Importance: Subsyndromal hypomanic symptoms are relatively common in the general population and are linked to the onset of bipolar disorder. Little is known about their etiology and whether this is shared with the etiology of bipolar disorder or other mental illnesses.
Objective: To examine the genetic and environmental architecture of hypomanic symptoms in a nonclinical youth sample and compare estimates at varying severity levels and their association with diagnosed bipolar disorder.
Design, Setting, And Participants: This cohort study used phenotypic and genetic data from the Child and Adolescent Twin Study in Sweden and included individuals with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis of psychiatric disorders from national registries for residents of Sweden. Associations between hypomania and polygenic risk scores for bipolar disorder, major depressive disorder and schizophrenia were also investigated. Analysis began November 2018 and ended October 2021.
Main Outcomes And Measures: Hypomanic symptoms were assessed using the parent-rated Mood Disorders Questionnaire when the twins were aged 18 years. Bipolar disorder diagnosis and/or lithium prescription were ascertained from national registries for residents of Sweden. Polygenic risk scores for psychiatric disorders were calculated using independent discovery genetic data.
Results: A total of 8568 twin pairs aged 18 years (9381 [54.7%] female) were included in the study. The hypomania heritability estimate was 59% (95% CI, 52%-64%) for male individuals and 29% (95% CI, 16%-44%) for female individuals. Unique environmental factors accounted for 41% (95% CI, 36%-47%) of the hypomania variance in male individuals and 45% (95% CI, 40%-50%) in female individuals. Shared environmental factors were only detected for female individuals and explained 26% (95% CI, 13%-38%) of the variance. The heritability estimates were fairly consistent across different hypomania severity groups. Moderate genetic (0.40; 95% CI, 0.21-0.58) and shared environmental (0.41; 95% CI, 0.03-0.75) correlations between hypomania and diagnosed bipolar disorder were found. Hypomania was significantly associated with the polygenic risk scores for schizophrenia (β = 0.08; SE = 0.026; P = .002) and major depressive disorder (β = 0.09; SE = 0.027; P = .001) but not bipolar disorder (β = 0.017; SE = 0.03; P = 0.57) (bipolar disorder I [β = 0.014; SE = 0.029; P = .64] or bipolar disorder II [β = 0.045; SE = 0.027; P = .10]).
Conclusions And Relevance: Higher heritability for hypomania was found for male compared with female individuals. The results highlight the shared etiologies between hypomanic symptoms, bipolar disorder, major depression, and schizophrenia in youths. Future research should focus on identifying specific shared genetic and environmental factors. These findings support a possible dimensional model of bipolar disorder, with hypomania representing a continuous trait underlying the disorder.
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http://dx.doi.org/10.1001/jamapsychiatry.2021.3654 | DOI Listing |
Background: Antibiomania is the manifestation of manic symptoms secondary to taking an antibiotic, which is a rare side effect. In these cases, the antibiotics most often incriminated are macrolides and quinolones, but to our knowledge, there are no published cases of antibiomania secondary to cotrimoxazole. Furthermore, we also provide an update of pharmacovigilance data concerning antibiomania through a search of the World Health Organization (WHO) database.
View Article and Find Full Text PDFNeuropsychopharmacology
January 2025
Neurocognition and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, Mental Health Services, Capital Region of Denmark, Frederiksberg, Denmark.
Individuals with bipolar disorder (BD) show heterogeneity in clinical, cognitive, and daily functioning characteristics, which challenges accurate diagnostics and optimal treatment. A key goal is to identify brain-based biomarkers that inform patient stratification and serve as treatment targets. The objective of the present study was to apply a data-driven, multivariate approach to quantify the relationship between multimodal imaging features and behavioral phenotypes in BD.
View Article and Find Full Text PDFMatrix Biol
January 2025
German Center for Neurodegenerative Diseases (DZNE), Helmholtz Association of German Research Centers, Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany; Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany. Electronic address:
The neural extracellular matrix (ECM) accumulates in the form of perineuronal nets (PNNs), particularly around fast-spiking GABAergic interneurons in the cortex and hippocampus, but also around synapses and in association with the axon initial segments (AIS) and nodes of Ranvier. Increasing evidence highlights the role of Neurocan (Ncan), a brain-specific component of ECM, in the pathophysiology of neuropsychiatric disorders like bipolar disorder and schizophrenia. Ncan localizes at PNNs, perisynaptically, and at the nodes of Ranvier and the AIS, highlighting its potential role in regulating axonal excitability.
View Article and Find Full Text PDFPsychiatry Res
December 2024
the Seventh People's Hospital of Wenzhou, Zhejiang Province, China.
Objective: A proportion of patients with bipolar disorder (BD) manifests with only Unipolar mania (UM). We conducted a follow-up study of patients diagnosed with Unipolar mania and compared them as a group if they had a mild depressive episode with those who did not.
Method: 248 subjects were prospectively followed-up to 15 years.
Comput Med Imaging Graph
January 2025
University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address:
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.
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