TY - JOUR
T1 - Establishing In-House Cutoffs of CSF Alzheimer's Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort
AU - Orellana, Adelina
AU - 0000-0003-0125-5403 García-gonzález, Pablo
AU - Valero, Sergi
AU - Montrreal, Laura
AU - De Rojas, Itziar
AU - Hernández, Isabel
AU - Rosende-roca, Maitee
AU - Vargas, Liliana
AU - Tartari, Juan Pablo
AU - Esteban de Antonio, Ester
AU - Bojaryn, Urszula
AU - Narvaiza, Leire
AU - Alarcón-Martín, Emilio
AU - Alegret, Montserrat
AU - Alcolea, Daniel
AU - Lleó, Alberto
AU - Tárraga, Lluís
AU - Pytel, Vanesa
AU - Cano, Amanda
AU - Marquié, Marta.
AU - Boada, Mercè
AU - Ruiz, Agustín
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cerebrospinal fluid
KW - Alzheimer's disease
KW - Chemiluminescent enzyme immunoassay
KW - Lumipulse
KW - MCI
U2 - 10.3390/ijms23136891
DO - 10.3390/ijms23136891
M3 - Article
C2 - 35805894
SN - 1422-0067
VL - 23
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 13
ER -