Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression

Abstract

Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. Design and Interventions: Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. Participants: Ninety-five individuals with LLD. Measurements: A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. Results: In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a

Association Between Major Depressive Disorder and the Development of Cerebral Small Vessel Disease: A Mendelian Randomization Study

“…This outcome is comparable to the results of two recent cohort studies: Clancy et al discovered that heightened WMH was not linked to depression (2023), whereas Ali et al illustrated that the load of WMH was not associated with depression 1 year after a stroke (2023). Another study, which implemented a machine learning algorithm, demonstrated that the severity of depression over time was unaffected by WMH volume (Ahmed et al, 2022).…”

“…Another study, which implemented a machine learning algorithm, demonstrated that the severity of depression over time was unaffected by WMH volume (Ahmed et al, 2022).…”

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“…However, a prospective long-term study did not confirm this relationship (den Heijer et al 2011 ). Others found that higher depression scores were associated with less age-related volumetric decreases in the right subiculum and CA1 sector of the cornu ammonis (Szymkowicz et al 2017 ), or did not observe effects of hippocampal or WMH volume on changes in depression severity (Ahmed et al 2022 ). Another study demonstrated that a considerable proportion of patients with LLD and MCI had hippocampal atrophy, but tested negative for brain Aβ (Wu et al 2018 ).…”