Dietary Total Antioxidant Capacity is Closely Associated with Skeletal Muscle Mass: A Cross-Sectional Study

Dietary Total Antioxidant Capacity is Closely Associated with Skeletal Muscle Mass: A Cross-Sectional Study | 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 Dietary Total Antioxidant Capacity is Closely Associated with Skeletal Muscle Mass: A Cross-Sectional Study Wendong Fang, Jie zhang, Lu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972809/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 Skeletal muscle is of great importance for human activity and quality of life, as its loss contributes greatly to immobilization, especially for aged individuals. An increased dietary intake of antioxidant vitamins may be beneficial for muscle loss because of aging. However, the quantitative relationship between total antioxidation capacity (TAC) of antioxidant vitamins and muscle mass is undetermined. 4009 participants from the National Health and Nutrition Examination Survey (NHANES) were included. Multivariate linear regression analysis was performed with demographic, lifestyle and dietary intake adjustment factors. The dose saturation effect was also determined by a saturation effect analysis. Subgroup analysis were performed forage and sex. In the fully adjusted model, per unit increase of dietary TAC was associated with an increase of 0.018 g/kg appendicular lean mass (95% CI: 0.007–0.029), 0.014 g/kg trunk lean mass (95% CI: 0.004–0.024) and 0.035 g/kg total lean mass (95% CI: 0.014–0.055). TAC was associated with an decrease of 0.004 kg/kg total percent fat (95% CI: -0.006–-0.002), 0.005 kg/kg trunk percent fat (95% CI: -0.007–-0.002) and 0.003 kg/m2 BMI (95% CI: -0.006–-0.001) at the same time. Subgroup analysis indicated that women and adults <50 years may experience the most significant association between TAC and skeletal muscle mass. We revealed a positive correlation between TAC and lean body mass, a negative association between TAC and body fat and BMI. Saturation values were found among people aged 40–59. Age and sex mediate these associations. Total antioxidation capacity Skeletal Muscle Mass Aging NHANES Figures Figure 1 Figure 2 Introduction Skeletal muscle, one of the most dynamic and plastic tissues in the human body, accounts for approximately 40% of the total body weight in humans and is fundamental to movement, energy homeostasis and overall quality of life [ 1 – 3 ]. However, skeletal muscle mass begins to decline in middle-aged and elderly people, and adults between the ages of 40 and 80 have already lost approximately 20% of their skeletal muscle mass during their lifetime [ 4 , 5 ]. Muscle mass decline makes middle-aged and elderly people vulnerable to bone fractures and chronic metabolic diseases, such as type 2 diabetes and obesity, leading to a significant increase in health care costs [ 6 , 7 ]. Apart from that, muscle loss has even been reported as an independent risk factor for high mortality in older individuals [ 8 , 9 ]. However, effective and strategic muscle-sparing intervention methods for older adults have not yet been revealed. In recent years, researchers have found that the level of oxidative stress in skeletal muscle increases with age, and the imbalance between increased reactive oxygen species (ROS) production and overall antioxidant defense is one of the leading causes of muscle damage [ 10 , 11 ]. At the same time, a series of studies have shown that dietary intake of antioxidant vitamins is associated with lower ROS and better-preserved muscle mass [ 12 – 14 ]; Additionally, exogenous supplementation of appropriate amounts of vitamins can protect against muscle loss during aging [ 15 , 16 ]. Total antioxidant capacity (TAC) is a term that reflects the antioxidant potential of dietary sources [ 17 , 18 ], which are mainly a combination of various vitamins [ 19 – 21 ]. Researchers believe that TAC participates in the progression of several diseases, such as hypertension and cancer [ 22 , 23 ]. However, the relationship between TAC and muscle loss has been scarcely studied. In a patient with liver cirrhosis, researchers found that TAC was positively correlated with grip strength and arm muscle area [ 24 ]. Other animal experiments have confirmed that antioxidant supplementation can improve skeletal muscle quality [ 25 , 26 ]. However, considering the subjects and population, there are still few studies focusing on the relationship between TAC and muscle loss in middle-aged and elderly people who are more vulnerable to muscle mass decline and its complications. Based on the National Health and Nutrition Screening Survey (NHANES) database, the purpose of this study was to investigate the association between dietary TAC of antioxidant vitamins and skeletal muscle mass in middle-aged and elderly individuals in the United States after adjusting for potential risk factors. Methods Study population The National Health and Nutrition Examination Survey (NHANES) is a representative U.S. population survey that uses complex multilevel probability sampling to provide information on the nutritional status and health status of the general U.S. population. The NHANES research programs were approved by the NCHS Research Ethics Review Committee and received written informed consent from the participants. This study uses the US NHANES database for the rolling period 2011–2018 (n = 39156). After excluding patients with missing information on demographics, diet, examination, and questionnaires, a total of 4009 subjects were included in the analysis. Figure 1 shows an example of a selection flow chart. Estimation of TAC from diet During the first-day interview, dietary antioxidant vitamin data from the 24-hour period prior to the interview (midnight to midnight) were collected. The antioxidant vitamins recorded in the NHANES dietary interview consisted of vitamin A, vitamin C, vitamin E, α-carotene, β-carotene, β-cryptoxanthin, lycopene and lutein-zeaxanthin. According to Floegel et al. [ 27 ], the individual antioxidant capacity of participants was determined by multiplying the individual amount of antioxidant compounds (antioxidant vitamins) by their antioxidant capacities: $$\text{Theoretical TAC=Σ(antioxidant content}\frac{\text{mg}}{\text{100g}}\text{*antioxidant capacity}\frac{\text{mg VCE}}{\text{100g}})$$ Antioxidant capacity is the vitamin C equivalent antioxidant capacity of the corresponding antioxidant vitamin, which was derived from the TAC database of the US diet. TAC was divided into the first quartile (0.236 to 22.188 mg VCE/100 g), second quartile (22.188 to 53.255 mg VCE/100 g), third quartile (53.255 to 112.933 mg VCE/100 g) and fourth quartile (112.933 to 779.247 mg VCE/100 g) according to the survey-weighted quartile. Covariates The demographic factors included age, sex (Men and Women), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race) and socioeconomic status (Low, PIR 3.5). Lifestyle factors consisted of alcohol consumption (yes and no), smoking status (never, previously and currently), physical activity (none, moderate and vigorous) and sedentary activity (supplementary Table S1 ).Other factors that may influence body mass were obtained from the first-day interview diet data and included protein, dietary fiber, calcium and phosphorus intake. Dependent variables There are six dependent variables in this study, including appendicular relative lean mass (relative to body weight, g/kg), trunk relative lean mass (relative to body weight, g/kg), total relative lean mass (relative to body weight, g/kg), total percent fat (percent of body weight, %), trunk percent fat (percent of body weight, %) and body mass index (BMI, kg/m 2 ). Appendicular lean body mass, more suitable for the evaluation of skeletal muscle mass, was calculated by summing the lean mass (excluding bone mineral content) of the right and left leg and the right and left arm as measured by dual-energy X-ray absorptiometry (DEXA). To account for the impact of body mass on variations in these outcomes, all variables assessed by DEXA were represented relative to body mass (g per kg of body mass for all lean mass; kg per kg of body mass for all fat mass). Statistical analysis The statistical analysis was conducted by using the statistical computing and graphics software R (version 4.2.1) and EmpowerStats (version 5.0). Baseline descriptive statistics of the study population were placed in a table and divided by subgroup, and continuous variables are described using means ± standard errors (SE). The beta values and 95% confidence intervals (CI) were determined using multivariate linear regression analysis between the TAC and all outcomes. The multivariate linear regression was built using three models: Model 1: not adjusted; Model 2: adjusted for sex, age, race and socioeconomic status; Model 3: adjusted for all covariates. Smoothed curve fits were carried out concurrently with the variable adjustments. We used a threshold effects analysis model to examine the relationship and saturation effect between TAC and body mass. Finally, subgroup analysis were used to determine the population who experienced the most benefit. We used dietary day one sample weight to analyze all the results, and P < 0.05 was considered statistically significant. Results 3.1 Descriptions of participants The characteristics of weighted demographics, dietary data and lifestyle of the participants are shown in Table 1 . A total of 4009 participants were included in this study. Of these participants, the average age was 49.69, and 50.18% were man. Among different groups of TAC (quartiles, Q1–Q4), age, sex, race, socioeconomic status, smoking, physical activity, appendicular relative lean mass, trunk relative lean mass, total relative lean mass, total percent fat, trunk percent fat, protein, dietary fiber, calcium and phosphorus were all significantly different (P < 0.05). The relationships between the dependent variable and the covariates can also be seen in Table S2. Table 1 Description of the participants Total Antioxidant Capacity All (N = 4009) Q1 Q2 Q3 Q4 P-value Age(year) 49.69 ± 0.17 48.91 ± 0.29 49.83 ± 0.21 49.81 ± 0.32 50.22 ± 0.32 0.025 Sex 0.044 Men 50.18 (1.21) 46.63 (2.35) 52.84 (2.23) 47.08 (2.55) 54.58 (2.54) Women 49.81(1.21) 53.37 (2.35) 47.16 (2.23) 52.92 (2.55) 45.42 (2.54) Race 0.027 Mexican American 8.39 (1.01) 6.76 (1.03) 9.1373 (1.39) 7.93(1.16) 9.87 (1.67) Other Hispanic 5.86 (0.78) 4.25 (0.87) 5.92 (0.93) 6.08 (1.04) 7.26 (1.08) Non-Hispanic White 66.19 (2.23) 70.50 (3.10) 66.23 (2.66) 66.87 (2.76) 60.84(2.98) Non-Hispanic Black 10.18 (1.02) 10.25 (1.52) 10.01 (1.30) 9.3991 (1.13) 11.12 (1.14) Other Race 9.38 (0.77) 8.25 (1.25) 8.70(1.10) 9.72 (1.09) 10.92 (1.48) Socio-economic status < 0.001 Low 18.58 (1.46) 24.07(2.68) 19.27 (1.47) 14.48 (1.68) 16.41 (1.86) Middle 31.60 (1.49) 35.94 (2.53) 33.36 (2.80) 30.99 (2.30) 25.85 (2.17) High 49.82(2.04) 39.99 (3.07) 47.37 (3.07) 54.53 (2.68) 57.74 (2.81) BMI (kg/m2) 0.054 Thin (< 18.5) 0.76 (0.19) 0.83 (0.34) 0.89 (0.42) 0.95 (0.53) 0.3663 (0.19) Normal (18.5–24.9) 24.81 (1.03) 21.18 (1.73) 21.23 (1.65) 27.99 (2.27) 28.90 (2.04) Overweight (25.0–29.9) 36.59 (1.24) 37.77 (2.14) 37.67 (2.03) 34.76 (2.67) 36.18 (2.20) Obesity (≥ 30.0) 37.84 (1.32) 40.22 (1.93) 40.21 (2.09) 36.29 (2.45) 34.55 (2.43) Alcohol 0.613 No 22.63 (1.10) 23.90 (2.19) 20.33 (1.68) 22.62 (2.23) 23.65 (2.30) Yes 77.37 (1.10) 76.10 (2.19) 79.67 (1.68) 77.38 (2.23) 76.35 (2.30) Smoke < 0.001 Never 52.56 (1.43) 45.96 (2.52) 50.25 (2.21) 57.41 (2.50) 56.76 (2.67) Former 24.65 (1.02) 21.19 (2.06) 24.93 (2.20) 24.64 (2.04) 28.05 (2.54) Now 22.79 (1.16) 32.85 (2.25) 24.82 (2.06) 17.95 (1.91) 15.19 (1.89) Physical Activity < 0.001 No 44.25 (1.3) 53.98 (2.63) 44.75 (2.12) 41.93 (2.74) 35.89 (2.55) Moderate 31.45 (1.39) 28.89 (2.58) 34.04 (2.29) 33.21 (2.62) 29.55 (2.33) Vigorous 24.32 (1.35) 17.13 (2.07) 21.21 (2.01) 24.86 (2.56) 34.56 (2.65) Sedentary Activity (min/day) 423.16 ± 13.78 457.07 ± 44.39 405.30 ± 13.81 433.80 ± 24.24 393.95 ± 10.93 0.288 Appendicular Relative Lean Mass (g/kg) 274.69 ± 1.10 268.91 ± 1.88 273.86 ± 2.03 275.11 ± 2.44 281.22 ± 1.97 < 0.001 Trunk Relative Lean Mass (g/kg) 321.92 ± 0.97 318.37 ± 1.83 322.33 ± 1.34 322.17 ± 1.91 324.99 ± 1.83 0.036 Total Relative Lean Mass (g/kg) 635.72 ± 1.95 625.74 ± 3.57 635.19 ± 3.14 636.75 ± 4.23 645.74 ± 3.59 0.001 Total Percent Fat 33.88 ± 0.20 34.91 ± 0.36 33.95 ± 0.32 33.74 ± 0.44 32.86 ± 0.37 < 0.001 Trunk Percent Fat 33.45 ± 0.21 34.47 ± 0.32 33.79 ± 0.32 33.02 ± 0.44 32.46 ± 0.39 < 0.001 Protein (g/day) 84.90 ± 0.91 68.47 ± 1.31 84.33 ± 1.72 92.21 ± 1.77 95.10 ± 2.00 < 0.001 Dietary fibre(g/d) 17.63 ± 0.30 11.25 ± 0.31 16.03 ± 0.36 20.17 ± 0.61 23.30 ± 0.54 < 0.001 Calcium (mg/day) 978.90 ± 15.96 768.33 ± 23.79 953.7123.92 1051.35 ± 31.77 1150.83 ± 34.66 < 0.001 Phosphorus (mg/day) 1435.37 ± 17.07 1159.40 ± 25.29 1409.23 ± 26.57 1558.90 ± 33.21 1622.60 ± 32.79 < 0.001 Data are %N (SE) for categorical variables or mean ± SE for continuous variables. 3.2. Relationship between TAC and skeletal muscle mass There was a significant positive association between dietary TAC and lean body mass in three weighted univariate and multivariate linear regression models (Table 2 ). In the fully adjusted model, each 1-unit increase in dietary TAC was associated with an increase of 0.018 g/kg appendicular lean mass (95% CI, 0.007 to 0.029), 0.014 g/kg trunk lean mass (95% CI, 0.004 to 0.024) and 0.035 g/kg total lean mass (95% CI, 0.014 to 0.055). Table 2 Simple and multiple linear regression analysis of Total Antioxidant Capacity and lean mass Appendicular Relative Lean Mass (g/kg) Trunk Relative Lean Mass (g/kg) Total Relative Lean Mass (g/kg) Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 TAC 0.052 (0.036, 0.068) < 0.001 0.029 (0.018, 0.039) < 0.001 0.018 (0.007, 0.029) < 0.001 0.026 (0.013, 0.039) < 0.001 0.017 (0.007, 0.026) < 0.001 0.014 (0.004, 0.024) 0.008 0.082 (0.053, 0.111) < 0.001 0.048 (0.028, 0.067) < 0.001 0.035 (0.014, 0.055) < 0.001 TAC (quartile) Q1 Ref Ref Ref Ref Ref Ref Ref Ref Ref Q2 4.948 (1.098, 8.797) 0.012 1.059 (-1.327, 3.444) 0.384 -0.036 (-2.37, 2.266) 0.976 3.962 (0.924, 7.000) 0.011 1.602 (-0.635, 3.839) 0.161 1.247 (-0.948, 3.442) 0.266 9.452 (2.681, 16.223) 0.006 3.046 (-1.521, 7.612) 0.191 1.752 (-2.673, 6.177) 0.438 Q3 6.201 (2.402, 10.001) 0.001 5.778 (3.413, 8.143) < 0.001 4.114 (1.736, 6.492) < 0.001 3.806 (0.808, 6.805) 0.013 4.237 (2.018, 6.455) < 0.001 3.931 (1.664,6.199) < 0.001 11.008 (4.324, 17.691) 0.001 10.841 (6.313, 15.369) < 0.001 9.153 (4.581, 13.724) 0.002 Q4 12.304 (8.431, 16.177) < 0.001 6.807 (4.374, 9.239) < 0.001 3.576 (1.039, 6.113) 0.006 6.619 (3.563, 9.675) < 0.001 4.108 (1.826, 6.390) < 0.001 3.087 (0.667, 5.506) 0.012 19.999 (13.187, 26.811) < 0.001 11.655 (6.998, 16.313) < 0.001 7.609 (2.732, 12.487) 0.002 P for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Model 1: without adjust. Model 2: age, sex, race and socio-economic status were adjusting. Model 3: model 2 plus smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, Calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented. Dietary TAC also showed a significant negative association with total percent fat, trunk percent fat and BMI (Table 3 ). Assuming linearity, each 1-unit increase in dietary TAC was associated with − 0.004 kg/kg total percent fat (95% CI: -0.006, -0.002), -0.005 kg/kg trunk percent fat (95% CI: -0.007, -0.002) and − 0.003 kg/m 2 BMI (95% CI: -0.006, -0.001). Table 3 Simple and multiple linear regression analysis of Total Antioxidant Capacity and fat/BMI. Total Relevant Fat(kg/kg) Trunk Relevant Fat(kg/kg) BMI(kg/m 2 ) Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 TAC -0.008 (-0.011, -0.005) < 0.001 -0.005 (-0.007, -0.003) < 0.001 -0.004 (-0.006, -0.002) < 0.001 -0.009 (-0.012, -0.006) < 0.001 -0.007 (-0.009, -0.004) < 0.001 -0.005 (-0.007, -0.002) < 0.001 -0.004 (-0.006, -0.001) 0.003 -0.003 (-0.005, -0.001) 0.008 -0.003 (-0.006, -0.001) 0.006 TAC (quartile) Q1 Ref Ref Ref Ref Ref Ref Ref Ref Ref Q2 -0.957 (-1.652, -0.262) 0.007 -0.316 (-0.795, 0.162) 0.195 -0.202 (-0.666, 0.261) 0.392 -0.686 (-1.363, -0.010) 0.047 -0.309 (-0.872, 0.255) 0.283 -0.152 (-0.697, 0.393) 0.585 -0.068 (-0.613, 0.478) 0.808 -0.008 (-0.534, 0.551) 0.976 -0.114 (-0.650, 0.422) 0.677 Q3 -1.168 (-1.854, -0.482) < 0.001 -1.152 (-1.626, -0.677) < 0.001 -1.011 (-1.490, -0.532) <0.001 -1.454 (-2.122, -0.786) <0.001 -1.525 (-2.083, -0.966) <0.001 -1.304 (-1.867, -0.741) < 0.001 -0.654 (-1.192, -0.115) 0.017 -0.502 (-1.040, 0.036) 0.068 -0.786 (-1.340, -0.233) 0.005 Q4 -2.048 (-2.747, -1.349) < 0.001 -1.209 (-1.697, -0.721) < 0.001 -0.832 (-1.343, -0.320) 0.001 -2.017 (-2.697, -1.336) < 0.001 -1.545 (-2.120, -0.971) < 0.001 -1.004 (-1.605, -0.403) 0.001 -0.989 (-1.507, -0.410) < 0.001 -0.817 (-1.370, -0.263) 0.004 -0.924 (-1.514, -0.333) 0.002 P for trend < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Model: without adjust. Model 2: age, sex, race and socio-economic status were adjusting. Model 3: model 2 plus smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, Calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented. 3.3 Dose–response relationships and their saturation effect Figure 2 shows the dose‒response relationship between dietary intake and total antioxidant capacity for all outcomes. Combining the smoothing curve and TAC quartile, a saturation effect was found between TAC and all outcomes. Then, a saturation effect analysis explored these turning points and the saturation effect value was 67.433 mg VCE/100 g in the appendicular relative lean mass, 64.072 mg VCE/100 g in the trunk relative lean mass, 64.809 mg VCE/100 g in the total relative lean mass, 67.433 mg VCE/100 g in the total percent fat, 65.955 mg VCE/100 g in the trunk percent fat and 71.167 mg VCE/100 g in BMI (Table 4 ). Table 4 Saturation effect analysis of TAC on all outcomes Outcomes TAC turning point (K),mg VCE/100 g K Appendicular Relative Lean Mass (g/kg) 67.433 0.077 (0.035, 0.118) < 0.001 0.006 (-0.008, 0.019) 0.396 Trunk Relative Lean Mass (g/kg) 64.072 0.071 (0.029, 0.112) 0.001 0.003 (-0.009, 0.016) 0.610 Total Relative Lean Mass (g/kg) 64.809 0.171 (0.087, 0.254) < 0.001 0.009 (-0.017, 0.034) 0.498 Total Percent Fat 67.433 -0.018 (-0.027, -0.010) < 0.001 -0.001 (-0.003, 0.002) 0.640 Trunk Percent Fat 65.955 -0.025 (-0.035, -0.015) < 0.001 -0.001 (-0.004, 0.002) 0.600 BMI(kg/m 2 ) 71.167 -0.016 (-0.025, -0.006) 0.0009 0.001 (-0.004, 0.002) 0.651 Age, sex, race, socio-economic status, smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented. 3.4 Subgroup analysis of the association between dietary TAC and skeletal muscle mass Our study population contained participants aged 40 to 59 years with a mix of both men and women participants, so we also explored how age and sex influenced the aforementioned associations (Table 5 , Figure S1 , Figure S2). When stratifying by age, the associations were significant in patients aged 40–50 years rather than in those aged 50–59 years. In the subgroup analysis of sex, women participants had significant associations between dietary TAC and skeletal muscle mass. Therefore, women younger than 50 years may experience the best benefits from dietary TAC. Table 5 Association of dietary TAC with all outcomes, stratified by age and sex. Appendicular Relative Lean Mass(g/kg) Trunk Relative Lean Mass(g/kg) Total Relative Lean Mass(g/kg) Total Percent Fat(kg/kg) Trunk Percent Fat(kg/kg) BMI(kg/m 2 ) Age(year) 50 0.014 (-0.000, 0.029) 0.052 0.014 (-0.000, 0.027) 0.053 0.031 (0.003, 0.058) 0.032 -0.003 (-0.006, -0.000) 0.025 -0.004 (-0.007, -0.001) 0.022 -0.003 (-0.006, 0.001) 0.134 Sex men 0.007 (-0.007, 0.021) 0.322 0.006 (-0.007, 0.019) 0.350 0.014 (-0.013, 0.040) 0.308 -0.002 (-0.004, 0.001) 0.281 -0.003 (-0.006, 0.000) 0.067 -0.002 (-0.005, 0.001) 0.202 women 0.032 (0.017, 0.047) < 0.001 0.023 (0.008, 0.037) 0.002 0.061 (0.032, 0.090) < 0.001 -0.006 (-0.009, -0.003) < 0.001 -0.007 (-0.011, -0.003) < 0.001 -0.005 (-0.009, -0.002) 0.003 Race, socio-economic status, smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented. Discussion The present analysis was conducted to determine the relationship between dietary TAC intake and body mass components in adults over 40 years old. The US population data were extracted from the NHANES database. The results showed that for adults who had an increased risk of skeletal muscle mass loss, higher dietary TAC is related to a greater preservation of appendicular lean mass, trunk lean mass and total lean mass. Also, higher dietary TAC intake is associated with lower total percent fat, trunk percent fat and BMI. To the best of our knowledge, the association between dietary TAC and skeletal muscle mass has not yet been investigated in a cohort with this size and scope [ 28 , 29 ]. Consistent with a previous cross-sectional study in cirrhotic outpatients, dietary TAC was positively associated with arm muscle area [ 6 ]. In a three-year-long cohort study, higher dietary antioxidant intake had positive effects on BMI and abdominal fat [ 10 ]. Another study of children and adolescents showed that dietary antioxidant intake had an inverse association with total body fat in obese subjects [ 11 ]. Above all, dietary TAC intake has an inspiring effect on lean body mass, fat and BMI [ 30 , 31 ]. Although some studies have been deployed to detect the association between antioxidant intake and body components in particular populations, including children and adolescents, women, and healthy young adults, they not only primarily focused on the effects of single antioxidant intake, which might not fully explain the synergistic effects of all antioxidant vitamins in the diet [ 12 ], but also provide less knowledge of the middle-aged population who suffer a higher risk of skeletal muscle mass loss [ 32 ]. In this study, we paid attention to the comprehensive TAC values rather than considering the effects of single compounds, and we focused on the people who may experience greater benefits from the above results. Dose–response curves suggest that all outcomes displayed a closely correlation with dietary TAC. However, there also displayed a saturation effect of correlation between dietary TAC and skeletal muscle mass. All these results indicated that higher dietary TAC would likely improve lean body mass and decrease body fat and BMI. The saturation effect revealed that there was a threshold effect between dietary TAC and all outcomes. A subsequent subgroup analysis indicated that women and individuals aged 40–50 years will experience maximum benefits from higher dietary TAC on skeletal muscle mass. However, there are still some limitations in our study. First, this study was a cross-sectional design, which means that the causal relationship between dietary TAC and skeletal muscle mass could not be clearly determined owing to its original survey. Second, vitamin supplementation, such as vitamin C supplementation, is not taken into consideration while only focusing on dietary TAC intake in this design[ 33 ]. Finally, the bioavailability of dietary vitamins in participants was not included in this study because of the defect value in NHANES dataset[ 34 ]. Furthermore, more work should be done to investigate the relationship between serum TAC levels and skeletal muscle mass both clinically and experimentally in the future to figure out their casual effect and potential mechanism. In conclusion, this is the first study to examine the association between dietary TAC and lean body mass and percent body fat in adults aged above 40 years old with a relatively large sample size. In this study, multiple linear regression models, smoothed curve fitting, saturation effect analysis and subgroup analysis were taken into consideration to examine the relationship between dietary TAC and body composition in US middle-aged adults. We found not only a simple linear positive correlation between TAC and lean body mass and negative association between TAC and percent body fat and BMI but also a saturation threshold. Our final subgroup analysis also indicate women younger than 50 years may experience the best benefits from higher dietary TAC. This work suggests that keeping dietary TAC under saturation value may provide its biggest benefits for middle-aged adults for their aging-related skeletal muscle loss. Declarations Ethics approval and consent to participate The NHANES research programs were approved by the NCHS Research Ethics Review Committee and received written informed consent from the participants. Consent for publication Written informed consent for publication was obtained from all participants. Availability of data and materials Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests The authors declare no competing interests. Funding There is no funding associated with the work featured in this article. Authors' contributions Lu Wang and Wendong Fang were in charge of proposing the topic, designing the research method and writing the first draft. Wendong Fang and Jie Zhang were in charge of the data processing data validation and review. All authors read and approved the final manuscript. References Frontera W RJ Ochala (2015) Skeletal muscle: a brief review of structure and function. Calcif Tissue Int 96:183-95. https://doi.org/10.1007/s00223-014-9915-y Tanaka H, N ShimizuN Yoshikawa (2017) Role of skeletal muscle glucocorticoid receptor in systemic energy homeostasis. Exp Cell Res 360:24-26. https://doi.org/10.1016/j.yexcr.2017.03.049 Bear D E, S M ParryZ A Puthucheary (2018) Can the critically ill patient generate sufficient energy to facilitate exercise in the ICU? Curr Opin Clin Nutr Metab Care 21:110-115. https://doi.org/10.1097/mco.0000000000000446 Marzetti E, A C Hwang, M Tosato, L N Peng, R Calvani, A Picca, et al (2018) Age-related changes of skeletal muscle mass and strength among Italian and Taiwanese older people: Results from the Milan EXPO 2015 survey and the I-Lan Longitudinal Aging Study. Exp Gerontol 102:76-80. https://doi.org/10.1016/j.exger.2017.12.008 Alcazar J, P Aagaard, B Haddock, R S Kamper, S K Hansen, E Prescott, et al (2020) Age- and Sex-Specific Changes in Lower-Limb Muscle Power Throughout the Lifespan. J Gerontol A Biol Sci Med Sci 75:1369-1378. https://doi.org/10.1093/gerona/glaa013 Al-Ozairi E, D Alsaeed, D Alroudhan, N Voase, A Hasan, J M R Gill, et al (2021) Skeletal Muscle and Metabolic Health: How Do We Increase Muscle Mass and Function in People with Type 2 Diabetes? J Clin Endocrinol Metab 106:309-317. https://doi.org/10.1210/clinem/dgaa835 Kaji H (2014) Interaction between Muscle and Bone. J Bone Metab 21:29-40. https://doi.org/10.11005/jbm.2014.21.1.29 Metter E J, L A Talbot, M SchragerR Conwit (2002) Skeletal muscle strength as a predictor of all-cause mortality in healthy men. J Gerontol A Biol Sci Med Sci 57:B359-65. https://doi.org/10.1093/gerona/57.10.b359 Rantanen T, T Harris, S G Leveille, M Visser, D Foley, K Masaki, et al (2000) Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. J Gerontol A Biol Sci Med Sci 55:M168-73. https://doi.org/10.1093/gerona/55.3.m168 Boengler K, M Kosiol, M Mayr, R SchulzS Rohrbach (2017) Mitochondria and ageing: role in heart, skeletal muscle and adipose tissue. J Cachexia Sarcopenia Muscle 8:349-369. https://doi.org/10.1002/jcsm.12178 Leitner L M, R J Wilson, Z YanA Gödecke (2017) Reactive Oxygen Species/Nitric Oxide Mediated Inter-Organ Communication in Skeletal Muscle Wasting Diseases. Antioxid Redox Signal 26:700-717. https://doi.org/10.1089/ars.2016.6942 Higgins M R, A IzadiM Kaviani (2020) Antioxidants and Exercise Performance: With a Focus on Vitamin E and C Supplementation. Int J Environ Res Public Health 17:https://doi.org/10.3390/ijerph17228452 Robinson S M, J Y Reginster, R Rizzoli, S C Shaw, J A Kanis, I Bautmans, et al (2018) Does nutrition play a role in the prevention and management of sarcopenia? Clin Nutr 37:1121-1132. https://doi.org/10.1016/j.clnu.2017.08.016 Cruz-Jentoft A J, B Dawson Hughes, D Scott, K M SandersR Rizzoli (2020) Nutritional strategies for maintaining muscle mass and strength from middle age to later life: A narrative review. Maturitas 132:57-64. https://doi.org/10.1016/j.maturitas.2019.11.007 Dekkers J C, L J van DoornenH C Kemper (1996) The role of antioxidant vitamins and enzymes in the prevention of exercise-induced muscle damage. Sports Med 21:213-38. https://doi.org/10.2165/00007256-199621030-00005 Nilsson M I, A Mikhail, L Lan, A Di Carlo, B Hamilton, K Barnard, et al (2020) A Five-Ingredient Nutritional Supplement and Home-Based Resistance Exercise Improve Lean Mass and Strength in Free-Living Elderly. Nutrients 12:https://doi.org/10.3390/nu12082391 Gupta S, R Finelli, A AgarwalR Henkel (2021) Total antioxidant capacity-Relevance, methods and clinical implications. Andrologia 53:e13624. https://doi.org/10.1111/and.13624 Bartosz G (2003) Total antioxidant capacity. Adv Clin Chem 37:219-92. https://doi.org/10.1016/s0065-2423(03)37010-6 Lindschinger M, F Tatzber, W Schimetta, I Schmid, B Lindschinger, G Cvirn, et al (2019) A Randomized Pilot Trial to Evaluate the Bioavailability of Natural versus Synthetic Vitamin B Complexes in Healthy Humans and Their Effects on Homocysteine, Oxidative Stress, and Antioxidant Levels. Oxid Med Cell Longev 2019:6082613. https://doi.org/10.1155/2019/6082613 Prieto P, M PinedaM Aguilar (1999) Spectrophotometric quantitation of antioxidant capacity through the formation of a phosphomolybdenum complex: specific application to the determination of vitamin E. Anal Biochem 269:337-41. https://doi.org/10.1006/abio.1999.4019 Mazdak H, Z Tolou GhamariM Gholampour (2020) Bladder cancer: total antioxidant capacity and pharmacotherapy with vitamin-E. Int Urol Nephrol 52:1255-1260. https://doi.org/10.1007/s11255-020-02411-3 Fateh H L, N Mirzaei, M I M Gubari, M Darbandi, F NajafiY Pasdar (2022) Association between dietary total antioxidant capacity and hypertension in Iranian Kurdish women. BMC Womens Health 22:255. https://doi.org/10.1186/s12905-022-01837-4 Han D, M ChungY Park (2022) Association of Dietary Total Antioxidant Capacity with Cancer Recurrence and Mortality among Breast Cancer Survivors: A Prospective Cohort Study. Nutr Cancer 74:3253-3262. https://doi.org/10.1080/01635581.2022.2074061 de Freitas Lima L, F de Faria Ghetti, H H M Hermsdorff, D G de Oliveira, G Teixeira, L de Castro Ferreira, et al (2020) Dietary total antioxidant capacity is positively associated with muscular strength in cirrhotic outpatients: a cross-sectional study. J Hum Nutr Diet 33:78-85. https://doi.org/10.1111/jhn.12698 Wen W, X Chen, Z Huang, D Chen, B Yu, J He, et al (2022) Dietary lycopene supplementation improves meat quality, antioxidant capacity and skeletal muscle fiber type transformation in finishing pigs. Anim Nutr 8:256-264. https://doi.org/10.1016/j.aninu.2021.06.012 Qin X, T Zhang, Y Cao, B Deng, J ZhangJ Zhao (2020) Effects of dietary sea buckthorn pomace supplementation on skeletal muscle mass and meat quality in lambs. Meat Sci 166:108141. https://doi.org/10.1016/j.meatsci.2020.108141 Floegel A, D O Kim, S J Chung, W O Song, M L Fernandez, R S Bruno, et al (2010) Development and validation of an algorithm to establish a total antioxidant capacity database of the US diet. Int J Food Sci Nutr 61:600-23. https://doi.org/10.3109/09637481003670816 Ratajczak M, D Skrypnik, P Bogdański, E Mądry, J Walkowiak, M Szulińska, et al (2019) Effects of Endurance and Endurance-Strength Training on Endothelial Function in Women with Obesity: A Randomized Trial. Int J Environ Res Public Health 16:https://doi.org/10.3390/ijerph16214291 Lim S H, S H FanY H Say (2012) Plasma total antioxidant capacity (TAC) in obese Malaysian subjects. Malays J Nutr 18:345-54. Besagil P S, S ÇalapkorurH Şahin (2020) Determination of the relationship between total antioxidant capacity and dietary antioxidant intake in obese patients. Niger J Clin Pract 23:481-488. https://doi.org/10.4103/njcp.njcp_212_19 Amani R, M Parohan, N JomehzadehM H Haghighizadeh (2019) Dietary and Biochemical Characteristics Associated with Normal-Weight Obesity. Int J Vitam Nutr Res 89:331-336. https://doi.org/10.1024/0300-9831/a000477 Bloom I, C Shand, C Cooper, S RobinsonJ Baird (2018) Diet Quality and Sarcopenia in Older Adults: A Systematic Review. Nutrients 10:https://doi.org/10.3390/nu10030308 Mansournia M A, V Ostadmohammadi, A Doosti-Irani, M Ghayour-Mobarhan, G Ferns, H Akbari, et al (2018) The Effects of Vitamin D Supplementation on Biomarkers of Inflammation and Oxidative Stress in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Horm Metab Res 50:429-440. https://doi.org/10.1055/a-0630-1303 Platel KK Srinivasan (2016) Bioavailability of Micronutrients from Plant Foods: An Update. Crit Rev Food Sci Nutr 56:1608-19. https://doi.org/10.1080/10408398.2013.781011 Additional Declarations No competing interests reported. Supplementary Files SupplymentMaterial.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-3972809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276079642,"identity":"fb8f51fd-5e49-4847-b5e6-d63055864678","order_by":0,"name":"Wendong Fang","email":"","orcid":"","institution":"the Lu'an People's Hospital, The Lu’an Hospital, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wendong","middleName":"","lastName":"Fang","suffix":""},{"id":276079643,"identity":"e23e7fc6-3c79-4c5c-9a73-f0462ee628aa","order_by":1,"name":"Jie zhang","email":"","orcid":"","institution":"the Lu'an People's Hospital, The Lu’an Hospital, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"zhang","suffix":""},{"id":276079645,"identity":"08859acf-3960-480f-9ab3-6f1e8c8b2039","order_by":2,"name":"Lu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACPghlwcDPzHz4AVFa2CCUBINkO1uaAWlaDM7zKEgQp4X9jOHngl8SiZsP8zAYMNTYRBPWwpNjLD2zTyJx22HeAw8YjqXlNhB2WO4Gad4ekBa+BAPGhsNEaOF/u/k3SMvmZh4DCeK0SORuk+b5IZG4gZl4Le+/WfM2SBjPOAwM5ARi/MLPn5Z8m+ePjWx//+HDDz7U2BDWAgaMbVBGAlHKweAP8UpHwSgYBaNgBAIAXik51YZLKWUAAAAASUVORK5CYII=","orcid":"","institution":"the Lu'an People's Hospital, The Lu’an Hospital, Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-20 12:50:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3972809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3972809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52032646,"identity":"b2466732-d358-496d-a955-35128c649afd","added_by":"auto","created_at":"2024-03-05 16:37:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":712033,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic flowchart of including and excluding.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3972809/v1/c81e08fa01eb05787b706cf8.jpg"},{"id":52032645,"identity":"1b5247a1-48fe-445b-ae1d-e0f0934c36ea","added_by":"auto","created_at":"2024-03-05 16:37:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2385562,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between dietary intake and total antioxidant capacity for all outcomes. (A)Appendicular Relative Lean Mass (g/kg); (B)Trunk Relative Lean Mass (g/kg); (C)Total Relative Lean Mass (g/kg); (D)Total Percent Fat (kg/kg); (E)Trunk Percent Fat (kg/kg); (F)BMI (kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3972809/v1/e824a573ba079dca83120b19.jpg"},{"id":67577301,"identity":"b2ca2b5e-ef16-4e0c-89fc-4810a68e0594","added_by":"auto","created_at":"2024-10-27 13:01:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3981046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3972809/v1/68579e50-5220-4a3c-9a65-9517a8a1887d.pdf"},{"id":52032647,"identity":"6e5b1035-58ca-4405-8289-7d815a3cf69b","added_by":"auto","created_at":"2024-03-05 16:37:52","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2901050,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3972809/v1/172a56f3acc3db0687fbb322.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary Total Antioxidant Capacity is Closely Associated with Skeletal Muscle Mass: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkeletal muscle, one of the most dynamic and plastic tissues in the human body, accounts for approximately 40% of the total body weight in humans and is fundamental to movement, energy homeostasis and overall quality of life [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, skeletal muscle mass begins to decline in middle-aged and elderly people, and adults between the ages of 40 and 80 have already lost approximately 20% of their skeletal muscle mass during their lifetime [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Muscle mass decline makes middle-aged and elderly people vulnerable to bone fractures and chronic metabolic diseases, such as type 2 diabetes and obesity, leading to a significant increase in health care costs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Apart from that, muscle loss has even been reported as an independent risk factor for high mortality in older individuals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, effective and strategic muscle-sparing intervention methods for older adults have not yet been revealed.\u003c/p\u003e \u003cp\u003eIn recent years, researchers have found that the level of oxidative stress in skeletal muscle increases with age, and the imbalance between increased reactive oxygen species (ROS) production and overall antioxidant defense is one of the leading causes of muscle damage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At the same time, a series of studies have shown that dietary intake of antioxidant vitamins is associated with lower ROS and better-preserved muscle mass [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; Additionally, exogenous supplementation of appropriate amounts of vitamins can protect against muscle loss during aging [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Total antioxidant capacity (TAC) is a term that reflects the antioxidant potential of dietary sources [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which are mainly a combination of various vitamins [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Researchers believe that TAC participates in the progression of several diseases, such as hypertension and cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the relationship between TAC and muscle loss has been scarcely studied. In a patient with liver cirrhosis, researchers found that TAC was positively correlated with grip strength and arm muscle area [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Other animal experiments have confirmed that antioxidant supplementation can improve skeletal muscle quality [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, considering the subjects and population, there are still few studies focusing on the relationship between TAC and muscle loss in middle-aged and elderly people who are more vulnerable to muscle mass decline and its complications.\u003c/p\u003e \u003cp\u003eBased on the National Health and Nutrition Screening Survey (NHANES) database, the purpose of this study was to investigate the association between dietary TAC of antioxidant vitamins and skeletal muscle mass in middle-aged and elderly individuals in the United States after adjusting for potential risk factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a representative U.S. population survey that uses complex multilevel probability sampling to provide information on the nutritional status and health status of the general U.S. population. The NHANES research programs were approved by the NCHS Research Ethics Review Committee and received written informed consent from the participants. This study uses the US NHANES database for the rolling period 2011\u0026ndash;2018 (n\u0026thinsp;=\u0026thinsp;39156). After excluding patients with missing information on demographics, diet, examination, and questionnaires, a total of 4009 subjects were included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an example of a selection flow chart.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of TAC from diet\u003c/h2\u003e \u003cp\u003eDuring the first-day interview, dietary antioxidant vitamin data from the 24-hour period prior to the interview (midnight to midnight) were collected. The antioxidant vitamins recorded in the NHANES dietary interview consisted of vitamin A, vitamin C, vitamin E, α-carotene, β-carotene, β-cryptoxanthin, lycopene and lutein-zeaxanthin. According to Floegel et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the individual antioxidant capacity of participants was determined by multiplying the individual amount of antioxidant compounds (antioxidant vitamins) by their antioxidant capacities:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{Theoretical TAC=\u0026Sigma;(antioxidant content}\\frac{\\text{mg}}{\\text{100g}}\\text{*antioxidant capacity}\\frac{\\text{mg VCE}}{\\text{100g}})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAntioxidant capacity is the vitamin C equivalent antioxidant capacity of the corresponding antioxidant vitamin, which was derived from the TAC database of the US diet. TAC was divided into the first quartile (0.236 to 22.188 mg VCE/100 g), second quartile (22.188 to 53.255 mg VCE/100 g), third quartile (53.255 to 112.933 mg VCE/100 g) and fourth quartile (112.933 to 779.247 mg VCE/100 g) according to the survey-weighted quartile.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe demographic factors included age, sex (Men and Women), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race) and socioeconomic status (Low, PIR\u0026thinsp;\u0026lt;\u0026thinsp;1.3; Middle, 1.3\u0026thinsp;\u0026le;\u0026thinsp;PIR\u0026thinsp;\u0026le;\u0026thinsp;3.5; High, PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5). Lifestyle factors consisted of alcohol consumption (yes and no), smoking status (never, previously and currently), physical activity (none, moderate and vigorous) and sedentary activity (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).Other factors that may influence body mass were obtained from the first-day interview diet data and included protein, dietary fiber, calcium and phosphorus intake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDependent variables\u003c/h2\u003e \u003cp\u003eThere are six dependent variables in this study, including appendicular relative lean mass (relative to body weight, g/kg), trunk relative lean mass (relative to body weight, g/kg), total relative lean mass (relative to body weight, g/kg), total percent fat (percent of body weight, %), trunk percent fat (percent of body weight, %) and body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eAppendicular lean body mass, more suitable for the evaluation of skeletal muscle mass, was calculated by summing the lean mass (excluding bone mineral content) of the right and left leg and the right and left arm as measured by dual-energy X-ray absorptiometry (DEXA). To account for the impact of body mass on variations in these outcomes, all variables assessed by DEXA were represented relative to body mass (g per kg of body mass for all lean mass; kg per kg of body mass for all fat mass).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted by using the statistical computing and graphics software R (version 4.2.1) and EmpowerStats (version 5.0). Baseline descriptive statistics of the study population were placed in a table and divided by subgroup, and continuous variables are described using means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors (SE). The beta values and 95% confidence intervals (CI) were determined using multivariate linear regression analysis between the TAC and all outcomes. The multivariate linear regression was built using three models: Model 1: not adjusted; Model 2: adjusted for sex, age, race and socioeconomic status; Model 3: adjusted for all covariates. Smoothed curve fits were carried out concurrently with the variable adjustments. We used a threshold effects analysis model to examine the relationship and saturation effect between TAC and body mass. Finally, subgroup analysis were used to determine the population who experienced the most benefit. We used dietary day one sample weight to analyze all the results, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptions of participants\u003c/h2\u003e \u003cp\u003eThe characteristics of weighted demographics, dietary data and lifestyle of the participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 4009 participants were included in this study. Of these participants, the average age was 49.69, and 50.18% were man. Among different groups of TAC (quartiles, Q1\u0026ndash;Q4), age, sex, race, socioeconomic status, smoking, physical activity, appendicular relative lean mass, trunk relative lean mass, total relative lean mass, total percent fat, trunk percent fat, protein, dietary fiber, calcium and phosphorus were all significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The relationships between the dependent variable and the covariates can also be seen in Table S2.\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\u003eDescription of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Antioxidant Capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4009)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.18 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.63 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.84 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.08 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.58 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.81(1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.37 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.16 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.92 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.42 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.39 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1373 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.93(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.87 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.86 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.08 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.26 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.19 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.50 (3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.23 (2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.87 (2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.84(2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.18 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.25 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.01 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3991 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.12 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.38 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.25 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.70(1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.72 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.92 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economic status\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.58 (1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.07(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.27 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.48 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.41 (1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.60 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.94 (2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.36 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.99 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.85 (2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.82(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.99 (3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.37 (3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.53 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.74 (2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThin (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3663 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.81 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.18 (1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.23 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.99 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.90 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25.0\u0026ndash;29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.59 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.77 (2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.67 (2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.76 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.18 (2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (\u0026ge;\u0026thinsp;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.84 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.22 (1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.21 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.29 (2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.55 (2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \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 \u003cp\u003e22.63 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.90 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.33 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.62 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.65 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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 \u003cp\u003e77.37 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.10 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.67 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.38 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.35 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.56 (1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.96 (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.25 (2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.41 (2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.76 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.65 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.19 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.93 (2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.64 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.05 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.79 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.85 (2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.82 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.95 (1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.19 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \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 \u003cp\u003e44.25 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.98 (2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.75 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.93 (2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.89 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.45 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.89 (2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.04 (2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.21 (2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.55 (2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.32 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.13 (2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.21 (2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.86 (2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.56 (2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary Activity (min/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457.07\u0026thinsp;\u0026plusmn;\u0026thinsp;44.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e405.30\u0026thinsp;\u0026plusmn;\u0026thinsp;13.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e433.80\u0026thinsp;\u0026plusmn;\u0026thinsp;24.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e393.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendicular Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e281.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrunk Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e322.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e322.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e635.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e625.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e635.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e636.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e645.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Percent Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrunk Percent Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fibre(g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978.90\u0026thinsp;\u0026plusmn;\u0026thinsp;15.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e768.33\u0026thinsp;\u0026plusmn;\u0026thinsp;23.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e953.7123.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1051.35\u0026thinsp;\u0026plusmn;\u0026thinsp;31.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1150.83\u0026thinsp;\u0026plusmn;\u0026thinsp;34.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1435.37\u0026thinsp;\u0026plusmn;\u0026thinsp;17.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1159.40\u0026thinsp;\u0026plusmn;\u0026thinsp;25.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1409.23\u0026thinsp;\u0026plusmn;\u0026thinsp;26.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1558.90\u0026thinsp;\u0026plusmn;\u0026thinsp;33.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1622.60\u0026thinsp;\u0026plusmn;\u0026thinsp;32.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eData are %N (SE) for categorical variables or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE for continuous variables.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Relationship between TAC and skeletal muscle mass\u003c/h2\u003e \u003cp\u003eThere was a significant positive association between dietary TAC and lean body mass in three weighted univariate and multivariate linear regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the fully adjusted model, each 1-unit increase in dietary TAC was associated with an increase of 0.018 g/kg appendicular lean mass (95% CI, 0.007 to 0.029), 0.014 g/kg trunk lean mass (95% CI, 0.004 to 0.024) and 0.035 g/kg total lean mass (95% CI, 0.014 to 0.055).\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\u003eSimple and multiple linear regression analysis of Total Antioxidant Capacity and lean mass\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAppendicular Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTrunk Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eTotal Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003cp\u003e(0.036, 0.068)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003cp\u003e(0.018, 0.039)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003cp\u003e(0.007, 0.029)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003cp\u003e(0.013, 0.039)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003cp\u003e(0.007, 0.026)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003cp\u003e(0.004, 0.024) 0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003cp\u003e(0.053, 0.111)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003cp\u003e(0.028, 0.067)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003cp\u003e(0.014, 0.055)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC (quartile)\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=\"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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.948\u003c/p\u003e \u003cp\u003e(1.098, 8.797)\u003c/p\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003cp\u003e(-1.327, 3.444)\u003c/p\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003cp\u003e(-2.37, 2.266) 0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.962\u003c/p\u003e \u003cp\u003e(0.924, 7.000)\u003c/p\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.602\u003c/p\u003e \u003cp\u003e(-0.635, 3.839)\u003c/p\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003cp\u003e(-0.948, 3.442) 0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.452\u003c/p\u003e \u003cp\u003e(2.681, 16.223)\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.046\u003c/p\u003e \u003cp\u003e(-1.521, 7.612)\u003c/p\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.752\u003c/p\u003e \u003cp\u003e(-2.673, 6.177)\u003c/p\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.201\u003c/p\u003e \u003cp\u003e(2.402, 10.001)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.778\u003c/p\u003e \u003cp\u003e(3.413, 8.143)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.114\u003c/p\u003e \u003cp\u003e(1.736, 6.492)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.806\u003c/p\u003e \u003cp\u003e(0.808, 6.805)\u003c/p\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.237\u003c/p\u003e \u003cp\u003e(2.018, 6.455)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.931\u003c/p\u003e \u003cp\u003e(1.664,6.199)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.008\u003c/p\u003e \u003cp\u003e(4.324, 17.691)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.841\u003c/p\u003e \u003cp\u003e(6.313, 15.369)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.153\u003c/p\u003e \u003cp\u003e(4.581, 13.724)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.304\u003c/p\u003e \u003cp\u003e(8.431, 16.177)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.807\u003c/p\u003e \u003cp\u003e(4.374, 9.239)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.576\u003c/p\u003e \u003cp\u003e(1.039, 6.113) 0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.619\u003c/p\u003e \u003cp\u003e(3.563, 9.675)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.108\u003c/p\u003e \u003cp\u003e(1.826, 6.390)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.087\u003c/p\u003e \u003cp\u003e(0.667, 5.506) 0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.999\u003c/p\u003e \u003cp\u003e(13.187, 26.811)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.655\u003c/p\u003e \u003cp\u003e(6.998, 16.313)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.609\u003c/p\u003e \u003cp\u003e(2.732, 12.487)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 1: without adjust.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 2: age, sex, race and socio-economic status were adjusting.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3: model 2 plus smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, Calcium and phosphorus were adjusting.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eβ, 95% confidence intervals (CIs) and P value are presented.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDietary TAC also showed a significant negative association with total percent fat, trunk percent fat and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Assuming linearity, each 1-unit increase in dietary TAC was associated with \u0026minus;\u0026thinsp;0.004 kg/kg total percent fat (95% CI: -0.006, -0.002), -0.005 kg/kg trunk percent fat (95% CI: -0.007, -0.002) and \u0026minus;\u0026thinsp;0.003 kg/m\u003csup\u003e2\u003c/sup\u003e BMI (95% CI: -0.006, -0.001).\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\u003eSimple and multiple linear regression analysis of Total Antioxidant Capacity and fat/BMI.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTotal Relevant Fat(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTrunk Relevant Fat(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003cp\u003e(-0.011, -0.005)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003cp\u003e(-0.007, -0.003)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(-0.006, -0.002)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003cp\u003e(-0.012, -0.006)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003cp\u003e(-0.009, -0.004)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003cp\u003e(-0.007, -0.002)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(-0.006, -0.001)\u003c/p\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(-0.005, -0.001)\u003c/p\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(-0.006, -0.001)\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC (quartile)\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=\"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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.957\u003c/p\u003e \u003cp\u003e(-1.652, -0.262)\u003c/p\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.316\u003c/p\u003e \u003cp\u003e(-0.795, 0.162)\u003c/p\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003cp\u003e(-0.666, 0.261)\u003c/p\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.686\u003c/p\u003e \u003cp\u003e(-1.363, -0.010)\u003c/p\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003cp\u003e(-0.872, 0.255)\u003c/p\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003cp\u003e(-0.697, 0.393)\u003c/p\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003cp\u003e(-0.613, 0.478)\u003c/p\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003cp\u003e(-0.534, 0.551)\u003c/p\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003cp\u003e(-0.650, 0.422)\u003c/p\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.168\u003c/p\u003e \u003cp\u003e(-1.854, -0.482)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.152\u003c/p\u003e \u003cp\u003e(-1.626, -0.677)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.011\u003c/p\u003e \u003cp\u003e(-1.490, -0.532)\u003c/p\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.454\u003c/p\u003e \u003cp\u003e(-2.122, -0.786)\u003c/p\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.525\u003c/p\u003e \u003cp\u003e(-2.083, -0.966)\u003c/p\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.304\u003c/p\u003e \u003cp\u003e(-1.867, -0.741)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.654\u003c/p\u003e \u003cp\u003e(-1.192, -0.115)\u003c/p\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.502\u003c/p\u003e \u003cp\u003e(-1.040, 0.036)\u003c/p\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.786\u003c/p\u003e \u003cp\u003e(-1.340, -0.233)\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.048\u003c/p\u003e \u003cp\u003e(-2.747, -1.349)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.209\u003c/p\u003e \u003cp\u003e(-1.697, -0.721)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.832\u003c/p\u003e \u003cp\u003e(-1.343, -0.320)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.017\u003c/p\u003e \u003cp\u003e(-2.697, -1.336)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.545\u003c/p\u003e \u003cp\u003e(-2.120, -0.971)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.004\u003c/p\u003e \u003cp\u003e(-1.605, -0.403)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.989\u003c/p\u003e \u003cp\u003e(-1.507, -0.410)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.817\u003c/p\u003e \u003cp\u003e(-1.370, -0.263)\u003c/p\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.924\u003c/p\u003e \u003cp\u003e(-1.514, -0.333)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel: without adjust.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 2: age, sex, race and socio-economic status were adjusting.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3: model 2 plus smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, Calcium and phosphorus were adjusting.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eβ, 95% confidence intervals (CIs) and P value are presented.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Dose\u0026ndash;response relationships and their saturation effect\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the dose‒response relationship between dietary intake and total antioxidant capacity for all outcomes. Combining the smoothing curve and TAC quartile, a saturation effect was found between TAC and all outcomes. Then, a saturation effect analysis explored these turning points and the saturation effect value was 67.433 mg VCE/100 g in the appendicular relative lean mass, 64.072 mg VCE/100 g in the trunk relative lean mass, 64.809 mg VCE/100 g in the total relative lean mass, 67.433 mg VCE/100 g in the total percent fat, 65.955 mg VCE/100 g in the trunk percent fat and 71.167 mg VCE/100 g in BMI (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eSaturation effect analysis of TAC on all outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAC turning point (K),mg VCE/100 g\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; K\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt; K\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendicular Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003cp\u003e(0.035, 0.118)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003cp\u003e(-0.008, 0.019)\u003c/p\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrunk Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003cp\u003e(0.029, 0.112)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003cp\u003e(-0.009, 0.016)\u003c/p\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Relative Lean Mass (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003cp\u003e(0.087, 0.254)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003cp\u003e(-0.017, 0.034)\u003c/p\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Percent Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003cp\u003e(-0.027, -0.010)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(-0.003, 0.002)\u003c/p\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrunk Percent Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003cp\u003e(-0.035, -0.015)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(-0.004, 0.002)\u003c/p\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003cp\u003e(-0.025, -0.006)\u003c/p\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(-0.004, 0.002)\u003c/p\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAge, sex, race, socio-economic status, smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analysis of the association between dietary TAC and skeletal muscle mass\u003c/h2\u003e \u003cp\u003eOur study population contained participants aged 40 to 59 years with a mix of both men and women participants, so we also explored how age and sex influenced the aforementioned associations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure S2). When stratifying by age, the associations were significant in patients aged 40\u0026ndash;50 years rather than in those aged 50\u0026ndash;59 years. In the subgroup analysis of sex, women participants had significant associations between dietary TAC and skeletal muscle mass. Therefore, women younger than 50 years may experience the best benefits from dietary TAC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of dietary TAC with all outcomes, stratified by age and sex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAppendicular Relative Lean Mass(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrunk Relative Lean Mass(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Relative Lean Mass(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Percent Fat(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrunk Percent Fat(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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 \u003cp\u003e0.023\u003c/p\u003e \u003cp\u003e(0.008, 0.037)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003cp\u003e(-0.000, 0.027)\u003c/p\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003cp\u003e(0.012, 0.066)\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(-0.007, -0.001)\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003cp\u003e(-0.009, -0.002)\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(-0.008, -0.001)\u003c/p\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003cp\u003e(-0.000, 0.029)\u003c/p\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003cp\u003e(-0.000, 0.027)\u003c/p\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003cp\u003e(0.003, 0.058)\u003c/p\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(-0.006, -0.000)\u003c/p\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(-0.007, -0.001)\u003c/p\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(-0.006, 0.001)\u003c/p\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(-0.007, 0.021)\u003c/p\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003cp\u003e(-0.007, 0.019)\u003c/p\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003cp\u003e(-0.013, 0.040)\u003c/p\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(-0.004, 0.001)\u003c/p\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(-0.006, 0.000)\u003c/p\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(-0.005, 0.001)\u003c/p\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003cp\u003e(0.017, 0.047)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003cp\u003e(0.008, 0.037)\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003cp\u003e(0.032, 0.090)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003cp\u003e(-0.009, -0.003)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003cp\u003e(-0.011, -0.003)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003cp\u003e(-0.009, -0.002)\u003c/p\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRace, socio-economic status, smoke, alcohol, physical activity, sedentary activity, protein, dietary fiber, calcium and phosphorus were adjusting. β, 95% confidence intervals (CIs) and P value are presented.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present analysis was conducted to determine the relationship between dietary TAC intake and body mass components in adults over 40 years old. The US population data were extracted from the NHANES database. The results showed that for adults who had an increased risk of skeletal muscle mass loss, higher dietary TAC is related to a greater preservation of appendicular lean mass, trunk lean mass and total lean mass. Also, higher dietary TAC intake is associated with lower total percent fat, trunk percent fat and BMI.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, the association between dietary TAC and skeletal muscle mass has not yet been investigated in a cohort with this size and scope [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent with a previous cross-sectional study in cirrhotic outpatients, dietary TAC was positively associated with arm muscle area [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In a three-year-long cohort study, higher dietary antioxidant intake had positive effects on BMI and abdominal fat [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another study of children and adolescents showed that dietary antioxidant intake had an inverse association with total body fat in obese subjects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Above all, dietary TAC intake has an inspiring effect on lean body mass, fat and BMI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough some studies have been deployed to detect the association between antioxidant intake and body components in particular populations, including children and adolescents, women, and healthy young adults, they not only primarily focused on the effects of single antioxidant intake, which might not fully explain the synergistic effects of all antioxidant vitamins in the diet [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but also provide less knowledge of the middle-aged population who suffer a higher risk of skeletal muscle mass loss [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, we paid attention to the comprehensive TAC values rather than considering the effects of single compounds, and we focused on the people who may experience greater benefits from the above results.\u003c/p\u003e \u003cp\u003eDose\u0026ndash;response curves suggest that all outcomes displayed a closely correlation with dietary TAC. However, there also displayed a saturation effect of correlation between dietary TAC and skeletal muscle mass. All these results indicated that higher dietary TAC would likely improve lean body mass and decrease body fat and BMI. The saturation effect revealed that there was a threshold effect between dietary TAC and all outcomes. A subsequent subgroup analysis indicated that women and individuals aged 40\u0026ndash;50 years will experience maximum benefits from higher dietary TAC on skeletal muscle mass.\u003c/p\u003e \u003cp\u003eHowever, there are still some limitations in our study. First, this study was a cross-sectional design, which means that the causal relationship between dietary TAC and skeletal muscle mass could not be clearly determined owing to its original survey. Second, vitamin supplementation, such as vitamin C supplementation, is not taken into consideration while only focusing on dietary TAC intake in this design[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Finally, the bioavailability of dietary vitamins in participants was not included in this study because of the defect value in NHANES dataset[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, more work should be done to investigate the relationship between serum TAC levels and skeletal muscle mass both clinically and experimentally in the future to figure out their casual effect and potential mechanism.\u003c/p\u003e \u003cp\u003eIn conclusion, this is the first study to examine the association between dietary TAC and lean body mass and percent body fat in adults aged above 40 years old with a relatively large sample size. In this study, multiple linear regression models, smoothed curve fitting, saturation effect analysis and subgroup analysis were taken into consideration to examine the relationship between dietary TAC and body composition in US middle-aged adults. We found not only a simple linear positive correlation between TAC and lean body mass and negative association between TAC and percent body fat and BMI but also a saturation threshold. Our final subgroup analysis also indicate women younger than 50 years may experience the best benefits from higher dietary TAC. This work suggests that keeping dietary TAC under saturation value may provide its biggest benefits for middle-aged adults for their aging-related skeletal muscle loss.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES research programs were approved by the NCHS Research Ethics Review Committee and received written informed consent from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehere: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLu Wang and Wendong Fang were in charge of proposing the topic, designing the research method and writing the first draft. Wendong Fang and Jie Zhang were in charge of the data processing data validation and review. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFrontera W RJ Ochala (2015) Skeletal muscle: a brief review of structure and function. Calcif Tissue Int 96:183-95. https://doi.org/10.1007/s00223-014-9915-y\u003c/li\u003e\n\u003cli\u003eTanaka H, N ShimizuN Yoshikawa (2017) Role of skeletal muscle glucocorticoid receptor in systemic energy homeostasis. Exp Cell Res 360:24-26. https://doi.org/10.1016/j.yexcr.2017.03.049\u003c/li\u003e\n\u003cli\u003eBear D E, S M ParryZ A Puthucheary (2018) Can the critically ill patient generate sufficient energy to facilitate exercise in the ICU? 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Nutrients 10:https://doi.org/10.3390/nu10030308\u003c/li\u003e\n\u003cli\u003eMansournia M A, V Ostadmohammadi, A Doosti-Irani, M Ghayour-Mobarhan, G Ferns, H Akbari, et al (2018) The Effects of Vitamin D Supplementation on Biomarkers of Inflammation and Oxidative Stress in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Horm Metab Res 50:429-440. https://doi.org/10.1055/a-0630-1303\u003c/li\u003e\n\u003cli\u003ePlatel KK Srinivasan (2016) Bioavailability of Micronutrients from Plant Foods: An Update. Crit Rev Food Sci Nutr 56:1608-19. https://doi.org/10.1080/10408398.2013.781011\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":"Total antioxidation capacity, Skeletal Muscle Mass, Aging, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-3972809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3972809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSkeletal muscle is of great importance for human activity and quality of life, as its loss contributes greatly to immobilization, especially for aged individuals. An increased dietary intake of antioxidant vitamins may be beneficial for muscle loss because of aging. However, the quantitative relationship between total antioxidation capacity (TAC) of antioxidant vitamins and muscle mass is undetermined. 4009 participants from the National Health and Nutrition Examination Survey (NHANES) were included. Multivariate linear regression analysis was performed with demographic, lifestyle and dietary intake adjustment factors. The dose saturation effect was also determined by a saturation effect analysis. Subgroup analysis were performed forage and sex. In the fully adjusted model, per unit increase of dietary TAC was associated with an increase of 0.018 g/kg appendicular lean mass (95% CI: 0.007–0.029), 0.014 g/kg trunk lean mass (95% CI: 0.004–0.024) and 0.035 g/kg total lean mass (95% CI: 0.014–0.055). TAC was associated with an decrease of 0.004 kg/kg total percent fat (95% CI: -0.006–-0.002), 0.005 kg/kg trunk percent fat (95% CI: -0.007–-0.002) and 0.003 kg/m2 BMI (95% CI: -0.006–-0.001) at the same time. Subgroup analysis indicated that women and adults \u0026lt;50 years may experience the most significant association between TAC and skeletal muscle mass. We revealed a positive correlation between TAC and lean body mass, a negative association between TAC and body fat and BMI. Saturation values were found among people aged 40–59. Age and sex mediate these associations.\u003c/p\u003e","manuscriptTitle":"Dietary Total Antioxidant Capacity is Closely Associated with Skeletal Muscle Mass: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 16:37:47","doi":"10.21203/rs.3.rs-3972809/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":"62477ff8-c516-49e1-8d5f-f41cb3335889","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-27T12:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 16:37:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3972809","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3972809","identity":"rs-3972809","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}