Hernández Lorenzo, LauraGil‐Moreno, Maria JoséOrtega‐Madueño, IsabelCárdenas Fernández, María CruzDiez‐Cirarda, MaríaDelgado Álvarez, AlfonsoPalacios‐Sarmiento, MartaMatías-Guiu Guía, JorgeCorrochano Sánchez, SilviaAyala Rodrigo, José LuisMatias‐Guiu Antem, Jordi2024-02-072024-02-072023-07-27Hernández‐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.143821755-593010.1111/cns.14382https://hdl.handle.net/20.500.14352/99840Aims: 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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal article1755-5949https://doi.org/10.1111/cns.14382open access616.894-053.9Alzheimer's diseaseCerebrospinal fluidClustering analysisEarly detectionMachine learningMild cognitive impairmentCiencias Biomédicas32 Ciencias Médicas