
==== Front
Redox Biol
Redox Biol
Redox Biology
2213-2317
Elsevier

S2213-2317(24)00305-7
10.1016/j.redox.2024.103327
103327
Research Paper
Association between the oxidative balance score and all-cause and cardiovascular mortality in patients with diabetes and prediabetes
Xu Zichen a1
Liu Daoqin b1
Zhai Ying a1
Tang Yu a
Jiang Luqing a
Li Lei a
Wu Qiwen 20141283@wnmc.edu.cn
a⁎
a Department of Laboratory Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, Anhui, China
b Department of Kidney Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, Anhui, China
⁎ Corresponding author. Department of Laboratory Medicine, The First Affiliated Hospital of Wannan Medical College, 2 Zheshan West Road, Wuhu, Anhui Province, China. 20141283@wnmc.edu.cn
1 These authors are contributed equally to this work.

23 8 2024
10 2024
23 8 2024
76 10332711 7 2024
7 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Few studies have examined the link between systemic oxidative stress and mortality risk in diabetes and prediabetes patients. The Oxidative Balance Score (OBS) is a novel measure of systemic oxidative stress, with higher scores indicating greater antioxidant exposure. This study investigates the relationship between OBS and all-cause and cardiovascular mortality in these patients.

Methods

This study analyzed 10,591 diabetes and prediabetes patients from the 1999–2018 National Health and Nutrition Examination Survey (NHANES). The endpoints were all-cause and cardiovascular mortality, determined from the National Death Index (NDI). OBS was calculated using 20 dietary and lifestyle factors. Kaplan-Meier survival analysis, multivariable Cox regression models, restricted cubic splines (RCS), and subgroup analyses were used to assess the relationship between OBS and mortality risks.

Results

Over an average follow-up of 99.8 months, 2900 (26.4 %) participants died, including 765 (8.9 %) from cardiovascular diseases. Kaplan-Meier analysis showed the lowest all-cause and cardiovascular mortality in the highest OBS quartile (Q4) and the highest mortality in the lowest quartile (Q1) (p < 0.001). In the fully adjusted model, multivariable Cox regression revealed that each unit increase in OBS was linked to a 1.8 % decrease in all-cause mortality risk (HR 0.982, 95 % CI 0.976–0.987, p < 0.0001) and a 4 % decrease in cardiovascular mortality risk (HR 0.960, 95 % CI 0.949–0.970, p < 0.0001). Compared to Q1, those in Q4 had significantly lower all-cause mortality (HR 0.719, 95 % CI 0.643–0.804, p < 0.0001, p for trend <0.0001) and cardiovascular mortality (HR 0.567, 95 % CI 0.455–0.705, p < 0.0001, p for trend <0.0001). These findings were consistent across subgroups. RCS curves showed a negative correlation between OBS and both mortality types.

Conclusion

Higher OBS is linked to reduced all-cause and cardiovascular mortality in diabetes and prediabetes patients.

Highlights

• The Oxidative Balance Score (OBS) is a comprehensive indicator of oxidative stress, calculated from 16 dietary nutrients and 4 lifestyle factors.

• An increase in OBS is associated with a reduced risk of all-cause mortality in patients with diabetes and prediabetes.

• An increase in OBS is associated with a reduced risk of cardiovascular mortality in patients with diabetes and prediabetes.

Keywords

Oxidative Balance Score (OBS)
All-cause mortality
Cardiovascular mortality
Diabetes
Prediabetes
==== Body
pmc1 Introduction

In recent decades, the prevalence of diabetes and its complications has emerged as a significant global public health concern [1,2]. According to the 10th edition of the International Diabetes Federation (IDF) Diabetes Atlas, the global number of adults with diabetes reached 536.6 million in 2021, accounting for 10.5 % of the global adult population [1]. It is projected that by 2045, this number will rise to 783.2 million [1]. Prediabetes is defined as blood glucose levels above normal but below the threshold for diabetes, associated with a high risk of developing diabetes [3]. Numerous epidemiological studies have linked diabetes with increased risks of cardiovascular diseases, cerebrovascular diseases, renal dysfunction, and retinopathy [[4], [5], [6]]. Furthermore, individuals with prediabetes experience damage to their heart, blood vessels, and kidneys [7,8]. Additionally, diabetes patients exhibit higher rates of cardiovascular and all-cause mortality [9]. Evidence suggests that oxidative stress plays a crucial role in the development and progression of diabetes and prediabetes [10]. Oxidative stress can mediate insulin resistance, which forms the physiological basis for the development of diabetes and prediabetes, through the inhibition of insulin signaling and the dysregulation of adipokine factors [11,12]. Moreover, oxidative stress may contribute to the development of microvascular and macrovascular complications of diabetes [13].

Oxidative stress levels in the body can be influenced by a multitude of factors. Antioxidant factors commonly include specific antioxidant nutrients and physical activity, whereas pro-oxidant factors encompass behaviors like smoking, alcohol consumption, obesity, and the consumption of certain pro-oxidant nutrients [14]. However, the impact of individual factors on the body's oxidative/antioxidative status is limited [14]. The Oxidative Balance Score (OBS), calculated from 20 different dietary and lifestyle components of antioxidants and pro-oxidants, highlights the overall balance between pro-oxidants and antioxidants at the dietary and lifestyle levels [14]. It serves as a comprehensive indicator to assess an individual's overall oxidative/antioxidant status [14]. Generally, a higher OBS indicates stronger antioxidant activity, while a lower OBS indicates stronger pro-oxidant activity. Recent studies have confirmed a lower incidence of diabetes associated with higher OBS scores [15]. Furthermore, OBS has been negatively correlated with the incidence of other diseases such as chronic kidney disease [16], hypertension [17], and depression [18].

However, no studies have explored the relationship between OBS and mortality risk among patients with diabetes and prediabetes. Therefore, this study aims to investigate the association between OBS and all-cause mortality as well as cardiovascular mortality among individuals with diabetes and prediabetes using data from the National Health and Nutrition Examination Survey.

2 Method

2.1 Study population

The National Health and Nutrition Examination Survey (NHANES) is a nationally representative survey conducted by the National Center for Health Statistics (NCHS), part of the Centers for Disease Control and Prevention (CDC) in the United States. Its primary objective is to assess the health and nutritional status of non-institutionalized individuals across the country. Data collection methods include structured household interviews, mobile examination center visits, and laboratory testing, utilizing a complex, stratified, multistage probability sampling design. The NHANES research protocol is approved by the NCHS Ethics Review Board, and all participants provide written informed consent.

The data for this study were sourced from the NHANES database spanning ten cycles (1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016 and 2017–2018). Initially, a total of 101,316 participants were included. Exclusions were made for the following individuals: (1) pregnant participants (n = 840); (2) age <18 years (n = 42,081); (3) missing mortality data (n = 140); (4) insufficient dietary and lifestyle data required for calculating the Oxidative Balance Score (n = 11,642); and (5) individuals without diabetes or prediabetes (n = 36,022). Ultimately, our study included 10,591 participants (Fig. 1).Fig. 1 Flowchart for selecting analyzed participants.

Fig. 1

2.2 Definition of diabetes and prediabetes

Diabetes is defined as individuals meeting any of the following criteria: (1) fasting plasma glucose ≥7.0 mmol/L or 2-h post-oral glucose tolerance test level ≥11.1 mmol/L; (2) random plasma glucose ≥11.1 mmol/L; (3) glycated hemoglobin (HbA1c) ≥6.5 %; (4) self-reported diagnosis of diabetes (“Doctor told you have diabetes.”) or currently using insulin.

Prediabetes is defined as individuals meeting any of the following criteria: (1) 2-h post-oral glucose tolerance test levels between 7.8 and 11.0 mmol/L; (2) fasting plasma glucose between 6.1 and 6.9 mmol/L; (3) HbA1c levels between 5.7 % and 6.4 %; (4) self-reported diagnosis of prediabetes (“Doctor told you have prediabetes.").

2.3 Exposure variable: oxidative balance score

The calculation of the Oxidative Balance Score (OBS) includes 16 dietary nutrients and 4 lifestyle factors, encompassing 15 antioxidative components and 5 pro-oxidative components [19]. The 16 nutrients are derived from the first dietary recall interview and include dietary fiber, carotenoids (retinol component, RE), riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E (Alpha-Tocopherol Equivalent, ATE), calcium, magnesium, zinc, copper, selenium, total fat, and iron. The 4 lifestyle factors are physical activity, Body Mass Index (BMI), alcohol consumption, and smoking, with the degree of smoking measured by cotinine levels. The 5 pro-oxidative components include total fat, iron, BMI, alcohol consumption, and cotinine, while the remaining factors are classified as antioxidative.

Alcohol consumption is categorized into three groups following previous literature [19]: non-drinkers, light-to-moderate drinkers (females 0–15g/day, males 0–30g/day), and heavy drinkers (females ≥15g/day, males ≥30g/day), scored as 2, 1, and 0 points, respectively. The other components are scored based on tertiles, stratified by gender. For antioxidative components, scores are assigned as 0, 1, and 2 points for the lowest to the highest tertiles, respectively. In contrast, pro-oxidative components are scored inversely, with the highest tertile receiving 0 points and the lowest receiving 2 points [19]. The overall OBS was calculated by summing the scores of each component, with scores ranging from 3 to 36. Higher scores indicate greater antioxidant exposure. The scoring scheme for OBS components is detailed in Supplementary Table 1.

2.4 Ascertainment of mortality

Mortality information was obtained from the National Death Index (NDI) database [https://www.cdc.gov/nchs/data-linkage/mortality-public.htm], maintained by the Centers for Disease Control and Prevention (CDC) in the United States. The follow-up period was calculated from the date of the baseline interview to the date of death or December 31, 2019, which is the most recent update in the NDI database. Cardiovascular mortality was identified using the International Classification of Diseases, 10th Revision (ICD-10) codes, specifically I00–I09, I11, I13, and I20–I51.

2.5 Covariates

The covariates considered in this study include age, gender, race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), education level (less than high school, high school or equivalent, and more than high school), marital status (married, single, living with a partner), poverty-to-income ratio (≤1.0, 1.0–3.0, >3.0), smoking history (defined as having smoked at least 100 cigarettes in one's lifetime), alcohol consumption history (defined as consuming at least 12 alcoholic drinks per year), and history of hypertension. The measurement procedures for these variables can be found on the CDC NHANES website at https://www.cdc.gov/nchs/nhanes/.

2.6 Statistical analysis

Statistical analyses were conducted in accordance with the guidelines provided by the CDC, available at https://www.cdc.gov/nchs/nhanes/tutorials/default.aspx. Baseline characteristics were expressed by quartiles of the OBS. Continuous variables were presented as mean ± standard deviation (SD), while categorical variables were presented as percentages. Chi-square tests were used to calculate p-values for categorical variables, and Kruskal-Wallis rank-sum tests were used for continuous variables. The log-rank test and Kaplan-Meier (K-M) survival analysis were applied to investigate differences in survival probabilities. Three multivariable Cox regression models (Model 1, Model 2, and Model 3) were developed to evaluate the relationship between OBS and all-cause mortality and cardiovascular mortality in patients with diabetes and prediabetes. Model 1 was unadjusted for covariates, Model 2 was adjusted for sex, age, and race/ethnicity, and Model 3 was adjusted for sex, age, race/ethnicity, education level, marital status, poverty-to-income ratio, smoking history, alcohol consumption history, and history of hypertension. The goodness-of-fit test using Schoenfeld residuals to validate the proportional hazards (PH) assumption. To visualize the association between OBS and all-cause mortality as well as cardiovascular mortality in patients with diabetes and prediabetes, restricted cubic splines with three knots based on the fully adjusted Cox model were constructed. Subgroup analyses and interaction tests were also conducted based on the fully adjusted model to investigate the relationship between OBS and mortality in different subgroups.

All statistical analyses were performed using Empower Stats software and R version 4.3.3. A two-tailed p-value of less than 0.05 was considered statistically significant.

3 Results

3.1 Baseline characteristics of the participants

Table 1 presents the baseline characteristics of the study population, categorized by quartiles of the OBS. The study included a total of 10,591 participants with diabetes or prediabetes. The average age of the selected participants was 59.78 years (±15.17), with non-Hispanic Whites constituting the majority of the study population. Compared to participants in the higher OBS quartiles (Q4), those in the lowest OBS quartile (Q1) were more likely to be male, older, non-Hispanic Black, have less than a high school education, be single, have a higher poverty-to-income ratio, have a history of hypertension, not consume alcohol, and be smokers. There was no statistically significant difference in alcohol consumption across the OBS groups.Table 1 Baseline characteristics of the selected participants.

Table 1OBS	Q1	Q2	Q3	Q4	P-value	
N	2486	2329	2919	2857		
Age, mean ± SD (years)	61.239 ± 15.097	60.795 ± 14.884	59.456 ± 15.350	58.020 ± 15.082	<0.001	
Gender (%)					<0.001	
Male	1387 (55.792 %)	1196 (51.353 %)	1440 (49.332 %)	1417 (49.597 %)		
Female	1099 (44.208 %)	1133 (48.647 %)	1479 (50.668 %)	1440 (50.403 %)		
Race/ethnicity (%)					<0.001	
Mexican American	434 (17.458 %)	462 (19.837 %)	585 (20.041 %)	591 (20.686 %)		
Other Hispanic	205 (8.246 %)	221 (9.489 %)	272 (9.318 %)	234 (8.190 %)		
Non-Hispanic White	930 (37.409 %)	881 (37.827 %)	1248 (42.754 %)	1319 (46.167 %)		
Non-Hispanic Black	789 (31.738 %)	596 (25.590 %)	573 (19.630 %)	444 (15.541 %)		
Other Race	128 (5.149 %)	169 (7.256 %)	241 (8.256 %)	269 (9.415 %)		
Education (%)					<0.001	
Less than high school	1154 (46.420 %)	854 (36.668 %)	915 (31.346 %)	679 (23.766 %)		
High school	592 (23.813 %)	572 (24.560 %)	682 (23.364 %)	641 (22.436 %)		
More than high school	735 (29.566 %)	898 (38.557 %)	1319 (45.187 %)	1535 (53.728 %)		
Not recorded	5 (0.201 %)	5 (0.215 %)	3 (0.103 %)	2 (0.070 %)		
Marital status (%)					<0.001	
Married	1221 (49.115 %)	1293 (55.517 %)	1595 (54.642 %)	1740 (60.903 %)		
Single	1077 (43.323 %)	914 (39.244 %)	1157 (39.637 %)	972 (34.022 %)		
Living with a partner	141 (5.672 %)	91 (3.907 %)	126 (4.317 %)	119 (4.165 %)		
Not recorded	47 (1.891 %)	31 (1.331 %)	41 (1.405 %)	26 (0.910 %)		
Poverty to income ratio (%)					<0.001	
≤1.0	678 (27.273 %)	491 (21.082 %)	552 (18.911 %)	409 (14.316 %)		
1.0–3.0	1095 (44.047 %)	1017 (43.667 %)	1216 (41.658 %)	1093 (38.257 %)		
>3.0	478 (19.228 %)	604 (25.934 %)	888 (30.421 %)	1142 (39.972 %)		
Not recorded	235 (9.453 %)	217 (9.317 %)	263 (9.010 %)	213 (7.455 %)		
Hypertension history (%)					<0.001	
Yes	1571 (63.194 %)	1403 (60.240 %)	1731 (59.301 %)	1553 (54.358 %)		
No	903 (36.323 %)	918 (39.416 %)	1181 (40.459 %)	1298 (45.432 %)		
Not recorded	12 (0.483 %)	8 (0.343 %)	7 (0.240 %)	6 (0.210 %)		
Drinking (%)					0.056	
Yes	1550 (62.349 %)	1479 (63.504 %)	1883 (64.508 %)	1911 (66.888 %)		
No	810 (32.582 %)	740 (31.773 %)	906 (31.038 %)	821 (28.736 %)		
Not recorded	126 (5.068 %)	110 (4.723 %)	130 (4.454 %)	125 (4.375 %)		
Smoking (%)					<0.001	
Yes	1415 (56.919 %)	1205 (51.739 %)	1403 (48.064 %)	1258 (44.032 %)		
No	1043 (41.955 %)	1106 (47.488 %)	1493 (51.148 %)	1581 (55.338 %)		
Not recorded	28 (1.126 %)	18 (0.773 %)	23 (0.788 %)	18 (0.630 %)		
Condition (%)					<0.001	
Diabetes	1894 (76.187 %)	1725 (74.066 %)	2070 (70.915 %)	1862 (65.173 %)		
Prediabetes	592 (23.813 %)	604 (25.934 %)	849 (29.085 %)	995 (34.827 %)		
Note: Q1–Q4: Grouped by quartile according to OBS.

Mean ± SD for continuous variables: P value was calculated by Kruskal-Wallis rank-sum test.

% for categorical variables: P value was calculated by chi-square test.

3.2 Associations of the OBS with all-cause and cardiovascular mortality

During an average follow-up period of 99.8 months, out of the 10,591 participants with diabetes or prediabetes, 2900 (26.4 %) died, with 765 (8.9 %) deaths attributed to cardiovascular disease. The Kaplan-Meier (K-M) survival analysis demonstrated significant differences in all-cause and cardiovascular mortality rates among the four groups. Participants in the highest quartile (Q4) of the OBS had the lowest mortality rates, whereas those in the lowest quartile (Q1) had the highest mortality rates (log-rank p < 0.001). More details on the K-M curves are shown in Fig. 2.Fig. 2 K–M analyses for mortality among the four groups. Q1–Q4 quartiles 1–4, OBS oxidative balance score A All-cause mortality. B cardiovascular mortality hyperglycemia ratio.

Fig. 2

Table 2 illustrates the Cox regression models examining the association between OBS and all-cause mortality. In the unadjusted model (Model 1), we found that with an increase in OBS, the risk of all-cause mortality significantly decreased (HR 0.968, 95 % CI 0.963–0.973, p < 0.0001). Compared to the lowest quartile (Q1) of OBS, the highest quartile (Q4) was associated with a 46.8 % reduction in the risk of all-cause mortality (HR 0.532, 95 % CI 0.478–0.592, p for trend <0.0001). After adjusting for sex, age, and race in Model 2, each unit increase in OBS was associated with a 2.7 % reduction in the risk of all-cause mortality (HR 0.973, 95 % CI 0.967–0.978, p < 0.0001). In this model, participants in the OBS Q4 group had a 39.3 % lower risk of all-cause mortality compared to those in the Q1 group (HR 0.607, 95 % CI 0.544–0.677, p for trend <0.0001). In the fully adjusted model (Model 3), each unit increase in OBS was linked to a 1.8 % reduction in the risk of all-cause mortality (HR 0.982, 95 % CI 0.976–0.987, p < 0.0001). When comparing Q4 to Q1, the risk of all-cause mortality was reduced by 28.1 % (HR 0.719, 95 % CI 0.643–0.804, p for trend <0.0001).Table 2 Multivariable Cox regression models analysis of the relationship between OBS and mortality in diabetes and prediabetes.

Table 2Exposure	Model 1	Model 2	Model 3	
HR (95 % CI) P value	HR (95 % CI) P value	HR (95 % CI) P value	
Cardiovascular death	
OBS	0.956 (0.946, 0.966) <0.00001	0.960 (0.949, 0.970) <0.00001	0.970 (0.959, 0.981) <0.00001	
OBS (Quartile)	
 Q1(3–12)	Reference	Reference	Reference	
 Q2(13–18)	0.648 (0.532, 0.789) 0.00002	0.671 (0.550, 0.817) 0.00008	0.739 (0.606, 0.903) 0.00300	
 Q3(19–23)	0.663 (0.553, 0.795) <0.00001	0.745 (0.619, 0.896) 0.00182	0.833 (0.690, 1.004) 0.05522	
 Q4(24–36)	0.417 (0.339, 0.514) <0.00001	0.465 (0.376, 0.575) <0.00001	0.567 (0.455, 0.705) <0.00001	
P for Trend	<0.00001	<0.00001	<0.00001	
All-cause death	
OBS	0.968 (0.963, 0.973) <0.00001	0.973 (0.967, 0.978) <0.00001	0.982 (0.976, 0.987) <0.00001	
OBS (Quartile)	
 Q1(3–12)	Reference	Reference	Reference	
 Q2(13–18)	0.778 (0.703, 0.861) <0.00001	0.783 (0.707, 0.867) <0.00001	0.833 (0.752, 0.923) 0.00049	
 Q3(19–23)	0.706 (0.640, 0.778) <0.00001	0.765 (0.693, 0.845) <0.00001	0.830 (0.751, 0.918) 0.00027	
 Q4(24–36)	0.532 (0.478, 0.592) <0.00001	0.607 (0.544, 0.677) <0.00001	0.719 (0.643, 0.804) <0.00001	
P for Trend	<0.00001	<0.00001	<0.00001	
Model 1, unadjusted; Model 2, adjusted for age, gender, race; Model 3, adjusted for age, gender, race, education, marital status, poverty to income ratio, hypertension history, drinking, smoking.

Table 2 also shows the Cox regression models assessing the relationship between OBS and cardiovascular mortality. In the unadjusted model (Model 1), we observed a significant reduction in the risk of cardiovascular mortality with increasing OBS (HR 0.956, 95 % CI 0.946–0.966, p < 0.0001). Compared to OBS Q1, the Q4 group exhibited a 58.3 % lower risk of cardiovascular mortality (HR 0.417, 95 % CI 0.339–0.514, p for trend <0.0001). After adjusting for sex, age, and race in Model 2, each unit increase in OBS was associated with a 4 % reduction in the risk of cardiovascular mortality (HR 0.960, 95 % CI 0.949–0.970, p < 0.0001). Participants in the Q4 group had a 53.5 % lower risk of cardiovascular mortality compared to those in the Q1 group (HR 0.465, 95 % CI 0.376–0.575, p for trend <0.0001). In the fully adjusted model (Model 3), each unit increase in OBS corresponded to a 3 % reduction in the risk of cardiovascular mortality (HR 0.970, 95 % CI 0.959–0.981, p < 0.0001). The risk of cardiovascular mortality in the OBS Q4 group was 43.3 % lower than in the Q1 group (HR 0.567, 95 % CI 0.455–0.705, p for trend <0.0001). The PH assumption was confirmed to be met using the Schoenfeld goodness-of-fit test (GLOBAL p = 0.3072 and 0.3637). We also conducted RCS analysis based on the fully adjusted Cox regression model (Model 3), revealing a negative correlation between OBS and both all-cause and cardiovascular mortality in patients with diabetes and prediabetes (Fig. 3).Fig. 3 The association of OBS with all-cause (A) and cardiovascular mortality (B) among diabetes and prediabetes visualized by restricted cubic spline.

Fig. 3

3.3 Subgroup analysis

We conducted a stratified analysis to assess the robustness of the regression results on the relationship between OBS and mortality among patients with diabetes and prediabetes across different subgroups. The results are presented in Table 3. The analysis indicated that in most subgroups, the relationship between OBS and mortality remained consistent, showing a negative correlation, and these associations were statistically significant. Additionally, education level appeared to interact with OBS, suggesting a potential moderating effect (p for interaction <0.05).Table 3 Stratified analysis of the relationship between OBS and mortality in diabetes and prediabetes.

Table 3	Cardiovascular mortality	All-cause mortality	
HR (95 % CI) P value	HR (95 % CI) P value	
Age	
 ≤60	0.947 (0.923, 0.972) <0.0001	0.976 (0.963, 0.988) 0.0002	
 >60	0.971 (0.960, 0.983) <0.0001	0.980 (0.974, 0.986) <0.0001	
P for interaction	0.0801	0.5448	
Gender	
Male	0.970 (0.956, 0.984) <0.0001	0.980 (0.973, 0.988) <0.0001	
Female	0.970 (0.954, 0.987) 0.0005	0.983 (0.975, 0.992) 0.0001	
P for interaction	0.9636	0.5933	
Race	
Mexican American	0.961 (0.929, 0.994) 0.0213	0.982 (0.967, 0.997) 0.0166	
Other Hispanic	1.019 (0.954, 1.088) 0.5835	0.995 (0.966, 1.024) 0.7243	
Non-Hispanic White	0.972 (0.958, 0.986) <0.0001	0.978 (0.971, 0.986) <0.0001	
Non-Hispanic Black	0.960 (0.936, 0.984) 0.0014	0.990 (0.977, 1.002) 0.1101	
Other Race	0.983 (0.910, 1.061) 0.6556	0.964 (0.931, 0.999) 0.0440	
P for interaction	0.5119	0.3854	
Education	
Less than high school	0.982 (0.965, 0.999) 0.0377	0.989 (0.980, 0.998) 0.0147	
High school	0.981 (0.960, 1.004) 0.0992	0.985 (0.974, 0.997) 0.0149	
More than high school	0.948 (0.930, 0.967) <0.0001	0.971 (0.962, 0.981) <0.0001	
P for interaction	0.0441	0.0646	
Marital status	
Married	0.970 (0.954, 0.985) 0.0002	0.980 (0.972, 0.988) <0.0001	
Single	0.975 (0.960, 0.991) 0.0025	0.983 (0.975, 0.992) 0.0001	
Living with a partner	0.857 (0.774, 0.949) 0.0031	0.982 (0.940, 1.026) 0.4210	
P for interaction	<0.0001	0.7672	
Poverty to income ratio	
Low	0.970 (0.946, 0.993) 0.0124	0.979 (0.966, 0.991) 0.0011	
Middle	0.976 (0.960, 0.992) 0.0032	0.984 (0.976, 0.992) 0.0001	
High	0.967 (0.945, 0.989) 0.0038	0.981 (0.969, 0.993) 0.0016	
P for interaction	0.4581	0.8882	
Smoking	
Yes	0.963 (0.948, 0.978) <0.0001	0.979 (0.972, 0.987) <0.0001	
No	0.978 (0.962, 0.995) 0.0096	0.986 (0.977, 0.995) 0.0020	
P for interaction	0.1649	0.7281	
Drinking	
Yes	0.964 (0.949, 0.978) <0.0001	0.979 (0.972, 0.987) <0.0001	
No	0.978 (0.960, 0.996) 0.0151	0.983 (0.973, 0.993) 0.0007	
P for interaction	0.2947	0.1042	
Hypertension history	
Yes	0.972 (0.965, 0.980) <0.0001	0.982 (0.975, 0.989) <0.0001	
No	0.967 (0.959, 0.975) <0.0001	0.982 (0.972, 0.992) 0.0004	
P for interaction	0.5017	0.1683	
Diabetes status	
Diabetes	0.968 (0.956, 0.980) <0.0001	0.983 (0.976, 0.989) <0.0001	
Prediabetes	0.982 (0.959, 1.006) 0.1393	0.978 (0.966, 0.990) 0.0004	
P for interaction	0.3275	0.4325	
Stratified analysis was constructed based on model 3. In each case, the model was not adjusted for the stratification variable itself.

4 Discussion

This study is the first to evaluate the relationship between the OBS and survival outcomes among patients with diabetes and prediabetes based on a large sample from the NHANES. Among the 10,591 participants with diabetes or prediabetes across 10 NHANES cycles (1999–2018), a negative correlation was observed between OBS and both all-cause and cardiovascular mortality. This relationship was found to be highly robust, indicating that higher OBS scores are associated with lower risks of both types of mortality. Our findings underscore the importance of adhering to an antioxidant-rich diet and lifestyle as a preventive strategy for managing diabetes and prediabetes.

Insulin resistance plays a pivotal role in the pathogenesis of diabetes and prediabetes [11,12]. Oxidative stress is considered a detrimental factor contributing to insulin resistance. Under physiological conditions, there is a balance between the production of reactive oxygen species (ROS) and cellular antioxidant defense mechanisms [20]. When this balance is disrupted, leading to an excess production of ROS beyond the neutralizing capacity of the antioxidant system, oxidative stress occurs, which can inhibit insulin signaling and reduce insulin sensitivity [21]. ROS can induce serine/threonine phosphorylation of insulin receptor substrate-1 (IRS-1), which inhibits its tyrosine phosphorylation and disrupts insulin signaling [22]. Normally, insulin binding to its receptor leads to tyrosine phosphorylation of IRS-1, activating the downstream PI3K/Akt pathway. However, ROS-induced aberrant phosphorylation hampers this process [23]. Additionally, ROS can activate both the NF-κB (nuclear factor κB) and JNK (c-Jun N-terminal kinase) signaling pathways, which play critical roles in regulating inflammatory responses and insulin signaling [24]. Activation of NF-κB induces the expression of various inflammatory cytokines, such as TNF-α and IL-6, which further inhibit insulin signaling [24]. Simultaneously, activation of JNK leads to serine/threonine phosphorylation of IRS-1, exacerbating insulin resistance [24]. Additionally, β-cells are particularly susceptible to oxidative stress due to their low levels of antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) [25]. Oxidative stress can diminish the expression of these critical β-cell genes, induce cell death, decrease insulin production, and impair glucose-stimulated insulin secretion [[25], [26], [27]]. Furthermore, oxidative stress can activate inflammatory responses, leading to the production of inflammatory mediators such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) [28,29]. These inflammatory mediators exacerbate oxidative stress, creating a vicious cycle that can trigger insulin resistance and promote the development of diabetes [30]. Under hyperglycemic conditions, this inflammatory response may further deteriorate, accelerating the progression of chronic complications associated with diabetes [30]. Moreover, this subclinical inflammation can damage β-cells in the pancreas, impairing normal insulin secretion and ultimately leading to elevated blood glucose levels [31]. Interestingly, chronic inflammation observed in various conditions, such as diabetes and atherosclerosis, is often driven by excessive mitochondrial ROS production [32,33].

Our study indicates that a high OBS, which reflects greater exposure to antioxidant factors, acts as a protective factor against mortality in patients with diabetes and prediabetes, aligning with findings from previous research. Recently, a cross-sectional study by Wu et al. involving 5233 individuals from the U.S. population found a gender-dependent negative association between OBS and diabetes prevalence [15]. Xu et al. reported that individuals with higher OBS had lower susceptibility to metabolic syndrome (MetS) in a study involving 11,171 participants, and that increased OBS was associated with reduced risk of all-cause mortality in MetS subjects [34]. Given that insulin resistance is a core feature of MetS, Xu's findings corroborate our results. Additionally, two cohort studies by Xuan et al. involving 2125 diabetes patients in Germany reported that levels of oxidative stress biomarkers were associated with major cardiovascular events and all-cause mortality, highlighting that an imbalance in the redox system is a significant contributor to premature death in diabetes patients [35]. Although research on the impact of OBS on mortality among diabetes and prediabetes patients is limited, several studies have examined the relationship between individual OBS components and mortality. A meta-analysis focusing on diabetic patients demonstrated that a high-fiber diet with antioxidant properties can improve blood glucose control, lipid profiles, and reduce premature mortality [36]. A cohort study by Liu et al. found that lower levels of serum vitamin B12 and folate were significantly associated with increased cardiovascular disease mortality risk in diabetes patients [37]. Additionally, there is evidence that diet and physical activity can reduce or delay the onset of diabetes in individuals with impaired glucose tolerance (IGT) [38]. Smoking has been shown to significantly increase the risk of total mortality and cardiovascular events in diabetes patients, whereas smoking cessation is associated with risk reduction [39].

This study has several strengths. First, we utilized a large sample size with a long follow-up period, resulting in stable and reliable outcomes with strong statistical power. Second, by using OBS, a composite index, to assess oxidative stress rather than relying on a single indicator, we could more comprehensively understand the relationship between oxidative stress and mortality in patients with diabetes and prediabetes. Third, the use of a nationally representative, high-quality sample enhances the generalizability of our findings. However, there are some limitations to this study. Due to database constraints, it was challenging to include all dietary and lifestyle factors related to oxidative stress in the OBS calculation. For instance, certain components like flavonoids were not fully recorded in the database. Moreover, the NHANES database does not provide specific data to distinguish between Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). Therefore, our results cannot be explicitly generalized to differentiate between T1DM and T2DM. Additionally, although we adjusted for several potential confounders in our multivariable models, OBS may still be influenced by other factors. Lastly, our investigation is based on NHANES data, which is specific to the U.S. population, and the results may not be generalizable to other populations.

5 Conclusion

In conclusion, our study of 10 NHANES cycles (1999–2018) involving 10,591 participants demonstrates a negative correlation between OBS and all-cause mortality as well as cardiovascular mortality among diabetes and prediabetes patients. Higher OBS scores, indicating greater exposure to antioxidant factors relative to pro-oxidant factors in diet and lifestyle, were associated with lower risks of all-cause and cardiovascular mortality. These findings underscore the importance of adhering to antioxidant-rich diets and lifestyles in strategies aimed at preventing and managing diabetes and prediabetes.

Funding

This study was supported by the Natural Science Foundation of Education Department of Anhui Province (No. 2022AH051221 ) and Anhui Province Key Laboratory of Biological Macromolecules Research of Wannan Medical College (No. LAB202204 ).

Ethics approval and consent to participate

The National Health and Nutrition Examination Survey (NHANES) is a publicly accessible database approved by the Institutional Review Board (IRB) of the National Center for Health Statistics. All participants provided written informed consent during their participation in the national survey. As secondary analysis does not require additional IRB approval, this study was exempt from further ethical review and approval.

Disclosure statement

The authors have nothing to disclose.

CRediT authorship contribution statement

Zichen Xu: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Daoqin Liu: Writing – review & editing, Software, Formal analysis, Data curation, Conceptualization. Ying Zhai: Writing – review & editing, Visualization, Validation, Supervision, Software, Formal analysis, Data curation, Conceptualization. Yu Tang: Writing – review & editing, Software, Resources, Project administration, Formal analysis, Data curation. Luqing Jiang: Validation, Software, Resources, Investigation, Formal analysis, Data curation. Lei Li: Writing – review & editing, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Qiwen Wu: Writing – review & editing, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no competing interests.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Data availability

Data will be made available on request.

Acknowledgments

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.redox.2024.103327.
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References

1 Sun H. Saeedi P. Karuranga S. Pinkepank M. Ogurtsova K. Duncan B.B. Stein C. Basit A. Chan J.C.N. Mbanya J.C. Pavkov M.E. Ramachandaran A. Wild S.H. James S. Herman W.H. Zhang P. Bommer C. Kuo S. Boyko E.J. Magliano D.J. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 Diabetes Res. Clin. Pract. 183 2022 109119 10.1016/j.diabres.2021.109119
2 Chan J.C.N. Lim L.-L. Wareham N.J. Shaw J.E. Orchard T.J. Zhang P. Lau E.S.H. Eliasson B. Kong A.P.S. Ezzati M. Aguilar-Salinas C.A. McGill M. Levitt N.S. Ning G. So W.-Y. Adams J. Bracco P. Forouhi N.G. Gregory G.A. Guo J. Hua X. Klatman E.L. Magliano D.J. Ng B.-P. Ogilvie D. Panter J. Pavkov M. Shao H. Unwin N. White M. Wou C. Ma R.C.W. Schmidt M.I. Ramachandran A. Seino Y. Bennett P.H. Oldenburg B. Gagliardino J.J. Luk A.O.Y. Clarke P.M. Ogle G.D. Davies M.J. Holman R.R. Gregg E.W. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives Lancet 396 2021 2019 2082 10.1016/S0140-6736(20)32374-6 33189186
3 Rooney M.R. Fang M. Ogurtsova K. Ozkan B. Echouffo-Tcheugui J.B. Boyko E.J. Magliano D.J. Selvin E. Global prevalence of prediabetes Diabetes Care 46 2023 1388 1394 10.2337/dc22-2376 37196350
4 Chi Z.S. Lee E.T. Lu M. Keen H. Bennett P.H. Vascular disease prevalence in diabetic patients in China: standardised comparison with the 14 centres in the WHO Multinational Study of Vascular Disease in Diabetes Diabetologia 44 Suppl 2 2001 S82 S86 10.1007/pl00002944 11587055
5 Forouhi N.G. Sattar N. Tillin T. McKeigue P.M. Chaturvedi N. Do known risk factors explain the higher coronary heart disease mortality in South Asian compared with European men? Prospective follow-up of the Southall and Brent studies, UK Diabetologia 49 2006 2580 2588 10.1007/s00125-006-0393-2 16972045
6 van Dieren S. Beulens J.W.J. van der Schouw Y.T. Grobbee D.E. Neal B. The global burden of diabetes and its complications: an emerging pandemic Eur. J. Cardiovasc. Prev. Rehabil. 17 Suppl 1 2010 S3 S8 10.1097/01.hjr.0000368191.86614.5a 20489418
7 Brannick B. Dagogo-Jack S. Prediabetes and cardiovascular disease: pathophysiology and interventions for prevention and risk reduction Endocrinol Metab. Clin. N. Am. 47 2018 33 50 10.1016/j.ecl.2017.10.001
8 Tabák A.G. Herder C. Rathmann W. Brunner E.J. Kivimäki M. Prediabetes: a high-risk state for diabetes development Lancet 379 2012 2279 2290 10.1016/S0140-6736(12)60283-9 22683128
9 Raghavan S. Vassy J.L. Ho Y.-L. Song R.J. Gagnon D.R. Cho K. Wilson P.W.F. Phillips L.S. Diabetes mellitus-related all-cause and cardiovascular mortality in a national cohort of adults J. Am. Heart Assoc. 8 2019 e011295 10.1161/JAHA.118.011295
10 Tangvarasittichai S. Oxidative stress, insulin resistance, dyslipidemia and type 2 diabetes mellitus World J. Diabetes 6 2015 456 480 10.4239/wjd.v6.i3.456 25897356
11 Macdonald I.A. A review of recent evidence relating to sugars, insulin resistance and diabetes Eur. J. Nutr. 55 2016 17 23 10.1007/s00394-016-1340-8 27882410
12 Chen Z. Franco O.H. Lamballais S. Ikram M.A. Schoufour J.D. Muka T. Voortman T. Associations of specific dietary protein with longitudinal insulin resistance, prediabetes and type 2 diabetes: the Rotterdam Study Clin. Nutr. 39 2020 242 249 10.1016/j.clnu.2019.01.021 30739809
13 Negre-Salvayre A. Salvayre R. Augé N. Pamplona R. Portero-Otín M. Hyperglycemia and glycation in diabetic complications Antioxidants Redox Signal. 11 2009 3071 3109 10.1089/ars.2009.2484
14 Hernández-Ruiz Á. García-Villanova B. Guerra-Hernández E.J. Carrión-García C.J. Amiano P. Sánchez M.-J. Molina-Montes E. Oxidative balance scores (OBSs) integrating nutrient, food and lifestyle dimensions: development of the NutrientL-OBS and FoodL-OBS Antioxidants 11 2022 300 10.3390/antiox11020300 35204183
15 Wu C. Ren C. Song Y. Gao H. Pang X. Zhang L. Gender-specific effects of oxidative balance score on the prevalence of diabetes in the US population from NHANES Front. Endocrinol. 14 2023 1148417 10.3389/fendo.2023.1148417
16 Wen H. Li X. Chen J. Li Y. Yang N. Tan N. Association of oxidative balance score with chronic kidney disease: NHANES 1999-2018 Front. Endocrinol. 15 2024 1396465 10.3389/fendo.2024.1396465
17 Lee J.-H. Son D.-H. Kwon Y.-J. Association between oxidative balance score and new-onset hypertension in adults: a community-based prospective cohort study Front. Nutr. 9 2022 1066159 10.3389/fnut.2022.1066159
18 Liu X. Liu X. Wang Y. Zeng B. Zhu B. Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005-2018 J. Affect. Disord. 337 2023 57 65 10.1016/j.jad.2023.05.071 37244542
19 Zhang W. Peng S.-F. Chen L. Chen H.-M. Cheng X.-E. Tang Y.-H. Association between the oxidative balance score and telomere length from the national health and nutrition examination survey 1999-2002 Oxid. Med. Cell. Longev. 2022 2022 1345071 10.1155/2022/1345071
20 Turrens J.F. Boveris A. Generation of superoxide anion by the NADH dehydrogenase of bovine heart mitochondria Biochem. J. 191 1980 421 427 10.1042/bj1910421 6263247
21 Sies H. Oxidative stress: oxidants and antioxidants Exp. Physiol. 82 1997 291 295 10.1113/expphysiol.1997.sp004024 9129943
22 Tanti J.-F. Jager J. Cellular mechanisms of insulin resistance: role of stress-regulated serine kinases and insulin receptor substrates (IRS) serine phosphorylation Curr. Opin. Pharmacol. 9 2009 753 762 10.1016/j.coph.2009.07.004 19683471
23 Parker L. Shaw C.S. Stepto N.K. Levinger I. Exercise and glycemic control: focus on redox homeostasis and redox-sensitive protein signaling Front. Endocrinol. 8 2017 10.3389/fendo.2017.00087
24 Evans J.L. Goldfine I.D. Maddux B.A. Grodsky G.M. Are oxidative stress-activated signaling pathways mediators of insulin resistance and beta-cell dysfunction? Diabetes 52 2003 1 8 10.2337/diabetes.52.1.1 12502486
25 Christensen A.A. Gannon M. The beta cell in type 2 diabetes Curr. Diabetes Rep. 19 2019 81 10.1007/s11892-019-1196-4
26 Gerber P.A. Rutter G.A. The role of oxidative stress and hypoxia in pancreatic beta-cell dysfunction in diabetes mellitus Antioxidants Redox Signal. 26 2017 501 518 10.1089/ars.2016.6755
27 Ježek P. Jabůrek M. Plecitá-Hlavatá L. Contribution of oxidative stress and impaired biogenesis of pancreatic β-cells to type 2 diabetes Antioxidants Redox Signal. 31 2019 722 751 10.1089/ars.2018.7656
28 Hussain T. Tan B. Yin Y. Blachier F. Tossou M.C.B. Rahu N. Oxidative stress and inflammation: what polyphenols can do for us? Oxid. Med. Cell. Longev. 2016 2016 7432797 10.1155/2016/7432797
29 Karam B.S. Chavez-Moreno A. Koh W. Akar J.G. Akar F.G. Oxidative stress and inflammation as central mediators of atrial fibrillation in obesity and diabetes Cardiovasc. Diabetol. 16 2017 120 10.1186/s12933-017-0604-9 28962617
30 Lontchi-Yimagou E. Sobngwi E. Matsha T.E. Kengne A.P. Diabetes mellitus and inflammation Curr. Diabetes Rep. 13 2013 435 444 10.1007/s11892-013-0375-y
31 Berbudi A. Rahmadika N. Tjahjadi A.I. Ruslami R. Type 2 diabetes and its impact on the immune system Curr. Diabetes Rev. 16 2020 442 449 10.2174/1573399815666191024085838 31657690
32 Poznyak A. Grechko A.V. Poggio P. Myasoedova V.A. Alfieri V. Orekhov A.N. The diabetes mellitus-atherosclerosis connection: the role of lipid and glucose metabolism and chronic inflammation Int. J. Mol. Sci. 21 2020 1835 10.3390/ijms21051835 32155866
33 Jin Q. Liu T. Qiao Y. Liu D. Yang L. Mao H. Ma F. Wang Y. Peng L. Zhan Y. Oxidative stress and inflammation in diabetic nephropathy: role of polyphenols Front. Immunol. 14 2023 1185317 10.3389/fimmu.2023.1185317
34 Xu Z. Lei X. Chu W. Weng L. Chen C. Ye R. Oxidative balance score was negatively associated with the risk of metabolic syndrome, metabolic syndrome severity, and all-cause mortality of patients with metabolic syndrome Front. Endocrinol. 14 2023 1233145 10.3389/fendo.2023.1233145
35 Xuan Y. Gào X. Anusruti A. Holleczek B. Jansen E.H.J.M. Muhlack D.C. Brenner H. Schöttker B. Association of serum markers of oxidative stress with incident major cardiovascular events, cancer incidence, and all-cause mortality in type 2 diabetes patients: pooled results from two cohort studies Diabetes Care 42 2019 1436 1445 10.2337/dc19-0292 31167893
36 Reynolds A.N. Akerman A.P. Mann J. Dietary fibre and whole grains in diabetes management: systematic review and meta-analyses PLoS Med. 17 2020 e1003053 10.1371/journal.pmed.1003053
37 Liu Y. Geng T. Wan Z. Lu Q. Zhang X. Qiu Z. Li L. Zhu K. Liu L. Pan A. Liu G. Associations of serum folate and vitamin B12 levels with cardiovascular disease mortality among patients with type 2 diabetes JAMA Netw. Open 5 2022 e2146124 10.1001/jamanetworkopen.2021.46124
38 Hemmingsen B. Gimenez-Perez G. Mauricio D. Roqué I Figuls M. Metzendorf M.-I. Richter B. Diet, physical activity or both for prevention or delay of type 2 diabetes mellitus and its associated complications in people at increased risk of developing type 2 diabetes mellitus Cochrane Database Syst. Rev. 12 2017 CD003054 10.1002/14651858.CD003054.pub4
39 Pan A. Wang Y. Talaei M. Hu F.B. Relation of smoking with total mortality and cardiovascular events among patients with diabetes mellitus: a meta-analysis and systematic review Circulation 132 2015 1795 1804 10.1161/CIRCULATIONAHA.115.017926 26311724
