Personality and bipolar disorder: dissecting state and trait associations between mood and personality

2010 ◽  
Vol 41 (8) ◽  
pp. 1593-1604 ◽  
Author(s):  
J. H. Barnett ◽  
J. Huang ◽  
R. H. Perlis ◽  
M. M. Young ◽  
J. F. Rosenbaum ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Latent Growth ◽  
Major Depressive ◽  
Course Of Illness ◽  
Personality Variables ◽  
Manic Symptoms ◽  
Increased Risk ◽  
Neo Five Factor Inventory ◽  
Depressed Symptoms ◽  
Trait Associations

BackgroundSome personality characteristics have previously been associated with an increased risk for psychiatric disorder. Longitudinal studies are required in order to tease apart temporary (state) and enduring (trait) differences in personality among individuals with bipolar disorder (BD). This study aimed to determine whether there is a characteristic personality profile in BD, and whether associations between BD and personality are best explained by state or trait effects.MethodA total of 2247 participants in the Systematic Treatment Enhancement Program for Bipolar Disorder study completed the NEO Five-Factor Inventory administered at study entry, and at 1 and 2 years.ResultsPersonality in BD was characterized by high neuroticism (N) and openness (O), and low agreeableness (A), conscientiousness (C) and extraversion (E). This profile was replicated in two independent samples, and openness was found to distinguish BD from major depressive disorder. Latent growth modeling demonstrated that manic symptoms were associated with increased E and decreased A, and depressed symptoms with higher N and lower E, A, C and O. During euthymic phases, high N and low E scores predicted a future depression-prone course.ConclusionsWhile there are clear state effects of mood on self-reported personality, personality variables during euthymia predict future course of illness. Personality disturbances in extraversion, neuroticism and openness may be enduring characteristics of patients with BD.

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2020 ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldasarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Depressive Disorders ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk ◽  
Chi Squared

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researcher. We explored whether genetic variation could identify individuals with different metabolic profiles. Loci previously associated with schizophrenia, bipolar disorder and major depressive disorder were identified from literature and those overlapping loci genotyped on the Illumina CardioMetabo and Immuno chips (representing cardiometabolic processes and diseases) were selected. In the IMPROVE study (high cardiovascular risk) and UK Biobank (general population) multidimensional scaling was applied to genetic variants implicated in both mental and cardiometabolic illness. Visual inspection of the resulting plots used to identify distinct clusters. Differences between clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both cardiometabolic disease and schizophrenia (but not bipolar or major depressive disorders) identified three groups of individuals with distinct metabolic profiles. The grouping was replicated in UK Biobank, albeit with less distinction between metabolic profiles. This study provides proof of concept that common biology underlying mental and physical illness can identify subsets of individuals with different cardiometabolic profiles.

scholarly journals Disruptive mood dysregulation disorder in offspring of parents with depression and bipolar disorder

2017 ◽  
Vol 210 (6) ◽  
pp. 408-412 ◽  
Author(s):  
Lukas Propper ◽  
Jill Cumby ◽  
Victoria C. Patterson ◽  
Vladislav Drobinin ◽  
Jacqueline M. Glover ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Family History ◽  
Depressive Disorder ◽  
Control Group ◽  
Major Depressive ◽  
Disruptive Mood Dysregulation Disorder ◽  
Increased Risk ◽  
History Of ◽  
Mood Dysregulation

BackgroundIt has been suggested that offspring of parents with bipolar disorder are at increased risk for disruptive mood dysregulation disorder (DMDD), but the specificity of this association has not been established.AimsWe examined the specificity of DMDD to family history by comparing offspring of parents with (a) bipolar disorder, (b) major depressive disorder and (c) a control group with no mood disorders.MethodWe established lifetime diagnosis of DMDD using the Schedule for Affective Disorders and Schizophrenia for School Aged Children for DSM-5 in 180 youth aged 6–18 years, including 58 offspring of parents with bipolar disorder, 82 offspring of parents with major depressive disorder and 40 control offspring.ResultsDiagnostic criteria for DMDD were met in none of the offspring of parents with bipolar disorder, 6 of the offspring of parents with major depressive disorder and none of the control offspring. DMDD diagnosis was significantly associated with family history of major depressive disorder.ConclusionsOur results suggest that DMDD is not specifically associated with a family history of bipolar disorder and may be associated with parental depression.

scholarly journals Chronic stressors and trauma: prospective influences on the course of bipolar disorder

2013 ◽  
Vol 43 (12) ◽  
pp. 2583-2592 ◽  
Author(s):  
A. Gershon ◽  
S. L. Johnson ◽  
I. Miller
Keyword(s):  
Bipolar Disorder ◽  
Support Groups ◽  
Life Stress ◽  
Bipolar I Disorder ◽  
Course Of Illness ◽  
College Life ◽  
Manic Symptoms ◽  
Chronic Stressors ◽  
Bipolar Patients ◽  
Exposure To Trauma

BackgroundExposure to life stress is known to adversely impact the course of bipolar disorder. Few studies have disentangled the effects of multiple types of stressors on the longitudinal course of bipolar I disorder. This study examines whether severity of chronic stressors and exposure to trauma are prospectively associated with course of illness among bipolar patients.MethodOne hundred and thirty-one participants diagnosed with bipolar I disorder were recruited through treatment centers, support groups and community advertisements. Severity of chronic stressors and exposure to trauma were assessed at study entry with in-person interviews using the Bedford College Life Event and Difficulty Schedule (LEDS). Course of illness was assessed by monthly interviews conducted over the course of 24 months (over 3000 assessments).ResultsTrauma exposure was related to more severe interpersonal chronic stressors. Multiple regression models provided evidence that severity of overall chronic stressors predicted depressive but not manic symptoms, accounting for 7.5% of explained variance.ConclusionsOverall chronic stressors seem to be an important determinant of depressive symptoms within bipolar disorder, highlighting the importance of studying multiple forms of life stress.

Mood (Affective) Disorders

2018 ◽  
pp. 15-60
Author(s):  
Carol S. North ◽  
Sean H. Yutzy
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Mood Disorders ◽  
Affective Disorders ◽  
Essential Feature ◽  
Severe Depression ◽  
Manic Episode ◽  
Major Depressive ◽  
Manic Symptoms ◽  
Mood Depression

Descriptions of mood disorders go back to the time of Hippocrates. Mood disorders are primarily characterized by depressed and/or elevated (manic) moods. The essential feature of mood disorders is an episode that is a distinct and persistent change from a person’s typical mood (depression or mania), accompanied by other depressive and manic symptoms, lasting 2 weeks for a major depressive episode and 1 week for a manic episode. Such episodes typically remit and recur over the course of time. Manic episodes define bipolar disorder. Severe depression without manic episodes is diagnosed as major depressive disorder. Mood disorders present a 10- to 30-fold risk for suicide. Effective treatments for mood disorders include medications, brain stimulation modalities, and psychotherapy.

scholarly journals Unrecognised bipolar disorder in primary care patients with depression

2011 ◽  
Vol 199 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Daniel J. Smith ◽  
Emily Griffiths ◽  
Mark Kelly ◽  
Kerry Hood ◽  
Nick Craddock ◽  
...  
Keyword(s):  
Primary Care ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Psychosocial Functioning ◽  
Unipolar Depression ◽  
Major Depressive ◽  
Manic Symptoms ◽  
History Of ◽  
Primary Care Patients

BackgroundBipolar disorder is complex and can be difficult to diagnose. It is often misdiagnosed as recurrent major depressive disorder.AimsWe had three main aims. To estimate the proportion of primary care patients with a working diagnosis of unipolar depression who satisfy DSM–IV criteria for bipolar disorder. To test two screening instruments for bipolar disorder (the Hypomania Checklist (HCL–32) and Bipolar Spectrum Diagnostic Scale (BSDS)) within a primary care sample. To assess whether individuals with major depressive disorder with subthreshold manic symptoms differ from those individuals with major depressive disorder but with no or little history of manic symptoms in terms of clinical course, psychosocial functioning and quality of life.MethodTwo-phase screening study in primary care.ResultsThree estimates of the prevalence of undiagnosed bipolar disorder were obtained: 21.6%, 9.6% and 3.3%. The HCL–32 and BSDS questionnaires had quite low positive predictive values (50.0 and 30.1% respectively). Participants with major depressive disorder and with a history of subthreshold manic symptoms differed from those participants with no or little history of manic symptoms on several clinical features and on measures of both psychosocial functioning and quality of life.ConclusionsBetween 3.3 and 21.6% of primary care patients with unipolar depression may have an undiagnosed bipolar disorder. The HCL–32 and BSDS screening questionnaires may be more useful for detecting broader definitions of bipolar disorder than DSM–IV-defined bipolar disorder. Subdiagnostic features of bipolar disorder are relatively common in primary care patients with unipolar depression and are associated with a more morbid course of illness. Future classifications of recurrent depression should include dimensional measures of bipolar symptoms.

scholarly journals Current issues: women and bipolar disorder

2008 ◽  
Vol 10 (2) ◽  
pp. 229-238 ◽  
Keyword(s):  
Bipolar Disorder ◽  
Oral Contraceptives ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
Course Of Illness ◽  
Her Family ◽  
Risks And Benefits ◽  
Increased Risk ◽  
Individualize Treatment ◽  
The Individual

While the treatment of bipolar disorder (BD) is typically complex, the treatment of women with bipolar disorder is even more challenging because clinicians must also individualize treatment based on the potential for pregnancy, drug interactions with oral contraceptives, and an increased risk of endocrine diseases that can either impact the course of illness or become manifest with some treatments. Women with BD should be checked for hypothyroidism, and if prescribed antidepressants, carefully watched for rapid cycling or a mood switch to mania, hypomania, or a mixed state. Several medications interact with oral contraceptives or increase the risk of developing polycystic ovary syndrome. Consideration of possible pregnancy is essential, and should be planned in advance whenever possible. Rates of recurrence have been shown to be equal in pregnant and nonpregnant women with BD. Risks of medication to the fetus at various points of development must be balanced against the risks of not treating, which is also detrimental to both fetus and mother. The postpartum period is a time of especially high risk; as many as 40% to 67% of women with BD report experiencing a postpartum mania or depression. The decision to breastfeed must also take into account the adverse impact of sleep deprivation in triggering mood episodes. In order to best address these issues, clinicians must be familiar with the data and collaborate with the patient to assess risks and benefits for the individual women and her family.

scholarly journals Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anastasiya Nestsiarovich ◽  
Jenna M. Reps ◽  
Michael E. Matheny ◽  
Scott L. DuVall ◽  
Kristine E. Lynch ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
External Validation ◽  
Severe Depression ◽  
Common Data Model ◽  
Major Depressive ◽  
National Network ◽  
Increased Risk ◽  
One Year

AbstractMany patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD’s timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five “training” US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model’s area under the curve (AUC) varied 0.633–0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570–0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

scholarly journals 117 Impact of Antipsychotic Treatment Switching in Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

CNS Spectrums ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 276-276
Author(s):  
Rajeev Ayyagari ◽  
Darren Thomason ◽  
Fan Mu ◽  
Michael Philbin ◽  
Benjamin Carroll
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Side Effects ◽  
Depressive Disorder ◽  
Antipsychotic Treatment ◽  
Disease Relapse ◽  
Major Depressive ◽  
Study Objective ◽  
Increased Risk ◽  
Risk Of Relapse

Abstract:Study Objective:To evaluate the risk of relapse for patients with schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder (MDD) who switched antipsychotics compared with those who did not switch.Background:Antipsychotics are commonly used for maintenance treatment of SZ, BP, and MDD but can have significant side effects, such as extrapyramidal symptoms (EPS). Adherence to treatment is important for reducing the risk of relapse, but fear of side effects may prompt medication switching.Methods:Medicaid claims from 6 US states spanning 6 years were retrospectively analyzed for antipsychotic switching versus non-switching. For all patients with SZ, BD or MDD and for the subset of patients who also had ≥1 EPS diagnosis during the baseline period, times to the following outcomes, during a 2-year study period were analyzed: underlying disease relapse, psychiatric relapse, all-cause emergency room (ER) visit, all-cause inpatient (IP) admission and EPS diagnosis.Results:Switchers (N=10,548) had a shorter time to disease relapse, other psychiatric relapse, IP admissions, ER visits, and EPS diagnosis (all, log-rank P<0.001) than non-switchers (N=31,644). Switchers reached the median for IP admission (21.50 months) vs non-switchers (not reached) and for ER visits (switchers, 9.07 months; non-switchers, 13.35 months). For disease relapse, other psychiatric relapse, and EPS diagnosis, <50% of patients had an event during the 2-year study period. Comparisons in a subgroup of patients with ≥1 EPS diagnosis revealed similar outcomes.Conclusions:These results show that disease and other psychiatric relapse, all-cause ER visits, IP admissions, and EPS diagnosis occurred earlier for switchers than for non-switchers, suggesting that switching is associated with an increased risk of relapse in patients with SZ, BP and MDD.Funding Acknowledgements:This study was supported by Teva Pharmaceuticals, Petach Tikva, Israel.

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