Association of Oxidative Stress-related Biomarkers and Atrial Fibrillation: A Cross- Sectional Study

doi:10.21203/rs.3.rs-3063179/v1

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Association of Oxidative Stress-related Biomarkers and Atrial Fibrillation: A Cross- Sectional Study

https://doi.org/10.21203/rs.3.rs-3063179/v1

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A growing number of reports have shown that oxidative stress is an important contributing factor for atrial fibrillation (AF). The identification of oxidative stress-related blood biomarkers for patients with AF has great significance for the early prevention and treatment of AF. The purpose of this study was to illuminate the relationship of several blood markers of oxidative stress with AF. This study enrolled hospitalized patients from January 2018 to December 2020 at the Department of Cardiology in a tertiary center in east China.Clinical data, with an emphasis on oxidative stress-related blood biomarkers, were collected to assess their relationship with AF. A total of 9452 patients were enrolled, including 1244 patients with AF (13.16%). Elevated total bilirubin (OR: 1.056; 95% CI: 1.046-1.065; P<0.001), uric acid (OR: 1.157; 95% CI: 1.112-1.204; P<0.001) and reduced superoxide dismutase(OR: 0.992; 95% CI: 0.987-0.997; P=0.001) were significantly associated with AF, which were also effective indicators for diagnosing AF (the area under the ROC curve model combined with uric acid, total bilirubin and superoxide dismutase was 69.1%). Furthermore, oxidative stress-related biomarkers were significantly associated with the prevalence of AF in different ages and sexes. As oxidative stress-related biomarkers, higher levels of total bilirubin and uric acid and lower levels of superoxide dismutase were independently associated with the prevalence of AF. These biomarkers may be used as potential indicators to predict AF, and multitarget antioxidant therapy may be a reasonable approach to prevent and treat AF.

Atrial fibrillation

Oxidative stress

Biomarkers

Antioxidants

Treatment

Atrial fibrillation (AF), as a common arrhythmia, has an increasing incidence year by year [1]. Multiple studies have shown that AF is associated with an increased risk of heart failure, stroke, and cognitive impairment [2, 3]. The current treatment of AF is not satisfactory, and the recurrence rate of AF is approximately 40%~50% despite the application of antiarrhythmic drugs or catheter ablation [4, 5]. This not only has an impact on patients' lives but also places great pressure on public medical resources. Therefore, early identification and screening of risk factors associated with AF is of great significance for the prevention and treatment of AF.

In recent years, a growing number of reports have shown the importance of oxidative stress in the pathophysiological mechanism of AF [6, 7]. During oxidative stress, there is an imbalance between the production of pro-oxidant reactive species and the antioxidant defenses of the cell, and excessive oxidation leads to oxidative damage and cell death [8]. Redox processes can lead to atrial fibrosis and cardiac remodeling [7], suggesting that strategies to address myocardial oxidative stress may constitute a reasonable approach for the treatment of AF. However, few studies have reported the relationship between comprehensive oxidative stress-related biomarkers and AF. The purpose of this study was to illuminate the relationship of several blood markers of oxidative stress with AF and to provide new clues for the prevention and treatment of AF.

Study population

This retrospective study enrolled all hospitalized patients from January 2018 to December 2020 at the Department of Cardiology in The First Affiliated Hospital of Shandong First Medical University. The diagnosis of AF was based on 12-lead electrocardiography (ECG) or 24-hour Holter monitoring, and classification was based on the published 2020 ESC guidelines for the diagnosis and management of AF. The exclusion criteria were as follows: (1) acute heart failure and acute myocardial infarction; (2) severe liver or kidney dysfunction (with aspartate aminotransferase or alanine aminotransferase levels three times higher than normal and estimated glomerular filtration rate < 30 ml/ (min*1.73 m²)); (3) malignant tumors; and (4) missing data of laboratory indicators at baseline. The Ethics Committee of The First Affiliated Hospital of Shandong First Medical University approved this study.

Data collection

Clinical data were collected retrospectively including age, sex, blood pressure, history of hypertension (systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or oral antihypertensive drugs) and diabetes mellitus (fasting glucose ≥ 7 mmol/L or use of diabetes medication such as oral hypoglycemic drugs or insulin) from the database. Venous blood was taken from patients after overnight fasting.

Biomarker measurements

Hematological indicators were measured using standard laboratory procedures. Total bilirubin (TBIL) and direct bilirubin (DBIL) concentrations were measured by the diazo method, while indirect bilirubin (IBIL) levels were calculated from TBIL and DBIL. Uric acid (UA), triglyceride (TG) and superoxide dismutase (SOD) concentration were measured by the colorimetric method. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and glycated hemoglobin A 1c (HbA1c) were measured by enzyme colorimetry, the direct method of the catalase clear method, selective elimination and high efficiency liquid chromatography, respectively. The data for this study were collected from The First Affiliated Hospital of Shandong First Medical University Health care Big Data Platform. The platform integrated multisource data from the hospital information system (HIS), electronic medical records (EMR), laboratory information management system (LIS), picture archiving and communication system (PACS), etc. All data in the platform were deidentified. The encrypted personal identification number was used as a unique identifier to interlink each person's data.

Statistical analysis

Statistical analysis was performed using R.3.6.3. The Kolmogorov‒Smirnov test was used to assess the normality for continuous data, and Bartlett test was used if the data conformed to normal distribution. If the data followed homogeneity of variance, continuous variables were described as the means ± standard deviations and analyzed by Student’s t test. If these tests were not satisfied, variables were described as the median (interquartile range), and the Wilcoxon rank sum test was used. Categorical variables were compared by using Fisher’s exact test and are described as frequencies and percentages. Univariate and multivariate binary logistic regression was conducted to identify oxidative stress-related biomarkers associated with AF. Significant biomarkers in logistic regression results were stratified into quartiles, and the prevalence of AF was calculated for each quartile. In addition, relationships between biomarkers and AF in different ages and sexes were also assessed by using logistic regression, and the results were adjusted for other clinical variables. To avoid collinearity, DBIL and IBIL were not included in the multivariate analysis. The diagnostic value of oxidative biomarkers in AF was analyzed by receiver operating characteristic (ROC) curve. Differences were considered significant at P < 0.05 (two-tailed).

Characteristics of the study population

Baseline characteristics are displayed in Table 1. A total of 9452 individuals, including 1244 (13.16%) patients with AF and 8208 patients in control group, were included in this study. The mean age of the study was 63 years (interquartile range 55–72), and the study comprised 4704 (49.8%) men. Regarding the clinical factors, the ages of patients with AF were significantly higher than those of patients in the control group (median: 70 years old vs. 63 years old; P < 0.001). Laboratory biomarkers showed that serum bilirubin (TBIL, DBIL and IBIL) and UA were elevated in patients with AF. However, serum lipid levels (including TC, TG, LDL-C and HDL-C), SOD and albumin in patients with AF were lower than in patients of the control group.

Association between oxidative stress-related biomarkers and AF

Table 2 presents the results of determining AF-associated biomarkers with logistic regression analysis. Multivariate analysis indicated that age (OR: 1.048; 95% CI: 1.042–1.055; P < 0.001), SBP (OR: 0.967; 95% CI: 0.983–0.990; P < 0.001), elevated TBIL (OR: 1.056; 95% CI: 1.046–1.065; P < 0.001), and elevated UA (OR: 1.157; 95% CI: 1.112–1.204; P < 0.001) and reduced SOD (OR: 0.992; 95% CI: 0.987–0.997; P = 0.001) were independently associated with AF.

Different levels of oxidative stress-related biomarkers and AF prevalence

Table 3 presents the association between AF prevalence and oxidative stress-related biomarkers by quartiles. According to UA quartile, the prevalence of AF increased from Q1 to Q4 (AF%: 10.26% in Q1; AF%: 12.26%, OR: 1.278, 95% CI: 1.059–1.545, P = 0.011 in Q2; AF%: 12.28%, OR: 1.346, 95% CI: 1.109–1.634, P = 0.003 in Q3; AF%: 17.79%, OR: 1.989, 95% CI: 1.650–2.402, P < 0.001 in Q4). Similarly, the prevalence of AF increased as TBIL rose from Q1 to Q4 (AF%: 8.53% in Q1; AF%: 10.45%, OR: 1.396, 95% CI: 1.139–1.712, P = 0.001 in Q2; AF%: 13.84%, OR: 1.954, 95% CI: 1.607–2.382, P < 0.001 in Q3; AF%: 19.68%, OR: 2.915, 95% CI: 2.410–3.537, P < 0.001 in Q4). However, the prevalence of AF decreased with increasing SOD levels. AF prevalence was lowest (AF%: 8.18%) at Q4 of SOD and significantly elevated when SOD levels were lower than 177 U/mL (AF%: 19.59%, OR: 1.547, 95% CI: 1.227–1.955, P < 0.001 in Q1; AF%: 14.61%, OR: 1.468; 95% CI: 1.194–1.808, P < 0.001 in Q2; AF%: 10.84%, OR: 1.228; 95% CI: 1.003–1.504, P = 0.047 in Q3).

The diagnostic value of oxidative biomarkers in AF was analyzed by ROC curve. The analysis results of the ROC curve are shown in Fig. 1. The area under the ROC curve (AUC) for using UA, TBIL and SOD to diagnose AF was 69.1%, and the AUC was 73.4% when the ROC model combined with age, SBP, UA, TBIL and SOD.

Table 1

Characteristics of the study population
Characteristics	Non-AF(n = 8208)	AF(n = 1244)	All(n = 9452)	P
Age (years)	63(54, 71)	70(62, 78)	63(55, 72)	< 0.001
Male (%)	4040(49.2)	664(53.4)	4704(49.8)	0.007
SBP (mmHg)	138(125, 152)	133(120, 148)	137(124, 152)	< 0.001
DBP (mmHg)	80(71, 89)	79(71, 89)	79(71, 89)	0.353
Smoking (%)	2505(30.5)	404(32.5)	2909(30.8)	0.162
Drinking (%)	2616(31.9)	404(32.5)	3020(32)	0.668
Hypertension (%)	5173(63)	751(60.4)	5924(62.7)	0.073
Diabetes (%)	2045(24.9)	294(23.6)	2339(24.7)	0.341
TBIL (µmol/L)	10.6(8, 14.03)	12.6(9.3, 17.83)	10.8(8.2, 14.4)	< 0.001
DBIL (µmol/L)	3.9(3, 5)	4.8(3.7, 6.8)	4(3.1, 5.2)	< 0.001
IBIL (µmol/L)	6.6(4.9, 9.1)	7.7(5.58, 11.1)	6.8(4.9, 9.3)	< 0.001
UA (mg/dl)	5.12(4.22, 6.17)	5.49(4.46, 6.79)	5.16(4.25, 6.23)	< 0.001
HbA1c (%)	5.9(5.6, 6.4)	6(5.6, 6.5)	5.9(5.6, 6.5)	< 0.001
TC (mmol/L)	4.29(3.56, 5.03)	3.84(3.27, 4.55)	4.22(3.52, 4.96)	< 0.001
TG (mmol/L)	1.22(0.9, 1.7)	1.05(0.81, 1.47)	1.2(0.89, 1.67)	< 0.001
HDL-C (mmol/L)	1.1(0.94, 1.29)	1.05(0.91, 1.23)	1.09(0.94, 1.28)	< 0.001
LDL-C (mmol/L)	2.5(1.93, 3.1)	2.25(1.74, 2.79)	2.46(1.9, 3.06)	< 0.001
SOD (U/mL)	165(152, 178)	157(143, 170)	164(151, 177)	< 0.001
ALB (g/L)	43.8(41, 46.3)	41.9(39, 44.6)	43.5(40.7, 46.2)	< 0.001
AF: atrial fibrillation, ALB: albumin, DBIL: direct bilirubin, DBP: diastolic blood pressure, HbA1c: glycosylated hemoglobin, HDL-C: high-density lipoprotein cholesterol, IBIL: indirect bilirubin, LDL-C: low density lipoprotein cholesterol, SBP: systolic blood pressure, SOD: superoxide dismutase, TBIL: total bilirubin, TC: total cholesterol, TG: triglycerides, UA: uric acid

Table 2

Association between oxidative stress-related biomarkers and AF
	Univariate			Multivariate
	OR	95%CI	P	OR	95%CI	P
Age(years)	1.052	1.046–1.058	< 0.001	1.048	1.042–1.055	< 0.001
Male (%)	0.847	0.751–0.954	0.006	1.01	0.885–1.161	0.85
SBP (mmHg)	0.988	0.985–0.991	< 0.001	0.967	0.983–0.990	< 0.001
DBP (mmHg)	1	0.996-1.000	0.708	——
Smoking (%)	1.098	0.965–1.249	0.154	——
Drinking (%)	1.03	0.905–1.171	0.654	——
Hypertension (%)	0.894	0.791–1.01	0.071	——
Diabetes (%)	0.933	0.81–1.072	0.329	——
TBIL (µmol/L)	1.055	1.047–1.063	< 0.001	1.056	1.046–1.065	< 0.001
DBIL (µmol/L)	1.169	1.145–1.193	< 0.001	——
IBIL (µmol/L)	1.063	1.051–1.075	< 0.001	——
UA (mg/dl)	1.18	1.139–1.223	< 0.001	1.157	1.112–1.204	< 0.001
HDL-C (mmol/L)	0.523	0.415–0.658	< 0.001	0.878	0.680–1.129	0.313
SOD (U/mL)	0.983	0.98–0.986	< 0.001	0.992	0.987–0.997	0.001
ALB (g/L)	0.911	0.899–0.924	< 0.001	0.983	0.962–1.005	0.135
CI: confidence interval, OR: odds ratio

Adjusted for age, gender, SBP, TBIL, UA, HDL-C, SOD, ALB

Table 3

Different levels of oxidative stress-related biomarkers and AF prevalence
	Q1	Q2	Q3	Q4
UA (mg/dl)	< 4.25	4.25–5.16	5.16–6.23	≥ 6.23
No.	242/2358	293/2390	285/2320	424/2384
AF (%)	10.26	12.26	12.28	17.79
OR (95%CI)	Ref.	1.278(1.059–1.545)	1.346(1.109–1.634)	1.989(1.650–2.402)
P		0.011	0.003	< 0.001
TBIL (µmol/L)	< 8.2	8.2–10.8	10.8–14.4	≥ 14.4
No.	200/2345	246/2354	325/2349	473/2404
AF (%)	8.53	10.45	13.84	19.68
OR (95%CI)	Ref.	1.396(1.139–1.712)	1.954(1.607–2.382)	2.915(2.410–3.537)
P		0.001	< 0.001	< 0.001
SOD (U/mL)	< 151	151–164	164–177	≥ 177
No.	455/2323	324/2218	258/2380	207/2531
AF (%)	19.59	14.61	10.84	8.18
OR (95%CI)	1.547(1.227–1.955)	1.468(1.194–1.808)	1.228(1.003–1.504)	Ref.
P	< 0.001	< 0.001	0.047

Adjusted for age, gender, SBP, TBIL, UA, HDL-C, SOD, ALB

Association between oxidative stress-related biomarkers and AF in younger and elderly patients.

To clarify the correlation between oxidative stress-related indicators and the occurrence of AF in younger and elderly patients, participants were divided into ≥ 65 years old and < 65 years old. As shown in the Table 4, according to UA levels, the prevalence of AF significantly increased at Q4 (AF%: 27.75%, OR: 1.77, 95% CI: 1.41–2.228, P < 0.001) in elderly group, and it significantly increased at Q3 (AF%: 8.41%, OR: 1.692, 95% CI: 1.195–2.411, P = 0.003) and Q4 (AF%: 11.12%, OR: 2.181, 95% CI: 1.541–3.115, P < 0.001) in young group. With regard to TBIL, a significantly higher

prevalence of AF was found at Q3 (AF%: 19.54%, OR: 1.666, 95% CI: 1.303–2.137, P < 0.001) and Q4 (AF%: 29.35%, OR: 2.619, 95% CI: 2.061–3.341, P < 0.001) in elderly patients, and a markedly high prevalence from Q2 to Q4 (AF%: 6.78%, OR: 1.6, 95% CI: 1.116–2.308, P = 0.011 in Q2; AF%: 8.51%, OR: 2.075, 95% CI: 1.469–2.963,

Table 4

Relationships between oxidative stress-related biomarkers and AF in younger and elderly patients
	Age ≥ 65 years				Age < 65 years
	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4
UA (mg/dl)	< 4.15	4.15–5.02	5.02–6.08	≥ 6.08	< 4.32	4.32–5.28	5.28–6.36	> 6.36
No.	172/1102	187/1077	175/1105	310/1117	66/1263	89/1278	106/1260	139/1250
AF (%)	15.61	17.36	15.84	27.75	5.23	6.96	8.41	11.12
OR	Ref.	1.156	1.031	1.77	Ref.	1.319	1.692	2.181
95%CI		0.91–1.468	0.808–1.315	1.41–2.228		0.93	1.195–2.411	1.541–3.115
P		0.235	0.805	< 0.001		0.123	0.003	< 0.01
TBIL (µmol/L)	< 8.2	8.2–10.9	10.9–14.7	> 14.7	< 8.1	8.1–10.6	10.6–14.3	> 14.3
No.	136/1073	164/1098	220/1126	324/1104	59/1216	85/1254	112/1316	144/1265
AF (%)	12.67	14.94	19.54	29.35	4.85	6.78	8.51	11.38
OR	Ref.	1.242	1.666	2.619	Ref.	1.6	2.075	2.57
95%CI		0.962–1.605	1.303–2.137	2.061–3.341		1.116–2.308	1.469–2.963	1.827–3.659
P		0.097	< 0.001	< 0.001		0.011	< 0.001	< 0.001
SOD (U/mL)	< 143	143–156	156–169	> 169	< 159	159–171	171–183	> 183
No.	253/1061	205/1048	208/1122	178/1170	132/1238	112/1266	79/1234	77/1313
AF (%)	23.85	19.56	18.54	15.21	10.66	8.85	6.4	5.86
OR	1.581	1.351	1.311	Ref.	1.264	1.259	0.986	Ref.
95%CI	1.188–2.107	1.048–1.742	1.037–1.66		0.866–1.856	0.899–1.77	0.697–1.392
P	0.002	0.02	0.024		0.228	0.183	0.934

P < 0.001 in Q3; AF%: 11.38%, OR: 2.57, 95% CI: 1.827–3.659, P < 0.001 in Q4) in the young group. Moreover, the prevalence of AF in elderly patients was significantly increased while SOD levels lower than 169 U/mL (AF%: 23.85%, OR: 1.581, 95% CI: 1.188–2.107, P = 0.002 in Q1; AF%: 19.56%, OR: 1.351, 95% CI: 1.048–1.742, P = 0.02 in Q2; AF%: 18.54%, OR: 1.311, 95% CI: 1.037–1.66, P = 0.024 in Q3). Although there was no significant difference in the young group, a clear trend was performed.

Association between oxidative stress-related biomarkers and AF in different sexes

Table 5 shows the relationship between oxidative stress-related biomarkers and AF in different sexes. After multivariate adjustments, compared with its lowest value in Q1 (AF%: 9.38%) of UA, AF prevalence was significantly increased in Q4 in females (AF%: 18.9%, OR: 1.871, 95% CI: 1.431–2.457, P < 0.001). Meanwhile, in males, AF prevalence was lowest in Q2 (AF%: 11.57%) of UA, and it was also significantly increased in Q4 (AF%: 17.93%, OR: 1.774, 95% CI: 1.39–2.27, P < 0.001). According to TBIL quartile, the prevalence of AF significantly increased from Q3 to Q4 in males (AF%: 13.85%, OR: 1.693, 95% CI: 1.297–2.217, P < 0.001 in Q3; AF%: 21.45%, OR: 2.285, 95% CI: 2.207–3.685, P < 0.001 in Q4) and from Q2 to Q4 in females (AF%: 9.87%, OR: 1.499, 95% CI: 1.104–2.044, P = 0.01 in Q2; AF%: 11.89%, OR: 1.854, 95% CI: 1.372–2.518, P < 0.001 in Q3; AF%: 19.27%, OR: 3.165, 95% CI: 2.385–4.234, P < 0.001 in Q4) compared with that in Q1. However, the prevalence of AF was the lowest when the SOD level was in Q4 (≥ 180 U/mL), and it gradually rose from Q4 to Q1 in males (AF%: 12.25%, OR: 1.346, 95% CI: 1.023–1.778, P = 0.035 in Q3; AF%: 16.13%, OR: 1.573, 95% CI: 1.184–2.099, P = 0.002 in Q2 and AF%: 20.11%, OR: 1.613,

Table 5

Relationships between oxidative stress-related biomarkers and AF in different genders
	Male patients				Female patients
	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4
UA (mg/dl)	< 4.8	4.8–5.73	5.73–6.77	≥ 6.77	< 3.88	3.88–4.64	4.64–5.54	≥ 5.54
No.	172/1167	138/1193	143/1167	211/1177	110/1173	116/1206	128/1173	226/1196
AF (%)	14.74	11.57	12.25	17.93	9.38	9.62	10.91	18.9
OR	1.068	Ref.	1.183	1.774	Ref.	1.121	1.163	1.871
95%CI	0.831–1.374		0.914–1.534	1.39–2.27		0.837–1.504	0.868–1.559	1.431–2.457
P	0.608		0.202	< 0.001		0.443	0.313	< 0.001
TBIL (µmol/L)	< 9	9–12	12–16	≥ 16	< 7.5	7.5–9.8	9.8–12.8	≥ 12.8
No.	112/1138	135/1205	163/1177	254/1184	89/1163	118/1195	141/1186	232/1204
AF (%)	9.84	11.2	13.85	21.45	7.65	9.87	11.89	19.27
OR	Ref.	1.223	1.693	2.285	Ref.	1.499	1.854	3.165
95%CI		0.93–1.609	1.297–2.217	2.207–3.685		1.104–2.044	1.372–2.518	2.385–4.234
P		0.15	< 0.001	< 0.001		0.01	< 0.001	< 0.001
SOD (U/mL)	< 152	152–166	166–180	≥ 180	< 149	149–162	162–175	≥ 175
No.	226/1124	182/1128	148/1208	107/1244	214/1103	161/1168	120/1273	85/1204
AF (%)	20.11	16.13	12.25	8.6	19.4	13.78	9.43	7.06
OR	1.613	1.573	1.346	Ref.	1.554	1.445	1.203	Ref.
95%CI	1.164–2.245	1.184–2.099	1.023–1.778	1.092–2.222	1.055–1.989	1.055–1.989	0.884–1.645
P	0.004	0.002	0.035		0.015	0.023	0.242

95%CI: 1.164–2.245, P = 0.004 in Q1). Meanwhile, in females the prevalence of AF was the lowest when the SOD level was in Q4 (≥ 175 U/mL), and it was significantly increased in Q2 and Q1(< 162 U/mL) (AF%: 13.78%, OR: 1.445, 95% CI: 1.055–1.989, P = 0.023 in Q2; AF%: 19.4%, OR: 1.554, 95% CI: 1.092–2.222, P = 0.015 in Q1).

This study showed that levels of UA and bilirubin were elevated in patients with AF, but HDL-C, SOD and albumin levels were lower than in patients with sinus rhythm. Logistic regression revealed that higher levels of UA and TBIL and lower levels of SOD were independently associated with the prevalence of AF. Furthermore, differences in

oxidative stress-related biomarker levels were significantly associated with the prevalence of AF in both males and females.

Although the underlying pathophysiological mechanisms of AF are complex and only partly known, recent studies have indicated that oxidative stress and AF are highly associated [6, 7]. A large prospective cohort study suggested that an elevated risk of AF is correlated with the redox potential of glutathione [9]. This study supported the concept that oxidative stress plays a key pathophysiological role in the development of AF. In addition, an increasing number of studies suggest that circulating blood markers play an important role on AF, including neurohormonal markers (BNP, NT-pro-BNP), inflammatory markers (CRP, IL-6), fibrosis markers (TGF-1 and matrix metalloproteinases) and cardiac embolism markers [10, 11]. However, few studies have reported comprehensive oxidative stress-related biomarkers. To identify oxidative stress-related markers for the risk of AF, we evaluated a comprehensive set of hematologic parameters.

As an oxidative stress-related biomarker, UA has received great attention regarding how it is related to AF. A meta-analysis including 527980 participants from 11 studies found that both moderate and high uric acid levels were associated with an increased risk of AF (RR: 1.36; 95% CI: 1.16–1.59; I² = 36% and RR: 1.9; 95% CI: 1.64–2.23; I² = 0%) [12]. Our study further confirmed that UA levels are associated with a significantly increased prevalence of AF in both males and females, especially in the upper quartile. Xanthine oxidase (XO) is not only a key enzyme in the production of uric acid, but also a major source of reactive oxygen species (ROS). Dudley et al. reported that AF increases superoxide (O²⁻) production in the left atrium and left atrial appendage, which is attributed to the activation of XO and NADPH. This indicated that urate-induced oxidative stress plays a major role in the pathophysiology of AF development [13].

Bilirubin is the product of heme catabolism and can be used as a diagnostic indicator of hepatobiliary and hematological disorders. Given the role of bilirubin as an endogenous antioxidant, elevated bilirubin levels are considered to be a protective factor for coronary heart disease [14]. However, the role of bilirubin in AF is controversial and in need of further investigation. A retrospective analysis of 102 patients with nonvalvular AF found that total bilirubin (0.82±0.8 vs. 0.48±0.5 mg/dL), direct bilirubin (0.30±0.20 vs. 0.19±0.10 mg/dL) and indirect bilirubin (0.19±0.10, 0.52±0.5 mg/dL) levels in these patients were significantly reduced (P < 0.001 in all) [15]. However, our results showed elevated bilirubin levels in patients with AF, which is consistent with the results of Chen et al [16] The study noted that higher bilirubin levels in patients with AF were similar to the clinical observations of congestive heart failure, and that the mechanism of this relationship may be more attributable to the heart-liver interaction. Elevated TBIL levels beyond the normal range may indicate underlying hepatocyte damage. A recent study showed that in a rat model of AF induced by rapid atrial pacing, the gene expression profile of the rat liver was significantly altered [17]. The positive correlation between bilirubin levels and the prevalence of AF may reflect the relationship between hepatobiliary dysfunction and the risk of AF. As reported by Soto Conti et al [18], such potent antioxidants sometimes exhibit toxic properties. Therefore, bilirubin is more likely to be of impact as a risk factor in oxidative stress-mediated disease than as a potential antioxidant.

SOD is the main cellular enzyme defense system against the damage of reactive oxygen species and can catalyze superoxide anion free radicals. SOD deficiency can lead to vascular abnormalities and impaired vascular function, including increased vasoconstriction and endothelial dysfunction, as well as various diseases such as hypertension and atherosclerosis. In addition, SOD deficiency may increase the risk of ischemic heart disease and arrhythmias in young people [19, 20]. Decreased SOD activity has been reported to be associated with increased oxidation levels [21]. In our study, the lower the level of superoxide, the higher the prevalence of AF. The prevalence of AF was lowest when the SOD level exceeded 177 U/mL. When the SOD level was lower than 151 U/mL, the prevalence of AF increased by approximately 11.41%. Although different levels of SOD did not significantly affect the prevalence of AF among young patients, a clear trend was performed. Several researches about the weakening of the enzymatic antioxidant defense in aging may confirm this[22, 23]. Kozakiewicz et al [23] found that activities of fundamental antioxidant enzymes including SOD-1, CAT, and GSH-Px decreased in the elderly people, which indicated the association between aging and the impairment of antioxidant defense. The mechanism of lower SOD levels in AF may be due to the excessive consumption of SOD caused by the increase in oxidation levels. Kliment et al [24] investigated the role of SOD in regulating cardiac fibrosis and cardiac function. In mice lacking SOD, decreased left ventricular wall thickness and increased end-diastolic volume have been observed. In addition, the levels of left ventricular fibrosis and free radicals were also increased in mice. However, after antioxidant treatment, the ejection fraction of SOD-deficient mice improved [24]. This study suggested that extracellular SOD is essential for normal cardiac morphology and protects the heart from oxidation-induced fibrosis, apoptosis and loss of function.

Although several studies have reported the relationship between lipid levels and AF, there were many inconsistencies in their results. A meta-analysis including 4032638 participants from 16 studies reported that higher levels of TC, HDL-C and LDL-C were correlated with AF while TG levels were not significantly associated with AF [25]. Recently, a large national cross-sectional study in Poland also reported that elevated TC, LDL-C, and HDL-C were associated with lower AF prevalence, and the relationship between TG and AF was not significant [26]. The mechanism by which TC and LDL-C are associated with AF risk is not fully understood, while HDL-C is considered to have anti-inflammatory and antioxidant properties in cardiovascular disease [27, 28]. In our study, TC, TG, LDL-C and HDL-C in patients with AF were lower than in patients of the control group, and the correlation between HDL-C and AF became nonsignificant differences after adjustment for confounding factors. In recent years, hypoproteinemia has become a prognostic indicator of cardiovascular diseases. Several studies indicated that low albumin levels were associated with the occurrence of AF [29, 30]. In terms of oxidative stress, serum albumin is the most abundant antioxidant in whole blood. The potential role of low albumin levels in AF may be attributed to its anti-inflammatory and antioxidant physiological properties [31–33]. Although HDL-C and albumin levels were significantly reduced in patients with AF in our study, the correlation became irrelevant after adjustment for confounding factors. There are many reasons for the inconsistencies, such as differences in population characteristics and adjustment for confounding factors. Further studies are needed to evaluate the pathophysiological mechanisms of these oxidative stress-related indicators in AF.

Currently, there is no clear consensus on uric acid-lowering therapy and other antioxidant therapies for AF. Singh et al [34] demonstrated that allopurinol reduced the risk of AF in elderly individuals who were 65 years of age or older. The study showed that elderly patients treated with allopurinol had a significantly reduced risk of AF (HR: 0.83; 95% CI: 0.74–0.93; P = 0.0013) and the risk of AF decreased with longer allopurinol use: the hazard of AF was reduced by 15% and 35% with used durations of 181 days to 2 years and over 2 years (HR: 0.85; 95% CI: 0.73–0.99; P = 0.04, HR: 0.65; 95% CI: 0.52–0.82; P = 0.0002, respectively). In a canine model of AF, allopurinol inhibited atrial fibrosis and decreased the expression of eNOS [35]. The research demonstrated that allopurinol inhibits AF by inhibiting electrical as well as structural remodeling, and further illustrated that oxidative stress is now considered a novel therapeutic target for AF. In our study, the prevalence of AF was significantly increased in the top quartile in both males and females. Therefore, according to our results, it is not necessary to target UA levels that are too low, especially in males. Considering the role of oxygen free radicals in the cellular mechanisms of AF, using antioxidants to prevent AF seems reasonable. A recent review by Rodrigo et al [36] elaborated the protective effects of vitamin C, vitamin E and omega-3 polyunsaturated fatty acids on AF. Statins are commonly used to treat hypercholesterolemia, but it remains to be seen whether minimal levels of cholesterol are better because of antioxidant properties [37]. Studies have shown that statin therapy reduced the risk of postoperative AF by 51% and reduced all-cause mortality and cardiovascular mortality by 41% and 25%, respectively [38, 39]. In addition, a recent review detailed the prospects and effectiveness of exogenous supplementation with SOD and SOD-catalase mimics in antioxidant therapy [40]. These studies have targeted the atrial redox state as a means of preventing AF. Relevant antioxidants are a safe, low-cost, and readily available adjunctive therapy, and we recommend further exploration of their efficacy in the prevention and treatment of AF.

There were several limitations in this study. First, this cross-sectional study cannot provide evidence of causal relationships between AF and biomarkers. Further studies with longitudinal designs would be conducted in our next study to investigate causality. Second, this observational study included all patients from January 2018 to December 2020 at the Department of Cardiology of the hospital. The sample size was relatively large and there were some imbalances in characteristics. Age and sex may be important factors in the prevalence of AF. Therefore, stratified analysis was used to adjust age and sex differences. Finally, we did not measure the use of medications that might influence the prevalence of AF. Therefore, further research needs to consider these factors.

TBIL, UA and SOD levels were independently associated with AF as markers of oxidative stress in blood. TBIL and UA were correlated with AF in patients of different ages, while SOD was associated with AF in patients aged 65 years or older. In addition, TBIL, UA and SOD were correlated with atrial fibrillation in patients of different genders. These results suggest that oxidative stress plays an important role in the development of AF. These indicators may be used as potential biomarkers to predict the occurrence of AF. In addition, multitarget antioxidant therapy may be a reasonable approach to prevent and treat AF, and further studies are needed to confirm this hypothesis.

Acknowledgements

We would like to thank the staff at the Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, for their valuable contribution.

Authors’ Contribution

Xuehan Wang and Yujiao Zhang: data curation, writing original draft and manuscript revise. Manyi Ren, Jing Guo and Zhan Li: data curation, formal analysis and supervision. Shaohua Zheng, Ximin Wang and Juanjuan Du: software and validation. Mei Gao and Yinglong Hou: writing review, editing and funding acquisition. Xuehan Wang and Yujiao Zhang contributed equally to this work and share first authorship. All authors read and approved the final manuscript.

Funding

The authors received the following support for the research, authorship, and/or publication of this article: National Natural Science Foundation of China [grant numbers 81970281], Natural Science Foundation of Shandong [grant number ZR2022MH228], Academic promotion programme of Shandong First Medical University [grant number 2019QL012], and Clinical research fund of Shandong Medical Association -- Qilu Special project [grant number YXH2022ZX02141].

Data Availability

The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

The study fully complied with the Declaration of Helsinki and was approved by the Ethics Committee of The First Affiliated Hospital of Shandong First Medical University, Shandong, China [Approval Number: YXLL-KY-2023(044)]. The ethics committee of The First Affiliated Hospital of Shandong First Medical University approved the request for a waiver of informed consent.

Consent for publication

Not applicable.

Competing interests

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.

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No competing interests reported.

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Association of Oxidative Stress-related Biomarkers and Atrial Fibrillation: A Cross- Sectional Study

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Association between oxidative stress-related biomarkers and AF

Different levels of oxidative stress-related biomarkers and AF prevalence

Association between oxidative stress-related biomarkers and AF in different sexes

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