Blood biomarkers for Alzheimer's disease with the Lumipulse automated platform: Age-effect and clinical value interpretation.

Alzheimer’s Disease Amyloid-β 1–42/1–40 ratio Blood biomarkers Chemiluminescence immunoassay Neurofilament light chain Phosphorylated tau 181

Journal

Clinica chimica acta; international journal of clinical chemistry
ISSN: 1873-3492
Titre abrégé: Clin Chim Acta
Pays: Netherlands
ID NLM: 1302422

Informations de publication

Date de publication:
21 Oct 2024
Historique:
received: 20 08 2024
revised: 20 10 2024
accepted: 20 10 2024
medline: 24 10 2024
pubmed: 24 10 2024
entrez: 23 10 2024
Statut: aheadofprint

Résumé

Advances in analytical methods have recently paved the way to Alzheimer's disease (AD) biomarkers testing in blood along with the more established CSF testing. To ensure a forthcoming application of this low-invasive diagnostic that might allow to recognize early onset of dementia, appropriate pathological cut-points need to be defined. In this cross-sectional study we measured blood and CSF neurofilament light chain (NFL), phosphorylated tau (pTau 181), Amyloid-β1-42 (AB 1-42) and Amyloid-β1-40 (AB 1-40) on a fully automated chemiluminescent platform (Lumipulse, Fujirebio) in 80 cognitively impaired patients and 55 cognitively unimpaired subjects. Clinical cut points were calculated with receiver-operator characteristic (ROC) curve analysis and a head-to-head comparison of blood and CSF testing was performed. Blood NFL best discriminant thresholds to distinguish neurodegenerative diseases from controls varied age-dependently, being 19 and 33 pg/mL in subjects 50-65 years and > 65 years respectively. AD was best framed by AB 1-42/1-40 ratio < 0.079 and ptau181 > 1 pg/mL. Though a strong correlation for all biomarkers, only blood AB ratio was equal to CSF testing for AD diagnosis. The specific context of use might be considered to define the cut-offs of blood biomarkers of neurodegenerative diseases. Future efforts towards reference materials for each AD blood biomarker will improve clinical cut-offs.

Sections du résumé

BACKGROUND BACKGROUND
Advances in analytical methods have recently paved the way to Alzheimer's disease (AD) biomarkers testing in blood along with the more established CSF testing. To ensure a forthcoming application of this low-invasive diagnostic that might allow to recognize early onset of dementia, appropriate pathological cut-points need to be defined.
METHODS METHODS
In this cross-sectional study we measured blood and CSF neurofilament light chain (NFL), phosphorylated tau (pTau 181), Amyloid-β1-42 (AB 1-42) and Amyloid-β1-40 (AB 1-40) on a fully automated chemiluminescent platform (Lumipulse, Fujirebio) in 80 cognitively impaired patients and 55 cognitively unimpaired subjects. Clinical cut points were calculated with receiver-operator characteristic (ROC) curve analysis and a head-to-head comparison of blood and CSF testing was performed.
RESULTS RESULTS
Blood NFL best discriminant thresholds to distinguish neurodegenerative diseases from controls varied age-dependently, being 19 and 33 pg/mL in subjects 50-65 years and > 65 years respectively. AD was best framed by AB 1-42/1-40 ratio < 0.079 and ptau181 > 1 pg/mL. Though a strong correlation for all biomarkers, only blood AB ratio was equal to CSF testing for AD diagnosis.
CONCLUSIONS CONCLUSIONS
The specific context of use might be considered to define the cut-offs of blood biomarkers of neurodegenerative diseases. Future efforts towards reference materials for each AD blood biomarker will improve clinical cut-offs.

Identifiants

pubmed: 39442787
pii: S0009-8981(24)02267-8
doi: 10.1016/j.cca.2024.120014
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

120014

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [G.M., C.G., C.C., A.C., G.S., M.Z., S.M., C.F.Z., M.P., M.C., D.B. declarations of interest: none. M.Pu. reports travel grants, consultancy, and board membership from Almirall, Teva, Sanofi Genzyme, Merck Serono, Biogen Italy, Novartis, Bristol Myers Squibb, Janssen, Sandoz and Alexion. P.G. reports grant, consultancy, and board membership for Almirall, Teva, Sanofi Genzyme, Merck Serono, Biogen Italy, Novartis, Roche, Bristol Myers Squibb, Janssen, Sandoz and Alexion. E. P. reports payments or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Biogen and Roche; support for attending meetings and/or travels from Roche, Biogen, and Alexion; payments for participation on a Data Safety Monitoring Board or Advisory Board from Alexion, UCB Biopharma, and Sanofi. A.A. has received compensation for consultancy and speaker related activities from UCB, Britannia, AbbVie, Zambon, Bial, Neuroderm, Theravance Biopharma, Roche; he receives research support from Horizon 2020 - Ministry of Education University and Research (MIUR), Ministry of Health, Cariparo Foundation, Fondazione Grigioni per il Morbo di Parkinson.].

Auteurs

Giulia Musso (G)

Department of Medicine - DIMED, University of Padova, via Giustiniani, 2, 35128 Padova Italy; Laboratory Medicine, University-Hospital of Padova, via Giustiniani, 2, 35128 Padova, Italy. Electronic address: giulia.musso@unipd.it.

Carlo Gabelli (C)

Regional Brain Aging Center, University-Hospital of Padova, via Giustiniani, 2, 35128 Padova, Italy.

Marco Puthenparampil (M)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Chiara Cosma (C)

Department of Medicine - DIMED, University of Padova, via Giustiniani, 2, 35128 Padova Italy.

Annachiara Cagnin (A)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Paolo Gallo (P)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Gianni Sorarù (G)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Elena Pegoraro (E)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Martina Zaninotto (M)

QI.LAB.MED, Spin-off of the University of Padova, via Antoniana, 220/E, 35011 Campodarsego, Italy.

Angelo Antonini (A)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Stefania Moz (S)

Laboratory Medicine, University-Hospital of Padova, via Giustiniani, 2, 35128 Padova, Italy.

Carlo Federico Zambon (C)

Department of Medicine - DIMED, University of Padova, via Giustiniani, 2, 35128 Padova Italy; Laboratory Medicine, University-Hospital of Padova, via Giustiniani, 2, 35128 Padova, Italy.

Mario Plebani (M)

Department of Medicine - DIMED, University of Padova, via Giustiniani, 2, 35128 Padova Italy; QI.LAB.MED, Spin-off of the University of Padova, via Antoniana, 220/E, 35011 Campodarsego, Italy.

Maurizio Corbetta (M)

Department of Neurosciences, University of Padova, via Giustiniani, 5, 35128 Padova, Italy.

Daniela Basso (D)

Department of Medicine - DIMED, University of Padova, via Giustiniani, 2, 35128 Padova Italy; Laboratory Medicine, University-Hospital of Padova, via Giustiniani, 2, 35128 Padova, Italy.

Classifications MeSH