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. 2021 Feb 22:14:495-503.
doi: 10.2147/JIR.S297930. eCollection 2021.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Shunjie Bai  1 , Liang Fang  2   3 , Jing Xie  4 , Huili Bai  1 , Wei Wang  5 , Jian-Jun Chen  6
Affiliations

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Shunjie Bai et al. J Inflamm Res. .

Abstract

Background: Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods: First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results: The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion: These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

Keywords: biomarkers; major depressive disorder; suicidal ideation.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Discrimination model built using MDD patients with SI and HCs in training set: (A) The built OPLS-DA model showing an obvious separation between MDD patients with SI (red dot) and HCs (green dot); (B) 399-iteration permutation test showed that the model was valid, as the Q2 and R2 values yielded by the permutation test (bottom left) were significantly lower than their original values (top right).
Figure 2
Figure 2
T-predicted scatter plot from the OPLS-DA model: (A) both MDD patients with SI (lemon dot) and HCs (purple dot) from testing set could be correctly predicted; (B) the model was effective in distinguishing MDD patients with SI (red dot) from MDD patients without SI (blue dot).
Figure 3
Figure 3
Potential biomarkers for diagnosing MDD patients with SI: (A) the differences of these four potential biomarkers among the three groups; (B) the levels of potential biomarkers in the 20 MDD patients with SI were increased after one-month treatment, and both HDRS score and BSI-CV score were also significantly improved.
Figure 4
Figure 4
Diagnostic performances of these potential biomarkers. The panel consisting of these potential biomarkers yielded the excellent diagnostic performances with AUC>9.0 in training set, testing set and blind set.
See this image and copyright information in PMC

References

    1. Bromet E, Andrade LH, Hwang I, et al. Cross-national epidemiology of DSM-IV major depressive episode. BMC Med. 2011;9(1):90. doi: 10.1186/1741-7015-9-90 - DOI - PMC - PubMed
    1. Wu M, Tian T, Mao Q, et al. Associations between disordered gut microbiota and changes of neurotransmitters and short-chain fatty acids in depressed mice. Transl Psychiatry. 2020;10(1):350. doi: 10.1038/s41398-020-01038-3 - DOI - PMC - PubMed
    1. Guilloux JP, Douillard-Guilloux G, Kota R, et al. Molecular evidence for BDNF- and GABA-related dysfunctions in the amygdala of female subjects with major depression. Mol Psychiatry. 2012;17(11):1130–1142. doi: 10.1038/mp.2011.113 - DOI - PMC - PubMed
    1. Chen JJ, He S, Fang L, et al. Age-specific differential changes on gut microbiota composition in patients with major depressive disorder. Aging (Albany NY). 2020;12(3):2764–2776. doi: 10.18632/aging.102775 - DOI - PMC - PubMed
    1. Dumais A, Lesage AD, Alda M, et al. Risk factors for suicide completion in major depression: a case-control study of impulsive and aggressive behaviors in men. Am J Psychiatry. 2005;162(11):2116–2124. doi: 10.1176/appi.ajp.162.11.2116 - DOI - PubMed