| 1. |
- Song, Jie, et al.
(författare)
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Bipolar disorder and its relation to major psychiatric disorders : a family-based study in the Swedish population
- 2015
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Ingår i: Bipolar Disorders. - Stockholm : Wiley-Blackwell. - 1398-5647 .- 1399-5618. ; 17:2, s. 184-193
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: Bipolar disorder (BPD) shares genetic components with other psychiatric disorders; however, uncertainty remains about where in the psychiatric spectra BPD falls. To understand the etiology of BPD, we studied the familial aggregation of BPD and co-aggregation between BPD and schizophrenia, depression, anxiety disorders, attention-deficit hyperactivity disorder, drug abuse, personality disorders, and autism spectrum disorders.METHODS: A population-based cohort was created by linking several Swedish national registers. A total of 54,723 individuals with BPD were identified among 8,141,033 offspring from 4,149,748 nuclear families. The relative risk of BPD in relatives and the co-occurrence of other psychiatric disorders in patients with BPD and their relatives were compared to those of matched-population controls. Structural equation modeling was used to estimate the heritability and tetrachoric correlation.RESULTS: The familial risks for relatives of BPD probands were 5.8-7.9 in first-degree relatives, and decreased with genetic distance. Co-occurrence risks for other psychiatric disorders were 9.7-22.9 in individuals with BPD and 1.7-2.8 in full siblings of BPD probands. Heritability for BPD was estimated at 58%. The correlations between BPD and other psychiatric disorders were considerable (0.37-0.62) and primarily due to genetic effects. The correlation with depression was the highest (0.62), and was 0.44 for schizophrenia.CONCLUSIONS: The high familial risks provide evidence that genetic factors play an important role in the etiology of BPD, and the shared genetic determinants suggest pleiotropic effects across different psychiatric disorders. Results also indicate that BPD is in both the mood and psychotic spectra, but possibly more closely related to mood disorders.
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| 4. |
- Di Simplicio, Martina, et al.
(författare)
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An investigation of mental imagery in bipolar disorder : Exploring "the mind's eye"
- 2016
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Ingår i: Bipolar Disorders. - : WILEY-BLACKWELL. - 1398-5647 .- 1399-5618. ; 18:8, s. 669-683
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Mental imagery abnormalities occur across psychopathologies and are hypothesized to drive emotional difficulties in bipolar disorder (BD). A comprehensive assessment of mental imagery in BD is lacking. We aimed to test whether (i) mental imagery abnormalities (abnormalities in cognitive stages and subjective domains) occur in BD relative to non-clinical controls; and (ii) to determine the specificity of any abnormalities in BD relative to depression and anxiety disorders. Methods: Participants included 54 subjects in the BD group (depressed/euthymic; n=27 in each subgroup), subjects with unipolar depression (n=26), subjects with anxiety disorders (n=25), and non-clinical controls (n=27) matched for age, gender, ethnicity, education, and premorbid IQ. Experimental tasks assessed cognitive (non-emotional) measures of mental imagery (cognitive stages). Questionnaires, experimental tasks, and a phenomenological interview assessed subjective domains including spontaneous imagery use, interpretation bias, and emotional mental imagery. Results: (i) Compared to non-clinical controls, the BD combined group reported a greater impact of intrusive prospective imagery in daily life, more vivid and "real" negative images (prospective imagery task), and higher self-involvement (picture-word task). The BD combined group showed no clear abnormalities in cognitive stages of mental imagery. (ii) When depressed individuals with BD were compared to the depressed or anxious clinical control groups, no significant differences remained-across all groups, imagery differences were associated with affective lability and anxiety. Conclusions: Compared to non-clinical controls, BD is characterized by abnormalities in aspects of emotional mental imagery within the context of otherwise normal cognitive aspects. When matched for depression and anxiety, these abnormalities are not specific to BD-rather, imagery may reflect a transdiagnostic marker of emotional psychopathology.
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| 5. |
- Kalman, Janos L, et al.
(författare)
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Investigating polygenic burden in age at disease onset in bipolar disorder: Findings from an international multicentric study.
- 2019
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Ingår i: Bipolar disorders. - : Wiley. - 1399-5618 .- 1398-5647. ; 21:1, s. 68-75
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early-onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD- and schizophrenia (SCZ)-associated risk variants is associated with an earlier AAO in BD patients.A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn-Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18years] vs adulthood [>18years]) or considered as a continuous measure. The associations between BD- and SCZ-PRSs and AAO were evaluated with regression models.BD- and SCZ-PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment.The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype.
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| 6. |
- Martin, Cederlöf, 1980-, et al.
(författare)
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The association between Darier disease, bipolar disorder, and schizophrenia revisited: a population-based family study.
- 2015
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Ingår i: Bipolar disorders. - Hoboken, USA : Wiley. - 1399-5618 .- 1398-5647. ; 17:3, s. 340-4
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Tidskriftsartikel (refereegranskat)abstract
- Darier disease is an autosomal dominant skin disorder caused by mutations in the ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 (ATP2A2) gene and previously reported to cosegregate with bipolar disorder and schizophrenia in occasional pedigrees. It is, however, unknown whether these associations exist also in the general population, and the objective of this study was to examine this question.
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| 10. |
- Ortiz, A., et al.
(författare)
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Episode forecasting in bipolar disorder : Is energy better than mood?
- 2018
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Ingår i: Bipolar Disorders. - : Blackwell Publishing Inc.. - 1398-5647 .- 1399-5618. ; 20:5, s. 470-476
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders. Methods: We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series. Results: Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically. Conclusions: Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
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