A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging.
Journal
Neurology
ISSN: 1526-632X
Titre abrégé: Neurology
Pays: United States
ID NLM: 0401060
Informations de publication
Date de publication:
16 08 2022
16 08 2022
Historique:
received:
08
09
2021
accepted:
30
03
2022
entrez:
15
8
2022
pubmed:
16
8
2022
medline:
18
8
2022
Statut:
ppublish
Résumé
To elaborate a new algorithm to establish a standardized method to define cutoffs for CSF biomarkers of Alzheimer disease (AD) by validating the algorithm against CSF classification derived from PET imaging. Low and high levels of CSF phosphorylated tau were first identified to establish optimal cutoffs for CSF β-amyloid (Aβ) peptide biomarkers. These Aβ cutoffs were then used to determine cutoffs for CSF tau and phosphorylated tau markers. We compared this algorithm to a reference method, based on tau and amyloid PET imaging status (ADNI study), and then applied the algorithm to 10 large clinical cohorts of patients. A total of 6,922 patients with CSF biomarker data were included (mean [SD] age: 70.6 [8.5] years, 51.0% women). In the ADNI study population (n = 497), the agreement between classification based on our algorithm and the one based on amyloid/tau PET imaging was high, with Cohen's kappa coefficient between 0.87 and 0.99. Applying the algorithm to 10 large cohorts of patients (n = 6,425), the proportion of persons with AD ranged from 25.9% to 43.5%. The proposed novel, pragmatic method to determine CSF biomarker cutoffs for AD does not require assessment of other biomarkers or assumptions concerning the clinical diagnosis of patients. Use of this standardized algorithm is likely to reduce heterogeneity in AD classification.
Sections du résumé
BACKGROUND AND OBJECTIVES
To elaborate a new algorithm to establish a standardized method to define cutoffs for CSF biomarkers of Alzheimer disease (AD) by validating the algorithm against CSF classification derived from PET imaging.
METHODS
Low and high levels of CSF phosphorylated tau were first identified to establish optimal cutoffs for CSF β-amyloid (Aβ) peptide biomarkers. These Aβ cutoffs were then used to determine cutoffs for CSF tau and phosphorylated tau markers. We compared this algorithm to a reference method, based on tau and amyloid PET imaging status (ADNI study), and then applied the algorithm to 10 large clinical cohorts of patients.
RESULTS
A total of 6,922 patients with CSF biomarker data were included (mean [SD] age: 70.6 [8.5] years, 51.0% women). In the ADNI study population (n = 497), the agreement between classification based on our algorithm and the one based on amyloid/tau PET imaging was high, with Cohen's kappa coefficient between 0.87 and 0.99. Applying the algorithm to 10 large cohorts of patients (n = 6,425), the proportion of persons with AD ranged from 25.9% to 43.5%.
DISCUSSION
The proposed novel, pragmatic method to determine CSF biomarker cutoffs for AD does not require assessment of other biomarkers or assumptions concerning the clinical diagnosis of patients. Use of this standardized algorithm is likely to reduce heterogeneity in AD classification.
Identifiants
pubmed: 35970577
pii: WNL.0000000000200735
doi: 10.1212/WNL.0000000000200735
pmc: PMC9484605
doi:
Substances chimiques
Amyloid beta-Peptides
0
Biomarkers
0
Peptide Fragments
0
tau Proteins
0
Types de publication
Journal Article
Validation Study
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e669-e678Subventions
Organisme : NIA NIH HHS
ID : R01 AG056477
Pays : United States
Informations de copyright
Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
Références
Alzheimers Dement. 2017 Mar;13(3):274-284
pubmed: 28341065
J Alzheimers Dis. 2013;33(3):807-22
pubmed: 23034521
J Neuropathol Exp Neurol. 2012 Apr;71(4):266-73
pubmed: 22437338
Nat Med. 2020 Aug;26(8):1256-1263
pubmed: 32572268
Alzheimers Dement. 2014 Nov;10(6):808-17
pubmed: 25150736
Alzheimers Dement. 2013 May;9(3):251-61
pubmed: 23622690
Alzheimers Res Ther. 2020 Aug 15;12(1):97
pubmed: 32799929
Alzheimers Dement. 2013 Jul;9(4):406-13
pubmed: 23141384
Neurology. 2020 Dec 8;95(23):e3104-e3116
pubmed: 32873693
Alzheimers Dement. 2015 Jan;11(1):58-69
pubmed: 24795085
J Neurol Neurosurg Psychiatry. 2019 Oct;90(10):1117-1123
pubmed: 31167811
Neurology. 2016 Jan 5;86(1):50-8
pubmed: 26468410
Alzheimers Dement. 2018 Apr;14(4):535-562
pubmed: 29653606
Nat Med. 2020 Mar;26(3):314-316
pubmed: 32132715
J Neurol. 2014 Jan;261(1):144-51
pubmed: 24162039
Front Aging Neurosci. 2017 Sep 12;9:289
pubmed: 28955218
Neurology. 2021 Feb 2;96(5):e650-e661
pubmed: 33262228
Alzheimers Dement. 2015 Feb;11(2):207-15
pubmed: 25022535
J Nucl Med. 2012 Mar;53(3):378-84
pubmed: 22331215
Biomark Med. 2012 Aug;6(4):391-400
pubmed: 22917141
Alzheimers Dement. 2011 May;7(3):270-9
pubmed: 21514249
Alzheimers Res Ther. 2019 Nov 23;11(1):91
pubmed: 31759396
Alzheimers Dement. 2015 Jul;11(7):865-84
pubmed: 26194320
Alzheimers Dement. 2015 Dec;11(12):1470-1479
pubmed: 26079415
Neurology. 2022 Mar 8;98(10):e1031-e1039
pubmed: 34937778
Arch Gen Psychiatry. 2004 Jan;61(1):95-102
pubmed: 14706948
Clin Chem. 2018 Mar;64(3):576-585
pubmed: 29208658
Acta Neuropathol. 2012 Jul;124(1):23-35
pubmed: 22526019
Neurology. 2021 Sep 14;97(11):543-544
pubmed: 34233942
Alzheimers Dement. 2021 Dec 22;:
pubmed: 34936194
Neurology. 2021 Apr 6;96(14):e1844-e1854
pubmed: 33589537
Neurology. 2021 Jul 15;:
pubmed: 34266917
Nat Med. 2020 Mar;26(3):379-386
pubmed: 32123385
Neurology. 2022 Feb 1;98(5):e506-e517
pubmed: 34810247
Alzheimers Res Ther. 2019 Oct 10;11(1):83
pubmed: 31601267
J Alzheimers Dis. 2012;31(1):13-20
pubmed: 22495345
Alzheimers Dement. 2018 Nov;14(11):1460-1469
pubmed: 29501462
Chest. 1998 Sep;114(3):946-7
pubmed: 9743196
Alzheimers Res Ther. 2019 Dec 7;11(1):100
pubmed: 31810489
Alzheimers Dement. 2017 Mar;13(3):205-216
pubmed: 27697430
Alzheimers Dement. 2012 Jan;8(1):1-13
pubmed: 22265587
Alzheimers Dement. 2011 May;7(3):263-9
pubmed: 21514250
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
Neurology. 2016 Aug 2;87(5):539-47
pubmed: 27371494
Neuroimage. 2017 Aug 15;157:448-463
pubmed: 28587897
Alzheimers Res Ther. 2019 Sep 12;11(1):78
pubmed: 31511058