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This study aims to compare the serum cytokine levels between controls, individuals with a current depressive episode (CDE) with childhood trauma and individuals with CDE without childhood trauma. This is a cross-sectional with paired sample nested in a population-based study. For the purposes of the current study, subjects who had psychotic symptoms, generalized anxiety disorder, and who refused to perform blood collection were excluded. Subsequently, only individuals who had a current depressive episode were selected (n = 76). Another 76 subjects were randomly paired by sex and age, constituting a population control group. The measurements of serum cytokine levels were performed using the multiplex analysis method. In the group with a CDE, when compared to the population control group, the following cytokines were high: IL-1β, IL-2, IL-4, IL-6, IL-17A, IFN-γ and TNF-α (p 0.05). On the other hand, there was a decrease in the levels of cytokines IL-10 (p = 0.027) and IL12p70 (p = 0.001). Bonferroni test demonstrates that there is no statistically significant difference in serum cytokine levels between subjects with a CDE, with and without trauma (p 0.05). In a multivariable logistic regression, adjusting for socioeconomic status, tobacco, alcohol and illicit drugs abuse/dependence, and use of psychiatric medication, we found that cytokines serum levels remained associated with CDE even when adjusted for these potential confounders. Our findings demonstrate that monitoring cytokine levels and immune function may be beneficial in preventing the development of a CDE. However, future research is necessary to investigate the impact of trauma on the relationship between inflammation and CDE.

Post-traumatic stress disorder (PTSD) has been associated with persistent, low-degree inflammation, which could explain the increased prevalence of autoimmune conditions and accelerated aging among patients. The aim of the present study is to assess which inflammatory and oxidative stress markers are associated with PTSD. We carried out a meta-analytic and meta-regression analysis based on a systematic review of studies comparing inflammatory and oxidative stress markers between patients with PTSD and controls. We undertook meta-analyses whenever values of inflammatory and oxidative stress markers were available in two or more studies. Overall, 28,008 abstracts were identified, and 54 studies were included, with a total of 8394 participants. The Newcastle-Ottawa Quality Assessment Scale was used to evaluate the quality of the studies. Concentrations of C-reactive protein (SMD = 0.64; 95% CI: 0.21 to 1.06; p = 0.0031; k = 12), interleukin 6 (SMD = 0.94; 95% CI: 0.36 to 1.52; p = 0.0014; k = 32), and tumor necrosis factor-α (SMD = 0.89; 95% CI: 0.23 to 1.55; p = 0.0080; k = 24) were significantly increased in patients with PTSD in comparison with healthy controls. Interleukin 1β levels almost reached the threshold for significance (SMD = 1.20; 95% CI: -0.04 to 2.44; p = 0.0569; k = 15). No oxidative stress marker was associated with PTSD. These findings may explain why PTSD is associated with accelerated aging and illnesses in which immune activation has a key role, such as cardiovascular diseases and diabetes. In addition, they pointed to the potential role of inflammatory markers as therapeutic targets.

Detecting substance use as a predictor of bipolar disorder (BD) is important for clinicians to perform accurate and early diagnosis, as well as better manage the treatment of individuals with BD. The aim of this systematic review was to describe whether substance use is a predictor of BD. A literature search was conducted using the following databases: PubMed, PsycINFO, and Embase. All eligible studies published up to February 9, 2021 were included. This systematic review included 22 studies. We found that 66.7% of the studies assessing overall substance use found that overall substance use was a risk factor for BD. Regarding the specific substances assessed, cannabis use was described as a risk factor for BD in 55.6% of the studies, nonmedical use of prescription medications was a risk factor for BD in 50% of the studies, nicotine was found as a risk factor for BD in 50% of the studies, and alcohol use was described as a risk factor for BD in 42.9% of the studies assessing it. Only one study assessed whether cocaine use was a risk factor for BD and found a significant association. Interestingly, some studies suggested that the greater frequency of cannabis use was associated with greater risk to develop BD or hypomanic/manic symptoms. In conclusion, there is evidence supporting that substance use is a risk factor for BD. Importantly, when assessing the risk factors for BD related to psychoactive substance use, special attention should be given for the frequency of cannabis use.

Background Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. Methods Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. Results The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. Limitations Proximal factors associated with the incidence of suicide risk were not assessed. Conclusions Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical non-linear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk.

Objectives To describe the cognitive and functional impairment in individuals with the first episode of major depressive disorder (MDD) as compared to controls and individuals with recurrent MDD. Also to describe the functional and cognitive trajectory after the first episode of MDD. Methods A total of 52 studies were included in our systematic review. 32 studies compared the cognitive performance between first episode of depression (FED) and controls, 11 studies compared the cognitive performance between recurrent depression (RD) and FED, 10 compared global functioning between RD and FED, four studies assessed cognition in FED over time, and two studies assessed global functioning in FED over time. Results The majority of studies (n = 22/32, 68.8%) found that FED subjects performed significantly worse than controls on cognitive tests, with processing speed (n = 12) and executive/working memory (n = 11) being the most commonly impaired domains. Seven out of 11 studies (63.6%) found that RD performed significantly worse than FED, with verbal learning and memory being the most commonly impaired domain (n = 4). Most studies (n = 7/10, 70%) did not find a significant difference in global functioning between RD and FED. In three of four longitudinal studies assessing cognition, subgroup analyses were used instead of directly assessing cognition in FED over time while the remaining study found significant cognitive declines over time in FED when compared to controls. The two longitudinal studies assessing functional trajectory found that functioning significantly improved over time, possibly due to the improvement of depressive symptoms. Conclusion There is strong evidence that cognitive impairment is present during the first episode of depression, and individuals with multiple episodes display greater cognitive impairment than individuals with a single episode. Future studies aimed at identifying predictors of cognitive and functional impairment after the first episode of depression are needed to describe the functional and cognitive trajectory of individuals with the first episode of MDD over time.

Objectives The 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) guidelines provided clinicians with pragmatic treatment recommendations for bipolar disorder (BD). While these guidelines included commentary on how mixed features may direct treatment selection, specific recommendations were not provided- a critical gap which the current update aims to address. Method Overview of research regarding mixed presentations in BD, with treatment recommendations developed using a modified CANMAT/ISBD rating methodology. Limitations are discussed, including the dearth of high-quality data and reliance on expert opinion. Results No agents met threshold for first line treatment of DSM-5 manic or depressive episodes with mixed features. For mania + mixed features second line treatment options include asenapine, cariprazine, divalproex, and aripiprazole. In depression + mixed features, cariprazine and lurasidone are recommended as second line options. For DSM-IV defined mixed episodes, with a longer history of research, asenapine and aripiprazole are first line, and olanzapine (monotherapy or combination), carbamazepine, and divalproex are second line. Research on maintenance treatments following a DSM-5 mixed presentation is extremely limited, with third-line recommendations based on expert opinion. For maintenance treatment following a DSM-IV mixed episode, quetiapine (monotherapy or combination) is first line, and lithium and olanzapine identified as second line options. Conclusion The CANMAT and ISBD groups hope these guidelines provide valuable support for clinicians providing care to patients experiencing mixed presentations, as well as further influence investment in research to improve diagnosis and treatment of this common and complex clinical state.

OBJECTIVES To detail the biological, clinical and neurocognitive characteristics differentiating bipolar disorder (BD) from frontotemporal dementia (FTD) and to investigate whether BD is a risk factor for FTD. METHODS A total of 16 studies were included in this systematic review. Five studies described biological and/or neurocognitive characteristics between patients with BD and FTD, and 11 studies investigated whether BD was a risk factor for FTD. RESULTS Individuals with FTD presented higher levels of serum neurofilament light chain, greater grey matter reduction in frontal, parietal and temporal lobes, and increased slow wave oscillations in channels F3, F4, T3, T5, T4 and T6 within an electroencephalogram (EEG), relative to individuals with BD. Patients with FTD presented greater deficits in executive function and theory of mind compared to patients with BD in a euthymic state, and more deficits in verbal fluency compared to patients with BD in a current mood episode. Patients with BD in a current mood episode showed greater impairment in attention, working memory, verbal memory and executive function relative to individuals with FTD. In addition, retrospective studies showed that 10.2%-11.6% of patients with behavioural variant FTD (bvFTD) had a preceding history of BD. CONCLUSION Biological and neurocognitive characteristics help to distinguish between BD and FTD, and it may help to reach a more precise diagnosis. In addition, individuals with BD are at higher risk of developing FTD. More studies are needed to identify the predictors of the conversion between BD to FTD.

Background Actuarial risk estimates are considered the gold-standard way to assess whether psychiatric patients are likely to commit prospective criminal offenses. However, these risk estimates cannot individually predict the type of criminal offense a patient will subsequently commit, and often simply assess the general likelihood of crime occurring in a group sample. In order to advance the predictive utility of risk assessments, better statistical strategies are required. Aim To develop a machine learning model to predict the type of criminal offense committed in a large transdiagnostic sample of psychiatry patients, at an individual level. Method Machine learning algorithms (Random Forest, Elastic Net, SVM), were applied to a representative and diverse sample of 1240 patients in the forensic mental health system. Clinical, historical, and sociodemographic variables were considered as potential predictors and assessed in a data-driven way. Separate models were created for each type of criminal offense, and feature selection methods were used to improve the interpretability and generalizability of our findings. Results Sexual offenses can be predicted from nonviolent and violent offenses at an individual level with a sensitivity of 82.44% and specificity of 60.00%, using only 36 variables. Furthermore, in a binary classification model, sexual and violent offenses can be predicted at an individual level with 83.26% sensitivity and 77.42% specificity using only 20 clinical variables. Likewise, non-violent and sexual offenses can be individually predicted with 74.60% sensitivity and 80.65% specificity using 30 clinical variables. Conclusion The current results suggest that machine learning models can show greater accuracy than gold-standard risk assessment tools (AUCs 0.70-0.80). However, unlike existing risk tools, this approach allows for the prediction of cases at an individual level, which is more clinically useful. Despite this, it is important to note that a large subset of patients in the sample were involved in the criminal system in the past, prior to an official diagnosis. Therefore, many of the variables that predict offenses may be derived from the issues of prior offenses. Irrespective of this, the accuracy of prospective models is expected to only improve with further refinement.

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90–89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747–0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05–88.70), and 84.60% (95% CI: 67.89–92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45–94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45–94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

BackgroundThere is still little knowledge of objective suicide risk stratification.MethodsThis study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2.ResultsThe model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance.ConclusionsRisk for suicide attempt can be estimated with high accuracy.

Background: Bipolar disorder may undertake a progressive course in a subset of patients, and research efforts have been made to understand the biological basis underlying this process. This systematic review examined the literature available on biological markers associated with illness progression in bipolar disorder. Methods: Peer-reviewed articles were assessed using Embase, PsycINFO and PubMed, as well as from external sources. After initial screening, a total of 871 citations from databases and other sources were identified. Participants with a diagnosis of bipolar disorder were included in our systematic review; however, studies with participants younger than 15 or older than 65 were excluded. All studies were assessed using the Newcastle-Ottawa Scale assessment tool, and data pertaining to the results were extracted into tabular form using Google Sheets and Google Documents. The systematic review was registered on PROSPERO international prospective register of systematic reviews (ID Number: CRD42020154305). Results: A total of 35 studies were included in the systematic review. Increased ventricular size and reduction of grey matter volume were the most common brain changes associated with illness progression in bipolar disorder. Among the several biomarkers evaluated in this systematic review, findings also indicate a role of peripheral inflammatory markers in this process. Discussion: The studies evaluating the biological basis of the illness progression in bipolar disorder are still scarce and heterogeneous. However, current evidence supports the notion of neuroprogression, the pathophysiological process related to progressive brain changes associated with clinical progression in patients with bipolar disorder. The increase in peripheral inflammatory biomarkers and the neuroanatomical changes in bipolar disorder suggest progressive systemic and structural brain alterations, respectively.

Access to postmortem brain tissue can be valuable in refining knowledge on the pathophysiology and genetics of neuropsychiatric disorders. Obtaining postmortem consent for the donation after death by suicide can be difficult, as families may be overwhelmed by a violent and unexpected death. Examining the process of brain donation can inform on how the request can best be conducted. This is a qualitative study with in-depth interviews with forty-one people that were asked to consider brain donation—32 who had consented to donation and 9 who refused it. Data collection and analyses were carried out according to grounded theory. Five key themes emerged from data analysis: the context of the families, the invitation to talk to the research team, the experience with the request protocol, the participants’ assessment of the experience, and their participation in the study as an opportunity to heal. The participants indicated that a brain donation request that is respectful and tactful can be made without adding to the family distress brought on by suicide and pondering brain donation was seen as an opportunity to transform the meaning of the death and invest it with a modicum of solace for being able to contribute to research.

Background Studies on the field of mood disorders has mainly focusing on the risk factors associated to develop the illness or the clinical factors associated with the clinical progression. Less attention was given to factors such as resilience that may be associated with better outcomes in the course of mood disorders. In this study, we assessed the mediation effect of resilience on the relationship between childhood trauma and mood disorders, as well as the severity of depressive symptoms in a population-based sample. Methods This is a cross-sectional study with a community sample of young adults with bipolar disorder (BD), major depressive disorder (MDD), and community controls without any mood disorder. The trauma experiences during childhood were assessed by Childhood Trauma Questionnaire (CTQ). The severity of depressive symptoms was assessed using the Montgomery-Asberg Depression Rating Scale (MADRS) and to assess the resilience was used the Resilience Scale (RS-25). Results All subtypes of trauma were associated with both MDD and BD, however, only physical and emotional abuse differentiated BD from MDD subjects. Bootstrapping-enhanced mediation analyses indicated that resilience partly mediated the association of childhood trauma to both mood disorder and severity of depression. Limitation The employed mediation analyses are cross-sectional in nature, which limits any firm conclusions regarding causality. Conclusions The findings support the clinical assumption that resilient subjects may be partly protected against the detrimental long-term effects of childhood trauma. This study provides important information regarding the relationships among childhood trauma, resilience, and mood disorder.

Suicide is the second leading cause of death among young adults worldwide, and few studies have assessed the factors associated with suicidality in this specific population. Thus, the aim of this study was to assess the association between mixed episodes and suicidality in a community sample of young adults. For this aim, we compared young adults in a mixed episode with individuals in a depressive or (hypo)manic episode, and community controls.This is a cross-sectional population-based study including young adults aged 18-24 years old. Information about participants' age, sex, self-reported skin color and study status were obtained using a sociodemographic questionnaire. Mixed, (hypo)manic and depressive episodes, as well as suicide risk were assessed using the Mini International Neuropsychiatric Interview.The sample included 1560 individuals. Young adults in a mixed episode showed a 13.50 times higher probability to have suicide risk, as compared to community controls (p 0.001), 5.67 higher probability to have suicide risk as compared to individuals in a (hypo)manic episode (p 0.001), and 2.18 times higher probability to have suicide risk as compared to young adults in a depressive episode (p 0.001).The assessment of suicide risk includes both suicide attempts and ideation.Young adults in a mixed episode showed a higher rate of suicide risk than individuals with depression or mania/hypomania. Considering the already increasing prevalence of suicide among young adults, there is an urgent need for the early diagnosis and treatment of mixed episodes in this specific population.

Background Emotional memory is a critical amygdala-dependent cognitive function characterized by enhanced memory for emotional events coupled with retrograde amnesia. Our study aims to assess the influence of bipolar disorder (BD), trauma, and the number of mood episodes on emotional memory. Methods 53 subjects (33 euthymic patients with BD and 20 healthy controls) answered a clinical assessment, childhood trauma questionnaire (CTQ), and an emotional memory test composed of lists of nouns, including neutral words, one emotional (E), one preceding (E-1) and one following word (E + 1). We assessed for the influence of type, position, diagnosis, trauma, and number of mood episodes in word recall using generalized estimating equations. Results Controlling for neutral words, BD had a higher recall for E-1 (p = 0.038) and a trend for a higher recall of E (p = 0.055). There was no difference between patients with and without trauma. Patients with BD who suffered multiple mood episodes had a higher recall of E compared to patients with fewer episodes (p = 0.016). Limitations Cross-sectional design and small sample size. Conclusion Our results indicate dysfunction in emotional memory in patients with BD, particularly after multiple mood episodes. While we expected an impaired emotional memory, patients with BD showed an increased recall for emotional stimuli and events preceding them. Childhood trauma does not seem to interfere with emotional memory changes in patients with BD. Emotional memory enhancement seems to be a promising marker of progression in BD.

Objective: To assess whether the effects of oral administration of 300 mg of Cannabidiol (CBD) for 28 days on mental health are maintained for a period after the medication discontinuation.Methods: This is a 3-month follow-up observational and clinical trial study. The data were obtained from two studies performed simultaneously by the same team in the same period and region with Brazilian frontline healthcare workers during the COVID-19 pandemic. Scales to assess emotional symptoms were applied weekly, in the first month, and at weeks eight and 12.Results: The primary outcome was that, compared to the control group, a significant reduction in General Anxiety Disorder-7 Questionnaire (GAD-7) from baseline values was observed in the CBD group on weeks two, four, and eight (Within-Subjects Contrasts, time-group interactions: F1-125 = 7.67; p = 0.006; ηp2 = 0.06; F1-125 = 6.58; p = 0.01; ηp2 = 0.05; F1-125 = 4.28; p = 0.04; ηp2 = 0.03, respectively) after the end of the treatment.Conclusions: The anxiolytic effects of CBD in frontline health care professionals during the COVID-19 pandemic were maintained up to 1 month after the treatment discontinuation, suggesting a persistent decrease in anxiety in this group in the real world. Future double-blind placebo-controlled clinical trials are needed to confirm the present findings and weigh the benefits of CBD therapy against potential undesired or adverse effects.

Introduction Changes in brain-derived neurotrophic factor (BDNF) have been linked to the neuroadaptative consequences of chronic alcohol use and associated with disease severity and prognosis. Few studies have evaluated the influence of drug withdrawal and clinical and sociodemographic data on BDNF levels in severe alcohol users. Objectives Our goals were (1) to evaluate variation in BDNF levels during alcohol withdrawal and, (2) to assess the influence of putative confounding factors on BDNF levels. Methods Our sample consists of 62 men with alcohol use disorder undergoing a detoxification process. Serum BDNF levels were measured using a commercial sandwich-ELISA kit, at two points: before and after the detoxification period. Results We found an increase in BDNF levels during alcohol withdrawal (25.4±9.6 at admission vs. 29.8±10.2 ng/ml at discharge; p < 0.001), even after controlling for potential confounders (positive family history, number of days between blood sample collections, and age) (Generalized Estimating Equation: coefficient = -4.37, 95% confidence interval [95%CI] -6.3; -2.4; p < 0.001). Moreover, individuals who had first-degree relative with alcohol dependence had smaller increases in BDNF levels than individuals with no family history (14.8 [95%CI -5.3; 35.6] vs. 35.3 [95%CI 15.4; 74.8]; p = 0.005). Conclusions In summary, variation in BDNF levels seems to be influenced by withdrawal in severe alcohol users. A positive family history of alcohol dependence could also be a factor that influences variation in this biomarker.

Importance: Owing to its anti-inflammatory properties and antiviral “in vitro” effect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cannabidiol (CBD) has been proposed as a ...

Background: Bipolar disorder (BD) is a recurrent chronic psychiatric disorder. Evidence indicates that many individuals with BD exhibit high serum levels of inflammation and oxidative stress markers, which are further associated with mood symptoms and cognitive dysfunction. Due to the crosstalk between the periphery and central nervous system in BD, the disruption of the blood-brain barrier (BBB) has been proposed as a key mechanism of the pathophysiology of the disorder. This study aimed to investigate claudin-5 expression – a major protein of the BBB – in the brain of an animal model of mania induced by d-amphetamine (AMPH) and evaluate the effects of treatment with lithium. Results: AMPH-injected animals exhibited increased overall activity in the open field test. In the serum, TBARS levels were augmented in the lithium-treated groups, regardless of AMPH injection, while TNFα was not detected. In the brain, TBARS and TNFα did not differ between groups but were positively and strongly correlated in the ST of AMPH-injected rats. Contrary to the primary hypothesis, AMPH and lithium injections did not affect brain claudin-5 protein levels.Conclusions: This is one of the first attempts to investigate the effects of AMPH on BBB integrity. Although no evidence of BBB disruption was found in the current study, our results provide evidence and rationale for future research to elucidate the importance of such alteration in BD.

Background : Clinical practice guidelines (CPG) are an important tool for implementation of evidence-based clinical care. Despite clinical trials showing lack of efficacy of some agents in bipolar disorder (BD), they are still frequently prescribed in clinical practice. The objective of this study was to systematically review the CPG recommendations on pharmacological interventions with evidence against their use due to lack of efficacy data and/or due to serious safety concerns. Methods : A systematic literature search identified 29 guidelines published by national and international organizations during the 1994-2020 period. Information was extracted regarding how the recommendations framed non-use of treatments in particular clinical situations as well as the actual recommendation in the guideline. Results : Twenty-three guidelines (79%) mentioned at least one non-recommended treatment. The terms used to qualify recommendations varied amongst guidelines and included: “not recommended” “no recommendation” and “negative evidence”. Lamotrigine, topiramate and gabapentin were commonly cited as non-recommended treatments for mania and most CPG did not recommend monotherapy with antidepressants, aripiprazole, risperidone, and ziprasidone for treatment of acute bipolar depression. Most guidelines made recommendations about lack of efficacy data or potential harm in treatments for BD but there is a significant variation in the way this information is conveyed to the reader. Limitations : Non-recommended treatments were based on their use for BD episodes or maintenance but specific medications may benefit patients when treating comorbid conditions. Conclusions : The absence of a uniform language and recommendations in current guidelines may be an additional complicating factor in the implementation of evidence-based treatments in BD.

BackgroundThe direct examination of postmortem brain tissue can provide valuable help in refining knowledge on the pathophysiology and genetics of neuropsychiatric disorders. Obtaining postmortem consent for the donation of brain tissue after death by suicide can be difficult, as families may be overwhelmed by a violent and unexpected death. Examining the process of brain donation can inform on how the request can best be conducted, examining the existing barriers and enhancing communication, to the benefit of proxy donors.MethodsThis is a qualitative study, in which we employed in-depth interviews to investigate the donation process. Family members of decedents who were eligible for brain tissue donation were asked to consider the donation; irrespective of their decision, they were invited for an interview on the donation process at least 2 months after the suicide. Data collection and analyses were carried out according to a grounded theory framework, and collection, coding, and theorization occurred simultaneously.FindingsForty-one people participated in this study — 32 family members who had consented to donation and 9 who refused it. Five key themes emerged from our data analysis: the context of the families of potential donors, the invitation to talk to the research team, the experience with the request protocol, the participants’ assessment of the experience, and their participation in the study as an opportunity to heal. We arranged the main categories around 3 central stages of the experience described by participants: before the donation request, the request, and after the request.ConclusionsThe participants indicated that a brain donation request that is respectful and tactful can be made without adding to the family distress brought on by suicide and is viewed in a highly positive lens in the months following the event. Having an adequate understanding of tissue donation for research was crucial for satisfactory decision-making. Meeting with the donation team was considered a chance to talk to mental health professionals about suicide. Pondering brain donation was seen as an opportunity to transform the meaning of the death and invest it with a modicum of solace for being able to contribute to research.

Introduction Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. Methods A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. Results The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. Conclusions Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

Despite the strong genetic component of psychiatric disorders, traditional genetic studies have failed to find individual genes of large effect size. Thus, alternative methods, using bioinformatics, have been proposed to solve these biological puzzles. Of these, here we employ systems biology-based approaches to identify potential master regulators (MRs) of bipolar disorder (BD), schizophrenia (SZ), and major depressive disorder (MDD), their association with biological processes and their capacity to differentiate disorders' phenotypes. High-throughput gene expression data was used to reconstruct standard human dorsolateral prefrontal cortex regulatory transcriptional network, which was then queried for regulatory units and MRs associated with the psychiatric disorders of interest. Furthermore, the activity status (active or repressed) of MR candidates was obtained and used in cluster analysis to characterize disease phenotypes. Finally, we explored the biological processes modulated by the MRs using functional enrichment analysis. Thirty-one, thirty-four, and fifteen MR candidates were identified in BD, SZ, and MDD, respectively. The activity state of these MRs grouped the illnesses in three clusters: MDD only, mostly BD, and a third one with BD and SZ. While BD and SZ share several biological processes related to ion transport and homeostasis, synapse, and immune function, SZ showed peculiar enrichment of processes related to cytoskeleton and neuronal structure. Meanwhile, MDD presented mostly processes related to glial development and fatty acid metabolism. Our findings suggest notable differences in functional enrichment between MDD and BD/SZ. Furthermore, similarities between BD and SZ may impose particular challenges in attempts to discriminate these pathologies based solely on their transcriptional profiles. Nevertheless, we believe that systems-oriented approaches are promising strategies to unravel the pathophysiology peculiarities underlying mental illnesses and reveal therapeutic targets.

Our previous studies showed reduced Ndel1 enzyme activity in patients with chronic schizophrenia (SCZ), and only a subtle NDEL1 mRNA increases in antipsychotic-naive first-episode psychosis (FEP) individuals compared to matched healthy controls (HC). Aiming to refine the evaluation of Ndel1 enzyme activity in early stages of psychosis, we compared 3 groups composed by (1) subjects at ultra-high-risk (UHR) for psychosis, (2) a cohort comprising antipsychotic-naive FEP individuals (assessed in three moments, at baseline (FEP-0), and after 2 months (FEP-2 M) and one year (FEP-1Y) of treatment with risperidone), and (3) a HC group. There was no significant difference in Ndel1 enzyme activity between UHR and HC, but this activity was significantly lower in FEP compared to HC. Conversely, Ndel1 activity in HC groups was higher than in FEP even before (FEP-0) or after the treatment with risperidone (FEP-2 M and FEP-1Y), and with progressive decrease of Ndel1 activity and significant improvement of symptoms observed after this treatment. In addition, a positive correlation was observed for Ndel1 activity with clinical symptoms as assessed by PANSS, while a negative correlation was seen for GAF scores. Our results suggest that reductions in Ndel1 activity in FEP may be possibly related to responses to the illness, rather than to the pharmacological effects of antipsychotics, which might be acting essentially in the symptoms suppression. This hypothesis might be further evaluated in prospective long-term follow-up studies with a larger sample cohort.

Objective: The purpose of this study was to assess the efficacy and tolerability of tianeptine as an adjunctive maintenance treatment for bipolar depression. Methods: This is a multicenter double-blind randomized placebo-controlled maintenance trial of adjunctive tianeptine 37.5 mg/day. Participants ( n=161) had a Montgomery-Asberg Depression Rating Scale ⩾12 at entry. After eight weeks of open-label tianeptine treatment, those who responded to tianeptine ( n=69) were randomized to adjunctive tianeptine ( n=36) or placebo ( n=33) in addition to usual treatment. Kaplan-Meier estimates and the Mantel-Cox log-rank test were used to evaluate differences in time to intervention for a mood episode between the tianeptine and placebo groups. We also assessed overall functioning, biological rhythms, quality of life, rates of manic switch and serum brain-derived neurotrophic factor levels. Results: There were no differences between adjunctive tianeptine or placebo regarding time to intervention or depression scores in the 24-week double-blind controlled phase. Patients in the tianeptine group showed better performance in the letter-number sequencing subtest from the Wechsler Adult Intelligence Scale at the endpoint ( p=0.014). Tianeptine was well tolerated and not associated with higher risk for manic switch compared to placebo. Conclusion: Tianeptine was not more effective than placebo in the maintenance treatment of bipolar depression. There is preliminary evidence suggesting a pro-cognitive effect of tianeptine in working memory compared to placebo.

Although the etiology of Bipolar Disorder (BD) remains unknown, a strong genetic component to the pathogenesis and risk for this disorder has been widely hypothesized. Several risk genes for BD have been identified; of these, the purinergic P2 × 7 receptor (P2 × 7R) constitutes a pro-inflammatory receptor and a potential risk gene candidate. The purpose of the present study was to assess the frequency of the 1513 A > C P2RX7 polymorphism (rs3751143; Glu496Ala), which leads to receptor loss-of-function, in 154 BD patients versus 184 control subjects. The existence of a differential modulation of P2 × 7R was also analyzed in 22 euthymic BD patients, in comparison to 18 healthy controls. Our data show a decrease in 1513C allele frequency (p = 0.045) and a potential increase in 1513 A A/AC (p = 0.055) genotype frequency in BD patients, compared to controls, indicating an enhanced function of the pro-inflammatory P2 × 7 receptor in BD subjects. Interestingly, no differences in P2RX7 gene and protein expression were found between euthymic BD patients and matched healthy controls. In conclusion, our results suggest that P2 × 7R might play a role in the pathophysiology of BD and add new information regarding this receptor as a potential biomarker for the prediction and diagnosis of the disorder.

The aim of this study was to assess the association between anhedonia and metabolic syndrome (MetS) in a well-characterized community sample of individuals with a current depressive episode. This is a cross-sectional study with young adults aged 24-30 years old. Depressive episode and the presence of anhedonia was assessed using the Mini International Neuropsychiatric Interview - Plus version (MINI Plus). The MetS was assessed using the National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III). The sample included 931 subjects, being 22 had depression without anhedonia, whereas 55 had depression with anhedonia. MetS was more prevalent among subjects with depression and anhedonia (43.6%) when compared to individuals without anhedonia and population control group. Moreover, subjects with depression and anhedonia have a significant increase of levels of glucose, triglycerides, total-cholesterol and LDL-cholesterol, as well as significant decreased in the HDL-cholesterol level. The present study showed that individuals with depression and anhedonia present higher prevalence of MetS. Our study suggests that the use of the concept of anhedonia may contribute to a better understanding of the complex relationship between depression and metabolic syndrome.

This study is aimed at assessing changes in functioning among young people from the general population with bipolar disorder and major depressive disorder over a period of five years. Specifically, we hypothesized that significant illness progression would take place during euthymia over time in bipolar disorder. We conducted a longitudinal study with 231 people, assessed at baseline and again at a five-year follow-up. A structured clinical interview was used to diagnose participants with mood disorders. A control group without mood disorders was also included. Functioning was assessed with the Functioning Assessment Short Test, and linear mixed models were used to analyze the effect of psychopathology on change in functioning. Mood disorders were associated with significant functional impairment, but functioning significantly improved in both groups over the 5-year follow-up period. Depressive episodes, however, were associated with worse functioning at follow-up, independently of depression severity. In contrast to our initial hypothesis, we found a worsening of functioning in a five-year period associated only with depressive episodes. This suggests that interventions focused on the prevention of mood episodes early in the course of illness may be particularly promising to reduce adverse functioning outcomes.

Objectives : To assess the differences in sleep impairments in major depressive disorder (MDD) and individuals recently diagnosed with bipolar disorder (BD) across different mood stages. Methods This is a cross-sectional study corresponding to the second wave of a prospective clinical cohort of a sample of outpatients. The first wave included subjects diagnosed with MDD aged 18 to 60 years. Averaging 3 years after the first phase (second wave), conversion from MDD to BD diagnosis was assessed using the Mini International Neuropsychiatric Interview. The total sample was divided into four groups: euthymic MDD, MDD in a current episode, euthymic BD, and BD in a current mood episode. The sleep alterations were assessed using the Pittsburgh Sleep Quality Index. Results The sample included 468 subjects (261 euthymic MDD, 149 MDD currently depressed, 16 euthymic BD, and 42 BDs currently in a (hypo)manic or depressive episode). Euthymic BD differed from euthymic MDD only in the domains of sleep efficiency and sleep disturbances, showing lower sleep efficiency (PR 4.91 [95%CI 1.94 – 12.42]) and higher sleep disturbances (PR 3.38 [95%CI 1.32 – 8.67]) in subjects recently diagnosed with BD during euthymia. These differences remained significant after adjusting for the potential confounding factors. Conclusions The findings point out the relevance of regular sleep assessments in individuals recently diagnosed with BD, since the differences in sleep quality observed could provide insights regarding prognosis, treatment, and the extent to which these individuals display significant subsyndromal symptomatology, even in the absence of a mood episode.

Importance Frontline health care professionals who work with patients with COVID-19 have an increased incidence of burnout symptoms. Cannabidiol (CBD) has anxiolytic and antidepressant properties and may be capable of reducing emotional exhaustion and burnout symptoms. Objective To investigate the safety and efficacy of CBD therapy for the reduction of emotional exhaustion and burnout symptoms among frontline health care professionals working with patients with COVID-19. Design, setting, and participants This prospective open-label single-site randomized clinical trial used a 1:1 block randomization design to examine emotional exhaustion and burnout symptoms among frontline health care professionals (physicians, nurses, and physical therapists) working with patients with COVID-19 at the Ribeirao Preto Medical School University Hospital in Sao Paulo, Brazil. Participants were enrolled between June 12 and November 12, 2020. A total of 214 health care professionals were recruited and assessed for eligibility, and 120 participants were randomized in a 1:1 ratio by a researcher who was not directly involved with data collection. Interventions Cannabidiol, 300 mg (150 mg twice per day), plus standard care or standard care alone for 28 days. Main outcomes and measures The primary outcome was emotional exhaustion and burnout symptoms, which were assessed for 28 days using the emotional exhaustion subscale of the Brazilian version of the Maslach Burnout Inventory-Human Services Survey for Medical Personnel. Results A total of 120 participants were randomized to receive either CBD, 300 mg, plus standard care (treatment arm; n = 61) or standard care alone (control arm; n = 59) for 28 days. Of those, 118 participants (59 participants in each arm; 79 women [66.9%]; mean age, 33.6 years [95% CI, 32.3-34.9 years]) received the intervention and were included in the efficacy analysis. In the treatment arm, scores on the emotional exhaustion subscale of the Maslach Burnout Inventory significantly decreased at day 14 (mean difference, 4.14 points; 95% CI, 1.47-6.80 points; partial eta squared [ηp2] = 0.08), day 21 (mean difference, 4.34 points; 95% CI, 0.94-7.73 points; ηp2 = 0.05), and day 28 (mean difference, 4.01 points; 95% CI, 0.43-7.59 points; ηp2 = 0.04). However, 5 participants, all of whom were in the treatment group, experienced serious adverse events: 4 cases of elevated liver enzymes (1 critical and 3 mild, with the mild elevations reported at the final 28-day assessment) and 1 case of severe pharmacodermia. In 2 of those cases (1 with critical elevation of liver enzymes and 1 with severe pharmacodermia), CBD therapy was discontinued, and the participants had a full recovery. Conclusions and relevance In this study, CBD therapy reduced symptoms of burnout and emotional exhaustion among health care professionals working with patients during the COVID-19 pandemic. However, it is necessary to balance the benefits of CBD therapy with potential undesired or adverse effects. Future double-blind placebo-controlled clinical trials are needed to confirm the present findings. Trial registration ClinicalTrials.gov Identifier: NCT04504877.

There is evidence suggesting clinical progression in a subset of patients with bipolar disorder (BD). This progression is associated with worse clinical outcomes and biological changes. Molecular pathways and biological markers of clinical progression have been identified and may explain the progressive changes associated with this disorder. The biological basis for clinical progression in BD is called neuroprogression. We propose that the following intertwined pathways provide the biological basis of neuroprogression: inflammation, oxidative stress, impaired calcium signaling, endoplasmic reticulum and mitochondrial dysfunction, and impaired neuroplasticity and cellular resilience. The nonlinear interaction of these pathways may worsen clinical outcomes, cognition, and functioning. Understanding neuroprogression in BD is crucial for identifying novel therapeutic targets, preventing illness progression, and ultimately promoting better outcomes.

This study aims to evaluate sociodemographic and clinical factors, quality of life (QoL) and functioning associated with history of suicide attempts (SA) in a sample of bipolar disorder (BD) type I patients.A total of 417 BD type I patients, with and without history of SA were recruited from two Brazilian specialized Mood Disorder Centers. They were assessed with a sociodemographic and clinical questionnaire, the Young Mania Rating Scale, the Hamilton Depression Rating Scale, the Structured Clinical Interviews for DSM-IV Axis I Disorders, the World Health Organization Quality of Life-BREF, and the Sheehan Disability Scale.One hundred and seventy-nine (42.9%) patients had a history of SA. There were no statistically significant sociodemographic differences between BD patients with and without a history of SA. Logistic regression found that lifetime hospitalization, comorbid anxiety disorders, depressive polarity in the first episode, current intensity of depressive symptoms, history of rapid cycling, family history of suicide and age at onset were significantly associated with SA in BD. Multiple linear regression showed that SA had no effect on QoL and functioning, which were affected mainly by comorbid anxiety disorders and current intensity of depressive symptoms, even in patients considered euthymic.Suicidal behavior in patients with BD is a complex phenomenon and reflects a more severe course of illness. Patients with history of SA may have worse QoL and functional impairment not because of its direct effect, but because of the greater association with clinical factors related to poor prognosis.

The aim of this study was to assess the risk factors for new-onset Bipolar Disorder (BD) in a community sample of young adults. This is a prospective cohort study including a population-based sample of young adults aged between 18-24 years. The baseline took place from 2007 to 2009, and 1560 subjects were included. Five years after, 1244 individuals were re-evaluated (79.7% retention). Substance abuse/dependence was assessed using the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), and mental disorders were assessed using the Mini International Neuropsychiatric Interview 5.0 (MINI) at both waves. The cumulative incidence of BD in five years was 4.6%. There was no significant association between sociodemographic factors and BD incidence. Tobacco, cannabis, cocaine/crack, other substances abuse/dependence increased the relative risk for BD. Depressive, anxiety, post-traumatic stress disorders, and the suicide risk increased the relative risk to BD. Depressive episode was the strongest risk factor for BD, followed by other mental disorders and substance abuse/dependence in a probabilistic community sample of young adults. Preventive actions in mental health directed at the non-clinical population are needed for early detection and better management of BD.

BackgroundSubjects with bipolar disorder (BD) show heterogeneous cognitive profile and that not necessarily the disease will lead to unfavorable clinical outcomes. We aimed to identify clinical markers of severity among cognitive clusters in individuals with BD through data-driven methods.MethodsWe recruited 167 outpatients with BD and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. Cognitive functions assessed were inhibitory control, processing speed, cognitive flexibility, verbal fluency, working memory, short- and long-term verbal memory. We performed hierarchical cluster analysis and discriminant function analysis to determine and confirm cognitive clusters, respectively. Then, we used classification and regression tree (CART) algorithm to determine clinical and sociodemographic variables of the previously defined cognitive clusters.ResultsWe identified three neuropsychological subgroups in individuals with BD: intact (35.3%), selectively impaired (34.7%), and severely impaired individuals (29.9%). The most important predictors of cognitive subgroups were years of education, the number of hospitalizations, and age, respectively. The model with CART algorithm showed sensitivity 45.8%, specificity 78.4%, balanced accuracy 62.1%, and the area under the ROC curve was 0.61. Of 10 attributes included in the model, only three variables were able to separate cognitive clusters in BD individuals: years of education, number of hospitalizations, and age.ConclusionThese results corroborate with recent findings of neuropsychological heterogeneity in BD, and suggest an overlapping between premorbid and morbid aspects that influence distinct cognitive courses of the disease.

Objective We set forth to build a prediction model of individuals who would develop bipolar disorder (BD) using machine learning techniques in a large birth cohort. Methods A total of 3748 subjects were studied at birth, 11, 15, 18, and 22 years of age in a community birth cohort. We used the elastic net algorithm with 10-fold cross-validation to predict which individuals would develop BD at endpoint (22 years) at each follow-up visit before diagnosis (from birth up to 18 years). Afterward, we used the best model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them. Results A total of 107 (2.8%) individuals within the cohort presented with BD type I, 26 (0.6%) with BD type II, and 87 (2.3%) with BD not otherwise specified. Frequency of female individuals was 58.82% (n = 150) in the BD sample and 53.02% (n = 1868) among the unaffected population. The model with variables assessed at the 18-year follow-up visit achieved the best performance: AUC 0.82 (CI 0.75-0.88), balanced accuracy 0.75, sensitivity 0.72, and specificity 0.77. The most important variables to detect BD at the 18-year follow-up visit were suicide risk, generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms. Conclusions We developed a risk calculator for BD incorporating both demographic and clinical variables from a 22-year birth cohort. Our findings support previous studies in high-risk samples showing the significance of suicide risk and generalized anxiety disorder prior to the onset of BD, and highlight the role of social factors and adverse life events.

Background The burden of opioid use disorder (OUD) has been increasing in North America. Administration of medication-assisted treatments (MATs) for OUD on an individual-dose basis has been shown to affect patient responses to treatment, proving to be, on occasion, dangerous. A genetic basis has been identified for some MAT responses in a candidate gene context, but consensus has not been reached for any genome-wide significant associations. This systematic review aims to identify and assess any genetic variants associated with MAT patient outcomes at genome-wide significance. Methods The databases searched by the authors will be: MEDLINE, Web of Science, EMBASE, CINAHL and Pre-CINAHL, GWAS Catalog, GWAS Central, and NIH Database of Genotypes and Phenotypes. A title and abstract screening, full-text screening, data extraction, and quality assessment will be completed in duplicate for each study via Covidence. Treatment outcomes of interest include continued opioid use or abstinence during treatment or at follow-up, time to relapse, treatment retention rates, opioid overdose, other substance use, comorbid psychiatric disorders, risk taking behaviors, MAT plasma concentrations, and mortality rates. Analysis methods applied, if appropriate, will include random effects meta-analysis with pooled odds ratios for all outcomes. Subgroup analyses will also be implemented, when possible. Discussion This systematic review can hopefully inform the direction of future research, aiding in the development of a safer and more patient-centered treatment. It will be able to highlight genome-wide significant variants that are replicable and associated with MAT patient outcomes. Systematic review registration This systematic review protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration ID CRD42020169121).

Objectives The purpose of this study is to assess the independent effects of depression and excess body weight (EBW) on cognition and functioning in a community sample of young adults. Methods This was a cross-sectional of 943 young adults. The diagnosis of a current depressive episode was performed using the Mini International Neuropsychiatric Interview (MINI). Cognition and functioning were assessed using the Montreal Cognitive Assessment (MoCA) and the Functional Assessment Short Test (FAST), respectively. The EBW was defined as BMI ≥ 25.0 kg/m2. The independent main effects of depression and EBW, as well as the analysis interaction were performed using two-way analysis of covariance (ANCOVA). Results The total sample comprised 943 adults, with 75 (8.0%) individuals diagnosed with a current depressive episode and 493 (52,6%) with EBW. Of the 75 subjects with depression, 40 were identified with EBW comorbidity. Subjects with depression and EBW comorbidity reported greater cognitive and functional impairment, as compared to individuals with depression without EBW. There was a significant interaction between depression and EBW on MoCA total (p Conclusions The adverse effects of depression and EBW are independent and cumulative with respect to cognition and functioning of individuals. The understanding of the complex interactions between cognition, functioning, EBW and depression are important for development of preventive and therapeutic strategies.

Aim To assess the differences in subjective cognitive dysfunction between major depressive disorder (MDD) and recently diagnosed Bipolar Disorder (BD) across euthymia and mood episodes. Methods This is a cross-sectional study corresponding to the second wave of a longitudinal study. The first wave consisted of subjects aged between 18 and 60 diagnosed with MDD. In the follow up after three years (second wave), conversion from MDD to BD diagnosis was assessed by qualified psychologists using the Mini International Neuropsychiatric Interview (MINI-Plus). Subjects were categorized in four diagnostic groups: euthymic MDD, MDD in a current mood episode, euthymic BD, and BD in a current mood episode. All subjects completed the Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA), an instrument specifically designed for detecting subjective cognitive deficits in BD. Results The total sample (n = 468) included 410 subjects with MDD and 58 individuals recently diagnosed with BD. We subdivided the 2 groups based on their current mood state, and found a significant difference in COBRA total scores between euthymic BD individuals (median 17.00 [IQR: 8.75 - 20.75]) and euthymic MDD subjects (median 8.00 [IRQ: 5.00 - 14.00], p = 0.002), showing higher subjective cognitive dysfunction in individuals recently diagnosed with BD. The differences remained significant after adjusting for the presence of lifetime psychotic symptoms. We found no differences between MDD and BD during an acute mood episode. Limitation The small sample size of individuals with BD. Conclusion The findings suggest a higher presence of subjective cognitive complaints among individuals recently diagnosed with BD in comparison to individuals with MDD during euthymia.

Background With the legalization of cannabis in Canada, there is an increase trend in use. Cannabis has been known to have several health implications, one of which is the development of cannabis use disorder (CUD). CUD is more common in males than females, as well as in certain ethnic groups such as Native Americans. Additionally, both environmental and genetic risk factors have been found for cannabis use. The objective of this systematic review will be to summarize the genetic variants associated with cannabis use which have reached borderline genome-wide significance. Methods This systematic review will incorporate articles that have performed a genome-wide association study (GWAS) investigating cannabis use. MEDLINE, Web of Science, EMBASE, GWAS Catalog, GWAS Central, and NIH Database of Genotype and Phenotype will be searched using a comprehensive search strategy. The quality of genetic association studies (Q-Genie) tool will be utilized to assess the quality of the included studies. All screening and data extraction will occur independently by two authors. If feasible, a random-effects meta-analysis will be conducted on pooled odds ratios of single nucleotide polymorphisms reaching borderline genome-wide significance. Discussion This systematic review will synthesize available GWAS on cannabis use. Results from this review will inform and direct further investigation of genetic variants associated with cannabis use. Systematic review registration PROSPERO CRD42020176016

OBJECTIVES: To assess whether bipolar disorder (BD) increases the rate of dementia and whether lithium is related to a lower risk of dementia in BD. METHODS: A total of 10 studies (6859 BD; 487 966 controls) were included in the meta-analysis to test whether BD is a risk factor for dementia. In addition, five studies (6483 lithium; 43 496 non-lithium) were included in the meta-analysis about the potential protective effect of lithium in BD. RESULTS: BD increases the risk of dementia (odds ratio (OR): 2.96 [95% CI: 2.09-4.18], P

Neuroprogression is associated with structural and functional brain changes that occur in parallel with cognitive and functioning impairments. There is substantial evidence showing early white matter changes, as well as trajectory-related gray matter alterations. Several structures, including prefrontal, parietal, temporal cortex, and limbic structures, seem to be altered over the course of bipolar disorder, especially associated with the number of episodes and length of the disease. An important limitation is that most of the studies used either a cross-sectional design or a short follow-up period, which may be insufficient to identify all neuroprogressive changes over time. In addition, the heterogeneity of patients with bipolar disorder is another challenge to determine which subjects will have a more pernicious trajectory. Larger studies and the use of new techniques, such as machine learning, may help to enable more discoveries and evidence on the role of neuroprogression in BD.

Background: Bipolar disorder (BD) is the sixth leading cause of disability worldwide. Early detection of BD may help clinicians to intervene early and to prevent illness progression. We aimed to build a BD prediction model using machine learning techniques in a large population-based birth cohort at the individual level. Methods: A total of 3,748 subjects were studied from birth up to the age of 22 years in a prospective population-based birth cohort. We used the Elastic Net algorithm with 10-fold cross-validation to predict who would develop BD by the age 22-years assessment using clinical and demographic variables at each follow-up visit before diagnosis (perinatal, 11 years, 15 years, and 18 years). After that, we used the best predictive model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them. Findings: A total of 107 (2∙8%) individuals within the cohort presented with BD type I, 26 (0∙6%) presented with BD type II, and 87 (2∙3%) presented with BD not otherwise specified. The model with variables assessed at the 18-years follow-up visit achieved the best performance: an AUC of 0∙82 (CI 0∙75–0∙88), balanced accuracy of 0∙75, sensitivity of 0∙72, and specificity of 0∙77. The most important variables to predict BD at the 18-years follow-up visit were suicide risk, a diagnosis of generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms. Interpretation: We developed a machine learning-based risk calculator for BD at the age of 22 years, which may be used as a tool to clinical decision making, incorporating both demographic and clinical variables. Future studies integrating data from different biological levels, such as genetics and metabolomics, could potentially help to build more accurate models. Furthermore, other researches should determine whether the features selected by the model as the most important have a causal relation with BD or whether they are prodromal symptoms. Funding Statement: This article is based on data from the study "Pelotas Birth Cohort − 1993" conducted by Graduate Program in Epidemiology at Universidade Federal de Pelotas with the collaboration of the Brazilian Public Health Association (ABRASCO). Furthermore, the 1993 birth cohort study was supported by the Wellcome Trust and Brazilian National Research Council (CNPq) with Brazilian Ministry of Health (DECIT – CNPq). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The 1993 Pelotas Birth Cohort study was approved by the local ethics committee. All participants provided written informed consent.

Background Essential workers have been shown to present a higher prevalence of positive screenings for anxiety and depression during the COVID-19 pandemic. Individuals from countries with socioeconomic inequalities may be at increased risk for mental health disorders. Objective We aimed to assess the prevalence and predictors of depression, anxiety, and their comorbidity among essential workers in Brazil and Spain during the COVID-19 pandemic. Methods A web survey was conducted between April and May 2020 in both countries. The main outcome was a positive screening for depression only, anxiety only, or both. Lifestyle was measured using a lifestyle multidimensional scale adapted for the COVID-19 pandemic (Short Multidimensional Inventory Lifestyle Evaluation–Confinement). A multinomial logistic regression model was performed to evaluate the factors associated with depression, anxiety, and the presence of both conditions. Results From the 22,786 individuals included in the web survey, 3745 self-reported to be essential workers. Overall, 8.3% (n=311), 11.6% (n=434), and 27.4% (n=1027) presented positive screenings for depression, anxiety, and both, respectively. After adjusting for confounding factors, the multinomial model showed that an unhealthy lifestyle increased the likelihood of depression (adjusted odds ratio [AOR] 4.00, 95% CI 2.72-5.87), anxiety (AOR 2.39, 95% CI 1.80-3.20), and both anxiety and depression (AOR 8.30, 95% CI 5.90-11.7). Living in Brazil was associated with increased odds of depression (AOR 2.89, 95% CI 2.07-4.06), anxiety (AOR 2.81, 95%CI 2.11-3.74), and both conditions (AOR 5.99, 95% CI 4.53-7.91). Conclusions Interventions addressing lifestyle may be useful in dealing with symptoms of common mental disorders during the strain imposed among essential workers by the COVID-19 pandemic. Essential workers who live in middle-income countries with higher rates of inequality may face additional challenges. Ensuring equitable treatment and support may be an important challenge ahead, considering the possible syndemic effect of the social determinants of health.

Background: Bipolar disorder is chronic and debilitating. Studies investigating resting-state functional connectivity in individuals with bipolar disorder may help to inform neurobiological models of illness. Methods: We conducted a systematic review with the following goals: to summarize the literature on resting-state functional connectivity in bipolar disorder during clinical remission (euthymia) compared with healthy controls; to critically appraise the literature and research gaps; and to propose directions for future research. We searched PubMed/MEDLINE, Embase, PsycINFO, CINAHL and grey literature up to April 2017. Results: Twenty-three studies were included. The most consistent finding was the absence of differences in resting-state functional connectivity of the default mode network (DMN), frontoparietal network (FPN) and salience network (SN) between people with bipolar disorder and controls, using independent component analysis. However, 2 studies in people with bipolar disorder who were positive for psychosis history reported DMN hypoconnectivity. Studies using seed-based analysis largely reported aberrant resting-state functional connectivity with the amygdala, ventrolateral prefrontal cortex, cingulate cortex and medial prefrontal cortex in people with bipolar disorder compared with controls. Few studies used regional homogeneity or amplitude of low-frequency fluctuations. Limitations: We found heterogeneity in the analysis methods used. Conclusion: Stability of the DMN, FPN and SN may reflect a state of remission. Further, DMN hypoconnectivity may reflect a positive history of psychosis in patients with bipolar disorder compared with controls, highlighting a potentially different neural phenotype of psychosis in people with bipolar disorder. Resting-state functional connectivity changes between the amygdala, prefrontal cortex and cingulate cortex may reflect a neural correlate of subthreshold symptoms experienced in bipolar disorder euthymia, the trait-based pathophysiology of bipolar disorder and/or a compensatory mechanism to maintain a state of euthymia.

Objective: This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls. Methods: We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model. Results: Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity. Conclusion: Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

ObjectiveThis study aimed to explore effects of adjunctive treatment with N-acetyl cysteine (NAC) on markers of inflammation and neurogenesis in bipolar depression.MethodsThis is a secondary analysis of a placebo-controlled randomised trial. Serum samples were collected at baseline, week 8, and week 32 of the open-label and maintenance phases of the clinical trial to determine changes in interleukin (IL)-6, IL-8, IL-10, tumour necrosis factor-α (TNF-α), C-reactive protein (CRP) and brain-derived neurotrophic factor (BDNF) following adjunctive NAC treatment, and to explore mediation and moderator effects of the listed markers.ResultsLevels of brain-derived neurotrophic factor (BDNF), tumour necrosis factor-α (TNF-α), C-reactive protein (CRP), interleukins (IL) -6, 8, or 10 were not significantly changed during the course of the trial or specifically in the open-label and maintenance phases. There were no mediation or moderation effects of the biological factors on the clinical parameters.ConclusionThe results suggest that these particular biological parameters may not be directly involved in the therapeutic mechanism of action of adjunctive NAC in bipolar depression.