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A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation
Julien Dumurgier1, Séverine Sabia2, Henrik Zetterberg2
1From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom. julien.dumurgier@inserm.fr.
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.
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