Symptom characteristics of depressive episodes prior to the onset of mania or hypomania

Patterns of Self-Reported Mental Health Symptoms and Treatment among People Booked into a Large Metropolitan County Jail

The Brief Jail Mental Health Screen (BJMHS) is one of the most well-known and frequently used tools to conduct routine mental health screening at jail intake. In prior research, the BJMHS results typically have been evaluated overall (i.e., yes/no positive screen). However, there is heterogeneity in symptom presentation and treatment histories among people with serious mental illness, and there are potential consequences of this heterogeneity for mental health administration and policy in jails. We conducted a latent class analysis of BJMHS item-level results using administrative data for 37,998 people booked into a southeastern, metropolitan, U.S. county jail over a 3.5-year period. A 4-class solution provided the best fitting and most interpretable model. The largest class (89.5%) comprised people unlikely to report symptoms or treatment histories (limited symptoms). The next class comprised people who were unlikely to report ongoing symptoms but reported medication and hospitalization (managed symptoms). The third class (2.5%) included people likely to report feeling useless/sinful, prior hospitalization, and current psychiatric medication (depressive symptoms). The fourth class (1.0%) comprised people likely to report thought control, paranoia, feeling useless/sinful, medication, and hospitalization (psychotic symptoms). Controlling for sociodemographic and booking characteristics, people in the managed, depressive, and psychotic symptoms classes had significantly longer jail stays compared to those in the limited symptoms class. People in the managed and depressive symptoms classes were at heightened risk of re-arrest compared to the limited symptoms class. Findings can inform case prioritization and the allocation of resources to support efficient and effective jail-based mental health services.

Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk.

Predictors of diagnostic conversion from major depression to bipolar disorder: a Swedish national longitudinal study

BACKGROUND

It is clinically important to predict the conversion of major depression (MD) to bipolar disorder (BD). Therefore, we sought to identify related conversion rates and risk factors.

METHODS

This cohort study included the Swedish population born from 1941 onward. Data were collected from Swedish population-based registers. Potential risk factors, including family genetic risk scores (FGRS), which were calculated based on the phenotypes of relatives in the extended family and not molecular data, and demographic/clinical characteristics from these registers were retrieved. Those with first MD registrations from 2006 were followed up until 2018. The conversion rate to BD and related risk factors were analyzed using Cox proportional hazards models. Additional analyses were performed for late converters and with stratification by sex.

RESULTS

The cumulative incidence of conversion was 5.84% [95% confidence interval (95% CI) 5.72-5.96] for 13 years. In the multivariable analysis, the strongest risk factors for conversion were high FGRS of BD [hazard ratio (HR) = 2.73, 95% CI 2.43-3.08], inpatient treatment settings (HR = 2.64, 95% CI 2.44-2.84), and psychotic depression (HR = 2.58, 95% CI 2.14-3.11). For late converters, the first registration of MD during the teenage years was a stronger risk factor when compared with the baseline model. When the interactions between risk factors and sex were significant, stratification by sex revealed that they were more predictive in females.

CONCLUSIONS

Family history of BD, inpatient treatment, and psychotic symptoms were the strongest predictors of conversion from MD to BD.

Associations among the workplace violence, burnout, depressive symptoms, suicidality, and turnover intention in training physicians: a network analysis of nationwide survey

Depression and anxiety are the most common mental disorders among physicians, who have a greater risk of suicide than those in other professional occupations. Relationships among a demanding workload, workplace violence, burnout, and intention to turnover have also been reported. The current study examined the principal components and propagating patterns of mental health and working environment interactions in training physicians. A total of 1981 training physicians completed online self-report questionnaires during September-October (midpoint of the training year) 2020. Regularized partial correlations in a mixed graphical model (MGM) and joint probability distributions (directed acyclic graph; DAG) were estimated for four subtypes of workplace violence (verbal abuse/physical violence perpetrated by clients/hospital staff), three burnout subdomains (Maslach Burnout Inventory), thoughts about quitting, and nine depressive symptoms, including suicidality, comprising the DSM-5 diagnostic criteria for major depressive disorder (assessed using the Patient Health Questionnaire-9). Thoughts of death/self-harm showed directional dependencies on the joint probability distributions of psychomotor agitation/retardation, concentration difficulty, self-reproach, and sadness in the DAG. In the MGM, a partial correlation with psychomotor agitation/retardation (r = 0.196) accounted for 56.5% of the variance in thoughts of death/self-harm. Partial correlations with concentration difficulties (r = 0.294), self-reproach (r = 0.257), changes in appetite (r = 0.184), and worker-on-worker physical violence (r = 0.240) in the MGM accounted for 54.4% of the variance in psychomotor agitation/retardation. Thoughts about quitting were partially correlated with and dependent upon the joint probability distributions of emotional exhaustion (r = 0.222), fatigue (r = 0.142), anhedonia (r = 0.178), and sadness (r = 0.237). In contrast, worker-on-worker (r = 0.417) and client-on-physician (r = 0.167) verbal abuse had regularized partial correlations with directional dependencies on thoughts about quitting. Organization-level interventions aiming to reduce the worker-on-worker violence and individual-level approaches of clinical screening program and psychiatric counseling clinic are required. Follow-up studies to verify the effectiveness of these interventions for training physicians are needed.

A symptom network model of misophonia: From heightened sensory sensitivity to clinical comorbidity

OBJECTIVES

Misophonia-an unusually strong intolerance of certain sounds-can cause significant distress and disruption to those who have it but is an enigma in terms of our scientific understanding. A key challenge for explaining misophonia is that, as with other disorders, it is likely to emerge from an interaction of traits that also occur in the general population (e.g., sensory sensitivity and anxiety) and that are transdiagnostic in nature (i.e., shared with other disorders).

METHODS

In this preregistered study with a large sample of participants (N = 1430), we performed a cluster analysis (based on responses to questions relating to misophonia) and identified two misophonia subgroups differing in severity, as well as a third group without misophonia. A subset of this sample (N = 419) then completed a battery of measures designed to assess sensory sensitivity and clinical comorbidities.

RESULTS

Clinical symptoms were limited to the most severe group of misophonics (including autistic traits, migraine with visual aura, anxiety sensitivity, obsessive-compulsive traits). Both the moderate and severe groups showed elevated attention-to-detail and hypersensitivity (across multiple senses). A novel symptom network model of the data shows the presence of a central hub linking misophonia to sensory sensitivity which, in turn, connects to other symptoms in the network (relating to autism, anxiety, etc.).

CONCLUSION

The core features of misophonia are sensory-attentional in nature with severity linked strongly to comorbidities.

Bipolar Chronobiology in Men and Mice: A Narrative Review

In patients with bipolar disorder, we do not only see a cycling of mood episodes, but also a shift in circadian rhythm. In the present overview, the circadian rhythm, the "internal clock", and their disruptions are briefly described. In addition, influences on circadian rhythms such as sleep, genetics, and environment are discussed. This description is conducted with a translational focus covering human patients as well as animal models. Concluding the current knowledge on chronobiology and bipolar disorder, implications for specificity and the course of bipolar disorder and treatment options are given at the end of this article. Taken together, circadian rhythm disruption and bipolar disorder are strongly correlated; the exact causation, however, is still unclear.

Distinguishing between bipolar depression and unipolar depression based on the reward circuit activities and clinical characteristics: A machine learning analysis

BACKGROUND

Differentiating bipolar depression (BD) from unipolar depression (UD) is a major clinical challenge. Identifying the potential classifying biomarkers between these two diseases is vital to optimize personalized management of depressed individuals.

METHODS

Here, we aimed to integrate neuroimaging and clinical data with machine learning method to classify BD and UD at the individual level. Data were collected from 31 healthy controls (HC group) and 80 depressive patients with an average follow-up period of 7.51 years. Of these patients, 32 got diagnosis conversion from major depressive disorder (MDD) to BD (BD group) and 48 remain persistent diagnosis of MDD (MDD group). Using graph theory and functional connectivity (FC) analysis, we investigated the differences in reward circuit properties among three groups. Then we applied a support vector machine and leave-one-out cross-validation methods to classify BD and UD patients based on neuroimaging and clinical data.

RESULTS

Compared with MDD and HC, BD showed decreased degree centrality of right mediodorsal thalamus (MD) and nodal efficiency (NE) of left ventral pallidum. Compared with BD and HC, MDD showed decreased NE of right MD and increased FC between right MD and bilateral dorsolateral prefrontal cortex and left ventromedial prefrontal cortex. Notably, the classifier obtained high classification accuracies (87.50 %) distinguishing BD and UD patients based on reward circuit properties and clinical features.

LIMITATIONS

The classifying model requires out-of-sample replication analysis.

CONCLUSION

The reward circuit dysfunction can not only provide additional information to assist clinical differential diagnosis, but also in turn informed treatment decision of depressive patients.

[Comorbidity patterns and structure of depressive episodes in patients with bipolar disorder and major depressive disorder]

OBJECTIVE

To conduct an exploratory analysis of comorbidity patterns and the structure of depressive episodes among Russian patients with bipolar disorder (BD) and major depressive disorder (MDD).

MATERIAL AND METHODS

This multicenter cross-sectional study included 178 patients with mood disorders, of which 78.1% (n=139) were women. The diagnosis of BD was made in 68.0% (n=121) patients, of them 37.1% (n=66) were diagnosed with BD type I. All study participants underwent a structured Mini International Neuropsychiatric Interview to verify the clinical diagnosis and identify concomitant mental disorders, and also filled out an electronic case report form. Statistical analysis was performed in RStudio v. 1.4.1717 using the standard R package and the «psych» package.

RESULTS

According to the results of stepwise regression, comorbid diagnoses of panic disorder (OR=5.3; 95% CI 1.9-19.1) and eating disorders (OR=7.7; 95% CI 2.8-27.4) were more associated with BD. In addition, depressive episodes in BD were more associated with symptoms of hypersomnia (OR=2.5; 95% CI 1.2-5.3) and psychomotor retardation (OR=3.2; 95% CI 1.5-7.6). Symptoms such as increased appetite (47.1% (n=57) vs 26.3% (n=15); p=0.009), ideas of guilt (92.6% (n=112) vs 7.2% (n=44); p=0.006) and thoughts of self-harm or death (70.2% (n=85) vs 45.6% (n=25); p=0.003) were also nominally more common in depressive episodes within the BD compared to MDD.

CONCLUSIONS

Mood disorders such as BD and MDD have significant differences in the patterns of comorbidity and the structure of depressive episodes, which is important to consider when conducting differential diagnosis of these disorders. The results also indicate the need for a comprehensive diagnostic interview with patients with mood disorders to assess the presence of comorbid mental disorders during life and the structure of depressive episodes throughout the clinical course from the moment of onset.

Neurobiological and behavioral mechanisms of circadian rhythm disruption in bipolar disorder: A critical multi-disciplinary literature review and agenda for future research from the ISBD task force on chronobiology

AIM

Symptoms of bipolar disorder (BD) include changes in mood, activity, energy, sleep, and appetite. Since many of these processes are regulated by circadian function, circadian rhythm disturbance has been examined as a biological feature underlying BD. The International Society for Bipolar Disorders Chronobiology Task Force (CTF) was commissioned to review evidence for neurobiological and behavioral mechanisms pertinent to BD.

METHOD

Drawing upon expertise in animal models, biomarkers, physiology, and behavior, CTF analyzed the relevant cross-disciplinary literature to precisely frame the discussion around circadian rhythm disruption in BD, highlight key findings, and for the first time integrate findings across levels of analysis to develop an internally consistent, coherent theoretical framework.

RESULTS

Evidence from multiple sources implicates the circadian system in mood regulation, with corresponding associations with BD diagnoses and mood-related traits reported across genetic, cellular, physiological, and behavioral domains. However, circadian disruption does not appear to be specific to BD and is present across a variety of high-risk, prodromal, and syndromic psychiatric disorders. Substantial variability and ambiguity among the definitions, concepts and assumptions underlying the research have limited replication and the emergence of consensus findings.

CONCLUSIONS

Future research in circadian rhythms and its role in BD is warranted. Well-powered studies that carefully define associations between BD-related and chronobiologically-related constructs, and integrate across levels of analysis will be most illuminating.

A systematic review and meta-analysis of sleep and circadian rhythms disturbances in individuals at high-risk of developing or with early onset of bipolar disorders

Sleep and circadian rhythms disturbances (SCRD) in young people at high risk or with early onset of bipolar disorders (BD) are poorly understood. We systematically searched for studies of self, observer or objective estimates of SCRD in asymptomatic or symptomatic offspring of parents with BD (OSBD), individuals with presentations meeting recognized BD-at-risk criteria (BAR) and youth with recent onset of full-threshold BD (FT-BD). Of 76 studies eligible for systematic review, 35 (46%) were included in random effects meta-analyses. Pooled analyses of self-ratings related to circadian rhythms demonstrated greater preference for eveningness and more dysregulation of social rhythms in BAR and FT-BD groups; analyses of actigraphy provided some support for these findings. Meta-analysis of prospective studies showed that pre-existing SCRD were associated with a 40% increased risk of onset of BD, but heterogeneity in assessments was a significant concern. Overall, we identified longer total sleep time (Hedges g: 0.34; 95% confidence intervals:.1,.57), especially in OSBD and FT-BD and meta-regression analysis indicated the effect sizes was moderated by the proportion of any sample manifesting psychopathology or receiving psychotropic medications. This evolving field of research would benefit from greater attention to circadian rhythm as well as sleep quality measures.

The German research consortium for the study of bipolar disorder (BipoLife): a magnetic resonance imaging study protocol

Background

Bipolar disorder is one of the most severe mental disorders. Its chronic course is associated with high rates of morbidity and mortality, a high risk of suicide and poor social and occupational outcomes. Despite the great advances over the last decades in understanding mental disorders, the mechanisms underlying bipolar disorder at the neural network level still remain elusive. This has severe consequences for clinical practice, for instance by inadequate diagnoses or delayed treatments. The German research consortium BipoLife aims to shed light on the mechanisms underlying bipolar disorders. It was established in 2015 and incorporates ten university hospitals across Germany. Its research projects focus in particular on individuals at high risk of bipolar disorder, young patients in the early stages of the disease and patients with an unstable highly relapsing course and/or with acute suicidal ideation.

Methods

Functional and structural magnetic resonance imaging (MRI) data was acquired across nine sites within three different studies. Obtaining neuroimaging data in a multicenter setting requires among others the harmonization of the acquisition protocol, the standardization of paradigms and the implementation of regular quality control procedures. The present article outlines the MRI imaging protocols, the acquisition parameters, the imaging paradigms, the neuroimaging quality assessment procedures and the number of recruited subjects.

Discussion

The careful implementation of a MRI study protocol as well as the adherence to well-defined quality assessment procedures is one key benchmark in the evaluation of the overall quality of large-scale multicenter imaging studies. This article contributes to the BipoLife project by outlining the rationale and the design of the MRI study protocol. It helps to set the necessary standards for follow-up analyses and provides the technical details for an in-depth understanding of follow-up publications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00240-6.

Predictors of conversion from major depressive disorder to bipolar disorder

The present study has two main aims: (1) To assess whether childhood trauma helps to differentiate Major Depressive Disorder (MDD) from Bipolar Disorder (BD) in a cross-sectional design; and (2) Describe the rate of conversion from MDD to BD, as well as the clinical and demographic predictors of conversion from MDD to BD in a prospective cohort design. We conducted a prospective cohort study in two phases, in the city of Pelotas, RS, Brazil. In the first phase, 565 subjects diagnosed with MDD, and 127 with BD according to the Mini International Neuropsychiatric Interview were included. In the second phase, only individuals with MDD were reevaluated for potential conversion to BD. The rate of conversion from MDD to BD in 3 years was 12.4%. Predictors of conversion from MDD to BD included lower educational level, use of illicit substances, younger age of the first depressive episode, and family history of BD. Childhood trauma was not a significant risk factor for conversion to BD in our prospective study. Our findings can contribute to the prevention and identification of conversion from MDD to BD, as well as to the establishment of more targeted therapeutic interventions, improving the prognosis of these individuals.

Sleep disturbances and first onset of major mental disorders in adolescence and early adulthood: A systematic review and meta-analysis

Despite several high-quality reviews of insomnia and incidence of mental disorders, prospective longitudinal relationships between a wider range of sleep disturbances and first onset of a depressive, bipolar, or psychotic disorders during the peak age range for onset of these conditions has not been addressed. Database searches were undertaken to identify publications on insomnia, but also on other sleep problems such as hypersomnia, short sleep duration, self-identified and/or generic 'sleep problems' and circadian sleep-wake cycle dysrhythmias. We discovered 36 studies that were eligible for systematic review and from these publications, we identified 25 unique datasets that were suitable for meta-analysis (Number>45,000; age ∼17). Individuals with a history of any type of sleep disturbance (however defined) had an increased odds of developing a mood or psychotic disorder in adolescence or early adulthood (Odds ratio [OR]:1.88; 95% Confidence Intervals:1.67, 2.25) with similar odds for onset of bipolar disorders (OR:1.72) or depressive disorders (OR:1.62). The magnitude of associations differed according to type of exposure and was greatest for sleep disturbances that met established diagnostic criteria for a sleep disorder (OR: 2.53). However, studies that examined observer or self-rated symptoms, also reported a significant association between hypersomnia symptoms and the onset of a major mental disorder (OR:1.39). Overall study quality was moderate with evidence of publication bias and meta-regression identified confounders such as year of publication. We conclude that evidence indicates that subjective, observer and objective studies demonstrate a modest but significant increase in the likelihood of first onset of mood and psychotic disorders in adolescence and early adulthood in individuals with broadly defined sleep disturbances. Although findings support proposals for interventions for sleep problems in youth, we suggest a need for greater consensus on screening strategies and for more longitudinal, prospective studies of circadian sleep-wake cycle dysrhythmias in youth.

Early identification of bipolar disorder among young adults - a 22-year community birth cohort

OBJECTIVE

We set forth to build a prediction model of individuals who would develop bipolar disorder (BD) using machine learning techniques in a large birth cohort.

METHODS

A total of 3748 subjects were studied at birth, 11, 15, 18, and 22 years of age in a community birth cohort. We used the elastic net algorithm with 10-fold cross-validation to predict which individuals would develop BD at endpoint (22 years) at each follow-up visit before diagnosis (from birth up to 18 years). Afterward, we used the best model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them.

RESULTS

A total of 107 (2.8%) individuals within the cohort presented with BD type I, 26 (0.6%) with BD type II, and 87 (2.3%) with BD not otherwise specified. Frequency of female individuals was 58.82% (n = 150) in the BD sample and 53.02% (n = 1868) among the unaffected population. The model with variables assessed at the 18-year follow-up visit achieved the best performance: AUC 0.82 (CI 0.75-0.88), balanced accuracy 0.75, sensitivity 0.72, and specificity 0.77. The most important variables to detect BD at the 18-year follow-up visit were suicide risk, generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms.

CONCLUSIONS

We developed a risk calculator for BD incorporating both demographic and clinical variables from a 22-year birth cohort. Our findings support previous studies in high-risk samples showing the significance of suicide risk and generalized anxiety disorder prior to the onset of BD, and highlight the role of social factors and adverse life events.

Rate of diagnostic conversion to bipolar disorder in adults with unipolar depression and psychopharmacological treatment in the republic of Korea: A nationwide register-based study

BACKGROUND

Patients with bipolar disorder (BD) and depressive episodes are usually diagnosed with major depressive disorder (MDD). It is clinically important to estimate diagnostic conversion rate from MDD to BD and identify factors characterizing MDD patients at highest risk. We investigated conversion rate from depression to BD, stratified by age and sex, and associated psychopharmacological treatment patterns.

METHODS

Data were extracted from the National Health Insurance to include 817,759 patients 18 years or older with new-onset antidepressant-treated depression between 2011 and 2015. We estimated rate of conversion from unipolar depression to BD stratified by age and sex during follow-up (357,343 person-years) and investigated the related medication patterns.

RESULTS

During follow-up (median, 155 days; range, 2-1,442 days), 19,179 patients had diagnostic conversion to BD. Conversion rate was highest for patients aged 18-29 years; it gradually decreased with age until 60-69 years. Overall conversion rate to BD was 58.48 per 1,000 person-years for males and 50.97 per 1,000 person-years for females. For patients aged 18-39 years, the rate was higher for females. Patients with conversion to BD were prescribed more antidepressants. When the diagnosis changed to BD, 51% of patients were prescribed additional antipsychotics and/or mood stabilizers.

LIMITATIONS

The limited follow-up period may have underestimated conversion rate to BD. Data were restricted to participants with pharmaceutically treated depression.

CONCLUSIONS

The rate of diagnostic conversion from depression to BD differed by sex and age and was highest for females aged 18-29 years. Diagnostic conversion was accompanied by relevant prescription changes.

Is suicide risk a predictor of diagnosis conversion to bipolar disorder?

To assess predictors of the diagnosis of bipolar disorder is important since it is known that the early diagnosis is associated with a better response to the treatment. Thus, the aim of this systematic review is to assess the role of the suicide risk in the diagnosis conversion to bipolar disorder. We searched Pubmed, Bireme, Scopus, and PsycINFO with no year restriction for articles containing the words (suicidal or suicide or suicide risk or suicide attempt) and (conversion or switch) and (bipolar disorder or mania or hypomania or bipolar disorders). The review included four studies, with only one confirming that subjects who converted to bipolar disorder had higher rates of suicide risk than subjects who did not convert to bipolar disorder. The main limitation of this review is that few longitudinal studies assessed the predictors of conversion to bipolar disorders. In conclusion, suicide risk appears to be a predictor of bipolar disorder; nevertheless, more studies are needed to confirm this association.

At-risk symptoms of bipolar disorder in a university student cohort

To assess the occurrence and frequency of bipolar at-risk symptoms in a large sample of previously undiagnosed students using the new screening tool Bochumer Screeningbogen Bipolar (BSB). 2329 students of the Ruhr-University Bochum, Germany completed online demographic data as well as various self-rating questionnaires (BSB; Hypomania Checklist 32; Altman Self-Rating Mania Scale; Beck Depression Inventory). Within the student cohort (64.4% female, mean age 24.3 years) every fifth student currently suffered from moderate to severe depressive symptoms; every sixth student had already thought about suicide and every other student reported a history of mood swings. The most frequently reported depressive symptoms included physical exhaustion, depressed mood, and tiredness. The most frequently reported (hypo)manic symptoms included physical agitation, feeling extremely energetic, and lack of concentration. The BSB showed good convergent validity with other established questionnaires capturing depressive or (hypo)manic symptoms, as well as a stable administration of underlying constructs. The BSB correlated significantly with the already established applied questionnaires. The predictive power of the BSB regarding the development of bipolar disorder cannot be correctly quantified at present. The further purpose of this exploratory web-based study should be to examine the validity of the presented measures in a longitudinal design.

Clinical predictors of conversion to bipolar disorder in a prospective longitudinal familial high-risk sample: focus on depressive features

BACKGROUND

Identifying clinical features that predict conversion to bipolar disorder (BD) in those at high familial risk (HR) would assist in identifying a more focused population for early intervention.

METHOD

In total 287 participants aged 12-30 (163 HR with a first-degree relative with BD and 124 controls (CONs)) were followed annually for a median of 5 years. We used the baseline presence of DSM-IV depressive, anxiety, behavioural and substance use disorders, as well as a constellation of specific depressive symptoms (as identified by the Probabilistic Approach to Bipolar Depression) to predict the subsequent development of hypo/manic episodes.

RESULTS

At baseline, HR participants were significantly more likely to report ⩾4 Probabilistic features (40.4%) when depressed than CONs (6.7%; p < .05). Nineteen HR subjects later developed either threshold (n = 8; 4.9%) or subthreshold (n = 11; 6.7%) hypo/mania. The presence of ⩾4 Probabilistic features was associated with a seven-fold increase in the risk of 'conversion' to threshold BD (hazard ratio = 6.9, p < .05) above and beyond the fourteen-fold increase in risk related to major depressive episodes (MDEs) per se (hazard ratio = 13.9, p < .05). Individual depressive features predicting conversion were psychomotor retardation and ⩾5 MDEs. Behavioural disorders only predicted conversion to subthreshold BD (hazard ratio = 5.23, p < .01), while anxiety and substance disorders did not predict either threshold or subthreshold hypo/mania.

CONCLUSIONS

This study suggests that specific depressive characteristics substantially increase the risk of young people at familial risk of BD going on to develop future hypo/manic episodes and may identify a more targeted HR population for the development of early intervention programs.

PTSD symptomics: network analyses in the field of psychotraumatology

Recent years have seen increasing attention on posttraumatic stress disorder (PTSD) research. While research has largely focused on the dichotomy between patients diagnosed with mental disorders and healthy controls - in other words, investigations at the level of diagnoses - recent work has focused on psychopathology symptoms. Symptomics research in the area of PTSD has been scarce so far, although several studies have focused on investigating the network structures of PTSD symptoms. The present special issue of EJPT adds to the literature by curating additional PTSD network studies, each looking at a different aspect of PTSD. We hope that this special issue encourages researchers to conceptualize and model PTSD data from a network perspective, which arguably has the potential to inform and improve the efficacy of therapeutic interventions.

Longitudinal changes in the antecedent and early manifest course of bipolar disorder-A narrative review of prospective studies

OBJECTIVE

Prospective study designs ideally allow patients to be followed from the first manifestations of the illness or even from an at-risk stage. It can thus provide data on the predictive value of changes in clinical symptomatology, cognition or further biological markers to broaden our understanding of the etiopathology and symptomatic trajectory of bipolar disorders. The scope of this narrative review is to summarize evidence from prospectively collected data on psychopathological and other clinical and biological changes in the early developmental course of bipolar disorders.

METHODS

The narrative review was based on a literature search conducted in February 2016 within the PubMed library for prospective study data of persons in antecedent and early manifest stages of manifest bipolar disorder published within the last 15 years.

RESULTS

A total of 19 prospective studies were included. Regarding psychopathological features; personality, temperament and character traits as well as changes in sleep and circadian rhythm, the evidence suggests that risk factors for the development of bipolar disorder can already be described and should be studied further to understand their interaction, mediation with other factors and timing in the developmental process of bipolar disorder. Apart from the positive family history, childhood anxiety, sleep problems, subthreshold (hypo)manic symptoms and certain character traits/emotionality should be identified and monitored already in clinical practice as their presence likely increases risk of bipolar disorder. Up to date no substantiated evidence was found from prospective studies addressing cognitive features, life events, immunological parameters and morphological central nervous system changes as potential risk factors for bipolar disorder.

CONCLUSION

For an improved understanding of episodic disorders, longitudinal data collection is essential. Since the etiology of bipolar disorders is complex, a number of potential risk factors have been proposed. Prospective studies addressing this spectrum and resilience factors are critical and will be best conducted within multi-site research networks or initiatives.

A systematic review and meta-analysis of prospective transition from major depression to bipolar disorder

OBJECTIVE

Some people with major depressive disorder (MDD) may be at a pre-onset stage for bipolar disorder (BD), where early identification or prevention efforts may be feasible. We aimed to identify rates and characteristics predictive of transition to BD in prospective follow-up studies of people with MDD.

METHODS

Using a systematic search strategy, we identified studies with a diagnostic ascertainment of MDD and BD of an adequate standard, and where the minimum length of follow-up was 6 months. We examined the incidence and point prevalence of BD and the pooled odds ratios (OR) for baseline predictors.

RESULTS

From 5554 unique publications, 56 were included. Nearly a quarter of adults (22.5%) and adolescents with MDD followed up for a mean length of 12-18 years developed BD, with the greatest risk of transition being in the first 5 years. The meta-analysis identified that transition from MDD to BD was predicted by family history of BD (OR = 2.89, 95% CI: 2.01-4.14, N = 7), earlier age of onset of depression (g = -0.33, SE = 0.05, N = 6) and presence of psychotic symptoms (OR = 4.76, 95% CI: 1.79-12.66, N = 5).

CONCLUSIONS

Participants with the identified risk factors merit closer observation and may benefit from prevention efforts, especially if outcomes broader than BD are considered.

Incidence, characteristics and course of narrow phenotype paediatric bipolar I disorder in the British Isles

OBJECTIVE

To estimate the surveillance incidence of first-time diagnosis of narrow phenotype bipolar I disorder (NPBDI) in young people under 16 years by consultants in child and adolescent psychiatry (CCAP) in the British Isles and describe symptoms, comorbidity, associated factors, management strategies and clinical outcomes at 1-year follow-up.

METHOD

Active prospective surveillance epidemiology was utilised to ask 730 CCAP to report cases of NPBDI using the child and adolescent psychiatry surveillance system.

RESULTS

Of the 151 cases of NPBDI reported, 33 (age range 10-15.11 years) met the DSM-IV analytical case definition with 60% having had previously undiagnosed mood episodes. The minimum 12-month incidence of NPBDI in the British Isles was 0.59/100 000 (95% CI 0.41-0.84). Irritability was reported in 72% cases and comorbid conditions in 51.5% cases with 48.5% cases requiring admission to hospital. Relapses occurred in 56.67% cases during the 1-year follow-up.

CONCLUSIONS

These rates suggest that the first-time diagnosis of NPBDI in young people <16 years of age by CCAP in the British Isles is infrequent; however, the rates of relapse and admission to hospital warrant close monitoring.

Aims and structure of the German Research Consortium BipoLife for the study of bipolar disorder

Background

Bipolar disorder is a severe and heterogeneous mental disorder. Despite great advances in neuroscience over the past decades, the precise causative mechanisms at the transmitter, cellular or network level have so far not been unraveled. As a result, individual treatment decisions cannot be tailor-made and the uncertain prognosis is based on clinical characteristics alone. Although a subpopulation of patients have an excellent response to pharmacological monotherapy, other subpopulations have been less well served by the medical system and therefore require more focused attention. In particular individuals at high risk of bipolar disorder, young patients in the early stages of bipolar disorder, patients with an unstable highly relapsing course and patients with acute suicidal ideation have been identified as those in need.

Structure

A research consortium of ten universities across Germany has therefore implemented a 4 year research agenda including three randomized controlled trials, one epidemiological trial and one cross-sectional trial to address these areas of unmet needs. The topics under investigation will be the improvement of early recognition, specific psychotherapy, and smartphones as an aid for early episode detection and biomarkers of lithium response. A subset of patients will be investigated utilizing neuroimaging (fMRI), neurophysiology (EEG), and biomaterials (genomics, transcriptomics).

Conclusions

This article aims to outline the rationale, design, and methods of these individual studies.

Bidirectional association between obstructive sleep apnea and depression: A population-based longitudinal study

An association may exist between obstructive sleep apnea (OSA) and depression. However, results regarding this association are inconsistent, and the direction of the association between OSA and depression remains unknown. Therefore, we used the Taiwan National Health Insurance Research Database to investigate the bidirectional association between OSA and depression.A total of 6427 OSA patients and 32,135 age and sex-matched control subjects were enrolled to analyze the risk of depression among patients with OSA, where 27,073 patients with depression and 135,365 control subjects were enrolled to address the risk of OSA among patients with depression. All subjects were followed to identify their outcomes of interest from January 1, 1997 to December 31, 2012.Cox proportional-hazards models, after adjusting for potential confounders, demonstrated that patients with OSA had an increased risk (adjusted hazard ratio 2.48, 95% confidence interval 2.20-2.79) of developing depression, whereas those with depression were associated with an increased risk of future OSA (adjusted hazard ratio 2.30, 95% confidence interval 2.11-2.50).Our results suggested that a strong bidirectional relationship exists between OSA and depression, with each disease influencing the development of the other. Health providers are recommended to ensure the early detection and management of depression among patients with OSA and vice versa.