Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

An empirical analysis of structural neuroimaging profiles in a staging model of depression

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

A Randomized Evaluation of MoodFX, a Patient-Centred e-Health Tool to Support Outcome Measurement for Depression: Une évaluation randomisée de MoodFX, un outil de santé en ligne centré sur le patient pour soutenir la mesure du résultat dans la dépression

BACKGROUND

e-Health tools using validated questionnaires to assess outcomes may facilitate measurement-based care for psychiatric disorders. MoodFX was created as a free online symptom tracker to support patients for outcome measurement in their depression treatment. We conducted a pilot randomized evaluation to examine its usability, and clinical utility.

METHODS

Patients presenting with a major depressive episode (within a major depressive or bipolar disorder) were randomly assigned to receive either MoodFX or a health information website as the intervention and control condition, respectively, with follow-up assessment surveys conducted online at baseline, 8 weeks and 6 months. The primary usability outcomes included the percentage of patients with self-reported use of MoodFX 3 or more times during follow up (indicating minimally adequate usage) and usability measures based on the System Usability Scale (SUS). Secondary clinical outcomes included the Quick Inventory of Depressive Symptomatology, Self-Rated (QIDS-SR) and Patient Health Questionnaire (PHQ-9).

RESULTS

Forty-nine participants were randomized (24 to MoodFX and 25 to the control condition). Of the 23 participants randomized to MoodFX who completed the user survey, 18 (78%) used MoodFX 3 or more times over the 6 months of the study. The mean SUS score of 72.7 (65th-69th percentile) represents good usability. Compared to the control group, the MoodFX group had significantly better improvement on QIDS-SR and PHQ-9 scores, with large effect sizes and higher response rates at 6 months. There were no differences between conditions on other secondary outcomes such as functioning and quality of life.

CONCLUSION

MoodFX demonstrated good usability and was associated with reduction in depressive symptoms. This pilot study supports the use of digital tools in depression treatment.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study

OBJECTIVES

Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample.

METHODS

A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models.

RESULTS

In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052).

CONCLUSIONS

CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Functional neuroimaging biomarkers of anhedonia response to escitalopram plus adjunct aripiprazole treatment for major depressive disorder

BACKGROUND

Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

AIMS

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

METHOD

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

RESULTS

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

CONCLUSIONS

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

Relation of hippocampal volume and SGK1 gene expression to treatment remission in major depression is moderated by childhood maltreatment: A CAN-BIND-1 report

Preclinical research implicates stress-induced upregulation of the enzyme, serum- and glucocorticoid-regulated kinase 1 (SGK1), in reduced hippocampal volume. In the current study, we tested the hypothesis that greater SGK1 mRNA expression in humans would be associated with lower hippocampal volume, but only among those with a history of prolonged stress exposure, operationalized as childhood maltreatment (physical, sexual, and/or emotional abuse). Further, we examined whether baseline levels of SGK1 and hippocampal volume, or changes in these markers over the course of antidepressant treatment, would predict treatment outcomes in adults with major depression [MDD]. We assessed SGK1 mRNA expression from peripheral blood, and left and right hippocampal volume at baseline, as well as change in these markers over the first 8 weeks of a 16-week open-label trial of escitalopram as part of the Canadian Biomarker Integration Network in Depression program (MDD [n = 161] and healthy comparison participants [n = 91]). Childhood maltreatment was assessed via contextual interview with standardized ratings. In the full sample at baseline, greater SGK1 expression was associated with lower hippocampal volume, but only among those with more severe childhood maltreatment. In individuals with MDD, decreases in SGK1 expression predicted lower remission rates at week 16, again only among those with more severe maltreatment. Decreases in hippocampal volume predicted lower week 16 remission for those with low childhood maltreatment. These results suggest that both glucocorticoid-related neurobiological mechanisms of the stress response and history of childhood stress exposure may be critical to understanding differential treatment outcomes in MDD. ClinicalTrials.gov: NCT01655706 Canadian Biomarker Integration Network for Depression Study.

Predicting recurrence of major depressive episodes using the Depression Implicit Association Test: A Canadian biomarker integration network in depression (CAN-BIND) report

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions.

Effects of intermittent theta-burst transcranial magnetic stimulation on cognition and hippocampal volumes in bipolar depression

INTRODUCTION

Repetitive transcranial magnetic stimulation (TMS) is increasingly used to treat neurocognitive symptoms in mood disorders. Intermittent theta burst stimulation (iTBS) is a brief version of TMS that may preferentially target cognitive functions. This study evaluated whether iTBS leads to cognitive improvements and associated increased hippocampal volumes in bipolar depression.

METHODS

In a two-site double-blind randomised sham controlled trial (NCT02749006), 16 patients received active iTBS to the Left Dorsolateral Prefrontal Cortex (DLPF) and 15 patients received sham stimulation across four weeks. A composite neuropsychological score and declarative memory scores served as the cognitive outcomes. Hippocampal volumes were derived from T1 weighted MRI scans using the longitudinal ComBat method to harmonise data across sites.

RESULTS

No significant improvements were observed in any cognitive variables in the active relative to the sham group; however, there was a trend for increased left hippocampal volume in the former. Left hippocampal volume increases were associated with improvements in nonverbal memory in the active group.

CONCLUSIONS

Although cognitive improvements were not associated with iTBS, the finding that hippocampal volume increases were associated with memory improvement suggests there may be some level of prefrontal-temporal neuroplasticity that could support cognitive change in future studies of iTBS in bipolar disorder.

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.

Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).

Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.

Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.

Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2-3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

Values and preferences related to workplace mental health programs and interventions: An international survey

INTRODUCTION

This study explores the perspectives of workers and managers on workplace programs and interventions that seek to promote mental wellbeing, and prevent and treat mental health conditions The results contributed supporting evidence for the development of the WHO's first global guidelines for mental health and work, which provide evidence-based recommendations to support the implementation of workplace mental health programs and supports, to improve their acceptability, appropriateness, and uptake.

METHODS

An international online survey was used to examine the values and preferences among workers and managers related to workplace mental health prevention, protection, promotion, and support programs and services. The survey was made available in English, French, and Spanish and recruitment consisted of convenience sampling. Descriptive statistics were used to analyse the survey data. Rapid thematic qualitative analysis was used to analyse the results of open-ended questions.

RESULTS

N = 451 responses representing all WHO regions were included in the analysis. These results provide a unique international perspective on programs and supports for mental health at work, from the standpoint of workers and managers. Results suggest that workers value interventions developed in consultation with workers (including indicated, selective and universal interventions), increased training and capacity building among managers, and targeted interventions to address the pervasive impact of stigma on perceptions about mental health at work and help-seeking.

CONCLUSION

The findings of this study seek to reflect the perspectives of workers and their managers, and therein to promote improved access, availability and uptake of mental health programs and supports at work and-ultimately- to support the potential of workplaces as environments that promote and support mental health.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Evaluating the effectiveness and cost-effectiveness of a digital, app-based intervention for depression (VMood) in community-based settings in Vietnam: Protocol for a stepped-wedge randomized controlled trial

The COVID-19 pandemic has amplified mental health problems and highlighted inequitable gaps in care worldwide. In response there has been an explosion of digital interventions such as smartphone applications ("apps") to extend care. The objective of this trial is to evaluate the effectiveness and cost-effectiveness of a digital depression intervention (VMood), delivered via a smartphone app. VMood is adapted from an in-person intervention that was delivered by non-specialist providers and shown to be effective in the Vietnamese context in our previous trial (2016-2019). A stepped-wedge, randomized controlled trial will be conducted across eight provinces in Vietnam. Adults aged 18 years and over will be recruited through community-based primary care centres and screened for depression using the embedded Patient Health Questionnaire-9 (primary outcome measure). Participants scoring 10-19, indicating depression caseness, will be randomly allocated to the intervention or control group until the target of 336 is reached. Secondary outcome measures will examine the effect of the intervention on commonly co-occuring anxiety, quality of life and work productivity, along with use of alcohol and tobacco products. Assessments will be administered through an online survey platform (REDCap) at baseline, and at every 3 months until 3 months post-intervention. Intervention-group participants will receive VMood for a 3-month period, with online support provided by social workers. Control-group participants will receive a limited version of the app until they cross into the intervention group. Generalized Linear Mixed-effect Models for clustered measures will be used for all outcomes data. We will conduct a cost-effectiveness analysis alongside the trial to capture VMood's costs and benefits. This trial will provide evidence on the effectiveness and cost-effectiveness of a digital mental health intervention adapted from an in-person intervention. This trial will also contribute important information to the growing and promising field of digital mental health. Trail regulation. Registered at ClinicalTrials.gov, identifier [NCT05783531].

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Importance

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.

Objective

To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.

Design, Setting, and Participants

This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.

Exposures

In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.

Main Outcomes and Measures

The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.

Results

The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response.

Conclusions and Relevance

In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Response trajectories during escitalopram treatment of patients with major depressive disorder

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report

BACKGROUND

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

METHODS

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

RESULTS

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

CONCLUSIONS

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Duration of Adjunctive Antidepressant Maintenance in Bipolar I Depression

BACKGROUND

Antidepressants are used to treat acute depression in patients with bipolar I disorder, but their effect as maintenance treatment after the remission of depression has not been well studied.

METHODS

We conducted a multisite, double-blind, randomized, placebo-controlled trial of maintenance of treatment with adjunctive escitalopram or bupropion XL as compared with discontinuation of antidepressant therapy in patients with bipolar I disorder who had recently had remission of a depressive episode. Patients were randomly assigned in a 1:1 ratio to continue treatment with antidepressants for 52 weeks after remission or to switch to placebo at 8 weeks. The primary outcome, assessed in a time-to-event analysis, was any mood episode, as defined by scores on scales measuring symptoms of hypomania or mania, depression, suicidality, and mood-episode severity; additional treatment or hospitalization for mood symptoms; or attempted or completed suicide. Key secondary outcomes included the time to an episode of mania or hypomania or depression.

RESULTS

Of 209 patients with bipolar I disorder who participated in an open-label treatment phase, 150 who had remission of depression were enrolled in the double-blind phase in addition to 27 patients who were enrolled directly. A total of 90 patients were assigned to continue treatment with the prescribed antidepressant for 52 weeks (52-week group) and 87 were assigned to switch to placebo at 8 weeks (8-week group). The trial was stopped before full recruitment was reached owing to slow recruitment and funding limitations. At 52 weeks, 28 of the patients in the 52-week group (31%) and 40 in the 8-week group (46%) had a primary-outcome event. The hazard ratio for time to any mood episode in the 52-week group relative to the 8-week group was 0.68 (95% confidence interval [CI], 0.43 to 1.10; P = 0.12 by log-rank test). A total of 11 patients in the 52-week group (12%) as compared with 5 patients in the 8-week group (6%) had mania or hypomania (hazard ratio, 2.28; 95% CI, 0.86 to 6.08), and 15 patients (17%) as compared with 35 patients (40%) had recurrence of depression (hazard ratio, 0.43; 95% CI, 0.25 to 0.75). The incidence of adverse events was similar in the two groups.

CONCLUSIONS

In a trial involving patients with bipolar I disorder and a recently remitted depressive episode, adjunctive treatment with escitalopram or bupropion XL that continued for 52 weeks did not show a significant benefit as compared with treatment for 8 weeks in preventing relapse of any mood episode. The trial was stopped early owing to slow recruitment and funding limitations. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00958633.).

The Differential Relation of Emotional, Physical, and Sexual Abuse Histories to Antidepressant Treatment Remission and Persistence of Anhedonia in Major Depression: A CAN-BIND-1 Report

OBJECTIVE

Childhood maltreatment is a potent enviromarker of risk for poor response to antidepressant medication (ADM). However, childhood maltreatment is a heterogeneous construct that includes distinct exposures that have distinct neurobiological and psychological correlates. The purpose of the current study is to examine the differential associations of emotional, physical, and sexual maltreatment to ADM outcome and to examine the unique role of anhedonia in driving poor response in patients with specific maltreatment histories.

METHODS

In a multicentre clinical trial of major depression, 164 individuals were assessed for childhood emotional, physical, and sexual maltreatment with a contextual interview with independent, standardized ratings. All individuals received 8 weeks of escitalopram, with nonresponders subsequently also receiving augmentation with aripiprazole, with outcomes measured with depression rating scales and an anhedonia scale.

RESULTS

Greater severity of emotional maltreatment perpetrated by the mother was a significant and direct predictor of lower odds of week 16 remission (odds ratio [OR] = 1.68, P = 0.02). In contrast, the relations of paternal-perpetrated emotional maltreatment and physical maltreatment to week 16 remission were indirect, mediated through greater severity of anhedonia at week 8.

CONCLUSIONS

We identify emotional maltreatment as a specific early exposure that places patients at the greatest risk for nonremission following pharmacological treatment. Further, we suggest that anhedonia is a key symptom domain driving nonremission in patients with particular maltreatment histories.

SNORD90 induces glutamatergic signaling following treatment with monoaminergic antidepressants

Pharmacotherapies for the treatment of major depressive disorder were serendipitously discovered almost seven decades ago. From this discovery, scientists pinpointed the monoaminergic system as the primary target associated with symptom alleviation. As a result, most antidepressants have been engineered to act on the monoaminergic system more selectively, primarily on serotonin, in an effort to increase treatment response and reduce unfavorable side effects. However, slow and inconsistent clinical responses continue to be observed with these available treatments. Recent findings point to the glutamatergic system as a target for rapid acting antidepressants. Investigating different cohorts of depressed individuals treated with serotonergic and other monoaminergic antidepressants, we found that the expression of a small nucleolar RNA, SNORD90, was elevated following treatment response. When we increased Snord90 levels in the mouse anterior cingulate cortex (ACC), a brain region regulating mood responses, we observed antidepressive-like behaviors. We identified neuregulin 3 (NRG3) as one of the targets of SNORD90, which we show is regulated through the accumulation of N6-methyladenosine modifications leading to YTHDF2-mediated RNA decay. We further demonstrate that a decrease in NRG3 expression resulted in increased glutamatergic release in the mouse ACC. These findings support a molecular link between monoaminergic antidepressant treatment and glutamatergic neurotransmission.

Rasch analyses of the Quick Inventory of Depressive Symptomatology Self-Report in neurodegenerative and major depressive disorders

Background

Symptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations. However, the measurement properties of the QIDS-SR have not been assessed in ND.

Aim

To use Rasch Measurement Theory to assess the measurement properties of the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) in ND and in comparison to major depressive disorder (MDD).

Methods

De-identified data from the Ontario Neurodegenerative Disease Research Initiative (NCT04104373) and Canadian Biomarker Integration Network in Depression (NCT01655706) were used in the analyses. Five hundred and twenty participants with ND (Alzheimer's disease or mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson's disease) and 117 participants with major depressive disorder (MDD) were administered the QIDS-SR. Rasch Measurement Theory was used to assess measurement properties of the QIDS-SR, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning.

Results

The QIDS-SR fit well to the Rasch model in ND and MDD, including unidimensionality, satisfactory category ordering and goodness-of-fit. Item-person measures (Wright maps) showed gaps in item difficulties, suggesting poor precision for persons falling between those severity levels. Differences between mean person and item measures in the ND cohort logits suggest that QIDS-SR items target more severe depression than experienced by the ND cohort. Some items showed differential item functioning between cohorts.

Conclusion

The present study supports the use of the QIDS-SR in MDD and suggest that the QIDS-SR can be also used to screen for depressive symptoms in persons with ND. However, gaps in item targeting were noted that suggests that the QIDS-SR cannot differentiate participants falling within certain severity levels. Future studies would benefit from examination in a more severely depressed ND cohort, including those with diagnosed clinical depression.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Canadian Network for Mood and Anxiety Treatments (CANMAT) Task Force Report: A Systematic Review and Recommendations of Cannabis use in Bipolar Disorder and Major Depressive Disorder

BACKGROUND

Given the increasing acceptability and legalization of cannabis in some jurisdictions, clinicians need to improve their understanding of the effect of cannabis use on mood disorders.

OBJECTIVE

The purpose of this task force report is to examine the association between cannabis use and incidence, presentation, course and treatment of bipolar disorder and major depressive disorder, and the treatment of comorbid cannabis use disorder.

METHODS

We conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials from inception to October 2020 focusing on cannabis use and bipolar disorder or major depressive disorder, and treatment of comorbid cannabis use disorder. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to evaluate the quality of evidence and clinical considerations were integrated to generate Canadian Network for Mood and Anxiety Treatments recommendations.

RESULTS

Of 12,691 publications, 56 met the criteria: 23 on bipolar disorder, 21 on major depressive disorder, 11 on both diagnoses and 1 on treatment of comorbid cannabis use disorder and major depressive disorder. Of 2,479,640 participants, 12,502 were comparison participants, 73,891 had bipolar disorder and 408,223 major depressive disorder without cannabis use. Of those with cannabis use, 2,761 had bipolar disorder and 5,044 major depressive disorder. The lifetime prevalence of cannabis use was 52%-71% and 6%-50% in bipolar disorder and major depressive disorder, respectively. Cannabis use was associated with worsening course and symptoms of both mood disorders, with more consistent associations in bipolar disorder than major depressive disorder: increased severity of depressive, manic and psychotic symptoms in bipolar disorder and depressive symptoms in major depressive disorder. Cannabis use was associated with increased suicidality and decreased functioning in both bipolar disorder and major depressive disorder. Treatment of comorbid cannabis use disorder and major depressive disorder did not show significant results.

CONCLUSION

The data indicate that cannabis use is associated with worsened course and functioning of bipolar disorder and major depressive disorder. Future studies should include more accurate determinations of type, amount and frequency of cannabis use and select comparison groups which allow to control for underlying common factors.

The course of insomnia symptoms during the acute treatment of major depressive disorder: A CAN-BIND-1 report

Despite considerable efforts to study the relationship between insomnia and depression, there is minimal research investigating whether insomnia symptoms change over time during a course of antidepressant pharmacotherapy. This study investigated the course of insomnia symptoms during the acute treatment of major depressive disorder (MDD) using a secondary analysis of data from MDD patients (N = 180) who were treated with open-label escitalopram (10-20 mg/day) for 8-weeks. Montgomery-Asberg Depression Rating Scale without sleep item (modified-MADRS) assessed depression and Self-reported Quick Inventory Depressive Scale (QIDS-SR) measured subjective sleep-onset, mid-nocturnal, and early-morning insomnia throughout 8-weeks of treatment. Pittsburgh Sleep Quality Index (PSQI) was used to assess subjective sleep quality, duration, onset latency, and efficiency throughout 8-weeks of treatment. Remission of depression was defined as modified-MADRS ≤10 at week-8. Mixed model repeated measures (MMRMs) were conducted with remission status as an independent variable and each sleep variable as a dependent variable. MMRMs demonstrated that remitters had significantly lower QIDS-SR sleep-onset and mid-nocturnal insomnia scores as well as a significantly lower PSQI sleep quality score than non-remitters throughout 8-weeks of treatment. Monitoring subjective sleep-onset and mid-nocturnal insomnia during the course of treatment with serotonergic antidepressants may be useful for predicting acute remission of depression.

Co-design of a Virtual Reality Cognitive Remediation Program for Depression (bWell-D) With Patient End Users and Clinicians: Qualitative Interview Study Among Patients and Clinicians

BACKGROUND

Major depressive disorder (MDD) is the leading cause of global disability; however, the existing treatments do not always address cognitive dysfunction-a core feature of MDD. Immersive virtual reality (VR) has emerged as a promising modality to enhance the real-world effectiveness of cognitive remediation.

OBJECTIVE

This study aimed to develop the first prototype VR cognitive remediation program for MDD ("bWell-D"). This study gathered qualitative data from end users early in the design process to enhance its efficacy and feasibility in clinical settings.

METHODS

Semistructured end-user interviews were conducted remotely (n=15 patients and n=12 clinicians), assessing the participants' perceptions and goals for a VR cognitive remediation program. Video samples of bWell-D were also shared to obtain feedback regarding the program. The interviews were transcribed, coded, and analyzed via thematic analysis.

RESULTS

End users showed an optimistic outlook toward VR as a treatment modality, and perceived it as a novel approach with the potential of having multiple applications. The participants expressed a need for an engaging VR treatment that included realistic and multisensorial settings and activities, as well as customizable features. Some skepticism regarding its effectiveness was also reported, especially when the real-world applications of the practiced skills were not made explicit, as well as some concerns regarding equipment accessibility. A home-based or hybrid (ie, home and clinic) treatment modality was preferred.

CONCLUSIONS

Patients and clinicians considered bWell-D interesting, acceptable, and potentially feasible, and provided suggestions to enhance its real-world applicability. The inclusion of end-user feedback is encouraged when developing future VR programs for clinical purposes.

Response Inhibition and Predicting Response to Pharmacological and Cognitive Behavioral Therapy Treatments for Major Depressive Disorder: A Canadian Biomarker Integration Network for Depression Study

BACKGROUND

Major depressive disorder (MDD) is associated with various cognitive impairments, including response inhibition. Deficits in response inhibition may also underlie poor antidepressant treatment response. Recent studies revealed that the neurobiological correlates of response inhibition can predict response to pharmacological treatments. However, the generalizability of this finding to first-line nonpharmacological treatments, particularly cognitive behavioral therapy, remains to be investigated.

METHODS

Data from two independent treatment protocols were combined, one in which 65 patients with MDD underwent treatment with escitalopram, and the other in which 41 patients with MDD underwent a course of cognitive behavioral therapy. A total of 25 healthy control subjects were also recruited. Neural correlates of response inhibition were captured by participants completing a Go/NoGo task during electroencephalography recording. Response inhibition-related measures of interest included the amplitudes of the N2 and P3 event-related potentials.

RESULTS

Pretreatment P3 amplitude, which has been linked to both the motor and cognitive aspects of response inhibition, was a significant predictor of change in depressive symptoms following escitalopram and cognitive behavioral therapy treatment. A greater pretreatment P3 amplitude was associated with a greater reduction in depressive severity. In addition, the pretreatment P3 amplitude was found to be significantly greater at baseline in remitters than in nonremitters and healthy control subjects.

CONCLUSIONS

The integrity of response inhibition may be critical for a successful course of pharmacological or psychological treatment for MDD. Electrophysiological correlates of response inhibition may have utility as a general prognostic marker of treatment response in MDD. Future studies may investigate the benefit of preceding first-line treatments with interventions that improve response inhibition in MDD.

ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study

The P-glycoprotein efflux pump, encoded by the ABCB1 gene, has been shown to alter concentrations of various antidepressants in the brain. In this study, we conducted a systematic review and meta-analysis to investigate the association between six ABCB1 single-nucleotide polymorphisms (SNPs; rs1045642, rs2032582, rs1128503, rs2032583, rs2235015, and rs2235040) and antidepressant treatment outcomes in individuals with major depressive disorder (MDD), including new data from the Canadian Biomarker and Integration Network for Depression (CAN-BIND-1) cohort. For the CAN-BIND-1 sample, we applied regression models to investigate the association between ABCB1 SNPs and antidepressant treatment response, remission, tolerability, and antidepressant serum levels. For the meta-analysis, we systematically summarized pharmacogenetic evidence of the association between ABCB1 SNPs and antidepressant treatment outcomes. Studies were included in the meta-analysis if they investigated at least one ABCB1 SNP in individuals with MDD treated with at least one antidepressant. We did not find a significant association between ABCB1 SNPs and antidepressant treatment outcomes in the CAN-BIND-1 sample. A total of 39 studies were included in the systematic review. In the meta-analysis, we observed a significant association between rs1128503 and treatment response (T vs. C-allele, odds ratio = 1.30, 95% confidence interval = 1.15-1.48, P value (adjusted) = 0.024, n = 2,526). We did not find associations among the six SNPs and treatment remission nor tolerability. Our findings provide limited evidence for an association between common ABCB1 SNPs and antidepressant outcomes, which do not support the implementation of ABCB1 genotyping to inform antidepressant treatment at this time. Future research, especially on rs1128503, is recommended.

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; p_corr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.

The Canadian Network for Mood and Anxiety Treatments (CANMAT) Task Force Report: Serotonergic Psychedelic Treatments for Major Depressive Disorder

OBJECTIVE

Serotonergic psychedelics are re-emerging as potential novel treatments for several psychiatric disorders including major depressive disorder. The Canadian Network for Mood and Anxiety Treatments (CANMAT) convened a task force to review the evidence and provide a consensus recommendation for the clinical use of psychedelic treatments for major depressive disorder.

METHODS

A systematic review was conducted to identify contemporary clinical trials of serotonergic psychedelics for the treatment of major depressive disorder and cancer-related depression. Studies published between January 1990 and July 2021 were identified using combinations of search terms, inspection of bibliographies and review of other psychedelic reviews and consensus statements. The levels of evidence for efficacy were graded according to the Canadian Network for Mood and Anxiety Treatments criteria.

RESULTS

Only psilocybin and ayahuasca have contemporary clinical trials evaluating antidepressant effects. Two pilot studies showed preliminary positive effects of single-dose ayahuasca for treatment-resistant depression (Level 3 evidence). Small randomized controlled trials of psilocybin combined with psychotherapy showed superiority to waitlist controls and comparable efficacy and safety to an active comparator (escitalopram with supportive psychotherapy) in major depressive disorder, with additional randomized controlled trials showing efficacy specifically in cancer-related depression (Level 3 evidence). There was only one open-label trial of psilocybin in treatment-resistant unipolar depression (Level 4 evidence). Small sample sizes and functional unblinding were major limitations in all studies. Adverse events associated with psychedelics, including psychological (e.g., psychotomimetic effects) and physical (e.g., nausea, emesis and headaches) effects, were generally transient.

CONCLUSIONS

There is currently only low-level evidence to support the efficacy and safety of psychedelics for major depressive disorder. In Canada, as of 2022, psilocybin remains an experimental option that is only available through clinical trials or the special access program. As such, Canadian Network for Mood and Anxiety Treatments considers psilocybin an experimental treatment and recommends its use primarily within clinical trials, or, less commonly, through the special access program in rare, special circumstances.

Barriers and facilitators to technology-enhanced measurement based care for depression among Canadian clinicians and patients: Results of an online survey

BACKGROUND

Measurement-based care (MBC) is an evidence-based approach that can improve management of depression, but its use in clinical settings remains low. The use of digital technology, or technology-enhanced MBC (eMBC), may address some of the barriers to implementation of MBC by enabling patients to easily complete outcome assessments. This study aims to identify the perceived facilitators and barriers to MBC and eMBC from both patient and provider perspectives in Canada.

METHODS

The study recruited 108 clinicians and 131 patients with a self-reported diagnosis of depression to participate in an online survey about the acceptability of both MBC and eMBC.

RESULTS

Most clinicians (90/108, 83.3 %) agreed that MBC is important for clinical judgement, and felt trained to interpret their scores; however, only 43/108 (39.8 %) use MBC routinely. Clinicians (95/108, 88.0 %) felt they would be more likely to use MBC if it was automated and available to use electronically. Most patients (117/131, 89.3 %) felt MBC would be helpful for their treatment and were willing to use a mobile app to track their symptoms.

LIMITATIONS

Recruitment was done online and, hence, this study may not capture the perspective of patients who are not already familiar with using online programs to complete questionnaires.

CONCLUSIONS

Clinicians and patients in Canada would be willing to utilize MBC and eMBC in their psychiatric care, but barriers to implementation include perceptions about efficiency, ease of use, and accessibility. These results highlight the importance of engaging both clinicians and patients to effectively implement MBC and eMBC for depression.

Association of total peripheral inflammation with lower frontal and temporal lobe volumes in early-stage bipolar disorder: A proof-of-concept study

BACKGROUND

We previously reported that in early-stage bipolar disorder (BD), frontal and temporal lobe volume reductions were more pronounced in patients with elevated BMI and more rapidly progressive in patients with additional weight gain. Elevated BMI is a pro-inflammatory state, and inflammation may contribute to brain volume reductions in BD. However, few studies have investigated the relationship between inflammation and brain volumes.

METHODS

We conducted a proof-of-concept analysis to investigate whether a composite measure of total peripheral inflammation derived from 9 cytokines predicted lower frontal and temporal lobe volumes, measured with 3 T MRI, in early-stage BD.

RESULTS

In 25 early-stage patients, linear regression models showed that greater total inflammation predicted lower white matter (WM) volumes in the left frontal lobe (β = -0.691, p = 0.001) and bilateral temporal lobes (left: β = -0.617, p = 0.003; right: β = -0.636, p = 0.001). Greater inflammation also predicted lower right frontal WM, although this did not survive correction for multiple comparisons (β = -0.557, p = 0.020). It did not predict frontal or temporal GM. Total inflammation was a stronger predictor of lower WM volumes than were individual cytokines.

LIMITATIONS

Although the magnitude of the association between total inflammation and lower WM volumes was large, our sample was small. Our findings require confirmation in further studies, with samples large enough to determine whether inflammation mediates the relationship between elevated BMI and brain volumes.

CONCLUSIONS

This study supports the hypothesis that inflammation contributes to brain volume reductions in BD and suggests that total inflammatory burden best captures the impact of inflammation on the brain.

The Effectiveness of Internet-Guided Self-help Interventions to Promote Physical Activity Among Individuals With Depression: Systematic Review

BACKGROUND

Depression is a prevalent and debilitating mental disorder and a leading cause of disability worldwide. Physical activity (PA) interventions have been shown to alleviate depressive symptoms. However, not all patients have access to PA programing tailored for depression. Internet-guided self-help (IGSH) interventions may be an effective option for increasing PA among people with depression who cannot or prefer not to access supervised exercise treatment.

OBJECTIVE

We aimed to evaluate the effectiveness of IGSH interventions in increasing PA and alleviating depressive symptoms in people with depression.

METHODS

A systematic literature search was conducted for randomized controlled trials and quasiexperimental studies using 9 electronic databases. The review was registered in PROSPERO (2020 CRD42020221713).

RESULTS

A total of 4 randomized controlled trials (430 participants) met the inclusion criteria. Of these, 3 were web-based and 1 was app-based. Three studies found IGSH interventions to have medium to large effects on decreasing depressive symptoms but not on increasing PA compared with waitlist or usual care. One study showed increased self-reported PA but no significant difference in depressive symptoms in the intervention group compared with the control group. Goal setting was the most common behavior change technique used in the interventions. Dropout rates within the intervention groups were relatively low (0%-19%).

CONCLUSIONS

Our findings suggested that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions with different combinations of behavior change techniques, particularly those targeting the emotion domain, are needed to assess the overall effectiveness and feasibility of using IGSH interventions to increase PA among people with depression.

TRIAL REGISTRATION

PROSPERO CRD42020221713; https://tinyurl.com/ysaua5bu.

A Positron Emission Tomography Study of Dopamine Transporter Density in Patients With Bipolar Disorder With Current Mania and Those With Recently Remitted Mania

Importance

Although dopamine is implicated in the pathophysiology of bipolar disorder (BD), the precise alterations in the dopaminergic system remain unknown.

Objective

To assess dopamine transporter (DAT) density in the striatum in patients with BD with current and recently remitted mania in comparison to healthy control individuals and its correlation with severity of manic symptoms.

Design, Setting, and Participants

This cross-sectional study conducted in a tertiary care referral center for mood disorders in Vancouver, British Columbia, Canada, recruited 26 patients with BD (9 with current mania; 17 with recently remitted mania) and 21 matched healthy control individuals. DAT density was measured using positron emission tomography with [11C]d-threo-methylphenidate (MP). The differences between the groups in nondisplaceable binding potential (BPND) for DAT was assessed using statistical parametric mapping. The study was conducted from November 2001 to February 2007 and the data were analyzed from November 2020 to December 2021.

Main Outcomes and Measures

DAT density as indexed by BPND for MP across groups; manic symptom severity as measured with the Young Mania Rating Scale (YMRS) and correlated with BPND values in patients with BD.

Results

Of 47 total participants (mean [SD] age, 37.8 [14.4] years), 27 (57.4%) were female; 26 individuals had BD (9 with current mania and 17 with recently remitted mania) and there were 21 healthy control individuals. MP BPND was significantly lower in patients with BD in the right putamen and nucleus accumbens (mean reduction [MR] = 22%; cluster level familywise error [FWE]-corrected P < .001) as well as left putamen and caudate (MR = 24%; cluster level FWE-corrected P < .001). The reduction in BPND was more extensive and pronounced in patients with current mania, while patients with recently remitted mania had lower BPND in the left striatum but not the right. There was a significant negative correlation between YMRS scores and MP BPND in the right striatum in patients with current mania (ρ = -0.93; 95% CI, -0.99 to -0.69; P < .001) and those with recently remitted mania (ρ = 0.64; 95% CI, -0.86 to -0.23; P = .005) but not in the left striatum in either group.

Conclusions and Relevance

These findings indicate that mania was associated with reduced DAT density and remitted mania was associated with DAT levels that approximated those present in individuals without BD. These results have potential implications for drug development for mania.

A detailed manual segmentation procedure for the hypothalamus for 3T T1-weighted MRI

The hypothalamus is a small grey matter structure which plays a crucial role in many physiological functions. Some studies have found an association between hypothalamic volume and psychopathology, which stresses the need for a standardized method to maximize segmentation accuracy. Here, we provide a detailed step-by-step method outlining the procedures to manually segment the hypothalamus using anatomical T1w images from 3T scanners, which many neuroimaging studies collect as a standard anatomical reference image. We compared volumes generated by manual segmentation and those generated by an automatic algorithm, observing a significant difference between automatically and manually segmented hypothalamus volumes on both sides (left: U = 222842, p-value < 2.2e-16; right: U = 218520, p- value < 2.2e-16).•Significant difference exists between existing automatic segmentation methods and the manual segmentation procedure.•We discuss potential drift effects, segmentation quality issues, and suggestions on how to mitigate them.•We demonstrate that the present manual segmentation procedure using standard T1-weighted MRI may be significantly more accurate than automatic segmentation outputs.

Blue-Light Therapy for Seasonal and Non-Seasonal Depression: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

OBJECTIVES

To determine the efficacy and safety of blue-light therapy in seasonal and non-seasonal major depressive disorder (MDD), by comparison to active and inactive control conditions.

METHODS

We searched Web of Science, EMBASE, Medline, PsycInfo, and Clinicaltrials.gov through January 17, 2022, for randomized controlled trials (RCTs) using search terms for blue/blue-enhanced, light therapy, and depression/seasonal affective disorder. Two independent reviewers extracted data. The primary outcome was the difference in endpoint scores on the Structured Interview Guide for the Hamilton Depression Rating Scale - Seasonal Affective Disorder (SIGH-SAD) or the Structured Interview Guide for the Hamilton Depression Rating Scale with Atypical Depression Supplement (SIGH-ADS) between blue light and comparison conditions. Secondary outcomes were response (≥ 50% improvement from baseline to endpoint on a depression scale) and remission rates (endpoint score in the remission range).

RESULTS

Of 582 articles retrieved, we included nine RCTs (n = 347 participants) assessing blue-light therapy. Seven studies had participants with seasonal MDD and two studies included participants with non-seasonal MDD. Four studies compared blue light to an inactive light condition (efficacy studies), and five studies compared it to an active condition (comparison studies). For the primary outcome, a meta-analysis with random-effects models found no evidence for the efficacy of blue-light conditions compared to inactive conditions (mean difference [MD] = 2.43; 95% confidence interval [CI], -1.28 to 6.14, P = 0.20); however, blue-light also showed no differences compared to active conditions (MD = -0.11; 95% CI, -2.38 to 2.16, P = 0.93). There were no significant differences in response and remission rates between blue-light conditions and inactive or active light conditions. Blue-light therapy was overall well-tolerated.

CONCLUSIONS

The efficacy of blue-light therapy in the treatment of seasonal and non-seasonal MDD remains unproven. Future trials should be of longer duration, include larger sample sizes, and attempt to better standardize the parameters of light therapy.

Acute and chronic stress predict anti-depressant treatment outcome and naturalistic course of major depression: A CAN-BIND report

BACKGROUND

In treatment studies of major depressive disorder (MDD), exposure to major life events predicts less symptom improvement and greater likelihood of relapse. In contrast, the impact of minor life events has received less attention. We hypothesized that the impact of minor events on symptom improvement and risk of relapse would be heightened in the presence of concurrent chronic stress. We also hypothesized that major events would predict less symptom improvement and greater risk of relapse independently of chronic stress.

METHODS

Adult patients experiencing an episode of MDD were enrolled into a 16-week trial with antidepressant treatments (n = 156). Forty-three fully remitted patients agreed to participate in a naturalistic 18-month follow-up, and 30 had full data for analyses. Life events and chronic stressors were assessed using a contextual life stress interview.

RESULTS

Greater exposure to minor events predicted greater improvement in symptoms during acute treatment, but this relation was specific to those who reported greater severity of chronic stress. During follow-up, however, major life events predicted increased risk of relapse, and this effect was not moderated by chronic stress.

LIMITATION

High attrition rates led to a small sample size for the follow-up analyses.

CONCLUSIONS

Exposure to minor events may provide an opportunity to practice problem-solving skills, thereby facilitating symptom improvement. Nevertheless, acute treatment did not protect patients from relapse when they subsequently faced major events during follow-up. Therefore, adjunctive strategies may be needed to enhance outcomes during pharmacotherapy, consolidating benefits from acute treatment and providing skills to prevent relapse.

Effects of CYP2C19 and CYP2D6 gene variants on escitalopram and aripiprazole treatment outcome and serum levels: results from the CAN-BIND 1 study

Cytochrome P450 drug-metabolizing enzymes may contribute to interindividual differences in antidepressant outcomes. We investigated the effects of CYP2C19 and CYP2D6 gene variants on response, tolerability, and serum concentrations. Patients (N = 178) were treated with escitalopram (ESC) from weeks 0-8 (Phase I), and at week 8, either continued ESC if they were responders or were augmented with aripiprazole (ARI) if they were non-responders (<50% reduction in Montgomery-Åsberg Depression Rating Scale from baseline) for weeks 8-16 (Phase II). Our results showed that amongst patients on ESC-Only, CYP2C19 intermediate and poor metabolizers (IM + PMs), with reduced or null enzyme function, trended towards significantly lower symptom improvement during Phase II compared to normal metabolizers (NMs), which was not observed in ESC + ARI. We further showed that CYP2D6 NMs and IM + PMs had a higher likelihood of reporting a treatment-related central nervous system side effect in ESC-Only and ESC + ARI, respectively. The differences in the findings between ESC-Only and ESC + ARI may be due to the altered pharmacokinetics of ESC by ARI coadministration in ESC + ARI. We provided evidence for this postulation when we showed that in ESC-Only, CYP2C19 and CYP2D6 IM + PMs demonstrated significantly higher ESC concentrations at Weeks 10 and 16 compared to NMs. In contrast, ESC + ARI showed an association with CYP2C19 but not with CYP2D6 metabolizer group. Instead, ESC + ARI showed an association between CYP2D6 metabolizer group and ARI metabolite-to-drug ratio suggesting potential competition between ESC and ARI for CYP2D6. Our findings suggest that dosing based on CYP2C19 and CYP2D6 genotyping could improve safety and outcome in patients on ESC monotherapy.

"One Degree of Separation": A Mixed-Methods Evaluation of Canadian Mental Health Care User and Provider Experiences With Remote Care During COVID-19

OBJECTIVES

The COVID-19 pandemic has contributed to a shift from in-person to remote mental health care. While remote care methods have long existed, their widespread use is unprecedented. There is little research about mental health care user and provider experiences with this transition, and no published studies to date have compared satisfaction between these groups.

METHODS

Canadian mental health care users (n = 332) and providers (n = 107) completed an online self-report survey from October 2020 to February 2021 hosted by the Canadian Biomarker Integration Network in Depression. Using a mixed-methods approach, participants were asked about their use of remote care, including satisfaction, barriers to use, helpful and unhelpful factors, and suggestions for improvement.

RESULTS

Overall, 59% to 63% of health care users and 59% of health care providers were satisfied with remote care. Users reported the greatest satisfaction with the convenience of remote care, while providers were most satisfied with the speed of provision of care; all groups were least satisfied with therapeutic rapport. Health care providers were less satisfied with the user-friendliness of remote care (P < 0.001) than users, while health care users were less satisfied than providers with continuity of care (P < 0.001). The use of a video-based platform was associated with remote care satisfaction among health care users (P < 0.02), and qualitative responses support the importance of visual cues in maintaining therapeutic rapport remotely. The majority of users (55%) and providers (87%) reported a likelihood of using remote care after the pandemic.

CONCLUSIONS

Remote mental health care is generally accepted by both users and providers, and the majority would consider using remote care following the pandemic. Suggestions for improvement include greater use of video, increased attention to body language and eye contact, consistency with in-person care, as well as increased provider training and administrative support.

Repeated subcutaneous racemic ketamine in treatment-resistant depression: case series

Interest in the use of parenteral ketamine has been increasing over the last 2 decades for the management of treatment-resistant depression (TRD). While intravenous (IV) ketamine has been the most common parenteral route of administration, subcutaneous (SC) and intramuscular options have been described. We developed a clinical treatment protocol for the use of repeated SC racemic ketamine (maximum six treatments, twice per week) in an inpatient psychiatric care setting with inclusion/exclusion criteria, dosing schedule, and description of treatment, assessment, and monitoring procedures. Results from the first 10 consecutive patients demonstrated the effectiveness of SC racemic ketamine in relieving symptoms of TRD as measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) and Quick Inventory of Depressive Symptomatology, Self-Report (QIDS-SR 16 ). Response (≥50% reduction in scores from baseline to endpoint) was achieved in 8/10 cases on the MADRS and 6/10 on the QIDS-SR 16 . Remission was achieved in 8/10 (based on MADRS ≤10) and 5/10 (based on QIDS-SR 16 ≤6). Patients tolerated the treatments well with only transient blood pressure changes and dissociative side effects. Repeated SC ketamine treatments could be a safe, feasible, and effective alternative to IV ketamine infusions for patients with TRD.

Association of intranasal esketamine, a novel 'standard of care' treatment and outcomes in the management of patients with treatment-resistant depression: protocol of a prospective cohort observational study of naturalistic clinical practice

INTRODUCTION

Esketamine is the S-enantiomer of racemic ketamine and has been approved by the Food and Drug Administration for the management of treatment resistant depression, demonstrating effective and long-lasting benefits. The objective of this observational study is to elucidate the association of intranasal (IN) esketamine with beneficial and negative outcomes in the management of treatment resistant major depressive disorder.

METHODS AND ANALYSIS

This is a multicentre prospective cohort observational study of naturalistic clinical practice. We expect to recruit 10 patients per research centre (6 centres, total 60 subjects). After approval to receive IN esketamine as part of their standard of care management of moderate to severe treatment resistant depression, patients will be invited to participate in this study. Association of esketamine treatment with outcomes in the management of depression will be assessed by measuring the severity of depression symptoms using the Montgomery-Åsberg Depression Rating Scale (MADRS), and tolerability by systematically tracking common side effects of ketamine treatment, dissociation using the simplified 6-Item Clinician Administered Dissociative Symptom Scale and potential for abuse using the Likeability and Craving Questionnaire (LCQ). Change in depressive symptoms (MADRS total scores) over time will be evaluated by within-subject repeated measures analysis of variance. We will calculate the relative risk associated with the beneficial (reduction in total scores for depression) outcomes, and the side effect and dropout rates (tolerability) of adding IN esketamine to patients' current pharmacological treatments. Covariate analysis will assess the impact of site and demographic variables on treatment outcomes.

ETHICS AND DISSEMINATION

Approval to perform this study was obtained through the Health Sciences Research Ethics Board at Queen's University. Findings will be shared among collaborators, through departmental meetings, presented on different academic venues and publishing our manuscript.

Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

BACKGROUND

The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established.

OBJECTIVES

To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators.

METHODS

We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668).

RESULTS

The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators.

CONCLUSION

We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

Mobile Health Applications for Depression in China: A Systematic Review

Mobile health (mHealth) applications (apps) have the potential to increase access to mental health care. In China, there is growing interest in mHealth apps for depression. Our objective was to systematically review research on mHealth for depression in China to identify benefits and challenges.

A systematic literature search was conducted using Chinese and English databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Randomized and nonrandomized clinical studies on mHealth apps and depression in China were included. Study quality was assessed using the Cochrane Risk of Bias tool.

Seven studies met the inclusion criteria with three randomized trials, two quasi-randomized trials, one clinical trial with an uncertain grouping method, and one study with a single-group design. All studies used the WeChat platform and included activities such as psychoeducation, self-management, supervised group chats, and/or remote contact with a healthcare team, in comparison to usual care. All studies reported significant and large benefits for outcomes, but the risk of bias was high.

There are few rigorous evaluations of mHealth apps for depression in China, with all included studies involving WeChat programs and most using WeChat to extend nursing discharge care for inpatients with depression. While these studies showed significant improvement in health outcomes as compared to usual care, the results remain inconclusive because of the high risk of bias. mHealth holds promise for increasing access to mental health care in China, but issues such as efficacy, scalability, patient and clinician acceptability, and data privacy must be addressed.

Identification of Endocannabinoid Predictors of Treatment Outcomes in Major Depressive Disorder: A Secondary Analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study

INTRODUCTION

An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes.

METHODS

The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels.

RESULTS

Lower methylation of CpG islands of the diacylglycerol lipase alpha gene (DAGLA) was associated with non-remission at week 16 (DAGLA; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes.

DISCUSSION

Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further.

Pretreatment anxious depression as a predictor of side effect frequency and severity in escitalopram and aripiprazole adjunctive therapy

OBJECTIVE

To report side effect frequency and severity in patients with major depressive disorder (MDD) receiving escitalopram and aripiprazole adjunctive therapy and to examine whether pretreatment anxious depression is associated with the number and presence of specific side effects.

METHODS

188 of the 211 trial participants provided information on side effects during treatment with escitalopram (10-20 mg) for 8 weeks, and nonresponders received further augmentation on aripiprazole (2-10 mg) adjunctive therapy for another 8 weeks, whereas responders remained on escitalopram. Participants completed the Toronto Side Effects Scale at weeks 2, 4, 10, and 12. Covariate-adjusted negative binomial regression and Wilcoxon tests examined the association between anxious depression (GAD-7 ≥ 10) and number of side effects. Covariate-adjusted logistic regression and chi-square tests explored the association between anxious depression and specific side effects.

RESULTS

For both therapies, the most frequent side effects were also the most severe. They mostly related to the central nervous system (CNS) (i.e., drowsiness and nervousness). Between baseline and week 2, the number of side effects participants experienced (incidence rate ratio [IRR] = 1.38, p = .010) or had trouble with (IRR = 1.34, p = .026) was significantly higher among those with anxious depression for escitalopram but not adjunctive aripiprazole. Further, odds of experiencing and having trouble with nervousness and agitation were also significantly higher in anxious depression for escitalopram only (p < .05).

CONCLUSION

Patients on escitalopram and aripiprazole adjunctive therapy may experience and have trouble with CNS side effects. Pretreatment anxious depression may predispose escitalopram recipients with MDD to developing side effects, especially those related to anxiety.

Males and females differ in reported sexual functioning with escitalopram treatment for major depressive disorder: A CAN-BIND-1 study report

BACKGROUND

Antidepressant use for major depressive disorder (MDD) is frequently associated with sexual dysfunction.

AIMS

Cross-sectional and longitudinal relationships between antidepressant treatment outcomes and sexual functioning (SF) were evaluated separately for males and females receiving escitalopram. We further assessed the association between pre- and posttreatment SF.

METHODS

In all, 208 of the 211 CAN-BIND-1 trial participants (77 males and 131 females) with MDD and detectable drug blood levels were eligible for the analyses. All received escitalopram (10-20 mg) for 8 weeks. At baseline and Week 8, participants completed the Montgomery-Åsberg Depression Rating Scale (MADRS) and the SexFx scale, which measures sexual satisfaction and SF frequency. Mixed-model repeated measures assessed baseline to Week 8 SF changes among participants with different response/remission statuses. Multiple linear regression analyses examined SF differences between treatment outcomes at Week 8 as well as associations between pretreatment and eventual SF.

RESULTS

For both sexes, overall sexual satisfaction improved among responders but not among nonresponders (p < 0.05). For females, overall SF frequency did not change significantly over time regardless of response status. For males, overall SF decreased significantly among nonresponders; orgasm decreased significantly among nonresponders and, to a lesser extent, among responders (p < 0.05). For both sexes, pretreatment SF was significantly associated with SF at Week 8 across all domains (p < 0.05).

CONCLUSION

For both sexes, sexual satisfaction improves with response to escitalopram. For females, the response does not correspond to improvements in SF frequency. For males, SF frequency, particularly that of orgasm, declines regardless of response/nonresponse.ClinicalTrials.gov identifier: NCT01655706.

Baseline anxiety, and early anxiety/depression improvement in anxious depression predicts treatment outcomes with escitalopram: A CAN-BIND-1 study report

INTRODUCTION

Although anxiety symptoms frequently co-occur with major depressive disorder, few studies examined the prediction of treatment outcomes among participants with anxious depression receiving antidepressants. We investigated whether baseline anxiety, and early improvements in anxiety and depression symptoms predict eventual treatment outcomes.

METHODS

111 participants with anxious depression, defined using ≥ 10 on GAD-7, received escitalopram (10-20 mg) for 8 weeks. Covariate-adjusted logistic regression was conducted to examine the impact of baseline anxiety, and to assess the extent week 2 anxiety (GAD-7) and depression (QIDS-SR) percentage improvement associates with week 8 anxiety (GAD-7) and depression (MADRS) response/remission. Optimum improvement thresholds were identified using receiving-operating-curve analysis and their predictive values assessed.

RESULTS

Greater percentage improvement in anxiety and depression after the first 2 weeks of treatment significantly increased odds of achieving week 8 anxiety and depression response/remission (OR:1.01-1.04, p<0.05). Early anxiety (68.4%/87.2%) and depression (52.2%/83.0%) improvement thresholds around 30 and 40% provided moderate to high positive predictive value (PPV) for predicting week 8 anxiety response/remission, as well as moderate to high negative predictive value (NPV) for predicting week 8 depression response/remission (anxiety:70.8%/91.7%; depression:72.2%/90.1%). Baseline anxiety severity predicted anxiety outcomes at weeks 2 and 8.

LIMITATIONS

Trial lacked placebo group.

CONCLUSION

In anxious depression, early improvement in anxiety may be better than depression in predicting anxiety outcomes, with similar or higher PPVs. Both improvement types perform similarly in predicting depression outcomes, with the lack of improvement predictive of non-response and non-remission. Finally, baseline anxiety predicts eventual anxiety but not depression outcomes.

Early Optimized Pharmacological Treatment in Patients With Depression and Chronic Pain

Major depressive disorder (MDD) is the leading cause of disability worldwide. Patients with MDD have high rates of comorbidity with mental and physical conditions, one of which is chronic pain. Chronic pain conditions themselves are also associated with significant disability, and the large number of patients with MDD who have chronic pain drives high levels of disability and compounds healthcare burden. The management of depression in patients who also have chronic pain can be particularly challenging due to underlying mechanisms that are common to both conditions, and because many patients with these conditions are already taking multiple medications. For these reasons, healthcare providers may be reluctant to treat such patients. The Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines provide evidence-based recommendations for the management of MDD and comorbid psychiatric and medical conditions such as anxiety, substance use disorder, and cardiovascular disease; however, comorbid chronic pain is not addressed. In this article, we provide an overview of the pathophysiological and clinical overlap between depression and chronic pain and review evidence-based pharmacological recommendations in current treatment guidelines for MDD and for chronic pain. Based on clinical experience with MDD patients with comorbid pain, we recommend rapidly and aggressively treating depression according to CANMAT treatment guidelines, using antidepressant medications with analgesic properties, while addressing pain with first-line pharmacotherapy as treatment for depression is optimized. We review options for treating pain symptoms that remain after response to antidepressant treatment is achieved.