Bipolar II disorder has the highest prevalence of seasonal affective disorder in early-onset mood disorders: Results from a prospective observational cohort study

Seasonal variation in mood among individuals with and without bipolar disorder

INTRODUCTION

Bipolar disorder is a disorder characterized by cyclic changes in mood, yet limited research has explored longitudinal patterns of seasonality on mood symptoms in this population. This study aimed to examine longitudinal mood symptoms in individuals with bipolar type I and II, and healthy controls to determine if seasonal patterns were present and to validate the Global Seasonality Score as a measure of seasonality.

METHODS

Participants from the Prechter Longitudinal Study of Bipolar Disorder were included. Seasonal variations in mood were determined from the Patient Health Questionnaire, Altman Self-Rating Mania scale, and the Seasonal Pattern Assessment Questionnaire. Mixed effects models were utilized to examine the effects of season and diagnostic group on patterns of mood over time.

RESULTS

All groups exhibited significant seasonal effects on mood symptoms, with evidence of decreased depressive symptoms and increased mania symptoms in longer daylight months. The Global Seasonality Score showed significant differences between diagnostic groups, with bipolar I and II groups demonstrating higher seasonality than healthy controls. High seasonality was associated with greater variance in mood symptoms.

CONCLUSION

The present study found evidence of seasonal patterns in mood symptoms in individuals with bipolar type I and II. These results highlight the need for consideration of seasonality in assessment and treatment in bipolar disorder and suggest that interventions such as light therapy during seasons of heightened risk could be beneficial. The validation of the Global Seasonality Score as a reliable measure further underscores the benefit of utilizing self-report measures to identify periods of vulnerability.

Subclinical hypomanic experiences in young adults after sleep deprivation are independent of depressive disorders, chronotype or 5-HTTLPR polymorphism

INTRODUCTION

The acute antidepressant effect of sleep deprivation (SD) in patients with depressive disorders has been studied for more than 60 years. However, hypomanic mood swings after partial or total SD have also been described in people without diagnosed mental disorders. Studying this phenomenon in the general population may yield insights about the mechanisms of therapeutic SD, mania and bipolar disorders.

METHODS

A cross-sectional sample of young adults was recruited and classified into those who described having regularly occurring subclinical hypomanic experiences (ROHE) after SD and those who did not. History of psychiatric and physical illness, with screening for depression and mania, as well as alcohol or drug consumption, family history of depressive disorders or suicide, 5-HTTLPR polymorphism, and MEQ-SA chronotype were collected.

RESULTS

A total of 251 participants were included; 39.0% indicated regularly having subclinical hypomanic experiences after SD. These experiences were not associated with depressive or mania screening, history of psychiatric illness, family history, 5-HTTLPR polymorphism, or MEQ-SA chronotype.

CONCLUSIONS

ROHE after non-therapeutic SD seem to be a relatively common phenomenon in young adults, independent of depressive mood state. Our results suggest that therapeutic SD may depend on a physiological phenomenon of subclinical affective disturbance after SD that affects a part of the general population, independent of psychiatric diagnosis. Further studies could elucidate associated factors and contribute to our understanding of (hypo-)manic mood states.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.

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.

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

Differences between bipolar disorder types 1 and 2 support the DSM two-syndrome concept

Objective

To compare characteristics of bipolar disorder patients diagnosed as DSM-5 types I (BD-1) vs. II (BD-2).

Methods

We compared descriptive, psychopathological, and treatment characteristics in a sample of 1377 consenting, closely and repeatedly evaluated adult BD patient-subjects from a specialty clinic, using bivariate methods and logistic multivariable modeling.

Results

Factors found more among BD-2 > BD-1 cases included: [a] descriptors (more familial affective disorder, older at onset, diagnosis and first-treatment, more education, employment and higher socioeconomic status, more marriage and children, and less obesity); [b] morbidity (more general medical diagnoses, less drug abuse and smoking, more initial depression and less [hypo]mania or psychosis, longer episodes, higher intake depression and anxiety ratings, less mood-switching with antidepressants, less seasonal mood-change, greater %-time depressed and less [hypo]manic, fewer hospitalizations, more depression-predominant polarity, DMI > MDI course-pattern, and less violent suicidal behavior); [c] specific item-scores with initial HDRS₂₁ (higher scores for depression, guilt, suicidality, insomnia, anxiety, agitation, gastrointestinal symptoms, hypochondriasis and weight-loss, with less psychomotor retardation, depersonalization, or paranoia); and [d] treatment (less use of lithium or antipsychotics, more antidepressant and benzodiazepine treatment).

Conclusions

BD-2 was characterized by more prominent and longer depressions with some hypomania and mixed-features but not mania and rarely psychosis. BD-2 subjects had higher socioeconomic and functional status but also high levels of long-term morbidity and suicidal risk. Accordingly, BD-2 is dissimilar to, but not necessarily less severe than BD-1, consistent with being distinct syndromes.

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.