학술논문
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
Document Type
article
Author
Irene Cumplido-Mayoral; Marina García-Prat; Grégory Operto; Carles Falcon; Mahnaz Shekari; Raffaele Cacciaglia; Marta Milà-Alomà; Luigi Lorenzini; Silvia Ingala; Alle Meije Wink; Henk JMM Mutsaerts; Carolina Minguillón; Karine Fauria; José Luis Molinuevo; Sven Haller; Gael Chetelat; Adam Waldman; Adam J Schwarz; Frederik Barkhof; Ivonne Suridjan; Gwendlyn Kollmorgen; Anna Bayfield; Henrik Zetterberg; Kaj Blennow; Marc Suárez-Calvet; Verónica Vilaplana; Juan Domingo Gispert; ALFA study; EPAD study; ADNI study; OASIS study
Source
eLife, Vol 12 (2023)
Subject
Language
English
ISSN
2050-084X
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.