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.
Auteur : Cumplido-Mayoral I, GarcÃa-Prat M, Operto G, Falcon C, Shekari M, Cacciaglia R, MilÃ -AlomÃ M, Lorenzini L, Ingala S, Meije Wink A, Mutsaerts HJMM, MinguillÃ³n C, Fauria K, Molinuevo JL, Haller S, Chetelat G, Waldman A, Schwarz AJ, Barkhof F, Suridjan I, Kollmorgen G, Bayfield A, Zetterberg H, Blennow K, SuÃ¡rez-Calvet M, Vilaplana V, Gispert LÃ³pez JD
Année : 2023
Journal : Elife 2050-084X
PubMed Id : 37067031
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.