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

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 mean = 71.3) to determine, for each subject, the model-specific predicted age and age gap. TL was most strongly associated with age gaps from the resting-state functional connectivity model after controlling for confounding variables. Overall, telomere shortening was significantly related to older brain but not cognitive age gaps. In particular, functional relative to structural brain-age gaps, were more strongly implicated in telomere shortening. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The study found that telomere shortening may be associated with biological aging of the brain, and using machine learning models can predict the age of the brain and cognition, with a clear correlation between telomere length and age discrepancies.