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Updated: Nov 23, 2025

Developing a Rat Model for Bipolar Disorder
Published on: May 2, 2025
Clinical predictors of recurrences in bipolar disorders type 1 and 2: A FACE-BD longitudinal study
B Etain1, F Bellivier1, E Olié2
1Université de Paris, Paris, France; AP-HP, Groupe Hospitalo-universitaire AP-HP Nord, DMU Neurosciences, Hôpital Fernand Widal, Département de Psychiatrie et de Médecine Addictologique, Paris, France; INSERM UMRS 1144, Paris, France; Fondation FondaMental, Créteil, France.
Objective:
To examine which characteristics predict the time to a first mood recurrence at three years in Bipolar Disorder type I (BD-I) and type II (BD-II).
Methods:
Individuals with BD were followed up to 3 years. Turbull's extension of the Kaplan-Meier analysis for interval-censored data was used to estimate the cumulative probability of recurrence over time. Separate models were performed according to BD subtype to determine which baseline factors were predictive of recurrences and were adjusted for age, gender and educational level.
Results:
We included 630 individuals with BD-I and 505 with BD-II. The first recurrence of any polarity occurred earlier in BD-II (p = 0.03). The first depressive recurrence occurred earlier in BD-II (p < 0.0001), whereas the first (hypo)manic recurrence occurred earlier in BD-I (p = 0.0003). In BD-I, the clinical variables that were associated to the time to a first mood recurrence were depressive symptoms, lifetime rapid cycling, global activation and the number of psychotropic medications at baseline. In BD-II, the time to a first recurrence was associated with a younger age at onset of BD and a higher number of lifetime mood episodes. The Areas Under the Curve for both models were moderate.
Conclusion:
Predictors of recurrences showed few specificities to BD-I or BD-II. The ability to predict recurrences in BD based on socio-demographic and clinical variables remained too moderate for a transfer in daily practice. This study highlights the need for further studies that would include other types of predictors, such as molecular, cognitive or neuro-imaging ones, to achieve an accurate level of prediction of recurrences in BD.
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