What this is
- This research examines the relationship between Oxidative Balance Scores () and () prevalence among U.S. adults.
- Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2018, the study analyzed 13,373 individuals aged 20 and older.
- The findings indicate a significant negative correlation between and , especially in mild and moderate-risk groups.
Essence
- Higher Oxidative Balance Scores () correlate with a lower prevalence of () in U.S. adults, particularly among those at mild and moderate risk.
Key takeaways
- Individuals in the highest quartile (Q4) have a lower likelihood of compared to those in the lowest quartile (Q1). This association is particularly significant in mild and moderate risk groups.
- The study reveals a non-linear inverse relationship between and prevalence, indicating that as increases, the odds of decrease.
- These findings support the potential for elevated as a preventative measure against , emphasizing the importance of dietary and lifestyle factors in managing risk.
Caveats
- The study's cross-sectional design limits the ability to establish causation between and . Further longitudinal studies are needed to clarify this relationship.
- Selection bias may affect results due to the exclusion of participants with incomplete data, potentially skewing the findings.
- The reliance on dietary recall for estimating introduces the possibility of bias, as it may not accurately reflect long-term dietary habits.
Definitions
- Oxidative Balance Score (OBS): A measure of an individual's antioxidant status, incorporating dietary and lifestyle factors that contribute to oxidative balance.
- Chronic Kidney Disease (CKD): A progressive decline in kidney function, classified into stages based on albumin-to-creatinine ratio and estimated glomerular filtration rate.
AI simplified
Introduction
Chronic kidney disease (CKD), marked by progressive kidney function decline, is increasingly recognized as a significant global health challenge with substantial health and economic impacts1,2. In the United States, CKD prevalence in the general population is approaching 15%3, representing a considerable burden to both individuals and society. Consequently, identifying and managing modifiable risk factors for CKD has become a crucial public health priority. Factors such as lifestyle changes, dietary habits, smoking, and obesity, which are linked to oxidative stress, have contributed to the rising annual prevalence of kidney diseases4–6.
Oxidative stress is defined as a physiological imbalance where an overabundance of free radicals surpasses the body's inherent antioxidant defenses7. When the balance between prooxidants and antioxidants is disrupted, oxidative stress occurs, leading to damage in cells, tissues, and organs8,9. The kidney, owing to its high metabolic demands, is especially vulnerable to damage inflicted by oxidative stress10, a critical factor implicated in the advancement of renal diseases7,11. Oxidative Balance Scores (OBS) serve as a measure of an individual’s antioxidant status by considering both the antioxidant and pro-oxidant elements in their diet and lifestyle12, and have been correlated with biomarkers of inflammation and oxidative stress, such as F2-isoprostanes and C-reactive protein13,14. Recent research suggests that OBS measurement may be pivotal in detecting pro- and antioxidant exposure in individuals at risk for CKD15. While several studies have established a connection between OBS and CKD, they primarily focused on middle-aged and older populations. Therefore, we utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 to conduct a comprehensive cross-sectional analysis, investigating the link between OBS and CKD prevalence among Americans, and further examining the relationship between OBS and different CKD risk stratification. Considering the influential roles of sodium and potassium in oxidative stress modulation and kidney function16–18, we included them as covariates in the examination of the relationship.
Results
Basic characteristics of the study population
| Overall (N = 13,373) | Oxidative balance score | P-value | ||||
|---|---|---|---|---|---|---|
| Q1 (N = 1853) | Q2 (N = 4438) | Q3 (N = 4373) | Q4 (N = 2709) | |||
| Age, years | 44.71 ± 16.47 | 42.62 ± 14.91 | 44.76 ± 16.07 | 45.6 ± 16.98 | 44.75 ± 17.43 | < 0.001 |
| Sex, n (%) | ||||||
| Men | 6906 (50.64%) | 1424 (77.36%) | 2634 (58.76%) | 1919 (39.87%) | 929 (30.87%) | < 0.001 |
| Women | 6467 (49.36%) | 429 (22.63%) | 1804 (41.24%) | 2454 (60.13%) | 1780 (69.13%) | |
| RACE, n (%) | ||||||
| Mexican American | 1725 (7.26%) | 267 (8.14%) | 572 (6.83%) | 538 (6.95%) | 348 (8.00%) | < 0.001 |
| Other Hispanic | 1259 (5.24%) | 166 (5.83%) | 367 (4.21%) | 440 (5.71%) | 286 (6.07%) | |
| Non-Hispanic White | 5949 (69.76%) | 901 (71.27%) | 2138 (73.56%) | 1880 (68.13%) | 1030 (63.54%) | |
| Non-Hispanic Black | 2641 (9.52%) | 322 (8.32%) | 749 (7.38%) | 884 (10.06%) | 686 (13.99%) | |
| Other Race | 1799 (8.22%) | 197 (6.44%) | 612 (8.02%) | 631 (9.15%) | 359 (8.41%) | |
| Education, n (%) | < 0.001 | |||||
| Less than high school education attainment | 1932 (8.67%) | 239 (8.00%) | 559 (7.25%) | 649 (9.05%) | 485 (11.52%) | |
| High school graduate | 2674 (19.03%) | 354 (17.87%) | 802 (17.01%) | 882 (18.95%) | 636 (24.32%) | |
| Has more than a high school education | 8767 (72.30%) | 1260 (74.13%) | 3077 (75.74%) | 2842 (72.00%) | 1588 (64.16%) | |
| Marital status, n (%) | < 0.001 | |||||
| Married | 6928 (55.05%) | 425 (55.62%) | 1068 (58.78%) | 1206 (54.45%) | 822 (47.86%) | |
| Never married | 2924 (21.97%) | 972 (23.43%) | 2461 (21.42%) | 2247 (20.42%) | 1248 (24.71%) | |
| Other status | 3521 (22.98%) | 456 (20.95%) | 909 (19.79%) | 920 (25.13%) | 639 (27.43%) | |
| HDL, mg/dL | 55.10 ± 16.87 | 51.64 ± 15.63 | 54.81 ± 16.46 | 56.50 ± 17.00 | 56.02 ± 17.98 | < 0.001 |
| TC, mg/dL | 193.90 ± 41.30 | 191.80 ± 41.23 | 194.00 ± 41.13 | 194.20 ± 40.16 | 195.00 ± 43.67 | 0.225 |
| Diabetes, n (%) | 0.056 | |||||
| Yes | 1247 (6.68%) | 143 (6.35%) | 384 (6.04%) | 408 (6.71%) | 312 (8.22%) | |
| No | 12,126 (93.32%) | 1710 (93.65%) | 4054 (93.96%) | 3965 (93.79%) | 2397 (91.78%) | |
| Hypertension, n (%) | 0.753 | |||||
| Yes | 3908 (25.35%) | 480 (25.13%) | 1256 (25.01%) | 1304 (25.16%) | 868 (26.59%) | |
| No | 9465 (74.65%) | 1373 (74.87%) | 3182 (74.99%) | 3069 (74.84%) | 1841 (73.41%) | |
| Serum potassium, mmol/L | 3.98 ± 0.33 | 4.03 ± 0.31 | 4.00 ± 0.32 | 3.97 ± 0.32 | 3.93 ± 0.34 | < 0.001 |
| Serum sodium, mmol/L | 139.30 ± 2.26 | 139.30 ± 2.24 | 139.30 ± 2.27 | 139.40 ± 2.21 | 139.30 ± 2.34 | 0.279 |
| eGFR, mL/min 1.73 m2 | 97.78 ± 19.41 | 99.75 ± 17.62 | 97.96 ± 18.55 | 97.07 ± 20.05 | 97.07 ± 21.24 | < 0.001 |
| ACR, mg/g | 22.38 ± 175.30 | 17.04 ± 92.26 | 21.98 ± 186.60 | 21.85 ± 120.20 | 28.65 ± 265.20 | 0.243 |
| CKD, n (%) | < 0.001 | |||||
| Yes | 1639 (9.86%) | 167 (7.40%) | 501 (9.06%) | 535 (9.83%) | 436 (13.62%) | |
| No | 11,734 (90.14%) | 1686 (92.60%) | 3937 (90.94%) | 3838 (90.87%) | 2273 (86.38%) | |
The association between oxidative balance score and chronic kidney disease
The dose–response relationships between oxidative balance score and chronic kidney disease. () RCS curves describing the dose–response relationship between oxidative balance score and CKD; () RCS curves describing the dose–response relationship between oxidative balance score and mild–risk stratification for CKD; () RCS curves describing the dose–response relationship between oxidative balance score and moderate–risk stratification for CKD. The solid lines and shaded areas represent the central risk estimates and 95% CIs. The following covariates were adjusted for: age, sex, race, marital status, education, HDL, TC, diabetes, hypertension, serum sodium and serum potassium. A B C
| Actual population numbers | Weighted population | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | |||
| Q1 | 1853 | 16,620,874 | Ref | Ref | Ref | |||
| Q2 | 4438 | 41,482,417 | 0.80 (0.61,1.06) | 0.12 | 0.93 (0.70,1.23) | 0.599 | 0.90 (0.67,1.21) | 0.468 |
| Q3 | 4373 | 36,064,965 | 0.73 (0.58,0.93) | 0.011 | 0.99 (0.76,1.30) | 0.959 | 0.96 (0.73,1.26) | 0.74 |
| Q4 | 2709 | 19,897,440 | 0.51 (0.39,0.66) | < 0.001 | 0.71 (0.54,0.94) | 0.016 | 0.70 (0.53,0.92) | 0.013 |
| P for trend | – | < 0.001 | < 0.001 | |||||
| Stratification factors | Cases/non-cases | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | ||
| CKD cases with mild risk | 461/11,734 | ||||||
| Q1 | Ref | ||||||
| Q2 | 0.73 (0.44,1.23) | 0.239 | 1.09 (0.63,1.9) | 0.755 | 1.05 (0.59,1.88) | 0.863 | |
| Q3 | 0.57 (0.37,0.89) | 0.014 | 1.12 (0.68,1.85) | 0.649 | 1.05 (0.63,1.76) | 0.843 | |
| Q4 | 0.36 (0.22,0.57) | < 0.001 | 0.70 (0.41,1.19) | 0.185 | 0.66 (0.38,1.13) | 0.131 | |
| P for trend | < 0.001 | < 0.001 | |||||
| CKD cases with moderate risk | 1001/11,734 | ||||||
| Q1 | Ref | ||||||
| Q2 | 0.77 (0.55,1.07) | 0.117 | 0.84 (0.61,1.16) | 0.292 | 0.81 (0.58,1.12) | 0.197 | |
| Q3 | 0.81 (0.61,1.07) | 0.137 | 1.01 (0.74,1.37) | 0.966 | 0.96 (0.71,1.31) | 0.811 | |
| Q4 | 0.54 (0.39,0.73) | < 0.001 | 0.71 (0.52,0.96) | 0.027 | 0.70 (0.51,0.96) | 0.029 | |
| P for trend | 0.002 | 0.002 | |||||
| CKD cases with high risk | 177/11,734 | ||||||
| Q1 | Ref | ||||||
| Q2 | 1.32 (0.64,2.74) | 0.446 | 1.44 (0.67,3.12) | 0.346 | 1.48 (0.70,3.14) | 0.304 | |
| Q3 | 0.76 (0.33,1.74) | 0.515 | 0.94 (0.39,2.27) | 0.892 | 0.87 (0.36,2.13) | 0.764 | |
| Q4 | 0.94 (0.41,2.17) | 0.888 | 1.28 (0.50,3.26) | 0.61 | 1.31 (0.52,3.30) | 0.557 | |
| P for trend | 0.24 | 0.24 | |||||
Subgroup analysis
Forest plot for subgroup analysis. Subgroup analysis was stratified by age, sex, race, marital status, education, diabetes, and hypertension.
Discussion
In our analysis of 13,373 participants from the NHANES 2007–2018, we explored the relationship between OBS and CKD prevalence among US adults. The study revealed that individuals in the highest OBS quartile (Q4) had a lower likelihood of prevalent CKD compared to those in the lowest quartile (Q1), after adjusting for all relevant covariates. Notably, this association was statistically significant in the mild and moderate CKD risk groups, but it was not observed in the high-risk group. Analysis using restricted cubic splines indicated a negative non-linear correlation between OBS and CKD prevalence. These findings suggest that elevated OBS may be beneficial in mitigating the risk of developing CKD, with a more pronounced effect observed in individuals with mild to moderate risk of the disease.
Our results are consistent with previous studies that have demonstrated an inverse connection between OBS and the prevalence of CKD. OBS, a multidimensional measure integrating various diet and lifestyle elements, acts as a barometer for an individual's antioxidant status12. In line with our results, Ilori et al. discovered a notable inverse link between higher OBS quartiles and CKD risk, though they did not find a significant association with albuminuria or the development of end-stage renal disease (ESRD)15. Similarly, a recent Korean study confirmed this trend, showing that increased OBS significantly lowers the risk of developing CKD in Asian populations19. Moreover, studies indicate a rise in oxidative markers in both atherosclerotic lesions and the circulating plasma of CKD patients, pointing to a pro-oxidant state in CKD20,21. These collective findings highlight the critical role of oxidative balance in both the onset and advancement of CKD. Interestingly, our study observed this relationship predominantly in individuals with mild to moderate CKD risk, rather than in those with high risk. This indicates the potential of a healthy diet and lifestyle, which mitigate oxidative stress, in curtailing the risk of new-onset CKD.
Although the etiology of CKD is complex, oxidative stress emerges as a pivotal factor in its pathogenesis. Oxidative stress is a consequence of the imbalance between the production of reactive oxygen species (ROS) and the antioxidant capacity of the cell22. The pathogenesis of CKD is significantly influenced by an imbalance between excessive ROS production and weakened antioxidant defense systems7,23. This imbalance originates from the activation of various enzyme systems, including nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, lipoxygenase, xanthine oxidase, uncoupled nitric oxide synthase (NOS), and the mitochondrial respiratory chain. At the same time, there's a reduction in the effectiveness of crucial antioxidants such as superoxide dismutase (SOD), catalase, selenium-based glutathione peroxidase, and paraoxonases (PON). This disparity, marked by increased pro-oxidant activities and reduced antioxidant capabilities, leads to the oxidation of vital macromolecules, resulting in tissue damage and functional decline in CKD24. Furthermore, the overproduction of ROS plays a direct role in the onset and progression of CKD, contributing to associated conditions like proteinuria, arterial hypertension, and diabetes mellitus23–26. Notably, oxidative stress manifests in the early stages of CKD, with increased ROS levels contributing to the progressive decline in GFR and vascular complications27,28 Thus, oxidative stress plays a key role in the pathophysiology of CKD development.
This study has several notable strengths. First, we utilize data from the NHANES database, ensuring the reliability of our findings through the application of appropriate weights and adjustments for confounding variables during statistical analysis. A key methodological strength is the use of the OBS, a comprehensive score combining dietary and lifestyle factors, to evaluate individuals' antioxidant status. We also conducted risk stratification for CKD cases to investigate the impact of OBS across different CKD risk categories. Nevertheless, the study is not without limitations. Being a cross-sectional analysis, it can only establish an association, not a causative link, between OBS and CKD. Furthermore, the utilization of OBS is constrained to contexts in which specific data are available, necessitating additional research to clarify its direct applicability in clinical practice. Another notable limitation is the potential for selection bias, attributed to the exclusion of numerous participants due to incomplete data. Additionally, our reliance on dietary information from the initial 24-h dietary recall interview for estimating dietary components introduces the possibility of bias. Moreover, the study does not incorporate endogenous measurements of antioxidants and oxidative stress, which may offer further insights into the OBS–CKD relationship.
In conclusion, our research found a negative correlation between OBS and the prevalence of CKD in the American adult population. Notably, this correlation was statistically significant within groups at mild and moderate risk of CKD, rather than high-risk. The analysis revealed a non-linear inverse trend between increasing OBS and the prevalence of CKD. Consequently, elevated OBS levels may serve as a primary preventative strategy against CKD, potentially contributing to a reduction in its overall prevalence.
Methods
Study population
The NHANES is a nationally representative, cross-sectional survey conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). This ongoing program with a 2-year reporting cycle targets the non-institutionalized civilian population of the United States and employs a complex multistage probability sampling design. Data collection in NHANES is carried out through a two-step process, starting with in-home interviews followed by physical examinations and laboratory tests at a Mobile Examination Center (MEC). Ethical approval for the survey was granted by the NCHS Research Ethics Review Board, and all participants provided written informed consent. Publicly accessible details on the NHANES' data collection methods and methodology are available on the official website (cdc.gov/nchs/nhanes.htm).
Flow chart of participant selection based on NHANES database from 2007 to 2018.
Oxidative balance scores
OBS were calculated by incorporating both dietary and lifestyle factors, using the method described in the related literatures29,30. This comprehensive approach involved selecting twenty components associated with oxidative stress—16 dietary and 4 lifestyle elements (Supplementary Table S1). These components were grouped as follows: (1) dietary antioxidants including fiber, β-carotene, riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E, and minerals like calcium, magnesium, zinc, copper, and selenium, (2) dietary pro-oxidants comprising total fat and iron, (3) lifestyle antioxidants represented by physical activity, and (4) lifestyle pro-oxidants such as alcohol consumption, smoking habits, and body mass index (BMI). The dietary component of OBS was derived from 24-h dietary recall interviews conducted in the NHANES Mobile Examination Center, without accounting for nutrients from dietary supplements or medications. Dietary components were categorized into tertiles, with dietary antioxidants scoring 0–2 points ascendingly and dietary pro-oxidants scoring reversely from 2–0 points. For lifestyle components, physical activity was classified as low (< 150 min/week), moderate (150–300 min/week), or high (> 300 min/week), with corresponding scores of 0, 1, and 2 points, based on the 2018 Physical Activity Guidelines for Americans31. Alcohol consumption was scored 0–2 points based on sex-specific consumption patterns, while smoking was evaluated using serum cotinine levels and assigned 0–2 points inversely across tertiles. BMI was calculated as weight in kilograms divided by the square of height in meters and categorized into normal (< 25 kg/m2), overweight (25–29.9 kg/m2), or obese (≥ 30 kg/m2), with scores of 2, 1, and 0 points, respectively. Overall, the OBS ranged from 0 to 40 points, with the total score being the sum of points from all sixteen dietary and four lifestyle components.
Chronic kidney disease
In this study, we defined the dependent variable as the presence of chronic kidney disease (CKD). CKD was primarily identified by two criteria: a urinary albumin-to-creatinine ratio (ACR) exceeding 30 mg/g or an estimated glomerular filtration rate (eGFR) falling below 60 mL/min/1.73 m232. 2021 CKD Epidemiology Collaboration equation is used to calculate eGFR based on serum creatinine levels33. The measurement of serum creatinine is employed the enzymatic method. Urine albumin levels were determined using a solid-phase fluorescence immunoassay, and urine creatinine was quantified through the modified Jaffé kinetic method. Additionally, we conducted a risk stratification of CKD patients based on the staging of CKD and the grading of albuminuria. These categories were as follows: mild risk, encompassing individuals with normal to mildly increased albuminuria (ACR < 30 mg/g); moderate risk, including those with moderately increased albuminuria (ACR: 30–300 mg/g); and high risk, comprising individuals with severely increased albuminuria (ACR > 300 mg/g)34.
Covariates
We further incorporated various factors potentially linked to OBS and the prevalence of CKD, drawing on existing clinical knowledge and research. These factors included sociodemographic variables, medical conditions, and biomedical data. All of sociodemographic variables were gathered from the NHANES questionnaire. Race was classified into Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race. Educational attainment was divided into three levels: less than high school, high school graduate (including those with a high school diploma or an equivalency diploma like the General Educational Development), and more than high school education. Marital status was categorized as married, not married, and other (including separated, widowed, or divorced). Biomedical data and medical conditions taken into consideration included HDL (mg/dL), TC (mg/dL), diabetes (yes or no), hypertension (yes or no), serum potassium (mmol/L), and serum sodium (mmol/L). Body mass index (BMI) was calculated by dividing weight in kilograms by the square of height in meters. The presence of hypertension and diabetes was determined based on self-reported diagnoses. Both HDL and TC levels were measured using an enzymatic method with the Roche Modular P chemistry analyzer at the University of Minnesota. Detailed information about these measurement procedures is available in the NHANES Procedure Manuals.
Statistical analysis
Our study used sample weights in all analyses to ensure representativeness, taking into account the complex multi-stage probabilistic sampling design of NHANES. Categorical variables were presented as unweighted counts and weighted percentages, whereas continuous variables were expressed as weighted means with standard errors. Weighted analysis of variance (ANOVA) and weighted chi-square tests for continuous and categorical variables, respectively. The OBS was categorized into quartiles (Q1, Q2, Q3, Q4), with Q1 serving as the reference group. Logistic regression models were utilized to explore the association between OBS and CKD prevalence. These models varied in terms of adjustment for confounding factors: Model 1 was unadjusted, Model 2 adjusted for sociodemographic variables (age, sex, ethnicity, marital status, and education level), and Model 3 further adjusted for potential correlates including age, sex, ethnicity, marital status, education level, high-density lipoprotein (HDL), total cholesterol (TC), diabetes, hypertension, serum potassium, and serum sodium. Our analysis also extended to the examination of OBS's association with eGFR and albuminuria, applying identical modeling approaches. Additionally, CKD patients were classified into mild, moderate, and high-risk categories. Logistic regression models were again utilized to evaluate the relationship between OBS and different CKD risk stratifications, using the lowest quartile of OBS (Q1) as the reference group. We explored dose–response dynamics between OBS and CKD prevalence using restricted cubic spline curves, with model adjustments mirroring those in Model 3.
Subgroup analyses were stratified according to age, sex, race, marital status, education, hypertension, and diabetes to assess the consistency of the OBS–CKD association across different demographic and clinical profiles. We assessed interactions among covariates using likelihood ratio tests, adjusting models for relevant covariates excluding the stratification variables.
All statistical analyses were conducted using R software, version 4.2.1 (https://www.r-project.org/↗). For all tests, a two-sided P-value of less than 0.05 was considered to indicate statistical significance.
Supplementary Information
Supplementary Tables.