Undetermined predominant polarity in a cohort of bipolar disorder patients: Prevalent, severe, and overlooked

Prevalence and correlates of manic/hypomanic and depressive predominant polarity in bipolar disorder: systematic review and meta-analysis

BACKGROUND

Identification of the predominant polarity, i.e. hypomanic/manic (mPP) or depressive predominant polarity (dPP), might help clinicians to improve personalised management of bipolar disorder.

AIMS

We performed a systematic review and meta-analysis to estimate prevalence and correlates of mPP and dPP in bipolar disorder.

METHOD

The protocol was registered in the Open Science Framework Registries (https://doi.org/10.17605/OSF.IO/8S2HU). We searched main electronic databases up to December 2023 and performed random-effects meta-analyses of weighted prevalence of mPP and dPP. Odds ratios and weighted mean differences (WMDs) were used for relevant correlates.

RESULTS

We included 28 studies, providing information on rates and/or correlates of mPP and dPP. We estimated similar rates of mPP (weighted prevalence = 30.0%, 95% CI: 23.1 to 37.4%) and dPP (weighted prevalence = 28.5%, 95% CI: 23.7 to 33.7%) in bipolar disorder. Younger age (WMD = -3.19, 95% CI: -5.30 to -1.08 years), male gender (odds ratio = 1.39, 95% CI: 1.10 to 1.76), bipolar-I disorder (odds ratio = 4.82, 95% CI: 2.27 to 10.24), psychotic features (odds ratio = 1.56, 95% CI: 1.01 to 2.41), earlier onset (WMD = -1.57, 95% CI: -2.88 to -0.26 years) and manic onset (odds ratio = 13.54, 95% CI: 5.83 to 31.46) were associated with mPP (P < 0.05). Depressive onset (odds ratio = 12.09, 95% CI: 6.38 to 22.90), number of mood episodes (WMD = 0.99, 95% CI: 0.28 to 1.70 episodes), history of suicide attempts (odds ratio = 2.09, 95% CI: 1.49 to 2.93) and being in a relationship (odds ratio = 1.98, 95% CI: 1.22 to 3.22) were associated with dPP (P < 0.05). No differences were estimated for other variables.

CONCLUSIONS

Despite some limitations, our findings support the hypothesis that predominant polarity might be a useful specifier of bipolar disorder. Evidence quality was mixed, considering effects magnitude, consistency, precision and publication bias. Different predominant polarities may identify subgroups of patients with specific clinical characteristics.

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.

Lamotrigine efficacy, safety, and tolerability for women of childbearing age with bipolar I disorder: Meta-analysis from four randomized, placebo-controlled maintenance studies

This meta-analysis investigated the efficacy, safety, and tolerability of lamotrigine versus placebo in preventing relapse and recurrence of mood episodes in women of childbearing age with bipolar I disorder. Following up to 16 weeks' open-label lamotrigine treatment, responders were randomized to double-blind treatment, including lamotrigine 100-400 mg/day or placebo, in four trials of up to 76 weeks. Women aged 18-45 years who received ≥ 1 dose of study treatment and had ≥ 1 efficacy assessment in the double-blind phase were pooled for efficacy analysis. The primary outcome was median time to intervention for any mood episode (TIME). Of 717 eligible women in the open-label phase, 287 responded and were randomized to lamotrigine (n = 153) or placebo (n = 134). The randomized group had a mean (SD) of 2.0(2.02) manic and 2.5(2.02) depressive episodes in the 3 years before screening. Median TIME was 323 days with lamotrigine and 127 days with placebo (HR 0.69; 95% CI 0.49, 0.96; p = 0.030). Lamotrigine delayed time to intervention for any depressive episode (HR 0.59; 95% CI 0.39, 0.90; p = 0.014) with no treatment difference for manic episodes (HR 0.91; 95% CI 0.52, 1.58; p = 0.732). 2/717 (< 1%) participants experienced serious rash-related adverse events (AEs) during the open-label phase, and 52/717 (7%) had non-serious rash-related events leading to study withdrawal. Incidence of AEs and AEs leading to withdrawal were similar between lamotrigine and placebo groups. Lamotrigine delayed relapse and recurrence of mood episodes, largely by preventing depressive episodes, and was well tolerated in women of childbearing age.

Seasonality of brain function: role in psychiatric disorders

Seasonality patterns are reported in various psychiatric disorders. The current paper summarizes findings on brain adaptations associated with seasonal changes, factors that contribute to individual differences and their implications for psychiatric disorders. Changes in circadian rhythms are likely to prominently mediate these seasonal effects since light strongly entrains the internal clock modifying brain function. Inability of circadian rhythms to accommodate to seasonal changes might increase the risk for mood and behavior problems as well as worse clinical outcomes in psychiatric disorders. Understanding the mechanisms that account for inter-individual variations in seasonality is relevant to the development of individualized prevention and treatment for psychiatric disorders. Despite promising findings, seasonal effects are still understudied and only controlled as a covariate in most brain research. Rigorous neuroimaging studies with thoughtful experimental designs, powered sample sizes and high temporal resolution alongside deep characterization of the environment are needed to better understand the seasonal adaptions of the human brain as a function of age, sex, and geographic latitude and to investigate the mechanisms underlying the alterations in seasonal adaptation in psychiatric disorders.

Case report: Rapid symptom resolution of a mixed affective state with high-frequency repetitive transcranial magnetic stimulation

INTRODUCTION

Bipolar major depressive episodes with mixed features are diagnosed in patients who meet the full criteria for a major depressive episode exhibiting three additional concurrent symptoms of hypomania or mania. Up to half of patients with bipolar disorder experience mixed episodes, which are more likely to be treatment-refractory than pure depression or mania/hypomania alone.

CASE

We present a 68-year-old female with Bipolar Type II Disorder with a four-month medication-refractory major depressive episode with mixed features referred for neuromodulation consultation. Previous failed medication trials over several years included lithium, valproate, lamotrigine, topiramate, and quetiapine. She had no history of treatment with neuromodulation. At the initial consultation, her baseline Montgomery-Asberg Depression Rating Scale (MADRS) was moderate in severity at 32. Her Young Mania Rating Scale (YMRS) was 22, with dysphoric hypomanic symptoms consisting of heightened irritability, verbosity and increased rate of speech, and decreased sleep. She declined electroconvulsive therapy but elected to receive repetitive transcranial magnetic stimulation (rTMS).

INTERVENTIONS

The patient underwent repetitive transcranial magnetic stimulation (rTMS) with a Neuronetics NeuroStar system, receiving nine daily sessions over the left dorsolateral prefrontal cortex (DLPFC). Standard settings of 120% MT, 10 Hz (4 sec on, 26 sec off), and 3,000 pulses/session were used. Her acute symptoms showed a brisk response, and at the final treatment, her repeat MADRS was 2, and YMRS was 0. The patient reported feeling "great," which she defined as feeling stable with minimal depression and hypomania for the first time in years.

CONCLUSION

Mixed episodes present a treatment challenge given their limited treatment options and diminished responses. Previous research has shown decreased efficacy of lithium and antipsychotics in mixed episodes with dysphoric mood such as the episode our patient experienced. One open-label study of low-frequency right-sided rTMS showed promising results in patients with treatment-refractory depression with mixed features, but the role of rTMS in the management of these episodes is largely unexplored. Given the concern for potential manic mood switches, further investigation into the laterality, frequency, anatomical target, and efficacy of rTMS for bipolar major depressive episodes with mixed features is warranted.

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.