Differences between multimodal brain-age and chronological-age are linked to telomere shortening.

ABSTRACT: Telomere shortening is theorized to accelerate biological aging, however, this has not been tested in the brain and cognitive contexts. We used machine learning age-prediction models to determine brain/cognitive age and quantified the degree of accelerated aging as the discrepancy between brain and/or cognitive and chronological ages (i.e., age gap). We hypothesized these age gaps are associated with telomere length (TL). Using healthy participants from the ADNI-3 cohort (N = 196, Age_mean=70.7), we trained age-prediction models using 4 modalities of brain features and cognitive scores, as well as a 'stacked' model combining all brain modalities. Then, these 6 age-prediction models were applied to an independent sample diagnosed with mild cognitive impairment (N = 91, Age_mea

SUBMITTER: Yu J 

PROVIDER: S-EPMC9133148 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature