Clinical and Prognostic Relevance of Cycle Pattern Recognition in Bipolar Disorder: A Further Step Toward...

Bipolar Disorder (BD) presents with heterogeneous longitudinal cycling patterns, including Manic-Depressive-free Interval (MDI), Depressive-Manic-free Interval (DMI), and Irregular (IRR) cycles. Studies investigating how these cycle types affect clinical course remain limited. This study aimed to examine a large, well-characterised sample of patients with BD types I (BD-I) and II (BD-II).

Life charts were used to determine the type of cycle, collecting information on first affective episode type, hospitalisations (presence or absence), suicidal ideation/attempts, psychiatric family history, seasonality, agitated depression, predominant polarity, and diagnostic subtype (BD-I vs. BD-II). The impact of cycle type on clinical course was analysed through univariate and multivariate models.

Of 378 BD patients, 140 (37.0%) had MDI cycles, 92 (24.3%) had DMI cycles, and 146 (38.6%) had IRR cycles. Multivariate analyses showed MDI patients were more likely to have a manic onset compared to DMI and IRR (p < 0.001). They also showed a higher likelihood of hypomanic onset compared to DMI (p < 0.001). Conversely, DMI was associated with a depressive onset relative to MDI and IRR (p < 0.001). Seasonality was more frequent in patients with regular cycles (MDI and DMI) compared to IRR (p < 0.001). Hospitalisations were more frequent in MDI and DMI cycles compared to IRR, but the association survived only for MDI in multivariate analysis. MDI patients had a higher prevalence of manic predominant polarity and lower rates of depressive predominant polarity compared to both DMI and IRR (p < 0.001). A BD-II diagnosis was significantly more frequent in DMI and IRR (p < 0.001), and a BD-I diagnosis was more prevalent in MDI (p < 0.001).

Cycle type affected the BD clinical course, with MDI tending to show more frequent hospitalisations, BD-I diagnosis, and manic predominant polarity, while DMI and IRR patients had more BD-II diagnoses.

Findings underscore the importance of classifying BD based on cycle patterns and suggest that taking into account these patterns may support more personalised treatment planning and improve clinical outcomes.