Confirmatory test of two factors and four subtypes of bipolar disorder based on lifetime psychiatric co-morbidity. Issue 10 (31st March 2015) - Monahan, P. O.; Stump, T.; Coryell, W. H.; Harezlak, J.; Marcoulides, G. A.; Liu, H.; Steeger, C. M.; Mitchell, P. B.; Wilcox, H. C.; Hulvershorn, L. A.; Glowinski, A. L.; ,; et al.
Abstract : Background: The first aim was to use confirmatory factor analysis (CFA) to test a hypothesis that two factors (internalizing and externalizing) account for lifetime co-morbid DSM-IV diagnoses among adults with bipolar I (BPI) disorder. The second aim was to use confirmatory latent class analysis (CLCA) to test the hypothesis that four clinical subtypes are detectible: pure BPI; BPI plus internalizing disorders only; BPI plus externalizing disorders only; and BPI plus internalizing and externalizing disorders. Method: A cohort of 699 multiplex BPI families was studied, ascertained and assessed (1998–2003) by the National Institute of Mental Health Genetics Initiative Bipolar Consortium: 1156 with BPI disorder (504 adult probands; 594 first-degree relatives; and 58 more distant relatives) and 563 first-degree relatives without BPI. Best-estimate consensus DSM-IV diagnoses were based on structured interviews, family history and medical records. MPLUS software was used for CFA and CLCA. Results: The two-factor CFA model fit the data very well, and could not be improved by adding or removing paths. The four-class CLCA model fit better than exploratory LCA models or post-hoc -modified CLCA models. The two factors and four classes were associated with distinctive clinical course and severity variables, adjusted for proband gender. Co-morbidity, especially more than one internalizing and/or externalizing disorder, was associated with a more severe and complicated course of illness. The four classes demonstrated significant familial aggregation, adjusted for gender and age of relatives. Conclusions: The BPI two-factor and four-cluster hypotheses demonstrated substantial confirmatory support. These models may be useful for subtyping BPI disorders, predicting course of illness and refining the phenotype in genetic studies.