bipolar affective disorder research paper

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Bipolar disorder.

Ankit Jain ; Paroma Mitra .

Affiliations

Last Update: February 20, 2023 .

Continuing Education Activity

Bipolar disorder, also known as bipolar affective disorder, is one of the top 10 leading causes of disability worldwide. Bipolar disorder is characterized by chronically occurring episodes of mania or hypomania alternating with depression and is often misdiagnosed initially. Treatment involves pharmacotherapy and psychosocial interventions, but mood relapse and incomplete response occur, particularly with depression. Continual reevaluation and treatment modification are commonly required during the long-term care of patients with bipolar disorder. Management of comorbid psychiatric and chronic medical conditions may also be necessary. This activity reviews the etiology, classification, evaluation, management, and prognosis of bipolar affective disorder, and it also highlights the role of the interprofessional team in managing and improving care for patients with this condition.

Recognize patterns of symptoms suggestive of bipolar disorder, its various subtypes, and related disorders.
Implement evidence-based management of bipolar disorder based on current published guidelines.
Select individualized pharmacotherapy plans and adjunct therapies for bipolar disorder and comorbidities.
Describe the necessity of an interprofessional holistic team approach that integrates psychiatric and medical healthcare in caring for patients with bipolar disorder to help achieve the best possible outcomes.
Introduction

Bipolar disorder (BD) is characterized by chronically occurring episodes of mania or hypomania alternating with depression and is often misdiagnosed initially.

Bipolar and related disorders include bipolar I disorder (BD-I), bipolar II disorder (BD-II), cyclothymic disorder, other specified bipolar and related disorders, and bipolar or related disorders, unspecified. The diagnostic label of "bipolar affective disorders" in the International Classification of Diseases 10th Revision (ICD-10) was changed to "bipolar disorders" in the ICD-11. The section on bipolar disorders in the ICD-11 is labeled "bipolar and related disorders," which is consistent with the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). [1]

A World Health Organization study showed "remarkably similar" international prevalence rates, severity, impact, and comorbidities of bipolar spectrum disorder, defined as BD-I, BD-II, and subthreshold bipolar. The aggregate lifetime prevalence of the bipolar spectrum was 2.4%. [2]

BD is often difficult to recognize because symptoms overlap with other psychiatric disorders, psychiatric and somatic comorbidity is common, and patients may lack insight into their conditions, particularly hypomania. Treatment involves pharmacotherapy and psychosocial interventions, but mood relapse and incomplete response occur, particularly with depression. Continual reevaluation and treatment modification are commonly required during the long-term care of these patients. Management of comorbid psychiatric and chronic medical conditions may also be necessary. This activity provides an overview of the etiology, classification, evaluation, and management of bipolar affective disorder.

Currently, the etiology of BD is unknown but appears to be due to an interaction of genetic, epigenetic, neurochemical, and environmental factors. Heritability is well established. [3] [4] [5] Numerous genetic loci have been implicated as increasing the risk of BD; the first was noted in 1987 with "DNA markers" on the short arm of chromosome 11. Since then, an association has been made between at least 30 genes and an increased risk of the condition. [6]

Although it is difficult to establish causation between life events and the development of BD, childhood maltreatment, particularly emotional abuse or neglect, has been linked to the later development of the condition. Other stressful life events associated with developing BD include childbirth, divorce, unemployment, disability, and early parental loss. [7] In adulthood, more than 60% of patients with BD report at least one "stressful life event" before a manic or depressive episode in the preceding 6 months. [6]

The etiology of BD is thought to involve imbalances in systems associated with monoaminergic neurotransmitters, particularly dopamine and serotonin, and intracellular signaling systems that regulate mood. However, no singular dysfunction of these neurotransmitter systems has been identified. [8]

In a recent neuroimaging review article, the ENIGMA Bipolar Disorder Working Group stated, "Overall, these studies point to a diffuse pattern of brain alterations including smaller subcortical volumes, lower cortical thickness and altered white matter integrity in groups of individuals with bipolar disorder compared to healthy controls." [9] Neuroimaging studies have also shown evidence of changes in functional connectivity. [10] [11]

Epidemiology

In the World Mental Health Survey Initiative, the use of mental health services for the bipolar spectrum (BD-I, BD-II, and subthreshold BD) concluded, “Despite cross-site variation in the prevalence rates of bipolar spectrum disorder, the severity, impact, and patterns of comorbidity were remarkably similar internationally.” The aggregate lifetime prevalence of BD-I was 0.6%, BD-II 0.4%, subthreshold BD 1.4%, and bipolar spectrum 2.4%. [2]

There are two peaks in the age of onset: 15-24 years and 45-54 years, with more than 70% of individuals manifesting clinical characteristics of the condition before 25 years of age. [12] [13] Bipolar disorder shows a relatively equal distribution across sex, ethnicity, and urban compared to rural areas. [7] [14]

Cyclothymia is associated with a lifetime prevalence of approximately 0.4-1% and a male-to-female ratio of 1:1. [15]

Pathophysiology

As with the etiology, the pathophysiology of BD is unknown and is thought to involve interactions between multiple genetic, neurochemical, and environmental factors. A recent neurobiology review article discusses in detail the “genetic components, signaling pathways, biochemical changes, and neuroimaging findings” in BD. [10]

Evidence supports a strong genetic component and an epigenetic contribution. Human studies have shown changes in brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), neurotrophin-3 (NT-3), and neurotrophin-4 (NT-4) in patients with BD, indicating neurotrophic signaling is a molecular mechanism associated with decreased neuroplasticity. Other proposed mechanisms include mitochondrial dysfunction, oxidative stress, immune-inflammatory imbalance, and compromised hypothalamic-pituitary-adrenal axis. Additionally, neuroimaging studies have shown “evidence of change in regional activity, functional connectivity, neuronal activity, and bioenergetics associated with BD,” and anatomic studies have revealed dendritic spine loss in the dorsolateral prefrontal cortex in the post-mortem brain tissue of patients with BD. [10] [16]

As mentioned, imbalances in systems associated with monoaminergic neurotransmitters, particularly dopamine and serotonin, and intracellular signaling systems that regulate mood are thought to be involved. However, no singular dysfunction of these neurotransmitter systems has been identified. [8]

History and Physical

Because bipolar disorder is a clinical diagnosis, making the correct diagnosis requires a comprehensive clinical assessment, including the directed patient interview, preferably supplemented by interviews of their relatives and the longitudinal course of their condition. Currently, there is no biomarker or neuroimaging study to aid in making the diagnosis.

Most patients with bipolar disorder are not correctly diagnosed until approximately 6 to 10 years after first contact with a healthcare provider, despite the presence of clinical characteristics of the condition. [17] Notably, misdiagnosing BD after first contact differs from not recognizing the transition from major depressive disorder (MDD), the most common index presentation, to BD. Estimates of patients transitioning to BD within three years of an MDD diagnosis range from 20-30%; therefore, clinicians must maintain an awareness of the potential for this transition when caring for patients with MDD who initially screened negative for BD. [18] Also, subthreshold hypomanic symptoms can occur in as many as 40% of patients with MDD. [19]

Although not highly sensitive and specific, self-report screening tools for BD may aid clinicians in making an accurate diagnosis. The most studied screening tools are the Mood Disorders Questionnaire (sensitivity 80%, specificity 70%) and the Hypomania Checklist 32 (sensitivity 82%, specificity 57%). [20] Positive results should motivate the clinician to conduct a thorough clinical assessment for bipolar disorder.

A significant diagnostic challenge is distinguishing between unipolar and bipolar depression because episodes of unipolar major depression and bipolar depression have the same general diagnostic criteria. Clinicians must inquire about past manic, hypomanic, and depressive episodes in patients presenting with symptoms of a depressive episode. Inquiry into past hypomanic or manic episodes is particularly important for patients with early onset of their first depressive episode (ie, in patients younger than 25 years), a high number of lifetime depressive episodes (5 or more episodes), and a family history of bipolar disorder. These findings in the patient’s history have been shown to increase the likelihood of a bipolar rather than a unipolar diagnosis. [21]

Other factors increasing the likelihood of a diagnostic change from MDD to BD include the presence of psychosis, unresponsiveness to antidepressants, the induction of manic or hypomanic symptoms by antidepressant drug treatment, and polymorbidity, defined as 3 or more comorbid conditions. [18] [22]

General DSM-5 Diagnostic Criteria for Bipolar and Related Disorders (American Psychiatric Association. Diagnostic and StatisticalManual of Mental Disorders 5th edition (DSM-5). Arlington, VA: American Psychiatric Publishing; 2013)

BD-I : Criteria met for at least one manic episode, which might have been preceded or followed by a hypomanic episode or major depressive episode (hypomanic or major depressive episodes are not required for the diagnosis).

BD-II : Criteria met for at least one current or past hypomanic episode and a major depressive episode; no manic episodes.

Cyclothymic disorder : Hypomanic symptoms that do not meet the criteria for hypomanic episodes and depressive symptoms that do not meet the criteria for major depressive episodes in numerousperiods (at least half the time) for at least 2 years (1 year in those aged ≤18 years); criteria for major depressive, manic, or hypomanic episodes have never been met.

Specified bipolar and related disorders : Bipolar-like phenomena that do not meet the criteria for BD-I, BD-II, or cyclothymic disorder due to insufficient duration or severity, ie, 1) short-duration hypomanic episodes and major depressive disorder, 2) hypomanic episodes with insufficient symptoms and major depressive episode, 3) hypomanic episode without a prior major depressive episode, and 4) short-duration cyclothymia.

Unspecified bipolar and related disorders : Characteristic symptoms of bipolar and related disorders that cause clinically significant distress or impairment in social, occupational, or other important areas of functioning but do not meet the full criteria for any category previously mentioned.

The symptoms and episodes used to diagnose these disorders must not be related to the physiological effects of a substance or general medical condition.

BD-I and BD-II can be further specified as rapid cycling or seasonal patterns and whether the episodes have psychotic features, catatonia, anxious distress, melancholic features, or peripartum onset. Rapid cycling refers to 4 or more distinct mood episodes during a 12-month period.

Mood-congruent delusions may be present in either a depressive or manic episode, including delusions of guilt or grandiose delusions of power and wealth. Psychotic features, by definition, are absent in hypomanic episodes.

To better account for "mixed features," the current diagnostic criteria implements specifiers. Manic or hypomanic episodes with mixed features meet the full criteria for mania or hypomania and have at least 3 of the following signs or symptoms: depressed mood, anhedonia, psychomotor retardation, fatigue, excessive guilt, or recurrent thoughts of death. Major depressive episodes with mixed features meet the full criteria for a major depressive episode and have at least 3 of the following signs or symptoms: expansive mood, grandiosity, increased talkativeness, flight of ideas, increased goal-directed activity, indulgence in activities with a high potential for "painful consequences," and decreased need for sleep. The mixed features must be present during "most days."

DSM-5 Diagnostic Criteria for Bipolar I Disorder

For a diagnosis of BD-I, it is necessary to meet the following criteria for a manic episode. The manic episode may have been preceded by and may be followed by hypomanic or major depressive episodes (hypomanic or major depressive episodes are not required for the diagnosis).

A manic episode is defined as a distinct period of persistently elevated or irritable mood with increased activity or energy lasting for at least 7 consecutive days or requiring hospitalization. The presence of 3 or more of the following is required to qualify as a manic episode. If the mood is irritable, at least 4 of the following must be present:

Inflated self-esteem or grandiosity
Decreased need for sleep
A compulsion to keep talking or being more talkative than usual
Flight of ideas or racing thoughts
High distractibility
Increased goal-directed activity (socially, at work or school, or sexually) or psychomotor agitation (non-goal-directed activity)
Excessive involvement in activities that have a high potential for painful consequences, such as engaging in unrestrained buying sprees, sexual indiscretions, or foolish business investments

The episode is not attributable to the physiological effects of a substance or general medical condition.

The symptoms of a manic episode are markedly more severe than those of a hypomanic episode and result in impaired social or occupational functioning or require hospitalization.

DSM-5 Diagnostic Criteria for Bipolar II Disorder

For a diagnosis of BD-II, it is necessary to have met the criteria for at least one current or past hypomanic episode and a major depressive episode without a manic episode (see below for major depressive episode criteria).

A hypomanic episode is defined as a distinct period of persistently elevated or irritable mood with increased activity or energy lasting for at least 4 consecutive days. The presence of 3 or more of the following is required to qualify as a hypomanic episode. If the mood is irritable, at least 4 of the following must be present:

The episode is an unequivocal change in functioning, uncharacteristic of the person and observable by others. Also, the episode is not severe enough to cause marked impairment, is not due to the physiological effects of a substance or general medical condition, and there is no psychosis (if present, this is mania by definition).

DSM-5 Diagnostic Criteria for a Major Depressive Episode

The presence of 5 or more of the following symptoms daily or nearly every day for a consecutive 2-week period that is a change from baseline or previous functioning:

Subjective report of depressed mood most of the day (or depressed mood observed by others)
Anhedonia most of the day
Significant weight loss when not dieting or weight gain or decrease or increase in appetite
Insomnia or hypersomnia
Psychomotor agitation or retardation
Fatigue or loss of energy
Feelings of worthlessness or excessive or inappropriate guilt
Decreased concentration or indecisiveness
Recurrent thoughts of death, recurrent suicidal ideation without a specific plan

To meet the criteria, at least one of the symptoms must be depressed mood or anhedonia, the symptoms must not be attributable to a substance or general medical condition, and it causes functional impairment (eg, social or occupational).

Possible Secondary Cause of Bipolar Disorder

The following characteristics may heighten the clinical suspicion for a possible secondary cause in patients with signs and symptoms associated with bipolar disorder: older than 50 at the first onset of symptoms, abnormal vital signs or neurological examination, a recent change in health status or medications temporally associated with symptom onset, unusual response or unresponsiveness to appropriate treatments, and no personal or family history of a psychiatric disorder.

Recommended initial evaluation for a possible secondary cause includes a urine drug screen, complete blood count with blood smear, comprehensive metabolic panel, thyroid function tests, and vitamin B and folate levels.

Treatment / Management

Although numerous clinical practice guidelines exist for the treatment and management of bipolar disorder, there is not enough consistency to generate a ‘meta-consensus’ model. [23] Authors of a recent systematic review concluded, “The absence of a uniform language and recommendations in current guidelines may be an additional complicating factor in the implementation of evidence-based treatments in BD.” [24] The following is an abbreviated synthesis of guidelines published by the National Institute for Health and Care Excellence (NICE), British Association for Psychopharmacology, International College of Neuro-Psychopharmacology (CINP), Canadian Network for Mood and Anxiety Treatments (CANMAT), International Society for Bipolar Disorders (ISBD), and Indian Psychiatric Society (IPS). [25] [26] [27] [28] [29]

Manic Episode

Mania is considered a medical emergency and often requires psychiatric hospitalization. Initial treatment is aimed at stabilization of the potentially or acutely agitated patient to help de-escalate distress, mitigate potentially dangerous behavior, and facilitate the patient assessment and evaluation. When possible, a calming environment with minimal stimuli should be provided. Adjunctive benzodiazepines may be used concomitantly with mood stabilizers and antipsychotic drugs to reduce agitation and promote sleep.

The patient’s current medications must be considered. For example, a second drug is recommended if the patient presents while the condition is already managed with lithium monotherapy. Also, antidepressants are usually tapered and discontinued in a manic phase. First-line monotherapy includes a mood stabilizer, such as lithium or valproate, or an antipsychotic, such as aripiprazole, asenapine, cariprazine, quetiapine, or risperidone.

Add another medication if symptoms are inadequately controlled, or the mania is very severe. Combination treatments include lithium or valproate with either aripiprazole, asenapine, olanzapine, quetiapine, or risperidone. Electroconvulsive therapy (ECT) may be considered as monotherapy or as part of combination therapy in patients whose mania is particularly severe or treatment-resistant and in women with severe mania who are pregnant.

Valproate should not be used for women of childbearing potential due to the unacceptable risk to the fetus of teratogenesis and impaired intellectual development.

Hypomanic Episodes

By definition, hypomanic episodes are not severe enough to cause marked impairment, and there is no psychosis; therefore, these episodes can be managed in an ambulatory setting. Pharmacotherapy is similar to that for mania, but higher doses may be required for the latter.

Acute Bipolar Depression

Suicidal and self-harm risk has priority in managing patients with bipolar disorder who present with an acute depressive episode because most suicide deaths in patients with BD occur during this phase. Patients may or may not require hospitalization.

For patients not already taking long-term medication for BD, first-line monotherapy includes quetiapine, olanzapine, or lurasidone (has not been studied in acute bipolar mania). Combination treatment with olanzapine-fluoxetine, lithium plus lamotrigine, and lurasidone plus lithium or valproate may also be considered.

Consider cognitive behavioral therapy (CBT) as an add-on to pharmacotherapy. However, never consider CBT as monotherapy because there is minimal evidence to support psychological treatments without pharmacotherapy in treating acute bipolar depression.

Also, consider adding ECT for refractory bipolar depression or as a first-line treatment in the presence of psychotic features and a high risk of suicide.

For patients presenting with a depressive episode while taking long-term medication (breakthrough episode), make sure their current treatments are likely to protect them from a manic relapse (eg, mood stabilizer or antipsychotic). When applicable, check the medication dose, patient adherence, drug-drug interactions, and serum concentrations. Also, inquire about current stressors, alcohol or substance use, and psychosocial intervention adherence.

Generally, treatment options for BD-II depression are similar to those for BD-I depression.

Antidepressant medications should not be used as monotherapy in most patients with bipolar disorder, as available evidence does not support their efficacy, and there is a risk of a switch to mania or mood instability during an episode of bipolar depression. Antidepressants can be administered adjunctively to mood stabilizers (eg, lithium and lamotrigine) and second-generation antipsychotics.

Maintenance Treatment

Most patients with bipolar disorder will require maintenance treatment for many years, possibly lifelong, to prevent recurrent episodes and restore their pre-illness functioning. The current recommendation is for continuous rather than intermittent treatment, with treatments that were effective during the acute phase often continued initially to prevent early relapse. Mood stabilizers and atypical antipsychotics alone or in combination are the mainstays of maintenance pharmacotherapy.

There is substantial evidence showing lithium monotherapy’s effectiveness against manic, depressive, and mixed relapse. Additionally, lithium is associated with a decreased risk of suicide in patients with BD. Monitoring during treatment, including serum lithium concentrations, is a standard of care.

In addition to the individualized pharmacotherapy plan, essential components of maintenance treatment include medication adherence, primary prevention and treatment for psychiatric and medical comorbidities, and psychotherapy when appropriate. Suicidality surveillance is critical throughout the maintenance phase.

Differential Diagnosis

The differential diagnosis of bipolar disorder includes other conditions characterized by depression, impulsivity, mood lability, anxiety, cognitive dysfunction, and psychosis. The most common differential diagnoses are MDD, schizophrenia, anxiety disorders, substance use disorders, borderline personality disorder, and in the pediatric age group, attention-deficit/hyperactivity disorder and oppositional defiant disorder. [18] [30]

Bipolar disorder is one of the top 10 leading causes of disability worldwide. [31] A recent meta-analysis showed that patients with BD “experienced reduced life expectancy relative to the general population, with approximately 13 years of potential life lost.” Additionally, patients with bipolar disorder showed a greater reduction in lifespan relative to the general population than patients with common mental health disorders, including anxiety and depressive disorders, and life expectancy was significantly lower in men with BD than in women with BD. [32] A different meta-analysis showed that all-cause mortality in patients with BD is double that expected in the general population. Natural deaths occurred over 1.5 times greater in BD, comprised of an “almost double risk of deaths from circulatory illnesses (heart attacks, strokes, etc) and 3 times the risk of deaths from respiratory illness (COPD, asthma, etc).” Unnatural deaths occurred approximately 7 times more often than in the general population, with an increased suicide risk of approximately 14 times and an increased risk of other violent deaths of almost 4 times. Deaths by all causes studied were similarly increased in men and women. [33] A more recent systematic review of the association between completed suicide and bipolar disorder showed an approximately 20- to 30-fold greater suicide rate in bipolar disorder than in the general population. [34]

Complications

Individuals with bipolar disorder show a markedly increased risk of premature death due to the increased risk of suicide and medical comorbidities, including cardiovascular, respiratory, and endocrine causes. [35] More than half of patients are overweight or obese, which appears to be independent of treatment with weight-promoting psychotropic medications. [36] One-third of patients with bipolar disorder also meet the criteria for metabolic syndrome, which increases the risks of heart disease and stroke. [37] Additionally, attempted suicides are more common among patients with concurrent metabolic syndrome. [37] Comorbid overweight and obesity are associated with a more severe course, an increased lifetime number of depressive and manic episodes, poorer response to pharmacotherapy, and heightened suicide risk. [22] [38] Migraine is also associated with bipolar disorder. [39]

Psychiatric comorbidity is present in 50 to 70% of patients with BD. Of those diagnosed with the condition, 70% to 90% meet the criteria for generalized anxiety disorder, social anxiety disorder, or panic disorder, and 30 to 50% for alcohol and other substance use disorders. [40] [41] [42] Psychiatric comorbidities in patients with bipolar disorder are associated with a more severe course, more frequent depressive and manic episodes, and reduced quality of life. [22] Up to half of patients with BD have a comorbid personality disorder, particularly borderline personality disorder, and 10 to 20% have a binge eating disorder, leading to more frequent mood episodes and higher rates of suicidality and alcohol and substance use disorders. [43] [44]

Deterrence and Patient Education

Psychoeducation delivered individually or in a group setting is recommended for patients and family members and may include teaching to detect and manage prodromes of depression and mania, enhance medication adherence, and improve lifestyle choices. Patients are encouraged to avoid stimulants like caffeine, minimize alcohol consumption, exercise regularly, and practice appropriate sleep hygiene. [28] Providers are encouraged to maximize the therapeutic alliance, convey empathy, allow patients to participate in treatment decisions, and consistently monitor symptoms, which have been shown to reduce suicidal ideation, improve treatment outcomes, and increase patient satisfaction with care. [28] [45] Patients may also benefit from case management or care coordination services to help connect them to community-based resources, such as support groups, mental health centers, and substance use treatment programs.

Enhancing Healthcare Team Outcomes

The goal of treatment for patients with bipolar disorder is a full functional recovery (a return to pre-illness baseline functioning). This goal can best be achieved by integrating psychiatric and medical healthcare using an interprofessional team approach to manage BD and comorbid psychiatric and medical conditions. [46] Interprofessional healthcare teams may consist of any combination of the following: case manager, primary care clinician, psychiatrist, psychiatric nurse practitioner, psychiatric physician assistant, psychiatric nurse specialist, social worker, psychologist, and pharmacist.

Ideally, a consistent long-term alliance will form between the patient, their family, and healthcare team members to provide pharmacotherapy management, psychoeducation, ongoing monitoring, and psychosocial support. [26] Also, patients with bipolar disorder and co-occurring alcohol or substance use disorders may benefit from the involvement of an addiction specialist, as there is evidence that effective treatment can improve outcomes. [47] Pharmacists must perform medication reconciliation to ensure there are no drug-drug interactions that could inhibit effective care and report any concerns they have to the prescriber or their nursing staff. Furthermore, collaborative care models have shown efficacy in improving outcomes when used to treat patients with BD. Key elements include patient psychoeducation, using evidence-based treatment guidelines; collaborative decision-making by patients and their healthcare provider(s); and supportive technology to support monitoring and patient follow-up. [46] [48] [49]

An interprofessional approach is a mainstay in treating patients with bipolar disorder. An interprofessional team that provides a holistic and integrated approach to patient care can help achieve the best possible outcomes with the fewest adverse events. [Level 5]

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Disclosure: Ankit Jain declares no relevant financial relationships with ineligible companies.

Disclosure: Paroma Mitra declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Jain A, Mitra P. Bipolar Disorder. [Updated 2023 Feb 20]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Diagnosis and...

Diagnosis and management of bipolar disorders

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Peer review
Fernando S Goes , associate professor of psychiatry and behavioral sciences 1 2
1 Precision Medicine Center of Excellence in Mood Disorders, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
2 Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
Correspondence to: F S Goes fgoes1{at}jhmi.edu

Bipolar disorders (BDs) are recurrent and sometimes chronic disorders of mood that affect around 2% of the world’s population and encompass a spectrum between severe elevated and excitable mood states (mania) to the dysphoria, low energy, and despondency of depressive episodes. The illness commonly starts in young adults and is a leading cause of disability and premature mortality. The clinical manifestations of bipolar disorder can be markedly varied between and within individuals across their lifespan. Early diagnosis is challenging and misdiagnoses are frequent, potentially resulting in missed early intervention and increasing the risk of iatrogenic harm. Over 15 approved treatments exist for the various phases of bipolar disorder, but outcomes are often suboptimal owing to insufficient efficacy, side effects, or lack of availability. Lithium, the first approved treatment for bipolar disorder, continues to be the most effective drug overall, although full remission is only seen in a subset of patients. Newer atypical antipsychotics are increasingly being found to be effective in the treatment of bipolar depression; however, their long term tolerability and safety are uncertain. For many with bipolar disorder, combination therapy and adjunctive psychotherapy might be necessary to treat symptoms across different phases of illness. Several classes of medications exist for treating bipolar disorder but predicting which medication is likely to be most effective or tolerable is not yet possible. As pathophysiological insights into the causes of bipolar disorders are revealed, a new era of targeted treatments aimed at causal mechanisms, be they pharmacological or psychosocial, will hopefully be developed. For the time being, however, clinical judgment, shared decision making, and empirical follow-up remain essential elements of clinical care. This review provides an overview of the clinical features, diagnostic subtypes, and major treatment modalities available to treat people with bipolar disorder, highlighting recent advances and ongoing therapeutic challenges.

Introduction

Abnormal states of mood, ranging from excesses of despondency, psychic slowness, diminished motivation, and impaired cognitive functioning on the one hand, and exhilaration, heightened energy, and increased cognitive and motoric activity on the other, have been described since antiquity. 1 However, the syndrome in which both these pathological states occur in a single individual was first described in the medical literature in 1854, 2 although its fullest description was made by the German psychiatrist Emil Kraepelin at the turn of the 19th century. 3 Kraepelin emphasized the periodicity of the illness and proposed an underlying trivariate model of mood, thought (cognition), and volition (activity) to account for the classic forms of mania and depression and the various admixed presentations subsequently know as mixed states. 3 These initial descriptions of manic depressive illness encompassed most recurrent mood syndromes with relapsing remitting course, minimal interepisode morbidity, and a wide spectrum of “colorings of mood” that pass “without a sharp boundary” from the “rudiment of more severe disorders…into the domain of personal predisposition.” 3 Although Kraepelin’s clinical description of bipolar disorder (BD) remains the cornerstone of today’s clinical description, more modern conceptions of bipolar disorder have differentiated manic depressive illness from recurrent depression, 4 partly based on differences in family history and the relative specificity of lithium carbonate and mood stabilizing anticonvulsants as anti-manic and prophylactic agents in bipolar disorder. While the boundaries of bipolar disorder remain a matter of controversy, 5 this review will focus on modern clinical conceptions of bipolar disorder, highlighting what is known about its causes, prognosis, and treatments, while also exploring novel areas of inquiry.

Sources and selection criteria

PubMed and Embase were searched for articles published from January 2000 to February 2023 using the search terms “bipolar disorder”, “bipolar type I”, “bipolar type II”, and “bipolar spectrum”, each with an additional search term related to each major section of the review article (“definition”, “diagnosis”, “nosology”, “prevalence”, “epidemiology”, “comorbid”, “precursor”, “prodrome”, “treatment”, “screening”, “disparity/ies”, “outcome”, “course”, “genetics”, “imaging”, “treatment”, “pharmacotherapy”, “psychotherapy”, “neurostimulation”, “convulsive therapy”, “transmagnetic”, “direct current stimulation”, “suicide/suicidal”, and “precision”). Searches were prioritized for systematic reviews and meta-analyses, followed by randomized controlled trials. For topics where randomized trials were not relevant, searches also included narrative reviews and key observational studies. Case reports and small observations studies or randomized controlled trials of fewer than 50 patients were excluded.

Modern definitions of bipolar disorder

In the 1970s, the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders reflected the prototypes of mania initially described by Kraepelin, following the “neo-Kraepelinian” model in psychiatric nosology. To meet the primary requirement for a manic episode, an individual must experience elevated or excessively irritable mood for at least a week, accompanied by at least three other typical syndromic features of mania, such as increased activity, increased speed of thoughts, rapid speech, changes in esteem, decreased need for sleep, or excessive engagement in impulsive or pleasurable activities. Psychotic symptoms and admission to hospital can be part of the diagnostic picture but are not essential to the diagnosis. In 1994, Diagnostic and Statistical Manual of Mental Disorders , fourth edition (DSM-IV) carved out bipolar disorder type II (BD-II) as a separate diagnosis comprising milder presentations of mania called hypomania. The diagnostic criteria for BD-II are similar to those for bipolar disorder type I (BD-I), except for a shorter minimal duration of symptoms (four days) and the lack of need for significant role impairment during hypomania, which might be associated with enhanced functioning in some individuals. While the duration criteria for hypomania remain controversial, BD-II has been widely accepted and shown to be as common as (if not more common than) BD-I. 6 The ICD-11 (international classification of diseases, 11th revision) included BD-II as a diagnostic category in 2019, allowing greater flexibility in its requirement of hypomania needing to last several days.

The other significant difference between the two major diagnostic systems has been their consideration of mixed symptoms. Mixed states, initially described by Kraepelin as many potential concurrent combinations of manic and depressive symptoms, were more strictly defined by DSM as a week or more with full syndromic criteria for both manic and depressive episodes. In DSM-5, this highly restrictive criterion was changed to encompass a broader conception of subsyndromal mixed symptoms (consisting of at least three contrapolar symptoms) in either manic, hypomanic, or depressive episodes. In ICD-11, mixed symptoms are still considered to be an episode, with the requirement of several prominent symptoms of the countervailing mood state, a less stringent requirement that more closely aligns with Kraepelin's broader conception of mixed states. 7

Epidemiology

Using DSM-IV criteria, the National Comorbidity Study replication 6 found similar lifetime prevalence rates for BD-I (1.0%) and BD-II (1.1%) among men and women. Subthreshold symptoms of hypomania (bipolar spectrum disorder) were more common, with prevalence rate estimates of 2.4%. 6 Incidence rates, which largely focus on BD-I, have been estimated at approximately 6.1 per 100 000 person years (95% confidence interval 4.7 to 8.1). 8 Estimates of the incidence and lifetime prevalence of bipolar disorder show moderate variations according to the method of diagnosis (performed by lay interviewers in a research context v clinically trained interviews) and the racial, ethnic, and demographic context. 9 Higher income, westernized countries have slightly higher rates of bipolar disorder, 10 which might reflect a combination of westernized centricity in the specific idioms used to understand and elicit symptoms, as well as a greater knowledge, acceptance, and conceptualization of emotional symptoms as psychiatric disorders.

Causes of bipolar disorder

Like other common psychiatric disorders, bipolar disorder is likely caused by a complex interplay of multiple factors, both at the population level and within individuals, 11 which can be best conceptualized at various levels of analysis, including genetics, brain networks, psychological functioning, social support, and other biological and environmental factors. Because knowledge about the causes of bipolar disorder remains in its infancy, for pragmatic purposes, most research has followed a reductionistic model that will ultimately need to be synthesized for a more coherent view of the pathophysiology that underlies the condition.

Insights from genetics

From its earliest descriptions, bipolar disorder has been observed to run in families. Indeed, family history is the strongest individual risk factor for developing the disorder, with first degree relatives having an approximately eightfold higher risk of developing bipolar disorder compared with the baseline population rates of ~1%. 12 While family studies cannot separate the effects of genetics from behavioral or cultural transmission, twin and adoption studies have been used to confirm that the majority of the familial risk is genetic in origin, with heritability estimates of approximately 60-80%. 13 14 There have been fewer studies of BD-II, but its heritability has been found to be smaller (~46%) 15 and closer to that of more common disorders such as major depressive disorder or generalized anxiety. 15 16 Nevertheless, significant heritability does not necessarily imply the presence of genes of large effect, since the genetic risk for bipolar disorder appears likely to be spread across many common variants of small effect sizes. 16 17 Ongoing studies of rare variations have found preliminary evidence for variants of slightly higher effect sizes, with initial evidence of convergence with common variations in genes associated with the synapse and the postsynaptic density. 18 19

While the likelihood that the testing of single variants or genes will be useful for diagnostic purposes is low, analyses known as polygenic risk studies can sum across all the risk loci and have some ability to discriminate cases from controls, albeit at the group level rather than the individual level. 20 These polygenic risk scores can also be used to identify shared genetic risk factors across other medical and psychiatric disorders. Bipolar disorder has strong evidence for common variant based coheritability with schizophrenia (genetic correlation (r g ) 0.69) and major depressive disorder (r g 0.48). BD-I has stronger coheritability with schizophrenia compared with BD-II, which is more strongly genetically correlated with major depressive disorder (r g 0.66). 16 Lower coheritability was observed with attention deficit hyperactivity disorder (r g 0.21), anorexia nervosa (0.20), and autism spectrum disorder (r g 0.21). 16 These correlations provide evidence for shared genetic risk factors between bipolar disorder and other major psychiatric syndromes, a pattern also corroborated by recent nationwide registry based family studies. 12 14 Nevertheless, despite their potential usefulness, polygenic risk scores must currently be interpreted with caution given their lack of populational representation and lingering concerns of residual confounds such as gene-environment correlations. 21

Insights from neuroimaging

Similarly to the early genetic studies, small initial studies had limited replication, leading to the formation of large worldwide consortiums such as ENIGMA (enhancing neuroimaging genetics through meta-analysis) which led to substantially larger sample sizes and improved reproducibility. In its volumetric analyses of subcortical structures from MRI (magnetic resonance imaging) of patients with bipolar disorder, the ENIGMA consortium found modest decreases in the volume of the thalamus (Cohen’s d −0.15), the hippocampus (−0.23), and the amygdala (−0.11), with an increased volume seen only in the lateral ventricles (+0.26). 22 Meta-analyses of cortical regions similarly found small reductions in cortical thickness broadly across the parietal, temporal, and frontal cortices (Cohen’s d −0.11 to −0.29) but no changes in cortical surface area. 23 In more recent meta-analyses of white matter tracts using diffuse tension imaging, widespread but modest decreases in white matter integrity were found throughout the brain in bipolar disorder, most notably in the corpus callosum and bilateral cinguli (Cohen’s d −0.39 to −0.46). 24 While these findings are likely to be highly replicable, they do not, as yet, have clinical application. This is because they reflect differences at a group level rather than an individual level, 25 and because many of these patterns are also seen across other psychiatric disorders 26 and could be either shared risk factors or the effects of confounding factors such as medical comorbidities, medications, co-occurring substance misuse, or the consequences (rather than causes) of living with mental illness. 27 Efforts to collate and meta-analyze large samples utilizing longitudinal designs 28 task based, resting state functional MRI measurents, 29 as well as other measures of molecular imaging (magnetic resonance spectroscopy and positron emission tomography) are ongoing but not as yet synthesized in large scale meta-analyses.

Environmental risk factors

Because of the difficulty in measuring and studying the relevant and often common environmental risk factors for a complex illness like bipolar disorder, there has been less research on how environmental risk factors could cause or modify bipolar disorder. Evidence for intrauterine risk factors is mixed and less compelling than such evidence in disorders like schizophrenia. 30 Preliminary evidence suggests that prominent seasonal changes in solar radiation, potentially through its effects on circadian rhythm, can be associated with an earlier onset of bipolar disorder 31 and a higher likelihood of experiencing a depressive episode at onset. 31 However, the major focus of environmental studies in bipolar disorder has been on traumatic and stressful life events in early childhood 32 and in adulthood. 33 The effects of such adverse events are complex, but on a broad level have been associated with earlier onset of bipolar disorder, a worse illness course, greater prevalence of psychotic symptoms, 34 substance misuse and psychiatric comorbidities, and a higher risk of suicide attempts. 32 35 Perhaps uniquely in bipolar disorder, evidence also indicates that positive life events associated with goal attainment can also increase the risk of developing elevated states. 36

Comorbidity

Bipolar disorder rarely manifests in isolation, with comorbidity rates indicating elevated lifetime risk of several co-occurring symptoms and comorbid disorders, particularly anxiety, attentional disorders, substance misuse disorders, and personality disorders. 37 38 The causes of such comorbidity can be varied and complex: they could reflect a mixed presentation artifactually separated by current diagnostic criteria; they might also reflect independent illnesses; or they might represent the downstream effects of one disorder increasing the risk of developing another disorder. 39 Anxiety disorders tend to occur before the frank onset of manic or hypomanic symptoms, suggesting that they could in part reflect prodromal symptoms that manifest early in the lifespan. 37 Similarly, subthreshold and syndromic symptoms of attention deficit/hyperactivity disorder are also observed across the lifespan of people with bipolar disorder, but particularly in early onset bipolar disorder. 40 On the other hand, alcohol and substance misuse disorders occur more evenly before and after the onset of bipolar disorder, consistent with a more bidirectional causal association. 41

The association between bipolar disorder and comorbid personality disorders is similarly complex. Milder manifestations of persistent mood instability (cyclothymia) or low mood (dysthymia) have previously been considered to be temperamental variants of bipolar disorder, 42 but are now classified as related but separate disorders. In people with persistent emotional dysregulation, making the diagnosis of bipolar disorder can be particularly challenging, 43 since the boundaries between longstanding mood instability and phasic changes in mood state can be difficult to distinguish. While symptom overlap can lead to artificially inflated prevalence rates of personality disorders in bipolar disorder, 44 the elevated rates of most personality disorders in bipolar disorder, particularly those related to emotional instability, are likely reflective of an important clinical phenomenon that is understudied, particularly with regard to treatment implications. 45 In general, people with comorbidities tend to have greater symptom burden and functional impairment and have lower response rates to treatment. 46 47 Data on approaches to treat specific comorbid disorders in bipolar disorder are limited, 48 49 and clinicians are often left to rely on their clinical judgment. The most parsimonious approach is to treat primary illness as fully as possible before considering additional treatment options for remaining comorbid symptoms. For certain comorbidities, such as anxiety symptoms and disorders of attention, first line pharmacological treatment—namely, antidepressants and stimulants, should be used with caution, since they might increase the long term risks of mood switching or overall mood instability. 50 51

Like other major mental illnesses, bipolar disorder is also associated with an increased prevalence of common medical disorders such as obesity, hyperlipidemia, coronary artery disease, chronic obstructive pulmonary disease, and thyroid dysfunction. 52 These have been attributed to increase risk factors such as physical inactivity, poor nutrition, smoking, and increased use of addictive substances, 53 but some could also be consequences of specific treatments, such as the atypical antipsychotics and mood stabilizers. 54 Along with poor access to care, this medical burden likely accounts for much of the increased standardized mortality (approximately 2.6 times higher) in people with bipolar disorder, 55 highlighting the need to utilize treatments with better long term side effect profiles, and the need for better integration with medical care.

Precursors and prodromes: who develops bipolar disorder?

While more widespread screening and better accessibility to mental health providers should in principle shorten the time to diagnosis and treatment, early manifestation of symptoms in those who ultimately go on to be diagnosed with bipolar disorder is generally non-specific. 56 In particular, high risk offspring studies of adolescents with a parent with bipolar disorder have found symptoms of anxiety and attentional/disruptive disorders to be frequent in early adolescence, followed by higher rates of depression and sleep disturbance in later teenage years. 56 57 Subthreshold symptoms of mania, such as prolonged increases in energy, elated mood, racing thoughts, and mood lability are also more commonly found in children with prodromal symptoms (meta-analytic prevalence estimates ranging from 30-50%). 58 59 Still, when considered individually, none of these symptoms or disorders are sensitive or specific enough to accurately identify individuals who will transition to bipolar disorder. Ongoing approaches to consider these clinical factors together to improve accuracy have a promising but modest ability to identify people who will develop bipolar disorder, 60 emphasizing the need for further studies before implementation.

Screening for bipolar disorder

Manic episodes can vary from easily identifiable prototypical presentations to milder or less typical symptoms that can be challenging to diagnose. Ideally, a full diagnostic evaluation with access to close informants is performed on patients presenting to clinical care; however, evaluations can be hurried in routine clinical care, and the ability to recall previous episodes might be limited. In this context, the use of screening scales can be a helpful addition to clinical care, although screening scales must be regarded as an impetus for a confirmatory clinical interview rather than a diagnostic instrument by themselves. The two most widely used and openly available screening scales are the mood disorders questionnaire (based on the DSM-IV criteria for hypomania) 61 and the hypomania check list (HCL-32), 62 that represent a broader overview of symptoms proposed to be part of a broader bipolar spectrum.

Racial/ethnic disparities

Although community surveys using structured or semi-structured diagnostic instruments, have provided little evidence for variation across ethnic groups, 63 64 observational studies based on clinical diagnoses in healthcare settings have found a disproportionately higher rate of diagnosis of schizophrenia relative to bipolar disorder in black people. 65 Consistent with similar disparities seen across medicine, these differences in clinical diagnoses are likely influenced by a complex mix of varying clinical presentations, differing rates of comorbid conditions, poorer access to care, greater social and economic burden, as well as the potential effect of subtle biases of healthcare professionals. 65 While further research is necessary to identify driving factors responsible for diagnostic disparities, clinicians should be wary of making a rudimentary diagnosis in patients from marginalized backgrounds, ensuring comprehensive data gathering and a careful diagnostic formulation that incorporates shared decision making between patient and provider.

Bipolar disorder is a recurrent illness, but its longitudinal course is heterogeneous and difficult to predict. 46 66 The few available long term studies of BD-I and BD-II have found a consistent average rate of recurrence of 0.40 mood episodes per year in historical studies 67 and 0.44 mood episodes per year in more recent studies. 68 The median time to relapse is estimated to be 1.44 years, with higher relapse rates seen in BD-I (0.81 years) than in BD-II (1.63 years) and no differences observed with respect to age or sex. 1 2 In addition to focusing on episodes, an important development in research and clinical care of bipolar disorder has been the recognition of the burden of subsyndromal symptoms. Although milder in severity, these symptoms can be long lasting, functionally impairing, and can themselves be a risk factor for episode relapse. 69 Recent cohort studies have also found that a substantial proportion of patients with bipolar disorder (20-30%) continue to have poor outcomes even after receiving guideline based care. 46 70 Risk factors that contribute to this poor outcome include transdiagnostic indicators of adversity such as substance misuse, low educational attainment, socioeconomic hardship, and comorbid disorders. As expected, those with more severe past illness activity, including those with rapid cycling, were also more likely to remain symptomatically and psychosocially impaired. 46 71 72

The primary focus of treating bipolar disorder has been to manage the manic, mixed, or depressive episodes that present to clinical care and to subsequently prevent recurrence of future episodes. Owing to the relapse remitting nature of the illness, randomized controlled trials are essential to determine treatment efficacy, as the observation of clinical improvement could just represent the ebbs and flows of the natural history of the illness. In the United States, the FDA (Food and Drug Administration) requires at least two large scale placebo controlled trials (phase 3) to show significant evidence of efficacy before approving a treatment. Phase 3 studies of bipolar disorder are generally separated into short term studies of mania (3-4 weeks), short term studies for bipolar depression (4-6 weeks), and longer term maintenance studies to evaluate prophylactic activity against future mood episodes (usually lasting one year). Although the most rigorous evaluation of phase 3 studies would be to require two broadly representative and independent randomized controlled trials, the FDA permits consideration of so called enriched design trials that follow participants after an initial response and tolerability has been shown to an investigational drug. Because of this initial selection, such trials can be biased against comparator agents, and could be less generalizable to patients seen in clinical practice.

A summary of the agents approved by the FDA for treatment of bipolar disorder is in table 1 , which references the key clinical trials demonstrating efficacy. Figure 1 and supplementary table 1 are a comparison of treatments for mania, depression, and maintenance. Effect sizes reflect the odds ratios or relative risks of obtaining response (defined as ≥50% improvement from baseline) in cases versus controls and were extracted from meta-analyses of randomized controlled trials for bipolar depression 86 and maintenance, 94 as well as a network meta-analysis of randomized controlled trials in bipolar mania. 73 Effect sizes are likely to be comparable for each phase of treatment, but not across the different phases, since methodological differences exist between the three meta-analytic studies.

FDA approved medications for bipolar disorder

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Summary of treatment response rates (defined as ≥50% improvement from baseline) of modern clinical trials for acute mania, acute bipolar depression, and long term recurrence. Meta-analytic estimates were extracted from recent meta-analyses or network meta-analyses of acute mania, 73 acute bipolar depression, 86 and bipolar maintenance studies 94

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Acute treatment of mania

As mania is characterized by impaired judgment, individuals can be at risk for engaging in high risk, potentially dangerous behaviors that can have substantial personal, occupational, and financial consequences. Therefore, treatment of mania is often considered a psychiatric emergency and is, when possible, best performed in the safety of an inpatient unit. While the primary treatment for mania is pharmacological, diminished insight can impede patients' willingness to accept treatment, emphasizing the significance of a balanced therapeutic approach that incorporates shared decision making frameworks as much as possible to promote treatment adherence.

The three main classes of anti-manic treatments are lithium, mood stabilizing anticonvulsants (divalproate and carbamazepine), and antipsychotic medications. Almost all antipsychotics are effective in treating mania, with the more potent dopamine D2 receptor antagonists such as risperidone and haloperidol demonstrating slightly higher efficacy ( fig 1 ). 73 In the United States, the FDA has approved the use of all second generation antipsychotics for treating mania except for lurasidone and brexpriprazole. Compared with mood stabilizing medications, second generation antipsychotics have a faster onset of action, making them a first line treatment for more severe manic symptoms that require rapid treatment. 99 The choice of which specific second generation antipsychotic to use depends on a balance of efficacy, tolerability concerns, and cost considerations (see table 1 ). Notably, the FDA has placed a black box warning on all antipsychotics for increasing the risk of cerebral vascular accidents in the elderly. 100 While this was primarily focused on the use of antipsychotics in dementia, this likely class effect should be taken into account when considering the use of antipsychotics in the elderly.

Traditional mood stabilizers, such as lithium, divalproate, and carbamazepine are also effective in the treatment of active mania ( fig 1 ). Since lithium also has a robust prophylactic effect (see section on prevention of mood episodes below) it is often recommended as first line treatment and can be considered as monotherapy when rapid symptom reduction is not clinically indicated. On the other hand, other anticonvulsants such as lamotrigine, gabapentin, topiramate, and oxcarbazepine have not been found to be effective for the treatment of mania or mixed episodes. 101 Although the empirical evidence for polypharmacy is limited, 102 combination treatment in acute mania, usually consisting of a mood stabilizer and a second generation antipsychotic, is commonly used in clinical practice despite the higher burden of side effects. Following resolution of an acute mania, consideration should be given to transitioning to monotherapy with an agent with proven prophylactic activity.

Pharmacological approaches to bipolar depression

Depressed episodes are usually more common than mania or hypomania, 103 104 and often represent the primary reason for individuals with bipolar disorder to seek treatment. Nevertheless, because early antidepressant randomized controlled trials did not distinguish between unipolar and bipolar depressive episodes, it has only been in the past two decades that large scale randomized controlled trials have been conducted specifically for bipolar depression. As such trials are almost exclusively funded by pharmaceutical companies, they have focused on the second generation antipsychotics and newer anticonvulsants still under patent. These trials have shown moderate but robust effects for most recent second generation antipsychotics, five of which have received FDA approval for treating bipolar depression ( table 1 ). No head-to-head trials have been conducted among these agents, so the choice of medication depends on expected side effects and cost considerations. For example, quetiapine has robust antidepressant efficacy data but is associated with sedation, weight gain, and adverse cardiovascular outcomes. 105 Other recently approved medications such as lurasidone, cariprazine, and lumateperone have better side effect profiles but show more modest antidepressant activity. 106

Among the mood stabilizing anticonvulsants, lamotrigine has limited evidence for acute antidepressant activity, 107 possibly owing to the need for an 8 week titration to reach the full dose of 200 mg. However, as discussed below, lamotrigine can still be considered for mild to moderate acute symptoms owing to its generally tolerable side effect profile and proven effectiveness in preventing the recurrence of depressive episodes. Divalproate and carbamazepine have some evidence of being effective antidepressants in small studies, but as there has been no large scale confirmatory study, they should be considered second or third line options. 86 Lithium has been studied for the treatment of bipolar depression as a comparator to quetiapine and was not found to have a significant acute antidepressant effect. 88

Antidepressants

Owing to the limited options of FDA approved medications for bipolar depression and concerns of metabolic side effects from long term second generation antipsychotic use, clinicians often resort to the use of traditional antidepressants for the treatment of bipolar depression 108 despite the lack of FDA approval for such agents. Indeed, recent randomized clinical trials of antidepressants in bipolar depression have not shown an effect for paroxetine, 89 109 bupropion, 109 or agomelatine. 110 Beyond the question of efficacy, another concern regarding antidepressants in bipolar disorder is their potential to worsen the course of illness by either promoting mixed or manic symptoms or inducing more subtle degrees of mood instability and cycle acceleration. 111 However, the risk of switching to full mania while being treated with mood stabilizers appears to be modest, with a meta-analysis of randomized clinical trials and clinical cohort studies showing the rates of mood switching over an average follow-up of five months to be approximately 15.3% in people with bipolar disorder treated on antidepressants compared with 13.8% in those without antidepressant treatment. 111 The risk of switching appears to be higher in the first 1-2 years of treatment in people with BD-I, and in those treated with a tricyclic antidepressant 112 or the dual reuptake inhibitor venlafaxine. 113 Overall, while the available data have methodological limitations, most guidelines do not recommend the use of antidepressants in bipolar disorder, or recommend them only after agents with more robust evidence have been tried. That they remain so widely used despite the equivocal evidence base reflects the unmet need for treatment of depression, concerns about the long term side effects of second generation antipsychotics, and the challenges of changing longstanding prescribing patterns.

Pharmacological approaches to prevention of recurrent episodes

Following treatment of the acute depressive or manic syndrome, the major focus of treatment is to prevent future episodes and minimize interepisodic subsyndromal symptoms. Most often, the medication that has been helpful in controlling the acute episode can be continued for prevention, particularly if clinical trial evidence exists for a maintenance effect. To show efficacy for prevention, studies must be sufficiently long to allow the accumulation of future episodes to occur and be potentially prevented by a therapeutic intervention. However, few long term treatment studies exist and most have utilized enriched designs that likely favor the drug seeking regulatory approval. As shown in figure 1 , meta-analyses 94 show prophylactic effect for most (olanzapine, risperidone, quetiapine, aripiprazole, asenapine) but not all (lurasidone, paliperidone) recently approved second generation antipsychotics. The effect sizes are generally comparable with monotherapy (odds ratio 0.42, 95% confidence interval 0.34 to 0.5) or as adjunctive therapy (odds ratio 0.37, 95% confidence interval 0.25 to 0.55). 94 Recent studies of lithium, which have generally used it as a (non-enriched) comparator drug, show a comparable protective effect (odds ratio 0.46, 95% confidence interval 0.28 to 0.75). 94 Among the mood stabilizing anticonvulsant drugs, a prophylactic effect has also been found for both divalproate and lamotrigine ( fig 1 and supplementary table 1), although only the latter has been granted regulatory approval for maintenance treatment. While there are subtle differences in effect sizes in drugs approved for maintenance ( fig 1 and table 1 ), the overlapping confidence intervals and methodological differences between studies prevent a strict comparison of the effect measures.

Guidelines often recommend lithium as a first line agent given its consistent evidence of prophylaxis, even when tested as the disadvantaged comparator drug in enriched drug designs. Like other medications, lithium has a unique set of side effects and ultimately the decision about which drug to use among those which are efficacious should be a decision carefully weighed and shared between patient and provider. The decision might be re-evaluated after substantial experience with the medication or at different stages in the long term treatment of bipolar disorder (see table 1 ).

Psychotherapeutic approaches

The frequent presence of residual symptoms, often associated with psychosocial and occupational dysfunction, has led to renewed interest in psychotherapeutic and psychosocial approaches to bipolar disorder. Given the impairment of judgment seen in mania, psychotherapy has more of a supportive and educational role in the treatment of mania, whereas it can be more of a primary focus in the treatment of depressive states. On a broad level, psychotherapeutic approaches effective for acute depression, such as cognitive behavioral therapy, interpersonal therapy, behavioral activation, and mindfulness based strategies, can also be recommended for acute depressive states in individuals with bipolar disorder. 114 Evidence for more targeted psychotherapy trials for bipolar disorder is more limited, but meta-analyses have found evidence for decreased recurrence (odds ratio 0.56; 95% confidence interval 0.43 to 0.74) 115 and improvement of subthreshold interepisodic depressive and manic symptoms with cognitive behavioral therapy, family based therapy, interpersonal and social rhythm therapy, and psychoeducation. 115 Recent investigations have also focused on targeted forms of psychotherapy to improve cognition 116 117 118 as well as psychosocial and occupational functioning. 119 120 Although these studies show evidence of a moderate effect, they remain preliminary, methodologically diverse, and require replication on a larger scale. 121

The implementation of evidence based psychotherapy as a treatment faces several challenges, including clinical training, fidelity monitoring, and adequate reimbursement. Novel approaches, leveraging the greater tractability of digital tools 122 and allied healthcare workers, 123 are promising means of lessening the implementation gap; however, these approaches require validation and evidence of clinical utility similar to traditional methods.

Neurostimulation approaches

For individuals with bipolar disorder who cannot tolerate or do not respond well to standard pharmacotherapy or psychotherapeutic approaches, neurostimulation techniques such as repetitive transcranial magnetic stimulation or electric convulsive therapy should be considered as second or third line treatments. Electric convulsive therapy has shown response rates of approximately 60-80% in severe acute depressions 124 125 and 50-60% in cases with treatment resistant depression. 126 These response rates compare favorably with those of pharmacological treatment, which are likely to be closer to ~50% and ~30% in subjects with moderate to severe depression and treatment resistant depression, respectively. 127 Although the safety of electric convulsive therapy is well established, relatively few medical centers have it available, and its acceptability is limited by cognitive side effects, which are usually short term, but which can be more significant with longer courses and with bilateral electrode placement. 128 While there have been fewer studies of electric convulsive therapy for bipolar depression compared with major depressive disorder, it appears to be similarly effective and might show earlier response. 129 Anecdotal evidence also suggests electric convulsive therapy that is useful in refractory mania. 130

Compared with electric convulsive therapy, repetitive transcranial magnetic stimulation has no cognitive side effects and is generally well tolerated. Repetitive transcranial magnetic stimulation acts by generating a magnetic field to depolarize local neural tissue and induce excitatory or inhibitory effects depending on the frequency of stimulation. The most studied FDA approved form of repetitive transcranial magnetic stimulation applies high frequency (10 Hz) excitatory pulses to the left prefrontal cortex for 30-40 minutes a day for six weeks. 131 Like electric convulsive therapy, repetitive transcranial magnetic stimulation has been primarily studied in treatment resistant depression and has been found to have moderate effect, with about one third of patients having a significant treatment response compared with those treated with pharmacotherapy. 131 Recent innovations in transcranial magnetic stimulation have included the use of a novel, larger coil to stimulate a larger degree of the prefrontal cortex (deep transcranial magnetic stimulation), 132 and a shortened (three minutes), higher frequency intermittent means of stimulation known as theta burst stimulation that appears to be comparable to conventional (10 Hz) repetitive transcranial magnetic stimulation. 133 A preliminary trial has recently assessed a new accelerated protocol of theta burst stimulation marked by 10 sessions a day for five days. It found that theta burst stimulation had a greater effect on people with treatment resistant depression compared with treatment as usual, although larger studies are needed to confirm these findings. 134

Conventional repetitive transcranial magnetic stimulation (10 Hz) studies in bipolar disorder have been limited by small sample sizes but have generally shown similar effects compared with major depressive disorder. 135 However, a proof of concept study of single session theta burst stimulation did not show efficacy in bipolar depression, 136 reiterating the need for specific trials for bipolar depression. Given the lack of such trials in bipolar disorder, repetitive transcranial magnetic stimulation should be considered a potentially promising but as yet unproven treatment for bipolar depression.

The other major form of neurostimulation studied in both unipolar and bipolar depression is transcranial direct current stimulation, an easily implemented method of delivering a low amplitude electrical current to the prefrontal area of the brain that could lead to local changes in neuronal excitability. 137 Like repetitive transcranial magnetic stimulation, transcranial direct current stimulation is well tolerated and has been mostly studied in unipolar depression, but has not yet generated sufficient evidence to be approved by a regulatory agency. 138 Small studies have been performed in bipolar depression, but the results have been mixed and require further research before use in clinical settings. 137 138 139 Finally, the evidence for more invasive neurostimulation studies such as vagal nerve stimulation and deep brain stimulation remains extremely limited and is currently insufficient for clinical use. 140 141

Treatment resistance in bipolar disorder

As in major depressive disorder, the use of term treatment resistance in bipolar disorder is controversial since differentiating whether persistent symptoms are caused by low treatment adherence, poor tolerability, the presence of comorbid disorders, or are the result of true treatment resistance, is an essential but often challenging clinical task. Treatment resistance should only be considered after two or three trials of evidence based monotherapy, adjunctive therapy, or both. 142 In difficult-to-treat mania, two or more medications from different mechanistic classes are typically used, with electric convulsive therapy 143 and clozapine 144 being considered if more conventional anti-manic treatments fail. In bipolar depression, it is common to combine antidepressants with anti-manic agents, despite limited evidence for efficacy. 145 Adjunctive therapies such as bright light therapy, 146 the dopamine D2/3 receptor agonist pramipexole, 147 and ketamine 148 149 have shown promising results in small open label trials that require further study.

Treatment considerations to reduce suicide in bipolar disorder

The risk of completed suicide is high across the subtypes of bipolar disorder, with estimated rates of 10-15% across the lifespan. 150 151 152 Lifetime rates of suicide attempts are much higher, with almost half of all individuals with bipolar disorder reporting at least one attempt. 153 Across a population and, often within individuals, the causes of suicide attempts and completed suicides are likely to be multifactorial, 154 affected by various risk factors, such as symptomatic illness, environmental stressors, comorbidities (particularly substance misuse), trait impulsivity, interpersonal conflict, loneliness, or socioeconomic distress. 155 156 Risk is highest in depressive and dysphoric/mixed episodes 157 158 and is particularly high in the transitional period following an acute admission to hospital. 159 Among the available treatments, lithium has potential antisuicidal properties. 160 However, since suicide is a rare event, with very few to zero suicides within a typical clinical trial, moderate evidence for this effect emerges only in the setting of meta-analyses of clinical trials. 160 Several observational studies have shown lower mortality in patients on lithium treatment, 161 but such associations might not be causal, since lithium is potentially fatal in overdose and is often avoided by clinicians in patients at high risk of suicide.

The challenge of studying scarce events has led most studies to focus on the reduction of the more common phenomena of suicidal ideation and behavior as a proxy for actual suicides. A recent such multisite study of the Veterans Affairs medical system included a mixture of unipolar and bipolar disorder and was stopped prematurely for futility, indicating no overall effect of moderate dose lithium. 162 Appropriate limitations of this study have been noted, 163 164 including difficulties in recruitment, few patients with bipolar disorder (rather than major depressive disorder), low levels of compliance with lithium therapy, high rates of comorbidity, and a follow-up of only one year. Nevertheless, while the body of evidence suggests that lithium has a modest antisuicidal effect, its degree of protection and utility in complex patients with comorbidities and multiple risk factors remain matters for further study. Treatment of specific suicidal risk in patients with bipolar disorder must therefore also incorporate broader interventions based on the individual’s specific risk factors. 165 Such an approach would include societal interventions like means restriction 166 and a number of empirically tested suicide focused psychotherapy treatments. 167 168 Unfortunately, the availability of appropriate training, expertise, and care models for such treatments remains limited, even in higher income countries. 169

More scalable solutions, such as the deployment of shortened interventions via digital means could help to overcome this implementation gap; however, the effectiveness of such approaches cannot be assumed and requires empirical testing. For example, a recent large scale randomized controlled trial of an abbreviated online dialectical behavioral therapy skills training program was paradoxically associated with slightly increased risk of self-harm. 170

Treatment consideration in BD-II and bipolar spectrum conditions

Because people with BD-II primarily experience depressive symptoms and appear less likely to switch mood states compared with individuals with BD-I, 50 171 there has been a greater acceptance of the use of antidepressants in BD-II depression, including as monotherapy. 172 However, caution should be exercised when considering the use of antidepressants without a mood stabilizer in patients with BD-II who might also experience high rates of mood instability and rapid cycling. Such individuals can instead respond better to newer second generation antipsychotic agents such as quetiapine 173 and lumateperone, 93 which are supported by post hoc analyses of these more recent clinical trials with more BD-II patients. In addition, despite the absence of randomized controlled trials, open label studies have suggested that lithium and other mood stabilizers can have similar efficacy in BD-II, especially in the case of lamotrigine. 174

Psychotherapeutic approaches such as psychoeducation, cognitive behavioral therapy, and interpersonal and social rhythm therapy have been found to be helpful 115 and can be considered as the primary form of treatment for BD-II in some patients, although in most clinical scenarios BD-II is likely to occur in conjunction with psychopharmacology. While it can be tempting to consider BD-II a milder variant of BD-I, high rates of comorbid disorders, rapid cycling, and adverse consequences such as suicide attempts 175 176 highlight the need for clinical caution and the provision of multimodal treatment, focusing on mood improvement, emotional regulation, and better psychosocial functioning.

Precision medicine: can it be applied to improve the care of bipolar disorder?

The recent focus on precision medicine approaches to psychiatric disorders seeks to identify clinically relevant heterogeneity and identify characteristics at the level of the individual or subgroup that can be leveraged to identify and target more efficacious treatments. 1 177 178

The utility of such an approach was originally shown in oncology, where a subset of tumors had gene expression or DNA mutation signatures that could predict response to treatments specifically designed to target the aberrant molecular pathway. 179 While much of the emphasis of precision medicine has been on the eventual identification of biomarkers utilizing high throughput approaches (genetics and other “omics” based measurements), the concept of precision medicine is arguably much broader, encompassing improvements in measurement, potentially through the deployment of digital tools, as well as better conceptualization of contextual, cultural, and socioeconomic mechanisms associated with psychopathology. 180 181 Ultimately, the goal of precision psychiatry is to identify and target driving mechanisms, be they molecular, physiological, or psychosocial in nature. As such, precision psychiatry seeks what researchers and clinicians have often sought: to identify clinically relevant heterogeneity to improve prediction of outcomes and increase the likelihood of therapeutic success. The novelty being not so much the goals of the overarching approach, but the increasing availability of large samples, novel digital tools, analytical advances, and an increasing armamentarium of biological measurements that can be deployed at scale. 177

Although not unique to bipolar disorder, several clinical decision points along the life course of bipolar disorder would benefit from a precision medicine approach. For example, making an early diagnosis is often not possible based on clinical symptoms alone, since such symptoms are usually non-specific. A precision medicine approach could also be particularly relevant in helping to identify subsets of patients for whom the use of antidepressants could be beneficial or harmful. Admittedly, precision medicine approaches to bipolar disorder are still in their infancy, and larger, clinically relevant, longitudinal, and reliable phenotypes are needed to provide the infrastructure for precision medicine approaches. Such data remain challenging to obtain at scale, leading to renewed efforts to utilize the extant clinical infrastructure and electronic medical records to help emulate traditional longitudinal analyses. Electronic medical records can help provide such data, but challenges such as missingness, limited quality control, and potential biases in care 182 need to be resolved with carefully considered analytical designs. 183

Emerging treatments

Two novel atypical antipsychotics, amilsupride and bifeprunox, are currently being tested in phase 3 trials ( NCT05169710 and NCT00134459 ) and could gain approval for bipolar depression in the near future if these pivotal trials show a significant antidepressant effect. These drugs could offer advantages such as greater antidepressant effects, fewer side effects, and better long term tolerability, but these assumptions must be tested empirically. Other near term possibilities include novel rapid antidepressant treatments, such as (es)ketamine that putatively targets the glutamatergic system, and has been recently approved for treatment resistant depression, but which have not yet been tested in phase 3 studies in bipolar depression. Small studies have shown comparable effects of intravenous ketamine, 149 184 in bipolar depression with no short term evidence of increased mood switching or mood instability. Larger phase 2 studies ( NCT05004896 ) are being conducted which will need to be followed by larger phase 3 studies. Other therapies targeting the glutamatergic system have generally failed phase 3 trials in treatment resistant depression, making them unlikely to be tested in bipolar depression. One exception could be the combination of dextromethorphan and its pharmacokinetic (CYP2D6) inhibitor bupropion, which was recently approved for treatment resistant depression but has yet to be tested in bipolar depression. Similarly, the novel GABAergic compound zuranolone is currently being evaluated by the FDA for the treatment of major depressive disorder and could also be subsequently studied in bipolar depression.

Unfortunately, given the general efficacy for most patients of available treatments, few scientific and financial incentives exist to perform large scale studies of novel treatment in mania. Encouraging results have been seen in small studies of mania with the selective estrogen receptor modulator 185 tamoxifen and its active metabolite endoxifen, both of which are hypothesized to inhibit protein kinase C, a potential mechanistic target of lithium treatment. These studies remain small, however, and anti-estrogenic side effects have potentially dulled interest in performing larger studies.

Finally, several compounds targeting alternative pathophysiological mechanisms implicated in bipolar disorder have been trialed in phase 2 academic studies. The most studied has been N -acetylcysteine, a putative mitochondrial modulator, which initially showed promising results only to be followed by null findings in larger more recent studies. 186 Similarly, although small initial studies of anti-inflammatory agents provided impetus for further study, subsequent phase 2 studies of the non-steroidal agent celecoxib, 187 the anti-inflammatory antibiotic minocycline, 187 and the antibody infliximab (a tumor necrosis factor antagonist) 188 have not shown efficacy for bipolar depression. Secondary analyses have suggested that specific anti-inflammatory agents might be effective only for a subset of patients, such as those with elevated markers of inflammation or a history of childhood adversity 189 ; however, such hypotheses must be confirmed in adequately powered independent studies.

Several international guidelines for the treatment of bipolar disorder have been published in the past decade, 102 190 191 192 providing a list of recommended treatments with efficacy in at least one large randomized controlled trial. Since effect sizes tend to be moderate and broadly comparable across classes, all guidelines allow for significant choice among first line agents, acknowledging that clinical characteristics, such as history of response or tolerability, severity of symptoms, presence of mixed features, or rapid cycling can sometimes over-ride guideline recommendations. For acute mania requiring rapid treatment, all guidelines prioritize the use of second generation antipsychotics such as aripiprazole, quetiapine, risperidone, asenapine, and cariprazine. 102 192 193 Combination treatment is considered based on symptom severity, tolerability, and patient choice, with most guidelines recommending lithium or divalproate along with a second generation antipsychotic for mania with psychosis, severe agitation, or prominent mixed symptoms. While effective, haloperidol is usually considered a second choice option owing to its propensity to cause extrapyramidal symptoms. 102 192 193 Uniformly, all guidelines agree on the need to taper antidepressants in manic or mixed episodes.

For maintenance treatment, guidelines are generally consistent in recommending lithium if tolerated and without relative contraindications, such as baseline renal disease. 194 The second most recommended maintenance treatment is quetiapine, followed by aripiprazole for patients with prominent manic episodes and lamotrigine for patients with predominant depressive episodes. 194 Most guidelines recommend considering prophylactic properties when initially choosing treatment for acute manic episodes, although others suggests that acute maintenance treatments can be cross tapered with maintenance medications after several months of full reponse. 193

For bipolar depression, recent guidelines recommend specific second generation antipsychotics such as quetiapine, lurasidone, and cariprazine 102 192 193 For more moderate symptoms, consideration is given to first using lamotrigine and lithium. Guidelines remain cautious about the use of antidepressants (selective serotonin reuptake inhibitors, venlafaxine, or bupropion) in patients with BP-I, restricting them to second or third line treatments and always in the context of an anti-manic agent. However, for patients with BP-II and no rapid cycling, several guidelines allow for the use of carefully monitored antidepressant monotherapy.

Bipolar disorder is a highly recognizable syndrome with many effective treatment options, including the longstanding gold standard therapy lithium. However, a significant proportion of patients do not respond well to current treatments, leading to negative consequences, poor quality of life, and potentially shortened lifespan. Several novel treatments are being developed but limited knowledge of the biology of bipolar disorder remains a major challenge for novel drug discovery. Hope remains that the insights of genetics, neuroimaging, and other investigative modalities could soon be able to inform the development of rational treatments aimed to mitigate the underlying pathophysiology associated with bipolar disorder. At the same time, however, efforts are needed to bridge the implementation gap and provide truly innovative and integrative care for patients with bipolar disorder. 195 Owing to the complexity of bipolar disorder, few patients can be said to be receiving optimized care across the various domains of mental health that are affected in those with bipolar disorder. Fortunately, the need for improvement is now well documented, 196 and concerted efforts at the scale necessary to be truly innovative and integrative are now on the horizon.

Questions for future research

Among adolescents and young adults who manifest common mental disorders such as anxiety or depressive or attentional disorders, who will be at high risk for developing bipolar disorder?

Can we predict the outcomes for patients following a first manic or hypomanic episode? This will help to inform who will require lifelong treatment and who can be tapered off medications after sustained recovery.

Are there reliable clinical features and biomarkers that can sufficiently predict response to specific medications or classes of medication?

What are the long term consequences of lifelong treatments with the major classes of medications used in bipolar disorder? Can we predict and prevent medical morbidity caused by medications?

Can we understand in a mechanistic manner the pathophysiological processes that lead to abnormal mood states in bipolar disorder?

Series explanation: State of the Art Reviews are commissioned on the basis of their relevance to academics and specialists in the US and internationally. For this reason they are written predominantly by US authors

Contributors: FSG performed the planning, conduct, and reporting of the work described in the article. FSG accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Competing interests: I have read and understood the BMJ policy on declaration of interests and declare no conflicts of interest.

Patient involvement: FSG discussed of the manuscript, its main points, and potential missing points with three patients in his practice who have lived with longstanding bipolar disorder. These additional viewpoints were incorporated during the drafting of the manuscript.

Provenance and peer review: Commissioned; externally peer reviewed.

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Open access
Published: 18 April 2024

A bibliometric and visual analysis of cognitive function in bipolar disorder from 2012 to 2022

Xiaohong Cui 4 , 5 na1 ,
Tailian Xue 1 na1 ,
Zhiyong Zhang 1 ,
Hong Yang 2 &
Yan Ren 3

Annals of General Psychiatry volume 23 , Article number: 13 ( 2024 ) Cite this article

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Introduction

Bipolar disorder (BD) is a chronic psychiatric disorder that combines hypomania or mania and depression. The study aims to investigate the research areas associated with cognitive function in bipolar disorder and identify current research hotspots and frontier areas in this field.

Methodology

Publications related to cognitive function in BD from 2012 to 2022 were searched on the Web of Science Core Collection (WoSCC) database. VOSviewer, CiteSpace, and Scimago Graphica were used to conduct this bibliometric analysis.

A total of 989 articles on cognitive function in BD were included in this review. These articles were mainly from the United States, China, Canada, Spain and the United Kingdom. Our results showed that the journal “ Journal of Affective Disorders ” published the most articles. Apart from “Biploar disorder” and “cognitive function”, the terms “Schizophrenia”, “Meta analysis”, “Rating scale” were also the most frequently used keywords. The research on cognitive function in bipolar disorder primarily focused on the following aspects: subgroup, individual, validation and pathophysiology.

Conclusions

The current concerns and hotspots in the filed are: “neurocognitive impairment”, “subgroup”, “1st degree relative”, “mania”, “individual” and “validation”. Future research is likely to focus on the following four themes: “Studies of the bipolar disorder and cognitive subgroups”, “intra-individual variability”, “Validation of cognitive function tool” and “Combined with pathology or other fields”.

Bipolar disorder presents a complex clinical presentation. It is characterized by alternating episodes of mania and depression, and is a serious mental health problem with a high rate of disability and difficult to cure [ 1 ]. The phenotypic manifestations of BD include not only core abnormalities in mood regulation, but also cognitive impairments, sleep/wake disturbances, and a high prevalence of comorbidities in both internal medicine and psychiatry. Cognitive function, also known as neurocognitive function, refers to the ability of the human brain to process information, including memory, executive function, space, time, language comprehension and expression. The current cognitive dysfunction in patients with bipolar disorder primarily affects memory, attention, executive function and so on [ 2 ].

In recent years, there has been increasing attention on cognitive functioning in patients with bipolar disorder. For example, a study has shown that individuals with bipolar disorder experience cognitive impairments during periods of remission as well as during acute episodes of depression or mania. Cognitive impairment is associated with multiple factors, including age of onset, duration of remission, and cognitive impairment, which are also intrinsic phenotypes of the disease [ 3 ]. Different diseases cause varying degrees of cognitive impairment. Patients with schizophrenia exhibit comprehensive cognitive decline, while those with bipolar disorder primarily experience impairment in memory, attention, and executive function, especially during acute episodes. The classification of bipolar disorder also corresponds to different areas of cognitive impairment. The impact of medication treatment on the cognitive function of bipolar disorder patients is contradictory, requiring a combined approach with other therapeutic methods to improve patient cognition [ 4 ]. The assessment of cognitive function is also a research prominent topic in this field. The assessment of cognitive function in bipolar disorder includes both objective and subjective aspects. Various neuropsychological tests, such as the Rey Auditory Verbal Learning Test (RAVLT) and Rapid Visual Information Processing (RVP) test, are used to assess objective cognitive function in individuals with bipolar disorder. Subjective cognitive function assessment can be performed using the Cognitive and Physical Functioning Questionnaire (CPFQ) [ 5 ]. In addition to the above assessment tools, the results of another study showed: for patients with BD in partial or full remission, the Screen for Cognitive Impairment in Psychiatry (SCIP) and the Cognitive Complaints in Bipolar Disorder Rating Scale (COBRA) are effective tools for screening objective and subjective cognitive impairments, respectively [ 6 ]. These findings indicate that we need to assess different aspects of cognitive impairment in patients using various scales. This will help us better understand their cognitive performance and provide assistance for clinical treatment.

Bibliometrics is the analysis of published information (books, journal articles, datasets, blogs) and associated metadata (abstracts, keywords, citations). It describes or shows the relationship between published works by using statistical data [ 7 ]. The characteristics of publications and the relationships between publications can be described by qualitative and quantitative analysis. Cognitive function is currently a hot topic of research in bipolar disorder, but there are no bibliometric articles yet, although there are many articles on cognitive function in bipolar disorder.

In this article, we use CiteSpace and VOSviewer software to review the research of cognitive function in bipolar disorder in the past 12 years (from 2012 to 2022) to learn the status of international research, the shift in research hotspots and emerging trends in this field.

Research methodology

Data sources and search strategy.

This study searched the related literature of cognitive function in bipolar disorder from the Web of Science (core collection) database, The reason for choosing WoSCC is that it is a high-quality digital literature resources database, suitable for the quantitative analysis of literature. The retrieval formula is TS="bipolar disorder” OR “bipolar affective disorder” OR “bipolar depressive disorder” OR “bipolar spectrum disorder” OR “biphasic disorder” AND TS = cognition OR “cognitive impairment” OR “cognitive decline” OR “cognitive function” OR “cognitive dysfunction” OR cognitive, selected over a period of 2012–2022, analyzed the type of literature as articles and reviews, and included the studies without regard to language. Through the analysis of the titles, abstracts and keywords of the article, a total of 4939 articles were searched, 1005 articles were preliminarily screened out, 989 articles were obtained (865 articles, 124 reviews) (Fig. 1 ).

Flow chart of scientometric analysis

Research tools

In this study, VOSviewer software (version 1.6.18) on WoSCC database is applied to conduct co-occurrence analysis, combined with Scimago Graphica software to achieve a country map visualization analysis. Using CiteSpace (version of 6.1.R6) software for the database author co-operation analysis, keyword cluster analysis, literature co-citations and keyword mutation analysis. VOSviewer application developed by Nees Jan van Eck and Ludo Waltman (Leiden University) in 2010, can be used for a variety of network analysis, including collaborative analysis, keyword co-occurrence analysis, citation and co-citation analysis, and bibliographic coupling. It can be used to conduct co-authorship analysis, keyword co-occurrence analysis, citation and co-citation analysis, and bibliographic coupling [ 8 ]. Citespace is a software developed by Professor Chaomei Chen of Drexel University (Philadelphia, USA) for the visual analysis of scientific references. With the software, we can generate a series of visual knowledge atlases to understand the research hotspots in the field, delve into the forefront of its development, and ascertain emerging trends [ 9 ].

The parameters used for co-occurrence analysis using VOSviewer are the default parameters for the software, and the parameters used in CiteSpace are as follows: time slices (2012–2022), number of years per slice (1), node types (author collaborations, co-citations, and keywords), pruning (pathfinder, pruning sliced network, pruning the merged network), g-index (k = 25, literature co-citations are k = 15).

Analysis of publication years

Distribution of publication from 2012 to 2022

There are a total of 989 articles were used in this study from 57 countries. Figure 2 shows the distribution of publication years for articles in the field of cognitive function in bipolar disorder. Overall, the volume of articles in this field is relatively balanced, with more than 60 articles published annually starting from 2012. The number of articles published in 2018–2020 remained relatively stable. The highest volume of articles was in 2021, with 129 articles published. The annual cumulative volume model aligns with the annual growth data y = 91.188e 0.244x (R 2 = 0.8956). This also shows that the field of cognitive function in bipolar disorder has garnered significant attention from scholars, with the development of society and technology, the study of cognition in the context of bipolar disorder has become an important and hot topic.

Analysis of author

By analyzing the authors of the literature cited in this paper, we aimed to gain insights into the prominent scholars and core strength within this research area. Famous scholar Price pointed out that, in the same subject, half of the papers are written by a group of high-productivity authors, and the number of authors in this group is approximately equal to the square root of the total number of authors [ 10 ].

According to the Price’s Law, the minimum number of core authors in the field is m = 4.79, so authors with 5 or more posts (including 5) are positioned as core authors in the field, where they are active professionals. Table 1 shows the top five productivity authors with contributions in this area. Top of the list was Vieta E, professor and chair of psychiatry at the University of Barcelona, with the highest number of published articles (41). He spearheads research focused on investigating cognitive function, cognitive impairment, and clinical manifestations associated with bipolar disorder, leading the Bipolar Disorder and Depression Project in Barcelona, Catalonia, Spain.

We then analyzed the author’s cooperation relationship. These studies published between 2012 and 2022, the year per slice for analysis is 1 year. The author cooperation network is shown in Fig. 3 (N = 419, E = 862). The circle size of the node represents the number of publications.

The centrality indicates that an author has a close cooperation relationship with other authors. According to Table 1 , the centrality of Vieta E is 0.14 (centrality > 0.1), indicating that the author has cooperation with multiple authors in the field, while the centrality of Vinberg M is 0.02. The author’s cooperation network graph also shows that Vinberg M is far away from other high-yielding authors, indicating that the author has less cooperation with other high-yielding authors in the research of cognitive function in bipolar disorder.

Author collaboration network analysis. The shorter the distance between two nodes the thicker the connection, indicating a higher level of collaboration between the two authors. Green nodes represent earlier published studies, while yellow nodes represent more recent studies

Analysis of the most productive journals

The analysis of the journals in literature shows that journals published in this field belong to the medical field except a few comprehensive journals in the past ten years. The top 10 most publication journals have shown in Table 2 . Journals with more than 60 published articles were Journal of Affective Disorders , Psychiatry Research and Bipolar Disorders , with 185, 72, and 66 articles, respectively. Among them, PLOS ONE is an open-source journal, with 20 citations, ranking 10th in the number of published journals.

Analyzing of journal citation founds that (Table 2 ) the most cited journals are the top medical journal “ Acta Psychiatrica Scandinavica ”, with a total of 27 articles cited up to 47 times. It indicates that the journal publishes high-quality articles and is of widespread interest in the field of cognitive function in bipolar disorder. The contents published in this journal include: empirical studies, factor studies, and the influence of variable indicators on cognitive function in patients with bipolar disorder.

Furthermore, a visual analysis of the journal co-citation network reveals the presence of three clusters (Fig. 4 , in supplementary material). According to the subject of the co-citation literature clustering, it divided into three different themes. The top 3 most cited journals are Journal of Affective Disorders (3807 citations), Bipolar Disorders (2186 citations), and American Journal of Psychiatry (1284 citations). All three of these journals are in the JCR1. The most cited journal, Journal of Affective Disorders , which includes articles on affective disorder. It covers a wide range of subjects, including neuroimaging, cognitive neuroscience, genetics, molecular biology, etc. This is in line with the research focus on cognitive function in bipolar disorder.

Co-citation resource

Analysis of the most productive countries/regions

In 2012–2022, a total of 57 countries have published articles on cognitive function in bipolar disorder. To gain an understanding of the countries that have made significant contributions to this field, the study utilized VOSviewer to visualize the countries with 5 or more articles, a total of 33 countries met this criterion. The data was then exported in HTML format and imported into Scimago Graphica for visual analysis to generate map. The result is shown in the Fig. 5 .

As we can see, most of articles are written by scholars from a few countries. Other countries such as Chile, Ireland, Colombia, etc. They have published fewer articles related to cognitive function in bipolar disorder in 2012–2022.

The visual map of countries. ( A ) The size of each node represents the number of publications from that country. ( B ) The number displayed below each country indicates the total number of publications

A further analysis of high-productivity countries in the field is presented in Table 3 , showing the Top 5 countries in the field. According to the data in Table 3 , American scholars have contributed the most research articles (287 articles published), accounting for 29% of the total number of articles published in this field. The second largest country is China, with 123 articles. The USA and the United Kingdom with a centrality value above 0.1, with centrality ratios of 0.25 and 0.36, respectively, indicating that these countries work closely with other countries in the study of cognitive function in bipolar disorder.

Analysis of keywords

We can classify keywords according to their frequency of occurrence and point out the links between high-frequency keywords. The analysis of keywords can help us understand the academic structure of a field and reveal the frontiers of research in the discipline. Figure 6 ( in supplementary material) presents the network and density of keywords that are referenced in the top 50. Keywords that are close to each other are classified into the same cluster, providing an overview of the main topics related to cognitive function in bipolar disorder.

Co-occurrence analysis of keywords. ( A ) The size of the node represets the frequency of the keyword. ( B ) The distance between two node represets the strength of their association

In terms of co-occurrence frequency, the most frequent keyword was “Biploar disorder” with 712 citation times, followed by “Schizophrenia”, “Cognition”, “Meta analysis”, “Rating scale”, “Impairment”, “Euthymic patients”, “Deficits”, “Depression” and “Cognitive impairment” (Table 4 ). These high-frequency keywords reflect the hot spots of cognitive function in bipolar disorder.

In CiteSpace, the LLR method was used to cluster the keywords (N = 473, E = 947) (Fig. 7 , in supplementary material). All keywords were divided into 15 clusters with co-citation relationship, including the largest cluster “cognitive function” (#0), followed by “Alzheimer disease” (#1), “cognitive control” (#2) and “executive function” (#3). Clustering results point to clinical symptoms (including #0 cognitive function, #1 Alzheimer disease, #2 cognitive control, #3 executive function, #15 affective response inhibition) and diagnostic and intervention strategies (#5 psychological testing, #10 clinical staging model, #11 antipsychotic).

The cluster map of keywords

Generally, Q = 0.6638 (Q > 0.3) means that the clustering structure is significant. Silhouette: S value is the average contour value of clustering, it is generally believed that S > 0.5 clustering is reasonable, and S = 0.8425 (S > 0.7) means clustering is convincing (Fig. 7 ).

Analysis of reference co-citation

The CiteSpace software analysis was used to analyze studies published between 2012 and 2022, with 1-year time slice for analysis. The network diagram of the document is shown in Fig. 8 , which consists of 216 nodes and 235 connections. The more times a document is cited, the greater the circle of the node. A circle with a purple outer ring indicates the document of intermediary centrality, meaning it is cited by several documents simultaneously. The color of the circle corresponds directly to the time slice, with yellow representing the earlier year and green representing more recent years. For example, light yellow represents co-cited studies in 2012, while dark green represents more recent co-references.

Co-occurrence analysis of references. ( A ) The nodes represent references. ( B ) The lines represent the relationships between the references and the common references of the collected studies

Table 5 shows the top 10 references, the most frequently cited is “Bourne C (2013)”, and the title of the article is “Neuropsychological testing of cognitive impairment in euthymic bipolar disorder: an individual patient data meta-analysis”, with 104 citations. This article is a meta-analysis describing reliable measures of cognitive impairment in bipolar patients: VLT, Digit Span, and TMT. Among the top ten cited articles, the highest centrality is “Bora E (2017)”, with the title “Meta-analysis of longitudinal studies of cognition in bipolar disorder: comparison with healthy controls and schizophrenia”, with a centrality of 0.22, which indicates that the article has important reference value in cognitive research of bipolar disorder.

Burst analysis of keywords

Burst words are the keywords that are frequently quoted and regarded as frontier topic indicators in a certain period. Figure 9 shows the top 25 keywords which are the most burst between 2012 and 2022. The most recent keywords are “pathophysiology”, “subgroup”, “individual”, “cognitive control” and “cluster analysis”. The keyword “neurocognitive impairment”, shows the reference burst to 5.55 began in 2012.

As can be seen from the figure, the research hotspots can be divided into the following three stages. The first stage was from 2012 to 2016. Focused on exploring cognitive impairment in patients with bipolar disorder, with keywords such as “neurocognitive impairment”, “cognitive impairment”, and“internal phenotype”,. The second stage was from 2016 to 2019, the keywords include “cognitive control”, “executive function”, “task”, and “risk factors”. This stage focused on investigating specific cognitive domains affected in bipolar disorder, utilizing cognitive task procedures and further integrating cognition with neurology. In the third stage, from 2019 to 2022, the keywords with bursts include “subgroup”、 “individual”、 “validation”、 “pathophysiology”. This stage emphasized the study of the neurocognitive subgroup within bipolar disorder, validating the scale for measuring cognitive function, and combining with pathophysiology. These studies provide practical evidence for the treatment of bipolar disorder.

Top 25 Keyword with the strongest citation. The blue and white squares in each row on the right side of the figure correspond to the year of hotspot. Red squares represent the year of hotspot, and blue squares represent non-hotspot year. The recent successive red squares represent the research hotspots in recent wears

This is the first bibliometric analysis of cognitive function in bipolar disorder. Our investigation covered 989 publications from WoSCC, most of them are original articles, and a few are review articles. The results show that during 2012–2022, the number of related publications increased significantly.

From the perspective of the authors. Vieta E, McIntyre RS, Vinberg M, and Miskowiak KW have published 20 or more articles. The articles published by Professor Eduard focus on cognitive dysfunction of schizophrenia and bipolar disorder. One of these articles points out that there is a widespread cognitive impairment in the first episode of mania, and its severity is lower than that of individuals with schizophrenia in their first episode. BD patients performed better than schizophrenia patients in verbal working memory, mental speed, executive control, and immediate verbal memory [ 11 ]. Both diseases have cognitive impairments, albeit with varying degrees of severity. Exploring the comorbidities between the two can lead to the development of more treatment options. In McIntyre RS’s articles, two of them examined the impact of obesity on the cognitive function of BD patients. The result shows that overweight/obese BD patients have significant cognitive defects and experience more severe cognitive impairment than normal-weight BD patients [ 12 , 13 ]. Approved for obesity treatment, Liraglutide, a GLP-1R agonist, has shown promise in enhancing objective measures of cognitive function in adults diagnosed with mood disorders. This research suggests that Liraglutide could potentially serve as a therapeutic intervention to improve cognitive function in individuals with mood disorders [ 14 ]. This research finding highlights the possibility of investigating factors like obesity, which contribute to cognitive abnormalities in patients, as potential avenues for developing interventions to enhance cognitive function.

According to the analysis of centrality and author’s cooperative relationship, it can be known that Miskowiak KW cooperated with two productive authors, Vieta E and Vinberg M. The five articles, produced in collaboration with Vieta E, are systematic reviews or meta-analyses. Two of them focus on cognitive dysfunction in patients with bipolar disorder across different age groups. Euthymic youths with BD exhibit significant cognitive dysfunction in verbal learning and memory, working memory, visual learning, and memory domains. Further research has shown that euthymic adults with BD have more widespread cognitive impairment [ 15 ]. Euthymic older adults with BD have important deficits in almost all cognitive domains, particularly in the memory domain [ 16 ]. The impact of the disease varies among different age groups, with cognitive decline typically occurring as individuals grow older, resulting in irreversible damage. Treatment options can only enhance cognitive performance in patients to a certain extent. One article explores the effects of various medications on cognitive function of BD patients. The efficacy of N-acetyl cysteine, pregnenolone, ketamine, and pramipexole did not demonstrate any cognitive benefits, while mifepristone, galantamine, and insulin were shown to improve different areas of cognition [ 17 ]. In addition, they also conducted a functional magnetic resonance imaging study on individuals with BD, and the results showed that the neural basis of cognitive impairment in BD patients was a failure to recruit key regions in the CCN and to suppress task-irrelevant DMN activity during cognitive performance [ 18 ]. Miskowiak KW has published eight articles with Vinberg M, one of which is meta-analysis. From the perspective of the ‘cold’ and ‘hot’ cognition and neuroimaging, they found that the most promising specific endophenotypic marker for BD is the abnormalities within ‘hot’ cognition, which is represented by impairments in emotion processing and regulation and reward processing [ 19 ]. The remaining seven articles are experimental studies. There are four articles related to cognitive remediation. They first conducted erythropoietin (EPO) trials, which showed that EPO effectively promotes cognitive recovery in patients [ 20 ]. Then based on the above research, they conducted a study on Action-Based Cognitive Remediation (ABCR). The result showed that the ABCR can improve executive function and subjective cognitive functioning in BD patients [ 21 , 22 ]. In another study, cognitive remediation (CR) was shown to improve subjective sharpness/mental acuity, verbal fluency and psychological quality of life in BD patients [ 23 ].

Most of the research on cognitive functioning in bipolar disorder was published in Journal of Affective Disorders (IF = 6.533, Q1), indicating it is currently the most popular journal in this research field. Among these journals, the journal with the highest impact factor is Psychiatry Research (IF = 11.225, Q1), followed by Psychological Medicine (IF = 10.592, Q1). Besides, PLOS ONE , which ranks 10th in terms of the number of published articles, is an open-source journal. This indicates that the presence of open-source journals has also promoted the development of this field, and it can obtain full-text documents for free, which facilitates knowledge dissemination and allows researchers to stay updated with the latest findings in the field of cognitive function in bipolar disorder. Through the co-citation analysis of journals, it can be found that the top cited journals are Q1 journals, which provide support for the study of cognitive function in bipolar disorder. More importantly, the most of research on cognitive function in bipolar disorder is published in clinical journals, which indicates that current research is in the clinical research stage.

In this bibliometric analysis, the majority of the related articles were published by authors from the United States, China, Canada, Spain, and Britain. According to the centrality, the United States and Britain had more cooperation with other countries, indicating their higher level of international cooperation. Although the number of articles published by China ranks second, showed that it had made extensive development in this field. However, due to a lack of collaboration with other productive countries, its influence is relatively low. Therefore, more international cooperation is needed in China. Spain ranks third in the number of articles published and had a significant presence due to the most active scholar, “Vieta E”, from the University of Barcelona. Vieta E set up a group to study bipolar disorder and depression, through experimental design and clinical trials, research on medication treatments, psychological therapy, and biophysical therapy, investigate the clinical development and progression of these disorders.

Keywords can be regarded as the core content of a specific article. Thus, keyword frequencies provide important clues about major trends in a research area [ 24 ]. Through the co-occurrence analysis of keywords, it can be found that apart from bipolar disorder and cognition, schizophrenia is also a high-frequency cited keyword, which indicates that one direction of research in this field is a comparative study of schizophrenia and bipolar disorder. This study compares the evolution of cognitive functioning in the same intervention mode and explores the fields of cognitive impairment. The research has shown that people with schizophrenia also perform significantly worse than people with bipolar disorder on social cognitive tasks such as theory of mind (ToM) and emotion recognition [ 25 ]. The keyword “Rating Scale” is also frequently cited, reflecting that a research topic in this research field is the analysis and measurement of cognitive scale for bipolar disorder, such as evaluating the degree of cognitive impairment in patients with bipolar disorder using different scales. Through cluster analysis, it can be found that the research mainly focuses on the cognitive impairments, diagnosis, and treatment of bipolar affective disorder in clinical settings.

Highly co-cited references are those that are frequently cited together by other articles, and thus, can be regarded as knowledge bases in a particular field [ 26 ]. In this article, the top ten cited literature are listed. The total number of citations can be regarded as an important indicator of interest in a specific research field. Nine of the articles in the top 10 co-cited references are meta-analyses. The tenth article is an empirical study published by Torrent Carla in 2012. This study points out the changes in cognitive impairment in BD patients during a longitudinal study: except for a worsening of executive function and slight improvement of attention, other cognitive fields remained stable [ 27 ]. The most frequently cited documents have been mentioned before and will not be explained here. The second most cited article is a meta-analysis published by Bora E in 2009. The results of this analysis show that response inhibition, set- shifting, verbal memory, and target detection impairments are potential candidate endophenotypes for BD. Euthymic patients may be associated with the medication they are taking and can also be influenced by disease-related factors [ 28 ]. The top 10 co-cited references focus on the following topics: meta-analysis, cognitive impairment, endophenotype, memory, executive function, neuropsychological test, and neurocognition. These are the research bases of cognitive function in bipolar disorder.

The burst detection analysis can show the interests of the research field and the changes in research hotspots over time. If a keyword is a high-frequency cited burst word, it indicates that the keyword has been actively discussed or used in a certain period [ 29 ]. From the citation bursts, we can conclude that the research on cognitive function in bipolar disorder mainly focuses on the following aspects: subgroup, individual, validation, and pathophysiology.

“subgroup”. There are three neurocognitive subgroups of BD. The “cognitively intact group” does not differ from HCs. The “Selective cognitive impairment group” has a lower cognitive score compared to HCs and one or two cognitive fields are damaged. The “Global cognitive impairment group” shows overall cognitive impairment [ 30 , 31 , 32 ]. There are differences in cognitive performance between subgroups of BD. BD-I performs significantly worse than BD-II in some cognitive performance, such as verbal memory, and processing speed [ 33 ]. However, BD-II has larger impairments in inhibition [ 34 ]. The study of cognitive subgroups and the varying impairments of the cognitive function in different subtypes of bipolar disorder will emerge as a prominent research focus in this field.

“individual”. Bipolar patients demonstrate increased intra-individual variability in cognitive processing, which can be observed through dispersion across tests within a single testing session or relative variability compared to their average performance over time [ 35 , 36 ]. In the future, we can study the intra-individual variability of cognitive ability of BD patients and its clinical significance。.

“validation”. The related content is the validation of cognitive assessment for bipolar disorder. The effectiveness of a cognitive assessment tool may vary among different individuals or regions, so it is necessary to verify its validity when applied in new situations. ICAT, an internet-based cognitive assessment tool, has been found to be feasible for large-scale assessment and monitoring of cognition in the clinical management of bipolar disorder [ 37 ]. The reliability and validity of the new cognitive assessment tools may be verified in future research. By utilizing existing cognitive assessment tools, we can determine the extent of cognitive impairment in BD patients, which can help identify therapeutic targets. For instance, a study has shown that many patients with BD have sleep problems, which can impact the process of cognitive testing and the accuracy of test results. It indicates that sleep maybe a potential target for treating cognitive disturbances in BD [ 38 ].

“pathophysiology”, major depressive disorder (MDD) and BD exhibit similar microstructural abnormalities in anterior callosal fibers, which can be considered as a basis for the neuropathy physiology of these two disorder [ 39 ]. Additionally, some evidence indicates that there is a connection between the dysfunction of the the hypothalamic-pituitary-adrenal (HPA) axis and impairments in neurocognitive function in BD. Dopamine neurotransmission is also believed to play an important role in the pathophysiology of BD [ 40 ]. By incorporating these findings into a coherent pathophysiological model, future research can generate testable hypotheses.

Strengths and limitations

Firstly, this study analyzes the research on the cognitive function of bipolar disorder using a bibliometrics system for the first time, providing valuable guidance for scholars interested in related research. Secondly, this article uses two bibliometrics tools, VOSviewer and CiteSpace, which have been widely used in the field of bibliometrics, ensuring an objective data analysis process.

At the same time, there are certain limitations. The data of this study only comes from the WoSCC database, other databases are ignored, and language restriction to English may result in the omission of relevant studies. Additionally, the time span considered in this study is from 2012 to 2022, excluding the year 2023 due to insufficient data.

Cognitive function has important research value and promising prospects for application in bipolar disorder. In general, the number of research papers is increasing, and there is a need for stronger collaboration among countries. Each journal has its scope, and researchers can choose an appropriate journal based on their article. On the one hand, the study of the neurocognitive subgroup of bipolar disorder helps us determine cognitive domains that are impaired in individuals with the disorder. This knowledge is valuable for diagnosing the disease process of bipolar disorder. On the other hand, with the development of society, the cognitive function assessment tools of bipolar disorder are also constantly updated, and these tools can help us understand the general situation of the patient before formal treatment. Therefore, it is of great application value to continuously verify the reliability and validity of new cognitive assessment tools. In addition, more treatment options can be explored in combination with other areas.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We thank all authors who participated in the study.

The study was supported by the Research Project Supported by Shanxi Scholarship Council of China (2021 − 167) and the Scientific Research Project of Health Commission of Shanxi Province (2019007).

Author information

Xiaohong Cui and Tailian Xue contributed equally to this work.

Authors and Affiliations

Department of Psychology, School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, Shanxi, China

Tailian Xue & Zhiyong Zhang

Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China

Department of Psychiatry, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital, Taiyuan, 030032, China

Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China

Xiaohong Cui

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China

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Study design: YR, HY; data collection, analysis, and interpretation: TLX, XHC, YZZ; drafting of the manuscript: TLX; critical revision of the manuscript: XHC. All authors contributed to the article and approved the submitted version.

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Correspondence to Zhiyong Zhang or Yan Ren .

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Cui, X., Xue, T., Zhang, Z. et al. A bibliometric and visual analysis of cognitive function in bipolar disorder from 2012 to 2022. Ann Gen Psychiatry 23 , 13 (2024). https://doi.org/10.1186/s12991-024-00498-x

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Published: 25 March 2022

Characterizing mood disorders in the AFFECT study: a large, longitudinal, and phenotypically rich genetic cohort in the US

Maria Dalby ORCID: orcid.org/0000-0001-6018-3468 1 , 2 ,
Morana Vitezic 1 ,
Niels Plath 1 ,
Lene Hammer-Helmich 1 ,
Yunxuan Jiang 3 ,
Chao Tian 3 ,
Devika Dhamija 3 ,
Catherine H. Wilson 3 ,
David Hinds ORCID: orcid.org/0000-0002-4911-803X 3 ,
23andMe Research Team ,
Patrick F. Sullivan 2 , 4 ,
Joshua W. Buckholtz 5 , 6 na1 &
Jordan W. Smoller 7 , 8 na1

Translational Psychiatry volume 12 , Article number: 121 ( 2022 ) Cite this article

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Bipolar disorder
Clinical genetics

There has recently been marked progress in identifying genetic risk factors for major depression (MD) and bipolar disorder (BD); however, few systematic efforts have been made to elucidate heterogeneity that exists within and across these diagnostic taxa. The Affective disorders, Environment, and Cognitive Trait (AFFECT) study presents an opportunity to identify and associate the structure of cognition and symptom-level domains across the mood disorder spectrum in a prospective study from a diverse US population.

Participants were recruited from the 23andMe, Inc research participant database and through social media; self-reported diagnosis of MD or BD by a medical professional and medication status data were used to enrich for mood-disorder cases. Remote assessments were used to acquire an extensive range of phenotypes, including mood state, transdiagnostic symptom severity, task-based measures of cognition, environmental exposures, personality traits. In this paper we describe the study design, and the demographic and clinical characteristics of the cohort. In addition we report genetic ancestry, SNP heritability, and genetic correlations with other large cohorts of mood disorders.

A total of 48,467 participants were enrolled: 14,768 with MD, 9864 with BD, and 23,835 controls. Upon enrollment, 47% of participants with MD and 27% with BD indicated being in an active mood episode. Cases reported early ages of onset (mean = 13.2 and 14.3 years for MD and BD, respectively), and high levels of recurrence (78.6% and 84.9% with >5 episodes), psychotherapy, and psychotropic medication use. SNP heritability on the liability scale for the ascertained MD participants (0.19–0.21) was consistent with the high level of disease severity in this cohort, while BD heritability estimates (0.16–0.22) were comparable to reports in other large scale genomic studies of mood disorders. Genetic correlations between the AFFECT cohort and other large-scale cohorts were high for MD but not for BD. By incorporating transdiagnostic symptom assessments, repeated measures, and genomic data, the AFFECT study represents a unique resource for dissecting the structure of mood disorders across multiple levels of analysis. In addition, the fully remote nature of the study provides valuable insights for future virtual and decentralized clinical trials within mood disorders.

Genome-wide association studies

Emil Uffelmann, Qin Qin Huang, … Danielle Posthuma

The serotonin theory of depression: a systematic umbrella review of the evidence

Joanna Moncrieff, Ruth E. Cooper, … Mark A. Horowitz

Emotions and brain function are altered up to one month after a single high dose of psilocybin

Frederick S. Barrett, Manoj K. Doss, … Roland R. Griffiths

Introduction

Mood disorders have a high lifetime prevalence in the general population and represent the leading cause of disability worldwide [ 1 , 2 ]. Moreover, mood disorders cause marked impairment in social and occupational functioning, resulting in a high burden for the individual and to society [ 3 , 4 ]. Twin and family studies show moderate-high heritability for these syndromes, indicating a prominent role for genetic variation in conferring susceptibility [ 5 , 6 , 7 , 8 ]. MD has a lifetime prevalence of 15% [ 9 ] and twin-heritability of 30–40% [ 5 , 10 ]. In contrast, BD has a lifetime prevalence of 2.4% [ 11 ] and twin-heritability ~70% [ 6 , 12 ]. Genomic analyses have shown that mood disorders are highly polygenic with likely thousands of small-effect loci contributing to susceptibility [ 13 , 14 ]. Significant progress has been made in identifying common genetic risk variants associated with MD and BD, most recently from the Psychiatric Genomics Consortium (PGC). The PGC Bipolar working group identified 40 independent BD loci in a sample of 40,000 BD cases [ 15 ], and the PGC MD working group identified 102 independent loci associated with MD from more than 246,000 cases [ 16 ]. Despite these successes, a major obstacle in psychiatric genetics is our inability to map these signals to the symptom patterns, cognitive deficits and maladaptive decision-making that characterize mood disorders.

One critical open question is how genetic risk affects human cognition to predispose the development of mood disorder symptoms and related behaviors. With up to 90% of patients with major depression (MD) or bipolar disorder (BD) exhibiting impairment in multiple domains of cognition, this represents an important diagnostic and symptomatic feature in mood disorders and a key determinant of functional recovery [ 17 , 18 ]. Much of the morbidity and mortality in mood disorders is due to behavioral factors, such as substance abuse, aggression, self-harm, and risky sexual behavior [ 19 , 20 , 21 ]. These behaviors, in turn, are thought to result from deficits in cognitive processes related to cost-benefit decision-making, reinforcement learning, social cognition, and executive function [ 22 ]. Many groups have reported phenotypic associations between mood disorders and some of these cognitive processes [ 23 , 24 ]. However, such studies are typically small in size, limited in scope, and genetically uninformative, limiting insight into the underlying causes of cognitive dysfunction and maladaptive behavior in mood disorders.

It is widely recognized that the DSM-based nosology of psychiatric illness poorly captures two important features of mental disorders: the high degree of comorbidity between diagnostic taxa, and the profound symptom-level heterogeneity that exists within a given diagnostic taxon [ 22 , 25 , 26 , 27 ]. These features suggest the existence of latent transdiagnostic symptom clusters in mood disorders and are consistent with evidence for shared genetic liability between otherwise categorically distinct psychiatric disorders [ 28 , 29 , 30 , 31 , 32 , 33 ]. To date, we know little about how much of the shared variance among mood disorder symptoms, cognitive function and maladaptive behavior is due to genetic factors. Likewise, GWAS estimate the proportion of variance in liability attributable to common variants genome-wide (SNP-heritability) to be ~9% for MD and 18% for BD [ 15 ], which are fractions of the pedigree-based estimated heritability. This accords with the significant role of non-genetic factors in mood disorder risk. In particular, a number of environmental risk factors have been identified for mood disorders, including poverty and traumatic life events, particularly in early life. Understanding the mechanisms through which such environmental influences interact with genetic susceptibility is key to elucidating the risk architecture of mood disorders. However, existent GWAS data sets are unable to answer these and other important open questions because of practical constraints that preclude the collection of an appropriately rich set of phenotypic data at scale.

To bridge these gaps, we leveraged technological advances in web-based participant recruitment, diagnostic assessment and cognitive testing to create the AFFECT study. The AFFECT study employed a longitudinal case-control design in nearly 50,000 US-based participants with BD, MD, and controls. Study participants were recruited from the 23andMe, Inc research participant database and through social media, representing a diverse sample that includes patients who may be underrepresented in clinical samples. A key innovation of this study is the depth of phenotypic data acquired, made practical through the use of online data collection. The study collected 9 months of remote phenotypic assessments, including recent and lifetime diagnostic evaluations, transdiagnostic symptom assessments, longitudinal measures of symptom state severity, and detailed medication profiling. Further, we obtained detailed information about environmental risk and protective factors, personality traits, and real-world maladaptive behaviors related to mood disorder morbidity and mortality. Finally, we measured task-based cognitive performance using an online testing battery. In this paper, we present the AFFECT study design, enrollment process, data collection, and characterize the MD, BD, and control groups based on baseline descriptive characteristics and genetic analysis. Lastly, we assess cohort representativeness and disorder severity and demonstrate the similarity of the case groups to those from prior large-scale genomic studies.

Cohort design

This genetic, case-control study was designed to enroll three cohorts: 15,000 participants with MD, 10,000 participants with BD, and 25,000 controls with no lifetime MD or BD. Of these, 1533 participants (3.06%) withdrew consent or failed to return the spit kit or intake survey before the study termination date and were excluded.

Participant eligibility criteria were: age between 18 and 50 years upon enrolment; residence in the United States; access to a desktop or laptop computer; and no reported diagnosis of Parkinson’s disease, essential tremor, schizophrenia, or Alzheimer’s disease. Enrollment required that the participants self-reported having been diagnosed with MD or BD by a medical professional and prescribed medication to treat such a disorder. Enrollment into the control cohort required that participants reported no lifetime diagnosis of BD, MD, generalized anxiety disorder, or post-traumatic stress disorder (PTSD) as well as never having been prescribed an antidepressant, mood stabilizer, or antipsychotic medication. All study participants had to provide informed consent and a saliva sample for SNP array genotyping, and be willing to complete the online study sessions over the course of 9 months.

The study was conducted between August 2017 and September 2019 and online recruitment of participants, genotyping, and survey data collection were performed by 23andMe. Figure 1 illustrates the enrollment flow and study setup . Participants were recruited through two channels: all controls and approximately one-fifth ( n = 4997) of all case participants were recruited from 23andMe’s existing customer database through email or logged-in website invitation. All other case participants ( n = 9635) were recruited through social media such as Facebook and enrolled as new 23andMe customers. Study participants who met the eligibility criteria received compensation depending on if they were existing or new 23andMe customers. Existing customers, who had purchased a 23andMe kit prior to joining the study, received a $20 Amazon gift card. New customers received the 23andMe ® Health + Ancestry Service, including a DNA test kit, at no cost.

The procedural steps were: Informed consent, apply for enrollment and meet study inclusion and no exclusion criteria, return a saliva kit for genotyping (except for excisting costumers who purchased and returned a 23andMe kit prior to joining the study), and answer the baseline questionnaire. In the 9 months after enrolment, participants were asked to complete monthly surveys and cognitive tests.

Study assessments

The study content was designed by the AFFECT investigators and administered by 23andMe. The self-reported survey and test battery (Table 1 ) was initiated at session 1 with an extensive background survey covering: demographics (i.e., age, gender, race, ethnicity), socioeconomic information (i.e., marital status, current employment, education, parental education, income), clinical details about the given disorder (cases only; e.g., age of onset, current and past episode characterization), family psychiatric history, the Self-rated Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Level 1 Cross-Cutting Symptom Measure, and adverse childhood experiences (scale and scoring details in Supplementary Materials).

The mood and medication survey was also given at session 1 and repeated in sessions 2–5, 7, and 9. This survey included: medication history (session 1), changes in medications (all follow-up surveys), life events/life style (e.g., alcohol use and sleep patterns), Altman Self-rating of Mania (ASRM) scale, and Patient-Reported Outcomes Measurement Information System (PROMIS)-Depression scale (scale and scoring details in supplementary materials). The study battery further included standardized behavioral tasks assessing risk, impulsivity and psychopathic traits and five cognitive tools designed to assess different domains of functioning. The cognitive tests were either given at one or two time points as noted in Table 1 .

SNP genotyping

We evaluated common variant genetic contributions to risk for MD and BD using SNP array data. DNA extraction and genotyping were performed on saliva samples by the National Genetics Institute, a CLIA-licensed clinical laboratory and a subsidiary of the Laboratory Corporation of America. Samples were genotyped, phased and imputed by 23andMe standardized pipeline, as described in detail in Supplementary Methods. Roughly 9.22 million high-quality genotyped and imputed SNPs on autosomal and X chromosome were tested.

For each GWAS, we restrict participants to a set of individuals who had a specified ancestry determined through an analysis of local ancestry estimation [ 34 ] and a maximal set of unrelated individuals was chosen for each GWAS analysis using a segmental identity-by-descent (IBD) estimation algorithm [ 35 ].

Genome-wide associations

GWAS was performed on MD versus controls, BD versus controls, mood disorder (MD, BD) versus controls and MD versus BD using a logistic regression model: case/control ~ age + sex + top 5 Principal Components (PCs) + genotyping platforms + genotype . GWAS was first performed separately on individuals of European, African American, East Asian, Latino ancestry, and combined by fixed-effect meta-analysis using METAL [ 36 ]. GWAS results were adjusted for the genomic control inflation factors, which can be found under each Manhattan plot in Supplementary Figures . Note that the study enrollment channel (existing/enrolled customers) was embedded in the genotype platform term, where around 80% of existing customers were genotyped on 23andMe’s genotype platform v4, while all newly enrolled participants were genotyped on platform v5 (Supplementary Table 1 ). Across all results, we removed SNPs that had an available sample size of less than 20% of the total GWAS sample size; where logistic regression results that did not converge due to complete separation, identified by absolute value of effect size or standard error greater than 10 on the log-odds scale; or that had MAF < 0.1%.

SNP-heritability and genetic correlations

We used LD score regression (LDSC) [ 37 ] v1.0.1 to estimate SNP-heritability ( $h_{SNP}^2$ ) from GWAS summary statistics for European ancestry MD and BD including variants with r 2 > 0.8 and minor allele frequency ≥0.01. Estimates of $h_{SNP}^2$ on the liability scale depend on the assumed lifetime prevalence of each disorder in the population (K). We report $h_{SNP}^2$ with K = [0.001–0.3] for MD and K = [0.001–0.03] for BD.

Genetic correlations (r g ) to external summary statistics were also performed using LDSC [ 37 ]. External data included; the PGC MDD meta-analysis samples PGC-MDD1 (2013) [ 10 ], PGC-MDD2 excluding the 23andMe sample (2018) [ 38 ], and PGC-MDD3 excluding the 23andMe sample (2019) [ 16 ]; the 23andMe discovery sample of MDD (herein Hyde et. al, 2016; where 5.0% of MD cases and 4.3% of controls from the AFFECT study were also included in Hyde et al.) [ 39 ]; the two most recent PGC BD meta-analysis samples PGC-BD2 (2019) [ 40 ] and PGC-BD3 (including the PGC-BD3 type I and type II sub-cohorts (2020) [ 15 ]); the most recent PGC SCZ meta-analysis samples PGC-SCZ2 (2014) [ 41 ] and PGC-SCZ3 (2020) [ 41 , 42 ]. Data was obtained from https://www.med.unc.edu/pgc/download-results/ and through the 23andMe data-access portal.

Statistical analyses

Sample comparisons were conducted using R (v3.5.2). Descriptive statistics were performed on the total participation pool and on subgroups: the three cohorts of MD, BD, and controls; within subtype of BD diagnosis (BD1 vs. BD2) and, within each cohort subgroups based on enrollment strategy (i.e., participants drawn from the 23andMe database and participants enrolled through social media for this study). For categorical variables, the number and percentage were reported for each value. For quantitative variables, the mean, median, standard deviation, and ranges were reported. Differences in demographic and clinical covariates were compared using regression models (continuous or categorical variables) and Fisher’s exact tests for categorical variables.

Cohort characteristics

A total of 48,467 participants were included in these analyses: 14,768 reported that they had been diagnosed and treated for MD, 9864 had been diagnosed and treated for BD, and 23,835 were controls with no lifetime history of MD or BD (Fig. 1 ). The BD cohort contained 3070 (31.2%) BD subtype I (BD1), 5053 (51.3%) BD subtype II (BD2), and 1718 (17.5%) did not specify the latest type of BD diagnosis received (BD unspecified-type). Among all participants, 72% were female and the mean age was 32.3 years (range 18–52 years). Most participants were of European ancestry (71.9%) followed by Latino (14.2%), African American (3.8%), and East Asian (3.6%) ancestry (Table 2 ).

Participant completion rates ranged from 28 to 100% (mean 42.6%) per session and were lower for cognitive assessments than for surveys (Supplementary Table 2 ). Study retention (i.e., number of assessments completed) was highest for MD cases (mean 50.2%, SD 30.6) followed by BD cases (mean 45.2%, SD 30.6), lowest for controls (mean 38.2%, SD 28.8), and higher in females (mean 45.0%, SD 30.1) than males (mean 38.7%, SD 29.9) (Supplementary Fig. 1 ). Study retention was positively correlated with educational level and age, and negatively correlated with reported adverse childhood experience score, BMI, ASRM score, and the DSM-5 cross-cutting domains of substance use, anxiety, depression, anger, suicidal ideation, and sleep problems (Supplementary Fig. 2 ).

Marital status, highest education achieved, and current socioeconomic status were reported at baseline and followed-up by a brief status assessment during each longitudinal assessment. Overall, socioeconomic status was significantly lower for cases, especially BD participants (Supplementary Table 3 ). In particular, we found that 19.5% and 26.9% of MD and BD participants, respectively, were currently not in paid employment as compared to only 7.3% of the control cohort. We observed an ascertainment effect in which case participants drawn from the 23andMe database (existing consumers) showed higher yearly salary and educational level than those enrolled through social media (multivariate analysis, P < 1.0 × 10 −16 ). After adjusting for enrollment method, however, significant socioeconomic differences remained between cases and controls (multivariate analysis, P < 1.0 × 10 −16 , Supplementary Table 3 ).

Disease history

Figure 2A and Supplementary Table 4 summarizes the clinical features of MD and BD cases and highlights that both MD and BD presented with high disease severity. Most participants reported symptom onset in adolescence (MD; mean 13.2 (SD 5.1), BD; mean 14.3 (SD 5.2)) while formal psychiatric diagnosis was not typically received until early adulthood (MD; mean 19.5 (SD 6.6), BD; mean 23.2 (SD 7.6)), consistent with prior studies [ 43 , 44 , 45 ]. The course of illness differed between the disorders; BD cases tended to report short but recurrent episodes: 52.2% of the participants had experienced >10 episodes and 80.0% reported a typical episode duration of <3 months. In contrast, MD cases had fewer episodes of longer duration: 59.7% had experienced ≤10 episodes, 47.7% reported a typical episode duration of 3–6 months or longer, and 10.0% reported episode duration ≥1 year (Fig. 2A , Supplementary Fig. 3A, B ).

A Summary of key clinical features in cases reporting a diagnosis of MDD, BD subtype 1 (BD1) and BD subtype 2 (BD2), as per latest diagnosis recieved. Mood disorders cases in this 23andMe sub-cohort show high burdens of illness. Any medication class refers to medication received over the last 5 years and during the study. Percentage of those who answered one or several treatment questions in the medication survey. B Transdiagnostic symptoms. Radar plot of median score pr. symptom domain within controls (purple), MD (blue), and BD (orange) participants. Scores are based on the DSM-5 cross-cutting symptom measures, where max item score (ranging from 0 to 4) within each domain is reported and summarized.

As expected, psychotropic medication use was common, since this was an inclusion criterion: 23,202 (96.4%) of cases reported having taken medication for a mood disorder in the prior 5 years, 17,292 (70.2 %) were taking medication at baseline, and 7726 (31.4%) began or restarted a medication during the study. MD and BD participants (respectively) reported use of the following treatments in the prior 5 years and/or at present: antidepressants 13,803 (95.4%) and 8508 (88.1%); mood stabilizers 4875 (33,8%) and 8394 (86.9%); antipsychotics 6133 (42.4%) and 7972 (82.5%); and electroconvulsive therapy 107 (0.9%) and 144 (2.0%). Most cases had received cognitive or behavioral psychotherapy in the past 5 years (MD 9770, 67.7%, BD 7235, 79.3%; Supplementary Fig. 3C, D ), most commonly 1–2 times a week. BD1 cases had the highest rates of symptom-related hospitalization (63.6%), although the rates were also high for the other mood disorder diagnoses (BD2, 46.1%; MD, 29.0%).

Symptom state

Nearly half of the MD cases ( N = 6971, 47.5%) and about a quarter of the BD cases (2729, 27.8%) reported that they were experiencing an episode at baseline (Table 3 , Supplementary Fig. 4 ). Most BD participants reported their current episode as depressive (1694, 62.7%). A current manic episode was reported in 219 (7.13%) BD1 participants, 106 (6.17%) unspecified-type BD participants, and a current hypomanic episode was reported across BD type: BD1 140 (16.0%), BD2 407 (29.4%), and 55 (11.9%) unspecified-type. We further observed that participants enrolled through social media exhibite greater disease burden (Supplementary Fig. 5 , Supplementary Table 4 ) and were more likely to be in active mood episode compared to participants drawn from the 23andMe research participant database (41.0% versus 34.0%).

We defined probable depressive episodes using the Level 1 DSM-5 cross-cutting measure—depressive domain (score ≥ 2) and the PROMIS-depression scale ( T -score ≥60), which identified 71.7% of all cases being in a depressive episode at baseline. Additionally, we defined a probable manic/hypomanic episode from the Level 1 DSM-5 cross-cutting measure—manic domain (score ≥ 2) and the ASRM scale (score > 5), identifying 28.5% of BD participants being in an episode at baseline (Table 3 ). When comparing the symptom scale-based episodes with the self-identified episodes at baseline, we found reasonable correspondence for depressive episodes (κ = 0.43 and κ = 0.36 respectively for MD and BD) and a more modest correspondence for manic or hypomanic episodes (κ = 0.22).

Symptom-level comorbidities

The DSM-5 self-rated cross-cutting symptom measure assesses 13 transdiagnostic symptom domains of relevance across psychiatric diagnosis [ 34 ] (scoring details given in Supplementary Materials). We found that both MD and BD participants exhibited a wide-range of transdiagnostic symptoms (median number of positively screened symptom domains = 9), a clear distinction to the control cohort (median number of positively screened symptom domains = 2) (Fig. 2B , Supplementary Table 5 ). The most common symptom domains in cases were depression, mania, somatic symptoms (i.e. aches and pains), and anxiety. Furthermore, sleep problems and substance use symptoms provided the strongest differentiation of BD from MD (multivariable analysis, coefficient 0.43 (95% CI ±0.03) P < 2.2e −16 , coefficient 0.41 (95% CI ±0.05) P < 2.2e −16 , respectively).

Regarding non-psychiatric conditions, MD and BD participants reported higher rates of comorbidities compared to controls. This was particularly evident for inflammatory and neurological disorders (multivariable analysis, OR ≥ 3.03 P < 0.001, Supplementary Table 3 ).

Family psychiatric history

Family history prevalence of anxiety disorder, MD, BD, or PTSD in first-degree relatives is shown in Supplementary Table 6 . Rates were significantly higher for all disorders among cases (78.4 %) compared to controls (Fisher’s exact OR = 4.2 (95% CI ±0.1), P < 2.2e −16 ), particularly for the same disorder and within BD subtypes (Fisher’s exact OR (95% CI) MD = 6.6 (0.6), BD1 = 3.1 (±0.4), OR = 5.0 (±1.0), P < 2.2e −16 , Supplementary Fig. 6 ). The prevalence of mental disorders in first-degree relatives of controls (33.0%) was comparable to rates reported in population-based samples [ 46 ].

Environmental influences

Reported adverse childhood experiences (ACE) were assessed across multiple domains (i.e., psychological and sexual abuse, neglect, and household dysfunction) [ 47 , 48 ]. Childhood adversity was common, with 63.9% of participants reporting at least one ACE. The total ACE score was significantly higher in cases than controls, with almost twice as many ACEs reported (case mean = 3.96, control mean = 2.00, P < 1.0 × 10 −16 ). Moreover, BD cases reported more ACEs than MD cases (Supplementary Table 7 ). Within ACE domains, physical and emotional neglect showed the largest association with mood disorders (OR = 5.6, 95% CI ±0.4); again, these associations were considerably stronger in BD cases (OR = 6.54, 95% CI ±0.34).

SNP-heritability and genetic comparability

GWAS was conducted in European ancestry participants for mood disorder (MD + BD), each disorder separately, BD subtypes, and comparing MD versus BD. Furthermore, a trans-ethnic meta-analysis of European, Latino, African American and East Asian GWAS was conducted for MD and for BD. Variant-level analysis, which was not the focus of this paper, is provided in Supplementary Figs. 7 – 22 and sample sizes for each GWAS can be found in Supplementary Table 8 .

The SNP-heritability ( $h_{SNP}^2$ ) on the liability scale for European ancestry MD was 0.19 (SE 0.02) and 0.21 (SE 0.03) for a population prevalence of 0.10 and 0.15, respectively. These estimates are higher than those reported in previous self-reported or broadly ascertained MD cohorts [ 39 , 49 ]. The SNP-heritability for European ancestry BD was comparable to previous large cohorts [ 1 , 15 , 40 ] with $h_{SNP}^2$ estimates of 0.16 (SE 0.02) and 0.22 (SE 0.02) on the liability scale assuming population prevalence of 0.005 and 0.02, respectively (Fig. 3A ).

A Liability-scale SNP-heritability of AFFECT BD and MD as a function of population prevalence, ranging from 0.001 to 0.03 for BD and 0.001–0.3 for MD with r g estimates at every 0.001 step-wise increase. Dotted line represents s.e. B Estimated genetic correlations of European ancestry AFFECT BD and MD with PGC GWAS of MDD3 (excluding the 23andMe cohort), the 23andMe MD discovery cohort (Hyde et al, 2016), PGC-BD2, and of PGC-BD3, which is further divided into BD3 type I and type II. Correlations in AFFECT BD were performed with the full cohort (BD) and within BD type (BD1, BD2). All correlations were significant, circle size and values indicate r g . P -values, Z -scores and s.e are reported in Supplementary Table 9 .

To further compare the MD and BD cohorts to other mood disorder studies, we estimated genetic correlations (r g ) to the most recent and largest meta-analysis samples (Fig. 3B , Supplementary Table 9 ). We found that r g for AFFECT-MD was highest with PGC MD2 (0.85 (SE 0,06), P = 2.1 × 10 −40 ), followed by significant correlations to the other MD cohorts, then PGC BD2 type II. We found significant, but moderate, genetic correlation between and the PGC3 BP cohort (0.43 (SE 0.04), P = 5.3 × 10 −22 ). Of note, stronger genetic correlations were observed between the AFFECT-BD cohort and prior MD samples (0.61 (SE 0.1) – 0.78 (SE 0.08)), suggesting that the current BD cohort is genetically different than previously published BD cohorts that used more traditional clinical ascertainment (see Discussion). Genetic correlations of AFFECT-BD1 and BD2 cases to external data showed an increased positive correlation between BD1 and external BD cohorts (0.42 (SE 0.07) – 0.59 (SE 0.12)) and SCZ cohorts (0.30 (SE 0.07) – 0.33 (SE 0.06)), while the genetic correlations of BD2 was greater for external MD cohorts (0.46 (SE 0.1) – 0.71 (SE 0.06) and the PGC3 BP type II cohort (0.56 (SE 0.08), P = 1.2e−10).

The AFFECT study was initiated to advance our understanding of phenotypic and genetic heterogeneity in MD and BD and to clarify the role of shared genomic and environmental risk factors that may transcend their diagnostic boundaries. Several aspects of AFFECT are notable including the administration of task-based measures indexing multiple domains of cognition (e.g. executive, motivational, and social) that capture key facets of the Research Domain Criteria (RDoC) [ 50 ] framework; transdiagnostic symptom assays; the assessment of trait and environmental risk and resilience factors; and the repeated measures design enabling analysis of change in symptoms and multi-domain cognitive task performance. Here, we have presented baseline characterization of the cohort and summarized the clinical features of MD and BD cases.

The US-based study participants were ascertained from the general 23andMe participant database and from social media. Control participants did not self-report diagnosis of or treatment for mood disorders. Case participants self-reported a clinican-ascertained diagnosis of MDD or BD (I or II) and were currently using one or more prescribed medications to manage their symptoms. Additional study ascertainment criteria pertained to age (18–50 years old) and the absence of of Parkinsons disease, Alzheimers disease, essential tremor, or schizophrenia diagnosis. Demographic and socio-economic features of BD and MD cases in the AFFECT study were largely comparable to those reported in epidemiologic and clinical samples [ 51 , 52 , 53 ] with a substantial female predominance among cases. Consistent with prior research [ 54 , 55 ], reported adverse childhood experiences were relatively common and associated with significantly increased risk of mood disorder.

Prior studies have shown that selective participation represents a potential source of bias in both epidemiological and genetic association studies [ 56 , 57 ]. Consistent with this, several features of the cohort differ from those seen in many clinically ascertained mood disorder cohorts. For example, educational attainment and income levels among MD cases were somewhat higher than reported in population-based samples [ 52 ] as might be expected given the ascertainment through a direct-to-consumer genomics company. Interestingly, we observed some differences within the sample: lower socioeconomic status and greater illness severity were observed among those recruited through social media compared to participants drawn from the existing 23andMe consumer database. Although it might be expected that cases recruited through direct-to-consumer genomics and social media platforms would have less burden of illness compared with those ascertained clinically, this was not the case. In fact, most mood disorder cases in this study reported early-onset illness, recurrent episodes, positive family history, and treatment with medication and psychotherapy. Indeed, a history of psychiatric hospitalization among MD cases was higher (29%) than that reported in a representative sample of US adults (12%) [ 52 ]. Together, these suggest a high disease burden (significant impairment and dysfunction) in our cohort.

Overall, 71.7% of AFFECT participants reported symptoms of a current depressive episode at baseline, and 28.1% of BD cases reported current manic or hypomanic symptoms. This likely reflects the fact that BD2 was overrepresented in our BD cohort (51.3%) relative to population-based samples [ 11 , 53 ], but may also suggest that remote study participation is more likely for euthymic and depressive BD patients. We found that the agreement between self-reported and mood scale ratings for mania was limited. This underlines the limitations of self-reported assessments and symptom-based outcomes as discussed elsewhere [ 58 ].

Despite these considerations, we expect the AFFECT study to contribute importantly to understanding the genetic basis of mood disorders. The incorporation of transdiagnostic symptom and behavior measures, longitudinal symptom assessments, and task-based measures of neuro- and social cognition, make this a unique resource for genomic studies. In the initial GWAS of the AFFECT mood disorders, we identified several genome-wide significant loci; the strongest association was between MD and SNPs within NEGR1 , a gene encoding a synaptic adhesion protein that has been robustly associated with depression in prior studies [ 16 , 59 ]. Recent analyses have found that GWAS of MD samples characterized by “minimal phenotyping” (e.g. based on self-report of prior diagnosis and/or treatment for depression) show lower heritability and are enriched for less specific genetic effects on MD compared with samples diagnosed using strict syndromal criteria [ 60 ]. In this context, it is notable that the estimated liability scale h 2 SNP for AFFECT MD (0.19–0.21) is in the same range as “strictly-defined lifetime MDD” in that analysis and higher than what is seen in broadly-defined MD cohorts, including the previous 23andMe self-reported depression cohort [ 16 , 38 , 39 ]. As demonstrated in previous work [ 61 , 62 ], SNP heritability is a consequence of several known and unknown effects, including the exclusion of specific comorbidities, disease severity, and the use of controls from which other psychiatric disorders have been excluded [ 63 ].

Genetic correlation analyses indicate that AFFECT MD is highly correlated (r g = 0.71–0.85) with MD ascertained in studies included in the PGC. Unexpectedly, however, genetic correlations between AFFECT-BD and published PGC GWAS of BD were relatively modest (r g s = 0.38–0.43) while the genetic correlation between the AFFECT MD and AFFECT BD was approximating 1. Indeed, the pattern of genetic correlations seen with AFFECT-BD closely resembled those of AFFECT-MD and did not vary substantially by AFFECT-BD subtype 1 or 2. While recent genetic studies have shown that depression and bipolar depression have a large genetic overlap and many symptoms co-occur [ 17 ], we speculate that study exclusion of comorbid SCZ diagnosis and the fully remote ascertainment and follow-up strategy might have affected study participation, e.g deselected BD cases with psychotic features. Furthermore, the high genetic correlation within the AFFECT study sub-cohorts may have been affected by the use of fully shared controls that were screened for both MD and BD (i.e. “extreme” controls). Together, these results suggest a large genetic overlap with depression and high variability between different BD samples, further underlining the importance of understanding heterogeneity within and across diagnostic taxa.

The AFFECT study represents a unique cohort of remotely recruited individuals with MD and BD and controls. The availability of repeated measures over time as well as task-based cognitive domains will provide an important opportunity to examine the genomic basis of mood disorders and underlying traits. More in-depth analyses of these phenotypes and shared or unique contributions to BD and MD are forthcoming.

Data availability

The top 10,000 SNPs for each GWAS are provided in Supplementary Tables 10 – 15 . Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review). Participants were included in the analysis on the basis of consent status as checked at the time data analyses were initiated.

The full GWAS summary statistics for the 23andMe discovery data set will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply for access. Individual-level data are not publicly available due to participant confidentiality, and in accordance with the IRB-approved protocol under which the study was conducted. Researchers interested in the study’s individual-level data may apply to the 23andMe Research Innovation Collaborations program.

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Acknowledgements

We thank the project coordinator STF (23andMe Inc.), Lars-Peder Haahr (former emplyee at Lundebck A/S), all AFFECT-study scientists from Lundbeck A/S, Massachusetts General Hospital, and 23andMe Inc. for valuable discussion and input. This work was supported by a Post Doc grant (8054-00026B) from the Innovation Fund Denmark (MD). Finally, we thank all study participants, who made this work possible.

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These authors contributed equally: Joshua W. Buckholtz, Jordan W. Smoller.

Authors and Affiliations

H. Lundbeck A/S, Valby, Denmark

Maria Dalby, Morana Vitezic, Niels Plath & Lene Hammer-Helmich

Department of Medical Epidemiology and Biostatistics, Karolinska Institutete, Stockholm, Sweden

Maria Dalby & Patrick F. Sullivan

23andMe Inc, Sunnyvale, CA, USA

Yunxuan Jiang, Chao Tian, Devika Dhamija, Catherine H. Wilson, David Hinds, Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar Partida, Sayantan Das, Sarah L. Elson, Teresa Filshtein, Kipper Fletez-Brant, Pierre Fontanillas, Will Freyman, Anna Faaborg, Shirin T. Fuller, Pooja M. Gandhi, Julie M. Granka, Karl Heilbron, Alejandro Hernandez, Barry Hicks, Ethan M. Jewett, Katelyn Kukar, Keng-Han Lin, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Joanna L. Mountain, Priyanka Nandakumar, Elizabeth S. Noblin, Jared O’Connell, Yunru Huang, Joanne S. Kim, Vanessa Lane, Aaron A. Petrakovitz, G. David Poznik, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Christophe Toukam Tchakouté, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, Peter Wilton & Corinna Wong

Department of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Patrick F. Sullivan

Department of Psychology, Harvard University, Cambridge, MA, USA

Joshua W. Buckholtz

Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Jordan W. Smoller

Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

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Stella Aslibekyan
, Adam Auton
, Elizabeth Babalola
, Robert K. Bell
, Jessica Bielenberg
, Katarzyna Bryc
, Emily Bullis
, Daniella Coker
, Gabriel Cuellar Partida
, Sayantan Das
, Sarah L. Elson
, Teresa Filshtein
, Kipper Fletez-Brant
, Pierre Fontanillas
, Will Freyman
, Anna Faaborg
, Shirin T. Fuller
, Pooja M. Gandhi
, Julie M. Granka
, Karl Heilbron
, Alejandro Hernandez
, Barry Hicks
, Ethan M. Jewett
, Katelyn Kukar
, Keng-Han Lin
, Maya Lowe
, Jey C. McCreight
, Matthew H. McIntyre
, Steven J. Micheletti
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, Joanna L. Mountain
, Priyanka Nandakumar
, Elizabeth S. Noblin
, Jared O’Connell
, Yunru Huang
, Joanne S. Kim
, Vanessa Lane
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, G. David Poznik
, Morgan Schumacher
, Anjali J. Shastri
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, Jingchunzi Shi
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, Christophe Toukam Tchakouté
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Conceptualization: CHW, JWS, JWB. Data curation: MD, YJ, DD. Formal analysis: MD, YJ. Funding acquisition: 23andMe Research Team, NP. Project administration: 23andMe Research Team, NP, JWS, JWB. Supervision: PFS, LH-H, MV, DH, JWS, JWB. Writing the original draft: MD, PFS, JWS, JWB. Reviewing and editing: all authors. 23andMe Research Team contributed to this study: SA, AA, EBabalola, RKB, JB, KB, EBullis, DC, GCP, DD, SD, SLE, TF, KF-B, PF, WF, AF, STF, PMG, KH, BH, EMJ, KK, K-HL, ML, JCMcC, MHM, SJM, MEM, JLM, PN, ESN, JO’C, YH, AAP, VL, JSK, GDP, MS, AJS, JFS, JS, SS, VT, JYT, XW, WW, PW, AH, CWong, CTT.

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Correspondence to Maria Dalby or David Hinds .

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Competing interests.

The study was funded by H. Lundbeck A/S and the Milken Institute. MD, MV, NP, and LH-H are employees of H. Lundbeck A/S. DH, YJ, CTT, DD, CHW, and members of the 23andMe Research Team are employees of 23andMe, Inc. JWS is a member of the Leon Levy Foundation Neuroscience Advisory Board and received an honorarium for an internal seminar at Biogen, Inc.

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Dalby, M., Vitezic, M., Plath, N. et al. Characterizing mood disorders in the AFFECT study: a large, longitudinal, and phenotypically rich genetic cohort in the US. Transl Psychiatry 12 , 121 (2022). https://doi.org/10.1038/s41398-022-01877-2

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Research Article

The Dynamics of Mood and Coping in Bipolar Disorder: Longitudinal Investigations of the Inter-Relationship between Affect, Self-Esteem and Response Styles

* E-mail: [email protected]

Affiliation School of Psychology, Bangor University, Bangor, United Kingdom

Affiliation School of Psychological Sciences, University of Manchester, Manchester, United Kingdom

Affiliation Greater Manchester West NHS Trust, Manchester, United Kingdom

Affiliation Department of Psychology and Neuropsychology, University of Maastricht, Maastricht, The Netherlands

Affiliation Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester, United Kingdom

Affiliation Institute of Psychology, Health and Society, University of Liverpool, Liverpool, United Kingdom

Hana Pavlickova,
Filippo Varese,
Angela Smith,
Inez Myin-Germeys,
Oliver H. Turnbull,
Richard Emsley,
Richard P. Bentall

Published: April 26, 2013
https://doi.org/10.1371/journal.pone.0062514
Reader Comments

Previous research has suggested that the way bipolar patients respond to depressive mood impacts on the future course of the illness, with rumination prolonging depression and risk-taking possibly triggering hypomania. However, the relationship over time between variables such as mood, self-esteem, and response style to negative affect is complex and has not been directly examined in any previous study – an important limitation, which the present study seeks to address.

In order to maximize ecological validity, individuals diagnosed with bipolar disorder (N = 48) reported mood, self-esteem and response styles to depression, together with contextual information, up to 60 times over a period of six days, using experience sampling diaries. Entries were cued by quasi-random bleeps from digital watches. Longitudinal multilevel models were estimated, with mood and self-esteem as predictors of subsequent response styles. Similar models were then estimated with response styles as predictors of subsequent mood and self-esteem. Cross-sectional associations of daily-life correlates with symptoms were also examined.

Cross-sectionally, symptoms of depression as well as mania were significantly related to low mood and self-esteem, and their increased fluctuations. Longitudinally, low mood significantly predicted rumination, and engaging in rumination dampened mood at the subsequent time point. Furthermore, high positive mood (marginally) instigated high risk-taking, and in turn engaging in risk-taking resulted in increased positive mood. Adaptive coping (i.e. problem-solving and distraction) was found to be an effective coping style in improving mood and self-esteem.

Conclusions

This study is the first to directly test the relevance of response style theory, originally developed to explain unipolar depression, to understand symptom changes in bipolar disorder patients. The findings show that response styles significantly impact on subsequent mood but some of these effects are modulated by current mood state. Theoretical and clinical implications are discussed.

Citation: Pavlickova H, Varese F, Smith A, Myin-Germeys I, Turnbull OH, Emsley R, et al. (2013) The Dynamics of Mood and Coping in Bipolar Disorder: Longitudinal Investigations of the Inter-Relationship between Affect, Self-Esteem and Response Styles. PLoS ONE 8(4): e62514. https://doi.org/10.1371/journal.pone.0062514

Editor: Xiang Yang Zhang, Baylor College of Medicine, United States of America

Received: November 22, 2012; Accepted: March 21, 2013; Published: April 26, 2013

Copyright: © 2013 Pavlickova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was supported by a studentship for A. Smith from the Economic and Social Research Council ( www.esrc.ac.uk ) and a studentship for H. Pavlickova from the National Institute for Social Care and Health Research, the Welsh Assembly Government ( www.wales.gov.uk/nischr ; Project ref.: HS/09/004), and Betsi Cadwaladr University Health Board (BCUHB) Charitable Funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Attempts to understand the psychological mechanisms underlying bipolar disorder are made difficult by the multidimensional, dynamic and fluctuating nature of the symptoms experienced by patients. For example, although the term ‘bipolar disorder’ implies that depression and mania lie at opposite ends on a spectrum of affect, cross-sectional comparisons indicate that these two groups of symptoms lie on separate dimensions of psychopathology, so that patients can be simultaneously depressed and manic [1] , explaining why patients sometimes present with mixed episodes [2] . It has been reported that mood in bipolar patients can fluctuate chaotically over short periods of time [3] , and longitudinal studies have shown that, within individuals, manic and depressive symptoms vary relatively independently with each other, although with a small but statistically significant positive correlation between them [4] , again explaining why mixed episodes are sometimes observed. The implication of these observations is that psychological studies of bipolar patients should ideally be conducted with sophisticated designs that take into account the complex cross-sectional and longitudinal structure of symptoms, so that covariations between symptoms and psychological processes can be adequately detected.

Problems of self-esteem and related processes seem to be particularly evident in bipolar disorder; almost a century ago, Kreapelin [5] described in detail how manic grandiosity sharply contrasts with low self-esteem and withdrawal during periods of depression. More recent research on the psychological mechanisms in bipolar disorder has focused on self-related cognitive processes already implicated in unipolar depression, for example as proposed in theories by Beck [6] and by Abramson et al. [7] . These studies have shown that individuals with bipolar disorder often present with a negative attributional (explanatory) style [8] , a negative self-concept, and dysfunctional attitudes towards the self [9] , [10] , [11] , [12] . In contrast to Kraepelin’s earlier observations, cross-sectional comparisons suggest that these pessimistic cognitive biases may be evident across all phases of bipolar disorder [13] .

However, a somewhat different picture has emerged from studies employing longitudinal designs or studies examining symptoms rather than episodes. These studies have indicated that bipolar disorder is associated with substantial instability in affective and self-related processes. Pronounced daily fluctuations in self-esteem have been observed in studies of remitted patients [14] , those in depressive episode [13] , and also in studies of individuals assessed by questionnaire measures to be at high-risk of bipolar disorder [15] . Further, low self-esteem in persons with bipolar disorder prospectively predicts worsening of affective, particularly depressive, symptoms [10] , [16] , [17] . In a longitudinal study [18] , where patients were assessed every 6 months, although self-esteem correlated positively with current mania and negatively with current depression, negative self-esteem predicted both future depressive and future manic symptoms. Other self-related cognitive measures administered in the study, although correlating with current symptoms, did not predict future symptoms.

In a similar vein, pronounced fluctuations of affect in bipolar disorder have been indicated by studies of high-risk student samples [15] , [19] , subsyndromal individuals [20] , remitted bipolar patients [14] , and those currently in manic and depressive episode [13] . Notably, affect and self-esteem appear to fluctuate in concert and hence to be tightly linked [21] , [22] .

One way of examining shifts in mood and self-esteem is in the context of the coping mechanisms or response styles individuals employ as a response to low, or elevated, mood. In her work on unipolar depression, Nolen-Hoeksema [23] argued that these mechanisms include rumination, problem solving, distraction activities and risk-taking. In a factor-analytic study by Knowles et al. [24] , problem-solving and distraction loaded on a single factor they labeled active coping.

A number of studies have found that rumination predicts onset and severity of depression in unipolar patients [25] , [26] , [27] . Expanding on the original theory, Thomas and Bentall [28] hypothesized that, whilst at times rumination may exacerbate depressive mood in bipolar patients, at other times it may instigate vigorous attempts to avoid negative mood by engaging in high-risk activities resulting, in turn, in hypomania or full-blown mania. Thomas et al. [29] found high levels of rumination in remitted bipolar patients compared to controls, and high levels of self-reported active coping (problem solving and distraction activities) and risk-taking in manic patients compared to controls. Van der Gucht et al. [13] found high levels of rumination in patients in all phases of bipolar disorder, including remission, but again that self-reported risk-taking was elevated only in currently manic patients. Only one study has examined response styles in relation to daily life experiences and fluctuations in mood and self-esteem [15] . In this experience sampling study of high-risk sample of students selected by questionnaire, higher levels of rumination were associated with lower self-esteem, even though no differences in rumination between the low-risk and high-risk groups were identified.

Insight into the temporal dynamics of response styles in relation to other variable psychological processes such as mood and self-esteem has been precluded by the cross-sectional designs employed in most previous studies of bipolar disorder.

Therefore, the aim of the present study was to examine processes specific to bipolar disorder. First, we investigated cross-sectional associations between symptoms of depression and mania with daily life correlates (i.e. affect and self-esteem) and coping styles (rumination, risk-taking and adaptive coping). We predicted that symptoms of depression would be associated with low mood and self-esteem, and more pronounced fluctuations of both. In addition, we expected depressive symptoms to be related to increased levels of rumination. As to symptoms of mania, we predicted associations with increased mood, self-esteem, and their fluctuations. Furhtermore, mania was expected to be associated with risk-taking.

Second, this study sought to examine prospective associations between mood, self-esteem and response styles in two ways: a) whether mood and self-esteem at time T−1 predict engagement in response styles at the subsequent time point. We expected that low mood and self-esteem at time T−1 would predict increased levels of rumination at time T. In turn, high mood and self-esteem would predict increased risk-taking at time T; b) whether engaging in coping styles at time T−1 influences mood and self-esteem at time T. We expected that engaging in rumination would lead to decreased mood and self-esteem, whilst engaging in risk-taking would improve mood and self-esteem.

Materials and Methods

Ethical approval was obtained from the Leeds (East) Research Ethics Committee and the University of Manchester Senate Ethics Committee. Inclusion criteria for inception into the study were a) diagnosis of bipolar affective disorder, b) currently receiving outpatient care, c) ability to speak/read English, and d) ability to complete the self-report measures independently. Participants were excluded from the study if they met diagnostic criteria for schizophrenia, schizoaffective disorder, primary substance misuse disorder, or had a history of post-natal depression with no hypomania/mania according to DSM-IV [30] . Potential participants were approached via secondary care and self-help groups: 129 covering letters were posted by consultant psychiatrists, resulting in 40 responses, out of which 7 individuals withdrew prior to interview, 5 after receiving further information. Out of the 28 participants commencing the study, 5 dropped out, and 23 completed the study. In addition, consultant psychiatrists approached prospective participants during clinics (N unknown), out of which 3 withdrew after gaining further information, and 24 completed the study. Only one participant was recruited via self-help groups. A total of 48 participants diagnosed with bipolar disorder provided written informed consent and were included into the study: 28 were in a remission, 12 were currently depressed, and 8 currently hypomanic. Participants’ characteristics are described in Table 1 . All participants completed the Structured Clinical Interview for Axis I DSM-IV Disorders [31] .

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https://doi.org/10.1371/journal.pone.0062514.t001

Instruments

1. clinical measures..

To assess symptom levels at the beginning of the study, participants completed two clinical measures in a face-to-face interview.

The Hamilton rating scale for depression [HRSD, 32] consists of 17 items rated by the interviewer on a 0–4 scale with higher scores indicating more sever depressive symptomatology. The HRSD shows inter-rater reliability coefficients up to 0.90 [32] , and good validity and reliability [33] .

The Bech-Refaelson Mania Scale, Modified Version [MAS, 34] is widely used to assess symptoms of mania and designed to be administered alongside the HRSD. Each of its 11 items is rated on a five-point scale, resulting in a total score ranging between 0–44. The scale shows a high inter-observer reliability and an acceptable level of consistency across items [34] .

2. Psychological measures.

All variables pertaining to the psychological processes of concern in this study were derived from experience sampling method (ESM) diaries that participants were asked to complete over a six-day period.

Experience sampling method. The experience sampling method (ESM, [35] ) is a repeated self-assessment procedure completed in participants’ natural environments and thus advantageous over classically administered self-report questionnaires for its high ecological validity [36] . Its validity, reliability and feasibility have been demonstrated in a number of clinical populations, such as in samples of individuals with diagnosis of schizophrenia [37] , [38] , depression [39] , [40] , panic disorder [41] and bipolar disorder [42] , [43] , [44] .

Participants received a pre-programmed digital wristwatch emitting 10 bleeps a day in quasi-random intervals (between 7.30 a.m. and 10.30 p.m.) and six pocketsize diaries to be completed over the period of six days (i.e. one dairy to be completed per each study day). The diary booklet consisted of 10 self-report forms (one per beep), and each comprised scales assessing mood, self-esteem, and styles of coping with depressive mood. Participants received a thorough explanation of the method during a briefing session. To ensure that participants understood the method, they were asked to fill in one form in a trial booklet during the briefing. During the 6-day study period, participants were contacted by telephone to ascertain that they had managed to comply with the procedure, and were thoroughly debriefed after completion of the study. Only participants who completed more than 20 valid responses (i.e. an entry between 5 minutes prior and 15 minutes after the beep) were included in the analyses [45] . This resulted in exclusion of two participants (both females, mean age 59, with depression ratings of 0, 0 and mania ratings of 1 and 2.

Experience Sampling Method Variables

The items included in the ESM self-assessment forms were all rated on 7-point Likert scales and used to define the following variables:

Momentary self-esteem and self-esteem fluctuations.

Four items in the self-report form assessed momentary self-esteem (i.e. “I am a failure”, “I am ashamed of myself”, “I like myself”, and “I am a good person”). Using the Kaiser criterion, principal component analysis (PCA) on the raw within-participant scores revealed one factor accounting for 63% of the total variance. Both negative and positive items showed a strong loading on the factor (positive items<−.68; negative items >.80) and high internal consistency after reversing the two negative items scores (Cronbach’s α = .79). The momentary self-esteem score was defined as the mean score of the four items. Each fluctuation in self-esteem was defined as the absolute difference in the ratings of self-esteem between consecutive time points, with higher scores reflecting more intense fluctuations.

Positive and negative affect, and mood fluctuations.

Nine items assessing momentary positive (e.g. “I feel cheerful”) and negative (e.g. “I feel sad”) affect were used. PCA confirmed two separate factors (eigenvalues >1) together accounting for 66% of variance. The positive affect (PA) factor consisted of four items (“cheerful”, “excited”, “relaxed” and “satisfied”; Cronbach’s α = .82) and the negative affect (NA) factor incorporated five items (“lonely”, “anxious”, “sad”, “irritated” and “guilty”; Cronbach’s α = .86). Fluctuation in mood was defined as the absolute moment-to-moment change in ratings of a) positive mood, and b) negative mood; that is, at each time point two variables were obtained, fluctuation in positive mood and fluctuation in negative mood; higher values reflected more pronounced fluctuations.

Assessment of responses to depression.

Based on the revised version of Nolen-Hoeksema’s Response Style Questionnaire [23] , [24] , the self-assessment forms contained eight items evaluating participants’ coping and response strategies for depression (e.g. “Since the last bleep I have thought about the bad things that have happened to me.”) rated on a 7-point Likert scale ranging from −3 (Disagree) to +3 (Agree). Due to bimodal distribution of the scores suggesting that a portion of participants misunderstood the scale as 0 indicating ‘no engagement’, we have recoded all responses rated negatively (i.e. −3, −2, and −1) as 0. Consistent with previous studies [13] , [24] , PCA confirmed three independent factors accounting for 72% of the variance: rumination (2 items with loadings >.90; Cronbach’s α = .82), adaptive coping (4 distraction and problem-solving items with loadings >.59; Cronbach’s α = .72) and risk-taking (2 items with loadings >.91; Cronbach’s α = .84).

Data Analyses

The structure of ESM data allows for the investigation of longitudinal associations between ESM variables using regression methods, i.e. testing whether ESM variables at a given beep (i.e. T) are predicted by responses at the previous beep (T−1). The longitudinal nature of these data implies that ESM data have a hierarchical structure (i.e. ESM entries at each beep are clustered within participants); therefore the assumption of the independence of residuals required for linear models is violated. Multilevel modeling adequately account for this type of violations [46] , [47] , [48] . Data were analyzed with the XTREG module of STATA version 12.0 using maximum likelihood estimation. As a number of variables (i.e. symptoms of depression and mania, and all response styles) were severely positively skewed, bootstrapping (1000 iterations) was utilized, the recommended procedure when the assumptions of normality are violated [49] .

Multilevel regression models were employed as follows:

We investigated the daily life correlates of depressive and manic symptoms measured at baseline. Separate multilevel regression models were estimated for the following dependent variables: PA, NA, SE, fluctuations of PA, fluctuations of NA, fluctuations of SE, rumination, active-copying and risk-taking. For each model, symptoms of depression and mania were entered as independent variables.
We examined whether PA, NA and SE predicted subsequent response style behaviors. Response style items were phrased “Since the last bleep…” in the diary booklets and as such, assessed coping behaviours between successive time points T−1 and T. For the purpose of the present analyses they were treated as time T items. Separate multilevel regression models were estimated for each independent variable (i.e. PA, NA and SE) as measured at T−1 and response styles (i.e. rumination, active copying and risk-taking) at time T were entered into the models as dependent variables. We controlled for the confounding effect of response style at the previous time point (T−1), as well as for the baseline symptoms of depression and mania.
We tested whether response styles predicted subsequent levels of PA, NA, and SE. Separate multilevel regression models were estimated for each dependent variable (i.e. PA, NA and SE) at time T with response styles (rumination, adaptive copying, and risk-taking) at time T−1 as predictors. We controlled for the confounding effect of PA, NA and SE at the previous beep, and symptoms of depression and mania measured at a baseline.

Are Symptoms of Depression (HRSD) and Mania (MAS) Associated?

In preliminary analyses, we first examined the distributions of depression (HRSD) and mania (MAS) scores, and their associations. As previous studies found a weak, but significant correlation between symptoms of depression and mania [4] , [50] , we first examined the relatedness of the two scores. Correlation analyses in the present study did not reach statistical significance, r s = 0.18, p = .23. Nevertheless, in the following analyses both symptoms were controlled for simultaneously.

i. Are symptoms of depression and mania associated with daily life correlates?

Although our main goal was to investigate the longitudinal relationship between variables, the cross-sectional associations were examined first, see Table 2 . First, we investigated whether positive and negative mood, and self-esteem were related to symptom ratings. Statistical analyses were carried out for momentary level of each variable (i.e. PA, NA, SE) as well as their fluctuations. We found that both depression and mania were associated with higher momentary negative affect (p<.001), lower momentary positive affect (p<.001), and lower momentary self-esteem (p<.01), as well as with more pronounced fluctuations of all variables (all p s <.001).

https://doi.org/10.1371/journal.pone.0062514.t002

We also examined the associations between symptom ratings and response style scores (i.e. rumination, adaptive coping, and risk-taking). Depression was significantly associated with higher levels of rumination, adaptive coping and risk-taking (all ps <.001), whilst mania was significantly associated only with increased levels of risk-taking (p<.001; Table 2 ).

ii. Does affect and self-esteem at time T-1 predict response styles at time T?

The main aim of the present study was to examine associations between affect, self-esteem, and response styles over time. We first examined how affect and self-esteem influenced the way individuals engaged in response styles, and then (in the next section), how response styles affected subsequent mood and self-esteem.

First, the predictive properties of each affect and self-esteem variable at each time point (T−1) on rumination at the subsequent time point (T) was investigated ( Table 3 , upper rows). Multilevel regression analyses revealed that negative affect was associated with increased rumination (p<.001), whereas positive affect (p<.001) and self-esteem (p<.001) were associated with decreases in ruminative thinking at the subsequent time point. When all predictors were entered into the model simultaneously, only affect remained a significant predictor of subsequent rumination: positive affect was associated with a decrease (p<.01), whilst negative affect with an increase (p<.001) of rumination ( Table 3 lower rows).

https://doi.org/10.1371/journal.pone.0062514.t003

None of the independent variables was significantly associated with adaptive coping (all p s = ns; Table 3 ).

Finally, we examined whether affect and self-esteem at time T−1 predicted risk-taking at time T ( Table 3 , upper rows). Risk-taking was significantly predicted by high positive (p<.01), and low negative mood (p<.01) at the previous time point, but only positive affect (p = .071) remained marginally associated with risk-taking when all predictors were entered into the model simultaneously ( Table 3 , lower rows).

iii. Do response styles assessed at T-1 predict affect and self-esteem at T?

Multilevel regression models were estimated to examine whether response styles to depression predicted changes in positive affect, negative affect and self-esteem at subsequent time points. When separate models were estimated for a model with positive affect as the dependent variable, adaptive coping (p<.05), and risk taking (p<.01) at the previous time point significantly predicted an increase in positive affect (both p s <.05), whilst rumination significantly predicted a decrease in self-esteem, and only marginally in positive affect (p = .05). All predictors were significantly associated with positive affect when entered into the model simultaneously (all p s <.05, Table 4 ).

https://doi.org/10.1371/journal.pone.0062514.t004

When separate models were estimated with negative affect as the outcome variable, no significant associations were revealed. Nevertheless, in a model with all response styles entered into the model simultaneously, a marginally significant relationship between rumination at time T−1 and negative affect at the subsequent time point was found (p = .079).

In a model with self-esteem as the dependent variable, no significant associations with response styles at the previous time point were revealed. When all predictors were entered into the model simultaneously, adaptive coping at time T-1 significantly predicted an increase in self-esteem at time T (p<.05).

iv. Follow-up analyses.

In order to examine whether any of the identified relationships were moderated by symptoms of depression or mania, an interaction term between each predictor and symptoms was added into each of the models described in ii) and iii) above with all relevant predictors entered simultaneously. Each model was calculated twice, first with interactions between symptoms of depression and the predictors, followed by a similar model with interactions between symptoms of mania and the predictors. For example, in the case of the model with positive affect as a dependent variable and all three response styles as predictors, three interaction terms were added (between each response style and ratings of depression). A similar model was then calculated with interaction terms between each response style and ratings of mania.

Only one model yielded a significant baseline symptom × predictor interaction. A significant interaction term between symptoms of mania and levels of rumination (β = 0.02, SE = 0.01, p<.01, CI [.01.04]), was found when positive affect was the dependent variable. Additional analyses indicated that rumination led to a decrease in positive affect in individuals with low symptoms of mania at baseline (β = −.27, SE = .04, p<.001, CI [−.35 −.19]) but not in those with high symptoms of mania at baseline. No other significant interaction terms were identified (all p s>.05).

The present study is a novel investigation of the prospective relationships between affect, self-esteem and response styles in individuals diagnosed with bipolar disorder. It tests Nolen-Hoeksema’s [23] response style theory and its later adaptations [24] , [28] , originally formulated to explain the course of unipolar depression using longitudinal data from bipolar patients to examine the impact of psychological variables on response styles and, subsequently, the effect of response styles on psychological variables. The experience sampling method employed in this study allowed the capture of these dynamic relationships, which cannot be assessed using more conventional cross-sectional designs.

Before reviewing the main results, we will comment first on the observed cross-sectional relationships between mood and self-esteem in daily life and baseline symptoms of depression and mania. It was expected that low self-esteem and high negative affect would be associated with symptoms of depression, whereas high positive affect and self-esteem would relate to symptoms of mania. Further, we predicted that increased fluctuations of these processes would be related to both symptoms. Our expectations regarding associations with depression were confirmed, and in line with previous literature. Here, associations between depression and negative mood, as well as its instability, have been consistently reported in studies of high risk students [19] , [24] , [51] , subclinical samples [20] and bipolar patients [13] , [52] . Similarly, previous findings have indicated an association between depression and self-esteem [16] , as well as instability of self-esteem in high risk student [15] and patient studies [14] .

Contrary to our expectations, symptoms of mania showed similar associations with mood and self-esteem as depression (i.e. mania was associated with low mood and self-esteem, and their increased instability), although the effect found was smaller. In contrast to our findings, previous studies have found mania to be related to high mood [51] , and self-esteem comparable to that of controls [13] . Yet, our findings are not the first of its kind. An earlier factor analytic study suggested dysphoria to be the strongest component of mania [53] , and underlying negativity of affect and self-concept during mania have been suggested by studies employing implicit assessments [14] , [54] .

The discrepancy between the present study and previous reports, both employing explicit assessments, might be related to methodological differences. For example, a number of studies employed comparisons of different phases of bipolar disorder, rather than investigating associations of psychological measures with symptoms (e.g. [13] ), an approach complicated by frequent co-existence of depressive and manic symptoms. Another explanation might be related to age differences between examined populations. Several previous studies employed high-risk student populations, and it is likely that personal context of students is considerably different to that of adults with a history of severe mental illness. Although both kinds of studies may be tapping the same underlying vulnerabilities, their expression might be changing across the course of life. The present study is methodologically advantageous in that it has employed patients, representative of bipolar phenomenology, and utilized a longitudinal and ecologically valid assessment and robust statistical methods controlling for covariation of symptoms and non-normality of data.

The increased fluctuations in affect and self-esteem seen in relation to symptoms of depression and mania in the present study suggests that the fluctuations we have observed in remitted patients in previous studies [13] , [24] may have been the consequence of subsyndromal symptoms.

In respect of associations between symptoms and response styles, we expected that rumination would be associated with depression, and risk-taking with mania. Indeed, symptoms of depression were related to increased rumination, an observation that is consistent with Nolen-Hoeksema’s [23] original response style theory, and with findings from bipolar high-risk [24] , [28] , [55] , and patient studies [13] , [29] . The association observed between depressive symptoms and adaptive coping was unexpected, as an earlier patient study found adaptive coping to be related to mania rather than depression [29] . The disparity might reflect the differences between the retrospective questionnaire assessments employed by Thomas et al. [29] and the more ecologically valid experience sampling method utilized in the current study. Finally, risk-taking was positively associated with symptoms of depression as well as mania. Although we did not predict an association between depression and risk-taking, similar cross-sectional relationships have been reported previously [14] , [24] , [29] .

The main aim of the present study was to examine the unique associations between momentary mood, self-esteem and coping styles, and vice versa, whilst controlling for symptoms of depression and mania. To our knowledge, this is the first study to prospectively investigate Nolen-Hoeksema’s [23] response style hypothesis, utilizing measures of response styles in daily life. It was predicted that both low mood and low self-esteem would prompt rumination at a subsequent time point, whilst positive mood and high self-esteem might trigger risky behaviors. The hypotheses were mostly confirmed, with a number of implications requiring comment. As noted, previous cross-sectional studies reported an association between rumination and symptoms of depression. The present findings suggest that high levels of negative, and low levels of positive affect instigate the subsequent engagement in rumination and that, in turn, rumination impacts most robustly via the dampening of positive mood. Furthermore, rumination led to decrease in positive affect only in individuals with few symptoms of mania, whilst no effect was found in those with manic symptoms. These findings are in line with Nolen-Hoeksema’s notion that rumination as such does not cause depression, but rather moderates already depressive mood [56] . The null finding regarding the causal role of self-esteem potentially points to the precedence of affect over cognitive psychological processes in affective disorders, but further investigations are warranted, and this conjecture should be viewed with caution.

The findings regarding risk-taking have both theoretical and clinical implications. Although risk-taking have been found to be related to symptoms of depression and mania cross-sectionally, in a prospective design, positive, rather than negative, mood led to greater risk taking when controlling for the effect of symptoms (although the association reached only marginal significance). In turn, engaging in risk-taking resulted in improvements of mood. In a similar vein, Thomas et al. [29] and Van der Gucht [13] reported higher levels of risk-taking, as measured by questionnaire, in manic participants compared to controls. The failure to detect an association between risk-taking and negative affect, then, implies that this response style might not necessarily act as a defense against low mood as proposed previously [28] , but rather is associated with an increased emotional and behavioral reactivity to reward stimuli as proposed by the behavioural activation theory of mania [57] , [58] , [59] . This account is consistent with recent neuroimaging studies, which have pointed to the abnormal processing of reward stimuli in bipolar patients and at-risk samples [60] , [61] , [62] .

In her original theory, Nolen-Hoeksama (1991) suggested that engaging in distraction (which, along with problem-solving, was incorporated into adaptive coping in this and some previous studies) ameliorates depressive symptoms. Moreover, Nolen-Hoeksema argued that employing healthy coping strategies such as problem solving may be prevented by rumination. Our findings support these hypotheses only partially. Although in the current study neither mood, nor self-esteem instigated subsequent engagement in adaptive coping, employing this coping style led to substantial improvements in mood and self-esteem at the following time point. Furthermore, adaptive coping was found to be an effective strategy even when controlling for other coping strategies. Hence, adaptive coping appears to be a top-down strategy, that can be deliberately employed to improve one’s affective state, an observation that is consistent with earlier studies showing its effectiveness in natural and laboratory conditions [25] , [56] .

A number of limitations should be acknowledged. Despite methodological advantages of experience sampling method over classical self-report assessments [45] , some authors have raised concerns regarding participants’ compliance with, and hence reliability of, the pencil-and-paper protocol of experience sampling, favoring the use of electronic diaries [63] , [64] , [65] . Whilst this might be an important limitation in studies employing predetermined entries, previous studies have demonstrated comparable, and relatively high, compliance in electronic and paper diary studies, when using a random-entry design [66] , [67] , [68] , also employed in the present study. Further, it is possible that utilizing different time lags in the predictive analyses would have led to different results.

The findings have a number of clinical implications. Various psychotherapies operate by means of modifying coping strategies – though often using different methods (for review, see [69] ); the response style theory has been found to provide a useful framework for understanding the utility of coping styles. Our findings highlight the importance of therapeutic strategies to ameliorate rumination in bipolar patients, and also the potential value of psychoeducational methods of reducing risk taking in response to incipient manic symptoms. The observation that risk-taking prompted by positive affect leads to a further escalation of affect points to the need to interrupt this cycle during the earliest phase of a hypomanic episode. Existing cognitive behavior therapy strategies which have been shown to be effective already address these issues to some degree [70] . The results regarding adaptive coping are promising as they imply that individuals with severe illness retain some ability to effectively regulate their mood.

Author Contributions

Contributed to revising manuscript critically for important intellectual content: IM-G AS FV OT RPB RE HP. Conceived and designed the experiments: AS IM-G RPB. Performed the experiments: AS. Analyzed the data: FV HP RE. Wrote the paper: HP RPB OT IM-G FV RE.

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Diagnosis and management of bipolar disorders

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A bibliometric and visual analysis of cognitive function in bipolar disorder from 2012 to 2022

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