Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures

Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures | 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 Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures Boubakari Ibrahimou, Kazi Tanvir Hasan, Shelbie Burchfield, Hamisu Salihu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4456611/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25 th percentile compared to their 50 th percentile and a positive association of 0.0285 units when all the metals were at their 75 th percentile compared to their 50 th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies. Bayesian kernel machine regression Heavy metal mixtures Cardiovascular disease Heart attack NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) is a serious global health issue. Despite recent breakthroughs in therapy, CVD remains the leading cause of death in the developed world, accounting for about one million deaths annually in the United States alone. About 17.7 million people died from CVDs globally in 2015, representing 31% of all deaths in the world [ 1 ]. By 2030, over 23.6 million people will have died from CVDs, primarily heart disease and stroke. These are expected to be the primary causes of death for the foreseeable future [ 2 ]. Traditional CVD risk factors aren't responsible for all deaths. Environmental, nutritional, and lifestyle factors appear to be crucial in explaining the dramatic recent changes in the prevalence, with the potential of widespread public health implications [ 3 ]. Recent research has shown that heavy metal exposure is related to an increased risk of cardiovascular diseases [ 1 , 4 , 5 ]. Heavy metals enter the human body through multiple routes including food, drinking water, and breathing. Heavy metals include toxic metals such as arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg), as well as vital trace elements such as chromium (Cr), cobalt (Co), copper (Cu), magnesium (Mg), manganese (Mn), nickel (Ni), selenium (Se) and zinc (Zn) [ 6 ]. Multiple heavy metal exposures can have additive, synergistic, antagonistic, or other effects on human health [ 7 , 8 ], however, most studies of heavy metals focus on single metal [ 9 ]. In addition, many earlier studies on heavy metals’ negative effects tended to focus on occupational exposure alone [ 5 , 10 , 11 ]. Heavy metal workers are exposed to higher amounts, while the general public in the United States is exposed to lower levels [ 12 ]. Low dosages of heavy metals produce epidemiological outcomes that are more in line with actual ambient exposure levels, and their exposure has been found in studies to be hazardous to the population [ 13 ]. Biologically active metals do, therefore, have a role across a range of physiological and pathological processes [ 14 ]. Evidence of the involvement of environmental exposure to heavy metals in CVD risk has quickly increased during the past two decades. Recent research points to evidence associating heavy metal exposure in the environment with an amplified risk for diabetes and hypertension, two major risk factors for CVD [ 3 ]. Higher amounts of barium in drinking water have been linked to increased cardiovascular mortality [ 15 ]. A Spanish study found that urine Cu, Zn, antimony (Sb), Cd, Cr, and vanadium (V) levels were all independently related to an elevated risk of cardiovascular diseases. Urine metals were similarly linked to an increased risk of cardiovascular diseases, with Cd and Sb being the most significant components [ 16 ]. In addition, heavy metals were found to interact with other diseases leading to CVD. An interaction between blood Cd and chronic bronchitis was reported to be associated with myocardial infarction, and interaction between blood Pb level and chronic obstructive pulmonary disease (COPD) was associated with a heart attack or stroke [ 1 , 5 ]. However, most of these studies only examined the exposure effects of one heavy metal at a time. Estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology and to regulatory agencies as humans are frequently exposed to many metals throughout their lifetimes. Using the weighted quantile sum (WQS) model, Duan et al. [ 8 ] investigated the correlations between a heavy metal combination and the risks of all-cause, CVD-related, and cancer-related death. Methods have been proposed for joint modelling of the data. Dunson [ 18 ] presented a new class of latent variables for grouping mixed outcome data. Nonetheless, several issues must be addressed in order to accurately quantify the health impacts of these multi-pollutant combinations. While current techniques for investigating mixtures [ 19 ] address some of these difficulties, they also have significant drawbacks. Bobb et al. [ 20 ] developed Bayesian kernel machine regression (BKMR) as a novel way to study mixtures, in which the health outcome is regressed on a flexible function of the mixture's components (e.g., air pollution or hazardous waste) that is described using a kernel function. A unique hierarchical variable selection strategy is used in high-dimensional situations to find essential mixture components and accounts for the associated structure of the mixture. This BKMR model is used in the current study to identify blood and urine heavy metals and heavy metal mixtures that may be associated with heart attacks. Materials and Methods Study population The data for this study comes from the National Health and Nutrition Examination Survey (NHANES) [ 21 ] covering the period from 2011 through 2016. It has been an ongoing national, population-based cross-sectional survey of the US population since the 1980s. We used Demographics Data, Medical Conditions, Laboratory Data, and Questionnaire Data in this analysis. Except for the levels of heavy metal, all variables were self-reported. We only considered participants who were 20 years or older. We also excluded subjects with missing data. The final sample contained 2972 participants. Measurement of diseases The survey question "Has a doctor or other health professional ever told you that you had a heart attack?" from the Medical Conditions data set was used to determine the study's main outcome: the occurrence of a heart attack. Some frequent comorbidities, such as high blood cholesterol, high blood pressure, poor kidney function, diabetes, and asthma, were also included as covariates [ 4 , 5 , 8 ]. These variables were determined by the question “Has a doctor or other health professional ever told you that you had high blood pressure/high cholesterol/weak or failing kidney/diabetes/asthma?” Measurement of Heavy Metals Blood lead, cadmium, mercury, and manganese level were all extracted from a lab data set called the lead, cadmium, total mercury, selenium, and manganese-blood. The Metals – Urine dataset of Laboratory Data was used to collect urine cobalt and barium information. More details, information measurement procedures, and quality control processes can be found on NHANES 2011–2012, 2013–2014, and 2015–2016 Data Documentation [ 21 ]. Measurement of smoking and alcohol intake The Smoking-Cigarette Usage dataset was utilized to determine smoking status with the queries "Smoked at least 100 cigarettes in your life?" and "Do you now smoke cigarettes?". Smokers were recoded as never smokers, former smokers, and current smokers. Alcohol was a continuous variable that showed the average number of alcoholic beverages drank per week by people in the preceding year. Measurement of other covariates Gender, Age, Marital Status, Household Income, Race, Educational Level, and Body Mass Index (BMI) were obtained from the NHANES Demographic, Examination, and Questionnaire data sets [ 21 ]. Based on earlier research [ 1 , 5 , 8 ] cut points were appropriately chosen. Statistical Analysis R statistical software, version 4.1.1 [ 22 ], was used for all analyses. To account for the complex, multistage survey design, a SURVEYLOGISTIC procedure was applied. We incorporated the weight, stratum, and cluster variables from NHANES data in the procedure [ 22 ]. Using survey logistic techniques, these three variables were included in the univariate models. to account for design features. The goal of the univariate logistic model investigation was to include multiple metals in the analysis to create models that more accurately represent real-life exposures. The chi-square test was performed to examine the relationship between heart attack history and categorical variables. The t-test was performed to compare the equality of means in continuous variables between groups with and without a heart attack. Pearson correlation test was used to find out the correlation between the metals. Adjusted Odds Ratios and 95% credible intervals (CI) were obtained from the univariate logistic regression and Bayesian kernel machine regression analysis respectively. The ‘BKMR' package [ 23 ] that implements Bayesian Kernel Machine Regression in R was used to see if there were any significant associations between the mixture of heavy metal levels and heart attack status. The BKMR model, a non-parametric Bayesian variable selection framework was used to evaluate the mixture effect of metals on heart attack. BKMR combines Bayesian and statistical learning methods to regress an exposure–response function iteratively by a Gaussian kernel function. BKMR can identify nonlinear and non-additive relationships within metals. In the current study, the outcome of interest (Y = 1) is heart attack (is binary), and the exposure variables z are blood lead, cadmium, mercury, and manganese and urine cobalt and barium, we used the following probit BKMR model. $${{\Phi }}^{-1}\left({{\mu }}_{\text{i}}\right)=\text{h}\left({\text{z}}_{\text{i}1}, {\text{z}}_{\text{i}2}, \dots , {\text{z}}_{\text{i}\text{M}}\right)+ {{\text{x}}_{\text{i}}}^{{\prime }}{\beta }$$ where \({\Phi }\) is the cumulative distribution function (CDF) for the standard normal distribution ( \({{\Phi }}^{-1}\) is the probit link function) and µ i is the probability that Y i equals 1. The h (⋅) is an exposure-response function that flexibly models the relationship between the exposures to multiple metals z 1 …z M , and the probit of the probability of a heart attack (Y = 1). The x is a vector of non-exposure covariates with a linear or non-linear relationship with the outcome, and β is a vector of respective coefficients of x [ 20 , 23 , 24 ]. Under BKMR, the kernel function used to represent h has several options. In this section, we concentrate on the Gaussian kernel, which captures a wide range of underlying functional forms for h and can be expressed as $$K\left(z,{z}^{{\prime }}\right)=\text{e}\text{x}\text{p}\left\{-\sum _{m=1}^{M}{r}_{m}{({z}_{m}- {{z}^{{\prime }}}_{m})}^{2}\right\}$$ In this case, both z and z′ represent a vector of exposure variables for two different individuals. In the present context, M is the number of metals, and \({r}_{m}\ge 0\) is the tuning parameter for the smoothness of h. With this kernel function, it is assumed that similar exposure profiles will have similar health effects. In the current study, this means two individuals with similar blood and urine heavy metal exposures will have a similar risk of a heart attack. To estimate h(z) at a certain exposure vector z, the posterior distribution of h is assumed to be normally distributed, with a posterior mean \({\mu }_{h}\left(\theta \right)\) and variance \({V}_{h}\left(\theta \right)\) , which depends on the model parameters denoted by \(\theta\) [ 20 , 23 ]. The probit model above can be expressed using a latent normal random variable formulation as follows: $${{Y}^{*}}_{i}=h\left({z}_{i1}, {z}_{i2}, \dots , {z}_{iM}\right)+ {{x}^{{\prime }}}_{i}\beta +{e}_{i}$$ where e i assumes standard normal distribution and $${Y}_{i}=I\left({{Y}^{*}}_{i}>0\right)=\left\{\begin{array}{c}1 if {{Y}^{*}}_{i}>0 \\ 0 otherwise\end{array}\right.$$ The Markov chain Monte Carlo (MCMC) algorithm is implemented to fit the BKMR model and allows for customization of the model with options such as continuous or binary outcomes, random or non-random intercepts, component wise variable selection, and hierarchical variable selection [ 20 , 23 , 24 ]. The model fit for the current study used binary outcomes, non-random intercepts, component wise variable selection, and the MCMC algorithm. The BKMR model is not able to accommodate sample weights yet, and thus we used unweighted estimation [ 25 ]. The R software and the required packages can be obtained from the CRAN website [ 22 ]. More details regarding the ‘bkmr’ package code and usage can be found in Bobb [ 23 ] and Bobb et al [ 24 ]. Results Table 1 shows the general features of our study population. Of the 2972 participants included in the study, 89 (3%) suffered from a heart attack. The prevalence of heart attack was higher in groups older than 60, men, who had high blood pressure, and high blood cholesterol and suffered from other comorbidities such as diabetes, asthma, and weak kidney functions. Smokers (including ex-smokers and current smokers) and heavy drinkers were more likely to have a heart attack (p-value <0.001). There were significantly different mean levels of heavy metals in the blood and urine of the heart attack group compared to the non-heart attack group (p-value <0.001). Those who had a heart attack exhibited higher mean levels of cadmium and lead in the blood as well as higher mean levels of cadmium, cobalt, and tin in the urine compared to those who did not report a heart attack. Conversely, the heart attack group had lower mean levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. Table 1. Baseline Characteristics from NHANES 2011–2016, of Adults More Than 20 Years Old Characteristics Heart Attack = No (N=2883) Heart Attack = Yes (N=89) Overall (N=2972) p-value Gender 0.001 Female 1357 (47.1%) 26 (29.2%) 1383 (46.5%) Male 1526 (52.9%) 63 (70.8%) 1589 (53.5%) Age < 0.001 20 – 39 1191 (41.3%) 5 (5.6%) 1196 (40.2%) 40 – 59 982 (34.1%) 19 (21.3%) 1001 (33.7%) 60 – 74 561 (19.5%) 43 (48.3%) 604 (20.3%) ≥ 75 149 (5.2%) 22 (24.7%) 171 (5.8%) Marital Status <0.001 Never Married 660 (22.9%) 12 (13.4%) 672 (22.6%) Married 1428 (49.5%) 44 (49.4%) 1472 (49.5%) Widowed 135 (4.7%) 13 (14.6%) 148 (5.0%) Divorced 303 (10.5%) 11 (12.4%) 314 (10.6%) Separated 78 (2.7%) 5 (5.6%) 83 (2.8%) Living with Partner 279 (9.7%) 4 (4.5%) 283 (9.5%) Household Income < 0.001 < USD 20,000 490 (17.0%) 33 (37.1%) 523 (17.6%) ≥ USD 20,000 2393 (83.0%) 56 (62.9%) 2449 (82.4%) Race 0.079 Mexican American 371 (12.9%) 7 (7.9%) 378 (12.7%) Other Hispanic 281 (9.7%) 9 (10.1%) 290 (9.8%) Non-Hispanic White 1234 (42.8%) 45 (50.6%) 1279 (43.0%) Non-Hispanic Black 603 (20.9%) 23 (25.8%) 626 (21.1%) Other Race 394 (13.7%) 5 (5.6%) 399 (13.4%) Education 0.003 High school 1824 (63.2%) 40 (44.9%) 1864 (62.7%) High Blood Pressure < 0.001 No 1956 (67.8%) 25 (28.1%) 1981 (66.7%) Yes 927 (32.2%) 64 (71.9%) 991 (33.3%) High Cholesterol < 0.001 No 1927 (66.8%) 30 (33.7%) 1957 (65.8%) Yes 956 (33.2%) 59 (66.3%) 1015 (34.2%) Diabetes No 2533 (87.9%) 53 (59.6%) 2586 (87.0%) < 0.001 Yes 280 (9.7%) 35 (39.3%) 315 (10.6%) Borderline 70 (2.4%) 1 (1.1%) 71 (2.4%) Kidney Conditions < 0.001 No 2815 (97.6%) 81 (91.0%) 2896 (97.4%) Yes 68 (2.4%) 8 (9.0%) 76 (2.6%) BMI 0.853 < 18.5 42 (1.5%) 1 (1.1%) 43 (1.4%) 18.5 – 24.9 809 (28.1%) 25 (28.1%) 834 (28.1%) 25 – 29.9 934 (32.4%) 26 (29.2%) 960 (32.3%) 30+ 1098 (38.1%) 37 (41.6%) 1135 (38.2%) Alcohol Consumption < 0.001 Mean (SD) 3.02 (18.7) 2.76 (2.60) 3.02 (18.4) Smoking Status < 0.001 Never 1533 (53.2%) 26 (29.2%) 1559 (52.5%) Current 632 (21.9%) 32 (36.0%) 664 (22.3%) Former 718 (24.9%) 31 (34.8%) 749 (25.2%) Asthma < 0.001 No 2426 (84.1%) 63 (70.8%) 2489 (83.7%) Yes 457 (15.9%) 26 (29.2%) 483 (16.3%) Blood lead (ug/L) < 0.001 Mean (SD) 13.5 (14.9) 21.1 (26.7) 13.7 (15.4) Median [Min, Max] 9.90 [1.10, 337] 15.1 [4.00, 246] 10.1 [1.10, 337] Blood cadmium (ug/L) < 0.001 Mean (SD) 0.480 (0.555) 0.741 (0.728) 0.487 (0.563) Median [Min, Max] 0.290 [0.070, 7.23] 0.510 [0.070, 4.00] 0.300 [0.070, 7.23] Blood mercury (ug/L) < 0.001 Mean (SD) 1.62 (2.64) 1.38 (1.56) 1.61 (2.62) Median [Min, Max] 0.860 [0.110, 50.8] 0.940 [0.200, 9.12] 0.860 [0.110, 50.8] Blood manganese (ug/L) < 0.001 Mean (SD) 9.78 (3.60) 9.53 (3.29) 9.77 (3.59) Median [Min, Max] 9.17 [1.88, 56.6] 8.87 [4.55, 23.5] 9.16 [1.88, 56.6] Blood selenium (ug/L) < 0.001 Mean (SD) 196 (24.2) 191 (23.5) 196 (24.2) Median [Min, Max] 195 [106, 391] 192 [120, 260] 195 [106, 391] Urine cadmium (ug/L) < 0.001 Mean (SD) 0.315 (0.431) 0.500 (0.399) 0.320 (0.431) Median [Min, Max] 0.181 [0.025, 6.94] 0.386 [0.041, 1.93] 0.186 [0.025, 6.94] Urine Lead (ug/L) < 0.001 Mean (SD) 5.52 (10.6) 6.70 (6.44) 5.56 (10.5) Median [Min, Max] 3.60 [0.200, 350] 4.40 [0.500, 39.4] 3.60 [0.200, 350] Urine cobalt (ug/L) < 0.001 Mean (SD) 0.503 (0.897) 0.680 (1.19) 0.508 (0.907) Median [Min, Max] 0.354 [0.016, 33.7] 0.358 [0.032, 9.91] 0.355 [0.016, 33.7] Urine manganese (ug/L) < 0.001 Mean (SD) 0.152 (0.490) 0.149 (0.248) 0.151 (0.485) Median [Min, Max] 0.0920 [0.057, 18.2] 0.0920 [0.057, 2.38] 0.0920 [0.057, 18.2] Urine barium (ug/L) < 0.001 Mean (SD) 1.78 (3.06) 1.53 (1.65) 1.77 (3.03) Median [Min, Max] 1.08 [0.042, 87.4] 0.900 [0.060, 9.06] 1.08 [0.042, 87.4] Urine tin (ug/L) < 0.001 Mean (SD) 1.23 (4.02) 2.51 (9.76) 1.27 (4.31) Median [Min, Max] 0.440 [0.064, 89.9] 0.770 [0.064, 91.0] 0.450 [0.064, 91.0] Urine tungsten (ug/L) < 0.001 Mean (SD) 0.121 (0.645) 0.115 (0.207) 0.121 (0.637) Median [Min, Max] 0.0580 [0.013, 32.9] 0.0770 [0.013, 1.85] 0.0590 [0.013, 32.9] Urine strontium (ug/L) < 0.001 Mean (SD) 120 (120) 108 (95.9) 119 (119) Median [Min, Max] 90.2 [1.77, 3220] 78.3 [5.6, 542] 89.8 [1.77, 3220] At the beginning of our analysis, we intended to include all metal exposures in our dataset in order to evaluate their associations with heart attack. However, during our initial assessment, we found that blood and urine lead were significantly associated (Pearson correlation coefficient of 0.78) as shown in figure 1. As a result of the potential of multicollinearity, we decided not to include urine lead in our study. Multicollinearity occurs when independent variables in a regression model have a strong correlation, which can lead to unstable or inaccurate estimates of their individual effects on the outcome variable. In this case, including both blood and urine lead in the analysis could have made it difficult to determine the independent effects of each exposure on the health outcome, as they would be highly correlated with each other. We aimed to reduce the risk of multicollinearity and obtain more reliable estimates of the associations between the remaining metal exposures and the heart attack by excluding urine lead from the analysis [1,5,15,16]. The results of the univariate survey logistic regression analysis are presented in table 2. Looking at individual heavy metal exposures, we observed that the odds of heart attack increased by about 10% in patients for every 1 µ/L increase in the average level of blood lead (OR = 1.014, CI = [1.005 – 1.022]) and by 91% for every 1 µ/L increase in the average level of blood cadmium (OR = 1.911, CI = [1.413 - 2.584). Several patient characteristics were associated with increased odds of heart attack. Males had 2.46 times the odds of heart attack compared to females (OR = 2.463, CI = [1.349 - 4.496]). Individuals in the 40-59 year (OR = 5.095, CI = [1.349 - 19.253]), 60-74 year (OR = 23.839, CI = [8.891- 72.022]), and ≥75 year (OR = 36.295, CI = [10.575 - 12.457]) age groups also had significantly greater odds of heart attack compared to those in the 20-39 year age group. Compared to those who were married, patients who were widowed (OR = 3.039, CI = [1.578, 5.852]) had greater odds of a heart attack. Patients with diabetes (OR = 8.687, CI = [4.389 - 17.195]), high blood pressure (OR = 6.08, CI = [3.659, 10.101]), and high blood cholesterol (OR = 6.686, CI = [3.641, 12.279]) had increased odds of having a heart attack. Current and former smokers had higher odds of heart attack compared to those who never smoked (OR = 2.797, CI = [1.440, 5.436]; OR = 1.929, CI = [0.932, 3.994] respectively). By contrast, other patient characteristics were associated with lower odds of a heart attack. For example, Mexican American (OR = 0.36, CI = [0.135, 0.959]) patients had lower odds of heart attack compared to non-Hispanic White patients. Patients whose household income was greater than or equal to $20,000 also had lower odds of heart attack than patients whose household income was less than $20,000 (OR = 0.475, CI = [0.314, 0.718]). Similarly, patients with more than a high school education (OR = 0.383, CI = [0.180, 0.810]) had lower odds of having a heart attack than patients with less than high school education. Table 2: Odds Ratio of Univariate Model Characteristics Odds Ratio (OR) 95% CI Blood lead level Mean 1.014 [1.005, 1.022] Blood cadmium level Mean 1.911 [1.413, 2.584] Blood mercury level Mean 0.999 [0.926, 1.077] Blood manganese level Mean 1.019 [0.936, 1.109] Blood selenium level Mean 0.992 [0.979, 1.006] Urine cadmium level Mean 1.892 [1.298, 2.758] Urine cobalt level Mean 1.284 [0.892, 1.848] Urine manganese level Mean 0.715 [0.230, 2.228] Urine barium level Mean 1.012 [0.955, 1.073] Urine tin level Mean 1.034 [1.010, 1.058] Urine tungsten level Mean 1.016 [0.882, 1.169] Urine strontium level Mean 0.023 [0.999, 1.002] Gender Female Male reference 2.463 [1.349, 4.496] Age 20 -39 40 – 59 60 - 74 ≥ 75 reference 5.095 23.839 36.295 [1.349, 19.253] [8.891, 72.022] [10.575, 12.457] Marital Status Married Never Married Widowed Divorced Separated Living with Partner reference 0.502 3.039 0.563 3.134 0.526 [0.206, 1.224] [1.578, 5.852] [0.236, 1.343] [0.912, 10.771] [0.117, 2.364] Household Income < USD 20,000 ≥ USD 20,000 reference 0.475 [0.314, 0.718] Race Non-Hispanic White Other Hispanic Mexican American Non-Hispanic Black Other Race reference 0.643 0.360 1.282 0.716 [0.282, 1.466] [0.135, 0.959] [0.738, 2.228] [0.161, 3.175] Education High school reference 0.557 0.383 [0.263, 1.179] [0.180, 0.810] Diabetes No Yes Borderline reference 8.687 0.159 [4.389, 17.195] [0.020, 1.269] High Blood Pressure No Yes reference 6.080 [3.659, 10.101] High Cholesterol No Yes reference 6.686 [3.641, 12.279] BMI < 18.5 18.5 – 24.9 25 – 29.9 30+ reference 1.405 1.571 1.725 [0.166, 11.879] [0.182, 13.561] [0.206, 14.483] Alcohol Consumption Mean 1.000 [0.996, 1.005] Smoking Status Never Current Former reference 2.797 1.929 [1.440, 5.436] [0.932, 3.994] Kidney Conditions No Yes reference 2.584 [1.013, 6.591] Asthma No Yes reference 2.194 [1.160, 4.147] Using Bayesian Kernel Machine Regression implemented in the R ‘bkmr package [23], which is a statistical approach for estimating the joint health effects of multiple concurrent exposures, we investigated the mixture effect of heavy metals on heart attack. We fitted the BKMR model to evaluate how the joint effect of metal exposures impacts heart attack risk. Table 3 summarizes the posterior inclusion probability (PIP) derived from the model, which measures variable importance. PIP is a ranking measure that indicates how strongly the data supports the inclusion of a variable in the regression. For example, a PIP value of 0.5104 for Lead in the Blood exposure column indicates a 51.04% possibility that Lead exposure is associated with an increased risk of heart attack. Similarly, the PIP value of 0.7872 for Cadmium in the Urine exposure column indicates that there is a 78.72% possibility that Cadmium exposure is associated to an increased risk of heart attack. Table 3: Posterior Inclusion Probability (PIP) Exposure PIP Blood Lead Cadmium Mercury Manganese Selenium 0.5104 0.4088 0.4376 0.3280 0.5200 Urine Cadmium Cobalt Manganese Barium Tin Tungsten Strontium 0.7872 0.4304 0.5272 0.4880 0.5208 0.4304 0.5504 Next, we estimated the Cumulative effects of metal mixtures on heart attack as the mixture exposure changes in the index (or linear predictor) h(z) (table 4). Changes in h are the results of combined changes in any components of the metal mixture. Table 4 summarizes the cumulative effect of the mixture exposure h(z) on the risk of a heart attack in comparison with that at the median level of h(z). The "Fraction of Risk Change (in Probit)" column indicates the change in risk relative to the median exposure level, measured in probits (a unit of measurement for standard deviations). The "Standard Deviation" column indicates the uncertainty or variability of the risk estimate. At the median exposure level (quantile 0.5), the risk change is 0.000, which means that there is no effect of metal mixtures on relative risk at this level of exposure. At the lower exposure quantiles (0.25, 0.3, and 0.4), the risk change is negative, indicating a decrease in risk with increasing metal mixture exposure. For example, the effect for the 0.25 quantile of exposure is -0.0030, indicating a small decrease in risk with increasing exposure to metal mixtures. The corresponding standard deviation of 0.0618 suggests that this effect is not statistically significant. At the higher exposure quantiles (0.6, 0.7, and 0.75), the risk change is positive, indicating an increase in risk with increasing metal mixture exposure. The effect for the 0.75 quantile of exposure is 0.0285, indicating an increase in risk with increasing exposure to metal mixtures. However, the standard deviation is relatively larger for these quantiles, which means that the risk estimates are less certain. Overall, the results suggest that the effect of metal mixtures on relative risk depends on the level of exposure, with lower exposure levels being associated with a decrease in risk and higher exposure levels being associated with an increase in risk. Table 4: Total effects of metal mixtures relative risk at median exposure Effect Quantile Fraction of Risk Change (in Probit) Standard Deviation 0.25 -0.0030 0.0618 0.3 -0.0027 0.0502 0.4 -0.0065 0.0263 0.5 0.000 0.000 0.6 0.0162 0.0291 0.7 0.0283 0.0623 0.75 0.0285 0.0800 Figure 2 displays the change in heart attack risks at different levels of the mixture exposure to the six metals when compared with each of the three metals at their median value (i.e., 50 th percentile). The risk of having a heart attack showed an increase when all the metals were at their 60 th , 70 th , and 75 th percentile compared to their 50 th percentile, indicating a positive association. Overall, we found a nonlinear relationship between the risk of heart attack and the mixture of heavy metals. Table 5 presents the estimated risk differences and their respective 95% confidence intervals for each metal exposure at different fixed quantiles of the remaining exposures. A positive risk difference indicates an increased risk of heart attack as metal exposure increases, whereas a negative difference indicates a decreased risk. For blood cadmium, there was a small positive risk difference at the 25th, 50th, and 75th quantiles, but the credible intervals included zero, indicating that the estimated risks were not statistically significant. The estimated risk differences for blood lead, blood manganese, blood mercury, and urine manganese were negative but not statistically significant at any quantile, as the credible intervals included zero. The estimated risk differences for urine barium were positive at the 25th quantile but negative at the 50th and 75th quantiles, however there was no statistically significant association between urine barium level and heart attack. For blood selenium, urine cadmium, urine cobalt and urine strontium the estimated risk were positive at all quantiles, but the credible intervals included zero, indicating that the estimated changes were not statistically significant. Figure 3 illustrates a visual representation of these numerical summaries, making it easier to identify the relative contributions of individual exposures to the heart attack. Table 5: Single Exposure Effects Metal Exposure Fixed Quantile of Remaining Exposures Estimated Risk Difference (change) 95% Credible Interval Blood Cadmium 0.25 0.0168 [-0.0315, 0.0621] 0.5 0.0168 [-0.0286, 0.0622] 0.75 0.0072 [-0.0457, 0.0602] Blood Lead 0.25 -0.0012 [-0.0684, 0.0660] 0.5 -0.0104 [-0.0722, 0.0514] 0.75 -0.0085 [-0.0841, 0.0672] Blood Manganese 0.25 -0.0089 [-0.089, 0.0711] 0.5 -0.0150 [-0.0894, 0.0594] 0.75 -0.0194 [-0.1140, 0.0752] Blood Mercury 0.25 -0.0097 [-0.0503, 0.0309] 0.5 -0.0104 [-0.0472, 0.0264] 0.75 -0.0062 [-0.0465, 0.0341] Blood Selenium 0.25 0.0246 [-0.1316, 0.1808] 0.5 0.0474 [-0.0946, 0.1894] 0.75 0.0530 [-0.1304, 0.2364] Urine Barium 0.25 0.0114 [-0.1726, 0.1954] 0.5 -0.0168 [-0.1611, 0.1274] 0.75 -0.0793 [-0.2546, 0.0961] Urine Cadmium 0.25 0.0598 [-0.0206, 0.1402] 0.50 0.0462 [-0.0263, 0.1187] 0.75 0.0063 [-0.0766, 0.0891] Urine Cobalt 0.25 0.0112 [-0.0437, 0.0662] 0.5 0.0076 [-0.0433, 0.0585] 0.75 0.0003 [-0.0538, 0.0544] Urine Manganese 0.25 -0.0036 [-0.0261, 0.0190] 0.5 -0.0039 [-0.0254, 0.0176] 0.75 -0.0033 [-0.0262, 0.0195] Urine Strontium 0.25 0.0532 [-0.1665, 0.2728] 0.5 0.0126 [-0.1576, 0.1827] 0.75 -0.1055 [-0.3275, 0.1165] Urine Tin 0.25 -0.0003 [-0.0271, 0.0264] 0.5 0.0001 [-0.0242, 0.0243] 0.75 -0.0013 [-0.0268, 0.0243] Urine Tungsten 0.25 0.0022 [-0.0333, 0.0377] 0.5 -0.0016 [-0.034, 0.0307] 0.75 -0.0075 [-0.0413, 0.0263] We also compared the health risks of each metal exposure when all the other exposures are fixed to their 75th percentile to when all of the other exposures are fixed to their 25th percentile. The analysis results showed no significant evidence of interactions between metal exposures and the outcome. The credible intervals for all metal exposures included zero, indicating that the estimated interaction effects were not statistically significant. These findings are presented in Figure 4, which provides a visual representation of the interaction effects between the metal exposures. Discussion The research investigates the association between heavy metal exposure and the likelihood of having a heart attack. Males, people over 60 years old, those with high blood pressure and cholesterol, diabetes, asthma, poor kidney function, smokers, and heavy drinkers have an increased risk of having a heart attack. The analyses show that people who have had a heart attack have greater blood levels of cadmium and lead, as well as higher urine levels of cadmium, cobalt, and tin. Males, patients over the age of 40, and those with diabetes, high blood pressure, and high blood cholesterol are more likely to suffer a heart attack, according to the univariate survey logistic regression analysis. The study also investigates the combined effect of heavy metals on heart attack using Bayesian Kernel Machine Regression. The findings suggest that exposure to lead, cadmium, and tin is associated to an increased risk of heart attack. Most of the past research has looked at the association between individual heavy metals and CVD. To our knowledge, this is the first study to investigate the association between the mixture of heavy metals from both blood and urine and the prevalence of heart attacks. Results of this study confirmed the primary hypothesis that exposure to a mixture of blood and urine heavy metals was significantly associated with a heart attack. Previous research has identified links between heavy metals exposure and heart attacks. Generally, just one single metal was included in these experiments, making the results easy to understand. However, to better represent real-life exposures, we must include a variety of heavy metal exposures as well as their complicated, nonlinear relationships [ 25 ]. Ignoring the combined impact of other metals may result in misleading positive or false negative results [ 26 ]. Nonetheless, caution must also be exercised when including all the metals of interest in a single multivariate regression model because this may lead to result distortion [ 27 , 28 ]. The approach of the multivariate survey logistic model presented here aimed to mindfully include multiple metals in the analysis to build models that more accurately represented real-life exposures. The BKMR model was developed to analyze the effects of exposure mixtures on health. Our findings contribute to the knowledge of how a mixture of heavy metals influences the risk of heart attack. This approach allows for the examination of the overall mixture effect as well as the impact of each mixture component in the context of the overall joint exposure. By applying hierarchical variable selection, the approach may be able to identify the most critical windows of susceptibility while allowing for highly correlated exposures. Finally, when estimating a high-dimensional collection of exposures, a Bayesian method is used to account for uncertainty. Using BKMR in this study population we predicted a joint effect of blood and urine heavy metals on the heart attack that showed a significant decline when all metals were at the lower percentile (e.g., 25th percentile) and an increase when all metals were at the higher percentile (e.g., 70th percentile) compared to the 50th percentile increment, with the highest risk noted at the 60th percentile. We also examined the interaction effect of social stressors (gender, age, household income, and education status) and metal mixtures, but no significant interactions were found (data not shown). A population-based study from Spain [ 16 ] found that increased levels of urine Cu, Zn, Sb, Cd, Cr, and V individually and as a mixture was associated with increased risk of any fatal or non-fatal cardiovascular incidents that collectively fall under the International Classification of Diseases 10th Revision (ICD-10) codes I00-I78, and BKMR analysis indicated that Cd and Sb were the main drivers of the association when considering the metals as a mixture. Another study of NHANES data from 1999–2014 found that heavy metal mixtures measured in blood and urine increased the odds of CVD-related death [ 8 ]. While studies investigating the effect of metal mixtures on cardiovascular disease outcomes are limited, many studies have highlighted the effects of individual heavy metals detected in blood and urine on CVD. For instance, one study found that higher levels of blood or urine cadmium increased the risk of stroke and heart failure [ 29 ]. Two studies among Pakistani myocardial infarction patients found that these patients had higher levels of blood mercury and urine manganese compared to healthy age-matched reference patients [ 30 , 31 ]. Another study of NHANES data from 1999–2006 found that higher levels of blood cadmium or cobalt were associated with higher odds of cardiovascular disease [ 4 ]. While many studies indicate an association between heavy metal exposure and cardiovascular disease, the mechanisms driving this association are still unclear. Research suggests that exposure to heavy metals or metal mixtures lead to increased oxidative stress, inflammation, and cardiac cell death [ 32 , 33 ], a biological process that could explain the increased risk of cardiovascular disease associated with these exposures. Our research provided helpful insights, but it is important to acknowledge its limitations. We did not collect samples prior to the onset of the diseases, making it difficult to identify the exact time of exposure. However, we made use of convenient samples that were available to us and attempted to extract as much information as possible from them. Although these samples may not be a direct indicator of prior exposure levels, we believe that the measurements we obtained reflect a relatively stable exposure situation that is not likely to have changed significantly. Another limitation of the study is that because it is cross-sectional, it is difficult to make causal assumptions. Another disadvantage is the likelihood of misclassification bias due to self-reporting (e.g., the outcome variable, myocardial infarction), a method that is vulnerable to variable degrees of inaccuracy Despite this, there are several beneficial aspects to the study. This is a population-based study that used data from the National Health and Nutrition Examination Survey (NHANES), which obtains high-quality data while adhering to strict criteria requirements to minimize errors. One other limitation of our study was the number of iterations was low due to the lack of computing power. Nonetheless, BKMR has the advantage of not only addressing the mixture effect but also of being able to extract the contributions of each component, with the caveat that these contributions are in the context of joint exposure at the exposure levels reported in the cohort. Finally, by using the BKMR method, we were able to overcome significant drawbacks of conventional analytic pathways, such as single metal effect estimate, model misspecification, and increased false discovery when fitting multiple regression models. Conclusions In conclusion, exposure to the mixture of heavy metals considered in this study was associated with an increased risk of a heart attack. Exposed adults aged 20 and above had a greater likelihood of heart attack related to levels of a heavy metal combination. When evaluating the entire mixture, lead, and selenium in the blood and cadmium, manganese, tin and strontium in the urine were found to be the most significant exposures associated with heart attacks. The BKMR model presented in this study can be used to explore new types of exposure in future studies, with the potential of yielding enhanced knowledge of how the environment as a whole influences’ health and disease in different population settings. While the current study found a significant association between exposure to mixtures of heavy metals and heart attack, additional studies using larger cohorts are needed to estimate the effects of heavy metal mixtures on a heart attack at exposure levels that are relevant for general populations. We also suggest using different approaches and interpreting their results together to draw more robust conclusions. Declarations Data Availability Data will be available upon contacting corresponding author. Conflicts of Interest The authors declare no conflict of interest. Funding Statement This work is supported by The National Heart Lung and Blood Institute [Grant number: K01 HL146944] Acknowledgments We would like to acknowledge that an earlier version of this manuscript was presented as a Speed presentation at the JSM 2022 Conference held at Washington, DC, USA on August 6, 2022 - August 11, 2022. The abstract of the presentation can be found at [https://ww2.amstat.org/meetings/jsm/2022/onlineprogram/AbstractDetails.cfm?abstractid=322512] All co-authors have meaningfully contributed to the production of this manuscript including funding, conception, data analysis, edition, revision, and final approval of the manuscript. References Ibrahimou B, Azim SI and Sun N. Interaction between blood lead level and chronic obstructive pulmonary disease (COPD) on risk of heart attack or stroke: USA NHANES, 2013–2014. Pulmonary Pharmacology & Therapeutics 2019; 58: 101805. Greenfield DM and Snowden JA. Cardiovascular Diseases and Metabolic Syndrome. In: Carreras E, Dufour C, Mohty M, et al. (eds) The EBMT Handbook: Hematopoietic Stem Cell Transplantation and Cellular Therapies . Cham (CH): Springer Copyright 2019, EBMT and the Author(s). 2019, pp.415-420. Alissa EM and Ferns GA. Heavy metal poisoning and cardiovascular disease. Journal of toxicology 2011; 2011. 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Levels of a mixture of heavy metals in blood and urine and all-cause, cardiovascular disease and cancer mortality: A population-based cohort study. Environmental Pollution 2020; 263: 114630. Yang G, Sun T, Han Y-Y, et al. Serum cadmium and lead, current wheeze, and lung function in a nationwide study of adults in the United States. The Journal of Allergy and Clinical Immunology: In Practice 2019; 7: 2653-2660. Liu Y, Zhou Y, Hnizdo E, et al. Total and cause-specific mortality risk associated with low-level exposure to crystalline silica: a 44-year cohort study from China. American Journal of Epidemiology 2017; 186: 481-490. Mandel JH, Alexander BH and Ramachandran G. A review of mortality associated with elongate mineral particle (EMP) exposure in occupational epidemiology studies of gold, talc, and taconite mining. American Journal of Industrial Medicine 2016; 59: 1047-1060. Gil F, Capitán-Vallvey LF, De Santiago E, et al. Heavy metal concentrations in the general population of Andalusia, South of Spain: a comparison with the population within the area of influence of Aznalcóllar mine spill (SW Spain). Science of the Total Environment 2006; 372: 49-57. Tellez-Plaza M, Navas-Acien A, Menke A, et al. Cadmium exposure and all-cause and cardiovascular mortality in the US general population. Environmental health perspectives 2012; 120: 1017-1022. Bloom AJ. Metal regulation of metabolism. Current opinion in chemical biology 2019; 49: 33-38. Wones RG, Stadler BL and Frohman LA. Lack of effect of drinking water barium on cardiovascular risk factors. Environmental health perspectives 1990; 85: 355-359. Domingo-Relloso A, Grau-Perez M, Briongos-Figuero L, et al. The association of urine metals and metal mixtures with cardiovascular incidence in an adult population from Spain: the Hortega Follow-Up Study. International journal of epidemiology 2019; 48: 1839-1849. Feng J, Gao Y, Ji Y, et al. Quantifying the interactions among metal mixtures in toxicodynamic process with generalized linear model. Journal of Hazardous Materials 2018; 345: 97-106. Dunson DB. Bayesian latent variable models for clustered mixed outcomes. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2000; 62: 355-366. Billionnet C, Sherrill D and Annesi-Maesano I. Estimating the health effects of exposure to multi-pollutant mixture. Annals of epidemiology 2012; 22: 126-141. Bobb JF, Valeri L, Claus Henn B, et al. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 2015; 16: 493-508. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, https://wwwn.cdc.gov/nchs/nhanes/ (2022). Team RC. R: A language and environment for statistical computing. Retrieved from. R Foundation for Statistical Computing, Vienna, Austria. 2017. Bobb JF and Bobb MJF. Package ‘bkmr’. Bayesian Kernel Machine Regression R package version 02 0 2017. Bobb JF, Claus Henn B, Valeri L, et al. Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environmental Health 2018; 17: 1-10. Kim S, Kim S, Won S, et al. Considering common sources of exposure in association studies-Urinary benzophenone-3 and DEHP metabolites are associated with altered thyroid hormone balance in the NHANES 2007–2008. Environment International 2017; 107: 25-32. Czarnota J, Gennings C, Colt JS, et al. Analysis of environmental chemical mixtures and non-Hodgkin lymphoma risk in the NCI-SEER NHL study. Environmental Health Perspectives 2015; 123: 965-970. Marill KA. Advanced statistics: linear regression, part II: multiple linear regression. Academic emergency medicine 2004; 11: 94-102. Zhang Y, Dong T, Hu W, et al. Association between exposure to a mixture of phenols, pesticides, and phthalates and obesity: comparison of three statistical models. Environment international 2019; 123: 325-336. Peters JL, Perlstein TS, Perry MJ, et al. Cadmium exposure in association with history of stroke and heart failure. Environmental research 2010; 110: 199-206. Afridi HI, Kazi TG, Kazi N, et al. Chromium and manganese levels in biological samples of Pakistani myocardial infarction patients at different stages as related to controls. Biological trace element research 2011; 142: 259-273. Afridi HI, Kazi TG, Talpur FN, et al. Interaction between selenium and mercury in biological samples of Pakistani myocardial infarction patients at different stages as related to controls. Biological trace element research 2014; 158: 143-151. Zhang Y, Ji X, Ku T, et al. Heavy metals bound to fine particulate matter from northern China induce season-dependent health risks: a study based on myocardial toxicity. Environmental Pollution 2016; 216: 380-390. Paithankar JG, Saini S, Dwivedi S, et al. Heavy metal associated health hazards: An interplay of oxidative stress and signal transduction. Chemosphere 2021; 262: 128350. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4456611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305166971,"identity":"5a1c97ca-e1ad-43f3-a34c-9bf3db5b83ad","order_by":0,"name":"Boubakari 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Metals\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4456611/v1/b4b3ab7e75693ddec8482436.png"},{"id":58590075,"identity":"99340a06-9ebe-4ad2-ade8-8e3c95753d5d","added_by":"auto","created_at":"2024-06-18 15:08:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18278,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Risk Summaries\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4456611/v1/a3424f369c1a07e41a33c54d.png"},{"id":58590077,"identity":"56713489-3393-4b31-9331-71cf42025a83","added_by":"auto","created_at":"2024-06-18 15:08:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45238,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual Exposures Effect on the Heart Attack\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4456611/v1/2378ead750f5adb9c825d5cf.png"},{"id":58590076,"identity":"85fdc01b-592e-4ac7-aac5-6924079865e9","added_by":"auto","created_at":"2024-06-18 15:08:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39920,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction Effect between the Metals\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4456611/v1/71791b94865f24188e73dfd2.png"},{"id":58591275,"identity":"dfe3e9b0-b27f-4e8c-a180-169050dcb201","added_by":"auto","created_at":"2024-06-18 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Despite recent breakthroughs in therapy, CVD remains the leading cause of death in the developed world, accounting for about one million deaths annually in the United States alone. About 17.7\u0026nbsp;million people died from CVDs globally in 2015, representing 31% of all deaths in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. By 2030, over 23.6\u0026nbsp;million people will have died from CVDs, primarily heart disease and stroke. These are expected to be the primary causes of death for the foreseeable future [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Traditional CVD risk factors aren't responsible for all deaths. Environmental, nutritional, and lifestyle factors appear to be crucial in explaining the dramatic recent changes in the prevalence, with the potential of widespread public health implications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent research has shown that heavy metal exposure is related to an increased risk of cardiovascular diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeavy metals enter the human body through multiple routes including food, drinking water, and breathing. Heavy metals include toxic metals such as arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg), as well as vital trace elements such as chromium (Cr), cobalt (Co), copper (Cu), magnesium (Mg), manganese (Mn), nickel (Ni), selenium (Se) and zinc (Zn) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Multiple heavy metal exposures can have additive, synergistic, antagonistic, or other effects on human health [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], however, most studies of heavy metals focus on single metal [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, many earlier studies on heavy metals\u0026rsquo; negative effects tended to focus on\u003c/p\u003e \u003cp\u003eoccupational exposure alone [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Heavy metal workers are exposed to higher amounts, while the general public in the United States is exposed to lower levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Low dosages of heavy metals produce epidemiological outcomes that are more in line with actual ambient exposure levels, and their exposure has been found in studies to be hazardous to the population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Biologically active metals do, therefore, have a role across a range of physiological and pathological processes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence of the involvement of environmental exposure to heavy metals in CVD risk has quickly increased during the past two decades. Recent research points to evidence associating heavy metal exposure in the environment with an amplified risk for diabetes and hypertension, two major risk factors for CVD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Higher amounts of barium in drinking water have been linked to increased cardiovascular mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A Spanish study found that urine Cu, Zn, antimony (Sb), Cd, Cr, and vanadium (V) levels were all independently related to an elevated risk of cardiovascular diseases. Urine metals were similarly linked to an increased risk of cardiovascular diseases, with Cd and Sb being the most significant components [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, heavy metals were found to interact with other diseases leading to CVD. An interaction between blood Cd and chronic bronchitis was reported to be associated with myocardial infarction, and interaction between blood Pb level and chronic obstructive pulmonary disease (COPD) was associated with a heart attack or stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, most of these studies only examined the exposure effects of one heavy metal at a time.\u003c/p\u003e \u003cp\u003eEstimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology and to regulatory agencies as humans are frequently exposed to many metals throughout their lifetimes. Using the weighted quantile sum (WQS) model, Duan et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] investigated the correlations between a heavy metal combination and the risks of all-cause, CVD-related, and cancer-related death. Methods have been proposed for joint modelling of the data. Dunson [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] presented a new class of latent variables for grouping mixed outcome data. Nonetheless, several issues must be addressed in order to accurately quantify the health impacts of these multi-pollutant combinations. While current techniques for investigating mixtures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] address some of these difficulties, they also have significant drawbacks. Bobb et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] developed Bayesian kernel machine regression (BKMR) as a novel way to study mixtures, in which the health outcome is regressed on a flexible function of the mixture's components (e.g., air pollution or hazardous waste) that is described using a kernel function. A unique hierarchical variable selection strategy is used in high-dimensional situations to find essential mixture components and accounts for the associated structure of the mixture. This BKMR model is used in the current study to identify blood and urine heavy metals and heavy metal mixtures that may be associated with heart attacks.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe data for this study comes from the National Health and Nutrition Examination Survey (NHANES) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] covering the period from 2011 through 2016. It has been an ongoing national, population-based cross-sectional survey of the US population since the 1980s. We used Demographics Data, Medical Conditions, Laboratory Data, and Questionnaire Data in this analysis. Except for the levels of heavy metal, all variables were self-reported. We only considered participants who were 20 years or older. We also excluded subjects with missing data. The final sample contained 2972 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of diseases\u003c/h2\u003e \u003cp\u003eThe survey question \"Has a doctor or other health professional ever told you that you had a heart attack?\" from the Medical Conditions data set was used to determine the study's main outcome: the occurrence of a heart attack. Some frequent comorbidities, such as high blood cholesterol, high blood pressure, poor kidney function, diabetes, and asthma, were also included as covariates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These variables were determined by the question \u0026ldquo;Has a doctor or other health professional ever told you that you had high blood pressure/high cholesterol/weak or failing kidney/diabetes/asthma?\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of Heavy Metals\u003c/h2\u003e \u003cp\u003eBlood lead, cadmium, mercury, and manganese level were all extracted from a lab data set called the lead, cadmium, total mercury, selenium, and manganese-blood. The Metals \u0026ndash; Urine dataset of Laboratory Data was used to collect urine cobalt and barium information. More details, information measurement procedures, and quality control processes can be found on NHANES 2011\u0026ndash;2012, 2013\u0026ndash;2014, and 2015\u0026ndash;2016 Data Documentation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of smoking and alcohol intake\u003c/h2\u003e \u003cp\u003eThe Smoking-Cigarette Usage dataset was utilized to determine smoking status with the queries \"Smoked at least 100 cigarettes in your life?\" and \"Do you now smoke cigarettes?\". Smokers were recoded as never smokers, former smokers, and current smokers. Alcohol was a continuous variable that showed the average number of alcoholic beverages drank per week by people in the preceding year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of other covariates\u003c/h2\u003e \u003cp\u003eGender, Age, Marital Status, Household Income, Race, Educational Level, and Body Mass Index (BMI) were obtained from the NHANES Demographic, Examination, and Questionnaire data sets [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on earlier research [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] cut points were appropriately chosen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eR statistical software, version 4.1.1 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], was used for all analyses. To account for the complex, multistage survey design, a SURVEYLOGISTIC procedure was applied. We incorporated the weight, stratum, and cluster variables from NHANES data in the procedure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Using survey logistic techniques, these three variables were included in the univariate models. to account for design features. The goal of the univariate logistic model investigation was to include multiple metals in the analysis to create models that more accurately represent real-life exposures. The chi-square test was performed to examine the relationship between heart attack history and categorical variables. The t-test was performed to compare the equality of means in continuous variables between groups with and without a heart attack. Pearson correlation test was used to find out the correlation between the metals. Adjusted Odds Ratios and 95% credible intervals (CI) were obtained from the univariate logistic regression and Bayesian kernel machine regression analysis respectively.\u003c/p\u003e \u003cp\u003eThe \u0026lsquo;BKMR' package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] that implements Bayesian Kernel Machine Regression in R was used to see if there were any significant associations between the mixture of heavy metal levels and heart attack status. The BKMR model, a non-parametric Bayesian variable selection framework was used to evaluate the mixture effect of metals on heart attack. BKMR combines Bayesian and statistical learning methods to regress an exposure\u0026ndash;response function iteratively by a Gaussian kernel function. BKMR can identify nonlinear and non-additive relationships within metals. In the current study, the outcome of interest (Y\u0026thinsp;=\u0026thinsp;1) is heart attack (is binary), and the exposure variables z are blood lead, cadmium, mercury, and manganese and urine cobalt and barium, we used the following probit BKMR model.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${{\\Phi }}^{-1}\\left({{\\mu }}_{\\text{i}}\\right)=\\text{h}\\left({\\text{z}}_{\\text{i}1}, {\\text{z}}_{\\text{i}2}, \\dots , {\\text{z}}_{\\text{i}\\text{M}}\\right)+ {{\\text{x}}_{\\text{i}}}^{{\\prime }}{\\beta }$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Phi }\\)\u003c/span\u003e\u003c/span\u003e is the cumulative distribution function (CDF) for the standard normal distribution (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Phi }}^{-1}\\)\u003c/span\u003e\u003c/span\u003e is the probit link function) and \u0026micro;\u003csub\u003ei\u003c/sub\u003e is the probability that Y\u003csub\u003ei\u003c/sub\u003e equals 1. The h (\u0026sdot;) is an exposure-response function that flexibly models the relationship between the exposures to multiple metals z\u003csub\u003e1\u003c/sub\u003e\u0026hellip;z\u003csub\u003eM\u003c/sub\u003e, and the probit of the probability of a heart attack (Y\u0026thinsp;=\u0026thinsp;1). The x is a vector of non-exposure covariates with a linear or non-linear relationship with the outcome, and β is a vector of respective coefficients of x [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnder BKMR, the kernel function used to represent h has several options. In this section, we concentrate on the Gaussian kernel, which captures a wide range of underlying functional forms for h and can be expressed as\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$K\\left(z,{z}^{{\\prime }}\\right)=\\text{e}\\text{x}\\text{p}\\left\\{-\\sum _{m=1}^{M}{r}_{m}{({z}_{m}- {{z}^{{\\prime }}}_{m})}^{2}\\right\\}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this case, both z and z\u0026prime; represent a vector of exposure variables for two different individuals. In the present context, M is the number of metals, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{m}\\ge 0\\)\u003c/span\u003e\u003c/span\u003eis the tuning parameter for the smoothness of h. With this kernel function, it is assumed that similar exposure profiles will have similar health effects. In the current study, this means two individuals with similar blood and urine heavy metal exposures will have a similar risk of a heart attack. To estimate h(z) at a certain exposure vector z, the posterior distribution of h is assumed to be normally distributed, with a posterior mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{h}\\left(\\theta \\right)\\)\u003c/span\u003e\u003c/span\u003eand variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{h}\\left(\\theta \\right)\\)\u003c/span\u003e\u003c/span\u003e, which depends on the model parameters denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe probit model above can be expressed using a latent normal random variable formulation as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${{Y}^{*}}_{i}=h\\left({z}_{i1}, {z}_{i2}, \\dots , {z}_{iM}\\right)+ {{x}^{{\\prime }}}_{i}\\beta +{e}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ee\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e assumes standard normal distribution and\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${Y}_{i}=I\\left({{Y}^{*}}_{i}\u0026gt;0\\right)=\\left\\{\\begin{array}{c}1 if {{Y}^{*}}_{i}\u0026gt;0 \\\\ 0 otherwise\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Markov chain Monte Carlo (MCMC) algorithm is implemented to fit the BKMR model and allows for customization of the model with options such as continuous or binary outcomes, random or non-random intercepts, component wise variable selection, and hierarchical variable selection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The model fit for the current study used binary outcomes, non-random intercepts, component wise variable selection, and the MCMC algorithm. The BKMR model is not able to accommodate sample weights yet, and thus we used unweighted estimation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe R software and the required packages can be obtained from the CRAN website [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. More details regarding the \u0026lsquo;bkmr\u0026rsquo; package code and usage can be found in Bobb [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Bobb et al [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 shows the general features of our study population. Of the 2972 participants included in the study, 89 (3%) suffered from a heart attack. The prevalence of heart attack was higher in groups older than 60, men, who had high blood pressure, and high blood cholesterol and suffered from other comorbidities such as diabetes, asthma, and weak kidney functions. Smokers (including ex-smokers and current smokers) and heavy drinkers were more likely to have a heart attack (p-value \u0026lt;0.001). There were significantly different mean levels of heavy metals in the blood and urine of the heart attack group compared to the non-heart attack group (p-value \u0026lt;0.001). Those who had a heart attack exhibited higher mean levels of cadmium and lead in the blood as well as higher mean levels of cadmium, cobalt, and tin in the urine compared to those who did not report a heart attack. Conversely, the heart attack group had lower mean levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline Characteristics from NHANES 2011\u0026ndash;2016, of Adults More Than 20 Years Old\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003eHeart Attack = No\u003cbr\u003e\u0026nbsp;(N=2883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003eHeart Attack = Yes\u003cbr\u003e\u0026nbsp;(N=89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003eOverall\u003cbr\u003e\u0026nbsp;(N=2972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1357 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e26 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1383 (46.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1526 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e63 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1589 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e20 \u0026ndash; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1191 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e5 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1196 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e40 \u0026ndash; 59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e982 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e19 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1001 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e60 \u0026ndash; 74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e561 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e43 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e604 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge; 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e149 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e22 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e171 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e660 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e12 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e672 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1428 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e44 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1472 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e135 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e13 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e148 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e303 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e11 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e314 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e78 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e5 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e83 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eLiving with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e279 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e283 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u0026lt; USD 20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e490 (17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e33 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e523 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge; USD 20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2393 (83.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e56 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2449 (82.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e371 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e7 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e378 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e281 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e9 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e290 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1234 (42.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e45 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1279 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e603 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e23 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e626 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e394 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e5 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e399 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\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 width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u0026lt; High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e463 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e22 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e485 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e596 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e27 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e623 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1824 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e40 (44.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1864 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Blood Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1956 (67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e25 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1981 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e927 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e64 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e991 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1927 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e30 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1957 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e956 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e59 (66.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1015 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2533 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e53 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2586 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e280 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e35 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e315 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e70 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e71 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2815 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e81 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2896 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e68 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e76 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u0026lt; 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e42 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e43 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e18.5 \u0026ndash; 24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e809 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e25 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e834 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e25 \u0026ndash; 29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e934 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e26 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e960 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e30+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1098 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e37 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1135 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.02 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2.76 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.02 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1533 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e26 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1559 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e632 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e32 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e664 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e718 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e31 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e749 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2426 (84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e63 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2489 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e457 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e26 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e483 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood lead (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e13.5 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e21.1 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e13.7 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e9.90 [1.10, 337]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e15.1 [4.00, 246]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e10.1 [1.10, 337]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood cadmium (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.480 (0.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.741 (0.728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.487 (0.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.290 [0.070, 7.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.510 [0.070, 4.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.300 [0.070, 7.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood mercury (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.62 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.38 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.61 (2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.860 [0.110, 50.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.940 [0.200, 9.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.860 [0.110, 50.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood manganese (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e9.78 (3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e9.53 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e9.77 (3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e9.17 [1.88, 56.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8.87 [4.55, 23.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e9.16 [1.88, 56.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood selenium (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e196 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e191 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e196 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e195 [106, 391]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e192 [120, 260]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e195 [106, 391]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine cadmium (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.315 (0.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.500 (0.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.320 (0.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.181 [0.025, 6.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.386 [0.041, 1.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.186 [0.025, 6.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine Lead (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e5.52 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e6.70 (6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e5.56 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.60 [0.200, 350]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e4.40 [0.500, 39.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.60 [0.200, 350]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine cobalt (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.503 (0.897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.680 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.508 (0.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.354 [0.016, 33.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.358 [0.032, 9.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.355 [0.016, 33.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine manganese (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.152 (0.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.149 (0.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.151 (0.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0920 [0.057, 18.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0920 [0.057, 2.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0920 [0.057, 18.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine barium (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.78 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.53 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.77 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 [0.042, 87.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.900 [0.060, 9.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 [0.042, 87.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine tin (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.23 (4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2.51 (9.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1.27 (4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.440 [0.064, 89.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.770 [0.064, 91.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.450 [0.064, 91.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine tungsten (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.121 (0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.115 (0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0.121 (0.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0580 [0.013, 32.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0770 [0.013, 1.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.0590 [0.013, 32.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine strontium (ug/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e120 (120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e108 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\"\u003e\n \u003cp\u003e119 (119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eMedian [Min, Max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e90.2 [1.77, 3220]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e78.3 [5.6, 542]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e89.8 [1.77, 3220]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.166666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;At the beginning of our analysis, we intended to include all metal exposures in our dataset in order to evaluate their associations with heart attack. However, during our initial assessment, we found that blood and urine lead were significantly associated (Pearson correlation coefficient of 0.78) as shown in figure 1. As a result of the potential of multicollinearity, we decided not to include urine lead in our study. Multicollinearity occurs when independent variables in a regression model have a strong correlation, which can lead to unstable or inaccurate estimates of their individual effects on the outcome variable. In this case, including both blood and urine lead in the analysis could have made it difficult to determine the independent effects of each exposure on the health outcome, as they would be highly correlated with each other. We aimed to reduce the risk of multicollinearity and obtain more reliable estimates of the associations between the remaining metal exposures and the heart attack by excluding urine lead from the analysis [1,5,15,16].\u003c/p\u003e\n\u003cp\u003eThe results of the univariate survey logistic regression analysis are presented in table 2. Looking at individual heavy metal exposures, we observed that the odds of heart attack increased by about 10% in patients for every 1 \u0026micro;/L increase in the average level of blood lead (OR = 1.014, CI = [1.005 \u0026ndash; 1.022]) and by 91% for every 1 \u0026micro;/L increase in the average level of blood cadmium (OR = 1.911, CI = [1.413 - 2.584). Several patient characteristics were associated with increased odds of heart attack. Males had 2.46 times the odds of heart attack compared to females (OR = 2.463, CI = [1.349 - 4.496]). Individuals in the 40-59 year (OR = 5.095, CI = [1.349 - 19.253]), 60-74 year (OR = 23.839, CI = [8.891- 72.022]), and \u0026ge;75 year (OR = 36.295, CI = [10.575 - 12.457]) age groups also had significantly greater odds of heart attack compared to those in the 20-39 year age group. Compared to those who were married, patients who were widowed (OR = 3.039, CI = [1.578, 5.852]) had greater odds of a heart attack. Patients with diabetes (OR = 8.687, CI = [4.389 - 17.195]), high blood pressure (OR = 6.08, CI = [3.659, 10.101]), and high blood cholesterol (OR = 6.686, CI = [3.641, 12.279]) had increased odds of having a heart attack. Current and former smokers had higher odds of heart attack compared to those who never smoked (OR = 2.797, CI = [1.440, 5.436]; OR = 1.929, CI = [0.932, 3.994] respectively). By contrast, other patient characteristics were associated with lower odds of a heart attack. For example, Mexican American (OR = 0.36, CI = [0.135, 0.959]) patients had lower odds of heart attack compared to non-Hispanic White patients. Patients whose household income was greater than or equal to $20,000 also had lower odds of heart attack than patients whose household income was less than $20,000 (OR = 0.475, CI = [0.314, 0.718]). Similarly, patients with more than a high school education (OR = 0.383, CI = [0.180, 0.810]) had lower odds of having a heart attack than patients with less than high school education.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Odds Ratio of Univariate Model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood lead level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.005, 1.022]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood cadmium level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.413, 2.584]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood mercury level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.926, 1.077]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood manganese level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.936, 1.109]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood selenium level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.979, 1.006]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine cadmium level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.298, 2.758]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine cobalt level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.892, 1.848]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine manganese level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e[0.230, 2.228]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine barium level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.955, 1.073]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine tin level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.010, 1.058]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine tungsten level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.882, 1.169]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine strontium level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.999, 1.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e2.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.349, 4.496]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e20 -39\u003c/p\u003e\n \u003cp\u003e40 \u0026ndash; 59\u003c/p\u003e\n \u003cp\u003e60 - 74\u003c/p\u003e\n \u003cp\u003e\u0026ge; 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e5.095\u003c/p\u003e\n \u003cp\u003e23.839\u003c/p\u003e\n \u003cp\u003e36.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.349, 19.253]\u003c/p\u003e\n \u003cp\u003e[8.891, 72.022]\u003c/p\u003e\n \u003cp\u003e[10.575, 12.457]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003cp\u003eLiving with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003cp\u003e3.039\u003c/p\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003cp\u003e3.134\u003c/p\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.206, 1.224]\u003c/p\u003e\n \u003cp\u003e[1.578, 5.852]\u003c/p\u003e\n \u003cp\u003e[0.236, 1.343]\u003c/p\u003e\n \u003cp\u003e[0.912, 10.771]\u003c/p\u003e\n \u003cp\u003e[0.117, 2.364]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; USD 20,000\u003c/p\u003e\n \u003cp\u003e\u0026ge; USD 20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.314, 0.718]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003cp\u003e1.282\u003c/p\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.282, 1.466]\u003c/p\u003e\n \u003cp\u003e[0.135, 0.959]\u003c/p\u003e\n \u003cp\u003e[0.738, 2.228]\u003c/p\u003e\n \u003cp\u003e[0.161, 3.175]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; High School\u003c/p\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003cp\u003e\u0026gt; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003e[0.263, 1.179]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e[0.180, 0.810]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e8.687\u003c/p\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[4.389, 17.195]\u003c/p\u003e\n \u003cp\u003e[0.020, 1.269]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Blood Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e6.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[3.659, 10.101]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e6.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[3.641, 12.279]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; 18.5\u003c/p\u003e\n \u003cp\u003e18.5 \u0026ndash; 24.9\u003c/p\u003e\n \u003cp\u003e25 \u0026ndash; 29.9\u003c/p\u003e\n \u003cp\u003e30+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003cp\u003e1.571\u003c/p\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.166, 11.879]\u003c/p\u003e\n \u003cp\u003e[0.182, 13.561]\u003c/p\u003e\n \u003cp\u003e[0.206, 14.483]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.996, 1.005]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e2.797\u003c/p\u003e\n \u003cp\u003e1.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.440, 5.436]\u003c/p\u003e\n \u003cp\u003e[0.932, 3.994]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKidney Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e2.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.013, 6.591]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.49180327868852%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.868852459016395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003cp\u003e2.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.639344262295083%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.160, 4.147]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUsing Bayesian Kernel Machine Regression implemented in the R \u0026lsquo;bkmr package [23], which is a statistical approach for estimating the joint health effects of multiple concurrent exposures, we investigated the mixture effect of heavy metals on heart attack.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe fitted the BKMR model to evaluate how the joint effect of metal exposures impacts heart attack risk. Table 3 summarizes the posterior inclusion probability (PIP) derived from the model, which measures variable importance. PIP is a ranking measure that indicates how strongly the data supports the inclusion of a variable in the regression. For example, a PIP value of 0.5104 for Lead in the Blood exposure column indicates a 51.04% possibility that Lead exposure is associated with an increased risk of heart attack. Similarly, the PIP value of 0.7872 for Cadmium in the Urine exposure column indicates that there is a 78.72% possibility that Cadmium exposure is associated to an increased risk of heart attack.\u003c/p\u003e\n\u003cp\u003eTable 3: Posterior Inclusion Probability (PIP)\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cadmium\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mercury\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Manganese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Selenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5104\u003c/p\u003e\n \u003cp\u003e0.4088\u003c/p\u003e\n \u003cp\u003e0.4376\u003c/p\u003e\n \u003cp\u003e0.3280\u003c/p\u003e\n \u003cp\u003e0.5200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cadmium\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Cobalt\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Manganese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Barium\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tungsten\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Strontium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.7872\u003c/p\u003e\n \u003cp\u003e0.4304\u003c/p\u003e\n \u003cp\u003e0.5272\u003c/p\u003e\n \u003cp\u003e0.4880\u003c/p\u003e\n \u003cp\u003e0.5208\u003c/p\u003e\n \u003cp\u003e0.4304\u003c/p\u003e\n \u003cp\u003e0.5504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNext, we estimated the Cumulative effects of metal mixtures on heart attack as the mixture exposure changes in the index (or linear predictor) h(z) (table 4). Changes in h are the results of combined changes in any components of the metal mixture. Table 4 summarizes the cumulative effect of the mixture exposure h(z) on the risk of a heart attack in comparison with that at the median level of h(z). The \u0026quot;Fraction of Risk Change (in Probit)\u0026quot; column indicates the change in risk relative to the median exposure level, measured in probits (a unit of measurement for standard deviations). The \u0026quot;Standard Deviation\u0026quot; column indicates the uncertainty or variability of the risk estimate.\u003c/p\u003e\n\u003cp\u003eAt the median exposure level (quantile 0.5), the risk change is 0.000, which means that there is no effect of metal mixtures on relative risk at this level of exposure. At the lower exposure quantiles (0.25, 0.3, and 0.4), the risk change is negative, indicating a decrease in risk with increasing metal mixture exposure. For example, the effect for the 0.25 quantile of exposure is -0.0030, indicating a small decrease in risk with increasing exposure to metal mixtures. The corresponding standard deviation of 0.0618 suggests that this effect is not statistically significant. At the higher exposure quantiles (0.6, 0.7, and 0.75), the risk change is positive, indicating an increase in risk with increasing metal mixture exposure. The effect for the 0.75 quantile of exposure is 0.0285, indicating an increase in risk with increasing exposure to metal mixtures. However, the standard deviation is relatively larger for these quantiles, which means that the risk estimates are less certain. Overall, the results suggest that the effect of metal mixtures on relative risk depends on the level of exposure, with lower exposure levels being associated with a decrease in risk and higher exposure levels being associated with an increase in risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Total effects of metal mixtures relative risk at median exposure Effect\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuantile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFraction of Risk Change (in Probit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e-0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e-0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e-0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e0.0283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.36802973977695%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.2453531598513%\"\u003e\n \u003cp\u003e0.0285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.386617100371744%\"\u003e\n \u003cp\u003e0.0800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure 2 displays the change in heart attack risks at different levels of the mixture exposure to the six metals when compared with each of the three metals at their median value (i.e., 50\u003csup\u003eth\u003c/sup\u003e percentile). The risk of having a heart attack showed an increase when all the metals were at their 60\u003csup\u003eth\u003c/sup\u003e, 70\u003csup\u003eth\u003c/sup\u003e, and 75\u003csup\u003eth\u003c/sup\u003e percentile compared to their 50\u003csup\u003eth\u003c/sup\u003e percentile, indicating a positive association. Overall, we found a nonlinear relationship between the risk of heart attack and the mixture of heavy metals.\u003c/p\u003e\n\u003cp\u003eTable 5 presents the estimated risk differences and their respective 95% confidence intervals for each metal exposure at different fixed quantiles of the remaining exposures. A positive risk difference indicates an increased risk of heart attack as metal exposure increases, whereas a negative difference indicates a decreased risk.\u003c/p\u003e\n\u003cp\u003eFor blood cadmium, there was a small positive risk difference at the 25th, 50th, and 75th quantiles, but the credible intervals included zero, indicating that the estimated risks were not statistically significant. The estimated risk differences for blood lead, blood manganese, blood mercury, and urine manganese were negative but not statistically significant at any quantile, as the credible intervals included zero. The estimated risk differences for urine barium were positive at the 25th quantile but negative at the 50th and 75th quantiles, however there was no statistically significant association between urine barium level and heart attack. For blood selenium, urine cadmium, urine cobalt and urine strontium the estimated risk were positive at all quantiles, but the credible intervals included zero, indicating that the estimated changes were not statistically significant. Figure 3 illustrates a visual representation of these numerical summaries, making it easier to identify the relative contributions of individual exposures to the heart attack.\u003c/p\u003e\n\u003cp\u003eTable 5: Single Exposure Effects\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"677\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetal Exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Quantile of Remaining Exposures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Risk Difference (change)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Credible Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood Cadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0315, 0.0621]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0286, 0.0622]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0457, 0.0602]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood Lead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e-0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0684, 0.0660]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0722, 0.0514]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0841, 0.0672]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood Manganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e-0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.089, 0.0711]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0894, 0.0594]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.1140, 0.0752]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood Mercury\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e-0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0503, 0.0309]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0472, 0.0264]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0465, 0.0341]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eBlood Selenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.1316, 0.1808]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0946, 0.1894]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.1304, 0.2364]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Barium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.1726, 0.1954]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.1611, 0.1274]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.2546, 0.0961]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Cadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0206, 0.1402]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0263, 0.1187]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0766, 0.0891]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Cobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0437, 0.0662]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0433, 0.0585]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0538, 0.0544]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Manganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e-0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0261, 0.0190]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0254, 0.0176]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0262, 0.0195]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Strontium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.1665, 0.2728]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.1576, 0.1827]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.3275, 0.1165]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Tin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e-0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0271, 0.0264]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0242, 0.0243]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0268, 0.0243]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.711964549483014%\" rowspan=\"3\"\u003e\n \u003cp\u003eUrine Tungsten\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.270310192023633%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.691285081240768%\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.326440177252586%\"\u003e\n \u003cp\u003e[-0.0333, 0.0377]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.034, 0.0307]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.57918552036199%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.56561085972851%\"\u003e\n \u003cp\u003e-0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.8552036199095%\"\u003e\n \u003cp\u003e[-0.0413, 0.0263]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe also compared the health risks of each metal exposure when all the other exposures are fixed to their 75th percentile to when all of the other exposures are fixed to their 25th percentile. The analysis results showed no significant evidence of interactions between metal exposures and the outcome. The credible intervals for all metal exposures included zero, indicating that the estimated interaction effects were not statistically significant. These findings are presented in Figure 4, which provides a visual representation of the interaction effects between the metal exposures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe research investigates the association between heavy metal exposure and the likelihood of having a heart attack. Males, people over 60 years old, those with high blood pressure and cholesterol, diabetes, asthma, poor kidney function, smokers, and heavy drinkers have an increased risk of having a heart attack. The analyses show that people who have had a heart attack have greater blood levels of cadmium and lead, as well as higher urine levels of cadmium, cobalt, and tin. Males, patients over the age of 40, and those with diabetes, high blood pressure, and high blood cholesterol are more likely to suffer a heart attack, according to the univariate survey logistic regression analysis. The study also investigates the combined effect of heavy metals on heart attack using Bayesian Kernel Machine Regression. The findings suggest that exposure to lead, cadmium, and tin is associated to an increased risk of heart attack.\u003c/p\u003e \u003cp\u003eMost of the past research has looked at the association between individual heavy metals and CVD. To our knowledge, this is the first study to investigate the association between the mixture of heavy metals from both blood and urine and the prevalence of heart attacks. Results of this study confirmed the primary hypothesis that exposure to a mixture of blood and urine heavy metals was significantly associated with a heart attack.\u003c/p\u003e \u003cp\u003ePrevious research has identified links between heavy metals exposure and heart attacks. Generally, just one single metal was included in these experiments, making the results easy to understand. However, to better represent real-life exposures, we must include a variety of heavy metal exposures as well as their complicated, nonlinear relationships [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Ignoring the combined impact of other metals may result in misleading positive or false negative results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nonetheless, caution must also be exercised when including all the metals of interest in a single multivariate regression model because this may lead to result distortion [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The approach of the multivariate survey logistic model presented here aimed to mindfully include multiple metals in the analysis to build models that more accurately represented real-life exposures.\u003c/p\u003e \u003cp\u003eThe BKMR model was developed to analyze the effects of exposure mixtures on health. Our findings contribute to the knowledge of how a mixture of heavy metals influences the risk of heart attack. This approach allows for the examination of the overall mixture effect as well as the impact of each mixture component in the context of the overall joint exposure. By applying hierarchical variable selection, the approach may be able to identify the most critical windows of susceptibility while allowing for highly correlated exposures. Finally, when estimating a high-dimensional collection of exposures, a Bayesian method is used to account for uncertainty. Using BKMR in this study population we predicted a joint effect of blood and urine heavy metals on the heart attack that showed a significant decline when all metals were at the lower percentile (e.g., 25th percentile) and an increase when all metals were at the higher percentile (e.g., 70th percentile) compared to the 50th percentile increment, with the highest risk noted at the 60th percentile. We also examined the interaction effect of social stressors (gender, age, household income, and education status) and metal mixtures, but no significant interactions were found (data not shown).\u003c/p\u003e \u003cp\u003eA population-based study from Spain [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] found that increased levels of urine Cu, Zn, Sb, Cd, Cr, and V individually and as a mixture was associated with increased risk of any fatal or non-fatal cardiovascular incidents that collectively fall under the International Classification of Diseases 10th Revision (ICD-10) codes I00-I78, and BKMR analysis indicated that Cd and Sb were the main drivers of the association when considering the metals as a mixture. Another study of NHANES data from 1999\u0026ndash;2014 found that heavy metal mixtures measured in blood and urine increased the odds of CVD-related death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While studies investigating the effect of metal mixtures on cardiovascular disease outcomes are limited, many studies have highlighted the effects of individual heavy metals detected in blood and urine on CVD. For instance, one study found that higher levels of blood or urine cadmium increased the risk of stroke and heart failure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Two studies among Pakistani myocardial infarction patients found that these patients had higher levels of blood mercury and urine manganese compared to healthy age-matched reference patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Another study of NHANES data from 1999\u0026ndash;2006 found that higher levels of blood cadmium or cobalt were associated with higher odds of cardiovascular disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While many studies indicate an association between heavy metal exposure and cardiovascular disease, the mechanisms driving this association are still unclear. Research suggests that exposure to heavy metals or metal mixtures lead to increased oxidative stress, inflammation, and cardiac cell death [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a biological process that could explain the increased risk of cardiovascular disease associated with these exposures.\u003c/p\u003e \u003cp\u003eOur research provided helpful insights, but it is important to acknowledge its limitations. We did not collect samples prior to the onset of the diseases, making it difficult to identify the exact time of exposure. However, we made use of convenient samples that were available to us and attempted to extract as much information as possible from them. Although these samples may not be a direct indicator of prior exposure levels, we believe that the measurements we obtained reflect a relatively stable exposure situation that is not likely to have changed significantly. Another limitation of the study is that because it is cross-sectional, it is difficult to make causal assumptions. Another disadvantage is the likelihood of misclassification bias due to self-reporting (e.g., the outcome variable, myocardial infarction), a method that is vulnerable to variable degrees of inaccuracy Despite this, there are several beneficial aspects to the study. This is a population-based study that used data from the National Health and Nutrition Examination Survey (NHANES), which obtains high-quality data while adhering to strict criteria requirements to minimize errors. One other limitation of our study was the number of iterations was low due to the lack of computing power. Nonetheless, BKMR has the advantage of not only addressing the mixture effect but also of being able to extract the contributions of each component, with the caveat that these contributions are in the context of joint exposure at the exposure levels reported in the cohort. Finally, by using the BKMR method, we were able to overcome significant drawbacks of conventional analytic pathways, such as single metal effect estimate, model misspecification, and increased false discovery when fitting multiple regression models.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, exposure to the mixture of heavy metals considered in this study was associated with an increased risk of a heart attack. Exposed adults aged 20 and above had a greater likelihood of heart attack related to levels of a heavy metal combination. When evaluating the entire mixture, lead, and selenium in the blood and cadmium, manganese, tin and strontium in the urine were found to be the most significant exposures associated with heart attacks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe BKMR model presented in this study can be used to explore new types of exposure in future studies, with the potential of yielding enhanced knowledge of how the environment as a whole influences’ health and disease in different population settings. While the current study found a significant association between exposure to mixtures of heavy metals and heart attack, additional studies using larger cohorts are needed to estimate the effects of heavy metal mixtures on a heart attack at exposure levels that are relevant for general populations. We also suggest using different approaches and interpreting their results together to draw more robust conclusions.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData will be available upon contacting corresponding author.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding Statement\u003c/p\u003e\n\u003cp\u003eThis work is supported by The National Heart Lung and Blood Institute [Grant number: K01 HL146944]\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge that an earlier version of this manuscript was presented as a Speed presentation at the JSM 2022 Conference held at Washington, DC, USA on August 6, 2022 - August 11, 2022. The abstract of the presentation can be found at [https://ww2.amstat.org/meetings/jsm/2022/onlineprogram/AbstractDetails.cfm?abstractid=322512]\u003c/p\u003e\n\u003cp\u003eAll co-authors have meaningfully contributed to the production of this manuscript including funding, conception, data analysis, edition, revision, and final approval of the manuscript. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIbrahimou B, Azim SI and Sun N. Interaction between blood lead level and chronic obstructive pulmonary disease (COPD) on risk of heart attack or stroke: USA NHANES, 2013\u0026ndash;2014. \u003cem\u003ePulmonary Pharmacology \u0026amp; Therapeutics\u003c/em\u003e 2019; 58: 101805.\u003c/li\u003e\n \u003cli\u003eGreenfield DM and Snowden JA. Cardiovascular Diseases and Metabolic Syndrome. In: Carreras E, Dufour C, Mohty M, et al. (eds) \u003cem\u003eThe EBMT Handbook: Hematopoietic Stem Cell Transplantation and Cellular Therapies\u003c/em\u003e. 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Heavy metal associated health hazards: An interplay of oxidative stress and signal transduction. \u003cem\u003eChemosphere\u003c/em\u003e 2021; 262: 128350.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bayesian kernel machine regression, Heavy metal mixtures, Cardiovascular disease, Heart attack, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4456611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4456611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25\u003csup\u003eth\u003c/sup\u003e percentile compared to their 50\u003csup\u003eth\u003c/sup\u003e percentile and a positive association of 0.0285 units when all the metals were at their 75\u003csup\u003eth\u003c/sup\u003e percentile compared to their 50\u003csup\u003eth\u003c/sup\u003e percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.\u003c/p\u003e","manuscriptTitle":"Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 15:08:10","doi":"10.21203/rs.3.rs-4456611/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ecb3809-860f-41a3-8e83-04dcedf709c3","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T17:17:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-18 15:08:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4456611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4456611","identity":"rs-4456611","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}