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
- Pasqual Maragall Foundation, Spain
- Amsterdam University Medical Centers, Netherlands
- CIRD Centre d'Imagerie Rive Droite, Switzerland
- Normandie Univ, UNICAEN, INSERM, U1237, France
- University of Edinburgh, United Kingdom
- Takeda Pharmaceutical Company Ltd, United States
- Roche Diagnostics International Ltd, Switzerland
- Roche Diagnostics GmbH, Germany
- University of Gothenburg, Sweden
- Universitat Politècnica de Catalunya, Spain
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-b, more advanced stages (AT) of AD pathology and APOE-e4 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.
Data availability
UKBiobank data availability at www.ukbiobank.ac.ukADNI data availability at https://adni.loni.usc.edu/EPAD data availability at www.ep-ad.org/ALFA: data availability through GAAIN at https://www.gaaindata.org/partners/online.htmlFully steps and instructions on data access can be found in the following links:UKBiobank: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-accessADNI: https://adni.loni.usc.edu/data-samples/access-data/EPAD: https://ep-ad.org/open-access-data/access/ALFA: https://www.gaaindata.org/partner/ALFA
Article and author information
Author details
Henk JMM Mutsaerts
Juan Domingo Gispert López
Funding
European Union's Horizon 2020 Research and Innovation (948677)
- Marc Suárez-Calvet
Instituto de Salud Carlos III (PI19/00155)
- Marc Suárez-Calvet
La Caixa Foundation (100010434)
- Marc Suárez-Calvet
European Union's Horizon 2020 Research and Innovation (847648)
- Marc Suárez-Calvet
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Karla L Miller, University of Oxford, United Kingdom
Ethics
Human subjects: ALFA ethics: All participants were enrolled in the ALFA (ALzheimer and FAmilies) study (Clinicaltrials.gov Identifier: NCT01835717. The study was approved by the Independent Ethics Committee "Parc de Salut Mar," Barcelona, and all participants gave written informed consent.
Version history
- Received: June 14, 2022
- Preprint posted: June 28, 2022 (view preprint)
- Accepted: April 10, 2023
- Accepted Manuscript published: April 17, 2023 (version 1)
- Version of Record published: May 12, 2023 (version 2)
Copyright
© 2023, Cumplido-Mayoral et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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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
eLife 12:e81067.
https://doi.org/10.7554/eLife.81067
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