Age‐at‐onset subsets of bipolar I disorders: A critical insight into admixture analyses

Montlahuc, Claire; Curis, Emmanuel; Jonas, Steven; Bellivier, Frank; Chevret, Sylvie

doi:10.1002/mpr.1536

Cited by 8 publications

(11 citation statements)

References 39 publications

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“…Concerning study design, Montlahuc et al (2016) tested whether cross-sectional designs (which cause right truncation), unreliable diagnosis for individuals younger than 10 years old (which causes left truncation), and the selection criterion used for admixture analysis impacted the number of identified AAO subgroups. Importantly, a combination of left and right truncation, which is common in previously published studies of AAO admixture analysis, appeared to significantly influence the number of AAO subgroups detected (Montlahuc et al 2016). Geographical location appears also to impact on AAO admixture Continuous cycling (%) 5 (3.0) 2 (1.4)…”

Section: Discussionmentioning

confidence: 99%

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Manchia¹

2016

Preprint

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Background: Admixture analysis of age at onset (AAO) has helped delineating the clinical profile of early onset (EO) bipolar disorder (BD). However, there is scarce evidence comparing the distributional properties of AAO as well as the clinical features of EO BD type 1 (BD1) with EO BD type 2 (BD2). To this end, we studied 515 BD patients (224 BD1, 279 BD2, and 12 BD not otherwise specified [NOS]) diagnosed according to DSM-IV-TR criteria. Methods: AAO was defined as the first reliably diagnosed hypo/manic or depressive episode according to diagnostic criteria. We used normal distribution mixture analysis to identify subgroups of patients according to AAO. Models were chosen according to the Schwarz's Bayesian information criteria (BIC). Clinical correlates of EO were analysed using univariate tests and multivariate logistic regression models. Results: A two normal components model best fitted the observed distribution of AAO in BD1 (BIC = −1599.3), BD2 (BIC = −2158.4), and in the whole sample (BIC = −3854.9). A higher number of EO BD2 patients had a depression-(hypo)mania-free interval (DMI) course, while a higher rate of (hypo)mania-depression-free interval (MDI) course was found in EO BD1. EO BD2 had also a higher rate of comorbidity with alcohol dependence compared to EO BD1. The latter finding was confirmed by multivariate logistic regression analysis. Conclusions: In conclusion, both BD1 and BD2 had bimodal AAO distributions, but EO subgroups had a diagnosticspecific clinical delineation.

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Section: Discussionmentioning

confidence: 99%

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Manchia¹

2016

Preprint

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“…percentages of patients attributed to the early onset subgroup varied between 21.4% (Bellivier et al 2001) and 79.7% (Lin et al 2006)]. Discrepancies in the identified AAO distributions, cut-off points, and proportions of patients in each AAO subgroups may depend on diverse assessment methods, recall bias, study design (Montlahuc et al 2016), and differences in characteristics of samples studied, including geographic location (Post et al 2008;Bellivier et al 2014) and birth cohort (Bauer et al 2015;Golmard et al 2015). Concerning study design, Montlahuc et al (2016) tested whether cross-sectional designs (which cause right truncation), unreliable diagnosis for individuals younger than 10 years old (which causes left truncation), and the selection criterion used for admixture analysis impacted the number of identified AAO subgroups.…”

Section: Discussionmentioning

confidence: 99%

“…Discrepancies in the identified AAO distributions, cut-off points, and proportions of patients in each AAO subgroups may depend on diverse assessment methods, recall bias, study design (Montlahuc et al 2016), and differences in characteristics of samples studied, including geographic location (Post et al 2008;Bellivier et al 2014) and birth cohort (Bauer et al 2015;Golmard et al 2015). Concerning study design, Montlahuc et al (2016) tested whether cross-sectional designs (which cause right truncation), unreliable diagnosis for individuals younger than 10 years old (which causes left truncation), and the selection criterion used for admixture analysis impacted the number of identified AAO subgroups. Importantly, a combination of left and right truncation, which is common in previously published studies of AAO admixture analysis, appeared to significantly influence the number of AAO subgroups detected (Montlahuc et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Manchia¹,

Maina²,

Carpiniello³

et al. 2016

European Neuropsychopharmacology

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Background: Admixture analysis of age at onset (AAO) has helped delineating the clinical profile of early onset (EO) bipolar disorder (BD). However, there is scarce evidence comparing the distributional properties of AAO as well as the clinical features of EO BD type 1 (BD1) with EO BD type 2 (BD2). To this end, we studied 515 BD patients (224 BD1, 279 BD2, and 12 BD not otherwise specified [NOS]) diagnosed according to DSM-IV-TR criteria.Methods: AAO was defined as the first reliably diagnosed hypo/manic or depressive episode according to diagnostic criteria. We used normal distribution mixture analysis to identify subgroups of patients according to AAO. Models were chosen according to the Schwarz's Bayesian information criteria (BIC). Clinical correlates of EO were analysed using univariate tests and multivariate logistic regression models. Results:A two normal components model best fitted the observed distribution of AAO in BD1 (BIC = −1599.3), BD2 (BIC = −2158.4), and in the whole sample (BIC = −3854.9). A higher number of EO BD2 patients had a depression-(hypo)mania-free interval (DMI) course, while a higher rate of (hypo)mania-depression-free interval (MDI) course was found in EO BD1. EO BD2 had also a higher rate of comorbidity with alcohol dependence compared to EO BD1. The latter finding was confirmed by multivariate logistic regression analysis. Conclusions:In conclusion, both BD1 and BD2 had bimodal AAO distributions, but EO subgroups had a diagnosticspecific clinical delineation.

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“…Several limitations must be considered when interpreting our findings. It has been suggested that admixture analysis is sensitive to the sample size and the characteristics of the data 80 . The studies that reported a bimodal AAO distribution had smaller sample sizes on average compared to those reporting a trimodal AAO distribution.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Bipolar disorder: Trimodal age‐at‐onset distribution

et al. 2020

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Bipolar disorder (BD) is a chronic mental health disorder with significant morbidity and mortality that affects between 1-4% of the population. 1 Age at onset in BD has been recognised as being important in the course and outcome of the disorder. Meta-analytic results suggest that an early (compared to late) age at onset in bipolar disorder is associated with a longer delay to treatment, greater severity of depression and higher levels of comorbid anxiety and substance abuse. 2,3 Given this differing clinical trajectory between early-versus late-onset BD, it has been proposed that age at onset (AAO) may be a key variable in delineating more

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Age‐at‐onset subsets of bipolar I disorders: A critical insight into admixture analyses

Cited by 8 publications

References 39 publications

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Bipolar disorder: Trimodal age‐at‐onset distribution

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