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

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-ss, 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.

Automated summary

Brain--age can be used as a marker of biological brain aging by comparing it with chronological age, and accelerated aging is found in neurodegenerative disorders like Alzheimer's disease. This study validates brain--age delta as a non-invasive marker of biological brain aging in cognitively unimpaired individuals with abnormal levels of biomarkers of AD and axonal injury. The study also found associations between brain--age delta and abnormal amyloid-ss, advanced stages of AD pathology, APOE-e4 status, and plasma neurofilament light.