Men show increased brain aging with respect to women among Cognitively Normal Individuals.

Background: There is an increasing interest in using machine learning and artificial intelligence to estimate chronological age using neuroimaging data. The gap between chronological age and estimated brain age (brain age gap, BAG) is used as a measure of accelerated/resilient brain aging. Previously, BAG has been associated with cognitive status. However, whether the BAG varies across sex and cognitive status have been less explored. The present study examines these associations and validates a voxel‐based machine learning approach based on the elastic net regression (ENR) for BAG calculation. Method: Using data from the Atherosclerosis Risk in Communities Study (ARIC) study, the Wake Forest School of Medicine Alzheimer's Disease Research Center (WFSM‐ADRC) clinical cohort and Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined associations of BAG across cognitive status and sex. We used structural MRI scans from 1853 ARIC participants (ages 67‐90, 60% females), 508 from the WFSM‐ADRC (55‐95 yo., 66% females) and 584 ADNI cognitively normal (CN) participants (55‐90 yo., 57% females). All images were aligned into a common template and the derived gray matter (GM) probability maps from ADNI MRIs were used as input to train the machine learning algorithm. Once the model was fitted the ARIC and WFSM‐ADRC GM probability maps were provided as input to the algorithm to estimate the BAG values. Finally, an age bias correction was applied. Linear regression methods were used to investigate differences between groups. Age, race, education, sex, and cognitive status were included in the model. Result: We found in both ARIC and WFSM‐ADRC participants that differences in BAG values between CN‐MCI and MCI‐Dementia participants were highly significant. In addition, when we examined differences in BAG values across sex per cognitive status, we found again in both cohorts that differences were only significant for CN individuals. See Table 1 for details. Conclusion: Our analyses show that our approach to estimate chronological age using high‐dimensional ENR, produces BAG values which are strongly associated with cognitive status. The increased severity of cognitive impairment is related to accelerated brain aging. Differences in BAG between men and women were significant for CN individuals only. [ABSTRACT FROM AUTHOR]