A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population

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.

Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers

BACKGROUND AND OBJECTIVE

Caused by shared genetic risk factors and similar neuropsychological symptoms, bipolar disorder (BD) and major depressive disorder (MDD) are at high risk of misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. We aimed to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients about to be admitted to a hospital and, hence, reduce the misdiagnosis of BD as MDD on admission. In addition, we examined to what extent our ML model could be made interpretable by quantifying and visualizing the features that drive the predictions.

METHODS

By identifying 16,311 patients admitted to a hospital located in western China between 2009 and 2018 with a recorded main diagnosis of MDD or BD, we established three sub-cohorts with different combinations of features for both the MDD-BD cohort and the MDD-BD depressive episodes cohort, respectively. Four different ML algorithms (logistic regression, extreme gradient boosting (XGBoost), random forest, and support vector machine) and four train-test splits were used to train and validate diagnostic models, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level, including feature importance, feature interaction, and feature effect on prediction decision for a specific subject.

RESULTS

The XGBoost algorithm provided the best test performance (AUC: 0.838 (0.810-0.867), PPV: 0.810 and NPV: 0.834) for separating patients with BD from those with MDD. Core predictors included symptoms (mood-up, exciting, bad sleep, loss of interest, talking, mood-down, provoke), along with age, job, myocardial enzyme markers (creatine kinase, hydroxybutyrate dehydrogenase), diabetes-associated marker (glucose), bone function marker (alkaline phosphatase), non-enzymatic antioxidant (uric acid), markers of immune/inflammation (white blood cell count, lymphocyte count, basophil percentage, monocyte count), cardiovascular function marker (low density lipoprotein), renal marker (total protein), liver biochemistry marker (indirect bilirubin), and vital signs like pulse. For separating patients with BD depressive episodes from those with MDD, the test AUC was 0.777 (0.732-0.822), with PPV 0.576 and NPV 0.899. Additional validation in models built with self-reported symptoms removed from the feature set, showed test AUC of 0.701 (0.666-0.736) for differentiating BD and MDD, and AUC of 0.564 (0.515-0.614) for detecting patients in BD depressive episodes from MDD patients. Validation in the datasets without removing the patients with comorbidity showed an AUC of 0.826 (0.806-0.846).

CONCLUSION

The diagnostic system accurately identified patients with BD in various clinical scenarios, and differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them.

Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers

Background

Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents.

Materials and methods

A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software.

Results

In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future.

Conclusion

This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.

Affective lability as a prospective predictor of subsequent bipolar disorder diagnosis: a systematic review

Objectives

The early pathogenesis and precursors of Bipolar Disorder (BD) are poorly understood. There is some cross-sectional and retrospective evidence of affective lability as a predictor of BD, but this is subject to recall biases. The present review synthesises the prospective evidence examining affective lability and the subsequent development of BD at follow-up.

Methods

The authors performed a systematic search of PubMed, PsycInfo and Embase (1960–June 2020) and conducted hand searches to identify studies assessing affective lability (according to a conceptually-inclusive definition) at baseline assessment in individuals without a BD diagnosis, and a longitudinal follow-up assessment of bipolar (spectrum) disorders. Results are reported according to the PRISMA guidelines, and the synthesis without meta-analysis (SWiM) reporting guidelines were used to strengthen the narrative synthesis. The Newcastle–Ottawa Scale was used to assess risk of bias (ROB).

Results

11 articles describing 10 studies were included. Being identified as having affective lability at baseline was associated with an increased rate of bipolar diagnoses at follow-up; this association was statistically significant in six of eight studies assessing BD type I/II at follow-up and in all four studies assessing for bipolar spectrum disorder (BSD) criteria. Most studies received a ‘fair’ or ‘poor’ ROB grade.

Conclusions

Despite a paucity of studies, an overall association between prospectively-identified affective lability and a later diagnosis of BD or BSD is apparent with relative consistency between studies. This association and further longitudinal studies could inform future clinical screening of those who may be at risk of BD, with the potential to improve diagnostic accuracy and facilitate early intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00237-1.

Development and validation of a risk calculator for major mood disorders among the offspring of bipolar parents using information collected in routine clinical practice

BACKGROUND

Family history is a significant risk factor for bipolar disorders (BD), but the magnitude of risk varies considerably between individuals within and across families. Accurate risk estimation may increase motivation to reduce modifiable risk exposures and identify individuals appropriate for monitoring over the peak risk period. Our objective was to develop and independently replicate an individual risk calculator for bipolar spectrum disorders among the offspring of BD parents using data collected in routine clinical practice.

METHODS

Data from the longitudinal Canadian High-Risk Offspring cohort study collected from 1996 to 2020 informed the development of a 5 and 10-year risk calculator using parametric time-to-event models with a cure fraction and a generalized gamma distribution. The calculator was then externally validated using data from the Lausanne-Geneva High-Risk Offspring cohort study collected from 1996 to 2020. A time-varying C-index by age in years was used to estimate the probability that the model correctly classified risk. Bias corrected estimates and 95% confidence limits were derived using a jackknife resampling approach.

FINDINGS

The primary outcome was age of onset of a major mood disorder. The risk calculator was most accurate at classifying risk in mid to late adolescence in the Canadian cohort (n = 285), and a similar pattern was replicated in the Swiss cohort (n = 128). Specifically, the time-varying C-index indicated that there was approximately a 70% chance that the model would correctly predict which of two 15-year-olds would be more likely to develop the outcome in the future. External validation within a smaller Swiss cohort showed mixed results.

INTERPRETATION

Findings suggest that this model may be a useful clinical tool in routine practice for improved individualized risk estimation of bipolar spectrum disorders among the adolescent offspring of a BD parent; however, risk estimation in younger high-risk offspring is less accurate, perhaps reflecting the evolving nature of psychopathology in early childhood. Based on external validation with a Swiss cohort, the risk calculator may not be as predictive in more heterogenous high-risk populations.

FUNDING

The Canadian High-Risk Study has been funded by consecutive operating grants from the Canadian Institutes for Health Research, currently CIHR PJT Grant 152796 he Lausanne-Geneva high-risk study was and is supported by five grants from the Swiss National Foundation (#3200-040,677, #32003B-105,969, #32003B-118,326, #3200-049,746 and #3200-061,974), three grants from the Swiss National Foundation for the National Centres of Competence in Research project "The Synaptic Bases of Mental Diseases" (#125,759, #158,776, and #51NF40 - 185,897), and a grant from GlaxoSmithKline Clinical Genetics.

Discriminating bipolar depression from major depressive disorder with polygenic risk scores

BACKGROUND

Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis.

METHODS

Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status.

RESULTS

We found a strong association between a BD diagnosis and PRSs drawn from BD (R2 = 3.5%, p = 4.94 × 10-12) and schizophrenia (R2 = 3.2%, p = 5.71 × 10-11) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19-5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10-4).

CONCLUSIONS

Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.

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.

Risk Calculators in Bipolar Disorder: A Systematic Review

INTRODUCTION

Early recognition of bipolar disorder improves the prognosis and decreases the burden of the disease. However, there is a significant delay in diagnosis. Multiple risk factors for bipolar disorder have been identified and a population at high-risk for the disorder has been more precisely defined. These advances have allowed the development of risk calculators to predict individual risk of conversion to bipolar disorder. This review aims to identify the risk calculators for bipolar disorder and assess their clinical applicability.

METHODS

A systematic review of original studies on the development of risk calculators in bipolar disorder was performed. The studies' quality was evaluated with the Newcastle-Ottawa Quality Assessment Form for Cohort Studies and according to recommendations of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Initiative.

RESULTS

Three studies met the inclusion criteria; one developed a risk calculator of conversion from major depressive episode to bipolar disorder; one of conversion to new-onset bipolar spectrum disorders in offspring of parents with bipolar disorder; and the last one of conversion in youths with bipolar disorder not-otherwise-specified.

CONCLUSIONS

The calculators reviewed in this article present good discrimination power for bipolar disorder, although future replication and validation of the models is needed.

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The goal of this study was to utilize prospective survey data and ensemble machine learning to develop algorithms predicting adult onset internalizing disorders. The data were from Waves 1-2 of the National Epidemiological Survey on Alcohol and Related Conditions (n = 34,653). Outcomes were incident occurrence of DSM-IV generalized anxiety, panic, social phobia, depression, and mania between Waves 1-2. In total, 213 risk factors (features) were operationalized based on their presence/occurrence at the time of or before Wave 1. For each of the five internalizing disorder outcomes, super learning was used to generate a composite algorithm from several linear and non-linear classifiers (e.g., random forests, k-nearest neighbors). AUCs achieved by the cross-validated super learner ensembles were in the range of 0.76 (depression) to 0.83 (mania), and were higher than AUCs achieved by the individual algorithms. Individuals in the top 10% of super learner predicted risk accounted for 37.97% (depression) to 53.39% (social anxiety) of all incident cases. Thus, the algorithms achieved acceptable-to-excellent prediction accuracy with a high concentration of incident cases observed among individuals predicted to be highest risk. In parallel with the development of effective preventive interventions, further validation, expansion, and dissemination of algorithms predicting internalizing disorder onset/trajectory could be of great value.

Hypersomnia and Bipolar Disorder: A systematic review and meta-analysis of proportion

BACKGROUND

Hypersomnia is a common problem amongst individuals with Bipolar Disorder (BD). The objective of this meta-analysis is to estimate the frequency of hypersomnia in individuals with BD, and identify associated factors METHODS: Our search focused on articles documenting the frequency of hypersomnia among individuals with BD indexed in PubMed database and in the Cochrane Library, following the recommendations from the Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) Group. A meta-analysis of proportion was conducted; funnel plot and Egger's test were used for the assessment of publication bias. Subgroups analyses were performed in order to evaluate possible confounders and associated factors.

RESULTS

We identified 10 studies, which included 1824 patients with BD. The overall estimate of the proportion of BD cases that reported hypersomnia was 29.9% [95% confidence interval (CI): 25.8 - 34.1%, I² = 59.2%; p < .05]. The funnel plot and the Egger's test suggest a low risk of publication bias (p = .527). The polarity of mood state, Bipolar Disorder type, use of medication, age, diagnostic criteria and hypersomnia criteria were not significantly related to hypersomnia.

LIMITATIONS

There is a possibility that smaller cross-sectional studies were not included. The high heterogeneity between studies is frequent in meta-analysis of both interventional and observational studies. Hypersomnia was not the primary outcome in some of the included studies.

CONCLUSIONS

To our knowledge, this is the first systematic review and meta-analysis of hypersomnia prevalence in patients with BD. Further studies focused on clinical correlates and implications for health outcomes in BD are warranted.

Can P300 aid in the differential diagnosis of unipolar disorder versus bipolar disorder depression? A meta-analysis of comparative studies

BACKGROUND

It is difficult to distinguish between bipolar disorder (BD) and unipolar disorder (UD) depression. Given the different pattern of cognitive impairments between BD and UD, P300 is potentially useful for the differential diagnosis. This meta-analysis was performed to estimate the extent of difference in P300 in patients with BD versus UD depression.

METHODS

Studies comparing P300 between depressed BD and UD patients with or without healthy controls (HCs) were retrieved from major English and Chinese databases. Studies with BD and UD samples that were comparable in terms of age, gender, and depression severity, were rated as having high quality. Standardized mean differences (SMDs) of P300 latency and amplitude were calculated.

RESULTS

In total, eight studies with a total of 397 depressed BD patients, 390 depressed UD patients, and 497 HCs, were included. Among included studies, six were rated as having good quality and three followed BD (n = 146) and UD (n = 144) patients during remission. BD patients had significantly longer P300 latency than UD patients during major depressive episode [SMD (95%CI): 0.580 (0.309, 0.850)] and remission [SMD (95%CI): 1.583 (1.322, 1.844)]. Compared to HCs, remitted BD patients still had significantly longer P300 latency [SMD (95%CI): 0.857 (0.059, 1.656)] but P300 latency of remitted UD patients had decreased to normal [SMD (95%CI): 0.536 (-0.272, 1.343)].

LIMITATIONS

Sample sizes of depressed and remitted patients with BD and UD of included studies are small.

CONCLUSIONS

P300 latency can be used as an auxiliary diagnostic marker for differentiating BD from UD depression.

Differential clinical characteristics and possible predictors of bipolarity in a sample of unipolar and bipolar inpatients

Major affective conditions including both unipolar (UD) and bipolar disorders (BD) are associated with significant disability throughout the life course. We aimed to investigate the most relevant socio-demographic/clinical differences between UD and BD subjects. Our sample included 180 inpatients, of which 82 (45.5%) participants were diagnosed with UD and 98 (54.5%) with BD. Relative to UD patients, BD individuals were more likely to report prior psychoactive medications, lifetime psychotic symptoms, nicotine abuse, a reduced ability to provide to their needs, gambling behavior, and fewer nonsuicidal self-harm episodes. Moreover, BD patients were more likely to report severe side effects related to medications, a younger age at illness onset and first hospitalization, higher illness episodes, and longer illness duration in years than UD subjects. In a multivariate logistic analysis accounting for age, gender, and socio-demographic characteristics, a significant positive contribution to bipolarity was found only for higher lifetime psychotic symptoms (β = 1.178; p ≤ .05) and number of illness episodes (β = .155; p ≤ .05). The present findings suggest that specific clinical factors may be used in order to better distinguish between UD and BD subgroups. Further studies are required to replicate these findings in larger samples.

A Risk Calculator for Bipolar Disorder in Youth: Improving the Odds for Personalized Prevention and Early Intervention?

There have been many longitudinal studies examining biological and environmental risk factors for developing bipolar disorder in youth. Specifically, well-established risk factors for bipolar disorder in children and adolescents include having a family history of bipolar disorder, depression, disruptive behavior disorders, psychosis, antidepressant-induced manic symptoms, anxiety, and subsyndromal symptoms of mania and depression.¹ In an effort to identify individuals at highest risk for developing bipolar disorder, several investigators have attempted to characterize a bipolar prodrome. A recent meta-analysis of early manifestations of bipolar disorder in youth found that the most common prodromal symptoms were increased energy, diminished ability to think, indecision, pressured speech, talkativeness, elated mood, academic or work difficulties, insomnia, depressed mood, and increased goal-directed activities.² The authors concluded that despite many of the participants having symptoms prior to their illness onset, there was significant heterogeneity in symptom presentation, making it difficult to define a consistent bipolar prodrome. Although it is important to explore risk factors and rates of early symptoms of incipient bipolar disorder, to date, most studies have examined risk within an entire group rather than quantified an individual's risk of having bipolar disorder, which is essential to advance personalized monitoring and treatment strategies.

Topological Properties of Brain Structural Networks Represent Early Predictive Characteristics for the Occurrence of Bipolar Disorder in Patients With Major Depressive Disorder: A 7-Year Prospective Longitudinal Study

Bipolar disorder (BD) and major depressive disorder (MDD) are associated with different brain functional and structural abnormalities, but BD is hard to distinguish from MDD until the first manic or hypomanic episode. The aim of this study was to examine whether the topological properties of the brain structural network could be used to differentiate BD from MDD patients before their first manic/hypomanic episode. Diffusion tensor images were collected from 80 MDD patients and 53 healthy controls (HCs); 78 patients completed the follow-up study lasting 7 years. Among them, 12 patients were converted to BD and 64 patients remained MDD. Topological properties of the brain structural networks at baseline were compared among patients who converted to BD, patients who did not develop BD, and HCs. Patients who converted to BD displayed reduced nodal local efficiency in the left inferior frontal gyrus(IFG) compared with HCs and patients who did not convert to BD. There was no significant difference in the nodal global efficiency among the three groups. The findings suggest that the nodal local efficiency in the left IFG could serve as a potential biomarker to predict the conversion of MDD to BD before the occurrence of the first manic or hypomanic episode.

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.

[Differences in Subjective Experience Between Unipolar and Bipolar Depression]

INTRODUCTION

It is important to make distinction between bipolar and unipolar depression because treatment and prognosis are different. Since the diagnosis of the two conditions is purely clinical, find symptomatic differences is useful.

OBJECTIVES

Find differences in subjective experience (first person) between unipolar and bipolar depression.

METHODS

Phenomenological-oriented qualitative exploratory study of 12 patients (7 with bipolar depression and 5 with unipolar depression, 3 men and 9 women). We used a semi-structured interview based on Examination of Anomalous Self-Experience (EASE).

RESULTS

The predominant mood in bipolar depression is emotional dampening, in unipolar is sadness. The bodily experience in bipolar is of a heavy, tired body; an element that inserts between the desires of acting and performing actions and becomes an obstacle to the movement. In unipolar is of a body that feels more comfortable with the stillness than activity, like laziness of everyday life. Cognition and the stream of consciousness: in bipolar depression, compared with unipolar, thinking is slower, as if to overcome obstacles in their course. There are more difficult to understand what is heard or read. Future perspective: in bipolar depression, hopelessness is stronger and broader than in unipolar, as if the very possibility of hope was lost.

CONCLUSIONS

Qualitative differences in predominant mood, bodily experience, cognition and future perspective were found between bipolar and unipolar depression.

Clinical assessment of bipolar depression: validity, factor structure and psychometric properties of the Korean version of the Bipolar Depression Rating Scale (BDRS)

BACKGROUND

The Bipolar Depression Rating Scale (BDRS) is a scale for assessment of the clinical characteristics of bipolar depression. The primary aims of this study were to describe the development of the Korean version of the BDRS (K-BDRS) and to establish more firmly its psychometric properties in terms of reliability and validity.

METHODS

The study included 141 patients (62 male and 79 female) who had been diagnosed with bipolar disorder, were currently experiencing symptoms of depression, and were interviewed using the K-BDRS. Other measures included the Montgomery and Asberg Depression Scale (MADRS), the 17-item Hamilton Depression Scale (HAMD), and the Young Mania Rating Scale (YMRS). Additionally, the internal consistency, concurrent validity, inter-rater reliability, and test-retest reliability of the K-BDRS were evaluated.

RESULTS

The Cronbach's α-coefficient for the K-BDRS was 0.866, the K-BDRS exhibited strong correlations with the HAMD (r = 0.788) and MADRS (r = 0.877), and the mixed symptoms score of the K-BDRS was significantly correlated with the YMRS (r = 0.611). An exploratory factor analysis revealed three factors that corresponded to psychological depressive symptoms, somatic depressive symptoms, and mixed symptoms.

CONCLUSIONS

The present findings suggest that the K-BDRS has good psychometric properties and is a valid and reliable tool for assessing depressive symptoms in patients with bipolar disorder.

Affective network and default mode network in depressive adolescents with disruptive behaviors

AIM

Disruptive behaviors are thought to affect the progress of major depressive disorder (MDD) in adolescents. In resting-state functional connectivity (RSFC) studies of MDD, the affective network (limbic network) and the default mode network (DMN) have garnered a great deal of interest. We aimed to investigate RSFC in a sample of treatment-naïve adolescents with MDD and disruptive behaviors.

METHODS

Twenty-two adolescents with MDD and disruptive behaviors (disrup-MDD) and 20 age- and sex-matched healthy control (HC) participants underwent resting-state functional magnetic resonance imaging (fMRI). We used a seed-based correlation approach concerning two brain circuits including the affective network and the DMN, with two seed regions including the bilateral amygdala for the limbic network and the bilateral posterior cingulate cortex (PCC) for the DMN. We also observed a correlation between RSFC and severity of depressive symptoms and disruptive behaviors.

RESULTS

The disrup-MDD participants showed lower RSFC from the amygdala to the orbitofrontal cortex and parahippocampal gyrus compared to HC participants. Depression scores in disrup-MDD participants were negatively correlated with RSFC from the amygdala to the right orbitofrontal cortex. The disrup-MDD participants had higher PCC RSFC compared to HC participants in a cluster that included the left precentral gyrus, left insula, and left parietal lobe. Disruptive behavior scores in disrup-MDD patients were positively correlated with RSFC from the PCC to the left insular cortex.

CONCLUSION

Depressive mood might be correlated with the affective network, and disruptive behavior might be correlated with the DMN in adolescent depression.

Time to lack of persistence with pharmacological treatment among patients with current depressive episodes: a natural study with 1-year follow-up

INTRODUCTION

Medication nonadherence remains a big challenge for depressive patients. This study aims to assess and compare the medication persistence between unipolar depression (UD) and bipolar depression (BD).

METHODS

A total of 146 UD and 187 BD patients were recruited at their first index prescription. Time to lack of persistence with pharmacological treatment (defined as a gap of at least 60 days without taking any medication) was calculated, and clinical characteristics were collected. Final diagnosis was made at the end of 1-year follow-up.

RESULTS

A total of 101 (69.2%) UD and 126 (67.4%) BD patients discontinued the treatment, with a median duration of 36 days and 27 days, respectively. No significant difference was found between UD and BD in terms of time to lack of persistence with pharmacological treatment. The highest discontinuation rate (>40%) occurred in the first 3 months for both groups of patients. For UD patients, those with a higher risk of suicide (odds ratio [OR] =0.696, P=0.035) or comorbidity of any anxiety disorder (OR =0.159, P<0.001) were less likely to prematurely drop out (drop out within the first 3 months), while those with onset in the summer (OR =4.702, P=0.049) or autumn (OR =7.690, P=0.012) were more likely to prematurely drop out than those with onset in the spring (OR =0.159, P<0.001). For BD patients, being female (OR =2.250, P=0.012) and having a history of spontaneous remission or switch to hypomania (OR =2.470, P=0.004) were risk factors for premature drop out, while hospitalization (OR =0.304, P=0.023) and misdiagnosis as UD (OR =0.283, P<0.001) at the first index prescription were protective factors.

LIMITATION

Conservative definition of nonadherence, low representativeness of sample.

CONCLUSION

Treatment discontinuation was frequently seen in patients with UD or BD, especially in the first 3 months of treatment. In spite of the similar pattern of medication persistence, UD and BD differ from each other in predictors of premature drop out.

Instruments that prospectively predict bipolar disorder - A systematic review

BACKGROUND

Identification of earlier stages of Bipolar Disorder (BD), even prior to the first manic episode, may help develop interventions to prevent or delay the onset of BD. However, reliable and valid instruments are necessary to ascertain such earlier stages of BD. The aim of the current review was to identify instruments that had predictive validity and utility for BD for use in early intervention (EI) settings for the prevention of BD.

METHODS

We undertook a systematic examination of studies that examined participants without BD I or II at baseline and prospectively explored the predictive abilities of instruments for BD onset over a period of 6 months or more. The instruments and the studies were rated with respect to their relative validity and utility predicting onset of BD for prevention or early intervention. Odds ratios and area under the curve (AUC) values were derived when not reported.

RESULTS

Six studies were included, identifying five instruments that examined sub-threshold symptoms, family history, temperament and behavioral regulation. Though none of the identified instruments had been examined in high-quality replicated studies for predicting BD, two instruments, namely the Child Behavioral Checklist - Pediatric BD phenotype (CBCL-PBD) and the General Behavioral Inventory - Revised (GBI-R), had greater levels of validity and utility.

LIMITATION

Non-inclusion of studies and instruments that incidentally identified BD on follow-up limited the breadth of the review.

CONCLUSION

Instruments that test domains such as subthreshold symptoms, behavioral regulation, family history, and temperament hold promise in predicting BD onset.

A diagnosis of bipolar spectrum disorder predicts diagnostic conversion from unipolar depression to bipolar disorder: a 5-year retrospective study

BACKGROUND

The major aims of this study were to identify factors that may predict the diagnostic conversion from major depressive disorder (MDD) to bipolar disorder (BP) and to evaluate the predictive performance of the bipolar spectrum disorder (BPSD) diagnostic criteria.

METHODS

The medical records of 250 patients with a diagnosis of MDD for at least 5 years were retrospectively reviewed for this study.

RESULTS

The diagnostic conversion from MDD to BP was observed in 18.4% of 250 MDD patients, and the diagnostic criteria for BPSD predicted this conversion with high sensitivity (0.870) and specificity (0.917). A family history of BP, antidepressant-induced mania/hypomania, brief major depressive episodes, early age of onset, antidepressant wear-off, and antidepressant resistance were also independent predictors of this conversion.

LIMITATIONS

This study was conducted using a retrospective design and did not include structured diagnostic interviews.

CONCLUSIONS

The diagnostic criteria for BPSD were highly predictive of the conversion from MDD to BP, and conversion was associated with several clinical features of BPSD. Thus, the BPSD diagnostic criteria may be useful for the prediction of bipolar diathesis in MDD patients.

First-episode medication-naive major depressive disorder is associated with altered resting brain function in the affective network

Background

Major depressive disorder (MDD) has been associated with abnormal structure and function of the brain's affective network, including the amygdala and orbitofrontal cortex (OFC). However, it is unclear if alterations of resting-state function in this affective network are present at the initial onset of MDD.

Aims

To examine resting-state function of the brain's affective network in first-episode, medication-naive patients with MDD compared to healthy controls (HCs).

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 32 first-episode, medication-naive young adult patients with MDD and 35 matched HCs. The amplitude of low-frequency fluctuations (ALFF) of the blood oxygen level-dependent (BOLD) signal and amygdala-seeded functional connectivity (FC) were investigated.

Results

Compared to HC, MDD patients showed reduced ALFF in the bilateral OFC and increased ALFF in the bilateral temporal lobe extending to the insular and left fusiform cortices. Enhanced anti-correlation of activity between the left amygdala seed and the left OFC was found in MDD patients but not in HCs.

Conclusions

Reduced ALFF in the OFC suggests hypo-functioning of emotion regulation in the affective network. Enhanced anti-correlation of activity between the amygdala and OFC may reflect dysfunction of the amygdala-OFC network and additionally represent a pathological process of MDD.

Clinical differences between unipolar and bipolar depression: interest of BDRS (Bipolar Depression Rating Scale)

OBJECTIVES

It is currently assumed that there are no important differences between the clinical presentations of unipolar and bipolar depression. Failure to distinguish bipolar from unipolar depression may lead to inappropriate treatment and poorer outcomes. We hereby compare unipolar and bipolar depressed subjects, in order to identify distinctive clinical specificities of bipolar depression.

METHODS

Two independent samples of depressed patients (unipolar and bipolar) were recruited, with 55 patients in one sample, and 49 in the other. In both samples, unipolar and bipolar patients were compared on a broad range of parameters, including sociodemographic characteristics, comorbidities, Montgomery and Asberg Depression Scale (MADRS; assessing depression severity), CORE (assessing psychomotor disturbance) and Bipolar Depression Rating Scale (assessing specific bipolar depression symptoms).

RESULTS

Results were similar in both samples. MADRS scores were similar in bipolar and unipolar subjects (median score 33 vs 34; p=0.74). On the CORE, there was a trend to higher scores among the bipolar subjects. BDRS scores were higher in bipolar than in unipolar subjects (median score 33 vs 27; p<0.001). The difference was particularly marked on the "mixed" subscale of the BDRS. We tested the ability of the mixed subscale of the BDRS to distinguish bipolar from unipolar depression, using different cut off points: a cut off point of 3 can predict bipolar depression, with a sensibility of 62% and a specificity of 82%.

CONCLUSIONS

Presence of mixed symptoms during a depressive episode is in favour of bipolar depression. The BDRS scale should be integrated in a probabilistic approach to distinguish bipolar from unipolar depression.

Validation of the Chinese version of the "Mood Disorder Questionnaire" for screening bipolar disorder among patients with a current depressive episode

BACKGROUND

The Mood Disorder Questionnaire (MDQ) is a well-recognized screening tool for bipolar disorder, but its Chinese version needs further validation. This study aims to measure the accuracy of the Chinese version of the MDQ as a screening instrument for bipolar disorder (BPD) in a group of patients with a current major depressive episode.

METHODS

142 consecutive patients with an initial DSM-IV-TR diagnosis of a major depressive episode were screened for BPD using the Chinese translation of the MDQ and followed up for one year. The final diagnosis, determined by a special committee consisting of three trained senior psychiatrists, was used as a 'gold standard' and ROC was plotted to evaluate the performance of the MDQ. The optimal cut-off was chosen by maximizing the Younden's index.

RESULTS

Of the 142 patients, 122 (85.9%) finished the one year follow-up. On the basis of a semi-structured clinical interview 48.4% (59/122) received a diagnosis of unipolar depression (UPD), 36.9% (45/122) BPDII and 14.8% (18/122) BPDI. At the end of the one year follow-up,9 moved from UPD to BPD, 2 from BPDII to UPD, 1 from BPDII to BPDI, the overall rate of initial misdiagnosis was 16.4%. MDQ showed a good accuracy for BPD: the optimal cut-off was 4, with a sensitivity of 0.72 and a specificity of 0.73. When BPDII and BPDI were calculated independently, the optimal cut-off for BPDII was 4, with a sensitivity of 0.70 and a specificity of 0.73; while the optimal cut-off for BPDI was 5, with a sensitivity of 0.67 and a specificity of 0.86.

CONCLUSIONS

Our results show that the Chinese version of MDQ is a valid tool for screening BPD in a group of patients with current depressive episode on the Chinese mainland.