Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

Dual-pocket inhibition of Nav channels by the antiepileptic drug lamotrigine.

Voltage-gated sodium (Nav) channels govern membrane excitability, thus setting the foundation for various physiological and neuronal processes. Nav channels serve as the primary targets for several classes of widely used and investigational drugs, including local anesthetics, antiepileptic drugs, antiarrhythmics, and analgesics. In this study, we present cryogenic electron microscopy (cryo-EM) structures of human Nav1.7 bound to two clinical drugs, riluzole (RLZ) and lamotrigine (LTG), at resolutions of 2.9 Å and 2.7 Å, respectively. A 3D EM reconstruction of ligand-free Nav1.7 was also obtained at 2.1 Å resolution. RLZ resides in the central cavity of the pore domain and is coordinated by residues from repeats III and IV. Whereas one LTG molecule also binds to the central cavity, the other is found beneath the intracellular gate, known as site BIG. Therefore, LTG, similar to lacosamide and cannabidiol, blocks Nav channels via a dual-pocket mechanism. These structures, complemented with docking and mutational analyses, also explain the structure-activity relationships of the LTG-related linear 6,6 series that have been developed for improved efficacy and subtype specificity on different Nav channels. Our findings reveal the molecular basis for these drugs' mechanism of action and will aid the development of novel antiepileptic and pain-relieving drugs.

Sex differences in bipolar disorder: The dorsolateral prefrontal cortex as an etiopathogenic region.

Bipolar disorder (BD) is worldwide a prevalent mental illness and a leading risk factor for suicide. Over the past three decades, it has been discovered that sex differences exist throughout the entire panorama of BD, but the etiologic regions and mechanisms that generate such differences remain poorly characterized. Available evidence indicates that the dorsolateral prefrontal cortex (DLPFC), a critical region that controls higher-order cognitive processing and mood, exhibits biological disparities between male and female patients with psychiatric disorders, which are highly correlated with the co-occurrence of psychotic symptoms. This review addresses the sex differences in BD concerning epidemiology, cognitive impairments, clinical manifestations, neuroimaging, and laboratory abnormalities. It also provides strong evidence linking DLPFC to the etiopathogenesis of these sex differences. We emphasize the importance of identifying gene signatures using human brain transcriptomics, which can depict sexually different variations, explain sex-biased symptomatic features, and provide novel targets for sex-specific therapeutics.

Genetics-informed precision treatment formulation in schizophrenia and bipolar disorder.

Genetically informed drug development and repurposing is an attractive prospect for improving patient outcomes in psychiatry; however, the effectiveness of these endeavors is confounded by heterogeneity. We propose an approach that links interventions implicated by disorder-associated genetic risk, at the population level, to a framework that can target these compounds to individuals. Specifically, results from genome-wide association studies are integrated with expression data to prioritize individual "directional anchor" genes for which the predicted risk-increasing direction of expression could be counteracted by an existing drug. While these compounds represent plausible therapeutic candidates, they are not likely to be equally efficacious for all individuals. To account for this heterogeneity, we constructed polygenic scores restricted to variants annotated to the network of genes that interact with each directional anchor gene. These metrics, which we call a pharmagenic enrichment score (PES), identify individuals with a higher burden of genetic risk, localized in biological processes related to the candidate drug target, to inform precision drug repurposing. We used this approach to investigate schizophrenia and bipolar disorder and reveal several compounds targeting specific directional anchor genes that could be plausibly repurposed. These genetic risk scores, mapped to the networks associated with target genes, revealed biological insights that cannot be observed in undifferentiated genome-wide polygenic risk score (PRS). For example, an enrichment of these partitioned scores in schizophrenia cases with otherwise low PRS. In summary, genetic risk could be used more specifically to direct drug repurposing candidates that target particular genes implicated in psychiatric and other complex disorders.

Shared genetic effect of kidney function on bipolar and major depressive disorders: a large-scale genome-wide cross-trait analysis.

BACKGROUND: Epidemiological studies have revealed a significant association between impaired kidney function and certain mental disorders, particularly bipolar disorder (BIP) and major depressive disorder (MDD). However, the evidence regarding shared genetics and causality is limited due to residual confounding and reverse causation. METHODS: In this study, we conducted a large-scale genome-wide cross-trait association study to investigate the genetic overlap between 5 kidney function biomarkers (eGFRcrea, eGFRcys, blood urea nitrogen (BUN), serum urate, and UACR) and 2 mental disorders (MDD, BIP). Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Psychiatric Genomics Consortium. RESULTS: Using LD score regression, we found moderate but significant genetic correlations between kidney function biomarker traits on BIP and MDD. Cross-trait meta-analysis identified 1 to 19 independent significant loci that were found shared among 10 pairs of 5 kidney function biomarkers traits and 2 mental disorders. Among them, 3 novel genes: SUFU, IBSP, and PTPRJ, were also identified in transcriptome-wide association study analysis (TWAS), most of which were observed in the nervous and digestive systems (FDR < 0.05). Pathway analysis showed the immune system could play a role between kidney function biomarkers and mental disorders. Bidirectional mendelian randomization analysis suggested a potential causal relationship of kidney function biomarkers on BIP and MDD. CONCLUSIONS: In conclusion, the study demonstrated that both BIP and MDD shared genetic architecture with kidney function biomarkers, providing new insights into their genetic architectures and suggesting that larger GWASs are warranted.

Neuromelanin-sensitive MRI of the substantia nigra distinguishes bipolar from unipolar depression.

Depression in bipolar disorder (BD-II) is frequently misdiagnosed as unipolar depression (UD) leading to inappropriate treatment and downstream complications for many bipolar sufferers. In this study, we evaluated whether neuromelanin-MR signal and volume changes in the substantia nigra (SN) can be used as potential biomarkers to differentiate BD-II from UD. The signal intensities and volumes of the SN regions were measured, and contrast-to-noise ratio (CNR) to the decussation of the superior cerebellar peduncles were calculated and compared between healthy controls (HC), BD-II and UD subjects. Results showed that compare to HC, both BD-II and UD subjects had significantly decreased CNR and increased volume on the right and left sides. Moreover, the volume in BD-II group was significantly increased compared to UD group. The area under the receiver operating characteristic curve (AUC) for discriminating BD from HC was the largest for the Volume-L (AUC, 0.85; 95% confidence interval [CI]: 0.77, 0.93). The AUC for discriminating UD from HC was the largest for the Volume-L (AUC, 0.76; 95% CI: 0.65, 0.86). Furthermore, the AUC for discriminating BD from UD was the largest for the Volume-R (AUC, 0.73; 95% CI: 0.62, 0.84). Our findings suggest that neuromelanin-sensitive magnetic resonance imaging techniques can be used to differentiate BD-II from UD.

Distinct functional patterns in child and adolescent bipolar and unipolar depression during emotional processing.

Accumulating evidence from functional magnetic resonance imaging studies supported brain dysfunction during emotional processing in bipolar disorder (BD) and major depressive disorder (MDD). However, child and adolescent BD and MDD could display different activation patterns, which have not been fully understood. This study aimed to investigate common and distinct activation patterns of pediatric BD (PBD) and MDD (p-MDD) during emotion processing using meta-analytic approaches. Literature search identified 25 studies, contrasting 252 PBD patients, and 253 healthy controls (HCs) as well as 311 p-MDD patients and 263 HCs. A total of nine meta-analyses were conducted pulling PBD and p-MDD experiments together and separately. The results revealed that PBD and p-MDD showed distinct patterns during negative processing. PBD patients exhibited activity changes in bilateral precuneus, left inferior parietal gyrus, left angular gyrus, and right posterior cingulate cortex while p-MDD patients showed functional disruptions in the left rectus, left triangular part of the inferior frontal gyrus, left orbital frontal cortex, left insula, and left putamen. In conclusion, the activity changes in PBD patients were mainly in regions correlated with emotion perception while the dysfunction among p-MDD patients was in the fronto-limbic circuit and reward-related regions in charge of emotion appraisal and regulation.

Using a generative model of affect to characterize affective variability and its response to treatment in bipolar disorder.

The affective variability of bipolar disorder (BD) is thought to qualitatively differ from that of borderline personality disorder (BPD), with changes in affect persisting longer in BD. However, quantitative studies have not been able to confirm this distinction. It has therefore not been possible to accurately quantify how treatments like lithium influence affective variability in BD. We assessed the affective variability associated with BD and BPD as well as the effect of lithium using a computational model that defines two subtypes of variability: affective changes that persist (volatility) and changes that do not (noise). We hypothesized that affective volatility would be raised in the BD group, noise would be raised in the BPD group, and that lithium would impact affective volatility. Daily affect ratings were prospectively collected for up to 3 y from patients with BD or BPD and nonclinical controls. In a separate experimental medicine study, patients with BD were randomized to receive lithium or placebo, with affect ratings collected from week -2 to +4. We found a diagnostically specific pattern of affective variability. Affective volatility was raised in patients with BD, whereas affective noise was raised in patients with BPD. Rather than suppressing affective variability, lithium increased the volatility of positive affect in both studies. These results provide a quantitative measure of the affective variability associated with BD and BPD. They suggest a mechanism of action for lithium, whereby periods of persistently low or high affect are avoided by increasing the volatility of affective responses.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Cardiovascular risk management among individuals with type 2 diabetes and severe mental illness: a cohort study.

AIMS/HYPOTHESIS: The aim of this study was to compare cardiovascular risk management among people with type 2 diabetes according to severe mental illness (SMI) status. METHODS: We used linked electronic data to perform a retrospective cohort study of adults diagnosed with type 2 diabetes in Scotland between 2004 and 2020, ascertaining their history of SMI from hospital admission records. We compared total cholesterol, systolic BP and HbA1c target level achievement 1 year after diabetes diagnosis, and receipt of a statin prescription at diagnosis and 1 year thereafter, by SMI status using logistic regression, adjusting for sociodemographic factors and clinical history. RESULTS: We included 291,644 individuals with type 2 diabetes, of whom 1.0% had schizophrenia, 0.5% had bipolar disorder and 3.3% had major depression. People with SMI were less likely to achieve cholesterol targets, although this difference did not reach statistical significance for all disorders. However, people with SMI were more likely to achieve systolic BP targets compared to those without SMI, with effect estimates being largest for schizophrenia (men: adjusted OR 1.72; 95% CI 1.49, 1.98; women: OR 1.64; 95% CI 1.38, 1.96). HbA1c target achievement differed by SMI disorder and sex. Among people without previous CVD, statin prescribing was similar or better in those with vs those without SMI at diabetes diagnosis and 1 year later. In people with prior CVD, SMI was associated with lower odds of statin prescribing at diabetes diagnosis (schizophrenia: OR 0.54; 95% CI 0.43, 0.68, bipolar disorder: OR 0.75; 95% CI 0.56, 1.01, major depression: OR 0.92; 95% CI 0.83, 1.01), with this difference generally persisting 1 year later. CONCLUSIONS/INTERPRETATION: We found disparities in cholesterol target achievement and statin prescribing by SMI status. This reinforces the importance of clinical review of statin prescribing for secondary prevention of CVD, particularly among people with SMI.

The emerging neuroimmune hypothesis of bipolar disorder: An updated overview of neuroimmune and microglial findings.

Bipolar disorder (BD) is a severe and multifactorial disease, with onset usually in young adulthood, which follows a progressive course throughout life. Replicated epidemiological studies have suggested inflammatory mechanisms and neuroimmune risk factors as primary contributors to the onset and development of BD. While not all patients display overt markers of inflammation, significant evidence suggests that aberrant immune signaling contributes to all stages of the disease and seems to be mood phase dependent, likely explaining the heterogeneity of findings observed in this population. As the brain's immune cells, microglia orchestrate the brain's immune response and play a critical role in maintaining the brain's health across the lifespan. Microglia are also highly sensitive to environmental changes and respond to physiological and pathological events by adapting their functions, structure, and molecular expression. Recently, it has been highlighted that instead of a single population of cells, microglia comprise a heterogeneous community with specialized states adjusted according to the local molecular cues and intercellular interactions. Early evidence has highlighted the contribution of microglia to BD neuropathology, notably for severe outcomes, such as suicidality. However, the roles and diversity of microglial states in this disease are still largely undermined. This review brings an updated overview of current literature on the contribution of neuroimmune risk factors for the onset and progression of BD, the most prominent neuroimmune abnormalities (including biomarker, neuroimaging, ex vivo studies) and the most recent findings of microglial involvement in BD neuropathology. Combining these different shreds of evidence, we aim to propose a unifying hypothesis for BD pathophysiology centered on neuroimmune abnormalities and microglia. Also, we highlight the urgent need to apply novel multi-system biology approaches to characterize the diversity of microglial states and functions involved in this enigmatic disorder, which can open bright perspectives for novel biomarkers and therapeutic discoveries.

The Role of the Gut Microbiome in Bipolar Disorder and its Common Medical Comorbidities.

Bipolar disorder is a decidedly heterogeneous and multifactorial disease, with significant psychosocial and medical disease burden. Much difficulty has been encountered in developing novel therapeutics and objective biomarkers for clinical use in this population. In that regard, gut-microbial homeostasis appears to modulate several key pathways relevant to a variety of psychiatric, metabolic, and inflammatory disorders. Microbial impact on immune, endocrine, endocannabinoid, kynurenine, and other pathways are discussed throughout this review. Emphasis is placed on this system's relevance to current pharmacology, diet, and comorbid illness in bipolar disorder. Despite the high level of optimism promoted in many reviews on this topic, substantial obstacles exist before any microbiome-related findings can provide meaningful clinical utility. Beyond a comprehensive overview of pathophysiology, this review hopes to highlight several key areas where progress is needed. As well, novel microbiome-associated suggestions are presented for future research.

HERV-W upregulation expression in bipolar disorder and schizophrenia: unraveling potential links to systemic immune/inflammation status.

BACKGROUND: Bipolar disorder (BD) and schizophrenia (SZ) are the two main mental disorders with unknown etiology that significantly impact individuals' quality of life. The potential pro-inflammatory role in their pathogenesis is postulated and Human Endogenous Retrovirus W (HERV-W) is an emerging candidate to modulate this pathogenic finding. HERVs, ancient retroviruses in the human genome, may play roles in inflammation and disease pathogenesis. Despite HERVs' involvement in autoimmune diseases, their influence on mental disorders remains underexplored. Therefore, the aim of this study was to assess the level of HERV-W-env expression and the systemic inflammatory profile through the concentration of IL-2, IL-4, IL-6, IL-10, TNF-α and INF-γ cytokines in BD and SZ patients. RESULTS: All participants showed HERV-W-env expression, but its expression was higher in mental disorder patients (p < 0.01) than in control. When separated, SZ individuals exhibited higher HERV-W expression than the control group (p < 0.01). Higher serum levels of TNF-α and IL-10 were found in BD (p = 0.0001 and p = 0.001, respectively) and SZ (p = 0.01) and p = 0.01, respectively) than in the control group, while SZ showed decreased levels IFN-γ and IL-2 as compared to controls (p = 0.05) and BD patients (p = 0.05), respectively. Higher TNF-α/IL-4 and TNF-α/IL-10 ratios, and lower IFN-γ/IL-10 were observed in BD and SZ patients than controls. Significant negative correlation between HERV-W-env expression and IL-10 (r=-0.47 p < 0.05), as well as positive correlations between HERV-W-env expression and TNF-α/IL-10 or IFN-γ/IL-10 ratios (r = 0.48 p < 0.05 and r = 0.46 p < 0.05, respectively) were found in BD patients. CONCLUSION: These findings suggest not only a potential link between HERV-W-env expression both in BD and SZ, but also a possible involvement of systemic inflammatory status in BD patients.

Using the Bayesian variational spike and slab model in a genome-wide association study for finding associated loci with bipolar disorder.

OBJECTIVE: The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. METHODS: This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. RESULTS: Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. CONCLUSIONS: By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined.

Chronotype and subjective sleep quality predict white matter integrity in young people with emerging mental disorders.

Protecting brain health is a goal of early intervention. We explored whether sleep quality or chronotype could predict white matter (WM) integrity in emerging mental disorders. Young people (N = 364) accessing early-intervention clinics underwent assessments for chronotype, subjective sleep quality, and diffusion tensor imaging. Using machine learning, we examined whether chronotype or sleep quality (alongside diagnostic and demographic factors) could predict four measures of WM integrity: fractional anisotropy (FA), and radial, axial, and mean diffusivities (RD, AD and MD). We prioritised tracts that showed a univariate association with sleep quality or chronotype and considered predictors identified by ≥80% of machine learning (ML) models as 'important'. The most important predictors of WM integrity were demographics (age, sex and education) and diagnosis (depressive and bipolar disorders). Subjective sleep quality only predicted FA in the perihippocampal cingulum tract, whereas chronotype had limited predictive importance for WM integrity. To further examine links with mood disorders, we conducted a subgroup analysis. In youth with depressive and bipolar disorders, chronotype emerged as an important (often top-ranking) feature, predicting FA in the cingulum (cingulate gyrus), AD in the anterior corona radiata and genu of the corpus callosum, and RD in the corona radiata, anterior corona radiata, and genu of corpus callosum. Subjective quality was not important in this subgroup analysis. In summary, chronotype predicted altered WM integrity in the corona radiata and corpus callosum, whereas subjective sleep quality had a less significant role, suggesting that circadian factors may play a more prominent role in WM integrity in emerging mood disorders.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

A Systematic Review of the Molecular and Cellular Alterations Induced by Cannabis That May Serve as Risk Factors for Bipolar Disorder.

BACKGROUND: Cannabis use is a risk factor of psychiatric illness, such as bipolar disorder type-I (BDI). Indeed, cannabis use strongly influences the onset and clinical course of BDI, although the biological mechanisms underlying this interaction remain unknown. Therefore, we have reviewed the biological mechanisms affected by cannabis use that may trigger BD. METHODS: A systematic review was carried out of articles in which gene expression was studied in cannabis users or human-derived cells exposed to tetrahydrocannabinol (THC) or cannabidiol (CBD). A second systematic review was then performed to identify articles in which gene expression was studied in BDI samples, highlighting those that described alterations to the same molecular and cellular mechanisms affected by cannabis/THC/CBD. RESULTS: The initial search identified 82 studies on cannabis and 962 on BDI. After removing duplicates and applying the inclusion/exclusion criteria, 9 studies into cannabis and 228 on BDI were retained. The molecular and cellular mechanisms altered by cannabis use or THC/CBD exposure were then identified, including neural development and function, cytoskeletal function, cell adhesion, mitochondrial biology, inflammatory related pathways, lipid metabolism, the endocannabinoid system, the hypocretin/orexin system, and apoptosis. Alterations to those activities were also described in 19 of 228 focused on BDI. CONCLUSIONS: The biological mechanisms described in this study may be good candidates to the search for diagnostic biomarkers and therapeutic targets for BDI. Because cannabis use can trigger the onset of BD, further studies would be of interest to determine whether they are involved in the early development of the disorder, prompting early treatment.

Mapping the global, regional and national burden of bipolar disorder from 1990 to 2019: trend analysis on the Global Burden of Disease Study 2019.

BACKGROUND: Data on trends in the epidemiological burden of bipolar disorder are scarce. AIMS: To provide an overview of trends in bipolar disorder burden from 1990 to 2019. METHOD: Revisiting the Global Burden of Disease Study 2019, we analysed the number of cases, calculated the age-standardised rate (per 100 000 population) and estimated annual percentage change (EAPC) of incidence, prevalence and years lived with disability (YLDs) for bipolar disorder from 1990 to 2019. The independent effects of age, period and cohort were estimated by the age-period-cohort modelling. RESULTS: Globally, the bipolar disorder-related prevalent cases, incident cases and number of YLDs all increased from 1990 to 2019. Regionally, the World Health Organization Region of the Americas accounted for the highest estimated YLD number and rate, with the highest age-standardised prevalence rate in 1990 and 2019 and highest EAPC of prevalence. By sociodemographic index (SDI) quintiles, all five SDI regions saw an increase in estimated incident cases. Nationally, New Zealand reported the highest age-standardised rate of incidence, prevalence and YLDs in 1990 and 2019. The most prominent age effect on incidence rate was in those aged 15-19 years. Decreased effects of period on incidence, prevalence and YLD rates was observed overall and in females, not in males. The incidence, prevalence and YLD rates showed an unfavourable trend in the younger cohorts born after 1990, with males reporting a higher cohort risk than females. CONCLUSIONS: From 1990 to 2019, the overall trend of bipolar disorder burden presents regional and national variations and differs by age, sex, period and cohort.

Adverse childhood experiences and psychological functioning among women with schizophrenia or bipolar disorder: population-based study.

BACKGROUND: Adverse childhood experiences (ACEs) are well-known risk factors for schizophrenia and bipolar disorder. AIMS: The aim was to study the associations between specific ACEs and psychological functioning in women with schizophrenia or bipolar disorder. METHOD: Among 29 367 women (mean age 44 years) from the Icelandic Stress-And-Gene-Analysis (SAGA) study, 534 (1.8%, mean age 40) reported having been diagnosed with schizophrenia or bipolar disorder, which were combined to 'severe mental disorders'. Participants reported on 13 types of ACEs, childhood deprivation and psychological functioning (defined as coping ability and current symptoms of depression, anxiety and sleep disturbances). Adjusted Poisson regression calculated prevalence ratios (PRs) between ACEs and severe mental disorders. Linear regression assessed the association between ACEs and psychological functioning among women with a severe mental disorder. RESULTS: Women with a severe mental disorder reported more ACEs (mean 4.57, s.d. = 2.82) than women without (mean 2.51, s.d. = 2.34) in a dose-dependent manner (fully-adjusted PR = 1.23 per ACE, 95% CI 1.20-1.27). After mutual adjustment for other ACEs, emotional abuse, sexual abuse, mental illness of a household member, emotional neglect, bullying and collective violence were associated with severe mental disorders. Among women with severe mental disorders, a higher number of ACEs was associated with increased symptom burden of depression (ß = 2.79, 95% CI = 1.19-4.38) and anxiety (ß = 2.04, 95% CI = 0.99-3.09) including poorer sleep quality (ß = 0.83, 95% CI = 0.07-1.59). Findings were similar for schizophrenia and bipolar disorder separately. CONCLUSION: Women with schizophrenia or bipolar disorder show a strong history of ACEs, which may interfere with their psychological functioning and, therefore, need to be addressed as part of their treatment, for example, with trauma-focused psychotherapy.

Neuroprogression in bipolar disorder: why right is wrong.

The controversy on whether bipolar disorder is a neurodevelopmental versus a neuroprogressive illness is still around, despite some reductionistic claims that only one model is right. The current diagnostic classifications are not helpful to address this issue, and there is conflicting evidence in favor and against either model. In practice, though, understanding that many patients may show a progressive cognitive and functional decline which may be correlated with the number and severity of episodes may lead to better outcomes through early intervention strategies.