The association between serum lead concentration and all-cause or cardiovascular disease mortality in hypertensive patients

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Methods and results In our study, 8364 subjects aged 20 years and older were included. We used a weighted COX proportional risk regression model to calculate risk ratios (HR) and 95% confidence intervals (CI) for blood lead and mortality. The relationship between serum lead levels and mortality was described by a restricted cubic spline curve. Kaplan Meier curves were used to describe the relationship between survival time and survival in study subjects, and all-cause mortality was analyzed in subgroups. Using the lowest quartile (Q1) as a reference, the HRs for all-cause mortality in model 3 (Q2, Q3, and Q4) were 1.05 (95% CI, 0.84-1.32), 1.10 (95% CI, 0.89-1.36), and 1.44 (95% CI, 1.16-1.79), respectively. For cardiovascular mortality, they were 1.03 (95% CI, 0.62-1.52), 1.30 (95% CI, 0.77-2.21), and 1.97 (95% CI, 1.31-2.97), respectively. Weighted restricted cubic spline regression confirmed a positive correlation between serum lead levels and risk ratio (HR) (P-overall < 0.001). Weighted Kaplan Meier curves showed a significant downward trend in survival in the hypertensive population with increasing serum lead levels. (P<0.0001 for all log-rankings). Subgroup analysis of all-cause mortality showed a strong positive association between serum lead levels and all-cause mortality in different populations. Conclusions Serum lead concentration showed a non-linear positive correlation with all-cause mortality and cardiovascular mortality in hypertensive patients. Biological sciences/Biochemistry Earth and environmental sciences/Environmental sciences Health sciences/Health care Lead Hypertension Subgroups All-cause mortality Cardiovascular mortality NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction Lead is a heavy metal that can cause serious damage to the nervous, digestive, and blood systems of the body. Hypertension is one of the most prominent risk factors for the occurrence of myocardial infarction, stroke and kidney failure, and even death. Lead poisoning causes heart and blood vessel damage and increases the risk of cardiovascular disease[ 1 ]. Potential mechanisms include enhanced lead-induced oxidative stress, disturbed lipid metabolism, and decreased production of nitric oxide and guanylate cyclase[ 2 , 3 ]. The relationship between hypertension and serum lead is unclear. Several studies have shown that elevated serum lead levels significantly increase the prevalence of hypertension [ 4 – 6 ], and a recent large cross-sectional study of a US population showed a nonsignificant correlation between blood lead concentrations and hypertension[ 7 ]. Another large study of a Gulf Coast follow-up population showed no significant association between lead concentration in the body and hypertension [ 8 ]. However, many studies have shown that higher lead concentrations are associated with increased mortality. A large classical cohort study based on the U.S. population showed that increased blood lead levels significantly increased all-cause mortality and cardiovascular disease mortality in the study population [ 9 ]. A study based on the NHANES database showed that blood lead levels were positively associated with the risk of total mortality and cardiovascular disease mortality in the population [ 10 – 12 ]. Studies in diabetic populations have shown that elevated blood lead concentrations increase the risk of death in diabetic patients [ 13 ]. The association between blood lead levels and all-cause mortality and cardiovascular disease mortality in the US hypertensive population has not been studied, so we designed this study. Based on data from the NHANES database of hypertensive adults from 2009–2016, the relationship between serum lead levels and mortality was analyzed using a weighted COX proportional risk regression model, and a subgroup analysis of all-cause mortality was performed to assess the robustness of this association. 2. Materials and Methods 2.1. Study Population We obtained data from the NHANES database for the 2009–2016 survey cycle for this retrospective cohort study with a total of 40,439 participants, and we included participants younger than or equal to 20 years of age (n = 17,173), non-hypertensives, and participants with missing information on hypertension (n = 11,102), participants with missing information on follow-up (n = 23), participants with serum participants with missing information or unreliable data on lead levels (n = 3777) were excluded. As a result, a total of 8364 participants were included in the final analysis, and Fig. 1 illustrates the specific inclusion and exclusion process. Approval of the study protocol was granted by the Institutional Review Board of the US Centers for Disease Control. Written informed consent was provided by each study subject. 2.2. Determination of serum lead Lead concentration in blood was selected as the subject of our study and was determined in serum by inductively coupled plasma-dynamic reaction cell-mass spectrometry (ICP-DRC-MS) after a standard blood sampling, and storage procedure. Detailed instructions for sample collection and handling are available in the NHANES Laboratory/Medical Technology Procedures Manual (LPM)[ 14 ] Measurements below the limit of detection (LOD) for blood lead were replaced by LOD divided by the square root of 2[ 15 ]. 2.3. Definition of covariates A standard questionnaire was used to collect demographic information on the study population. Other confounders included: smokers were those who had consumed at least 100 cigarettes over their lifetime [ 16 , 17 ]. People with fasting glucose levels higher than 126 mg/dL or 2-hour blood glucose levels above 200 mg/dL (measured by oral glucose tolerance test) or treated with insulin or antidiabetic drugs were diabetic [ 18 ]. Participants with hypertension are those who have an average systolic blood pressure of more than or equal to 130 mm Hg and average diastolic blood pressure of more than or equal to 80 mm Hg, or who have been medically diagnosed with hypertension or are taking hypertensive drugs to reduce their blood pressure [ 12 ]. BMI is measured by taking the weight in kilograms and dividing it by the square of the height in square meters.[ 19 ]. Drinkers were defined as participants who consumed alcohol more than or equal to 12 times per year. Standard biochemical techniques were used to assess total cholesterol, high-density lipoprotein cholesterol (HDL) and glycated hemoglobin levels in the NHANES database. The laboratory methods documentation section of NHANES contains a comprehensive overview of the laboratory procedures performed. Random forest interpolation was performed for covariates with missing values [ 20 ] 2.4. Definition of Death Mortality data for study subjects were downloaded through the NHANES database's public use link mortality file [ 21 ]. All-cause mortality was determined as a death from any cause, and when codes I00-I09, I11, I13, I20-I51, or I60-I69 were used, study subjects were defined as cardiovascular disease deaths[ 22 ]. 2.5. Statistical analysis The NHANES database uses a complex sampling design method, so we used appropriate weights in the statistical analysis. In the baseline information, the mean ± standard deviation was used to represent continuous variables and percentages were used for the categorical variables. Weighted ANOVA and weighted chi-square tests were used for comparing between-group differences in continuous and categorical variables, respectively. Furthermore, the study subjects were classified into four groups based on the quartiles of their serum lead concentrations, The lowest quartile group Q1 was used as the reference group. To adjust for the effect of confounding factors, we calculated risk ratios (HR) and 95% confidence intervals (CI) for blood lead and mortality using a weighted COX proportional risk regression model. Model adjustments were performed, Model 1: no adjustment. Model 2: adjusted for demographic information (age, gender, and race), and Model 3: adjusted for information on all covariates (age, gender, race, marriage, education level, BMI, HDL, triglycerides, glycohemoglobin, smoking status and diabetes.). A weighted restricted cubic spline curve was used to visualize the relationship between serum lead concentrations and total and cardiovascular mortality. We used weighted Kaplan Meier curves to describe the relationship between survival time and survival of the study subjects. A subgroup analysis of all-cause mortality was also performed with the purpose of discussing the stability of the connection between serum lead concentrations and all-cause mortality in different populations and discussing the interaction between different covariates by likelihood ratio tests. We used R4.21 version for all analyses[ 13 , 23 – 25 ]. 3. Results 3.1. Baseline information The study ultimately included 8364 patients with hypertension who met the criteria, of whom 48.3% were male and 51.7% were female, with a mean BMI of 30.66 ± 7.15 kg/m2, 48.1% were smokers, 22.3% were diabetics, and 56.5% were alcohol drinkers. The study population was divided into four groups based on serum lead levels Q1 (< 0.85umol/l), Q2 (0.85 to 1.28umol/l), Q3 (1.28 to 2.00umol/l), and Q4 (≥ 2.00umol/l). For the indicators we focused on, all-cause mortality and cardiovascular mortality in the total population were 11.9% and 3.6%, respectively, with increasing serum lead levels, all-cause mortality (Q1: 5.2%, Q2: 10.3%, Q3: 13.2%, Q4. 21.3%) and cardiovascular mortality (Q1: 1.4%, Q2: 2.5%, Q3: 4.1%, Q4. 7.3%) showed a continuous upward trend. The baseline information is shown in Table 1 . Table 1 Baseline information, weighted Characteristic Serum lead Overall Q1 Q2 Q3 Q4 N 8364 2070 2105 2095 2094 Age,n (%) 20–40 years 20.5 40.3 19.3 10.3 7.6 40-60years 41.0 39.8 42.3 42.5 39.5 > 60years 38.4 19.9 38.4 47.2 52.9 Gender,n(%) Male 48.3 56.0 51.2 45.6 37.8 Female 51.7 44.0 48.8 54.4 62.2 Race,n(%) Mexican America 6.5 8.0 6.2 6.1 5.4 Nonhis panic Black 12.9 12.9 11.7 12.8 14.2 Nonhis panic White 69.1 67.1 69.5 70.2 69.9 Other races 11.7 12.5 12.6 11.0 10.5 Marriage,n(%) Married/living with partner 63.3 63.0 61.4 66.2 62.6 Widowed/divorced/separated 23.9 18.1 25.7 24.4 28.9 Never married 12.8 18.8 12.9 9.4 8.5 Education,n (%) High school 58.5 63.8 59.5 57.9 50.9 BMI,kg/m 2 30.66 ± 7.15 32.99 ± 8.09 30.91 ± 6.99 29.77 ± 6.32 28.32 ± 5.86 Hdl,mg/dl 52.45 ± 17.24 49.23 ± 14.61 51.36 ± 15.57 54.54 ± 19.25 55.63 ± 18.99 TC,mg/dl 197.78 ± 41.94 191.84 ± 39.42 199.44 ± 43.80 200.86 ± 42.36 200.13 ± 41.89 Triglyceride,mg/dl 144.84 ± 91.02 149.86 ± 92.68 150.31 ± 104.62 134.82 ± 80.55 139.18 ± 78.58 GFR,ml/mim1.73m 2 86.59 ± 25.19 94.65 ± 27.36 85.86 ± 23.05 83.63 ± 22.92 80.22 ± 24.41 Smoking status,n(%) NO 51.9 63.6 55.1 48.5 36.5 YES 48.1 36.4 44.9 51.5 63.5 Diabetes,n(%) NO 77.7 77.0 74.6 80.3 79.4 YES 22.3 23.0 25.4 19.7 20.6 Alcohol status,n(%) NO 43.5 50.8 44.1 40.4 35.8 YES 56.5 49.2 55.9 59.6 64.2 All deaths,n(%) 11.9 5.2 10.3 13.2 21.3 CVD deaths,n(%) 3.6 1.4 2.5 4.1 7.3 Mean ± standard error (SE) for continuous variables, percentages (%) for categorical variables. BMI, body mass index; HDL, high-density lipoprotein; TC, Total cholesterol; GFR, Glomerular rate filtration; CVD death, cardiovascular disease death 3.2. Association Between Serum Lead and All-Cause and Cardiovascular Mortality Table 2 shows the weighted Cox proportional risk regression results. The weighted Cox proportional risk regression showed that the risk ratio (HR) for that for all-cause mortality and cardiovascular mortality gradually increased with increasing serum lead levels. Using the lowest quartile (Q1) as a reference, the HRs for all-cause mortality were 1.05 (95% CI, 0.84–1.32), 1.10 (95% CI, 0.89–1.36), and 1.44 (95% CI, 1.16–1.79) for model 3 (Q2, Q3, and Q4), respectively. For cardiovascular mortality, they were 1.03 (95% CI, 0.62–1.52), 1.30 (95% CI, 0.77–2.21), and 1.97 (95% CI, 1.31–2.97), respectively. All the above P trends were < 0.001. As shown in Fig. 2 , restricted cubic spline regression confirmed a positive correlation between serum lead concentration and risk ratio (HR) (P-overall < 0.05). Blood lead levels were positively associated with both all-cause mortality and cardiovascular. Table 2 All-cause mortality and CVD-mortality hazard ratios (HRs) for participants aged 20 years and older according to malnutrition status. (weighted) Mortality outcome Dealth/o Weight/death (%) Hazard ratio (95%CI) Model1 Model2 Model3 All-cause mortality 1231 11.9 Q1 128 5.2 Ref Ref Ref Q2 250 10.3 1.86(1.47,2.34) 0.98(0.78,1.22) 1.05(0.84,1.32) Q3 319 13.2 2.31(1.85,2.89) 0.93(0.75,1.16) 1.10(0.89,1.36) Q4 534 21.3 3.87(3.09,4.85) 1.27(1.02,1.58) 1.44(1.16,1.79) P for trend < 0.001 0.02 0.001 CVD-mortality 399 3.6 Q1 39 1.4 Ref Ref Ref Q2 72 2.5 1.76(1.15,2.68) 0.84(0.54,1.32) 1.03(0.62,1.52) Q3 105 4.1 2.76(1.61,4.72) 1.00(0.59,1.69) 1.30(0.77,2.21) Q4 183 7.3 5.11(3.25,8.04) 1.43(0.95,2.15) 1.97(1.31,2.97) P for trend < 0.001 0.004 < 0.001 CVD-mortality; cardiovascular disease mortality Q1(< 0.85umol/l), Q2(0.85to 1.28umol/l), Q3(1.28 to 2.00umol/l). Q4(≥ 2.00umol/l). Model1: No adjustment Model2: Adjusts for basic information such as age, ethnicity and gender. Model3: Adjust for all confounding factors. 3.3. Survival analysis As shown in Fig. 3 , Kaplan Meier curves showed a significant trend of decreasing survival in the hypertensive population with increasing serum lead levels (All Log-rank P < 0.0001). 3.4. Subgroup Analysis The subgroup analysis of the all-cause mortality is presented in Table 3 . HR was greater than 1 in all subgroups and the p-value for the interaction was greater than 0.01 in all subgroups except the education level and smoking status subgroups. This represents a strong positive link between serum lead levels and all-cause mortality in different populations. Table 3 Sub-group analysis HR (95%CI) P for interaction Stratified by age 0.12 Age20-40 1.84(1.30,2.59) Age40-60 1.32(1.16,1.50) Age60-100 1.29(1.21,1.38) Stratified by gender 0.94 Female 1.48(1.39,1.58) Male 1.49(1.37,1.61) Stratified by race 0.74 Mexican America 1.40(1.22,1.60) Other races 1.61(1.41,1.85) Non hispanic White 1.48(1.37,1.60) Non hispanic Black 1.53(1.43,1.65) Stratified by educational level 0.002 High school 1.56(1.44,1.70) Stratified by BMI 0.57 < 25 kg/m 2 1.40(1.25,1.58) 25–30 kg/m 2 1.49(1.35,1.63) ≥ 30 kg/m 2 1.48(1.37,1.61) Stratified by Smoking status < 0.01 Yes 1.62(1.48,1.76) No 1.62(1.48,1.76) Stratified by Alcohol status 0.34 YES 1.56(1.44,1.69) NO 1.48(1.38,1.60) Stratified by Diabetes 0.04 Yes 1.41(1.31,1.52) No 1.53(1.44,1.63) Subgroup analyses were performed on the following covariates (age, sex, race, marital status, education level, body mass index; smoking status; alcohol consumption status; diabetes mellitus; OR,odds ratio; 95% CI, 95% confidence interval. BMI, body mass index; 4. Discussion In this retrospective NHANES-based cohort study, in which 8364 hypertensive patients participated, all-cause mortality and cardiovascular mortality were 15.9% and 5.9% higher, respectively, in the highest quartile of serum lead study population than in the lowest quartile study population. Weighted COX proportional risk regression models (adjusting for all confounders) showed an increasing trend in the risk ratio (HR) for all-cause mortality to cardiovascular mortality in the lowest quartile, second, third, and highest quartiles. In addition, by plotting restricted cubic spline curves, we found a positive correlation between serum lead concentration and HR (P-overall < 0.001). As serum lead concentrations increased, the risk of all-cause mortality and of cardiovascular death also increased. In terms of survival curves, there was a significant downward trend in survival in the hypertensive population as serum lead levels increased. In terms of educational stratification, hypertensive patients with high school education or higher had a greater risk of death due to high blood lead levels than others. People with higher education levels usually have higher social status and higher standards of living, and although a higher standard of living is usually associated with better health status, this association is variable across ethnic groups [ 26 – 29 ]. Therefore, we need further research to illustrate the impact of blood lead on the more educated among different ethnic groups with hypertension. In terms of smoking status stratification, hypertensive patients who did not smoke had a greater risk of death due to high blood pressure lead levels than others. This may be due to the fact that hypertensive patients who smoke are more likely to have chronic diseases, and those with chronic diseases are more likely to be concerned about their health and therefore reduce the intake of harmful substances, thus reducing mortality [ 30 ]. Our findings suggest that blood lead levels in hypertensive populations are significantly and positively associated with mortality. Studies based on the Third National Nutrition and Health Survey (NHANES) have shown that blood lead levels remain a significant contributor to mortality in study populations with low blood lead [ 31 ], and the latest studies have shown a positive correlation between blood lead concentrations and cardiovascular mortality[ 32 ]. A study based on the NHANES database from 2009–2014 showed that high exposure to lead ions was significantly associated with death from hypertension, heart disease and chronic lower respiratory disease [ 33 ]. This is consistent with our findings. The potential possible mechanism is as follows, in a hypertensive state, the bioavailability of antioxidants is reduced, and excessive reactive oxygen species (ROS) production eventually leads to oxidative stress, and cellular and tissue damage [ 34 ]. Lead exposure causes oxidative stress in cardiovascular tissues in vivo and endothelial cells and vascular smooth muscle cells (VSMC) in vitro[ 3 ]. In addition, lead reduces nitric oxide and guanylate cyclase production in the vasculature, thereby remodeling blood vessels and inhibiting vascular relaxation [ 2 ]. Through the action of these two mechanisms, increased serum lead levels exacerbate the level of oxidative stress in hypertensive patients, and oxidative stress can promote inflammation, fibrosis, and apoptosis, leading to an elevated risk of death. Our study has several benefits. To ensure the validity of the results, we used appropriate weights and confounder adjustments during the analysis. Second, the large sample size of our study from a nationally representative population that strictly adhered to the protocol, as well as the use of a national registry to identify deaths Third, our study is the first to study the influence of serum lead concentrations on all-cause mortality as well as cardiovascular mortality in hypertensive populations and is highly innovative. However, limitations are inevitable. First, although we have adjusted for most relevant confounders, we could not exclude residual or unknown confounders. Second, we excluded numerous subjects due to missing data for some covariates, which may have led to selection bias. Third, some factors may influence lead metabolism, including dietary habits, occupational exposure, and environmental factors, which were not collected in our data. 5. Conclusions Serum lead concentration showed a non-linear positive correlation with all-cause mortality and cardiovascular mortality in hypertensive patients. Declarations Ethical Approval: The study was conducted in accordance with the Declaration of Helsinki and approved by the National Center for Health Statistics (NCHS) Ethics Review Board (ERB) of the Centers for Disease Control (CDC). This study utilized publicly available NHANES data and did not involve new interventions on human subjects. Therefore, clinical trial registration was not required. Consent to Participate: Informed consent was obtained from all individual participants included in the study. Consent to Publish: Participants signed an informed consent form for the release of their data. Informed Consent Statement : Informed consent was obtained from all subjects involved in NHANES. Competing Interests: The authors declare no conflict of interest. Funding: This work was supported by the research program of Changsha Natural Science Foundation (num: kq2208313) and the Fundamental Research Funds for the Central Universities of Central South University [No. 2025ZZTS0961] . Author Contribution Conceptualization and formal analysis:Yan Pu. Methodology, software, and survey: Yan Pu , Qiang Zhen and Ting Yi. Data collation, and writing-original draft preparation: Yan Pu and Xianming Tang. Writing-review and editing and supervision: Xianming Tang. Xianming Tang was the guarantor of this work, and as such, had full access to all the data in the study and assumes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and agreed to the published version of the manuscript. Data Availability The data that support the findings of this study are available from the first author, Yan Pu, upon reasonable request. References ALMEIDA LOPES, A. C. B. D., NAVAS-ACIEN, S. I. L. B. E. R. G. E. L. D. E. K., ZAMOISKI, A., CAMARGO, R. M. A. R. T. I. N. S. A. D. C., PAOLIELLO, M. & A. E. I., URBANO, M. R., MESAS, A. E. & M. B. Association between blood lead and blood pressure: a population-based study in Brazilian adults. 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K. et al. B., SMITH, S. C., SPENCER, C. C., STAFFORD, R. S., TALER, S. J., THOMAS, R. J., WILLIAMS, K. A., WILLIAMSON, J. D.WRIGHT,J.T (2018). ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Journal of the American College of Cardiology. 71 2199–2269. XU, J. SANDLER, D. P. The association between blood metals and hypertension in the GuLF study. Environ. Res. 202 , 111734 (2021). YAO, X. et al. Stratification of population in NHANES 2009–2014 based on exposure pattern of lead, cadmium, mercury, and arsenic and their association with cardiovascular, renal and respiratory outcomes. Environ. Int. 149 , 106410 (2021). ZHANG, Y. et al. Association between blood lead levels and hyperlipidemiais: Results from the NHANES (1999–2018). Front. Public. Health . 10 , 981749 (2022). ZHU, K. et al. M., LIU, L., PAN, A. Associations of exposure to lead and cadmium with risk of all-cause and cardiovascular disease mortality among patients with type 2 diabetes. Environmental Science and Pollution Research International. 29 76805–76815. (2022). ZHANG, X., GOBBO, X. I. A. J. D. E. L., HRUBY, L. C., DAI, A., SONG, Y. & Q. & Serum magnesium concentrations and all-cause, cardiovascular, and cancer mortality among U.S. adults: Results from the NHANES I Epidemiologic Follow-up Study. Clin. Nutr. 37 , 1541–1549 (2018). Additional Declarations No competing interests reported. Supplementary Files file.csv Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 24 Jun, 2025 Editor invited by journal 18 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 16 Jun, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6904376","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477505199,"identity":"fe1abdde-a649-4dc3-bae4-7850e1bc8228","order_by":0,"name":"Yan Pu","email":"","orcid":"","institution":"Central South University，Changsha","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Pu","suffix":""},{"id":477505200,"identity":"9bd4278a-b34d-4c58-9d6b-ff8c7d1ebd85","order_by":1,"name":"Qiang Zhen","email":"","orcid":"","institution":"Sichuan Academy of Medical Sciences \u0026 Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhen","suffix":""},{"id":477505201,"identity":"81302e75-c7aa-4058-94d9-e153977e30f8","order_by":2,"name":"Ting Yi","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Yi","suffix":""},{"id":477505202,"identity":"caeda81c-8fcf-48f9-9b57-ed849863f5f0","order_by":3,"name":"Xianming Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYHAC9t8/DGx4+NkbwDzGBkLqeYBYmqEiTUay5wBJWs4ctjGYkUCkFnuJ7ATjwrbDPAaSjx9/5mGwkd1wgPnZA7y28JzdkDyzLZ3HXDrNTJqHIc14wwE2cwO8Wth7NxzgbbPmsZydw8bMw3A4ccMBHjYJvFqYeTc28LYx8xjcPMMMdNh/IrSw925m5jnjzGNwg4cB6LADRGg5c3Yb44yKNB7JnjQzyTkGycYzD7OZ4dXCPiN3G8MHAxt7fvbDjz+8qbCT7Tve/AyvFjQACipmEtSPglEwCkbBKMAOAEOtQ0d7vch8AAAAAElFTkSuQmCC","orcid":"","institution":"Central South University，Changsha","correspondingAuthor":true,"prefix":"","firstName":"Xianming","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-06-16 10:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6904376/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6904376/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22009-y","type":"published","date":"2025-11-03T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85757818,"identity":"7f6158e5-ae58-4b52-8197-c4ac59530af1","added_by":"auto","created_at":"2025-07-01 10:59:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6904376/v1/162f36d61ce796cd7b2a47f4.jpg"},{"id":85755345,"identity":"91c0700e-7e10-48be-a01b-44d451c5f571","added_by":"auto","created_at":"2025-07-01 10:43:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of serum lead levels with the all-cause (a) and cardiovascular mortality (b) performed by restricted cubic spline analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6904376/v1/d32bad6b3af57b4192a27517.jpg"},{"id":85755346,"identity":"28100b23-50f0-4b3d-a035-102d6d661a9c","added_by":"auto","created_at":"2025-07-01 10:43:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curve for all-cause (a) and cardiovascular mortality(b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6904376/v1/8b7dbaa920ec1ee73371acd8.jpg"},{"id":95564042,"identity":"e3f3f6ca-3d28-4599-8ef5-f1dfb9f6e0e3","added_by":"auto","created_at":"2025-11-10 16:06:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1454585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6904376/v1/30e605b5-b9a0-4113-8081-f06cca84bfd4.pdf"},{"id":85756743,"identity":"38339f21-ef2f-4079-a57e-860647f32fa0","added_by":"auto","created_at":"2025-07-01 10:51:46","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1051105,"visible":true,"origin":"","legend":"","description":"","filename":"file.csv","url":"https://assets-eu.researchsquare.com/files/rs-6904376/v1/7a4e5c603f041396105e24d5.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between serum lead concentration and all-cause or cardiovascular disease mortality in hypertensive patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLead is a heavy metal that can cause serious damage to the nervous, digestive, and blood systems of the body. Hypertension is one of the most prominent risk factors for the occurrence of myocardial infarction, stroke and kidney failure, and even death. Lead poisoning causes heart and blood vessel damage and increases the risk of cardiovascular disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Potential mechanisms include enhanced lead-induced oxidative stress, disturbed lipid metabolism, and decreased production of nitric oxide and guanylate cyclase[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The relationship between hypertension and serum lead is unclear. Several studies have shown that elevated serum lead levels significantly increase the prevalence of hypertension [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and a recent large cross-sectional study of a US population showed a nonsignificant correlation between blood lead concentrations and hypertension[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another large study of a Gulf Coast follow-up population showed no significant association between lead concentration in the body and hypertension [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, many studies have shown that higher lead concentrations are associated with increased mortality. A large classical cohort study based on the U.S. population showed that increased blood lead levels significantly increased all-cause mortality and cardiovascular disease mortality in the study population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A study based on the NHANES database showed that blood lead levels were positively associated with the risk of total mortality and cardiovascular disease mortality in the population [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies in diabetic populations have shown that elevated blood lead concentrations increase the risk of death in diabetic patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The association between blood lead levels and all-cause mortality and cardiovascular disease mortality in the US hypertensive population has not been studied, so we designed this study. Based on data from the NHANES database of hypertensive adults from 2009\u0026ndash;2016, the relationship between serum lead levels and mortality was analyzed using a weighted COX proportional risk regression model, and a subgroup analysis of all-cause mortality was performed to assess the robustness of this association.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Population\u003c/h2\u003e \u003cp\u003eWe obtained data from the NHANES database for the 2009\u0026ndash;2016 survey cycle for this retrospective cohort study with a total of 40,439 participants, and we included participants younger than or equal to 20 years of age (n\u0026thinsp;=\u0026thinsp;17,173), non-hypertensives, and participants with missing information on hypertension (n\u0026thinsp;=\u0026thinsp;11,102), participants with missing information on follow-up (n\u0026thinsp;=\u0026thinsp;23), participants with serum participants with missing information or unreliable data on lead levels (n\u0026thinsp;=\u0026thinsp;3777) were excluded. As a result, a total of 8364 participants were included in the final analysis, and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the specific inclusion and exclusion process. Approval of the study protocol was granted by the Institutional Review Board of the US Centers for Disease Control. Written informed consent was provided by each study subject.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Determination of serum lead\u003c/h2\u003e \u003cp\u003eLead concentration in blood was selected as the subject of our study and was determined in serum by inductively coupled plasma-dynamic reaction cell-mass spectrometry (ICP-DRC-MS) after a standard blood sampling, and storage procedure. Detailed instructions for sample collection and handling are available in the NHANES Laboratory/Medical Technology Procedures Manual (LPM)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Measurements below the limit of detection (LOD) for blood lead were replaced by LOD divided by the square root of 2[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Definition of covariates\u003c/h2\u003e \u003cp\u003eA standard questionnaire was used to collect demographic information on the study population. Other confounders included: smokers were those who had consumed at least 100 cigarettes over their lifetime [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. People with fasting glucose levels higher than 126 mg/dL or 2-hour blood glucose levels above 200 mg/dL (measured by oral glucose tolerance test) or treated with insulin or antidiabetic drugs were diabetic [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Participants with hypertension are those who have an average systolic blood pressure of more than or equal to 130 mm Hg and average diastolic blood pressure of more than or equal to 80 mm Hg, or who have been medically diagnosed with hypertension or are taking hypertensive drugs to reduce their blood pressure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. BMI is measured by taking the weight in kilograms and dividing it by the square of the height in square meters.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Drinkers were defined as participants who consumed alcohol more than or equal to 12 times per year. Standard biochemical techniques were used to assess total cholesterol, high-density lipoprotein cholesterol (HDL) and glycated hemoglobin levels in the NHANES database. The laboratory methods documentation section of NHANES contains a comprehensive overview of the laboratory procedures performed. Random forest interpolation was performed for covariates with missing values [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Definition of Death\u003c/h2\u003e \u003cp\u003eMortality data for study subjects were downloaded through the NHANES database's public use link mortality file [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All-cause mortality was determined as a death from any cause, and when codes I00-I09, I11, I13, I20-I51, or I60-I69 were used, study subjects were defined as cardiovascular disease deaths[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe NHANES database uses a complex sampling design method, so we used appropriate weights in the statistical analysis. In the baseline information, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used to represent continuous variables and percentages were used for the categorical variables. Weighted ANOVA and weighted chi-square tests were used for comparing between-group differences in continuous and categorical variables, respectively. Furthermore, the study subjects were classified into four groups based on the quartiles of their serum lead concentrations, The lowest quartile group Q1 was used as the reference group. To adjust for the effect of confounding factors, we calculated risk ratios (HR) and 95% confidence intervals (CI) for blood lead and mortality using a weighted COX proportional risk regression model. Model adjustments were performed, Model 1: no adjustment. Model 2: adjusted for demographic information (age, gender, and race), and Model 3: adjusted for information on all covariates (age, gender, race, marriage, education level, BMI, HDL, triglycerides, glycohemoglobin, smoking status and diabetes.). A weighted restricted cubic spline curve was used to visualize the relationship between serum lead concentrations and total and cardiovascular mortality. We used weighted Kaplan Meier curves to describe the relationship between survival time and survival of the study subjects. A subgroup analysis of all-cause mortality was also performed with the purpose of discussing the stability of the connection between serum lead concentrations and all-cause mortality in different populations and discussing the interaction between different covariates by likelihood ratio tests. We used R4.21 version for all analyses[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline information\u003c/h2\u003e \u003cp\u003eThe study ultimately included 8364 patients with hypertension who met the criteria, of whom 48.3% were male and 51.7% were female, with a mean BMI of 30.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15 kg/m2, 48.1% were smokers, 22.3% were diabetics, and 56.5% were alcohol drinkers. The study population was divided into four groups based on serum lead levels Q1 (\u0026lt;\u0026thinsp;0.85umol/l), Q2 (0.85 to 1.28umol/l), Q3 (1.28 to 2.00umol/l), and Q4 (\u0026ge;\u0026thinsp;2.00umol/l). For the indicators we focused on, all-cause mortality and cardiovascular mortality in the total population were 11.9% and 3.6%, respectively, with increasing serum lead levels, all-cause mortality (Q1: 5.2%, Q2: 10.3%, Q3: 13.2%, Q4. 21.3%) and cardiovascular mortality (Q1: 1.4%, Q2: 2.5%, Q3: 4.1%, Q4. 7.3%) showed a continuous upward trend. The baseline information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline information, weighted\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSerum lead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge,n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40-60years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonhis panic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonhis panic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation,n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI,kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.32\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHdl,mg/dl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.23\u0026thinsp;\u0026plusmn;\u0026thinsp;14.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.36\u0026thinsp;\u0026plusmn;\u0026thinsp;15.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.54\u0026thinsp;\u0026plusmn;\u0026thinsp;19.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.63\u0026thinsp;\u0026plusmn;\u0026thinsp;18.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC,mg/dl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197.78\u0026thinsp;\u0026plusmn;\u0026thinsp;41.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191.84\u0026thinsp;\u0026plusmn;\u0026thinsp;39.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199.44\u0026thinsp;\u0026plusmn;\u0026thinsp;43.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.86\u0026thinsp;\u0026plusmn;\u0026thinsp;42.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200.13\u0026thinsp;\u0026plusmn;\u0026thinsp;41.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride,mg/dl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144.84\u0026thinsp;\u0026plusmn;\u0026thinsp;91.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.86\u0026thinsp;\u0026plusmn;\u0026thinsp;92.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.31\u0026thinsp;\u0026plusmn;\u0026thinsp;104.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.82\u0026thinsp;\u0026plusmn;\u0026thinsp;80.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139.18\u0026thinsp;\u0026plusmn;\u0026thinsp;78.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFR,ml/mim1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.59\u0026thinsp;\u0026plusmn;\u0026thinsp;25.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.65\u0026thinsp;\u0026plusmn;\u0026thinsp;27.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.63\u0026thinsp;\u0026plusmn;\u0026thinsp;22.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.22\u0026thinsp;\u0026plusmn;\u0026thinsp;24.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol status,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll deaths,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD deaths,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE) for continuous variables, percentages (%) for categorical variables. BMI, body mass index; HDL, high-density lipoprotein; TC, Total cholesterol; GFR, Glomerular rate filtration; CVD death, cardiovascular disease death\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Association Between Serum Lead and All-Cause and Cardiovascular Mortality\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the weighted Cox proportional risk regression results. The weighted Cox proportional risk regression showed that the risk ratio (HR) for that for all-cause mortality and cardiovascular mortality gradually increased with increasing serum lead levels. Using the lowest quartile (Q1) as a reference, the HRs for all-cause mortality were 1.05 (95% CI, 0.84\u0026ndash;1.32), 1.10 (95% CI, 0.89\u0026ndash;1.36), and 1.44 (95% CI, 1.16\u0026ndash;1.79) for model 3 (Q2, Q3, and Q4), respectively. For cardiovascular mortality, they were 1.03 (95% CI, 0.62\u0026ndash;1.52), 1.30 (95% CI, 0.77\u0026ndash;2.21), and 1.97 (95% CI, 1.31\u0026ndash;2.97), respectively. All the above P trends were \u0026lt;\u0026thinsp;0.001. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, restricted cubic spline regression confirmed a positive correlation between serum lead concentration and risk ratio (HR) (P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Blood lead levels were positively associated with both all-cause mortality and cardiovascular.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll-cause mortality and CVD-mortality hazard ratios (HRs) for participants aged 20 years and older according to malnutrition status. (weighted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eDealth/o\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e \u003cp\u003eWeight/death (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003eHazard ratio (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.86(1.47,2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.98(0.78,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.05(0.84,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.31(1.85,2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.93(0.75,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.10(0.89,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.87(3.09,4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.27(1.02,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.44(1.16,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP for trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD-mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.76(1.15,2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.84(0.54,1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.03(0.62,1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.76(1.61,4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.00(0.59,1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.30(0.77,2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.11(3.25,8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.43(0.95,2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.97(1.31,2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP for trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eCVD-mortality; cardiovascular disease mortality\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eQ1(\u0026lt;\u0026thinsp;0.85umol/l), Q2(0.85to 1.28umol/l), Q3(1.28 to 2.00umol/l). Q4(\u0026ge;\u0026thinsp;2.00umol/l).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel1: No adjustment\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel2: Adjusts for basic information such as age, ethnicity and gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel3: Adjust for all confounding factors.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Survival analysis\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Kaplan Meier curves showed a significant trend of decreasing survival in the hypertensive population with increasing serum lead levels (All Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Subgroup Analysis\u003c/h2\u003e \u003cp\u003eThe subgroup analysis of the all-cause mortality is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. HR was greater than 1 in all subgroups and the p-value for the interaction was greater than 0.01 in all subgroups except the education level and smoking status subgroups. This represents a strong positive link between serum lead levels and all-cause mortality in different populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSub-group analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge20-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84(1.30,2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge40-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32(1.16,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge60-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29(1.21,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48(1.39,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49(1.37,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by race\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMexican America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.22,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61(1.41,1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48(1.37,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(1.43,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by educational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(1.17,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50(1.33,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56(1.44,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.25,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49(1.35,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48(1.37,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by Smoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62(1.48,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62(1.48,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by Alcohol status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56(1.44,1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48(1.38,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratified by Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41(1.31,1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(1.44,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSubgroup analyses were performed on the following covariates (age, sex, race, marital status, education level, body mass index; smoking status; alcohol consumption status; diabetes mellitus; OR,odds ratio; 95% CI, 95% confidence interval. BMI, body mass index;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this retrospective NHANES-based cohort study, in which 8364 hypertensive patients participated, all-cause mortality and cardiovascular mortality were 15.9% and 5.9% higher, respectively, in the highest quartile of serum lead study population than in the lowest quartile study population. Weighted COX proportional risk regression models (adjusting for all confounders) showed an increasing trend in the risk ratio (HR) for all-cause mortality to cardiovascular mortality in the lowest quartile, second, third, and highest quartiles. In addition, by plotting restricted cubic spline curves, we found a positive correlation between serum lead concentration and HR (P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As serum lead concentrations increased, the risk of all-cause mortality and of cardiovascular death also increased. In terms of survival curves, there was a significant downward trend in survival in the hypertensive population as serum lead levels increased.\u003c/p\u003e \u003cp\u003eIn terms of educational stratification, hypertensive patients with high school education or higher had a greater risk of death due to high blood lead levels than others. People with higher education levels usually have higher social status and higher standards of living, and although a higher standard of living is usually associated with better health status, this association is variable across ethnic groups [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, we need further research to illustrate the impact of blood lead on the more educated among different ethnic groups with hypertension. In terms of smoking status stratification, hypertensive patients who did not smoke had a greater risk of death due to high blood pressure lead levels than others. This may be due to the fact that hypertensive patients who smoke are more likely to have chronic diseases, and those with chronic diseases are more likely to be concerned about their health and therefore reduce the intake of harmful substances, thus reducing mortality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our findings suggest that blood lead levels in hypertensive populations are significantly and positively associated with mortality. Studies based on the Third National Nutrition and Health Survey (NHANES) have shown that blood lead levels remain a significant contributor to mortality in study populations with low blood lead [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and the latest studies have shown a positive correlation between blood lead concentrations and cardiovascular mortality[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A study based on the NHANES database from 2009\u0026ndash;2014 showed that high exposure to lead ions was significantly associated with death from hypertension, heart disease and chronic lower respiratory disease [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This is consistent with our findings.\u003c/p\u003e \u003cp\u003eThe potential possible mechanism is as follows, in a hypertensive state, the bioavailability of antioxidants is reduced, and excessive reactive oxygen species (ROS) production eventually leads to oxidative stress, and cellular and tissue damage [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Lead exposure causes oxidative stress in cardiovascular tissues in vivo and endothelial cells and vascular smooth muscle cells (VSMC) in vitro[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, lead reduces nitric oxide and guanylate cyclase production in the vasculature, thereby remodeling blood vessels and inhibiting vascular relaxation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Through the action of these two mechanisms, increased serum lead levels exacerbate the level of oxidative stress in hypertensive patients, and oxidative stress can promote inflammation, fibrosis, and apoptosis, leading to an elevated risk of death.\u003c/p\u003e \u003cp\u003eOur study has several benefits. To ensure the validity of the results, we used appropriate weights and confounder adjustments during the analysis. Second, the large sample size of our study from a nationally representative population that strictly adhered to the protocol, as well as the use of a national registry to identify deaths Third, our study is the first to study the influence of serum lead concentrations on all-cause mortality as well as cardiovascular mortality in hypertensive populations and is highly innovative.\u003c/p\u003e \u003cp\u003eHowever, limitations are inevitable. First, although we have adjusted for most relevant confounders, we could not exclude residual or unknown confounders. Second, we excluded numerous subjects due to missing data for some covariates, which may have led to selection bias. Third, some factors may influence lead metabolism, including dietary habits, occupational exposure, and environmental factors, which were not collected in our data.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eSerum lead concentration showed a non-linear positive correlation with all-cause mortality and cardiovascular mortality in hypertensive patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval:\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the National Center for Health Statistics (NCHS) Ethics Review Board (ERB) of the Centers for Disease Control (CDC). This study utilized publicly available NHANES data and did not involve new interventions on human subjects. Therefore, clinical trial registration was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u0026nbsp;\u003c/strong\u003eParticipants signed an informed consent form for the release of their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatement\u003c/strong\u003e: Informed consent was obtained from all subjects involved in NHANES.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the research program of Changsha Natural Science Foundation (num: kq2208313) and the Fundamental Research Funds for the Central Universities of Central South University [No. 2025ZZTS0961] .\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization and formal analysis:Yan Pu. Methodology, software, and survey: Yan Pu , Qiang Zhen and Ting Yi. Data collation, and writing-original draft preparation: Yan Pu and Xianming Tang. Writing-review and editing and supervision: Xianming Tang. Xianming Tang was the guarantor of this work, and as such, had full access to all the data in the study and assumes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the first author, Yan Pu, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eALMEIDA LOPES, A. C. B. D., NAVAS-ACIEN, S. I. L. B. E. R. G. E. L. D. E. K., ZAMOISKI, A., CAMARGO, R. M. A. R. T. I. N. S. A. D. C., PAOLIELLO, M. \u0026amp; A. E. I., URBANO, M. R., MESAS, A. E. \u0026amp; M. B. Association between blood lead and blood pressure: a population-based study in Brazilian adults. \u003cem\u003eEnviron. Health: Global Access. Sci. Source\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 27 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssociation, A. D. Accessed May 31,. \u003cem\u003eUnderstanding A1c Diagnosis\u003c/em\u003e. Available online at: (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diabetes.org/diabetes/a1c/diagnosis\u003c/span\u003e\u003cspan address=\"https://www.diabetes.org/diabetes/a1c/diagnosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCASE, A. D., YAO, E. A. G. L. E. D. E., PROESCHOLD-BELL, R. J. \u0026amp; J. \u0026amp; Disentangling Race and Socioeconomic Status in HealthDisparities Research: an Examination of Black and White Clergy. \u003cem\u003eJ. 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(2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG, X., GOBBO, X. I. A. J. D. E. L., HRUBY, L. C., DAI, A., SONG, Y. \u0026amp; Q. \u0026amp; Serum magnesium concentrations and all-cause, cardiovascular, and cancer mortality among U.S. adults: Results from the NHANES I Epidemiologic Follow-up Study. \u003cem\u003eClin. Nutr.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 1541\u0026ndash;1549 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lead, Hypertension, Subgroups, All-cause mortality, Cardiovascular mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6904376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6904376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this study was to examine the relationship between serum lead levels and mortality in a hypertensive population in the United States.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, 8364 subjects aged 20 years and older were included. We used a weighted COX proportional risk regression model to calculate risk ratios (HR) and 95% confidence intervals (CI) for blood lead and mortality. The relationship between serum lead levels and mortality was described by a restricted cubic spline curve. Kaplan Meier curves were used to describe the relationship between survival time and survival in study subjects, and all-cause mortality was analyzed in subgroups. Using the lowest quartile (Q1) as a reference, the HRs for all-cause mortality in model 3 (Q2, Q3, and Q4) were 1.05 (95% CI, 0.84-1.32), 1.10 (95% CI, 0.89-1.36), and 1.44 (95% CI, 1.16-1.79), respectively. For cardiovascular mortality, they were 1.03 (95% CI, 0.62-1.52), 1.30 (95% CI, 0.77-2.21), and 1.97 (95% CI, 1.31-2.97), respectively. Weighted restricted cubic spline regression confirmed a positive correlation between serum lead levels and risk ratio (HR) (P-overall \u0026lt; 0.001). Weighted Kaplan Meier curves showed a significant downward trend in survival in the hypertensive population with increasing serum lead levels. (P\u0026lt;0.0001 for all log-rankings). Subgroup analysis of all-cause mortality showed a strong positive association between serum lead levels and all-cause mortality in different populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum lead concentration showed a non-linear positive correlation with all-cause mortality and cardiovascular mortality in hypertensive patients.\u003c/p\u003e","manuscriptTitle":"The association between serum lead concentration and all-cause or cardiovascular disease mortality in hypertensive patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:43:41","doi":"10.21203/rs.3.rs-6904376/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T07:44:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T15:59:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T13:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90939196728574030235113162643191513365","date":"2025-07-11T16:22:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67752291713414047446665672788781943532","date":"2025-07-02T02:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T14:03:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T17:28:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-18T04:08:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T11:56:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-16T10:04:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bcdbcbbc-b669-4961-9fde-58e838315efb","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50708518,"name":"Biological sciences/Biochemistry"},{"id":50708519,"name":"Earth and environmental sciences/Environmental sciences"},{"id":50708520,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-11-10T16:01:53+00:00","versionOfRecord":{"articleIdentity":"rs-6904376","link":"https://doi.org/10.1038/s41598-025-22009-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-03 15:57:43","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-07-01 10:43:41","video":"","vorDoi":"10.1038/s41598-025-22009-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22009-y","workflowStages":[]},"version":"v1","identity":"rs-6904376","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6904376","identity":"rs-6904376","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}