Associations of Dietary Trace Elements With Muscle Quality Index and Metabolic Syndrome: Evidence from a NHANES Cross-Sectional Study

Associations of Dietary Trace Elements With Muscle Quality Index and Metabolic Syndrome: Evidence from a NHANES 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 Associations of Dietary Trace Elements With Muscle Quality Index and Metabolic Syndrome: Evidence from a NHANES Cross-Sectional Study Tian Peng, Rui Wang, Siwei Hao, Juan Li, Xiaochen Li, Zhenping Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8728355/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 Objective This study aimed to examine the associations of dietary trace elements with muscle quality index (MQI) and metabolic syndrome (MetS), and to assess potential mediating effects. Methods This cross-sectional study included adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (NHANES) 2011–2018. Dietary intake of trace elements, including iron, copper, selenium, and iodine, was assessed. Muscle quality index (MQI) was defined as the ratio of combined handgrip strength to appendicular skeletal muscle mass. Metabolic syndrome (MetS) was identified according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Weighted multivariable linear and logistic regression models were applied to examine the associations between dietary trace element intake and MQI and MetS. Trend analyses and restricted cubic spline (RCS) models were further used to assess dose–response relationships and potential nonlinear associations. Subgroup analyses were conducted according to age, sex, body mass index (BMI), and physical activity level. Additionally, MQI was treated as a mediator to explore its potential mediating role in the associations between dietary trace elements and MetS. Results A total of 1,111 participants were included in the analysis. Weighted multivariable analyses showed that dietary intakes of iron, zinc, and copper were negatively associated with muscle quality index (MQI). In addition, lower intakes of zinc and iodine were associated with a higher prevalence of metabolic syndrome (MetS). In the fully adjusted models, zinc intake remained inversely associated with MetS risk (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.50–0.92), suggesting a protective association of higher zinc intake. Iodine intake also demonstrated a stable inverse association with MetS (OR = 0.97, 95% CI: 0.94–1.00), indicating that adequate iodine intake may be associated with a reduced risk of MetS. In contrast, the association between iron intake and MetS was attenuated after multivariable adjustment and did not reach statistical significance. Restricted cubic spline (RCS) analyses revealed significant nonlinear associations between intakes of iron, zinc, and iodine and MetS risk (P for nonlinearity < 0.05). Subgroup analyses further indicated that these associations were more pronounced among older adults, women, and individuals with obesity. Conclusions Higher dietary intakes of zinc were associated with better muscle quality and a lower prevalence of metabolic syndrome. These findings highlight the potential public health relevance of trace elements in supporting musculoskeletal health and reducing metabolic risk. Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic syndrome (MetS) is a clinical condition characterized by a cluster of metabolic abnormalities, including insulin resistance, central obesity, impaired glucose metabolism, hypertension, and dyslipidemia. In the United States, the prevalence of MetS among adults has remained persistently high, increasing from 32.9% in 2003 to 34.7% in 2011, and more recent epidemiological evidence indicates that this figure rose further to 41.8% by 2018. According to the 2021 Global Burden of Disease study, MetS and its related metabolic abnormalities have shown a continuously increasing contribution to disability-adjusted life years (DALYs) among 25 level-three risk factors. A substantial body of evidence consistently demonstrates that MetS markedly increases the risk of cardiovascular disease, type 2 diabetes, and stroke, and is associated with a wide range of adverse chronic health outcomes. Collectively, MetS and its metabolic components have emerged as major contributors to the global disease burden, representing a pressing public health challenge. Existing epidemiological and clinical studies have primarily focused on the associations between skeletal muscle characteristics and health outcomes. Prior research has shown that skeletal muscle mass is inversely associated with obesity, and that lower muscle mass is linked to a significantly elevated risk of cardiovascular disease and metabolic-related mortality. Declines in muscle mass and muscle strength have also been closely associated with impaired glucose metabolism, chronic obstructive pulmonary disease, and increased cancer risk. Compared with muscle mass or muscle strength alone, the muscle quality index (MQI), which integrates handgrip strength (dominant and nondominant) and appendicular skeletal muscle mass (ASM), captures both the functional and quantitative aspects of skeletal muscle and is therefore considered a more comprehensive indicator. Consequently, MQI has been widely applied in population-based studies. Previous studies have reported associations between lower MQI and an increased risk of periodontitis and sleep-related problems. Moreover, reductions in skeletal muscle mass may exacerbate insulin resistance and hyperglycemia, thereby accelerating the progression of MetS. Despite growing interest in skeletal muscle health, evidence regarding the associations between dietary trace element intake, muscle quality index, and MetS remains limited. Most existing studies have focused on single trace elements, providing insufficient insight into the combined and potentially interacting effects of multiple micronutrients. Therefore, using data from the National Health and Nutrition Examination Survey (NHANES), the present study aimed to systematically examine the associations between dietary intakes of multiple trace elements—including zinc, copper, selenium, iron, and magnesium—and muscle quality index and metabolic syndrome. Methods Study population: This study employed a cross-sectional design using data from the National Health and Nutrition Examination Survey (NHANES). NHANES is a nationally representative survey of the noninstitutionalized civilian population in the United States, conducted biennially by the National Center for Health Statistics (NCHS). The survey collects comprehensive information on dietary intake and health status through standardized procedures, which have been described in detail elsewhere. All NHANES protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from all participants prior to participation. The initial study population consisted of 88,308 participants aged ≥ 18 years from the NHANES 2011–2018 survey cycles. Participants without dietary intake data were excluded (n = 86,840). Subsequently, 222 individuals with missing data on muscle quality index (MQI) or metabolic syndrome (MetS) were excluded. An additional 135 participants were excluded due to missing key covariates, including body mass index (BMI), race/ethnicity, educational attainment, smoking status, alcohol consumption, and other relevant variables. Pregnant women and participants with unreliable or implausible dietary recall data, defined as total daily energy intake 5,000 kcal, were also excluded. After applying all exclusion criteria, a total of 1,111 participants were included in the final analytical sample (Fig. 1 ). Variables Exposure variables The exposure variables in this study were dietary trace elements, including zinc, copper, selenium, and iron. Dietary intake data were obtained from the NHANES dietary interview component, which estimates the intake of foods and beverages (including all types of water) consumed during the 24-hour period prior to the interview (midnight to midnight). Based on these data, intakes of energy, nutrients, and other food components were calculated. All nutrient intake values were derived using the food and nutrient databases developed by the United States Department of Agriculture (USDA). These dietary data were collected and processed through a collaborative effort between the USDA and the Department of Health and Human Services, with the USDA Food Surveys Research Group (FSRG) responsible for dietary data collection methodology, database maintenance, and quality control procedures. Outcome variables Outcome variables Muscle quality index (MQI) was calculated as the ratio of handgrip strength (kg) to appendicular skeletal muscle mass (ASM, kg). Handgrip strength was measured using a standardized electronic dynamometer, and ASM was assessed by dual-energy X-ray absorptiometry (DXA). MQI was treated as a continuous variable in all statistical analyses. Metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Participants meeting at least three of the following five components were classified as having MetS: ( 1 ) waist circumference ≥ 102 cm in men or ≥ 88 cm in women; ( 2 ) triglycerides ≥ 150 mg/dL; ( 3 ) high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL in men or < 50 mg/dL in women; ( 4 ) blood pressure ≥ 130/85 mmHg or current use of antihypertensive medication; and ( 5 ) fasting plasma glucose ≥ 100 mg/dL or use of glucose-lowering medication. Covariates Covariates were selected based on prior literature and biological plausibility. These included demographic characteristics (age, sex, race/ethnicity, and educational attainment), lifestyle factors (smoking status, alcohol consumption, and physical activity level), and clinical variables. Clinical covariates comprised body mass index (BMI) and history of diabetes, which was defined by any of the following criteria: a physician or health professional diagnosis, use of glucose-lowering medication or insulin, random plasma glucose ≥ 11.1 mmol/L, glycated hemoglobin (HbA1c) ≥ 6.5%, fasting plasma glucose ≥ 7.0 mmol/L, or 2-hour plasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test (OGTT). Information on medication use and clinical measurements was obtained from NHANES standardized questionnaires and examination data. Unless otherwise specified, all covariates were treated as categorical variables in the analyses. Statistical analysis All statistical analyses were performed using R software (version 4.4.1). In accordance with recommendations for analyses of National Health and Nutrition Examination Survey (NHANES) data, the complex multistage sampling design was accounted for by incorporating sampling weights, strata, and primary sampling units. Continuous variables are presented as weighted means with standard errors, whereas categorical variables are expressed as weighted percentages. Differences in baseline characteristics between participants with and without metabolic syndrome (MetS) were compared using weighted t tests for continuous variables and weighted chi-square tests for categorical variables. Dietary intakes of zinc, iron, and selenium were included as continuous variables and categorized into weighted population-based tertiles. Weighted multivariable linear regression models were applied to examine differences in muscle quality index (MQI) across levels of trace element intake and to assess their associations with MQI. Metabolic syndrome (MetS) was treated as a binary outcome and analyzed using weighted multivariable logistic regression models. Three hierarchical adjustment models were constructed: Model 1 was unadjusted; Model 2 was adjusted for sex and race/ethnicity; and Model 3 was further adjusted for educational attainment, smoking status, alcohol consumption, physical activity level, body mass index (BMI), and total energy intake. To explore potential nonlinear associations, restricted cubic spline (RCS) functions with three knots were incorporated into the weighted regression models, and nonlinearity was evaluated using Wald tests for dietary trace elements (zinc, iron, and iodine). Subgroup analyses were conducted according to age (< 40 vs. ≥40 years), sex, and physical activity level. Sensitivity analyses were performed by excluding participants with extreme trace element intakes, those with chronic conditions (e.g., diabetes), and those with unreliable dietary recall data to assess the robustness of the findings. All statistical tests were two-sided, and a p value < 0.05 was considered statistically significant. Results 1. Baseline characteristics of participants A total of 1,111 participants aged ≥ 18 years were included in the final analysis. Participants were categorized into tertiles (Q1–Q3) based on total muscle quality index (MQI_total) (Table 1 ). As MQI increased across tertiles, body mass index (BMI) showed a decreasing trend (from 31.19 to 23.86 kg/m² from Q1 to Q3, P = 0.005). With respect to dietary intake, iodine intake increased across increasing MQI tertiles, and intakes of other trace elements, including iron, zinc, copper, and selenium, also exhibited overall upward trends (Table 1 ). Table 1 Characteristics of participants with metabolic syndrome across different muscle quality statuses in NHANES 2011–2018 Characteristic Overall Q1 Q2 Q3 p-value 3 Drinking,% 0.9 No 229.00 (12.49%) 61.00 (12.97%) 72.00 (9.54%) 96.00 (15.02%) Yes 882.00 (87.51%) 297.00 (87.03%) 338.00 (90.46%) 247.00 (84.98%) Serum Creatine Kinase (IU/L) 136.06 (89.50) 197.41 (96.39) 101.43 (46.62) 92.32 (66.58) 0.004 BMI,kg/m 2 28.80 (7.09) 31.19 (7.85) 30.48 (6.55) 23.86 (2.93) 0.005 Gender,% 0.046 Male 517.00 (51.10%) 250.00 (75.32%) 140.00 (34.05%) 127.00 (37.44%) Female 594.00 (48.90%) 108.00 (24.68%) 270.00 (65.95%) 216.00 (62.56%) Age,y 43.05 (12.19) 44.15 (12.42) 41.51 (11.15) 43.26 (12.81) 0.8 Race/Ethnicity,% 0.076 Non-Hispanic White 536.00 (74.33%) 200.00 (82.47%) 134.00 (60.45%) 202.00 (78.44%) Non-Hispanic Black 133.00 (5.49%) 114.00 (11.49%) 19.00 (3.24%) 0.00 (0.00%) Other Race 442.00 (20.18%) 44.00 (6.04%) 257.00 (36.32%) 141.00 (21.56%) Education Level,% 0.083 Under high school 114.00 (8.19%) 0.00 (0.00%) 35.00 (6.19%) 79.00 (21.09%) High school 144.00 (10.50%) 40.00 (6.46%) 52.00 (12.37%) 52.00 (13.81%) Above high school 853.00 (81.31%) 318.00 (93.54%) 323.00 (81.44%) 212.00 (65.11%) Vitamin K,mcg 24.69 (19.08) 31.37 (22.11) 24.13 (18.27) 16.52 (10.42) 0.029 Iron Intake,mg 11.92 (6.16) 15.04 (4.35) 11.27 (5.71) 8.50 (6.64) 0.007 Zinc Intake,mg 8.21 (4.50) 9.92 (3.59) 8.01 (4.82) 6.18 (4.34) 0.011 Copper Intake,mg 0.77 (0.63) 0.85 (0.61) 0.95 (0.72) 0.47 (0.41) 0.024 Iodine Intake,mg 104.63 (50.43) 126.82 (33.91) 104.15 (48.91) 75.98 (55.50) 0.012 Blood Cadmium,ug/dL 0.37 (0.34) 0.24 (0.26) 0.35 (0.22) 0.55 (0.45) 0.003 25(OH)D, nmol/L 75.23 (22.05) 84.53 (21.47) 71.60 (19.97) 66.87 (20.40) 0.015 Moderate Physical Activities,% 0.4 No 434.00 (40.74%) 132.00 (34.79%) 123.00 (34.71%) 179.00 (55.00%) Yes 677.00 (59.26%) 226.00 (65.21%) 287.00 (65.29%) 164.00 (45.00%) Arm muscle quality index,g/kg 12.63 (2.38) 10.78 (1.09) 12.71 (1.87) 14.99 (1.93) < 0.001 Appendicular muscle quality index,g/kg 1.74 (0.29) 1.46 (0.14) 1.76 (0.14) 2.08 (0.13) < 0.001 Total muscle quality index,g/kg 3.36 (0.56) 2.80 (0.26) 3.39 (0.23) 4.05 (0.19) < 0.001 Appendicular skeletal muscle mass,g/kg 23.41 (7.22) 29.06 (5.65) 21.49 (5.38) 18.01 (5.34) < 0.001 Appendicular skeletal muscle mass index,g/kg 7.83 (1.74) 9.02 (1.42) 7.65 (1.41) 6.45 (1.28) < 0.001 MetS,% 0.5 No 915.00 (78.23%) 269.00 (83.55%) 373.00 (83.56%) 273.00 (65.54%) Yes 196.00 (21.77%) 89.00 (16.45%) 37.00 (16.44%) 70.00 (34.46%) Continuous variables are presented as weighted means ± standard errors and compared using Student’s t test. Categorical variables are expressed as weighted percentages (95% confidence intervals) and compared using the Cochran–Mantel–Haenszel χ² test. Trends across MQI tertiles were assessed using weighted linear regression or trend tests, as appropriate. 2. Associations between dietary trace elements and total muscle quality index (MQI_total) Table 2 presents the associations between dietary trace elements and total muscle quality index (MQI_total). In the unadjusted Model 1, dietary intakes of iron, zinc, and copper were negatively associated with MQI_total, with statistically significant associations observed for iron and zinc, while the association for copper was marginally significant. After adjustment for sex and race/ethnicity, these negative associations were substantially attenuated. Following further adjustment for educational attainment, body mass index (BMI), and alcohol consumption, the associations between iron, zinc, and copper intake and MQI_total were no longer statistically significant (Table 2 ). Iodine intake showed a positive association with MQI_total in Models 1 and 2. Blood cadmium levels were positively associated with MQI_total in the unadjusted and partially adjusted models. In contrast, serum 25-hydroxyvitamin D [25(OH)D] exhibited a negative trend with MQI_total; however, this association did not reach statistical significance in the fully adjusted model (Table 2 ). Table 2 Linear regression associations between different trace elements and overall muscle quality index(MQI.total) Variables Model1 (Beta,95%CI) P Model2 (Beta,95%CI) P Model3 (Beta,95%CI)) P Iron Intake,mg -0.04(-0.06, -0.01) 0.004** -0.03(-0.06,-0.01) 0.021* -0.02(-0.04,0.00) 0.065 Zinc Intake,mg -0.04(-0.07,-0.01) 0.024* -0.03(-0.06,-0.01) 0.022* -0.01(-0.04,0.02) 0.4 Copper Intake,mg -0.20(-0.41, 0.00) 0.054 -0.24(-0.42,-0.05) 0.018* -0.05(-0.29,0.18) 0.6 Iodine Intake,mg 0.00(-0.01, 0.00) 0.002** 0.00(-0.01, 0.00) 0.008** 0.00(0.00, 0.00) 0.1 BloodCadmium,ug/dL 0.60(0.15,1.1) 0.013* 0.55(0.05,1.0) 0.034* 0.19(-0.29,0.66) 0.4 25(OH)D,nmol/L -0.01(-0.01,0.00) 0.001** -0.01(-0.01,0.00) 0.022* -0.01(-0.01,0.00) 0.074 *,P < 0.05; **, P < 0.01; ***, P < 0.001.MetS_ATP,Metabolic Syndrome by Adult Treatment Panel III criteria; OR, odds ratio; CI, confidence interval. 3. Associations between trace elements and metabolic syndrome Table 3 presents the associations between dietary trace elements and metabolic syndrome (MetS). In the unadjusted Model 1, higher intakes of iron, zinc, and iodine were associated with a lower risk of MetS. These associations remained after adjustment for sex and race/ethnicity. In the fully adjusted Model 3, the association between iron intake and MetS was no longer statistically significant, whereas dietary intakes of zinc and iodine remained significantly associated with MetS risk.Specifically, each 1 mg/day increase in dietary zinc intake was associated with an approximately 32% lower odds of MetS (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.50–0.92, p = 0.021). Similarly, each 1 mg/day increase in dietary iodine intake was associated with an approximately 3% lower odds of MetS (OR = 0.97, 95% CI: 0.94–1.00, p = 0.040) (Table 3 ). Table 3 Logistic regression analyses of associations between different trace elements and metabolic syndrome risk Variables Model1 (OR,95%CI) P Model2 (OR,95%CI) P Model3 (OR,95%CI)) P Iron Intake,mg 0.90(0.81,1.00) 0.042* 0.880.78, 0.99 0.037* 0.77(0.58, 1.02) 0.062 Zinc Intake,mg 0.82(0.70,0.97) 0.023* 0.80(0.68, 0.95) 0.016* 0.68(0.50, 0.92) 0.021* Iodine Intake,mg 0.99(0.97,1.00) 0.019* 0.98(0.97, 1.00) 0.017* 0.97(0.94,1.00) 0.040* *,P < 0.05; **, P < 0.01; ***, P < 0.001.MetS_ATP,Metabolic Syndrome by Adult Treatment Panel III criteria; OR, odds ratio; CI, confidence interval. 4. Dose–response relationships based on restricted cubic spline analyse Restricted cubic spline (RCS) analyses indicated heterogeneous dose–response relationships between different trace elements and the risk of metabolic syndrome (MetS) (Fig. 2 ). Intakes of zinc and iodine exhibited significant nonlinear associations with MetS risk (P for nonlinearity < 0.001). Iron and copper intake were also nonlinearly associated with MetS risk, with P values for nonlinearity < 0.05 for both elements. Although nonlinear associations were observed for all examined trace elements, the ranges and patterns of these associations differed across elements. 5. Subgroup analyses Subgroup analyses indicated that dietary intakes of iron, zinc, and copper were negatively associated with total muscle quality index (MQI_total), with these associations being more pronounced among women, participants aged ≥ 40 years, and alcohol drinkers. However, these negative associations were primarily observed in Models 1 and 2 and were no longer statistically significant in most subgroups after full adjustment in Model 3.Blood cadmium levels showed a positive trend with MQI_total in several subgroups, but this association was limited to the unadjusted and partially adjusted models and lacked stability after full multivariable adjustment in Model 3 (Table 4 ). Table 4 Subgroup linear regression analyses of associations between different trace elements and the muscle quality index (MQI.total) Variables Subgroup Model1 (Beta,95%CI) P Model2 (Beta,95%CI) P Model3 (Beta,95%CI) P Iron Intake,mg Gender male -0.01(-0.07,0.05) 0.6 -0.01(-0.09,0.06) 0.6 -0.01 0.5 female -0.04(-0.09,0.01) 0.036* -0.04(-0.10,0.03) 0.023* -0.02 0.2 Age(years) < 40 -0.04(-0.11,0.04) 0.2 -0.04(-0.18,0.10) 0.2 -0.01 0.7 ≥ 40 -0.04(-0.09, 0.01) 0.008** -0.04(-Inf, Inf) < 0.001*** -0.01 0.4 Alcohol Drinking yes -0.04(-0.08,-0.01) 0.009** -0.04(-0.08,0.00) 0.011* -0.02(-0.06,0.02) 0.2 no 0.02 0.007** 0.03 0.027* Zinc Intake,mg Gender male -0.01(-0.05, 0.03) 0.4 -0.01(-0.07,0.05) 0.5 0.00 0.9 female -0.04(-0.10,0.01) 0.026* -0.05(-0.11,0.02) 0.016* -0.01 0.6 Age(years) < 40 -0.01(-0.09,0.07) 0.7 -0.01(-0.17,0.15) 0.8 0.03 0.068 ≥ 40 -0.06(-0.13,0.01) 0.008** -0.06(-Inf, Inf) < 0.001*** -0.02 0.2 Alcohol Drinking yes -0.05(-0.09,0.00) 0.015** -0.04(-0.09,0.00) 0.018* -0.01(-0.05,0.03) 0.6 no -0.45 0.007** -0.56 0.027* ref ref Copper Intake,mg Gender male -0.18(-0.43,0.06) 0.051 -0.18(-0.48, 0.12) 0.06* 0.03 0.7 female -0.26(-0.52,0.00) 0.011* -0.26(-0.56,0.04) 0.006** 0.09 0.5 Age(years) < 40 -0.09(-0.29,0.11) 0.2 -0.09(-0.41,0.24) 0.3 0.22 < 0.001*** ≥ 40 -0.36(-0.91,0.19) 0.038* -0.40(-Inf, Inf) 0.014* -0.16 0.2 Alcohol Drinking yes -0.21(-0.44, 0.01) 0.036* -0.21(-0.42,0.00) 0.025* 0.06(-0.17,0.28) 0.5 no -0.23 0.2 -0.34 0.2 ref ref Iodine Intake,mg Gender male 0.00(-0.01,0.01) 0.9 0.00(-0.01, 0.01) > 0.9 0.00 > 0.9 female -0.01(-0.01,0.00) 0.009** -0.01(-0.01, 0.00) 0.003** 0.00 0.3 Age(years) < 40 0.00(-0.01,0.00) 0.2 0.00(-0.02,0.01) 0.3 0.00 0.4 ≥ 40 0.00-0.01, 0.00 0.008** -0.01(-Inf, Inf) < 0.001*** 0.00 0.4 Alcohol Drinking yes 0.00-0.01, 0.00 0.006** 0.00-0.01, 0.00 0.007** 0.00-0.01, 0.00 0.3 no 0.00 0.007** 0.01 0.027 ref ref Blood Cadmium,ug/dL Gender male 0.52 − 0.15, 1.2 0.047* 0.52(-0.22,1.3) 0.024* 0.48 < 0.001*** female 0.53(0.03, 1.0) 0.006** 0.59(-0.24, 1.4) 0.02* 0.17 0.5 Age(years) < 40 0.45(-0.25, 1.1) 0.077 0.49(-0.61,1.6) 0.057 0.40 0.053 ≥ 40 0.650.03, 1.3 < 0.001*** 0.66(-Inf,Inf) 0.002** -0.24 0.5 Alcohol Drinking yes 0.59(0.33, 0.84) < 0.001*** 0.59(0.30, 0.87) < 0.001*** 0.12(-0.27,0.52) 0.4 no 0.44 0.2 2.2 < 0.001*** ref fer 25(OH)D,nmol/L Gender male -0.01(-0.02,0.00) 0.016* -0.01(-0.02,0.00) 0.028* 0.00 0.069 female 0.00(-0.01, 0.01) 0.4 -0.01(-0.02, 0.01) 0.2 -0.01 0.2 Age(years) < 40 0.00(-0.01, 0.00) 0.049* -0.01(-0.03, 0.02) 0.3 -0.01 0.003** ≥ 40 -0.01(-0.02, 0.00) < 0.001*** -0.01(-Inf, Inf) < 0.001*** 0.00 0.4 Alcohol Drinking yes -0.01(-0.01, 0.00) < 0.001*** -0.01(-0.02, 0.00) 0.001** -0.01(-0.01,0.00) < 0.001*** no 0.01 0.063 0.02 < 0.001*** ref ref Subgroup analyses showed that higher intakes of zinc and iodine were associated with lower odds of metabolic syndrome (MetS) among alcohol drinkers and participants with lower to moderate levels of physical activity. In contrast, the direction and magnitude of the associations between iron intake and MetS varied across subgroups. In some subgroup analyses, iodine intake was positively associated with MetS risk, indicating potential heterogeneity across populations and warranting further investigation. Nevertheless, across the majority of subgroups, the associations of zinc and iodine intake with MetS were consistently inverse (Table 5 ). Table 5 Subgroup logistic regression analyses of associations between trace elements and the risk of metabolic syndrome Variables Subgroup Model1 Model2 Model3 OR,95%CI P OR,95%CI P OR,95%CI P Iron Intake,mg Gender male 0.98(0.68,1.42) > 0.9 0.99(0.64,1.53) > 0.9 0.89 ref female 0.79(0.58,1.0) 0.10 0.64(0.38,1.06) 0.067 ref Alcohol Drinking Yes 0.90(0.80,1.0) 0.054 0.88(0.78, 1.00) 0.050 0.78(0.53,1.14) 0.2 No 1.01 1.01 1.0 Age(years) < 40 0.970.78, 1.20 0.7 0.90(0.50,1.62) 0.5 0.91 ≥ 40 0.86(0.71,1.05) 0.093 0.86(0.00,Inf) 0.2 0.00 ref Moderate Physical Activity Yes 1.14(0.78,1.67) 0.4 1.13(0.76,1.68) 0.5 1.47(1.11, 1.95) 0.023* No 0.77(0.64,0.94) 0.024* 0.73(0.00, Inf) 0.086 0.00 Zinc Intake,mg Gender male 0.90(0.62,1.2) 0.5 0.91(0.59,1.40) 0.5 0.78 female 0.73(0.48,1.10) 0.11 0.58(0.33, 1.01) 0.053 0.00 Drinking Yes 0.83(0.70, 0.9) 0.036* 0.81(0.67,0.97) 0.028* 0.69(0.41,1.18) 0.14 No 0.84 0.59(0.00,Inf) 0.2 0.00 Age(years) < 40 0.85(0.61,1.19) 0.2 0.74(0.23,2.40) 0.4 0.73 ≥ 40 0.82(0.60,1.11) 0.13 0.80(0.00,Inf) 0.2 0.00 Moderate Physical Activity Yes 1.07(0.81, 1.41) 0.6 1.050.79, 1.41 0.7 1.30(0.85, 2.00) 0.14 No 0.58(0.33, 1.0) 0.049* 0.59(0.00,Inf) 0.2 0.00 Iodine Intake,mg Gender male 1.00(0.96, 1.04) 0.8 1.00(0.95,1.05) 0.9 0.99 female 0.97(0.94, 1.00) 0.075 0.95(0.90, 1.01) 0.070 0.17 Alcohol Drinking Yes 0.99(0.97, 1.00) 0.028* 0.98(0.97,1.00) 0.028* 0.96(0.92,1.01) 0.13 No 1.00 1.00 1.01 Age(years) < 40 0.99(0.96,1.02) 0.4 0.98(0.90,1.07) 0.4 0.97 ≥ 40 0.98(0.96,1.01) 0.093 0.98(0.00,Inf) 0.2 0.06 Moderate Physical Activity Yes 1.01(0.97,1.06) 0.5 1.010.97, 1.06 0.5 1.051.01, 1.09 0.022* No 0.96(0.93, 0.99) 0.017* 0.960.00, Inf 0.2 0.42 6. Mediating Role of MQI and ASMI Mediation analyses revealed heterogeneous mediating roles of muscle quality indicators (MQI_total and MQI_app) and muscle mass (ASMI) in the associations between dietary trace elements and metabolic syndrome (MetS) (Fig. 3 ). Across most models, the indirect effects of certain mediation pathways reached statistical significance; however, the corresponding mediation proportions were predominantly negative, indicating primarily suppressive (inverse) mediation effects (Fig. 3 ). Specifically, dietary intakes of iron and iodine demonstrated mediation patterns mainly characterized by suppressive effects through muscle quality indicators. In contrast, for dietary zinc intake, MQI_app and MQI_total exhibited partial positive mediation effects, whereas no stable mediating effects were observed for the remaining muscle-related indicators. Overall, neither muscle function nor muscle mass consistently acted as positive mediators in the associations between most trace elements and MetS (Fig. 3 ). Discussion This study examined the associations among dietary trace elements, muscle quality index (MQI), and metabolic syndrome (MetS) in a nationally representative sample of U.S. adults. We observed inverse associations between dietary intakes of zinc and iodine and MetS risk, whereas the association between iron intake and MetS was attenuated at higher intake levels. In addition, significant nonlinear dose–response relationships were identified for zinc, iodine, and iron in relation to MetS risk, with zinc exhibiting a U-shaped pattern and iron and iodine showing threshold-like characteristics. These findings extend previous evidence indicating that declines in muscle quality and metabolic dysregulation have emerged as major public health concerns. Skeletal muscle plays a central role not only in locomotion but also in glucose uptake, lipid oxidation, and whole-body energy metabolism. Age-related declines in muscle mass and function often occur alongside increased insulin resistance, chronic low-grade inflammation, and mitochondrial dysfunction, thereby accelerating the disruption of metabolic homeostasis and the development of MetS. In the present study, several trace elements were significantly associated with MQI in minimally adjusted models; however, these associations were largely attenuated after further adjustment for body mass index (BMI) and lifestyle-related factors. This pattern suggests that MQI may reflect an integrated phenotype of body composition and metabolic status rather than serving as a direct downstream effect of individual micronutrient intake. The inverse association between zinc intake and MetS risk observed in this study is biologically plausible. Zinc is involved in insulin signaling, antioxidant defense, and mitochondrial energy metabolism, and zinc deficiency has been linked to impaired insulin sensitivity, dysregulated lipid metabolism, and elevated inflammatory responses. Our findings further revealed a nonlinear association between zinc intake and MetS, with the strongest inverse association observed within a moderate intake range. Moreover, MQI_app and MQI_total exhibited limited but directionally consistent positive mediation effects in the zinc–MetS association, suggesting that improvements in muscle function may represent one potential, albeit modest, pathway through which zinc intake influences metabolic health. This interpretation is consistent with prior evidence linking zinc status to mitochondrial efficiency, oxidative stress regulation, and insulin sensitivity. The association between iodine intake and MetS appears to be mediated primarily through systemic endocrine regulation. Iodine is essential for thyroid hormone synthesis, and insufficient iodine intake can lead to reduced basal metabolic rate, impaired lipid metabolism, and decreased energy expenditure. In this study, iodine intake demonstrated a nonlinear association with MetS risk that plateaued beyond a certain intake level, consistent with the physiological saturation characteristics of thyroid hormone regulation. However, mediation analyses indicated that muscle-related pathways associated with iodine intake were predominantly suppressive rather than positively mediating, suggesting that the metabolic effects of iodine are unlikely to be primarily transmitted through muscle mass or muscle function. Iron exhibited a more complex, bidirectional association with metabolic health. While iron is essential for oxygen transport and mitochondrial energy production, excessive iron accumulation may exacerbate oxidative stress and metabolic burden. Our results indicated an inverse association between iron intake and MetS risk at lower intake levels, which weakened as intake increased, reflecting a threshold-dependent pattern. Consistently, iron-related mediation pathways through muscle indices were largely suppressive, further supporting the notion that iron influences metabolic risk mainly through systemic redox balance and oxidative mechanisms rather than via muscle-mediated pathways. Notably, although several indirect effects reached statistical significance in the mediation analyses, MQI and ASMI did not function as conventional positive mediators in most trace element–MetS associations. Instead, negative mediation proportions were frequently observed, indicating suppressive mediation effects. This suggests that muscle-related indicators may modulate the direction or magnitude of micronutrient–MetS associations rather than simply transmitting their effects. These findings highlight the importance of distinguishing between skeletal muscle as a metabolic target tissue and as a regulatory or modifying factor when interpreting nutrition–metabolism relationships. Subgroup analyses further suggested heterogeneity in the associations between trace elements and MQI across sex, age, and drinking status; however, the stability of these associations was limited after full covariate adjustment, underscoring the influential role of lifestyle factors and overall body composition in shaping these relationships. Overall, our findings indicate that higher intakes of zinc and iodine are associated with a lower risk of MetS, with zinc potentially exerting modest effects through muscle function, whereas iron shows a clear intake-dependent pattern. Muscle quality and muscle mass did not consistently serve as traditional mediators in most trace element–MetS pathways but instead exhibited predominantly suppressive mediation effects, suggesting that the metabolic impacts of trace elements are more likely driven by systemic metabolic and endocrine mechanisms. These results underscore the importance of balanced micronutrient intake and provide new insights into the complex interplay among nutrition, skeletal muscle, and metabolic health. Several limitations should be acknowledged. First, the cross-sectional design precludes causal inference. Second, dietary intake was assessed using 24-hour recall data, which may be subject to measurement error. Third, certain potential confounders, such as inflammatory biomarkers and thyroid function indicators, were not available for inclusion. Future longitudinal and mechanistic studies are warranted to further elucidate these relationships. Conclusions This study demonstrates that dietary trace elements, particularly zinc, selenium, and iodine, are significantly associated with muscle quality index (MQI) and metabolic syndrome (MetS) among U.S. adults. Dietary intakes of zinc and iodine showed consistent associations with MetS risk. Mediation analyses further indicated that MQI was not a primary mediator in most trace element–MetS associations, with a mediating effect observed only for zinc. These findings suggest that the influence of dietary trace elements on metabolic risk may operate largely through pathways independent of muscle quality, highlighting the complexity of the underlying mechanisms linking micronutrient intake and metabolic health. Data availability statement All data are freely available in the NHANES: https://www.cdc.gov/nchs/nhanes/ . Abbreviations NHANES National Health and Nutrition Examination Survey ASMI Appendicular Skeletal Muscle Mass Index MQI Muscle Quality Index MQI.total Total muscle quality index MQI.app Appendicular muscle quality index MetS Metabolic Syndrome BMI Body Mass Index OR Odds Ratio CI Confidence Interval RCS Restricted Cubic Spline Declarations Acknowledgements The authors acknowledge the participants and investigators of the National Health and Nutrition Examination Survey (NHANES) for providing the data used in this study. Funding Not applicable. Authors and Affiliations Department of Physical Education, Zhejiang University of Science and Technology, Hangzhou 310023, China Tian Peng College of Physical Education, Guizhou Normal University, Guiyang 550025, China Rui Wang Hanyang University ERICA, Ansan 15588, Republic of Korea Siwei Hao, Juan Li, Xiaochen Li, Zhenping Jiang Authors’ contributions Conceptualization and methodology: Tian Peng. Formal analysis: Rui Wang, Siwei Hao. Data curation: Juan Li, Xiaochen Li. Writing—review and editing: Tian Peng, Zhenping Jiang. Corresponding authors Correspondence to Tian Peng or Zhenping Jiang. Ethics declarations The data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available database. The NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent prior to participation. The present study involved secondary analysis of de-identified public-use data and therefore did not require additional institutional review board approval. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are publicly available from the NHANES database. Competing interests The authors declare that they have no competing interests. Clinical trial registration Not applicable. 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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-8728355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584680795,"identity":"323c91e3-63be-4f7e-832e-914a72d146a5","order_by":0,"name":"Tian 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University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":584680806,"identity":"8e83415c-5424-4cdd-b023-0eb870f0f0e0","order_by":2,"name":"Siwei Hao","email":"","orcid":"","institution":"Hanyang University ERICA","correspondingAuthor":false,"prefix":"","firstName":"Siwei","middleName":"","lastName":"Hao","suffix":""},{"id":584680808,"identity":"68c4210f-7c8e-44ff-b9c5-5c7225d79c19","order_by":3,"name":"Juan Li","email":"","orcid":"","institution":"Hanyang University ERICA","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Li","suffix":""},{"id":584680809,"identity":"7655d5b4-f6e1-43e3-ac50-9c4e3747a7ef","order_by":4,"name":"Xiaochen Li","email":"","orcid":"","institution":"Hanyang University ERICA","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Li","suffix":""},{"id":584680811,"identity":"5f499cea-90c0-4919-a108-b1d736544d61","order_by":5,"name":"Zhenping Jiang","email":"","orcid":"","institution":"Hanyang University ERICA","correspondingAuthor":false,"prefix":"","firstName":"Zhenping","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-01-29 07:25:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8728355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8728355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101833777,"identity":"df723b6b-b251-4f83-85e4-c626f74393c3","added_by":"auto","created_at":"2026-02-04 07:06:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant selection from the NHANES 2011–2018 cycles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8728355/v1/ef5de02bbe98c18617e7d924.png"},{"id":101833779,"identity":"7a491eb5-0cd8-4ed1-b5fc-22a5abf972d8","added_by":"auto","created_at":"2026-02-04 07:06:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":442725,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear associations between dietary intakes of zinc, iodine, and iron and metabolic syndrome (MetS)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8728355/v1/4e4852d5e712aad445cd5e55.jpeg"},{"id":101833778,"identity":"7d98db9f-30b7-4da6-8d28-cc2f15de9d83","added_by":"auto","created_at":"2026-02-04 07:06:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654812,"visible":true,"origin":"","legend":"\u003cp\u003eMediation pathway models of muscle-related indicators in the associations between dietary intakes of iron, zinc, and iodine and metabolic syndrome（(A) The mediating effect of total muscle quality index (MQI_total) in the association between dietary iron intake and metabolic syndrome (MetS), showing a significant suppressive effect (mediation proportion = −36.0%).(B) The mediating effect of MQI_total in the association between dietary zinc intake and MetS, with a mediation proportion of 11.7%, indicating a partial mediation trend.(C) The mediating effect of MQI_total in the association between dietary iodine intake and MetS, characterized by a suppressive effect (mediation proportion = −34.4%).(D) The mediating effect of appendicular muscle quality index (MQI_app) in the association between dietary iron intake and MetS, also demonstrating a significant suppressive effect (mediation proportion = −61.7%).(E) The mediating effect of MQI_app in the association between dietary zinc intake and MetS, with a mediation proportion of 15.3%.(F) The mediating effect of MQI_app in the association between dietary iodine intake and MetS, showing a significant suppressive effect (mediation proportion = −47.4%).(G) The mediating effect of appendicular skeletal muscle mass index (ASMI) in the association between dietary iron intake and MetS, exhibiting a pronounced suppressive effect (mediation proportion = −38.3%).(H) The mediating effect of ASMI in the association between dietary zinc intake and MetS was not statistically significant (mediation proportion = 9.1%, P = 0.16).(I) The mediating effect of ASMI in the association between dietary iodine intake and MetS.All models were estimated using weighted mediation analysis. Negative mediation proportions indicate that the direction of the indirect effect is opposite to that of the direct effect, representing suppressive mediation.）\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8728355/v1/1adbdde61441b7aa1bb06068.jpeg"},{"id":105737168,"identity":"3d806535-ab02-4881-ba42-ad97e505b7b9","added_by":"auto","created_at":"2026-03-30 12:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2433369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8728355/v1/2fd609ab-379c-4c51-afe8-b16295621122.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Dietary Trace Elements With Muscle Quality Index and Metabolic Syndrome: Evidence from a NHANES Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is a clinical condition characterized by a cluster of metabolic abnormalities, including insulin resistance, central obesity, impaired glucose metabolism, hypertension, and dyslipidemia. In the United States, the prevalence of MetS among adults has remained persistently high, increasing from 32.9% in 2003 to 34.7% in 2011, and more recent epidemiological evidence indicates that this figure rose further to 41.8% by 2018. According to the 2021 Global Burden of Disease study, MetS and its related metabolic abnormalities have shown a continuously increasing contribution to disability-adjusted life years (DALYs) among 25 level-three risk factors. A substantial body of evidence consistently demonstrates that MetS markedly increases the risk of cardiovascular disease, type 2 diabetes, and stroke, and is associated with a wide range of adverse chronic health outcomes. Collectively, MetS and its metabolic components have emerged as major contributors to the global disease burden, representing a pressing public health challenge.\u003c/p\u003e \u003cp\u003eExisting epidemiological and clinical studies have primarily focused on the associations between skeletal muscle characteristics and health outcomes. Prior research has shown that skeletal muscle mass is inversely associated with obesity, and that lower muscle mass is linked to a significantly elevated risk of cardiovascular disease and metabolic-related mortality. Declines in muscle mass and muscle strength have also been closely associated with impaired glucose metabolism, chronic obstructive pulmonary disease, and increased cancer risk. Compared with muscle mass or muscle strength alone, the muscle quality index (MQI), which integrates handgrip strength (dominant and nondominant) and appendicular skeletal muscle mass (ASM), captures both the functional and quantitative aspects of skeletal muscle and is therefore considered a more comprehensive indicator. Consequently, MQI has been widely applied in population-based studies. Previous studies have reported associations between lower MQI and an increased risk of periodontitis and sleep-related problems. Moreover, reductions in skeletal muscle mass may exacerbate insulin resistance and hyperglycemia, thereby accelerating the progression of MetS.\u003c/p\u003e \u003cp\u003eDespite growing interest in skeletal muscle health, evidence regarding the associations between dietary trace element intake, muscle quality index, and MetS remains limited. Most existing studies have focused on single trace elements, providing insufficient insight into the combined and potentially interacting effects of multiple micronutrients. Therefore, using data from the National Health and Nutrition Examination Survey (NHANES), the present study aimed to systematically examine the associations between dietary intakes of multiple trace elements\u0026mdash;including zinc, copper, selenium, iron, and magnesium\u0026mdash;and muscle quality index and metabolic syndrome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population:\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design using data from the National Health and Nutrition Examination Survey (NHANES). NHANES is a nationally representative survey of the noninstitutionalized civilian population in the United States, conducted biennially by the National Center for Health Statistics (NCHS). The survey collects comprehensive information on dietary intake and health status through standardized procedures, which have been described in detail elsewhere. All NHANES protocols were approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from all participants prior to participation.\u003c/p\u003e \u003cp\u003eThe initial study population consisted of 88,308 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years from the NHANES 2011\u0026ndash;2018 survey cycles. Participants without dietary intake data were excluded (n\u0026thinsp;=\u0026thinsp;86,840). Subsequently, 222 individuals with missing data on muscle quality index (MQI) or metabolic syndrome (MetS) were excluded. An additional 135 participants were excluded due to missing key covariates, including body mass index (BMI), race/ethnicity, educational attainment, smoking status, alcohol consumption, and other relevant variables. Pregnant women and participants with unreliable or implausible dietary recall data, defined as total daily energy intake\u0026thinsp;\u0026lt;\u0026thinsp;500 kcal or \u0026gt;\u0026thinsp;5,000 kcal, were also excluded. After applying all exclusion criteria, a total of 1,111 participants were included in the final analytical sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposure variables\u003c/h2\u003e \u003cp\u003eThe exposure variables in this study were dietary trace elements, including zinc, copper, selenium, and iron. Dietary intake data were obtained from the NHANES dietary interview component, which estimates the intake of foods and beverages (including all types of water) consumed during the 24-hour period prior to the interview (midnight to midnight). Based on these data, intakes of energy, nutrients, and other food components were calculated. All nutrient intake values were derived using the food and nutrient databases developed by the United States Department of Agriculture (USDA). These dietary data were collected and processed through a collaborative effort between the USDA and the Department of Health and Human Services, with the USDA Food Surveys Research Group (FSRG) responsible for dietary data collection methodology, database maintenance, and quality control procedures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome variables\u003c/div\u003e \u003cp\u003eMuscle quality index (MQI) was calculated as the ratio of handgrip strength (kg) to appendicular skeletal muscle mass (ASM, kg). Handgrip strength was measured using a standardized electronic dynamometer, and ASM was assessed by dual-energy X-ray absorptiometry (DXA). MQI was treated as a continuous variable in all statistical analyses.\u003c/p\u003e \u003cp\u003eMetabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Participants meeting at least three of the following five components were classified as having MetS: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102 cm in men or \u0026ge;\u0026thinsp;88 cm in women; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL in men or \u0026lt;\u0026thinsp;50 mg/dL in women; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or current use of antihypertensive medication; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL or use of glucose-lowering medication.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates were selected based on prior literature and biological plausibility. These included demographic characteristics (age, sex, race/ethnicity, and educational attainment), lifestyle factors (smoking status, alcohol consumption, and physical activity level), and clinical variables. Clinical covariates comprised body mass index (BMI) and history of diabetes, which was defined by any of the following criteria: a physician or health professional diagnosis, use of glucose-lowering medication or insulin, random plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L during an oral glucose tolerance test (OGTT). Information on medication use and clinical measurements was obtained from NHANES standardized questionnaires and examination data. Unless otherwise specified, all covariates were treated as categorical variables in the analyses.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.4.1). In accordance with recommendations for analyses of National Health and Nutrition Examination Survey (NHANES) data, the complex multistage sampling design was accounted for by incorporating sampling weights, strata, and primary sampling units. Continuous variables are presented as weighted means with standard errors, whereas categorical variables are expressed as weighted percentages. Differences in baseline characteristics between participants with and without metabolic syndrome (MetS) were compared using weighted t tests for continuous variables and weighted chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eDietary intakes of zinc, iron, and selenium were included as continuous variables and categorized into weighted population-based tertiles. Weighted multivariable linear regression models were applied to examine differences in muscle quality index (MQI) across levels of trace element intake and to assess their associations with MQI. Metabolic syndrome (MetS) was treated as a binary outcome and analyzed using weighted multivariable logistic regression models.\u003c/p\u003e \u003cp\u003eThree hierarchical adjustment models were constructed: Model 1 was unadjusted; Model 2 was adjusted for sex and race/ethnicity; and Model 3 was further adjusted for educational attainment, smoking status, alcohol consumption, physical activity level, body mass index (BMI), and total energy intake. To explore potential nonlinear associations, restricted cubic spline (RCS) functions with three knots were incorporated into the weighted regression models, and nonlinearity was evaluated using Wald tests for dietary trace elements (zinc, iron, and iodine).\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted according to age (\u0026lt;\u0026thinsp;40 vs. \u0026ge;40 years), sex, and physical activity level. Sensitivity analyses were performed by excluding participants with extreme trace element intakes, those with chronic conditions (e.g., diabetes), and those with unreliable dietary recall data to assess the robustness of the findings. All statistical tests were two-sided, and a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e1. Baseline characteristics of participants\u003c/p\u003e\n\u003cp\u003eA total of 1,111 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years were included in the final analysis. Participants were categorized into tertiles (Q1\u0026ndash;Q3) based on total muscle quality index (MQI_total) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). As MQI increased across tertiles, body mass index (BMI) showed a decreasing trend (from 31.19 to 23.86 kg/m\u0026sup2; from Q1 to Q3, P\u0026thinsp;=\u0026thinsp;0.005). With respect to dietary intake, iodine intake increased across increasing MQI tertiles, and intakes of other trace elements, including iron, zinc, copper, and selenium, also exhibited overall upward trends (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of participants with metabolic syndrome across different muscle quality statuses in NHANES 2011\u0026ndash;2018\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e229.00 (12.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.00 (12.97%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e72.00 (9.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.00 (15.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e882.00 (87.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e297.00 (87.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e338.00 (90.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e247.00 (84.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum Creatine Kinase (IU/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e136.06 (89.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e197.41 (96.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101.43 (46.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92.32 (66.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.80 (7.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.19 (7.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.48 (6.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.86 (2.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e517.00 (51.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e250.00 (75.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e140.00 (34.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e127.00 (37.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e594.00 (48.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108.00 (24.68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e270.00 (65.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e216.00 (62.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge,y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.05 (12.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.15 (12.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.51 (11.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.26 (12.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace/Ethnicity,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e536.00 (74.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e200.00 (82.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e134.00 (60.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e202.00 (78.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e133.00 (5.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e114.00 (11.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.00 (3.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther Race\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e442.00 (20.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.00 (6.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e257.00 (36.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e141.00 (21.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation Level,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.083\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnder high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e114.00 (8.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.00 (6.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.00 (21.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144.00 (10.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.00 (6.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.00 (12.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.00 (13.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbove high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e853.00 (81.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e318.00 (93.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e323.00 (81.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e212.00 (65.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVitamin K,mcg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.69 (19.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.37 (22.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.13 (18.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.52 (10.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIron Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.92 (6.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.04 (4.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.27 (5.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.50 (6.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.21 (4.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.92 (3.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.01 (4.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.18 (4.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCopper Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77 (0.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85 (0.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95 (0.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.47 (0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIodine Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.63 (50.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e126.82 (33.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104.15 (48.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.98 (55.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood Cadmium,ug/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.37 (0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.24 (0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.35 (0.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55 (0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(OH)D, nmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.23 (22.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.53 (21.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e71.60 (19.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.87 (20.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate Physical Activities,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e434.00 (40.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e132.00 (34.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e123.00 (34.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e179.00 (55.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e677.00 (59.26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e226.00 (65.21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e287.00 (65.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e164.00 (45.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArm muscle quality index,g/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.63 (2.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.78 (1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.71 (1.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.99 (1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAppendicular muscle quality index,g/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.74 (0.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.46 (0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.76 (0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.08 (0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal muscle quality index,g/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.36 (0.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.80 (0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.39 (0.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.05 (0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAppendicular skeletal muscle mass,g/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.41 (7.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.06 (5.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.49 (5.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.01 (5.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAppendicular skeletal muscle mass index,g/kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.83 (1.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.02 (1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.65 (1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.45 (1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetS,%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e915.00 (78.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e269.00 (83.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e373.00 (83.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e273.00 (65.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e196.00 (21.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.00 (16.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.00 (16.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.00 (34.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eContinuous variables are presented as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors and compared using Student\u0026rsquo;s t test. Categorical variables are expressed as weighted percentages (95% confidence intervals) and compared using the Cochran\u0026ndash;Mantel\u0026ndash;Haenszel \u0026chi;\u0026sup2; test. Trends across MQI tertiles were assessed using weighted linear regression or trend tests, as appropriate.\u003c/p\u003e\n\u003cp\u003e2. Associations between dietary trace elements and total muscle quality index (MQI_total)\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between dietary trace elements and total muscle quality index (MQI_total). In the unadjusted Model 1, dietary intakes of iron, zinc, and copper were negatively associated with MQI_total, with statistically significant associations observed for iron and zinc, while the association for copper was marginally significant. After adjustment for sex and race/ethnicity, these negative associations were substantially attenuated. Following further adjustment for educational attainment, body mass index (BMI), and alcohol consumption, the associations between iron, zinc, and copper intake and MQI_total were no longer statistically significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIodine intake showed a positive association with MQI_total in Models 1 and 2. Blood cadmium levels were positively associated with MQI_total in the unadjusted and partially adjusted models. In contrast, serum 25-hydroxyvitamin D [25(OH)D] exhibited a negative trend with MQI_total; however, this association did not reach statistical significance in the fully adjusted model (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLinear regression associations between different trace elements and overall muscle quality index(MQI.total)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel1\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel2\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel3\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI))\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIron Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.06, -0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03(-0.06,-0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02(-0.04,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.07,-0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.024*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.03(-0.06,-0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.04,0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCopper Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.20(-0.41, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24(-0.42,-0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05(-0.29,0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIodine Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(0.00, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBloodCadmium,ug/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60(0.15,1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.55(0.05,1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.034*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19(-0.29,0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(OH)D,nmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.022*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*,P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.MetS_ATP,Metabolic Syndrome by Adult Treatment Panel III criteria; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e3. Associations between trace elements and metabolic syndrome\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the associations between dietary trace elements and metabolic syndrome (MetS). In the unadjusted Model 1, higher intakes of iron, zinc, and iodine were associated with a lower risk of MetS. These associations remained after adjustment for sex and race/ethnicity. In the fully adjusted Model 3, the association between iron intake and MetS was no longer statistically significant, whereas dietary intakes of zinc and iodine remained significantly associated with MetS risk.Specifically, each 1 mg/day increase in dietary zinc intake was associated with an approximately 32% lower odds of MetS (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.68, 95% confidence interval [CI]: 0.50\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.021). Similarly, each 1 mg/day increase in dietary iodine intake was associated with an approximately 3% lower odds of MetS (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.94\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.040) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression analyses of associations between different trace elements and metabolic syndrome risk\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel1\u003c/p\u003e\n\u003cp\u003e(OR,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel2\u003c/p\u003e\n\u003cp\u003e(OR,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel3\u003c/p\u003e\n\u003cp\u003e(OR,95%CI))\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIron Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90(0.81,1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.042*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.880.78, 0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.037*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77(0.58, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82(0.70,0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80(0.68, 0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68(0.50, 0.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIodine Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99(0.97,1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98(0.97, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97(0.94,1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.040*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*,P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.MetS_ATP,Metabolic Syndrome by Adult Treatment Panel III criteria; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e4. Dose\u0026ndash;response relationships based on restricted cubic spline analyse\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline (RCS) analyses indicated heterogeneous dose\u0026ndash;response relationships between different trace elements and the risk of metabolic syndrome (MetS) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Intakes of zinc and iodine exhibited significant nonlinear associations with MetS risk (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Iron and copper intake were also nonlinearly associated with MetS risk, with P values for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for both elements. Although nonlinear associations were observed for all examined trace elements, the ranges and patterns of these associations differed across elements.\u003c/p\u003e\n\u003cp\u003e5. Subgroup analyses\u003c/p\u003e\n\u003cp\u003eSubgroup analyses indicated that dietary intakes of iron, zinc, and copper were negatively associated with total muscle quality index (MQI_total), with these associations being more pronounced among women, participants aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, and alcohol drinkers. However, these negative associations were primarily observed in Models 1 and 2 and were no longer statistically significant in most subgroups after full adjustment in Model 3.Blood cadmium levels showed a positive trend with MQI_total in several subgroups, but this association was limited to the unadjusted and partially adjusted models and lacked stability after full multivariable adjustment in Model 3 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubgroup linear regression analyses of associations between different trace elements and the muscle quality index (MQI.total)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSubgroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel1\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel2\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel3\u003c/p\u003e\n\u003cp\u003e(Beta,95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIron Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.07,0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.09,0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.09,0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.10,0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.11,0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.18,0.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.09, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-Inf, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.08,-0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.08,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02(-0.06,0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZinc Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.05, 0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.07,0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.10,0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.026*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05(-0.11,0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.09,0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.17,0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06(-0.13,0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06(-Inf, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.05(-0.09,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04(-0.09,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.05,0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCopper Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18(-0.43,0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18(-0.48, 0.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.26(-0.52,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.26(-0.56,0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.09(-0.29,0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.09(-0.41,0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.36(-0.91,0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.40(-Inf, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21(-0.44, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21(-0.42,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06(-0.17,0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIodine Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01,0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.02,0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00-0.01, 0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-Inf, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00-0.01, 0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00-0.01, 0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00-0.01, 0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlood Cadmium,ug/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52\u0026thinsp;\u0026minus;\u0026thinsp;0.15, 1.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52(-0.22,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53(0.03, 1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59(-0.24, 1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45(-0.25, 1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49(-0.61,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.650.03, 1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66(-Inf,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59(0.33, 0.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59(0.30, 0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12(-0.27,0.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efer\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(OH)D,nmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.02,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.02,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.02, 0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00(-0.01, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.03, 0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.02, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-Inf, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.02, 0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.01(-0.01,0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSubgroup analyses showed that higher intakes of zinc and iodine were associated with lower odds of metabolic syndrome (MetS) among alcohol drinkers and participants with lower to moderate levels of physical activity. In contrast, the direction and magnitude of the associations between iron intake and MetS varied across subgroups. In some subgroup analyses, iodine intake was positively associated with MetS risk, indicating potential heterogeneity across populations and warranting further investigation. Nevertheless, across the majority of subgroups, the associations of zinc and iodine intake with MetS were consistently inverse (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubgroup logistic regression analyses of associations between trace elements and the risk of metabolic syndrome\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSubgroup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR,95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR,95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR,95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"12\" align=\"left\"\u003e\n\u003cp\u003eIron Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.68,1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.64,1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79(0.58,1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64(0.38,1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.80,1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88(0.78, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78(0.53,1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970.78, 1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.50,1.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86(0.71,1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86(0.00,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eref\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eModerate Physical Activity\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14(0.78,1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13(0.76,1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47(1.11, 1.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77(0.64,0.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73(0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"12\" align=\"left\"\u003e\n\u003cp\u003eZinc Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90(0.62,1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91(0.59,1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73(0.48,1.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58(0.33, 1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eDrinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83(0.70, 0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81(0.67,0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69(0.41,1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59(0.00,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85(0.61,1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74(0.23,2.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82(0.60,1.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80(0.00,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eModerate Physical Activity\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07(0.81, 1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.050.79, 1.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30(0.85, 2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58(0.33, 1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59(0.00,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"12\" align=\"left\"\u003e\n\u003cp\u003eIodine Intake,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.96, 1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00(0.95,1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97(0.94, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95(0.90, 1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAlcohol Drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.97, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.97,1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.92,1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99(0.96,1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.90,1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.96,1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98(0.00,Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eModerate Physical Activity\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01(0.97,1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.010.97, 1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.051.01, 1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96(0.93, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.960.00, Inf\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e6. Mediating Role of MQI and ASMI\u003c/p\u003e\n\u003cp\u003eMediation analyses revealed heterogeneous mediating roles of muscle quality indicators (MQI_total and MQI_app) and muscle mass (ASMI) in the associations between dietary trace elements and metabolic syndrome (MetS) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Across most models, the indirect effects of certain mediation pathways reached statistical significance; however, the corresponding mediation proportions were predominantly negative, indicating primarily suppressive (inverse) mediation effects (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSpecifically, dietary intakes of iron and iodine demonstrated mediation patterns mainly characterized by suppressive effects through muscle quality indicators. In contrast, for dietary zinc intake, MQI_app and MQI_total exhibited partial positive mediation effects, whereas no stable mediating effects were observed for the remaining muscle-related indicators. Overall, neither muscle function nor muscle mass consistently acted as positive mediators in the associations between most trace elements and MetS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the associations among dietary trace elements, muscle quality index (MQI), and metabolic syndrome (MetS) in a nationally representative sample of U.S. adults. We observed inverse associations between dietary intakes of zinc and iodine and MetS risk, whereas the association between iron intake and MetS was attenuated at higher intake levels. In addition, significant nonlinear dose\u0026ndash;response relationships were identified for zinc, iodine, and iron in relation to MetS risk, with zinc exhibiting a U-shaped pattern and iron and iodine showing threshold-like characteristics. These findings extend previous evidence indicating that declines in muscle quality and metabolic dysregulation have emerged as major public health concerns.\u003c/p\u003e \u003cp\u003eSkeletal muscle plays a central role not only in locomotion but also in glucose uptake, lipid oxidation, and whole-body energy metabolism. Age-related declines in muscle mass and function often occur alongside increased insulin resistance, chronic low-grade inflammation, and mitochondrial dysfunction, thereby accelerating the disruption of metabolic homeostasis and the development of MetS. In the present study, several trace elements were significantly associated with MQI in minimally adjusted models; however, these associations were largely attenuated after further adjustment for body mass index (BMI) and lifestyle-related factors. This pattern suggests that MQI may reflect an integrated phenotype of body composition and metabolic status rather than serving as a direct downstream effect of individual micronutrient intake.\u003c/p\u003e \u003cp\u003eThe inverse association between zinc intake and MetS risk observed in this study is biologically plausible. Zinc is involved in insulin signaling, antioxidant defense, and mitochondrial energy metabolism, and zinc deficiency has been linked to impaired insulin sensitivity, dysregulated lipid metabolism, and elevated inflammatory responses. Our findings further revealed a nonlinear association between zinc intake and MetS, with the strongest inverse association observed within a moderate intake range. Moreover, MQI_app and MQI_total exhibited limited but directionally consistent positive mediation effects in the zinc\u0026ndash;MetS association, suggesting that improvements in muscle function may represent one potential, albeit modest, pathway through which zinc intake influences metabolic health. This interpretation is consistent with prior evidence linking zinc status to mitochondrial efficiency, oxidative stress regulation, and insulin sensitivity.\u003c/p\u003e \u003cp\u003eThe association between iodine intake and MetS appears to be mediated primarily through systemic endocrine regulation. Iodine is essential for thyroid hormone synthesis, and insufficient iodine intake can lead to reduced basal metabolic rate, impaired lipid metabolism, and decreased energy expenditure. In this study, iodine intake demonstrated a nonlinear association with MetS risk that plateaued beyond a certain intake level, consistent with the physiological saturation characteristics of thyroid hormone regulation. However, mediation analyses indicated that muscle-related pathways associated with iodine intake were predominantly suppressive rather than positively mediating, suggesting that the metabolic effects of iodine are unlikely to be primarily transmitted through muscle mass or muscle function.\u003c/p\u003e \u003cp\u003eIron exhibited a more complex, bidirectional association with metabolic health. While iron is essential for oxygen transport and mitochondrial energy production, excessive iron accumulation may exacerbate oxidative stress and metabolic burden. Our results indicated an inverse association between iron intake and MetS risk at lower intake levels, which weakened as intake increased, reflecting a threshold-dependent pattern. Consistently, iron-related mediation pathways through muscle indices were largely suppressive, further supporting the notion that iron influences metabolic risk mainly through systemic redox balance and oxidative mechanisms rather than via muscle-mediated pathways.\u003c/p\u003e \u003cp\u003eNotably, although several indirect effects reached statistical significance in the mediation analyses, MQI and ASMI did not function as conventional positive mediators in most trace element\u0026ndash;MetS associations. Instead, negative mediation proportions were frequently observed, indicating suppressive mediation effects. This suggests that muscle-related indicators may modulate the direction or magnitude of micronutrient\u0026ndash;MetS associations rather than simply transmitting their effects. These findings highlight the importance of distinguishing between skeletal muscle as a metabolic target tissue and as a regulatory or modifying factor when interpreting nutrition\u0026ndash;metabolism relationships.\u003c/p\u003e \u003cp\u003eSubgroup analyses further suggested heterogeneity in the associations between trace elements and MQI across sex, age, and drinking status; however, the stability of these associations was limited after full covariate adjustment, underscoring the influential role of lifestyle factors and overall body composition in shaping these relationships.\u003c/p\u003e \u003cp\u003eOverall, our findings indicate that higher intakes of zinc and iodine are associated with a lower risk of MetS, with zinc potentially exerting modest effects through muscle function, whereas iron shows a clear intake-dependent pattern. Muscle quality and muscle mass did not consistently serve as traditional mediators in most trace element\u0026ndash;MetS pathways but instead exhibited predominantly suppressive mediation effects, suggesting that the metabolic impacts of trace elements are more likely driven by systemic metabolic and endocrine mechanisms. These results underscore the importance of balanced micronutrient intake and provide new insights into the complex interplay among nutrition, skeletal muscle, and metabolic health.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the cross-sectional design precludes causal inference. Second, dietary intake was assessed using 24-hour recall data, which may be subject to measurement error. Third, certain potential confounders, such as inflammatory biomarkers and thyroid function indicators, were not available for inclusion. Future longitudinal and mechanistic studies are warranted to further elucidate these relationships.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that dietary trace elements, particularly zinc, selenium, and iodine, are significantly associated with muscle quality index (MQI) and metabolic syndrome (MetS) among U.S. adults. Dietary intakes of zinc and iodine showed consistent associations with MetS risk. Mediation analyses further indicated that MQI was not a primary mediator in most trace element\u0026ndash;MetS associations, with a mediating effect observed only for zinc. These findings suggest that the influence of dietary trace elements on metabolic risk may operate largely through pathways independent of muscle quality, highlighting the complexity of the underlying mechanisms linking micronutrient intake and metabolic health.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eAll data are freely available in the NHANES: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAppendicular Skeletal Muscle Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMuscle Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMQI.total\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal muscle quality index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMQI.app\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAppendicular muscle quality index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the participants and investigators of the National Health and Nutrition Examination Survey (NHANES) for providing the data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Physical Education, Zhejiang University of Science and Technology, Hangzhou 310023, China \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTian Peng \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollege of Physical Education, Guizhou Normal University, Guiyang 550025, China \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRui Wang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHanyang University ERICA, Ansan 15588, Republic of Korea \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSiwei Hao, Juan Li, Xiaochen Li, Zhenping Jiang \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and methodology: Tian Peng. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFormal analysis: Rui Wang, Siwei Hao. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation: Juan Li, Xiaochen Li. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting—review and editing: Tian Peng, Zhenping Jiang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Tian Peng or Zhenping Jiang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which is a publicly available database. The NHANES protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent prior to participation. The present study involved secondary analysis of de-identified public-use data and therefore did not require additional institutional review board approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the NHANES database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaklayen MG. The global epidemic of the metabolic syndrome. 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Journals Gerontology: Ser A. 2012;67(1):28\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-8728355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8728355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to examine the associations of dietary trace elements with muscle quality index (MQI) and metabolic syndrome (MetS), and to assess potential mediating effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years from the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2018. Dietary intake of trace elements, including iron, copper, selenium, and iodine, was assessed. Muscle quality index (MQI) was defined as the ratio of combined handgrip strength to appendicular skeletal muscle mass. Metabolic syndrome (MetS) was identified according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Weighted multivariable linear and logistic regression models were applied to examine the associations between dietary trace element intake and MQI and MetS. Trend analyses and restricted cubic spline (RCS) models were further used to assess dose\u0026ndash;response relationships and potential nonlinear associations. Subgroup analyses were conducted according to age, sex, body mass index (BMI), and physical activity level. Additionally, MQI was treated as a mediator to explore its potential mediating role in the associations between dietary trace elements and MetS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,111 participants were included in the analysis. Weighted multivariable analyses showed that dietary intakes of iron, zinc, and copper were negatively associated with muscle quality index (MQI). In addition, lower intakes of zinc and iodine were associated with a higher prevalence of metabolic syndrome (MetS). In the fully adjusted models, zinc intake remained inversely associated with MetS risk (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.68, 95% confidence interval [CI]: 0.50\u0026ndash;0.92), suggesting a protective association of higher zinc intake. Iodine intake also demonstrated a stable inverse association with MetS (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.94\u0026ndash;1.00), indicating that adequate iodine intake may be associated with a reduced risk of MetS. In contrast, the association between iron intake and MetS was attenuated after multivariable adjustment and did not reach statistical significance. Restricted cubic spline (RCS) analyses revealed significant nonlinear associations between intakes of iron, zinc, and iodine and MetS risk (P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subgroup analyses further indicated that these associations were more pronounced among older adults, women, and individuals with obesity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher dietary intakes of zinc were associated with better muscle quality and a lower prevalence of metabolic syndrome. These findings highlight the potential public health relevance of trace elements in supporting musculoskeletal health and reducing metabolic risk.\u003c/p\u003e","manuscriptTitle":"Associations of Dietary Trace Elements With Muscle Quality Index and Metabolic Syndrome: Evidence from a NHANES Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 07:06:00","doi":"10.21203/rs.3.rs-8728355/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":"99f70bbc-4da9-4308-9968-c197064b0218","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T12:11:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 07:06:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8728355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8728355","identity":"rs-8728355","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}