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. 2020 Dec 1:277:620-630.
doi: 10.1016/j.jad.2020.08.078. Epub 2020 Sep 1.

Identifying Plasma Biomarkers with high specificity for major depressive disorder: A multi-level proteomics study

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Identifying Plasma Biomarkers with high specificity for major depressive disorder: A multi-level proteomics study

Yachen Shi et al. J Affect Disord. .

Erratum in

Abstract

Background: There are currently no objective diagnostic biomarkers for major depressive disorder (MDD) due to the biological complexity of the disorder. The existence of blood-based biomarkers with high specificity would be convenient for the clinical diagnosis of MDD.

Methods: A comprehensive plasma proteomic analysis was conducted in a highly homogeneous cohort [7 drug-naïve MDD patients and 7 healthy controls (HCs)], with bioinformatics analysis combined with machine learning used to screen candidate proteins. Verification of reproducibility and specificity was conducted in independent cohorts [60 HCs and 74 MDD, 42 schizophrenia (SZ) and 39 bipolar I disorder (BD-I) drug-naïve patients]. Furthermore, verification of consistency was accomplished by proteomic analysis of postmortem brain tissue from 16 MDD patients and 16 HCs.

Results: Levels of C-reactive protein (CRP), antithrombin III (ATIII), inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) and vitamin D-binding protein (VDB) were significantly higher in MDD patients, both in the discovery cohort and independent replication cohort. In comparison with SZ or BD-I patients, two proteins (VDB and ITIH4) were significantly elevated only in MDD patients. In addition, increased VDB and ITIH4 were observed consistently in both plasma and postmortem dorsolateral prefrontal cortex tissues of MDD patients. Furthermore, a panel consisting of all four plasma proteins was able to distinguish MDD patients from HCs or SZ or BD-I patients with the highest accuracy.

Conclusion: Plasma ITIH4 and VDB may be potential plasma biomarkers of MDD with high specificity. The four-protein panel is more suitable as a potential clinical diagnostic marker for MDD.

Keywords: Machine learning; Major depressive disorder; Plasma; Postmortem brain tissue; Proteomics; Vitamin D-binding protein.

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