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. 2025 May 19;15(5):102618.
doi: 10.5498/wjp.v15.i5.102618.

Alterations of serum metabolic profile in major depressive disorder: A case-control study in the Chinese population

Affiliations

Alterations of serum metabolic profile in major depressive disorder: A case-control study in the Chinese population

Bing Cao et al. World J Psychiatry. .

Abstract

Background: Major depressive disorder (MDD) is characterized by persistent depressed mood and cognitive symptoms. This study aimed to discover biomarkers for MDD, explore its pathological mechanisms, and examine the associations of the identified biomarkers with clinical and psychological variables.

Aim: To discover candidate biomarkers for MDD identification and provide insight into the pathological mechanism of MDD.

Methods: The current study adopted a single-center cross-sectional case-control design. Serum samples were obtained from 100 individuals diagnosed with MDD and 97 healthy controls (HCs) aged between 18 to 60 years. Metabolomics was performed on an Ultimate 3000 UHPLC system coupled with Q-Exactive MS (Thermo Scientific). The online software Metaboanalyst 6.0 was used to process and analyze the acquired raw data of peak intensities from the instrument.

Results: The study included 100 MDD patients and 97 HCs. Metabolomic profiling identified 35 significantly different metabolites (e.g., cortisol, sebacic acid, and L-glutamic acid). Receiver operating characteristic curve analysis highlighted 8-HETE, 10-HDoHE, cortisol, 12-HHTrE, and 10-hydroxydecanoic acid as top diagnostic biomarkers for MDD. Significant correlations were found between metabolites (e.g., some lipids, steroids, and amino acids) and clinical and psychological variables.

Conclusion: Our study reported metabolites (some lipids, steroids, amino acids, carnitines, and alkaloids) responsible for discriminating MDD patients and HCs. This metabolite profile may enable the development of a laboratory-based diagnostic test for MDD. The mechanisms underlying the association between psychological or clinical variables and differential metabolites deserve further exploration.

Keywords: Clinical variables; Major depressive disorder; Metabolites; Psychological variables; Serum metabolic profiling.

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Conflict of interest statement

Conflict-of-interest statement: The authors declare no conflict of interests for this article.

Figures

Figure 1
Figure 1
Metabolomic analysis of serum samples from major depressive disorder and healthy control groups. A: 2D partial least squares discriminant analysis scores plot of top 2 components; B: Overview of the performance of the top 5 components; C: The heatmap of representative metabolites to distinguish major depressive disorder and healthy control. 1The model with three components has the highest Q2 value, thus demonstrating the best prediction performance. HC: Healthy control; MDD: Major depressive disorder; FA: Fatty acid; CMPF: 3-Carboxy-4-methyl-5-propyl-2-furanpropanoic acid.
Figure 2
Figure 2
Total 35 differential metabolites between major depressive disorder and healthy control groups. The abscissa represents each feature, and the ordinate represents the fold change after the log2 transformation. Red: Major depressive disorder (MDD) > healthy control (HC); Blue: MDD < HC. FC: Fold change; CMPF: 3-Carboxy-4-methyl-5-propyl-2-furanpropanoic acid.
Figure 3
Figure 3
Receiver operating characteristic analysis revealing candidate metabolomic biomarkers for major depressive disorder diagnosis. A-E: Receiver operating characteristic (ROC) curves of 8-HETE, 10-HDoHE, cortisol, 12-HHTrE, and 10-hydroxydecanoic acid. The left panel of each picture represents the ROC for differentiating the major depressive disorder (MDD) group from the healthy control (HC) group, and the boxplots on the right side are the feature intensities of the two groups. The y-axis refers to the relative value after normalization of the peak intensities; F: Multivariate ROC curves constructed with 2-100 metabolites based on the cross-validation performance. Each curve represents the potential of the top 5, 10, 15, 25, 50, and 100 features in differentiating the MDD group from the HC group; G: Predicted class probabilities (average of the cross-validations) for each sample using the 5-feature model of metabolites; H: Corresponding predictive accuracy of each partial least squares discriminant analysis model constructed with different numbers of features. The predictive accuracy of 5 to 100 features is from 90% to 95.7%, respectively. HC: Healthy control; MDD: Major depressive disorder; AUC: Area under the receiver operating characteristic curve.
Figure 4
Figure 4
Spearman correlations of metabolites and clinical variables in patients with major depressive disorder and healthy controls. The red background represents the positive correlations between the two compared variables, while the blue background represents the negative correlations. FA: Fatty acid; SHAPS: Snaith-Hamilton Pleasure Scale; GAD-7: Generalized Anxiety Disorder 7-item scale; WHO-5: 5-item World Health Organization Well-Being Index; HAMD-24: Hamilton Depression Rating Scale-24 items; BMI: Body mass index; BUN: Blood urea nitrogen; CREA: Creatinine; URIC: Uric acid; CMPF: 3-Carboxy-4-methyl-5-propyl-2-furanpropanoic acid.

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References

    1. Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, Mohr DC, Schatzberg AF. Major depressive disorder. Nat Rev Dis Primers. 2016;2:16065. - PubMed
    1. Zheng P, Wang Y, Chen L, Yang D, Meng H, Zhou D, Zhong J, Lei Y, Melgiri ND, Xie P. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Mol Cell Proteomics. 2013;12:207–214. - PMC - PubMed
    1. Fekadu A, Demissie M, Birhane R, Medhin G, Bitew T, Hailemariam M, Minaye A, Habtamu K, Milkias B, Petersen I, Patel V, Cleare AJ, Mayston R, Thornicroft G, Alem A, Hanlon C, Prince M. Under detection of depression in primary care settings in low and middle-income countries: a systematic review and meta-analysis. Syst Rev. 2022;11:21. - PMC - PubMed
    1. Zhang X, Zhang Z, Diao W, Zhou C, Song Y, Wang R, Luo X, Liu G. Early-diagnosis of major depressive disorder: From biomarkers to point-of-care testing. TrAC Trend Anal Chem. 2023;159:116904.
    1. Li Z, Ruan M, Chen J, Fang Y. Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications. Neurosci Bull. 2021;37:863–880. - PMC - PubMed

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