The nonenzymatic antioxidant profile as a biomarker for adolescent mood disorders: Cross-sectional and longitudinal analyses

Abstract

Objective

This study aimed to investigate the associations between serum nonenzymatic antioxidants and major depressive disorder or bipolar disorder in adolescents.

Methods

Cross-sectional (410 adolescents: 178 patients with major depressive disorder, 54 patients with bipolar disorder, and 178 healthy controls) and longitudinal (72 adolescents: 55 patients with major depressive disorder and 17 patients with bipolar disorder) analyses were conducted. Antioxidant levels were measured and correlated with diagnosis and Hamilton Depression Rating Scale scores. Treatment-related changes in uric acid, albumin, and total bilirubin were assessed in the longitudinal cohort.

Results

Patients with major depressive disorder and bipolar disorder showed higher uric acid (p = 0.019) and lower albumin and total bilirubin (p < 0.001) than in controls. Multivariate regression analysis identified uric acid as a risk factor for major depressive disorder (odds ratio = 2.52, p = 0.003) and bipolar disorder (odds ratio = 4.66, p = 0.001), whereas albumin and total bilirubin were protective factors (p < 0.001). Post-treatment, uric acid decreased in patients with major depressive disorder (336.7 ± 82.5 to 314.1 ± 76.5 µmol/L, p = 0.017) and bipolar disorder (341.9 ± 106.8 to 314.5 ± 102.4 µmol/L, p = 0.013), whereas total bilirubin increased in patients with major depressive disorder (8.8 ± 3.4 to 11.0 ± 4.6 µmol/L, p = 0.001). Uric acid was correlated positively with Hamilton Depression Rating Scale scores in patients with major depressive disorder (r = 0.32, p < 0.001) and showed a trend in patients with bipolar disorder (r = 0.33, p = 0.06). Total bilirubin showed an inverse correlation with Hamilton Depression Rating Scale scores in patients with major depressive disorder (r = −0.30, p = 0.002).

Conclusion

Adolescent patients with major depressive disorder and bipolar disorder exhibit antioxidant imbalances. Elevated uric acid may act as a risk factor, whereas reduced albumin and total bilirubin appear protective, suggesting their role as biomarkers for disease status and treatment response.

Keywords

Nonenzymatic antioxidants oxidative stress major depressive disorder bipolar disorder adolescents

Introduction

Major depressive disorder (MDD) and bipolar disorder (BD) are highly prevalent psychiatric disorders, particularly among adolescents.¹ Approximately one in five children and adolescents experience depression or depressive symptoms worldwide, and this incidence continues to rise.² BD is a chronic and recurrent condition, with relapse rates approaching 90%,^3,4 and it often has an onset during adolescence.^5,6 The emergence of MDD and BD at this developmental stage is associated with substantial functional impairments, including interpersonal difficulties; academic underachievement; reduced quality of life; and an increased risk of physical comorbidities, self-harm, and suicide.^7–10 These disorders significantly impact adolescent development and place a considerable burden on families and society.^11,12

Emerging evidence implicates oxidative stress in the pathophysiology of MDD and BD.^13–15 Oxidative stress arises from an imbalance between reactive oxygen species (ROS) and antioxidant defenses.¹⁶ The antioxidant system comprises both enzymatic and nonenzymatic components.¹⁷ However, enzymatic antioxidants are challenging to measure in routine clinical settings. Nonenzymatic antioxidants comprise a wide range of circulating molecules, including uric acid (UA), bilirubin, glutathione, vitamins, and abundant plasma proteins such as albumin (Alb). Among these, UA, Alb, and total bilirubin (Tbil) are routinely measured in standardized clinical chemistry panels and collectively contribute to more than 85% of plasma antioxidant capacity,^18,19 making them practical indicators of systemic oxidative balance. This advantage is particularly relevant for multicenter adolescent studies, where short-term dietary variability can influence vitamin indices,²⁰ and glutathione assays often require stringent preanalytical handling and specialized laboratory procedures.²¹ Accordingly, we prioritized UA, Alb, and Tbil as primary nonenzymatic antioxidant markers in the present study. Among these, UA has been the most extensively studied, although findings remain inconsistent. Some studies have reported lower UA levels in patients with depression,^22–24 suggesting a neuroprotective role.²⁵ In contrast, Tao et al.²⁶ reported significantly higher UA levels in adolescents with depression than in healthy controls (HC). However, few studies have simultaneously investigated the profiles of UA, Alb, and Tbil in adolescents with MDD and BD, which limits our understanding of their collective relevance in disease onset and progression.

Methods

Study population

For the cross-sectional analysis, 232 adolescent inpatients were retrospectively selected from the Case-Data Management Platform of The First Affiliated Hospital of Chongqing Medical University and Chongqing Mental Health Center between January 2020 and December 2022. This group included 178 patients with MDD and 54 patients with BD during depressive episodes. An age- and sex-matched HC group comprising 178 individuals was also included. For the longitudinal analysis, 72 adolescent inpatients were retrospectively enrolled between January and August 2024 from the same two hospitals, including 55 patients with MDD and 17 patients with BD during depressive episodes. All diagnoses were confirmed by two experienced psychiatrists in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Notably, there was no overlap between the participants in the cross-sectional study and those in the subsequent longitudinal analysis.

Participants in both the patient and HC groups were aged 12–18 years. Inclusion criteria for the patient groups were as follows: (a) meeting DSM-5 criteria for MDD (including recurrent depressive disorder) or bipolar depressive episodes and (b) no prior use of antipsychotic drugs, antidepressants, or mood stabilizers and no documented history of such drug use, with a withdrawal period of at least 1 month. Exclusion criteria for both patient and HC groups were as follows: (a) severe physical illnesses, including but not limited to cardiovascular, neurological, respiratory, hepatic, renal, endocrine, or immune system diseases; (b) diagnosis of any other psychiatric disorder; (c) history of substance abuse or dependence; (d) current use of medications known to affect oxidative stress markers, such as acetylsalicylic acid, allopurinol, thiazide diuretics, corticosteroids, ibuprofen, or vitamin E; and (e) abnormal findings in physical or laboratory examinations indicating other medical conditions.

This study was conducted in accordance with the Declaration of Helsinki, as revised in 2024. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (K2023-130-02) and the Ethics Committee of Chongqing Mental Health Center (2025 Lunshen Medical No. 004), and all procedures were implemented according to relevant ethical requirements. Due to the retrospective nature of the study, the requirement for written informed consent was waived for the patient group. For the HC group, participants were recruited voluntarily and provided written informed consent. All controls were screened through clinical interviews conducted by qualified psychiatrists to exclude any psychiatric disorders. All collected data were anonymized and kept confidential, and personal identifiers were not disclosed.

Data collection and measurements

Data were collected by physicians from electronic case records. Height and weight were measured with participants wearing lightweight clothing and no shoes, and body mass index (BMI) was subsequently calculated. Approximately 5 mL of fasting venous blood was collected from each participant after at least 8 h of fasting. Blood samples were centrifuged within 2 h of collection at 3500 × g for 5 min. The serum levels of Tbil, Alb, and UA were quantitatively determined using the diazo method for Tbil, the bromocresol green colorimetric assay for Alb, and the uricase–peroxidase–coupled technique for UA on a fully automated biochemical analyzer.

Statistical analyses

All statistical analyses were conducted using Statistical Package for Social Sciences (SPSS) version 26.0 (IBM Corp.; Armonk, NY, USA). Continuous variables were presented as mean ± SD, and normality was assessed using the Kolmogorov–Smirnov test (n ≥ 50) or the Shapiro–Wilk test (n < 50). For the cross-sectional analysis, continuous variables were compared among groups using the Kruskal–Wallis test. Categorical variables were expressed as counts and percentages, and group comparisons were performed using one-way analysis of variance (ANOVA). In the longitudinal analysis, differences in antioxidant levels before and after treatment were evaluated using the paired t-test for normally distributed variables and the Wilcoxon signed-rank test for non-normally distributed variables.

To examine the associations between nonenzymatic antioxidants and MDD or BD, antioxidant levels (Tbil, Alb, and UA) were categorized into tertiles (“low,” “medium,” and “high”) based on their distribution in the cross-sectional dataset. Logistic regression analyses were performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the medium- and high-tertile groups, using the low-tertile group as the reference. Three multivariate binary logistic regression models were constructed to estimate independent associations, adjusting for potential confounders. In the longitudinal analysis, changes in antioxidant levels before and after antidepressant treatment were assessed, and correlations between antioxidant levels and Hamilton Depression Rating Scale (HAMD) scores were analyzed. A p-value <0.05 was considered statistically significant.

Reporting guidelines

This study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.²⁷ The completed checklist has been provided as the research data.

Results

Characteristics of study participants in the cross-sectional study

The general characteristics of the cross-sectional study are shown in Table 1. The sample includes 178 individuals with MDD, 54 with BD, and 178 HCs. These participants were ultimately selected from approximately 6000 initial cases after applying stringent inclusion and exclusion criteria. Participants were matched for sex and age, ensuring comparability across the three groups in terms of physical and sociodemographic characteristics. No significant differences were observed among the groups in terms of sex distribution, age, and BMI (all p > 0.05). However, significant group differences were observed in serum nonenzymatic antioxidant levels. UA levels were significantly higher in patients with MDD (347.7 ± 80.7 µmol/L) and BD (359.9 ± 88.5 µmol/L) than in HCs (328.0 ± 71.6 µmol/L; p = 0.019) (Figure 1(a)). In contrast, Alb levels were significantly lower in patients with MDD (44.8 ± 2.8 g/L) and BD (44.9 ± 3.1 g/L) than in HCs (46.9 ± 2.8 g/L; p < 0.001) (Figure 1(b)). Similarly, Tbil levels were significantly lower in patients with MDD (12.1 ± 6.5 µmol/L) and BD (10.6 ± 5.2 µmol/L) compared with HCs (14.7 ± 6.2 µmol/L; p < 0.001) (Figure 1(c)). It should be noted that the BD group exhibited six missing values for Alb and one missing value for Tbil. Participants with incomplete data were excluded from the respective statistical analyses.

Table 1.

Demographic and clinical characteristics of patients with depression, patients with BD, and the healthy control group in the cross-sectional study.

	HC	MDD	BD	p-value
Variables	n = 178	n = 178	n = 54	p-value
Sex, %				0.995
Male	44 (24.7%)	44 (24.7%)	13 (24.1%)
Female	134 (75.3%)	134 (75.3%)	41 (75.9%)
Age, years	15.5 ± 1.7	15.4 ± 1.7	14.9 ± 1.7	0.079
BMI, kg/m²	20.7 ± 2.5	20.6 ± 3.9	20.1 ± 3.0	0.144
UA, μmol/L	328.0 ± 71.6	347.7 ± 80.7	359.9 ± 88.5	0.019
Alb, g/L	46.9 ± 2.8	44.8 ± 2.8	44.9 ± 3.1	<0.001
Tbil, μmol/L	14.7 ± 6.2	12.1 ± 6.5	10.6 ± 5.2	<0.001

The BD group exhibited six missing values for Alb and one missing value for Tbil.

Alb: albumin; BD: bipolar disorder; BMI: body mass index; HC: healthy control; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Figure 1.

Comparison of nonenzymatic antioxidant levels among groups. (a) Comparison of UA levels among groups; (b) comparison of Alb levels among groups; (c) comparison of Tbil levels among groups. *p < 0.05, **p < 0.01, ***p < 0.001. HC: Healthy control, MDD: major depressive disorder, BD: bipolar disorder. UA: uric acid, Alb: albumin, Tbil: total bilirubin.

Univariate binary logistic regression analysis for identifying significant risk and protective factors

To investigate potential factors associated with MDD and BD, univariate binary logistic regression analyses were performed using data from the cross-sectional cohort. As shown in Figure 2, MDD and BD were analyzed as dependent variables, whereas sex, age, BMI, and serum levels of nonenzymatic antioxidants (UA, Alb, and Tbil) were included as independent variables. The results revealed that elevated UA levels were significantly associated with an increased risk of both MDD (OR = 1.003, p = 0.016) and BD (OR = 1.005, p = 0.009). In contrast, higher levels of Tbil and Alb were significantly associated with a reduced risk of MDD and BD (p < 0.001 for all), suggesting a protective role.

Figure 2.

Univariate binary logistic regression analysis for MDD and BD. (a) Univariate binary logistic regression analysis for MDD; (b) univariate binary logistic regression analysis for BD. Alb: albumin; BD: bipolar disorder; BMI: body mass index; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Nonenzymatic antioxidants for predicting incident MDD and BD

To further investigate the associations between nonenzymatic antioxidants and the risk of MDD and BD, multivariate binary logistic regression analyses were performed, as shown in Figure 3. After adjusting for age, sex, and BMI, participants in the highest tertile of UA had a significantly increased risk of MDD (OR = 2.518, 95% CI: 1.374–4.614; p = 0.003) and BD (OR = 4.663, 95% CI: 1.819–11.952; p = 0.001) compared with those in the lowest tertile. In contrast, participants in the highest tertile of Alb had a significantly reduced risk of MDD (OR = 0.167, 95% CI: 0.093–0.301; p < 0.001) and BD (OR = 0.146, 95% CI: 0.051–0.419; p < 0.001), indicating a protective effect. Similarly, Tbil was inversely associated with both MDD (OR = 0.284, 95% CI: 0.163–0.495; p < 0.001) and BD (OR = 0.147, 95% CI: 0.057–0.381; p < 0.001).

Figure 3.

Multivariate binary logistic regression analysis for MDD and BD. (a) Multivariate binary logistic regression analysis with UA for MDD and BD; (b) multivariate binary logistic regression analysis with Alb for MDD and BD; (c) multivariate binary logistic regression analysis with Alb for MDD and BD. Alb: albumin; BD: bipolar disorder; BMI: body mass index; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Longitudinal changes in nonenzymatic antioxidants following treatment

As shown in Table 2, the longitudinal analysis included 55 patients with MDD and 17 patients with BD. Patients in the MDD group had a mean hospitalization duration of 20.9 days and all received antidepressant treatment, including selective serotonin reuptake inhibitors (SSRIs; n = 32) and serotonin–norepinephrine reuptake inhibitors (SNRIs; n = 23). Patients in the BD group had a mean hospitalization duration of 23.8 days and received comprehensive pharmacotherapy based on mood stabilizers administered as monotherapy or in combination. The specific regimens were as follows: (a) mood stabilizers augmented with second-generation antipsychotics (n = 2); (b) mood stabilizers combined with antidepressants (SSRIs or SNRIs; n = 6); and (c) triple therapy comprising mood stabilizers, second-generation antipsychotics, and antidepressants (n = 9). No structured psychotherapy was administered in either group. In both groups, serum UA levels significantly decreased after treatment compared with baseline (MDD: 336.7 ± 82.5 vs. 314.1 ± 76.5 µmol/L, p = 0.017; BD: 341.9 ± 106.8 vs. 314.5 ± 102.4 µmol/L, p = 0.013). In the MDD group, Tbil levels significantly increased following treatment (11.0 ± 4.6 vs. 8.8 ± 3.4 µmol/L, p = 0.001). No significant changes were observed in Alb levels in either group, and Tbil levels in the BD group also remained unchanged (p > 0.05). The dynamic changes in nonenzymatic antioxidants are shown in Figure 4.

Table 2.

Clinical characteristics of patients with MDD and BD in the longitudinal study.

Variable	MDD (n = 55)		Z/t	p-value	BD (n = 17)		Z/t	p-value
Variable	Pretreatment	Post-treatment			Pretreatment	Post-treatment
Age, years	14.8 ± 1.5		–	–	14.4 ± 1.4		–	–
BMI, kg/m²	21.4 ± 4.7		–	–	20.0 ± 3.0		–	–
UA, μmol/L^a	336.7 ± 82.5	314.1 ± 76.5	−2.380	0.017	341.9 ± 106.8	314.5 ± 102.4	−2.485	0.013
Alb, g/L^b	43.6 ± 3.4	43.0 ± 2.6	1.307	0.197	43.8 ± 2.2	42.2 ± 3.5	1.119	0.285
Tbil, μmol/L^a	8.8 ± 3.4	11.0 ± 4.6	−3.294	0.001	9.6 ± 4.3	8.9 ± 3.7	−1.160	0.246
HAMD, score^b	37.3 ± 3.1	15.8 ± 7.5	21.161	<0.001	34.6 ± 4.6	20.2 ± 6.7	10.323	<0.001

Analyzed using the paired-samples Wilcoxon signed-rank test.

Analyzed using the paired t-test.

Alb: albumin; BD: bipolar disorder; BMI: body mass index; HAMD: Hamilton Depression Scale; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Figure 4.

Comparison of nonenzymatic antioxidant levels pretreatment and post-treatment in patients with MDD and BD. (a) Comparison of UA levels pretreatment and post-treatment in MDD; (b) comparison of UA levels pretreatment and post-treatment in BD; (c) comparison of Tbil levels pretreatment and post-treatment in MDD. BD: bipolar disorder; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Correlations between nonenzymatic antioxidant levels and HAMD scores

We examined the relationships between serum levels of nonenzymatic antioxidants and depressive symptom severity, as assessed by the HAMD. HAMD scores were treated as the dependent variable, and UA, Alb, and Tbil were entered as independent variables. Separate univariate correlation analyses were conducted for the MDD and BD groups. As shown in Figure 5, HAMD scores were positively correlated with UA levels in patients with MDD (r = 0.322, p < 0.001; Figure 5(a)) and BD (r = 0.326, p = 0.060; Figure 5(b)). In contrast, a significant negative correlation was observed between HAMD scores and Tbil levels in patients with MDD (r = −0.298, p = 0.002; Figure 5(c)). No significant correlations were found between HAMD scores and Alb levels in either group.

Figure 5.

Spearman linear correlation analysis between nonenzymatic antioxidant levels and HAMD scores in patients with MDD and BD. (a) Spearman linear correlation analysis between UA levels and HAMD scores in MDD; (b) Spearman linear correlation analysis between UA levels and HAMD scores in BD; (c) Spearman linear correlation analysis between Tbil levels and HAMD scores in MDD. Alb: albumin; BD: bipolar disorder; BMI: body mass index; HAMD: Hamilton Depression Scale; MDD: major depressive disorder; Tbil: total bilirubin; UA: uric acid.

Discussion

This study provides novel evidence that elevated serum UA is independently associated with an increased risk of MDD and BD in adolescents, whereas higher levels of Tbil and Alb appear to exert protective effects. Furthermore, longitudinal data indicate that effective treatment of patients with MDD and BD is associated with a reduction in UA levels. These findings suggest that mood disorders in adolescents are linked to disruptions in the peripheral nonenzymatic antioxidant defense system.

Consistent with our results, significantly higher UA levels were observed in patients with MDD and BD than in HCs. Both univariate and multivariate logistic regression analyses confirmed UA as a risk factor for MDD and BD. However, previous findings have been inconsistent. For example, Kesebir et al.²⁸ reported decreased UA levels in patients with depression and increased levels in those with bipolar depression. Wium-Andersen et al.,²⁹ in a large adult cohort (n = 96,989), found that higher UA levels were associated with a reduced risk of hospitalization for depression. Similarly, Black et al.³⁰ reported a negative correlation between UA levels and depression severity. These discrepancies may be attributable to differences in study populations, as most existing studies have focused on adults, whereas our study exclusively investigated adolescents aged 12–18 years.

To date, only one relevant study has focused on adolescents. Ran et al.²⁶ reported elevated UA levels in male adolescents with depression, suggesting its role as a diagnostic biomarker. However, their study was limited by sex specificity and absence of longitudinal data. Our study extends these findings by including both sexes and a treatment follow-up. The observed post-treatment decrease in UA further supports its role as a dynamic biomarker of clinical improvement. The underlying pathophysiological mechanisms contributing to elevated serum UA levels in adolescents with MDD and BD remain unclear. Current evidence suggests that multiple pathways may contribute to these findings. First, UA elevation may reflect a compensatory response to increased oxidative stress, as oxidative injury can enhance nucleic acid turnover and purine catabolism, thereby increasing UA production.³¹ Second, UA may represent an inflammation-related metabolic byproduct, given that mood disorders are often accompanied by systemic and neuroinflammatory activation that can perturb purine metabolism.³² Third, lifestyle and dietary factors commonly associated with adolescent mood disorders may further influence purine load and UA levels. These mechanisms likely interact to influence UA metabolism in adolescents. These findings also raise the possibility that UA may exert age-dependent effects, consistent with previous research demonstrating age-related variability in antioxidant systems.^33–35 Future prospective studies are warranted to further explore the causal pathways and age-specific mechanisms.

In addition to UA, we found that Tbil and Alb levels were significantly lower in patients with MDD and BD than in HCs. As potent endogenous antioxidants, Alb and Tbil play essential roles in protecting tissues, including the brain, from oxidative damage.^36,37 Our regression models identified both as protective factors, and Tbil levels were significantly elevated following treatment in patients with MDD. These results are consistent with previous studies reporting reduced Tbil levels in patients with MDD^38,39 and support its role in free radical scavenging and the reduction of oxidative stress.^40,41 Interestingly, no significant post-treatment changes in Alb were observed in either group, possibly due to the short hospitalization period or the limited sample size, particularly for the BD group (n = 17). This finding highlights the greater sensitivity of UA and Tbil as potential biomarkers of treatment response.

In addition to the three nonenzymatic antioxidants investigated in this study, other compounds such as glutathione, vitamins, and coenzyme Q10 also contribute to the body’s antioxidant defenses. These molecules are involved in diverse physiological processes, including mitochondrial oxidation, lipid peroxidation,⁴² cell proliferation,⁴³ neuronal differentiation,⁴⁴ and neurotransmitter regulation.⁴⁵ Notably, various antioxidants can interact with and modulate one another,⁴⁶ suggesting a complex network of oxidative balance that may influence the onset and progression of mood disorders. However, the specific molecular mechanisms remain to be elucidated.

Despite the strengths of our study, including the focus on a pediatric population and the combined cross-sectional and longitudinal design, several limitations should be acknowledged. First, the short duration of the longitudinal component limits our ability to establish causality. Larger and longer-term cohort studies are needed to assess temporal relationships. Second, the sample size, particularly in the BD group, was relatively small, which may have reduced statistical power of the analysis. Finally, although changes in UA levels were observed post-treatment, the underlying mechanisms remain unclear. Future mechanistic studies are needed to explore how antioxidant systems interact with the pathophysiology and treatment of mood disorders.

Conclusion

This study suggests that an imbalance in nonenzymatic antioxidants may be associated with MDD and BD in adolescents. Particularly, elevated UA levels were associated with an increased risk of MDD and BD, whereas higher levels of Tbil and Alb appeared to have protective effects. Additionally, effective treatment of patients with MDD and BD was associated with a reduction in UA levels, suggesting its role as a biomarker of treatment response.

Footnotes

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Funding

This work was supported by the National Key R&D Program of China (2024YFC2707800); STI2030-Major Projects (2022ZD0212900); the National Natural Science Foundation of China (82271565, 82301714); the China Postdoctoral Science Foundation (2023TQ0398, GZB20230916, 2023MD734124); Natural Science Foundation of Chongqing, China (CSTB2023NSCQ-BHX0106); and Postdoctoral Innovation Talents Support Program of Chongqing, China (2208013341918508).

ORCID iD

Xinyu Zhou

References

Bowden

Krishnan

AA.

Pharmacotherapy for bipolar depression: an economic assessment. Expert Opin Pharmacother 2004; 5: 1101–1107.

Kieling

Buchweitz

Caye

, et al. Worldwide prevalence and disability from mental disorders across childhood and adolescence: evidence from the global burden of disease study. JAMA Psychiatry 2024; 81: 347–356.

Sajatovic

Bipolar disorder: disease burden. Am J Manag Care 2005; 11: S80–S84.

Lin

Global burden of depression or depressive symptoms in children and adolescents: a systematic review and meta-analysis. J Affect Disord 2024; 354: 553–562.

Freund

Juckel

Bipolar disorder: its etiology and how to model in rodents. Methods Mol Biol 2019; 2011: 61–77.

Carvalho

Firth

Vieta

Bipolar disorder. N Engl J Med 2020; 383: 58–66.

Dong

Zhang

, et al. Prevalence of suicide attempts in bipolar disorder: a systematic review and meta-analysis of observational studies. Epidemiol Psychiatr Sci 2019; 29: e63.

Fitzpatrick

Lemieux

Smith

, et al. Is adolescent internet use a risk factor for the development of depression symptoms or vice-versa? Psychol Med 2023; 53: 6773–6779.

Hawrilenko

Masyn

Cerutti

, et al. Individual differences in the stability and change of childhood depression: a growth mixture model with structured residuals. Child Dev 2021; 92: e343–e363.

10.

Hetrick

McKenzie

Bailey

, et al. New generation antidepressants for depression in children and adolescents: a network meta-analysis. Cochrane Database Syst Rev 2021; 5: CD013674.

11.

Centers for Disease Control and Prevention (CDC).

Suicide trends among youths and young adults aged 10–24 years–United States, 1990-2004

. MMWR Morb Mortal Wkly Rep 2007; 56: 905–908.

12.

Zhong

Chen

, et al. Global, regional and national burdens of bipolar disorders in adolescents and young adults: a trend analysis from 1990 to 2019. Gen Psychiatr 2024; 37: e101255.

13.

Halliwell

Understanding mechanisms of antioxidant action in health and disease. Nat Rev Mol Cell Biol 2024; 25: 13–33.

14.

Jimenez-Fernandez

Gurpegui

Garrote-Rojas

, et al. Oxidative stress parameters and antioxidants in patients with bipolar disorder: results from a meta-analysis comparing patients, including stratification by polarity and euthymic status, with healthy controls. Bipolar Disord 2021; 23: 117–129.

15.

Saleem

Sabir

Niazi

, et al. Role of oxidative stress and antioxidant defense biomarkers in neurodegenerative diseases. Crit Rev Eukaryot Gene Expr 2020; 30: 311–322.

16.

Sies

Oxidative stress: concept and some practical aspects. Antioxidants (Basel) 2020; 9: 852.

17.

Kozlov

Javadov

Sommer

Cellular ROS and antioxidants: physiological and pathological role. Antioxidants (Basel) 2024; 13: 602.

18.

Miller

Rice-Evans

Davies

, et al. A novel method for measuring antioxidant capacity and its application to monitoring the antioxidant status in premature neonates. Clin Sci (Lond) 1993; 84: 407–412.

19.

Maxwell

Dietrich

Chapple

IL.

Prediction of serum total antioxidant activity from the concentration of individual serum antioxidants. Clin Chim Acta 2006; 372: 188–194.

20.

Padayatty

Levine

Vitamin C: the known and the unknown and Goldilocks. Oral Dis 2016; 22: 463–493.

21.

Giustarini

Rossi

Milzani

, et al. Pitfalls in the analysis of the physiological antioxidant glutathione (GSH) and its disulfide (GSSG) in biological samples: an elephant in the room. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1019: 21–28.

22.

Jimenez-Fernandez

Gurpegui

Diaz-Atienza

, et al. Oxidative stress and antioxidant parameters in patients with major depressive disorder compared to healthy controls before and after antidepressant treatment: results from a meta-analysis. J Clin Psychiatry 2015; 76: 1658–1667.

23.

Liu

Zhong

Liao

, et al. A meta-analysis of oxidative stress markers in depression. PlOS One 2015; 10: e0138904.

24.

Bartoli

Trotta

Crocamo

, et al. Antioxidant uric acid in treated and untreated subjects with major depressive disorder: a meta-analysis and meta-regression. Eur Arch Psychiatry Clin Neurosci 2018; 268: 119–127.

25.

Irizarry

Raman

Schwarzschild

, et al. Plasma urate and progression of mild cognitive impairment. Neurodegener Dis 2009; 6: 23–28.

26.

Tao

High serum uric acid level in adolescent depressive patients. J Affect Disord 2015; 174: 464–466.

27.

Von Elm

Altman

Egger

; STROBE Initiativeet al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147: 573–577.

28.

Kesebir

Tatlidil

Suner

, et al. Uric acid levels may be a biological marker for the differentiation of unipolar and bipolar disorder: the role of affective temperament. J Affect Disord 2014; 165: 131–134.

29.

Wium-Andersen

Kobylecki

Afzal

, et al. Association between the antioxidant uric acid and depression and antidepressant medication use in 96 989 individuals. Acta Psychiatr Scand 2017; 136: 424–433.

30.

Black

Bot

Scheffer

, et al. Uric acid in major depressive and anxiety disorders. J Affect Disord 2018; 225: 684–690.

31.

Correia

Cardoso

Vale

Oxidative stress in depression: the link with the stress response, neuroinflammation, serotonin, neurogenesis and synaptic plasticity. Antioxidants (Basel) 2023; 12: 470.

32.

Ferencova

Visnovcova

Ondrejka

, et al. Evaluation of inflammatory response system (IRS) and compensatory immune response system (CIRS) in adolescent major depression. J Inflamm Res 2022; 15: 5959–5976.

33.

Zhou

Liu

Lan

, et al. Polyunsaturated fatty acids metabolism, purine metabolism and inosine as potential independent diagnostic biomarkers for major depressive disorder in children and adolescents. Mol Psychiatry 2019; 24: 1478–1488.

34.

Maciejczyk

Zalewska

Ładny

JR.

Salivary antioxidant barrier, redox status, and oxidative damage to proteins and lipids in healthy children, adults, and the elderly. Oxid Med Cell Longev 2019; 2019: 4393460.

35.

Canadananovic

Latinovic

Barisic

, et al. Age-related changes of vitamin C levels in aqueous humour. Vojnosanit Pregl 2015; 72: 823–826.

36.

Stocker

Yamamoto

McDonagh

, et al. Bilirubin is an antioxidant of possible physiological importance. Science 1987; 235: 1043–1046.

37.

Van Hunsel

Wauters

Vandoolaeghe

, et al. Lower total serum protein, albumin, and beta- and gamma-globulin in major and treatment-resistant depression: effects of antidepressant treatments. Psychiatry Res 1996; 65: 159–169.

38.

Huang

TL.

Lower serum albumin levels in patients with mood disorders. Chang Gung Med J 2002; 25: 509–513.

39.

Al-Marwani

Batieha

Khader

, et al. Association between albumin and depression: a population-based study. BMC Psychiatry 2023; 23: 780.

40.

Gong

Kang

, et al. Peripheral non-enzymatic antioxidants as biomarkers for mood disorders: evidence from a machine learning prediction model. Front Psychiatry 2022; 13: 1019618.

41.

Yao

Reddy

Van Kammen

DP.

Abnormal age-related changes of plasma antioxidant proteins in schizophrenia. Psychiatry Res 2000; 97: 137–151.

42.

Bhatt

Nagappa

Patil

CR.

Role of oxidative stress in depression. Drug Discov Today 2020; 25: 1270–1276.

43.

Zhang

Han

, et al. Impairment of autophagy mediates the uric-acid-induced phenotypic transformation of vascular smooth muscle cells. Pharmacology 2024; 109: 34–42.

44.

De Lima

De Paula

AGP

Yuasa

, et al. Genetic and epigenetic regulation of the innate immune response to gout. Immunol Invest 2023; 52: 364–397.

45.

Schmelzer

Lindner

Rimbach

, et al. Functions of coenzyme Q10 in inflammation and gene expression. Biofactors 2008; 32: 179–183.

46.

Maes

Galecki

Chang

, et al. A review on the oxidative and nitrosative stress (O&NS) pathways in major depression and their possible contribution to the (neuro)degenerative processes in that illness. Prog Neuropsychopharmacol Biol Psychiatry 2011; 35: 676–692.