Factor analyses differentiate clinical phenotypes of idiopathic and interferon-alpha-induced depression.

The discovery that prolonged administration of interferon-alpha (a pro-inflammatory cytokine) readily precipitates depressive symptoms has played a key role in development of the inflammation theory of major depressive disorder (MDD). However, it remains unclear whether the clinical phenotype of patients with inflammation-associated depression significantly overlaps with, or can be distinguished from that of patients with 'idiopathic' depression. Here we explored the Hamilton depression scale factor structure of 172 patients undergoing interferon-alpha treatment for hepatitis-C at the point of transition to a depressive episode of DSM IV defined major depression severity. The resulting factor structure was first compared with a model derived from 6 previous studies of 'idiopathic' MDD (Cole et al., 2004). This confirmatory factor analysis revealed that the factor structure of HAMD scores in our interferon-alpha treated cohort did not plausibly fit that previously described for 'idiopathic' MDD. Instead, subsequent exploratory factor analysis revealed a distinct four factor model with a novel primary factor grouping cognitive symptoms of depression and anxiety (HAMD items 1, 2, 9, 10, 11, 15). The second sleep disorder factor (items 4, 5, 6) replicated previous findings in 'idiopathic' depression. A third and unique factor grouped somatic symptoms and function (items 7, 12, 13, 14 and item 1). The final factor (also common in idiopathic depression studies), grouped gastrointestinal symptoms and weight loss (items 12 and 16). Severe depression items (3, 8, and 17) were excluded from analysis due to very low variance. At transition, interferon-alpha induced major depressive episodes therefore appears to have more associated anxiety features that covary with depressed mood than classical or 'idiopathic' MDD and a low likelihood of severe features such as suicidal ideation. Identification of this clinical phenotype may help identify patients with an inflammatory depression etiology and support the development of more effective and personalized therapies.