RESUMEN
BACKGROUND: The heterogenous nature of depression continues to stymie efforts to identify biomarkers or predict treatment response. Efforts leveraging large datasets to define more uniform subtypes of depression or subgroups of depressed patients have considered only small subsets of symptoms. We aimed to understand how inclusion of more diverse complaints would impact data-emergent symptom and patient clusters. METHODS: We applied principal components analysis to baselineInventory of Depressive Symptomatology data from 1491 patients with major depressive disorder to derive naturally co-occurring symptom subsets before utilizing k-means clustering to divide patients into groups based on standardized residuals of each symptom subset score. We evaluated the clinical utility of our approach by comparing how cluster membership impacted response to citalopram. RESULTS: Prinicpal components analysis identified nine naturally co-occurring symptom subsets: core affective symptoms, appetite/weight loss, anxiety, somatic symptoms, insomnia, negative intrusive thoughts, leaden paralysis/mood quality, diurnal mood variation, and irritability. Cluster analysis identified two patient groups, differing significantly in 7 of 9 c symptom subsets. Patients distinguished by the prominence of somatic versus core affective symptoms exhibited less reduction in depression severity with citalopram treatment. LIMITATIONS: Results depend not only on raw data, but also parameter selection, and interpretation. Replication is indicated. CONCLUSIONS: Findings are consistent with previous reports linking somatic symptoms to treatment resistance and demonstrating that SSRIs are most effective in treating affective symptoms. A novel distinction between physical somatic symptoms and psychic anxiety highlights the utility of assessing a broad spectrum of symptoms when exploring heterogeneity in depression and the need for treatments targeting physical somatic symptoms specifically.