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

Abstract

BackgroundOxidative 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.MethodsPeripheral 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.ResultsAll 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.ConclusionPeripheral non-enzymatic antioxidants, especially the UA, might be a potential biomarker capable of distinguishing between BD and MDD.

Clinical Application of Machine Learning Methods in Psychiatric Disorders

“…The samples for the clinical trials are obtained from peripheral blood, which is less invasive, reinforcing the validity of the proposed method. Similarly, Gong et al [12] use XGBoost, an ML prediction model, to improve classification ability. The technique successfully differentiates major depressive disorder from bipolar disorder with a 0.849 accuracy [12].…”

“…Similarly, Gong et al [12] use XGBoost, an ML prediction model, to improve classification ability. The technique successfully differentiates major depressive disorder from bipolar disorder with a 0.849 accuracy [12]. Furthermore, the model distinguishes major depressive disorder from bipolar disorder with depressive episodes with a 0.899 accuracy [12].…”

“…The technique successfully differentiates major depressive disorder from bipolar disorder with a 0.849 accuracy [12]. Furthermore, the model distinguishes major depressive disorder from bipolar disorder with depressive episodes with a 0.899 accuracy [12]. Differentiating the maladies, including bipolar disorder, depressive episodes, major depressive disorder, and schizophrenia, facilitates accurate and responsive treatment.…”

Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis

“…Excessive concentrations of uric acid deposited in the kidneys and joints can not only lead to kidney stones and gout, but also cause cardiovascular diseases such as hypertension and atherosclerosis ( Deb and Sakharkar, 2021 ; Shin and Lee, 2021 ). Consequently, although uric acid was once considered to be simply an end product of purine metabolism with no physiological value, new knowledge has led to the understanding that it is actually a major endogenous water-soluble antioxidant whose antioxidant effects are similar to those of vitamin C ( Gong et al, 2022 ). Hence, increases in the body’s uric acid levels could reflect the body’s attempt to increase the levels of endogenous antioxidants to eliminate free radicals and prevent toxicity and DNA damage as well as lipid peroxidation.…”