Age at onset in bipolar I disorder: two may be better than three subgroups

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

“…When adjusting for the birth cohort, or considering only those born after 1959, only two subgroups were found. As in prior studies in which data were unadjusted for the birth cohort, the youngest subgroup was more likely to have a family history of mood disorders [3,22,24], and to have a first episode with a polarity of depression [39,40] when compared to either the middle or older subgroup. However, there was no significant difference between the middle and older subgroups for either family history or polarity of first episode suggesting that the two older subgroups may not be clinically distinct.…”

“…The first approach uses a clustering methodology (mixture analysis) to determine the optimal number of distinct subgroups in a sample based on the age of onset distribution [8]. Using this clustering methodology, researchers have identified three onset subgroups, with the youngest subgroup having the most severe course of illness and highest likelihood of a family history of mood disorders [2,8,9,22,24,35,37]. The second approach groups the data in a sample by patient year of birth and analyzes for a birth cohort effect [21].…”

Bipolar disorder: Trimodal age‐at‐onset distribution

“…Heterogeneous definitions of AAO were used across studies including: age at which diagnostic criteria for an affective episode was first met according to medial case notes, interviews or self‐report 5 , 30 , 32 , 33 , 34 , 35 ; age at first impairment due to an affective episode according to self‐report 8 ; age at first contact with psychiatric services for symptoms of mania 14 , 22 ; age at first treatment for an affective disorder 28 and age at first psychiatric hospitalisation. 36 Across all studies, AAO was determined retrospectively using information gathered from medical records and/or interviews with participants and their relatives.…”

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

“…The majority of studies in the literature were consistent with the identification of three subsets of patients regarding AAO of bipolar I disorders; however, we need to carefully interpret these results because they could be more closely related to the sample size, the sampling design, or the choice of criterion rather than the existence of three truly different clinical‐phenotype subsets of patients. González Pinto et al () already pointed the fact that even if they found, using admixture analyses, three subgroups of patients regarding AAO, clinical analysis showed that the early and intermediate onset groups had no significant differences for any of the clinical variables studied, which according to them was in favor of the existence of two different AAO groups instead of three. Moreover, Consoli et al () identified a GM of two AAO subgroups using admixture analysis which may be explained in part by the population sample which was different from other studies including patients with Cotard's syndrome with comorbid bipolar disorder but probably also by the very small sample of the study ( n = 27), in fact too small to allow fitting any model with more than two subsets.…”

“…In psychiatric applications, used techniques are primarily based on the likelihood ratio test (LRT), which is assumed to be chi‐square distributed. This widely used approach (Azorin et al, ; Bellivier et al, , 2014; González Pinto et al, ; Hamshere et al, ; Manchia et al, ) consistently identified three different subsets in terms of the AAO of type I disorders, with slight differences in terms of mean and prevalence of these subsets (Figure ).…”