Associations between metal-metal interaction and the risk of hypertension: A case-control study in Chinese community-dwelling elderly

Associations between metal-metal interaction and the risk of hypertension: A case-control study in Chinese community-dwelling elderly | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between metal-metal interaction and the risk of hypertension: A case-control study in Chinese community-dwelling elderly Meiyan Li, Siyu Duan, Rui Wang, Pei He, Zhongyuan Zhang, Yuqing Dai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3880760/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 Fewer studies have focused on the interaction of metal mixtures with hypertension, especially in Chinese community-dwelling elderly. In addition, the relationship between metals exposure and hypertension may be weakened or strengthened due to the presence of multiple chronic diseases in the elderly. Methods In this study, inductively coupled plasma mass spectrometry was used to detect the levels of 12 metals in the urine of 693 elderly people in the Yinchuan community. Conditional logistic regression model and restricted cubic spline analysis (RCS) were used to explore the association between urinary metal concentration and hypertension and dose-response relationship. Quantile g-computation and Bayesian kernel machine regression (BKMR) to analyze the association of individual urinary metal concentrations and metal mixtures with hypertension risk. Results Urinary concentrations of 12 metals (vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, thallium, and lead) were higher in the hypertension group than in the non-hypertension group. In the RCS models, the urinary concentrations of vanadium, iron, and lead showed a linear dose-response relationship with hypertension risk. Quantile g-computation analyses showed cadmium contributed the largest positive weights. The BKMR models showed that the positive slope of lead became steep at higher concentrations of urinary iron when the other three metals were at the median. Conclusion We found that exposure to metal mixtures was associated with the risk of hypertension and a significant positive interaction between urinary iron and lead. Further research is needed to confirm our findings and elucidate the underlying mechanisms of the interaction between metals and hypertension. metal mixtures hypertension community-dwelling elderly Bayesian kernel machine regression interaction effect Figures Figure 1 Figure 2 Figure 3 1. Introduction Hypertension is a common chronic non-communicable disease that significantly increases the risk of heart, brain, kidney disease, and other diseases, which has become one of the major public health problems. Over the past few decades, the burden of hypertension in China has been increasing (Shi et al., 2020 ). According to the data from the China National Hypertension Survey in 1991 and 2002, the prevalence of hypertension among the elderly over 60 years old in China was 40.4% and 49.1% respectively (Hua et al., 2019 ). A cross-sectional survey of stratified multi-stage random sampling of hypertension in China from 2012 to 2015 showed that the prevalence of hypertension was 53.2% (Wang et al., 2018 ). With the arrival of an aging society, the prevalence of hypertension increases with age (Pimenta et al., 2012). Therefore, the prevention and treatment of hypertension have become increasingly important in the field of public health and clinical medicine (Tan et al., 2023 ). Identifying and controlling the risk factors of hypertension is of great significance for reducing the burden of hypertension and related diseases and improving the quality of life. There are many reasons for an increase in hypertension. Environmental factors (such as metals, pesticides, etc.) are considered to be one of the causes of hypertension (Habeeb et al., 2022 ). Metals exist in free states in air, water, soil, animals, and plants. The main exposure pathways of environmental metals include respiratory inhalation, skin contact, and oral intake (Tchounwou et al., 2012 ), and as a result, people are exposed to low-dose metal mixtures for a long time, which leads to the interaction between metals (Ma et al., 2022 ). Laboratory studies have shown that there is an interaction between metals (Katsnelson et al., 2016 ). Although numerous epidemiological studies have shown that environmental metal exposure is associated with the risk of hypertension (Qu et al., 2022 ; Miao et al., 2020 ; Wang et al., 2020 ; Vinceti et al., 2019 ), most of the related studies mainly focus on the effects of single metal on occupational population, general population, children, or pregnant women (Shi et al., 2019 ; Geldsetzer et al., 2018 ; Liu et al., 2022 ; Liu et al., 2021 ). A few studies have focused on the joint association between metal mixtures and the risk of hypertension and the interaction between metals (Zhong et al.,2021). And the role of heavy metals in hypertension in Chinese community-dwelling elderly has not been well studied, and results from studies are inconsistent. One study has found blood arsenic (As) concentration and blood manganese (Mn) level were negatively correlated with the risk of hypertension in the elderly (Zhang et al., 2022 ). However, another study in Wuhan, China showed As and Mn increased the risk of hypertension (Wu et al., 2018a ). Therefore, it is necessary to explore the interaction between metal exposure and the risk of hypertension in Chinese community-dwelling elderly. In addition to the effects of metal pollution concentrations in different regions, different populations, and different environments on the association between metals and the risk of hypertension, multiple chronic diseases also affect the association between metal exposure and hypertension. The relationship between metals exposure and hypertension may be weakened or strengthened due to multiple chronic diseases in the study population (especially in the elderly). It is reported that diabetes can easily lead to hypertension and related cardiovascular diseases and dyslipidemia is also one of the risk factors for hypertension (Jia et al., 2021; Tang N et al., 2022 ). So, diabetes and dyslipidemia would affect the accuracy of the analysis on the association between metal exposure and hypertension. Therefore, considering the different concentrations of metal pollution in different regions, different populations, and different environments, as well as the effects of multiple chronic diseases, it is necessary to explore the relationship between the risk of hypertension excluding multiple chronic diseases (such as diabetic, dyslipidemia) and individual metals and mixtures, as well as the effects of metal-metal synergy. In our study, we performed a case-control study (including 231 hypertensive patients and 462 non-hypertensive individuals, all excluding diabetes, dyslipidemia, cerebrovascular disease, and other serious diseases) to evaluate the association between 13 urinary metals and the risk of hypertension in Chinese community-dwelling elderly by using quantile g-computation and BKMR approach to explore the joint effect and potential interactions of multiple metals. Finally, generalized linear models were used to evaluate the correlation between monometallic exposure and blood pressure. 2. Methods 2.1. Study population The study population was derived from the chronic disease cohort study of urban elderly people in Yinchuan from June 2020 to October 2021. Two community health service centers in three districts and two counties of Yinchuan City were randomly selected, and 5037 research subjects over 60 years old were recruited in each community health service center. All participants underwent a physical examination and 10 mL of mid-morning urine was collected, aliquoted and stored in a -20°C freezer. Of the 5037 participants, 4144 had urine metal concentration measurements. We excluded participants with missing data (n = 363), dyslipidemia, diabetes, cerebrovascular disease, and other serious diseases (n = 2242). Then, 231 patients with hypertension were included. For each hypertensive patient, two healthy individuals were matched by age (± 5 years) and sex. Finally, 231 hypertensive patients and 462 non-hypertensive individuals were included in this study. Figure 1 shows the inclusion-exclusion process. This study was approved by the Ethics Committee of Ningxia Medical University. All participants signed the informed consent. 2.2. Ascertainment of hypertension, diabetes, and dyslipidemia The definition of hypertension was as follows: either systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg (Joint Committee for Guideline Revision, 2019). The diagnostic criteria for diabetes were: typical symptoms (polydipsia, polyuria, polyphagia, and weight loss) combined with random plasma glucose ≥ 11.1 mmol/L or FPG ≥ 7.0 mmol/L or OGTT 2hPG ≥ 11.1 mmol/L (Jia et al.,2019). The diagnostic criteria for dyslipidemia were: TC ≥ 6.22 mmol/L, or LDL-C ≥ 4.14 mmol/L, or HDL-C < 1.04 mmol/L, or TG ≥ 2.26 mmol/L(Joint Committee for Developing Chinese guidelines., 2016). 2.3. Metal exposure We used an inductively coupled plasma mass spectrometer(ICP-MS) (Agilent, 7800X) to measure vanadium(V), iron(Fe), cobalt(Co), zinc(Zn), copper(Cu), arsenic (As), selenium(Se), molybdenum(Mo), cadmium(Cd), tellurium(Te), thallium(Tl), lead(Pb) in the urine of the subjects. The specific experimental steps refer to the method part of the article published by our research group (Wang et al., 2023 ). 2.4. Quality Control To ensure the accuracy of this method, 1 urine QC sample (ClinChekR-Control urine, grade II.) and 3 random blank samples were processed for every 28 samples. In addition, the recovery rate of metals was 85.20% ~ 115.05% (Table S1 ). The limit of detection (LOD) for all metals was in the range of 0.00113 µg/L − 0.8589 µg/L (Table S2). Undetected sample concentrations are recorded as one-half of the LOD value. Urine creatinine (mQi -- graphs per liter, g/L) was determined using a fully automated clinical chemistry analyzer (Beckman Coulter, AU480) and urine dilution was adjusted. 2.5. Covariates The data of gender, age, body mass index (BMI), smoking status, alcohol drinking status, exercise frequency, and eating habits were collected by questionnaire survey, among which smoking status, alcohol drinking status, exercise frequency, and eating habits were classified data. We categorized the frequency of exercise as every day, ≥once a week, < once a week, and never; and the smoking status was classified as never, former, and active; alcohol drinking was divided into every day, ≥once a week, < once a week, and never; dietary habits are divided into meat-vegetables balanced diet, plant-based diet, meat-based diet. 2.6. Statistical analyses Continuous data and categorical data were used to compare the mean ± standard deviation (SD) and odds ratio of baseline characteristics between hypertensive patients and non-hypertensive individuals by t-test and chi-square test, respectively. After adjusting for urinary creatinine, the Wilcoxon rank-sum test was used to analyze the difference in metal concentration between the hypertensive group and the non-hypertensive group. Since the distribution of urinary metals is skewed, we performed a logarithmic transformation, and the correlation coefficient between multiple metals is estimated by using the Spearman rank correlation. We used the quartile g-computation estimates the mixture odds ratio (OR) and relative contribution (weight) of each metal, and the joint effect of the mixture when all metals increase by one quantile at the same time, to investigate the association between the metal mixture and hypertension. Since obesity is one of the risk factors for hypertension, stratified analysis was performed according to BMI, and obesity was defined as BMI ≥ 28 (Chen et al., 2004 ). Then, we used a conditional logistic regression model to assess the association between hypertension and exposure to multiple metals. The adjusted covariates were age, sex (male or female), BMI (continuous), smoking status, drinking status, exercise frequency, and eating habits. The false discovery rate (FDR) correction is used to adjust the trend p-value. The Lasso regression model was used to screen the risk factors of hypertension and represent the importance of each factor. The λ value with the smallest error was calculated by the cross-validation method, which corresponded to the selected risk factors. In the subsequent analysis, we included only the metals selected for the Lasso regression model. The RCS regression model estimated the dose-response relationship between metal concentration and hypertension. Given the possible nonlinearities and interactions between metal mixtures, we use the BKMR model to explore the direction of the expose-response relationship for a single metal and the possible joint effects of multiple exposures. In this model, statistical data that quantify the corresponding exposure measures can be used to gain insight into the cumulative effects of the mixture. We run the model to 10,000 iterations by using the Markov chain Monte Carlo algorithm. The following estimates were reported: when the metal mixture is fixed at a specific percentile compared to the median, the overall relationship between the metal mixture and each result. Compared with the median, the increase in the quartile range (IQR, from the 25th percentile to the 75th percentile) of each metal was associated with each result. When all other metals are exposed to each metal, the univariate exposure-response relationship between each result is quantile. The remaining metals are fixed in the median and the predictive response function of one metal to another metal at different quantiles. Finally, the generalized linear models were employed to investigate the associations of metal exposure with blood pressure. Statistical analyses were performed using Stata MP17.0 and R 4.2.2. Quantile g-computation and BKMR were fit in R using the” qgcomp” and “bkmr” packages, respectively. The two-sided statistical significance level was set to α = 0.05. 3. Result 3.1. General characteristics of the study population Table 1 presents the general characteristics of the 231 hypertensive patients and 462 non-hypertensive individuals. The mean age of all the participants was 71.22±5.33 years old. Compared with the non-hypertension group, the hypertension group had a higher BMI ( P < 0.05). 3.2. Urinary metal concentration distributions After adjusting urinary creatinine levels, we found that urine concentrations of V, Fe, Co, Zn, Cu, As, Se, Mo, Cd, Te, Tl, and Pb were significantly higher in the hypertensive group than in the non-hypertensive group (P < 0.05) (Table 2). Spearman correlation was between 0.21 and 0.63(Figure S1). 3.3. Quantile g-computation analysis The joint effect of increasing 12 metals by one quartile on the risk of hypertension was 0.76 (95%CI: 0.53, 1.00, P < 0.001). The metal summary ORs positively correlated with hypertension was 1.29, and the metal summary ORs negatively correlated with hypertension was -0.53(Table S3). The positive weight of Cd is the largest, followed by Zn; Se is the metal with the largest negative weight (Figure S2). 3.4. Conditional logistic regression models for hypertension and urinary metal levels In the crude models, 12 metals were shown to be significantly associated with hypertension (p-trend<0.05). After adjusting for potential confounding factors and multiple comparison corrections by the FDR method, 11 metals retained statistical significance. V (OR =1.33, 95% CI: 1.15, 1.55), Fe (OR = 1.42, 95% CI: 1.22,1.65), Co(OR = 1.25, 95% CI: 1.08,1.45), Zn(OR = 1.43, 95% CI: 1.23,1.67), Cu(1.21(1.05,1.41), As(OR= 1.30, 95%CI: 1.12,1.52), Mo(OR = 1.27, 95% CI: 1.10,1.48), Cd(OR = 1.98, 95% CI: 1.67,2.34), Te(OR = 1.27, 95% CI: 1.09,1.48), Tl(OR = 1.26, 95% CI: 1.09,1.47), Pb(OR = 1.44, 95% CI: 1.24,1.68) (p-trend<0.05) were associated with an increased risk of hypertension (Table 3). In analyses stratified by BMI, among obese (BMI≥28) participants, we found significant associations between urinary Fe, Co, Zn, Cd and Pb concentrations (Q4vs1) (P 0.05). In non-obese (BMI<28) participants, we observed that urinary concentrations of V, Co, Zn, As, Mo, Te, Tl and Pb ( Q4vs1 ) were significantly associated with hypertension(P 0.05). (Table S4) 3.5. Dose-response relationship between urinary metals and hypertension risk V, Fe, Co, Zn, Cu, As, Se, Mo, Cd, Te, Tl, Pb, age, sex, BMI, exercise frequency, smoking status, alcohol drinking status, and dietary habit were included in the Lasso regression model for screening variables. The Lambda(λ) value was shown by the cross-validation method in Figure S3(A). The range between the two dashed lines represents the range of the positive standard deviation of the lambda value. In the range, the deviation of the regression model fluctuates slightly. Therefore, the lambda value with the smallest mean-square error in this range is selected, which contains four variables. As the lambda value increases, the coefficients of each risk factor are compressed (Figure S3(B)). The earlier the variable is compressed, the lower its importance. Finally, the top four risk factors V, Fe, Cd, and Pb were retained. Thus, the 4 selected variables were included in the RCS regression model to assess the dose-response relationship between each metal and the risk of hypertension. The associations between urinary metal concentrations and hypertension risk were explored separately, as shown in Figure 2. Dose-response analysis using the RCS method found that urinary concentrations of V, Fe, and Pb there was a linear dose-response relationship with the risk of hypertension (Poverall 0.05), Cd concentration showed a nonlinear dose-response relationship (Poverall < 0.001, Pnonlinear < 0.01). 3.6. Analysis of polymetallic exposure using BKMR model To further study the effects of simultaneous exposure to V, Fe, Cd, and Pb on hypertension, four metals were included in the BKMR model. (Figure 3A) shows the overall effect of these four metals on hypertension. Compared with the 50th percentile, when all metals were in or above the 55th percentile, the joint effect of the four metals was significant. The PIP of Cd (1.000) was the highest in their group (the PIP value indicates the importance of the effect on the outcome, and the higher the value, the more important it is for the outcome). In addition, when the other three metals were fixed at different percentiles (25th, 50th, or 75th), it was found that Cd exposure was positively correlated with hypertension (75th vs 25th) (Figure 3B). When fixing the remaining 4 metals at the median, we observed a linear association between V, Fe, Pb and hypertension (Figure 3C). Figure 3D showed the potential interaction between Fe and Pb, which is associated with an increased risk of hypertension. When the other three metals were in the median, the positive slope of Pb became steeper at higher urinary Fe concentrations. 3.7. Associations between urinary metals and blood pressure In the generalized linear model, the relationship between urinary metal and SBP and DBP levels was studied (Figure S4 and S5). SBP and DBP increased with increasing quartiles of urinary Cd and Pb. (both p for trend < 0.05). We found that the quartiles of V and Fe were associated with elevated SBP levels(both p for trend 0.05). 4. Discussion Our study provides epidemiological evidence that supports many previous studies on the association between urinary metal levels and hypertension. Quantile g-computation showed that urinary metal mixture concentrations were significantly associated with the risk of hypertension. Cd contributed the largest positive weights and followed by Zn, Tl, and V. Logistic regression models showed that V, Fe, Co, Zn, Cu, As, Mo, Cd, Te, Tl, Pb were associated with increased risk of hypertension. As the quartiles of V, Fe, Cd, and Pb increase, so does the risk of hypertension. We used the RCS model to study the shape of the dose-response relationship curve of these associations and observed a positive linear relationship of V, Fe, and Pb with hypertension. The BKMR analysis estimated the effects of joint exposure of four metals. We also found an interaction between urinary Fe and Pb for increasing risk of hypertension. 4.1. V V is essential for microorganisms and animals, and the overall physiological role of vanadate in humans is critical (Rehder et al., 2016). There is increasing evidence that essential metals play a beneficial role only within a certain range, and too low or too high concentrations of essential metals pose a health threat to the body (Budi et al., 2022 ). As a transition metal element with a variety of oxidation states, V has redox activity. It is easy to be oxidized to V 2 O 5 after the redox reaction. and the toxicity of V increases with the increase of valence, and V 2 O 5 is the most toxic, leading to oxidative stress and cellular senescence (He et al., 2022 ). Previous studies have shown that the effect of V on the cardiovascular system is bidirectional, which has both advantages and disadvantages. Accumulation of transition metal V may cause inflammation and endothelial dysfunction, which can lead to elevated blood pressure levels and the development of hypertension (Rines et al., 2012). In this study, urinary V was positively correlated with an increased risk of hypertension in the elderly( P < 0.05). However, some studies have shown that V compounds exert a range of insulin-like effects in the cardiovascular system to improve hypertension (Othman et al., 2022 ). An animal study showed that V compounds led to significant reductions in plasma insulin concentrations and blood pressure (Bhanot et al., 2022). The results of studies on pregnant women suggest that exposure to V during pregnancy is inversely correlated with blood pressure (Qiu et al., 2020 ). These inconsistent results may reflect the potential bidirectional effects of V on blood pressure. This may be related to the type and dose of V compounds and the different laboratory animals and study populations. Since the dose of vanadium determines whether it has beneficial or harmful effects, its exact effective dose remains unknown. Therefore, further research on vanadium exposure in the environment is necessary. In our study, the median concentrations of urinary V (2.32 µg/g creatinine) in the hypertension group (community-dwelling elderly) were significantly higher than in pregnant Chinese women (0.76µg/L) and in the general Chinese population (1.95 µg/g creatinine) (Ma et al., 2022 ; Jiang et al., 2021 ). These comparisons may indicate biogeochemical differences in vanadium exposure and sources. Stratified according to BMI, the results showed that the risk of hypertension increases with an increase in the quartile of V concentration in obese people (BMI ≥ 28). In the RCS model, a significant linear dose-response relationship between V exposure and the odds of hypertension also was suggested (Poverall < 0.001, Pnon-linear = 0.462) 4.2. Fe Fe is a key cofactor in a variety of physiological processes (Evstatiev et al., 2012). It is an important component of hemoglobin, myoglobin, and others, and plays an important physiological function in the human body(Simpson et al., 2015), affecting oxygen delivery and uptake(Evstatiev et al., 2012). Fe deficiency and excessive intake are harmful to the human body. The most common form of iron deficiency is iron deficiency anemia and iron overload is associated with an increased risk of cardiovascular disease (Kiechl et al., 1997 ). The accumulation of Fe in the body can lead to oxidative stress. Epidemiological and laboratory data suggest that cellular responses induced by oxidative stress following heavy metal exposure are associated with an increased risk of tumors, diabetes, renal failure, and 36 cardiovascular diseases (Taucher et al., 2022 ; Ighodaro et al., 2018; Yaribeygi et al., 2012; Yi et al., 2022 ). Iron-induced oxidative stress has the following significance: 1) DNA damage, lipid peroxidation and oxidative protein damage caused by the failure of redox regulation; 2) free radical-induced signal transduction pathway activation (Valko et al., 2005 ). The results of this study indicate that increased Fe content in urine is a risk factor for hypertension, which is consistent with previous studies (Wu et al., 2018a ; Wu et al., 2018b). Wu et al. found positive trends for increased odds of hypertension with increasing Fe quartiles (Wu et al., 2018a ). In this study, the urinary Fe concentration (141.97 µg/g creatinine) in the hypertension group was also significantly higher than in the above study (69.9µg/g creatinine). In obese and non-obese populations, there was a significant positive correlation between high levels of urinary Fe and hypertension, suggesting that the association between iron and hypertension risk may not be affected by BMI. Finally, we found a significant positive correlation between iron and SBP levels, with no significant correlation with DBP (P < 0.05). Therefore, it can be inferred that higher concentrations of Fe in urine may affect SBP, thereby increasing the risk of hypertension. Therefore, effective prevention and control of hypertension can start with improving lifestyle and reasonably controlling iron intake and excretion. 4.3. Cd and Pb Cd and Pb are common environmental toxic metals. Human exposure to Cd and Pb is mainly through the consumption of contaminated water or food and the inhalation of contaminated air. Exposure to Cd and Pb is harmful to humans. Some cross-sectional studies have shown significant associations between Cd and Pb exposure and hypertension prevalence in the US, Korea, and Kentucky (Tang et al.,2022; Kwon et al.,2021; Walker et al.,2022). Furthermore, both Cd and Pb are non-Fenton metals, which indirectly produce reactive oxygen species by disrupting cellular antioxidant defense systems, inhibiting mitochondrial electron respiratory chains, and replacing metals with redox activity, thereby inducing lipid peroxidation, DNA damage, and toxic effects (Paithankar et al.,2020). Heavy metal exposure will produce excessive ROS, thus damaging the balance of oxidation and antioxidant systems in the vascular system, leading to oxidative stress (Münzel et al.,2017). Mechanistic studies suggest that Cd and Pb may cause increased blood pressure through oxidative stress (Perfus-Barbeoch et al.,2002; Kasperczyk et al., 2005 ). Our study identified a positive increasing trend of OR for hypertension with Cd and Pb exposure. Zhang et al. showed that SBP and DBP levels increased with increased exposure to Cd and Pb, suggesting that cadmium exposure may increase the risk of hypertension (Zhang et al., 2022 ), which is consistent with our findings. Kim et al. found that high blood Cd and Pb concentrations were positively correlated with elevated blood pressure levels and the prevalence of hypertension (Kim et al., 2022). In our study, the BKMR model proposed that as urinary concentrations of Cd and Pb increase, the risk of hypertension also increases. To further understand the effect of this effect on blood pressure levels, we analyzed the association between Cd, Pb exposure and SBP, DBP separately. The results showed that increasing quartiles of Cd and Pb were significantly associated with higher SBP and DBP. These results suggest that joint metal exposure may play an important role in the occurrence and development of hypertension. 4.4. Strengths and limitations This study has several advantages. First, we simultaneously measured up to 12 metals, which allowed us to discover the interaction between Fe and Pb. Secondly, we use quantile g-computation analysis and the BKMR model, which helps to estimate the joint effects and interactions between complex exposures. Finally, we used the generalized linear model to further verify the correlation between metals and SBP and DBP. However, there are some limitations. Firstly, this is a case-control study with a weak ability to verify causal associations. Urine samples and blood pressure measurements were performed simultaneously, so we could not assess the time relationship between metal exposure and hypertension. Therefore, this result needs to be further confirmed in other populations. 5. Conclusions In conclusion, our study explored a potential association between urinary concentrations of metal mixtures and the risk of hypertension. Our study found that urinary Cd concentration was identified as the most critical factor associated with hypertension. Urinary Fe and urinary Pb may have a synergistic effect. Given the increase of heavy metal pollution in the environment, it is necessary to determine the relationship between heavy metal exposure and risk factors of hypertension in different populations and regions and to determine ways to minimize heavy metal exposure. Further studies are needed to confirm our findings and elucidate the potential mechanism of the link between metals and hypertension. Declarations CRediT authorship contribution statement Meiyan Li: Conceptualization, Methodology, Investigation, Formal Analysis, Writing - Original Draft, Visualization; Siyu Duan: Methodology, Visualization, Formal analysis, Resources; Rui Wang: Methodology, Investigation, Data Curation, Conceptualization; Pei He: Visualization, Data Curation, Investigation, Formal analysis; Zhongyuan Zhang: Visualization, Data Curation, Investigation; Yuqing Dai: Formal analysis, Investigation, Validation; Zhuoheng Shen: Validation, Investigation, Formal analysis; Yue Chen: Validation, Investigation, Formal analysis; Huifang Yang: Visualization, Data Curation, Formal analysis; Xiaoyu Li: Conceptualization, Writing - review & editing, Funding acquisition; Jian Sun: Conceptualization, Writing - review & editing, Funding acquisition, Validation, Methodology; Rui Zhang: Writing - review & editing, Supervision, Validation. Funding This work was supported by the “Light of the West” Talent Training Plan Project of Chinese Academy of Sciences (XAB2022YW18), the National Natural Science Foundation of China (No.82202431 and U22A20360), the Natural Science Foundation Project of Ningxia, China (2022AAC05024, LNZR202305 and 2022AAC05028), and the Key Research and Development Project of Ningxia (2021BEG02026 and 2021BEG02030), the Key Research and Development Project of Ningxia (Grant No. 2023BEG02028). Acknowledgments The authors acknowledge the support from the “Light of the West” Talent Training Plan Project of Chinese Academy of Sciences (XAB2022YW18), the National Natural Science Foundation of China (No.82202431 and U22A20360), the Natural Science Foundation Project of Ningxia, China (2022AAC05024, LNZR202305 and 2022AAC05028), and the Key Research and Development Project of Ningxia (2021BEG02026 and 2021BEG02030), the Key Research and Development Project of Ningxia (Grant No. 2023BEG02028). We thank all participants for their support of our study. Ethics approval and consent to participate The study was approved by the Ningxia Medical University Medical Ethical Committee (No. 2021-N0098, No. 2022-N013). Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose. Supplementary data See supplementary materials. References Bhanot S, Michoulas A, McNeill JH. Antihypertensive effects of vanadium compounds in hyperinsulinemic, hypertensive rats. Mol Cell Biochem. 1995 Dec 6–20;153(1–2):205–9. doi: 10.1007/BF01075939 . Budi HS, Catalan Opulencia MJ, Afra A, Abdelbasset WK, Abdullaev D, Majdi A, Taherian M, Ekrami HA, Mohammadi MJ. Source, toxicity and carcinogenic health risk assessment of heavy metals. Rev Environ Health. 2022 Oct 3. doi: 10.1515/reveh-2022-0096 . Chen C, Lu FC; Department of Disease Control Ministry of Health, PR China. The guidelines for prevention and control of overweight and obesity in Chinese adults. 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Tables Table 1 Characteristics of hypertensive and non-hypertensive participants [n (%)] Characteristic Total Hypertension Non-hypertension (t) P N (%) 693(100%) 231 (33.3%) 462 (66.7%) Age(years) 71.22±5.33 71.25±5.46 71.20±5.27 -1.101 0.920 Sex 0.000 1.000 Male 291(42.0) 97(42.0) 194(42.0) Female 402(58.0) 134(58.0) 268(58.0) BMI(kg/m 2 ) 24.43±3.41 25.21±3.32 24.04±3.38 -4.296 ＜0.001* <18.5 21(3.0) 4(1.6) 17(3.7) 18.5~ 310(44.7) 82(35.3) 228(49.3) 24.0~ 261(37.7) 97(41.8) 164(35.5) ≥28.0 101(14.6) 48(21.3) 53(11.5) Exercise frequency （ % ） 8.120 0.044* Every day 511(73.7) 157(68.0) 354(76.6) ≥once a week 35(5.1) 17(7.4) 18(3.9) ＜once a week 45(6.5) 20(8.6) 25(5.4) Never 102(14.7) 37(16.0) 65(14.1) Smoking status （ % ） 1.689 0.430 Never 581(83.8) 192(83.1) 389(84.2) Former 43(6.2) 18(7.8) 25(5.4) Active 69(10.0) 21(9.1) 48(10.4) Alcohol drinking status （ % ） 6.724 0.081 Never 592(81.7) 207(89.6) 385(83.3) ＜once a week 82(15.4) 17(7.4) 65(14.1) ≥once a week 13(1.5) 5(2.2) 8(1.7) Everyday 6(1.4) 2(0.8) 4(0.9) Dietary habits （ % ） 0.136 0.934 Meat-vegetables balanced diet 618(89.2) 205(88.7) 413(89.4) Plant-based diet 70(10.1) 24(10.4) 46 (10.0) Meat-based diet 5(0.7) 2(0.9) 3(0.6) *P < 0.05 Table 2 Metal concentration distributions standardized by creatinine (μg/g Cr) in urine Urinary metals (μg/L) Full population (n=693) Hypertension (n=231) Non-hypertension (n=462) Z P V 1.34(0.09,5.52) 2.32(0.15,9.32) 1.08(0.07,4.42) -4.123 <0.001* Fe 99.96(20.99,277.22) 141.97(40.51,424.18) 80.54(7.64,245.23) -4.642 <0.001* Co 0.24(0.13,0.48) 0.33(0.16,0.64) 0.21(0.12,0.38) -4.592 <0.001* Zn 370.50(179.43,689.29) 503.79(231.95,1082.01) 323.32(164.87,565.63) -4.911 <0.001* Cu 10.54(6.47,18.98) 12.93(6.46,26.06) 9.69(6.52,17.07) -3.395 <0.001* As 58.39(33.67,110.12) 74.52(35.62,150.49) 53.29(32.20,95.19) -3.935 <0.001* Se 16.25(10.72,24.49) 17.78(10.68.30.04) 15.47(10.73,22.17) -2.595 <0.05* Mo 78.38(49.02,128.05) 91.64(51.49,171.32) 73.25(48.34,115.77) -3.338 <0.05* Cd 0.34(0.20,0.56) 0.49（0.27,0.95） 0.29(0.19,0.45) -7.778 <0.001* Te 58.89(22.14,156.32) 77.47(28.80,215.12) 54.07(19.67,124.82) -3.305 <0.001* Tl 0.22(0.14,0.36) 0.25(0.16,0.46) 0.21(0.14,0.32) -0.038 <0.05* Pb 0.89(0.38,2.19) 1.32(0.58,3.38) 0.81(0.31,1.73) -4.921 <0.001* Abbreviations: V, Vanadium; Fe, Iron ; Co, Cobalt; Zn, zinc; Cu, Copper; As, Arsenic; Se, Selenium; Mo, Molybdenum; Cd, Cadmium; Te, Tellurium; Tl, Thallium; Pb, Lead. Z : Wilcoxon rank sum test * P < 0.05 Table 3 Odds ratios (95%CI) for hypertension associated with quartiles of urinary metal concentrations Urinary metals (μg/L) Quartiles of urinary metals (μg/L) Linear models a p -trend b p -FDR c Q1 Q2 Q3 Q4 V <0.09 0.09~ 1.34~ 5.52~ Model 1 1.0 1.54(0.97,2.47) 1.38(0.86,1.31) 2.64(1.67,4.17) 1.33(1.15,1.53) <0.001* <0.001* Model 2 1.0 1.54(0.94,2.49) 1.30(0.80,2.11) 2.72(1.70,4.37) 1.33(1.15,1.55) <0.001* <0.001* Fe <20.99 20.99~ 99.96~ 277.22~ Model 1 1.0 1.80(1.11,2.91) 1.71(1.05,2.77) 3.01(1.88,4.80) 1.38(1.19,1.60) <0.001* <0.001* Model 2 1.0 1.90(1.16,3.10) 1.72(1.05,2.82) 3.23(2.04,5.43) 1.42(1.22,1.65) <0.001* <0.001* Co <0.13 0.13~ 0.24~ 0.48~ Model 1 1.0 0.84(0.52,1.36) 1.25(0.79,1.99) 2.53(1.62,3.96) 1.39(1.21,1.61) <0.001* <0.001* Model 2 1.0 0.80(0.49,1.32) 1.21(0.76,1.95) 2.50 (1.57,4.00) 1.39(1.19,1.62) <0.001* <0.001* Zn <179.43 179.43~ 370.50~ 689.29~ Model 1 1.0 0.92(0.56,1.49) 1.33(0.84,2.12) 2.75(1.75,4.31) 1.43(1.23,1.65) <0.001* <0.001* Model 2 1.0 0.93(0.57,1.52) 1.36(0.84,2.20) 2.78(1.74,4.45) 1.43(1.23,1.67) <0.001* <0.001* Cu <6.47 6.47~ 10.54~ 18.98~ Model 1 1.0 0.51(0.32,0.83) 1.03(0.66,1.61) 1.52(0.99,2.36) 1.22(1.06,1.41) 0.006* 0.007* Model 2 1.0 0.49(0.30,0.80) 1.04(0.66,1.64) 1.48(0.94,2.32) 1.21(1.05,1.41) 0.010* 0.012* As <33.67 33.67~ 58.39~ 110.12~ Model 1 1.0 0.82(0.51,1.32) 1.38(0.88,2.18) 1.94(1.25,3.03) 1.29(1.12,1.49) <0.001* <0.001* Model 2 1.0 0.83(0.51,1.35) 1.35(0.85,2.15) 2.02(1.26,3.24) 1.30(1.12,1.52) 0.001* 0.0018* Se <10.72 10.72~ 16.25~ 24.49~ Model 1 1.0 0.64(0.40,1.03) 0.93(0.60,1.45) 1.42,(0.92,2.20) 1.16(1.00,1.33) 0.044* 0.044* Model 2 1.0 0.63(0.39,1.01) 0.91(0.57,1.44) 1.37(0.86,2.16) 1.14(0.98,1.33) 0.078 0.078 Mo <49.02 49.02~ 78.38~ 128.05~ Model 1 1.0 0.62(0.39,1.01) 1.09(0.70,1.70) 1.81(1.17,2.81) 1.27(1.10,1.47) 0.001* 0.0016* Model 2 1.0 0.65(0.40,1.06) 1.05(0.66,1.66) 1.88(1.19,2.95) 1.27(1.10,1.48) 0.001* 0.0018* Cd <0.20 0.20~ 0.34~ 0.56~ Model 1 1.0 0.81(0.48,1.37) 1.76(1.08,2.84) 5.39(3.36,8.64) 1.86(1.59,2.17) <0.001* <0.001* Model 2 1.0 0.80(0.46,1.37) 1.82(1.11,3.00) 6.68(4.01,11.11) 1.98(1.67,2.34) <0.001* <0.001* Te <22.14 22.14~ 58.89~ 156.32~ Model 1 1.0 0.95(0.59,1.52) 0.98(0.61,1.56) 2.12(1.36,3.31) 1.27(1.10,1.47) 0.001* 0.0016* Model 2 1.0 0.93(0.58,1.50) 0.89(0.55,1.44) 2.15(1.36,3.42) 1.27(1.09,1.48) 0.002* 0.0031* Tl <0.14 0.14~ 0.22~ 0.36~ Model 1 1.0 0.92(0.58,1.47) 0.92(0.58,1.47) 2.06(1.32,3.21) 1.26(1.09,1.45) 0.002* 0.0028* Model 2 1.0 0.96(0.60,1.55) 0.93(0.58,1.50) 2.13(1.34,3.38) 1.26(1.09,1.47) 0.004* 0.0051* Pb <0.38 0.38~ 0.89~ 2.19~ Model 1 1.0 0.95(0.59,1.53) 1.33(1.71,2.12) 2.68(1.71,4.21) 1.41(1.22,1.63) <0.001* <0.001* Model 2 1.0 0.97(0.59,1.58) 1.38(0.85,2.23) 2.87(1.81,4.59) 1.44(1.24,1.68) <0.001* <0.001* Note: a Linear model: The metal concentration transformed by the interquartile range was incorporated into the regression model, representing the OR (95% CI) of increased b p- trend the median of each metal quartile ( natural log-transformation urinary metal concentrations) as a continuous variable, and the p -value for the trend test was obtained from the conditional logistic regression model. c FDR corrections were performed to adapt to multiple tests. Model 1: Unadjusted odds ratio. Model 2: Adjusted for age, sex, BMI, exercise frequency, smoking status, alcohol drinking status, dietary habit * P < 0.05 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":268559349,"identity":"05517801-3fda-46ac-bc92-7f0d1f7bc88c","order_by":8,"name":"Huifang Yang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Yang","suffix":""},{"id":268559350,"identity":"eff89f1e-fb49-483a-bd52-37ba268745c7","order_by":9,"name":"Xiaoyu Li","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Li","suffix":""},{"id":268559351,"identity":"213f5517-d440-42dc-aace-6d04d75e20dc","order_by":10,"name":"Jian Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACfmbGxgcJBhJy/FABxgZCWiTbm5sNPlRYGEs2EKvF4MzxNskZZyoSDQ4Qq4XhRmKzMW+bRILx7R7TzTwMNrIbDjA/e4BPB+OMxMbHQC15ZneOpd3mYUgz3nCAzdwAnxZmCYgtxWY3ko8BtRxO3HCAh00CnxY2icQ2aaCWxM0zEtuAWv4T1sLDcxDkfYnEDRJgWw4Q1iLB3ggKZAljiRtpaTfnGCQbzzzMZoZXi/1h9ofAqKyT45+RY3bjTYWdbN/x5md4taABUFAxk6B+FIyCUTAKRgF2AACq70ypbK12DwAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Sun","suffix":""},{"id":268559352,"identity":"892cdf6b-0177-49eb-98cd-7cd80d65cca5","order_by":11,"name":"Rui Zhang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-20 05:50:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3880760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3880760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50051591,"identity":"422cf91d-b395-4e2b-aab0-7fe32a31af58","added_by":"auto","created_at":"2024-01-23 16:47:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164734,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study population selection\u003c/p\u003e","description":"","filename":"AllFiguresPage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3880760/v1/56a9490bf40425d51a69c4d6.png"},{"id":50051589,"identity":"fc6101a8-b876-4727-86e3-5153aa91f254","added_by":"auto","created_at":"2024-01-23 16:47:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161965,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose-response relationship between the concentration of metals in urine and the prevalence of hypertension\u003c/p\u003e\n\u003cp\u003eNote: The curve shows the dose-response relationship between the elements in urine and the risk of hypertension after logarithmic transformation. The solid line of the curve represents the adjusted OR value, and the shaded area represents the 95% CI of the OR value.\u003c/p\u003e","description":"","filename":"AllFiguresPage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3880760/v1/e9fd36f66c83e76bc4bb9ce7.png"},{"id":50051590,"identity":"3080eecd-3c9f-4e2a-8121-2752072c18dc","added_by":"auto","created_at":"2024-01-23 16:47:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159076,"visible":true,"origin":"","legend":"\u003cp\u003eThe BKMR model was used to study the combined effects of metal mixtures (V, Fe, Cd and Pb) on hypertension. Age, gender, BMI, exercise frequency, smoking status, drinking status and eating habits were adjusted in the model. (A) Overall effects of five metals (estimate and 95 % CI). (B) The effect of single metal (estimated value and 95 % CI). (C) Univariate dose-response function (95 % CI) between selected metal concentration and hypertension. (D) Estimate the bivariate association between each pair of metal mixtures and hypertension.\u003c/p\u003e","description":"","filename":"AllFiguresPage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3880760/v1/2ea505e7077a8bb6e0531962.png"},{"id":53004854,"identity":"7fca9bb7-413e-485b-9d59-5dd7abbc4516","added_by":"auto","created_at":"2024-03-19 14:55:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3880760/v1/887ffe19-6f4c-4d22-8a8c-10a318441bd5.pdf"},{"id":50051592,"identity":"1b3edc12-6225-4a7c-8c62-4fc27975467c","added_by":"auto","created_at":"2024-01-23 16:47:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":973887,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3880760/v1/a8b8724cab308666a09a15f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between metal-metal interaction and the risk of hypertension: A case-control study in Chinese community-dwelling elderly","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHypertension is a common chronic non-communicable disease that significantly increases the risk of heart, brain, kidney disease, and other diseases, which has become one of the major public health problems. Over the past few decades, the burden of hypertension in China has been increasing (Shi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the data from the China National Hypertension Survey in 1991 and 2002, the prevalence of hypertension among the elderly over 60 years old in China was 40.4% and 49.1% respectively (Hua et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A cross-sectional survey of stratified multi-stage random sampling of hypertension in China from 2012 to 2015 showed that the prevalence of hypertension was 53.2% (Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With the arrival of an aging society, the prevalence of hypertension increases with age (Pimenta et al., 2012). Therefore, the prevention and treatment of hypertension have become increasingly important in the field of public health and clinical medicine (Tan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Identifying and controlling the risk factors of hypertension is of great significance for reducing the burden of hypertension and related diseases and improving the quality of life.\u003c/p\u003e \u003cp\u003eThere are many reasons for an increase in hypertension. Environmental factors (such as metals, pesticides, etc.) are considered to be one of the causes of hypertension (Habeeb et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Metals exist in free states in air, water, soil, animals, and plants. The main exposure pathways of environmental metals include respiratory inhalation, skin contact, and oral intake (Tchounwou et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and as a result, people are exposed to low-dose metal mixtures for a long time, which leads to the interaction between metals (Ma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Laboratory studies have shown that there is an interaction between metals (Katsnelson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although numerous epidemiological studies have shown that environmental metal exposure is associated with the risk of hypertension (Qu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vinceti et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), most of the related studies mainly focus on the effects of single metal on occupational population, general population, children, or pregnant women (Shi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Geldsetzer et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A few studies have focused on the joint association between metal mixtures and the risk of hypertension and the interaction between metals (Zhong et al.,2021). And the role of heavy metals in hypertension in Chinese community-dwelling elderly has not been well studied, and results from studies are inconsistent. One study has found blood arsenic (As) concentration and blood manganese (Mn) level were negatively correlated with the risk of hypertension in the elderly (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, another study in Wuhan, China showed As and Mn increased the risk of hypertension (Wu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Therefore, it is necessary to explore the interaction between metal exposure and the risk of hypertension in Chinese community-dwelling elderly.\u003c/p\u003e \u003cp\u003eIn addition to the effects of metal pollution concentrations in different regions, different populations, and different environments on the association between metals and the risk of hypertension, multiple chronic diseases also affect the association between metal exposure and hypertension. The relationship between metals exposure and hypertension may be weakened or strengthened due to multiple chronic diseases in the study population (especially in the elderly). It is reported that diabetes can easily lead to hypertension and related cardiovascular diseases and dyslipidemia is also one of the risk factors for hypertension (Jia et al., 2021; Tang N et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). So, diabetes and dyslipidemia would affect the accuracy of the analysis on the association between metal exposure and hypertension.\u003c/p\u003e \u003cp\u003eTherefore, considering the different concentrations of metal pollution in different regions, different populations, and different environments, as well as the effects of multiple chronic diseases, it is necessary to explore the relationship between the risk of hypertension excluding multiple chronic diseases (such as diabetic, dyslipidemia) and individual metals and mixtures, as well as the effects of metal-metal synergy. In our study, we performed a case-control study (including 231 hypertensive patients and 462 non-hypertensive individuals, all excluding diabetes, dyslipidemia, cerebrovascular disease, and other serious diseases) to evaluate the association between 13 urinary metals and the risk of hypertension in Chinese community-dwelling elderly by using quantile g-computation and BKMR approach to explore the joint effect and potential interactions of multiple metals. Finally, generalized linear models were used to evaluate the correlation between monometallic exposure and blood pressure.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThe study population was derived from the chronic disease cohort study of urban elderly people in Yinchuan from June 2020 to October 2021. Two community health service centers in three districts and two counties of Yinchuan City were randomly selected, and 5037 research subjects over 60 years old were recruited in each community health service center. All participants underwent a physical examination and 10 mL of mid-morning urine was collected, aliquoted and stored in a -20\u0026deg;C freezer.\u003c/p\u003e \u003cp\u003eOf the 5037 participants, 4144 had urine metal concentration measurements. We excluded participants with missing data (n\u0026thinsp;=\u0026thinsp;363), dyslipidemia, diabetes, cerebrovascular disease, and other serious diseases (n\u0026thinsp;=\u0026thinsp;2242). Then, 231 patients with hypertension were included. For each hypertensive patient, two healthy individuals were matched by age (\u0026plusmn;\u0026thinsp;5 years) and sex. Finally, 231 hypertensive patients and 462 non-hypertensive individuals were included in this study. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the inclusion-exclusion process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Ningxia Medical University. All participants signed the informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Ascertainment of hypertension, diabetes, and dyslipidemia\u003c/h2\u003e \u003cp\u003e The definition of hypertension was as follows: either systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg (Joint Committee for Guideline Revision, 2019).\u003c/p\u003e \u003cp\u003eThe diagnostic criteria for diabetes were: typical symptoms (polydipsia, polyuria, polyphagia, and weight loss) combined with random plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L or FPG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or OGTT 2hPG\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L (Jia et al.,2019).\u003c/p\u003e \u003cp\u003eThe diagnostic criteria for dyslipidemia were: TC\u0026thinsp;\u0026ge;\u0026thinsp;6.22 mmol/L, or LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.14 mmol/L, or HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.04 mmol/L, or TG\u0026thinsp;\u0026ge;\u0026thinsp;2.26 mmol/L(Joint Committee for Developing Chinese guidelines., 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Metal exposure\u003c/h2\u003e \u003cp\u003eWe used an inductively coupled plasma mass spectrometer(ICP-MS) (Agilent, 7800X) to measure vanadium(V), iron(Fe), cobalt(Co), zinc(Zn), copper(Cu), arsenic (As), selenium(Se), molybdenum(Mo), cadmium(Cd), tellurium(Te), thallium(Tl), lead(Pb) in the urine of the subjects. The specific experimental steps refer to the method part of the article published by our research group (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quality Control\u003c/h2\u003e \u003cp\u003eTo ensure the accuracy of this method, 1 urine QC sample (ClinChekR-Control urine, grade II.) and 3 random blank samples were processed for every 28 samples. In addition, the recovery rate of metals was 85.20% ~ 115.05% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The limit of detection (LOD) for all metals was in the range of 0.00113 \u0026micro;g/L \u0026minus;\u0026thinsp;0.8589 \u0026micro;g/L (Table S2). Undetected sample concentrations are recorded as one-half of the LOD value. Urine creatinine (mQi -- graphs per liter, g/L) was determined using a fully automated clinical chemistry analyzer (Beckman Coulter, AU480) and urine dilution was adjusted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Covariates\u003c/h2\u003e \u003cp\u003eThe data of gender, age, body mass index (BMI), smoking status, alcohol drinking status, exercise frequency, and eating habits were collected by questionnaire survey, among which smoking status, alcohol drinking status, exercise frequency, and eating habits were classified data. We categorized the frequency of exercise as every day, \u0026ge;once a week, \u0026lt; once a week, and never; and the smoking status was classified as never, former, and active; alcohol drinking was divided into every day, \u0026ge;once a week, \u0026lt; once a week, and never; dietary habits are divided into meat-vegetables balanced diet, plant-based diet, meat-based diet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analyses\u003c/h2\u003e \u003cp\u003eContinuous data and categorical data were used to compare the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and odds ratio of baseline characteristics between hypertensive patients and non-hypertensive individuals by t-test and chi-square test, respectively. After adjusting for urinary creatinine, the Wilcoxon rank-sum test was used to analyze the difference in metal concentration between the hypertensive group and the non-hypertensive group.\u003c/p\u003e \u003cp\u003eSince the distribution of urinary metals is skewed, we performed a logarithmic transformation, and the correlation coefficient between multiple metals is estimated by using the Spearman rank correlation. We used the quartile g-computation estimates the mixture odds ratio (OR) and relative contribution (weight) of each metal, and the joint effect of the mixture when all metals increase by one quantile at the same time, to investigate the association between the metal mixture and hypertension. Since obesity is one of the risk factors for hypertension, stratified analysis was performed according to BMI, and obesity was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Then, we used a conditional logistic regression model to assess the association between hypertension and exposure to multiple metals. The adjusted covariates were age, sex (male or female), BMI (continuous), smoking status, drinking status, exercise frequency, and eating habits. The false discovery rate (FDR) correction is used to adjust the trend p-value.\u003c/p\u003e \u003cp\u003eThe Lasso regression model was used to screen the risk factors of hypertension and represent the importance of each factor. The λ value with the smallest error was calculated by the cross-validation method, which corresponded to the selected risk factors. In the subsequent analysis, we included only the metals selected for the Lasso regression model. The RCS regression model estimated the dose-response relationship between metal concentration and hypertension. Given the possible nonlinearities and interactions between metal mixtures, we use the BKMR model to explore the direction of the expose-response relationship for a single metal and the possible joint effects of multiple exposures. In this model, statistical data that quantify the corresponding exposure measures can be used to gain insight into the cumulative effects of the mixture. We run the model to 10,000 iterations by using the Markov chain Monte Carlo algorithm. The following estimates were reported: when the metal mixture is fixed at a specific percentile compared to the median, the overall relationship between the metal mixture and each result. Compared with the median, the increase in the quartile range (IQR, from the 25th percentile to the 75th percentile) of each metal was associated with each result. When all other metals are exposed to each metal, the univariate exposure-response relationship between each result is quantile. The remaining metals are fixed in the median and the predictive response function of one metal to another metal at different quantiles. Finally, the generalized linear models were employed to investigate the associations of metal exposure with blood pressure.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using Stata MP17.0 and R 4.2.2. Quantile g-computation and BKMR were fit in R using the\u0026rdquo; qgcomp\u0026rdquo; and \u0026ldquo;bkmr\u0026rdquo; packages, respectively. The two-sided statistical significance level was set to α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cstrong\u003e3.1. General characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 1 presents the general characteristics of the 231 hypertensive patients and 462 non-hypertensive individuals. The mean age of all the participants was 71.22\u0026plusmn;5.33 years old. Compared with the non-hypertension group, the hypertension group had a higher BMI (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Urinary metal concentration distributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusting urinary creatinine levels, we found that urine concentrations of V, Fe, Co, Zn, Cu, As, Se, Mo, Cd, Te, Tl, and Pb were significantly higher in the hypertensive group than in the non-hypertensive group (P \u0026lt; 0.05) (Table 2).\u0026nbsp;Spearman correlation was between 0.21 and 0.63(Figure S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Quantile g-computation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe joint effect of increasing 12 metals by one quartile on the risk of hypertension was 0.76 (95%CI: 0.53, 1.00, P \u0026lt; 0.001). The metal summary ORs positively correlated with hypertension was 1.29, and the metal summary ORs negatively correlated with hypertension was\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-0.53(Table S3). The positive weight of Cd is the largest, followed by Zn; Se is the metal with the largest negative weight (Figure S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Conditional logistic regression models for hypertension and urinary metal levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the crude models, 12 metals were shown to be significantly associated with hypertension (p-trend\u0026lt;0.05). After adjusting for potential confounding factors and multiple comparison corrections by the FDR method, 11 metals retained statistical significance. V (OR =1.33, 95% CI: 1.15, 1.55), Fe (OR = 1.42, 95% CI: 1.22,1.65), Co(OR = 1.25, 95% CI: 1.08,1.45), Zn(OR = 1.43, 95% CI: 1.23,1.67), Cu(1.21(1.05,1.41), As(OR= 1.30, 95%CI: 1.12,1.52), Mo(OR = 1.27, 95% CI: 1.10,1.48), Cd(OR = 1.98, 95% CI: 1.67,2.34), Te(OR = 1.27, 95% CI: 1.09,1.48), Tl(OR = 1.26, 95% CI: 1.09,1.47), Pb(OR = 1.44, 95% CI: 1.24,1.68) (p-trend\u0026lt;0.05) \u0026nbsp;were associated with an increased risk of hypertension (Table 3).\u003c/p\u003e\n\u003cp\u003eIn analyses stratified by BMI, among obese (BMI\u0026ge;28) participants, we found significant\u0026nbsp;associations\u0026nbsp;between urinary Fe, Co, Zn, Cd and Pb concentrations (Q4vs1) (P\u0026lt;0.05). For Cu, Se, Mo, Te, and Tl, there was no evidence of significant associations with hypertension (P \u0026gt; 0.05). In non-obese (BMI\u0026lt;28) participants, we observed that urinary concentrations of V, Co, Zn, As, Mo, Te, Tl and Pb ( Q4vs1 ) were significantly associated with hypertension(P\u0026lt;0.05). For Se, no evidence was found to be significantly associated with hypertension (P \u0026gt; 0.05). (Table S4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Dose-response relationship between urinary metals and hypertension risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV, Fe, Co, Zn, Cu, As, Se, Mo, Cd, Te, Tl, Pb, age, sex, BMI, exercise frequency, smoking status, alcohol drinking status, and dietary habit were included in the Lasso regression model for screening variables. The Lambda(\u0026lambda;) value was shown by the cross-validation method in Figure S3(A). The range between the two dashed lines represents the range of the positive standard deviation of the lambda value. In the range, the deviation of the regression model fluctuates slightly. Therefore, the lambda value with the smallest mean-square error in this range is selected, which contains four variables. As the lambda value increases, the coefficients of each risk factor are compressed (Figure S3(B)). The earlier the variable is compressed, the lower its importance. Finally, the top four risk factors V, Fe, Cd, and Pb were retained.\u003c/p\u003e\n\u003cp\u003eThus, the 4 selected variables were included in the RCS regression model to assess the dose-response relationship between each metal and the risk of hypertension. The associations between urinary metal concentrations and hypertension risk were explored separately, as shown in Figure 2. Dose-response analysis using the RCS method found that urinary concentrations of V, Fe, and Pb there was a linear dose-response relationship with the risk of hypertension (Poverall\u0026lt;0.05, Pnonlinear \u0026gt; 0.05), Cd concentration showed a nonlinear dose-response relationship (Poverall \u0026lt; 0.001, Pnonlinear \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Analysis of polymetallic exposure using\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBKMR model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further study the effects of simultaneous exposure to V, Fe, Cd, and Pb on hypertension, four metals were included in the BKMR model. (Figure 3A) shows the overall effect of these four metals on hypertension. Compared with the 50th percentile, when all metals were in or above the 55th percentile, the joint effect of the four metals was significant. The PIP of Cd (1.000) was the highest in their group (the PIP value indicates the importance of the effect on the outcome, and the higher the value, the more important it is for the outcome). In addition, when the other three metals were fixed at different percentiles (25th, 50th, or 75th), it was found that Cd exposure was positively correlated with hypertension (75th vs 25th) (Figure 3B). When fixing the remaining 4 metals at the median, we observed a linear association between V, Fe, Pb and hypertension (Figure 3C). Figure 3D showed the potential interaction between Fe and Pb, which is associated with an increased risk of hypertension. When the other three metals were in the median, the positive slope of Pb became steeper at higher urinary Fe concentrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7. Associations between urinary metals and blood pressure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the generalized linear model, the relationship between urinary metal and SBP and DBP levels was studied (Figure S4 and S5). SBP and DBP increased with increasing quartiles of urinary Cd and Pb. (both \u003cem\u003ep\u003c/em\u003e for trend \u0026lt; 0.05). We found that the quartiles of V and Fe were associated with elevated SBP levels(both \u003cem\u003ep\u003c/em\u003e for trend \u0026lt; 0.05), but not with the increasing of DBP levels (both \u003cem\u003ep\u003c/em\u003e for trend \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study provides epidemiological evidence that supports many previous studies on the association between urinary metal levels and hypertension. Quantile g-computation showed that urinary metal mixture concentrations were significantly associated with the risk of hypertension. Cd contributed the largest positive weights and followed by Zn, Tl, and V. Logistic regression models showed that V, Fe, Co, Zn, Cu, As, Mo, Cd, Te, Tl, Pb were associated with increased risk of hypertension. As the quartiles of V, Fe, Cd, and Pb increase, so does the risk of hypertension. We used the RCS model to study the shape of the dose-response relationship curve of these associations and observed a positive linear relationship of V, Fe, and Pb with hypertension. The BKMR analysis estimated the effects of joint exposure of four metals. We also found an interaction between urinary Fe and Pb for increasing risk of hypertension.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. V\u003c/h2\u003e \u003cp\u003eV is essential for microorganisms and animals, and the overall physiological role of vanadate in humans is critical (Rehder et al., 2016). There is increasing evidence that essential metals play a beneficial role only within a certain range, and too low or too high concentrations of essential metals pose a health threat to the body (Budi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a transition metal element with a variety of oxidation states, V has redox activity. It is easy to be oxidized to V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e after the redox reaction. and the toxicity of V increases with the increase of valence, and V\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e is the most toxic, leading to oxidative stress and cellular senescence (He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have shown that the effect of V on the cardiovascular system is bidirectional, which has both advantages and disadvantages. Accumulation of transition metal V may cause inflammation and endothelial dysfunction, which can lead to elevated blood pressure levels and the development of hypertension (Rines et al., 2012). In this study, urinary V was positively correlated with an increased risk of hypertension in the elderly(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, some studies have shown that V compounds exert a range of insulin-like effects in the cardiovascular system to improve hypertension (Othman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An animal study showed that V compounds led to significant reductions in plasma insulin concentrations and blood pressure (Bhanot et al., 2022). The results of studies on pregnant women suggest that exposure to V during pregnancy is inversely correlated with blood pressure (Qiu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These inconsistent results may reflect the potential bidirectional effects of V on blood pressure. This may be related to the type and dose of V compounds and the different laboratory animals and study populations. Since the dose of vanadium determines whether it has beneficial or harmful effects, its exact effective dose remains unknown. Therefore, further research on vanadium exposure in the environment is necessary.\u003c/p\u003e \u003cp\u003eIn our study, the median concentrations of urinary V (2.32 \u0026micro;g/g creatinine) in the hypertension group (community-dwelling elderly) were significantly higher than in pregnant Chinese women (0.76\u0026micro;g/L) and in the general Chinese population (1.95 \u0026micro;g/g creatinine) (Ma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These comparisons may indicate biogeochemical differences in vanadium exposure and sources. Stratified according to BMI, the results showed that the risk of hypertension increases with an increase in the quartile of V concentration in obese people (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28). In the RCS model, a significant linear dose-response relationship between V exposure and the odds of hypertension also was suggested (Poverall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Pnon-linear\u0026thinsp;=\u0026thinsp;0.462)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Fe\u003c/h2\u003e \u003cp\u003eFe is a key cofactor in a variety of physiological processes (Evstatiev et al., 2012). It is an important component of hemoglobin, myoglobin, and others, and plays an important physiological function in the human body(Simpson et al., 2015), affecting oxygen delivery and uptake(Evstatiev et al., 2012). Fe deficiency and excessive intake are harmful to the human body. The most common form of iron deficiency is iron deficiency anemia and iron overload is associated with an increased risk of cardiovascular disease (Kiechl et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The accumulation of Fe in the body can lead to oxidative stress. Epidemiological and laboratory data suggest that cellular responses induced by oxidative stress following heavy metal exposure are associated with an increased risk of tumors, diabetes, renal failure, and 36 cardiovascular diseases (Taucher et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ighodaro et al., 2018; Yaribeygi et al., 2012; Yi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Iron-induced oxidative stress has the following significance: 1) DNA damage, lipid peroxidation and oxidative protein damage caused by the failure of redox regulation; 2) free radical-induced signal transduction pathway activation (Valko et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of this study indicate that increased Fe content in urine is a risk factor for hypertension, which is consistent with previous studies (Wu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Wu et al., 2018b). Wu et al. found positive trends for increased odds of hypertension with increasing Fe quartiles (Wu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). In this study, the urinary Fe concentration (141.97 \u0026micro;g/g creatinine) in the hypertension group was also significantly higher than in the above study (69.9\u0026micro;g/g creatinine). In obese and non-obese populations, there was a significant positive correlation between high levels of urinary Fe and hypertension, suggesting that the association between iron and hypertension risk may not be affected by BMI. Finally, we found a significant positive correlation between iron and SBP levels, with no significant correlation with DBP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, it can be inferred that higher concentrations of Fe in urine may affect SBP, thereby increasing the risk of hypertension. Therefore, effective prevention and control of hypertension can start with improving lifestyle and reasonably controlling iron intake and excretion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Cd and Pb\u003c/h2\u003e \u003cp\u003eCd and Pb are common environmental toxic metals. Human exposure to Cd and Pb is mainly through the consumption of contaminated water or food and the inhalation of contaminated air. Exposure to Cd and Pb is harmful to humans. Some cross-sectional studies have shown significant associations between Cd and Pb exposure and hypertension prevalence in the US, Korea, and Kentucky (Tang et al.,2022; Kwon et al.,2021; Walker et al.,2022). Furthermore, both Cd and Pb are non-Fenton metals, which indirectly produce reactive oxygen species by disrupting cellular antioxidant defense systems, inhibiting mitochondrial electron respiratory chains, and replacing metals with redox activity, thereby inducing lipid peroxidation, DNA damage, and toxic effects (Paithankar et al.,2020). Heavy metal exposure will produce excessive ROS, thus damaging the balance of oxidation and antioxidant systems in the vascular system, leading to oxidative stress (M\u0026uuml;nzel et al.,2017). Mechanistic studies suggest that Cd and Pb may cause increased blood pressure through oxidative stress (Perfus-Barbeoch et al.,2002; Kasperczyk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study identified a positive increasing trend of OR for hypertension with Cd and Pb exposure. Zhang et al. showed that SBP and DBP levels increased with increased exposure to Cd and Pb, suggesting that cadmium exposure may increase the risk of hypertension (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which is consistent with our findings. Kim et al. found that high blood Cd and Pb concentrations were positively correlated with elevated blood pressure levels and the prevalence of hypertension (Kim et al., 2022). In our study, the BKMR model proposed that as urinary concentrations of Cd and Pb increase, the risk of hypertension also increases. To further understand the effect of this effect on blood pressure levels, we analyzed the association between Cd, Pb exposure and SBP, DBP separately. The results showed that increasing quartiles of Cd and Pb were significantly associated with higher SBP and DBP. These results suggest that joint metal exposure may play an important role in the occurrence and development of hypertension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several advantages. First, we simultaneously measured up to 12 metals, which allowed us to discover the interaction between Fe and Pb. Secondly, we use quantile g-computation analysis and the BKMR model, which helps to estimate the joint effects and interactions between complex exposures. Finally, we used the generalized linear model to further verify the correlation between metals and SBP and DBP. However, there are some limitations. Firstly, this is a case-control study with a weak ability to verify causal associations. Urine samples and blood pressure measurements were performed simultaneously, so we could not assess the time relationship between metal exposure and hypertension. Therefore, this result needs to be further confirmed in other populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our study explored a potential association between urinary concentrations of metal mixtures and the risk of hypertension. Our study found that urinary Cd concentration was identified as the most critical factor associated with hypertension. Urinary Fe and urinary Pb may have a synergistic effect. Given the increase of heavy metal pollution in the environment, it is necessary to determine the relationship between heavy metal exposure and risk factors of hypertension in different populations and regions and to determine ways to minimize heavy metal exposure. Further studies are needed to confirm our findings and elucidate the potential mechanism of the link between metals and hypertension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeiyan Li:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Investigation, Formal Analysis, Writing - Original Draft, Visualization; \u003cstrong\u003eSiyu Duan:\u003c/strong\u003e Methodology, Visualization, Formal analysis, Resources; \u003cstrong\u003eRui Wang:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Data Curation, Conceptualization;\u003cstrong\u003e\u0026nbsp;Pei He:\u0026nbsp;\u003c/strong\u003eVisualization, Data Curation, Investigation, Formal analysis; \u003cstrong\u003eZhongyuan Zhang:\u0026nbsp;\u003c/strong\u003eVisualization, Data Curation, Investigation;\u003cstrong\u003e\u0026nbsp;Yuqing Dai:\u0026nbsp;\u003c/strong\u003eFormal analysis, Investigation, Validation; \u003cstrong\u003eZhuoheng Shen:\u0026nbsp;\u003c/strong\u003eValidation,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Formal analysis;\u003cstrong\u003e\u0026nbsp;Yue Chen:\u0026nbsp;\u003c/strong\u003eValidation,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Formal analysis; \u003cstrong\u003eHuifang Yang:\u0026nbsp;\u003c/strong\u003eVisualization, Data Curation, Formal analysis;\u003cstrong\u003e\u0026nbsp;Xiaoyu Li:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - review \u0026amp; editing, Funding acquisition; \u003cstrong\u003eJian Sun:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - review \u0026amp; editing, Funding acquisition, Validation, Methodology; \u003cstrong\u003eRui Zhang:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing, Supervision, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the \u0026ldquo;Light of the West\u0026rdquo; Talent Training Plan Project of Chinese Academy of Sciences (XAB2022YW18), the National Natural Science Foundation of China (No.82202431 and U22A20360), the Natural Science Foundation Project of Ningxia, China (2022AAC05024, LNZR202305 and 2022AAC05028), and the Key Research and Development Project of Ningxia (2021BEG02026 and 2021BEG02030), the Key Research and Development Project of Ningxia (Grant No. 2023BEG02028).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the support from the \u0026ldquo;Light of the West\u0026rdquo; Talent Training Plan Project of Chinese Academy of Sciences (XAB2022YW18), the National Natural Science Foundation of China (No.82202431 and U22A20360), the Natural Science Foundation Project of Ningxia, China (2022AAC05024, LNZR202305 and 2022AAC05028), and the Key Research and Development Project of Ningxia (2021BEG02026 and 2021BEG02030), the Key Research and Development Project of Ningxia (Grant No. 2023BEG02028). We thank all participants for their support of our study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ningxia Medical University Medical Ethical Committee (No. 2021-N0098, No. 2022-N013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSee supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhanot S, Michoulas A, McNeill JH. Antihypertensive effects of vanadium compounds in hyperinsulinemic, hypertensive rats. 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Environ Int. 2021; 153:106538. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envint.2021.106538\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2021.106538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eCharacteristics of hypertensive and non-hypertensive participants [n (%)]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.231197771587743%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003eNon-hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(t)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e693(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.231197771587743%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e231 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e462 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e71.22\u0026plusmn;5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.231197771587743%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e71.25\u0026plusmn;5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.384401114206128%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e71.20\u0026plusmn;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e291(42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e97(42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e194(42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e402(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e134(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e268(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e24.43\u0026plusmn;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e25.21\u0026plusmn;3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e24.04\u0026plusmn;3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-4.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e21(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e17(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e18.5~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e310(44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e82(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e228(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e24.0~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e261(37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e97(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e164(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e101(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e48(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e53(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise frequency\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eEvery day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e511(73.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e157(68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e354(76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;once a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e35(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e17(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e18(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e＜once a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e45(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e20(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e25(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e102(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e37(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e65(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e581(83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e192(83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e389(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e43(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e18(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e25(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eActive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e69(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e21(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e48(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol drinking status\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e592(81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e207(89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e385(83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e＜once a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e82(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e17(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e65(14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;once a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e13(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e5(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e8(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eEveryday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary habits\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eMeat-vegetables balanced diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e618(89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e205(88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e413(89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003ePlant-based diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e70(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e24(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e46 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.57660167130919%\" valign=\"top\"\u003e\n \u003cp\u003eMeat-based diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.42339832869081%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e5(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.484679665738161%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.73816155988858%\" valign=\"top\"\u003e\n \u003cp\u003e3(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.584958217270195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.192200557103064%\" 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\u003cp\u003e\u003cstrong\u003e*P \u0026lt; 0.05\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eMetal concentration distributions standardized by creatinine (\u0026mu;g/g Cr) in urine\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"769\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.263979193758127%\" valign=\"top\"\u003e\n \u003cp\u003eUrinary metals\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u0026mu;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" valign=\"top\"\u003e\n \u003cp\u003eFull population\u0026nbsp;(n=693)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.96749024707412%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;(n=231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNon-hypertension\u003c/p\u003e\n \u003cp\u003e(n=462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.152145643693108%\" valign=\"top\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.842652795838752%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e1.34(0.09,5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.32(0.15,9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e1.08(0.07,4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-4.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e99.96(20.99,277.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e141.97(40.51,424.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e80.54(7.64,245.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-4.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.24(0.13,0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.33(0.16,0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e0.21(0.12,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-4.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e370.50(179.43,689.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e503.79(231.95,1082.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e323.32(164.87,565.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-4.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e10.54(6.47,18.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e12.93(6.46,26.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e9.69(6.52,17.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-3.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e58.39(33.67,110.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e74.52(35.62,150.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e53.29(32.20,95.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-3.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e16.25(10.72,24.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e17.78(10.68.30.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e15.47(10.73,22.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-2.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eMo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e78.38(49.02,128.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e91.64(51.49,171.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e73.25(48.34,115.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.34(0.20,0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.49（0.27,0.95）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e0.29(0.19,0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.402597402597403%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-7.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eTe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e58.89(22.14,156.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e77.47(28.80,215.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e54.07(19.67,124.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-3.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003eTl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.22(0.14,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.25(0.16,0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e0.21(0.14,0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.246753246753247%\" valign=\"top\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.89(0.38,2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.324675324675326%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.32(0.58,3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e0.81(0.31,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-4.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.831168831168831%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: V, Vanadium; Fe,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIron\u003c/strong\u003e\u003cstrong\u003e; Co, Cobalt; Zn, zinc; Cu, Copper; As, Arsenic; Se, Selenium; Mo, Molybdenum; Cd, Cadmium; Te, Tellurium; Tl, Thallium; Pb, Lead.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWilcoxon rank sum test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Odds ratios (95%CI) for hypertension associated with quartiles of urinary metal concentrations\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"763\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUrinary metals (\u0026mu;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.39056356487549%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eQuartiles of urinary metals (\u0026mu;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003eLinear models \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-trend \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-FDR \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.431164901664145%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.431164901664145%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.431164901664145%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49016641452345%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.733736762481088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.497730711043873%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.984871406959153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.09~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.34~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.52~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.54(0.97,2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(0.86,1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.64(1.67,4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.33(1.15,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.54(0.94,2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.30(0.80,2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.72(1.70,4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.33(1.15,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e20.99~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e99.96~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e277.22~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.80(1.11,2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.71(1.05,2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.01(1.88,4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(1.19,1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.90(1.16,3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.72(1.05,2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.23(2.04,5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.42(1.22,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.13~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.24~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.48~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.84(0.52,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.25(0.79,1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.53(1.62,3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.39(1.21,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.80(0.49,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.21(0.76,1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.50 (1.57,4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.39(1.19,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;179.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e179.43~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e370.50~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e689.29~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.92(0.56,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.33(0.84,2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.75(1.75,4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.43(1.23,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.57,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.36(0.84,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.78(1.74,4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.43(1.23,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e6.47~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e10.54~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e18.98~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.51(0.32,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.03(0.66,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.52(0.99,2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.22(1.06,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.49(0.30,0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.04(0.66,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.48(0.94,2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.21(1.05,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e33.67~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e58.39~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e110.12~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.82(0.51,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(0.88,2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.94(1.25,3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.29(1.12,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.83(0.51,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.35(0.85,2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.02(1.26,3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.30(1.12,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e10.72~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e16.25~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e24.49~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.64(0.40,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.60,1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.42,(0.92,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.16(1.00,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.63(0.39,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.91(0.57,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.37(0.86,2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.14(0.98,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eMo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;49.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e49.02~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e78.38~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e128.05~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.62(0.39,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.09(0.70,1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.81(1.17,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.27(1.10,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.65(0.40,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.05(0.66,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.88(1.19,2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.27(1.10,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.20~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.34~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.56~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.81(0.48,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.76(1.08,2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.39(3.36,8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.86(1.59,2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.80(0.46,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.82(1.11,3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6.68(4.01,11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.98(1.67,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eTe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;22.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e22.14~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e58.89~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e156.32~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.95(0.59,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.98(0.61,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.12(1.36,3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.27(1.10,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.58,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.89(0.55,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.15(1.36,3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.27(1.09,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0031*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eTl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.14~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.22~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.36~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.92(0.58,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.92(0.58,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.06(1.32,3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.26(1.09,1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.96(0.60,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.58,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.13(1.34,3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.26(1.09,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e0.0051*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.38~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.89~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.19~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.95(0.59,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.33(1.71,2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.68(1.71,4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.41(1.22,1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e0.97(0.59,1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.368283093053735%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(0.85,2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.87(1.81,4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.63040629095675%\" valign=\"top\"\u003e\n \u003cp\u003e1.44(1.24,1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.960681520314548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.650065530799475%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eLinear model: The metal concentration transformed by the interquartile range was incorporated into the regression model, representing the OR (95% CI) of increased\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003etrend the median of each metal quartile ( natural log-transformation urinary metal concentrations) as a continuous variable, and the \u003cem\u003ep\u003c/em\u003e-value for the trend test was obtained from the conditional logistic regression model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;FDR corrections were performed to adapt to multiple tests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1: Unadjusted odds ratio.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2: Adjusted for age, sex, BMI, exercise frequency, smoking status, alcohol drinking status, dietary habit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"metal mixtures, hypertension, community-dwelling elderly, Bayesian kernel machine regression, interaction effect","lastPublishedDoi":"10.21203/rs.3.rs-3880760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3880760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFewer studies have focused on the interaction of metal mixtures with hypertension, especially in Chinese community-dwelling elderly. In addition, the relationship between metals exposure and hypertension may be weakened or strengthened due to the presence of multiple chronic diseases in the elderly.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, inductively coupled plasma mass spectrometry was used to detect the levels of 12 metals in the urine of 693 elderly people in the Yinchuan community. Conditional logistic regression model and restricted cubic spline analysis (RCS) were used to explore the association between urinary metal concentration and hypertension and dose-response relationship. Quantile g-computation and Bayesian kernel machine regression (BKMR) to analyze the association of individual urinary metal concentrations and metal mixtures with hypertension risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUrinary concentrations of 12 metals (vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, thallium, and lead) were higher in the hypertension group than in the non-hypertension group. In the RCS models, the urinary concentrations of vanadium, iron, and lead showed a linear dose-response relationship with hypertension risk. Quantile g-computation analyses showed cadmium contributed the largest positive weights. The BKMR models showed that the positive slope of lead became steep at higher concentrations of urinary iron when the other three metals were at the median.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe found that exposure to metal mixtures was associated with the risk of hypertension and a significant positive interaction between urinary iron and lead. Further research is needed to confirm our findings and elucidate the underlying mechanisms of the interaction between metals and hypertension.\u003c/p\u003e","manuscriptTitle":"Associations between metal-metal interaction and the risk of hypertension: A case-control study in Chinese community-dwelling elderly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 16:47:40","doi":"10.21203/rs.3.rs-3880760/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":"9345cf48-3fe5-40b0-8575-3c038f5f9c4f","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T15:35:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 16:47:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3880760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3880760","identity":"rs-3880760","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}