Abstract
Objective
Oxidative stress is known to play a key role in the progression of asthma. The Oxidative Balance Score (OBS), derived from an analysis of diet and lifestyle choices, provides a measure of the body’s oxidative stress levels. This study aims to investigate the clinical relevance of OBS in asthma.
Methods
The study included National Health and Nutrition Examination Survey (NHANES) data from 10,038 individuals between the years 2007 and 2018. We assessed associations of asthma prevalence with the OBS, Dietary Oxidative Balance Score (DOBS), and Lifestyle Oxidative Balance Score (LOBS) using logistic regression and restricted cubic spline (RCS) analyses. Lung function was evaluated through correlation analysis. The Cox proportional hazards models were employed to assess all-cause and cardiovascular-specific mortality by OBS components. Subgroup and interaction analyses were conducted to examine heterogeneity and to validate the findings.
Results
After adjusting for confounders, higher OBS [OR = 0.98 (0.97, 0.99), p = 0.003], DOBS [OR = 0.99 (0.97, 0.99), p = 0.016], and LOBS [OR = 0.91 (0.87, 0.96), p < 0.001] were inversely associated with asthma prevalence. A nonlinear relationship was observed between OBS and asthma prevalence (p-for-nonlinear = 0.028). OBS showed positive correlations with lung function parameters, including FEV1 (r = 0.12, p < 0.001) and FEF25-75% (r = 0.06, p < 0.001). Additionally, higher OBS was associated with reduced risks of all-cause mortality [HR = 0.92 (0.89, 0.96), p < 0.001] and cardiovascular mortality [HR = 0.86 (0.80, 0.93), p < 0.001] in asthma individuals.
Conclusion
Our findings suggest that an antioxidant-rich diet and lifestyle may lower the risk of developing asthma and improve the prognosis for individuals with asthma.
Keywords: NHANES, OBS, dietary, lifestyle, asthma, mortality
Key Messages
The Oxidative Balance Score (OBS), Dietary Oxidative Balance Score (DOBS), and Lifestyle Oxidative Balance Score (LOBS) were negatively correlated with asthma prevalence.
OBS exhibited a negative association with all-cause and cardiovascular mortality in asthma.
An antioxidant-rich diet and lifestyle may lower the risk of developing asthma and improve the prognosis for individuals with asthma.
1. Introduction
Asthma is a chronic respiratory disorder characterized by reversible bronchoconstriction, heightened airway responsiveness, and persistent bronchial inflammation [1,2]. According to the World Health Organization (WHO), asthma affects 262 million people globally, with over 460,000 annual deaths [3]. This indicates that, despite the availability of effective treatment approaches for the majority of cases, a subset of patients still confront significant challenges. Consequently, there remains an ongoing need for evidence-based preventive strategies and therapeutic advancements to further improve long-term outcomes.
Oxidative stress is thought to result from an imbalance between the production of reactive oxygen species (ROS) and the body’s antioxidant defenses, potentially leading to cellular and tissue damage [1]. It has been implicated in the pathogenesis of various diseases, including asthma, where excessive ROS may contribute to inflammation, airway hyperresponsiveness, and structural changes in the airways [4]. Emerging evidence from epidemiological and experimental studies has established a significant association between modifiable lifestyle factors (including dietary patterns) and asthma pathogenesis, potentially mediated through oxidative stress pathways [4,5]. The pathophysiological significance of oxidative stress in asthma development may stem from disrupted redox homeostasis, characterized by excessive ROS production overwhelming endogenous antioxidant capacity [4,6].
The Oxidative Balance Score (OBS), a comprehensive metric developed by Zhang et al., quantitatively integrates 20 dietary and lifestyle components based on their antioxidant properties [7]. This innovative scoring system has demonstrated biological plausibility through its association with leukocyte telomere dynamics, suggesting that modifiable lifestyle factors may regulate cellular aging processes via oxidative stress modulation [7]. Epidemiological studies have consistently shown that individuals with lower OBS values exhibit diminished antioxidant capacity and elevated oxidative damage biomarkers, potentially predisposing them to poorer health trajectories [8]. While substantial evidence supports the protective role of optimal oxidative balance against various chronic conditions [9–13], the specific clinical implications of OBS in asthma pathogenesis and progression remain underexplored.
We hypothesized that elevated OBS levels would be inversely correlated with asthma prevalence, symptom severity, and all-cause mortality in adults. To evaluate this hypothesis, we conducted a comprehensive analysis of data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cohort. This study seeks to clarify the role of oxidative stress in asthma pathophysiology and investigate its potential for informing individualized management approaches through targeted dietary and lifestyle modifications.
2. Materials and methods
2.1. Study design and subjects
This study utilized data from the NHANES, a comprehensive program evaluating health and nutritional parameters in the U.S. population. The NHANES protocol incorporates standardized interviews, physical examinations, and laboratory assessments, generating five core data domains: demographic characteristics, dietary intake patterns, medical history, biochemical measurements, and health-related questionnaires [14]. The study received approval from the Ethics Review Board of the National Center for Health Statistics (NCHS), and every participant signed a document form following the Declaration of Helsinki indicating their informed consent. This study was granted an ethical exemption by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (approval No. ES-MS-2024-008, exemption letter provided as supplementary file).
Our analysis incorporated six consecutive NHANES cycles from 2007 to 2018, which were selected to ensure both consistent measurement of OBS components (particularly physical activity metrics) and complete mortality follow-up data. The initial cohort comprised 59,842 participants, from which we excluded individuals aged <20 years (n = 25,072), those with incomplete OBS data (n = 10,450), missing asthma status (n = 22), unavailable mortality follow-up (n = 39), incomplete covariate information (n = 13,768), and invalid sampling weights (n = 453). The final analytical sample included 10,038 eligible participants. Of these, participants with available spirometry data from 2007 to 2012 were included in the pulmonary function analysis. The participant selection flow is shown in Figure 1.
Figure 1.
Flow chart of participant selection. NHANES, National Health and Nutrition Examination Survey; OBS, Oxidative balance score.
2.2. Definition of OBS
The OBS calculation followed established protocols [15], incorporating 16 dietary components, including macronutrients (total fats), micronutrients (folate, β-carotene, niacin), vitamins (B2, B6, B12, C, and E), and minerals (calcium, magnesium, zinc, copper, selenium, iron)—along with four lifestyle factors: smoking status, alcohol consumption, physical activity, and body mass index (BMI). The scoring system classified five components as pro-oxidants (total fat, iron, BMI, alcohol, cotinine) and fifteen as antioxidants.
Weekly physical exercise was quantified using metabolic equivalent tasks (METs) [16]. Alcohol consumption was categorized: participants consuming >2 drinks/day (men) or >1 drink/day (women) received 0 points; those below these thresholds scored 1 point; and individuals with <12 drinks/year were assigned 2 points [17]. Pro-oxidant factors (excluding alcohol) were stratified into tertiles and scored 0–2, while antioxidant components were inversely scored (2–0) across tertiles [18].
The comprehensive OBS was derived by aggregating scores from both dietary and lifestyle components, enabling the separate calculation of two sub-scores: the Dietary Oxidative Balance Score (DOBS) and Lifestyle Oxidative Balance Score (LOBS). The OBS values were stratified into three groups based on weighted distributions, facilitating subsequent comparative analyses.
2.3. Asthma assessment
Asthma prevalence was assessed based on participant-reported history of physician-diagnosed asthma. Respondents acknowledging a previous clinical diagnosis were categorized as asthmatic, whereas those reporting no such medical history were classified as the non-asthmatic controls.
2.4. Assessment of pulmonary function
Pulmonary function data were obtained from NHANES 2007–2012. Eligible participants met the rigorous inclusion criteria specified in the NHANES protocol. Spirometry testing was conducted using Ohio 822/827 dry-rolling seal volume spirometers, with procedures adhering to the joint technical standards published by the American Thoracic Society and European Respiratory Society [19]. Five lung-function indices were extracted for analysis: FEV1, FVC, the FEV1/FVC ratio, peak expiratory flow (PEF), and the forced expiratory flow at 25%–75% of FVC (FEF25-75%).
2.5. Mortality assessment
Mortality outcomes were categorized as all-cause mortality (any death) and cardiovascular disease (CVD)-related mortality, with the latter identified through ICD-10 codes I00-I09, I11, I13, and I20-I51 [20]. Cause of death classification was performed according to the standardized ICD-10 coding framework [20].
2.6. Involvement of covariates
This study identified potential covariates and confounding variables, including demographic characteristics, laboratory findings, and pre-existing health conditions. Demographic factors included gender (male or female), age (categorized as 20–39, 40–59, and ≥60 years), race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Others), education level (less than high school, high school or equivalent, and beyond high school), marital status (Married/Cohabiting, Widowed/Divorced/Separated, and Unmarried), and household poverty-to-income ratio (PIR) classified as low income (PIR <1.3), moderate income (PIR 1.3–3.5), and high income (PIR >3.5). Laboratory findings primarily included low-density lipoprotein (LDL) and triglycerides (TG), which have been linked to diet-associated oxidative stress in asthma. Diabetes and hypertension were defined by specific diagnostic criteria, including laboratory test thresholds, medication use, and self-reported diagnoses [18,19].
2.7. Statistical analysis
To ensure the representativeness of our findings, sample weights were applied, as NHANES employs a complex multi-stage probability sampling design. Continuous variables with normal distributions were analyzed using ANOVA, with results expressed as mean ± standard error. For non-normally distributed continuous variables, the Kruskal–Wallis rank sum test was used, with outcomes reported as median and interquartile range (IQR). Categorical variables were assessed using the chi-square test and presented as weighted percentages with sample counts.
Multicollinearity was evaluated using the variance inflation factor (VIF), with all VIF values below 10, indicating no significant multicollinearity. Clinical significance was assessed using weighted logistic and COX regression models for OBS, DOBS, and LOBS: Model 1 was unadjusted; Model 2 adjusted for age, race, and gender; and Model 3 further adjusted for marital status, education level, poverty-to-income ratio, hypertension, diabetes, low-density lipoprotein, and triglycerides. Nonlinear relationships between OBS, DOBS, and LOBS with asthma incidence and mortality (total and CVD-related) were examined using restricted cubic splines (RCS). Threshold effects were analyzed using two-stage linear regression if nonlinearity was detected. The Spearman correlation analysis was performed to assess the relationships between OBS and lung function parameters. Stratified analyses and interaction tests were conducted to explore potential heterogeneity and interactions across subgroups. Kaplan-Meier analysis was used to evaluate the impact of OBS on overall and cardiovascular disease mortality in asthma patients.
To ensure robustness, sensitivity analyses were performed: (1) 1:1 propensity score matching (PSM) using the ‘MatchIt’ package to balance confounders between asthma and non-asthma groups; (2) application of a two-stage missing data framework employing the Multivariate Imputation by Chained Equations (MICE) algorithm for variables with ≤30% missingness, with core analyses replicated across five imputed datasets [21]; and (3) additional adjustments for medication use (e.g. oral glucocorticoids) and medical history, including cardiovascular diseases (stroke, coronary heart disease, congestive heart failure, angina pectoris, heart attack) and respiratory diseases (emphysema, chronic obstructive pulmonary disease, chronic bronchitis). Statistical analyses were conducted in R software (version 4.4.0, https://www.r-project.org/), with significance defined as p-value < 0.05.
3. Results
3.1. Basic attributes
The demographic characteristics of the 10,038 participants are shown in Table 1. Among them, 48.3% were male, 38.4% were ages of 20–39 years, and 69.0% identified as non-Hispanic white. The majority (63.7%) had completed high school or higher education, were married or cohabiting (63.8%), and had a PIR greater than 3.5 (43.4%). Furthermore, 34.9%reported a prior diagnosis of hypertension. Among the total participants, 1471 individuals were identified as having asthma. Compared to non-asthma participants, those with asthma were more likely to be younger (20–39 years), female, non-Hispanic white, married or cohabiting, have a PIR > 3.5, and have a history of hypertension (all p-values < 0.05). Notably, non-asthma participants had significantly higher OBS (20.00 vs. 19.00, p < 0.001), DOBS (16.00 vs. 15.00, p = 0.003), and LOBS (4.00 vs. 3.00, p < 0.001) scores than those with asthma.
Table 1.
Basic characteristics of participants (n = 10038) in the NHANES 2007–2018.
| Characteristic | Overall | Participants without asthma | Participants with asthma | p-value |
|---|---|---|---|---|
| N | 10038(100.0%) | 8567(85.3%) | 1471(14.7%) | |
| Age | 0.002 | |||
| 20–39 | 3475(38.4%) | 2891(37.3%) | 584(44.3%) | |
| 40–59 | 3323(36.4%) | 2854(37.0%) | 469(32.9%) | |
| ≥60 | 3240(25.2%) | 2822(25.7%) | 418(22.8%) | |
| Gender | 0.001 | |||
| Male | 4817(48.3%) | 4194(49.3%) | 623(42.3%) | |
| Female | 5221(51.7%) | 4373(50.7%) | 848(57.8%) | |
| Race | <0.001 | |||
| Mexican American | 1478(7.9%) | 1350(8.5%) | 130(5.0%) | |
| Other Hispanic | 1020(5.3%) | 866(5.3%) | 154(5.4%) | |
| Non-Hispanic White | 4457(69.0%) | 3774(69.1%) | 683(68.5%) | |
| Non-Hispanic Black | 1960(10.7%) | 1604(10.2%) | 356(13.3%) | |
| Other Race | 1121(7.1%) | 973(7.0%) | 148(7.8%) | |
| Education | 0.900 | |||
| <High school diploma | 2114(13.8%) | 1834(13.9%) | 280(13.4%) | |
| High school diploma/equivalent | 2276(22.4%) | 1940(22.5%) | 336(22.1%) | |
| >High school diploma | 5648(63.7%) | 4793(63.6%) | 855(64.5%) | |
| Marriage | <0.001 | |||
| Married/cohabitation | 6020(63.8%) | 5245(64.7%) | 775(58.3%) | |
| Widow/divorce/separation | 2145(17.6%) | 1792(17.2%) | 353(19.7%) | |
| Unmarried | 1873(18.7%) | 1530(18.1%) | 343(22.1%) | |
| PIR | 0.002 | |||
| <1.3 | 2996(20.6%) | 2475(19.8%) | 521(25.3%) | |
| 1.3-3.5 | 3826(36.0%) | 3309(36.2%) | 517(34.8%) | |
| >3.5 | 3216(43.4%) | 2783(44.1%) | 433(39.8%) | |
| Hypertension | 0.001 | |||
| No | 6066(65.1%) | 5229(65.7%) | 837(61.7%) | |
| Yes | 3972(34.9%) | 3338(34.3%) | 634(38.3%) | |
| Diabetes | 0.300 | |||
| No | 8041(85.2%) | 6888(85.5%) | 1153(84.1%) | |
| Yes | 1997(14.8%) | 1679(14.6%) | 318(15.9%) | |
| TG | 98.00(68.00,144.00) | 98.00(68.00,143.00) | 99.00(68.00,147.00) | 0.500 |
| LDL | 111.00(89.00,135.00) | 111.00(89.00,135.00) | 111.00(88.00,136.45) | 0.900 |
| OBS | 20.00(14.00,25.00) | 20.00(14.00,26.00) | 19.00(12.00,25.00) | <0.001 |
| DOBS | 16.00(10.00,22.00) | 16.00(11.00,22.00) | 15.00(9.00,21.00) | 0.003 |
| LOBS | 4.00(3.00,5.00) | 4.00(3.00,5.00) | 3.00(2.00,4.00) | <0.001 |
Abbreviations: NHANES, National Health and Nutrition Examination Survey; PIR, Poverty Income Ratio; TG, triglyceride; LDL, low-density lipoprotein; OBS, oxidative balance score; DOBS, dietary oxidative balance score; LOBS, lifestyle oxidative balance score. Continuous variables that follow a normal distribution are represented by their means along with standard errors, while those that do not conform to a normal distribution are shown as medians with interquartile ranges. Categorical data is shown in numerical form (as percentages). The letter N indicates the sample size of the study, whereas the percentages represent the weighted results from the survey.
3.2. Associationbetween OBS and asthma prevalence and lung function
A weighted multivariate logistic regression model showed that higher OBS levels were consistently associated with reduced asthma risk (Table 2). In the fully adjusted model (Model 3), each unit increase in OBS correlated with a 2.0% decrease in asthma prevalence [OR = 0.98 (0.97, 0.99), p < 0.001], with Q3 showing a 26.0% reduction versus Q1 [OR = 0.74 (0.61, 0.89), p for trend = 0.007]. Similar trends were observed for DOBS and LOBS.
Table 2.
Assessing links between OBS, DOBS, LOBS, and asthma via weighted logistic regression.
| Participants | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| OBS | ||||||
| Continuous | 0.98(0.97,0.99) | <0.001 | 0.98(0.97, 0.99) | <0.001 | 0.98(0.97, 0.99) | 0.003 |
| Tertiles | ||||||
| Q1 (<16) | Ref | Ref | Ref | |||
| Q2 (16–24) | 0.80(0.67, 0.94) | 0.008 | 0.81(0.69, 0.96) | 0.015 | 0.84(0.71, 1.00) | 0.050 |
| Q3 (>24) | 0.69(0.57, 0.83) | <0.001 | 0.70(0.58, 0.85) | <0.001 | 0.74(0.61, 0.89) | 0.002 |
| p for trend | <0.001 | <0.001 | 0.007 | |||
| Dietary OBS | ||||||
| Continuous | 0.98(0.97, 0.99) | 0.003 | 0.98(0.97, 0.99) | 0.005 | 0.99(0.97, 0.99) | 0.016 |
| Tertiles | ||||||
| Q1 (<13) | Ref | Ref | Ref | |||
| Q2 (13–20) | 0.87(0.74, 1.02) | 0.083 | 0.89(0.75, 1.04) | 0.140 | 0.91(0.78, 1.08) | 0.300 |
| Q3 (>20) | 0.77(0.64, 0.92) | 0.004 | 0.78(0.66, 0.93) | 0.006 | 0.81(0.68, 0.97) | 0.019 |
| p for trend | 0.012 | 0.018 | 0.056 | |||
| Lifestyle OBS | ||||||
| Continuous | 0.89(0.85, 0.93) | <0.001 | 0.89(0.85, 0.94) | <0.001 | 0.91(0.87, 0.96) | <0.001 |
| Tertiles | ||||||
| Q1 (<3) | Ref | Ref | Ref | |||
| Q2 (3–4) | 0.90(0.75, 1.07) | 0.200 | 0.90(0.76, 1.06) | 0.200 | 0.93(0.78, 1.11) | 0.400 |
| Q3 (>4) | 0.71(0.59, 0.85) | <0.001 | 0.72(0.60, 0.86) | <0.001 | 0.78(0.65, 0.93) | 0.008 |
| p for trend | <0.001 | 0.001 | 0.021 | |||
Model 1 lacked any adjustments. Model 2 incorporated age, race, and gender into the analysis. Model 3 included adjustments for all variables. Ref denotes the reference group. OBS, oxidative balance score; DOBS, dietary oxidative balance score; LOBS, lifestyle oxidative balance score; OR, odds ratio; CI, confidence interval.
RCS analysis revealed a nonlinear dose-response relationship between OBS and asthma (P-non-linear = 0.028), with an inflection point at OBS = 18.95 (Figure 2A). Below this threshold, each unit increase in OBS was linked to a 3.0% reduction in asthma risk, while no significant association was observed above 18.95 (p-value = 0.002, Table 3). DOBS and LOBS showed consistent negative associations but no significant nonlinear relationships (Figure 2B, C).
Figure 2.
(A) OBS non-linear relationship with asthma prevalence. (B, C) DOBS/LOBS linear relationship with asthma morbidity rates. OBS, Oxidative balance score; DOBS, Dietary oxidative balance score; LOBS, Lifestyle oxidative balance score; OR, odds ratio; CI, confidence interval.
Table 3.
Threshold effect analysis of OBS in asthma risk: a segmented regression approach.
| OR (95%CI) | p-value | |
|---|---|---|
| Prevalence of asthma | ||
| Univariable linear regression | 0.97 (0.95, 0.99) | 0.002 |
| Segmented regression | ||
| Knot point | 18.95 | |
| Below threshold | 0.97 (0.95, 0.99) | 0.002 |
| Above threshold | 1.00 (0.98, 1.02) | 0.800 |
| Likelihood ratio test | 0.033 | |
Abbreviations: OBS, oxidative balance score; OR, odds ratio; CI, confidence interval.
Spearman correlation analysis indicated significant positive associations between OBS and lung function parameters, including FEF25-75%, FEV1, FVC, and PEF (all p < 0.001) (Supplementary Figure 1A–D).
3.3. Association of OBS with mortality in asthma individuals
Kaplan-Meier analysis revealed that asthma patients in the highest OBS tertile (Q3) had the lowest all-cause and cardiovascular mortality rates, while those in the lowest tertile (Q1) had the highest (Log-rank p < 0.001) (Supplementary Figure 2). Similar trends were observed for DOBS and LOBS (Supplementary Figure 3 and 4).
Cox models (Table 4) showed that in the fully adjusted model, each unit increase in OBS was associated with an 8.0% reduction in all-cause mortality [HR = 0.92 (0.89, 0.96), p < 0.001] and a 14.0% reduction in cardiovascular mortality [HR = 0.86 (0.80, 0.93), p < 0.001]. Similar protective associations were noted for DOBS and LOBS (Supplementary Table 1).
Table 4.
The relationship between OBS and all-cause mortality and CVD mortality in individuals with asthma.
| Participants | Model 1 | Model 2 | Model 3 | |||
| HR (95% CI) | p-value | HR (95% CI) | p-value | HR (95% CI) | p-value | |
| OBS All-cause mortality | ||||||
| Continuous | 0.92(0.89, 0.95) | <0.001 | 0.91(0.88, 0.94) | <0.001 | 0.92(0.89, 0.96) | <0.001 |
| Tertiles | ||||||
| Q1 (<15) | Ref | Ref | Ref | |||
| Q2 (15–22) | 0.40(0.22, 0.75) | 0.004 | 0.37(0.20, 0.69) | 0.002 | 0.44(0.23, 0.84) | 0.013 |
| Q3 (>22) | 0.22(0.11, 0.42) | <0.001 | 0.19(0.10, 0.37) | <0.001 | 0.24(0.12, 0.49) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
| CVD mortality | ||||||
| Continuous | 0.90(0.85, 0.94) | <0.001 | 0.88(0.83, 0.93) | <0.001 | 0.86(0.80, 0.93) | <0.001 |
| Tertiles | ||||||
| Q1 (<15) | Ref | Ref | Ref | |||
| Q2 (15–22) | 0.47(0.16, 1.34) | 0.200 | 0.43(0.15,1.25) | 0.120 | 0.43(0.13, 1.46) | 0.200 |
| Q3 (>22) | 0.06(0.01, 0.30) | <0.001 | 0.05(0.01, 0.25) | <0.001 | 0.05(0.01, 0.22) | <0.001 |
| p for trend | 0.002 | 0.002 | 0.002 | |||
Model 1 lacked any adjustments. Model 2 incorporated age, race, and gender into the analysis. Model 3 included adjustments for all variables. Ref denotes the reference group. OBS, oxidative balance score; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.
RCS analysis demonstrated linear relationships between OBS, DOBS, LOBS, and asthma-related mortality (Figure 3), underscoring the protective role of higher OBS levels in reducing mortality risks among asthma patients.
Figure 3.
Associations between OBS/DOBS/LOBS with all-cause mortality and CVD mortality by RCS after adjustment for all covariates. (A) OBS linear relationship with asthma all-cause mortality rates. (B) DOBS linear relationship with asthma all-cause mortality rates. (C) LOBS linear relationship with asthma all-cause mortality rates. (D) OBS linear relationship with asthma CVD mortality rates. (E) DOBS linear relationship with asthma CVD mortality rates. (F) LOBS linear relationship with asthma CVD mortality rates. OBS, oxidative balance score; DOBS, dietary oxidative balance score; LOBS, lifestyle oxidative balance score. CVD, Cardiovascular disease; HR, hazard ratio; CI, confidence interval.
3.4. Stratified analysis
Stratified analyses revealed consistent inverse associations of OBS, DOBS, and LOBS with asthma risk across key demographic subgroups (Figure 4). All three scores showed significant negative associations in individuals aged 20–39, those with lower education levels, a PIR < 1.3, and without diabetes (all p < 0.05). Additionally, OBS and LOBS demonstrated similar inverse associations in females, while DOBS and LOBS shared significant results in Other Hispanic populations. No significant interactions were observed.
Figure 4.
Forest plot of the relationship between OBS/DOBS/LOBS and the prevalence of asthma. OBS, oxidative balance score; DOBS, dietary oxidative balance score; LOBS, lifestyle oxidative balance score; CVD, cardiovascular disease; PIR, family income to poverty ratio; OR, odds ratio; CI, confidence interval.
3.5. Sensitivity analysis
We validated our findings through sensitivity analyses. Propensity score matching (PSM) generated balanced asthma-non-asthma pairs, showing consistent associations between OBS and asthma prevalence, all-cause mortality, and cardiovascular mortality (Supplementary Tables 2, 3 and Supplementary Figure 5). Second, multiple imputation using the MICE algorithm for covariates with ≤30.0% missingness confirmed the robustness of our findings (Supplementary Table 4). Additionally, excluding individuals with comorbidities supported the study’s conclusions (Supplementary Table 5).
4. Discussion
In this comprehensive nationwide study, we systematically examined the associations between OBS, DOBS, and LOBS with asthma clinical outcomes. Our findings revealed that the values of OBS, DOBS, and LOBS were significantly lower in the asthma group compared to the non-asthma group. These scores were negatively linked to asthma prevalence and positively linked to lung function parameters. Additionally, higher OBS and DOBS were linked to significantly reduced mortality rates. Therefore, applying OBS to evaluate diet and lifestyle-related oxidative stress in asthma may provide valuable insights. It suggests potential intervention areas that warrant further investigation in real-life studies to elucidate their impact on asthma management and outcomes.
Oxidative stress is hypothesized to contribute to asthma pathogenesis through endogenous sources (e.g. mitochondrial respiration and inflammatory cells) and exogenous factors (e.g. environmental ozone) [22]. In asthmatic patients, oxidative stress levels may be heightened due to inhaled oxidants and reactive species produced by airway and inflammatory cells [21,22], potentially overwhelming the body’s antioxidant defenses. Antioxidants, including enzymatic systems (e.g. catalases and superoxide dismutase) and dietary compounds (e.g. vitamins C and E), can counteract free radicals and reduce cellular damage. However, the precise relationship between oxidative stress and asthma remains unclear, as antioxidant imbalances might skew immune responses toward a TH2-dominant profile [22]. While the potential role of antioxidants in asthma is suggested, the relationship between dietary and lifestyle factors and asthma outcomes is still debated, with evidence supporting both protective and neutral effects.
Several real-world studies provide valuable insights into the influence of nutritional and lifestyle factors on asthma. For example, Kazaks et al. found that oral magnesium supplementation improved airway resistance and subjective asthma-control scores [23]. In an open-label pilot study, Guo et al. reported that combined selenium, zinc, β-carotene, and vitamins C and E attenuated inflammation, enhanced immune response, and improved lung function and quality of life in asthma patients [24]. Similarly, Halnes et al. showed that a single soluble-fibre supplement reduced airway inflammation and improved lung function relative to controls [25]. Epidemiological studies have also provided important insights. Patel et al. linked low manganese intake to symptomatic asthma [26]. In contrast, Berthon et al. reported that individuals with severe persistent asthma consumed more fat and less fibre, associations paralleled by lower lung function and elevated airway inflammation [27]. Conversely, Picado et al. found no significant relationship between micronutrient or antioxidant intake and asthma outcomes [28]. The impact of lifestyle on asthma prognosis has also been examined in epidemiological investigations. De Lima et al. demonstrated, in a multi-centre cross-sectional study, that greater physical activity was associated with better asthma control in adults with moderate-to-severe disease [29]. Regarding mortality, Zhang et al. observed that a higher Composite Dietary Antioxidant Index (CDAI) was associated with lower all-cause mortality in asthma patients [30]. Similarly, Yang et al. based on a study of 7884 NHANES III participants, found a negative correlation between serum carotenoids and respiratory morbidity and mortality [31]. Additionally, a meta-analysis of 41 prospective observational studies concluded that adherence to an antioxidant-rich diet may reduce all-cause mortality risk [32].
The OBS is a comprehensive method that evaluates both antioxidant and pro-oxidant dietary and lifestyle factors, reflecting biomarkers of inflammation and oxidative stress [33]. Unlike isolated analyses, OBS provides a holistic assessment by considering combined effects. This large-scale longitudinal study supports the potential of OBS as a marker for guiding dietary and lifestyle interventions in asthma, though further real-world studies are needed to confirm its translational impact.
Recent studies have highlighted a significant inverse association between the OBS and the risk of all-cause mortality, including CVD [34]. For instance, Kong et al. found higher OBS levels linked to reduced mortality from all causes, cancer, and non-cancer-related deaths [35], while Mao et al. highlighted the protective role of antioxidant-rich lifestyles in older women [36]. Although asthma-related mortality is low due to advanced treatments, understanding factors influencing mortality in asthma patients remains crucial. Asthma, as a chronic inflammatory condition, can lead to long-term complications like CVD, indirectly affecting overall mortality. Our study found that higher OBS levels were associated with reduced all-cause and CVD-related mortality in asthma patients, emphasizing the potential benefits of dietary and lifestyle modifications, especially for those with severe or poorly controlled asthma. These findings underscore the importance of maintaining a balanced oxidative state to improve health outcomes in asthma patients.
The study has several limitations that should be acknowledged. First, the reliance on self-reported asthma data may introduce recall bias, requiring cautious interpretation of the results. Second, the absence of a longitudinal design limits the ability to establish causal relationships between OBS and asthma prevalence. Third, the limited number of respiratory-related deaths reduces the reliability of risk estimates for respiratory-specific mortality. Additionally, the findings, based on the U.S. population, may not be generalizable to other populations. Furthermore, our analysis was based on a subsample of 10,038 participants from the NHANES dataset. Although NHANES survey weights were applied to maintain national representativeness, the restriction to participants with complete data may still introduce some selection bias. Future prospective studies should explore the direct impact of improving OBS on asthma exacerbations, symptom control, and disease progression, potentially establishing OBS as a standardized metric in clinical practice.
5. Conclusion
This study suggests potential benefits of an antioxidant-rich diet and lifestyle in reducing asthma prevalence, improving lung function in asthma patients, and lowering all-cause mortality among adults with asthma. These findings provide insights that could inform future prevention strategies and personalized interventions. However, further observational and controlled studies are needed to validate these results and more thoroughly explore their clinical implications.
Supplementary Material
Glossary
Abbreviations
- OBS
Oxidative Balance Score
- NHANES
National Health and Nutrition Examination
- DOBS
Dietary Oxidative Balance Score
- LOBS
Lifestyle Oxidative Balance Score
- RCS
restricted cubic spline
- ROS
reactive oxygen species
- NCHS
National Center for Health Statistics
- BMI
body mass index
- MET
metabolic equivalent task
- CVD
cardiovascular disease
- PIR
poverty-to-income ratio
- LDL
low-density lipoprotein
- TG
triglycerides
- VIF
variance inflation factor
- PEF
peak expiratory flow
- FEF25-75%
forced expiratory flow at 25%–75% of FVC
- PSM
propensity score matching
Funding Statement
This study was supported by the National Natural Science Foundation of China (82161138020), the Major Project of Guangzhou National Laboratory (GZNL2024A02002), and the Guangdong Innovation Team Project of General College and University (2023KCXTD024).
Ethical approval
The NHANES program received approval from the Ethics Review Board of the NCHS, and every participant signed a document form following the Declaration of Helsinki indicating their informed consent. This study was granted an ethical exemption by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (approval No. ES-MS-2024-008, exemption letter provided as supplementary file).
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.