Depressive disorders are the most common diagnosis among individuals who die by suicide, and intermittent theta-burst stimulation (iTBS) is a noninvasive treatment for those with difficult-to-treat depression who are at higher risk for suicide. Previous data suggests that pairing iTBS with D-cycloserine, a partial N-methyl-D-aspartate (NMDA) receptor agonist, improves antidepressant outcomes. However, its impact on suicide risk is not known.

We examine suicidal ideation and implicit suicide risk after iTBS+D-cycloserine in two clinical trials (open-label trial [n = 12] and randomized placebo-controlled trial [RCT, n = 50]) involving adults with major depressive disorder and the acute effects of D-cycloserine on implicit suicide risk in a crossover trial (n = 18). Implicit suicide risk was assessed using the computerized death/suicide implicit association test (IAT), and depressive symptoms and suicidal ideation were assessed using the clinician-rated Montgomery–Asberg Depression Rating Scale (MADRS).

Open-label iTBS+D-cycloserine was associated with a rapid reduction in suicidal ideation, and iTBS+D-cycloserine was superior to iTBS+placebo in reducing suicidal ideation. Similarly, open-label iTBS+D-cycloserine was associated with decreased implicit suicide risk as measured by the death/suicide IAT, and iTBS+D-cycloserine was associated with greater decreases in death/suicide IAT scores compared to iTBS+placebo. A single acute dose of D-cycloserine in the absence of iTBS had no effect on implicit suicide risk.

Adjunctive D-cycloserine with iTBS is a promising strategy to reduce suicidal ideation and implicit suicide risk in depression.

The aim of this study is to analyse complementary feeding practices, to assess the extent to which minimum dietary diversity (MDD) recommendations are being met in the population studied and to study factors that influence the achievement of MDD.

We pooled individual level data form the Demographic and Health Surveys (DHS) and Multi Indicator Cluster Surveys (MICS). We apply methods from poverty measurement to identify individual gaps towards achieving MDD. We further identify food groups that separate children who achieve MDD from those who do not.

West and Central Africa.

62 257 children aged between 6 and 23 months.

82·0 per cent of children do not achieve MDD and on average are lacking 2·5 out of five required food groups. For 19·0 per cent of children, the gap to MDD is one food group and for 23·7 per cent of children the gap is two food groups. Consumption of eggs, other fruits and vegetables as well as legumes and nuts is particularly low among children who are not achieving MDD. More than 90·0 per cent of children who do not achieve MDD do not consume these food groups compared to around half of children who achieve MDD.

Overall MDD is low, but there is large potential for improving MDD achievement if food consumption can be increased by one or two food groups. Available, affordable and culturally accepted food groups are identified that could be prioritised in interventions to close this gap.

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

To investigate the association between pre-trial expectancy, suggestibility, and response to treatment in a trial of escitalopram and investigational drug, COMP360, psilocybin, in the treatment of major depressive disorder (ClinicalTrials.gov registration: NCT03429075).

We used data (n = 55) from our recent double-blind, parallel-group, randomized head-to-head comparison trial of escitalopram and investigational drug, COMP360, psilocybin. Mixed linear models were used to investigate the association between pre-treatment efficacy-related expectations, as well as baseline trait suggestibility and absorption, and therapeutic response to both escitalopram and COMP360 psilocybin.

Patients had significantly higher expectancy for psilocybin relative to escitalopram; however, expectancy for escitalopram was associated with improved therapeutic outcomes to escitalopram, expectancy for psilocybin was not predictive of response to psilocybin. Separately, we found that pre-treatment trait suggestibility was associated with therapeutic response in the psilocybin arm, but not in the escitalopram arm.

Overall, our results suggest that psychedelic therapy may be less vulnerable to expectancy biases than previously suspected. The relationship between baseline trait suggestibility and response to psilocybin therapy implies that highly suggestible individuals may be primed for response to this treatment.

Major depressive disorder (MDD) often involves immune dysregulation with high peripheral levels of pro-inflammatory cytokines that might have an impact on the clinical course and treatment response. Moreover, MDD patients show brain volume changes and white matter (WM) alterations that are already existing in the early stage of illness.

The aim of the present review is to elucidate the association between inflammation and WM integrity and its impact on the pathophysiology and progression of MDD as well as the role of possible novel biomarkers of treatment response to improve MDD prevention and treatment strategies.

We conducted an electronic literature search of PubMed on studies that examined the role of inflammation in depression and that focused on WM integrity and pro-inflammatory cytokines as predictors of antidepressant response.

There is evidence for central effects of peripheral inflammation which could activate microglia which, in turn, might trigger a cascade of inflammatory processes leading to neurotransmitter imbalances. Numerous studies indicated that both altered levels of peripheral inflammatory markers, particularly TNF-α, IL-6, and CRP as well as WM integrity might predict antidepressant treatment outcome.

Despite mounting evidence on the impact of the immune system on WM microstructure, no study has yet addressed the interaction between the two factors in influencing antidepressant response. There is a lack of reproducible biomarkers predicting treatment response on an individual basis. The availability of such biomarkers would enable more efficient and personalized treatments with a faster treatment response and better prevention of treatment resistance.

No significant relationships.

The challenge for primary care physicians (PCPs) is keeping up to date in managing major depressive disorder (MDD) and psychiatric emergencies.

We evaluated whether an online educational video lecture directed at PCPs, could improve knowledge and confidence regarding management of psychiatric emergencies associated with MDD.

Educational effect was assessed using a 3-question repeated pairs, pre/post assessment survey. A paired-samples t-test was conducted to assess overall number correct and confidence change. A McNemar’s test was conducted to assess question-level significance. P values < 0.05 are statistically significant. Cohen’s d test was used to estimate the magnitude of effect of education. The activity launched on 8 April 2021, and preliminary data analysed as of 24 June 2021.

511 PCPs participated in the programme, of which 86 PCPs completed the pre- and post-assessment test. An average overall correct response rate of 28% pre- increased to 64% post- (129% relative increase, P<0.001; Cohen’s d = 1.13). Knowledge on the burden of suicide and MDD improved from 23% pre- to 87% post- (278% relative increase,P<0.001). Knowledge regarding clinical data for novel therapies for use in psychiatric emergencies improved from 29% pre- to 50% post- (72% relative increase, P<0.01). Knowledge regarding signs of suicidal intent in patients with MDD improved from 31% pre- to 53% (71% relative increase, P<0.001) following education.

This study demonstrates the positive effect of online medical education on PCPs’ knowledge and confidence in contemporary management of psychiatric emergencies associated with MDD in Europe.

The results of this study were derived from an educational programme which was developed through independent educational funding from Janssen Neuroscience

Psilocybin is a psychedelic drug found in mushrooms, often referred to as magic mushrooms due to its visual and auditory hallucinations effects upon ingestion. It is a Schedule I drug per DEA, and the FDA has not approved psilocybin for medicinal purposes. However, recent studies have shown promising therapeutic use to treat depression.

To identify current use, prevalence, and its association with depression in adolescents.

The National Survey on Drug Use and Health survey data from 2008-18 studied adolescent data (12-17 years), who responded, “ever used psilocybin (mushrooms)” and “lifetime major depressive episode (MDE).” The association between the psilocybin use and MDE status was analyzed in SAS 9.4 through multivariate logistic regression for odds ratio (OR) and 95% confidence interval (CI).

A total of 172745 adolescents were included in this study, of which 2469 ever used psilocybin in their lifetime, and 170276 responded no lifetime use. The psilocybin ever lifetime users were 17 years old (42%vs.17%,p<0.001), male (60%vs.51%,p<0.001), and non-Hispanic White (71%vs.55%,p<0.001) in comparison to non-users. Among psilocybin user group, 31% of respondents had lifetime MDE, compared to 16% of the lifetime psilocybin non-user group participants (p<0.001). The odds of association of psilocybin use among participants with MDE were 2.17 times compared to those without MDE (CI: 1.93-2.44,p<0.001).

We identified a significant association between psilocybin use and MDE among adolescents, which raises public health concerns about its illegal use, abuse, and toxicity potential. Future clinical studies should assess its clinical safety, efficacy, and addictive properties.

No significant relationships.

1.Introduction MDD is a heterogeneous disorder, with a wide variety of symptoms and inconsistent treatment response, and is not completely understood. A dysregulated stress system is a consistent finding, however, and exhaustion is a consistent trait in adolescent patients. In order to open up our thinking about MDD we take up the challenge to reframe depression, specifically focusing on the possible role of the Microbiota-Gut-Brain-Axis in the etiology of MDD.

We propose a ‘bidirectional feedback hypothesis’: microbiota can promote or inhibit a pro-inflammatory state, (in)directly altering the hypothalamic pituitary adrenal (HPA) axis response and the microbiome and further increasing or decreasing its pro-inflammatory state. The aim is to show that the pro-inflammatory state is an integral part of a HPA axis stress spiralling mechanism that plays a role in the etiology of MDD.

A systematic review based on publications from PubMed, Embase and PsycInfo

The etiology of MDD can be understood as sliding down a spiral. This stress spiralling mechanism can be promoted or inhibited by: 1.factors such as a poor lifestyle or (pre-existing) illness 2.bettering someone’s lifestyle, coping behavior or providing pro-/prebiotics in combination with personalised therapeutics.

We argue that an interdisciplinary One Health approach is the most promising preventive and therapeutic option for MDD.

No significant relationships.

The challenge for psychiatrists is keeping up to date with the latest clinical trial data in managing major depressive disorder (MDD) and psychiatric emergencies.

We evaluated whether an online educational video lecture directed at psychiatrists, could improve knowledge and confidence regarding management of psychiatric emergencies associated with MDD.

Educational effect was assessed using a 3-question repeated pairs, pre/post assessment survey. A paired-samples t-test was conducted to assess overall number correct and confidence change. A McNemar’s test was conducted to assess question-level significance. P values < 0.05 are statistically significant. Cohen’s d test was used to estimate the magnitude of effect of education. The activity launched on 8 April 2021, and preliminary data analysed as of 24 June 2021.

807 psychiatrists participated in the programme, of which 150 completed the pre- and post-assessment test. An average overall correct response rate of 44% pre- increased to 74% post- (67% relative increase, P<0.001; Cohen’s d = 0.91). Knowledge on the burden of suicide and MDD improved from 38% pre- to 85% post- (124% relative increase,P<0.001). Knowledge regarding clinical data for novel therapies for use in psychiatric emergencies improved from 47% pre- to 68% post- (45% relative increase, P<0.01). Knowledge regarding signs of suicidal intent in patients with MDD improved from 49% pre- to 71% (45% relative increase, P<0.001) following education.

This study demonstrates the positive effect of online medical education on psychiatrists’ knowledge in contemporary management of psychiatric emergencies associated with MDD in Europe.

The results of this study were from an educational programme that was developed through independent educational funding from Janssen Neuroscience.

Major Depressive Disorder (MDD) is a common mood disorder diagnosed in children and adolescents. Bright light therapy has been effective for seasonal affective disorders, however its role in the treatment of MDD is under studied.

Our objective is to evaluate if bright light therapy (BLT) is a practical approach in treating Child and Adolescents having MDD.

We performed an extensive literature search using a wide range of MeSH terms in PubMed, PubMed Central and Google Scholar. We reviewed the literature for studies (published between 1983-2021) assessing the efficacy of BLT in the treatment of MDD in children and adolescents.

The final search results yielded 8 randomized clinical trials and 1 case report from 1983 to 2021. BLT showed a superior effect in children and adolescents with MDD compared to the control group in the majority of the randomized trials and a case report. In six studies BLT showed good effect, however in a study by Magnusson et al. and Sonis et al., found a milder degree of improvement in depression symptoms when compared to the control group. In the majority of the studies, patients’ age range was 7 years 18 and in most of the studies, patients were not on antidepressants.

The use of BLT in children and adolescents suffering from MDD can be a promising alternative method of biological treatment, which is effective as well as well tolerated. Future long-term studies on large sample size are necessary in this field.

No significant relationships.

The corticotropin-releasing hormone receptor 2 (CRHR2) gene encodes CRHR2, which is an important element in the hypothalamic-pituitary-adrenal physiologic response towards stress culminating in hyperglycemia, insulin resistance, mood disorders and depression (MDD). CRHR2-/- mice are hypersensitive to stress, and the CRHR2 locus in humans has been linked to type 2 diabetes (T2D) and MDD.

Several variants in the CRHR2 gene have been reported in patients with bipolar disorder, post-traumatic stress disorder, and T2D, but variants in the gene have not been investigated in families with T2D and MDD.

We genotyped 212 Italian families with T2D and MDD. We tested 17 SNPs in the CRHR2 gene using two-point parametric-linkage and linkage-disequilibrium (LD) analysis with the following models: dominant with complete-penetrance (D1), dominant with incomplete-penetrance (D2), recessive with complete-penetrance (R1) and recessive with incomplete-penetrance (R2).

We detected linkage to and/or LD with: MDD for 3 SNPs/D1, 2 SNPs/D2, 3 SNPs/R1, and 3 SNPs/R2; and, T2D for 3 SNPs/D1, 2 SNPs/D2, 2 SNPs/R1 and 1 SNP/R2. Two independent SNPs were comorbid. Interestingly, the variants linked to or in LD with MDD had in general higher statistical significance level than the variants linked to T2D, despite that the families were primarily ascertained for T2D.

Our study shows for the first time that the CRHR2 gene which encodes CRHR2 is in linkage to and linkage disequilibrium with MDD and T2D, thereby contributing, in families with T2D, to both disorders and underlying the shared genetic pathogenesis of their comorbidity

No significant relationships.

No significant relationships.

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.

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.

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.

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.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Major depressive disorder (MDD) is closely related to obesity, inflammation, and insulin resistance, all together being etiologically linked to metabolic syndrome (MetS) development. The depressive disorder has a neuroendocrinological component, co-influencing the MetS, while MetS is characterised by increased cytokine levels, which are known to cause a depressed mood. This study aimed to establish biological subtypes of the depressive disorder based on researched clinical, laboratory, and anthropometric variables.

We performed a cross-sectional study on a sample of 293 subjects (145 suffering from a depressive disorder and 148 healthy controls). Results were analysed with multivariate statistical methods as well as with cluster and discriminant analysis. In order to classify depressive disorder on the grounds of laboratory, anthropometric, and clinical parameters, we performed cluster analysis, which resulted in three clusters.

The first cluster is characterised by low platelet serotonin, high cortisol levels, high blood glucose levels, high triglycerides levels, high Hamilton Depression Rating Scale score, high waist circumference, high C-Reactive Protein values, and a high number of previous depressive episodes, was named Combined (Metabolic) depression. The inflammatory depression cluster is defined with average platelet serotonin values, normal cortisol, and all other parameter levels, except for increased IL-6 levels. The serotoninergic depression cluster is characterised by markedly low platelet serotonin, and all other parameters are within the normal range.

From a biological point of view, depressive disorder is not uniform, and as such, these findings suggest potential clinically useful and generalisable biological subtypes of depressive disorder.

There is a burgeoning body of evidence suggesting that arginine vasopressin (AVP) acts as a neuromodulator of the stress response. AVP stimulates the release of adrenocorticotropic hormone, synergistic to corticotropin-releasing hormone, which might explain AVP’s role in resilience. Personal hardiness is the bedrock of resilience. Numerous studies have demonstrated elevated plasma levels of AVP in patients with major depressive disorder (MDD), suggesting an etiopathogenetic role as well as a novel therapeutic target.

The aim of this study was to examine the relationship between AVP and resilience in patients with MDD and to determine AVP levels in serum of patients with MDD.

Forty patients with MDD and 40 healthy control subjects were studied using the Dispositional Resilience (Hardiness) Scale by Barton, the Quality of Life Scale, the Social Readjustment Rating Scale, and the Beck Depression Inventory. Biochemical analysis of plasma levels of AVP, using the enzyme-linked immunosorbent assay (ELISA), was performed for all participants.

Levels of AVP were statistically significantly elevated in patients with MDD compared with healthy controls. Psychological hardiness was decreased in patients with MDD compared with healthy controls, a finding also statistically significant. There was a negative correlation between plasma AVP level and psychological hardiness.

AVP and psychological hardiness are negatively correlated, reflecting lower stress resilience. AVP levels are indeed higher in patients struggling with MDD.