Supplemental Material for Emotion–Behavior Decoupling in Individuals With Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

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.

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Predictors of conversion of major depressive disorder to bipolar disorder

Psychiatry Research ◽

10.1016/j.psychres.2021.113939 ◽

2021 ◽

pp. 113939

Author(s):

Satish Suhas ◽

Abha Thakurdesai ◽

Amal Jolly Joseph ◽

Chittaranjan Andrade

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive

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A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

Translational Psychiatry ◽

10.1038/s41398-020-01181-x ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Jakub Tomasik ◽

Sung Yeon Sarah Han ◽

Giles Barton-Owen ◽

Dan-Mircea Mirea ◽

Nayra A. Martin-Key ◽

...

Keyword(s):

Mental Health ◽

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Diagnostic Algorithm ◽

Health Questionnaire ◽

Major Depressive ◽

Blood Biomarker ◽

Mental Health Questionnaire ◽

Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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Attention-deficit/hyperactivity disorder and other neurodevelopmental disorders in offspring of parents with depression and bipolar disorder

Psychological Medicine ◽

10.1017/s0033291721001951 ◽

2021 ◽

pp. 1-8

Author(s):

L. Propper ◽

A. Sandstrom ◽

S. Rempel ◽

E. Howes Vallis ◽

S. Abidi ◽

...

Keyword(s):

Bipolar Disorder ◽

Attention Deficit Hyperactivity Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Mood Disorder ◽

Attention Deficit ◽

Neurodevelopmental Disorders ◽

Autism Spectrum ◽

Major Depressive ◽

Hyperactivity Disorder

Abstract Background Offspring of parents with major mood disorders (MDDs) are at increased risk for early psychopathology. We aim to compare the rates of neurodevelopmental disorders in offspring of parents with bipolar disorder, major depressive disorder, and controls. Method We established a lifetime diagnosis of neurodevelopmental disorders [attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disabilities, specific learning disorders, and motor disorders] using the Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetime Version in 400 participants (mean age 11.3 + s.d. 3.9 years), including 93 offspring of parents with bipolar disorder, 182 offspring of parents with major depressive disorder, and 125 control offspring of parents with no mood disorder. Results Neurodevelopmental disorders were elevated in offspring of parents with bipolar disorder [odds ratio (OR) 2.34, 95% confidence interval (CI) 1.23–4.47, p = 0.010] and major depressive disorder (OR 1.87, 95% CI 1.03–3.39, p = 0.035) compared to controls. This difference was driven by the rates of ADHD, which were highest among offspring of parents with bipolar disorder (30.1%), intermediate in offspring of parents with major depressive disorder (24.2%), and lowest in controls (14.4%). There were no significant differences in frequencies of other neurodevelopmental disorders between the three groups. Chronic course of mood disorder in parents was associated with higher rates of any neurodevelopmental disorder and higher rates of ADHD in offspring. Conclusions Our findings suggest monitoring for ADHD and other neurodevelopmental disorders in offspring of parents with MDDs may be indicated to improve early diagnosis and treatment.

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BDgene: A Genetic Database for Bipolar Disorder and Its Overlap With Schizophrenia and Major Depressive Disorder

Biological Psychiatry ◽

10.1016/j.biopsych.2013.04.016 ◽

2013 ◽

Vol 74 (10) ◽

pp. 727-733 ◽

Cited By ~ 33

Author(s):

Su-Hua Chang ◽

Lei Gao ◽

Zhao Li ◽

Wei-Na Zhang ◽

Yang Du ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive ◽

Genetic Database

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Twelve-month prevalence and disability of DSM-IV bipolar disorder in an Australian general population survey

Psychological Medicine ◽

10.1017/s0033291703001636 ◽

2004 ◽

Vol 34 (5) ◽

pp. 777-785 ◽

Cited By ~ 99

Author(s):

P. B. MITCHELL ◽

T. SLADE ◽

G. ANDREWS

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

National Survey ◽

Large Scale ◽

Well Being ◽

Major Depressive ◽

Co Morbidity ◽

Dsm Iv ◽

Days Out Of Role

Background. There have been few large-scale epidemiological studies which have examined the prevalence of bipolar disorder. The authors report 12-month prevalence data for DSM-IV bipolar disorder from the Australian National Survey of Mental Health and Well-Being.Method. The broad methodology of the Australian National Survey has been described previously. Ten thousand, six hundred and forty-one people participated. The 12-month prevalence of euphoric bipolar disorder (I and II) – similar to the euphoric-grandiose syndrome of Kessler and co-workers – was determined. Those so identified were compared with subjects with major depressive disorder and the rest of the sample, on rates of co-morbidity with anxiety and substance use disorders as well as demographic features and measures of disability and service utilization. Polychotomous logistic regression was used to study the relationship between the three samples and these dependent variables.Results. There was a 12-month prevalence of 0·5% for bipolar disorder. Compared with subjects with major depressive disorder, those with bipolar disorder were distinguished by a more equal gender ratio; a greater likelihood of being widowed, separated or divorced; higher rates of drug abuse or dependence; greater disability as measured by days out of role; increased rates of treatment with medicines; and higher lifetime rates of suicide attempts.Conclusions. This large national survey highlights the marked functional impairment caused by bipolar disorder, even when compared with major depressive disorder.

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Emotional availability in women with bipolar disorder and major depression: A longitudinal pregnancy cohort study

Australian & New Zealand Journal of Psychiatry ◽

10.1177/0004867421998796 ◽

2021 ◽

pp. 000486742199879

Author(s):

Pavitra Aran ◽

Andrew J Lewis ◽

Stuart J Watson ◽

Thinh Nguyen ◽

Megan Galbally

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Symptoms ◽

Major Depression ◽

Depressive Disorder ◽

Emotional Availability ◽

Major Depressive ◽

Interaction Quality ◽

Pregnancy Cohort ◽

The Impact

Objective: Poorer mother–infant interaction quality has been identified among women with major depression; however, there is a dearth of research examining the impact of bipolar disorder. This study sought to compare mother–infant emotional availability at 6 months postpartum among women with perinatal major depressive disorder, bipolar disorder and no disorder (control). Methods: Data were obtained for 127 mother–infant dyads from an Australian pregnancy cohort. The Structured Clinical Interview for the DSM-5 was used to diagnose major depressive disorder ( n = 60) and bipolar disorder ( n = 12) in early pregnancy (less than 20 weeks) and review diagnosis at 6 months postpartum. Prenatal and postnatal depressive symptoms were measured using the Edinburgh Postnatal Depression Scale, along with self-report psychotropic medication use. Mother and infant’s interaction quality was measured using the Emotional Availability Scales when infants reached 6 months of age. Multivariate analyses of covariance examining the effects of major depressive disorder and bipolar disorder on maternal emotional availability (sensitivity, structuring, non-intrusiveness, non-hostility) and child emotional availability (responsiveness, involvement) were conducted. Results: After controlling for maternal age and postpartum depressive symptoms, perinatal disorder (major depressive disorder, bipolar disorder) accounted for 17% of the variance in maternal and child emotional availability combined. Compared to women with major depressive disorder and their infants, women with bipolar disorder and their infants displayed lower ratings across all maternal and child emotional availability qualities, with the greatest mean difference seen in non-intrusiveness scores. Conclusions: Findings suggest that perinatal bipolar disorder may be associated with additional risk, beyond major depressive disorder alone, to a mother and her offspring’s emotional availability at 6 months postpartum, particularly in maternal intrusiveness.

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