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. 2022 Nov 7:13:1019618.
doi: 10.3389/fpsyt.2022.1019618. eCollection 2022.

Peripheral non-enzymatic antioxidants as biomarkers for mood disorders: Evidence from a machine learning prediction model

Yuandong Gong  1 , Zhe Lu  2   3   4 , Zhewei Kang  2   3   4 , Xiaoyang Feng  2   3   4 , Yuyanan Zhang  2   3   4 , Yaoyao Sun  2   3   4 , Weimin Chen  1 , Guanglei Xun  1 , Weihua Yue  2   3   4   5   6
Affiliations

Peripheral non-enzymatic antioxidants as biomarkers for mood disorders: Evidence from a machine learning prediction model

Yuandong Gong et al. Front Psychiatry. .

Abstract

Background: Oxidative stress is related to the pathogenesis of mood disorders, and the level of oxidative stress may differ between bipolar disorder (BD) and major depressive disorder (MDD). This study aimed to detect the differences in non-enzymatic antioxidant levels between BD and MDD and assess the predictive values of non-enzymatic antioxidants in mood disorders by applying a machine learning model.

Methods: Peripheral uric acid (UA), albumin (ALB), and total bilirubin (TBIL) were measured in 1,188 participants (discover cohort: 157 with BD and 544 with MDD; validation cohort: 119 with BD and 95 with MDD; 273 healthy controls). An extreme gradient boosting (XGBoost) model and a logistic regression model were used to assess the predictive effect.

Results: All three indices differed between patients with mood disorders and healthy controls; in addition, the levels of UA in patients with BD were higher than those of patients with MDD. After treatment, UA levels increased in the MDD group, while they decreased in the BD group. Finally, we entered age, sex, UA, ALB, and TBIL into the XGBoost model. The area under the curve (AUC) of the XGBoost model for distinguishing between BD and MDD reached 0.849 (accuracy = 0.808, 95% CI = 0.719-0.878) and for distinguishing between BD with depression episode (BD-D) and MDD was 0.899 (accuracy = 0.891, 95% CI = 0.856-0.919). The models were validated in the validation cohort. The most important feature distinguishing between BD and MDD was UA.

Conclusion: Peripheral non-enzymatic antioxidants, especially the UA, might be a potential biomarker capable of distinguishing between BD and MDD.

Keywords: bipolar disorder; machine learning model; major depressive disorder; non-enzymatic antioxidants; uric acid.

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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
Peripheral non-enzymatic antioxidants at baseline among BD, MDD, and HC. (A) UA levels among BD, MDD, and HC groups. (B) ALB levels among BD, MDD, and HC groups. (C) TBIL levels among BD, MDD, and HC groups. UA, uric acid; ALB, albumin; TBIL, total bilirubin; BD, bipolar disorder; BD-M, bipolar disorder with mania/hypomania/mixed episode; BD-D, bipolar disorder with depression episode; MDD, major depressive disorder; HC, healthy control; *p < 0.05; ***p < 0.001.
Figure 2
Figure 2
Changing of three non-enzymatic antioxidants after treatment. (A) Changing of UA levels after treatment. (B) Changing of ALB levels after treatment. (C) Changing of TBIL levels after treatment. UA, uric acid; ALB, albumin; TBIL, total bilirubin; BD, bipolar disorder; MDD, major depressive disorder; HC, healthy control.
Figure 3
Figure 3
XGBoost model for the predictive effect of non-enzymatic antioxidants. (A–D) The result of discovery data. (E–H) The result of validation data. (A,E) BD vs. HC. (B,F) MDD vs. HC. (C,G) BD vs. MDD. (D,H) BD-D vs. MDD. UA, uric acid; ALB, albumin; TBIL, total bilirubin; BD, bipolar disorder; MDD, major depressive disorder; BD-D, bipolar disorder with depression episode; HC, healthy control; ROC, receiver operating characteristic curve; AUC, area under the curve.
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