Inflammatory biomarkers in Alzheimer's disease plasma
Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers. A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. Th...
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| Published in | Alzheimer's & dementia Vol. 15; no. 6; pp. 776 - 787 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.06.2019
Alzheimer's Association / Wiley Elsevier, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1552-5260 1552-5279 1552-5279 |
| DOI | 10.1016/j.jalz.2019.03.007 |
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| Abstract | Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers.
A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.
Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).
Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. |
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| AbstractList | Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers.
A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.
Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).
Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers.INTRODUCTIONPlasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers.A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.METHODSA hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).RESULTSTen analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.DISCUSSIONPlasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers.A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. Introduction Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well‐established plasma markers. Methods A hypothesis‐led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. Results Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP‐1, eotaxin‐1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP‐1, eotaxin‐1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). Discussion Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. Introduction: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers.Methods: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.Results: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).Discussion: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. |
| Author | Kettunen, Petronella Zajkowska, Zuzanna Engelborghs, Sebastiaan Deith, Catherine Reynolds, Richard Drevets, Wayne C. Sporn, Jonathan Legido-Quigley, Cristina Benjamin, Jithen Bos, Isabelle Sala, Isabel Simmons, Camilla Leckey, Claire O'Hagan, Caroline Oladejo, Akintayo Knight, Clare Byrom, Heidi Plath, Niels Clare, Stuart Nye, Jeffrey S. Correia, Marta M. O'Connor, Jason Bhattacharya, Anindya Jones, Declan Thomsen, Christian Campbell, Brian Turkheimer, Federico Fryatt, Gemma Van Loo, Victoria Cavanagh, Jonathan Hughes, Zoe Zorn, Stevin Ribe, Elena Molinuevo, José Luis Mada, Francisca Nevado-Holgado, Alejo Martinez-Lage, Pablo Vandenberghe, Rik Randall, Karen Vicente-Rodriguez, Marta Parker, Christine A. Richardson, Jill C. Egebjerg, Jan Duerr, James Gastambide, Francois Teunissen, Charlotte Boddeke, H.W.G.M. Morgan, Angharad R. Scouller, Paul Jeggo, Ross Turner, Lorinda Mondelli, Valeria Patel, Jai Bullmore, Edward T. Dobricic, Valerija Carruthers, Nick Foster, Andrew C. Westwood, Sarah Morgan, B. Paul Cattaneo, Annamaria Worrell, Courtney Willi |
| AuthorAffiliation | z Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain a Systems Immunity Research Institute and UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Cardiff, UK n Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium s Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain m Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany c Department of Radiology and Nuclear Medicine, VU University Medical, Amsterdam, the Netherlands hh Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands p University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden cc Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland ff 1st Department of Neurology, AHEPA University Hospital, Mak |
| AuthorAffiliation_xml | – name: x Hopitaux Universitaires Geneve and Universite de Geneve, Geneva, Switzerland – name: c Department of Radiology and Nuclear Medicine, VU University Medical, Amsterdam, the Netherlands – name: l IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy – name: p University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden – name: ff 1st Department of Neurology, AHEPA University Hospital, Makedonia, Thessaloniki, Greece – name: k University of Geneva, Geneva, Switzerland – name: n Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium – name: v Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, Barcelona, Spain – name: e Max Planck Institute for Molecular Genetics, Berlin, Germany – name: z Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain – name: cc Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland – name: g Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands – name: nn UK Dementia Research Institute, London, UK – name: w Department of Psychiatry, Old Age Psychiatry, Lausanne University Hospital, Lausanne, Switzerland – name: gg Department of Clinical Chemistry, Neurochemistry lab, Amsterdam University Medical Centers, Amsterdam, the Netherlands – name: o Division of Clinical Geriatrics, Department of Neurobiology, Caring Sciences and Society, Karolinska Institutet, Stockholm, Sweden – name: y Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK – name: t CITA-Alzheimer Foundation, San Sebastian, Spain – name: dd Center for Research and Advanced Therapies. CITA-Alzheimer Foundation, San Sebastian, Spain – name: kk Clinical Neurochemistry Lab, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, Mölndal, Sweden – name: mm Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK – name: f Aix-Marseille University, APHM, Institute Neurosci System, Pharmacology, Marseille, France – name: s Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain – name: ee University Hospital Leuven, Leuven, Belgium – name: q Department of Old Age Psychiatry & Psychotic Disorders, Medical University of Lodz, Lodz, Poland – name: ll Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden – name: a Systems Immunity Research Institute and UK Dementia Research Institute Cardiff, School of Medicine, Cardiff University, Cardiff, UK – name: u Department of Medicine, Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy – name: hh Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands – name: aa Alzheimer Center, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the Netherlands – name: h Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany – name: b Department of Psychiatry, University of Oxford, Oxford, UK – name: bb Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium – name: m Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany – name: ii NVS-Department, Section of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden – name: i Department of Neurology, Hospital Network Antwerp (ZNA), Antwerp, Belgium – name: j Reference Center for Biological Markers of Dementia, Institute Born-Bunge, Antwerp, Belgium – name: r School of Public Health, Imperial College London, London, UK – name: jj Section for Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden – name: d UCL Institutes of Neurology and Healthcare Engineering, University College London, London, UK |
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| Copyright | 2019 The Authors 2019 The Alzheimer's Association Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License 2019 The Authors 2019 |
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| Publisher | Elsevier Inc Alzheimer's Association / Wiley Elsevier, Inc |
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| Snippet | Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no... Introduction Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there... Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no... Introduction: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there... |
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| SubjectTerms | Aged Alzheimer Disease - blood Alzheimer Disease - diagnosis Alzheimer's disease Amyloid beta-Peptides - blood Biomarker Biomarkers - blood Clinical Medicine Cognitive Dysfunction - blood Cognitive Dysfunction - diagnosis Cognitive science Cohort Studies Complement Complement Factor B Complement Factor H Humans Inflammation Internationality Klinisk medicin Neuroscience Plasma Prognosis |
| Title | Inflammatory biomarkers in Alzheimer's disease plasma |
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