Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort

• Published in: International journal of molecular sciences 2022-07, Vol.23 (13), p.6891
• Main Authors: Orellana, Adelina, García-González, Pablo, Valero, Sergi, Montrreal, Laura, de Rojas, Itziar, Hernández, Isabel, Rosende-Roca, Maitee, Vargas, Liliana, Tartari, Juan Pablo, Esteban-De Antonio, Ester, Bojaryn, Urszula, Narvaiza, Leire, Alarcón-Martín, Emilio, Alegret, Montserrat, Alcolea, Daniel, Lleó, Alberto, Tárraga, Lluís, Pytel, Vanesa, Cano, Amanda, Marquié, Marta, Boada, Mercè, Ruiz, Agustín
• Format: Article
• Language: English
• Subjects: Accuracy; Agreements; Alzheimer's disease; Amyloid; Amyloidosis; Automation; Biomarkers; Categories; Cerebrospinal fluid; Chemiluminescence; Dementia; Dementia disorders; Enzyme immunoassay; Enzyme-linked immunosorbent assay; Immunoassay; Laboratories; Medical imaging; Patients; Positron emission; Positron emission tomography; Statistical models; Tau protein; Tomography
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms23136891

Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815−27). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD