Clinical models for predicting the association between dietary oxidative balance score and sarcopenia

Clinical models for predicting the association between dietary oxidative balance score and sarcopenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical models for predicting the association between dietary oxidative balance score and sarcopenia Hualing Xiao, Minwen Lian, Jinshen He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7084098/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Previous investigations have suggested a potential link between Dietary Oxidative Balance Score (DOBS) and sarcopenia, but about the clinic prediction models evaluating the risk of sarcopenia are rare. This study highlighted that the clinic prediction models construction would be also a novel tool to help diagnose sarcopenia clinically. Methods According to the data collected between 2011 and 2014 by the National Health and Nutrition Examination Survey (NHANES), the cross-sectional analysis evaluated DOBS, consisted of scores of sixteen dietary factors. Analytical approaches, including multivariable logistic regression, subgroup analysis, and sensitivity analysis, were applied to examine the association between DOBS and sarcopenia. The clinical prediction models were used to predict the risk of sarcopenia. Results Among 10,732 participants, higher DOBS exhibited a closer correlation with sarcopenia (OR: 0.97; 95% CI: 0.95–0.98, P < 0.001). Furthermore, stratification by quartiles revealed that each unit increase in DOBS (range: 16–21) reduced sarcopenia likelihood by 33% (OR: 0.67; 95% CI: 0.52–0.87, P 21 corresponded to a 41% risk reduction (OR: 0.59; 95% CI: 0.43–0.82, P < 0.05). Both the training and validation cohorts demonstrated statistically significant results ( P < 0.05) in Hosmer-Lemeshow test. The predictive performance was consistent across datasets, with the area under the curve values (AUC) values of 0.80 (95% CI 0.79–0.81) for the training set and 0.80 (95% CI 0.78–0.82) for the validation set. Conclusions Diet might influence sarcopenia through modulating oxidative stress mechanisms. Antioxidant diets played a crucial role in reducing the possibility of sarcopenia and this study firstly provided a novel predictive assessment instrument for clinical diagnosis of sarcopenia. Level of Evidence Level IV, crosssectional study. DOBS oxidative stress prediction model nomogram NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sarcopenia, a disease linked to aging, is characterized by the progressive loss of muscle mass, strength, and function[1]. Sarcopenia leading to loss of mobility, risk of falling and even death[1], which are common phenotypes in older people, links to a heightened risk of mortality from all causes[2]. With the growing global elderly population, sarcopenia is emerging as a significant public health problem worldwide. A meta-analysis reveals that the frequency of sarcopenia stands at 10% overall[3]. A forecast indicates that sarcopenia, which currently affects more than 80 million individuals, is anticipated to affect over 320 million people in the next three decades[4]. One of the main reasons of sarcopenia is malnutrition[5]. Many studies show that oxidative stress is one of the blood biomarkers of sarcopenia[6]. Numerous studies concentrate solely on the association between the Oxidative Balance Score (OBS) and sarcopenia[7, 8]. OBS is a tool to assess oxidative stress which evaluates the balance between antioxidant and pro-oxidant elements present in diets and lifestyles. To examine the effects of diet on oxidative, the Dietary Oxidation Balance Score (DOBS) is utilized. DOBS is an instrument that enables the assessment of antioxidant status by ranking the dietary components associated with both antioxidants and pro-oxidants. A higher DOBS score indicates that antioxidants are superior than pro-oxidants[9]. However, the relationship between DOBS and sarcopenia remains unknown. Meanwhile, there is no study applying the clinical prediction models to predict the risk of sarcopenia. As a result, it’s worthwhile to discuss the relationship between DOBS and sarcopenia. This study took use of information from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014 to examine the relationship between DOBS and sarcopenia. Additionally, the study constructed clinical prediction models to evaluate the risk of sarcopenia. The present study hypothesized that the increase in DOBS was associated with a low possibility of sarcopenia, and it might take advantage of the models to predict sarcopenia. Materials and methods Study Population The data utilized in this research were obtained from the NHANES spanning the years 2011 to 2014, for the participants having completed the relevant questionnaires during this time frame. This investigation utilized data gathered from two consecutive NHANES cycles, spanning from 2011 through 2014. A total of 14935 participants with dietary DOBS components were recruited in both two-year survey cycles. Subsequently, this study excluded 4,202 participants due to incomplete or missing sarcopenia-related data, resulting in a final data of 10,733 individuals (Fig. 1 ). Exposure Definitions According to the past research[10], there are two types of DOBS, including the contributions of 16 dietary factors and 4 lifestyle factors. However, in this study, because of some missing data, this study only used sixteen nutrients to calculate DOBS based on a previous article[9]. The contributions of 16 dietary factors include 14 antioxidants and 2 pro-oxidants. Previous studies always combined OBS with diets and lifestyles to get more accurate indicator of the Oxidative Stress[11]. Diet is a key factor affecting inflammation and Oxidative Stress, which plays important roles in sarcopenia[12]. DOBS is a measure to calculate oxidative stress based on dietary intake of nutrients, which is necessary to search the influence that diet do to the incidence rate of sarcopenia. In the NHANES study, dietary intake data is obtained through 48-hour dietary recall interviews (48HR), which takes place in the Mobile Examination Center. Throughout these survey sessions, respondents reported details regarding the variety and amounts of food and drink they had consumed during the previous two days. This data is then captured and documented using the NHANES computer-assisted dietary interview system. Meanwhile, this study got the average total nutrient intakes between the first day and the second day. It demonstrated the detailed scoring scheme of the DOBS (Table 1 ). This study divided each dietary factors into three groups according to gender. Each nutritional component was rated on a scale from 0 to 2, where 0 indicated the highest level of pro-oxidants and 2 represented the lowest level of pro-oxidants. Table 1 Ingredients that make up the oxidative balance score. Dietary OBS components Male Female 0 1 2 0 1 2 Dietary fiber (g/d) < 12.75 12.75–19.95 ≥ 19.95 < 11.10 11.10–16.70 ≥ 16.70 Carotene (RE/d) < 490.00 490.00-1621.67 ≥ 1621.67 < 499.50 499.50-1787.26 ≥ 1787.26 Riboflavin (mg/d) < 1.72 1.72–2.45 ≥ 2.45 < 1.37 1.37–1.95 ≥ 1.95 Niacin (mg/d) < 21.60 21.60-30.89 ≥ 30.89 < 16.42 16.42–23.15 ≥ 23.15 Vitamin B6 (mg/d) < 1.67 1.67–2.49 ≥ 2.49 < 1.30 1.30–1.90 ≥ 1.90 Total folate (mcg/d) < 322.00 322.00-483.50 ≥ 483.50 < 262.00 262.00-389.26 ≥ 389,26 Vitamin B12 (mcg/d) < 3.62 3.62–6.61 ≥ 6.61 < 2.70 2.70–4.58 ≥ 4.58 Vitamin C (mg/d) < 42.64 42.64–97.80 ≥ 97.80 < 40.02 40.02–89.78 ≥ 89.78 Calcium (mg/d) < 775.00 775.00-1175.50 ≥ 1175.50 < 641.00 641.00-958.00 ≥ 958.00 Vitamin E (ATE) (mg/d) < 6.03 6.03–9.41 ≥ 9.41 < 5.21 5.21–8.04 ≥ 8.04 Magnesium (mg/d) < 238,00 238.00-336.50 ≥ 336.50 < 198.50 198.50-274.76 ≥ 274.76 Zinc (mg/d) < 9.21 9.21–13.52 ≥ 13.52 < 7.20 7.20-10.23 ≥ 10.23 Copper (mg/d) < 0.94 0.94–1.35 ≥ 1.35 < 0.80 0.80–1.13 ≥ 1.13 Selenium (mcg/d) < 97.74 97.74-138.71 ≥ 138.71 < 76.07 76.07-106.45 ≥ 106.45 Total fat (g/d) ≥ 95.91 65.59–95.91 < 65.59 ≥ 75.71 52.55–75.71 < 52.55 Iron (mg/d) ≥ 17.96 12.49–17.96 < 12.49 ≥ 14.18 9.87–14.18 < 9.87 Dietary OBS components listed on the table are antioxidant except total fat and iron. Sarcopenia Measures and Outcome Definitions The diagnosis of sarcopenia, as recommended by the European Working Group on Sarcopenia in Older People (EWGSOP), should be based on the assessment of both reduced muscle mass and impaired muscle function, which includes either diminished strength or decreased physical performance[13]. To measure sarcopenia, as the previous studies, the study employed a straightforward and efficient method to defined sarcopenia, which was characterized as possessing a hand grip strength below 28 kg for men and below 18 kg for women[14]. The outcome of sarcopenia is determined by the participants' responses of either "Yes" or "No". More detailed information can be found at https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2011/DataFiles/MGX_G.htm . Covariate Definitions The covariates contained age, smoking, content of cotinine, BMI(Body Mass Index), sex, race (Mexican American, other Hispanics, Non-Hispanic white, Non-Hispanic black, other race), liver disease, heart disease, kidney disease, arthritis, kinds of arthritis (Osteoarthritis or degenerative arthritis, Rheumatoid Arthritis, Psoriatic arthritis, or others)[15–21]. The previous knowledge and descriptive statistic were using from the cohort through the use of directed acyclic graphs to evaluate those con-founders(Fig. 2 ). Statistical Analysis The 2011–2012, 2013–2014 NHANES data was downloaded to get the data needed. And the 4-year sample weights could be calculated by dividing the 2-year MEC exam weights by two. The study stratified the baseline characteristics of the population according to DOBS quartiles. Continuous variables are presented as depicted means, while categorical variables are presented as frequencies. The research constructed weighted linear models and weighted logistic regression in order to evaluate the association of DOBS with sarcopenia. And through dividing DOBS into quartile, it could get the p -values and OR. By adjusting the covariates, three different models can be obtained: Model 1 was not adjusted; Model 2 was adjusted for gender, age, and race; Model 3 was adjusted for age, sex, race, content of cotinine, BMI, heart disease, kidney disease, liver disease, arthritis, and types of arthritis. This study also performed subgroup analyses based on gender, race, BMI, kidney disease, liver disease, heart disease, arthritis, types of arthritis, and sarcopenia. In addition, sensitivity assessment was performed by gradually removing each DOBS component from the model to avoid any single DOBS component has a significant impact on the experimental result(Supplement Table 1 ). Independent predictors of sarcopenia were established through univariate and multivariate logistic regression analyses. The predictive accuracy of the nomogram was evaluated using both the area under the receiver operating characteristic curve (AUC - ROC) and calibration plots.[22]. The discrimination ability was evaluated by AUC of the ROC curve, with the AUC value of > 0.7 suggesting good discrimination ability of the nomogram[23]. And calibration plots were used to evaluate the calibration ability of the nomogram model using the Hosmer–Lemeshow test. The Hosmer–Lemeshow test yielded a p -value greater than 0.05, suggesting a satisfactory agreement between the probabilities predicted by the nomogram and the actual probabilities[24]. The 45°straight line represents the model had a perfect fit[24]. Results Baseline Characteristic 10732 individuals participated in the study, including 5218 males and 5514 females. The baseline characteristics of the participants categorized according to DOBS quartiles was summarized (Table 2 , Supplement Table 2 , Fig. 3 ). Relative to those in the bottom DOBS quartile, individuals in the top DOBS quartile may more frequently be female, older, and non-Hispanic white. It was of great importance to note that a lower proportion of higher DOBS participants was diagnosed with sarcopenia. And a significant trend in the prevalence of sarcopenia from the bottom DOBS quartile to the top DOBS quartile could be observed ( P < 0.001). Table 2 Characteristics by quartile of the OBS. Mean + SD for continuous variables: the P-value was calculated by the weighted linear regression model. (%) for categorical variables: the P-value was calculated by the weighted chi- square test.Q, quartile; BMI, body mass index. Characteristic Oxidative Balance Score P Q1 Q2 Q3 Q4 N = 2339 N = 2744 N = 2891 N = 2758 Sarcopenia (%) < 0.001 * Yes 18.62 16.76 11.87 8.81 Age 48.00[47.16,48.85] 48.48[47.74,49.23] 48.43[47.27,49.60] 47.39[46.26,48.52] 0.189 BMI (kg/m 2 ) 27.72[27.35,28.08] 27.48[26.96,28.00] 27.76[27.34,28.17] 27.54[27.10,27.97] 0.558 Sex (%) 0.226 Male 46.23 49.66 47.2 48.68 Female 53.77 50.34 57.8 51.32 Race (%) < 0.001 * Mexican American 5.03 5.08 6.05 7.29 Other Hispanics 4.1 4.22 4.19 3.93 Non-Hispanic white 45.58 51.52 58.13 62.72 African-American 12.35 9.15 7.45 6.49 Other race 32.94 30.03 24.18 19.57 Kidney Disease (%) < 0.001 * Yes 3.02 2.03 1.64 1.41 Borderline 28.41 24.73 18.82 13.63 Association between Dietary Oxidative Balance Score and Sarcopenia Through utilizing a weighted linear model and weighted logistic regression, the relationship between DOBS and sarcopenia was assessed and shown (Table 3 ). In adjusted model 3, elevated DOBS demonstrated an inverse relationship with sarcopenia risk (OR:0.97, 95% CI: 0.95–0.98). Then the DOBS was changed from a continuous to a categorical variable. In the model 3, the first DOBS quartile serving as a reference point, the adjusted odds ratios (95% CI) were 0.95 (0.71–1.26) for the second quartile and 0.67 (0.52–0.87) for the third quartile, with the P for trend was 0.002. Specifically, within the 16-21unit range of DOBS, each one-unit increase in DOBS means a 33% decrease in the probability of sarcopenia. Similarly, participants in the highest DOBS group had 41% lower risk of sarcopenia (OR:0.59, 95% CI: 0.43–0.82). Table 3 The relationship between dietary oxidative balance score and sarcopenia. Exposure Model 1 Model 2 Model 3 OR(95%CI) P OR(95%CI) P OR(95%CI) P OBS(continuous) 0.95(0.94,0.96) < 0.001 * 0.97(0.96,0.98) < 0.001 * 0.97(0.95,0.98) < 0.001 * OBS(quartile) Quartile 1(2–9) Reference Reference Reference Quartile 2(10–15) 0.88(0.70,1.11) 0.281 0.94(0.73,1.20) 0.614 0.95(0.71,1.26) 0.720 Quartile 3(16–21) 0.59(0.48,0.73) < 0.001 * 0.71(0.57,0.89) 0.007 * 0.67(0.52,0.87) 0.013 * Quartile 4(22–31) 0.42(0.32,0.55) < 0.001 * 0.58(0.42,0.80) 0.003 * 0.59(0.43,0.82) 0.010 * P for trend < 0.001 * < 0.001 * 0.002 * OR, odds ratio; CI, confidence intervals; OBS, Oxidative Balance Score. Subgroup Analysis It showed the subgroup analysis and interaction tests stratified by sex, age and kidney disease (Table 4 ). And a significant interaction effect among sex, age, kidney disease could be observed ( P for interaction < 0.05). Relative to other groups, the negative association effect between DOBS and sarcopenia was significantly greater in group Q4. Regardless of the value of DOBS, this effect was more readily observed in men and in those who were older, specifically those over the age of 50. Furthermore, statistical significance was observed that DOBS was more likely to affect people without kidney disease in group Q3( P < 0.05) and group Q4( P < 0.05). But there were inconsistent effect values for the association between DOBS and sarcopenia in female in group Q2 and Q4, as well as the people younger than 50 in group Q2. Still, the results indicate that the inverse relationship between DOBS and sarcopenia was consistent across all subgroups. Table 4 Subgroup analysis of the association between Dietary Oxidative Balance Score and sarcopenia. Variables Q1 Q2 Q3 Q4 P for interaction OR(95%CI) P OR(95%CI) P OR(95%CI) P Sarcopenia Sex Ref. < 0.0001 Male 0.80(0.57,1.14) 0.2561 0.63(0.47,0.84) 0.0159 0.33(0.23,0.47) 0.0005 Female 1.15(0.81,1.64) 0.4523 0.74(0.52,1.05) 0.1343 1.08(0.73,1.60) 0.7237 Age Ref. 0.0145 <50 1.12(0.86,1.45) 0.4456 0.85(0.68,1.06) 0.2002 0.63(0.47,0.85) 0.0223 ≥50 0.68(0.41,1.12) 0.1768 0.43(0.27,0.68) 0.0120 0.50(0.30,0.81) 0.0307 Kidney Disease Ref. 0.0021 Yes 0.58(0.23,1.47) 0.3112 0.53(0.18,1.60) 0.3261 0.35(0.07,1.61) 0.2470 No 0.74 (0.44,1.25) 0.3231 0.37 (0.24,0.56) 0.0097 0.45(0.28,0.71) 0.0275 Clinical Prediction Models 2250 population with sarcopenia accounted for 20.97% in total, consisting 660 people in training cohort and 1590 people in validation cohort (Supplement Table 3 ). Through uni-variate analysis and multivariate analysis, it was found that DOBS, sex, age, race, heart disease, kidney disease and BMI could be used to predict independently the possibility of sarcopenia. It showed that the seven independent predictors were parts of a non-invasive clinical nomogram construction (Fig. 4 ). By taking advantage of the Youden Index to calculate the best cut-off score for each variable, the study obtained separate scores for each one, and then summed them up to get a total score. The prediction risk corresponding to the total score represented the risk of sarcopenia. The calibration plot demonstrated that the predicted probabilities were close to the actual DOBS observed outcomes in the training and validation cohorts. The Hosmer–Lemeshow test of the training ( P < 0.001) and validation ( P < 0.001) cohort models indicated a good fit of the nomogram. The AUC values of 0.80 (95% CI, 0.79–0.81) in the training and 0.80 (95% CI, 0.78–0.82) in the validation cohorts. This robust performance across both cohorts substantiates the model's high precision in stratifying the risk of sarcopenia (Fig. 5 ). Discussion A cross-sectional study, involving 10732 participants from the NHANES cohort, showed a negative connection between DOBS and sarcopenia. And sex, age and kidney disease significantly influenced the association between DOBS and sarcopenia. The construction of clinic prediction models was well-calibrated for assessing the risk of sarcopenia(Fig. 6 ). Recent investigations have revealed that the OBS is negatively correlated with sarcopenia among US adults in the NHANES dataset[25]. This link has also been observed in older Iranian adults[8]. However, the specific mechanisms by which diet influences sarcopenia through oxidative stress remain unclear. The results highlighted the importance to prevent sarcopenia through managing an antioxidant diet. Though this is the first study to evaluate the relationship between DOBS and sarcopenia, its results are credible and consistent with the majority of previous studies. A wide range of ingredients are included in the DOBS, with many found to influence sarcopenia based on prior research. Studies have proved that vitamin E contributed to promoting myocyte plasma membrane repair when exposed to oxidant environment by cell experiments[26, 27]. The Korean ageing study, older adults aged 65–93 years participating, has shown Vitamin D was also related to muscle mass and physical function[28].Additionally, there is positive association between vitamin C and skeletal muscle mass evaluations in middle-aged and older individuals in another cross-sectional study[29]. Except for the ingredients, many nutrients in the serum also have been proven to be helpful for the research. It is seemed that magnesium, selenium and calcium are likely to be the most effective minerals for preventing or treating sarcopenia[30–32]. And zinc plays an active role on preventing sarcopenia[33]. However, animal studies show that the rise in non-heme iron (NHI) concentration is likely to the development of sarcopenia[34]. It was obvious from the subgroup analysis results that there were sex differences in the impact of DOBS on sarcopenia. And the finding concluded that sarcopenia in community-dwelling individuals occurred more frequently in males than females[35]. And there were some studies indicated that males were more irresistant compared to females due to e3Estrogen and other reasons[36]. The interaction test revealed that the inverse relationship between DOBS and sarcopenia was more evident in the males. Furthermore, increased catabolic processes in chronic kidney disease were effective in the development of sarcopenia[37]. Meanwhile, hormonal changes were key players in sarcopenia in chronic kidney disease[37]. Besides, liver disease, heart disease and arthritis significantly affect the negative association between DOBS and sarcopenia by the release of pro-inflammatory mediators, oxidative stress, and other psychophysiology processes[38–41]. The study showed that people with diseases above were more likely to suffer from sarcopenia. The former studies have furthermore proved that these diseases all were closely associated the oxidative stress score negatively[42–44]. Currently, most researches took advantage of malnutrition, cachexia, and frailty to differentially diagnosis sarcopenia directly[5, 45–47]. Or just by examining some inflammatory elements, such as CRP, TNF-α and IL-6, indicated it indirectly[48–51]. And DOBS serves as a valuable instrument for evaluating the cumulative effects of pro-oxidant and antioxidant exposures in the research of chronic disease[11]. With shed new light on this, here eleven predictors were screened out and a non-invasive clinical nomogram was constructed, including DOBS, sex, age, racial, liver disease, heart disease, kidney disease, arthritis, kinds of arthritis, BMI and smoke. The clinical prediction model that contributed was proven could accurately predict the risk of sarcopenia. It might be a useful measure to help us investigate and diagnosis the sarcopenia, which further demonstrated the relationship between DOBS and sarcopenia. There are several advantages in the study. Firstly, the study used a large, nationally representative sample, which increases the generality of the findings. Secondly, a variety of complex statistical methods was used, such as appropriate covariate adjustment, subgroup analysis, interaction test and sensitivity analysis, to improve the credibility and practicality of the study. Meanwhile, the risk assessment prediction model is used to predict the risk of sarcopenia. Undoubtedly, the study has several limitations. First, since the study adopted a cross - sectional study, determining a causal relationship between sarcopenia and DOBS is unfeasible. Secondly, due to the scarcity of data, the research failed to take into account some covariates that might influence the results, such as altitude, region and gene. Finally, the data the study used to calculate DOBS scores were obtained in the form of questionnaires, so there may be problems with participants' inaccurate recall. This reduced the accuracy of the findings. As a result, the findings may not apply to all patients with sarcopenia. And whether the study will yield the same results in other populations remains uncertain. So it is necessary to take these factors into account in future studies. List of abbreviations NHANES, National Health and Nutrition Examination Survey; BMI, Body Mass Index; OBS, Oxidative Balance Score; DOBS, Dietary Oxidative Balance Score; EWGSOP, European Working Group on Sarcopenia in Older People; AUC-ROC, area under the receiver operating characteristic curve; NHI, non-heme iron; DAG, Directed Acyclic Graph; CRP, C-reactive protein; TNF-α, tumor necrosis factor-α; IL-6, interleukin-6. Declarations Ethics approval and consent to participate Approval of this study was obtained from the ethics review board of the National Center for Health Statistics. All participants gave written informed consent. The experimental protocol was established according to the ethical guidelines of the Declaration of Helsinki. Consent for publication Written informed consent has been obtained from the participants to publish this paper. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The detailed information on the data is available at https://www.cdc.gov/nchs/nhanes/. Competing interests The authors declare that they have no competing interests. Funding None. Authors' contributions Hualing Xiao was responsible for the design of this study and performed the experiments. Hualing Xiao and Minwen Lian analyzed/interpreted the results and wrote the manuscript. Jinshen He supervised the article as corresponding author and contacted the submission editor. All authors participated in the design of the research and the review of the manuscript. Human Ethics and Consent to Participate declarations Centers for Disease Control and Prevention research on human participants complies with the Health and Human Services Policy for Protection of Human Research Subjects. 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Supplementary Files SupplementTable1.docx SupplementTable2.docx SupplementTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7084098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528232604,"identity":"0e53971b-d3d6-4d48-8224-9026ef107e86","order_by":0,"name":"Hualing Xiao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Hualing","middleName":"","lastName":"Xiao","suffix":""},{"id":528232605,"identity":"8704c983-f440-44ee-90c4-2551b5550909","order_by":1,"name":"Minwen Lian","email":"","orcid":"","institution":"Central South 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10:26:22","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114294,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/8cd9e595965266f0178bf97e.html"},{"id":93581082,"identity":"cd220931-bac4-4249-b2be-4126ce610312","added_by":"auto","created_at":"2025-10-15 10:26:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":746084,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of sample selection from the NHANES 2011-2014.\u003c/p\u003e","description":"","filename":"figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/178dec8ab89b6e86f95fc722.jpg"},{"id":93581086,"identity":"bb2963ed-320f-49f2-a54b-073b4d16baec","added_by":"auto","created_at":"2025-10-15 10:26:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355552,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of people in the four grades of DOBS scores on gender and age, NHANES 2011-2014. (A) It illustrates the proportions of men and women across the four categories of DOBS. (B) It illustrates the proportions of individuals aged between 20 and 50, as well as those over 50, across the four categories of DOBS.\u003c/p\u003e","description":"","filename":"figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/e47809be667809e9961761b5.jpg"},{"id":93581497,"identity":"943fa39e-1389-4355-805c-1e1cec9011a6","added_by":"auto","created_at":"2025-10-15 10:34:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197180,"visible":true,"origin":"","legend":"\u003cp\u003eDirected Acyclic Graph (DAG). DAG plot shows the effects of confounding on the primary exposures and outcomes. Circle with a triangle represent primary exposure; Circle with a \"I\" represent primary outcome; Green lines represent causal paths. The final minimally sufficient adjustment set com prised sex, age, race, cotinine, liver disease, kidney disease, heart disease, arthritis/arthritis kind, BMI, body mass index.\u003c/p\u003e","description":"","filename":"figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/92e5aba2c373c7274d90482b.jpg"},{"id":93581091,"identity":"9f9e9a2b-a4c3-46d1-8bd9-bad82ae32da4","added_by":"auto","created_at":"2025-10-15 10:26:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":250444,"visible":true,"origin":"","legend":"\u003cp\u003eA constructed nomogram for clinic prediction of a patient with sarcopenia. The patient, who was female, Non-Hispanic white, less than 50 years of age, without heart and kidney disease, BMI less than 30, DOBS value 24, is shown in below nomogram. To use the nomogram, the specific points (black dots) of individual patients are located on each variable axis. Red lines and dots are drawn upward to determine the points received by each variable; the sum (152) of these points is located on the Total Points axis, and a line is drawn downward to the survival axes to determine the probability of sarcopenia diagnosed.\u003c/p\u003e","description":"","filename":"figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/493df58fce7063f4ab63bcb6.jpg"},{"id":93581503,"identity":"2d444d42-133e-44a6-8d67-873d69de157b","added_by":"auto","created_at":"2025-10-15 10:34:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":318365,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the non-invasive nomogram. Calibration curves for the non-invasive nomogram in the training (A) and validation (B) cohorts. The calibration plot illustrates the accuracy of the original prediction (‘Apparent’; light dotted line) and bootstrap models (‘Bias-corrected’; solid line) in predicting the probability of sarcopenia. The 45◦straight line represents the perfect match between the actual and nomogram-predicted probabilities. A closer distance between the two curves indicates higher accuracy. ROC curves of the non-invasive nomogram in the training (C) and validation (D) cohorts. Orange and blue lines represent the non-invasive nomogram.\u003c/p\u003e","description":"","filename":"figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/3588e86b1af97e4a27cad09f.jpg"},{"id":93581501,"identity":"1d4a8d00-67ab-4bf1-8893-d0d4c1db8354","added_by":"auto","created_at":"2025-10-15 10:34:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":777056,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of people in the four grades of DOBS scores on gender and age, NHANES 2011-2014.\u003c/p\u003e","description":"","filename":"figure6.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/dc766fa0eaa57fd42b16e411.jpg"},{"id":95312579,"identity":"54a09ea2-13aa-4eb8-b615-2195e3ec0681","added_by":"auto","created_at":"2025-11-06 15:49:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3790010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/a0a0878f-65da-438e-b1d7-8d4e765156ca.pdf"},{"id":93581083,"identity":"8d9dd6b5-e094-4f1e-89f9-03cd1a3b20de","added_by":"auto","created_at":"2025-10-15 10:26:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14918,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/d363acf6b98eaf254adc9dff.docx"},{"id":93582582,"identity":"a810f08a-154c-4658-aa99-c589906c3113","added_by":"auto","created_at":"2025-10-15 10:42:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16568,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/939c8f1498331c5064849909.docx"},{"id":93581087,"identity":"8f925f52-14f0-4f28-b0b6-8cdae188198b","added_by":"auto","created_at":"2025-10-15 10:26:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26902,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7084098/v1/99aaf80fc7d91767bd71e158.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical models for predicting the association between dietary oxidative balance score and sarcopenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, a disease linked to aging, is characterized by the progressive loss of muscle mass, strength, and function[1]. Sarcopenia leading to loss of mobility, risk of falling and even death[1], which are common phenotypes in older people, links to a heightened risk of mortality from all causes[2]. With the growing global elderly population, sarcopenia is emerging as a significant public health problem worldwide. A meta-analysis reveals that the frequency of sarcopenia stands at 10% overall[3]. A forecast indicates that sarcopenia, which currently affects more than 80\u0026nbsp;million individuals, is anticipated to affect over 320\u0026nbsp;million people in the next three decades[4].\u003c/p\u003e\u003cp\u003eOne of the main reasons of sarcopenia is malnutrition[5]. Many studies show that oxidative stress is one of the blood biomarkers of sarcopenia[6]. Numerous studies concentrate solely on the association between the Oxidative Balance Score (OBS) and sarcopenia[7, 8]. OBS is a tool to assess oxidative stress which evaluates the balance between antioxidant and pro-oxidant elements present in diets and lifestyles. To examine the effects of diet on oxidative, the Dietary Oxidation Balance Score (DOBS) is utilized. DOBS is an instrument that enables the assessment of antioxidant status by ranking the dietary components associated with both antioxidants and pro-oxidants. A higher DOBS score indicates that antioxidants are superior than pro-oxidants[9]. However, the relationship between DOBS and sarcopenia remains unknown. Meanwhile, there is no study applying the clinical prediction models to predict the risk of sarcopenia. As a result, it\u0026rsquo;s worthwhile to discuss the relationship between DOBS and sarcopenia.\u003c/p\u003e\u003cp\u003eThis study took use of information from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014 to examine the relationship between DOBS and sarcopenia. Additionally, the study constructed clinical prediction models to evaluate the risk of sarcopenia. The present study hypothesized that the increase in DOBS was associated with a low possibility of sarcopenia, and it might take advantage of the models to predict sarcopenia.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data utilized in this research were obtained from the NHANES spanning the years 2011 to 2014, for the participants having completed the relevant questionnaires during this time frame. This investigation utilized data gathered from two consecutive NHANES cycles, spanning from 2011 through 2014. A total of 14935 participants with dietary DOBS components were recruited in both two-year survey cycles. Subsequently, this study excluded 4,202 participants due to incomplete or missing sarcopenia-related data, resulting in a final data of 10,733 individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccording to the past research[10], there are two types of DOBS, including the contributions of 16 dietary factors and 4 lifestyle factors. However, in this study, because of some missing data, this study only used sixteen nutrients to calculate DOBS based on a previous article[9]. The contributions of 16 dietary factors include 14 antioxidants and 2 pro-oxidants. Previous studies always combined OBS with diets and lifestyles to get more accurate indicator of the Oxidative Stress[11]. Diet is a key factor affecting inflammation and Oxidative Stress, which plays important roles in sarcopenia[12]. DOBS is a measure to calculate oxidative stress based on dietary intake of nutrients, which is necessary to search the influence that diet do to the incidence rate of sarcopenia. In the NHANES study, dietary intake data is obtained through 48-hour dietary recall interviews (48HR), which takes place in the Mobile Examination Center. Throughout these survey sessions, respondents reported details regarding the variety and amounts of food and drink they had consumed during the previous two days. This data is then captured and documented using the NHANES computer-assisted dietary interview system. Meanwhile, this study got the average total nutrient intakes between the first day and the second day. It demonstrated the detailed scoring scheme of the DOBS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study divided each dietary factors into three groups according to gender. Each nutritional component was rated on a scale from 0 to 2, where 0 indicated the highest level of pro-oxidants and 2 represented the lowest level of pro-oxidants.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIngredients that make up the oxidative balance score.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDietary OBS components\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDietary fiber (g/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.75\u0026ndash;19.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;19.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;11.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.10\u0026ndash;16.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;16.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarotene (RE/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;490.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e490.00-1621.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1621.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;499.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e499.50-1787.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1787.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiboflavin (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.72\u0026ndash;2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.37\u0026ndash;1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiacin (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;21.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.60-30.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;16.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.42\u0026ndash;23.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;23.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B6 (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.67\u0026ndash;2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.30\u0026ndash;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal folate (mcg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;322.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e322.00-483.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;483.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;262.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e262.00-389.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;389,26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin B12 (mcg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.62\u0026ndash;6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.70\u0026ndash;4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin C (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;42.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.64\u0026ndash;97.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;97.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;40.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.02\u0026ndash;89.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;89.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;775.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e775.00-1175.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1175.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;641.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e641.00-958.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;958.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin E (ATE) (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.03\u0026ndash;9.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;9.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.21\u0026ndash;8.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;8.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;238,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238.00-336.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;336.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;198.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e198.50-274.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;274.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.21\u0026ndash;13.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;13.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;7.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.20-10.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;10.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCopper (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.80\u0026ndash;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelenium (mcg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;97.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.74-138.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;138.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;76.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76.07-106.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;106.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal fat (g/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;95.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.59\u0026ndash;95.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;75.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e52.55\u0026ndash;75.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;52.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron (mg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;17.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.49\u0026ndash;17.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;14.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.87\u0026ndash;14.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;9.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eDietary OBS components listed on the table are antioxidant except total fat and iron.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSarcopenia Measures and Outcome Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diagnosis of sarcopenia, as recommended by the European Working Group on Sarcopenia in Older People (EWGSOP), should be based on the assessment of both reduced muscle mass and impaired muscle function, which includes either diminished strength or decreased physical performance[13]. To measure sarcopenia, as the previous studies, the study employed a straightforward and efficient method to defined sarcopenia, which was characterized as possessing a hand grip strength below 28 kg for men and below 18 kg for women[14]. The outcome of sarcopenia is determined by the participants' responses of either \"Yes\" or \"No\". More detailed information can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2011/DataFiles/MGX_G.htm\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2011/DataFiles/MGX_G.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariate Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe covariates contained age, smoking, content of cotinine, BMI(Body Mass Index), sex, race (Mexican American, other Hispanics, Non-Hispanic white, Non-Hispanic black, other race), liver disease, heart disease, kidney disease, arthritis, kinds of arthritis (Osteoarthritis or degenerative arthritis, Rheumatoid Arthritis, Psoriatic arthritis, or others)[15\u0026ndash;21]. The previous knowledge and descriptive statistic were using from the cohort through the use of directed acyclic graphs to evaluate those con-founders(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe 2011\u0026ndash;2012, 2013\u0026ndash;2014 NHANES data was downloaded to get the data needed. And the 4-year sample weights could be calculated by dividing the 2-year MEC exam weights by two. The study stratified the baseline characteristics of the population according to DOBS quartiles. Continuous variables are presented as depicted means, while categorical variables are presented as frequencies. The research constructed weighted linear models and weighted logistic regression in order to evaluate the association of DOBS with sarcopenia. And through dividing DOBS into quartile, it could get the \u003cem\u003ep\u003c/em\u003e-values and OR. By adjusting the covariates, three different models can be obtained: Model 1 was not adjusted; Model 2 was adjusted for gender, age, and race; Model 3 was adjusted for age, sex, race, content of cotinine, BMI, heart disease, kidney disease, liver disease, arthritis, and types of arthritis. This study also performed subgroup analyses based on gender, race, BMI, kidney disease, liver disease, heart disease, arthritis, types of arthritis, and sarcopenia. In addition, sensitivity assessment was performed by gradually removing each DOBS component from the model to avoid any single DOBS component has a significant impact on the experimental result(Supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIndependent predictors of sarcopenia were established through univariate and multivariate logistic regression analyses. The predictive accuracy of the nomogram was evaluated using both the area under the receiver operating characteristic curve (AUC - ROC) and calibration plots.[22]. The discrimination ability was evaluated by AUC of the ROC curve, with the AUC value of \u0026gt;\u0026thinsp;0.7 suggesting good discrimination ability of the nomogram[23]. And calibration plots were used to evaluate the calibration ability of the nomogram model using the Hosmer\u0026ndash;Lemeshow test. The Hosmer\u0026ndash;Lemeshow test yielded a \u003cem\u003ep\u003c/em\u003e-value greater than 0.05, suggesting a satisfactory agreement between the probabilities predicted by the nomogram and the actual probabilities[24]. The 45\u0026deg;straight line represents the model had a perfect fit[24].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristic\u003c/b\u003e\u003c/p\u003e\u003cp\u003e10732 individuals participated in the study, including 5218 males and 5514 females. The baseline characteristics of the participants categorized according to DOBS quartiles was summarized (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplement Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Relative to those in the bottom DOBS quartile, individuals in the top DOBS quartile may more frequently be female, older, and non-Hispanic white. It was of great importance to note that a lower proportion of higher DOBS participants was diagnosed with sarcopenia. And a significant trend in the prevalence of sarcopenia from the bottom DOBS quartile to the top DOBS quartile could be observed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics by quartile of the OBS. Mean\u0026thinsp;+\u0026thinsp;SD for continuous variables: the P-value was calculated by the weighted linear regression model. (%) for categorical variables: the P-value was calculated by the weighted chi- square test.Q, quartile; BMI, body mass index.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eOxidative Balance Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2339\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2744\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2891\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2758\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSarcopenia\u003c/b\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.00[47.16,48.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.48[47.74,49.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.43[47.27,49.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.39[46.26,48.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.72[27.35,28.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.48[26.96,28.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.76[27.34,28.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.54[27.10,27.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic white\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrican-American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKidney Disease\u003c/b\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorderline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between Dietary Oxidative Balance Score and Sarcopenia\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThrough utilizing a weighted linear model and weighted logistic regression, the relationship between DOBS and sarcopenia was assessed and shown (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In adjusted model 3, elevated DOBS demonstrated an inverse relationship with sarcopenia risk (OR:0.97, 95% CI: 0.95\u0026ndash;0.98). Then the DOBS was changed from a continuous to a categorical variable. In the model 3, the first DOBS quartile serving as a reference point, the adjusted odds ratios (95% CI) were 0.95 (0.71\u0026ndash;1.26) for the second quartile and 0.67 (0.52\u0026ndash;0.87) for the third quartile, with the \u003cem\u003eP\u003c/em\u003e for trend was 0.002. Specifically, within the 16-21unit range of DOBS, each one-unit increase in DOBS means a 33% decrease in the probability of sarcopenia. Similarly, participants in the highest DOBS group had 41% lower risk of sarcopenia (OR:0.59, 95% CI: 0.43\u0026ndash;0.82).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe relationship between dietary oxidative balance score and sarcopenia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOBS(continuous)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95(0.94,0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97(0.96,0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97(0.95,0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOBS(quartile)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuartile 1(2\u0026ndash;9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuartile 2(10\u0026ndash;15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88(0.70,1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94(0.73,1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.95(0.71,1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuartile 3(16\u0026ndash;21)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59(0.48,0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71(0.57,0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67(0.52,0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQuartile 4(22\u0026ndash;31)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42(0.32,0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58(0.42,0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.59(0.43,0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP for trend\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eOR, odds ratio; CI, confidence intervals; OBS, Oxidative Balance Score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIt showed the subgroup analysis and interaction tests stratified by sex, age and kidney disease (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). And a significant interaction effect among sex, age, kidney disease could be observed (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Relative to other groups, the negative association effect between DOBS and sarcopenia was significantly greater in group Q4. Regardless of the value of DOBS, this effect was more readily observed in men and in those who were older, specifically those over the age of 50. Furthermore, statistical significance was observed that DOBS was more likely to affect people without kidney disease in group Q3(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and group Q4(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). But there were inconsistent effect values for the association between DOBS and sarcopenia in female in group Q2 and Q4, as well as the people younger than 50 in group Q2. Still, the results indicate that the inverse relationship between DOBS and sarcopenia was consistent across all subgroups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis of the association between Dietary Oxidative Balance Score and sarcopenia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eSarcopenia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.80(0.57,1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.63(0.47,0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.33(0.23,0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15(0.81,1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74(0.52,1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.08(0.73,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.7237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.0145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12(0.86,1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85(0.68,1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.63(0.47,0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68(0.41,1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43(0.27,0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.50(0.30,0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKidney Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.0021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58(0.23,1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53(0.18,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.35(0.07,1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.44,1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37 (0.24,0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.45(0.28,0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Prediction Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003e2250 population with sarcopenia accounted for 20.97% in total, consisting 660 people in training cohort and 1590 people in validation cohort (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Through uni-variate analysis and multivariate analysis, it was found that DOBS, sex, age, race, heart disease, kidney disease and BMI could be used to predict independently the possibility of sarcopenia. It showed that the seven independent predictors were parts of a non-invasive clinical nomogram construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). By taking advantage of the Youden Index to calculate the best cut-off score for each variable, the study obtained separate scores for each one, and then summed them up to get a total score. The prediction risk corresponding to the total score represented the risk of sarcopenia.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe calibration plot demonstrated that the predicted probabilities were close to the actual DOBS observed outcomes in the training and validation cohorts. The Hosmer\u0026ndash;Lemeshow test of the training (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and validation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) cohort models indicated a good fit of the nomogram. The AUC values of 0.80 (95% CI, 0.79\u0026ndash;0.81) in the training and 0.80 (95% CI, 0.78\u0026ndash;0.82) in the validation cohorts. This robust performance across both cohorts substantiates the model's high precision in stratifying the risk of sarcopenia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA cross-sectional study, involving 10732 participants from the NHANES cohort, showed a negative connection between DOBS and sarcopenia. And sex, age and kidney disease significantly influenced the association between DOBS and sarcopenia. The construction of clinic prediction models was well-calibrated for assessing the risk of sarcopenia(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRecent investigations have revealed that the OBS is negatively correlated with sarcopenia among US adults in the NHANES dataset[25]. This link has also been observed in older Iranian adults[8]. However, the specific mechanisms by which diet influences sarcopenia through oxidative stress remain unclear. The results highlighted the importance to prevent sarcopenia through managing an antioxidant diet.\u003c/p\u003e\u003cp\u003eThough this is the first study to evaluate the relationship between DOBS and sarcopenia, its results are credible and consistent with the majority of previous studies. A wide range of ingredients are included in the DOBS, with many found to influence sarcopenia based on prior research. Studies have proved that vitamin E contributed to promoting myocyte plasma membrane repair when exposed to oxidant environment by cell experiments[26, 27]. The Korean ageing study, older adults aged 65\u0026ndash;93 years participating, has shown Vitamin D was also related to muscle mass and physical function[28].Additionally, there is positive association between vitamin C and skeletal muscle mass evaluations in middle-aged and older individuals in another cross-sectional study[29].\u003c/p\u003e\u003cp\u003eExcept for the ingredients, many nutrients in the serum also have been proven to be helpful for the research. It is seemed that magnesium, selenium and calcium are likely to be the most effective minerals for preventing or treating sarcopenia[30\u0026ndash;32]. And zinc plays an active role on preventing sarcopenia[33]. However, animal studies show that the rise in non-heme iron (NHI) concentration is likely to the development of sarcopenia[34].\u003c/p\u003e\u003cp\u003eIt was obvious from the subgroup analysis results that there were sex differences in the impact of DOBS on sarcopenia. And the finding concluded that sarcopenia in community-dwelling individuals occurred more frequently in males than females[35]. And there were some studies indicated that males were more irresistant compared to females due to e3Estrogen and other reasons[36]. The interaction test revealed that the inverse relationship between DOBS and sarcopenia was more evident in the males. Furthermore, increased catabolic processes in chronic kidney disease were effective in the development of sarcopenia[37]. Meanwhile, hormonal changes were key players in sarcopenia in chronic kidney disease[37].\u003c/p\u003e\u003cp\u003eBesides, liver disease, heart disease and arthritis significantly affect the negative association between DOBS and sarcopenia by the release of pro-inflammatory mediators, oxidative stress, and other psychophysiology processes[38\u0026ndash;41]. The study showed that people with diseases above were more likely to suffer from sarcopenia. The former studies have furthermore proved that these diseases all were closely associated the oxidative stress score negatively[42\u0026ndash;44].\u003c/p\u003e\u003cp\u003eCurrently, most researches took advantage of malnutrition, cachexia, and frailty to differentially diagnosis sarcopenia directly[5, 45\u0026ndash;47]. Or just by examining some inflammatory elements, such as CRP, TNF-α and IL-6, indicated it indirectly[48\u0026ndash;51]. And DOBS serves as a valuable instrument for evaluating the cumulative effects of pro-oxidant and antioxidant exposures in the research of chronic disease[11]. With shed new light on this, here eleven predictors were screened out and a non-invasive clinical nomogram was constructed, including DOBS, sex, age, racial, liver disease, heart disease, kidney disease, arthritis, kinds of arthritis, BMI and smoke. The clinical prediction model that contributed was proven could accurately predict the risk of sarcopenia. It might be a useful measure to help us investigate and diagnosis the sarcopenia, which further demonstrated the relationship between DOBS and sarcopenia.\u003c/p\u003e\u003cp\u003eThere are several advantages in the study. Firstly, the study used a large, nationally representative sample, which increases the generality of the findings. Secondly, a variety of complex statistical methods was used, such as appropriate covariate adjustment, subgroup analysis, interaction test and sensitivity analysis, to improve the credibility and practicality of the study. Meanwhile, the risk assessment prediction model is used to predict the risk of sarcopenia.\u003c/p\u003e\u003cp\u003eUndoubtedly, the study has several limitations. First, since the study adopted a cross - sectional study, determining a causal relationship between sarcopenia and DOBS is unfeasible. Secondly, due to the scarcity of data, the research failed to take into account some covariates that might influence the results, such as altitude, region and gene. Finally, the data the study used to calculate DOBS scores were obtained in the form of questionnaires, so there may be problems with participants' inaccurate recall. This reduced the accuracy of the findings. As a result, the findings may not apply to all patients with sarcopenia. And whether the study will yield the same results in other populations remains uncertain. So it is necessary to take these factors into account in future studies.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eNHANES, National Health and Nutrition Examination Survey;\u003c/p\u003e\n\u003cp\u003eBMI, Body Mass Index;\u003c/p\u003e\n\u003cp\u003eOBS, Oxidative\u0026nbsp;Balance Score;\u003c/p\u003e\n\u003cp\u003eDOBS, Dietary Oxidative\u0026nbsp;Balance Score;\u003c/p\u003e\n\u003cp\u003eEWGSOP, European Working Group on Sarcopenia in Older People;\u003c/p\u003e\n\u003cp\u003eAUC-ROC, area under the receiver operating characteristic curve;\u003c/p\u003e\n\u003cp\u003eNHI, non-heme iron;\u003c/p\u003e\n\u003cp\u003eDAG, Directed Acyclic Graph;\u003c/p\u003e\n\u003cp\u003eCRP, C-reactive protein;\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;, tumor necrosis factor-\u0026alpha;;\u003c/p\u003e\n\u003cp\u003eIL-6, interleukin-6.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval of this study was obtained from the ethics review board of the National Center for Health Statistics.\u0026nbsp;All participants gave written informed consent. The experimental protocol was established according to the ethical guidelines of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent has been obtained from the participants to publish this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The detailed information on the data is available at https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHualing Xiao was responsible for the design of this study and performed the experiments. Hualing Xiao and Minwen Lian analyzed/interpreted the results and wrote the manuscript. Jinshen He supervised the article as corresponding author and contacted the submission editor. All authors participated in the design of the research and the review of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCenters for Disease Control and Prevention research on human participants complies with the Health and Human Services Policy for Protection of Human Research Subjects. All National Health and Nutrition Examination Survey procedures and protocols have been reviewed and approved by the National Center for Health Statistics Research Ethics Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang, A., et al., \u003cem\u003eThe effect of vitamin D on sarcopenia depends on the level of physical activity in older adults.\u003c/em\u003e J Cachexia Sarcopenia Muscle, 2020. \u003cstrong\u003e11\u003c/strong\u003e(3): p. 678-689.\u003c/li\u003e\n\u003cli\u003eBenz, E., et al., \u003cem\u003eSarcopenia and Sarcopenic Obesity and Mortality Among Older People.\u003c/em\u003e JAMA Netw Open, 2024. \u003cstrong\u003e7\u003c/strong\u003e(3): p. e243604.\u003c/li\u003e\n\u003cli\u003eShafiee, G., et al., \u003cem\u003ePrevalence of sarcopenia in the world: a systematic review and meta- analysis of general population studies.\u003c/em\u003e J Diabetes Metab Disord, 2017. \u003cstrong\u003e16\u003c/strong\u003e: p. 21.\u003c/li\u003e\n\u003cli\u003eJang, J.Y., D. 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Kwon, \u003cem\u003eAssociation between oxidative balance score and new-onset hypertension in adults: A community-based prospective cohort study.\u003c/em\u003e Front Nutr, 2022. \u003cstrong\u003e9\u003c/strong\u003e: p. 1066159.\u003c/li\u003e\n\u003cli\u003eLa, R., et al., \u003cem\u003eAssociation between oxidative balance score and rheumatoid arthritis in female: a cross-sectional study.\u003c/em\u003e BMC Womens Health, 2024. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 225.\u003c/li\u003e\n\u003cli\u003eThomas, D.R., \u003cem\u003eLoss of skeletal muscle mass in aging: examining the relationship of starvation, sarcopenia and cachexia.\u003c/em\u003e Clin Nutr, 2007. \u003cstrong\u003e26\u003c/strong\u003e(4): p. 389-99.\u003c/li\u003e\n\u003cli\u003eJeejeebhoy, K.N., \u003cem\u003eMalnutrition, fatigue, frailty, vulnerability, sarcopenia and cachexia: overlap of clinical features.\u003c/em\u003e Curr Opin Clin Nutr Metab Care, 2012. \u003cstrong\u003e15\u003c/strong\u003e(3): p. 213-9.\u003c/li\u003e\n\u003cli\u003eTer Beek, L., et al., \u003cem\u003eUnsatisfactory knowledge and use of terminology regarding malnutrition, starvation, cachexia and sarcopenia among dietitians.\u003c/em\u003e Clin Nutr, 2016. \u003cstrong\u003e35\u003c/strong\u003e(6): p. 1450-1456.\u003c/li\u003e\n\u003cli\u003eTuttle, C.S.L., L.A.N. Thang, and A.B. Maier, \u003cem\u003eMarkers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis.\u003c/em\u003e Ageing Res Rev, 2020. \u003cstrong\u003e64\u003c/strong\u003e: p. 101185.\u003c/li\u003e\n\u003cli\u003eChhetri, J.K., et al., \u003cem\u003eChronic inflammation and sarcopenia: A regenerative cell therapy perspective.\u003c/em\u003e Exp Gerontol, 2018. \u003cstrong\u003e103\u003c/strong\u003e: p. 115-123.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eP\u003c/em\u003eicca, A., et al., \u003cem\u003eInflammatory, mitochondrial, and senescence-related markers: Underlying biological pathways of muscle aging and new therapeutic targets.\u003c/em\u003e Exp Gerontol, 2023. \u003cstrong\u003e178\u003c/strong\u003e: p. 112204.\u003c/li\u003e\n\u003cli\u003eSchaap, L.A., et al., \u003cem\u003eInflammatory markers and loss of muscle mass (sarcopenia) and strength.\u003c/em\u003e Am J Med, 2006. \u003cstrong\u003e119\u003c/strong\u003e(6): p. 526.e9-17.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DOBS, oxidative stress, prediction model, nomogram, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7084098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7084098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePrevious investigations have suggested a potential link between Dietary Oxidative Balance Score (DOBS) and sarcopenia, but about the clinic prediction models evaluating the risk of sarcopenia are rare. This study highlighted that the clinic prediction models construction would be also a novel tool to help diagnose sarcopenia clinically.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAccording to the data collected between 2011 and 2014 by the National Health and Nutrition Examination Survey (NHANES), the cross-sectional analysis evaluated DOBS, consisted of scores of sixteen dietary factors. Analytical approaches, including multivariable logistic regression, subgroup analysis, and sensitivity analysis, were applied to examine the association between DOBS and sarcopenia. The clinical prediction models were used to predict the risk of sarcopenia.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 10,732 participants, higher DOBS exhibited a closer correlation with sarcopenia (OR: 0.97; 95% CI: 0.95\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, stratification by quartiles revealed that each unit increase in DOBS (range: 16\u0026ndash;21) reduced sarcopenia likelihood by 33% (OR: 0.67; 95% CI: 0.52\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while values\u0026thinsp;\u0026gt;\u0026thinsp;21 corresponded to a 41% risk reduction (OR: 0.59; 95% CI: 0.43\u0026ndash;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both the training and validation cohorts demonstrated statistically significant results (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Hosmer-Lemeshow test. The predictive performance was consistent across datasets, with the area under the curve values (AUC) values of 0.80 (95% CI 0.79\u0026ndash;0.81) for the training set and 0.80 (95% CI 0.78\u0026ndash;0.82) for the validation set.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDiet might influence sarcopenia through modulating oxidative stress mechanisms. Antioxidant diets played a crucial role in reducing the possibility of sarcopenia and this study firstly provided a novel predictive assessment instrument for clinical diagnosis of sarcopenia.\u003c/p\u003e\u003ch2\u003eLevel of Evidence\u003c/h2\u003e\u003cp\u003eLevel IV, crosssectional study.\u003c/p\u003e","manuscriptTitle":"Clinical models for predicting the association between dietary oxidative balance score and sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 10:26:17","doi":"10.21203/rs.3.rs-7084098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f10cfe2-9cb2-4023-bb17-5d4578f38f55","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T17:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 10:26:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7084098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7084098","identity":"rs-7084098","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}