Mood and anxiety spectrum as a means to identify clinically relevant subtypes of bipolar I disorder.

OBJECTIVES: Latent class analysis of demographic and clinical variables can help identify subtypes of patients with bipolar disorder type I (BD I). Classification of patients into clinically relevant and homogeneous subtypes may have implications for further research. We examine the structure of mood and anxiety spectrum features in patients with BD I to identify subtypes with similar profiles.
METHODS: Adult patients diagnosed with BD I, who were also participants in the Bipolar Disorder Center for Pennsylvanians (BDCP) Study, were followed for a median time of 448 days. Data from self-report instruments of BD I patients were used to derive dichotomous indicators of four spectrum conditions. Latent class analysis was applied to these indicators. Demographic and clinical variables were used as external validators of the classes.
RESULTS: A 3-class solution provided a satisfactory data fit and outlined three classes of subjects. Members of the three groups differed in terms of demographic and clinical variables, such as gender, age of onset, mean Clinical Global Impression (CGI) depressive ratings and overall CGI ratings at entry, weighted mean CGI ratings for the period between the first and last evaluation in the BDCP Study and mean Global Assessment of Functioning scores at entry and during the BDCP Study.
CONCLUSIONS: We found substantial clinical heterogeneity among individuals with BD I and found that the levels of lifetime depressive, manic, panic-agoraphobic, and obsessive-compulsive spectrum symptoms identify three distinct subtypes characterized by differences in demographic and clinical variables. These results may have implications for research on the neurobiology, genetics, and treatment of BD I.