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. 2022 Feb 21:12:810302.
doi: 10.3389/fpsyt.2021.810302. eCollection 2021.

Non-targeted Metabolomics Profiling of Plasma Samples From Patients With Major Depressive Disorder

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Non-targeted Metabolomics Profiling of Plasma Samples From Patients With Major Depressive Disorder

Zhonghao Wu et al. Front Psychiatry. .

Abstract

Background: Major depressive disorder (MDD) is a neuropsychiatric disorder caused by multiple factors. Although there are clear guidelines for the diagnosis of MDD, the direct and objective diagnostic methods remain inadequate thus far.

Methods: This study aims to discover peripheral biomarkers in patients with MDD and promote the diagnosis of MDD. Plasma samples of healthy controls (HCs, n = 52) and patients with MDD (n = 38) were collected, and then, metabolism analysis was performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Heatmap analysis was performed to identify the different metabolites. Meanwhile, receiver operating characteristic (ROC) curves of these differential metabolites were generated.

Results: Six differential metabolites were found by LC-MS/MS analysis. Three of these were increased, including L-aspartic acid (Asp), diethanolamine, and alanine. Three were decreased, including O-acetyl-L-carnitine (LAC), cystine, and fumarate. In addition, LAC, Asp, fumarate, and alanine showed large areas under the curve (AUCs) by ROC analysis.

Conclusion: The study explored differences in peripheral blood between depressed patients and HCs. These results indicated that differential metabolites with large AUCs may have the potential to be promising biomarkers for the diagnosis of MDD.

Keywords: LC-MS/MS; biomarkers; diagnosis; major depressive disorder; non-targeted metabolomics; plasma; receiver operating characteristic curve.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Metabolomics analysis of plasma samples from HCs and patients with MDD. (A) Discriminant analysis of the orthogonal partial least squares (OPLS-DA) method (R2X = 0.796, R2Y = 0.883, Q2 = 0.671). (B) Permutation tests performed with 200 random permutations in OPLS-DA models showing R2 (green circles) and Q2 (blue squares) values from the permuted analysis (left) as significantly lower than the corresponding original R2 and Q2 values (right) (R2 = 0.433, Q2 = −0.812).
Figure 2
Figure 2
Differential metabolites in plasma of HC and MDD. (A) Levels of metabolites were determined by LC–MS/MS, including LAC, Asp, cystine, diethanolamine, alanine, and fumarate, in the HC group (n = 52) and the MDD group (n = 38). (B) Heatmap of the differential metabolites in plasma (HC vs. MDD). Blue, positive correlation; red, negative correlation. Student's t-test and Mann–Whitney U-test were used for the comparison of two groups in which data obeyed normal and abnormal distribution, respectively. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 3
Figure 3
Heatmap of the correlation matrix among differential metabolites.
Figure 4
Figure 4
Judgment of the diagnostic value of differential metabolites. Receiver operating characteristic (ROC) analysis showing the diagnostic performances of these differential metabolites: the areas under the curve (AUCs) values of LAC, Asp, fumarate, and alanine were 0.871, 0.873, 0.858, and 0.859, respectively (A–D).

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References

    1. Nobis A, Zalewski D, Waszkiewicz N. Peripheral markers of depression. J Clin Med. (2020) 9:3793. 10.3390/jcm9123793 - DOI - PMC - PubMed
    1. World Health . Global Burden of Mental Disorders and the Need for a Comprehensive, Coordinated Response from Health and Social Sectors at the Country Level: Report by The Secretariat. Geneva: World Health Organization; (2012).
    1. Pham TH, Gardier AM. Fast-acting antidepressant activity of ketamine: highlights on brain serotonin, glutamate, and GABA neurotransmission in preclinical studies. Pharmacol Ther. (2019) 199:58–90. 10.1016/j.pharmthera.2019.02.017 - DOI - PubMed
    1. Berger T, Lee H, Young AH, Aarsland D, Thuret S. Adult hippocampal neurogenesis in major depressive disorder and Alzheimer's disease. Trends Mol Med. (2020) 26:803–18. 10.1016/j.molmed.2020.03.010 - DOI - PubMed
    1. Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman ATF, Penninx B. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatr. (2013) 18:692–9. 10.1038/mp.2012.144 - DOI - PubMed

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