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Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

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dc.contributor.author Cumplido-Mayoral, Irene
dc.contributor.author Operto, Grégory
dc.contributor.author Falcón, Carles
dc.contributor.author Shekari, Mahnaz
dc.contributor.author Cacciaglia, Raffaele
dc.contributor.author Milà Alomà, Marta
dc.contributor.author Minguillón, Carolina
dc.contributor.author Suárez-Calvet, Marc
dc.contributor.author Gispert López, Juan Domingo
dc.contributor.author OASIS study
dc.date.accessioned 2024-03-01T07:31:01Z
dc.date.available 2024-03-01T07:31:01Z
dc.date.issued 2023
dc.identifier.citation Cumplido-Mayoral I, García-Prat M, Operto G, Falcon C, Shekari M et al. Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex. Elife. 2023 Apr 17;12:e81067. DOI: 10.7554/eLife.81067
dc.identifier.issn 2050-084X
dc.identifier.uri http://hdl.handle.net/10230/59299
dc.description.abstract Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.
dc.description.sponsorship The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17–519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892 and the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033. FB is supported by the NIHR biomedical research center at UCLH. MSC receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948677), the Instituto de Salud Carlos III (PI19/00155), and from a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher eLife
dc.relation.ispartof Elife. 2023 Apr 17;12:e81067
dc.rights © 2023, Cumplido-Mayoral et al. This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.7554/eLife.81067
dc.subject.keyword Alzheimer's disease
dc.subject.keyword Brain age prediction
dc.subject.keyword Human
dc.subject.keyword Medicine
dc.subject.keyword Neuroimaging
dc.subject.keyword Neuroscience
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/948677
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116907RB-I00
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/847648
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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