Citation

Cassano GB, Benvenuti A, Miniati M, Calugi S, Mula M, Maggi L, Rucci P, Fagiolini A, Perris F, Frank E. J. Affect. Disord. 2009; 115(1-2): 87-99.

Affiliation

Department of Psychiatry, Neurobiology, Pharmacology and Biotechnology, School of Medicine, University of Pisa, Italy. gcassano@psico.med.unipi.it

Copyright

(Copyright © 2009, Elsevier Publishing)

DOI

10.1016/j.jad.2008.09.006

PMID

18947882

Abstract

BACKGROUND: While previous attempts to elucidate the factor structure of depression tended to agree on a central focus on depressed mood, other factors were not replicated across studies. By examining data from a large number of items covering the range of depressive symptoms, the aim of the present study is to contribute to the identification of the structure of depression on a lifetime perspective. METHODS: The study sample consisted of 598 patients with unipolar depression who were administered the Mood Spectrum Self-Report (lifetime version) in Italian (N=415) or English (N=183). In addition to classical exploratory factor analysis using tetrachoric correlation coefficients, an IRT-based factor analysis approach was adopted to analyze the data on 74 items of the instrument that explore cognitive, mood and energy/activity features associated with depression. RESULTS: Six factors were identified, including 'Depressive Mood', 'Psychomotor Retardation', 'Suicidality', 'Drug/Illness related depression', 'Psychotic Features' and 'Neurovegetative Symptoms', accounting overall for 48.3% of the variance of items. LIMITATIONS: Clinical information on onset of depression and duration of illness is available only for 350 subjects. Therefore, differences between sites can only be partially accounted using available data. CONCLUSIONS: Our study confirms the central role of depressed mood, psychomotor retardation and suicidality and identifies the factors 'Drug/Illness related depression', 'Psychotic features' and the neurovegetative dysregulation not captured by the instruments most frequently used in previous studies. The identification of patients with specific profiles on multiple factors may be useful in achieving greater precision in neuroimaging studies and in informing treatment selection.

Language: en