0cb3840c696af234cdb7e22924db1344 284458.pdf 450d8bfd0f5e238319137efb68cd4efe6a37009d 284458.pdf 15c133270ca3fea4b0ff94a270f2dd10065fbda64df5af867b7b51f618bef513 284458.pdf Title: Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort Subject: 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 A1-42, A1-40, 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 A1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and A1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and A1-42/A1-40 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 A1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for A1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for A1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the A1-42/A1-40 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 individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects. Keywords: cerebrospinal fluid; Alzheimer’s disease; chemiluminescent enzyme immunoassay; Lumipulse; MCI Author: Adelina Orellana, Pablo García-González, Sergi Valero, Laura Montrreal, Itziar de Rojas, Isabel Hernández, Maitee Rosende-Roca, Liliana Vargas, Juan Pablo Tartari, Ester Esteban-De Antonio, Urszula Bojaryn, Leire Narvaiza, Emilio Alarcón-Martín, Montserrat Alegret, Daniel Alcolea, Alberto Lleó, Lluís Tárraga, Vanesa Pytel, Amanda Cano, Marta Marquié, Mercè Boada and Agustín Ruiz Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Jun 23 10:18:58 2022 CEST ModDate: Thu Jun 23 10:25:16 2022 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2107223 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- XYDCIJ+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 MFWPRN+VnURWPalladioL Type 1 Custom yes yes no 15 0 CSBDNX+EURM10 Type 1 Builtin yes yes no 19 0 LOZKTA+URWPalladioL-Bold Type 1 Custom yes yes no 23 0 ABKIQK+URWPalladioL-Roma Type 1 Custom yes yes no 28 0 MNLEZY+URWPalladioL-Ital Type 1 Custom yes yes no 32 0 QMDDXF+CMSY10 Type 1 Builtin yes yes no 36 0 IRWHZX+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 54 0 CKUPDE+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 57 0 WBVKDJ+URWPalladioL-BoldItal Type 1 Custom yes yes no 82 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-02-21 02:39:14 CET RepresentationInformation: 284458.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-02-20 11:14:10 CET Size: 2107223 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 341 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Results Destination: section.2 Children: Item: Title: Cutoffs of CSF Biomarkers for Both Immunoassays Destination: subsection.2.1 Item: Title: Comparison of Immunoassays Destination: subsection.2.2 Item: Title: Concordance between Immunoassays Destination: subsection.2.3 Item: Title: MCI Progression in AT(N) Categories Destination: subsection.2.4 Item: Title: Discussion Destination: section.3 Item: Title: Methods Destination: section.4 Children: Item: Title: Participants Destination: subsection.4.1 Item: Title: CSF Sampling and Analysis Destination: subsection.4.2 Item: Title: Ethical Considerations Destination: subsection.4.3 Children: Item: Title: Study 1: Determination of Cutoff for Both Immunoassays Destination: subsubsection.4.3.1 Item: Title: Study 2: Comparison of Immunoassays Destination: subsubsection.4.3.2 Item: Title: Study 3: Concordance between Immunoassays Destination: subsubsection.4.3.3 Item: Title: Study 4: MCI Progression Destination: subsubsection.4.3.4 Item: Title: Statistical Analysis Destination: subsection.4.4 Item: Title: References Destination: subsection.4.4 Info: Title: Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort Author: Adelina Orellana, Pablo García-González, Sergi Valero, Laura Montrreal, Itziar de Rojas, Isabel Hernández, Maitee Rosende-Roca, Liliana Vargas, Juan Pablo Tartari, Ester Esteban-De Antonio, Urszula Bojaryn, Leire Narvaiza, Emilio Alarcón-Martín, Montserrat Alegret, Daniel Alcolea, Alberto Lleó, Lluís Tárraga, Vanesa Pytel, Amanda Cano, Marta Marquié, Mercè Boada and Agustín Ruiz Subject: 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 A1-42, A1-40, 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 A1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and A1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and A1-42/A1-40 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 A1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for A1-40 and 0.96 for p181TAU. Passing Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for A1-40. Bland Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the A1-42/A1-40 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 individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects. 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