The Effects of Accelerated Brain Aging on the Antidepressant Response in Late-Life Depression

Abstract

Introduction Late-Life Depression (LLD) is characterized by functional and structural brain alterations and accelerated brain aging. Our past work utilized a brain-based marker of accelerated aging to examine LLD and demonstrated a cross-sectional association between accelerated brain aging and impaired cognitive ability (poor episodic memory, slow processing speed, and poor executive function) as well as disability (measured by WHODAS). However, it is unclear whether or not accelerated brain aging influences the antidepressant response in depressed elders. Methods We examined depressed adults over 60 years-old with current Major Depressive Disorder (MDD) without psychotic features (N=95) randomized to receive either escitalopram up to 20mg daily or identical placebo (2:1 subject ratio) for 8 weeks. Subjects underwent baseline depression assessment (MADRS) and MRI prior to randomization. After using an automated deep learning tool to estimate brain age from cranial MRI, the brain age gap (BAG) was calculated by subtracting the participant's chronological age from his or her calculated brain age estimation. The brain MRI estimating tool uses ROI volumes derived from multiple atlases and the BrainAGE algorithm - the algorithm applies a deep convolutional neural network regression model trained on 5000 healthy controls. For statistical analysis, the dependent outcome variable was change in MADRS over 8 weeks. Initial models examined the effects of BAG with baseline MADRS, age, sex, and treatment assignment as covariates. Subsequent models tested for a statistical interaction between treatment assignment and BAG to observe whether or not the effects of brain aging on change in MADRS differed between arms. Results In the primary effects model examining change in MADRS, although there was a strong effect of treatment assignment (t = -3.75, p = 0.0003), there was no significant relationship between BAG and antidepressant response (t = -0.02, p = 0.9819). In the subsequent model, we observed a statistical interaction between BAG and treatment assignment on change in MADRS (t = -2.38, p = 0.0196). Analysis of subgroup data demonstrated that a larger BAG (indicating an older appearing brain) was associated with less change in MADRS for those who received placebo. In contrast, a larger BAG (indicating an older appearing brain) was associated with greater change in MADRS for those who received escitalopram. Conclusions The effect of accelerated brain aging, measured with the BAG, on change in MADRS have opposite patterns based on treatment assignment. Although a larger BAG is associated with poorer cognitive performance and greater disability, a larger BAG was associated with greater change in MADRS in the active treatment arm of our study. This is somewhat discrepant with prior work that associates poor cognitive performance with worse antidepressant response. As a global brain metric, BAG is not specific to discrete functional and/or structural brain impairments. Therefore, the elements that jointly contribute to the brain age metric may act in discrete, separate ways, with some influencing cognitive performance, and with others having less of a functional impact, serving as a marker of positive response to antidepressant medication. Further work must examine the complex relationships between accelerated brain aging, cognition, and response to antidepressants. Funding R01 MH102246, K24 MH110598