Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes.

BACKGROUND: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults. METHODS: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process. RESULTS: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted. CONCLUSIONS: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD.

The association of blood biomarkers with cerebral white matter and myelin content in bipolar disorder: a systematic review.

OBJECTIVES: Evidence from diffusion tensor imaging (DTI) and postmortem studies has demonstrated white-matter (WM) deficits in bipolar disorder (BD). Changes in peripheral blood biomarkers have also been observed; however, studies evaluating the potential relationship between brain alterations and the periphery are scarce. The objective of this systematic review is to investigate the relationship between blood-based biomarkers and WM in BD. METHODS: PubMed, Embase, and PsycINFO were used to conduct literature searches. Cross-sectional or longitudinal studies reporting original data which investigated both a blood-based biomarker and WM (by neuroimaging) in BD were included. RESULTS: Of 3,750 studies retrieved, 23 were included. Several classes of biomarkers were found to have a significant relationship with WM in BD. These included cytokines and growth factors (interleukin-8 [IL-8], tumor necrosis factor alpha [TNF-a], and insulin-like growth factor binding protein 3 [IGFBP-3]), innate immune system (natural killer cells [NK]), metabolic markers (lipid hydroperoxidase, cholesterol, triglycerides), the kynurenine (Kyn) pathway (5-hydroxyindoleacetic acid, kynurenic acid [Kyna]), and various gene polymorphisms (serotonin-transporter-linked promoter region). CONCLUSION: This systematic review revealed that blood-based biomarkers are associated with markers of WM deficits observed in BD. Longitudinal studies investigating the potential clinical utility of these specific biomarkers are encouraged.

Greater within- and between-day instability is associated with worse anxiety and depression symptoms.

BACKGROUND: Depression and anxiety affect hundreds of millions of people worldwide, and their prevalence increased during the COVID-19 pandemic as social schedules were disrupted. This study explores the associations between anxiety and depression and within- and between-day instability of affective, somatic, and cognitive symptoms during the early pandemic stages. METHODS: Participants (n = 153, ages 18-77, 72 % female) reported daily levels of affective (anxiety/sadness), somatic (appetite/sleepiness), and cognitive (concentration/energy) symptoms for 14-44 days at five timepoints: 0, 3, 6, 9, and 12 h after awakening. At the end of the study, participants completed validated scales for anxiety (GAD-7) and depression (PHQ-9). Symptom instability was assessed using the Absolute Real Variability (ARV) index. Regression models examined within-day instability (WD-I) and between-day instability (BD-I) with GAD-7 and PHQ-9 scores as outcomes. RESULTS: Greater instability (both WD-I and BD-I) of affective symptoms correlated with elevated GAD-7 and PHQ-9 scores. For somatic and cognitive symptoms, greater BD-I was associated with higher scores. LIMITATIONS: The study used retrospective daily data, which could benefit from real-time assessments for improved accuracy. CONCLUSIONS: This study provides empirical evidence of a connection between greater anxiety and depression severity and increased instability in daily mood and physiological symptoms. The findings underscore the importance of consistent symptom monitoring to understand overall mental health trajectories. Additionally, it highlights the role of daily routines in stabilizing the circadian system, potentially regulating physiological and psychological processes and reducing symptom instability.

Atypical brain aging and its association with working memory performance in major depressive disorder.

BACKGROUND: Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. METHODS: 304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. RESULTS: Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS: Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.

Routine regularity during a global pandemic: Impact on mental health outcomes and influence of chronotype.

Among the public health recommendations for supporting mental health during the COVID-19 pandemic, many strategies had an impact on biological rhythms, like sleep hygiene, physical exercise and healthy eating habits. Considering the known relationship between circadian organization and mental health, our aim was to test the association between behavioral regularity and mental health, and its interaction with chronotype, in a large sample surveyed in Brazil. We collected longitudinal data using online questionnaires that assessed sociodemographic characteristics, behavioral routines, mental health (PHQ-9, GAD-7, WHO-5 scales), and chronotype estimation based on midpoint of sleep on free days - MSF (µMCTQ), in a sample of 1390 participants (81% females). We computed a Routine Regularity Score (RRS) that reflects regularity across four behaviors: sleep, eating, working, exercising. There was a strong negative association between RRS and the severity of anxiety and depressive symptoms (GAD-7 and PHQ-9 scores), which was weaker among participants with late MSF, and a strong positive association with well-being (WHO-5 scores). RRS was a mediator of the MSF-mental health association and a predictor of mental health states. This study provides empirical evidence that maintaining behavioral routines during times of hardship may serve as tools to alleviate the negative impact on mental health.

Factors associated with suicide risk in young women with premenstrual dysphoric disorder: A population-based study.

PURPOSE: Women with premenstrual dysphoric disorder (PMDD) are more likely to report suicide ideation and behavior when compared to women without PMDD. However, there is a lack of studies investigating the risk factors for suicide risk in women with PMDD. Thus, the aim of this study is to assess the factors associated with suicide risk in young women with PMDD. METHODS: This is a cross-sectional study including 128 young women with PMDD who were recruited from the community. PMDD and suicide risk were assessed by trained psychologists using the Mini International Neuropsychiatric Interview (MINI-PLUS). Suicide risk evaluation includes six questions that assess suicidal intention, planning and previous attempts. Subjects who answer yes to any of the six questions are classified as having current suicide risk. RESULTS: The prevalence of current suicide risk in women with PMDD was 28.1%. The factors associated with suicide risk in this population were: presenting current panic disorder (OR: 18.71 [95% CI: 1.02 - 343.27], p=0.048), a non-white skin color (OR: 4.18 [CI 95%: 1.28 - 13.61], p=0.018), greater severity of depressive symptoms (OR: 1.22 [95% CI: 1.12 - 1.32], < 0.001), and history of childhood trauma (OR: 1.04 [95% CI: 1.01 - 1.08], 0.010). CONCLUSION: Our findings indicate that there are key sociodemographic and clinical factors associated with suicide risk in young women with PMDD, enabling clinicians to identify at-risk individuals who could benefit from further screening and interventions.

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.

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.

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.

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.

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.

A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study.

Introduction: Real-time fMRI-based neurofeedback (rt-fMRI-NFB) is a non-invasive technology that enables individuals to self-regulate brain activity linked to neuropsychiatric symptoms, including those associated with post-traumatic stress disorder (PTSD). Selecting the target brain region for neurofeedback-mediated regulation is primarily informed by the neurobiological characteristics of the participant population. There is a strong link between PTSD symptoms and multiple functional disruptions in the brain, including hyperactivity within both the amygdala and posterior cingulate cortex (PCC) during trauma-related processing. As such, previous rt-fMRI-NFB studies have focused on these two target regions when training individuals with PTSD to regulate neural activity. However, the differential effects of neurofeedback target selection on PTSD-related neural activity and clinical outcomes have not previously been investigated. Methods: Here, we compared whole-brain activation and changes in PTSD symptoms between PTSD participants (n = 28) that trained to downregulate activity within either the amygdala (n = 14) or the PCC (n = 14) while viewing personalized trauma words. Results: For the PCC as compared to the amygdala group, we observed decreased neural activity in several regions implicated in PTSD psychopathology - namely, the bilateral cuneus/precuneus/primary visual cortex, the left superior parietal lobule, the left occipital pole, and the right superior temporal gyrus/temporoparietal junction (TPJ) - during target region downregulation using rt-fMRI-NFB. Conversely, for the amygdala as compared to the PCC group, there were no unique (i.e., over and above that of the PCC group) decreases in neural activity. Importantly, amygdala downregulation was not associated with significantly improved PTSD symptoms, whereas PCC downregulation was associated with reduced reliving and distress symptoms over the course of this single training session. In this pilot analysis, we did not detect significant between-group differences in state PTSD symptoms during neurofeedback. As a critical control, the PCC and amygdala groups did not differ in their ability to downregulate activity within their respective target brain regions. This indicates that subsequent whole-brain neural activation results can be attributed to the effects of the neurofeedback target region selection in terms of neurophysiological function, rather than as a result of group differences in regulatory success. Conclusion: In this study, neurofeedback-mediated downregulation of the PCC was differentially associated with reduced state PTSD symptoms and simultaneous decreases in PTSD-associated brain activity during a single training session. This novel analysis may guide researchers in choosing a neurofeedback target region in future rt-fMRI-NFB studies and help to establish the clinical efficacy of specific neurofeedback targets for PTSD. A future multi-session clinical trial of rt-fMRI-NFB that directly compares between PCC and amygdala target regions is warranted.

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.

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.

Delphi consensus study to develop guidelines for the management of adults with borderline personality disorder in the emergency department: a protocol.

INTRODUCTION: Clinicians caring for adults with borderline personality disorder (BPD) in acute settings such as the emergency department (ED) have little evidence/guidance to base decisions on. Specific/detailed guidance for managing BPD in the ED is needed given the morbidity and mortality risks, high service utilisation, unique challenges and risk of iatrogenic interventions. The primary objective of this study is to use a consensus method to develop a guideline for managing adults with BPD in the ED. This protocol and the key questions for the guideline were developed with the advice of people with BPD and their family members/support persons. METHODS AND ANALYSIS: We will perform a four-phase Delphi study of an expert panel of clinicians, researchers, adults with BPD and their family members/support persons. Various disciplines (psychiatry, psychology, emergency medicine, nursing, social work) and treatment approaches will be included in the expert panel. An online questionnaire will be developed from systematic reviews, qualitative assessments of pivotal literature, and opinions suggested by the panel (phase 1). The panel will rate their agreement on opinions for each key question covering areas of emergency care of adults with BPD using two rounds of this questionnaire (phases 2 and 3). Opinions meeting predefined thresholds for consensus will be brought to consensus meetings moderated by an independent chair (phase 4). The purpose of these meetings is to finalise the set and phrasing of the opinions for each area of emergency care. These final opinions will be the recommendations in the guideline. If there are significant differences of opinion, the guideline will present both recommendations alongside one another. ETHICS AND DISSEMINATION: This study has received ethics approval by the Hamilton Integrated Research Ethics Board in Hamilton, Ontario, Canada. The results of this study will be disseminated through peer-reviewed publications, conferences and national professional and patient/family/support associations.

Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression.

OBJECTIVE: Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD. METHOD: Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system. RESULTS: No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD. CONCLUSIONS: While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).

Lifestyle in bipolar disorder: A cross-sectional study.

BACKGROUND: Modifiable lifestyle behaviors are important factors for improving mental health, yet there has been a lack of research studying lifestyle as a multidimensional construct in bipolar disorder (BD). The aim of this cross-sectional study was to compare the lifestyle patterns of individuals with BD in a current mood episode with healthy controls (HCs) using the Short Multidimensional Inventory Lifestyle Evaluation (SMILE). MATERIALS AND METHODS: The sample consisted of 46 individuals with BD currently experiencing a depressive or manic episode and 50 HC, assessed using the MINI International Neuropsychiatric Interview, Montgomery-Åsberg Depression Rating Scale (MADRS), and the Young Mania Rating Scale (YMRS). The SMILE scale assesses lifestyle across seven domains: diet and nutrition, substance abuse, physical activity, stress management, restorative sleep, social support, and environmental exposures. Between-groups comparisons were performed based on the presence of a psychiatric diagnosis and the type of BD episode. RESULTS: We found significant differences in the total SMILE score (r=0.75, p<0.001) and in scores from each domain of the scale between BD and HC (p<0.05), where individuals with BD in a depressive or manic episode with or without mixed features reported worse lifestyle across all domains. Differences between individuals with BD in different mood episodes across domains on the SMILE scale were non-significant. CONCLUSION: Findings from this study highlight the presence of unhealthy lifestyle patterns in people with BD regardless of the polarity of their mood episode. Implementation of multidimensional lifestyle assessments is an essential step toward detecting the clustering of unhealthy lifestyle patterns in BD.

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.

Changes in posttraumatic stress disorder symptom severity during the COVID-19 pandemic: Ten-wave findings from a longitudinal observational cohort study of community adults.

Few studies have examined changes in posttraumatic-stress disorder (PTSD) symptomatology across an extended time period during the COVID-19 pandemic. This study used a longitudinal cohort design to examine: (1) changes in overall PTSD symptoms and symptom clusters; (2) moderators of change; (3) the clinical significance of observed changes; and (4) correlates of clinically meaningful changes. Community adults (N = 1412) were assessed using the PTSD Checklist for DSM-5 (PCL-5) at 10 timepoints (October 2018 - April 2022). Changes in overall PCL-5 score and symptom clusters were substantially moderated by pre-pandemic clinical severity (i.e., above/below PCL-5 cut-off). Pre-pandemic non-clinical participants exhibited increases in overall scores, Cluster D (negative cognitions), and Cluster E (arousal), while clinically elevated participants exhibited decreases overall and in all clusters. Regarding clinical significance, 12% of pre-pandemic non-clinical participants exhibited clinically meaningful increases, and 4% exhibited decreases. Conversely, 42% of the pre-pandemic elevated group exhibited clinically meaningful decreases, while 6% exhibited increases. Pandemic impacts in numerous psychosocial domains were associated with clinically meaningful change. Collectively, these findings reveal substantively divergent trajectories by pre-pandemic severity and PTSD symptom cluster. The large proportion of pre-pandemic high-severity participants exhibiting sizable decreases was an unexpected notable observation.

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.