Behdin Nowrouzi1, Roger S McIntyre2, Glenda MacQueen3, Sidney H Kennedy2, James L Kennedy4, Arun Ravindran4, Lakshmi Yatham5, Vincenzo De Luca6. 1. Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Centre for Research in Occupational Safety and Health, Laurentian University, Sudbury, Ontario, Canada. 2. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; University Health Network in Toronto, Ontario, Canada. 3. University of Calgary in Calgary, Alberta, Canada. 4. Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. 5. University of British Columbia, Vancouver, British Columbia, Canada. 6. Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Electronic address: Vincenzo_deluca@camh.net.
Abstract
BACKGROUND: Many studies have used the admixture analysis to separate age-at-onset (AAO) subgroups in bipolar disorder, but none of them examined first episode patients. OBJECTIVE: The purpose of this study was to investigate the influence of clinical variables on AAO in first episode bipolar patients. METHODS: The admixture analysis was applied to identify the model best fitting the observed AAO distribution of a sample of 194 patients with DSM-IV diagnosis of bipolar disorder and the finite mixture model was applied to assess the effect of clinical covariates on AAO. RESULTS: Using the BIC method, the model that was best fitting the observed distribution of AAO was a mixture of three normal distributions. We identified three AAO groups: early age-at-onset (EAO) (µ=18.0, σ=2.88), intermediate-age-at-onset (IAO) (µ=28.7, σ=3.5), and late-age-at-onset (LAO) (µ=47.3, σ=7.8), comprising 69%, 22%, and 9% of the sample respectively. Our first episode sample distribution model was significantly different from most of the other studies that applied the mixture analysis. LIMITATIONS: The main limitation is that our sample may have inadequate statistical power to detect the clinical associations with the AAO subgroups. CONCLUSIONS: This study confirms that bipolar disorder can be classified into three groups based on AAO distribution. The data reported in our paper provide more insight into the diagnostic heterogeneity of bipolar disorder across the three AAO subgroups.
BACKGROUND: Many studies have used the admixture analysis to separate age-at-onset (AAO) subgroups in bipolar disorder, but none of them examined first episode patients. OBJECTIVE: The purpose of this study was to investigate the influence of clinical variables on AAO in first episode bipolarpatients. METHODS: The admixture analysis was applied to identify the model best fitting the observed AAO distribution of a sample of 194 patients with DSM-IV diagnosis of bipolar disorder and the finite mixture model was applied to assess the effect of clinical covariates on AAO. RESULTS: Using the BIC method, the model that was best fitting the observed distribution of AAO was a mixture of three normal distributions. We identified three AAO groups: early age-at-onset (EAO) (µ=18.0, σ=2.88), intermediate-age-at-onset (IAO) (µ=28.7, σ=3.5), and late-age-at-onset (LAO) (µ=47.3, σ=7.8), comprising 69%, 22%, and 9% of the sample respectively. Our first episode sample distribution model was significantly different from most of the other studies that applied the mixture analysis. LIMITATIONS: The main limitation is that our sample may have inadequate statistical power to detect the clinical associations with the AAO subgroups. CONCLUSIONS: This study confirms that bipolar disorder can be classified into three groups based on AAO distribution. The data reported in our paper provide more insight into the diagnostic heterogeneity of bipolar disorder across the three AAO subgroups.
Authors: Bo H Jonsson; Funda Orhan; Sanna Bruno; Ana Osório Oliveira; Timea Sparding; Mikael Landen; Carl M Sellgren Journal: Brain Behav Date: 2021-12-30 Impact factor: 2.708