Diagnostic subgrouping of depressed patients by principal component analysis and visualized pattern recognition

A data-analytical method is described for identifying behavioral and biological variables in psychiatric patients with predictive value in defining clinical subgroups. The procedure, based on principal component analysis (PCA) and graphical analysis, was applied in a group of 28 depressed patients. The 28 depressed patients of unipolar type were observed for up to 15 years for re-evaluation of the diagnoses at the start of the study. Platelet monoamine oxidase activity, post-dexamethasone serum cortisol and serum melatonin predicted two main clinical subgroups as well as a smaller subgroup of bipolar patients. The selection procedure revealed which of several variables were predictive of subgroups that were not possible to identify by univariate methods. The three biological variables may thus be useful in further assessment of clinical subgroups of unipolar depressed patients studied by other research groups.