Personalized treatment for functional outcome in depression






Pim CUIJPERS,PhD
Department of Clinical Psychology
VU University, Amsterdam
THE NETHERLANDS
and
EMGO Institute for Health and
Care Research, VU University and
VU University Medical Center
Amsterdam, THE NETHERLANDS

Personalized treatment for functional outcome in depression


by P. Cuijpers, The Netherlands



Although effective treatments for major depressive disorders are available, better treatments are needed. Personalized medicine may improve the outcomes of current treatments; its aim is to identify which characteristics of an individual can predict the outcome of a specific treatment in order to get a better match between the individual and the treatment received. In the past few years, several different approaches have attempted to develop personalized treatments. Pharmacogenetic studies have not yet been successful, but it is expected that combined data from genomics, proteomics, metabolomics, neuroimaging, and neuroendocrinology may eventually lead to effective personalized antidepressant treatments. Randomized trials comparing different therapies in specific target groups have resulted in some preliminary knowledge regarding who benefits from which treatment. For example, pharmacotherapy is probably more effective than psychotherapy in dysthymia, and combined treatments are more effective in older adults. New data mining techniques are now emerging that may constitute a new approach to personalized treatments. Finally, clinical staging has been proposed as a model for personalized treatment of depression. Although there is little evidence as yet that it will indeed lead to better outcomes, it is a good framework to guide our thinking about personalized treatments. However, it is clear that much more research is needed before all of these different approaches lead to personalized treatments that can be used in practice.

Medicographia. 2014;36:476-481 (see French abstract on page 481)



Major depressive disorders constitute one of the great challenges for health care in the next decades.1,2 Major depression is currently ranked fourth worldwide in terms of disease burden, and is expected to rank first in high-income countries by the year 2030.3 Depressive disorders are associated with a substantial loss of quality of life for patients and their relatives,4 huge economic costs, and an increased risk of dying.5

It is well established that psychological and pharmacological therapies are effective in the treatment of adult depression.6 Several types of antidepressant medication have been found to be effective, including tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), serotonin-noradrenalin reuptake inhibitors (SNRIs), and several others.7 Several hundreds of trials directly comparing different types of medication have also shown that all these medications are most likely to be about equally effective.8,9

Several types of psychotherapy have also been shown to be effective, including cognitive behavior therapy,10,11 interpersonal psychotherapy,12 behavioral activation therapy,13 problem- solving therapy,14 counseling,15 and possibly psychodynamic therapy.16 Dozens of trials directly comparing different types of psychotherapy have also shown that there are no differences— or only small ones—between the effects of these therapies and that all therapies seem to be equally effective.

In addition, dozens of trials with direct comparisons between psychotherapies and pharmacotherapies have shown that psychotherapies and pharmacotherapies are equally effective or approximately equally effective.17 This suggests that all psychological and pharmacological treatments of adult depression are equally effective or about equally effective for mild to moderate depression, although this may not pertain to more chronic forms of depression and dysthymia.18

Although these current treatments are considered to be effective, there is also much room for improvement. Modeling studies have shown that pharmacological and psychological treatments together can reduce the disease burden of depression in only about 33% of patients.19 More than 40% of patients do not, or only partially, respond to treatment and less than one-third of all patients have completely recovered after completing their treatment.20 Furthermore, relapse rates are estimated to be 50% after 2 years and up to 85% within 15 years after recovery from an initial episode.21 Therefore, it is very important to improve the outcomes of treatment.

So, we have several pharmacological and psychological treatments that are on average equally effective, but have only limited effects. The fact that these treatments are equally effective does not mean, however, that there are no differences in the response of individual patients to them. Individuals can vary widely in their response to specific treatments,22 and they may benefit from one treatment, but not from another. Ultimately, outcome research should not aim to answer the question of whether a treatment is effective, but rather the question: “what treatment, by whom, is most effective for this individual with that specific problem and under which set of circumstances?”23

Toward personalized treatments for depression

One important way to improve the outcome of treatment in depression is to develop personalized treatments. “Personalized medicine” aims at identifying which characteristics of an individual will predict the outcome of a specific treatment in order to get a better match between the individual and the treatment received.24,25 These characteristics may include sociodemographic characteristics and clinical characteristics of the depressive disorder, as well as biological markers. The development of personalized treatments is considered by many to be one of the major challenges for health care research in the next decades.26,27 The development of personalized treatments for depression is especially important because at the moment there is very little evidence that, on average, one treatment of depression is more effective than another.

Although the overall effects of different psychological and pharmacological treatments are comparable, it is very likely that specific patients with specific characteristics may respond better to one treatment than to another. For example, preliminary research shows that pharmacotherapy is probably more effective than psychotherapy in patients with dysthymia and chronic depression, and combined treatments are more effective than each treatment alone in older adults and mental health outpatients.18 But overall, our knowledge of outcome predictors and moderators is very limited, and consequently it is largely unknown which individual patient will respond to which treatment.24,28,29

Predictors can be defined as the characteristics that predict whether patients will respond to a treatment or not.30,31 Specific predictors indicate whether a specific characteristic predicts the outcome of therapy compared with a no-treatment control, while nonspecific predictors indicate variables that are related to improvement, regardless of comparison or control groups (within-group improvement). Moderators indicate whether participants respond better to one treatment than to another (examined in trials in which two treatments are directly compared with each other). For example, sex might not be a predictor (men and women might benefit equally from therapy) but it might be a moderator (women respond better to treatment X and men better to treatment Y). Research on moderators and predictors of outcome is of vital importance for the development of personalized treatments of depression.

Personalized antidepressant medications and pharmacogenetics

It has long been expected—especially in the field of pharmacogenetics— that the efficacy of pharmacological treatments could be improved by our increasing knowledge of genotypes,32 as about 50% of the response to antidepressants can be attributed to genetic factors.33 However, it has also become clear that the regulation of gene transcription and interactions between genes and environmental factors are extremely complicated. It should, therefore, not be expected that individual genetic information can easily be translated into personalized treatments for depression.32

Most pharmacogenetic studies in the field of antidepressant drugs have focused on candidate genes that are involved in monoaminergic pathways.32 Candidate genes that have been examined include genes for the cytochrome P450 superfamily, P-glycoprotein (ABCB1), tryptophan hydroxylase, catechol- O-methyltransferase, monoamine oxidase A, the serotonin transporter (5-HTTLPR), the norepinephrine transporter (NET), the dopamine transporter (DAT), variants of the 5-hydroxytryptamine receptors (5-HT1A, 5-HT2A, 5-HT3A, 5-HT3B, and 5-HT6), adrenoreceptor beta-1 and alpha-2, the dopamine receptors (D2), the G protein beta 3 subunit (GSK-3β), corticotropin releasing hormone receptors (CRHR1 and CRHR2), glucocorticoid receptors, c-AMP response-element binding protein, and brain-derived neurotrophic factor (BDNF).33 However, although there is some modest evidence that common genetic variations may contribute to individual differences in antidepressant response, no reliable predictors of antidepressant treatment outcome have been found until now.33-34

There is also some evidence that genetic variations can contribute to variability in response to medication, with an impact on adverse effects. However, research on this topic has shown that the explained variance from single gene polymorphisms was actually very small, which again suggests that only combinations of various gene polymorphisms can contribute to individual variability in response to treatment. As yet, no large and replicable findings on the impact of genetic variations have been found.35

It is expected, however, that in the future it will become possible to combine data from genomics, proteomics, metabolomics, neuroimaging, and neuroendocrinology and that this combined knowledge may lead to the development of effective personalized antidepressant treatment based on both genotypes and biomarkers.32

Randomized trials in specific populations

It is not only in the field of pharmacogenetics that researchers are trying to develop personalized treatments for depression. Individual characteristics such as sociodemographic characteristics, clinical characteristics, and biological markers that could reliably predict differences in benefits or adverse effects in response to various depression treatments need to be identified.24

Two types of study design could produce the evidence needed to identify characteristics that could lead to personalized treatment selection.24,25 In the first one, two treatments are compared in an unselected group of participants, and the researchers examine whether a specific characteristic of the participants moderates the relationship between treatment type and outcome.36 For example, in the NIMH (National Institute of Mental Health) Treatment of Depression Collaborative Research Program it was found that severity of depression at baseline could significantly predict differential treatment effects.37 Pharmacotherapy appeared to be more effective than psychotherapy in the more severely depressed patients, while there was no difference between pharmacotherapy and psychotherapy in the less severely depressed. In the second type of study design a group of patients with a specific characteristic is selected, and they are randomized to alternative treatments. For example, in a study among patients with multiple sclerosis it was found that cognitive behavior therapy was more effective than supportive-expressive group therapy.38

It is important to note that if a study does not include a direct comparison between alternative treatments, it is not possible to identify moderators or predictors of differential treatment response.24 If, for example, a psychological treatment is compared with an untreated control group, a characteristic that significantly predicts outcome can be a true moderator, but it can also be a predictor of response to any treatment. So, only studies in which two or more treatments are directly compared with each other can be used to examine moderators of treatments.

In the past decades, many such comparative studies have been conducted in specific patient samples. Recently, we conducted a meta-analytic review of studies in specific target groups comparing antidepressant medication with psychotherapy, medication with combined treatment, and psychotherapy with combined treatment.25 The target group had to have a predefined sociodemographic characteristic, a specific type of depression, a comorbid mental or somatic disorder, or it had to come from a specific setting (outpatients, primary care). These studies can find evidence that psychotherapy, pharmacotherapy, or combined treatment is more effective in a specific target group. We included 52 studies with 4734 depressed patients. In these studies, 20 characteristics of the target groups were examined. The results showed that medication is probably the best treatment for dysthymia, and combined treatments are more effective in depressed outpatients, as well as in depressed older adults. However, in order to examine the 20 characteristics in the three categories of comparisons, 254 studies would be needed to have sufficient statistical power to show an effect size of g=0.5. Currently, only 20.1% of these studies have been conducted.

We concluded in our review that although a considerable number of studies have compared medication, psychotherapy, and combined treatments, and some preliminary results are useful for deciding which treatment is best for which patient, the development of personalized treatment of depression has only just begun and much more research is needed.

Decision trees and personalized treatment of depression

In the past few years a number of studies have used data mining techniques for the development of decision trees to predict who will benefit from a specific treatment or to choose between two treatments. In large samples of patients these techniques identify subgroups that benefit more or less from a specific treatment and these data are used to develop decision trees to determine who should get which treatment. For example, in one study of the STAR*D trial (Sequenced Treatment Alternatives to Relieve Depression) it was found that the overall response rate to treatment was 47%, but that in the profiled patient subgroups the response rate ranged from 31% to 63%.39 In another study, classification and regression trees (CART analysis) showed that patients with HAM-D (Hamilton Depression scale) scores lower than 13 by day 7 were more likely to be responders to fluoxetine or venlafaxine treatment than nonresponders.40 In a third recent example among inpatients with depression,41 it was shown that the presence of suicidality, a higher initial HAMD-21 total score, an episode length of less than 24 months, fewer previous hospitalizations, and absence of any ICD-10 F4 comorbidity were factors that predicted response to treatment.

A new method to predict differential response to various treatments was recently developed by DeRubeis and colleagues.42 Data from a previous randomized trial comparing cognitive behavior therapy with pharmacotherapy43 were used to identify pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials). These variables were included in regression models aimed at the calculation of each patient’s Personalized Advantage Index (PAI). In 60% of the patients, one of the two treatments was predicted to be superior to the other treatment, and if all patients had been assigned to the optimal treatment the outcomes would have been significantly better (effect size d=0.58).

Although this approach is relatively new to the field of psychiatry and mental health, it can be expected that new applications of this approach will lead to meaningful decision trees that will result in personalized treatments and better outcomes for patients.

Personalized treatment and clinical staging in depression

Clinical staging can be seen as another attempt to develop personalized treatments for major depression.44 It is a tried and tested mode of diagnosis elsewhere in medicine, classifying complex diseases in terms of their stage of development.45-47 Major depression can also vary in individual patients from a single episode with minimal impact on the life course of the patient to a chronic relapsing disorder that causes lifelong and serious suffering or even premature death.44 The idea of clinical staging is that a treatment should be aimed at the specific stage that the patient is in. For example, an intervention aimed at a patient with subthreshold depression may be aimed at preventing further development of these symptoms into a full-blown major depressive disorder and could consist of a brief psychological training in cognitive behavioral skills.48 On the other hand, an intervention aimed at a patient with chronic depression may involve a combination of pharmacotherapy and a psychological treatment specifically aimed at chronic patients.49 And a patient who has had several episodes of major depressive disorder may not only need acute treatment for a new episode, but also an intervention aimed at preventing relapse after a successful acute treatment.50

Several possible advantages of clinical staging in depression have been proposed, such as a focus on prevention, a reduction of the heterogeneity of depression, including a reduction of the heterogeneity of treatment response, and a move toward personalized treatments and away from the notion that all treatments are equally effective in all patients. Perhaps the most important benefit of staging is that it orients both clinicians and patients toward thinking about depression as a developmental disorder44 with a high risk of relapse and seeing it as a chronic disorder.

Until now there has not been sufficient evidence to show that clinical staging results in better outcomes of treatment, and although it is an interesting concept that helps to organize our thinking about depression, more research is needed to validate the different clinical phases and the differential effectiveness of treatments in these phases.

Discussion

Although effective treatments for major depressive disorders are available, their overall effects are limited and better treatments are needed. One way to improve the effects of current therapies is to develop personalized treatments. This is important because until now the evidence has shown that all psychological and pharmacological treatments are equally effective. At the same time, clinical practice shows that individuals respond better to one therapy than to another. Personalized medicine aims to identify which characteristics of an individual predict the outcome of a specific treatment in order to get a better match between the individual and the treatment received.

In the past years several different approaches have attempted to develop personalized treatments. Pharmacogenetic studies have not been successful yet, but it is expected that combined data from genomics, proteomics, metabolomics, neuroimaging, and neuroendocrinology may lead to the development of effective personalized antidepressant treatments. Randomized trials comparing different therapies in specific target groups have resulted in some preliminary knowledge regarding who can benefit from which treatment. For example, pharmacotherapy is probably more effective than psychotherapy in dysthymia, and combined treatments are more effective in depressed outpatients, as well as in depressed older adults. New data mining techniques are now emerging as another method to predict who will benefit from which treatment, and the first studies have shown that this may be a feasible approach to personalize treatments. Finally, clinical staging has been proposed as a model for personalizing the treatment of depression. Although there is little evidence as yet that this will indeed lead to better outcomes, it is a good framework to guide our thinking about personalized treatments.

One important problem for the development of personalized treatments of depression is that depression is a very heterogeneous disorder and that many different effective treatments are available. If we want to show that one treatment is better in an individual with a specific characteristic, we have to choose between the many treatments that may be more effective than others in that individual. This implies that the number of trials that can be—and eventually will be—performed is very large. If we wanted to differentiate between the many available antidepressant medications and psychotherapies, and all possible combinations (in combined treatments), we would probably need many thousands of studies—only a fraction of which have so far been conducted—and millions of participating patients.25 This problem is multiplied if we focus on other characteristics that have not yet been examined in trials until now, such as biomarkers. And if we really want to develop personalized treatments for depression, we should not only look at individual characteristics of patients and treatments, but also at combinations of characteristics, such as older adults with atypical depression and a specific biomarker.25 Furthermore, we may want to look at other outcomes, such as side effects of medications, long-term outcomes, patient preferences, and prediction of treatment dropout rates. And we could also choose a more precise effect size of g=0.3 or even 0.2. This would require an almost endless number of randomized trials and even more patients who would be willing to participate in such trials. There is no doubt that the path toward personalized treatments is a long one, requiring considerable resources.25

In conclusion, developing personalized treatments for depression is one of the most important challenges for mental health researchers in the next decades. There are several useful approaches that may, in the future, lead to giving personalized advice to patients about the treatment from which they will most likely benefit. In all of these different approaches it is clear that much more research is needed. There is no doubt, however, that the development of personalized treatments for depression has begun.


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Keywords: depression; outcome; personalized medicine; treatment