Serum lipidomic analysis for the discovery of biomarkers for major depressive disorder in drug-free patients. (July 2018)

• Record Type: Journal Article
• Authors: Kim, Eun Young; Lee, Jae Won; Lee, Min Young; Kim, Se Hyun; Mok, Hyuck Jun; Ha, Kyooseob; Ahn, Yong Min; Kim, Kwang Pyo
• Is Part Of: Psychiatry research. Volume 265(2018)
• Page Start: 174
• Page End: 182
• Publication Date: 2018-07
• Subjects: Major depressive disorder -- Lipidomics -- Biomarker -- Peripheral lipid signature; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2018.04.029
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: We performed lipidome profiling to identify novel peripheral biomarkers for depression. Differently regulated lipids were verified using multiple-reaction monitoring. Lysophosphatidic acid and triglycerides were identified as biomarker candidates. Abstract: Lipidomic analysis can be used to efficiently identify hundreds of lipid molecular species in biological materials and has been recently established as an important tool for biomarker discovery in various neuropsychiatric disorders including major depressive disorder (MDD). In this study, quantitative targeted serum lipidomic profiling was performed on female subjects using liquid chromatography–mass spectrometry. Global lipid profiling of pooled serum samples from 10 patients currently with MDD (cMDD), 10 patients with remitted MDD (rMDD), and 10 healthy controls revealed 37 differentially regulated lipids (DRLs). DRLs were further verified using multiple-reaction monitoring (MRM) in each of the 25 samples from the three groups of independent cohorts. Using multivariate analysis and MRM data we identified serum biomarker panels of discriminatory lipids that differentiated between pairs of groups: lysophosphatidic acid (LPA)(16:1), triglycerides (TG)(44:0), and TG(54:8) distinguished cMDD from controls with 76% accuracy; lysophosphatidylcholines(16:1), TG(44:0), TG(46:0), and TG(50:1) distinguished between cMDD and rMDD at 65% accuracy; and LPA(16:1), TG(52:6), TG(54:8), and TG(58:10) distinguished between rMDD and controls with 60% accuracy. Our lipidomic analysis identified peripheral lipid signatures of MDD, which thereby provides providing important biomarker candidates for MDD.