Increased Serum Levels of Cadmium are Associated with an Elevated Risk of Cardiovascular Disease in Adults

Increased Serum Levels of Cadmium are Associated with an Elevated Risk of Cardiovascular Disease in Adults | 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 Increased Serum Levels of Cadmium are Associated with an Elevated Risk of Cardiovascular Disease in Adults Siyu Ma, Jie Zhang, Cheng Xu, Min Da, Yang Xu, Yong Chen, Xuming Mo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-537965/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Aug, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract Previous studies have determined the effects of exposure to some heavy metals on cardiovascular disease (CVD); however, the association between exposure to cadmium and CVD in adults remains unclear. The relationship between serum levels of cadmium and the risk of CVD was studied by analysing available data from 38,223 participants who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2016. After adjusting for all covariates, we found that higher serum cadmium concentrations were positively related to both the overall risk of CVD (odds ratio (OR): 1.45; 95% confidence interval (CI): 1.22, 1.72; p for trend <0.001) and the risks of its subtypes, including congestive heart failure, coronary heart disease, heart attack and stroke. Elevated levels of cadmium were associated with increased levels of lipids and inflammation parameters, including blood triglycerides, total cholesterol, white blood cells (WBCs) and C-reactive protein (CRP). Our study provided epidemiological evidence that cadmium may increase the risk of CVD by elevating blood lipids and inflammation. Capsule High blood levels of Cd are associated with increased risks of overall CVD and four of the CVD subtypes Environmental Engineering Environmental Policy Cadmium Cardiovascular disease Blood lipids Inflammation Adults Figures Figure 1 1. Introduction Cardiovascular diseases (CVDs), which include heart and vasculature diseases such as coronary heart disease (CHD), angina, heart attack (HA), heart failure (HF) and stroke, is a leading cause of death worldwide.(Xu et al. 2020a ) According to a report from the American Heart Association (AHA) in 2010, CVD mortality accounted for nearly 33% of all mortality; and one person died from CVD every 38 seconds. Moreover, more than 785,000 people were estimated to have new or recurrent CVD every year.(Lloyd-Jones et al. 2010 ) Therefore, determining and controlling the risk factors for CVD are critical.(Phillips &Guazzi 2015 , Thiara 2015 ) Some new risk factors were recently identified in addition to traditional factors, such as smoking, high cholesterol, physical inactivity, obesity and diabetes.(Aw et al. 2020 , Koller &Agyemang 2020 , Lloyd-Jones et al. 2010 , Zhang et al. 2020 ). In particular, environmental pollution was found to contribute to the development of CVD and its risk factors.(Bi et al. 2020 , Li et al. 2020c , So et al. 2020 , Xu et al. 2020a ) Of these, heavy metal pollutants (methylmercury, lead, chromium, etc.), which constitute one type of environmental pollutant, are associated with CVD and its risk factors.(Ali et al. 2020 , Cao et al. 2020 , Orisakwe et al. 2020 ) However, few studies have addressed the correlation between cadmium and CVD. Cadmium (Cd) is a toxic heavy metal that is found in soil, water, seafood and vegetables.(Dennis et al. 2020 , Gemeda et al. 2020 , Koker et al. 2020 , Orisakwe et al. 2015 , Zhao et al. 2016 ) Many regions have reported levels of Cd exceeding the maximum permissible limit of 0.3 mg.kg − 1 established by the World Health Organization (WHO).(Li et al. 2020a , Orisakwe et al. 2015 , Orisakwe et al. 2020 , Pan et al. 2016 , Wang et al. 2018 ) In addition to natural sources, various human activities can increase levels of Cd, including smoking, traffic emissions, metallurgical processes, nuclear energy production, mining, coal combustion and chemical manufacturing.(Li et al. 2019 , Li et al. 2018 , Sall et al. 2020 , Wu et al. 2019 ) Furthermore, similar to other heavy metals, the stability and permeation of Cd lead to its persistence and accumulation in vivo.(Wang et al. 2015 , Wu et al. 2019 ) Therefore, the relationships between Cd and many diseases have attracted considerable attention. Cd was found to increase not only the risk of carcinogenesis but also noncancer-related mortality;(Al Amin et al. 2020 , Amadou et al. 2020 , Suwazono et al. 2020 ) exposure to Cd was shown to be associated with kidney function decline, the development of neurodevelopmental disorders and inflammation of the airways.(Ijomone et al. 2020 , Klein et al. 2020 , Sotomayor et al. 2020 ) In addition, Cd was associated with elevated lipid levels and atherogenic indices, which might induce CVD in susceptible people.(Igharo et al. 2020 , Xu et al. 2020b ) However, few studies have identified the relationship between Cd and CVD. A study in a Korean population showed that Cd was associated with the risk of stroke in people under the age of 60 years. An investigation in a larger and more representative population was needed to determine the correlation between the levels of Cd and CVD.(Jeong et al. 2020 ) Therefore, 38,223 subjects were included in this large population-based study based on data from the National Health and Nutrition Examination Survey (NHANES). Interestingly, the results showed that serum levels of Cd were positively related to CVD and its risk factors in adults. 2. Material And Methods 2.1 Subjects We included subjects who had participated in the NHANES, which is a program of studies involving members of the general, non-institutionalized population in the United States. The detailed survey design, methods, and available data are accessible on the NHANES website. ( https://www.cdc.gov/nchs/nhanes/ ) The subjects who participated in the NHANES, had available serum heavy metal concentrations and had CVD from 1999 to 2016 were enrolled in our study. In total, 92,062 adults completed the interviews and examinations, and those who were pregnant or had missing data were excluded. Figure 1 shows the participant selection process. Finally, our study enrolled 38,223 participants. 2.2 Evaluation of outcomes The participants were evaluated by both a standardized medical questionnaire and self-reported physician diagnoses. The participants reported whether doctors or other health professionals had ever diagnosed them with CHD, congestive HF, angina, stroke or HA. A participant was considered to have CVD if a positive response was given to any of the relevant questions. The blood concentrations of lipids were measured by Roche Modular P and Roche Cobas 6000 chemistry analysers and the Friedewald equation. The Beckman Coulter method was used to measure the parameters of inflammation. 2.3 Exposure to cadmium Whole-blood samples were collected by a trained investigator and frozen before analysis. First, the blood samples were diluted. Then the serum cadmium levels were measured with an inductively coupled plasma mass spectrometer with dynamic reaction cell technology (ELAN® DRC II; PerkinElmer Norwalk, CT, USA). The quality assurance and quality control protocols followed the 1988 Clinical Laboratory Improvement Act mandates ( https://www.cdc.gov/nchs/nhanes/index.htm ). 2.4 Covariate The covariates included age, sex, race, physical activity level, education level, poverty to income ratio (PIR), past-year alcohol consumption status, category of serum cotinine level, body mass index (BMI), family history of CVD and fish consumption; these covariates were selected based on factors that could affect the correlation between Cd levels and CVD risk. We treated age and PIR as continuous variables. Other variables were treated as categorical variables. 2.5 Statistical analysis Continuous variables are presented as the means with standard deviations (SDs), and categorical variables are presented as frequencies and percentages. We compared continuous variables and categorical variables between groups with and without CVD with the Mann-Whitney U test and χ 2 tests, respectively, and analysed the correlations between the serum levels of heavy metals and the risk of CVD by survey-weighted multiple logistic regression analysis with three separate models. Model 1 was a crude model; Model 2 was adjusted for age, sex, race and education level; and Model 3 was adjusted for the variables included in Model 2 and BMI, PIR, physical activity, past-year alcohol consumption, serum cotinine, history of CVD, and fish consumption. We further investigated the relationships between serum concentrations of heavy metals and five CVD subtypes. Finally, multivariate analysis was used to explore the associations between serum Cd levels and blood lipids and inflammation parameters. Sampling weights were adjusted in all statistical analyses in SAS (version 9.2) and R software (version 3.5.0). We considered P<0.05 to indicate statistical significance in this study. 3. Results The demographics of the participants are shown in Table 1 . Significant differences were found between subjects with and without CVD for age, sex, race, education level, PIR, physical activity level, past-year alcohol consumption, family history of CVD, serum cotinine category, BMI category and fish consumption. Table 1 Participants characteristics (N = 38,223) in NHANES 1999–2016. Non-CVD CVD P value Age (years) 45.30 ± 0.20 64.60 ± 0.30 < 0.001 Gender (%) < 0.001 Male 48.40 53.20 Race (%) < 0.001 Mexican American 8.12 4.08 Other Hispanic 5.63 3.22 Non-Hispanic White 69.39 76.74 Non-Hispanic Black 10.65 10.96 Other Race - Including Multi-Racial 6.21 4.99 Education Level (%) < 0.001 Less Than 9th Grade 5.97 12.16 9-11th Grade 11.56 16.86 High School Grad/GED or Equivalent 23.73 26.98 Some College or AA degree 30.79 26.37 College Graduate or above 27.85 17.49 Missing 1.05 1.34 Family PIR (%) 0.001 =1 80.49 77.01 Missing 6.71 7.05 Physical activity (%) < 0.001 Never 44.77 54.23 Moderate 26.01 26.29 Vigorous 28.47 14.34 Missing 0.76 5.15 Past-year alcohol drinking (%) < 0.001 No 22.25 30.08 Yes 70.43 63.45 Missing 7.32 6.47 Family history of CVD (%) < 0.001 No 77.52 66.34 Yes 20.36 30.24 Missing 2.12 3.42 Serum cotinine category (%) < 0.001 10 26.60 25.98 Missing 1.28 2.05 BMI category (%) < 0.001 =30 32.57 41.93 Missing 1.19 3.81 Fish consumption < 0.001 No 14.05 13.08 Yes 66.00 60.22 Missing 19.96 26.70 Mean ± SD. Percentage. NHANES, National Health and Nutrition Examination Survey; BMI, body mass index; CVD, cardiovascular disease; PIR, poverty to income ratio; LOD, limit of detection. Table 2 shows the correlations between the quartiles of the serum concentrations of three heavy metals and the risk of overall CVD according to the multivariate logistic regression model after adjustment for covariates. After adjusting for all covariates (model 3), we found that the risk of overall CVD was 1.45 times (95% CI: 1.22, 1.72; p for trend < 0.001) higher in the group with the highest quartile of serum Cd concentrations than in the group with the lowest quartile of serum Cd concentrations. No significant association was found between the other heavy metals and the risk of CVD. Table 2 Multivariable correlations of selected heavy metals with cardiovascular disease (CVD) risk. Q1 Q2 Q3 Q4 P for trend Lead Model 1 Ref 1.79 (1.54, 2.08) 2.91 (2.51, 3.36) 4.00 (3.46, 4.63) < 0.001 Model 2 Ref 0.93 (0.79, 1.08) 1.10 (0.94, 1.29) 1.09 (0.94, 1.27) 0.025 Model 3 Ref 0.91 (0.76, 1.08) 1.05 (0.88, 1.25) 0.98 (0.82, 1.18) 0.432 Cadmium Model 1 Ref 1.66 (1.47, 1.86) 2.53 (2.24, 2.86) 2.73 (2.38, 3.12) < 0.001 Model 2 Ref 0.99 (0.88, 1.13) 1.18 (1.03, 1.36) 1.58 (1.37, 1.82) < 0.001 Model 3 Ref 0.97 (0.83, 1.12) 1.21 (1.04, 1.42) 1.45 (1.22, 1.72) < 0.001 Mercury Model 1 Ref 0.92 (0.81, 1.05) 0.80 (0.70, 0.90) 0.76 (0.65, 0.89) 0.003 Model 2 Ref 0.88 (0.76, 1.02) 0.73 (0.63, 0.83) 0.70 (0.59, 0.82) 0.002 Model 3 Ref 0.94 (0.81, 1.08) 0.81 (0.70, 0.93) 0.82 (0.69, 0.96) 0.059 model 1: crude model; model 2: adjust for age, sex, race, education; model 3: model 2 plus, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption and cycle. We further analysed the correlations between the risk of the five common subtypes of CVD (congestive heart failure, coronary heart disease, angina, heart attack and stroke) and the quartiles of the serum concentrations of the three heavy metals (Table 3 ). After adjusting for all covariates, the serum levels of Cd were positively related to the risk of congestive heart disease (OR = 1.53, 95% CI: 1,17, 2.00, p for trend < 0.001), CHD (OR = 1.24, 95% CI: 0.98, 1.55, p for trend = 0.005), HA (OR = 1.61, 95% CI: 1.28, 2.02, p for trend < 0.001) and stroke (OR = 1.68, 95% CI: 1.26, 2.23, p for trend < 0.001). Table 3 Multivariable correlations of selected heavy metals with individual cardiovascular disease (CVD) risk. Congestive heart failure Coronary heart disease Angina Heart attack Stroke Lead Q1 Ref Ref Ref Ref Ref Q2 1.09 (0.84, 1.41) 0.98 (0.77, 1.25) 0.86 (0.65, 1.14) 1.22 (0.95, 1.58) 0.98 (0.78, 1.23) Q3 0.97 (0.72, 1.30) 0.99 (0.78, 1.27) 0.87 (0.66, 1.15) 1.22 (0.94, 1.57) 1.16 (0.90, 1.49) Q4 1.06 (0.81, 1.38) 0.99 (0.76, 1.29) 0.85 (0.62, 1.17) 1.26 (0.99, 1.60) 1.17 (0.89, 1.54) P for trend 0.781 0.565 0.005 0.152 0.086 Cadmium Q1 Ref Ref Ref Ref Ref Q2 0.98 (0.77, 1.24) 0.90 (0.72, 1.13) 1.03 (0.82, 1.30) 0.90 (0.71, 1.15) 1.20 (0.90, 1.58) Q3 1.35 (1.06, 1.72) 1.19 (0.95, 1.47) 1.11 (0.90, 1.38) 1.36 (1.09, 1.71) 1.32 (1.00, 1.73) Q4 1.53 (1.17, 2.00) 1.24 (0.98, 1.55) 1.07 (0.81, 1.40) 1.61 (1.28, 2.02) 1.68 (1.26, 2.23) P for trend < 0.001 0.005 0.161 < 0.001 < 0.001 Mercury Q1 Ref Ref Ref Ref Ref Q2 0.89 (0.72, 1.09) 0.95 (0.75, 1.18) 0.99 (0.78, 1.26) 0.99 (0.81, 1.21) 0.86 (0.70, 1.06) Q3 0.66 (0.50, 0.86) 0.95 (0.74, 1.23) 0.92 (0.71, 1.19) 0.83 (0.66, 1.04) 0.81 (0.67, 0.99) Q4 0.55 (0.44, 0.68) 1.18 (0.90, 1.54) 0.92 (0.70, 1.20) 1.03 (0.83, 1.27) 0.61 (0.47, 0.80) P for trend 0.041 0.019 0.419 0.634 0.003 Adjust for age, gender, race, education, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption and cycle. Table 4 shows the relationships between levels of Cd and concentrations of blood lipids and inflammation parameters. After adjusting for all covariates, cadmium levels were positively related to the levels of serum triglycerides (Beta = 7.85, 95% CI: 2.78, 12.93, p = 0.003), total cholesterol (Beta = 0.03, 95% CI: 0.01,0.06, p = 0.021), and C-reactive protein (CRP, Beta = 0.03, 95% CI: 0.01,0.05, p = 0.009) and the WBC count (Beta = 0.26, 95% CI: 0,20,0.32, p < 0.001). Table 4 Multivariate analysis of the association of the serum cadmium levels and concentration changes (95% CI) in the blood lipids and inflammation parameters. Beta 95% CI P value Serum triglyceride (mg/dL) 7.85 2.78, 12.93 0.003 HDL-cholesterol (mg/dL) 0.18 -0.32, 0.67 0.471 LDL-cholesterol (mg/dL) 0.54 -0.83, 1.91 0.438 Total cholesterol (mg/dL) 0.03 0.01, 0.06 0.021 WBC (10 9 /mL) 0.26 0.20, 0.32 < 0.001 CRP (mg/dL) 0.03 0.01, 0.05 0.009 Adjust for age, sex, race, education, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption, lead, mercury and cycle. CI, confidence interval; HDL, high density lipid; LDL, low density lipid; WBC, white blood cell; CRP, C-reactive protein. 4. Discussion Our large population-based study is the first to show a dose-response relationship between cadmium (Cd) and cardiovascular disease (CVD) in adults. Furthermore, the serum levels of Cd were positively related to the overall risk of CVD and the risks of four of the subtypes. The underlying mechanism may involve the increases in blood lipid levels and activation of the inflammatory response induced by Cd. Few previous studies have focused on the relationship between the levels of Cd and CVD in adults. Although a study involving 15,624 United States (US) adults showed that urinary levels of Cd might be associated with all-cause mortality, more than 30% of which was attributed to CVD, no significant association was observed between Cd levels and CVD mortality.(Kim et al. 2019 ) Given that the levels of urinary Cd are sensitive to kidney function and physical activity, they cannot be used to accurately reflect exposure levels.(Li et al. 2020b , Munoz et al. 2020 ) The association of blood levels of Cd with CVD was studied in another population, and it was shown that elevated levels of Cd were associated with an elevated risk of CVD in adults under 60 years old.(Jeong et al. 2020 ) Nevertheless, given that the investigated population consisted of a single ethnicity, had fewer subtypes of CVD and was not adjusted for the confounding effects of smoking,(Li et al. 2019 ) this finding lacks generalizability. Therefore, our study provided valid evidence of the relationship between Cd levels and CVD risk after overcoming the abovementioned limitations. Further analysis is needed to investigate the underlying mechanisms by which Cd affects CVD; these mechanisms may involve the relationships between Cd and the levels of triglycerides, total cholesterol, and CRP and the WBC count. Our results showed that the levels of Cd were positively correlated with the blood levels of triglycerides and total cholesterol, which indicated that elevated levels of triglyceride and total cholesterol may play important mediating roles in Cd-related CVD. Consistent with our findings, there is a substantial amount of evidence that exposure to Cd can result in dyslipidaemia, which has been identified as a risk factor for CVD.(Samarghandian et al. 2015 , Zhu et al. 2020 ) Indications of the possible underlying mechanisms can be found in the results of this study and previous studies. An animal study showed that Cd could increase triglyceride levels by reducing lipid uptake receptors in the liver.(Liu et al. 2020a ) In addition, exposure to Cd also initiated the endoplasmic reticulum (ER) stress process, which negatively affected lipid homeostasis and metabolic gene expression.(Rajakumar et al. 2020 ) Furthermore, high levels of Cd could also increase the production of lipids by markedly elevating the activity of serum lipase, reduce lipid degradation by reducing fatty acid β oxidation and promote lipid synthesis by modifying many liver enzymes, such as hydroxyl-methyl-glutaryl CoA reductase (HMG-CoA).(Aja et al. 2020 , Ali et al. 2020 , Pawlak et al. 2015 , Wu et al. 2017 ) Our study also suggested that WBC counts and CRP levels were positively associated with Cd levels, which provides insight into another possible mechanism underlying Cd-related CVD. Although many studies have reported high levels of WBCs in patients with CVD, the reason is unclear.(Koren-Morag et al. 2005 , Lassale et al. 2018 , Xu et al. 2020a ) Interestingly, some studies showed that changes in WBC counts and CRP levels indicated systemic inflammation.(Baek &Chung 2017 , Fagerberg et al. 2017 , Saggu et al. 2019 ) It is worth noting that reactive oxygen species (ROS), autophagy, and immune-related and apoptosis-related genes were found to be involved in this process, which possibly increased the risk of CVD by inducing cytotoxicity, vascular toxicity, nephrotoxicity and cardiotoxicity.(Kwok &Chan 2020 , Kwok et al. 2020 , Liu et al. 2020b , Reyes-Becerril et al. 2019 , Roy et al. 2020 , Wang et al. 2020 ) Our study had some limitations. First, due to its long biological half-life and low excretion rate, it was difficult to determine whether the timing of exposure to Cd influenced the CVD risk in our study.(Bhardwaj et al. 2020 , Kabamba &Tuakuila 2020 ) In addition, genetic factors also contribute to the risk of CVD; however, there were no genetic data collected in the NHANES. Although the results also showed that levels of mercury were positively related to the risks of congestive HF and stroke, our study mainly addressed the relationship between Cd levels and the risk of CVD. Finally, as this was a cross-sectional study, it can only provide epidemiological evidence of a correlation between Cd levels and the risk of CVD, and further functional experiments and prospective cohort studies are needed to verify this correlation. 5. Conclusion In our study, high serum levels of Cd are associated with increased risks of overall CVD and four of the CVD subtypes and that the concentrations of Cd are also positively related to the levels of lipids and inflammation parameters, which might provide insight into the possible mechanism underlying Cd-related CVD. Declarations Availability of data and material Data will be available on request. Authors’ contributions Xuming Mo: Conceptualization, Validation, Supervision and Funding acquisition. Siyu Ma: Methodology, Writing - Original Draft and Visualization. Cheng Xu: Methodology, Formal analysis and Visualization. Jie Zhang: Supervision and Project administration. Min Da, Yang Xu and Yong Chen: Resources. Acknowledgements Not applicable Funding This work was supported by Key Project supported by the Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau (ZKX19039), the National Key Research and Development Program of China (2017YFC1308105), Clinical Frontier Technology of Clinical Medicine of Jiangsu Provincial Science and Technology Department (BE2017608), the National Natural Science Foundation of China (81970265,82000303), the Natural Science Foundation of Jiangsu Province (BK20180144), Nanjing Science and Technology Development Project (2019060007) and Key Medical Discipline of Science & Education Project of Jiangsu Province (ZDXKB2016017). Conflicts of interests The authors declare that they have no competing interests. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. 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Chemosphere 148, 248-54 Pawlak M, Lefebvre P, Staels B (2015): Molecular mechanism of PPARalpha action and its impact on lipid metabolism, inflammation and fibrosis in non-alcoholic fatty liver disease. J Hepatol 62, 720-33 Phillips SA, Guazzi M (2015): The vasculature in cardiovascular diseases: will the vasculature tell us what the future holds? Prog Cardiovasc Dis 57, 407-8 Rajakumar S, Vijayakumar R, Abhishek A, Selvam GS, Nachiappan V (2020): Loss of ERAD bridging factor UBX2 modulates lipid metabolism and leads to ER stress-associated apoptosis during cadmium toxicity in Saccharomyces cerevisiae. Curr Genet 66, 1003-1017 Reyes-Becerril M, Angulo C, Sanchez V, Cuesta A, Cruz A (2019): Methylmercury, cadmium and arsenic(III)-induced toxicity, oxidative stress and apoptosis in Pacific red snapper leukocytes. Aquat Toxicol 213, 105223 Roy A, Nethi SK, Suganya N, Raval M, Chatterjee S, Patra CR (2020): Attenuation of cadmium-induced vascular toxicity by pro-angiogenic nanorods. 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Environ Int 143, 105983 Sotomayor CG, Groothof D, Vodegel JJ, Eisenga MF, Knobbe TJ, J IJ, Lammerts RGM, de Borst MH, Berger SP, Nolte IM, Rodrigo R, Slart R, Navis GJ, Touw DJ, Bakker SJL (2020): Plasma cadmium is associated with increased risk of long-term kidney graft failure. Kidney Int Suwazono Y, Nogawa K, Sakurai M, Watanabe Y, Nishijo M, Ishizaki M, Morikawa Y, Kido T, Nakagawa H (2020): Environmental cadmium exposure and noncancer mortality in a general Japanese population in cadmium nonpolluted regions. J Appl Toxicol Thiara B (2015): Cardiovascular disease. Nurs Stand 29, 60 Wang C, Liu X, Chen JP, Li K (2015): Superior removal of arsenic from water with zirconium metal-organic framework UiO-66. Sci Rep 5, 16613 Wang C, Nie G, Zhuang Y, Hu R, Wu H, Xing C, Li G, Hu G, Yang F, Zhang C (2020): Inhibition of autophagy enhances cadmium-induced apoptosis in duck renal tubular epithelial cells. Ecotoxicol Environ Saf 205, 111188 Wang J, Zhang X, Yang Q, Zhang K, Zheng Y, Zhou G (2018): Pollution characteristics of atmospheric dustfall and heavy metals in a typical inland heavy industry city in China. J Environ Sci (China) 71, 283-291 Wu C, Zhang Y, Chai L, Wang H (2017): Histological changes, lipid metabolism and oxidative stress in the liver of Bufo gargarizans exposed to cadmium concentrations. Chemosphere 179, 337-346 Wu Y, Pang H, Liu Y, Wang X, Yu S, Fu D, Chen J, Wang X (2019): Environmental remediation of heavy metal ions by novel-nanomaterials: A review. Environ Pollut 246, 608-620 Xu C, Liang J, Xu S, Liu Q, Xu J, Gu A (2020a): Increased serum levels of aldehydes are associated with cardiovascular disease and cardiovascular risk factors in adults. J Hazard Mater 400, 123134 Xu C, Weng Z, Zhang L, Xu J, Dahal M, Basnet TB, Gu A (2020b): HDL cholesterol: A potential mediator of the association between urinary cadmium concentration and cardiovascular disease risk. Ecotoxicol Environ Saf 208, 111433 Zhang YB, Pan XF, Chen J, Cao A, Xia L, Zhang Y, Wang J, Li H, Liu G, Pan A (2020): Combined lifestyle factors, all-cause mortality and cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. J Epidemiol Community Health Zhao M, Xu Y, Zhang C, Rong H, Zeng G (2016): New trends in removing heavy metals from wastewater. Appl Microbiol Biotechnol 100, 6509-6518 Zhu X, Fan Y, Sheng J, Gu L, Tao Q, Huang R, Liu K, Yang L, Chen G, Cao H, Li K, Tao F, Wang S (2020): Association Between Blood Heavy Metal Concentrations and Dyslipidemia in the Elderly. Biol Trace Elem Res Supplementary Files graphicalabstract.tif Cite Share Download PDF Status: Published Journal Publication published 06 Aug, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 17 Jun, 2021 Reviews received at journal 27 May, 2021 Reviewers invited by journal 26 May, 2021 Editor invited by journal 26 May, 2021 Editor assigned by journal 19 May, 2021 First submitted to journal 17 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-537965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":29578084,"identity":"27a79211-1512-457c-b9f5-d74de4a57cf0","order_by":0,"name":"Siyu Ma","email":"","orcid":"","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Ma","suffix":""},{"id":29578085,"identity":"222327a5-2526-46e0-b65a-48afd0dac18b","order_by":1,"name":"Jie Zhang","email":"","orcid":"","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":29578086,"identity":"067cfd0e-dba4-4e39-8e24-5d2e8d73881f","order_by":2,"name":"Cheng Xu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Xu","suffix":""},{"id":29578087,"identity":"1c32a3d5-4d5c-409a-8b44-be04cb178d30","order_by":3,"name":"Min Da","email":"","orcid":"","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Da","suffix":""},{"id":29578088,"identity":"5aaa9ccf-76cc-4656-a4d1-631fb2fbac05","order_by":4,"name":"Yang Xu","email":"","orcid":"","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Xu","suffix":""},{"id":29578089,"identity":"47f6785b-48c8-4033-8335-a44c44fd5b3b","order_by":5,"name":"Yong Chen","email":"","orcid":"","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Chen","suffix":""},{"id":29578090,"identity":"8289024f-5ff2-4daf-96ac-a0a891115588","order_by":6,"name":"Xuming Mo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFACHhAhwcDAzP/wAYTDYECkFnYeZgNStAABPw+bBJSJX4tue+/hz7w5FnnyzrzHqgtktiU2sDdvk2CouYNTi9mZc2nSvNskig0P86XdnsFzO7GB51iZBMOxZ7i13MgxYwZqSdzYzGB2mwekRSLHTIKx4TA+LcafYVqKwVrk3xDUYgByWOJ8Zh4zZogtPAS0nDljJjkXqGUDM1uyNFCLcRtPWrFFwjE8Wo73GH94u60ucX7/4YOfeXtuy/azH95440MNbi1wYHAASDD2MDCwgXgJhDUwMMg3gMgfxCgdBaNgFIyCkQYAbdxQsQvAmZQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2264-6123","institution":"Children's Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xuming","middleName":"","lastName":"Mo","suffix":""}],"badges":[],"createdAt":"2021-05-19 05:31:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-537965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-537965/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-021-15732-2","type":"published","date":"2021-08-06T15:04:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9727486,"identity":"d8bc97f1-bd65-441a-abe7-f337ce7e0a0f","added_by":"auto","created_at":"2021-05-28 19:29:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":632179,"visible":true,"origin":"","legend":"Participant selection process for the analyses of the relationships between serum cadmium levels and the risk of cardiovascular disease (CVD) in adults.","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-537965/v1/158570d86b87fd7318772b33.png"},{"id":13695873,"identity":"1dc3d50f-b010-4d0c-8b5e-6aad78d73bac","added_by":"auto","created_at":"2021-09-17 13:00:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":469868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-537965/v1/07a6e11f-548b-4481-9ad7-de49eb7e6236.pdf"},{"id":9727485,"identity":"899731dd-5fb2-49c5-a2aa-a2195a506619","added_by":"auto","created_at":"2021-05-28 19:29:02","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":435836,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-537965/v1/7cae14a0ffdf681a482fdec7.tif"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIncreased Serum Levels of Cadmium are Associated with an Elevated Risk of Cardiovascular Disease in Adults\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eCardiovascular diseases (CVDs), which include heart and vasculature diseases such as coronary heart disease (CHD), angina, heart attack (HA), heart failure (HF) and stroke, is a leading cause of death worldwide.(Xu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) According to a report from the American Heart Association (AHA) in 2010, CVD mortality accounted for nearly 33% of all mortality; and one person died from CVD every 38 seconds. Moreover, more than 785,000 people were estimated to have new or recurrent CVD every year.(Lloyd-Jones et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) Therefore, determining and controlling the risk factors for CVD are critical.(Phillips \u0026amp;Guazzi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Thiara \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) Some new risk factors were recently identified in addition to traditional factors, such as smoking, high cholesterol, physical inactivity, obesity and diabetes.(Aw et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Koller \u0026amp;Agyemang \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Lloyd-Jones et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, environmental pollution was found to contribute to the development of CVD and its risk factors.(Bi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e, So et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Xu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) Of these, heavy metal pollutants (methylmercury, lead, chromium, etc.), which constitute one type of environmental pollutant, are associated with CVD and its risk factors.(Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Cao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Orisakwe et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) However, few studies have addressed the correlation between cadmium and CVD.\u003c/p\u003e \u003cp\u003eCadmium (Cd) is a toxic heavy metal that is found in soil, water, seafood and vegetables.(Dennis et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Gemeda et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Koker et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Orisakwe et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Zhao et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) Many regions have reported levels of Cd exceeding the maximum permissible limit of 0.3 mg.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e established by the World Health Organization (WHO).(Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, Orisakwe et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Orisakwe et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Pan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) In addition to natural sources, various human activities can increase levels of Cd, including smoking, traffic emissions, metallurgical processes, nuclear energy production, mining, coal combustion and chemical manufacturing.(Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Sall et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Furthermore, similar to other heavy metals, the stability and permeation of Cd lead to its persistence and accumulation in vivo.(Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Therefore, the relationships between Cd and many diseases have attracted considerable attention. Cd was found to increase not only the risk of carcinogenesis but also noncancer-related mortality;(Al Amin et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Amadou et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Suwazono et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) exposure to Cd was shown to be associated with kidney function decline, the development of neurodevelopmental disorders and inflammation of the airways.(Ijomone et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Klein et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Sotomayor et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) In addition, Cd was associated with elevated lipid levels and atherogenic indices, which might induce CVD in susceptible people.(Igharo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Xu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eHowever, few studies have identified the relationship between Cd and CVD. A study in a Korean population showed that Cd was associated with the risk of stroke in people under the age of 60 years. An investigation in a larger and more representative population was needed to determine the correlation between the levels of Cd and CVD.(Jeong et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Therefore, 38,223 subjects were included in this large population-based study based on data from the National Health and Nutrition Examination Survey (NHANES). Interestingly, the results showed that serum levels of Cd were positively related to CVD and its risk factors in adults.\u003c/p\u003e "},{"header":"2. Material And Methods","content":"\u003ch2\u003e2.1 Subjects\u003c/h2\u003e\n\u003cp\u003eWe included subjects who had participated in the NHANES, which is a program of studies involving members of the general, non-institutionalized population in the United States. The detailed survey design, methods, and available data are accessible on the NHANES website. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003c/span\u003e) The subjects who participated in the NHANES, had available serum heavy metal concentrations and had CVD from 1999 to 2016 were enrolled in our study. In total, 92,062 adults completed the interviews and examinations, and those who were pregnant or had missing data were excluded. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the participant selection process. Finally, our study enrolled 38,223 participants.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2 Evaluation of outcomes\u003c/h2\u003e\n\u003cp\u003eThe participants were evaluated by both a standardized medical questionnaire and self-reported physician diagnoses. The participants reported whether doctors or other health professionals had ever diagnosed them with CHD, congestive HF, angina, stroke or HA. A participant was considered to have CVD if a positive response was given to any of the relevant questions. The blood concentrations of lipids were measured by Roche Modular P and Roche Cobas 6000 chemistry analysers and the Friedewald equation. The Beckman Coulter method was used to measure the parameters of inflammation.\u003c/p\u003e\n\u003ch2\u003e2.3 Exposure to cadmium\u003c/h2\u003e\n\u003cp\u003eWhole-blood samples were collected by a trained investigator and frozen before analysis. First, the blood samples were diluted. Then the serum cadmium levels were measured with an inductively coupled plasma mass spectrometer with dynamic reaction cell technology (ELAN\u0026reg; DRC II; PerkinElmer Norwalk, CT, USA). The quality assurance and quality control protocols followed the 1988 Clinical Laboratory Improvement Act mandates (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003e2.4 Covariate\u003c/h2\u003e\n\u003cp\u003eThe covariates included age, sex, race, physical activity level, education level, poverty to income ratio (PIR), past-year alcohol consumption status, category of serum cotinine level, body mass index (BMI), family history of CVD and fish consumption; these covariates were selected based on factors that could affect the correlation between Cd levels and CVD risk. We treated age and PIR as continuous variables. Other variables were treated as categorical variables.\u003c/p\u003e\n\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eContinuous variables are presented as the means with standard deviations (SDs), and categorical variables are presented as frequencies and percentages. We compared continuous variables and categorical variables between groups with and without CVD with the Mann-Whitney U test and \u0026chi;\u003csup\u003e2\u003c/sup\u003e tests, respectively, and analysed the correlations between the serum levels of heavy metals and the risk of CVD by survey-weighted multiple logistic regression analysis with three separate models. Model 1 was a crude model; Model 2 was adjusted for age, sex, race and education level; and Model 3 was adjusted for the variables included in Model 2 and BMI, PIR, physical activity, past-year alcohol consumption, serum cotinine, history of CVD, and fish consumption. We further investigated the relationships between serum concentrations of heavy metals and five CVD subtypes. Finally, multivariate analysis was used to explore the associations between serum Cd levels and blood lipids and inflammation parameters. Sampling weights were adjusted in all statistical analyses in SAS (version 9.2) and R software (version 3.5.0). We considered P\u0026lt;0.05 to indicate statistical significance in this study.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe demographics of the participants are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences were found between subjects with and without CVD for age, sex, race, education level, PIR, physical activity level, past-year alcohol consumption, family history of CVD, serum cotinine category, BMI category and fish consumption.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eParticipants characteristics (N\u0026thinsp;=\u0026thinsp;38,223) in NHANES 1999\u0026ndash;2016.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-CVD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCVD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMexican American\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther Hispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation Level (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLess Than 9th Grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9-11th Grade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh School Grad/GED or Equivalent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSome College or AA degree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollege Graduate or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily PIR (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;=1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVigorous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePast-year alcohol drinking (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of CVD (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum cotinine category (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLOD-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI category (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;=30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFish consumption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Percentage.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNHANES, National Health and Nutrition Examination Survey; BMI, body mass index; CVD, cardiovascular disease; PIR, poverty to income ratio; LOD, limit of detection.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the correlations between the quartiles of the serum concentrations of three heavy metals and the risk of overall CVD according to the multivariate logistic regression model after adjustment for covariates. After adjusting for all covariates (model 3), we found that the risk of overall CVD was 1.45 times (95% CI: 1.22, 1.72; p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher in the group with the highest quartile of serum Cd concentrations than in the group with the lowest quartile of serum Cd concentrations. No significant association was found between the other heavy metals and the risk of CVD.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable correlations of selected heavy metals with cardiovascular disease (CVD) risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLead\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.79 (1.54, 2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.91 (2.51, 3.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.00 (3.46, 4.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.93 (0.79, 1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.10 (0.94, 1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.09 (0.94, 1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91 (0.76, 1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.05 (0.88, 1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98 (0.82, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCadmium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.66 (1.47, 1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.53 (2.24, 2.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.73 (2.38, 3.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99 (0.88, 1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.18 (1.03, 1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.58 (1.37, 1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97 (0.83, 1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.21 (1.04, 1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.45 (1.22, 1.72)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMercury\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.92 (0.81, 1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80 (0.70, 0.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76 (0.65, 0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88 (0.76, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73 (0.63, 0.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.70 (0.59, 0.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94 (0.81, 1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81 (0.70, 0.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82 (0.69, 0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003emodel 1: crude model;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003emodel 2: adjust for age, sex, race, education;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003emodel 3: model 2 plus, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption and cycle.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe further analysed the correlations between the risk of the five common subtypes of CVD (congestive heart failure, coronary heart disease, angina, heart attack and stroke) and the quartiles of the serum concentrations of the three heavy metals (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for all covariates, the serum levels of Cd were positively related to the risk of congestive heart disease (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1,17, 2.00, p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CHD (OR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 0.98, 1.55, p for trend\u0026thinsp;=\u0026thinsp;0.005), HA (OR\u0026thinsp;=\u0026thinsp;1.61, 95% CI: 1.28, 2.02, p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and stroke (OR\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.26, 2.23, p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable correlations of selected heavy metals with individual cardiovascular disease (CVD) risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCongestive heart failure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoronary heart disease\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAngina\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHeart attack\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStroke\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLead\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09 (0.84, 1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.77, 1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 (0.65, 1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22 (0.95, 1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.78, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97 (0.72, 1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.78, 1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 (0.66, 1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22 (0.94, 1.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 (0.90, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0.81, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.76, 1.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85 (0.62, 1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26 (0.99, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17 (0.89, 1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCadmium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.77, 1.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.72, 1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.82, 1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.71, 1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20 (0.90, 1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35 (1.06, 1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19 (0.95, 1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 (0.90, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36 (1.09, 1.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32 (1.00, 1.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.53 (1.17, 2.00)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.24 (0.98, 1.55)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (0.81, 1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.61 (1.28, 2.02)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.68 (1.26, 2.23)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMercury\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89 (0.72, 1.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95 (0.75, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.78, 1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 (0.81, 1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 (0.70, 1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66 (0.50, 0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95 (0.74, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 (0.71, 1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83 (0.66, 1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81 (0.67, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.55 (0.44, 0.68)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (0.90, 1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 (0.70, 1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.83, 1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.61 (0.47, 0.80)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.634\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAdjust for age, gender, race, education, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption and cycle.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the relationships between levels of Cd and concentrations of blood lipids and inflammation parameters. After adjusting for all covariates, cadmium levels were positively related to the levels of serum triglycerides (Beta\u0026thinsp;=\u0026thinsp;7.85, 95% CI: 2.78, 12.93, p\u0026thinsp;=\u0026thinsp;0.003), total cholesterol (Beta\u0026thinsp;=\u0026thinsp;0.03, 95% CI: 0.01,0.06, p\u0026thinsp;=\u0026thinsp;0.021), and C-reactive protein (CRP, Beta\u0026thinsp;=\u0026thinsp;0.03, 95% CI: 0.01,0.05, p\u0026thinsp;=\u0026thinsp;0.009) and the WBC count (Beta\u0026thinsp;=\u0026thinsp;0.26, 95% CI: 0,20,0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariate analysis of the association of the serum cadmium levels and concentration changes (95% CI) in the blood lipids and inflammation parameters.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerum triglyceride (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e7.85\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.78, 12.93\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.32, 0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.471\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL-cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.83, 1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.01, 0.06\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/mL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.20, 0.32\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.01, 0.05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eAdjust for age, sex, race, education, BMI, PIR, physical activity, past-year alcohol drinking, serum cotinine, family history of CVD, fish consumption, lead, mercury and cycle.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eCI, confidence interval; HDL, high density lipid; LDL, low density lipid; WBC, white blood cell; CRP, C-reactive protein.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":" \u003cp\u003eOur large population-based study is the first to show a dose-response relationship between cadmium (Cd) and cardiovascular disease (CVD) in adults. Furthermore, the serum levels of Cd were positively related to the overall risk of CVD and the risks of four of the subtypes. The underlying mechanism may involve the increases in blood lipid levels and activation of the inflammatory response induced by Cd.\u003c/p\u003e \u003cp\u003eFew previous studies have focused on the relationship between the levels of Cd and CVD in adults. Although a study involving 15,624 United States (US) adults showed that urinary levels of Cd might be associated with all-cause mortality, more than 30% of which was attributed to CVD, no significant association was observed between Cd levels and CVD mortality.(Kim et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Given that the levels of urinary Cd are sensitive to kidney function and physical activity, they cannot be used to accurately reflect exposure levels.(Li et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e, Munoz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) The association of blood levels of Cd with CVD was studied in another population, and it was shown that elevated levels of Cd were associated with an elevated risk of CVD in adults under 60 years old.(Jeong et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Nevertheless, given that the investigated population consisted of a single ethnicity, had fewer subtypes of CVD and was not adjusted for the confounding effects of smoking,(Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) this finding lacks generalizability. Therefore, our study provided valid evidence of the relationship between Cd levels and CVD risk after overcoming the abovementioned limitations. Further analysis is needed to investigate the underlying mechanisms by which Cd affects CVD; these mechanisms may involve the relationships between Cd and the levels of triglycerides, total cholesterol, and CRP and the WBC count.\u003c/p\u003e \u003cp\u003eOur results showed that the levels of Cd were positively correlated with the blood levels of triglycerides and total cholesterol, which indicated that elevated levels of triglyceride and total cholesterol may play important mediating roles in Cd-related CVD. Consistent with our findings, there is a substantial amount of evidence that exposure to Cd can result in dyslipidaemia, which has been identified as a risk factor for CVD.(Samarghandian et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Zhu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Indications of the possible underlying mechanisms can be found in the results of this study and previous studies. An animal study showed that Cd could increase triglyceride levels by reducing lipid uptake receptors in the liver.(Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) In addition, exposure to Cd also initiated the endoplasmic reticulum (ER) stress process, which negatively affected lipid homeostasis and metabolic gene expression.(Rajakumar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Furthermore, high levels of Cd could also increase the production of lipids by markedly elevating the activity of serum lipase, reduce lipid degradation by reducing fatty acid β oxidation and promote lipid synthesis by modifying many liver enzymes, such as hydroxyl-methyl-glutaryl CoA reductase (HMG-CoA).(Aja et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Pawlak et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOur study also suggested that WBC counts and CRP levels were positively associated with Cd levels, which provides insight into another possible mechanism underlying Cd-related CVD. Although many studies have reported high levels of WBCs in patients with CVD, the reason is unclear.(Koren-Morag et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Lassale et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Xu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) Interestingly, some studies showed that changes in WBC counts and CRP levels indicated systemic inflammation.(Baek \u0026amp;Chung \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Fagerberg et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Saggu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) It is worth noting that reactive oxygen species (ROS), autophagy, and immune-related and apoptosis-related genes were found to be involved in this process, which possibly increased the risk of CVD by inducing cytotoxicity, vascular toxicity, nephrotoxicity and cardiotoxicity.(Kwok \u0026amp;Chan \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kwok et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Liu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e, Reyes-Becerril et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Roy et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOur study had some limitations. First, due to its long biological half-life and low excretion rate, it was difficult to determine whether the timing of exposure to Cd influenced the CVD risk in our study.(Bhardwaj et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kabamba \u0026amp;Tuakuila \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) In addition, genetic factors also contribute to the risk of CVD; however, there were no genetic data collected in the NHANES. Although the results also showed that levels of mercury were positively related to the risks of congestive HF and stroke, our study mainly addressed the relationship between Cd levels and the risk of CVD. Finally, as this was a cross-sectional study, it can only provide epidemiological evidence of a correlation between Cd levels and the risk of CVD, and further functional experiments and prospective cohort studies are needed to verify this correlation.\u003c/p\u003e "},{"header":"5. Conclusion","content":" \u003cp\u003eIn our study, high serum levels of Cd are associated with increased risks of overall CVD and four of the CVD subtypes and that the concentrations of Cd are also positively related to the levels of lipids and inflammation parameters, which might provide insight into the possible mechanism underlying Cd-related CVD.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eData will be available on request.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eXuming Mo: Conceptualization, Validation, Supervision and Funding acquisition. Siyu Ma: Methodology, Writing - Original Draft and Visualization. Cheng Xu: Methodology, Formal analysis and Visualization. Jie Zhang: Supervision and Project administration. Min Da, Yang Xu and Yong Chen: Resources.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Key Project supported by the Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau (ZKX19039), the National Key Research and Development Program of China (2017YFC1308105), Clinical Frontier Technology of Clinical Medicine of Jiangsu Provincial Science and Technology Department (BE2017608), the National Natural Science Foundation of China (81970265,82000303), the Natural Science Foundation of Jiangsu Province (BK20180144), Nanjing Science and Technology Development Project (2019060007) and Key Medical Discipline of Science \u0026amp; Education Project of Jiangsu Province (ZDXKB2016017).\u003c/p\u003e\n\u003ch2\u003eConflicts of interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAja PM, Izekwe FI, Famurewa AC, Ekpono EU, Nwite FE, Igwenyi IO, Awoke JN, Ani OG, Aloke C, Obasi NA, Udeh KU, Ale BA (2020): Hesperidin protects against cadmium-induced pancreatitis by modulating insulin secretion, redox imbalance and iNOS/NF-kB signaling in rats. 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Biol Trace Elem Res\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cadmium, Cardiovascular disease, Blood lipids, Inflammation, Adults","lastPublishedDoi":"10.21203/rs.3.rs-537965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-537965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies have determined the effects of exposure to some heavy metals on cardiovascular disease (CVD); however, the association between exposure to cadmium and CVD in adults remains unclear. The relationship between serum levels of cadmium and the risk of CVD was studied by analysing available data from 38,223 participants who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2016. After adjusting for all covariates, we found that higher serum cadmium concentrations were positively related to both the overall risk of CVD (odds ratio (OR): 1.45; 95% confidence interval (CI): 1.22, 1.72; p for trend \u0026lt;0.001) and the risks of its subtypes, including congestive heart failure, coronary heart disease, heart attack and stroke. Elevated levels of cadmium were associated with increased levels of lipids and inflammation parameters, including blood triglycerides, total cholesterol, white blood cells (WBCs) and C-reactive protein (CRP). Our study provided epidemiological evidence that cadmium may increase the risk of CVD by elevating blood lipids and inflammation. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCapsule\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eHigh blood levels of Cd are associated with increased risks of overall CVD and four of the CVD subtypes\u003c/p\u003e","manuscriptTitle":"Increased Serum Levels of Cadmium are Associated with an Elevated Risk of Cardiovascular Disease in Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-28 19:29:00","doi":"10.21203/rs.3.rs-537965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2021-06-17T04:56:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-05-27T18:49:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-05-26T11:30:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2021-05-26T10:54:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-20T01:37:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-05-17T22:52:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"365b4f23-53ab-47f5-92ed-fbd83255e243","owner":[],"postedDate":"May 28th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4651708,"name":"Environmental Engineering"},{"id":4651709,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-08-22T15:21:32+00:00","versionOfRecord":{"articleIdentity":"rs-537965","link":"https://doi.org/10.1007/s11356-021-15732-2","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2021-08-06 15:04:48","publishedOnDateReadable":"August 6th, 2021"},"versionCreatedAt":"2021-05-28 19:29:00","video":"","vorDoi":"10.1007/s11356-021-15732-2","vorDoiUrl":"https://doi.org/10.1007/s11356-021-15732-2","workflowStages":[]},"version":"v1","identity":"rs-537965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-537965","identity":"rs-537965","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}