Is a vegetarian diet beneficial for bipolar disorder? Relationship between dietary patterns, exercise and pharmacological treatments with metabolic syndrome and course of disease in bipolar disorder.

BACKGROUND: Lifestyle factors are being increasingly studied in bipolar disorder (BD) due to their possible effects on both course of disease and physical health. The aim of this study was to jointly describe and explore the interrelations between diet patterns, exercise, pharmacological treatment with course of disease and metabolic profile in BD. METHODS: The sample consisted of 66 euthymic or mild depressive individuals with BD. Clinical and metabolic outcomes were assessed, as well as pharmacological treatment or lifestyle habits (diet and exercise). Correlations were explored for different interrelations and a factor analysis of dietary patterns was performed. RESULTS: Adherence to the Mediterranean diet was low, seen in 37.9% of the patients and was positively associated with perceived quality of life. The amount of exercise was negatively associated with cholesterol levels, with 32.8% of participants rated as low active by International Physical Activity Questionnaire. There was a high prevalence of obesity (40.6%) and metabolic syndrome (29.7%). Users of lithium showed the best metabolic profile. Interestingly, three dietary patterns were identified: "vegetarian," "omnivore" and "Western." The key finding was the overall positive impact of the "vegetarian" pattern in BD, which was associated with reduced depression scores, better psychosocial functioning, and perceived quality of life, decreased body mass index, cholesterol, LDL and diastolic blood pressure. Nuts consumption was associated with a better metabolic profile. CONCLUSIONS: A vegetarian diet pattern was associated with both, better clinical and metabolic parameters, in patients with BD. Future studies should prioritize prospective and randomized designs to determine causal relationships, and potentially inform clinical recommendations.

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions.

BACKGROUND: Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). OBJECTIVE: An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. METHODS: The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. RESULTS: Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. CONCLUSIONS: The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Aerobic capacity and mitochondrial function in bipolar disorder: a longitudinal study during acute phases and after clinical remission.

Background: Aerobic capacity has shown to predict physical and mental health-related quality of life in bipolar disorder (BD). However, the correlation between exercise respiratory capacity and mitochondrial function remains understudied. We aimed to assess longitudinally intra-individual differences in these factors during mood episodes and remission in BD. Methods: This study included eight BD patients admitted to an acute psychiatric unit. Incremental cardiopulmonary exercise test (CPET) was conducted during acute episodes (T0), followed by constant work rate cycle ergometry (CWRCE) to evaluate endurance time, oxygen uptake at peak exercise (VO2peak) and at the anaerobic threshold. The second test was repeated during remission (T1). Mitochondrial respiration rates were assessed at T0 and T1 in peripheral blood mononuclear cells. Results: Endurance time, VO2peak, and anaerobic threshold oxygen consumption showed no significant variations between T0 and T1. Basal oxygen consumption at T1 tended to inversely correlate with maximal mitochondrial respiratory capacity (r=-0.690, p=0.058), and VO2peak during exercise at T1 inversely correlated with basal and minimum mitochondrial respiration (r=-0.810, p=0.015; r=-0.786, p=0.021, respectively). Conclusions: Our preliminary data showed that lower basal oxygen consumption may be linked to greater mitochondrial respiratory capacity, and maximum oxygen uptake during the exercise task was associated with lower basal mitochondrial respiration, suggesting that lower oxygen requirements could be associated with greater mitochondrial capacity. These findings should be replicated in larger samples stratified for manic and depressive states.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Pharmacological treatments for psychotic depression: a systematic review and network meta-analysis.

BACKGROUND: There are no recommendations based on the efficacy of specific drugs for the treatment of psychotic depression. To address this evidence gap, we did a network meta-analysis to assess and compare the efficacy and safety of pharmacological treatments for psychotic depression. METHODS: In this systematic review and network meta-analysis, we searched ClinicalTrials.gov, CENTRAL, Embase, PsycINFO, PubMed, Scopus, and Web of Science from inception to Nov 23, 2023 for randomised controlled trials published in any language that assessed pharmacological treatments for individuals of any age with a diagnosis of a major depressive episode with psychotic features, in the context of major depressive disorder or bipolar disorder in any setting. We excluded continuation or maintenance trials. We screened the study titles and abstracts identified, and we extracted data from relevant studies after full-text review. If full data were not available, we requested data from study authors twice. We analysed treatments for individual drugs (or drug combinations) and by grouping them on the basis of mechanisms of action. The primary outcomes were response rate (ie, the proportion of participants who responded to treatment) and acceptability (ie, the proportion who discontinued treatment for any reason). We calculated risk ratios and did separate frequentist network meta-analyses by using random-effects models. The risk of bias of individual studies was assessed with the Cochrane risk-of-bias tool and the confidence in the evidence with the Confidence-In-Network-Meta-Analysis (CINeMA). This study was registered with PROSPERO, CRD42023392926. FINDINGS: Of 6313 reports identified, 16 randomised controlled trials were included in the systematic review, and 14 were included in the network meta-analyses. The 16 trials included 1161 people with psychotic depression (mean age 50·5 years [SD 11·4]). 516 (44·4%) participants were female and 422 (36·3%) were male; sex data were not available for the other 223 (19·2%). 489 (42·1%) participants were White, 47 (4·0%) were African American, and 12 (1·0%) were Asian; race or ethnicity data were not available for the other 613 (52·8%). Only the combination of fluoxetine plus olanzapine was associated with a higher proportion of participants with a treatment response compared with placebo (risk ratio 1·91 [95% CI 1·27-2·85]), with no differences in terms of safety outcomes compared with placebo. When treatments were grouped by mechanism of action, the combination of a selective serotonin reuptake inhibitor with a second-generation antipsychotic was associated with a higher proportion of treatment responses than was placebo (1·89 [1·17-3·04]), with no differences in terms of safety outcomes. In head-to-head comparisons of active treatments, a significantly higher proportion of participants had a response to amitriptyline plus perphenazine (3·61 [1·23-10·56]) and amoxapine (3·14 [1·01-9·80]) than to perphenazine, and to fluoxetine plus olanzapine compared with olanzapine alone (1·60 [1·09-2·34]). Venlafaxine, venlafaxine plus quetiapine (2·25 [1·09-4·63]), and imipramine (1·95 [1·01-3·79]) were also associated with a higher proportion of treatment responses overall. In head-to-head comparisons grouped by mechanism of action, antipsychotic plus antidepressant combinations consistently outperformed monotherapies from either drug class in terms of the proportion of participants with treatment responses. Heterogeneity was low. No high-risk instances were identified in the bias assessment for our primary outcomes. INTERPRETATION: According to the available evidence, the combination of a selective serotonin reuptake inhibitor and a second-generation antipsychotic-and particularly of fluoxetine and olanzapine-could be the optimal treatment choice for psychotic depression. These findings should be taken into account in the development of clinical practice guidelines. However, these conclusions should be interpreted cautiously in view of the low number of included studies and the limitations of these studies. FUNDING: None.

Exploring Clinical Subgroups of Participants with Major Depressive Disorder that may Benefit from Adjunctive Minocycline Treatment.

Objective: : To explore illness-related factors in patients with major depressive disorder (MDD) recipients of adjunctive minocycline (200 mg/day) treatment. The analysis included participants experiencing MDD from a 12-week, double blind, placebo-controlled, randomized clinical trial (RCT). Methods: : This is a sub-analysis of a RCT of all 71 participants who took part in the trial. The impact of illness chronicity (illness duration and number of depressive episodes), systemic illness (endocrine, cardiovascular and obesity), adverse effects and minocycline were evaluated as change from baseline to endpoint (12-week) using ANCOVA. Results: : There was a consistent but statistically non-significant trend on all outcomes in favour of the use of adjunctive minocycline for participants without systemic illness, less illness chronicity, and fewer adverse effects. Conclusion: : Understanding the relationship between MDD and illness chronicity, comorbid systemic illness, and adverse effects, can potentially better characterise those individuals who are more likely to respond to adjunctive anti-inflammatory medications.

Impact of cognitive reserve in clinical, neurocognitive and lifestyle factors in chronic schizophrenia and early stages of schizophrenia.

INTRODUCTION: Although there is evidence that higher cognitive reserve (CR) is a protective factor and it has been related to better prognosis, there have been no studies to date that have explored the CR level and its impact in clinical, neurocognitive and lifestyle outcomes according to the stage of the disease: early stage of psychosis (ESP) or chronic schizophrenia (SCZ). MATERIAL AND METHODS: A total of 60 patients in the ESP and 225 patients with SCZ were enrolled in the study. To test the predictive capacity of CR for each diagnostic group, a logistic regression analysis was conducted. Hierarchical linear regression analyses were performed to explore the associations between CR and different outcomes. The mediation analyses were performed according to the principles of Baron and Kenny. RESULTS: Patients with SCZ showed lower CR than those in the ESP (p<0.001). CR correctly classified 79.6% of the cases (p<0.001; Exp(B)=1.062). In ESP group, CR was related to working memory (p=0.030) and negative symptoms (p=0.027). CR (t=3.925, p<0.001) and cannabis use (t=2.023, p=0.048) explained 26.7% of the variance on functioning (p=0.003). In patients with SCZ, CR predicted all cognitive domains, negative symptoms (R2=0.091, p=0.001) and functioning (R2=0.074, p=0.005). In both ESP and SCZ groups, higher CR was associated with lower body mass index and circumference. In ESP group, the effect of adherence to Mediterranean diet on functioning (p=0.037) was mediated by CR level (p=0.003). CONCLUSIONS: The implications of CR depend on the stage of the disease (ESP vs. SCZ), with a greater effect on neurocognition and negative symptoms in patients with chronic SCZ.

From gut to brain: A network model of intestinal permeability, inflammation, and psychotic symptoms in schizophrenia.

Impaired intestinal permeability has recently been suggested as a possible source of chronic inflammation in schizophrenia, but its association with specific psychopathological features remains uncertain. This study aimed to explore the interaction between intestinal permeability, inflammation, and positive and negative symptoms in schizophrenia using a network analysis approach. The study sample comprised 281 adults with schizophrenia (age 40.29 ± 13.65 years, 63.0 % males), enrolled in a cross-sectional observational study assessing intestinal permeability. We estimated the network with a Gaussian graphical model, incorporating scores from 14 individual items of the Positive and Negative Syndrome Scale (PANSS), along with body mass index (BMI), and plasma C-reactive protein (CRP) and lipopolysaccharide-binding protein (LBP) levels. We calculated strength centrality and expected influence and used bridge centrality statistics to identify the bridge nodes. Distinct but highly interconnected clusters emerged for positive and negative symptoms. The biological variables were closely associated with each other. LBP was positively linked with CRP and BMI, but only indirectly connected to psychopathology. CRP exhibited direct positive relationships with various PANSS items and bridged LBP and BMI with psychopathology. Bridge nodes included Conceptual Disorganisation (P2), Active Social Avoidance (G16), Suspiciousness/Persecution (P6), and CRP. These findings support the role of gut-derived inflammation as a mechanism underlying greater symptom severity in schizophrenia and emphasise the importance of addressing dietary habits not only to enhance physical health but also to contribute to improving psychotic symptoms.

Reduced mitochondrial respiratory capacity in patients with acute episodes of bipolar disorder: Could bipolar disorder be a state-dependent mitochondrial disease?

BACKGROUND: Bipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes and periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might underpin the neurobiology of BD. However, most studies focused on inter-subject differences rather than intra-subject variations between different mood states. To test this hypothesis, in this preliminary proof-of-concept study, we measured in vivo mitochondrial respiration in patients with BD during a mood episode and investigated differences compared to healthy controls (HC) and to the same patients upon clinical remission. METHODS: This longitudinal study recruited 20 patients with BD admitted to our acute psychiatric ward with a manic (n = 15) or depressive (n = 5) episode, and 10 matched HC. We assessed manic and depressive symptoms using standardized psychometric scales. Different mitochondrial oxygen consumption rates (OCRs: Routine, Leak, electron transport chain [ETC], Rox) were assessed during the acute episode (T0) and after clinical remission (T1) using high-resolution respirometry at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system in one million of peripheral blood mononuclear cells (PMBC). Specific OCRs were expressed as mean ± SD in picomoles of oxygen per million cells. Significant results were adjusted for age, sex, and body mass index. RESULTS: The longitudinal analysis showed a significant increase in the maximal oxygen consumption capacity (ETC) in clinical remission (25.7 ± 16.7) compared to the acute episodes (19.1 ± 11.8, p = 0.025), and was observed separately for patients admitted with a manic episode (29.2 ± 18.9 in T1, 22.3 ± 11.9 in T0, p = 0.076), and at a trend-level for patients admitted with a depressive episode (15.4 ± 3.9 in T1 compared to 9.4 ± 3.2 in T0, p = 0.107). Compared to HC, significant differences were observed in ETC in patients with a bipolar mood episode (H = 11.7; p = 0.003). Individuals with bipolar depression showed lower ETC than those with a manic episode (t = -3.7, p = 0.001). Also, significant differences were observed in ETC rates between HC and bipolar depression (Z = 1.000, p = 0.005). CONCLUSIONS: Bioenergetic and mitochondrial dysregulation could be present in both manic and depressive phases in BD and, importantly, they may restore after clinical remission. These preliminary results suggest that mitochondrial respiratory capacity could be a biomarker of illness activity and clinical response in BD. Further studies with larger samples and similar approaches are needed to confirm these results and identify potential biomarkers in different phases of the disease.

The Mixed Tendency in Bipolar Disorder: An Operational Proposal for the Integration of Mixed Episodes in Predominant Polarity.

Predominant Polarity (PP) is an established specifier of Bipolar Disorder (BD), holding significant clinical implications. Nevertheless, there exists no consensus on how to incorporate mixed states into PP, leaving patients prone to mixed recurrences that are unclassified. In a comprehensive study involving 701 euthymic BD patients, we sought to redefine PP by introducing a novel metric, the "mixed tendency", and establish a practical threshold to identify patients with a "mixed phenotype". Furthermore, we investigated potential associations between the mixed phenotype and specific PP categories. Our findings revealed that the mixed tendency correlated significantly with early BD type I, lifetime suicide attempts, self-aggressive behaviour, and lifetime number of affective episodes (>5). Using a ROC curve analysis, we determined an optimal cut-off point for the mixed tendency at 0.228, suggesting that patients with ~25% of lifetime mixed episodes relative to total affective episodes should be identified as having a mixed phenotype. Notably, the mixed phenotype was positively associated with undetermined PP and negatively with manic and depressive PP. This study introduces a promising approach to incorporating mixed episodes into the PP definition, potentially enabling tailored interventions for patients with a substantial history of mixed episodes. However, further research in large, longitudinal cohorts is essential to validate these findings.

Emotion dysregulation in bipolar disorder compared to other mental illnesses: a systematic review and meta-analysis.

People with bipolar disorder (BD) often present emotion dysregulation (ED), a pattern of emotional expression interfering with goal-directed behavior. ED is a transdiagnostic construct, and it is unclear whether it manifests itself similarly in other conditions, such as major depressive disorder (MDD) or borderline personality disorder (BPD), or has specific features in BD. The present systematic review and meta-analysis explored ED and adopted emotion regulation (ER) strategies in BD compared with other psychiatric conditions. PubMed/MEDLINE, EMBASE, Scopus, and PsycINFO databases were systematically searched from inception to April 28th, 2022. Studies implementing validated instruments assessing ED or ER strategies in BD and other psychiatric disorders were reviewed, and meta-analyses were conducted. Twenty-nine studies yielding multiple comparisons were included. BD was compared to MDD in 20 studies (n = 2451), to BPD in six studies (n = 1001), to attention deficit hyperactivity disorder in three studies (n = 232), to anxiety disorders in two studies (n = 320), to schizophrenia in one study (n = 223), and to post-traumatic stress disorder in one study (n = 31). BD patients did not differ from MDD patients in adopting most adaptive and maladaptive ER strategies. However, small-to-moderate differences in positive rumination and risk-taking behaviors were observed. In contrast, patients with BPD presented an overall higher degree of ED and more maladaptive ER strategies. There were insufficient data for a meta-analytic comparison with other psychiatric disorders. The present report further supports the idea that ED is a transdiagnostic construct spanning a continuum across different psychiatric disorders, outlining specific clinical features that could represent potential therapeutic targets.

Correlation between emotion dysregulation and mood symptoms of bipolar disorder: A systematic review and meta-analysis.

BACKGROUND: Emotion dysregulation (ED) is a transdiagnostic construct characterized by difficulties regulating intense emotions. People with bipolar disorder (BD) are more likely to show ED and use maladaptive emotion regulation strategies than adaptive ones. However, little is known about whether ED in BD is a trait or it is rather an epiphenomenon of mood symptoms. METHODS: We conducted a systematic review and meta-analysis of the evidence across major literature databases reporting correlations between measures of emotion regulation (overall ED and different emotion regulation strategies) and measures of depressive and (hypo)manic symptoms in BD from inception until April 12th, 2022. RESULTS: Fourteen studies involving 1371 individuals with BD were included in the qualitative synthesis, of which 11 reported quantitative information and were included in the meta-analysis. ED and maladaptive strategies were significantly higher during periods with more severe mood symptoms, especially depressive ones, while adaptive strategies were lower. CONCLUSION: ED significantly correlates with BD symptomatology, and it mainly occurs during mood alterations. ED may be a target for specific psychotherapeutic and pharmacological treatments, according to precision psychiatry. However, further studies are needed, including patients with mood episodes and longitudinal design, to provide more robust evidence and explore the causal direction of the associations.

Differences in facial emotion recognition between bipolar disorder and other clinical populations: A systematic review and meta-analysis.

Facial emotion (or expression) recognition (FER) is a domain of affective cognition impaired across various psychiatric conditions, including bipolar disorder (BD). We conducted a systematic review and meta-analysis searching for eligible articles published from inception to April 26, 2023, in PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO to examine whether and to what extent FER would differ between people with BD and those with other mental disorders. Thirty-three studies comparing 1506 BD patients with 1973 clinical controls were included in the present systematic review, and twenty-six of them were analyzed in random-effects meta-analyses exploring the discrepancies in discriminating or identifying emotional stimuli at a general and specific level. Individuals with BD were more accurate in identifying each type of emotion during a FER task compared to individuals diagnosed with schizophrenia (SCZ) (SMD = 0.27; p-value = 0.006), with specific differences in the perception of anger (SMD = 0.46; p-value = 1.19e-06), fear (SMD = 0.38; p-value = 8.2e-04), and sadness (SMD = 0.33; p-value = 0.026). In contrast, BD patients were less accurate than individuals with major depressive disorder (MDD) in identifying each type of emotion (SMD = -0.24; p-value = 0.014), but these differences were more specific for sad emotional stimuli (SMD = -0.31; p-value = 0.009). No significant differences were observed when BD was compared with children and adolescents diagnosed with attention-deficit/hyperactivity disorder. FER emerges as a potential integrative instrument for guiding diagnosis by enabling discrimination between BD and SCZ or MDD. Enhancing the standardization of adopted tasks could further enhance the accuracy of this tool, leveraging FER potential as a therapeutic target.

Anxiety and depression played a central role in the COVID-19 mental distress: A network analysis.

INTRODUCTION: Psychological, socio-demographics, and clinical factors play an important role in patients with COVID-19, but their relationship is complex. The network approach might be used to disentangle complex interactions in different systems. Using data from a multicentre, cross-sectional, survey among patients with COVID-19 in Spain (July-November 2020), we investigated the network structure of mental disorders symptoms, social support, and psychological resilience, and changes in network structures according to the presence of a pre-existing mental disorder or hospitalization for COVID-19. METHODS: Subjects completed a survey to evaluate sociodemographic characteristics, COVID-19 infection status, resilience, social support, and symptoms of depression, anxiety disorders, post-traumatic stress disorder, panic attacks, and substance use disorder. 2084 patients with COVID-19 were included in the analysis. Network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between COVID-19 patients with and without a history of lifetime mental disorder, and between hospitalized and non-hospitalized patients. LIMITATIONS: Generalization of our findings may be difficult since differences in network connectivity may exist in different populations or samples. RESULTS: Anxiety and depression showed high centrality in patients with COVID-19 and anxiety showed the highest bridge influence in the network. Resilience and social support showed a low influence on mental disorder symptoms. Global network estimations show no statistically significant changes between patients with and without pre-existing mental disorders or between hospitalized and non-hospitalized patients. CONCLUSIONS: Anxiety might be a key treatment target in patients with COVID-19 since its treatment might prevent other mental health adverse outcomes.