A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging

OBJECTIVE: To elaborate a new algorithm to establish a standardized method to define cuff-offs for CSF biomarkers of Alzheimer's 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 cut-offs for CSF amyloid-β peptide (Aβ) biomarkers. These Aβ cut-offs were then used to determine cut-offs 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 subjects with CSF biomarkers 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 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 biomarkers cut-offs 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