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.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.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.doi http://dx.doi.org/10.7554/eLife.81067
  • dc.identifier.issn 2050-084X
  • dc.identifier.uri http://hdl.handle.net/10230/59299
  • dc.language.iso eng
  • dc.publisher eLife
  • dc.relation.ispartof Elife. 2023 Apr 17;12:e81067
  • 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 © 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.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • 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.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.type.version info:eu-repo/semantics/publishedVersion

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