RT Journal Article
T1 A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers
A1 Hernández Lorenzo, Laura
A1 Gil‐Moreno, Maria José
A1 Ortega‐Madueño, Isabel
A1 Cárdenas Fernández, María Cruz
A1 Diez‐Cirarda, María
A1 Delgado Álvarez, Alfonso
A1 Palacios‐Sarmiento, Marta
A1 Matías-Guiu Guía, Jorge
A1 Corrochano Sánchez, Silvia
A1 Ayala Rodrigo, José Luis
A1 Matias‐Guiu Antem, Jordi
AB Aims: The AT(N) classification system not only improved the biological characteriza-tion of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.Methods: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1–  42), Aβ(1–  42 ) /Aβ(1–  40) ratio, tTau, and pTau.Results: The optimal solution yielded three clusters in both cohorts, significantly dif-ferent in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impair-ment subjects with faster progression to dementia.Conclusion: We propose this data-driven three- group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, com-plementary to the AT(N) system classification.
PB Wiley Open Access
SN 1755-5930
YR 2023
FD 2023-07-27
LK https://hdl.handle.net/20.500.14352/99840
UL https://hdl.handle.net/20.500.14352/99840
LA eng
NO Hernández‐Lorenzo, L., Gil‐Moreno, M. J., Ortega‐Madueño, I., Cárdenas, M. C., Diez‐Cirarda, M., Delgado‐Álvarez, A., Palacios‐Sarmiento, M., Matias‐Guiu, J., Corrochano, S., Ayala, J. L., Matias‐Guiu, J. A., & for the Alzheimer’s Disease Neuroimaging Initiative. (2024). A data‐driven approach to complement the A/T/(N) classification system using CSF biomarkers. CNS Neuroscience & Therapeutics, 30(2), e14382. https://doi.org/10.1111/cns.14382
NO Universidad Complutense de Madrid
NO Instituto de Salud Carlos III
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 18 abr 2025