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.

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.

Trial registration: ClinicalTrials.gov NCT01835717.

Keywords: alzheimer's disease; brain age prediction; human; medicine; neuroimaging; neuroscience.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alzheimer Disease* / diagnostic imaging
  • Alzheimer Disease* / pathology
  • Amyloid beta-Peptides / metabolism
  • Biomarkers
  • Brain / metabolism
  • Female
  • Humans
  • Machine Learning
  • Male
  • Neuroimaging / methods

Substances

  • Amyloid beta-Peptides
  • Biomarkers

Associated data

  • ClinicalTrials.gov/NCT01835717