Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT02752542 |
| Other study ID # |
1021193 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
Phase 4
|
| First received |
|
| Last updated |
|
| Start date |
October 31, 2016 |
| Est. completion date |
May 2023 |
Study information
| Verified date |
February 2023 |
| Source |
Nova Scotia Health Authority |
| Contact |
Rudolf Uher, MD, PhD |
| Phone |
1-902-473-7209 |
| Email |
rudolf.uher[@]nshealth.ca |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
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.
Description:
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.
