{"paper_id":"005584bf-ab0e-408c-8e6b-b287c8fed998","body_text":"Barium Exposure as a Major Risk Factor for Elevated Cardiovascular Risk within Heavy Metal Mixtures: Integrating Population Analysis with Experimental Approach | 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 Barium Exposure as a Major Risk Factor for Elevated Cardiovascular Risk within Heavy Metal Mixtures: Integrating Population Analysis with Experimental Approach Hongya Wang, Haonan Cui, Butuo Xu, Min Sun, Wei Zhang, Daoyan Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9359383/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Exposure to environmental heavy metals is a significant modifiable risk factor for cardiovascular disease (CVD). The plasma atherogenic index (AIP) and cardiometabolic index (CMI) are integrated biomarkers that robustly reflect CVD risk. A critical unanswered question is how exposure to heavy metal mixtures exacerbates CVD risk and which specific components are the primary toxic drivers. Methods This study leveraged data from NHANES 2011–2018. Logistic regression, grouped weighted quantile sum (GWQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze associations between heavy metals and AIP/CMI. In parallel, in vitro experiments examined the direct effects of barium on vascular smooth muscle cells (VSMCs) and endothelial cells (ECs), and indirect effects mediated by barium chloride-injured myotube cells. Results A total of 2,834 participants were included. Population analyses revealed a significant positive association between mixed heavy metal exposure and elevated levels of both AIP and CMI. Single-pollutant models specifically linked urinary Ba, Cd and Sb to higher AIP; Ba, Cs and Sb to higher CMI. Notably, mixture analyses using GWQS and BKMR consistently identified barium as the primary contributing factor. Complementary in vitro experiments demonstrated that barium exposure directly increased oxidative stress in VSMCs and ECs. Consistently, in an indirect co-culture model, barium-injured myotube cells also promoted ROS generation in VSMCs and ECs. Conclusion Heavy metal exposure is associated with higher AIP and CMI, with barium playing a key role. These findings underscore the need for environmental monitoring and interventions to mitigate CVD risk, advancing the understanding of environment-metabolism interactions. Metal components Cardiovascular disease BKMR Barium Oxidative stress Figures Figure 1 Figure 3 Figure 4 Figure 5 1. Introduction Cardiovascular disease (CVD) remains one of the predominant causes of mortality worldwide, contributing to millions of deaths each year. The development of CVD is a multifactorial process involving a combination of genetic predisposition, environmental exposures, and lifestyle-related factors [ 1 ]. Of these environmental determinants, various pollutants including air pollutants, heavy metals, and other chemical agents have been recognized as important risk factors for CVD pathogenesis [ 2 ]. Heavy metals are persistent environmental pollutants released through industrial, agricultural, and urban activities [ 3 ]. Their low biodegradability facilitates accumulation in ecosystems and the human body via the food chain [ 4 ]. Following exposure, heavy metals undergo systemic distribution with preferential accumulation in the vascular system, and are subsequently cleared primarily via renal excretion [ 5 ]. Chronic low-level accumulation in the body underlies their persistent toxicity and public health impact. Accumulating evidence suggests that heavy metal exposure is associated with greater CVD. Specific metals, such as cadmium, directly impair vascular function [ 6 ], while combined exposures (e.g., Mn/Cr/Mo) may influence blood pressure through mechanisms like lipid peroxidation [ 7 ]. Although composite biomarkers such as the atherogenic index of plasma (AIP) and the cardiometabolic index (CMI) offer sensitive early risk stratification, research on their association with heavy metal exposure remains limited, and the key toxic components and their direct vascular mechanisms are not yet fully elucidated. Both AIP and CMI are recognized as integrated biomarkers of CVD and metabolic risk. AIP, proposed as a measure of atherogenic risk [ 8 ], predicts diverse outcomes including CVD events [ 9 ], vascular calcification [ 10 ], mortality in cardiorenal-metabolic (CKM) syndrome and metabolic dysfunction-associated fatty liver disease (MAFLD) [ 11 , 12 ]. CMI, which reflects visceral adiposity and lipid dysregulation, demonstrates superior predictive performance over conventional measures (e.g., BMI) for metabolic syndrome, kidney stones, insulin resistance, and atherosclerosis [ 13 – 15 ]. Therefore, compared to traditional obesity or lipid indicators, AIP and CMI may be more suitable for assessing CVD risk. Although AIP and CMI show only a moderate correlation [ 16 ], their complementary predictive value supports combined use for more comprehensive risk assessment. Sarcopenia is defined as a syndrome involving the gradual reduction in muscle mass, muscular strength, and physical performance, and is closely linked to impaired mobility, functional disability, and elevated mortality risk [ 17 ]. Its global prevalence is approximately 5–10%, imposing a substantial healthcare burden [ 18 ]. A growing body of research indicates a comorbid link between sarcopenia and cardiovascular disease: prospective cohort studies show that the progression of sarcopenia status increases the risk of various cardiovascular diseases, while its improvement reduces corresponding risks, with a causal relationship existing between them and no apparent reverse association [ 19 – 21 ]. Current evidence on the comorbidity mechanisms between the two is limited, making it urgent to explore new environmental factors to prevent their co-occurrence. Given the pervasive environmental presence of heavy metals, elucidating their link to CVD risk is crucial for mitigating health impacts. This two-stage study first evaluates associations of heavy metal exposure with AIP and CMI using NHANES 2011–2018 data, and then validates specific toxic effects in vascular cells through in vitro experiments. This study may improve mechanistic insights into heavy metal‑associated CVD and support risk evaluation strategies for environmental contaminants. 2. Methods 2.1 Study population We analyzed publicly available data from the NHANES 2011–2018 cycles, a cross-sectional, population-based survey. From the initial 39,156 participants, 22,607 adults aged ≥20 years were included for further screening. We further excluded 12,968 subjects with incomplete lipid profiles (including missing triglycerides [TG] and high-density lipoprotein cholesterol [HDL-C]), leaving 9,639 participants with complete lipid data. Subsequent exclusions removed 1,291 individuals due to missing data on body mass index (BMI), systolic/diastolic blood pressure (SBP/DBP), or the poverty-income ratio (PIR). Additional exclusions were applied for pregnancy or cancer history (n = 838), missing urinary heavy metal data (n = 4,452), and missing other key covariates (n = 224). Finally, 2,834 participants with a key covariate missing rate below 5% were included in the study sample (Figure. 1). 2.2 Heavy metal measurement Urinary concentrations of ten metals (barium [Ba], cadmium [Cd], cobalt [Co], cesium [Cs], molybdenum [Mo], lead [Pb], antimony [Sb], thallium [Tl], tungsten [Tu], mercury [Hg]) were analyzed via inductively coupled plasma mass spectrometry (ICP-MS) at CDC’s National Center for Environmental Health (NCEH); metals with inconsistent inter-survey measurements or <60% detection rate were excluded, values below the detection limit (BDL) were imputed as detection limit/√2, and concentrations were creatinine-corrected (μg/g creatinine) and natural log-transformed for improved distributional properties. 2.3 AIP and CMI calculation Two lipid-related indices were constructed: a. Atherogenic Index of Plasma: AIP = log (TG/HDL‑C) b. Cardiometabolic Index: CMI = (Waist circumference/Height) × (TG/HDL‑C) 2.4 Sarcopenia definition Sarcopenia was defined using the FNIH criteria: appendicular skeletal muscle mass adjusted for BMI < 0.512 in women and < 0.789 in men. Skeletal muscle mass was measured by dual‑energy X‑ray absorptiometry (DXA) [22]. 2.5 Covariates Covariates included: a. Demographics: age, sex, race/ethnicity, education, poverty‑income ratio, marital status b. Lifestyle: smoking status, alcohol consumption c. Comorbidities: diabetes, hypertension, cardiovascular disease d. Renal function: estimated glomerular filtration rate (eGFR) e. Disease definitions followed established clinical thresholds and self-reported physician diagnosis. Complete covariate definitions are provided in the Supplementary Materials 2.6 Cell culture and treatment Vascular smooth muscle cells (VSMCs), mouse aortic endothelial cells (MAECs), and C2C12 myoblasts were cultured under standard conditions. Cells were exposed to graded concentrations of BaCl₂ for 24 or 48 h to determine appropriate doses for subsequent experiments. Conditioned medium from BaCl₂‑treated myotubes was collected and used to treat VSMCs and ECs to assess paracrine effects. Specific cell treatment parameters are provided in the Supplementary Materials. 2.7 Cell viability Cell viability was measured using the CCK-8 assay. After incubation with CCK-8 reagent, the absorbance at 450 nm was detected using a Varioskan microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), and the relative viability was calculated. 2.8 ROS detection Cells were incubated with dihydroethidium (DHE, S0064S, Beyotime) for 20 minutes, then fixed with 4% paraformaldehyde (PFA). After washing with PBS, slides were mounted and observed under an inverted fluorescence microscope (TE2000-U; Nikon, Tokyo, Japan). Images were captured and analyzed using ImageJ 9.0 software. 2.9 Statistical analysis Baseline characteristics of participants were presented according to AIP and CMI quartiles. Continuous variables were shown as median (IQR), and categorical variables as frequency (%). Between-group comparisons were performed using Rao–Scott chi-square or Kruskal–Wallis tests. Multivariate logistic regression was used to examine the associations of urinary heavy metals with AIP, CMI and sarcopenia, with progressive adjustment for demographics, lifestyle factors and comorbidities. Grouped weighted quantile sum (GWQS) regression [25] and Bayesian kernel machine regression (BKMR) [26,27] were further applied to explore the mixture and joint effects of heavy metal exposure. All statistical analyses were conducted using R 4.3.3 and Prism 10.1.2, with P < 0.05 as statistically significant. Detailed methodological procedures are provided in the supplementary file. 3. Results 3.1. Sociodemographic and clinical attributes of the enrolled subjects The present study comprised 2,834 participants in total. An analysis using AIP and CMI as categorical variables (quartiles) showed the baseline characteristics stratified by AIP and CMI status, as presented in Table 1 . Compared to the low AIP groups (Q1–Q3), the high AIP group (Q4) had a higher proportion of males, a higher proportion of current and former smokers, lower education levels and lower PIR, while exhibiting higher BMI, SBP, DBP, and TC levels, and a lower eGFR (all P < 0.05). Meanwhile, the high AIP group presented a significantly higher prevalence of hypertension, diabetes, and cardiovascular disease (all P < 0.05). The baseline characteristics of the high CMI group (Q4) were similar to those of the high AIP group, but the high CMI group was older than the low CMI groups. Table 1 Characteristics of the study participants in U.S. adults: results from NHANES 2011–2018. Variable Overall (n = 2834) Low AIP (n = 2125) High AIP (n = 709) P value Low CMI (n = 2088) High CMI (n = 746) P value Age (years) 48 (34–61) 48 (33–62) 49 (37–60) 0.168 47 (33–61) 50 (38–62) < 0.001 Gender (male,%) 1492 (52.6%) 1026 (48.3%) 466 (65.7%) < 0.001 1043 (50%) 449 (60.2%) < 0.001 BMI (kg/m 2 ) 27.9 (24.1-32.58) 27.1 (23.5–31.7) 30.2 (26.8–35.1) < 0.001 26.85 (23.4–31.1) 31 (27.6-36.27) < 0.001 SBP (mmHg) 121.33 (111.33–134) 120.67 (111.33-133.33) 124 (114–134) < 0.001 120.67 (110.67-132.67) 124 (114-134.67) < 0.001 DBP (mmHg) 70.67 (63.33–77.33) 69.33 (62.67–76.67) 72.67 (66-79.33) < 0.001 70 (62.67–76.67) 72.33 (65.33–79.33) < 0.001 eGFR (mL/min/1.73m²) 90.66 (76.18-106.94) 91.58 (77-107.53) 88.28 (73.74-105.16) 0.004 91.81 (77.55-107.82) 87.49 (71.7-105.05) < 0.001 Diabetes (n,%) 580 (20.5%) 345 (16.2%) 235 (33.1%) < 0.001 327 (15.7%) 253 (33.9%) < 0.001 Hypertension (n,%) 1515 (53.5%) 1085 (51.1%) 430 (60.6%) < 0.001 1039 (49.8%) 476 (63.8%) < 0.001 CVD (n,%) 302 (10.7%) 199 (9.4%) 103 (14.5%) < 0.001 183 (8.8%) 119 (16%) < 0.001 Smoking status (n,%) < 0.001 < 0.001 Never 1281 (45.2%) 1015 (47.8%) 266 (37.5%) 993 (47.6%) 288 (38.6%) Former 540 (19.1%) 402 (18.9%) 138 (19.5%) 384 (18.4%) 156 (20.9%) Current 1013 (35.7%) 708 (33.3%) 305 (43%) 711 (34.1%) 302 (40.5%) Drinking status (n,%) 0.098 0.283 Never 309 (10.9%) 247 (11.6%) 62 (8.7%) 237 (11.4%) 72 (9.7%) Former 888 (31.3%) 664 (31.2%) 224 (31.6%) 641 (30.7%) 247 (33.1%) Current 1637 (57.8%) 1214 (57.1%) 423 (59.7%) 1210 (58%) 427 (57.2%) Race (n,%) < 0.001 < 0.001 Mexican American 367 (12.9%) 251 (11.8%) 116 (16.4%) 243 (11.6%) 124 (16.6%) Other Hispanic 297 (10.5%) 202 (9.5%) 95 (13.4%) 199 (9.5%) 98 (13.1%) Non-Hispanic White 1129 (39.8%) 808 (38%) 321 (45.3%) 787 (37.7%) 342 (45.8%) Non-Hispanic Black 636 (22.4%) 545 (25.6%) 91 (12.8%) 542 (26%) 94 (12.6%) Other races 405 (14.3%) 319 (15%) 86 (12.1%) 317 (15.2%) 88 (11.8%) Education (n,%) < 0.001 0.001 Less than high school 651 (23%) 457 (21.5%) 194 (27.4%) 449 (21.5%) 202 (27.1%) High school College or above 681 (24%) 499 (23.5%) 182 (25.7%) 490 (23.5%) 191 (25.6%) College or above 1501 (53%) 1169 (55%) 332 (46.8%) 1148 (55%) 353 (47.3%) PIR (n,%) 0.004 < 0.001 < 1.3 1026 (36.2%) 741 (34.9%) 285 (40.2%) 718 (34.4%) 308 (41.3%) 1.3–3.5 1053 (37.2%) 787 (37%) 266 (37.5%) 768 (36.8%) 285 (38.2%) > 3.5 755 (26.6%) 597 (28.1%) 158 (22.3%) 602 (28.8%) 153 (20.5%) Marital status (n,%) 0.003 < 0.001 Married/living with partner 1335 (47.1%) 976 (45.9%) 359 (50.6%) 958 (45.9%) 377 (50.5%) Widowed/divorced/separated 607 (21.4%) 487 (22.9%) 120 (16.9%) 487 (23.3%) 120 (16.1%) Never married 892 (31.5%) 662 (31.2%) 230 (32.4%) 643 (30.8%) 249 (33.4%) AIP -0.07 (-0.3-0.15) -0.18 (-0.37–0.02) 0.32 (0.23–0.47) < 0.001 -0.18 (-0.37–0.02) 0.3 (0.21–0.46) < 0.001 CMI 0.5 (0.28–0.89) 0.38 (0.23–0.57) 1.32 (1.06–1.93) < 0.001 0.38 (0.23–0.57) 1.33 (1.06–1.93) < 0.001 SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; BMI, body mass index; eGFR, estimated Glomerular Filtration Rate; CVD, cardiovascular disease; PIR, the family income to poverty ratio; NHANES, National Health and Nutrition Examination Survey 3.2. Logistic regression models of urinary heavy metals in relation to high AIP and elevated CMI The associations of urinary heavy metals with AIP and CMI are shown in Table 2 and Table 3 . An analysis treating urinary heavy metals as categorical variables (quartiles) revealed that, in the fully adjusted model, the associations of urinary Ba, Cd, and Sb with AIP remained statistically significant (all P -trend < 0.05). Specifically, the highest quartile groups of urinary Ba, Cd, Tl were associated with an increased risk of high AIP, with ORs of 1.426 (95% CI: 1.100–1.850, P = 0.007), 1.548 (95% CI: 1.096–2.188, P = 0.013), and 1.386 (95% CI: 1.058–1.816, P = 0.018), respectively. In contrast, urinary Sb was significantly negatively associated with high AIP risk (OR = 0.766, 95% CI: 0.591–0.992, P = 0.044). Table 2 Multivariate regression models of the association between heavy metals and high AIP risk. Metals Q1 Q2 Q3 Q4 P _Trend OR (95% CI) OR (95% CI) OR (95% CI) Ba Model 1 1.00 (reference) 0.779 (0.607–0.998) 1.209 (0.956–1.529) 1.11 (0.876–1.406) 0.053 Model 2 1.00 (reference) 0.772 (0.598–0.996) 1.288 (1.012–1.642) 1.219 (0.955–1.557) 0.006 Model 3 1.00 (reference) 0.856 (0.655–1.118) 1.557 (1.205–2.017) 1.426 (1.1–1.85) < 0.001 Cd Model 1 1.00 (reference) 1.024 (0.808–1.299) 0.944 (0.742–1.199) 0.978 (0.77–1.242) 0.700 Model 2 1.00 (reference) 1.179 (0.913–1.524) 1.148 (0.871–1.515) 1.321 (0.985–1.775) 0.096 Model 3 1.00 (reference) 1.207 (0.92–1.583) 1.227 (0.906–1.664) 1.548 (1.096–2.188) 0.020 Co Model 1 1.00 (reference) 0.954 (0.755–1.204) 0.874 (0.69–1.106) 0.705 (0.553–0.898) 0.004 Model 2 1.00 (reference) 0.966 (0.76–1.227) 0.965 (0.753–1.237) 0.888 (0.681–1.158) 0.418 Model 3 1.00 (reference) 1.144 (0.891–1.47) 1.129 (0.87–1.464) 1.096 (0.825–1.457) 0.525 Cs Model 1 1.00 (reference) 0.873 (0.693–1.099) 0.658 (0.517–0.836) 0.69 (0.543–0.874) < 0.001 Model 2 1.00 (reference) 0.85 (0.67–1.079) 0.679 (0.526–0.876) 0.763 (0.588–0.988) 0.014 Model 3 1.00 (reference) 0.908 (0.707–1.165) 0.76 (0.581–0.993) 0.992 (0.75–1.311) 0.605 Mo Model 1 1.00 (reference) 0.931 (0.733–1.181) 0.945 (0.745–1.198) 0.929 (0.732–1.179) 0.590 Model 2 1.00 (reference) 0.909 (0.713–1.16) 0.93 (0.728–1.188) 0.926 (0.724–1.186) 0.602 Model 3 1.00 (reference) 0.97 (0.752–1.25) 1.059 (0.819–1.37) 1.099 (0.844–1.43) 0.390 Pb Model 1 1.00 (reference) 0.875 (0.689–1.111) 1.108 (0.878–1.397) 0.759 (0.594–0.967) 0.146 Model 2 1.00 (reference) 0.831 (0.648–1.064) 1.023 (0.796–1.314) 0.649 (0.496–0.85) 0.013 Model 3 1.00 (reference) 0.845 (0.651–1.096) 1.117 (0.852–1.464) 0.861 (0.64–1.157) 0.746 Sb Model 1 1.00 (reference) 0.846 (0.67–1.068) 0.749 (0.591–0.949) 0.725 (0.571–0.919) 0.004 Model 2 1.00 (reference) 0.85 (0.669–1.079) 0.741 (0.58–0.944) 0.734 (0.573–0.94) 0.007 Model 3 1.00 (reference) 0.859 (0.669–1.102) 0.72 (0.557–0.929) 0.766 (0.591–0.992) 0.018 Tl Model 1 1.00 (reference) 1.045 (0.829–1.318) 0.74 (0.58–0.942) 0.826 (0.65–1.047) 0.017 Model 2 1.00 (reference) 1.068 (0.842–1.355) 0.846 (0.657–1.088) 1.049 (0.816–1.348) 0.828 Model 3 1.00 (reference) 1.199 (0.934–1.541) 1.025 (0.785–1.338) 1.386 (1.058–1.816) 0.063 Tu Model 1 1.00 (reference) 1.105 (0.869–1.407) 1.047 (0.822–1.334) 1.205 (0.95–1.531) 0.187 Model 2 1.00 (reference) 1.114 (0.871–1.425) 1.069 (0.834–1.37) 1.266 (0.991–1.619) 0.091 Model 3 1.00 (reference) 1.102 (0.854–1.423) 1.008 (0.778–1.306) 1.233 (0.954–1.593) 0.191 Hg Model 1 1.00 (reference) 0.761 (0.582–0.994) 0.689 (0.525–0.904) 0.71 (0.541–0.929) 0.008 Model 2 1.00 (reference) 0.836 (0.634-1.1) 0.814 (0.614–1.076) 0.937 (0.703–1.246) 0.567 Model 3 1.00 (reference) 0.864 (0.648–1.152) 0.964 (0.718–1.293) 1.291 (0.954–1.749) 0.103 *OR (95% CI) for P values < 0.05 are in bold. Ba: barium; Cd: cadmium; Co: cobalt; Cs: cesium; Mo: molybdenum; Pb: lead; Sb: antimony; Tl: thallium; Tu: tungsten; Hg: hydrargyrum. Model 1 adjust for: None; Model 2 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty. Model 3 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty; BMI, eGFR, smoking status, alcohol status, diabetes, hypertension, CVD. Table 3 Multivariate regression models of the association between heavy metals and high CMI risk. Metals Q1 Q2 Q3 Q4 P _Trend OR (95% CI) OR (95% CI) OR (95% CI) Ba Model 1 1.00 (reference) 0.78 (0.60-1.00) 1.24 (0.97–1.58) 1.15 (0.90–1.46) 0.025 Model 2 1.00 (reference) 0.69 (0.53–0.89) 1.14 (0.89–1.48) 1.02 (0.79–1.32) 0.147 Model 3 1.00 (reference) 0.73 (0.55–0.98) 1.42 (1.07–1.89) 1.12 (0.84–1.49) 0.029 Cd Model 1 1.00 (reference) 1.09 (0.85–1.39) 1.05 (0.82–1.33) 1.07 (0.84–1.36) 0.687 Model 2 1.00 (reference) 1.13 (0.87–1.47) 1.05 (0.79–1.39) 1.09 (0.81–1.47) 0.723 Model 3 1.00 (reference) 1.23 (0.92–1.64) 1.21 (0.88–1.68) 1.49 (1.03–2.16) 0.050 Co Model 1 1.00 (reference) 1.08 (0.85–1.36) 0.98 (0.77–1.25) 0.76 (0.59–0.97) 0.023 Model 2 1.00 (reference) 0.92 (0.72–1.19) 0.82 (0.63–1.07) 0.66 (0.50–0.88) 0.003 Model 3 1.00 (reference) 1.13 (0.86–1.48) 0.95 (0.72–1.27) 0.81 (0.59–1.11) 0.131 Cs Model 1 1.00 (reference) 0.94 (0.74–1.19) 0.75 (0.59–0.96) 0.79 (0.62-1.00) 0.016 Model 2 1.00 (reference) 0.79 (0.61–1.01) 0.60 (0.46–0.78) 0.61 (0.46–0.80) < 0.001 Model 3 1.00 (reference) 0.75 (0.57–0.98) 0.59 (0.44–0.79) 0.72 (0.53–0.98) 0.017 Mo Model 1 1.00 (reference) 0.94 (0.74–1.20) 1.11 (0.87–1.41) 1.06 (0.83–1.35) 0.388 Model 2 1.00 (reference) 0.89 (0.69–1.14) 1.00 (0.78–1.29) 0.92 (0.71–1.19) 0.758 Model 3 1.00 (reference) 0.88 (0.67–1.16) 1.14 (0.86–1.50) 1.09 (0.82–1.45) 0.256 Pb Model 1 1.00 (reference) 0.86 (0.68–1.09) 1.09 (0.86–1.38) 0.70 (0.55–0.90) 0.044 Model 2 1.00 (reference) 0.72 (0.56–0.92) 0.83 (0.65–1.08) 0.46 (0.35–0.61) < 0.001 Model 3 1.00 (reference) 0.69 (0.52–0.91) 0.92 (0.69–1.23) 0.64 (0.47–0.88) 0.053 Sb Model 1 1.00 (reference) 0.88 (0.70–1.11) 0.74 (0.58–0.94) 0.71 (0.56–0.90) 0.002 Model 2 1.00 (reference) 0.86 (0.68–1.10) 0.71 (0.55–0.91) 0.66 (0.52–0.86) < 0.001 Model 3 1.00 (reference) 0.85 (0.65–1.10) 0.68 (0.52–0.90) 0.69 (0.52–0.91) 0.003 Tl Model 1 1.00 (reference) 1.01 (0.80–1.28) 0.75 (0.59–0.96) 0.77 (0.61–0.99) 0.006 Model 2 1.00 (reference) 0.98 (0.77–1.26) 0.76 (0.59–0.99) 0.82 (0.63–1.06) 0.044 Model 3 1.00 (reference) 1.07 (0.82–1.40) 0.88 (0.66–1.17) 1.05 (0.78–1.41) 0.900 Tu Model 1 1.00 (reference) 1.14 (0.89–1.45) 1.01 (0.79–1.29) 1.27 (1.00-1.62) 0.118 Model 2 1.00 (reference) 1.15 (0.89–1.48) 1.03 (0.79–1.33) 1.25 (0.98–1.61) 0.154 Model 3 1.00 (reference) 1.15 (0.88–1.51) 0.98 (0.74–1.30) 1.24 (0.94–1.62) 0.259 Hg Model 1 1.00 (reference) 0.80 (0.61–1.04) 0.71 (0.54–0.93) 0.70 (0.53–0.92) 0.007 Model 2 1.00 (reference) 0.83 (0.63–1.10) 0.77 (0.58–1.02) 0.79 (0.58–1.06) 0.086 Model 3 1.00 (reference) 0.88 (0.65–1.19) 0.94 (0.69–1.28) 1.16 (0.84–1.61) 0.368 *OR (95% CI) for P values < 0.05 are in bold. Ba: barium; Cd: cadmium; Co: cobalt; Cs: cesium; Mo: molybdenum; Pb: lead; Sb: antimony; Tl: thallium; Tu: tungsten; Hg: hydrargyrum. Model 1 adjust for: None; Model 2 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty. Model 3 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty; BMI, eGFR, smoking status, alcohol status, diabetes, hypertension, CVD. In the fully adjusted model, the associations of urinary Ba, Cs, and Sb with CMI remained statistically significant (all P -trend < 0.05). The highest quartile group of urinary Cd was associated with an increased risk of high CMI (OR = 1.490, 95% CI: 1.030–2.160, P = 0.032). Conversely, a decreased risk of high CMI was observed among those in the top quartile of urinary Cs, Pb, and Sb concentrations (OR = 0.720, 95% CI: 0.530–0.980, P = 0.037), 0.640 (95% CI: 0.470–0.880, P = 0.006), and 0.690 (95% CI: 0.520–0.910, P = 0.008), respectively. After full adjustment, positive correlations were observed between Ba and both high AIP and high CMI (all P-trend < 0.05), suggesting a consistent risk-increasing effect on these metabolic indicators. 3.3 Association of individual heavy metal exposure with sarcopenia Given the potential role of divalent barium ions in muscle injury [ 23 ], we analyzed the association between urinary heavy metals and sarcopenia in the same participants. In the fully adjusted model, the associations of urinary Ba and Co with sarcopenia remained statistically significant ( P -trend = 0.016 and 0.037, respectively). The highest quartile group of urinary Ba was associated with a 110% increased risk of sarcopenia (95% CI: 1.16–3.90, P = 0.016) (Table S1 ). 3.4. Barium as the primary driver of heavy metal-associated AIP and CMI risk in GWQS analysis We employed the GWQS model to assess the association of a mixture of 10 urinary heavy metals with AIP and CMI treated as continuous variables. Figure. 2a-b shows that after adjusting for all covariates, barium had the highest weight and showed a positive contribution to the risk of elevated AIP and elevated CMI. As shown in Fig. 2 c, the GWQS index indicated that the heavy metal mixture was positively linked to AIP treated as a continuous variable (Model 1: OR = 0.027, 95% CI: 0.003–0.050, P = 0.026; Model 2: OR = 0.032, 95% CI: 0.012–0.052, P < 0.0001; Model 3: OR = 0.054, 95% CI: 0.027–0.080, P < 0.0001). Similarly, the heavy metal mixture was also positively associated with the continuous variable CMI (Model 1: OR = 0.021, 95% CI: −0.082–0.124, P = 0.687; Model 2: OR = 0.021, 95% CI: −0.046–0.089, P = 0.540; Model 3: OR = 0.123, 95% CI: 0.024–0.223, P = 0.015). 3.5. BKMR-derived PIP values for urinary metals in relation to AIP and CMI BKMR analysis identified the ten urinary metals most strongly associated with elevated AIP and CMI based on posterior inclusion probability (PIP). For AIP, the PIP ranking was: Ba (0.7740), Mo (0.7428), Hg (0.6968), Cs (0.6770), Sb (0.6760), W (0.6666), Co (0.6382), Tl (0.6380), Pb (0.5898), and Cd (0.5782) (Figure.3a). For CMI, the order was: Mo (0.9592), Cd (0.7558), Hg (0.7342), Ba (0.7310), W (0.7182), Cs (0.7068), Sb (0.7024), Tl (0.6914), Pb (0.6568), and Co (0.6274) (Figure.3b). In both indices, Ba was a prominent risk factor, consistent with logistic regression and GWQS results. For CMI, Mo exhibited the highest PIP value, suggesting its potential distinct or interactive role in cardiometabolic risk. 3.6. Effects of heavy metal barium on oxidative stress in VSMCs and ECs To elucidate the mechanism of action of barium exposure on VSMCs and endothelial cells, this study conducted systematic in vitro experiments. We found that relatively low concentrations of barium chloride markedly inhibited VSMC growth, with effects dependent on both dose and treatment duration (Figure. 4a, Figure. S1a). The inhibitory effect was most pronounced at a concentration of 2 mM, which was therefore selected for subsequent experiments. Similarly, the inhibitory effect of barium chloride on EC proliferation was most significant at a concentration of 1 mM (Figure. 4b, Figure. S1b), and this concentration was used for subsequent mechanistic studies. In contrast, C2C12 myotube cells exhibited stronger tolerance to barium chloride (Figure. 4c, Figure. S1c), showing the most significant proliferation inhibition at a concentration of 10 mM. Detection of reactive oxygen species levels by DHE fluorescence staining revealed that barium chloride treatment significantly induced ROS generation in VSMCs and ECs (Figure. 4d, e). To investigate the effect of secreted factors from injured myotube cells on vascular cells, the supernatant from barium chloride-treated myotube cells was used to culture VSMCs and ECs. It was found that this supernatant could also promote ROS production in both cell types (Figure. 4d, e). Furthermore, the oxidative stress level in the barium chloride-treated C2C12 cells themselves was also significantly elevated (Figure. 4f). The quantitative results of DHE fluorescence intensity for each experimental group are shown in Figure. 4g. 4. Discussion This study is the first to systematically evaluate the individual and joint effects of 10 urinary heavy metals on cardiometabolic indicators and sarcopenia risk among US adults, employing a combination of multivariate adjustment and multiple statistical models. Single-pollutant logistic regression indicated that urinary Ba, Cd, and Sb were independently linked to higher levels of AIP; urinary Ba, Cs, and Sb showed independent positive associations with CMI; and urinary Ba and Co were associated with an increased risk of sarcopenia. The multi-pollutant GWQS model further indicated that mixed heavy metal exposure was positively associated with the risk of AIP and CMI. Notably, all statistical methods consistently suggested a significant and robust positive association between barium exposure and adverse risks of AIP and CMI (Figure. 5). This study supports the view that heavy metal exposure is associated with CVD risk. Previous large-scale studies have emphasized the cardiovascular toxicity of metals such as cadmium and lead. For instance, individual and mixed exposure to urinary Cd, CS, Co, Pb and other metals were significantly associated with accelerated vascular aging, and men may be more sensitive to the combined toxicity of heavy metal [ 24 ]. Lead and cadmium exposure were associated with increased arterial stiffness [ 25 ], and mixed heavy metal exposure was also associated with an increased risk of heart failure in the elderly [ 26 ]. Mixed exposure to metals such as Cd, Tu, Co and Sb was significantly associated with an increased risk of CVD [ 27 ]. However, systematic research on the effects of mixed heavy metal exposure on AIP and CMI is still relatively scarce, suggesting that this area warrants further in-depth investigation. In addition to traditional risk factors such as age, physical inactivity, metabolic imbalance, and neuromuscular dysfunction [ 28 , 29 ], emerging evidence suggests that environmental pollution (e.g., ozone, PM2.5, ozone, and phthalates) may also increase the risk of sarcopenia [ 22 ]. Although many risk factors for sarcopenia have been identified, the condition remains highly prevalent worldwide. A population‑based study in the United States found that higher AIP levels are independently associated with an increased risk of sarcopenia in adults [ 30 ]. Moreover, individuals with both sarcopenia and elevated CMI face a significantly higher risk of developing multiple cardiometabolic disorders [ 31 , 32 ], suggesting that sarcopenia may share common pathological mechanisms with elevated AIP and CMI. Notably, this study reveals a novel positive association between urinary Ba levels and Ba-induced oxidative stress in both vascular and muscle cells. Although studies based on NHANES data have indicated a positive correlation between heavy metal mixtures (blood Pb, Cd, Hg, Se and Mn) and sarcopenia prevalence, with manganese as the main driver and the association mediated by inflammation [ 22 ], evidence for the association between heavy metals and sarcopenia remains insufficient. It has been reported that divalent barium ions can cause calcium overload and excessive contraction in muscle fibers, leading to widespread muscle proteolysis and membrane structural damage[ 23 ]. Oxidative stress is a key link in skeletal muscle atrophy, inflammation, and mitochondrial dysfunction, and can impair cellular function through lipid peroxidation and DNA damage [ 33 ]. Based on these shared pathological mechanisms, it is reasonable to hypothesize that barium exposure may increase CVD risk by inducing oxidative stress. Oxidative stress is important drivers in the occurrence and development of cardiovascular disease. They can activate endothelial dysfunction, promote monocyte infiltration and foam cell formation, thereby initiating atherosclerotic plaque formation [ 34 , 35 ], and stimulate vascular smooth muscle cell proliferation, accelerating CVD progression [ 36 ]. Heavy metals can induce oxidative stress by depleting glutathione and generating ROS [ 37 ]. The in vitro experiments in this study also confirmed that barium exposure significantly increased ROS generation in VSMCs and ECs. To further investigate the mechanisms of \"muscle-vascular\" interaction under injury conditions, our indirect co-culture experiments found that factors secreted or upregulated by injured myotube cells could lead to increased oxidative stress levels in VSMCs and ECs. This finding suggests that in the context of muscle injury, intercellular communication within the microenvironment is of great significance for understanding the mechanisms of multiple diseases (such as myopathies and vascular dysfunction accompanying sepsis, chronic kidney disease, heart failure, etc.). This study has the following strengths: First, it is the first to explore the comorbid relationship between barium exposure, sarcopenia, and cardiovascular disease. In addition, multiple statistical models were applied, with adjustment for relevant confounding variables. All analyses were based on a large, rigorously quality-controlled population database, enhancing the robustness of the results. Third, in vitro experiments further revealed that skeletal muscle cells after barium exposure may serve as an important mediator affecting vascular function—damaged myotubes can exacerbate oxidative stress in vascular cells, while the study also confirmed the direct toxicity of Ba to vascular cells. The limitations of this study include: at the population level, the cross-sectional design makes it difficult to establish causality, and the data did not cover all potential confounding exposures (such as sewage, cosmetics, and cumulative exposure to barium); at the experimental level, in vitro results still require further validation in more complex in vivo models. 5. Conclusion In summary, by integrating population‑based epidemiological data with in vitro mechanistic evidence, this study demonstrates that exposure to heavy metals, particularly barium, constitutes a common and independent risk factor for both cardiovascular disease and sarcopenia. This finding deepens the understanding of the pathophysiological mechanisms of age-related comorbidities and suggests that interventions targeting barium exposure may provide dual benefits in preventing these two debilitating diseases. Declarations Ethics and approval All procedures involving NHANES data followed the Declaration of Helsinki. Ethical approval was obtained from the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB) (Protocols #2011-17, #2018-01). Written informed consent was obtained from all participants. Clinical trial number Not applicable. Data availability The datasets analyzed in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) database, conducted by the U.S. Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics (NCHS). Data from the 2011–2018 survey cycles were used and are accessible at the official website: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx Funding This work was supported by the National Natural Science Foundation of China (82270440, 82570554 and 82495173), Wenzhou Science and Technology Program (Y20240819), and the National Key R&D Program of China (2022YFA1104204). CRediT authorship contribution statement Hongya Wang: Data Extraction, Writing, Software and Statistical Analysis. Haonan Cui: Software, Data Extraction and Statistical Analysis. Butuo Xu: Software and Statistical Analysis. Min Sun: Methodology, Data curation. Wei Zhang: Visualization, Data curation. Daoyan Liu: Methodology. Zhiming Zhu: Methodology, Investigation. Peng Gao: Conceptualization and Supervision. All authors have read and approved the final version of this manuscript. Conflict of interest statement The authors declare no conflicts of interest. References Blaustein, J. R., Quisel, M. J., Hamburg, N. 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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-9359383\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":639800527,\"identity\":\"7371e622-207d-4930-bf06-8a89871195fb\",\"order_by\":0,\"name\":\"Hongya Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hongya\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":639800528,\"identity\":\"fb1967a5-85a9-4e4e-9d11-0b2aaa69b321\",\"order_by\":1,\"name\":\"Haonan Cui\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haonan\",\"middleName\":\"\",\"lastName\":\"Cui\",\"suffix\":\"\"},{\"id\":639800529,\"identity\":\"533dec37-b314-4c6d-b279-1e64c8cf1503\",\"order_by\":2,\"name\":\"Butuo Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Butuo\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":639800530,\"identity\":\"e7618245-9605-4c9a-8a54-29a63c47d06c\",\"order_by\":3,\"name\":\"Min Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":639800531,\"identity\":\"2daafd34-3781-4422-9937-d11d517137c7\",\"order_by\":4,\"name\":\"Wei Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wei\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":639800532,\"identity\":\"f327bc26-fa7c-4c65-9763-e1827cbd1762\",\"order_by\":5,\"name\":\"Daoyan Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daoyan\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":639800533,\"identity\":\"fc369880-bd54-4997-8a41-023e17b1ecbe\",\"order_by\":6,\"name\":\"Zhiming Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhiming\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":639800534,\"identity\":\"b18383fd-8a71-47b0-8770-84159418a096\",\"order_by\":7,\"name\":\"Peng Gao\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBADHn725oMPoBwDorTISPYcSzY4QIoWG4MbOWYSRGmRdz987MHHHbU8BmcOmFV/zNmW2MDevE2CoeYOTi2GZ9LSDWeeOc4jebwh7cbBbbcTG3iOlUkwHHuGW0tDjpk0b9sxHr4zB45BtEgAXcjYcBi3lv43ZtJ/gVoYbiS2FYC1yL/Br0UeaKY0Y1sNj8CNZDYGiC08+LUYSDxLN+xtO8ADDGRmibPbbhu38aQVWyQcw2NLf/KxBz/b6uz52fs/fqjcdlu2n/3wxhsfavDYcoCBDUghKQBxGRJwagDa0gBWU4dHySgYBaNgFIx4AAB05l+uGNXZgQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Army Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Peng\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-08 16:23:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9359383/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9359383/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109303898,\"identity\":\"e4aa9872-2511-4a51-8e5c-c80a5f2c0bc8\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:02\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":138359,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of study population.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/e33bc26ce8855e64905be99d.png\"},{\"id\":109303939,\"identity\":\"497570e1-ab12-432d-b8dd-d60121f873d4\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:07\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":50846,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationship between complex heavy metal exposure and risk of high AIP and CMI analyzed using the BKMR model. (a) Posterior Inclusion Probabilities (PIPs) for urine heavy metals (log-transformed) associated with high AIP. (b) PIPs for heavy metals associated with high CMI. Adjusted for age, gender, ethnicity, education level, marital and ratio of family income to poverty; BMI, eGFR, smoking status, alcohol status, diabetes, hypertension, CVD.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/7fe70cc04a392fd263cc5643.png\"},{\"id\":109303943,\"identity\":\"dd04ad60-8db6-4ad3-8a63-16db1bbaf980\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:08\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":460040,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBarium promotes oxidative stress in smooth muscle, endothelial, and skeletal muscle cells. (a) VSMCs, (b) ECs, and (c) mature differentiated C2C12 skeletal muscle cells treated with barium ions for 24 hours, as determined by CCK-8 assay (n=4). (d-f) Representative images showing ROS production detected by Dihydroethidium (DHE) staining in VSMCs (d), ECs (e), and C2C12 cells (f). Cells were either treated directly with barium ions (direct induction) or co-cultured with Myotubes (indirect induction). Scale bar = 100 μm, n=3. (g) Statistical analysis of DHE fluorescence intensity corresponding to panels d-e. For comparisons between groups, Welch's corrected tests (Welch's t-test or Brown-Forsythe ANOVA) were applied as appropriate when the assumption of equal variances was violated.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/d1f2c829f9b8009229e93eee.png\"},{\"id\":109303917,\"identity\":\"b0c7ca19-ff03-4c68-9426-eb42ff5d1931\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:07\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":610251,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGraphical abstract\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/ac2e5fc13c1699ea458029bc.png\"},{\"id\":109303896,\"identity\":\"591df818-c579-4432-81aa-f5d6ec854b04\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:02\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":553679,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/f43c38ae-fb15-4836-90a1-0b07b9e6468d.pdf\"},{\"id\":109303916,\"identity\":\"96a528a1-e7db-4566-a1c0-ba3dd73b5962\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 09:41:07\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":243217,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9359383/v1/5677d563b3673d46391b0fa6.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Barium Exposure as a Major Risk Factor for Elevated Cardiovascular Risk within Heavy Metal Mixtures: Integrating Population Analysis with Experimental Approach\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eCardiovascular disease (CVD) remains one of the predominant causes of mortality worldwide, contributing to millions of deaths each year. The development of CVD is a multifactorial process involving a combination of genetic predisposition, environmental exposures, and lifestyle-related factors [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Of these environmental determinants, various pollutants including air pollutants, heavy metals, and other chemical agents have been recognized as important risk factors for CVD pathogenesis [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eHeavy metals are persistent environmental pollutants released through industrial, agricultural, and urban activities [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Their low biodegradability facilitates accumulation in ecosystems and the human body via the food chain [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Following exposure, heavy metals undergo systemic distribution with preferential accumulation in the vascular system, and are subsequently cleared primarily via renal excretion [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Chronic low-level accumulation in the body underlies their persistent toxicity and public health impact.\\u003c/p\\u003e \\u003cp\\u003eAccumulating evidence suggests that heavy metal exposure is associated with greater CVD. Specific metals, such as cadmium, directly impair vascular function [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], while combined exposures (e.g., Mn/Cr/Mo) may influence blood pressure through mechanisms like lipid peroxidation [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Although composite biomarkers such as the atherogenic index of plasma (AIP) and the cardiometabolic index (CMI) offer sensitive early risk stratification, research on their association with heavy metal exposure remains limited, and the key toxic components and their direct vascular mechanisms are not yet fully elucidated.\\u003c/p\\u003e \\u003cp\\u003eBoth AIP and CMI are recognized as integrated biomarkers of CVD and metabolic risk. AIP, proposed as a measure of atherogenic risk [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], predicts diverse outcomes including CVD events [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], vascular calcification [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], mortality in cardiorenal-metabolic (CKM) syndrome and metabolic dysfunction-associated fatty liver disease (MAFLD) [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. CMI, which reflects visceral adiposity and lipid dysregulation, demonstrates superior predictive performance over conventional measures (e.g., BMI) for metabolic syndrome, kidney stones, insulin resistance, and atherosclerosis [\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Therefore, compared to traditional obesity or lipid indicators, AIP and CMI may be more suitable for assessing CVD risk. Although AIP and CMI show only a moderate correlation [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], their complementary predictive value supports combined use for more comprehensive risk assessment.\\u003c/p\\u003e \\u003cp\\u003eSarcopenia is defined as a syndrome involving the gradual reduction in muscle mass, muscular strength, and physical performance, and is closely linked to impaired mobility, functional disability, and elevated mortality risk [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Its global prevalence is approximately 5\\u0026ndash;10%, imposing a substantial healthcare burden [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. A growing body of research indicates a comorbid link between sarcopenia and cardiovascular disease: prospective cohort studies show that the progression of sarcopenia status increases the risk of various cardiovascular diseases, while its improvement reduces corresponding risks, with a causal relationship existing between them and no apparent reverse association [\\u003cspan additionalcitationids=\\\"CR20\\\" citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Current evidence on the comorbidity mechanisms between the two is limited, making it urgent to explore new environmental factors to prevent their co-occurrence.\\u003c/p\\u003e \\u003cp\\u003eGiven the pervasive environmental presence of heavy metals, elucidating their link to CVD risk is crucial for mitigating health impacts. This two-stage study first evaluates associations of heavy metal exposure with AIP and CMI using NHANES 2011\\u0026ndash;2018 data, and then validates specific toxic effects in vascular cells through in vitro experiments. This study may improve mechanistic insights into heavy metal‑associated CVD and support risk evaluation strategies for environmental contaminants.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1 Study population\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe analyzed publicly available data from the NHANES 2011–2018 cycles, a cross-sectional, population-based survey. From the initial 39,156 participants, 22,607 adults aged ≥20 years were included for further screening. We further excluded 12,968 subjects with incomplete lipid profiles (including missing triglycerides [TG] and high-density lipoprotein cholesterol [HDL-C]), leaving 9,639 participants with complete lipid data. Subsequent exclusions removed 1,291 individuals due to missing data on body mass index (BMI), systolic/diastolic blood pressure (SBP/DBP), or the poverty-income ratio (PIR). Additional exclusions were applied for pregnancy or cancer history (n = 838), missing urinary heavy metal data (n = 4,452), and missing other key covariates (n = 224). Finally, 2,834 participants with a key covariate missing rate below 5% were included in the study sample (Figure.\\u0026nbsp;1).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2 Heavy metal measurement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUrinary concentrations of ten metals (barium [Ba], cadmium [Cd], cobalt [Co], cesium [Cs], molybdenum [Mo], lead [Pb], antimony [Sb], thallium [Tl], tungsten [Tu], mercury [Hg]) were analyzed via inductively coupled plasma mass spectrometry (ICP-MS) at CDC’s National Center for Environmental Health (NCEH); metals with inconsistent inter-survey measurements or \\u0026lt;60% detection rate were excluded, values below the detection limit (BDL) were imputed as detection limit/√2, and concentrations were creatinine-corrected (μg/g creatinine) and natural log-transformed for improved distributional properties.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3 AIP and CMI calculation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTwo lipid-related indices were constructed:\\u003c/p\\u003e\\n\\u003cp\\u003ea.\\u0026nbsp; \\u0026nbsp;Atherogenic Index of Plasma: AIP = log (TG/HDL‑C)\\u003c/p\\u003e\\n\\u003cp\\u003eb.\\u0026nbsp; \\u0026nbsp;Cardiometabolic Index: CMI = (Waist circumference/Height) × (TG/HDL‑C)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4 Sarcopenia definition\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSarcopenia was defined using the FNIH criteria: appendicular skeletal muscle mass adjusted for BMI \\u0026lt; 0.512 in women and \\u0026lt; 0.789 in men. Skeletal muscle mass was measured by dual‑energy X‑ray absorptiometry (DXA) [22].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.5 Covariates\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCovariates included:\\u003c/p\\u003e\\n\\u003cp\\u003ea.\\u0026nbsp; \\u0026nbsp;Demographics: age, sex, race/ethnicity, education, poverty‑income ratio, marital status\\u003c/p\\u003e\\n\\u003cp\\u003eb.\\u0026nbsp; \\u0026nbsp;Lifestyle: smoking status, alcohol consumption\\u003c/p\\u003e\\n\\u003cp\\u003ec.\\u0026nbsp; \\u0026nbsp;Comorbidities: diabetes, hypertension, cardiovascular disease\\u003c/p\\u003e\\n\\u003cp\\u003ed.\\u0026nbsp; \\u0026nbsp;Renal function: estimated glomerular filtration rate (eGFR)\\u003c/p\\u003e\\n\\u003cp\\u003ee.\\u0026nbsp; \\u0026nbsp;Disease definitions followed established clinical thresholds and self-reported physician diagnosis.\\u003c/p\\u003e\\n\\u003cp\\u003eComplete covariate definitions are provided in the Supplementary Materials\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.6 Cell culture and treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVascular smooth muscle cells (VSMCs), mouse aortic endothelial cells (MAECs), and C2C12 myoblasts were cultured under standard conditions. Cells were exposed to graded concentrations of BaCl₂ for 24 or 48 h to determine appropriate doses for subsequent experiments. Conditioned medium from BaCl₂‑treated myotubes was collected and used to treat VSMCs and ECs to assess paracrine effects. Specific cell treatment parameters are provided in the Supplementary Materials.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.7 Cell viability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCell viability was measured using the CCK-8 assay. After incubation with CCK-8 reagent, the absorbance at 450 nm was detected using a Varioskan microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), and the relative viability was calculated.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.8 ROS detection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCells were incubated with dihydroethidium (DHE, S0064S, Beyotime) for 20 minutes, then fixed with 4% paraformaldehyde (PFA). After washing with PBS, slides were mounted and observed under an inverted fluorescence microscope (TE2000-U; Nikon, Tokyo, Japan). Images were captured and analyzed using ImageJ 9.0 software.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.9 Statistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBaseline characteristics of participants were presented according to AIP and CMI quartiles. Continuous variables were shown as median (IQR), and categorical variables as frequency (%). Between-group comparisons were performed using Rao–Scott chi-square or Kruskal–Wallis tests. Multivariate logistic regression was used to examine the associations of urinary heavy metals with AIP, CMI and sarcopenia, with progressive adjustment for demographics, lifestyle factors and comorbidities. Grouped weighted quantile sum (GWQS) regression [25] and Bayesian kernel machine regression (BKMR) [26,27] were further applied to explore the mixture and joint effects of heavy metal exposure. All statistical analyses were conducted using R 4.3.3 and Prism 10.1.2, with \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05 as statistically significant. Detailed methodological procedures are provided in the supplementary file.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Sociodemographic and clinical attributes of the enrolled subjects\\u003c/h2\\u003e \\u003cp\\u003eThe present study comprised 2,834 participants in total. An analysis using AIP and CMI as categorical variables (quartiles) showed the baseline characteristics stratified by AIP and CMI status, as presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Compared to the low AIP groups (Q1\\u0026ndash;Q3), the high AIP group (Q4) had a higher proportion of males, a higher proportion of current and former smokers, lower education levels and lower PIR, while exhibiting higher BMI, SBP, DBP, and TC levels, and a lower eGFR (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Meanwhile, the high AIP group presented a significantly higher prevalence of hypertension, diabetes, and cardiovascular disease (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The baseline characteristics of the high CMI group (Q4) were similar to those of the high AIP group, but the high CMI group was older than the low CMI groups.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCharacteristics of the study participants in U.S. adults: results from NHANES 2011\\u0026ndash;2018.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2834)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLow AIP\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2125)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHigh AIP\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;709)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLow CMI\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2088)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eHigh CMI\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;746)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e48 (34\\u0026ndash;61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48 (33\\u0026ndash;62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49 (37\\u0026ndash;60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.168\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e47 (33\\u0026ndash;61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e50 (38\\u0026ndash;62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender (male,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1492 (52.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1026 (48.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e466 (65.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1043 (50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e449 (60.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27.9 (24.1-32.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.1 (23.5\\u0026ndash;31.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30.2 (26.8\\u0026ndash;35.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e26.85 (23.4\\u0026ndash;31.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e31 (27.6-36.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121.33 (111.33\\u0026ndash;134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e120.67 (111.33-133.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e124 (114\\u0026ndash;134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e120.67 (110.67-132.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e124 (114-134.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70.67 (63.33\\u0026ndash;77.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.33 (62.67\\u0026ndash;76.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.67 (66-79.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e70 (62.67\\u0026ndash;76.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e72.33 (65.33\\u0026ndash;79.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR (mL/min/1.73m\\u0026sup2;)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e90.66 (76.18-106.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e91.58 (77-107.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e88.28 (73.74-105.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e91.81 (77.55-107.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e87.49 (71.7-105.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e580 (20.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e345 (16.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e235 (33.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e327 (15.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e253 (33.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1515 (53.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1085 (51.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e430 (60.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1039 (49.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e476 (63.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCVD (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e302 (10.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e199 (9.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103 (14.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e183 (8.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e119 (16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmoking status (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1281 (45.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1015 (47.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e266 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e993 (47.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e288 (38.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFormer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e540 (19.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e402 (18.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e138 (19.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e384 (18.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e156 (20.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1013 (35.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e708 (33.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e305 (43%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e711 (34.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e302 (40.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDrinking status (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.098\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.283\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e309 (10.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e247 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62 (8.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e237 (11.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e72 (9.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFormer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e888 (31.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e664 (31.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e224 (31.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e641 (30.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e247 (33.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1637 (57.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1214 (57.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e423 (59.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1210 (58%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e427 (57.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRace (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMexican American\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e367 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e251 (11.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e116 (16.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e243 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e124 (16.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther Hispanic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e297 (10.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e202 (9.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95 (13.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e199 (9.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e98 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1129 (39.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e808 (38%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e321 (45.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e787 (37.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e342 (45.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-Hispanic Black\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e636 (22.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e545 (25.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91 (12.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e542 (26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (12.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther races\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e405 (14.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e319 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86 (12.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e317 (15.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e88 (11.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEducation (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLess than high school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e651 (23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e457 (21.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e194 (27.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e449 (21.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e202 (27.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh school College or above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e681 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e499 (23.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e182 (25.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e490 (23.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e191 (25.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCollege or above\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1501 (53%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1169 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e332 (46.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1148 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e353 (47.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePIR (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1026 (36.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e741 (34.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e285 (40.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e718 (34.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e308 (41.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1.3\\u0026ndash;3.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1053 (37.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e787 (37%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e266 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e768 (36.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e285 (38.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;3.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e755 (26.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e597 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e158 (22.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e602 (28.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e153 (20.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarital status (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/living with partner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1335 (47.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e976 (45.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e359 (50.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e958 (45.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e377 (50.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWidowed/divorced/separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e607 (21.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e487 (22.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e120 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e487 (23.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e120 (16.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNever married\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e892 (31.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e662 (31.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e230 (32.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e643 (30.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e249 (33.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAIP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.07 (-0.3-0.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.18 (-0.37\\u0026ndash;0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.32 (0.23\\u0026ndash;0.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.18 (-0.37\\u0026ndash;0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.3 (0.21\\u0026ndash;0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCMI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.5 (0.28\\u0026ndash;0.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.38 (0.23\\u0026ndash;0.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.32 (1.06\\u0026ndash;1.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.38 (0.23\\u0026ndash;0.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.33 (1.06\\u0026ndash;1.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eSBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; BMI, body mass index; eGFR, estimated Glomerular Filtration Rate; CVD, cardiovascular disease; PIR, the family income to poverty ratio; NHANES, National Health and Nutrition Examination Survey\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Logistic regression models of urinary heavy metals in relation to high AIP and elevated CMI\\u003c/h2\\u003e \\u003cp\\u003eThe associations of urinary heavy metals with AIP and CMI are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. An analysis treating urinary heavy metals as categorical variables (quartiles) revealed that, in the fully adjusted model, the associations of urinary Ba, Cd, and Sb with AIP remained statistically significant (all \\u003cem\\u003eP\\u003c/em\\u003e-trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Specifically, the highest quartile groups of urinary Ba, Cd, Tl were associated with an increased risk of high AIP, with ORs of 1.426 (95% CI: 1.100\\u0026ndash;1.850, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.007), 1.548 (95% CI: 1.096\\u0026ndash;2.188, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.013), and 1.386 (95% CI: 1.058\\u0026ndash;1.816, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.018), respectively. In contrast, urinary Sb was significantly negatively associated with high AIP risk (OR\\u0026thinsp;=\\u0026thinsp;0.766, 95% CI: 0.591\\u0026ndash;0.992, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.044).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMultivariate regression models of the association between heavy metals and high AIP risk.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMetals\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e_Trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.779 (0.607\\u0026ndash;0.998)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.209 (0.956\\u0026ndash;1.529)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.11 (0.876\\u0026ndash;1.406)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.053\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.772 (0.598\\u0026ndash;0.996)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.288 (1.012\\u0026ndash;1.642)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.219 (0.955\\u0026ndash;1.557)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.006\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.856 (0.655\\u0026ndash;1.118)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.557 (1.205\\u0026ndash;2.017)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.426 (1.1\\u0026ndash;1.85)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCd\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.024 (0.808\\u0026ndash;1.299)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.944 (0.742\\u0026ndash;1.199)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.978 (0.77\\u0026ndash;1.242)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.700\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.179 (0.913\\u0026ndash;1.524)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.148 (0.871\\u0026ndash;1.515)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.321 (0.985\\u0026ndash;1.775)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.096\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.207 (0.92\\u0026ndash;1.583)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.227 (0.906\\u0026ndash;1.664)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.548 (1.096\\u0026ndash;2.188)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.020\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.954 (0.755\\u0026ndash;1.204)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.874 (0.69\\u0026ndash;1.106)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.705 (0.553\\u0026ndash;0.898)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.966 (0.76\\u0026ndash;1.227)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.965 (0.753\\u0026ndash;1.237)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.888 (0.681\\u0026ndash;1.158)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.418\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.144 (0.891\\u0026ndash;1.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.129 (0.87\\u0026ndash;1.464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.096 (0.825\\u0026ndash;1.457)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.525\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.873 (0.693\\u0026ndash;1.099)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.658 (0.517\\u0026ndash;0.836)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.69 (0.543\\u0026ndash;0.874)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.85 (0.67\\u0026ndash;1.079)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.679 (0.526\\u0026ndash;0.876)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.763 (0.588\\u0026ndash;0.988)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.014\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.908 (0.707\\u0026ndash;1.165)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.76 (0.581\\u0026ndash;0.993)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.992 (0.75\\u0026ndash;1.311)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.605\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.931 (0.733\\u0026ndash;1.181)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.945 (0.745\\u0026ndash;1.198)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.929 (0.732\\u0026ndash;1.179)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.590\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.909 (0.713\\u0026ndash;1.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.93 (0.728\\u0026ndash;1.188)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.926 (0.724\\u0026ndash;1.186)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.602\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.97 (0.752\\u0026ndash;1.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.059 (0.819\\u0026ndash;1.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.099 (0.844\\u0026ndash;1.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.390\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.875 (0.689\\u0026ndash;1.111)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.108 (0.878\\u0026ndash;1.397)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.759 (0.594\\u0026ndash;0.967)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.146\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.831 (0.648\\u0026ndash;1.064)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.023 (0.796\\u0026ndash;1.314)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.649 (0.496\\u0026ndash;0.85)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.013\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.845 (0.651\\u0026ndash;1.096)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.117 (0.852\\u0026ndash;1.464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.861 (0.64\\u0026ndash;1.157)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.746\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.846 (0.67\\u0026ndash;1.068)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.749 (0.591\\u0026ndash;0.949)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.725 (0.571\\u0026ndash;0.919)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.85 (0.669\\u0026ndash;1.079)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.741 (0.58\\u0026ndash;0.944)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.734 (0.573\\u0026ndash;0.94)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.859 (0.669\\u0026ndash;1.102)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.72 (0.557\\u0026ndash;0.929)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.766 (0.591\\u0026ndash;0.992)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.018\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.045 (0.829\\u0026ndash;1.318)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.74 (0.58\\u0026ndash;0.942)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.826 (0.65\\u0026ndash;1.047)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.017\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.068 (0.842\\u0026ndash;1.355)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.846 (0.657\\u0026ndash;1.088)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.049 (0.816\\u0026ndash;1.348)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.828\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.199 (0.934\\u0026ndash;1.541)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.025 (0.785\\u0026ndash;1.338)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.386 (1.058\\u0026ndash;1.816)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.063\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.105 (0.869\\u0026ndash;1.407)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.047 (0.822\\u0026ndash;1.334)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.205 (0.95\\u0026ndash;1.531)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.114 (0.871\\u0026ndash;1.425)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.069 (0.834\\u0026ndash;1.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.266 (0.991\\u0026ndash;1.619)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.091\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.102 (0.854\\u0026ndash;1.423)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.008 (0.778\\u0026ndash;1.306)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.233 (0.954\\u0026ndash;1.593)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.191\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.761 (0.582\\u0026ndash;0.994)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.689 (0.525\\u0026ndash;0.904)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.71 (0.541\\u0026ndash;0.929)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.836 (0.634-1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.814 (0.614\\u0026ndash;1.076)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.937 (0.703\\u0026ndash;1.246)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.567\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.864 (0.648\\u0026ndash;1.152)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.964 (0.718\\u0026ndash;1.293)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.291 (0.954\\u0026ndash;1.749)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.103\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e*OR (95% CI) for \\u003cem\\u003eP\\u003c/em\\u003e values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 are in bold.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBa: barium; Cd: cadmium; Co: cobalt; Cs: cesium; Mo: molybdenum; Pb: lead; Sb: antimony; Tl: thallium; Tu: tungsten; Hg: hydrargyrum.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 1 adjust for: None;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 2 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 3 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty; BMI, eGFR, smoking status, alcohol status, diabetes, hypertension, CVD.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMultivariate regression models of the association between heavy metals and high CMI risk.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMetals\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e_Trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.78 (0.60-1.00)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.24 (0.97\\u0026ndash;1.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.15 (0.90\\u0026ndash;1.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.025\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.69 (0.53\\u0026ndash;0.89)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.14 (0.89\\u0026ndash;1.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.02 (0.79\\u0026ndash;1.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.147\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.73 (0.55\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.42 (1.07\\u0026ndash;1.89)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.12 (0.84\\u0026ndash;1.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.029\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCd\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.09 (0.85\\u0026ndash;1.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05 (0.82\\u0026ndash;1.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.07 (0.84\\u0026ndash;1.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.687\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.13 (0.87\\u0026ndash;1.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.05 (0.79\\u0026ndash;1.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.09 (0.81\\u0026ndash;1.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.723\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.23 (0.92\\u0026ndash;1.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.21 (0.88\\u0026ndash;1.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.49 (1.03\\u0026ndash;2.16)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.050\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.08 (0.85\\u0026ndash;1.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.77\\u0026ndash;1.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.76 (0.59\\u0026ndash;0.97)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92 (0.72\\u0026ndash;1.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.82 (0.63\\u0026ndash;1.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.66 (0.50\\u0026ndash;0.88)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.13 (0.86\\u0026ndash;1.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.95 (0.72\\u0026ndash;1.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81 (0.59\\u0026ndash;1.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.131\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.94 (0.74\\u0026ndash;1.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.75 (0.59\\u0026ndash;0.96)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.79 (0.62-1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.016\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.79 (0.61\\u0026ndash;1.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.60 (0.46\\u0026ndash;0.78)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.61 (0.46\\u0026ndash;0.80)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.75 (0.57\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.59 (0.44\\u0026ndash;0.79)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.72 (0.53\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.017\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.94 (0.74\\u0026ndash;1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.11 (0.87\\u0026ndash;1.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.06 (0.83\\u0026ndash;1.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.388\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.89 (0.69\\u0026ndash;1.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00 (0.78\\u0026ndash;1.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.92 (0.71\\u0026ndash;1.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.758\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.67\\u0026ndash;1.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.14 (0.86\\u0026ndash;1.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.09 (0.82\\u0026ndash;1.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.256\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.86 (0.68\\u0026ndash;1.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.09 (0.86\\u0026ndash;1.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.70 (0.55\\u0026ndash;0.90)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.044\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.72 (0.56\\u0026ndash;0.92)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.83 (0.65\\u0026ndash;1.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.46 (0.35\\u0026ndash;0.61)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.69 (0.52\\u0026ndash;0.91)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.92 (0.69\\u0026ndash;1.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.64 (0.47\\u0026ndash;0.88)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.053\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.70\\u0026ndash;1.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.74 (0.58\\u0026ndash;0.94)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.71 (0.56\\u0026ndash;0.90)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.86 (0.68\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.71 (0.55\\u0026ndash;0.91)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.66 (0.52\\u0026ndash;0.86)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.85 (0.65\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.68 (0.52\\u0026ndash;0.90)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.69 (0.52\\u0026ndash;0.91)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.01 (0.80\\u0026ndash;1.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.75 (0.59\\u0026ndash;0.96)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.77 (0.61\\u0026ndash;0.99)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.006\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.77\\u0026ndash;1.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.76 (0.59\\u0026ndash;0.99)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.82 (0.63\\u0026ndash;1.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.044\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.07 (0.82\\u0026ndash;1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.88 (0.66\\u0026ndash;1.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.05 (0.78\\u0026ndash;1.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.900\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.14 (0.89\\u0026ndash;1.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01 (0.79\\u0026ndash;1.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.27 (1.00-1.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.118\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.15 (0.89\\u0026ndash;1.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.03 (0.79\\u0026ndash;1.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.25 (0.98\\u0026ndash;1.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.154\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.15 (0.88\\u0026ndash;1.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.74\\u0026ndash;1.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.24 (0.94\\u0026ndash;1.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.259\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.80 (0.61\\u0026ndash;1.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.71 (0.54\\u0026ndash;0.93)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.70 (0.53\\u0026ndash;0.92)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.83 (0.63\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.77 (0.58\\u0026ndash;1.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.79 (0.58\\u0026ndash;1.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.086\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.65\\u0026ndash;1.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.94 (0.69\\u0026ndash;1.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.16 (0.84\\u0026ndash;1.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.368\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e*OR (95% CI) for \\u003cem\\u003eP\\u003c/em\\u003e values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 are in bold.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBa: barium; Cd: cadmium; Co: cobalt; Cs: cesium; Mo: molybdenum; Pb: lead; Sb: antimony; Tl: thallium; Tu: tungsten; Hg: hydrargyrum.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 1 adjust for: None;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 2 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eModel 3 adjust for: age, gender, ethnicity, education level, marital and ratio of family income to poverty; BMI, eGFR, smoking status, alcohol status, diabetes, hypertension, CVD.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn the fully adjusted model, the associations of urinary Ba, Cs, and Sb with CMI remained statistically significant (all \\u003cem\\u003eP\\u003c/em\\u003e-trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The highest quartile group of urinary Cd was associated with an increased risk of high CMI (OR\\u0026thinsp;=\\u0026thinsp;1.490, 95% CI: 1.030\\u0026ndash;2.160, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.032). Conversely, a decreased risk of high CMI was observed among those in the top quartile of urinary Cs, Pb, and Sb concentrations (OR\\u0026thinsp;=\\u0026thinsp;0.720, 95% CI: 0.530\\u0026ndash;0.980, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.037), 0.640 (95% CI: 0.470\\u0026ndash;0.880, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.006), and 0.690 (95% CI: 0.520\\u0026ndash;0.910, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008), respectively.\\u003c/p\\u003e \\u003cp\\u003eAfter full adjustment, positive correlations were observed between Ba and both high AIP and high CMI (all \\u003cem\\u003eP-trend\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), suggesting a consistent risk-increasing effect on these metabolic indicators.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Association of individual heavy metal exposure with sarcopenia\\u003c/h2\\u003e \\u003cp\\u003eGiven the potential role of divalent barium ions in muscle injury [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], we analyzed the association between urinary heavy metals and sarcopenia in the same participants. In the fully adjusted model, the associations of urinary Ba and Co with sarcopenia remained statistically significant (\\u003cem\\u003eP\\u003c/em\\u003e-trend\\u0026thinsp;=\\u0026thinsp;0.016 and 0.037, respectively). The highest quartile group of urinary Ba was associated with a 110% increased risk of sarcopenia (95% CI: 1.16\\u0026ndash;3.90, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.016) (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. Barium as the primary driver of heavy metal-associated AIP and CMI risk in GWQS analysis\\u003c/h2\\u003e \\u003cp\\u003eWe employed the GWQS model to assess the association of a mixture of 10 urinary heavy metals with AIP and CMI treated as continuous variables. Figure. 2a-b shows that after adjusting for all covariates, barium had the highest weight and showed a positive contribution to the risk of elevated AIP and elevated CMI. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec, the GWQS index indicated that the heavy metal mixture was positively linked to AIP treated as a continuous variable (Model 1: OR\\u0026thinsp;=\\u0026thinsp;0.027, 95% CI: 0.003\\u0026ndash;0.050, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.026; Model 2: OR\\u0026thinsp;=\\u0026thinsp;0.032, 95% CI: 0.012\\u0026ndash;0.052, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Model 3: OR\\u0026thinsp;=\\u0026thinsp;0.054, 95% CI: 0.027\\u0026ndash;0.080, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Similarly, the heavy metal mixture was also positively associated with the continuous variable CMI (Model 1: OR\\u0026thinsp;=\\u0026thinsp;0.021, 95% CI: \\u0026minus;0.082\\u0026ndash;0.124, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.687; Model 2: OR\\u0026thinsp;=\\u0026thinsp;0.021, 95% CI: \\u0026minus;0.046\\u0026ndash;0.089, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.540; Model 3: OR\\u0026thinsp;=\\u0026thinsp;0.123, 95% CI: 0.024\\u0026ndash;0.223, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.015).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5. BKMR-derived PIP values for urinary metals in relation to AIP and CMI\\u003c/h2\\u003e \\u003cp\\u003eBKMR analysis identified the ten urinary metals most strongly associated with elevated AIP and CMI based on posterior inclusion probability (PIP). For AIP, the PIP ranking was: Ba (0.7740), Mo (0.7428), Hg (0.6968), Cs (0.6770), Sb (0.6760), W (0.6666), Co (0.6382), Tl (0.6380), Pb (0.5898), and Cd (0.5782) (Figure.3a).\\u003c/p\\u003e \\u003cp\\u003eFor CMI, the order was: Mo (0.9592), Cd (0.7558), Hg (0.7342), Ba (0.7310), W (0.7182), Cs (0.7068), Sb (0.7024), Tl (0.6914), Pb (0.6568), and Co (0.6274) (Figure.3b). In both indices, Ba was a prominent risk factor, consistent with logistic regression and GWQS results. For CMI, Mo exhibited the highest PIP value, suggesting its potential distinct or interactive role in cardiometabolic risk.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6. Effects of heavy metal barium on oxidative stress in VSMCs and ECs\\u003c/h2\\u003e \\u003cp\\u003eTo elucidate the mechanism of action of barium exposure on VSMCs and endothelial cells, this study conducted systematic in vitro experiments. We found that relatively low concentrations of barium chloride markedly inhibited VSMC growth, with effects dependent on both dose and treatment duration (Figure. 4a, Figure. S1a). The inhibitory effect was most pronounced at a concentration of 2 mM, which was therefore selected for subsequent experiments. Similarly, the inhibitory effect of barium chloride on EC proliferation was most significant at a concentration of 1 mM (Figure. 4b, Figure. S1b), and this concentration was used for subsequent mechanistic studies. In contrast, C2C12 myotube cells exhibited stronger tolerance to barium chloride (Figure. 4c, Figure. S1c), showing the most significant proliferation inhibition at a concentration of 10 mM.\\u003c/p\\u003e \\u003cp\\u003eDetection of reactive oxygen species levels by DHE fluorescence staining revealed that barium chloride treatment significantly induced ROS generation in VSMCs and ECs (Figure. 4d, e). To investigate the effect of secreted factors from injured myotube cells on vascular cells, the supernatant from barium chloride-treated myotube cells was used to culture VSMCs and ECs. It was found that this supernatant could also promote ROS production in both cell types (Figure. 4d, e). Furthermore, the oxidative stress level in the barium chloride-treated C2C12 cells themselves was also significantly elevated (Figure. 4f). The quantitative results of DHE fluorescence intensity for each experimental group are shown in Figure. 4g.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study is the first to systematically evaluate the individual and joint effects of 10 urinary heavy metals on cardiometabolic indicators and sarcopenia risk among US adults, employing a combination of multivariate adjustment and multiple statistical models. Single-pollutant logistic regression indicated that urinary Ba, Cd, and Sb were independently linked to higher levels of AIP; urinary Ba, Cs, and Sb showed independent positive associations with CMI; and urinary Ba and Co were associated with an increased risk of sarcopenia. The multi-pollutant GWQS model further indicated that mixed heavy metal exposure was positively associated with the risk of AIP and CMI. Notably, all statistical methods consistently suggested a significant and robust positive association between barium exposure and adverse risks of AIP and CMI (Figure. 5).\\u003c/p\\u003e \\u003cp\\u003eThis study supports the view that heavy metal exposure is associated with CVD risk. Previous large-scale studies have emphasized the cardiovascular toxicity of metals such as cadmium and lead. For instance, individual and mixed exposure to urinary Cd, CS, Co, Pb and other metals were significantly associated with accelerated vascular aging, and men may be more sensitive to the combined toxicity of heavy metal [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Lead and cadmium exposure were associated with increased arterial stiffness [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], and mixed heavy metal exposure was also associated with an increased risk of heart failure in the elderly [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Mixed exposure to metals such as Cd, Tu, Co and Sb was significantly associated with an increased risk of CVD [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. However, systematic research on the effects of mixed heavy metal exposure on AIP and CMI is still relatively scarce, suggesting that this area warrants further in-depth investigation.\\u003c/p\\u003e \\u003cp\\u003eIn addition to traditional risk factors such as age, physical inactivity, metabolic imbalance, and neuromuscular dysfunction [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], emerging evidence suggests that environmental pollution (e.g., ozone, PM2.5, ozone, and phthalates) may also increase the risk of sarcopenia [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Although many risk factors for sarcopenia have been identified, the condition remains highly prevalent worldwide. A population‑based study in the United States found that higher AIP levels are independently associated with an increased risk of sarcopenia in adults [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Moreover, individuals with both sarcopenia and elevated CMI face a significantly higher risk of developing multiple cardiometabolic disorders [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e], suggesting that sarcopenia may share common pathological mechanisms with elevated AIP and CMI. Notably, this study reveals a novel positive association between urinary Ba levels and Ba-induced oxidative stress in both vascular and muscle cells.\\u003c/p\\u003e \\u003cp\\u003eAlthough studies based on NHANES data have indicated a positive correlation between heavy metal mixtures (blood Pb, Cd, Hg, Se and Mn) and sarcopenia prevalence, with manganese as the main driver and the association mediated by inflammation [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e], evidence for the association between heavy metals and sarcopenia remains insufficient. It has been reported that divalent barium ions can cause calcium overload and excessive contraction in muscle fibers, leading to widespread muscle proteolysis and membrane structural damage[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Oxidative stress is a key link in skeletal muscle atrophy, inflammation, and mitochondrial dysfunction, and can impair cellular function through lipid peroxidation and DNA damage [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Based on these shared pathological mechanisms, it is reasonable to hypothesize that barium exposure may increase CVD risk by inducing oxidative stress.\\u003c/p\\u003e \\u003cp\\u003eOxidative stress is important drivers in the occurrence and development of cardiovascular disease. They can activate endothelial dysfunction, promote monocyte infiltration and foam cell formation, thereby initiating atherosclerotic plaque formation [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e], and stimulate vascular smooth muscle cell proliferation, accelerating CVD progression [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. Heavy metals can induce oxidative stress by depleting glutathione and generating ROS [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. The in vitro experiments in this study also confirmed that barium exposure significantly increased ROS generation in VSMCs and ECs. To further investigate the mechanisms of \\\"muscle-vascular\\\" interaction under injury conditions, our indirect co-culture experiments found that factors secreted or upregulated by injured myotube cells could lead to increased oxidative stress levels in VSMCs and ECs. This finding suggests that in the context of muscle injury, intercellular communication within the microenvironment is of great significance for understanding the mechanisms of multiple diseases (such as myopathies and vascular dysfunction accompanying sepsis, chronic kidney disease, heart failure, etc.).\\u003c/p\\u003e \\u003cp\\u003eThis study has the following strengths: First, it is the first to explore the comorbid relationship between barium exposure, sarcopenia, and cardiovascular disease. In addition, multiple statistical models were applied, with adjustment for relevant confounding variables. All analyses were based on a large, rigorously quality-controlled population database, enhancing the robustness of the results. Third, in vitro experiments further revealed that skeletal muscle cells after barium exposure may serve as an important mediator affecting vascular function\\u0026mdash;damaged myotubes can exacerbate oxidative stress in vascular cells, while the study also confirmed the direct toxicity of Ba to vascular cells. The limitations of this study include: at the population level, the cross-sectional design makes it difficult to establish causality, and the data did not cover all potential confounding exposures (such as sewage, cosmetics, and cumulative exposure to barium); at the experimental level, in vitro results still require further validation in more complex in vivo models.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eIn summary, by integrating population‑based epidemiological data with in vitro mechanistic evidence, this study demonstrates that exposure to heavy metals, particularly barium, constitutes a common and independent risk factor for both cardiovascular disease and sarcopenia. This finding deepens the understanding of the pathophysiological mechanisms of age-related comorbidities and suggests that interventions targeting barium exposure may provide dual benefits in preventing these two debilitating diseases.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics and approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll procedures involving NHANES data followed the Declaration of Helsinki. Ethical approval was obtained from the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB) (Protocols #2011-17, #2018-01). Written informed consent was obtained from all participants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets analyzed in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) database, conducted by the U.S. Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics (NCHS). Data from the 2011–2018 survey cycles were used and are accessible at the official website: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (82270440, 82570554 and 82495173), Wenzhou Science and Technology Program (Y20240819), and the National Key R\\u0026amp;D Program of China (2022YFA1104204).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHongya Wang:\\u003c/strong\\u003e Data Extraction, Writing, Software and Statistical Analysis. \\u003cstrong\\u003eHaonan Cui:\\u003c/strong\\u003e Software, Data Extraction and Statistical Analysis. \\u003cstrong\\u003eButuo Xu:\\u003c/strong\\u003e Software and Statistical Analysis. \\u003cstrong\\u003eMin Sun:\\u003c/strong\\u003e Methodology, Data curation. \\u003cstrong\\u003eWei Zhang:\\u003c/strong\\u003e Visualization, Data curation. \\u003cstrong\\u003eDaoyan Liu:\\u003c/strong\\u003e Methodology. \\u003cstrong\\u003eZhiming Zhu:\\u003c/strong\\u003e Methodology, Investigation.\\u0026nbsp;\\u003cstrong\\u003ePeng Gao:\\u003c/strong\\u003e Conceptualization and Supervision. All authors have read and approved the final version of this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflicts of interest.\\u003cstrong\\u003e\\u003cbr clear=\\\"all\\\"\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eBlaustein, J. R., Quisel, M. J., Hamburg, N. M. \\u0026amp; Wittkopp, S. Environmental Impacts on Cardiovascular Health and Biology: An Overview. Circ Res. 134 (2024) 1048-1060. doi:10.1161/circresaha.123.323613.\\u003c/li\\u003e\\n \\u003cli\\u003ePan, Z., Gong, T. \\u0026amp; Liang, P. Heavy Metal Exposure and Cardiovascular Disease. Circulation Research. 134 (2024) 1160-1178. doi:10.1161/CIRCRESAHA.123.323617.\\u003c/li\\u003e\\n \\u003cli\\u003eRoth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., . . . Fuster, V. 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Contribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease. JHEP Reports : Innovation In Hepatology. 2 (2020) 100171. doi:10.1016/j.jhepr.2020.100171.\\u003c/li\\u003e\\n \\u003cli\\u003eBing, S., Chen, Z., Wu, D., Yu, B., Qiu, H., Zhang, Y. \\u0026amp; Wang, S. Evolution of sarcopenia status and risk of incident cardiovascular disease. European Journal of Preventive Cardiology. (2025). doi:10.1093/eurjpc/zwaf115.\\u003c/li\\u003e\\n \\u003cli\\u003eChen, Y., Zhong, Z., Prokopidis, K., Gue, Y., McDowell, G., Liu, Y., . . . Lip, G. Y. H. Associations of Sarcopenia and Its Components With Cardiovascular Risk: Five-Year Longitudinal Evidence From China Health and Retirement Longitudinal Study. Journal of the American Heart Association. 14 (2025) e040099. doi:10.1161/JAHA.124.040099.\\u003c/li\\u003e\\n \\u003cli\\u003eWei, Y. \\u0026amp; Hu, X. Sarcopenia and cardiovascular disease among adults with cardiovascular-kidney-metabolic syndrome stages 0-3: A prospective cohort study. American Journal of Preventive Cardiology. 23 (2025) 101060. doi:10.1016/j.ajpc.2025.101060.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang, Q., Wan, J., Nan, W., Li, S., He, B. \\u0026amp; Peng, Z. Association between manganese exposure in heavy metals mixtures and the prevalence of sarcopenia in US adults from NHANES 2011-2018. Journal of Hazardous Materials. 464 (2023) 133005. doi:10.1016/j.jhazmat.2023.133005.\\u003c/li\\u003e\\n \\u003cli\\u003eMorton, A. B., Norton, C. E., Jacobsen, N. L., Fernando, C. A., Cornelison, D. D. W. \\u0026amp; Segal, S. S. Barium chloride injures myofibers through calcium-induced proteolysis with fragmentation of motor nerves and microvessels. Skeletal Muscle. 9 (2019) 27. doi:10.1186/s13395-019-0213-2.\\u003c/li\\u003e\\n \\u003cli\\u003eFeng, Y., Liu, C., Huang, L., Qian, J., Li, N., Tan, H. \\u0026amp; Liu, X. Associations between heavy metal exposure and vascular age: a large cross-sectional study. Journal of Translational Medicine. 23 (2025) 4. doi:10.1186/s12967-024-06021-w.\\u003c/li\\u003e\\n \\u003cli\\u003eLi, P., Ma, J., Jiang, Y., Yang, X., Luo, Y., Tao, L., . . . Gao, B. Association between Mixed Heavy Metal Exposure and Arterial Stiffness, with Alkaline Phosphatase Identified as a Mediator. Biological Trace Element Research. 203 (2024) 3457-3469. doi:10.1007/s12011-024-04359-2.\\u003c/li\\u003e\\n \\u003cli\\u003eYuting, Y. \\u0026amp; Shan, D. Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003-2020). International Journal of Cardiology. Cardiovascular Risk and Prevention. 25 (2025) 200418. doi:10.1016/j.ijcrp.2025.200418.\\u003c/li\\u003e\\n \\u003cli\\u003eGuo, X., Li, N., Wang, H., Su, W., Song, Q., Liang, Q., . . . Sun, Y. Combined exposure to multiple metals on cardiovascular disease in NHANES under five statistical models. Environmental Research. 215 (2022) 114435. doi:10.1016/j.envres.2022.114435.\\u003c/li\\u003e\\n \\u003cli\\u003eJyv\\u0026auml;korpi, S. K., Urtamo, A., Kivim\\u0026auml;ki, M. \\u0026amp; Strandberg, T. E. Macronutrient composition and sarcopenia in the oldest-old men: The Helsinki Businessmen Study (HBS). Clinical Nutrition (Edinburgh, Scotland). 39 (2020) 3839-3841. doi:10.1016/j.clnu.2020.04.024.\\u003c/li\\u003e\\n \\u003cli\\u003eKim, D., Wijarnpreecha, K., Sandhu, K. K., Cholankeril, G. \\u0026amp; Ahmed, A. Sarcopenia in nonalcoholic fatty liver disease and all-cause and cause-specific mortality in the United States. Liver International : Official Journal of the International Association For the Study of the Liver. 41 (2021) 1832-1840. doi:10.1111/liv.14852.\\u003c/li\\u003e\\n \\u003cli\\u003ePan, R., Wang, T., Tang, R. \\u0026amp; Qian, Z. Association of atherogenic index of plasma and triglyceride glucose-body mass index and sarcopenia in adults from 20 to 59: a cross-sectional study. Frontiers In Endocrinology. 15 (2024) 1437379. doi:10.3389/fendo.2024.1437379.\\u003c/li\\u003e\\n \\u003cli\\u003eHe, L., Lin, C., Tu, Y., Yang, Y., Lin, M., Tu, H. \\u0026amp; Li, J. Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study. Frontiers In Endocrinology. 15 (2024) 1387374. doi:10.3389/fendo.2024.1387374.\\u003c/li\\u003e\\n \\u003cli\\u003eHu, X., Yang, Y., Gao, K., Zhang, Z., Guan, G., Zhang, G., . . . Zhang, Y. Lipid accumulation product and cardiometabolic index as indicators for sarcopenia: A cross-sectional study from NHANES 2011-2018. Scientific Reports. 15 (2025) 21982. doi:10.1038/s41598-025-09123-7.\\u003c/li\\u003e\\n \\u003cli\\u003eZhang, H., Qi, G., Wang, K., Yang, J., Shen, Y., Yang, X., . . . Sun, H. Oxidative stress: Roles in skeletal muscle atrophy. Biochemical Pharmacology. 214 (2023) 115664. doi:10.1016/j.bcp.2023.115664.\\u003c/li\\u003e\\n \\u003cli\\u003eLee, J., Lee, S., Zhang, H., Hill, M. A., Zhang, C. \\u0026amp; Park, Y. Interaction of IL-6 and TNF-\\u0026alpha; contributes to endothelial dysfunction in type 2 diabetic mouse hearts. PloS One. 12 (2017) e0187189. doi:10.1371/journal.pone.0187189.\\u003c/li\\u003e\\n \\u003cli\\u003eKanter, J. E., Hsu, C.-C. \\u0026amp; Bornfeldt, K. E. Monocytes and Macrophages as Protagonists in Vascular Complications of Diabetes. Frontiers In Cardiovascular Medicine. 7 (2020) 10. doi:10.3389/fcvm.2020.00010.\\u003c/li\\u003e\\n \\u003cli\\u003eSenoner, T. \\u0026amp; Dichtl, W. Oxidative Stress in Cardiovascular Diseases: Still a Therapeutic Target? Nutrients. 11 (2019). doi:10.3390/nu11092090.\\u003c/li\\u003e\\n \\u003cli\\u003eMilnerowicz, H., Ściskalska, M. \\u0026amp; Dul, M. Pro-inflammatory effects of metals in persons and animals exposed to tobacco smoke. Journal of Trace Elements In Medicine and Biology : Organ of the Society For Minerals and Trace Elements (GMS). 29 (2014). doi:10.1016/j.jtemb.2014.04.008.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cardiovascular-disorders\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcar\",\"sideBox\":\"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcar/default.aspx\",\"title\":\"BMC Cardiovascular Disorders\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Metal components, Cardiovascular disease, BKMR, Barium, Oxidative stress\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9359383/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9359383/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eExposure to environmental heavy metals is a significant modifiable risk factor for cardiovascular disease (CVD). The plasma atherogenic index (AIP) and cardiometabolic index (CMI) are integrated biomarkers that robustly reflect CVD risk. A critical unanswered question is how exposure to heavy metal mixtures exacerbates CVD risk and which specific components are the primary toxic drivers.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis study leveraged data from NHANES 2011\\u0026ndash;2018. Logistic regression, grouped weighted quantile sum (GWQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze associations between heavy metals and AIP/CMI. In parallel, in vitro experiments examined the direct effects of barium on vascular smooth muscle cells (VSMCs) and endothelial cells (ECs), and indirect effects mediated by barium chloride-injured myotube cells.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA total of 2,834 participants were included. Population analyses revealed a significant positive association between mixed heavy metal exposure and elevated levels of both AIP and CMI. Single-pollutant models specifically linked urinary Ba, Cd and Sb to higher AIP; Ba, Cs and Sb to higher CMI. Notably, mixture analyses using GWQS and BKMR consistently identified barium as the primary contributing factor. Complementary in vitro experiments demonstrated that barium exposure directly increased oxidative stress in VSMCs and ECs. Consistently, in an indirect co-culture model, barium-injured myotube cells also promoted ROS generation in VSMCs and ECs.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eHeavy metal exposure is associated with higher AIP and CMI, with barium playing a key role. These findings underscore the need for environmental monitoring and interventions to mitigate CVD risk, advancing the understanding of environment-metabolism interactions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Barium Exposure as a Major Risk Factor for Elevated Cardiovascular Risk within Heavy Metal Mixtures: Integrating Population Analysis with Experimental Approach\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-15 09:40:31\",\"doi\":\"10.21203/rs.3.rs-9359383/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T07:16:26+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"114810118232667782364935006324351665949\",\"date\":\"2026-05-15T14:09:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-10T12:53:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"190070511426013845198362714615948814552\",\"date\":\"2026-05-09T06:23:41+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"167391235654952778753315664828518919675\",\"date\":\"2026-05-08T16:09:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-05-06T10:26:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-04T08:53:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-04-15T18:28:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-15T03:57:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Cardiovascular Disorders\",\"date\":\"2026-04-15T03:52:35+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cardiovascular-disorders\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcar\",\"sideBox\":\"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcar/default.aspx\",\"title\":\"BMC Cardiovascular Disorders\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"33ffc0ec-d3ab-478e-a8d1-d06abdede43c\",\"owner\":[],\"postedDate\":\"May 15th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T07:16:26+00:00\",\"index\":69,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"114810118232667782364935006324351665949\",\"date\":\"2026-05-15T14:09:14+00:00\",\"index\":68,\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-10T12:53:01+00:00\",\"index\":49,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"190070511426013845198362714615948814552\",\"date\":\"2026-05-09T06:23:41+00:00\",\"index\":48,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"167391235654952778753315664828518919675\",\"date\":\"2026-05-08T16:09:14+00:00\",\"index\":47,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"30\",\"date\":\"2026-05-06T10:26:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-04T08:53:31+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-15T09:40:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-15 09:40:31\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9359383\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9359383\",\"identity\":\"rs-9359383\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}