Purpose: There are great demands for identifying biomarkers of major depressive disorder (MDD), a common mental illness with a prevalence of approximately 6%. Finding potential biomarkers to aid MDD diagnosis is in high demand.
Experimental design: In this study, a combination of pretreatment methods named salt-out assisted liquid-liquid extraction (SALLE) and nontargeted peptidomics based on nano-LC-Orbitrap/MS was primarily employed to discover the candidate peptide markers from the plasma of 238 subjects.
Results: Many peptides were enriched and identified from the plasma, 42 of which showed significant differences between MDD patients and controls by univariate statistical analysis. A binary logistic regression (BLR) model combined four peptide markers (P1, P9, P17, P29) was established, yielding an overall prediction accuracy of 91.7% and 82.2% in the discovery and validation sets, respectively.
Conclusions and clinical relevance: In conclusion, the excellent performance of the BLR model in both discovery and validation sets demonstrates the robustness of the four peptide markers panel. It is very valuable for quantification of the absolute content of four peptides and further verification.
Keywords: binary logistic regression model; biomarkers; major depressive disorder; peptidomics; plasma.
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