A five-times repeated verbal memory test revealed difference between bipolar disorder and major depressive disorder

2010 ◽  
Vol 68 ◽  
pp. e318
Author(s):  
Jun Iwatani ◽  
Masahiro Yamamoto ◽  
Satoshi Ukai ◽  
Tomikimi Tsuji ◽  
Sayuri Kubo ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Verbal Memory ◽  
Memory Test ◽  
Major Depressive

Are major depression and bipolar disorder neuropsychologically distinct? A meta-analysis of comparative studies

2017 ◽  
Vol 39 ◽  
pp. 17-26 ◽  
Author(s):  
C. Samamé ◽  
A.G. Szmulewicz ◽  
M.P. Valerio ◽  
D.J. Martino ◽  
S.A. Strejilevich
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Verbal Memory ◽  
Comparative Studies ◽  
Negative Impact ◽  
Cognitive Outcomes ◽  
Major Depressive ◽  
Depressive Episodes

AbstractBackgroundNeuropsychological deficits are present in both major depression and bipolar disorder. So far, however, reports directly comparing these mood disorders with regard to cognitive outcomes have been scant and yielded inconsistent results. This work aims to combine the findings of comparative studies of cognition in major depression and bipolar disorder in order to explore whether these neuropsychiatric conditions present with distinct cognitive features.MethodsThe main online databases were extensively searched to retrieve reports assessing neurocognitive functioning in two groups of mood disorder patients, one with major depressive disorder and another with bipolar disorder, both in the same phase of illness. Between-group effect sizes for cognitive variables were obtained from selected studies and pooled by means of meta-analytic procedures.ResultsDuring euthymia, a significant overall effect size (Hedges’g = 0.64, P < 0.001) favoring major depressive disorder was found for verbal memory as assessed with list learning tests, whereas no significant between-group differences were found for the remaining variables analyzed. During depressive episodes, similar cognitive outcomes were observed between groups.ConclusionAt present, it is not possible to postulate specific neuropsychological profiles for major depression and bipolar disorder in light of available evidence. It remains to be ascertained whether the differences found for verbal memory constitute an expression of distinct underlying mechanisms or whether they are best explained by sample characteristics or differential exposure to variables with a negative impact on cognition.

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.

Predictors of conversion of major depressive disorder to bipolar disorder

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

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

2021 ◽  
Vol 11 (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.

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

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.

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

2013 ◽  
Vol 74 (10) ◽  
pp. 727-733 ◽  
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

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

2004 ◽  
Vol 34 (5) ◽  
pp. 777-785 ◽  
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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