A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

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dc.contributor.authorHernández Lorenzo, Laura
dc.contributor.authorGil‐Moreno, Maria José
dc.contributor.authorOrtega‐Madueño, Isabel
dc.contributor.authorCárdenas Fernández, María Cruz
dc.contributor.authorDiez‐Cirarda, María
dc.contributor.authorDelgado Álvarez, Alfonso
dc.contributor.authorPalacios‐Sarmiento, Marta
dc.contributor.authorMatías-Guiu Guía, Jorge
dc.contributor.authorCorrochano Sánchez, Silvia
dc.contributor.authorAyala Rodrigo, José Luis
dc.contributor.authorMatias‐Guiu Antem, Jordi
dc.date.accessioned2024-02-07T09:59:57Z
dc.date.available2024-02-07T09:59:57Z
dc.date.issued2023-07-27
dc.description.abstractAims: 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.
dc.description.departmentDepto. de Psicobiología y Metodología en Ciencias del Comportamiento
dc.description.facultyFac. de Psicología
dc.description.refereedTRUE
dc.description.sponsorshipUniversidad Complutense de Madrid
dc.description.sponsorshipInstituto de Salud Carlos III
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades (España)
dc.description.statuspub
dc.identifier.citationHernández‐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
dc.identifier.doi10.1111/cns.14382
dc.identifier.essn1755-5949
dc.identifier.issn1755-5930
dc.identifier.officialurlhttps://doi.org/10.1111/cns.14382
dc.identifier.urihttps://hdl.handle.net/20.500.14352/99840
dc.journal.titleCNS Neuroscience and Therapeutics
dc.language.isoeng
dc.publisherWiley Open Access
dc.relation.projectIDinfo:eu-repo/grantAgreement/CT63/19-CT64/19
dc.relation.projectIDinfo:eu-repo/grantAgreement/PID2019-110866RB-I00
dc.relation.projectIDinfo:eu-repo/grantAgreement/CD22/00043
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu616.894-053.9
dc.subject.keywordAlzheimer's disease
dc.subject.keywordCerebrospinal fluid
dc.subject.keywordClustering analysis
dc.subject.keywordEarly detection
dc.subject.keywordMachine learning
dc.subject.keywordMild cognitive impairment
dc.subject.ucmCiencias Biomédicas
dc.subject.unesco32 Ciencias Médicas
dc.titleA data-driven approach to complement the A/T/(N) classification system using CSF biomarkers
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
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