Proteomic Profiling as a Diagnostic Biomarker for Discriminating Between Bipolar and Unipolar Depression

Abstract

Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.

Material and methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAP^TM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.

Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.

Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.

Keywords: affective disorder; biomarker; bipolar disorder; blood; machine learning; major depression (MD); major depressive disorder (MDD); proteome.

Figure 1

Receiver operating characteristic (ROC) curve depicting true positive versus false positive rates for the major depressive disorder (MDD) vs. bipolar disorder (BD) classification. Note that MDD was denoted as the positive class.