GENETIC AND ENVIRONMENTAL PREDICTORS OF TREATMENT RESISTANT DEPRESSION

A range of pharmacological and psychological treatments for depression exist. However, across these treatment types, outcomes are variable and many individuals do not experience any remission of symptoms. Approximately one third of individuals diagnosed with major depressive disorder (MDD) are refractory to treatment, often termed ‘treatment-resistant depression (TRD)’. The complex and heterogeneous features and pathophysiology of MDD, influenced by various environmental and biological factors, is likely a major contributor to the high rates of treatment failure. Consequently, identifying predictors associated with treatment-resistant depression may help optimize therapy and mitigate the risk of poor treatment outcomes for individuals with depression.

The Australian genetics of depression study (AGDS) comprises ∼16 000 genotyped participants that report a diagnosis of depression. Using AGDS data, we defined TRD cases using prescription record data according to the following criteria: i) at least three unique antidepressant medications; ii) each prescription was prescribed for a period of at least two months before switching medications; iii) the time between the prescriptions of two consecutive drugs was no longer than 14 weeks and iv) prescriptions did not overlap when switching medications. Controls were defined as i) individuals with two or less antidepressant prescriptions, prescribed at least twice (for 2 months or longer) and ii) if two antidepressant were prescribed, the time between the two antidepressant prescriptions was > 14 weeks. This lead to a final sample size of 1,411 TRD cases and 8,711 controls. We used regression analysis to explore the association of TRD with biological predictors such a polygenic score (PGS) and CYP2C19 and CYP2D16 metaboliser profiles, measured personality traits, and environmental predictors such as social support and exposure to stressful life events. Lastly, we tested for any gene-environment interactions across our predictors.

Individuals with TRD were more likely to be male, have an earlier age of onset and report more lifetime depressive episodes in our cohort. Preliminary analyses show that PGS for depression, bipolar disorder, ADHD and PTSD were all significantly associated with TRD (p < 0.001). CYP gene metaboliser profiles did not differ significantly between TRD and non-TRD groups. We found a nominally significant association between individuals with high levels of neuroticism and increased TRD risk. In line with previous studies, we show a significant interaction effect between depression PGS and stressful life event exposure on TRD risk as well as and interaction between bipolar disorder PGS and social support.

Given the limited resources available to the thousands of individuals seeking treatment for depression, there is a strong rationale to understand who is most likely to benefit from certain types of treatment. The incorporation of genetic information alongside demographic and clinical predictors is a promising avenue in this endeavor. Here we show that PGS for various mental health disorders are associated with TRD risk, and provide preliminary evidence of the contribution of gene-environment interactions. However, given the complexity of MDD, further large, well-phenotyped studies of depression that collect genetic, environmental, and treatment outcome data are vital to thoroughly explore the genetic underpinnings of TRD.