Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

Abstract

Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain-age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ² = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ² = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ² = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ² = 5.89, p = 0.0152) and working memory performance (Wald χ² = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ² = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

Fig. 1. Comparison of structural MRI of participant brains in midlife and older adult cohorts.

Each image is a separate participant, displaying coronal and axial images and actual (chronological) age and estimated (calculated) age. The top row is from the midlife adult cohort and the bottom row is from the geriatric cohort.

Fig. 2. Association between brain-age gap and episodic memory in geriatric subjects.

BAG was associated with episodic memory in general linear models (Wald Χ² = 4.10, 132 df, p = 0.0430), including covariates of diagnosis, age, sex, education, and medical morbidity. Episodic memory has no units, presented as an average z-score across tests. Brain-age gap (BAG) is in years, calculated as the difference between the calculated estimated age and the chronological age.

Fig. 3. Association between brain-age gap and disability (WHODAS) in older adults.

BAG was associated with disability in general linear models (Wald Χ² = 6.00, 93 df, p = 0.0143), including covariates of diagnosis, age, sex, education, and medical morbidity. Disability measured by the WHODAS 2.0, calculated as percent disabled. Brain-age gap (BAG) is in years, calculated as the difference between the calculated estimated age and the chronological age.

Fig. 4. Association between brain-age gap and processing speed in older depressed subjects.

BAG was associated with processing speed in depressed elders in general linear models (Wald Χ² = 4.43, 96 df, p = 0.0354), including covariates of depression severity (MADRS), age, sex, education, and medical morbidity. Processing speed has no units, presented as an average z-score across tests. Brain-age gap (BAG) is in years, calculated as the difference between the calculated estimated age and the chronological age.