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.

Machine learning for brain age prediction: Introduction to methods and clinical applications

“…Studies have begun to explore deep learning approaches for brain age prediction, which are potentially more complex and powerful than supervised methods [28] , [29] , [30] , [31] , [32] , [33] , [34] . Nevertheless, in direct comparison to the commonly used shallow machine learning approaches like relevance vector regression, deep learning approaches appear to be comparable (MAE 4-5 years) [ 28 , 29 ] or superior (MAE 7-8 years versus MAE 5-6 years) [33] .…”

“…2020 [6] MDD 2675 18-75 +1.08 * Christman et al. 2020 [32] MDD (adult) 76 20-50 nonsignificant * MDD (geriatric) 118 >60 approx. 4-5 * Besteher et al.…”

“…Brain age has a range of potential applications for the clinical assessment of individual patients at various stages of health and disease, including the support of diagnosis, prognosis, and treatment decisions ( Figure 3 ). Brain age studies are being conducted in a wide range of clinical populations including neurological conditions such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) [ 1 , 14 , 15 , 25 , 37 , [56] , [57] , [58] , [59] ], traumatic brain injury [ 60 , 61 ], epilepsy [ 18 , 19 , 62 ], multiple sclerosis [ 37 , 54 , 63 ], and stroke [ 64 , 65 ], as well as psychiatric disorders such as schizophrenia [ 10 , 12 , 69 , 20 , 26 , 37 , 38 , 45 , [66] , [67] , [68] ], including clinical high-risk for psychosis (CHR) and first-episode psychosis (FEP), bipolar disorder [ 37 , 38 , 66 , 67 , 70 ], major depressive disorder (MDD) [ 6 , 20 , 32 , 37 , 71 , 72 ], borderline personality disorder [20] , autism spectrum disorder [ 8 , 37 , 73 ], and attention deficit hyperactivity disorder [37] . The results of the clinical studies are summarised in Tables 1 and 2 .…”

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report

“…Recently, brainAGE studies have begun to investigate this hypothesis of accelerated brain aging in MDD. The findings so far have been inconclusive, with some studies claiming to have identified signs of accelerated aging in MDD and others indicating the opposite ( Besteher et al, 2019 , Christman et al, 2020 , Kaufmann et al, 2019 , Schmaal et al, 2020 ). A subgroup comparison of medication-free individuals with MDD versus those currently on medications found no differences in brain-PAD ( Han et al, 2020 ) and there is scarce information regarding associations with other clinical characteristics.…”

“…In all significant associations between brain-PAD and clinical symptoms, a larger brain-PAD was associated with worse clinical outcomes ( Kaufmann et al, 2019 ). There may also be an effect of age on brain-PAD, such that it is more pronounced in older compared to young/mid-life individuals ( Christman et al, 2020 ).…”