Tuesday, December 5, 2023
12/5/2023
DESCRIPTION: Over the past decades, antidepressants and psychotherapy have been the first-line treatments for LLD. Despite being safe and well-tolerated, a large number of patients do not achieve full and persistent remission after initial treatment. About 50% of patients with LLD do not respond after two antidepressant trials, meeting the consensus definition of treatment resistance (TR-LLD). The persistence of chronic and elevated depressive symptoms in older adults has significant clinical and public health implications. This has been correlated to poor general health, reduced quality of life, and a higher risk of mortality when compared to those with sustained remission after treatment. Despite the relevance to public health of TR-LLD, there is little information about the biological mechanisms and no robust clinical prediction model to evaluate at the outset of antidepressant therapy who will or will not respond to treatment. Leveraging an NIMH funded clinical trial, the Incomplete Response in Late-Life Depression: Getting to Remission” (IRL-GREY), across 3 sites, in this study, we propose to evaluate the biological mechanisms related to treatment response in late-life depression and to develop a machine learning based algorithm for prediction of treatment response in these subjects. We will carry out a comprehensive, multiplexed proteomic analysis on 542 samples from patients who completed phase 1 and phase 2 of the clinical trial. We hypothesise that ageing-related biological pathways (i.e. inflammatory response control, proteostasis control, cell damage response, endothelial function) will be associated with poorer treatment response in LLD. Moreover, we hypothesize that a machine learning derived biomarker panel will have sensitivity and specificity greater than 80% to predict treatment response in LLD. Finally, we will evaluate the biological mechanisms related to different depressive symptoms trajectories after treatment. This work will set the stage for a biologically-driven model of treatment response that will be useful to guide, at the outset of antidepressant treatment, those who will benefit more from a specific treatment. If successful, our work can accelerate therapeutic efforts and innovation targeting depression and reduce suffering for large numbers of elderly and their families.
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