Yu, JunhongKanchi, MadhuRawtaer, IrisFeng, LeiKumar, Alan PremKua, Ee HeokMahendran, Rathi2024-05-130197-4580http://hdl.handle.net/1885/317472Telomere 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, Agemean=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, Agemean=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.Junhong Yu is supported by the Nanyang Assistant Professorship (Award no. 021080-0 0 0 01 ). The work was supported by a grant from the Singapore Ministry of Education Tier 2 ( MOE- T2EP30120-0016 ) to A.P.K. The ADNI data collection and sharing was funded by the ADNI ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI ( Department of Defense award number W81XWH-12-2-0012 ). ADNI is funded by the National Institute on Agingapplication/pdfen-AUBrain-ageCognitive-ageTelomereResting-state functional connectivityStructural connectivitySubcortical gray matterCortical thickness202210.1016/j.neurobiolaging.2022.03.0152023-01-15