Center Amsterdam (PJV, JEFM, WMF), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Lotte Gerritsen
- Department of Psychology (LG) Utrecht University, Utrecht, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam (PJV, JEFM, WMF), Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands; Amsterdam UMC Location Vrije Universiteit Amsterdam (MH, AALK, WMF), Epidemiology and Data Science, Amsterdam, The Netherlands
| | - Mirjam I Geerlings
- Julius Center for Health Sciences and Primary Care (ELT, MK, WMMV, MIG), University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands; Amsterdam UMC (MIG), Location University of Amsterdam, Department of General Practice, Amsterdam Public Health, Amsterdam Neuroscience, Amsterdam, The Netherlands.
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Chen Y, Zeng X, Diaz JL, Sehrawat A, Lafferty TK, Boslett JJ, Klunk WE, Pascoal TA, Villemagne VL, Cohen AD, Lopez O, Yates NA, Karikari TK. Effect of blood collection tube containing protease inhibitors on the pre-analytical stability of Alzheimer's disease plasma biomarkers. J Neurochem 2024; 168:2736-2750. [PMID: 38814273 PMCID: PMC11449657 DOI: 10.1111/jnc.16130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/03/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
The reliability of plasma biomarkers of Alzheimer's disease (AD) can be compromised by protease-induced degradation. This can limit the feasibility of conducting plasma biomarker studies in environments that lack the capacity for immediate processing and appropriate storage of blood samples. We hypothesized that blood collection tube supplementation with protease inhibitors can improve the stability of plasma biomarkers at room temperatures (RT). In this study, we conducted a comparative analysis of blood biomarker stability in traditional ethylenediaminetetraacetic acid (EDTA) tubes versus BD™ P100 collection tubes, the latter being coated with a protease inhibitor cocktail. The stability of six plasma AD biomarkers was evaluated over time under RT conditions. We evaluated three experimental approaches. In Approach 1, pooled plasma samples underwent storage at RT for up to 96 h. In Approach 2, plasma samples isolated upfront from whole blood collected into EDTA or P100 tubes were stored at RT for 0 h or 24 h before biomarker measurements. In Approach 3, whole blood samples were collected into paired EDTA and P100 tubes, followed by storage at RT for 0 h or 24 h before isolating the plasma for analyses. Biomarkers were measured with Single Molecule Array (Simoa) and immunoprecipitation-mass spectrometry (IP-MS) assays. Both the IP-MS and Simoa methods revealed that the use of P100 tubes significantly improves the stability of Aβ42 and Aβ40 across all approaches. However, the Aβ42/Aβ40 ratio levels were significantly stabilized only in the IP-MS assay in Approach 3. No significant differences were observed in the levels of plasma p-tau181, GFAP, and NfL for samples collected using either tube type in any of the approaches. Supplementation of blood collection tubes with protease inhibitors could reduce the protease-induced degradation of plasma Aβ42 and Aβ40, and the Aβ42/40 ratio for the IP-MS assay. These findings have crucial implications for preanalytical procedures, particularly in resource-limited settings.
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Affiliation(s)
- Yijun Chen
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Xuemei Zeng
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jihui L. Diaz
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Anuradha Sehrawat
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Tara K. Lafferty
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - James J. Boslett
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - William E. Klunk
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Tharick A. Pascoal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Victor L. Villemagne
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Annie D. Cohen
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Oscar Lopez
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Nathan A. Yates
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Thomas K. Karikari
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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Rajendran K, Krishnan UM. Biomarkers in Alzheimer's disease. Clin Chim Acta 2024; 562:119857. [PMID: 38986861 DOI: 10.1016/j.cca.2024.119857] [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: 05/13/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/12/2024]
Abstract
Alzheimer's disease (AD) is among the most common neurodegenerative disorders. AD is characterized by deposition of neurofibrillary tangles and amyloid plaques, leading to associated secondary pathologies, progressive neurodegeneration, and eventually death. Currently used diagnostics are largely image-based, lack accuracy and do not detect early disease, ie, prior to onset of symptoms, thus limiting treatment options and outcomes. Although biomarkers such as amyloid-β and tau protein in cerebrospinal fluid have gained much attention, these are generally limited to disease progression. Unfortunately, identification of biomarkers for early and accurate diagnosis remains a challenge. As such, body fluids such as sweat, serum, saliva, mucosa, tears, and urine are under investigation as alternative sources for biomarkers that can aid in early disease detection. This review focuses on biomarkers identified through proteomics in various biofluids and their potential for early and accurate diagnosis of AD.
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Affiliation(s)
- Kayalvizhi Rajendran
- Centre for Nanotechnology & Advanced Biomaterials, SASTRA Deemed University, Thanjavur, India; School of Chemical & Biotechnology, SASTRA Deemed University, Thanjavur, India
| | - Uma Maheswari Krishnan
- Centre for Nanotechnology & Advanced Biomaterials, SASTRA Deemed University, Thanjavur, India; School of Arts, Sciences, Humanities, & Education, SASTRA Deemed University, Thanjavur, India.
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Lv H, Tang L, Jian C, Wei A, Li D, Jiang Y, Yang C, Mo S, Shang J, Li X. Prognostic value of plasma Aβ1-40 for Alzheimer's disease. Am J Transl Res 2024; 16:1962-1968. [PMID: 38883359 PMCID: PMC11170593 DOI: 10.62347/piyv4216] [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: 03/12/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
OBJECTIVE To investigate the clinical significance of plasma p-amyloid 1-40 (Aβ1-40) in patients with Alzheimer's disease (AD). METHODS In this retrospective study, the clinical data of 305 patients, with or without Alzheimer's disease (AD), who were treated at the Affiliated Hospital of Youjiang Medical University for Nationalities and the People's Hospital of Baise between January 2018 and December 2021 were analyzed. Patients were divided into two groups: an AD group (n=147) and a non-AD group (without AD, n=158 cases). Blood test indices, including serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine (CRE), high-sensitivity C-reactive protein (hsCRP), and plasma β-amyloid 1-40 were collected and compared between the two groups. RESULTS The plasma β-amyloid 1-40 in the AD group was (3.71±3.45) mol/L, which was significantly higher than (2.8±1.35) mmol/L in the non-AD group (P<0.05). Similarly, hsCRP expression was significantly higher in the AD group than that in the non-AD group (P<0.05). There were no significant differences in AST, ALT, UA, T-tau, NFL or Cr levels between the two groups (all P>0.05). Moreover, univariate regression analysis showed that plasma β-amyloid 1-40 and hsCRP were significantly correlated with AD. Multiple regression analysis demonstrated that plasma p-amyloid 1-40 (P<0.0001) and hsCRP (P=0.002) were independent predictors of AD. CONCLUSION Plasma p-amyloid 1-40 and hsCRP are closely related to AD, and may serve as important clinical predictors of AD.
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Affiliation(s)
- Hui Lv
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
- College of Nursing of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Lingjiao Tang
- College of Nursing of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Chongdong Jian
- Affiliated Hospital of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Anshang Wei
- The Second People's Hospital of Baise Baise 533000, Guangxi, China
| | - Dengxing Li
- The People's Hospital of Baise Baise 533000, Guangxi, China
| | - Yongming Jiang
- Affiliated Hospital of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Chengmin Yang
- Affiliated Hospital of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Shenglong Mo
- The Graduate College of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Jingwei Shang
- Affiliated Hospital of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
| | - Xinzhou Li
- Affiliated Hospital of Youjiang Medical University for Nationalities Baise 533000, Guangxi, China
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Oosthoek M, Vermunt L, de Wilde A, Bongers B, Antwi-Berko D, Scheltens P, van Bokhoven P, Vijverberg EGB, Teunissen CE. Utilization of fluid-based biomarkers as endpoints in disease-modifying clinical trials for Alzheimer's disease: a systematic review. Alzheimers Res Ther 2024; 16:93. [PMID: 38678292 PMCID: PMC11055304 DOI: 10.1186/s13195-024-01456-1] [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: 10/27/2023] [Accepted: 04/12/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND Clinical trials in Alzheimer's disease (AD) had high failure rates for several reasons, including the lack of biological endpoints. Fluid-based biomarkers may present a solution to measure biologically relevant endpoints. It is currently unclear to what extent fluid-based biomarkers are applied to support drug development. METHODS We systematically reviewed 272 trials (clinicaltrials.gov) with disease-modifying therapies starting between 01-01-2017 and 01-01-2024 and identified which CSF and/or blood-based biomarker endpoints were used per purpose and trial type. RESULTS We found that 44% (N = 121) of the trials employed fluid-based biomarker endpoints among which the CSF ATN biomarkers (Aβ (42/40), p/tTau) were used most frequently. In blood, inflammatory cytokines, NFL, and pTau were most frequently employed. Blood- and CSF-based biomarkers were used approximately equally. Target engagement biomarkers were used in 26% (N = 72) of the trials, mainly in drugs targeting inflammation and amyloid. Lack of target engagement markers is most prominent in synaptic plasticity/neuroprotection, neurotransmitter receptor, vasculature, epigenetic regulators, proteostasis and, gut-brain axis targeting drugs. Positive biomarker results did not always translate to cognitive effects, most commonly the small significant reductions in CSF tau isoforms that were seen following anti-Tau treatments. On the other hand, the positive anti-amyloid trials results on cognitive function were supported by clear effect in most fluid markers. CONCLUSIONS As the field moves towards primary prevention, we expect an increase in the use of fluid-based biomarkers to determine disease modification. Use of blood-based biomarkers will rapidly increase, but CSF markers remain important to determine brain-specific treatment effects. With improving techniques, new biomarkers can be found to diversify the possibilities in measuring treatment effects and target engagement. It remains important to interpret biomarker results in the context of the trial and be aware of the performance of the biomarker. Diversifying biomarkers could aid in the development of surrogacy biomarkers for different drug targets.
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Affiliation(s)
- Marlies Oosthoek
- Department of Laboratory Medicine, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - Lisa Vermunt
- Department of Laboratory Medicine, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Arno de Wilde
- EQT Life Sciences, Johannes Vermeersplein 9, 1071 DV, Amsterdam, The Netherlands
| | - Bram Bongers
- Department of Laboratory Medicine, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Daniel Antwi-Berko
- Department of Laboratory Medicine, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Philip Scheltens
- EQT Life Sciences, Johannes Vermeersplein 9, 1071 DV, Amsterdam, The Netherlands
- Alzheimer Center, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | | | - Everard G B Vijverberg
- Alzheimer Center, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Department of Laboratory Medicine, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
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11
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Dark HE, Duggan MR, Walker KA. Plasma biomarkers for Alzheimer's and related dementias: A review and outlook for clinical neuropsychology. Arch Clin Neuropsychol 2024; 39:313-324. [PMID: 38520383 PMCID: PMC11484593 DOI: 10.1093/arclin/acae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 03/25/2024] Open
Abstract
Recent technological advances have improved the sensitivity and specificity of blood-based biomarkers for Alzheimer's disease and related dementias. Accurate quantification of amyloid-ß peptide, phosphorylated tau (pTau) isoforms, as well as markers of neurodegeneration (neurofilament light chain [NfL]) and neuro-immune activation (glial fibrillary acidic protein [GFAP] and chitinase-3-like protein 1 [YKL-40]) in blood has allowed researchers to characterize neurobiological processes at scale in a cost-effective and minimally invasive manner. Although currently used primarily for research purposes, these blood-based biomarkers have the potential to be highly impactful in the clinical setting - aiding in diagnosis, predicting disease risk, and monitoring disease progression. Whereas plasma NfL has shown promise as a non-specific marker of neuronal injury, plasma pTau181, pTau217, pTau231, and GFAP have demonstrated desirable levels of sensitivity and specificity for identification of individuals with Alzheimer's disease pathology and Alzheimer's dementia. In this forward looking review, we (i) provide an overview of the most commonly used blood-based biomarkers for Alzheimer's disease and related dementias, (ii) discuss how comorbid medical conditions, demographic, and genetic factors can inform the interpretation of these biomarkers, (iii) describe ongoing efforts to move blood-based biomarkers into the clinic, and (iv) highlight the central role that clinical neuropsychologists may play in contextualizing and communicating blood-based biomarker results for patients.
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Affiliation(s)
- Heather E Dark
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
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12
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Nazir S. Salivary biomarkers: The early diagnosis of Alzheimer's disease. Aging Med (Milton) 2024; 7:202-213. [PMID: 38725701 PMCID: PMC11077336 DOI: 10.1002/agm2.12282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/25/2023] [Accepted: 12/19/2023] [Indexed: 05/12/2024] Open
Abstract
The precise identification of Alzheimer's disease and other prevalent neurodegenerative diseases remains a difficult issue that requires the development of early detection of the disease and inexpensive biomarkers that can replace the present cerebrospinal fluid and imaging biomarkers. Blood biomarkers, such as amyloid and neurofilament light, have been emphasized as an important and practical tool in a testing or examination procedure thanks to advancements in ultra-sensitive detection techniques. Although saliva is not currently being researched for neurodegenerative diseases, it is an important source of biomarkers that can be used for the identification of diseases and has some advantages over other biofluids. While this may be true for most people, getting saliva from elderly people presents some significant challenges. In this overview, we will first discuss how saliva is created and how aging-related illnesses may affect the amount and kind of saliva produced. The findings support the use of salivary amyloid protein, tau species, and novel biomarkers in the diagnosis of Alzheimer's disease.
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Affiliation(s)
- Sophia Nazir
- Wolfson Nanomaterials and Devices Laboratory, School of Computing, Electronics and MathematicsPlymouth UniversityDevonUK
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13
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Brum WS, Cullen NC, Therriault J, Janelidze S, Rahmouni N, Stevenson J, Servaes S, Benedet AL, Zimmer ER, Stomrud E, Palmqvist S, Zetterberg H, Frisoni GB, Ashton NJ, Blennow K, Mattsson-Carlgren N, Rosa-Neto P, Hansson O. A blood-based biomarker workflow for optimal tau-PET referral in memory clinic settings. Nat Commun 2024; 15:2311. [PMID: 38486040 PMCID: PMC10940585 DOI: 10.1038/s41467-024-46603-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
Blood-based biomarkers for screening may guide tau positrion emissition tomography (PET) scan referrals to optimize prognostic evaluation in Alzheimer's disease. Plasma Aβ42/Aβ40, pTau181, pTau217, pTau231, NfL, and GFAP were measured along with tau-PET in memory clinic patients with subjective cognitive decline, mild cognitive impairment or dementia, in the Swedish BioFINDER-2 study (n = 548) and in the TRIAD study (n = 179). For each plasma biomarker, cutoffs were determined for 90%, 95%, or 97.5% sensitivity to detect tau-PET-positivity. We calculated the percentage of patients below the cutoffs (who would not undergo tau-PET; "saved scans") and the tau-PET-positivity rate among participants above the cutoffs (who would undergo tau-PET; "positive predictive value"). Generally, plasma pTau217 performed best. At the 95% sensitivity cutoff in both cohorts, pTau217 resulted in avoiding nearly half tau-PET scans, with a tau-PET-positivity rate among those who would be referred for a scan around 70%. And although tau-PET was strongly associated with subsequent cognitive decline, in BioFINDER-2 it predicted cognitive decline only among individuals above the referral cutoff on plasma pTau217, supporting that this workflow could reduce prognostically uninformative tau-PET scans. In conclusion, plasma pTau217 may guide selection of patients for tau-PET, when accurate prognostic information is of clinical value.
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Affiliation(s)
- Wagner S Brum
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Nicholas C Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Joseph Therriault
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Quebec, QC, Canada
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Nesrine Rahmouni
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Quebec, QC, Canada
| | - Jenna Stevenson
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Quebec, QC, Canada
| | - Stijn Servaes
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Quebec, QC, Canada
| | - Andrea L Benedet
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Eduardo R Zimmer
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Graduate Program in Biological Sciences: Pharmacology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Henrik Zetterberg
- 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 Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Giovanni B Frisoni
- Memory Center, Geneva University and University Hospital, Geneva, Switzerland
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Pedro Rosa-Neto
- McGill Centre for Studies in Aging, McGill University, Verdun, Quebec, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Quebec, QC, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
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Chen Y, Zeng X, Lee J, Sehrawat A, Lafferty TK, Boslett JJ, Klunk WE, Pascoal TA, Villemagne VL, Cohen AD, Lopez O, Yates NA, Karikari TK. Effect of blood collection tube containing protease inhibitors on the pre-analytical stability of Alzheimer's disease plasma biomarkers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.05.24303504. [PMID: 38496591 PMCID: PMC10942510 DOI: 10.1101/2024.03.05.24303504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
INTRODUCTION The reliability of plasma Alzheimer's disease (AD) biomarkers can be compromised by protease-induced degradation. This limits the feasibility of conducting plasma biomarker studies in environments that lack the capacity for immediate processing and appropriate storage of blood samples. We hypothesized that blood collection tube supplementation with protease inhibitors can improve the stability of plasma biomarkers at room temperatures (RT). This study conducted a comparative analysis of blood biomarker stability in traditional ethylenediaminetetraacetic acid (EDTA) tubes versus BD™ P100 collection tubes, the latter being coated with a protease inhibitor cocktail. The stability of six plasma AD biomarkers was evaluated over time under RT conditions. METHODS We evaluated three experimental approaches. In Approach 1, pooled plasma samples underwent storage at RT for up to 96 hours. In Approach 2, plasma samples isolated upfront from whole blood collected into EDTA or P100 tubes were stored at RT for 0h or 24h before biomarker measurements. In Approach 3, whole blood samples were collected into paired EDTA or P100 tubes, followed by storage at RT for 0h or 24h before isolating the plasma for analyses. Biomarkers were measured with Single Molecule Array (Simoa) and immunoprecipitation-mass spectrometry (IP-MS) assays. RESULTS Both the IP-MS and Simoa methods revealed that the use of P100 tubes significantly improved the stability of Aβ42 and Aβ40 across all approaches. Additionally, the Aβ42/Aβ40 ratio levels were significantly stabilized only in the IP-MS assay in Approach 3. No significant differences were observed in the levels of plasma p-tau181, GFAP, and NfL for samples collected using either tube type in any of the approaches. CONCLUSION Supplementation of blood collection tubes with protease inhibitors could reduce the protease-induced degradation of plasma Aβ42 and Aβ40, and the Aβ ratio for IP-MS assay. This has crucial implications for preanalytical procedures, particularly in resource-limited settings.
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15
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De Meyer S, Blujdea ER, Schaeverbeke J, Reinartz M, Luckett ES, Adamczuk K, Van Laere K, Dupont P, Teunissen CE, Vandenberghe R, Poesen K. Longitudinal associations of serum biomarkers with early cognitive, amyloid and grey matter changes. Brain 2024; 147:936-948. [PMID: 37787146 DOI: 10.1093/brain/awad330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 10/04/2023] Open
Abstract
Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-β (Aβ) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [n = 185, mean age (range) = 69 (53-84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aβ1-42/Aβ1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aβ-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aβ-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3-11.0) years]. Matching plasma measurements were available for Aβ1-42/Aβ1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (βGFAP×Time = -0.021, PFDR = 0.007 and βNfL×Time = -0.031, PFDR = 0.002) and language (βGFAP×Time = -0.021, PFDR = 0.002 and βNfL×Time = -0.018, PFDR = 0.03) domains. Low serum Aβ1-42/Aβ1-40 equally but independently predicted memory decline (βAβ1-42/Aβ1-40×Time = -0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aβ1-42/Aβ1-40 predicted Aβ accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aβ1-42/Aβ1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aβ1-42/Aβ1-40 decreased only in Aβ-PET-negative elderly. NfL increases associated with declining memory (βNfLchange×Time = -0.030, PFDR = 0.006) and language (βNfLchange×Time = -0.021, PFDR = 0.02) function and serum Aβ1-42/Aβ1-40 decreases associated with declining language function (βAβ1-42/Aβ1-40×Time = -0.020, PFDR = 0.04). GFAP increases associated with Aβ accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aβ accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aβ1-42/Aβ1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aβ-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aβ-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aβ1-42/Aβ1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Elena R Blujdea
- Neurochemistry Laboratory, Amsterdam UMC, 1081 HZ Amsterdam, The Netherlands
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
| | - Koen Van Laere
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium
- Division of Nuclear Medicine, UZ Leuven, 3000 Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
| | | | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Neurology, UZ Leuven, 3000 Leuven, Belgium
| | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of Neurosciences, KU Leuven, 3000 Leuven, Belgium
- Alzheimer Research Centre, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Department of Laboratory Medicine, UZ Leuven, 3000 Leuven, Belgium
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16
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Noda K, Lim Y, Goto R, Sengoku S, Kodama K. Cost-effectiveness comparison between blood biomarkers and conventional tests in Alzheimer's disease diagnosis. Drug Discov Today 2024; 29:103911. [PMID: 38311028 DOI: 10.1016/j.drudis.2024.103911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/06/2024]
Abstract
Dementia management has evolved with drugs such as lecanemab, shifting management from palliative care to early diagnosis and intervention. However, the administration of these drugs presents challenges owing to the invasiveness, high cost and limited availability of amyloid-PET and cerebrospinal fluid tests for guiding drug administration. Our manuscript explores the potential of less invasive blood biomarkers as a diagnostic method, with a cost-effectiveness analysis and a comparison with traditional tests. Our findings suggest that blood biomarkers are a cost-effective alternative, but with lower accuracy, indicating the need for multiple specific biomarkers for precision. This underscores the importance of future research on new blood biomarkers and their clinical efficacy.
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Affiliation(s)
- Kenta Noda
- Graduate School of Design and Architecture, Nagoya City University, Nagoya 464-0083, Japan
| | | | - Rei Goto
- Graduate School of Health Management, Keio University, Fujisawa 252-0883, Kanagawa, Japan; Graduate School of Business Administration, Keio University, Yokohama 223-8526, Japan
| | - Shintaro Sengoku
- School of Environment and Society, Tokyo Institute of Technology, Tokyo 108-0023, Japan
| | - Kota Kodama
- Graduate School of Design and Architecture, Nagoya City University, Nagoya 464-0083, Japan; Ritsumeikan University, Osaka 567-8570, Japan; Faculty of Data Science, Nagoya City University, Nagoya 467-8501, Japan; Center for Research and Education on Drug Discovery, The Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan.
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17
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Muir RT, Ismail Z, Black SE, Smith EE. Comparative methods for quantifying plasma biomarkers in Alzheimer's disease: Implications for the next frontier in cerebral amyloid angiopathy diagnostics. Alzheimers Dement 2024; 20:1436-1458. [PMID: 37908054 PMCID: PMC10916950 DOI: 10.1002/alz.13510] [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: 03/12/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 11/02/2023]
Abstract
Plasma amyloid beta (Aβ) and tau are emerging as accessible biomarkers for Alzheimer's disease (AD). However, many assays exist with variable test performances, highlighting the need for a comparative assessment to identify the most valid assays for future use in AD and to apply to other settings in which the same biomarkers may be useful, namely, cerebral amyloid angiopathy (CAA). CAA is a progressive cerebrovascular disease characterized by deposition of Aβ40 and Aβ42 in cortical and leptomeningeal vessels. Novel immunotherapies for AD can induce amyloid-related imaging abnormalities resembling CAA-related inflammation. Few studies have evaluated plasma biomarkers in CAA. Identifying a CAA signature could facilitate diagnosis, prognosis, and a safer selection of patients with AD for emerging immunotherapies. This review evaluates studies that compare the diagnostic test performance of plasma biomarker techniques in AD and cerebrovascular and plasma biomarker profiles of CAA; it also discusses novel hypotheses and future avenues for plasma biomarker research in CAA.
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Affiliation(s)
- Ryan T. Muir
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
| | - Zahinoor Ismail
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of PsychiatryUniversity of CalgaryCalgaryAlbertaCanada
| | - Sandra E. Black
- Division of NeurologyDepartment of MedicineSunnybrook Health Sciences CentreTorontoOntarioCanada
- LC Campbell Cognitive Neurology Research UnitDr Sandra Black Centre for Brain Resilience and Recovery, and Hurvitz Brain Sciences ProgramSunnybrook Research InstituteUniversity of TorontoTorontoOntarioCanada
| | - Eric E. Smith
- Calgary Stroke ProgramDepartment of Clinical NeurosciencesUniversity of CalgaryCalgaryAlbertaCanada
- Department of Community Health SciencesUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
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18
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Wojdała AL, Bellomo G, Toja A, Gaetani L, Parnetti L, Chiasserini D. CSF and plasma Aβ42/40 across Alzheimer's disease continuum: comparison of two ultrasensitive Simoa ® assays targeting distinct amyloid regions. Clin Chem Lab Med 2024; 62:332-340. [PMID: 37656487 DOI: 10.1515/cclm-2023-0659] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES Decreased cerebrospinal fluid (CSF) amyloid beta 42/40 ratio (Aβ42/40) is one of the core Alzheimer's disease (AD) biomarkers. Measurement of Aβ42/40 in plasma has also been proposed as a surrogate marker for amyloidosis, however the validity and the diagnostic performance of this biomarker is still uncertain. Here we evaluated two immunoassays targeting distinct regions of the amyloid peptides by (a) performing a method comparison in both CSF and plasma, and (b) assessing the diagnostic performance across the AD continuum. METHODS We used N4PE and N3PA Simoa® assays to measure Aβ42/40 in CSF and plasma of 134 patients: preclinical AD (pre-AD, n=19), mild cognitive impairment due to AD (MCI-AD, n=41), AD at the dementia stage (AD-dem, n=35), and a control group (CTRL, n=39). The N4PE includes a detector antibody targeting the amyloid N-terminus, while the N3PA uses a detector targeting amyloid mid-region. RESULTS Method comparison of N4PE and N3PA assays revealed discrepancies in assessment of plasma Aβ42/Aβ40. While the diagnostic performance of the two assays did not significantly differ in CSF, in plasma, N4PE assay provided better accuracy for AD discrimination than N3PA assay (AUC AD-dem vs. CTRL 0.77 N4PE, 0.68 N3PA). CONCLUSIONS While both Aβ42/40 assays allowed for an effective discrimination between CTRL and different AD stages, the assay targeting amyloid N-terminal region provided the best diagnostic performance in plasma. Differences observed in technical and diagnostic performance of the two assays may depend on matrix-specific amyloid processing, suggesting that further studies should be carried to standardize amyloid ratio measurement in plasma.
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Affiliation(s)
- Anna Lidia Wojdała
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Giovanni Bellomo
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Andrea Toja
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Lorenzo Gaetani
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Lucilla Parnetti
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Davide Chiasserini
- Section of Physiology and Biochemistry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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19
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Wunderlin M, Zeller CJ, Senti SR, Fehér KD, Suppiger D, Wyss P, Koenig T, Teunissen CE, Nissen C, Klöppel S, Züst MA. Acoustic stimulation during sleep predicts long-lasting increases in memory performance and beneficial amyloid response in older adults. Age Ageing 2023; 52:afad228. [PMID: 38163288 PMCID: PMC10758173 DOI: 10.1093/ageing/afad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Sleep and neurodegeneration are assumed to be locked in a bi-directional vicious cycle. Improving sleep could break this cycle and help to prevent neurodegeneration. We tested multi-night phase-locked acoustic stimulation (PLAS) during slow wave sleep (SWS) as a non-invasive method to improve SWS, memory performance and plasma amyloid levels. METHODS 32 healthy older adults (agemean: 68.9) completed a between-subject sham-controlled three-night intervention, preceded by a sham-PLAS baseline night. RESULTS PLAS induced increases in sleep-associated spectral-power bands as well as a 24% increase in slow wave-coupled spindles, known to support memory consolidation. There was no significant group-difference in memory performance or amyloid-beta between the intervention and control group. However, the magnitude of PLAS-induced physiological responses were associated with memory performance up to 3 months post intervention and beneficial changes in plasma amyloid. Results were exclusive to the intervention group. DISCUSSION Multi-night PLAS is associated with long-lasting benefits in memory and metabolite clearance in older adults, rendering PLAS a promising tool to build upon and develop long-term protocols for the prevention of cognitive decline.
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Affiliation(s)
- Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, 3012 Bern, Switzerland
| | - Céline Jacqueline Zeller
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, 3012 Bern, Switzerland
| | - Samira Rafaela Senti
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
| | - Kristoffer Daniel Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
| | - Debora Suppiger
- Department of Neonatology, University Hospital Zurich and University of Zurich, 8006 Zürich, Switzerland
| | - Patric Wyss
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
| | - Charlotte Elisabeth Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
- Division of Psychiatric Specialties, Geneva University Hospitals (HUG), 1205 Geneva, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
| | - Marc Alain Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland
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20
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Liu J, Li X, Qu J. Plasma MMP-9/TIMP-1 ratio serves as a novel potential biomarker in Alzheimer's disease. Neuroreport 2023; 34:767-772. [PMID: 37695608 DOI: 10.1097/wnr.0000000000001952] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
This study aimed to explore the diagnostic potential of plasma MMP-9, TIMP-1 and MMP-9/TIMP-1 ratio for Alzheimer's disease (AD). This retrospective study was performed in a cohort consisting of patients with AD (AD group) and cognitive normal subjects (HC group). Cerebrospinal fluid (CSF) classic biomarkers including Aβ42, Aβ40, total tau (t-tau), and phosphorylated tau (p-tau) levels, and plasma MMP-9 and TIMP-1 levels were measured by commercially available ELISA kits, respectively. The differential diagnostic potential of plasma MMP-9, TIMP-1 and MMP-9/TIMP-1 ratio was evaluated using the receiver operating characteristic curve analysis. It was observed that significantly elevated levels of plasma MMP-9 and MMP-9/TIMP-1 ratio in patients with AD than HC. Both MMP-9 and MMP-9/TIMP-1 ratios were negatively correlated with CSF Aβ42/Aβ40 ratio and positively correlated with CSF p-tau in AD group. ROC curve analysis showed better diagnostic accuracy of MMP-9/TIMP-1 ratio than MMP-9 for AD at a cutoff value of 1.35 with an area under the curve of 0.906 and sensitivity and specificity of 95.8% and 75%, respectively. Our findings encourage the use of plasma MMP-9/TIMP-1 ratio as a biomarker in the diagnosis of AD.
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Affiliation(s)
| | - Xing Li
- Department of Neurology, Beijing Hepingli Hospital, Beijing, China
| | - Ji Qu
- Department of Neurology, Beijing Hepingli Hospital, Beijing, China
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21
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Teunissen CE, Kimble L, Bayoumy S, Bolsewig K, Burtscher F, Coppens S, Das S, Gogishvili D, Fernandes Gomes B, Gómez de San José N, Mavrina E, Meda FJ, Mohaupt P, Mravinacová S, Waury K, Wojdała AL, Abeln S, Chiasserini D, Hirtz C, Gaetani L, Vermunt L, Bellomo G, Halbgebauer S, Lehmann S, Månberg A, Nilsson P, Otto M, Vanmechelen E, Verberk IMW, Willemse E, Zetterberg H. Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias. Mol Cell Proteomics 2023; 22:100629. [PMID: 37557955 PMCID: PMC10594029 DOI: 10.1016/j.mcpro.2023.100629] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 08/11/2023] Open
Abstract
Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.
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Affiliation(s)
- Charlotte E Teunissen
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands.
| | - Leighann Kimble
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Sherif Bayoumy
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Katharina Bolsewig
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Felicia Burtscher
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Salomé Coppens
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; National Measurement Laboratory at LGC, Teddington, United Kingdom
| | - Shreyasee Das
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; ADx NeuroSciences, Gent, Belgium
| | - Dea Gogishvili
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bárbara Fernandes Gomes
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Nerea Gómez de San José
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany
| | - Ekaterina Mavrina
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Francisco J Meda
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Pablo Mohaupt
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Sára Mravinacová
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Katharina Waury
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anna Lidia Wojdała
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Sanne Abeln
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Davide Chiasserini
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Physiology and Biochemistry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Christophe Hirtz
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Lorenzo Gaetani
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Lisa Vermunt
- Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Giovanni Bellomo
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Steffen Halbgebauer
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany; German Center for Neurodegenerative Diseases (DZNE e.V.), Ulm, Germany
| | - Sylvain Lehmann
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; LBPC-PPC, IRMB CHU Montpellier, INM INSERM, Université de Montpellier, Montpellier, France
| | - Anna Månberg
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Peter Nilsson
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Markus Otto
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Neurology, University of Ulm, Ulm, Germany; Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Eugeen Vanmechelen
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; ADx NeuroSciences, Gent, Belgium
| | - Inge M W Verberk
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Eline Willemse
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
| | - Henrik Zetterberg
- MIRIADE Consortium, Multiomics Interdisciplinary Research Integration to Address DEmentia diagnosis, Amsterdam, The Netherlands; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; UK Dementia Research Institute at UCL, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
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22
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Züst MA, Mikutta C, Omlin X, DeStefani T, Wunderlin M, Zeller CJ, Fehér KD, Hertenstein E, Schneider CL, Teunissen CE, Tarokh L, Klöppel S, Feige B, Riemann D, Nissen C. The Hierarchy of Coupled Sleep Oscillations Reverses with Aging in Humans. J Neurosci 2023; 43:6268-6279. [PMID: 37586871 PMCID: PMC10490476 DOI: 10.1523/jneurosci.0586-23.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/11/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
A well orchestrated coupling hierarchy of slow waves and spindles during slow-wave sleep supports memory consolidation. In old age, the duration of slow-wave sleep and the number of coupling events decrease. The coupling hierarchy deteriorates, predicting memory loss and brain atrophy. Here, we investigate the dynamics of this physiological change in slow wave-spindle coupling in a frontocentral electroencephalography position in a large sample (N = 340; 237 females, 103 males) spanning most of the human life span (age range, 15-83 years). We find that, instead of changing abruptly, spindles gradually shift from being driven by slow waves to driving slow waves with age, reversing the coupling hierarchy typically seen in younger brains. Reversal was stronger the lower the slow-wave frequency, and starts around midlife (age range, ∼40-48 years), with an established reversed hierarchy between 56 and 83 years of age. Notably, coupling strength remains unaffected by age. In older adults, deteriorating slow wave-spindle coupling, measured using the phase slope index (PSI) and the number of coupling events, is associated with blood plasma glial fibrillary acidic protein levels, a marker for astrocyte activation. Data-driven models suggest that decreased sleep time and higher age lead to fewer coupling events, paralleled by increased astrocyte activation. Counterintuitively, astrocyte activation is associated with a backshift of the coupling hierarchy (PSI) toward a "younger" status along with increased coupling occurrence and strength, potentially suggesting compensatory processes. As the changes in coupling hierarchy occur gradually starting at midlife, we suggest there exists a sizable window of opportunity for early interventions to counteract undesirable trajectories associated with neurodegeneration.SIGNIFICANCE STATEMENT Evidence accumulates that sleep disturbances and cognitive decline are bidirectionally and causally linked, forming a vicious cycle. Improving sleep quality could break this cycle. One marker for sleep quality is a clear hierarchical structure of sleep oscillations. Previous studies showed that sleep oscillations decouple in old age. Here, we show that, rather, the hierarchical structure gradually shifts across the human life span and reverses in old age, while coupling strength remains unchanged. This shift is associated with markers for astrocyte activation in old age. The shifting hierarchy resembles brain maturation, plateau, and wear processes. This study furthers our comprehension of this important neurophysiological process and its dynamic evolution across the human life span.
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Affiliation(s)
- Marc Alain Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Christian Mikutta
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Private Clinic Meiringen, 3860 Meiringen, Switzerland
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom
| | - Ximena Omlin
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Tatjana DeStefani
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Céline Jacqueline Zeller
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Kristoffer Daniel Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Division of Psychiatric Specialties, Geneva University Hospitals (HUG), 1201 Geneva, Switzerland
| | - Elisabeth Hertenstein
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Carlotta L Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Charlotte Elisabeth Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Leila Tarokh
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Bernd Feige
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, 79104 Freiburg, Germany
| | - Dieter Riemann
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, 79104 Freiburg, Germany
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Division of Psychiatric Specialties, Geneva University Hospitals (HUG), 1201 Geneva, Switzerland
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23
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Yuan J, Pedrini S, Thota R, Doecke J, Chatterjee P, Sohrabi HR, Teunissen CE, Verberk IMW, Stoops E, Vanderstichele H, Meloni BP, Mitchell C, Rainey-Smith S, Goozee K, Tai ACP, Ashton N, Zetterberg H, Blennow K, Gao J, Liu D, Mastaglia F, Inderjeeth C, Zheng M, Martins RN. Elevated plasma sclerostin is associated with high brain amyloid-β load in cognitively normal older adults. NPJ AGING 2023; 9:17. [PMID: 37666862 PMCID: PMC10477312 DOI: 10.1038/s41514-023-00114-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/07/2023] [Indexed: 09/06/2023]
Abstract
Osteoporosis and Alzheimer's disease (AD) mainly affect older individuals, and the possibility of an underlying link contributing to their shared epidemiological features has rarely been investigated. In the current study, we investigated the association between levels of plasma sclerostin (SOST), a protein primarily produced by bone, and brain amyloid-beta (Aβ) load, a pathological hallmark of AD. The study enrolled participants meeting a set of screening inclusion and exclusion criteria and were stratified into Aβ- (n = 65) and Aβ+ (n = 35) according to their brain Aβ load assessed using Aβ-PET (positron emission tomography) imaging. Plasma SOST levels, apolipoprotein E gene (APOE) genotype and several putative AD blood-biomarkers including Aβ40, Aβ42, Aβ42/Aβ40, neurofilament light (NFL), glial fibrillary acidic protein (GFAP), total tau (t-tau) and phosphorylated tau (p-tau181 and p-tau231) were detected and compared. It was found that plasma SOST levels were significantly higher in the Aβ+ group (71.49 ± 25.00 pmol/L) compared with the Aβ- group (56.51 ± 22.14 pmol/L) (P < 0.01). Moreover, Spearman's correlation analysis showed that plasma SOST concentrations were positively correlated with brain Aβ load (ρ = 0.321, P = 0.001). Importantly, plasma SOST combined with Aβ42/Aβ40 ratio significantly increased the area under the curve (AUC) when compared with using Aβ42/Aβ40 ratio alone (AUC = 0.768 vs 0.669, P = 0.027). In conclusion, plasma SOST levels are elevated in cognitively unimpaired older adults at high risk of AD and SOST could complement existing plasma biomarkers to assist in the detection of preclinical AD.
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Grants
- 2018-02532 Vetenskapsrådet (Swedish Research Council)
- KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), and the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495).
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Affiliation(s)
- Jun Yuan
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Orthopaedic Translational Research, Medical School, The University of Western Australia, Nedlands, WA, Australia
| | - Steve Pedrini
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Rohith Thota
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
| | - James Doecke
- Australian E-Health Research Centre, CSIRO, Brisbane, QLD, Australia
| | - Pratishtha Chatterjee
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
| | - Hamid R Sohrabi
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- Centre for Healthy Ageing, Health Future Institute, Murdoch University, Perth, WA, Australia
- The Centre of Excellence for Alzheimer's Disease Research and Care, Edith Cowan University, Joondalup, WA, Australia
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Inge M W Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Erik Stoops
- ADx NeuroSciences, Technologiepark 94, 9052, Gent, Belgium
| | | | - Bruno P Meloni
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Christopher Mitchell
- Centre for Orthopaedic Translational Research, Medical School, The University of Western Australia, Nedlands, WA, Australia
| | - Stephanie Rainey-Smith
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Centre for Healthy Ageing, Health Future Institute, Murdoch University, Perth, WA, Australia
| | - Kathryn Goozee
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- KaRa Institute of Neurological Disease, Macquarie Park, NSW, Australia
| | - Andrew Chi Pang Tai
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Orthopaedic Translational Research, Medical School, The University of Western Australia, Nedlands, WA, Australia
| | - Nicholas Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Junjie Gao
- Department of Orthopaedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Delin Liu
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
- Centre for Orthopaedic Translational Research, Medical School, The University of Western Australia, Nedlands, WA, Australia
| | - Frank Mastaglia
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia
| | - Charles Inderjeeth
- School of Medicine, The University of Western Australia, Perth, WA, Australia
- Sir Charles Gairdner and Osborne Park Health Care Group, Perth, Australia
| | - Minghao Zheng
- Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia.
- Centre for Orthopaedic Translational Research, Medical School, The University of Western Australia, Nedlands, WA, Australia.
| | - Ralph N Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- Centre for Healthy Ageing, Health Future Institute, Murdoch University, Perth, WA, Australia
- The Centre of Excellence for Alzheimer's Disease Research and Care, Edith Cowan University, Joondalup, WA, Australia
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24
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Bellaver B, Puig-Pijoan A, Ferrari-Souza JP, Leffa DT, Lussier FZ, Ferreira PCL, Tissot C, Povala G, Therriault J, Benedet AL, Ashton NJ, Servaes S, Chamoun M, Stevenson J, Rahmouni N, Vermeiren M, Macedo AC, Fernández-Lebrero A, García-Escobar G, Navalpotro-Gómez I, Lopez O, Tudorascu DL, Cohen A, Villemagne VL, Klunk WE, Gauthier S, Zimmer ER, Karikari TK, Blennow K, Zetterberg H, Suárez-Calvet M, Rosa-Neto P, Pascoal TA. Blood-brain barrier integrity impacts the use of plasma amyloid-β as a proxy of brain amyloid-β pathology. Alzheimers Dement 2023; 19:3815-3825. [PMID: 36919582 PMCID: PMC10502181 DOI: 10.1002/alz.13014] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/08/2022] [Accepted: 01/25/2023] [Indexed: 03/16/2023]
Abstract
INTRODUCTION Amyloid-β (Aβ) and tau can be quantified in blood. However, biological factors can influence the levels of brain-derived proteins in the blood. The blood-brain barrier (BBB) regulates protein transport between cerebrospinal fluid (CSF) and blood. BBB altered permeability might affect the relationship between brain and blood biomarkers. METHODS We assessed 224 participants in research (TRIAD, n = 96) and clinical (BIODEGMAR, n = 128) cohorts with plasma and CSF/positron emission tomography Aβ, p-tau, and albumin measures. RESULTS Plasma Aβ42/40 better identified CSF Aβ42/40 and Aβ-PET positivity in individuals with high BBB permeability. An interaction between plasma Aβ42/40 and BBB permeability on CSF Aβ42/40 was observed. Voxel-wise models estimated that the association of positron emission tomography (PET), with plasma Aβ was most affected by BBB permeability in AD-related brain regions. BBB permeability did not significantly impact the relationship between brain and plasma p-tau levels. DISCUSSION These findings suggest that BBB integrity may influence the performance of plasma Aβ, but not p-tau, biomarkers in research and clinical settings. HIGHLIGHTS BBB permeability affects the association between brain and plasma Aβ levels. BBB integrity does not affect the association between brain and plasma p-tau levels. Plasma Aβ was most affected by BBB permeability in AD-related brain regions. BBB permeability increases with age but not according to cognitive status.
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Affiliation(s)
- Bruna Bellaver
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Albert Puig-Pijoan
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - João Pedro Ferrari-Souza
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Douglas T Leffa
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Firoza Z Lussier
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Pamela C L Ferreira
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Guilherme Povala
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Andréa L Benedet
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Marie Vermeiren
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Arthur C Macedo
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Aida Fernández-Lebrero
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | | | - Irene Navalpotro-Gómez
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Oscar Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Dana L Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ann Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Eduardo R Zimmer
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Graduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Brain Institute of Rio Grande do Sul, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Marc Suárez-Calvet
- Cognitive Decline and Movement Disorders Unit, Neurology Department, Hospital del Mar, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Weiner S, Blennow K, Zetterberg H, Gobom J. Next-generation proteomics technologies in Alzheimer's disease: from clinical research to routine diagnostics. Expert Rev Proteomics 2023; 20:143-150. [PMID: 37701966 DOI: 10.1080/14789450.2023.2255752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/18/2023] [Indexed: 09/14/2023]
Abstract
INTRODUCTION Clinical proteomics studies of Alzheimer's disease (AD) research aim to identify biomarkers useful for clinical research, diagnostics, and improve our understanding of the pathological processes involved in the disease. The rapidly increasing performance of proteomics technologies is likely to have great impact on AD research. AREAS COVERED We review recent proteomics approaches that have advanced the field of clinical AD research. Specifically, we discuss the application of targeted mass spectrometry (MS), labeling-based and label-free MS-based as well as affinity-based proteomics to AD biomarker development, underpinning their importance with the latest impactful clinical studies. We evaluate how proteomics technologies have been adapted to meet current challenges. Finally, we discuss the limitations and potential of proteomics techniques and whether their scope might extend beyond current research-based applications. EXPERT OPINION To date, proteomics technologies in the AD field have been largely limited to AD biomarker discovery. The recent development of the first successful disease-modifying treatments of AD will further increase the need for blood biomarkers for early, accurate diagnosis, and CSF biomarkers that reflect specific pathological processes. Proteomics has the potential to meet these requirements and to progress into clinical routine practice, provided that current limitations are overcome.
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Affiliation(s)
- Sophia Weiner
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the 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 at the University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Dementia Research Institute at UCL, London, UK
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Johan Gobom
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
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Noda K, Lim Y, Sengoku S, Kodama K. Global biomarker trends in Alzheimer's research: a bibliometric analysis. Drug Discov Today 2023:103677. [PMID: 37390962 DOI: 10.1016/j.drudis.2023.103677] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/25/2023] [Accepted: 06/16/2023] [Indexed: 07/02/2023]
Abstract
Alzheimer's disease (AD) has no effective treatment, although antibody drugs targeting beta-amyloid, mainly aducanumab, have produced useful clinical results. Biomarkers can be used to determine drug regimens effectively and to monitor the effects of drugs. A concept in which biomarkers reflect disease states is emerging. Although several AD biomarker studies have been reported, measurement methods and target molecules are still being validated, and various biomarkers are being explored. This study analyzed trends in research on AD biomarkers using bibliometric methods, revealing an exponential increase in research reports in this field, with the US most active in research. Analysis of the 'Burst' biomarkers using CiteSpace revealed that networks centered on authors, rather than networks among countries, drive new research trends in this area.
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Affiliation(s)
- Kenta Noda
- Graduate School of Design and Architecture, Nagoya City University, Nagoya 464-0083, Japan
| | | | - Shintaro Sengoku
- School of Environment and Society, Tokyo Institute of Technology, Tokyo 108-0023, Japan
| | - Kota Kodama
- Graduate School of Design and Architecture, Nagoya City University, Nagoya 464-0083, Japan; Ritsumeikan University, Osaka 567-8570, Japan; School of Data Science, Nagoya City University, Nagoya 467-8501, Japan; Center for Research and Education on Drug Discovery, The Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan.
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27
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Hirtz C, Busto GU, Bennys K, Kindermans J, Navucet S, Tiers L, Lista S, Vialaret J, Gutierrez LA, Dauvilliers Y, Berr C, Lehmann S, Gabelle A. Comparison of ultrasensitive and mass spectrometry quantification of blood-based amyloid biomarkers for Alzheimer's disease diagnosis in a memory clinic cohort. Alzheimers Res Ther 2023; 15:34. [PMID: 36800984 PMCID: PMC9938625 DOI: 10.1186/s13195-023-01188-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/09/2023] [Indexed: 02/20/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a complex neurodegenerative disorder with β-amyloid pathology as a key underlying process. The relevance of cerebrospinal fluid (CSF) and brain imaging biomarkers is validated in clinical practice for early diagnosis. Yet, their cost and perceived invasiveness are a limitation for large-scale implementation. Based on positive amyloid profiles, blood-based biomarkers should allow to detect people at risk for AD and to monitor patients under therapeutics strategies. Thanks to the recent development of innovative proteomic tools, the sensibility and specificity of blood biomarkers have been considerably improved. However, their diagnosis and prognosis relevance for daily clinical practice is still incomplete. METHODS The Plasmaboost study included 184 participants from the Montpellier's hospital NeuroCognition Biobank with AD (n = 73), mild cognitive impairments (MCI) (n = 32), subjective cognitive impairments (SCI) (n = 12), other neurodegenerative diseases (NDD) (n = 31), and other neurological disorders (OND) (n = 36). Dosage of β-amyloid biomarkers was performed on plasma samples using immunoprecipitation-mass spectrometry (IPMS) developed by Shimadzu (IPMS-Shim Aβ42, Aβ40, APP669-711) and Simoa Human Neurology 3-PLEX A assay (Aβ42, Aβ40, t-tau). Links between those biomarkers and demographical and clinical data and CSF AD biomarkers were investigated. Performances of the two technologies to discriminate clinically or biologically based (using the AT(N) framework) diagnosis of AD were compared using receiver operating characteristic (ROC) analyses. RESULTS The amyloid IPMS-Shim composite biomarker (combining APP669-711/Aβ42 and Aβ40/Aβ42 ratios) discriminated AD from SCI (AUC: 0.91), OND (0.89), and NDD (0.81). The IPMS-Shim Aβ42/40 ratio also discriminated AD from MCI (0.78). IPMS-Shim biomarkers have similar relevance to discriminate between amyloid-positive and amyloid-negative individuals (0.73 and 0.76 respectively) and A-T-N-/A+T+N+ profiles (0.83 and 0.85). Performances of the Simoa 3-PLEX Aβ42/40 ratio were more modest. Pilot longitudinal analysis on the progression of plasma biomarkers indicates that IPMS-Shim can detect the decrease in plasma Aβ42 that is specific to AD patients. CONCLUSIONS Our study confirms the potential usefulness of amyloid plasma biomarkers, especially the IPMS-Shim technology, as a screening tool for early AD patients.
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Affiliation(s)
- Christophe Hirtz
- grid.157868.50000 0000 9961 060XUniversity of Montpellier, IRMB-PPC, INM, CHU Montpellier, INSERM CNRS, Montpellier, France
| | - Germain U. Busto
- grid.157868.50000 0000 9961 060XResource and Research Memory Center (CMRR), Department of Neurology, Montpellier University Hospital, 80 avenue Augustin Fliche, 34000 Montpellier, France ,grid.121334.60000 0001 2097 0141Institute for Neurosciences of Montpellier (INM), Univ Montpellier, INSERM, Montpellier, France
| | - Karim Bennys
- grid.157868.50000 0000 9961 060XResource and Research Memory Center (CMRR), Department of Neurology, Montpellier University Hospital, 80 avenue Augustin Fliche, 34000 Montpellier, France
| | - Jana Kindermans
- grid.157868.50000 0000 9961 060XUniversity of Montpellier, IRMB-PPC, INM, CHU Montpellier, INSERM CNRS, Montpellier, France
| | - Sophie Navucet
- grid.157868.50000 0000 9961 060XResource and Research Memory Center (CMRR), Department of Neurology, Montpellier University Hospital, 80 avenue Augustin Fliche, 34000 Montpellier, France
| | - Laurent Tiers
- grid.157868.50000 0000 9961 060XUniversity of Montpellier, IRMB-PPC, INM, CHU Montpellier, INSERM CNRS, Montpellier, France
| | - Simone Lista
- grid.157868.50000 0000 9961 060XResource and Research Memory Center (CMRR), Department of Neurology, Montpellier University Hospital, 80 avenue Augustin Fliche, 34000 Montpellier, France
| | - Jérôme Vialaret
- grid.157868.50000 0000 9961 060XUniversity of Montpellier, IRMB-PPC, INM, CHU Montpellier, INSERM CNRS, Montpellier, France
| | - Laure-Anne Gutierrez
- grid.121334.60000 0001 2097 0141Institute for Neurosciences of Montpellier (INM), Univ Montpellier, INSERM, Montpellier, France
| | - Yves Dauvilliers
- grid.121334.60000 0001 2097 0141Institute for Neurosciences of Montpellier (INM), Univ Montpellier, INSERM, Montpellier, France ,grid.121334.60000 0001 2097 0141Sleep and Wake Disorders Center, Department of Neurology, Gui de Chauliac Hospital, University of Montpellier, Montpellier, France
| | - Claudine Berr
- grid.121334.60000 0001 2097 0141Institute for Neurosciences of Montpellier (INM), Univ Montpellier, INSERM, Montpellier, France
| | - Sylvain Lehmann
- University of Montpellier, IRMB-PPC, INM, CHU Montpellier, INSERM CNRS, Montpellier, France.
| | - Audrey Gabelle
- grid.157868.50000 0000 9961 060XResource and Research Memory Center (CMRR), Department of Neurology, Montpellier University Hospital, 80 avenue Augustin Fliche, 34000 Montpellier, France ,grid.121334.60000 0001 2097 0141Institute for Neurosciences of Montpellier (INM), Univ Montpellier, INSERM, Montpellier, France
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Review of Technological Challenges in Personalised Medicine and Early Diagnosis of Neurodegenerative Disorders. Int J Mol Sci 2023; 24:ijms24043321. [PMID: 36834733 PMCID: PMC9968142 DOI: 10.3390/ijms24043321] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Neurodegenerative disorders are characterised by progressive neuron loss in specific brain areas. The most common are Alzheimer's disease and Parkinson's disease; in both cases, diagnosis is based on clinical tests with limited capability to discriminate between similar neurodegenerative disorders and detect the early stages of the disease. It is common that by the time a patient is diagnosed with the disease, the level of neurodegeneration is already severe. Thus, it is critical to find new diagnostic methods that allow earlier and more accurate disease detection. This study reviews the methods available for the clinical diagnosis of neurodegenerative diseases and potentially interesting new technologies. Neuroimaging techniques are the most widely used in clinical practice, and new techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have significantly improved the diagnosis quality. Identifying biomarkers in peripheral samples such as blood or cerebrospinal fluid is a major focus of the current research on neurodegenerative diseases. The discovery of good markers could allow preventive screening to identify early or asymptomatic stages of the neurodegenerative process. These methods, in combination with artificial intelligence, could contribute to the generation of predictive models that will help clinicians in the early diagnosis, stratification, and prognostic assessment of patients, leading to improvements in patient treatment and quality of life.
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Planche V, Bouteloup V, Pellegrin I, Mangin JF, Dubois B, Ousset PJ, Pasquier F, Blanc F, Paquet C, Hanon O, Bennys K, Ceccaldi M, Annweiler C, Krolak-Salmon P, Godefroy O, Wallon D, Sauvee M, Boutoleau-Bretonnière C, Bourdel-Marchasson I, Jalenques I, Chene G, Dufouil C. Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort. Neurology 2023; 100:e473-e484. [PMID: 36261295 PMCID: PMC9931079 DOI: 10.1212/wnl.0000000000201479] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/13/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Blood biomarkers for Alzheimer disease (AD) have consistently proven to be associated with CSF or PET biomarkers and effectively discriminate AD from other neurodegenerative diseases. Our aim was to test their utility in clinical practice, from a multicentric unselected prospective cohort where patients presented with a large spectrum of cognitive deficits or complaints. METHODS The MEMENTO cohort enrolled 2,323 outpatients with subjective cognitive complaint (SCC) or mild cognitive impairment (MCI) consulting in 26 French memory clinics. Participants had neuropsychological assessments, MRI, and blood sampling at baseline. CSF sampling and amyloid PET were optional. Baseline blood Aβ42/40 ratio, total tau, p181-tau, and neurofilament light chain (NfL) were measured using a Simoa HD-X analyzer. An expert committee validated incident dementia cases during a 5-year follow-up period. RESULTS Overall, 2,277 individuals had at least 1 baseline blood biomarker available (n = 357 for CSF subsample, n = 649 for PET subsample), among whom 257 were diagnosed with clinical AD/mixed dementia during follow-up. All blood biomarkers but total tau were mildly correlated with their equivalence in the CSF (r = 0.33 to 0.46, p < 0.0001) and were associated with amyloid-PET status (p < 0.0001). Blood p181-tau was the best blood biomarker to identify amyloid-PET positivity (area under the curve = 0.74 [95% CI = 0.69; 0.79]). Higher blood and CSF p181-tau and NfL concentrations were associated with accelerated time to AD dementia onset with similar incidence rates, whereas blood Aβ42/40 was less efficient than CSF Aβ42/40. Blood p181-tau alone was the best blood predictor of 5-year AD/mixed dementia risk (c-index = 0.73 [95% CI = 0.69; 0.77]); its accuracy was higher in patients with clinical dementia rating (CDR) = 0 (c-index = 0.83 [95% CI = 0.69; 0.97]) than in patients with CDR = 0.5 (c-index = 0.70 [95% CI = 0.66; 0.74]). A "clinical" reference model (combining demographics and neuropsychological assessment) predicted AD/mixed dementia risk with a c-index = 0.88 [95% CI = 0.86-0.91] and performance increased to 0.90 [95% CI = 0.88; 0.92] when adding blood p181-tau + Aβ42/40. A "research" reference model (clinical model + apolipoprotein E genotype and AD signature on MRI) had a c-index = 0.91 [95% CI = 0.89-0.93] increasing to 0.92 [95% CI = 0.90; 0.93] when adding blood p181-tau + Aβ42/40. Chronic kidney disease and vascular comorbidities did not affect predictive performances. DISCUSSION In a clinic-based cohort of patients with SCC or MCI, blood biomarkers may be good hallmarks of underlying pathology but add little to 5-year dementia risk prediction models including traditional predictors.
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Affiliation(s)
- Vincent Planche
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand.
| | - Vincent Bouteloup
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Pellegrin
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Jean-Francois Mangin
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Bruno Dubois
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Pierre-Jean Ousset
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Florence Pasquier
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Frederic Blanc
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Claire Paquet
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Olivier Hanon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Karim Bennys
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Mathieu Ceccaldi
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Cédric Annweiler
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Pierre Krolak-Salmon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Olivier Godefroy
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - David Wallon
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Mathilde Sauvee
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Claire Boutoleau-Bretonnière
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Bourdel-Marchasson
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Isabelle Jalenques
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Genevieve Chene
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
| | - Carole Dufouil
- From the Univ. Bordeaux (V.P.), CNRS UMR 5293, Institut des Maladies Neurodégénératives; CHU de Bordeaux (V.P.), Pôle de Neurosciences Cliniques, Centre Mémoire de Ressources et de Recherche; Univ. Bordeaux (V.B., G.C., C.D.), Inserm U1219, PHARes Team, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED); CHU Bordeaux (V.B., G.C., C.D.), CIC 1401 EC, Pôle Santé Publique; CHU de Bordeaux (I.P.), Département d'Immunologie et d'Immunogénétique; Univ. Paris-Saclay (J.-F.M.), CEA, CNRS, Baobab UMR9027, Neurospin, CATI Multicenter Neuroimaging Platform, US52, UAR 9031, Gif-sur-Yvette; Sorbonne-Université (B.D.), Service des Maladies Cognitives et Comportementales et Institut de La Mémoire et de La Maladie d'Alzheimer (IM2A), Hôpital de La Salpêtrière, AP-PH, Paris; Univ. Toulouse (P.-J.O.), Inserm U1027, Gérontopôle, Departement de Gériatrie, CHU Purpan, Toulouse; Univ. Lille (F.P.), Inserm U1171, Centre Mémoire de Ressources et de Recherche, CHU Lille, DISTAlz, Lille; Univ. Strasbourg (F.B.), CNRS, ICube Laboratory, UMR 7357, Fédération de Médecine Translationnelle de Strasbourg, Centre Mémoire de Ressources et de Recherche, Pôle de Gériatrie, Strasbourg; Univ. Paris (C.P.), Inserm U1144, Groupe Hospitalier Lariboisière Fernand-Widal, AP-HP; Univ. Paris Cité (O.H.), EA 4468, AP-HP, Hôpitaux Universitaires Paris Centre, Service de Gériatrie, Hôpital Broca; CHU de Montpellier (K.B.), Pôle de Neurosciences, Département de Neurologie, Centre Mémoire de Ressources et de Recherche, Montpellier; Univ. Aix Marseille (M.C.), Inserm UMR 1106, Institut de Neurosciences des Systèmes, Centre Mémoire de Ressources et de Recherche, Département de Neurologie et de Neuropsychologie, AP-HM, Marseille; Univ. Angers (C.A.), UPRES EA 4638, Centre Mémoire de Ressources et de Recherche, Département de Gériatrie, CHU d'Angers, Angers; Univ. Lyon (P.K.-S.), Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon; Univ. Picardie (O.G.), UR UPJV4559, Laboratoire de Neurosciences Fonctionnelles et Pathologies, Service de Neurologie, CHU Amiens; Univ. Normandie (D.W.), UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen; Centre Mémoire de Ressources et de Recherche Grenoble Arc Alpin (M.S.), Pôle de Psychiatrie et Neurologie, CHU Grenoble Alpes; CHU de Nantes (C.B.-B.), Département de Neurologie, Centre Mémoire de Ressources et Recherche, Nantes; Univ. Bordeaux (I.B.-M.), CNRS UMR 5536, Centre de Résonance Magnétique des Systèmes Biologiques, Pôle de Gérontologie Clinique, CHU de Bordeaux; and Univ. Clermont Auvergne (I.J.), CNRS, CHU Clermont-Ferrand, Centre Mémoire de Ressources et de Recherche, Service de Psychiatrie de L'Adulte A et Psychologie Médicale, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand
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Krebs C, Brill E, Minkova L, Federspiel A, Kellner-Weldon F, Wyss P, Teunissen CE, van Harten AC, Seydell-Greenwald A, Klink K, Züst MA, Brem AK, Klöppel S. Investigating Compensatory Brain Activity in Older Adults with Subjective Cognitive Decline. J Alzheimers Dis 2023; 93:107-124. [PMID: 36970895 DOI: 10.3233/jad-221001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
BACKGROUND Preclinical Alzheimer's disease (AD) is one possible cause of subjective cognitive decline (SCD). Normal task performance despite ongoing neurodegeneration is typically considered as neuronal compensation, which is reflected by greater neuronal activity. Compensatory brain activity has been observed in frontal as well as parietal regions in SCD, but data are scarce, especially outside the memory domain. OBJECTIVE To investigate potential compensatory activity in SCD. Such compensatory activity is particularly expected in participants where blood-based biomarkers indicated amyloid positivity as this implies preclinical AD. METHODS 52 participants with SCD (mean age: 71.00±5.70) underwent structural and functional neuroimaging (fMRI), targeting episodic memory and spatial abilities, and a neuropsychological assessment. The estimation of amyloid positivity was based on plasma amyloid-β and phosphorylated tau (pTau181) measures. RESULTS Our fMRI analyses of the spatial abilities task did not indicate compensation, with only three voxels exceeding an uncorrected threshold at p < 0.001. This finding was not replicated in a subset of 23 biomarker positive individuals. CONCLUSION Our results do not provide conclusive evidence for compensatory brain activity in SCD. It is possible that neuronal compensation does not manifest at such an early stage as SCD. Alternatively, it is possible that our sample size was too small or that compensatory activity may be too heterogeneous to be detected by group-level statistics. Interventions based on the individual fMRI signal should therefore be explored.
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Affiliation(s)
- Christine Krebs
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Esther Brill
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Lora Minkova
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Ber, Bern, Switzerland
| | - Frauke Kellner-Weldon
- Section Neuroradiology of the Department of Radiology, Cantonal Hospital Lucerne, Lucerne, Switzerland
| | - Patric Wyss
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Vrije University, Amsterdam, the Netherlands
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Katharina Klink
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Marc A Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Anna-Katharine Brem
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
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Green ZD, Kueck PJ, John CS, Burns JM, Morris JK. Blood Biomarkers Discriminate Cerebral Amyloid Status and Cognitive Diagnosis when Collected with ACD-A Anticoagulant. Curr Alzheimer Res 2023; 20:557-566. [PMID: 38047367 PMCID: PMC10792989 DOI: 10.2174/0115672050271523231111192725] [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: 08/14/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND The development of biomarkers that are easy to collect, process, and store is a major goal of research on current Alzheimer's Disease (AD) and underlies the growing interest in plasma biomarkers. Biomarkers with these qualities will improve diagnosis and allow for better monitoring of therapeutic interventions. However, blood collection strategies have historically differed between studies. We examined the ability of various ultrasensitive plasma biomarkers to predict cerebral amyloid status in cognitively unimpaired individuals when collected using acid citrate dextrose (ACD). We then examined the ability of these biomarkers to predict cognitive impairment independent of amyloid status. METHODS Using a cross-sectional study design, we measured amyloid beta 42/40 ratio, pTau-181, neurofilament-light, and glial fibrillary acidic protein using the Quanterix Simoa® HD-X platform. To evaluate the discriminative accuracy of these biomarkers in determining cerebral amyloid status, we used both banked plasma and 18F-AV45 PET cerebral amyloid neuroimaging data from 140 cognitively unimpaired participants. We further examined their ability to discriminate cognitive status by leveraging data from 42 cognitively impaired older adults. This study is the first, as per our knowledge, to examine these specific tests using plasma collected using acid citrate dextrose (ACD), as well as the relationship with amyloid PET status. RESULTS Plasma AB42/40 had the highest AUC (0.833, 95% C.I. 0.767-0.899) at a cut-point of 0.0706 for discriminating between the two cerebral amyloid groups (sensitivity 76%, specificity 78.5%). Plasma NFL at a cut-point of 20.58pg/mL had the highest AUC (0.908, 95% CI 0.851- 0.966) for discriminating cognitive impairment (sensitivity 84.8%, specificity 89.9%). The addition of age and apolipoprotein e4 status did not improve the discriminative accuracy of these biomarkers. CONCLUSION Our results suggest that the Aβ42/40 ratio is useful in discriminating clinician-rated elevated cerebral amyloid status and that NFL is useful for discriminating cognitive impairment status. These findings reinforce the growing body of evidence regarding the general utility of these biomarkers and extend their utility to plasma collected in a non-traditional anticoagulant.
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Affiliation(s)
- Zachary D. Green
- Alzheimer’s Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States
| | - Paul J. Kueck
- Alzheimer’s Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States
| | - Casey S. John
- Alzheimer’s Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States
| | - Jeffrey M. Burns
- Alzheimer’s Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, 66160, United States
| | - Jill K. Morris
- Alzheimer’s Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, 66160, United States
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32
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Dahri M, Miri Jahromi A, Nikzad A, Mohammadgholian M, Rahmanian M, Abolmaali SS, Maleki R. Novel bioengineered MBenes for the treatment of Alzheimer's disease: An in-Sillico study. J Biomol Struct Dyn 2022; 40:12268-12276. [PMID: 34427178 DOI: 10.1080/07391102.2021.1969288] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease is a neurodegenerative disease caused by the deposition and accumulation of amyloid-β (Aβ) peptides in the brain neurons. Current medications are not a definitive cure for this disease, but they can hamper the signs and symptoms of Alzheimer's disease. Therefore, prevention is the best way to deal with this disease. In this study, the novel structures based on MBenes (such as Cd2B, Mo2B, Cu2B, and Ta2B) are proposed to prevent amyloid-β accumulation in Alzheimer's disease. Regarding the remarkable MBene properties such as tunability, biocompatibility, and low manufacturing cost, the effect of these structures on amyloid-β deformation was explored using molecular dynamics simulation. To provide an atomic analysis of Beta-amyloid behavior in the presence of these structures, the compaction, contact area, and stability of Beta-amyloid were investigated. The results indicated the satisfactory performance of MBenes on the destabilization of amyloid-β structures. Moreover, given the higher interactions between Cd2B and amyloid-β, the instability, compaction, and the contact area of amyloid-β particles were investigated in this complex. The findings confirmed Cd2B as the best structure to prevent amyloid-β accumulation. The results of this investigation paved the way for the development of these structures as a medicinal agent to prevent Alzheimer's disease.
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Affiliation(s)
- Mohammad Dahri
- Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Department of Physics, Tehran University, Tehran, Iran.,Center for Nanotechnology in Drug Delivery, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ahmad Miri Jahromi
- Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Department of Physics, Tehran University, Tehran, Iran
| | - Arash Nikzad
- The University of British Columbia, Vancouver, Canada
| | - Maryam Mohammadgholian
- Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Department of Physics, Tehran University, Tehran, Iran
| | - Mohammad Rahmanian
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Samira Sadat Abolmaali
- Center for Nanotechnology in Drug Delivery, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Pharmaceutical Nanotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Maleki
- Computational Biology and Chemistry Group (CBCG), Universal Scientific Education and Research Network (USERN), Department of Physics, Tehran University, Tehran, Iran
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Yamashita K, Miura M, Watanabe S, Ishiki K, Arimatsu Y, Kawahira J, Kubo T, Sasaki K, Arai T, Hagino K, Irino Y, Nagai K, Verbel D, Koyama A, Dhadda S, Niiro H, Iwanaga S, Sato T, Yoshida T, Iwata A. Fully automated and highly specific plasma β-amyloid immunoassays predict β-amyloid status defined by amyloid positron emission tomography with high accuracy. Alzheimers Res Ther 2022; 14:86. [PMID: 35739591 PMCID: PMC9219197 DOI: 10.1186/s13195-022-01029-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/15/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Clinicians, researchers, and patients alike would greatly benefit from more accessible and inexpensive biomarkers for neural β-amyloid (Aβ). We aimed to assess the performance of fully automated plasma Aβ immunoassays, which correlate significantly with immunoprecipitation mass spectrometry assays, in predicting brain Aβ status as determined by visual read assessment of amyloid positron emission tomography (PET).
Methods
The plasma Aβ42/Aβ40 ratio was measured using a fully automated immunoassay platform (HISCL series) in two clinical studies (discovery and validation studies). The discovery and validation sample sets were retrospectively and randomly selected from participants with early Alzheimer’s disease (AD) identified during screening for the elenbecestat Phase 3 program.
Results
We included 197 participants in the discovery study (mean [SD] age 71.1 [8.5] years; 112 females) and 200 in the validation study (age 70.8 [7.9] years; 99 females). The plasma Aβ42/Aβ40 ratio predicted amyloid PET visual read status with areas under the receiver operating characteristic curves of 0.941 (95% confidence interval [CI] 0.910–0.973) and 0.868 (95% CI 0.816–0.920) in the discovery and validation studies, respectively. In the discovery study, a cutoff value of 0.102 was determined based on maximizing the Youden Index, and the sensitivity and specificity were calculated to be 96.0% (95% CI 90.1–98.9%) and 83.5% (95% CI 74.6–90.3%), respectively. Using the same cutoff value, the sensitivity and specificity in the validation study were calculated to be 88.0% (95% CI 80.0–93.6%) and 72.0% (95% CI 62.1–80.5%), respectively.
Conclusions
The plasma Aβ42/Aβ40 ratio measured using the HISCL series achieved high accuracy in predicting amyloid PET status. Since our blood-based immunoassay system is less invasive and more accessible than amyloid PET and cerebrospinal fluid testing, it may contribute to the diagnosis of AD in routine clinical practice.
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Fowler CJ, Stoops E, Rainey‐Smith SR, Vanmechelen E, Vanbrabant J, Dewit N, Mauroo K, Maruff P, Rowe CC, Fripp J, Li Q, Bourgeat P, Collins SJ, Martins RN, Masters CL, Doecke JD. Plasma p-tau181/Aβ 1-42 ratio predicts Aβ-PET status and correlates with CSF-p-tau181/Aβ 1-42 and future cognitive decline. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12375. [PMID: 36447478 PMCID: PMC9695763 DOI: 10.1002/dad2.12375] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 09/28/2022] [Indexed: 11/26/2022]
Abstract
Background In Alzheimer's disease (AD), plasma amyloid beta (Aβ)1-42 and phosphorylated tau (p-tau) predict high amyloid status from Aβ positron emission tomography (PET); however, the extent to which combination of these plasma assays can predict remains unknown. Methods Prototype Simoa assays were used to measure plasma samples from participants who were either cognitively normal (CN) or had mild cognitive impairment (MCI)/AD in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Results The p-tau181/Aβ1-42 ratio showed the best prediction of Aβ-PET across all participants (area under the curve [AUC] = 0.905, 95% confidence interval [CI]: 0.86-0.95) and in CN (AUC = 0.873; 0.80-0.94), and symptomatic (AUC = 0.908; 0.82-1.00) adults. Plasma p-tau181/Aβ1-42 ratio correlated with cerebrospinal fluid (CSF) p-tau181 (Elecsys, Spearman's ρ = 0.74, P < 0.0001) and predicted abnormal CSF Aβ (AUC = 0.816; 0.74-0.89). The p-tau181/Aβ1-42 ratio also predicted future rates of cognitive decline assessed by AIBL Preclinical Alzheimer Cognitive Composite or Clinical Dementia Rating Sum of Boxes (P < 0.0001). Discussion Plasma p-tau181/Aβ1-42 ratio predicted both Aβ-PET status and cognitive decline, demonstrating potential as both a diagnostic aid and as a screening and prognostic assay for preclinical AD trials.
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Affiliation(s)
| | | | - Stephanie R. Rainey‐Smith
- School of Medical and Health SciencesCentre of Excellence for Alzheimer's Disease Research & CareEdith Cowan UniversityJoondalupWestern AustraliaAustralia
| | | | | | | | | | | | - Christopher C. Rowe
- The Florey Institute of Neuroscience and Mental HealthMelbourneVictoriaAustralia
- Austin Health, Molecular Imaging Researchand The Florey Department of NeuroscienceUniversity of MelbourneMelbourneVictoriaAustralia
| | - Jurgen Fripp
- Australian E‐Health Research CentreCSIROHerstonQueenslandAustralia
| | - Qiao‐Xin Li
- The Florey Institute of Neuroscience and Mental HealthMelbourneVictoriaAustralia
| | | | - Steven J. Collins
- Department of Medicine (RMH)The University of MelbourneMelbourneVictoriaAustralia
| | - Ralph N. Martins
- School of Medical and Health SciencesCentre of Excellence for Alzheimer's Disease Research & CareEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Department of Biological SciencesMacquarie UniversityNorth RydeNew South WalesAustralia
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthMelbourneVictoriaAustralia
| | - James D. Doecke
- Australian E‐Health Research CentreCSIROHerstonQueenslandAustralia
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Abstract
Alzheimer's disease (AD) characterization has progressed from being indexed using clinical symptomatology followed by neuropathological examination at autopsy to in vivo signatures using cerebrospinal fluid (CSF) biomarkers and positron emission tomography. The core AD biomarkers reflect amyloid-β plaques (A), tau pathology (T) and neurodegeneration (N), following the ATN schedule, and are now being introduced into clinical routine practice. This is an important development, as disease-modifying treatments are now emerging. Further, there are now reproducible data on CSF biomarkers which reflect synaptic pathology, neuroinflammation and common co-pathologies. In addition, the development of ultrasensitive techniques has enabled the core CSF biomarkers of AD pathophysiology to be translated to blood (e.g., phosphorylated tau, amyloid-β and neurofilament light). In this chapter, we review where we stand with both core and novel CSF biomarkers, as well as the explosion of data on blood biomarkers. Also, we discuss potential applications in research aiming to better understand the disease, as well as possible use in routine clinical practice and therapeutic trials.
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Affiliation(s)
- Joel Simrén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Anders Elmgren
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Neurodegenerative Disease, Institute of Neurology, University College London, London, United Kingdom; UK Dementia Research Institute, University College London, London, United Kingdom; Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
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Pontecorvo MJ, Lu M, Burnham SC, Schade AE, Dage JL, Shcherbinin S, Collins EC, Sims JR, Mintun MA. Association of Donanemab Treatment With Exploratory Plasma Biomarkers in Early Symptomatic Alzheimer Disease: A Secondary Analysis of the TRAILBLAZER-ALZ Randomized Clinical Trial. JAMA Neurol 2022; 79:1250-1259. [PMID: 36251300 PMCID: PMC9577883 DOI: 10.1001/jamaneurol.2022.3392] [Citation(s) in RCA: 120] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Importance Plasma biomarkers of Alzheimer disease may be useful as minimally invasive pharmacodynamic measures of treatment outcomes. Objective To analyze the association of donanemab treatment with plasma biomarkers associated with Alzheimer disease. Design, Setting, and Participants TRAILBLAZER-ALZ was a randomized, double-blind, placebo-controlled clinical trial conducted from December 18, 2017, to December 4, 2020, across 56 sites in the US and Canada. Exploratory biomarkers were prespecified with the post hoc addition of plasma glial fibrillary acidic protein and amyloid-β. Men and women aged 60 to 85 years with gradual and progressive change in memory function for at least 6 months were included. A total of 1955 participants were assessed for eligibility. Key eligibility criteria include Mini-Mental State Examination scores of 20 to 28 and elevated amyloid and intermediate tau levels. Interventions Randomized participants received donanemab or placebo every 4 weeks for up to 72 weeks. The first 3 doses of donanemab were given at 700 mg and then increased to 1400 mg with blinded dose reductions as specified based on amyloid reduction. Main Outcomes and Measures Change in plasma biomarker levels after donanemab treatment. Results In TRAILBLAZER-ALZ, 272 participants (mean [SD] age, 75.2 [5.5] years; 145 [53.3%] female) were randomized. Plasma levels of phosphorylated tau217 (pTau217) and glial fibrillary acidic protein were significantly lower with donanemab treatment compared with placebo as early as 12 weeks after the start of treatment (least square mean change difference vs placebo, -0.04 [95% CI, -0.07 to -0.02]; P = .002 and -0.04 [95% CI, -0.07 to -0.01]; P = .01, respectively). No significant differences in plasma levels of amyloid-β 42/40 and neurofilament light chain were observed between treatment arms at the end of treatment. Changes in plasma pTau217 and glial fibrillary acidic protein were significantly correlated with the Centiloid percent change in amyloid (Spearman rank correlation coefficient [R] = 0.484 [95% CI, 0.359-0.592]; P < .001 and R = 0.453 [95% CI, 0.306-0.579]; P < .001, respectively) following treatment. Additionally, plasma levels of pTau217 and glial fibrillary acidic protein were significantly correlated at baseline and following treatment (R = 0.399 [95% CI, 0.278-0.508], P < .001 and R = 0.393 [95% CI, 0.254-0.517]; P < .001, respectively). Conclusions and Relevance Significant reductions in plasma biomarkers pTau217 and glial fibrillary acidic protein compared with placebo were observed following donanemab treatment in patients with early symptomatic Alzheimer disease. These easily accessible plasma biomarkers might provide additional evidence of Alzheimer disease pathology change through anti-amyloid therapy. Usefulness in assessing treatment response will require further evaluation. Trial Registration ClinicalTrials.gov Identifier: NCT03367403.
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Affiliation(s)
- Michael J. Pontecorvo
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania,Eli Lilly and Company, Indianapolis, Indiana
| | - Ming Lu
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania,Eli Lilly and Company, Indianapolis, Indiana
| | - Samantha C. Burnham
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania,Eli Lilly and Company, Indianapolis, Indiana
| | | | - Jeffrey L. Dage
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis
| | | | - Emily C. Collins
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania,Eli Lilly and Company, Indianapolis, Indiana
| | | | - Mark A. Mintun
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania,Eli Lilly and Company, Indianapolis, Indiana
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Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease. Pharmaceutics 2022; 14:pharmaceutics14081532. [PMID: 35893788 PMCID: PMC9330777 DOI: 10.3390/pharmaceutics14081532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 02/04/2023] Open
Abstract
The key to current Alzheimer’s disease (AD) therapy is the early diagnosis for prompt intervention, since available treatments only slow the disease progression. Therefore, this lack of promising therapies has called for diagnostic screening tests to identify those likely to develop full-blown AD. Recent AD diagnosis guidelines incorporated core biomarker analyses into criteria, including amyloid-β (Aβ), total-tau (T-tau), and phosphorylated tau (P-tau). Though effective, the accessibility of screening tests involving conventional cerebrospinal fluid (CSF)- and blood-based analyses is often hindered by the invasiveness and high cost. In an attempt to overcome these shortcomings, biomarker profiling research using non-invasive body fluid has shown the potential to capture the pathological changes in the patients’ bodies. These novel non-invasive body fluid biomarkers for AD have emerged as diagnostic and pathological targets. Here, we review the potential peripheral biomarkers, including non-invasive peripheral body fluids of nasal discharge, tear, saliva, and urine for AD.
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Casas-Fernández E, Peña-Bautista C, Baquero M, Cháfer-Pericás C. Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease. Curr Neuropharmacol 2022; 20:1613-1631. [PMID: 34727857 PMCID: PMC9881089 DOI: 10.2174/1570159x19666211102150955] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/09/2021] [Accepted: 10/19/2021] [Indexed: 11/22/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.
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Affiliation(s)
| | | | - Miguel Baquero
- Division of Neurology, University and Polytechnic Hospital La Fe, Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Health Research Institute La Fe, Valencia, Spain;,Address correspondence to this author at the Health Research Institute La Fe, Avenida Fernando Abril Martorell 106, Valencia E46026, Spain;, Tel: +34-96 1246721; E-mail:
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Higher cerebrospinal fluid biomarkers of neuronal injury in HIV-associated neurocognitive impairment. J Neurovirol 2022; 28:438-445. [PMID: 35674935 PMCID: PMC9470698 DOI: 10.1007/s13365-022-01081-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/18/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
We evaluated whether biomarkers of age-related neuronal injury and amyloid metabolism are associated with neurocognitive impairment (NCI) in people with and without HIV (PWH, PWoH). This was a cross-sectional study of virally suppressed PWH and PWoH. NCI was assessed using a validated test battery; global deficit scores (GDS) quantified overall performance. Biomarkers in cerebrospinal fluid (CSF) were quantified by immunoassay: neurofilament light (NFL), total Tau (tTau), phosphorylated Tau 181 (pTau181), amyloid beta (Aβ)42, and Aβ40. Factor analysis was used to reduce biomarker dimensionality. Participants were 256 virally suppressed PWH and 42 PWoH, 20.2% female, 17.1% Black, 7.1% Hispanic, 60.2% non-Hispanic White, and 15.6% other race/ethnicities, mean (SD) age 56.7 (6.45) years. Among PWH, the best regression model for CSF showed that higher tTau (β = 0.723, p = 3.79e-5) together with lower pTau181 (β = −0.510, p = 0.0236) best-predicted poor neurocognitive performance. In univariable analysis, only higher tTau was significantly correlated with poor neurocognitive performance (tTau r = 0.214, p = 0.0006; pTau181 r = 0.00248, p = 0.969). Among PWoH, no CSF biomarkers were significantly associated with worse NCI. Predicted residual error sum of squares (PRESS) analysis showed no evidence of overfitting. Poorer neurocognitive performance in aging PWH was associated with higher CSF tTau, a marker of age-related neuronal injury, but not with biomarkers of amyloid metabolism. The findings suggest that HIV might interact with age-related neurodegeneration to contribute to cognitive decline in PWH.
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40
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Thijssen EH, Verberk IMW, Kindermans J, Abramian A, Vanbrabant J, Ball AJ, Pijnenburg Y, Lemstra AW, van der Flier WM, Stoops E, Hirtz C, Teunissen CE. Differential diagnostic performance of a panel of plasma biomarkers for different types of dementia. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12285. [PMID: 35603139 PMCID: PMC9107685 DOI: 10.1002/dad2.12285] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/07/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
Introduction We explored what combination of blood‐based biomarkers (amyloid beta [Aβ]1‐42/1‐40, phosphorylated tau [p‐tau]181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP]) differentiates Alzheimer's disease (AD) dementia, frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Methods We measured the biomarkers with Simoa in two separate cohorts (n = 160 and n = 152). In one cohort, Aβ1‐42/1‐40 was also measured with mass spectrometry (MS). We assessed the differential diagnostic value of the markers, by logistic regression with Wald's backward selection. Results MS and Simoa Aβ1‐42/1‐40 similarly differentiated AD from controls. The Simoa panel that optimally differentiated AD from FTD consisted of NfL and p‐tau181 (area under the curve [AUC] = 0.94; cohort 1) or NfL, GFAP, and p‐tau181 (AUC = 0.90; cohort 2). For AD from DLB, the panel consisted of NfL, p‐tau181, and GFAP (AUC = 0.88; cohort 1), and only p‐tau181 (AUC = 0.81; cohort 2). Discussion A combination of plasma p‐tau181, NfL, and GFAP, but not Aβ1‐42/1‐40, might be useful to discriminate AD, FTD, and DLB.
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Affiliation(s)
- Elisabeth H Thijssen
- Neurochemistry Laboratory Department of Clinical Chemistry Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Inge M W Verberk
- Neurochemistry Laboratory Department of Clinical Chemistry Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Jana Kindermans
- IRMB-PPC, INM, Univ Montpellier, CHU Montpellier, INSERM CNRS Montpellier France
| | - Adlin Abramian
- Neurochemistry Laboratory Department of Clinical Chemistry Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | | | | | - Yolande Pijnenburg
- Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Afina W Lemstra
- Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
| | | | - Christophe Hirtz
- IRMB-PPC, INM, Univ Montpellier, CHU Montpellier, INSERM CNRS Montpellier France
| | - Charlotte E Teunissen
- Neurochemistry Laboratory Department of Clinical Chemistry Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam the Netherlands
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Li TR, Yao YX, Jiang XY, Dong QY, Yu XF, Wang T, Cai YN, Han Y. β-Amyloid in blood neuronal-derived extracellular vesicles is elevated in cognitively normal adults at risk of Alzheimer's disease and predicts cerebral amyloidosis. Alzheimers Res Ther 2022; 14:66. [PMID: 35550625 PMCID: PMC9097146 DOI: 10.1186/s13195-022-01010-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/27/2022] [Indexed: 02/08/2023]
Abstract
Background Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+). Methods The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification. Results Only nEV Aβ was included in the subsequent analysis. We focused on Aβ42 in the current study. After normalization of nEVs, the levels of Aβ42 were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ42 was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals. Conclusions Our findings suggest the potential use of plasma nEV Aβ42 levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01010-x.
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Affiliation(s)
- Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yun-Xia Yao
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Xue-Yan Jiang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.,School of Biomedical Engineering, Hainan University, Haikou, 570228, China
| | - Qiu-Yue Dong
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Xian-Feng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ting Wang
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yan-Ning Cai
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,School of Biomedical Engineering, Hainan University, Haikou, 570228, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China. .,National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
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De Meyer S, Vanbrabant J, Schaeverbeke JM, Reinartz M, Luckett ES, Dupont P, Van Laere K, Stoops E, Vanmechelen E, Poesen K, Vandenberghe R. Phospho-specific plasma p-tau181 assay detects clinical as well as asymptomatic Alzheimer's disease. Ann Clin Transl Neurol 2022; 9:734-746. [PMID: 35502634 PMCID: PMC9082389 DOI: 10.1002/acn3.51553] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Plasma phosphorylated-tau-181 (p-tau181) reliably detects clinical Alzheimer's disease (AD) as well as asymptomatic amyloid-β (Aβ) pathology, but is consistently quantified with assays using antibody AT270, which cross-reacts with p-tau175. This study investigates two novel phospho-specific assays for plasma p-tau181 and p-tau231 in clinical and asymptomatic AD. METHODS Plasma p-tau species were quantified with Simoa in 44 AD patients, 40 spouse controls and an independent cohort of 151 cognitively unimpaired (CU) elderly who underwent Aβ-PET. Simoa plasma Aβ42 measurements were available in a CU subset (N = 69). Receiver operating characteristics and Aβ-PET associations were used to evaluate biomarker validity. RESULTS The novel plasma p-tau181 and p-tau231 assays did not show cross-reactivity. Plasma p-tau181 accurately detected clinical AD (area under the curve (AUC) = 0.98, 95% CI 0.95-1.00) as well as asymptomatic Aβ pathology (AUC = 0.84, 95% CI 0.76-0.92), while plasma p-tau231 did not (AUC = 0.74, 95% CI 0.63-0.85 and 0.61, 95% CI 0.52-0.71, respectively). Plasma p-tau181, but not p-tau231, detected asymptomatic Aβ pathology more accurately than age, sex and APOE combined (AUC = 0.64). In asymptomatic elderly, correlations between plasma p-tau181 and Aβ pathology were observed throughout the cerebral cortex (ρ = 0.40, p < 0.0001), with focal associations within AD-vulnerable regions, particularly the precuneus. The plasma Aβ42/p-tau181 ratio did not reflect asymptomatic Aβ pathology better than p-tau181 alone. INTERPRETATION The novel plasma p-tau181 assay is an accurate tool to detect clinical as well as asymptomatic AD and provides a phospho-specific alternative to currently employed immunoassays.
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Affiliation(s)
- Steffi De Meyer
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | | | - Jolien M. Schaeverbeke
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Mariska Reinartz
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Emma S. Luckett
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Koen Van Laere
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and PathologyKU LeuvenLeuvenBelgium
- Division of Nuclear MedicineUZ LeuvenLeuvenBelgium
| | | | | | - Koen Poesen
- Laboratory for Molecular Neurobiomarker Research, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Laboratory Medicine DepartmentUZ LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
- Alzheimer Research CentreLeuven Brain Institute (LBI), KU LeuvenLeuvenBelgium
- Neurology DepartmentUZ LeuvenLeuvenBelgium
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Álvarez-Sánchez L, Peña-Bautista C, Baquero M, Cháfer-Pericás C. Novel Ultrasensitive Detection Technologies for the Identification of Early and Minimally Invasive Alzheimer's Disease Blood Biomarkers. J Alzheimers Dis 2022; 86:1337-1369. [PMID: 35213367 DOI: 10.3233/jad-215093] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Single molecule array (SIMOA) and other ultrasensitive detection technologies have allowed the determination of blood-based biomarkers of Alzheimer's disease (AD) for diagnosis and monitoring, thereby opening up a promising field of research. OBJECTIVE To review the published bibliography on plasma biomarkers in AD using new ultrasensitive techniques. METHODS A systematic review of the PubMed database was carried out to identify reports on the use of blood-based ultrasensitive technology to identify biomarkers for AD. RESULTS Based on this search, 86 works were included and classified according to the biomarker determined. First, plasma amyloid-β showed satisfactory accuracy as an AD biomarker in patients with a high risk of developing dementia. Second, plasma t-Tau displayed good sensitivity in detecting different neurodegenerative diseases. Third, plasma p-Tau was highly specific for AD. Fourth, plasma NfL was highly sensitive for distinguishing between patients with neurodegenerative diseases and healthy controls. In general, the simultaneous determination of several biomarkers facilitated greater accuracy in diagnosing AD (Aβ42/Aβ40, p-Tau181/217). CONCLUSION The recent development of ultrasensitive technology allows the determination of blood-based biomarkers with high sensitivity, thus facilitating the early detection of AD through the analysis of easily obtained biological samples. In short, as a result of this knowledge, pre-symptomatic and early AD diagnosis may be possible, and the recruitment process for future clinical trials could be more precise. However, further studies are necessary to standardize levels of blood-based biomarkers in the general population and thus achieve reproducible results among different laboratories.
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Affiliation(s)
| | - Carmen Peña-Bautista
- Alzheimer Disease Research Group, Health Research Institute La Fe, Valencia, Spain
| | - Miguel Baquero
- Division of Neurology, University and Polytechnic Hospital La Fe, Valencia, Spain
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Leuzy A, Mattsson‐Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood-based biomarkers for Alzheimer's disease. EMBO Mol Med 2022; 14:e14408. [PMID: 34859598 PMCID: PMC8749476 DOI: 10.15252/emmm.202114408] [Citation(s) in RCA: 179] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 12/01/2022] Open
Abstract
Neurodegenerative disorders such as Alzheimer's disease (AD) represent a mounting public health challenge. As these diseases are difficult to diagnose clinically, biomarkers of underlying pathophysiology are playing an ever-increasing role in research, clinical trials, and in the clinical work-up of patients. Though cerebrospinal fluid (CSF) and positron emission tomography (PET)-based measures are available, their use is not widespread due to limitations, including high costs and perceived invasiveness. As a result of rapid advances in the development of ultra-sensitive assays, the levels of pathological brain- and AD-related proteins can now be measured in blood, with recent work showing promising results. Plasma P-tau appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and AD dementia) and preclinical AD when combined with Aβ42/Aβ40. Though not AD-specific, blood NfL appears promising for the detection of neurodegeneration and could potentially be used to detect the effects of disease-modifying therapies. This review provides an overview of the progress achieved thus far using AD blood-based biomarkers, highlighting key areas of application and unmet challenges.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Niklas Mattsson‐Carlgren
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Department of NeurologySkåne University HospitalLundSweden
- Wallenberg Centre for Molecular MedicineLund UniversityLundSweden
| | - Sebastian Palmqvist
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Shorena Janelidze
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Jeffrey L Dage
- Stark Neuroscience Research InstituteIndiana University School of MedicineIndianapolisINUSA
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
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McCarter SJ, Lesnick TG, Lowe VJ, Rabinstein AA, Przybelski SA, Algeciras-Schimnich A, Ramanan VK, Jack CR, Petersen RC, Knopman DS, Boeve BF, Kantarci K, Vemuri P, Mielke MM, Graff-Radford J. Association Between Plasma Biomarkers of Amyloid, Tau, and Neurodegeneration with Cerebral Microbleeds. J Alzheimers Dis 2022; 87:1537-1547. [PMID: 35527558 PMCID: PMC9472282 DOI: 10.3233/jad-220158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Cerebral microbleeds (CMBs) are a common vascular pathology associated with future intracerebral hemorrhage. Plasma biomarkers of amyloid, tau, and neurodegeneration may provide a screening avenue to identify those with CMBs, but evidence is conflicting. OBJECTIVE To determine the association between plasma biomarkers (Aβ40, Aβ42, t-tau, p-tau181, p-tau217, neurofilament light chain (NfL)) and CMBs in a population-based study of aging and whether these biomarkers predict higher signal on Aβ-PET imaging in patients with multiple CMBs. METHODS 712 participants from the Mayo Clinic Study of Aging with T2* GRE MRI and plasma biomarkers were included. Biomarkers were analyzed utilizing Simoa (Aβ40, Aβ42, t-tau, NfL) or Meso Scale Discovery (p-tau181, p-tau217) platforms. Cross-sectional associations between CMBs, plasma biomarkers and Aβ-PET were evaluated using hurdle models and multivariable regression models. RESULTS Among the 188 (26%) individuals with≥1 CMB, a lower plasma Aβ42/Aβ40 ratio was associated with more CMBs after adjusting for covariables (IRR 568.5 95% CI 2.8-116,127). No other biomarkers were associated with risk or number CMBs. In 81 individuals with≥2 CMBs, higher plasma t-tau, p-tau181, and p-tau217 all were associated with higher Aβ-PET signal, with plasma p-tau217 having the strongest predictive value (r2 0.603, AIC -53.0). CONCLUSION Lower plasma Aβ42/Aβ40 ratio and higher plasma p-tau217 were associated with brain amyloidosis in individuals with CMBs from the general population. Our results suggest that in individuals with multiple CMBs and/or lobar intracranial hemorrhage that a lower plasma Aβ42/Aβ40 ratio or elevated p-tau217 may indicate underlying cerebral amyloid angiopathy.
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Affiliation(s)
- Stuart J. McCarter
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Center for Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M. Mielke
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
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Teunissen CE, Verberk IMW, Thijssen EH, Vermunt L, Hansson O, Zetterberg H, van der Flier WM, Mielke MM, Del Campo M. Blood-based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurol 2021; 21:66-77. [PMID: 34838239 DOI: 10.1016/s1474-4422(21)00361-6] [Citation(s) in RCA: 492] [Impact Index Per Article: 123.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/06/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022]
Abstract
For many years, blood-based biomarkers for Alzheimer's disease seemed unattainable, but recent results have shown that they could become a reality. Convincing data generated with new high-sensitivity assays have emerged with remarkable consistency across different cohorts, but also independent of the precise analytical method used. Concentrations in blood of amyloid and phosphorylated tau proteins associate with the corresponding concentrations in CSF and with amyloid-PET or tau-PET scans. Moreover, other blood-based biomarkers of neurodegeneration, such as neurofilament light chain and glial fibrillary acidic protein, appear to provide information on disease progression and potential for monitoring treatment effects. Now the question emerges of when and how we can bring these biomarkers to clinical practice. This step would pave the way for blood-based biomarkers to support the diagnosis of, and development of treatments for, Alzheimer's disease and other dementias.
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Affiliation(s)
- Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.
| | - Inge M W Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Elisabeth H Thijssen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Sölvegatan, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, UK; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Hong Kong Center for Neurodegenerative Diseases, Hong Kong Special Administrative Region, China
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, and Department of Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, and Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Marta Del Campo
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands; Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
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47
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Verberk IMW, Misdorp EO, Koelewijn J, Ball AJ, Blennow K, Dage JL, Fandos N, Hansson O, Hirtz C, Janelidze S, Kang S, Kirmess K, Kindermans J, Lee R, Meyer MR, Shan D, Shaw LM, Waligorska T, West T, Zetterberg H, Edelmayer RM, Teunissen CE. Characterization of pre-analytical sample handling effects on a panel of Alzheimer's disease-related blood-based biomarkers: Results from the Standardization of Alzheimer's Blood Biomarkers (SABB) working group. Alzheimers Dement 2021; 18:1484-1497. [PMID: 34845818 PMCID: PMC9148379 DOI: 10.1002/alz.12510] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 09/10/2021] [Accepted: 09/15/2021] [Indexed: 12/11/2022]
Abstract
Introduction Pre‐analytical sample handling might affect the results of Alzheimer's disease blood‐based biomarkers. We empirically tested variations of common blood collection and handling procedures. Methods We created sample sets that address the effect of blood collection tube type, and of ethylene diamine tetraacetic acid plasma delayed centrifugation, centrifugation temperature, aliquot volume, delayed storage, and freeze–thawing. We measured amyloid beta (Aβ)42 and 40 peptides with six assays, and Aβ oligomerization‐tendency (OAβ), amyloid precursor protein (APP)699‐711, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), total tau (t‐tau), and phosphorylated tau181. Results Collection tube type resulted in different values of all assessed markers. Delayed plasma centrifugation and storage affected Aβ and t‐tau; t‐tau was additionally affected by centrifugation temperature. The other markers were resistant to handling variations. Discussion We constructed a standardized operating procedure for plasma handling, to facilitate introduction of blood‐based biomarkers into the research and clinical settings.
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Affiliation(s)
- Inge M W Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Els O Misdorp
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Jannet Koelewijn
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Andrew J Ball
- Quanterix Corporation, Billerica, Massachusetts, USA
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, The Salhgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Christophe Hirtz
- IRMB-LBPC/PPC, INM, Univ Montpellier, CHU Montpellier, INSERM CNRS, Montpellier, France
| | | | | | | | - Jana Kindermans
- IRMB-LBPC/PPC, INM, Univ Montpellier, CHU Montpellier, INSERM CNRS, Montpellier, France
| | - Ryan Lee
- PeopleBio, Seongnam, South Korea
| | | | - Dandan Shan
- Quanterix Corporation, Billerica, Massachusetts, USA
| | - Leslie M Shaw
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Teresa Waligorska
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Tim West
- C2N Diagnostics, St. Louis, Missouri, USA
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, The Salhgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,UK Dementia Research Institute at UCL, London, UK.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | | | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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Yun G, Kim HJ, Kim HG, Lee KM, Hong IK, Kim SH, Rhee HY, Jahng GH, Yoon SS, Park KC, Hwang KS, Lee JS. Association Between Plasma Amyloid-β and Neuropsychological Performance in Patients With Cognitive Decline. Front Aging Neurosci 2021; 13:736937. [PMID: 34759814 PMCID: PMC8573146 DOI: 10.3389/fnagi.2021.736937] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/07/2021] [Indexed: 01/10/2023] Open
Abstract
Objective: To investigate the association between plasma amyloid-β (Aβ) levels and neuropsychological performance in patients with cognitive decline using a highly sensitive nano-biosensing platform. Methods: We prospectively recruited 44 patients with cognitive decline who underwent plasma Aβ analysis, amyloid positron emission tomography (PET) scanning, and detailed neuropsychological tests. Patients were classified into a normal control (NC, n = 25) or Alzheimer’s disease (AD, n = 19) group based on amyloid PET positivity. Multiple linear regression was performed to determine whether plasma Aβ (Aβ40, Aβ42, and Aβ42/40) levels were associated with neuropsychological test results. Results: The plasma levels of Aβ42/40 were significantly different between the NC and AD groups and were the best predictor of amyloid PET positivity by receiver operating characteristic curve analysis [area under the curve of 0.952 (95% confidence interval, 0.892–1.000)]. Although there were significant differences in the neuropsychological performance of cognitive domains (language, visuospatial, verbal/visual memory, and frontal/executive functions) between the NC and AD groups, higher levels of plasma Aβ42/40 were negatively correlated only with verbal and visual memory performance. Conclusion: Our results demonstrated that plasma Aβ analysis using a nano-biosensing platform could be a useful tool for diagnosing AD and assessing memory performance in patients with cognitive decline.
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Affiliation(s)
- Gyihyaon Yun
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Hye Jin Kim
- Department of Clinical Pharmacology and Therapeutics, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Il Ki Hong
- Department of Nuclear Medicine, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Sang Hoon Kim
- Department of Otorhinolaryngology, Head and Neck Surgery, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Sung Sang Yoon
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Key-Chung Park
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Kyo Seon Hwang
- Department of Clinical Pharmacology and Therapeutics, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea
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Janelidze S, Teunissen CE, Zetterberg H, Allué JA, Sarasa L, Eichenlaub U, Bittner T, Ovod V, Verberk IMW, Toba K, Nakamura A, Bateman RJ, Blennow K, Hansson O. Head-to-Head Comparison of 8 Plasma Amyloid-β 42/40 Assays in Alzheimer Disease. JAMA Neurol 2021; 78:1375-1382. [PMID: 34542571 PMCID: PMC8453354 DOI: 10.1001/jamaneurol.2021.3180] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Importance Blood-based tests for brain amyloid-β (Aβ) pathology are needed for widespread implementation of Alzheimer disease (AD) biomarkers in clinical care and to facilitate patient screening and monitoring of treatment responses in clinical trials. Objective To compare the performance of plasma Aβ42/40 measured using 8 different Aβ assays when detecting abnormal brain Aβ status in patients with early AD. Design, Setting, and Participants This study included 182 cognitively unimpaired participants and 104 patients with mild cognitive impairment from the BioFINDER cohort who were enrolled at 3 different hospitals in Sweden and underwent Aβ positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) and plasma collection from 2010 to 2014. Plasma Aβ42/40 was measured using an immunoprecipitation-coupled mass spectrometry developed at Washington University (IP-MS-WashU), antibody-free liquid chromatography MS developed by Araclon (LC-MS-Arc), and immunoassays from Roche Diagnostics (IA-Elc); Euroimmun (IA-EI); and Amsterdam University Medical Center, ADx Neurosciences, and Quanterix (IA-N4PE). Plasma Aβ42/40 was also measured using an IP-MS-based method from Shimadzu in 200 participants (IP-MS-Shim) and an IP-MS-based method from the University of Gothenburg (IP-MS-UGOT) and another immunoassay from Quanterix (IA-Quan) among 227 participants. For validation, 122 participants (51 cognitively normal, 51 with mild cognitive impairment, and 20 with AD dementia) were included from the Alzheimer Disease Neuroimaging Initiative who underwent Aβ-PET and plasma Aβ assessments using IP-MS-WashU, IP-MS-Shim, IP-MS-UGOT, IA-Elc, IA-N4PE, and IA-Quan assays. Main Outcomes and Measures Discriminative accuracy of plasma Aβ42/40 quantified using 8 different assays for abnormal CSF Aβ42/40 and Aβ-PET status. Results A total of 408 participants were included in this study. In the BioFINDER cohort, the mean (SD) age was 71.6 (5.6) years and 49.3% of the cohort were women. When identifying participants with abnormal CSF Aβ42/40 in the whole cohort, plasma IP-MS-WashU Aβ42/40 showed significantly higher accuracy (area under the receiver operating characteristic curve [AUC], 0.86; 95% CI, 0.81-0.90) than LC-MS-Arc Aβ42/40, IA-Elc Aβ42/40, IA-EI Aβ42/40, and IA-N4PE Aβ42/40 (AUC range, 0.69-0.78; P < .05). Plasma IP-MS-WashU Aβ42/40 performed significantly better than IP-MS-UGOT Aβ42/40 and IA-Quan Aβ42/40 (AUC, 0.84 vs 0.68 and 0.64, respectively; P < .001), while there was no difference in the AUCs between IP-MS-WashU Aβ42/40 and IP-MS-Shim Aβ42/40 (0.87 vs 0.83; P = .16) in the 2 subcohorts where these biomarkers were available. The results were similar when using Aβ-PET as outcome. Plasma IPMS-WashU Aβ42/40 and IPMS-Shim Aβ42/40 showed highest coefficients for correlations with CSF Aβ42/40 (r range, 0.56-0.65). The BioFINDER results were replicated in the Alzheimer Disease Neuroimaging Initiative cohort (mean [SD] age, 72.4 [5.4] years; 43.4% women), where the IP-MS-WashU assay performed significantly better than the IP-MS-UGOT, IA-Elc, IA-N4PE, and IA-Quan assays but not the IP-MS-Shim assay. Conclusions and Relevance The results from 2 independent cohorts indicate that certain MS-based methods performed better than most of the immunoassays for plasma Aβ42/40 when detecting brain Aβ pathology.
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Affiliation(s)
- Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Henrik Zetterberg
- Institute of Neuroscience & 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, University College London Institute of Neurology, London, United Kingdom,United Kingdom Dementia Research Institute at University College London, London, United Kingdom
| | | | - Leticia Sarasa
- Mass Spectrometry Laboratory, Araclon Biotech, Zaragoza, Spain
| | | | | | - Vitaliy Ovod
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | - Inge M. W. Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Kenji Toba
- National Center for Geriatrics and Gerontology, Obu, Aichi, Japan,Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Akinori Nakamura
- Center for Development of Advanced Medicine for Dementia, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden,Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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50
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Fully automated chemiluminescence enzyme immunoassays showing high correlation with immunoprecipitation mass spectrometry assays for β-amyloid (1-40) and (1-42) in plasma samples. Biochem Biophys Res Commun 2021; 576:22-26. [PMID: 34478915 DOI: 10.1016/j.bbrc.2021.08.066] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/23/2021] [Indexed: 11/24/2022]
Abstract
Blood based β-amyloid (Aβ) assays that can predict amyloid positivity in the brain are in high demand. Current studies that utilize immunoprecipitation mass spectrometry assay (IP-MS), which has high specificity for measuring analytes, have revealed that precise plasma Aβ assays have the potential to detect amyloid positivity in the brain. In this study, we developed plasma Aβ40 and Aβ42 immunoassays using a fully automated immunoassay platform that is used in routine clinical practice. Our assays showed high sensitivity (limit of quantification: 2.46 pg/mL [Aβ40] and 0.16 pg/mL [Aβ42]) and high reproducibility within-run (coefficients of variation [CVs]: <3.7% [Aβ40] and <2.0% [Aβ42]) and within-laboratory (CVs: <4.6% [Aβ40] and <5.3% [Aβ42]). The interference from plasma components was less than 10%, and the cross-reactivity with various lengths of Aβ peptides was less than 0.5%. In addition, we found a significant correlation between the IP-MS method and our immunoassay (correlation coefficients of Pearson's r: 0.91 [Aβ40] and 0.82 [Aβ42]). Our new method to quantify plasma Aβ40 and Aβ42 provides clinicians and patients with a way to continuously monitor disease progression.
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