Grant: Project Grant

The absolute health care cost of depression currently equals $1.2 billion in Ontario alone. Treating older adults with the first onset of major depressive disorder (age 50 years or older, affecting up to 16% of older adults) results in a 30-50% increase in health care costs. The biology of late-life depression (LLD) is distinct from early-onset depression. LLD patients present with greater concurrent illness, in large part due to underlying cerebrovascular (e.g., stroke) and neurodegenerative (e.g., Alzheimer's disease, dementia) changes. Globally, more than 50% of LLD patients do not recover sufficiently after treatment with ongoing antidepressant medications, which further predisposes them to cognitive decline, representing an increased risk for dementia and stroke. Our proposed investigation will use Polygenic Risk Scores (PRS) and genetic Machine Learning (ML) models, both of which use algorithms based on genetic patterns associated with the disease. The goal is to identify older adults who are likely to achieve antidepressant remission, prior to treatment selection. The genomic data for PRS is publicly available and will be applied to well-characterized clinical samples of older adults treated with antidepressants. By leveraging genomic data with ML, we aim to discover factors that differentiate and predict remission in LLD. We also hypothesize that comorbidities (e.g. neurodegeneration, cardiovascular disease) and risk of such comorbidities may affect LLD patients' response to antidepressants. Ultimately, such models will aid in substantially reducing the functional, economic and social burden associated with poorly managed LLD. Within the next years, these findings will provide a platform that will allow us to develop a genetic test to personalize and optimize treatment of LLD.