Personalized Indications for CBT and Antidepressants in Treating Depression

Depression currently affects close to 2 million Canadians and is the leading cause of disability worldwide. Pharmacological treatments (antidepressant medication) and psychological treatments such as cognitive-behavioural therapy are available for depression, but the majority of those who receive treatment have an unsatisfactory response. On average, the combination of pharmacological and psychological treatment achieves better results than either treatment alone. However, the apparently superior results of combination treatment may be due to the fact that different individuals preferentially respond to pharmacological or psychological treatment. The invesitagtors have discovered several clinical factors and biomarkers that predict poor response to commonly used antidepressant medication: history of childhood maltreatment, loss of interest and reduced activity, a biomarker of systemic inflammation, and a genetic marker of sensitivity to environment. Indirect evidence suggests that the same factors may indicate the need for psychological treatment, but their usefulness as differential predictors of psychological and pharmacological treatment outcomes remains to be established.

The investigators will test the hypothesis that a pre-determined set of clinical variables (history of childhood maltreatment, loss of interest and reduced activity) and biomarkers (serum C-reactive protein, a marker of systemic inflammation, and short alleles of the serotonin transporter gene promoter polymorphism) differentially predicts response to antidepressants and to cognitive-behavioural psychotherapy with clinically significant accuracy.

If this hypothesis is supported, the resulting predictor will allow personalized selection of treatment for depression, leading to improved outcomes and healthcare efficiency. Additional objectives include replication of additional predictors and integrative analyses aimed at refining the treatment choice algorithms.

Depression is among the most common and burdensome diseases worldwide and in Canada. It is treatable with effective pharmacological and psychological treatments, but fewer than 50% of individuals achieve remission with the first treatment. The unsatisfactory outcomes are at least partly due to a gap in evidence on which treatment benefits which individual.

The two most commonly used treatment modalities for depression are antidepressant medication and psychotherapy. However, only a minority of individuals with depression achieve remission with each type of treatment. On average, combination of antidepressants and psychotherapy achieves better results than either alone, but using two treatments at the same time may be wasteful and unnecessary. Several lines of evidence suggest that the apparently superior results of combination may be due to some individuals preferentially responding to antidepressants and others to psychological treatment. These findings point to the possibility that it may be possible to improve outcomes of depression by matching psychological and pharmacological treatment to individuals who are most likely to benefit from each one. The investigators propose to close this gap using a combination of clinical predictors and biomarkers to optimize the choice between psychological and pharmacological treatment.

The investigators have identified that a symptom profile with prominent reduction in interest and activity, history of maltreatment in childhood, increase in systemic inflammation, and a genetic variant increasing sensitivity to environment predict poor response to antidepressant medication. Indirect evidence suggests that the same factors may predict better response to cognitive-behavioural psychotherapy.

The investigators focus on these four predictors with substantial prior evidence:

Reduced interest and activity. A symptom dimension of interest and activity emerged as the strongest predictor of poor outcome of antidepressant medication treatment. This prediction replicated with undiminished effect size in the largest study of antidepressant treatment carried out to date,4 and additional independent replications confirmed its robustness. Cognitive behavioural therapy improves activity and interest better than antidepressant medication.

Childhood maltreatment. A meta-analysis established that a history of maltreatment in childhood predicts non-response to antidepressants but not to psychotherapy in adults with depression. History of maltreatment may actually predict greater benefits from psychological treatment.

Systemic inflammation. Inflammation may be a pathogenic mechanism in a proportion of depression cases. The invesigators found that increased C-reactive protein (CRP), a marker of systemic inflammation, predicted non-response to a commonly used antidepressant. Cognitive behavioural therapy can decrease CRP and may be the preferred treatment option for individuals with high levels of systemic inflammation.

Genetic sensitivity to environment. A functional polymorphism in the serotonin transporter gene makes some individuals more sensitive to positive and negative effects of the environment. This gene encodes the molecular target of the most commonly used antidepressants and the polymorphism that confers sensitivity to environment also predicts non-response to antidepressants. A hypothesis that genetic sensitivity to environment may extend to effects of psychotherapy has been proposed and supported by preliminary data.

Combination of predictors. The above predictors are at least partly independent and may combine to a clinically significant prediction of treatment outcomes. Reports of gene-environment interactions suggest that a combination of environmental and genetic factors may add unique value. Therefore, the investigators propose to evaluate the predictive validity of a pre-determined combination of the four factors in predicting differential outcomes of psychological and pharmacological treatment.

Other predictors. The selection of the four predictors described above represents a balance between comprehensiveness and complexity, but it is not exhaustive. To maximize the potential for integrative analysis, the investigators will also evaluate addition potential predictors including anxiety, dysfunctional attitudes, and personality disorders.

The investigators propose to test whether these four predictors in combination differentially predict the outcomes cognitive-behavioural psychotherapy and antidepressant medication with clinically significant accuracy. If this hypothesis is supported, the resulting predictor will allow personalized selection of treatment for depression, leading to improved outcomes and healthcare efficiency. Additional objectives include replication of additional predictors and integrative analyses aimed at refining the treatment choice algorithms.

Depression has been reclassified into two categories: major depressive disorder (MDD), and persistent depressive disorder (PDD), which may be associated with distinct etiology and treatment response. The investigators will include individuals with MDD and PDD to provide results that generalize to both conditions and keep the option of exploring each separately.

Objective: The invesitagtors' primary objective is to establish whether a predictive score based on a pre-determined set of clinical variables and biomarkers differentially predicts response to antidepressant medication and to cognitive-behavioural psychotherapy with a clinically significant effect size. Secondary objectives include testing each predictor separately, testing a gene-environment interaction between the serotonin transporter polymorphism and childhood maltreatment, and integrative analyses using multiple predictors to derive optimized prediction algorithms.

Primary hypothesis: A score reflecting loss of interest and activity, history of childhood maltreatment, systemic inflammation and genetic sensitivity to environment will predict a better response to cognitive-behavioural therapy relative to antidepressant medication among adults with major depressive disorder or persistent depressive disorder. If this hypothesis is supported, the resulting predictor will allow personalized selection of treatment for depression, leading to improved outcomes and healthcare efficiency.

Eligibility Criteria:
Inclusion Criteria:

* a diagnosis of MDD or PDD established with the Structured Clinical Interview for DSM-5 (SCID-5), and depression being the primary problem requiring clinical attention (judgement of intake clinician).
* a minimum current severity of 14 on the 17-item Hamilton Rating Scale for Depression (HRSD-17)
* a cumulative duration of depression of at least two months (this will exclude short-lasting first depressive episodes that do not require treatment of this intensity), age 18 or more (no upper limit)
* capacity to provide informed consent.

Exclusion Criteria

* lifetime diagnosis of bipolar disorder, schizophrenia, schizophreniform disorder, schizoaffective disorder, or current alcohol or drug use disorder
* pregnancy
* recent receipt of adequate trial of psychological treatment (10 or more sessions in the past 12 months)
* recently introduced antidepressant medication (new antidepressant in past 12 weeks or dose increase in the past 6 weeks)
* previous non-response to two or more of study medications
* acute suicide risk (MADRS suicide item≥4)
* current psychotic symptoms.