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Oxidative balance score is inversely associated with low muscle mass in young and middle-aged adults: a cross-sectional NHANES study

BMC Musculoskeletal Disorders volume 26, Article number: 398 (2025) Cite this article

Abstract

Background

Low muscle mass is a critical indicator of frailty and adverse health outcomes. However, the potential link between systemic oxidative stress and low muscle mass remains underexplored. This study aims to investigate the association between the Oxidative Balance Score (OBS) and low muscle mass in U.S. adults.

Methods

In this cross-sectional study, data from 4096 adults aged 20 to 59 years from National Health and Nutritional Examination Survey (NHANES) 2011 to 2018 were analyzed. Low muscle mass, the primary outcome, was evaluated utilizing the Foundation for the National Institutes of Health (FNIH) definition. Analysis involved the application of restricted cubic splines and weighted multivariate regression techniques.

Results

A nonlinear association was observed between OBS and low muscle mass (p for nonlinearity < 0.0049). Compared to the lowest OBS quartile, individuals in the highest quartile had an adjusted OR of 0.26 (95% CI: 0.14–0.48) for low muscle mass (P for trend < 0.001). Additionally, the adjusted β value for ALM/BMI was 0.067 (95% CI: 0.053–0.082), P for trend < 0.001. Both dietary and lifestyle OBS also showed negative associations with low muscle mass, with fully adjusted ORs of 0.38 (95% CI: 0.19–0.76) and 0.17 (95% CI: 0.05–0.62), respectively (both P for trends < 0.01). Furthermore, in stratified analyses, this relationship was particularly prominent in the 40–59 years age group (P for interaction = 0.048).

Conclusion

Higher OBS, indicative of greater antioxidant exposure, was robustly associated with a lower risk of low muscle mass, particularly in 40–59 old adults. These findings underscore the potential role of oxidative balance in preserving muscle health and highlight the need for targeted interventions in this demographic. Further longitudinal studies are warranted to confirm these associations and evaluate potential clinical applications.

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Introduction

Sarcopenia, a medical condition characterized by the progressive reduction in muscle mass, is intricately associated with the aging process. As individuals advance in age and reduce their physical activity levels, an inevitable decline in both muscle mass and strength ensues. The precise delineation of sarcopenia remains a contentious subject, subject to variations across disparate standards [1,2,3,4]. Nevertheless, a predominant hallmark in the majority of these definitions revolves centered on skeletal muscle atrophy, often described as low muscle mass [4]. This characteristic is now recognized as a key factor shaping health outcomes and increasing mortality risk [5, 6]. Extensive research has revealed that low muscle mass is influenced by various factors, including genetics, early-life conditions such as birth weight and lactation history, physical activity levels, dietary habits, socioeconomic status, and underlying diseases [7, 8]. While sarcopenia and low muscle mass have been traditionally viewed as aging-related conditions, recent evidence suggests that non-aging-related factors, including lifestyle, environmental exposures, and chronic diseases, are contributing to their increasing prevalence among young and middle-aged adults [9]. These findings highlight the need to investigate modifiable factors influencing muscle health, not only in older adults but also among young and middle-aged populations.

Moreover, a wealth of evidence underscores the central role played by oxidative stress in the realm of skeletal muscle health [10]. Foundational studies have demonstrated that heightened levels of oxidative stress disrupt cellular homeostasis, giving rise to processes such as protein degradation, compromised protein synthesis, perturbed cellular signaling, and mitochondrial dysfunction, collectively culminating in muscle atrophy [11]. Oxidative stress can be conceptualized as an imbalance between the production of reactive oxygen species and the body’s capacity to neutralize them through its antioxidant defense systems [12]. The extent of oxidative stress within the body is susceptible to modulation by a plethora of factors, including dietary constituents, physical activity, obesity, smoking habits, and other behavioral factors [12]. It is imperative to recognize that a singular factor proves insufficient in comprehensively assessing the entirety of the oxidation/antioxidant system. Consequently, in order to capture the multifaceted effects of diverse dietary regimes and lifestyles on the overarching oxidative/antioxidant equilibrium, the Oxidative Balance Score (OBS) was conceived [13]. This metric serves to quantify an individual’s prooxidant and overall antioxidant exposure, with a higher OBS denoting a preponderance of antioxidants over prooxidants.

Previous investigations have demonstrated that higher OBS is inversely associated with a variety of health conditions in adults, including metabolic syndrome, respiratory disorders, cardiovascular diseases, neurodegenerative conditions, and type 2 diabetes [14,15,16,17,18]. Despite its established importance in health research, no studies to date have assessed the association between OBS and low muscle mass. This gap in the literature represents a critical opportunity to investigate whether oxidative balance plays a protective role against skeletal muscle atrophy, a condition influenced by oxidative stress. So, this study aims to evaluate the relationship between OBS and low muscle mass in a representative adult population in the United States, using data from the National Health and Nutrition Examination Survey (NHANES). By exploring this novel association, this study seeks to provide valuable insights into the role of oxidative balance in muscle health and identify potential intervention strategies. Additionally, the findings may serve as a basis for future research to further elucidate the interplay between oxidative stress, diet, and skeletal muscle health across different populations.

Methods

Design

In this cross-sectional investigation, publicly accessible data from NHANES were applied to examine the relationship between OBS and low muscle mass. Individuals aged 20–59 years who provided responses to pertinent demographic, socioeconomic, dietary, and health-related inquiries during the survey cycles spanning from 2011 to 2018 were considered for inclusion. Notably, adults aged ≥ 60 years were excluded due to ineligibility for Dual-energy X-ray Absorptiometry (DXA) examination. Following the application of specific inclusion and exclusion criteria, the final cohort comprised 4,096 participants, as delineated in Fig. 1, providing a comprehensive dataset for analysis.

Fig. 1
figure 1

Participants flow chart

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Oxidative balance score

The OBS is a comprehensive metric derived from the assessment of 16 nutrients and 4 lifestyle factors, were categorized into 5 pro-oxidants and 15 antioxidants, with the scoring system based on established insights into the complex interplay between oxidative stress and dietary and lifestyle elements [19, 20]. The OBS is derived from the analysis of dietary intake, incorporating 16 key nutrients such as vitamins (e.g., C, E, B6, and B12), minerals (e.g., calcium, magnesium, zinc, copper, selenium), dietary fiber, carotene, riboflavin, niacin, folate, total fat, and iron. This information is obtained through two 24-hour dietary recalls and is determined by averaging data from two separate 24-hour dietary recalls.

Simultaneously, the OBS calculation incorporates four key lifestyle factors: physical activity, BMI, alcohol consumption, and smoking. These lifestyle factors are meticulously assessed to gauge their impact on oxidative balance. Among these factors, total fat, iron, BMI, alcohol consumption, and smoking are classified as pro-oxidants, whereas the others are categorized as antioxidants. For instance, alcohol consumption is stratified into three distinct levels: heavy drinkers (≥ 15 g/d for women and ≥ 30 g/d for men), non-heavy drinkers (0 ~ 15 g/d for women and 0 ~ 30 g/d for men), and non-drinkers. These groups are assigned scores of 0, 1, and 2 points, respectively, as per the methodology outlined by Zhang et al. [21]. Following this initial categorization, the other OBS components are further stratified by gender and subsequently divided into three groups based on tertiles. In this classification, antioxidants receive scores of 0 ~ 2 in groups 1 ~ 3, while pro-oxidants are assigned scores of 2 ~ 0 in groups 1 ~ 3, as elaborated in Supplementary Table 1 [21]. It is important to note that a higher OBS score indicates greater exposure to antioxidants.

Low muscle mass

For all surveys conducted at the Mobile Examination Center between 2011 and 2018, BMI was measured and recorded (expressed in kg/m2). In our study, all eligible participants had their Appendicular Lean Mass (ALM) assessed through DXA. ALM, a widely recognized indicator of skeletal muscle mass, was calculated by summing the lean mass (excluding bone mineral content) of both legs and arms. It’s worth noting that various definitions for low muscle mass, which is a critical determinant in the diagnosis of sarcopenia, exist and have stirred ongoing debates within the scientific community. For the purposes of our investigation, we opted for the cutout points provided by the Foundation for the National Institutes of Health (FNIH) [4] - defining low muscle mass as ALM (kg)/ BMI (kg/m2) being less than 0.789 for males and less than 0.512 for females. This choice was motivated by the alignment of this definition with the data source utilized in our research.

Covariates

The choice of potential confounding variables was based on established findings in prior literature [22]. These variables were subsequently incorporated into our multivariate models to ensure a comprehensive assessment. The covariates included socio-demographic factors such as sex, age, and race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, and others), as well as socio-economic status (poverty income ratio, marital status, and home status). Chronic diseases, including hypertension, diabetes mellitus, cardiovascular disease, and cancer, were also considered. Additionally, we included the Urinary Albumin to Creatinine Ratio (UACR), estimated glomerular filtration rate (eGFR), total energy intake (kcal), and protein intake (g) to ensure a comprehensive analysis [22].

Statistical analysis

The NHANES survey uses a non-random, stratified sampling design to capture specific subgroups within the population, with sample weights assigned to address non-response and design complexities. Our analytical approach adheres to NHANES guidelines and involves consolidating data from four distinct survey cycles spanning the years 2011 to 2018 into a unified 8-year dataset.

We employed the Taylor Series Linearization method to estimate standard errors for continuous variables. Subsequently, associations among categorical variables were examined by Student’s t-test. For categorical variables, we employed weighted percentages, calculated means with 95% confidence intervals (CI), and utilized survey-weighted chi-squared tests.

Restricted cubic splines with three knots (at the 25th, 50th, and 75th percentiles) were used to model the association between OBS and low muscle mass. Multiple regression models computed adjusted odds ratios (OR) for low muscle mass across OBS quartiles, and estimated adjusted differences in ALM/BMI.

To investigate potential variations within selected subgroups, we conducted subgroup and interactive analyses. All data analyses were executed using R software (version 4.3.1) in conjunction with the “survey” package.

Results

Characteristics of study participants

The characteristics of participants, categorized into OBS quartiles, are presented in Table 1. Subjects had a mean age of 39.44 ± 0.50 years, with 48.12% being female. The majority of participants were non-Hispanic white (64.90%). The weighted prevalence of low muscle mass across all US adults stood at 6.06%. Individuals in the highest OBS quartile had a higher level of education, greater wealth (as indicated by PIR and home status), increased protein intake, higher total energy intake, and a predominant non-Hispanic White ethnicity compared to those in the lowest quartile. Notably, the prevalence of both low muscle mass and hypertension displayed a gradual decrease as OBS values increased.

Table 1 The baseline characteristics by quartiles of the OBS: NHANES 2011–2018
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Associations between OBS and low muscle mass

The results of the weighted multiple regression analysis, shown in Table 2, illustrate the associations between OBS, low muscle mass, and ALM/BMI. In model 3, individuals in the highest OBS quartile showed a more pronounced negative relationship with low muscle mass and ALM/BMI than those in the lowest OBS quartile. The observed relationship maintained relative stability across models, with OR = 0.26 (95% CI: 0.14–0.48, p for trend < 0.001) and β = 0.067 (95% CI: 0.053–0.082, p for trend < 0.001), respectively.

Table 2 Weighted multivariable associations between OBS and low muscle mass defined by FNIH
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Associations between dietary obs/lifestyle OBS and low muscle mass

The association between dietary OBS, lifestyle OBS, and low muscle mass through multiple regression analysis, as presented in Table 3. After adjusting for all variables, both dietary and lifestyle components of the OBS were significantly and negatively associated with low muscle mass. For dietary OBS, the association remained statistically significant (OR = 0.38, 95% CI: 0.19–0.76, p for trend < 0.012), and a similar pattern was observed for ALM/BMI (β = 0.037, 95% CI: 0.021–0.053, p for trend < 0.001). Likewise, higher lifestyle OBS was strongly related to a reduced risk of low muscle mass (OR = 0.17, 95% CI: 0.05–0.62, p for trend < 0.01), with a corresponding negative association observed for ALM/BMI (β = 0.085, 95% CI: 0.068–0.102, p for trend < 0.001).

Table 3 Weighted multivariable associations between dietary/ lifestyle OBS and low muscle mass
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Subgroup and interaction analysis

Table 4 displays the results of our analysis on the association between total OBS and low muscle mass, assessed through multiple logistic regression across different subgroups. In the fully adjusted model, we identified consistent and statistically significant negative associations between total OBS and low muscle mass across various subgroups, with the exception of the age group. Notably, in the fully adjusted model, we observed that OR exhibited a decreasing trend in the 40–59 age group compared to the 20–39 age group, evident across different quartiles of OBS. Furthermore, our analysis delved into the interaction between age group and OBS, revealing a significant interaction effect (P for interaction = 0.048). This indicates that age group significantly influences the relationship between total OBS and low muscle mass.

Table 4 The association between OBS and low muscle mass, stratified by selected subgroups
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Dose-response relationships between OBS and low muscle mass

The dose-response relationships between OBS and the occurrence of low muscle mass as well as ALM/BMI were shown in Fig. 2. Our analysis revealed a negative and non-linear association between OBS and low muscle mass (P for non-linear = 0.0049, P for total < 0.001), while a negative and linear relationship was found between OBS and ALM/BMI (P for non-linear = 0.0613, P for total < 0.001).

Fig. 2
figure 2

Dose–response relationship between OBS and low muscle mass (A) and ALM/BMI (B) in US adults 20-59years (n = 4096), NHANES 2011 to 2018. Red solid lines and red dotted line represent restricted cubic spline models and 95%CI, respectively. Multivariable logistic regression model is used to estimate the fully adjusted OR in low muscle mass (FNIH definition) and corresponding 95% CI and the fully adjusted β value in ALM/BMI and corresponding 95CI. Models were adjusted by age, ethnicity, PIR, marital status, home status, education, eGFR, UACR, hypertension, DM, CVD, cancer, energy (Kcal), and protein(g). ALM/BMI was additionally adjusted for sex

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Discussion

In this cross-sectional analysis, a negative association between total OBS, dietary OBS, lifestyle OBS, and the prevalence of low muscle mass was revealed, with these findings being particularly pronounced among individuals aged 40–59. Consequently, both antioxidant-rich diets and healthy lifestyles may play pivotal roles in skeletal muscle health. In essence, higher OBS scores were associated with a reduced risk of low muscle mass.

Numerous prior studies have explored the relationship between oxidative stress and muscle atrophy [23,24,25]. Under conditions of muscle atrophy, an excessive generation of reactive oxygen species within skeletal muscle is observed, accompanied by compromised antioxidant defense mechanisms. This oxidative stress inflicts damage on cellular components, primarily affecting proteins. Proteins are particularly susceptible to oxidation, resulting in alterations in their structure and functionality. These modifications involve oxidative changes to amino acid residues, protein cross-linking, and carbonylation. Oxidative damage impairs enzymatic activity, disrupts cell signaling, and ultimately leads to protein degradation [12, 26].

Previous research has already established the antioxidant properties of individual components concerning skeletal muscle health. For example, an increased intake of dietary fiber has been demonstrated to enhance muscle mass [27]. Naturally occurring antioxidant flavonoid apigenin, through its role in reducing oxidative stress, inhibiting excessive autophagy, and attenuating apoptosis, mitigates age-related skeletal muscle atrophy [24]. Higher intake levels of energy, carbohydrates, proteins, fiber, zinc, carotenoids, and vitamin B6 are associated with a lower incidence of muscle atrophy [28]. Consequently, obtaining sufficient nutrition from diverse protein sources and vegetables contributes to the prevention of muscle atrophy in the elderly. Additionally, some trace elements such as selenium and magnesium [29], as well as dietary zinc intake [30], have been shown to have potential associations with muscle mass and strength. Moreover, specific dietary patterns have been correlated with muscle mass. For instance, among elderly individuals residing in Japanese communities, a dietary pattern characterized by a high consumption of fish, legumes, potatoes, most vegetables, mushrooms, seaweed, and fruits, coupled with low rice intake, exhibited a negative correlation with muscle wasting. These dietary patterns are often closely linked to oxidative stress [31].

Numerous studies have elucidated the influence of lifestyle factors on skeletal muscle health. Beyond physical activity, it is noteworthy that in males, smoking demonstrates a positive association with sarcopenic non-obesity, with heavy smokers exhibiting a heightened likelihood of presenting with sarcopenic obesity [32]. Individuals with chronic obstructive pulmonary disease display significantly reduced plasma levels of glycine-l-histidine-l-lysine, which are closely related to muscle mass. Exogenous administration of glycine-l-histidine-l-lysine-Cu2 + can prevent smoking-induced skeletal muscle dysfunction through sirtuin 1 [33]. Excessive alcohol consumption, when combined with liver fibrosis, may result in a more substantial reduction in muscle mass than observed in individual cases [34]. However, there are also inconsistent findings, with no association between alcohol consumption and muscle measurements observed in longitudinal results [35].

Despite these findings, the utility of the OBS lies in its ability to aggregate the effects of both dietary and lifestyle factors into a single measure, providing a more comprehensive reflection of the overall oxidative/antioxidant balance. While prior research has examined individual components in isolation, the OBS enables a holistic assessment of oxidative stress, which cannot be fully captured by studying components independently. The integration of multiple factors into the OBS allows for the evaluation of cumulative and synergistic effects, offering unique insights into the complex interplay between oxidative stress and skeletal muscle health. In addition, prior research has noted that muscle mass begins to decline gradually from around age 30, with an annual decrease of 1–2% after age 50, accelerating to approximately 8% per year after age 60 [36]. Consequently, focusing on the 20–59 age range offers a crucial opportunity to address oxidative stress before significant muscle atrophy occurs. By intervening at this life stage, prevention strategies can potentially mitigate muscle loss before it becomes severe.

Furthermore, our research demonstrated a significantly stronger negative correlation between OBS and low muscle mass among individuals aged 40–59, with a 92% reduced risk of low muscle mass in the highest OBS quartile. In contrast, no such effect was observed in individuals under 40 years. This age-specific difference suggests that middle-aged individuals may experience unique metabolic changes or accumulate oxidative stress over time, making them particularly susceptible to muscle loss [37, 38]. Addressing oxidative stress during this period may therefore serve as a critical intervention point for preserving muscle health.

This study represents a pioneering endeavor within its cohort, investigating the relationship between OBS and low muscle mass in young to middle-aged U.S. adults. However, we must acknowledge certain limitations. Firstly, the cross-sectional nature of the study precludes any inference of causality, as the temporal sequence between OBS and low muscle mass cannot be established. Future longitudinal studies are needed to confirm the causal pathways and directionality of this association. Additionally, the OBS-related components in this dataset may exhibit temporal variations, which could potentially influence the final outcomes but were not elucidated in this study. For example, changes in dietary and lifestyle habits over time or seasonal fluctuations in antioxidant intake might introduce variability in OBS, thereby impacting its relationship with muscle mass. Addressing these dynamics in future research could enhance the robustness of findings. Nevertheless, the observed robust correlation between low muscle mass and current OBS remains relatively stable, suggesting minimal impact from unaccounted factors.

Conclusion

In conclusion, our study highlights the potentially significant role of OBS in skeletal muscle health. We observed a significant negative correlation between OBS, encompassing dietary and lifestyle components, and the prevalence of low muscle mass, particularly among those aged 40–59. These results highlight the crucial role of a balanced oxidative status, integrating both dietary and lifestyle factors, in preserving muscle mass and preventing muscle atrophy. Given the impact of oxidative stress on muscle health, especially with aging, future research should focus on longitudinal studies to confirm causality and explore intervention strategies that target oxidative stress as a means to preserve muscle mass and function across the lifespan.

Data availability

The datasets ANALYZED for this study can be found in the [CDC.gov website] [https://www.cdc.gov/nchs/nhanes].

Abbreviations

OBS:

Oxidative Balance Score

DXA:

Dual-Energy X-ray Absorptiometry

NHANES:

National Health and Nutrition Examination Survey

FNIH:

Foundation for the National Institutes of Health

BMI:

Body Mass Index (weight [kg]/height[m]2)

OR:

Odds Ratio

PIR:

Poverty Income Ratio

CI:

Confidence Intervals

eGFR:

Estimations of Glomerular Filtration Rate

FNDDS:

Food and Nutrient Database for Dietary Studies

UACR:

Urinary Albumin to Creatinine Ratio

ALM:

Appendicular Lean Mass

DM:

Diabetes Mellitus

CVD:

Cardiovascular Disease

References

  1. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, van Abellan G, Andrieu S, Bauer J, Breuille D, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011;12(4):249–56.

    Article  PubMed  Google Scholar 

  3. Liang CK, Peng LN, Lin MH, Loh CH, Lee WJ, Hsiao FY, Chen LK. Long-Term mortality risk in older adults with sarcopenia: an 11-Year prospective cohort study comparing AWGS 2014 and AWGS 2019 guidelines for enhanced clinical utility and accurate risk prediction. J Nutr Health Aging. 2023;27(7):507–13.

    Article  PubMed  Google Scholar 

  4. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, Ferrucci L, Guralnik JM, Fragala MS, Kenny AM et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014, 69(5):547–558.

  5. Kiss N, Prado CM, Daly RM, Denehy L, Edbrooke L, Baguley BJ, Fraser SF, Khosravi A, Abbott G. Low muscle mass, malnutrition, sarcopenia, and associations with survival in adults with cancer in the UK biobank cohort. J Cachexia Sarcopenia Muscle. 2023;14(4):1775–88.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tessier AJ, Wing SS, Rahme E, Morais JA, Chevalier S. Association of low muscle mass with cognitive function during a 3-Year Follow-up among adults aged 65 to 86 years in the Canadian longitudinal study on aging. JAMA Netw Open. 2022;5(7):e2219926.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Staatz CB, Kelly Y, Lacey RE, Hardy R. Area-level and family-level socioeconomic position and body composition trajectories: longitudinal analysis of the UK millennium cohort study. Lancet Public Health. 2021;6(8):e598–607.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ganmaa D, Bromage S, Khudyakov P, Erdenenbaatar S, Delgererekh B, Martineau AR. Influence of vitamin D supplementation on growth, body composition, and pubertal development among School-aged children in an area with a high prevalence of vitamin D deficiency: A randomized clinical trial. JAMA Pediatr. 2023;177(1):32–41.

    Article  PubMed  Google Scholar 

  9. Jung HN, Jung CH, Hwang YC. Sarcopenia in youth. Metabolism. 2023;144:155557.

    Article  CAS  PubMed  Google Scholar 

  10. Duranti G. Oxidative stress and skeletal muscle function. Int J Mol Sci 2023, 24(12).

  11. Lian D, Chen M, Wu H, Deng S, Hu X. The role of oxidative stress in skeletal muscle myogenesis and muscle disease. Antioxid (Basel Switzerland) 2022, 11(4).

  12. Agrawal S, Chakole S, Shetty N, Prasad R, Lohakare T, Wanjari M. Exploring the role of oxidative stress in skeletal muscle atrophy: mechanisms and implications. Cureus. 2023;15(7):e42178.

    PubMed  PubMed Central  Google Scholar 

  13. Kong SY, Bostick RM, Flanders WD, McClellan WM, Thyagarajan B, Gross MD, Judd S, Goodman M. Oxidative balance score, colorectal adenoma, and markers of oxidative stress and inflammation. Cancer Epidemiol Biomarkers Prev. 2014;23(3):545–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Park HM, Han TH, Kwon YJ, Lee JH. Oxidative balance score inversely associated with the prevalence and incidence of metabolic syndrome: analysis of two studies of the Korean population. Front Nutr. 2023;10:1226107.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lee YJ, Hwang IC, Ahn HY. The association between oxidative balance score and lung function: A nationwide cross-sectional survey. Heliyon. 2023;9(3):e14650.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu Z, Xue Y, Wen H, Chen C. Association of oxidative balance score and lung health from the National health and nutrition examination survey 2007–2012. Front Nutr. 2022;9:961950.

    Article  PubMed  Google Scholar 

  17. Li Y, Yuan H, Li Q, Geng S, Chen X, Zhu Y, Jiang H. Lifestyle-based oxidative balance score and its association with cardiometabolic health of the community-dwelling elderly: A cross-sectional secondary analysis. Front Cardiovasc Med. 2022;9:1000546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Golmohammadi M, Ayremlou P, Zarrin R. Higher oxidative balance score is associated with better glycemic control among Iranian adults with type-2 diabetes. Int J Vitam Nutr Res. 2021;91(1–2):31–9.

    Article  CAS  PubMed  Google Scholar 

  19. Hernández-Ruiz Á, García-Villanova B, Guerra-Hernández E, Amiano P, Ruiz-Canela M, Molina-Montes E. A review of A priori defined oxidative balance scores relative to their components and impact on health outcomes. Nutrients 2019, 11(4).

  20. Song L, Li H, Fu X, Cen M, Wu J. Association of the oxidative balance score and cognitive function and the mediating role of oxidative stress: evidence from the National health and nutrition examination survey (NHANES) 2011–2014. J Nutr. 2023;153(7):1974–83.

    Article  PubMed  Google Scholar 

  21. Zhang W, Peng SF, Chen L, Chen HM, Cheng XE, Tang YH. 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.

  22. Kong W, Xie Y, Hu J, Ding W, Cao C. Higher ultra processed foods intake is associated with low muscle mass in young to middle-aged adults: a cross-sectional NHANES study. Front Nutr. 2024;11:1280665.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhang H, Qi G, Wang K, Yang J, Shen Y, Yang X, Chen X, Yao X, Gu X, Qi L, et al. Oxidative stress: roles in skeletal muscle atrophy. Biochem Pharmacol. 2023;214:115664.

    Article  CAS  PubMed  Google Scholar 

  24. Wang D, Yang Y, Zou X, Zhang J, Zheng Z, Wang Z. Antioxidant apigenin relieves Age-Related muscle atrophy by inhibiting oxidative stress and hyperactive mitophagy and apoptosis in skeletal muscle of mice. J Gerontol Biol Sci Med Sci. 2020;75(11):2081–8.

    Article  CAS  Google Scholar 

  25. Ren W, Xu Z, Pan S, Ma Y, Li H, Wu F, Bo W, Cai M, Tian Z. Irisin and ALCAT1 mediated aerobic exercise-alleviated oxidative stress and apoptosis in skeletal muscle of mice with myocardial infarction. Free Radic Biol Med. 2022;193(Pt 2):526–37.

    Article  CAS  PubMed  Google Scholar 

  26. Yin L, Li N, Jia W, Wang N, Liang M, Yang X, Du G. Skeletal muscle atrophy: from mechanisms to treatments. Pharmacol Res. 2021;172:105807.

    Article  CAS  PubMed  Google Scholar 

  27. Frampton J, Murphy KG, Frost G, Chambers ES. Higher dietary fibre intake is associated with increased skeletal muscle mass and strength in adults aged 40 years and older. J cachexia Sarcopenia Muscle. 2021;12(6):2134–44.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Park SJ, Park J, Won CW, Lee HJ. The inverse association of sarcopenia and Protein-Source food and vegetable intakes in the Korean elderly: the Korean frailty and aging cohort study. Nutrients 2022, 14(7).

  29. van Dronkelaar C, Fultinga M, Hummel M, Kruizenga H, Weijs PJM, Tieland M. Minerals and sarcopenia in older adults: an updated systematic review. J Am Med Dir Assoc. 2023;24(8):1163–72.

    Article  PubMed  Google Scholar 

  30. Kong FS, Ma CM. Dietary zinc intakes are associated with skeletal muscle mass and strength in children and adolescents: zinc and muscle in children. Clin Pediatr (Phila) 2023:99228231171242.

  31. Yokoyama Y, Kitamura A, Seino S, Kim H, Obuchi S, Kawai H, Hirano H, Watanabe Y, Motokawa K, Narita M, et al. Association of nutrient-derived dietary patterns with sarcopenia and its components in community-dwelling older Japanese: a cross-sectional study. Nutr J. 2021;20(1):7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jo Y, Linton JA, Choi J, Moon J, Kim J, Lee J, Oh S. Association between cigarette smoking and sarcopenia according to obesity in the Middle-Aged and elderly Korean population: the Korea National health and nutrition examination survey (2008–2011). Korean J Fam Med. 2019;40(2):87–92.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Deng M, Zhang Q, Yan L, Bian Y, Li R, Gao J, Wang Y, Miao J, Li J, Zhou X, et al. Glycyl-l-histidyl-l-lysine-Cu(2+) rescues cigarette smoking-induced skeletal muscle dysfunction via a Sirtuin 1-dependent pathway. J Cachexia Sarcopenia Muscle. 2023;14(3):1365–80.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Onishi S, Miyai N, Zhang Y, Oka T, Nogami E, Utsumi M, Arita M. Excessive alcohol intake and liver fibrosis are associated with skeletal muscle mass reduction in elderly men: the Wakayama study. Aging Clin Exp Res. 2022;34(1):185–92.

    Article  PubMed  Google Scholar 

  35. Hong SH, Bae YJ. Association between alcohol consumption and the risk of sarcopenia: A systematic review and Meta-Analysis. Nutrients 2022, 14(16).

  36. Melton LJ 3rd, Khosla S, Crowson CS, O’Connor MK, O’Fallon WM, Riggs BL. Epidemiology of sarcopenia. J Am Geriatr Soc. 2000;48(6):625–30.

  37. Jackson MJ, McArdle A. Age-related changes in skeletal muscle reactive oxygen species generation and adaptive responses to reactive oxygen species. J Physiol. 2011;589(Pt 9):2139–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liguori I, Russo G, Curcio F, Bulli G, Aran L, Della-Morte D, Gargiulo G, Testa G, Cacciatore F, Bonaduce D, et al. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018;13:757–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Data from NHANES collection was sponsored by the CDC.

Funding

This work was supported by Zhejiang Traditional Chinese Medicine Science and Technology Plan(2024ZF027).

Author information

Author notes

  1. Weiliang Kong and Jiayuan Ye contributed equally to this work.

Authors and Affiliations

  1. Department of Respiratory and Critical Care Medicine, Key Laboratory of Respiratory Disease of Ningbo, The First Affiliated Hospital of Ningbo University, No.59 Liuting Street, Ningbo, Zhejiang, 315000, China

    Weiliang Kong, Jingjing Hu, Weiping Ding & Chao Cao

  2. Department of Infectious Diseases, Shangyu People’s Hospital of Shaoxing, Shaoxing, Zhejiang Province, 312399, China

    Jiayuan Ye

  3. Department of General Internal Medicine, The Third Hospital of Ninghai County, Ningbo, China

    Shuaiqin Dai

  4. Department of General Medicine, The Third Hospital of Ninghai County, Ningbo, China

    Xiaowei Xia

  5. Health Science Center, Ningbo University, Ningbo, China

    Hui Li

  6. Department of Hepatology, The First Affiliated Hospital of Ningbo University, No.59 Liuting Street, Ningbo, Zhejiang, 315000, China

    Yilian Xie

Authors
  1. Weiliang Kong
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  2. Jiayuan Ye
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  3. Shuaiqin Dai
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  4. Xiaowei Xia
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  5. Jingjing Hu
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  6. Weiping Ding
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  7. Hui Li
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  8. Yilian Xie
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  9. Chao Cao
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Contributions

WLK, YLX and CC conceived and designed the study. XWX and SQD completed statistical analyses. WPD, JJH, and HL analyzed the data. WLK and JYY contributed to drafting and editing the paper. WLK, YLX and CC had full access to all the data in the study and took responsibility for the integrity of the data and accuracy of the data analysis. All authors have given final approval of the manuscript.

Corresponding authors

Correspondence to Weiliang Kong, Yilian Xie or Chao Cao.

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Ethics approval and consent to participate

Detailed methods and protocols for the NHANES study were approved by the CDC/NCHS Research Ethics Review Board. They are publicly available through the CDC.gov website; this includes informed consent procedures for all participants. All methods in this study were performed by the relevant guidelines and regulations. This study was exempt from human subject ethical review as the data is freely available in the public domain.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Kong, W., Ye, J., Dai, S. et al. Oxidative balance score is inversely associated with low muscle mass in young and middle-aged adults: a cross-sectional NHANES study. BMC Musculoskelet Disord 26, 398 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-025-08459-5

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-025-08459-5

Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

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