Objective: Unipolar mania is a clinical reality in our daily practice. Many authors suggested that bipolar patients can have only manic episodes without depressions. These findings lead us to explore more this particularity.
Methods: We conduct a retrospective, descriptive and comparative study including 173 patients, followed for bipolar disorder type I, according to the Diagnostic and Statistical Manual of Mental Disorders fifth edition criteria, during the period between January 2008 and December 2015. Two groups were identified. The first one was composed of 98 patients who had presented only manic episodes. The second group contained the rest of the sample. Unipolar mania was defined as the presence of three or more manic states without a depressive episode during the period of the study.
Results: One hundred seventy three patients were included in the study. The average age of the sample was 43 years old. The first episode was manic in 129 patients (74.6%). The dominant polarity was manic in 90.8% of the cases. Seasonal characteristic and psychotic symptoms were observed in respectively 11.0% and 53.2% of the sample. Rapid cycling evolution was observed among 2.3% of patients. The unipolar manic profile accounted for 56.6% of the population. This result is equivalent to an annual incidence of 8%. Comparing the two groups, we did not find a significant difference concerning the sociodemographic and clinical variables except for the number of suicide attempts (=0.014).
Conclusion: Our study shows that unipolar mania is clinical evidence. More studies should be conducted in order to understand its nosological and psychopathological foundations.
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http://dx.doi.org/10.9758/cpn.2018.16.2.209 | DOI Listing |
J Affect Disord
November 2024
Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Objectives: We examined associations between polygenic risk scores (PRS) for depression (PRS-MDD), psychosis (PRS-SCZ), bipolar disorders (PRS-BD) and neuroticism (PRS-NEU) and (i) help-seeking, and (ii) new onset cases of full-threshold mood or psychotic disorders in youth.
Methods: Help-seeking for mental health problems was assessed by self-report and mood and psychotic disorders were identified using the Composite International Diagnostic Interview. A principal component analysis of the four selected PRS identified two dimensions (BD-SCZ; MDD-NEU) that accounted for 69.
Int J Bipolar Disord
November 2024
Department of Psychiatry, Carl Gustav Carus University Hospital, Medical Faculty, Technische Universität Dresden, Dresden, Germany.
Background: Lithium is our oldest continuously prescribed medication in psychopharmacology, with its history as an agent for treating mood disorders extending from the 19th century. Although clinicians prescribe it less frequently than in the past, its utility in treating bipolar disorder is unquestionable. Novel potential indications for its use in psychiatry have created excitement about broader roles for lithium in treating and preventing other disorders.
View Article and Find Full Text PDFJ Psychopharmacol
November 2024
International Consortium for Mood and Psychotic Disorders Research, Mailman Research Center, McLean Hospital, Belmont, MA, USA.
Background: Whether responses to treatment of major depressive episodes differ between women and men or with bipolar (BD) and major depressive disorders (MDD) remains unresolved.
Aims: To test for diagnostic and sex differences in responses to treatment of depression.
Methods: We compared changes in the 21-item Hamilton Depression Rating Scale (HDRS) ratings of depression ( = 3243) between women (64.
J Med Internet Res
October 2024
Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States.
Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.
Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.
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