Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.

Prevalence and correlates of common mental disorders among participants of the Uganda Genome Resource: Opportunities for psychiatric genetics research

Genetics research has potential to alleviate the burden of mental disorders in low- and middle-income-countries through identification of new mechanistic pathways which can lead to efficacious drugs or new drug targets. However, there is currently limited genetics data from Africa. The Uganda Genome Resource provides opportunity for psychiatric genetics research among underrepresented people from Africa. We aimed at determining the prevalence and correlates of major depressive disorder (MDD), suicidality, post-traumatic stress disorder (PTSD), alcohol abuse, generalised anxiety disorder (GAD) and probable attention-deficit hyperactivity disorder (ADHD) among participants of the Uganda Genome Resource. Standardised tools assessed for each mental disorder. Prevalence of each disorder was calculated with 95% confidence intervals. Multivariate logistic regression models evaluated the association between each mental disorder and associated demographic and clinical factors. Among 985 participants, prevalence of the disorders were: current MDD 19.3%, life-time MDD 23.3%, suicidality 10.6%, PTSD 3.1%, alcohol abuse 5.7%, GAD 12.9% and probable ADHD 9.2%. This is the first study to determine the prevalence of probable ADHD among adult Ugandans from a general population. We found significant association between sex and alcohol abuse (adjusted odds ratio [AOR] = 0.26 [0.14,0.45], p < 0.001) and GAD (AOR = 1.78 [1.09,2.49], p = 0.019) respectively. We also found significant association between body mass index and suicidality (AOR = 0.85 [0.73,0.99], p = 0.041), alcohol abuse (AOR = 0.86 [0.78,0.94], p = 0.003) and GAD (AOR = 0.93 [0.87,0.98], p = 0.008) respectively. We also found a significant association between high blood pressure and life-time MDD (AOR = 2.87 [1.08,7.66], p = 0.035) and probable ADHD (AOR = 1.99 [1.00,3.97], p = 0.050) respectively. We also found a statistically significant association between tobacco smoking and alcohol abuse (AOR = 3.2 [1.56,6.67], p = 0.002). We also found ever been married to be a risk factor for probable ADHD (AOR = 2.12 [0.88,5.14], p = 0.049). The Uganda Genome Resource presents opportunity for psychiatric genetics research among underrepresented people from Africa.

Program of algorithm for pharmacological treatment of major depressive disorder in China: Benefits or not?

Background

This research was designed to investigate Algorithm Guided Treatment (AGT) and clinical traits for the prediction of antidepressant treatment outcomes in Chinese patients with major depressive disorder (MDD).

Methods

This study included 581 patients who had reached treatment response and 406 patients remained non-responded observed after three months of treatment. Sociodemographic factors, clinical traits, and psychiatric rating scales for evaluating therapeutic responses between the two groups were compared. Logistic regression analysis was adopted to determine the risk factors of unresponsive to antidepressant (URA) in MDD. Kaplan-Meier survival analysis was utilized to compare the therapeutic response between AGT and treatment as usual (TAU).

Results

Compared to the MDD responsive to antidepressant (RA) group, the URA group had significantly lower rates of the following clinical traits: married status, anxious distress, moderate to severe depressive symptoms, and higher rates of comorbidity (p-value < 0.05). Logistic Regression Analysis showed that eight clinical traits from psychiatric rating scales, such as anxious characteristics, were correlated positively with URA, while the other eight symptoms, such as autonomic symptoms, were negatively correlated. Time to symptomatic remission was longer in TAU without statistically significant (p-value = 0.11) by log-rank testing.

Conclusions

The factors may affect the therapeutic responses and compliance of patients, increasing the non-response risk for antidepressants. Therapeutic responses might be improved by increasing the clarification and elucidation of different symptom clusters of patients. Benefits on treatment response to AGT were not found in our study, indicating a one-size-fits-all approach may not work.Trial Registration: We registered as a clinical trial at the International Clinical Trials Registry Platform (No. NCT01764867) and obtained ethical approval 2012-42 from SMHC.

Self-processing in relation to emotion and reward processing in depression

BACKGROUND

Depression is characterised by a heightened self-focus, which is believed to be associated with differences in emotion and reward processing. However, the precise relationship between these cognitive domains is not well understood. We examined the role of self-reference in emotion and reward processing, separately and in combination, in relation to depression.

METHODS

Adults experiencing varying levels of depression (n = 144) completed self-report depression measures (PHQ-9, BDI-II). We measured self, emotion and reward processing, separately and in combination, using three cognitive tasks.

RESULTS

When self-processing was measured independently of emotion and reward, in a simple associative learning task, there was little association with depression. However, when self and emotion processing occurred in combination in a self-esteem go/no-go task, depression was associated with an increased positive other bias [b = 3.51, 95% confidence interval (CI) 1.24-5.79]. When the self was processed in relation to emotion and reward, in a social evaluation learning task, depression was associated with reduced positive self-biases (b = 0.11, 95% CI 0.05-0.17).

CONCLUSIONS

Depression was associated with enhanced positive implicit associations with others, and reduced positive learning about the self, culminating in reduced self-favouring biases. However, when self, emotion and reward processing occurred independently there was little evidence of an association with depression. Treatments targeting reduced positive self-biases may provide more sensitive targets for therapeutic intervention and potential biomarkers of treatment responses, allowing the development of more effective interventions.

Symptoms that remain after depression treatment in patients with coronary heart disease

OBJECTIVE

Symptoms which commonly remain after treatment for major depression increase the risk of relapse and recurrence in medically well patients. The same symptoms predict major adverse cardiac events in observational studies of patients with coronary heart disease (CHD). The purpose of this study was to determine the prevalence and predictors of residual depression symptoms in depressed patients with CHD-.

METHODS

Beck Depression Inventory-II data from two randomized clinical trials and an uncontrolled treatment study of depression in patients with CHD were combined to determine the prevalence and predictors of residual symptoms.

RESULTS

Loss of energy, loss of pleasure, loss of interest, fatigue, and difficulty concentrating were the five most common residual symptoms in all three studies. They are also among the most common residual symptoms in medically well patients who are treated for depression. The severity of pre-treatment anxiety predicted the post-treatment persistence of all these symptoms except for loss of energy.

CONCLUSIONS

The most common post-treatment residual symptoms found in this study of patients with coronary heart disease and comorbid major depression are the same as those that have been reported in previous studies of medically-well depressed patients. This suggests that they may be resistant to standard depression treatments across diverse patient populations. More effective treatments for these symptoms are needed.

Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning

BACKGROUND

Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application.

METHODS

In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection.

RESULTS

Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data.

CONCLUSIONS

The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients.

[Prospects for the use of drugs with anti-inflammatory properties for the treatment of depression]

The review briefly summarizes experimental and preclinical data of the role of pro-inflammatory cytokines in triggering pathophysiological changes associated with depression, primarily major depressive disorder (MDD), as well as the possibility of using anti-inflammatory drugs as antidepressants.

The relationship between depressive symptoms, general psychopathology, and well-being in patients with major depressive disorder

OBJECTIVE

In mental health care, treatment effects are commonly monitored by symptom severity measures. This study aimed to investigate the relationship between symptom severity and well-being in the treatment of patients with major depressive disorder (MDD).

METHODS

Adult MDD outpatients (n = 77) were administered the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR), the Outcome Questionnaire (OQ-45), and the Mental Health Continuum-Short Form (MHC-SF) before treatment and 6 months later.

RESULTS

Symptom severity correlated moderately with well-being at baseline and strongly at follow-up. Reliable change index scores showed improvement on the QIDS-SR, OQ-45, and MHC-SF in 65%, 59%, and 40%, respectively. A quarter of patients improved in symptom severity but not well-being (Inventory of Depressive Symptomatology-Self-Report [IDS-SR]: 25%; OQ-45: 24%).

CONCLUSION

Findings suggest that symptom severity and subjective well-being are related, but distinct concepts. Several reasons for the stronger improvements in symptoms than in well-being are discussed.

Depression: Biological markers and treatment

Nowadays depression is considered as a systemic illness with different biological mechanisms involved in its etiology, including inflammatory response, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and neurotransmitter and neurotrophic systems imbalance. Novel "omics" approaches, such as metabolomics and glycomics provide information about altered metabolic pathways and metabolites, as well as disturbances in glycosylation processes affected by or causing the development of depression. The clinical diagnosis of depression continues to be established based on the presence of the specific symptoms, but due to its heterogeneous underlying biological background, that differs according to the disease stage, there is an unmet need for treatment response biomarkers which would facilitate the process of appropriate treatment selection. This paper provides an overview of the role of major stress response system, the HPA axis, and its dysregulation in depression, possible involvement of neurotrophins, especially brain-derived neurotrophic factor, glial cell line-derived neurotrophic factor and insulin-like growth factor-1, in the development of depression. Article discusses how activated inflammation processes and increased cytokine levels, as well as disturbed neurotransmitter systems can contribute to different stages of depression and could specific metabolomic and glycomic species be considered as potential biomarkers of depression. The second part of the paper includes the most recent findings about available medical treatment of depression. The described biological factors impose an optimistic conclusion that they could represent easy obtainable biomarkers potentially predicting more personalized treatment and diagnostic options.

Predictors of poor 6-week outcome in a cohort of major depressive disorder patients treated with antidepressant medication: the role of entrapment

BACKGROUND

Only a small number of consistent processes predict which depressed patients will achieve remission with antidepressant medication. One set of processes is that of social ranking strategies/variables that are related to life events and severe difficulties. Particularly, defeat and entrapment predict poorer response to antidepressants. However, results are inconsistent.

AIM

The current study aimed to evaluate evolutionary strategies, childhood maltreatment, neglect and life events and difficulties (LEDs) as predictors of remission in depressed patients undergoing pharmacological treatment in a psychiatric outpatient sample.

METHODS

A cohort of 139 depressed outpatients undergoing pharmacological treatment was followed prospectively in a naturalistic study for 6 weeks. Two major evaluations were considered at baseline and 6 weeks. We allocated patients to a pharmacological treatment algorithm for depression - the Texas Medication Algorithm Project. Variables evaluated at baseline and tested as predictors of remission included demographic and clinical data, severity of depression, social ranking, evolution informed variables, LEDs and childhood maltreatment.

RESULTS

Of the 139 patients, only 24.5% were remitted at week 6. In univariate analyses, non-remitted patients scored significantly higher in all psychopathology and vulnerability scales except for submissive behaviour and internal entrapment. For the logistic regression, a higher load of LEDs of the entrapment and humiliation dimension in the year before the index episode (OR = 6.62), and higher levels external entrapment in the Entrapment Scale (OR = 1.10) predicted non-remission. These variables accounted for 28.7% of the variance.

CONCLUSIONS

Multivariate analysis revealed that external entrapment was the only predictor of non-remission.

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients' response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation-electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches - and then widespread incorporation of the precision clinical trial framework - could help achieve the vision of precision medicine for neurobehavioural conditions.

On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study

Background

The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect.

Methods

Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo.

Results

The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed.

Conclusions

The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

Improving Therapeutic Interventions of Schizophrenia with Advances in Stem Cell Technology

Although historic documents posit schizophrenia to the beginnings of mankind, its diagnosis remains poorly defined, currently relying on unspecific clinical symptoms; and controversies still maintain its origin under intense debate. This review aimed at quantitatively assessing the preferential forefronts of clinical trials towards the treatment of schizophrenia from inception till present, according to clinicaltrials.gov database registry. Towards that end study status and study phase classifications were used as criteria for progress in the field. Study groups by sex and age together with countries and organisms involved in the studies were used as indicators of the populations studied and as evidence of main promoter institutions, in both, pharmacological and drug-free protocols. The findings clearly show a decline of active clinical research with small synthetic compounds and limited numbers of novel initiatives, mostly based on drug-free alternatives with expected reduced secondary effects. A paucity of sex- and age-oriented designs is detected, and it is proposed that future clinical trials should set their basis on data obtained from patient-derived induced pluripotent stem cells, brain organoid systems and human brain circuitry platforms. Only individual precision medical approaches may turn effective for the treatment of this complex and highly incapacitating disease.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Symptom-based selection of antidepressants

Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.