Abstract
Background
Sarcopenia and obesity, two prevalent health conditions, often coexist and exacerbate each other’s impact, increasing the risk of chronic diseases and mortality. This dual condition is termed “sarcopenic obesity.” The correlation between oxidative stress (OS) and sarcopenia or obesity was established, and the oxidative balance score (OBS) can serve as an indicator of overall dietary or lifestyle-related OS exposure within an individual. Prior reports have not addressed the relationship between OBS and sarcopenia or sarcopenic obesity in adults under 60. This study endeavors to explore these associations and to identify potential dietary and lifestyle risk factors.
Methods
We performed a cross-sectional analysis utilizing data from 4,241 participants in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. OBS is a cumulative score derived from 16 dietary components and 4 lifestyle components, where higher scores indicate greater exposure to antioxidants and lower exposure to pro-oxidant factors, reflecting a reduced oxidative stress burden. Weighted multivariate logistic regression was employed to investigate the association of OBS and sarcopenia and sarcopenic obesity. Further subgroup analyses was conducted to examine interactions with various covariates. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify significant components of OBS associated with sarcopenia and sarcopenic obesity, which were subsequently integrated into a risk prediction nomogram model. The model’s predictive accuracy was evaluated using the receiver operating characteristic (ROC) curve.
Results
After adjusting for potential confounders, the weighted logistic regression analyses demonstrated a significant negative association between OBS and the prevalence of sarcopenia (odds ratio [OR] = 0.954, 95% confidence interval [CI] = 0.925–0.984, P = 0.004) and sarcopenic obesity (OR = 0.948, 95% CI = 0.918–0.980, P = 0.002). The nomogram models, informed by key OBS components identified through LASSO regression, exhibited considerable predictive value for sarcopenia (area under the ROC curve [AUC] = 0.813, 95% CI = 0.792–0.833) and sarcopenic obesity (AUC = 0.894, 95% CI = 0.879–0.909).
Conclusion
This study reveals a robust inverse correlation between OBS and both sarcopenia and sarcopenic obesity in adults aged 20–59. These results suggest that an antioxidant-rich diet and healthy lifestyle practices, including low-fat diets, adequate vitamin B intake, regular physical activity, and weight management, may help mitigate the risk of sarcopenia and sarcopenic obesity. Further research is warranted to confirm these associations and determine causality.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12986-025-00914-3.
Keywords: NHANES, Oxidative balance score, Oxidative stress, Sarcopenia, Sarcopenic obesity
Introduction
Sarcopenia, characterized by a pathological decline in skeletal muscle mass, strength, and function [1], has evolved from a condition once thought to be predominantly affect older adults to one that spans a broader age range [2]. The global prevalence of sarcopenia is projected to surge from 50 million to over 200 million within the next four decades [3]. Concurrently, obesity has become a global epidemic, affecting individuals across all socioeconomic backgrounds [4]. According to the World Health Organization (WHO), obesity affected one in eight individuals worldwide in 2022 [4]. Although traditionally considered separately, there is an increasing recognition of the frequent coexistence of sarcopenia and obesity, which share common pathophysiological mechanisms such as oxidative stress (OS), proinflammatory cytokines, insulin resistance (IR), hormonal imbalances, decreased physical activity, and dysfunction in liver, adipose, and skeletal muscle [5]. This co-occurrence, termed sarcopenic obesity, is associated with higher mortality rates and a more adverse impact on metabolic profiles and physical function than either condition alone [6]. Early identification and intervention are paramount in mitigating the progression of sarcopenia and sarcopenic obesity.
OS arises from an imbalance between the production of free radicals, such as reactive oxygen species(ROS) and reactive nitrogenous species(RNS), and the body’s antioxidant defenses. A variety of factors can modulate OS levels within the body. Among these, antioxidant factors typically include regular physical activity and the consumption of specific foods or nutrients, such as plant fiber, vitamins C, D, and E [9]. Conversely, prooxidant factors may include behaviors like smoking and alcohol consumption, as well as conditions such as obesity and a diet high in fats [9]. However, the individual impact of these factors on the body’s oxidative or antioxidative status is often limited and complex, as they do not operate in isolation but interact biologically with one another. To address this limitation, the oxidative balance score (OBS) has been developed as a comprehensive metric that integrates exposure to both antioxidants and prooxidants from diet and lifestyle, providing a nuanced assessment of the body’s overall OS status [9]. Recent studies have demonstrated significant correlations between OBS and biomarkers of OS, including F2-isoprostane [10], inflammatory biomarkers such as C-reactive protein (CRP) [11] and gamma glutamyl transferase(GGT) [12], validating OBS as a reliable tool for assessing redox homeostasis. The OBS has become a valuable tool in epidemiological studies, aiding in the exploration of the associations between OS and the risk of chronic diseases, including cardiovascular disease [13] and cancer [14].
Several mechanisms, such as OS, inflammation, mitochondrial dysfunction, endoplasmic reticulum stress and muscle regeneration have been implicated in the pathophysiology of sarcopenia and sarcopenic obesity [7, 8]. This underscores the potential utility of the OBS in assessing OS status, thereby facilitating early detection and intervention strategies. Across a lifetime, muscle mass and strength generally increase during youth and plateau in midlife, with a decline typically occurring in later years [15]. However, sedentary behaviors and unhealthy dietary patterns characteristic of modern, fast-paced living have become prevalent among younger individuals, exacerbating muscle degeneration and accelerating the onset of functional impairments and disabilities [16]. The identification of early-onset sarcopenia is crucial for the prevention of more severe reduction in muscle mass and function in later life for its longer duration. Despite the importance of early detection and prevention, existing research has not gotten raised sufficient attention in non-elderly adults [17, 18]. This focus has left a significant gap in understanding how OBS relates to sarcopenia and sarcopenic obesity in younger to middle-aged populations. Herein, our study aims to explore the associations between OBS and both sarcopenia and sarcopenic obesity in the general population aged 20–59, and to identify potential dietary and lifestyle risk factors using data from the National Health and Nutrition Examination Survey (NHANES).
Methods
Study population and ethics
The National Health and Nutrition Examination Survey (NHANES) is a nationwide research program conducted by the National Center for Health Statistics (NCHS) that mainly aims to assess the nutritional status and general health of US civilians at two-year intervals. This study program consists of five main data sets collected through interviews and physical examinations, including demographic, dietary, medical assessment, laboratory, and questionnaire data. Data were analyzed to evaluate nutritional status and how it relates to disease prevention and health promotion. All surveys were approved by the Ethical Review Board (IRB) of the NCHS and written consent was obtained from the participants.
In this study, cross-sectional data from 39,156 participants, collected over four consecutive cycles of the NHANES (2011–2018), were included initially. The exclusion criteria included: participants aged less than 20 years or more than 60 years (n = 24,222); those without relevant information on sarcopenia (n = 4,977) and OBS (n = 901); and those without data for covariates (n = 4,815). Consequently, a total of 4,241 participants were eligible for subsequent analyses (Fig. 1).
Fig. 1.
A detailed flow chart of participant recruitment
Exposure variable
The Oxidative Balance Score (OBS) was calculated by integrating pro-oxidant and antioxidant elements derived from 16 dietary and 4 lifestyle factors, following the most commonly used OBS framework in the literature [9, 19, 20]. The dietary components included a range of nutrients associated with OS, such as dietary fiber, β-carotene, riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E, calcium, magnesium, zinc, copper, selenium, total fat, and iron. The lifestyle factors comprised physical activity, alcohol consumption, smoking, and body mass index (BMI).
The dietary and alcohol intake were extracted from 24-hour dietary recalls at the mobile examination center (MEC), employing the computer-assisted dietary interview system to estimate the types and amounts of foods and beverages consumed and to estimate intakes of energy, nutrients, and other foods components. Nutrient intake assessment relied upon the University of Texas Food Intake Analysis System Nutrients Database. To simultaneously assess both active and passive smoking, serum cotinine levels were utilized, as they serve as a metabolite of nicotine. Physical activity information was gathered through the Physical Activity Questionnaire. According to the NHANES guidelines, the metabolic equivalent (MET) score was used to quantify physical activity levels, encompassing work-related (vigorous and moderate) and leisure-time physical activities (vigorous and moderate), as well as walking or bicycling for transportation. The MET score for each physical activity was calculated by multiplying its weekly frequency, duration per session, and the MET value assigned to that specific activity. BMI, calculated from height and weight measurements recorded by trained examiners, served as a proxy for adipose tissue dysfunction, a major source of pro-oxidant signaling, and was included as an indirect lifestyle-related pro-oxidant factor in the OBS framework [21].
Alcohol comsumption is categorized into three groups following previous literature: nondrinkers, nonheavy drinkers (0 to 30 g/d for men and 0 to 15 g/d for women), and heavy drinkers (≥ 30 g/d for men and ≥ 15 g/d for women), scored as 2, 1, or 0 points, respectively [22]. Other components were scored based on their nature (antioxidant or prooxidant) and the participant’s gender, with antioxidants receiving scores of 2, 1, and 0 for the highest to lowest tertiles, while prooxidants were scored inversely [23]. The final comprehensive OBS was compiled by integrating scores from both antioxidant and prooxidant components. Higher scores of the OBS indicate a greater exposure to antioxidants and lower exposure to pro-oxidant factors. Table 1 details the scoring system for OBS components.
Table 1.
Oxidative balance score assignment scheme (N = 4,241)
| OBS | Men | Women | ||||
|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 0 | 1 | 2 | |
| Dietary OBS components | ||||||
| A Dietary fiber (g/d) | <13.20 | 13.20–21.50 | >21.50 | <11.20 | 11.20–17.40 | >17.40 |
| Aβ-carotene (mcg/d) | <564.00 | 564.00-1858.00 | >1858.00 | <585.00 | 585.00-2092.00 | >2092.00 |
| A Riboflavin (Vitamin B2) (mg/d) | <1.74 | 1.74–2.55 | >2.55 | <1.35 | 1.35–1.93 | >1.93 |
| A Niacin (mg/d) | <24.10 | 24.10–34.30 | >34.30 | <17.20 | 17.20–24.40 | >24.40 |
| A Vitamin B6 (mg/d) | <1.79 | 1.79–2.63 | >2.63 | <1.32 | 1.32–1.95 | >1.95 |
| A Total folate (mcg/d) | <333.00 | 333.00-508.00 | >508.00 | <261.00 | 261.00-392.00 | >392.00 |
| A Vitamin B12 (mcg/d) | <3.56 | 3.56–6.14 | >6.14 | <2.47 | 2.47–4.40 | >4.40 |
| A Vitamin C (mg/d) | <37.10 | 37.10–93.50 | >93.50 | <36.30 | 36.30–86.40 | >86.40 |
| A Vitamin E as alpha-tocopherol (mg/d) | <6.33 | 6.33–10.10 | >10.10 | <5.63 | 5.63–8.76 | >8.76 |
| A Calcium (mg/d) | <781.00 | 781.00-1210.00 | >1210.00 | <616.00 | 616.00-942.00 | >942.00 |
| A Magnesium (mg) | <262.00 | 262.00-372.00 | >372.00 | <212.00 | 212.00-293.00 | >293.00 |
| A Zinc (mg/d) | <9.77 | 9.77–14.30 | >14.30 | <7.33 | 7.33–10.40 | >10.40 |
| A Copper (mg/d) | <1.02 | 1.02–1.47 | >1.47 | <0.83 | 0.83–1.20 | >1.20 |
| A Selenium (mcg/d) | <109.00 | 109.00-154.00 | >154.00 | <79.30 | 79.30–113.00 | >113.00 |
| P Total fat (g/d) | >104.00 | 70.70–104.00 | <70.70 | >81.50 | 56.00-81.50 | <56.00 |
| P Iron (mg/d) | >18.20 | 12.50–18.20 | <12.50 | >14.10 | 9.71–14.10 | <9.71 |
| Lifestyle OBS components | ||||||
| A Physical activity (MET-minute/week) | <1120.00 | 1120.00-5340.00 | >5340.00 | <400 | 400–1000 | >1000 |
| P Alcohol (g/d) | >30 | 0–30 | 0 | >15 | 0–15 | 0 |
| P Cotinine (ng/mL) | >11.50 | 0.02–11.50 | <0.02 | >0.14 | 0.02–0.14 | <0.02 |
| P Body mass index (kg/m2) | >29.60 | 25.30–29.60 | <25.30 | >31.80 | 25.00-31.80 | <25.00 |
Abbreviation: OBS, oxidative balance score; A antioxidant; P prooxidant; MET, metabolic equivalent. The dietary components did not include nutrients obtained from dietary supplements or medications
Outcome variables
Total appendicular lean mass (ALM) was assessed using a dual-energy X-ray absorptiometry (DEXA) within the NHANES program. Individuals who were pregnant or exceeding 192.5 cm in height, weighted exceeding 204.1 kg did not receive DEXA examination and thus were not included in the current study. The definition of sarcopenia currently used by the scientific community was proposed by the European Working Group on Sarcopenia in Older People (EWGSOP2) in 2018 [3] or by the Foundation for the National Institutes of Health Biomarkers Consortium Sarcopenia (FNIH) Project [24]. Sarcopenia was defined by the FNIH criteria (ALM/BMI ≤ 0.789 for men and ≤ 0.512 for women) in this study for its high sensitivity to differentiate patients with sarcopenic obesity [25]. Sarcopenic obesity was defined as the coexistence of sarcopenia and obesity, where obesity was defined as body mass index (BMI) ≥ 30 kg/m2 [6].
Covariates
Based on previous publications [17, 26], we collected as many covariates as possible. Demographic features including age, gender, race/ethnicity (non-Hispanic Black, non-Hispanic White, Mexican American, Other Hispanic, or others), education level (under high school, completed high school, high school or above), marital status (never married, married/living with partner, separated/divorced/widowed), poverty-income ratio (PIR; <1.3, 1.3–3.5, >3.5) were obtained from standardized questionnaires and face-to-face interviews. For medical history, we considered diabetes, cardiovascular disease (CVD, including coronary heart disease, angina, heart failure, heart attack and stroke), arthritis, chronic kidney disease (CKD) and cancer. In addition, we incorporated high-density lipoprotein cholesterol (HDL-C; mg/dL), total cholesterol (TC, mg/dL), serum 25(OH)D (nmol/L), and total energy intake (kcal). More details on variable collection methods can be found in the NHANES survey methods and analysis guide.
Statistical analysis
All data were weighted according to the NHANES analysis guidelines, considering the complex sampling design of NHANES. The baseline characteristics were presented based on the presence or absence of sarcopenia or sarcopenic obesity. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR); categorical variables were presented as number (percentage). Analysis of variance (ANOVA) was used for comparing continuous variables, and chi-square tests and the Kruskal-Wallis rank sum test were employed to examine statistical differences in categorical variables between groups.
We constructed three weighted multifactorial logistic regression models to estimate the odds ratio (ORs) and 95% confidence intervals (CIs) for the association of OBS with sarcopenia and sarcopenic obesity: a crude model without adjustments; Model I adjusted for age, gender, race/ethnicity, education levels, marital status, PIR; and Model II further adjusted for diabetes, CVD, arthritis, CKD, metabolic syndrome, cancer, HDL-C, TC, serum vitamin D. Subsequently, the association between OBS and sarcopenia as well as sarcopenic obesity in individuals of age (20–39, 40–59), gender (men/women), race/ethnicity (non-Hispanic Black, non-Hispanic White, Mexican American, Other Hispanic, Other race), CVD (no/yes), arthritis (no/yes), CKD (no/yes), metabolic syndrome (no/yes), cancer (no/yes) was examined using subgroup analysis. Interaction tests were used to examine the presence of significant interactions of these covariates with the association between OBS and sarcopenia and sarcopenic obesity. Additionally, we performed sensitivity analyses under unweighted conditions to assess the stability of the association between OBS and sarcopenia.
To screen the most critical dietary and lifestyle-related predictors of sarcopenia as well as sarcopenic obesity and eliminate collinearity among different variables, we applied the least absolute shrinkage and selection operator (LASSO) regression model; in this model, coefficients of variables that make only a negligible contribution to the whole model are shrunk to zero, ensuring the predictive performance of the model [27]. In the LASSO models, we used the method of cross-validation for model evaluation and parameter selection; the dataset was divided into 10 subsets, and the model was trained and tested on these subsets multiple times. This helps assess the model’s performance and determine suitable parameter values. During cross-validation, it is common to plot a curve with respect to lambda values; this allows observation of the model’s performance at different lambda settings. “Minimum deviance” refers to the lambda value with the smallest bias found during the cross-validation process, which means it provides the best fit to the data. We selected a lambda value slightly larger than the one with minimum deviance, typically by adding one standard deviation to the minimum deviance lambda value. This approach aims to make the model more stable and prevent overfitting, enhancing its generalization to new data. Besides, a risk prediction nomogram model was developed based on several key sarcopenia-related or sarcopenic obesity-related variables; its discriminatory power for forecasting the risk of sarcopenia or sarcopenic obesity was assessed using the receiver operating characteristic (ROC) curve. R software version 4.2.3 was used for all statistical analyses; a two-tailed P-value < 0.05 was considered statistically significant.
Results
Baseline characteristics of the study participants
A total of 4,241 participants aged 20–59 were ultimately included in the analysis, with a weighted average age of 39.52±11.69 years, including 2,160 men (50.93%) and 2,081 women (49.07%). The overall prevalence of sarcopenia and sarcopenic obesity among all participants was 8.54% and 5.61%, respectively. The weighted median OBS (IQR) was 21.00 [15.00, 27.00].
Given the findings that the sarcopenia group had lower OBS scores than those without sarcopenia (18.00 [13.00, 23.00] vs. 22.00 [16.00, 27.00], P < 0.05), and the same was observed for the sarcopenic obesity group (18.00 [13.00, 23.00] vs. 21.00 [16.00, 27.00], P < 0.05), we further investigated the differences in the OBS components involved in estimating OBS between the four groups, and found lower scores on several diet or lifestyle antioxidant components in participants with sarcopenia or sarcopenic obesity than those without, as detailed in Supplementary Table S1. Individuals with sarcopenia were older, non-Hispanic Black or non-Hispanic White, less educated, with lower income, higher levels of TC, lower HDL-C levels, lower total energy intake, and lower serum 25(OH)D (all P < 0.05) compared to those without sarcopenia. Furthermore, individuals with sarcopenia were more likely to have comorbidities such as diabetes, CVD, arthritis, and CKD. Similar characteristics were observed in participants with sarcopenic obesity. Detailed information about the baseline characteristics of all participants, grouped by sarcopenia or sarcopenic obesity, is presented in Table 2 and Supplementary Table S2.
Table 2.
Baseline characteristics of the study participants grouped by sarcopenia and sarcopenic obesity status
| Variables | Overall (N = 4,241) | Non-Sarcopenia (N = 3,879) | Sarcopenia (N = 362) | P value | Non-Sarcopenic-obesity (N = 4,003) | Sarcopenic-obesity (N = 238) | P value |
|---|---|---|---|---|---|---|---|
| Weighted Number | 51,411,973 | 47,803,325 | 3,608,648 | 48,767,244 | 2,644,729 | ||
| OBS (median [IQR]) | 21.00 [15.00, 27.00] | 22.00 [16.00, 27.00] | 18.00 [13.00, 23.00] | < 0.001 | 21.00 [16.00, 27.00] | 18.00 [13.00, 23.00] | < 0.001 |
| Age, mean(SD) | 39.52 (11.69) | 39.23 (11.67) | 43.42 (11.19) | < 0.001 | 39.31 (11.66) | 43.53 (11.46) | 0.001 |
| Age group, n (%) | < 0.001 | 0.001 | |||||
| 20–40 | 25,276,617.9 (49.2) | 23,994,990.5 (52.2) | 1,281,627.4 (35.5) | 24,344,378.4 (49.9) | 932,239.5 (35.2) | ||
| 41–60 | 26,135,354.9 (50.8) | 23,808,334.4 (49.8) | 2,327,020.5 (64.5) | 24,422,865.5 (50.1) | 1,712,489.4 (64.8) | ||
| Gender, n (%) | 0.271 | 0.698 | |||||
| Men | 25,744,000.6 (50.1) | 27,399,415.6 (49.8) | 49,756,557.8 (54.1) | 24,344,378.4 (49.9) | 932,239.5 (35.2) | ||
| Women | 25,667,972.2 (49.9) | 24,012,557.2 (50.2) | 1,655,415.0 (45.9) | 24,388,160.2 (50.0) | 1,279,812.1 (48.4) | ||
| Race/Ethnicity, n (%) | < 0.001 | < 0.001 | |||||
| Non-Hispanic Black | 5,163,921.2 (10.0) | 4,336,586.9 (9.1) | 827,334.3 (22.9) | 4,575,899.2 (9.4) | 588,022.1 (22.2) | ||
| Non-Hispanic White | 3,637,236.3 (7.1) | 3,178,661.5 (6.6) | 458,574.8 (12.7) | 3,319,273.7 (6.8) | 317,962.6 (12.0) | ||
| Mexican American | 32,649,164.5 (63.5) | 30,765,975.4 (64.4) | 1,883,189.1 (52.2) | 31,142,005.1 (63.9) | 1,507,159.4 (57.0) | ||
| Other Hispanic | 5,255,989.8 (10.2) | 5,139,135.9 (10.8) | 116,853.9 (3.2) | 5,139,135.9 (10.5) | 116,853.9 (4.4) | ||
| Other Race | 4,705,661.0 (9.2) | 4,382,965.2 (9.2) | 322,695.8 (8.9) | 4,590,930.0 (9.4) | 114,731.0 (4.3) | ||
| Education, n (%) | < 0.001 | 0.001 | |||||
| Under high school | 6591025.2 (12.8) | 5,709,602.0 (11.9) | 881,423.2 (24.4) | 5,990,094.2 (12.3) | 600,931.1 (22.7) | ||
| Completed high school | 11168129.5 (21.7) | 10,233,681.5 (21.4) | 934,448.0 (25.9) | 10,510,469.1 (21.6) | 657,660.4 (24.9) | ||
| High school or above | 33,652,818.0 (65.5) | 31,860,041.4 (66.6) | 1,792,776.7 (49.7) | 32,266,680.6 (66.2) | 1,386,137.5 (52.4) | ||
| Marital, n (%) | 0.769 | 0.987 | |||||
| Never married | 32,319,097.4 (62.9) | 29,987,740.1 (62.7) | 2,331,357.3 (64.6) | 30,644,559.9 (62.8) | 1,674,537.6 (63.3) | ||
| Married / Living with partner | 6,745,362.4 (13.1) | 6,310,873.8 (13.2) | 434,488.6 (12.0) | 6,405,575.6 (13.1) | 339,786.8 (12.8) | ||
| Separated / Divorced / Widowed | 12,347,513.0 (24.0) | 11,504,711.0 (24.1) | 842,802.0 (23.4) | 11,717,108.4 (24.0) | 630,404.6 (23.8) | ||
| PIR Category, n (%) | < 0.001 | < 0.001 | |||||
| < 1.3 | 12,452,328.1 (24.2) | 11,140,333.9 (23.3) | 1,311,994.2 (36.4) | 11,530,792.1 (23.6) | 921,536.0 (34.8) | ||
| 1.3–3.5 | 18,186,408.1 (35.4) | 16,776,962.5 (35.1) | 1,409,445.6 (39.1) | 17,092,338.4 (35.0) | 1,094,069.7 (41.4) | ||
| > 3.5 | 20,773,236.5 (40.4) | 19,886,028.5 (41.6) | 887,208.0 (24.6) | 20,144,113.3 (41.3) | 629,123.2 (23.8) | ||
| Diabetes, n (%) | < 0.001 | < 0.001 | |||||
| No | 46,714,938.5 (90.9) | 43,893,023.5 (91.8) | 2,821,914.9 (78.2) | 44,758,207.7 (91.8) | 1,956,730.8 (74) | ||
| Yes | 4,697,034.3 (9.1) | 3,910,301.4 (8.2) | 786,733.0 (21.8) | 4,009,036.2 (8.2) | 687,998.1 (26.0) | ||
| CVD, n (%) | < 0.001 | 0.007 | |||||
| No | 49,652,153.1 (96.6) | 46,343,109.4 (96.9) | 3,309,043.8 (91.7) | 47,224,194.5 (96.8) | 2,427,958.6 (91.8) | ||
| Yes | 1,759,819.7 (3.4) | 1,460,215.5 (3.1) | 299,604.1 (8.3) | 1,543,049.4 (3.2) | 216,770.3 (8.2) | ||
| Arthritis, n (%) | 0.024 | 0.003 | |||||
| No | 43,871,858.3 (85.3) | 40,987,041.7 (85.7) | 2,884,816.7 (79.9) | 41,848,288.4 (85.8) | 2,023,569.9 (76.5) | ||
| Yes | 7,540,114.5 (14.7) | 6,816,283.2 (14.3) | 723,831.2 (20.1) | 6,918,955.5 (14.2) | 621,159.0 (23.5) | ||
| CKD, n (%) | < 0.004 | 0.002 | |||||
| No | 47,651,312.2 (92.7) | 44,505,189 (93.1) | 3,146,123.2 (87.2) | 45,387,657.1 (93.1) | 2,263,655.0 (85.6) | ||
| Yes | 3,760,660.6 (7.3) | 3,298,135.9 (6.9) | 462,524.7 (12.8) | 3,379,586.8 (6.9) | 381,073.9 (14.4) | ||
| Metabolic Syndrome, n (%) | < 0.001 | < 0.001 | |||||
| No | 13,743,947.1 (26.7) | 13,530,123.0 (28.3) | 213,823.2 (5.9) | 13733852.6 (28.2) | 10094.5 (0.4) | ||
| Yes | 37,668,025.7 (73.3) | 34,273,201.0 (71.7) | 3,394,824.7 (94.1) | 35033391.3 (71.8) | 2634634.4 (99.6) | ||
| Cancer, n (%) | 0.610 | 0.581 | |||||
| No | 49,151,814.8 (95.6) | 45,727,918.4 (95.7) | 3,423,896.4 (94.9) | 46,647,503.8 (95.7) | 2,504,311.0 (94.7) | ||
| Yes | 2,260,158.0 (4.4) | 2,075,406.5 (4.3) | 184,751.5 (5.1) | 2,119,740.1 (4.3) | 140,417.9 (5.3) | ||
| HDL-C (median [IQR]) | 51.00 [42.00, 61.56] | 51.00 [42.87, 62.00] | 45.00 [39.00, 56.00] | < 0.001 | 51.00 [42.00, 62.00] | 45.00 [39.00, 55.00] | < 0.001 |
| TC (median [IQR]) | 187.00 [163.00,214.00] | 186.00 [163.00,214.00] | 195.00 [173.00,221.47] | 0.001 | 187.00 [163.00,214.00] | 195.00 [173.00,222.00] | 0.023 |
| Total energy intake (median [IQR]) | 2062.44 [1594.69, 2609.47] | 2069.50[1600.89, 2626.33] | 1880.05 [1481.79, 2461.39] | 0.005 | 2068.02 [1598.04, 2622.00] | 1935.17 [1504.66, 2552.11] | 0.137 |
| Serum 25(OH)D (median [IQR]) | 64.30 [49.50, 81.20] | 64.60 [49.91, 81.24] | 60.06 [44.49, 78.43] | 0.007 | 64.60 [49.92, 81.22] | 58.18 [44.25, 77.99] | 0.008 |
| ALM/BMI (median [IQR]) | 0.80 [0.64, 0.97] | 0.83 [0.65, 0.99] | 0.65 [0.49, 0.76] | 0.027 | 0.82 [0.65, 0.98] | 0.60 [0.49, 0.75] | < 0.001 |
Continuous variables are presented as weighted mean (SD) or weighted median (IQR), and categorical variables are presented as weighted number (weighted frequencies or percentages). Abbreviation: CI, confidence interval; PIR, income to poverty ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; HDL-C, high-density lipoprotein; TC, total cholesterol; ALM, appendix lean mass; BMI, body mass index
Association between OBS and sarcopenia as well as sarcopenic obesity
Table 3 shows examining the relationship between OBS and the risk of sarcopenia and sarcopenic obesity using weighted logistic regression. Analyzing OBS as a continuous variable showed a significant association between OBS and lower incidence of developing sarcopenia and sarcopenic obesity in both the crude model [sarcopenia: OR = 0.943 (0.926, 0.961), P < 0.001; sarcopenic obesity: OR = 0.944 (0.925, 0.963), P < 0.001] and Model II [sarcopenia: OR = 0.954 (0.925, 0.984), P = 0.004; sarcopenic obesity: OR = 0.948 (0.918, 0.980), P = 0.002]. Additionally, OBS quartile was consistently linked to a decreased risk of sarcopenia and sarcopenic obesity across all three models, with statistical significance (all P < 0.05). When OBS was treated as a categorical variable, the overall trend indicated a reduction in the risk of sarcopenia and sarcopenic obesity with increasing quartiles in all models (P for trend <0.05). Specifically, individuals with a higher quartile of OBS were generally associated with a lower risk of sarcopenia and sarcopenic obesity compared with the lowest quartile (reference group). In particular, the highest quartile of OBS was significantly associated with a lower prevalence of sarcopenia [OR = 0.451 (95% CI: 0.242–0.841), P = 0.013] and sarcopenic obesity [OR = 0.390 (95% CI: 0.199–0.762), P = 0.007] in the fully adjusted model (Model II). These trends were consistent across various models, except that in Model II, the second quartile of OBS did not show a significant association with the risk of sarcopenia, and the second quartile of OBS in both Model I and Model II did not show a significant association with sarcopenic obesity.
Table 3.
Weighted logistic regression analysis on the association between OBS and sarcopenia as well as sarcopenic obesity
| Sarcopenia | Sarcopenic obesity | |||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Non-adjusted model | 0.943 (0.926,0.961) | < 0.001 | 0.944 (0.925,0.963) | < 0.001 |
| OBS quartile 1 | Reference | - | Reference | - |
| OBS quartile 2 | 0.622 (0.452,0.856) | 0.004 | 0.620 (0.412,0.935) | 0.023 |
| OBS quartile 3 | 0.395 (0.277,0.565) | < 0.001 | 0.404 (0.268.0.608) | < 0.001 |
| OBS quartile 4 | 0.365 (0.240,0.556) | < 0.001 | 0.360 (0.221,0.586) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | ||
| Model I | 0.943 (0.926,0.961) | < 0.001 | 0.945 (0.925, 0.965) | < 0.001 |
| OBS quartile 1 | Reference | - | Reference | - |
| OBS quartile 2 | 0.649 (0.455,0.926) | 0.018 | 0.655 (0.417,1.027) | 0.065 |
| OBS quartile 3 | 0.394 (0.272,0.570) | < 0.001 | 0.409 (0.267,0.625) | < 0.001 |
| OBS quartile 4 | 0.359 (0.233,0.554) | < 0.001 | 0.360 (0.216,0.600) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | ||
| Model II | 0.954 (0.925,0.984) | 0.004 | 0.948 (0.918,0.980) | 0.002 |
| OBS quartile 1 | Reference | - | Reference | - |
| OBS quartile 2 | 0.726 (0.495,1.066) | 0.100 | 0.708 (0.448,1.119) | 0.135 |
| OBS quartile 3 | 0.460 (0.292,0.723) | 0.001 | 0.430 (0.266,0.695) | 0.001 |
| OBS quartile 4 | 0.451 (0.242,0.841) | 0.013 | 0.390 (0.199,0.762) | 0.007 |
| P for trend | 0.005 | 0.002 | ||
Model I was adjusted for age, gender, race/ethnicity, education levels, marital status, PIR, education level; Model II was further adjusted for diabetes, cardiovascular diseases, arthritis, chronic kidney disease, metabolic syndrome, cancer, HDL-C, TC, serum vitamin D based on Model I. Abbreviation: OR, odds ratio; CI, confidence interval; OBS, oxidative balance score
Subgroup analysis
This study investigated the link between OBS and sarcopenia as well as sarcopenic obesity in diverse populations stratified by age, gender, race, CVD, arthritis, CKD, metabolic syndrome, and cancer. As shown in Table 4, slightly stronger negative associations between OBS and sarcopenia or sarcopenic obesity were observed in individuals with comorbidities such as diabetes, CVD, arthritis, and CKD compared to those without. The differences were generally not statistically significant as no significant interactions were observed between comorbidities and OBS. Notably, the interaction between metabolic syndrome and OBS was statistically significant (P for interaction < 0.001). To be specific, for individuals without metabolic syndrome, higher OBS was related to higher risk of sarcopenic obesity [OR = 1.403 (1.333, 1.477)].
Table 4.
Subgroup analyses for the association between OBS and sarcopenia/sarcopenic obesity
| Number | Sarcopenia | Sarcopenic obesity | |||||
|---|---|---|---|---|---|---|---|
| OR [95% CI] | P value | P for interaction | OR [95% CI] | P value | P for interaction | ||
| Age | 0.552 | 0.411 | |||||
| 20–40 | 2142 | 0.961 (0.927,0.995) | 0.032 | 0.960 (0.920,1.002) | 0.068 | ||
| 41–60 | 2099 | 0.950 (0.917,0.984) | 0.007 | 0.942 (0.908,0.976) | 0.002 | ||
| Gender | 0.914 | 0.827 | |||||
| Men | 2081 | 0.955 (0.924,0.987) | 0.009 | 0.945 (0.908,0.984) | 0.009 | ||
| Women | 2160 | 0.953 (0.915,0.992) | 0.024 | 0.950 (0.913,0.989) | 0.017 | ||
| Race | 0.430 | 0.887 | |||||
| Non-Hispanic Black | 621 | 0.958 (0.923,0.994) | 0.028 | 0.945 (0.901,0.992) | 0.029 | ||
| Non-Hispanic White | 436 | 0.987 (0.942,1.033) | 0.571 | 0.961 (0.904,1.021) | 0.202 | ||
| Mexican American | 1576 | 0.945 (0.908,0.983) | 0.008 | 0.945 (0.908,0.982) | 0.007 | ||
| Other Hispanic | 836 | 0.963 (0.905,1.024) | 0.236 | 0.950 (0.895,1.009) | 0.101 | ||
| Other Race | 772 | 0.951 (0.902,1.002) | 0.066 | 0.976 (0.923,1.031) | 0.384 | ||
| Diabetes | 0.091 | 0.066 | |||||
| No | 3738 | 0.961 (0.930,0.993) | 0.021 | 0.958 (0.925,0.993) | |||
| 0.907 (0.862,0.955) | 0.025 | ||||||
| Yes | 503 | 0.918 (0.875,0.964) | 0.001 | < 0.001 | |||
| Cardiovascular Disease | 0.451 | 0.899 | |||||
| No | 4075 | 0.955 (0.927,0.985) | 0.006 | 0.948 (0.918,0.979) | 0.002 | ||
| Yes | 166 | 0.931 (0.867,0.999) | 0.054 | 0.952 (0.883,1.027) | 0.213 | ||
| Arthritis | 0.598 | 0.905 | |||||
| No | 3644 | 0.957 (0.928,0.987) | 0.007 | 0.949 (0.917,0.983) | 0.006 | ||
| Yes | 597 | 0.942 (0.889,0.999) | 0.054 | 0.946 (0.893,1.001) | 0.062 | ||
| Chronic Kidney Disease | 0.437 | 0.266 | |||||
| No | 3865 | 0.956 (0.926,0.986) | 0.008 | 0.952 (0.922,0.984) | 0.005 | ||
| Yes | 376 | 0.937 (0.892.0.985) | 0.014 | 0.922 (0.872,0.975) | 0.007 | ||
| Metabolic Syndrome | 0.193 | < 0.001 | |||||
| No | 1110 | 0.908 (0.835,0.988) | 0.031 | 1.403 (1.333,1.477) | < 0.001 | ||
| Yes | 3131 | 0.957 (0.930,0.985) | 0.005 | 0.947 (0.918,0.978) | 0 002 | ||
| Cancer | 0.801 | 0.355 | |||||
| No | 4084 | 0.953 (0.925,0.982) | 0.003 | 0.945 (0.916,0.975) | 0.001 | ||
| Yes | 157 | 0.963 (0.881,1.053) | 0.418 | 0.990 (0.896,1.093) | 0.837 | ||
Analyses were stratified for age (20–40, 41–60), gender (men/women), race/ethnicity (non-Hispanic Black, non-Hispanic White, Mexican American, Other Hispanic, Other race), cardiovascular diseases (no/yes), arthritis (no/yes), chronic kidney disease (no/yes), metabolic syndrome (no/yes), cancer (no/yes). After being adjusted for age, gender, race/ethnicity, education levels, marital status, PIR, education level, diabetes, cardiovascular diseases, arthritis, chronic kidney disease, metabolic syndrome, cancer, HDL-C, TC, and serum vitamin D. Abbreviation: OR, odds ratio; CI, confidence interval; OBS, oxidative balance score
Identification of key sarcopenia or sarcopenic obesity related dietary and lifestyle factors
LASSO penalized regressions were constructed to identify OBS components intimately associated with sarcopenia and sarcopenic obesity, incorporating all OBS components and 3 covariates (age, gender, and race/ethnicity), comorbidities (diabetes, CVD, arthritis, CKD, metabolic syndrome, cancer), total energy intake, and the level of serum 25(OH)D (Fig. 2). In LASSO regression, coefficient shrinkage occurs when minimizing the loss function along with the L1 regularization term, thereby setting some coefficients to zero and effectively excluding the corresponding features (Fig. 2A and C). Vitamin B6, total fat, riboflavin, physical activity, and BMI were identified as the OBS-related factors most strongly associated with sarcopenia. Similarly, vitamin B6, physical activity, and BMI were identified as the OBS-related factors most strongly associated with sarcopenic obesity. To establish a risk prediction nomogram model for sarcopenia, including age category, race/ethnicity, vitamin B6, total fat, riboflavin, physical activity, BMI, diabetes, CVD, and metabolic syndrome as ten initial variables, the final model included nine of the ten variables, which contributed significantly to the model: age category, race/ethnicity, vitamin B6, total fat, riboflavin, physical activity, BMI, CVD, and metabolic syndrome, which demonstrated good predictive performance for sarcopenia, as validated by the ROC curve [AUC = 0.813 (0.792–0.833)] (Fig. 3A and B). Similar analyses for sarcopenic obesity identified age category, race/ethnicity, vitamin B6, physical activity, and BMI as variables that contributed significantly, with considerable predictive performance for sarcopenic obesity being validated by the ROC curve [AUC = 0.894 (0.879–0.909)] (Fig. 3C and D).
Fig. 2.
LASSO Penalized Regression Analysis for Identifying Key Factors Related to Sarcopenia and Sarcopenic Obesity. (A, C) Coefficient shrinkage plots for sarcopenia (A) and sarcopenic obesity (C). The plots illustrate the behavior of coefficients for all observed study components, including three covariates (age, gender, race/ethnicity), comorbidities (diabetes, cardiovascular disease, arthritis, chronic kidney disease, metabolic syndrome, cancer), total energy intake, and serum 25(OH)D levels. The coefficients are shown to change under various levels of shrinkage, represented by lines of different colors. (B, D) Binomial deviance plots for 10-fold cross-validation of the LASSO regression model for sarcopenia (B) and sarcopenic obesity (D). Abbreviation: LASSO, least absolute shrinkage and selection operator
Fig. 3.
Establishment and validation of a risk prediction model for sarcopenia and sarcopenic obesity. (A) A nomogram model for sarcopenia based on age category, race/ethnicity, vitamin B6, total fat, riboflavin, physical activity, BMI, cardiovascular disease, metabolic syndrome identified by LASSO regression analysis. (B) ROC curve for evaluating the predictive power of the nomogram model for sarcopenia. (C) A nomogram model for sarcopenic obesity based on age category, race/ethnicity, vitamin B6, physical activity, BMI identified by LASSO regression analysis. (D) ROC curve for evaluating the predictive power of the nomogram model for sarcopenic obesity. Abbreviation: LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; BMI, body mass index
Sensitivity analysis
In line with the findings of weighted logistic regression, sensitivity analysis under unweighted conditions also confirmed a negative association between OBS and sarcopenia and sarcopenic obesity, consistently observed in both unadjusted and adjusted models. In Model II, the odds of having sarcopenia and sarcopenic obesity decreased with increasing OBS, indicating that higher OBS quartiles were associated with lower susceptibility to sarcopenia and sarcopenic obesity (Supplementary Table S3). Overall, the sensitivity analysis confirmed the stability and reliability of the results obtained from population-weighted logistic regression analysis.
Discussion
This study examined the relationship between the OBS and the risk of sarcopenia and sarcopenic obesity among 4,241 young and middle-aged adults from the NHANES database. Our findings revealed a robust inverse correlation between OBS and the prevalence of both conditions, with the highest OBS quartile showing a significantly reduced risk compared to the lowest. This association remained robust even after accounting for potential confounding factors, highlighting the modulatory role of diet and lifestyle in the risk of sarcopenia and sarcopenic obesity, particularly in the context of OS. Subgroup analyses reinforced the consistency of this association across diverse demographic and health-related categories. Interestingly, within the subgroup without metabolic syndrome, an unexpected positive correlation was observed between OBS and sarcopenic obesity. While individuals with high OBS typically show low oxidative stress and better intake of antioxidants, this finding suggests that in this subgroup, a lower level of oxidative stress may not necessarily be associated with optimal muscle maintenance or fat distribution. It is possible that, for example, individuals with higher OBS in this subgroup may lack adequate exercise or protein intake, which are critical for muscle preservation. While low oxidative stress may indicate a generally healthier lifestyle, it may not fully capture the complexity of factors contributing to sarcopenic obesity. Utilizing LASSO penalized regression, we identified five key components of OBS—total fat, vitamin B6, riboflavin, physical activity, BMI—that were significantly associated with sarcopenia. For sarcopenic obesity, vitamin B6, physical activity, and BMI were similarly found to be significantly associated with the condition. By integrating these factors with demographic and health indices, we developed a predictive nomogram model that exhibited excellent predictive accuracy for both sarcopenia and sarcopenic obesity. The interplay between OS and the development of sarcopenia and obesity has been extensively studied. Under physiological conditions, a delicate balance exists between the generation of reactive oxygen species (ROS) and the cellular antioxidant defense mechanisms [28]. However, a shift in this balance towards ROS can lead to oxidative damage to cellular compoenents, including organelles, carbohydrates, proteins, nucleic acids and lipids, which impairs muscle stem cell function, and hampers muscle repair and growth [29]. Additionally, OS can disrupt the immune system, induce inflammation, and establish a self-perpetuating cycle of OS-Inflammation-OS that damages various structures and tissues [30]; increases protein carbonylation [31]; impaires myogenic protein [32]; and inhibites skeletal muscle cell differentiation [33]. Besides, accumulated ROS can also trigger apoptotic signaling cascades [34] and is the cause of neuromuscular junction dysfunction in myopenia [35]. Furthermore, ROS can enhance anabolic resistance in sarcopenia by suppressing the phosphorylation of key signaling molecules such as mTOR, Akt, p70S6K and 4E-BP1 [36]. Taken together, the disruption of redox homeostasis is a significant contributor to the pathogenesis of sarcopenia and sarcopenic obesity. Given the multifactorial etiology of sarcopenia and sarcopenic obesity, a comprehensive therapeutic approach is warranted. While no pharmacological agents have been specifically approved for these conditions, there is a growing consensus on the utility of nonpharmacological interventions [37, 38]. Diet and lifestyle, as key modifiable factors influencing OS, have been shown to significantly impact the development of sarcopenia and sarcopenic obesity [39 ]. Our study suggests that a higher OBS, indicative of greater antioxidant exposure and lower prooxidant exposure, may serve as a protective factor against these conditions, which is consistent with previous research. For instance, a population-based study by Zhao et al. demonstrated that a higher OBS is significantly associated with a reduced risk of low skeletal muscle mass and low handgrip strength [40]. Similarly, Xu et al. reported that a negative association existed between OBS and sarcopenia, with robust associations also observed between dietary and lifestyle-related OBS components [26]. Since OS plays a pivotal role in the pathogenesis of sarcopenia and sarcopenic obesity, adopting healthy eating habits accompanied by regular exercise is essential for maintaining muscle mass and quality and for the primary and secondary prevention of these conditions.
The specific dietary and lifestyle components contributing to OS are of particular mechanistic and clinical interest. Our findings highlight the importance of total fat, vitamin B6, riboflavin, physical activity, and BMI in the context of sarcopenia. Similarly, vitamin B6, physical activity, and BMI were identified as key factors associated with sarcopenic obesity. Vitamins have been shown to play a protective role against sarcopenia and sarcopenic obesity by mitigating ROS-mediated oxidative stress, promoting mitochondrial biogenesis, and suppressing the expression of muscle atrophy-related genes such as MAFbx and MuRF1. Our findings are consistent with a prior Dutch study highlighting the importance of vitamin B in both conditions [41], warranting further investigation into their mechanistic roles. Specifically, the Gothenburg H70 birth cohort study demonstrated a positive association between riboflavin intake and appendicular lean soft tissue index [42], while Son et al. reported that inadequate riboflavin and vitamin C intake increased sarcopenic obesity risk by 36.6% and 32.6%, respectively [43]. Additionally, the present study corroborates previous findings that dietary total fat is a significant factor associated with sarcopenia. Preclinical studies have shown that a high-fat diet (HFD) induces phenotypic changes in skeletal muscle, including decreased muscle strength and fiber size, fiber type transitions (e.g., IIb→IIa), downregulation of myosin heavy chain levels, the upregulation of atrogenes, and increased OS and myonuclear apoptosis [44]. Notably, HFD-driven metabolic dysregulation extends beyond sarcopenia, contributing to obesity—a condition closely intertwined with muscle deterioration. BMI, a widely used obesity marker (BMI ≥ 30 defining obesity) [4], is identified as a key OBS-related factor in both sarcopenia and sarcopenic obesity in our analysis. Weight gain often occurs centrally, promoting the deposition of fat in various organs, including skeletal muscle [45]. This excess intramyocellular lipids deposition promotes OS, which in turn induces lipotoxicity, inflammation, decreases the number of mitochondria in the muscle and impairs muscle fibre contractility [46, 47]. Moreover, this lipotoxicity interferes with muscle protein synthesis and new muscle tissue regeneration [47]. These mechanisms collectively exacerbate sarcopenia, creating a vicious cycle between obesity and muscle deterioration. However, the interpretation of BMI in this context requires caution. While our analysis incorporated BMI as a proxy for adiposity-related OS within the OBS framework, it is imperative to emphasize that BMI serves as an indirect composite marker rather than a direct causal factor. As a composite measure influenced by multiple factors (e.g., energy intake, sedentary behavior), BMI may conflate the effects of adiposity with other unmeasured confounders. Therefore, its inclusion in our model underscores the systemic impact of excess adiposity on oxidative pathways, but does not preclude the need for future studies to disentangle these relationships using direct body composition assessments (e.g., dual-energy X-ray absorptiometry, computed tomography). Despite these limitations, our findings underscore the clinical relevance of weight management in mitigating OS-related muscle damage. Physical activity diminishes lipotoxicity by increasing mitochondrial fatty acid beta-oxidation by muscle cells and increases the synthesis of muscle protein [48]. Randomized controlled trials have shown the benefits of exercise on muscle strength and mass as well as on obesity markers [49, 50]. Our findings further support these studies, reinforcing the pivotal role of physical activity in addressing both sarcopenia and sarcopenic obesity. In conclusion, our study highlights the interplay between dietary nutrients, adiposity, and physical activity in the pathogenesis of sarcopenia and sarcopenic obesity. A combined therapeutic approach integrating physical therapy and nutritional interventions may offer an effective strategy for managing these conditions.
As age advances, there is a notable deterioration in muscle quality, primarily attributed to a decrease in muscle fiber size, number, and contractility, alongside an increase in adipose tissue infiltration [15, 51]. While most epidemiology studies on sarcopenia and sarcopenic obesity have focused on older adults, our study is the first to demonstrate an inverse correlation between OBS and those conditions in a large, nationally representative sample of US adults aged 20–59. By employing LASSO regression and developing nomogram models, we have identified key OBS factors that are most strongly associated with sarcopenia and sarcopenic obesity. These models offer a valuable tool for assessing the impact of various diets and lifestyles on the development of these conditions and for identifying at-risk patients at an early stage, which may aid in prevention and improve the clinical outcomes. Despite the strengths of our study, several limitations must be acknowledged. First, the cross-sectional design precludes causal inferences and limits the predictive value of our nomogram model, emphasizing the need for large-scale prospective studies. Second, while we controlled for numerous covariates, the complex nature of sarcopenia and sarcopenic obesity may involve unidentified confounders, possibly due to incomplete data in the NHANES database. Third, the constraints of the NHANES database in providing comprehensive data on sarcopenia assessment, such as limitations on age, height, and weight, have confined our study to an eligible adult population. For example, aging is associated with a gradual decline in muscle mass and function, and an increase in fat mass with redistribution to metabolically deleterious depots [52]. These alterations in body composition make older individuals more prone to sarcopenia and sarcopenic obesity.
Conclusions
In summary, this study demonstrated significant negative correlations between OBS and the prevalence of sarcopenia and sarcopenic obesity in US adults aged 20–59. Specifically, we identified specific dietary or lifestyle-related OBS components impacting the risk of sarcopenia or sarcopenic obesity, with implications for following an antioxidant diet and lifestyle to prevent and mitigat the early onset of these conditions. Further prospective studies and large-scale epidemiological investigations are needed to confirm the findings of this study, which are expected to provide clearer guidance for the prevention of sarcopenia and sarcopenic obesity.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We acknowledge the NHANES database for providing the platform and contributors for uploading datasets. And we are sincerely grateful to all the participants in our study.
Abbreviations
- NHANES
National Health and Nutrition Examination Survey
- WHO
World Health Organization
- OS
Oxidative stress
- IR
Insulin resistance
- ROS
Reactive oxygen species
- RNS
Reactive nitrogenous species
- OBS
Oxidative balance score
- CRP
C-reactive protein
- GGT
Gamma glutamyl transferase
- NCHS
National Center for Health Statistics
- IRB
Ethical Review Board
- MEC
Mobile examination center
- MET
Metabolic equivalent
- BMI
Body mass index
- ALM
Appendicular lean mass
- DEXA
Dual-energy X-ray absorptiometry
- EWGSOP
European Working Group on Sarcopenia in Older People
- FNIH
Foundation for the National Institutes of Health Biomarkers Consortium Sarcopenia
- PIR
Poverty-income ratio
- CVD
Cardiovascular disease
- CKD
Chronic kidney disease
- HDL-C
High-density lipoprotein cholesterol
- TC
Total cholesterol
- LASSO
Least absolute shrinkage and selection operator
- ROC
Receiver operating characteristic
Author contributions
ZW, QX, and YZ: Conceptualization, Formal analysis; ZW: Methodology, Writing – original draft, Visualization; QX and YZ: Data Curation; RW, JC: Validation; JH, ZJ: Writing – Reviewing & Editing; JH: Supervision, project administration, and Funding acquisition. The final manuscript was read and approved by all authors.
Funding
This work was supported by the National Natural Science Foundation of China (82272926); Humanities and Social Sciences Research of Anhui Provincial Higher Education Institutions (SK2020ZD13); Promoting scientific research cooperation and high-level talent training projects with Canada, Australia, New Zealand, and Latin America of the National Scholarship Foundation ((2022)1007).
Data availability
Data is provided within the manuscript or supplementary information files.The datasets generated and analysed during the current study are available on the NHANES website, https://www.cdc.gov/nchs/nhanes/index.htm.
Declarations
Ethics approval and consent to participate
This study was conducted according to the guideline laid down in the Declaration of Helsinki, and all procedures involving study participants were approved by the Institutional Review Board of the NCHS. Ethical review and approval were waived for this study as it solely used publicly available data for research and publication. Informed consent was obtained from all subjects involved in the NHANES.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhu-zhu Wang, Qin Xu and Yu-han Zhang have contributed equally to this work.
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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
Data is provided within the manuscript or supplementary information files.The datasets generated and analysed during the current study are available on the NHANES website, https://www.cdc.gov/nchs/nhanes/index.htm.