The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account.

Within the field of psychiatry the development of biomarker based assay methods is relatively young. Recent efforts focused on combining several biomarkers within a panel to increase discriminative power. However, most biomarker panels have failed to advance to the stage of clinical application. An important prerequisite is a proper sampling and storage procedure, based on a priori identified stability properties of all biomarker/body fluid combinations present in the panel. Second, is the performance requisites of the assays in use, such as Enzyme-Linked Immunosorbent Assays (ELISA), in order to assure reliable results within and between runs. In this study, we analyzed 24 biomarker assays in 32 biomarker/body fluid combinations. Each biomarker body fluid combination was tested for stability and assay performance. We found hampering stability in almost all cases expect three biomarkers in urine and three in serum. Variability in biomarker stability either indicates decreased biomarker stability or issues in assay performance. This study indicates that basic biomarker/body fluid combination stability provides a good starting point for biomarker panel assay development. However, assay performance plays an important role in the correct interpretation of those results. Along the way of assay development, other quality assurance parameters might be implemented focused on a fit for purpose principle ultimately providing reliable data necessary for diagnostical method implementation.