Differential efficacy of escitalopram and nortriptyline on dimensional measures of depression

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment

OBJECTIVE

Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment.

METHOD

A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted.

RESULTS

Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters.

CONCLUSION

The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials

BACKGROUND

Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

AIMS

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

METHOD

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

RESULTS

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

CONCLUSIONS

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review

Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.

SuPAR in major depression: Association with 26 weeks antidepressant response and 10-year depression outcomes

Introduction

Inflammation has been associated with depression and differential antidepressant (AD) treatment response. Soluble urokinase plasminogen activator receptor (suPAR) is a novel measure of chronic inflammation. We investigated whether suPAR is associated with depression severity and AD response.

Methods

We included 90 patients with major depressive disorder (MDD) who participated in a part-randomized clinical trial of 26 weeks of treatment with escitalopram or nortriptyline. suPAR levels were measured in serum samples collected at baseline and after 8, 12 and 26 weeks. Mixed effects models for the association between suPAR levels and AD response were performed. By merging with Danish nationwide registers, we included information on psychiatric hospital contacts during ten years after the GENDEP trial. Cox regression analyses calculated the hazard rate ratios between suPAR levels and subsequent hospitalizations.

Results

At baseline, higher suPAR levels were not associated with overall depression severity but with greater severity of neurovegetative depressive symptoms, specifically appetite and weight changes. 57 (63.3%) patients responded positively to treatment. Among 57 (63.3%) patients who achieved response, those who responded had significantly higher baseline suPAR levels levels, and response was associated with a significant decrease in suPAR during AD treatment. Remitters decreased from 3.1 ng/ml at baseline to 2.8 ng/ml after 26 weeks (p = 0.003) and responders from 3.0 to 2.8 ng/ml (p = 0.02), whereas non-remitters and non-responders showed unchanged suPAR levels. We found no correlation between a change in suPAR and a change in MADRS, but a lowering of suPAR correlated with a decrease in neurovegetative symptoms. We found no association between suPAR levels and 10-year risk for hospitalizations.

Discussion

The present study suggests that an elevated level of chronic inflammation, measured as the suPAR level, is associated with better response to AD treatment.

Psychopharmacology of smoking cessation medications: focus on patients with mental health disorders

The adverse effects of smoking cessation in individuals with mental health disorders have been a point of concern, and progress in the development of treatment has been slow. The primary first-line treatments for smoking cessation are Nicotine Replacement Therapy, Bupropion, Varenicline, and behavioural support. Nortriptyline and Clonidine are second-line treatments used when the first-line treatments are not effective or are contraindicated. Smoking cessation medications have been shown to be effective in reducing nicotine cravings and withdrawal symptoms and promoting smoking cessation among patients living with mental disorders. However, these medications may have implications for patients' mental health and need to be monitored closely. The efficacy and side effects of these medications may vary depending on the patient's psychiatric condition, medication regimen, substance use, or medical comorbidities. The purpose of this review is to synthesise the pharmacokinetics, pharmacodynamics, therapeutic effects, adverse effects, and pharmacological interactions of first- and second-line smoking cessation drugs, with an emphasis on patients suffering from mental illnesses. Careful consideration of the risks and benefits of using smoking cessation medications is necessary, and treatment plans must be tailored to individual patients' needs. Monitoring symptoms and medication regimens is essential to ensure optimal treatment outcomes.

Depressive symptom dimensions predict the treatment effect of repetitive transcranial magnetic stimulation for post-stroke depression

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) has attracted considerable attention because of its non-invasiveness, minimal side effects, and treatment efficacy. Despite an adequate duration of rTMS treatment, some patients with post-stroke depression (PSD) do not achieve full symptom response or remission.

METHODS

This was a prospective randomized controlled trial. Participants receiving rTMS were randomly assigned to the ventromedial prefrontal cortex (VMPFC), left dorsolateral prefrontal cortex (DLPFC), or contralateral motor area (M1) groups in a ratio of 1:1:1. Enrollment assessments and data collection were performed in weeks 0, 2, 4, and 8. The impact of depressive symptom dimensions on treatment outcomes were tested using a linear mixed-effects model fitted with maximum likelihood. Univariate analysis of variance (ANOVA) and back-testing were used to analyze the differences between the groups.

RESULTS

In total, 276 patients were included in the analysis. Comparisons across groups showed that 17-item Hamilton Rating Scale for Depression (HAMD-17) scores of the DLPFC group significantly differed from those of the VMPFC and M1 groups at 2, 4, and 8 weeks after treatment (p < 0.05). A higher observed mood score (β = -0.44, 95% confidence interval [CI]: -0.85-0.04, p = 0.030) could predict a greater improvement in depressive symptoms in the DLPFC group. Higher neurovegetative scores (β = 0.60, 95% CI: 0.25-0.96, p = 0.001) could predict less improvement of depressive symptoms in the DLPFC group.

CONCLUSION

Stimulation of the left DLPFC by high-frequency rTMS (HF-rTMS) could significantly improve depressive symptoms in the subacute period of subcortical ischemic stroke, and the dimension of depressive symptoms at admission might predict the treatment effect.

Dimensions of temperament and character as predictors of antidepressant discontinuation, response and adverse reactions during treatment with nortriptyline and escitalopram

BACKGROUND

Personality traits may predict antidepressant discontinuation and response. However, previous studies were rather small, only explored a few personality traits and did not include adverse drug effects nor the interdependency between antidepressant discontinuation patterns and response.

METHODS

GENDEP included 589 patients with unipolar moderate-severe depression treated with escitalopram or nortriptyline for 12 weeks. Seven personality dimensions were measured using the self-reported 240-item Temperament and Character Inventory-Revised (TCI-R). We applied Cox proportional models to study discontinuation patterns, logistic and linear regression to investigate response and remission after 8 and 12 weeks, and mixed-effects linear models regarding time-varying treatment response and adverse drug reactions.

RESULTS

Low harm avoidance, low cooperativeness, high self-transcendence and high novelty seeking were associated with higher risks for antidepressant discontinuation, independent of depressed mood, adverse drug reactions, drug, sex and age. Regression analyses showed that higher novelty seeking and cooperativeness scores were associated with a greater likelihood of response and remission after 8 and 12 weeks, respectively, but we found no correlations with response in the mixed-effects models. Only high harm avoidance was associated with more self-reported adverse effects.

CONCLUSIONS

This study, representing the largest investigation between several personality traits and response to two different antidepressants, suggests that correlations between personality traits and antidepressant treatment response may be confounded by differential rates of discontinuation. Future trials on personality in the treatment of depression need to consider this interdependency and study whether interventions aiming at improving compliance for some personality types may improve response to antidepressants.

The complex clinical response to selective serotonin reuptake inhibitors in depression: a network perspective

The clinical response to selective serotonin reuptake inhibitors (SSRIs) in depression takes weeks to be fully developed and is still not entirely understood. This study aimed to determine the direct and indirect effects of SSRIs relative to a placebo control condition on clinical symptoms of depression. We included data of 8262 adult patients with major depression participating in 28 industry-sponsored US Food and Drug Administration (FDA) registered trials on the efficacy of SSRIs. Clinical symptoms of depression were assessed by the 17 separate items of the Hamilton Depression Rating Scale (HDRS) after 1, 2, 3, 4 and 6 weeks of treatment. Network estimation techniques showed that SSRIs had quick and strong direct effects on the two affective symptoms, i.e., depressed mood and psychic anxiety; direct effects on other symptoms were weak or absent. Substantial indirect effects were found for all four cognitive symptoms, which showed larger reductions in the SSRI condition but mainly in patients reporting larger reductions in depressed mood. Smaller indirect effects were found for two arousal/somatic symptoms via the direct effect on psychic anxiety. Both direct and indirect effects on sleep problems and most arousal/somatic symptoms were weak or absent. In conclusion, our study revealed that SSRIs primarily caused reductions in affective symptoms, which were related to reductions in mainly cognitive symptoms and some specific arousal/somatic symptoms. The results can contribute to disclosing the mechanisms of action of SSRIs, and has the potential to facilitate early detection of responders and non-responders in clinical practice.

Differential symptom cluster responses to repetitive transcranial magnetic stimulation treatment in depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) can target specific neural circuits, which may allow for personalized treatment of depression. Treatment outcome is typically determined using sum scores from validated measurement scales; however, this may obscure differential improvements within distinct symptom domains. The objectives for this work were to determine: (1) whether a standard depression measure can be represented using a four symptom cluster model and (2) whether these symptom clusters had a differential response to rTMS treatment.

METHODS

Data were obtained from two multi-centre randomized controlled trials of rTMS delivered to the left dorsolateral prefrontal cortex (DLPFC) for participants with treatment-resistant depression (TRD) conducted in Canada (THREE-D [Conducted between Sept 2013, and Oct 2016] and CARTBIND [Conducted between Apr 2016 and Feb 2018]). The first objective used confirmatory factor analytic techniques, and the second objective used a linear mixed effects model. Trial Registration: NCT01887782, NCT02729792.

FINDINGS

In the total sample of 596 participants with TRD, we found a model consisting of four symptom clusters adequately fit the data. The primary analysis using the THREE-D treatment trial found that symptom clusters demonstrated a differential response to rTMS treatment (F(3,5984) = 31.92, p < 0.001). The anxiety symptom cluster was significantly less responsive to treatment than other symptom clusters (t(6001) = -8.02, p < 0.001). These findings were replicated using data from the CARTBIND trial.

INTERPRETATION

There are distinct symptom clusters experienced by individuals with TRD that have a differential response to rTMS. Future work will determine whether differing rTMS treatment targets have distinct patterns of symptom cluster responses with the eventual goal of personalizing rTMS protocols based on an individual's clinical presentation.

FUNDING

Canadian Institutes of Health Research, Brain Canada.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

BACKGROUND

The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo.

METHODS

The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment.

RESULTS

Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants.

CONCLUSIONS

PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Biomarkers as predictors of treatment response to tricyclic antidepressants in major depressive disorder: A systematic review

Tricyclic antidepressants (TCAs) are frequently prescribed in case of non-response to first-line antidepressants in Major Depressive Disorder (MDD). Treatment of MDD often entails a trial-and-error process of finding a suitable antidepressant and its appropriate dose. Nowadays, a shift is seen towards a more personalized treatment strategy in MDD to increase treatment efficacy. One of these strategies involves the use of biomarkers for the prediction of antidepressant treatment response. We aimed to summarize biomarkers for prediction of TCA specific (i.e. per agent, not for the TCA as a drug class) treatment response in unipolar nonpsychotic MDD. We performed a systematic search in PubMed and MEDLINE. After full-text screening, 36 papers were included. Seven genetic biomarkers were identified for nortriptyline treatment response. For desipramine, we identified two biomarkers; one genetic and one nongenetic. Three nongenetic biomarkers were identified for imipramine. None of these biomarkers were replicated. Quality assessment demonstrated that biomarker studies vary in endpoint definitions and frequently lack power calculations. None of the biomarkers can be confirmed as a predictor for TCA treatment response. Despite the necessity for TCA treatment optimization, biomarker studies reporting drug-specific results for TCAs are limited and adequate replication studies are lacking. Moreover, biomarker studies generally use small sample sizes. To move forward, larger cohorts, pooled data or biomarkers combined with other clinical characteristics should be used to improve predictive power.

Specific depression dimensions are associated with a faster rate of cognitive decline in older adults

Introduction

Understanding the relationship between different depression presentations and cognitive outcome may elucidate high-risk sub-groups for cognitive decline.

Methods

In this study we utilized longitudinal data from the National Alzheimer's Coordinating Center (NACC) on 16,743 initially not demented older adults followed every 12 months for an average of 5 years. Depression dimensions were defined based on the 15-item Geriatric Depression Scale (GDS-15), that is, dysphoric mood, Withdrawal-Apathy-Vigor (WAV), anxiety, hopelessness, and subjective memory complaint (SMC).

Results

After adjustment for sociodemographic and clinical covariates, SMC and hopelessness were associated with faster decline in global cognition and all cognitive domains and WAV with decline executive function. Dysphoric mood and anxiety were not associated with a faster cognitive decline in any of the cognitive domains.

Discussion

Different depression dimensions had different associations with the rate of cognitive decline, suggesting distinct pathophysiology and the need for more targeted interventions.

Depression and inflammation: Correlation between changes in inflammatory markers with antidepressant response and long-term prognosis

Inflammation may correlate with a specific subgroup of depression and differential antidepressant response, but no trial has studied changes of many inflammatory markers over several time points and evaluated symptom-specific antidepressant response and long-term prognosis. We performed secondary analyses among 90 outpatients with moderate-severe depression (71% female, mean age 38 years) treated for 26 weeks with escitalopram or nortriptyline. We measured 27 pro- and anti-inflammatory markers at week 0, 8, 12, and 26 and calculated composite inflammation scores. Three depression rating scales were applied and symptom dimensions of depression calculated. Via Danish nationwide registers, 10 years follow-up were included on psychiatric hospital contacts, indicating relapse. Pearson correlation analyses were performed between baseline inflammatory markers and depressive symptom severity, mixed effects models during the 26-week trial, and Cox regression analyses for the register-based outcomes, adjusted for age, sex, BMI, and smoking. Baseline inflammatory markers correlated with differential severity on specific symptom dimensions but not with overall depression severity. A total of 17 of 27 inflammatory markers decreased significantly during treatment. We found no correlation between baseline nor change in inflammatory markers nor composite inflammation scores with differential treatment response on the MADRS, but small correlations between changes in inflammatory markers and differential response on neurovegetative symptoms. Findings were similar among 59 treatment-naïve patients. Inflammatory markers were not associated with differential risks for 10-year relapse. These findings support the importance of studying specific depressive symptoms to further explore the correlation between inflammation with differential antidepressant response in a subgroup of depression. Clinical Trial Registration number: GENDEP is registered at EudraCT2004-001723-38 (http://eudract.emea.europa.eu) and ISRCTN03693000 (www.controlled-trials.com).

Post-Stroke Depressive Symptoms: Varying Responses to Escitalopram by Individual Symptoms and Lesion Location

OBJECTIVE

The efficacy of antidepressants in post-stroke depressive symptoms (PSD) varies. We aimed to examine whether the effect of escitalopram on PSD differs according to individual depressive symptoms and stroke lesion location.

METHODS

This is a post hoc analysis of EMOTION (ClinicalTrials.gov, NCT01278498), a randomized, placebo-controlled, double-blind trial that examined the efficacy of escitalopram on depression in acute stroke patients (237 with placebo, 241 with escitalopram). Depressive symptoms were evaluated with the 10-item Montgomery-Åsberg Depression Rating Scale (MADRS). Changes in MADRS and individual item scores at 12 weeks were compared between the treatment groups and among the stroke lesion location groups. Stroke lesion locations were grouped according to the anatomical distribution of serotonin fibers that originate from the midbrain/pons and spread to the forebrain via subcortical structures: "Midbrain-Pons," "Frontal-Subcortical," and "Others." Least-squares means were calculated to demonstrate the independent effect of lesion location.

RESULTS

Total MADRS scores decreased more significantly in the escitalopram than in the placebo group, while a significant effect of escitalopram was observed in only 3 items: apparent sadness, reported sadness, pessimistic thoughts. In the lesion location analyses, escitalopram users in the Frontal-Subcortical group showed significant improvement in total MADRS scores (placebo [n = 130] vs. escitalopram [n = 148], least-square mean [95% CI]: -2.3 [-3.5 to -0.2] vs. -4.5 [-5.5 to -3.4], p = .005), while those in the Midbrain-Pons and Others groups did not.

CONCLUSIONS

The effect of escitalopram on PSD may be more prominent in patients with particular depressive symptoms and stroke lesion locations, suggesting the need for tailored treatment strategies.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Sport Psychiatry and Its Research Agenda

Athletes commonly experience mental health symptoms. However, prevalence estimates require refinement so that symptoms are interpreted in context and diagnostic labels are accurately applied. Further prevalence studies are also needed in subgroups within sport, in particular female athletes, athletes with disabilities, and coaches. Existing consensus-based and evidence-based therapies must be adapted not only to the individual athlete but also to the ecology of sports. Filling the gaps in our knowledge on what treatment modifications may be required for the individual athlete and how services should be designed to deliver treatment most effectively will require well-designed studies that use standardized terminology and defined outcome measures.

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Parsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS

BACKGROUND

Transcranial direct current stimulation (tDCS) presents small antidepressant efficacy at group level and considerable inter-individual variability of response. Its heterogeneous effects bring the need to investigate whether specific groups of patients submitted to tDCS could present comparable or larger improvement compared to pharmacotherapy. Aggregate measurements might be insufficient to address its effects.

OBJECTIVE

/Hypothesis: To determine the efficacy of tDCS, compared to pharmacotherapy and placebo, in depressive symptom clusters.

METHODS

Data from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Therapy for Treating Depression Clinical Study, ClinicalTrials.gov, NCT01894815), in which antidepressant-free, depressed patients were randomized to receive 22 bifrontal tDCS (2 mA, 30 min) sessions (n = 94), escitalopram 20 mg/day (n = 91), or placebo (n = 60) over 10 weeks. Agglomerative hierarchical clustering identified "sleep/insomnia", "core depressive", "guilt/anxiety", and "atypical" clusters that were the dependent measure. Trajectories were estimated using linear mixed regression models. Effect sizes are expressed in raw HAM-D units. P-values were adjusted for multiple comparisons.

RESULTS

For core depressive symptoms, escitalopram was superior to tDCS (ES = -0.56; CI_95% = -0.94 to -0.17, p = .009), which was superior to placebo (ES = 0.49; CI_95% = 0.06 to 0.92, p = .042). TDCS but not escitalopram was superior to placebo in sleep/insomnia symptoms (ES = 0.87; CI_95% = 0.22 to 1.52, p = .015). Escitalopram but not tDCS was superior to placebo in guilt/anxiety symptoms (ES = 1.66; CI_95% = 0.58 to 2.75, p = .006). No active intervention was superior to placebo for atypical symptoms.

CONCLUSIONS

Pharmacotherapy and non-invasive brain stimulation produce distinct effects in depressive symptoms. TDCS or escitalopram could be chosen according to specific clusters of symptoms for a bigger response.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01894815.

Effects of dietary interventions on depressive symptom profiles: results from the MooDFOOD depression prevention study

BACKGROUND

Dietary interventions did not prevent depression onset nor reduced depressive symptoms in a large multi-center randomized controlled depression prevention study (MooDFOOD) involving overweight adults with subsyndromal depressive symptoms. We conducted follow-up analyses to investigate whether dietary interventions differ in their effects on depressive symptom profiles (mood/cognition; somatic; atypical, energy-related).

METHODS

Baseline, 3-, 6-, and 12-month follow-up data from MooDFOOD were used (n = 933). Participants received (1) placebo supplements, (2) food-related behavioral activation (F-BA) therapy with placebo supplements, (3) multi-nutrient supplements (omega-3 fatty acids and a multi-vitamin), or (4) F-BA therapy with multi-nutrient supplements. Depressive symptom profiles were based on the Inventory of Depressive Symptomatology.

RESULTS

F-BA therapy was significantly associated with decreased severity of the somatic (B = -0.03, p = 0.014, d = -0.10) and energy-related (B = -0.08, p = 0.001, d = -0.13), but not with the mood/cognition symptom profile, whereas multi-nutrient supplementation was significantly associated with increased severity of the mood/cognition (B = 0.05, p = 0.022, d = 0.09) and the energy-related (B = 0.07, p = 0.002, d = 0.12) but not with the somatic symptom profile.

CONCLUSIONS

Differentiating depressive symptom profiles indicated that food-related behavioral interventions are most beneficial to alleviate somatic symptoms and symptoms of the atypical, energy-related profile linked to an immuno-metabolic form of depression, although effect sizes were small. Multi-nutrient supplements are not indicated to reduce depressive symptom profiles. These findings show that attention to clinical heterogeneity in depression is of importance when studying dietary interventions.

Remote Digital Measurement of Facial and Vocal Markers of Major Depressive Disorder Severity and Treatment Response: A Pilot Study

Objectives: Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine uptake inhibitors (SNRIs). Methods: Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT (n = 18). MDD diagnosis was confirmed using the Mini-International Neuropsychiatric Interview (MINI), and the Montgomery-Åsberg Depression Rating Scale (MADRS) was collected concordantly to assess changes in MDD severity. Results: Patient responses to ADT demonstrated clinically and statistically significant changes in the MADRS [F (2, 34) = 51.62, p < 0.0001]. Additionally, patients demonstrated significant increases in multiple digital markers including facial expressivity, head movement, and amount of speech. Finally, patients demonstrated significantly decreased frequency of fear and anger facial expressions. Conclusion: Digital markers associated with MDD demonstrate validity as measures of treatment response.

How Can Drug Metabolism and Transporter Genetics Inform Psychotropic Prescribing?

Many genetic variants in drug metabolizing enzymes and transporters have been shown to be relevant for treating psychiatric disorders. Associations are strong enough to feature on drug labels and for prescribing guidelines based on such data. A range of commercial tests are available; however, there is variability in included genetic variants, methodology, and interpretation. We herein provide relevant background for understanding clinical associations with specific variants, other factors that are relevant to consider when interpreting such data (such as age, gender, drug-drug interactions), and summarize the data relevant to clinical utility of pharmacogenetic testing in psychiatry and the available prescribing guidelines. We also highlight areas for future research focus in this field.

The clinical characterization of the adult patient with depression aimed at personalization of management

Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial-and-error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta-analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self-report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above-mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

Depression symptoms and cognitive impairment in older nursing home residents in the USA: A latent class analysis

OBJECTIVES

To identify subgroups of nursing home (NH) residents in the USA experiencing homogenous depression symptoms and evaluate if subgroups vary by cognitive impairment.

METHODS

We identified 104 465 newly admitted, long-stay residents with depression diagnosis at NH admission in 2014 using the Minimum Data Set 3.0. The Patient Health Questionnaire-9 was used to measure depression symptoms and the Brief Interview of Mental Status for cognitive impairment (intact; moderately impaired; severely impaired). Latent class analysis (LCA) with logistic regression was used to: (a) construct the depression subgroups and (b) estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) of the associations between the subgroups and cognitive impairment level, adjusting for demographic and clinical characteristics.

RESULTS

The best-fitted LCA model suggested four subgroups of depression: minimal symptoms (latent class prevalence: 42.4%), fatigue (32.0%), depressed mood (14.5%), and multiple symptoms (11.2%). Odds of subgroup membership varied by cognitive impairment. Compared to residents with intact cognition, those with moderate or severe cognitive impairment were less likely to belong to the fatigue subgroup [aOR(95% CI): moderate: 0.75 (0.71-0.80); severe: 0.26 (0.23-0.29)] and more likely to belong to the depressed mood subgroup [aOR (95% CI): moderate: 4.54 (3.55-5.81); severe: 6.41 (4.86-8.44)]. Residents with moderate cognitive impairment had increased odds [aOR (95% CI): 1.19 (1.12-1.27)] while those with severe impairment had reduced odds of being in the multiple symptoms subgroup [aOR (95% CI): 0.63 (0.58-0.68)].

CONCLUSIONS

Findings provide a basis for improving depression management with consideration of both subgroups of depression symptoms and levels of cognitive function.

A functional variant in the serotonin receptor 7 gene (HTR7), rs7905446, is associated with good response to SSRIs in bipolar and unipolar depression

Predicting antidepressant response has been a clinical challenge for mood disorder. Although several genome-wide association studies have suggested a number of genetic variants to be associated with antidepressant response, the sample sizes are small and the results are difficult to replicate. Previous animal studies have shown that knockout of the serotonin receptor 7 gene (HTR7) resulted in an antidepressant-like phenotype, suggesting it was important to antidepressant action. In this report, in the first stage, we used a cost-effective pooled-sequencing strategy to sequence the entire HTR7 gene and its regulatory regions to investigate the association of common variants in HTR7 and clinical response to four selective serotonin reuptake inhibitors (SSRIs: citalopram, paroxetine, fluoxetine and sertraline) in a retrospective cohort mainly consisting of subjects with bipolar disorder (n = 359). We found 80 single-nucleotide polymorphisms (SNPs) with false discovery rate < 0.05 associated with response to paroxetine. Among the significant SNPs, rs7905446 (T/G), which is located at the promoter region, also showed nominal significance (P < 0.05) in fluoxetine group. GG/TG genotypes for rs7905446 and female gender were associated with better response to two SSRIs (paroxetine and fluoxetine). In the second stage, we replicated this association in two independent prospective samples of SSRI-treated patients with major depressive disorder: the MARS (n = 253, P = 0.0169) and GENDEP studies (n = 432, P = 0.008). The GG/TG genotypes were consistently associated with response in all three samples. Functional study of rs7905446 showed greater activity of the G allele in regulating expression of HTR7. The G allele displayed higher luciferase activity in two neuronal-related cell lines, and estrogen treatment decreased the activity of only the G allele. Electrophoretic mobility shift assay suggested that the G allele interacted with CCAAT/enhancer-binding protein beta transcription factor (TF), while the T allele did not show any interaction with any TFs. Our results provided novel pharmacogenomic evidence to support the role of HTR7 in association with antidepressant response.

Hamilton scale and MADRS are interchangeable in meta-analyses but can disagree at trial level

BACKGROUND AND OBJECTIVE

Major depressive disorder is a multidimensional disease, in which demonstrating the efficacy of treatments is difficult. The Hamilton Rating Scale for Depression (HRSD) and the Montgomery-Asberg Depression Rating Scale (MADRS) cover different domains but are used interchangeably as primary measures of the outcome in trials and-with standardized measures-in meta-analyses. We aimed at understanding (i) whether the choice of the outcome measurement tool can influence the outcome of a trial, and if so, (ii) whether one systematically outperforms the other, and (iii) whether using standardized measures of the effect in meta-analysis is justified.

METHODS

Short-term randomized trials in patients with major depressive disorder that used both the scales were systematically searched and the results were collected. To quantify the differences in the results-both in terms of the standardized mean difference (SMD) and odds ratio (OR) for response-and their range, data were analyzed and plotted with the Bland-Altman method.

RESULTS

161 comparisons from 80 studies were included, involving a total of 18,189 patients. Neither of the two scales appears systematically more sensitive to the treatment effect than the other in terms of SMDs (P-value = 0.06, 95% CI -0.044 to 0.001) or ORs (P-value = 0.15, 95% CI -0.25 to 0.04). However, the variability of differences between the HRSD and MADRS largely depends on the number of patients included in the comparison.

CONCLUSION

No systematic differences between the two scales were found supporting the use of standardized measures in meta-analyses. However, the same trial may give very different results with either scale, especially in small trials. Further research is needed to understand the causes of this variability.

Concordance between clinician-rated and patient reported outcome measures of depressive symptoms in treatment resistant depression

BACKGROUND

Calls to implement measurement-based care (MBC) in psychiatry are increasing. A recent Cochrane meta-analysis concluded that there is insufficient evidence that routine application of patient reported outcomes (PROs) improves treatment outcomes for common psychiatric disorders. There is a particular paucity of this information in patients with treatment resistant depression (TRD).

METHODS

A TRD sample (n = 302) and a treatment-naïve sample with major depression (n = 344) were assessed for the level of agreement in depression severity between two PROs (the Beck Depression Inventory, BDI, and the Quick Inventory of Depressive Symptomatology Self-report, QIDS-SR) and two Clinician Rated (CRs) measures (Hamilton Depression Rating Scale, HDRS, and the Montgomery-Asberg Depression Rating Scale, MADRS).

RESULTS

Correlations between CR and PRO total scores in the TRD sample ranged from 0.57 (HDRS-QIDS-SR) to 0.68 (MADRS-BDI), reflecting a moderate-to-strong relationship between assessment tools. Correlations in the treatment naïve sample were non-significantly lower for most comparisons, ranging from 0.51 (HDRS-QIDS-SR) to 0.64 (MADRS-BDI). Few predictors of discordance between CRs and PROs were identified, though chronicity of the current episode in treatment-naïve patients was associated with greater agreement.

LIMITATIONS

Inter-rater reliability of the clinician interviews was conducted separately within the two studies so we could not determine the reliability between the two groups of raters used in the studies.

CONCLUSION

Findings generally supported acceptably high levels of agreement between patient and clinician ratings of baseline depression severity. More work is needed to determine the extent to which PROs can improve outcomes in MBC for depression and, more specifically, TRD.

Pharmacological management of depression: Japanese expert consensus

BACKGROUND

Clinically relevant issues in the real-world treatment of depression have not always been captured by conventional treatment guidelines.

METHODS

Certified psychiatrists of the Japanese Society of Clinical Neuropsychopharmacology were asked to evaluate treatment options regarding 23 clinical situations in the treatment of depression using a 9-point Likert scale (1="disagree" and 9="agree"). According to the responses of 114 experts, the options were categorized into first-, second-, and third-line treatments.

RESULTS

First-line antidepressants varied depending on predominant symptoms: escitalopram (mean ± standard deviation score, 7.8 ± 1.7) and sertraline (7.3 ± 1.7) were likely selected for anxiety; duloxetine (7.6 ± 1.9) and venlafaxine (7.2 ± 2.1) for loss of interest; mirtazapine for insomnia (8.2 ± 1.6), loss of appetite (7.9 ± 1.9), agitation and severe irritation (7.4 ± 2.0), and suicidal ideation (7.5 ± 1.9). While first-line treatment was switched to either an SNRI (7.7 ± 1.9) or mirtazapine (7.4 ± 2.0) in the case of non-response to an SSRI, switching to mirtazapine (7.1 ± 2.2) was recommended in the case of non-response to an SNRI, and vice versa (switching to an SNRI (7.0 ± 2.0) in the case of non-response to mirtazapine). Augmentation with aripiprazole was considered the first-line treatment for partial response to an SSRI (7.1 ± 2.3) or SNRI (7.0 ± 2.5).

LIMITATIONS

The evidence level of expert consensus is considered low. All included experts were Japanese.

CONCLUSIONS

Recommendations made by experts in the field are useful and can supplement guidelines and informed decision making in real-world clinical practice. We suggest that pharmacological strategies for depression be flexible and that each patient's situational needs as well as the pharmacotherapeutic profile of medications be considered.

Symptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial

BACKGROUND

Better understanding of the heterogeneity of treatment responses could help to improve care for adolescents with depression. We analysed data from a clinical trial to assess whether specific symptom clusters responded differently to various treatments.

METHODS

For this secondary analysis, we used data from the Treatment for Adolescents with Depression Study (TADS), in which 439 US adolescents aged 12-17 with a DSM-IV diagnosis of major depressive disorder and a minimum score of 45 on the Children's Depression Rating Scale-Revised (CDRS-R) were randomly assigned (1:1:1:1) to treatment with fluoxetine, cognitive behavioural therapy (CBT), fluoxetine plus CBT, or pill placebo. Our analysis focuses on the acute phase of the trial (ie, the first 12 weeks). Groups of co-occurring symptoms were established by clustering scores for each CDRS-R item at baseline with Ward's method, with Euclidean distances for hierarchical agglomerative clustering. We then used a linear mixed-effects model to investigate the relationship between symptom clusters and treatment efficacy, with the sum of symptom scores within each cluster as the dependent measure. As fixed effects, we entered cluster, time, and treatment assignment, with all two-way and three-way interactions, into the model. The random effect providing better fit was established to be a by-subject random slope for cluster based on improvement in the Schwarz-Bayesian information criterion.

OUTCOMES

We identified two symptom clusters: cluster 1 comprised depressed mood, difficulty having fun, irritability, social withdrawal, sleep disturbance, impaired schoolwork, excessive fatigue, and low self-esteem, and cluster 2 comprised increased appetite, physical complaints, excessive weeping, decreased appetite, excessive guilt, morbid ideation, and suicidal ideation. For cluster 1 symptoms, CDRS-R scores were reduced by 5·8 points (95% CI 2·8-8·9) in adolescents treated with fluoxetine plus CBT, and by 4·1 points (1·1-7·1) in those treated with fluoxetine, compared with those given placebo. For cluster 2 symptoms, no significant differences in improvements in CDRS-R scores were detected between the active treatment and placebo groups.

INTERPRETATION

Response to fluoxetine and CBT among adolescents with depression is heterogeneous. Clinicians should consider clinical profile when selecting therapeutic modality. The contrast in response patterns between symptom clusters could provide opportunities to improve treatment efficacy by gearing the development of new therapies towards the resolution of specific symptoms.

FUNDING

Conselho Nacional de Desenvolvimento Científico e Tecnológico.

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches

OBJECTIVE

Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of study clinicians. The extent to which findings from such studies generalize to naturalistic psychiatric settings is unclear. This study sought to predict depression outcomes for patients seeking treatment within an intensive psychiatric hospital setting and while comparing the performance of a range of machine learning approaches.

METHOD

Depressed patients (N = 484; ages 18-72; 89% White) receiving treatment within a psychiatric partial hospital program delivering pharmacotherapy and cognitive behavioral therapy were split into a training sample and holdout sample. First, within the training sample, 51 pretreatment variables were submitted to 13 machine learning algorithms to predict, via cross-validation, posttreatment Patient Health Questionnaire-9 depression scores. Second, the best performing modeling approach (lowest mean squared error; MSE) from the training sample was selected to predict outcome in the holdout sample.

RESULTS

The best performing model in the training sample was elastic net regularization (ENR; MSE = 20.49, R2 = .28), which had comparable performance in the holdout sample (MSE = 11.26; R2 = .38). There were 14 pretreatment variables that predicted outcome. To demonstrate the translation of an ENR model to personalized prediction of treatment outcome, a patient-specific prognosis calculator is presented.

CONCLUSIONS

Informed by pretreatment patient characteristics, such predictive models could be used to communicate prognosis to clinicians and to guide treatment planning. Identified predictors of poor prognosis may suggest important targets for intervention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Estimating dose-response for time to remission with instrumental variable adjustment: the obscuring effects of drug titration in Genome Based Therapeutic Drugs for Depression Trial (GENDEP): clinical trial data

Background

Threshold regression, in which time to remission is modelled as a stochastic drift towards a boundary, is an alternative to the proportional hazards survival model and has a clear conceptual mechanism for examining the effects of drug dose. However, for both threshold regression and proportional hazard models, when dose titration occurs during treatment, the estimated causal effect of dose can be biased by confounding. An instrumental variable analysis can be used to minimise such bias.

Method

Weekly antidepressant dose was measured in 380 men and women with major depression treated with escitalopram or nortriptyline for 12 weeks as part of the Genome Based Therapeutic Drugs for Depression (GENDEP) study. The averaged dose relative to maximum prescribing dose was calculated from the 12 trial weeks and tested for association with time to depression remission. We combined the instrumental variable approach, utilising randomised treatment as an instrument, with threshold regression and proportional hazard survival models.

Results

The threshold model was constructed with two linear predictors. In the naïve models, averaged daily dose was not associated with reduced time to remission. By contrast, the instrumental variable analyses showed a clear and significant relationship between increased dose and faster time to remission, threshold regression (velocity estimate: 0.878, 95% confidence interval [CI]: 0.152–1.603) and proportional hazards (log hazards ratio: 3.012, 95% CI: 0.086–5.938).

Conclusions

We demonstrate, using the GENDEP trial, the benefits of these analyses to estimate causal parameters rather than those that estimate associations. The results for the trial dataset show the link between antidepressant dose and time to depression remission. The threshold regression model more clearly distinguishes the factors associated with initial severity from those influencing treatment effect. Additionally, applying the instrumental variable estimator provides a more plausible causal estimate of drug dose on treatment effect. This validity of these results is subject to meeting the assumptions of instrumental variable analyses.

Trial registration

EudraCT, 2004–001723-38; ISRCTN, 03693000. Registered on 27 September 2007.

Comparing sensitivity to change using the 6-item versus the 17-item Hamilton depression rating scale in the GUIDED randomized controlled trial

BACKGROUND

Previous research suggests that the 17-item Hamilton Depression Rating Scale (HAM-D17) is less sensitive in detecting differences between active treatment and placebo for major depressive disorder (MDD) than is the HAM-D6 scale, which focuses on six core depression symptoms. Whether HAM-D6 shows greater sensitivity when comparing two active MDD treatment arms is unknown.

METHODS

This post hoc analysis used data from the intent-to-treat (ITT) cohort (N = 1541) of the Genomics Used to Improve DEpression Decisions (GUIDED) trial, a rater- and patient-blinded randomized controlled trial. GUIDED compared combinatorial pharmacogenomics-guided care with treatment as usual (TAU) in patients with MDD. Percent of symptom improvement, response rate and remission rate from baseline to week 8 were evaluated using both scales. Analyses were performed for the full cohort and for the subset of patients who at baseline were taking medications predicted by the test to have moderate or significant gene-drug interactions. A Mokken scale analysis was conducted to compare the homogeneity of HAM-D17 with that of HAM-D6.

RESULTS

At week 8, the guided-care arm demonstrated statistically significant benefit over TAU when the HAM-D6 (∆ = 4.4%, p = 0.023) was used as the continuous measure of symptom improvement, but not when using the HAM-D17 (∆ = 3.2%, p = 0.069). Response rates increased significantly for guided-care compared with TAU when evaluated using both HAM-D6 (∆ = 7.0%, p = 0.004) and HAM-D17 (∆ = 6.3%, p = 0.007). Remission rates also were significantly greater for guided-care versus TAU using both measures (HAM-D6 ∆ = 4.6%, p = 0.031; HAM-D17 ∆ = 5.5%, p = 0.005). Patients in the guided-care arm who at baseline were taking medications predicted to have gene-drug interactions showed further increased benefit over TAU at week 8 for symptom improvement (∆ = 7.3%, p = 0.004) response (∆ = 10.0%, p = 0.001) and remission (∆ = 7.9%, p = 0.005) using HAM-D6. All outcomes showed continued improvement through week 24. Mokken scale analysis demonstrated the homogeneity and unidimensionality of HAM-D6, but not of HAM-D17, across treatment arms.

CONCLUSIONS

The HAM-D6 scale identified a statistically significant difference in symptom improvement between combinatorial pharmacogenomics-guided care and TAU, whereas the HAM-D17 did not. The demonstrated utility of pharmacogenomics-guided treatment over TAU as detected by the HAM-D6 highlights its value for future biomarker-guided trials comparing active treatment arms.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT02109939. Registered 10 April 2014.

Sertraline and Mirtazapine Versus Placebo in Subgroups of Depression in Dementia: Findings From the HTA-SADD Randomized Controlled Trial

OBJECTIVE

Studies have shown that antidepressants are no better than placebo in treating depression in dementia. The authors examined antidepressant efficacy in subgroups of depression in dementia with different depressive symptom profiles.

METHODS

This study focuses on exploratory secondary analyses on the randomized, parallel-group, double-blind, placebo-controlled Health Technology Assessment Study of the Use of Antidepressants for Depression in Dementia (HTA-SADD) trial. The setting included old-age psychiatry services in nine centers in England. The participants included 326 patients meeting National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association probable/possible Alzheimer disease criteria, and Cornell Scale for Depression in Dementia (CSDD) scores of 8 or more. Intervention was placebo (n = 111), sertraline (n = 107), or mirtazapine (n = 108). Latent class analyses (LCA) on baseline CSDD items clustered participants into symptom-based subgroups. Mixed-model analysis evaluated CSDD improvement at 13 and 39 weeks by randomization in each subgroup.

RESULTS

LCA yielded 4 subgroups: severe (n = 34), psychological (n = 86), affective (n = 129), and somatic (n = 77). Mirtazapine, but not sertraline, outperformed placebo in the psychological subgroup at week 13 (adjusted estimate: -2.77 [standard error (SE) 1.16; 95% confidence interval: -5.09 to -0.46]), which remained, but lost statistical significance at week 39 (adjusted estimate: -2.97 [SE 1.59; 95% confidence interval: -6.15 to 0.20]). Neither sertraline nor mirtazapine outperformed placebo in the other subgroups.

CONCLUSION

Because of the exploratory nature of the analyses and the small sample sizes for subgroup analysis there is the need for caution in interpreting these data. Replication of the potential effects of mirtazapine in the subgroup of those with depression in dementia with "psychological" symptoms would be valuable. These data should not change clinical practice, but future trials should consider stratifying types of depression in dementia in secondary analyses.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

Effect of antidepressant switching between nortriptyline and escitalopram after a failed first antidepressant treatment among patients with major depressive disorder

BACKGROUND

For patients with major depressive disorder (MDD) experiencing side-effects or non-response to their first antidepressant, little is known regarding the effect of switching between a tricyclic antidepressant (TCA) and a selective serotonin reuptake inhibitor (SSRI).AimsTo compare the switch between the TCA nortriptyline and the SSRI escitalopram.

METHOD

Among 811 adults with MDD treated with nortriptyline or escitalopram for up to 12 weeks, 108 individuals switched from nortriptyline to escitalopram or vice versa because of side-effects or non-response (trial registration: EudraCT No.2004-001723-38 (https://eudract.ema.europa.eu/) and ISRCTN No.03693000 (http://www.controlled-trials.com)). Patients were followed for up to 26 weeks after switching and response was measured with the Montgomery-Åsberg Depression Rating scale (MADRS). We performed adjusted mixed-effects linear regression models with full information maximum likelihood estimation reporting β-coefficients with 95% CIs.

RESULTS

Switching antidepressants resulted in a significant decrease in MADRS scores. This was present for switchers from escitalopram to nortriptyline (n = 36, β = -0.38, 95% CI -0.51 to -0.25, P&lt;0.001) and from nortriptyline to escitalopram (n = 72, β = -0.34, 95% CI -0.41 to -0.26, P&lt;0.001). Both switching options resulted in significant improvement among individuals who switched because of non-response or side-effects. The results were supported by analyses on other rating scales and symptom dimensions.

CONCLUSIONS

These results suggest that switching from a TCA to an SSRI or vice versa after non-response or side-effects to the first antidepressant may be a viable approach to achieve response among patients with MDD.Declarations of interestK.J.A. holds an Alberta Centennial Addiction and Mental Health Research Chair, funded by the Government of Alberta. K.J.A. has been a member of various advisory boards, received consultancy fees and honoraria, and has received research grants from various companies including Johnson and Johnson Pharmaceuticals Research and Development and Bristol-Myers Squibb Pharmaceuticals Limited. D.S. has served on advisory boards for, and received unrestricted grants from, Lundbeck and AstraZeneca. A.F. and P.M. have received honoraria for participating in expert panels for Lundbeck and GlaxoSmithKline.

Genetic endophenotypes for insomnia of major depressive disorder and treatment-induced insomnia

Major depressive disorder (MDD) is primarily hinged on the presence of either low mood and/or anhedonia to previously pleasurable events for a minimum of 2 weeks. Other clinical features that characterize MDD include disturbances in sleep, appetite, concentration and thoughts. The combination of any/both of the primary MDD symptoms as well as any four of the other clinical features has been referred to as MDD. The challenge for replicating gene association findings with phenotypes of MDD as well as its treatment outcome is putatively due to stratification of MDD patients. Likelihood for replication of gene association findings is hypothesized with specificity in symptoms profile (homogenous clusters of symptom/individual symptoms) evaluated. The current review elucidates the genetic factors that have been associated with insomnia symptom of MDD phenotype, insomnia symptom as a constellation of neuro-vegetative cluster of MDD symptom, insomnia symptom of MDD as an individual entity and insomnia feature of treatment outcome. Homozygous CC genotype of 3111T/C, GSK3B-AT/TT genotype of rs33458 and haplotype of TPH1 218A/C were associated with insomnia symptom of MDD. Insomnia symptom of MDD was not resolved in patients with the A/A genotype of HTR2A-rs6311 when treated with SSRI. Homozygous short (SS) genotype-HTTLPR, GG genotype of HTR2A-rs6311 and CC genotype of HTR2A-rs6313 were associated with AD treatment-induced insomnia, while val/met genotype of BDNF-rs6265 and the TT genotype of GSK-3beta-rs5443 reduced it. Dearth of association studies may remain the bane for the identification of robust genetic endophenotypes in line with findings for genotypes of HTR2A-rs6311.

Venlafaxine Mitigates Depressive-Like Behavior in Ovariectomized Rats by Activating the EPO/EPOR/JAK2 Signaling Pathway and Increasing the Serum Estradiol Level

Reduced estradiol levels are associated with depression in women during the transition to and after menopause. A considerable number of studies focusing on the theme of treating depression through the activation of erythropoietin (EPO)-induced signaling pathways have been published. Venlafaxine is an approved antidepressant drug that inhibits both serotonin and norepinephrine transporters. The aim of the present study was to investigate the effects of venlafaxine on the depressive-like behaviors and serum estradiol levels in female rats following ovariectomy (OVX) and the possible roles of EPO-induced signaling pathways. Venlafaxine (10 mg/kg/day) was orally administered to OVX rats over a period of 4 weeks using two different treatment regimens: either starting 24 h or 2 weeks after OVX. Venlafaxine showed a superior efficacy in inducing antidepressant-like effects after an acute treatment (24 h post-OVX) than after the delayed treatment (2 weeks post-OVX) and was characterized by a decreased immobility time in the forced swimming test. In parallel, venlafaxine induced EPO and EPO receptor mRNA expression and increased levels of phospho-Janus kinase 2 (p-JAK2), phospho-signal transducer and activator of transcription 5, and phospho-extracellular signal-regulated kinase 1/2 in the hippocampus of OVX rats. Meanwhile, rats exhibited a marked reduction in the hippocampal Bax/Bcl2 ratio, caspase-3 activity, and tumor necrosis factor alpha levels after venlafaxine treatment. Venlafaxine also increased the hippocampal brain-derived neurotrophic factor and serum estradiol levels. Based on these findings, venlafaxine exerts a neuroprotective effect on OVX rats that is at least partially attributed to the activation of EPO/EPOR/JAK2 signaling pathways, anti-apoptotic activities, anti-inflammatory activities, and neurotrophic activities, as well as an increase in serum estradiol level. Graphical Abstract ᅟ.

Latent toxoplasmosis and psychiatric symptoms - A role of tryptophan metabolism?

Toxoplasma gondii (TOX) is a common parasite which infects approximately one third of the human population. In recent years, it has been suggested that latent toxoplasmosis may be a risk factor for the development of mental disorders, particularly schizophrenia and anxiety. With regards to depression the results have been varied. The main objective of this study was to examine subpopulations from the Danish PRISME and GENDEP populations for TOX IgG antibodies. These consisted of: a group with symptoms of anxiety, a group suffering from burnout syndrome, as well as two different subpopulations with depression of differing severity. The secondary objective of this study was to examine whether tryptophan metabolism was altered in TOX-positive subjects within each subpopulation. Our results show that the anxiety and burnout populations were more likely to be TOX IgG seropositive. Furthermore, we find that the moderate-severe but not mild-moderate depressive subpopulation were associated with TOX seropositivety, suggesting a possible role of symptom severity. Additionally, we found that TOX positive subjects in the anxiety and burnout subpopulations had altered tryptophan metabolism. This relationship did not exist in the mild-moderate depressive subpopulation. These results suggest that TOX seropositivity may be related to anxiety, burnout and potentially to severity of depression. We furthermore show that the psychiatric symptoms could be associated with an altered tryptophan metabolism.

Point estimation following two-stage adaptive threshold enrichment clinical trials

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.

Genes associated with anhedonia: a new analysis in a large clinical trial (GENDEP)

A key feature of major depressive disorder (MDD) is anhedonia, which is a predictor of response to antidepressant treatment. In order to shed light on its genetic underpinnings, we conducted a genome-wide association study (GWAS) followed by investigation of biological pathway enrichment using an anhedonia dimension for 759 patients with MDD in the GENDEP study. The GWAS identified 18 SNPs associated at genome-wide significance with the top one being an intronic SNP (rs9392549) in PRPF4B (pre-mRNA processing factor 4B) located on chromosome 6 (P = 2.07 × 10-9) while gene-set enrichment analysis returned one gene ontology term, axon cargo transport (GO: 0008088) with a nominally significant P value (1.15 × 10-5). Furthermore, our exploratory analysis yielded some interesting, albeit not statistically significant genetic correlation with Parkinson's Disease and nucleus accumbens gray matter. In addition, polygenic risk scores (PRSs) generated from our association analysis were found to be able to predict treatment efficacy of the antidepressants in this study. In conclusion, we found some markers significantly associated with anhedonia, and some suggestive findings of related pathways and biological functions, which could be further investigated in other studies.

Exploring the sortilin related receptor, SorLA, in depression

BACKGROUND

Studies of individual biomarkers for depression have shown insufficient sensitivity and specificity for clinical use, and most likely combinations of biomarkers may provide a better signature. The sorting-related receptor with A-type repeats (SorLA) is a well-studied pathogenic factor for Alzheimer's. SorLA belongs to the Vps10p domain receptor family, which also encompasses sortilin and SorCS1-3. All family members have been implicated in neurological and mental disorders. Notably, the SORCS3 gene is genome-wide significantly associated with depression and serum protein levels of sortilin are reduced in depressed individuals. SorLA regulates the activity of neurotrophic factors and cytokines and we hence speculated that SorLA might be implicated in depression.

METHODS

Serum SorLA levels were measured in two well-defined clinical samples using ELISA. Generalized linear models were used in the statistical analyses.

RESULTS

We identified a multivariate model to discriminate depressed individuals from healthy controls. Interestingly, the model consisted of serum SorLA levels and additional four predictors: previous depressive episode, stressful life events, serum levels of sortilin and VEGF. However, as an isolated factor, we observed no significant difference in SorLA levels between 140 depressed individuals and 140 healthy controls. Nevertheless, we observed a significant increase in SorLA levels following 12 weeks of treatment with nortriptyline, but not escitalopram.

LIMITATIONS

The number of biomarkers included in the multivariate model for depression and lack of replication limit our study.

CONCLUSIONS

Our results suggest SorLA as one of five factors that in combination may support the depression diagnosis, but not as an individual biomarker for depression or treatment response.

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.

Physical exercise for late-life depression: Effects on symptom dimensions and time course

BACKGROUND

Physical exercise is increasingly recognized as a treatment for major depression, even among older patients. However, it is still unknown which depressive symptoms exercise affects most, (e.g. somatic vs. affective) and the timing of its effects. Thus, the aim of this study was to examine the changes of depressive symptoms after treatment with exercise.

METHODS

We analyzed data from the SEEDS study, a trial comparing the antidepressant effectiveness of sertraline (S) and sertraline plus exercise (S+EX). Exercise was delivered thrice weekly in small groups and monitored by heart rate meters. Patients with late life depression (n=121) were assessed at baseline, 4, 8, 12 and 24 weeks with the Hamilton Depression Scale. Scores of affective, vegetative, anxiety and agitation/insight factors were analyzed using Multilevel Growth Curve Models and sensitivity analyses (multiple imputation).

RESULTS

Compared with the S group, patients in the S+EX group displayed significantly greater improvements of the affective symptom dimension (total effect size = 0.79) with largest changes in the first 4 weeks and last 12 weeks. Improvements were mainly driven by depressed mood and psychomotor retardation.

LIMITATIONS

Sample size; lack of an exercise only treatment arm CONCLUSIONS: Adding exercise to antidepressant drug treatment may offer significant advantages over affective symptoms of depression, rather than somatic symptoms or other dimensions of depression. Compared with standard antidepressant treatment, clinical advantages should be expected both at an early (first 4 weeks) and later stage (after 12 weeks).

Investigation of serum levels of sortilin in response to antidepressant treatment

BACKGROUND

The identification of biomarkers for depression is of great clinical relevance as the diagnosis is currently subjective. Recent research points towards sortilin as a potential biomarker for depression, and the aim of the current study was to investigate the serum sortilin level in response to antidepressant treatment.

METHODS

The study included 56 depressed individuals of which 41 responded to treatment. Depression scores and serum levels of sortilin were measured at baseline and after 12 weeks of antidepressant treatment. Statistical analyses were performed using Stata 13.

RESULTS

The depression score and response to treatment were not predicted by the sortilin level. Likewise, we observed no significant change in serum sortilin levels following 12 weeks of antidepressant treatment. Furthermore, no association between the serum sortilin level and depression score was observed.

CONCLUSION

The results do not point towards sortilin as a state-dependent biomarker.

Differential change on depressive symptom factors with antidepressant medication and cognitive behavior therapy for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous condition and individual patients are likely to be differentially responsive to specific treatments. In an exploratory factor analysis of three rating scales, the Genome-based Therapeutic Drugs for Depression (GENDEP) trial identified three factors that were differentially associated with outcome to nortriptyline and escitalopram. However, this factor analysis has neither been replicated or applied to a psychotherapy treatment.

METHODS

We replicated the GENDEP analytic method in the Emory Predictors of Remission to Individual and Combined Treatments (PReDICT) study. The 17-item Hamilton Depression Rating Scale, Montgomery Asberg Depression Rating Scale, and Beck Depression Inventory were administered to 306 MDD patients in the PReDICT study, which randomized previously untreated adults to 12 weeks of treatment with cognitive behavior therapy (CBT), escitalopram, or duloxetine. Utilizing Item Response Theory methodologies, factor scores were derived from the three scales and the efficacy of the three treatments was compared for the identified factor scores.

RESULTS

Four factors were identified: "Despair," "Mood and Interest," "Sleep," and "Appetite." These factors closely aligned with the factors identified in GENDEP. Compared to CBT, escitalopram and duloxetine produced more rapid but ultimately similar improvement on the Despair and Mood and Interest factors; no significant differences between treatments emerged on the other factors.

LIMITATIONS

The scales contained differing numbers of items pertaining to specific depressive symptoms.

CONCLUSION

The heterogeneity of MDD can be parsed into a consistent factor structure, with the factors showing differential rapidity, but ultimately similar, improvement across treatments.

The potential of predictive analytics to provide clinical decision support in depression treatment planning

PURPOSE OF REVIEW

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD).

RECENT FINDINGS

Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem.

SUMMARY

Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Management of post-stroke mood and emotional disturbances

INTRODUCTION

Mood and emotional disturbances are common in stroke patients. Out of diverse post-stroke emotional disturbances, depression, anxiety, emotional incontinence, anger proneness, and fatigue are frequent and important symptoms. These symptoms are distressing for both the patients and their caregivers, and negatively influence the patient's quality of life. The emotional symptoms are not apparent and are therefore often neglected by neurologists. Their phenomenology, predicting factors, and pathophysiology have been under-studied, and are under-recognized. In addition, well-designed clinical trials targeting on these symptoms are rare. Areas covered: This review will describe some of the most common or relevant post-stroke mood and emotional disturbances. The phenomenology, factors or predictors, and presumed etio-pathogenesis will be described. Current pharmacological and non-pharmacological management strategies of these diverse emotional disturbances will be discussed based on different pathophysiological mechanisms. Expert commentary: It is fortunate that these mood and emotional disturbances can be treated by various methods, including pharmacological and non-pharmacological therapy. To administer the appropriate therapy, we must understand the similarities and differences in the pathophysiological mechanisms associated with these emotional symptoms.

MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems.

Antidepressant drugs are the most common treatment for depressive episodes but only a fraction of patients experience adequate response. Here the authors find dysregulation of miRNAs in peripheral blood samples from depressed patients after antidepressant treatment, and show that the miRNAs are regulators of psychiatrically relevant signalling pathways.