Is a delay in the diagnosis of bipolar disorder inevitable?

Efficacy of life goals collaborative care for bipolar disorder: A systematic review

BACKGROUND

Bipolar disorder remains a disabling mental health condition despite the availability of effective treatments. Collaborative chronic care models (CCMs) represent an evidence-based way to structure care for conditions like bipolar disorder. Life Goals Collaborative Care (LGCC) was designed specifically for bipolar disorder, featuring psychoeducation alongside collaborative components (e.g. nurse care management or expert psychiatric consultation). Despite the use of Life Goals across health systems, a systematic review summarizing its effectiveness has never been conducted.

METHODS

We conducted a systematic review of randomized controlled trials (RCTs) of LGCC through December 2023 to help guide the field in treating bipolar disorder (PROSPERO: #404581). We evaluated study quality and outcomes in several symptom and quality of life domains.

RESULTS

Ten articles describing eight studies met inclusion criteria. All studies featured group-based LGCC; most were compared to treatment as usual (TAU). Three of eight studies found LGCC to be associated with statistically significant effects for the prevention of manic episodes. Most studies finding positive effects featured additional collaborative care components beyond psychoeducation and were conducted in capitated healthcare systems.

LIMITATIONS

Limitations include: several types of potential bias in included studies; exclusion of observational studies of LGCC; lack of generalizability to pediatric populations; insufficient studies to conduct subgroup analyses; and low confidence in the quality of the evidence.

CONCLUSIONS

In this systematic review, group-based LGCC demonstrated some positive effects for reducing mania recurrence; results for other outcome domains were equivocal. Future studies should investigate one-on-one LGCC, both in person and virtually, to enhance well-being for people with bipolar disorder.

Comorbidity of bipolar disorder and borderline personality disorder: Phenomenology, course, and treatment considerations

AIM

Bipolar disorder (BD) and borderline personality disorder (BPD) are both serious psychiatric conditions that elevate the risk for harmful outcomes. Although these conditions represent distinct diagnostic entities, existing research suggests that approximately 20% of individuals with BD meet the criteria for comorbid BPD. Individuals with comorbid BD/BPD appear to have a markedly more severe and phenomenologically distinct clinical course when compared with those with BD alone. However, treatments have generally not been tested in this specific population, and currently, no formal treatment guidelines exist for this subgroup of patients.

METHOD

In the current paper, we review the epidemiological and descriptive research characterizing those with comorbid BD/BPD and discuss the impact of this comorbidity on psychosocial treatment. We also review current findings on evidence-based treatments for BD and BPD that show promise in treating those with comorbid BD/BPD.

RESULTS

In our review of the literature, we highlight the importance of recognizing this comorbidity and discuss avenues for developing and integrating evidence-based treatment approaches for this understudied clinical population.

CONCLUSIONS

Although formal trials of interventions targeted to comorbid BD/BPD are limited, there is promising evidence regarding the possibility of using or integrating existing evidence-based approaches for this population. There are also several areas of clinical practice improvement and future research directions that stem from this literature.

AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Combining predominant polarity and affective spectrum concepts in bipolar disorder: towards a novel theoretical and clinical perspective

This is an overview of recent advances on predominant polarity conceptualization in bipolar disorder (BD). Current evidence on its operationalized definitions, possible contextualization within the affective spectrum, along with its epidemiological impact, and treatment implications, are summarized. Predominant polarity identifies three subgroups of patients with BD according to their mood recurrencies: (i) those with depressive or (ii) manic predominance as well as (iii) patients without any preponderance ('nuclear' type). A predominant polarity can be identified in approximately half of patients, with similar rates for depressive and manic predominance. Different factors may influence the predominant polarity, including affective temperaments. More generally, affective disorders should be considered as existing on a spectrum ranging from depressive to manic features, also accounting for disorders with 'ultrapredominant' polarity, i.e., unipolar depression and mania. While mixed findings emerge on its utility in clinical practice, it is likely that the construct of predominant polarity, in place of conventional differentiation between BD-I and BD-II, may be useful to clarify the natural history of the disorder and select the most appropriate interventions. The conceptualization of predominant polarity seems to reconcile previous theoretical views of both BD and affective spectrum into a novel perspective. It may provide useful information to clinicians for the early identification of possible trajectories of BD and thus guide them when selecting interventions for maintenance treatment. However, further research is needed to clarify the specific role of predominant polarity as a key determinant of BD course, outcome, and treatment response.

Diagnostic error in mental health: a review

Diagnostic errors are associated with patient harm and suboptimal outcomes. Despite national scientific efforts to advance definition, measurement and interventions for diagnostic error, diagnosis in mental health is not well represented in this ongoing work. We aimed to summarise the current state of research on diagnostic errors in mental health and identify opportunities to align future research with the emerging science of diagnostic safety. We review conceptual considerations for defining and measuring diagnostic error, the application of these concepts to mental health settings, and the methods and subject matter focus of recent studies of diagnostic error in mental health. We found that diagnostic error is well understood to be a problem in mental healthcare. Although few studies used clear definitions or frameworks for understanding diagnostic error in mental health, several studies of missed, wrong, delayed and disparate diagnosis of common mental disorders have identified various avenues for future research and development. Nevertheless, a lack of clear consensus on how to conceptualise, define and measure errors in diagnosis will pose a barrier to advancement. Further research should focus on identifying preventable missed opportunities in the diagnosis of mental disorders, which may uncover generalisable opportunities for improvement.

White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls - exploring associations with disease course and polygenic risk

Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data

Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Ten-Year Trends in Lithium Prescribing in Alberta, Canada

AIMS

Despite lithium's clinical efficacy, it is commonly thought that its use is declining. The objective of this study is to describe the new and prevalent lithium users as well as rates of discontinuation of lithium use over a 10-year period.

METHODS

This study used provincial administrative health data from Alberta, Canada between January 1, 2009 and December 31, 2018. Lithium prescriptions were identified within the Pharmaceutical Information Network database. Total and subgroup specific frequencies of new and prevalent lithium use were determined over the 10-year study period. Lithium discontinuation was also estimated through survival analysis.

RESULTS

Between the calendar years of 2009 and 2018, 580,873 lithium prescriptions were dispensed in Alberta to 14,008 patients. The total number of new and prevalent lithium users appears to be decreasing over the 10-year timeframe, although the decline may have stopped or reversed in the latter years of the study period. Prevalent use of lithium was lowest among individuals between the ages of 18-24 years while the highest number of prevalent users were in the 50-64 age group, particularly among females. New lithium use was lowest amongst those 65 years and older. More than 60% (8,636) of patients prescribed lithium, discontinued use during the study timeframe. Lithium users between ages of 18-24 years were at the highest risk of discontinuations.

CONCLUSIONS

Rather than a general decline in prescribing, trends in lithium use are dependent on age and sex. Further, the period soon after lithium initiation appears to be a key time period in which many lithium trials are abandoned. Detailed studies using primary data collection are needed to confirm and further explore these findings. These population-based results not only confirm a decline in lithium use, but also suggest that this may have stopped or even reversed. Population-based data on discontinuation pinpoint the period soon after initiation as the time when trials are most often discontinued.

Toward explainable AI (XAI) for mental health detection based on language behavior

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a "black-box" domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress).

Early longitudinal changes in brain structure and cognitive functioning in remitted patients with recently diagnosed bipolar disorder

BACKGROUND

Patients with bipolar disorder (BD) who are presenting with cognitive impairment and associated structural brain abnormalities have generally a poorer clinical outcome. This study aims to map the early longitudinal trajectories in brain structure and cognition in patients with recently diagnosed BD.

METHODS

Fully or partially remitted patients with a recent diagnosis of BD and matched healthy controls (HC) underwent structural MRI and neuropsychological testing at baseline (BD n = 97; HC n = 66) and again following an average of 16 (range 6-27) months (BD n = 50; HC n = 38). We investigated the differential trajectories in BD vs. HC in cortical gray matter volume and thickness, total cerebral white matter, hippocampal and amygdala volumes, estimated brain age, and cognitive functioning using linear mixed models. Within patients, we further investigated whether brain structural abnormalities detected at baseline were associated with subsequent mood episodes.

RESULTS

Compared to HC, patients showed a decline in total white matter volume over time and they had a larger amygdala volume, both at baseline and at follow-up time. Patients further showed lower cognitive performance at both times of investigation with no significant change over time. There were no differences between patients and HC in cortical gray matter volume or thickness, hippocampal volume, or brain-aging patterns.

CONCLUSIONS

Cognitive impairment and amygdala enlargement may represent stable markers of BD early in the course of illness, whereas subtle white matter decline may result from illness progression.

The diagnosis of bipolar disorder in children and adolescents: Past, present and future

AIMS

This article examines the ongoing debate concerning the diagnosis of bipolar disorder in children and adolescents. This contentious issue has generated robust discussion over the past two decades without consensus, and as such the true prevalence of so-called paediatric bipolar disorder (PBD) remains unknown. In this article we offer a solution to break this deadlock.

METHODS

Recent meta-analyses and additional literature concerning the definition and prevalence of PBD was critically reviewed with a view to understanding the perspectives of those developing the taxonomy of PBD, and those engaged in research and clinical practice.

RESULTS

A key finding is the lack of iteration and meaningful communication between the various groups interested in PBD that stems from deep-seated problems within our classificatory systems. This undermines our research efforts and complicates clinical practice. These problems make the already difficult diagnosis of bipolar disorder in adults even more challenging to transpose to younger populations, and additional complexities arise when parsing clinical phenomenology from normative developmental changes in youth. Therefore, in those manifesting bipolar symptoms post-puberty, we argue for the use of adolescent bipolar disorder to describe bipolar symptoms whereas in pre-pubertal children, we propose a reconceptualisation that allows symptomatic treatment to be advanced whilst requiring critical review of these symptoms over time.

CONCLUSION

Significant changes in our current taxonomy are necessary and to be clinically meaningful, these revisions to our diagnoses need to be developmentally-informed.

Barriers to and facilitators of success for early and Mid-Career professionals focused on bipolar disorder: A global needs survey by the International Society for Bipolar Disorders

INTRODUCTION

The International Society for Bipolar Disorders created the Early Mid-Career Committee (EMCC) to support career development of the next generation of researchers and clinicians specializing in bipolar disorder (BD). To develop new infrastructure and initiatives, the EMCC completed a Needs Survey of the current limitations and gaps that restrict recruitment and retention of researchers and clinicians focused on BD.

METHODS

The EMCC Needs Survey was developed through an iterative process, relying on literature and content expertise of workgroup members. The survey included 8 domains: navigating transitional career stages, creating and fostering mentorship, research activities, raising academic profile, clinical-research balance, networking and collaboration, community engagement, work-life balance. The final survey was deployed from May to August 2022 and was available in English, Spanish, Portuguese, Italian, and Chinese.

RESULTS

Three hundred participants across six continents completed the Needs Survey. Half of the participants self-identified as belonging to an underrepresented group in health-related sciences (i.e., from certain gender, racial, ethnic, cultural, or disadvantaged backgrounds including individuals with disabilities). Quantitative results and qualitative content analysis revealed key barriers to pursuing a research career focused on BD with unique challenges specific to scientific writing and grant funding. Participants highlighted mentorship as a key facilitator of success in research and clinical work.

CONCLUSION

The results of the Needs Survey are a call to action to support early- and midcareer professionals pursuing a career in BD. Interventions required to address the identified barriers will take coordination, creativity, and resources to develop, implement, and encourage uptake but will have long-lasting benefits for research, clinical practice, and ultimately those affected by BD.

Job retention for people with bipolar disorder: A qualitative analysis

At least half of people with bipolar disorder, whose global prevalence is greater than 1% of the general population, do not have a stable occupation. The objective of this study is to identify the factors perceived by bipolar people as having an impact on retention in employment. Semi-directed individual interviews were offered to subjects with bipolar disorder, of working age, who had been seen by three different hospital departments in the south of France. These people had to be medically stable and have at least one occupational experience. Each interview was recorded and transcribed manually and then analyzed according to a thematic classification of verbatims. Nineteen people participated in the interviews. Three major themes identified were classified into intrinsic (working routine and working environment) and extrinsic (external aids) factors for the bipolar person. The majority believe that working regular hours is a balancing factor. For everyone, night work and stress factors can lead to relapses. All stressed the need to destigmatize the disease. Most believe that it is necessary to develop employment support organizations specific to mental illnesses. This research helps to inform people with bipolar disorder about the occupational factors that help balance their condition. Personalized multidisciplinary care pathways involving occupational medicine must be developed in order to promote the balance of the disease and job retention.

Neural responses during down-regulation of negative emotion in patients with recently diagnosed bipolar disorder and their unaffected relatives

BACKGROUND

Aberrant emotion regulation has been posited as a putative endophenotype of bipolar disorder (BD). We therefore aimed to compare the neural responses during voluntary down-regulation of negative emotions in a large functional magnetic resonance imaging study of BD, patients' unaffected first-degree relatives (URs), and healthy controls (HCs).

METHODS

We compared neural activity and fronto-limbic functional connectivity during emotion regulation in response to aversive v. neutral pictures in patients recently diagnosed with BD (n = 78) in full/partial remission, their URs (n = 35), and HCs (n = 56).

RESULTS

Patients showed hypo-activity in the left dorsomedial, dorsolateral, and ventrolateral prefrontal cortex (DMPFC and DLPFC) during emotion regulation while viewing aversive pictures compared to HCs, with URs displaying intermediate neural activity in these regions. There were no significant differences between patients with BD and HCs in functional connectivity from the amygdala during emotion regulation. However, exploratory analysis indicated that URs displayed more negative amygdala-DMPFC coupling compared with HCs and more negative amygdala-cingulate DLPFC coupling compared to patients with BD. At a behavioral level, patients and their URs were less able to dampen negative emotions in response aversive pictures.

CONCLUSIONS

The findings point to deficient recruitment of prefrontal resources and more negative fronto-amygdala coupling as neural markers of impaired emotion regulation in recently diagnosed remitted patients with BD and their URs, respectively.

Development and Multi-Site External Validation of a Generalizable Risk Prediction Model for Bipolar Disorder

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

The Mood Disorder Assessment Schedule: Initial validation of a new measure for early identification of bipolar spectrum disorders in inpatient adolescents

Due to the significant impairment associated with subthreshold bipolar symptomatology and the harmful effects of delayed diagnosis, there is a great need for diagnostic tools that can facilitate early identification of bipolar spectrum disorders. The Mood Disorder Assessment Schedule (MDAS) is a newly developed measure that focuses on autonomous changes in mood and energy, a key indicator of bipolar spectrum problems which is not included in current diagnostic tools for bipolar disorders. The current study tested the ability of the MDAS to identify individuals at risk for bipolar spectrum disorders. In a cross-sectional sample of 396 inpatient adolescents, the MDAS identified a group of individuals with several bipolar spectrum disorder (BSD) indicators, including greater manic and depressive symptoms, affective lability, suicidal behavior, adverse reactions to antidepressants, and a family history of bipolar disorder and suicidal behavior. When compared to a standard diagnostic interview for bipolar disorders (i.e., Kiddie Schedule for Affective Disorders and Schizophrenia [KSADS]), the MDAS yielded stronger clinical utility in its ability to identify individuals with BSD indicators. Therefore, the MDAS appears to be a promising diagnostic tool for identifying adolescents at risk for BSDs and may help facilitate earlier diagnosis and prevent harmful effects of improper treatment.

A systematic review and meta-analysis of delayed help-seeking, delayed diagnosis and duration of untreated illness in bipolar disorders

OBJECTIVES

To examine the time delay between the age at onset of symptoms or episodes of bipolar disorders (BD) and the age at diagnosis of and/or receipt of clinical practice guideline recommended interventions for BD.

METHODS

Systematic search of five databases to identify publications from January 2000 to July 2022 that reported one or more of the following reliable and valid estimates of latency: delay in help seeking (DHS), delay in diagnosis (DD) and duration of untreated BD (DUB). Eligible studies were included in random effects meta-analyses and multivariate meta-regression was used to assess factors associated with each latency construct.

RESULTS

Screening of 1074 publications identified 59 eligible studies (reported in 66 publications) of >40,000 individuals that estimated DHS (8 studies), DD (20 studies) and/or DUB (45 studies). The median DHS, DD and DUB were 3.5 (IQR: 2.8, 8.48), 6.7 (IQR: 5.6, 8.9) and 5.9 years (IQR: 1.1, 8.2), respectively. Key factors associated with shorter DD included older age and residing outside North America; shorter DUB was associated with psychotic or manic onset and access to early intervention services.

CONCLUSIONS

Greater consensus on definitions of latency constructs and better-quality targeted research is required regarding DHS, DD and DUB. This review suggests that, while the peak age at onset of BD is 15-25, diagnosis and guideline recommended interventions (e.g., mood stabilizers) are likely to be delayed until age 25-35 years except for a minority of individuals with access to early intervention services.

Neuroinflammation and neuroprogression produced by oxidative stress in euthymic bipolar patients with different onset disease times

Bipolar disorder (BD) is associated with systemic toxicity, represented by changes in biomarkers associated with mood episodes, leading to neurological damage, which may reflect cognitive functions and functionality and the progression of the disease. We aimed to analyze the effect of four biomarkers, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and thiobarbituric acid reactive substances (TBA-RS), related to oxidative stress in BD and to correlate them with cognitive functions and functionality. We studied 50 bipolar types I/II patients in the euthymic phase, which was divided into two subgroups with 25 patients each (≤ 3 years and ≥ 10 years of diagnosis, from the first episode of mania) and 25 control patients. To analyze frontal cognitive functions and functionality, we used the Frontal Assessment Battery (FAB) and Functioning Assessment Short Test (FAST) tests, respectively. The scores of the FAST and FAB tests showed an increase and decrease respectively, in both bipolar groups, when compared to the control group, demonstrating impairment in cognitive functions and functionality since the disease onset. In addition, changes occurred in all six domains of the FAST test, and in four domains of the FAB test in bipolar patients when compared to the control group. Regarding oxidative stress biomarkers, we did not find changes in SOD and GSH-Px activities; however, a significant increase in CAT activity and lipid peroxidation was observed in both groups, although the patients were euthymic and medicated. These results allow us to raise the hypothesis that since the beginning of the disease, the euthymic bipolar patient has presented a level of oxidative stress, which gets worse with the evolution of the disease, promoting impairments in the frontal cognitive functions and functionality gradually.

Identifying transdiagnostic biological subtypes across schizophrenia, bipolar disorder, and major depressive disorder based on lipidomics profiles

Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.

Association between polarity of first episode and solar insolation in bipolar I disorder

OBJECTIVE

Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar insolation at the onset location was associated with the polarity of the first episode of BD I. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area of the Earth.

METHODS

Data from 7488 patients with BD I were collected at 75 sites in 42 countries. The first episode occurred at 591 onset locations in 67 countries at a wide range of latitudes in both hemispheres. Solar insolation values were obtained for every onset location, and the ratio of the minimum mean monthly insolation to the maximum mean monthly insolation was calculated. This ratio is largest near the equator (with little change in solar insolation over the year), and smallest near the poles (where winter insolation is very small compared to summer insolation). This ratio also applies to tropical locations which may have a cloudy wet and clear dry season, rather than winter and summer.

RESULTS

The larger the change in solar insolation throughout the year (smaller the ratio between the minimum monthly and maximum monthly values), the greater the likelihood the first episode polarity was depression. Other associated variables were being female and increasing percentage of gross domestic product spent on country health expenditures. (All coefficients: P ≤ 0.001).

CONCLUSION

Increased awareness and research into circadian dysfunction throughout the course of BD is warranted.

Differential trajectory of cognitive functions in neurocognitive subgroups of newly diagnosed patients with bipolar disorder and their unaffected first-degree relatives - A longitudinal study

BACKGROUND

Cognitive impairments exist in a large proportion of remitted patients with bipolar disorder (BD). However, no study has investigated the cognitive trajectories across neurocognitive subgroups of patients or their unaffected first-degree relatives (UR).

METHODS

Newly diagnosed BD patients, UR and healthy controls (HC) underwent comprehensive cognitive testing at baseline and at 16-months follow-up. Hierarchical cluster analysis was conducted to identify homogeneous subgroups of patients based on their neurocognitive profile at baseline. Cognitive change across subgroups of patients and UR was assessed with linear mixed-model analyses.

RESULTS

Data from baseline and follow-up were collected from 152 patients, 53 UR and 135 HC. Patients were clustered into three discrete neurocognitive subgroups: 'cognitively normal' (43%), 'mild-moderately impaired' (33%) and 'globally impaired' (24%). While 'mild-moderately impaired' patients and HC showed normative cognitive improvement over time in global cognition (p < .001), 'globally impaired' patients showed greater improvement than all groups (p < .001), whereas 'cognitively normal' patients showed a lack of normative improvement (p = .17). UR of impaired patients showed a lack of normative improvement in executive functions (p = .01). 'Globally impaired' patients also presented with stable impairments in facial expression recognition and emotion regulation.

LIMITATIONS

Follow-up data was available for 62% of participants, possibly reflecting a selection bias.

CONCLUSIONS

The greater cognitive improvement in 'globally impaired' patients partly speaks against neuroprogression. However, the lack of normative improvement in 'cognitively normal' patients could indicate negative effects of illness. Further follow-up assessments are warranted to clarify whether lack of normative improvement in executive function in UR represents an illness risk-marker.

Psychosocial markers of age at onset in bipolar disorder: a machine learning approach

BACKGROUND

Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the psychosocial predictors associated with AAO are unknown.

AIMS

We aim to identify psychosocial factors associated with bipolar disorder AAO.

METHOD

Using data from the Bipolar Disorder Research Network UK, we employed least absolute shrinkage and selection operator regression to identify psychosocial factors associated with bipolar disorder AAO. Twenty-eight factors were entered into our model, with AAO as our outcome measure.

RESULTS

We included 1022 participants with bipolar disorder (μ = 23.0, s.d. ± 9.86) in our model. Six variables predicted an earlier AAO: childhood abuse (β = -0.2855), regular cannabis use in the year before onset (β = -0.2765), death of a close family friend or relative in the 6 months before onset (β = -0.2435), family history of suicide (β = -0.1385), schizotypal personality traits (β = -0.1055) and irritable temperament (β = -0.0685). Five predicted a later AAO: the average number of alcohol units consumed per week in the year before onset (β = 0.1385); birth of a child in the 6 months before onset (β = 0.2755); death of parent, partner, child or sibling in the 6 months before onset (β = 0.3125); seeking work without success for 1 month or more in the 6 months before onset (β = 0.3505) and a major financial crisis in the 6 months before onset (β = 0.4575).

CONCLUSIONS

The identified predictor variables have the potential to help stratify high-risk individuals into likely AAO groups, to inform treatment provision and early intervention.

Characteristics prior to and at time of diagnosis in pediatric bipolar disorder

OBJECTIVES

Describe symptoms before and at time of register-diagnosis in children and adolescents.

METHODS

A random sample was selected for chart-review from a Danish nationwide cohort of patients <18 years registered with an incident ICD-10 register-diagnosis of single hypomanic/manic episode or bipolar disorder between 1995 and 2014. Patients with symptoms which adequately documented a BD diagnosis in the chart were included for analysis.

RESULTS

521 were diagnosed in the study period. A random sample of 25% were selected, and 106 charts were possible to retrieve, with 48 chart reviews resulting in confirmation of diagnosis. Time from first reported affective symptoms to diagnosis was 2.6 ± 2.7 years for depressive symptoms, 2.5 ± 2.9 years for mixed symptoms, 1.4 ± 1.6 years for hypomanic symptoms, and 0.4 ± 0.5 years for manic symptoms. A hierarchical clustering analysis revealed three patient-profiles: primarily hypomanic/manic, primarily depressive, and more rare, primarily mixed profile. Frequently reported symptoms prior to diagnosis include anhedonia (79%), irritability (71%), hyperactivity (62.5%), decreased energy (62.5%), and psychotic symptoms (52%).Symptoms of ADHD (19%), comorbid ADHD (15%), symptoms of anxiety (52%), comorbid anxiety (4%), suicidal thoughts (50%), suicide attempts (8%), cutting (23%), substance misuse (21%), and criminal activity (10%) were reported before incident BD diagnosis.

CONCLUSION

The observed patient-profiles leading to diagnosis were primarily manic or depressive, resembling presentations in adults. The presence of ADHD, anxiety, suicide attempts, cutting, and criminal activity prior to diagnosis emphasizes the need for treatment of children and adolescents with affective symptoms. The gap from appearance of the symptoms to diagnosis suggests a window for earlier treatment.

Neural underpinnings of emotion regulation subgroups in remitted patients with recently diagnosed bipolar disorder

Neuroimaging studies of bipolar disorder (BD) generally involve comparison with healthy controls (HC), which may mask neurobiological variability within the disorder. This study aims to assess the neural underpinnings of potential subgroups of BD patients based on functional activity in the emotion regulation network and its relation to illness characteristics and relapse risk. Eighty-seven remitted patients with recently diagnosed BD and 66 HC underwent functional magnetic resonance imaging (fMRI) while performing an emotion regulation task. Patients were re-assessed with clinical interviews after 16 (±5) months. Data-driven hierarchical cluster analysis was employed to investigate 'neuronal subgroups' of patients based on their neuronal activity in a pre-defined emotion regulation network. Relations between neuronal subgroups and illness characteristics and relapse rates were examined. Patients were allocated into two subgroups. Subgroup 1 (n=62, 75%) was characterized by exaggerated bilateral amygdala reactivity but normal prefrontal and temporo-parietal activation. Subgroup 2 (n= 22, 25%) showed widespread hypo-activity within all emotion regulation regions. Both subgroups were less successful at down-regulating their emotions than HC (F(2,146)=5.33, p=.006, η_p²=.07). Patients in subgroup 2 had a history of more and longer mixed episodes (ps≤.01). Importantly, heightened amygdala activity across all patients was associated with increased risk of relapse during a 16-month follow-up period (β=3.36, 95% CI [1.49;550.35], N=60, p=.03). The identified neuronal subgroups of patients with either amygdala hyper-activity or broad network hypo-activity during emotion regulation points to neurobiological heterogeneity among remitted patients with BD. Heightened amygdala reactivity may be a neuronal target for personalized treatments to prevent relapse.

A comparison of symptoms of bipolar and unipolar depression in postpartum women

BACKGROUND

Distinguishing postpartum women with bipolar from unipolar depression remains challenging, particularly in obstetrical and primary care settings. The post-birth period carries the highest lifetime risk for the onset or recurrence of Bipolar Disorder (BD). Characterization of differences between unipolar and bipolar depression symptom presentation and severity is critical to differentiate the two disorders.

METHODS

We performed a secondary analysis of a study of 10,000 women screened by phone with the Edinburgh Postnatal Depression Scale at 4-6 weeks post-birth. Screen-positive mothers completed the Structured Clinical Interview for DSM-4 and those diagnosed with BD and unipolar Major Depressive Disorder (UD) were included. Depressive symptoms were assessed with the 29-item Structured Interview Guide for the Hamilton Rating Scale for Depression (SIGH-ADS).

RESULTS

The sample consisted of 728 women with UD and 272 women with BD. Women with BD had significantly elevated levels of depression severity due to the higher scores on 8 of the 29 SIGH-ADS symptoms. Compared to UD, women with BD had significantly higher rates of comorbid anxiety disorders and were twice as likely to report sexual and/or physical abuse.

LIMITATIONS

Only women who screened positive for depression were included in this analysis. Postpartum women with unstable living situations, who were hospitalized or did not respond to contact attempts did not contribute data.

CONCLUSIONS

Severity of specific symptom constellations may be a useful guide for interviewing postpartum depressed women along with the presence of anxiety disorder comorbidity and physical and/or sexual abuse.

Differences in mitochondrial DNA copy number between patients with bipolar I and II disorders

Mitochondria play a critical role in energy metabolism. Genetic, postmortem brain, and brain imaging studies of bipolar disorder (BD) patients indicated that mitochondrial dysfunction might explain BD pathophysiology. Mitochondrial function can be indirectly evaluated by measuring mitochondrial DNA (mtDNA) copy numbers. We recruited 186 bipolar I disorder (BD1) and 95 bipolar II disorder (BD2) patients, and age- and sex-matched controls. MtDNA copy numbers in peripheral blood cells were measured via quantitative polymerase chain reaction. We explored parameters (including age and clinical features) that might affect mtDNA copy numbers. We found that BD1 patients had a lower mtDNA copy number than controls and that mtDNA copy number was negatively associated with the number of mood episodes. BD2 patients had a higher mtDNA copy number than controls. Thus, changes in mitochondrial function may influence BD pathophysiology. The opposite directions of the association with mtDNA copy number in BD1 and BD2 patients suggests that the difference in pathophysiology may be associated with mitochondrial function.

Learnings from user feedback of a novel digital mental health assessment

Digital mental health interventions (DMHI) have the potential to address barriers to face-to-face mental healthcare. In particular, digital mental health assessments offer the opportunity to increase access, reduce strain on services, and improve identification. Despite the potential of DMHIs there remains a high drop-out rate. Therefore, investigating user feedback may elucidate how to best design and deliver an engaging digital mental health assessment. The current study aimed to understand 1304 user perspectives of (1) a newly developed digital mental health assessment to determine which features users consider to be positive or negative and (2) the Composite International Diagnostic Interview (CIDI) employed in a previous large-scale pilot study. A thematic analysis method was employed to identify themes in feedback to three question prompts related to: (1) the questions included in the digital assessment, (2) the homepage design and reminders, and (3) the assessment results report. The largest proportion of the positive and negative feedback received regarding the questions included in the assessment (n = 706), focused on the quality of the assessment (n = 183, 25.92% and n = 284, 40.23%, respectively). Feedback for the homepage and reminders (n = 671) was overwhelmingly positive, with the largest two themes identified being positive usability (i.e., ease of use; n = 500, 74.52%) and functionality (i.e., reminders; n = 278, 41.43%). The most frequently identified negative theme in results report feedback (n = 794) was related to the report content (n = 309, 38.92%), with users stating it was lacking in-depth information. Nevertheless, the most frequent positive theme regarding the results report feedback was related to wellbeing outcomes (n = 145, 18.26%), with users stating the results report, albeit brief, encouraged them to seek professional support. Interestingly, despite some negative feedback, most users reported that completing the digital mental health assessment has been worthwhile (n = 1,017, 77.99%). Based on these findings, we offer recommendations to address potential barriers to user engagement with a digital mental health assessment. In summary, we recommend undertaking extensive co-design activities during the development of digital assessment tools, flexibility in answering modalities within digital assessment, customizable additional features such as reminders, transparency of diagnostic decision making, and an actionable results report with personalized mental health resources.

Clinical and demographic factors associated with delayed diagnosis of bipolar disorder: Data from Health Outcomes and Patient Evaluations in Bipolar Disorder (HOPE-BD) study

BACKGROUND

The diagnosis of Bipolar Disorder (BD) is frequently delayed. In this study, we aimed to examine the clinical and demographic factors associated with delayed diagnosis of BD, defined as the difference between the age at first mood episode (depressive, manic, or hypomanic) and the age at the correct diagnosis of BD, using data from a Canadian multicentre naturalistic study.

METHODS

The sample included 192 patients with Bipolar I Disorder (BD-I) and 127 with Bipolar II Disorder (BP-II) who participated in the Health Outcomes and Patient Evaluations in Bipolar Disorder (HOPE-BD) study. Sociodemographic characteristics and clinical features that had been previously associated with delayed diagnosis of BD were included in the analysis.

RESULTS

The median delay in diagnosis was 5.0 years in BD-I and 11.0 years in BD-II. Clinical factors such as earlier age of onset, lifetime suicide attempts and comorbid anxiety disorders were associated with a longer delay, whereas the presence of lifetime psychotic symptoms and psychiatric hospitalizations were associated with a shorter delay. Quantile regression analysis showed older age at which professional help was first sought and younger age of onset as predictors of increased delay in diagnosis of BD-I and BD-II. Depression as first episode predicted longer delay in diagnosis of BD-I but not BD-II.

CONCLUSION

Our findings identified the ongoing lag in identification of a BD diagnosis and the clinical markers most associated with this delay, highlighting the need for implementation of strategies for early identification and interventions in BD.

Impact of bipolar disorder on health-related quality of life and work productivity: Estimates from the national health and wellness survey in Japan

BACKGROUND

Previous studies have shown that bipolar disorder (BD) patients frequently present with difficulties in interpersonal relationships, education or employment and suffer poorer quality of life. This study aimed to estimate the impact of BD on health-related quality of life (HRQOL), work productivity loss and indirect costs.

METHODS

Data was from the online, self-administered 2019 National Health and Wellness Survey. Outcomes were compared for those who self-reported a physician diagnosis of BD (N=179), major depressive disorder (MDD, N=1,549) and controls who have never experienced BD, MDD and schizophrenia (N=27,485).

RESULTS

The lifetime prevalence was estimated to be 0.60% for BD and 5.16% for MDD. Significantly lower Mental Component Summary (MCS), Role Component Summary (RCS) scores and EuroQol 5-dimension scale (EQ-5D-5L) summary index and significantly higher presenteeism, total work productivity impairment and activity impairment assessed by Work Productivity and Activity Impairment questionnaire and indirect costs for BD versus controls and BD PHQ-9≥10 versus PHQ-9<10 were observed. Compared to MDD patients, BD patients had significantly lower RCS score and greater work productivity loss and activity impairment. The national morbidity cost of BD in Japan was estimated to be Japanese yen 1,236 billion using a human-capital approach.

LIMITATIONS

The data used were self-reported and is cross-sectional in nature, thus causal relationship cannot be assumed.

CONCLUSION

BD patients and those with severe depressive symptoms experience significantly poorer HRQOL and greater work productivity loss and indirect costs. These findings highlight the importance of proper screening, diagnosis and treatment of BD and bipolar depression.

Substance use as a risk factor for bipolar disorder: A systematic review

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.

Lithium as a Neuroprotective Agent for Bipolar Disorder: An Overview

Lithium (Li⁺) is a first option treatment for adult acute episodes of Bipolar Disorder (BD) and for the prophylaxis of new depressed or manic episodes. It is also the preferred choice as maintenance treatment. Numerous studies have shown morphological abnormalities in the brains of BD patients, suggesting that this highly heritable disorder may exhibit progressive and deleterious changes in brain structure. Since treatment with Li⁺ ameliorates these abnormalities, it has been postulated that Li⁺ is a neuroprotective agent in the same way atypical antipsychotics are neuroprotective in patients diagnosed with schizophrenia spectrum disorders. Li⁺'s neuroprotective properties are related to its modulation of nerve growth factors, inflammation, mitochondrial function, oxidative stress, and programmed cell death mechanisms such as autophagy and apoptosis. Notwithstanding, it is not known whether Li⁺-induced neuroprotection is related to the inhibition of its putative molecular targets in a BD episode: the enzymes inositol-monophosphatase, (IMPase), glycogen-synthase-kinase 3β (GSK3), and Protein kinase C (PKC). Furthermore, it is uncertain whether these neuroprotective mechanisms are correlated with Li⁺'s clinical efficacy in maintaining mood stability. It is expected that in a nearby future, precision medicine approaches will improve diagnosis and expand treatment options. This will certainly contribute to ameliorating the medical and economic burden created by this devastating mood disorder.

Diagnostic delay in ADHD: Duration of untreated illness and its socio-demographic and clinical predictors in a sample of adult outpatients

AIM

To investigate the Duration of Untreated Illness (DUI) and its socio-demographic and clinical predictors in a sample of adult ADHD outpatients.

METHODS

The DUI of 150 adult outpatients with a confirmed diagnosis of DSM-IV ADHD was calculated. Non-parametric tests were used to evaluate differences in DUI among subgroups and to build a correlation matrix. Subsequently, a multiple linear regression model was performed.

RESULTS

The median DUI was 17 years (interquartile range [IQR] = 14). DUI was longer in employed patients, those with a family history of ADHD, those with a history of major depressive disorder and those who had predominantly inattentive ADHD in childhood. The current age, age at administration of the first proper treatment and education level were correlated with DUI. Current age (63.3% of total variance), family history of ADHD and the presence of a predominantly inattentive type in childhood (together, 2.6% of total variance) were all predictors of DUI.

CONCLUSIONS

Information programmes for caregivers and training for health care professionals should be promoted to foster the early recognition of covert inattentive symptoms and shorten DUI.

Navigating the diagnostic challenges of bipolar disorder in youth

ABSTRACT

Bipolar disorder in youth is difficult to diagnose and treat, but early detection is important to mitigate risks and improve patient outcomes. This article evaluates the unique challenges of diagnosing and treating bipolar disorder in children and adolescents. Bipolar disorder is associated with a significant personal and societal health burden and frequently is misdiagnosed and incorrectly treated. More research is needed to understand the pathophysiology of bipolar disorder and which treatment options are best throughout the lifespan.

Exploring real-world evidence to uncover unknown drug benefits and support the discovery of new treatment targets for depressive and bipolar disorders

BACKGROUND

Major depressive and bipolar disorders are associated with impaired quality of life and high economic burden. Although progress has been made in our understanding of the underlying pathophysiology and the development of novel pharmacological treatments, a large unmet need remains for finding effective treatment options. The purpose of this study was to identify potential new mechanisms of actions or treatment targets that could inform future research and development opportunities for major depressive and bipolar disorders.

METHODS

A self-controlled cohort study was conducted to examine associations between 1933 medications and incidence of major depressive and bipolar disorders across four US insurance claims databases. Presence of incident depressive or bipolar disorders were captured for each patient prior to or after drug exposure and incident rate ratios were calculated. Medications that demonstrated ≥50% reduction in risk for both depressive and bipolar disorders within two or more databases were evaluated as potential treatment targets.

RESULTS

Eight medications met our inclusion criteria, which fell into three treatment groups: drugs used in substance use disorders; drugs that affect the cholinergic system; and drugs used for the management of cardiovascular-related conditions.

LIMITATIONS

This study was not designed to confirm a causal association nor inform current clinical practice. Instead, this research and the methods employed intended to be hypothesis generating and help uncover potential treatment pathways that could warrant further investigation.

CONCLUSIONS

Several potential drug targets that could aid further research and discovery into novel treatments for depressive and bipolar disorders were identified.

Editorial: Moving From Innovation to Validation With Independent Sample Testing

It is well established that family history is the most important known risk factor for the development of a mood disorder.¹ Approximately 25% of at-risk offspring will develop a mood disorder^1,2; yet, this is only slightly higher than the lifetime rate of a mood disorder overall of 21.4%.³ These statistics suggest heterogeneity within mood disorders generally and within people at familial risk. More information, particularly in the form of trait-level biomarkers, is sorely needed to accurately predict mood disorder risk and development. Trait-level biomarker identification may also help to decrease the well-known delay in accurately diagnosing bipolar disorders specifically,⁴ which could ultimately improve treatment and outcomes. Nimarko et al.⁵ aimed to identify trait-level biological markers of social reward-related neural vulnerability that distinguish youths genetically at risk for mood disorder. Nimarko and colleagues reported a well-designed study consisting of extremely well-matched youths who, by virtue of parentage, were at risk of bipolar disorder, were at risk of major depressive disorder, or were healthy (control group).⁵ The sample sizes reflected the challenges associated with doing this type of research with children, but the importance of the relationships was clear and strong. Specifically, the results showed reduced bilateral putamen activity (left: z = 4.39, p = .003; right: z = 4.56, p < .001) and reduced left putamen connectivity with right posterior cingulate cortex (z = 4.04, p < .01) and left anterior cingulate cortex (z > 3.64, p < .001) during an emotional face social reward task as potential trait markers that distinguished at-risk samples.⁵ In another study, similarly reduced reward-related connectivity in genetically at-risk youths for bipolar disorder relative to at-risk and healthy groups was shown to monetary reward and loss.⁶ Importantly, in both of these studies, reward-related connectivity group differences were observed before threshold symptoms developed, suggesting that early identification and intervention may be possible. In addition, this reduced reward-related connectivity was not related to subthreshold symptoms in either study. Taken together, these findings suggest that social reward and monetary reward and loss may be interconnected at a neural level in youths genetically at risk for bipolar disorder before symptom development. Exploratory analyses in Nimarko et al.⁵ also showed that reduced reward-related connectivity may be a trait-level biomarker of risk of future conversion to an Axis I disorder over time (bipolar disorder risk: hazard ratio = 8.28, p < .01); major depressive disorder risk: hazard ratio = 2.31, p = .02). Although Acuff et al.⁶ did not address the longitudinal question of conversion directly, the authors speculated that reduced reward-related connectivity was related to increased risk of future bipolar disorder. This serendipitous independent replication of reward-related connectivity is a promising lead, but repeated testing in independent samples is needed. However, direct independent sample testing is uncommon in psychiatry and in psychiatric neuroscience.^7,8 This is partially due to factors such as time and money, but also to the value placed on innovation, which is prized by funding agencies, journal editors, and society. Independent sample testing is not, by definition, innovative, and while innovation is an important cornerstone of science and should be encouraged, we also need to test previously identified findings using independent samples.

Deficient LEF1 expression is associated with lithium resistance and hyperexcitability in neurons derived from bipolar disorder patients

Bipolar disorder (BD) is a psychiatric condition characterized by depressive and manic episodes that affect 2% of the world population. The first-line long-term treatment for mood stabilization is lithium (Li). Induced pluripotent stem cell modeling of BD using hippocampal dentate gyrus-like neurons derived from Li-responsive (LR) and Li-non-responsive (NR) patients previously showed neuronal hyperexcitability. Li treatment reversed hyperexcitability only on the LR neurons. In this study we searched for specific targets of Li resistance in NR neurons and found that the activity of Wnt/β-catenin signaling pathway was severely affected, with a significant decrease in expression of LEF1. Li targets the Wnt/β-catenin signaling pathway by inhibiting GSK-3β and releasing β-catenin that forms a nuclear complex with TCF/LEF1, activating the Wnt/β-catenin transcription program. Therefore, we propose that downregulation of LEF1 may account for Li resistance in NR neurons. Our results show that valproic acid (VPA), a drug used to treat NR patients that also acts downstream of GSK-3β, upregulated LEF1 and Wnt/β-catenin gene targets, increased transcriptional activity of complex β-catenin/TCF/LEF1, and reduced excitability in NR neurons. In addition, decreasing LEF1 expression in control neurons using shLEF1 caused hyperexcitability, confirming that the impact of VPA on excitability in NR neurons was connected to changes in LEF1 and in the Wnt/β-catenin pathway. Our results suggest that LEF1 may be a useful target for the discovery of new drugs for BD treatment.

Clinical characteristics of bipolar disorders with postpartum depressive onset

BACKGROUND

Postpartum period is associated with an increased risk of bipolar disorder diagnosis and relapse, mainly major depressive episode. Onset during this period might be associated with specific characteristics.

AIM

To compare the socio-demographic and clinical characteristics of parous women presenting with bipolar disorder and an index depressive episode occurring during or outside the postpartum period.

METHODS

Using the multicenter cohort FACE-BD (FondaMental Academic Centers of Expertise for Bipolar Disorders), we considered all women who started their BD with a major depressive episode and have at least one child. We compared two groups depending on the onset: in or outside the postpartum period.

RESULTS

Among the 759 women who started BD with a major depressive episode, 93 (12.2%) had a postpartum onset, and 666 (87.8%) had not. Women who started BD in the postpartum period with a major depressive episode have a more stable family life, more children, an older age at onset, more Bipolar 2 disorder, less history of suicide attempts, less depressive episodes and more mood stabilizer treatments as compared to those who started with a major depressive episode outside the postpartum period. The multivariable logistic regression showed that women with an onset in the postpartum period had significantly more children, less lifetime depressive episodes and a lower rate of history of suicide attempts as compared to women with an onset outside the postpartum period.

DISCUSSION

Our results suggest that women starting their BD with postpartum depression have a more favorable course of BD, especially less history of suicide attempt and less lifetime depressive episodes.

Differences in Demographic and Clinical Characteristics of Patients With Depressive vs. Manic First Episode of Bipolar Disorder

Background: Bipolar disorder is a serious mental disease marked by episodes of depression, mania, hypomania, or mixed states. Patients with bipolar disorder may present with different symptoms at first onset. The aim of this study is to compare demographic and clinical variables based on a patient's first episode of bipolar disorder, including risk of recurrence over a 2-year period. Methods: A large cohort (N = 742) of patients with bipolar disorder in China was analyzed. Patients were divided into two groups according to their first episode of bipolar disorder, either depression or mania. Patients in mixed state first episode were classified based on predominant symptoms. Three hundred eighteen patients of the cohort had a first episode of mania and 424 patients had initial symptoms of depression. Demographic and clinical data were collected. All patients were followed up for 24 months. Data on compliance with follow-up appointments and recurrence of symptoms after 6, 12, 18, and 24 months were collected. Clinical characteristics (course of disease, age of onset, psychiatric family history, etc.) were compared between the mania group and depression groups. Results: More patients with bipolar disorder had a first episode of depression than mania (57.14 vs. 42.86%). Compared with the depression group, the mania group had later age of diagnosis of bipolar disorder [(38.64 ± 13.50) vs. (36.34 ± 14.94), P = 0.028], lower education level [(9.37 ± 4.34) vs. (10.17 ± 4.81), P = 0.017] and longer latency between an initial episode of psychiatric symptoms and formal bipolar diagnosis [(10.80 ± 10.76) vs. (8.85 ± 9.90), P = 0.012]. More patients in the mania group were male and without psychotic symptoms (all P < 0.05). In comparison with the mania group, more patients in the depression group were female, with higher frequency of a reported precipitating event before first mood episode (all P < 0.05). Compared with the depression group, the mania group had more recurrences of illness at the end of 12 months (Z =-2.156, P = 0.031), 18 months (Z =-2.192, P = 0.028), and 24 months (Z = -2.364, P = 0.018). Conclusions: In our study, there are a number of differences in demographic and clinical characteristics of patients with different onset syndromes of bipolar disorder. These differences include gender, education level, diagnosis age, the rate of recurrences, and others. These data of a cohort of Chinese patients add to the growing international literature on the relationship between index episode of bipolar disorder and clinical variables and outcomes. These results and further study may allow clinicians to offer patients and families more reliable prognostic information at the onset of disease.

Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool

Background: Sleep disturbances are common early warning signs of an episode of bipolar disorder, and early recognition can favorably impact the illness course. Symptom monitoring via a smartphone app is an inexpensive and feasible method to detect an early indication of changes such as sleep. The study aims were (1) to assess the acceptance of apps and (2) to validate sleeping times measured by the smartphone app UP!. Methods:UP! was used by 22 individuals with bipolar disorder and 23 controls. Participants recorded their time of falling asleep and waking-up using UP! for 3 weeks. Results were compared to a validated accelerometer and the Pittsburgh Sleep Quality Index. Additionally, participants were interviewed regarding early warning signs and their feedback for apps as monitoring tools in bipolar disorder (NCT03275714). Results: With UP!, our study did not find strong reservations concerning data protection or continual smartphone usage. Correlation analysis demonstrates UP! to be a valid tool for measuring falling asleep and waking-up times. Discussion: Individuals with bipolar disorder assessed the measurement of sleep disturbances as an early warning sign with a smartphone as positive. The detection of early signs could change an individual's behavior and strengthen self-management. The study showed that UP! can be used to measure changes in sleep durations accurately. Further investigation of smartphone apps' impact to measure other early signs could significantly contribute to clinical treatment and research in the future through objective, continuous, and individual data collection.

Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults

Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD. Methods: A final sample of 185 young, high-risk German adults (aged 18-35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes. Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls. Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.

Neurophysiological markers of attention distinguish bipolar disorder and unipolar depression

BACKGROUND

Attentional deficits are common in both symptomatic and symptom-remitted patients with bipolar disorder (BP) and major depressive disorder (MDD). However, whether the level of neurocognitive impairment in attentional processing is different between these two disorders, or not, is still unclear. Thus, we investigated the P300 event-related potential component as a biomarker of cognitive dysfunction to differentiate BP and MDD.

METHODS

Twenty-three age and gender matched BP, 20 MDD and 23 healthy controls (HC) were part of a discovery cohort to identify neurophysiological differences between groups and build a classification model of these disorders. The replication of this model was then tested in an independent second cohort of 17 BP, 19 MDD and 19 HC. All participants were symptom-remitted for at least two weeks. We compared neural responses to target stimuli during an auditory oddball task, computing peak amplitude and latency of the P300 component extracted from the midline centro-parietal electrode.

RESULTS

BP had significantly smaller P300 amplitudes compared to both MDD and HC, whereas there were no differences between MDD and HC. The differences between groups were replicated in the second cohort, however the accuracy level of the classification model was only 53.5%.

LIMITATIONS

Small sample sizes may have led to low accuracy levels of the classification model.

CONCLUSION

Specific neural mechanisms of attention and context updating seem not to recover with symptom remission in BP. These findings contribute to the detection of a potential electrophysiological marker for BP, which may allow its differentiation from unipolar major depressive disorder.

A prospective longitudinal study searching for predictors of response to group psychoeducation in bipolar disorder

BACKGROUND

The efficacy of adjunctive group psychoeducation in bipolar disorder has been proven although treatment response differ among individuals. The aim of this study was to characterize responders and non-responders to group psychoeducation in order to identify baseline variables that could predict treatment response.

METHODS

The sample was composed of 103 medicated euthymic patients with bipolar disorder referred to 21 sessions of group psychoeducation (6 months). Sociodemographic and clinical variables, temperament, circadian rhythms, BDNF, cognitive and psychosocial functioning were collected. At the 18-month endpoint, the patients were split in two groups on the basis of having suffered any recurrence. Significant group differences were included in a logistic regression analysis.

RESULTS

Ninety patients out of 103 engaged in group psychoeducation, 47 of whom (52.2%) responded to psychoeducation and 43 (47.8%) did not. Recurrences occurred more often in the follow-up, the most common being depression. Responders and non-responders differed in gender, age at diagnosis, latency of diagnosis, temperament, attention composite score and BDNF. Lower age at diagnosis of bipolar disorder, lower cyclothimic temperament scores and being male -which was associated with bipolar type I and a trend to more previous manic episodes- were significantly related to a better response to psychoeducation in the regression analysis.

LIMITATIONS

No control group.

CONCLUSIONS

This study identifies age at diagnosis as a significant modifiable risk factor of treatment response, highlighting the need for early identification of bipolar disorder. Existing programs should be adjusted to the characteristics of specific subpopulations in the framework of a personalized approach.

Changes in Non-Coding RNA in Depression and Bipolar Disorder: Can They Be Used as Diagnostic or Theranostic Biomarkers?

The similarities between the depressive symptoms of Major Depressive Disorders (MDD) and Bipolar Disorders (BD) suggest these disorders have some commonality in their molecular pathophysiologies, which is not apparent from the risk genes shared between MDD and BD. This is significant, given the growing literature suggesting that changes in non-coding RNA may be important in both MDD and BD, because they are causing dysfunctions in the control of biochemical pathways that are affected in both disorders. Therefore, understanding the changes in non-coding RNA in MDD and BD will lead to a better understanding of how and why these disorders develop. Furthermore, as a significant number of individuals suffering with MDD and BD do not respond to medication, identifying non-coding RNA that are altered by the drugs used to treat these disorders offer the potential to identify biomarkers that could predict medication response. Such biomarkers offer the potential to quickly identify patients who are unlikely to respond to traditional medications so clinicians can refocus treatment strategies to ensure more effective outcomes for the patient. This review will focus on the evidence supporting the involvement of non-coding RNA in MDD and BD and their potential use as biomarkers for treatment response.

Lifetime cocaine use is a potential predictor for conversion from major depressive disorder to bipolar disorder: A prospective study

AIM

We aimed to identify whether lifetime cocaine use is a risk factor for conversion from major depressive disorder (MDD) to bipolar disorder (BD) in an outpatient sample of adults.

METHODS

This prospective cohort study included 585 subjects aged 18 to 60 years who had been diagnosed with MDD as assessed by the Mini International Neuropsychiatric Interview (MINI-Plus) at baseline (2012-2015). Subjects were reassessed a mean of 3 years later (2017-2018) for potential conversion to BD as assessed by the MINI-Plus. Lifetime cocaine use was assessed using the Alcohol, Smoking, and Substance Involvement Screening Test.

RESULTS

In the second wave, we had 117 (20%) losses, and 468 patients were reassessed. The rate of conversion from MDD to BD in 3 years was 12.4% (n = 58). A logistic regression analysis showed that the risk for conversion from MDD to BD was 3.41-fold higher (95% confidence interval, 1.11-10.43) in subjects who reported lifetime cocaine use at baseline as compared to individuals who did not report lifetime cocaine use at baseline, after adjusting for demographic and clinical confounders.

CONCLUSION

These findings showed that lifetime cocaine use is a potential predictor of conversion to BD in an MDD cohort. Further studies are needed to assess the possible underlying mechanisms linking exposure to cocaine with BD conversion.

Investigating the neural basis of cognitive control dysfunction in mood disorders

OBJECTIVES

Dysfunction of cognitive control is a feature of both bipolar disorder (BP) and major depression (MDD) and persists through to remission. However, it is unknown whether these disorders are characterized by common or distinct disruptions of cognitive control function and its neural basis. We investigated this gap in knowledge in asymptomatic BP and MDD participants, interpreted within a framework of normative function.

METHODS

Participants underwent fMRI scans engaging cognitive control through a working memory task and completed a cognitive battery evaluating performance across multiple subdomains of cognitive control, including attention, impulsivity, processing speed, executive function, and memory. Analysis was performed in two stages: (i) cognitive control-related brain activation and deactivation were correlated with cognitive control performance in 115 healthy controls (HCs), then, (ii) significantly correlated regions from (i) were compared between 25 asymptomatic BP, 25 remitted MDD, and with 25 different HCs, matched for age and gender.

RESULTS

Impulsivity and executive function performance were significantly worse in BP compared to both MDD and HCs. Both BP and MDD had significantly poorer memory performance compared to HCs. Greater deactivation of the medial prefrontal cortex (MPFC) during the fMRI task was associated with better executive function in healthy controls. Significantly less deactivation in this region was present in both BP and MDD compared to HCs.

CONCLUSIONS

Failure to deactivate the MPFC, a key region of the default mode network, during working memory processing is a shared neural feature present in both bipolar and major depression and could be a source of common cognitive dysfunction.

Remission and recurrence in bipolar disorder: The data from health outcomes and patient evaluations in bipolar disorder (HOPE-BD) study

BACKGROUND

The HOPE-BD was a naturalistic study established to follow individuals in Canada seeking treatment for bipolar disorder (BD). The study aimed to examine the course of BD and describe how clinical and sociodemographic factors are associated with outcomes.

METHODS

Individuals with BD had their clinical data recorded at enrolment and were naturalistically treated. Participant were followed for up to four years, and visits occurred at least once every three months. We investigated the longitudinal outcomes with logistic, Cox, and quantile regressions.

RESULTS

Among the 354 participants, 57.3% had BD type I. Depression as first episode, younger ages at onset and older ages of the first professional help predicted longer delays in correct diagnosis. Among the symptomatic patients at baseline, the median time to remission was 10.9 months. Comorbid alcohol use disorder and the severity of baseline depressive symptoms predicted longer times to remission. Among the euthymic participants, the median time to recurrence was 14.5 months. History of anxiety disorder and younger ages at onset predicted shorter times to recurrence. Baseline depression scores predicted recurrence in euthymic patients.

LIMITATIONS

We did not investigate the predictors of each polarity. Our findings may not apply to individuals followed in non-specialised outpatient services.

CONCLUSION

Our study reinforces the necessity of early diagnosis and interventions, as well as the importance of treating depressive symptoms and comorbidities.

Incidence of Depression and First-Line Antidepressant Therapy in People with Obesity and Depression in Primary Care

OBJECTIVE

The aim of this study was to describe the age- and gender-specific incidence of depression, the dose-response relationship between BMI and risk of depression (Cox proportional hazards), and antidepressant drug prescribing in adults with overweight or obesity.

METHODS

A retrospective electronic health record study using the Clinical Practice Research Datalink was conducted to identify adults with overweight and obesity (≥ 18 years) with incident depression (no prior depression diagnosis in their records), followed up from 2000 to 2019.

RESULTS

Among 519,513 adults, incidence of depression was 9.2 per 1,000 person-years and was higher in women and in 40- to 59-year-old men who had severe obesity. Compared with having overweight, the hazard of depression increased with each BMI category as follows: 1.13 (30-34 kg/m² ; 95% CI: 1.10-1.16), 1.34 (35-39 kg/m² ; 1.29-1.40), 1.51 (40-44 kg/m² ; 1.41-1.61), and 1.67 (45-49 kg/m² ; 1.48-1.87), attenuating at BMI 50+ kg/m² (1.54; 2.91-1.84). Antidepressants were prescribed as first-line therapy in two-thirds (66.3%) of cases. Prescriptions for fluoxetine reduced over time (20.4% [2000]; 8.8% [2018]), and prescriptions for sertraline increased (4.3% [2000]; 38.9% [2018]).

CONCLUSIONS

We recommend guidance on antidepressant drug prescribing and specific services for people with obesity and depression that address both symptoms and behaviors.