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| Pàgina inicial > Articles > Articles publicats > Establishing In-House Cutoffs of CSF Alzheimer's Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort |
(Universitat Internacional de Catalunya. Ace Alzheimer Center Barcelona) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Universitat Internacional de Catalunya. Ace Alzheimer Center Barcelona) (Universitat Internacional de Catalunya. Ace Alzheimer Center Barcelona) (Universitat Internacional de Catalunya. Ace Alzheimer Center Barcelona) (Universitat Internacional de Catalunya. Ace Alzheimer Center Barcelona) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Institut d'Investigació Biomèdica Sant Pau) (Institut d'Investigació Biomèdica Sant Pau) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas) (Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas)| Data: | 2022 |
| Resum: | 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). 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. |
| Ajuts: | Instituto de Salud Carlos III FI20/00215 Instituto de Salud Carlos III PI13/02434 Instituto de Salud Carlos III PI16/01861 Instituto de Salud Carlos III PI17/01474 Instituto de Salud Carlos III PI19/01240 Instituto de Salud Carlos III PI19/01301 Instituto de Salud Carlos III PI19/0335 Ministerio de Ciencia, Innovación y Universidades FJC2018-036012-I European Commission 115975 European Commission 115985 European Commission AC17/00100 European Commission AC19/00097 |
| Nota: | Altres ajuts: FACEHBI private sponsors (Grifols SA, Life Molecular Imaging GmbH, Araclon Biotech, Laboratorios Echevarne SA i ACE Alzheimer Center Barcelona). |
| Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.  |
| Llengua: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Matèria: | Cerebrospinal fluid ; Alzheimer's disease ; Chemiluminescent enzyme immunoassay ; Lumipulse ; MCI |
| Publicat a: | International journal of molecular sciences, Vol. 23 Núm. 13 (july 2022) , p. 6891, ISSN 1422-0067 |
