Aswin Ratheesh1, Susan M Cotton2, Jennifer K Betts2, Andrew Chanen2, Barnaby Nelson2, Christopher G Davey2, Patrick D McGorry2, Michael Berk3, Andreas Bechdolf4. 1. Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, Australia; Centre for Youth Mental Health, the University of Melbourne, Parkville, Australia. Electronic address: aswinr@unimelb.edu.au. 2. Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, Australia; Centre for Youth Mental Health, the University of Melbourne, Parkville, Australia. 3. Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, Australia; IMPACT Strategic Research Centre, Deakin University, Geelong, Australia; Florey Institute of Neurosciences and Mental Health, Parkville, Australia. 4. Centre for Youth Mental Health, the University of Melbourne, Parkville, Australia; Department of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Klinikum am Urban, Charite Medical University, Berlin, Germany; Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.
Abstract
BACKGROUND: Identification of risk factors within precursor syndromes, such as depression, anxiety or substance use disorders (SUD), might help to pinpoint high-risk stages where preventive interventions for Bipolar Disorder (BD) could be evaluated. METHODS: We examined baseline demographic, clinical, quality of life, and temperament measures along with risk clusters among 52 young people seeking help for depression, anxiety or SUDs without psychosis or BD. The risk clusters included Bipolar At-Risk (BAR) and the Bipolarity Index as measures of bipolarity and the Ultra-High Risk assessment for psychosis. The participants were followed up for 12 months to identify conversion to BD. Those who converted and did not convert to BD were compared using Chi-Square and Mann Whitney U tests. RESULTS: The sample was predominantly female (85%) and a majority had prior treatment (64%). Four participants converted to BD over the 1-year follow up period. Having an alcohol use disorder at baseline (75% vs 8%, χ(2)=14.1, p<0.001) or a family history of SUD (67% vs 12.5%, χ(2)=6.0, p=0.01) were associated with development of BD. The sub-threshold mania subgroup of BAR criteria was also associated with 12-month BD outcomes. The severity of depressive symptoms and cannabis use had high effects sizes of association with BD outcomes, without statistical significance. CONCLUSIONS AND LIMITATIONS: The small number of conversions limited the power of the study to identify associations with risk factors that have previously been reported to predict BD. However, subthreshold affective symptoms and SUDs might predict the onset of BD among help-seeking young people with high-prevalence disorders.
BACKGROUND: Identification of risk factors within precursor syndromes, such as depression, anxiety or substance use disorders (SUD), might help to pinpoint high-risk stages where preventive interventions for Bipolar Disorder (BD) could be evaluated. METHODS: We examined baseline demographic, clinical, quality of life, and temperament measures along with risk clusters among 52 young people seeking help for depression, anxiety or SUDs without psychosis or BD. The risk clusters included Bipolar At-Risk (BAR) and the Bipolarity Index as measures of bipolarity and the Ultra-High Risk assessment for psychosis. The participants were followed up for 12 months to identify conversion to BD. Those who converted and did not convert to BD were compared using Chi-Square and Mann Whitney U tests. RESULTS: The sample was predominantly female (85%) and a majority had prior treatment (64%). Four participants converted to BD over the 1-year follow up period. Having an alcohol use disorder at baseline (75% vs 8%, χ(2)=14.1, p<0.001) or a family history of SUD (67% vs 12.5%, χ(2)=6.0, p=0.01) were associated with development of BD. The sub-threshold mania subgroup of BAR criteria was also associated with 12-month BD outcomes. The severity of depressive symptoms and cannabis use had high effects sizes of association with BD outcomes, without statistical significance. CONCLUSIONS AND LIMITATIONS: The small number of conversions limited the power of the study to identify associations with risk factors that have previously been reported to predict BD. However, subthreshold affective symptoms and SUDs might predict the onset of BD among help-seeking young people with high-prevalence disorders.
Authors: Jan Scott; Steven Marwaha; Aswin Ratheesh; Iain Macmillan; Alison R Yung; Richard Morriss; Ian B Hickie; Andreas Bechdolf Journal: Schizophr Bull Date: 2017-07-01 Impact factor: 9.306
Authors: Alexander Denissoff; Antti Mustonen; Anni-Emilia Alakokkare; James G Scott; Musa B Sami; Jouko Miettunen; Solja Niemelä Journal: Addiction Date: 2022-04-08 Impact factor: 7.256
Authors: Ralph Kupka; Anne Duffy; Jan Scott; Jorge Almeida; Vicent Balanzá-Martínez; Boris Birmaher; David J Bond; Elisa Brietzke; Ines Chendo; Benicio N Frey; Iria Grande; Danella Hafeman; Tomas Hajek; Manon Hillegers; Marcia Kauer-Sant'Anna; Rodrigo B Mansur; Afra van der Markt; Robert Post; Mauricio Tohen; Hailey Tremain; Gustavo Vazquez; Eduard Vieta; Lakshmi N Yatham; Michael Berk; Martin Alda; Flávio Kapczinski Journal: Bipolar Disord Date: 2021-07-23 Impact factor: 5.345
Authors: Mauro G Carta; Alessandra Perra; Michela Atzeni; Silvia D'Oca; Maria F Moro; Peter K Kurotschka; Daniela Moro; Federica Sancassiani; Luigi Minerba; Maria V Brasesco; Gustavo Mausel; Antonio E Nardi; Leonardo Tondo Journal: Braz J Psychiatry Date: 2017-03-09 Impact factor: 2.697