Association between Blood Urea Nitrogen-to-Serum Albumin Ratio and Cardiovascular Disease Risk: A NHANES Study

Association between Blood Urea Nitrogen-to-Serum Albumin Ratio and Cardiovascular Disease Risk: A NHANES Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Blood Urea Nitrogen-to-Serum Albumin Ratio and Cardiovascular Disease Risk: A NHANES Study Wei Chen, Xinghong Zhou, Bailing Zhang, Yue Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6204802/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular disease (CVD) ranks among the top causes of mortality worldwide, particularly impacting the US. The Blood Urea Nitrogen-to-Serum Albumin Ratio (BAR) has recently been identified as a promising biomarker that integrates indicators of both renal and nutritional health. However, the association between BAR and CVD has not been thoroughly explored. Methods This study utilized cross-sectional data from individuals aged ≥ 20 years who participated in the NHANES from 2011–2018. To evaluate the stability of the findings, cubic spline models with restricted parameters along with logistic regression were employed, and both subgroup analyses and Receiver Operating Characteristic (ROC) curve analyses were conducted. Results There were 19770 participants, 10.5% (2085/19770) were diagnosed with CVD. When BAR was analyzed as a continuous variable, the full model-adjusted OR was 1.06 (95% CI: 1.02 ~ 1.09, p = 0.003). When compared with Q2, the OR values for Q1, Q3, and Q4 groups were 1.21 (95% CI: 1.01 ~ 1.46), 1.09 (95% CI: 0.92 ~ 1.29), and 1.31 (95% CI: 1.11 ~ 1.53), respectively. The correlation between BAR and CVD showed a U-shaped curve (p for non−linear 0.05). The ROC curve demonstrated an area under the curve (AUC) of 0.685 (95% CI: 0.672–0.698), indicating BAR's ability to predict CVD. Conclusion BAR is a potential predictor of CVD risk with a U-shaped association. Further prospective studies are required to validate our findings. Association blood urea nitrogen (BUN) albumin National Health and Nutrition Examination Survey (NHANES) cardiovascular disease (CVD) Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Cardiovascular disease (CVD) is a primary factor contributing to mortality and disability globally, with its prevalence increasing, especially in the United States [ 1 ] . Over the past decade, global CVD deaths have increased by 12.5%, accounting for nearly one-third of global deaths [ 2 ] . The exact causes of CVD are complex and involve interactions between dietary, nutritional, genetic, and environmental factors. Although significant advances have been made in the early detection and management of CVD in recent years, there remains a need to explore new biomarkers to improve the effectiveness of risk assessment and early intervention. The blood urea nitrogen to serum albumin ratio, commonly referred to as BAR, has emerged as a novel prognostic biomarker that has garnered attention in recent years. This innovative ratio brings together two significant predictors: blood urea nitrogen (BUN) and serum albumin levels. By integrating these two critical components, BAR offers a more comprehensive tool for assessing patient prognosis, highlighting its potential utility in clinical settings. BUN reflects renal function and protein metabolism status, whereas albumin is an important indicator for assessing nutritional status and liver function. In recent years, it has been found that this biomarker may be important in assessing a patient's systemic status, as well as being a good predictor of mortality in patients with acute kidney injury, pneumonia, and acute pulmonary embolism [ 3 – 6 ] . While BUN is not as sensitive to renal insufficiency as serum creatinine, earlier research has validated that a rise in BUN correlates with a worse prognosis in individuals suffering from heart failure [ 7 , 8 ] . Furthermore, hypoalbuminemia, which is believed to be mainly the result of cachexia, renal and hepatic insufficiency, and inflammation, has now been identified as an independent risk factor for various cardiovascular conditions [ 9 , 10 ] . While previous studies have examined the connection between cardiovascular health and both BUN and albumin levels separately, investigations focusing on BAR are still quite sparse. Therefore, this study will systematically assess the correlation between BAR and CVD using the 2011–2018 National Health and Nutrition Examination Survey (NHANES) database to identify new biomarkers for the early identification of CVD and provide a valuable reference for clinical practice. 2. Methods 2.1 Study population: This study utilized information from the NHANES covering four cycles: 2011–2012, 2013–2014, 2015–2016, and 2017–2018. The NHANES is a comprehensive and nationally representative survey that meticulously collects a wide array of health and nutritional data from a diverse sample of the American adult population. The Research Ethics Review Board of the Centers for Disease Control and Prevention (CDC) granted ethical approval for the NHANES study. To safeguard participants’ rights, the NHANES requires informed written consent. The datasets from NHANES, utilized in our research, are available to the public on the official NHANES website ( www.cdc.gov/nchs/nhanes/ ). This study exclusively focused on adult participants aged ≥ 20 years. At the outset, those without serum BUN or albumin measurements were excluded from the sample (n = 14,472). Subsequently, 4,406 individuals were dismissed due to missing data concerning CVD, and an additional 508 participants were excluded because of incomplete covariate information. Ultimately, 19,770 individuals were included in the final analysis (Fig. 1). This research followed the protocols established by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). 2.2 Study variables and Outcomes BAR (mg/g) was calculated by dividing the initial BUN (mg/dl) by the serum albumin (g/dl), with both values sourced directly from laboratory data files [ 11 ] . The main outcome variable was the diagnosis of CVD (either present or absent). Participants were evaluated for the occurrence of CVD by inquiring, “Has a healthcare provider or doctor ever told you that you have angina, experienced a heart attack, suffered a stroke, or been diagnosed with coronary heart disease?” during the Medical Conditions Questionnaire. Data on covariates were gathered using a combination of questionnaires, physical examinations, and laboratory tests. The demographic data collected included age, gender, race, education level, poverty income ratio (PIR), and body mass index (BMI). Hypertension is recognized when the systolic blood pressure reads ≥ 140 mmHg, or the diastolic pressure ≥ 90 mmHg, or if there is an existing documented diagnosis of the condition. The assessment of previous health conditions, such as diabetes mellitus (DM) and hyperlipidemia, relied on responses provided in the questionnaire regarding whether the individual had previously disclosed these conditions to a healthcare provider [ 12 ] . In alignment with the definitions established in previous literature [ 13 ] , individuals' smoking status was divided into three categories: current smokers, former smokers, and those who have never smoked. Physical activity levels were categorized into three distinct types: sedentary, moderately active, and vigorously active. To ascertain alcohol consumption, participants were asked a straightforward question: “Have you had at least 12 alcohol drinks in your lifetime?” Additionally, the laboratory measurements comprised the counts of white blood cells (WBC), red blood cells (RBC), platelets, hemoglobin concentrations, as well as levels of creatinine (Cr), BUN, albumin, blood glucose, uric acid, and serum potassium. 2.3 Statistical analysis This paper provides a comprehensive secondary review of datasets that are publicly accessible for research purposes. To ascertain whether the continuous variables within these datasets adhered to a normal distribution, the authors employed the Shapiro-Wilk statistical test. For the categorical variables, the findings are represented using proportions, expressed as percentages (%), while the continuous variables are summarized through their mean values along with standard deviations (SD), where it is deemed appropriate. A variety of statistical methodologies were implemented to assess differences between the various groups included in the study. These methodologies encompassed one-way analyses of variance, which were utilized for data that were normally distributed; Kruskal-Wallis tests, which were employed for data exhibiting skewed distributions; and chi-square tests, which were applied to evaluate categorical variables. Furthermore, logistic regression modeling was carried out to calculate the OR and the corresponding 95 percent confidence intervals (95% CIs) related to the associations with BAR and CVD. The selection of confounding variables for this analysis was methodically based on factors of clinical interest as well as insights drawn from prior scientific literature [ 7 , 11 , 14 ] . We constructed three models: Model 1 adjusted for age, gender, race, education level, PIR, and BMI. Model 2 was additionally adjusted for hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking. Model 3 was additionally adjusted for Cr, glucose, K, UA, RBC, WBC, hemoglobin, and platelets. Furthermore, we conducted restricted cubic spline (RCS) regressions with four sections at the 5th, 35th, 65th, and 95th percentiles of BAR. This was done to evaluate the linear relationship and examine the dose-response curves linking BAR to CVD, taking into account the adjustments made for the variables in Model 3. Following the adjustments in Model 3, a smoothed two-segment logistic regression model was utilized to investigate the threshold of the association between BAR and CVD. To identify inflection points, we employed likelihood ratio tests and self-supporting weight sampling techniques. In addition, subgroup analyses regarding the relationship between BAR and CVD were conducted, which included variables such as age (65 years), gender, BMI (< 25 vs. ≥25 Kg/m2), hypertension, diabetes mellitus, smoking status (current vs. former vs. never), and alcohol consumption (No vs. Yes). Multivariate logistic regression was employed to assess the heterogeneity present among the various subgroups involved in the study. In addition, likelihood ratio tests were conducted to examine the interactions between these subgroups and BAR factors. To further enhance the validity and reliability of our results, participants who exhibited extreme blood potassium levels—defined as those below 3.5 mmol/L or above 5.5 mmol/L—were excluded from the sensitivity analyses. This careful exclusion was implemented to reinforce the robustness of our findings. Furthermore, ROC curves were generated to evaluate the effectiveness of BAR in predicting the risk of CVD. These curves provide a graphical representation of the diagnostic performance, allowing for a more thorough understanding of BAR's predictive capability in relation to CVD outcomes. The sample size was exclusively based on existing data, implying that no preliminary statistical power assessments were available to inform this procedure. Analyses were conducted using the R statistical software ( http://www.R-project.org , The R Foundation) and Free Statistics software version 2.1 [ 15 ] . A significance threshold of p < 0.05, established through a two-tailed test and demonstrated statistical significance. 3. Results 3.1 Study population characteristics The study included 19,770 participants who were selected based on a set of specific inclusion and exclusion criteria. The detailed screening process used in this study is illustrated in Fig. 1 . To facilitate the analysis, the participants were categorized into four distinct groups according to the quartiles of the BAR (Q1: 3.90); the baseline characteristics of these four groups are presented in Table 1 , providing an overview of the demographic and health-related factors associated with each quartile. Participants with higher BAR were typically older; had fewer years of education; higher proportions of non-Hispanic whites; higher BMI; higher prevalence of hypertension, hyperlipidemia, and diabetes; lower proportions of current smokers and sedentary individuals; and higher proportions of alcohol drinkers. RBC, hemoglobin, and platelet counts were lower than those in the group with lower BAR, whereas WBC, UA, Cr, blood glucose, and potassium levels were higher than those in the group with lower BAR. The incidence of CVD increased progressively with increasing BAR (5.6% vs. 6% vs. 9.2% vs. 21.2%, p < 0.001). 3.2 Factors associated with CVD In the univariate analysis, every covariate showed a correlation with CVD, and age, race, hypertension, hyperlipidemia, DM, drinking, Cr, blood glucose, potassium, and WBC count were positively associated with myocardial infarction ( Table 2 ). 3.3 Association between BAR and CVD Table 3 presents the OR along with their corresponding 95% CIs that are associated with CVD in relation to the BAR. In the analysis where BAR was treated as a continuous variable, a statistically significant correlation was established between BAR and the occurrence of CVD in the unadjusted model, revealing an OR of 1.44 with a 95% CI ranging from 1.41 to 1.48, and a p-value of less than 0.001. Further examination involved multiple regression models that incorporated stepwise adjustments for various covariates. In Model 1, which accounted for factors such as age, gender, race, education level, PIR, and BMI, the resulting OR was 1.15, with a 95% CI of 1.12 to 1.18 and a p-value of less than 0.001, indicating a meaningful relationship between these demographic factors and CVD risk in relation to BAR. Model 2 enhanced this analysis by including additional variables, specifically hypertension, hyperlipidemia, DM, smoking status, physical activity, and alcohol consumption. This model produced an OR of 1.11, with a 95% CI of 1.08 to 1.14 and a p-value of less than 0.001, suggesting that these lifestyle and health factors also significantly contributed to the likelihood of developing CVD. Finally, Model 3 expanded the scope of analysis even further by integrating laboratory values such as Cr, glucose, K+, uric acid, RBC, WBC, hemoglobin, and platelet. This comprehensive model yielded an OR of 1.06 (95% CI :1.02–1.09, p = 0.003). This finding indicates that for every 1 unit increase in BAR, there is a 6% increase in the likelihood of developing CVD, highlighting the continued relevance of BAR as a significant predictor of cardiovascular risk even after accounting for various health and demographic factors. Moreover, when regarded as a categorical variable, the association between BAR and the risk of CVD reflected the trends identified in the continuous analyses. The subjects were classified into four categories based on the quartiles of BAR. The positive relationship between BAR and CVD risk continued to be significant following thorough adjustments. In comparison to Q2, the OR was 1.21 (95% CI: 1.01–1.46, p = 0.041) for Q1, 1.09 (95% CI: 0.92–1.29, p = 0.311) for Q3, and 1.31 (95% CI: 1.11–1.53, p = 0.001) for Q4 ( Table 3 ). In Fig. 2, Using RCS with combined adjustment for potential confounding variables, we found a U-shaped curve (p for non−linear < 0.001) for the association between BAR and CVD. We also performed a threshold analysis, the OR for the occurrence of CVD was 0.619 (95% CI: 0.439–0.872, p = 0.0061) at BAR 2.49. This means that for BAR 2.49, the risk of CVD increased by 8.2% for each unit increase in BAR( Table 4 ). 3.4 Subgroup analyses outcomes To determine if there was a significant interaction effect among subgroups, we conducted stratified subgroup analyses. After stratification by age, gender, BMI, hypertension, DM, smoking habits, and alcohol consumption, no notable interactions were identified within any of the subgroups (Fig. 3). 3.5 Sensitivity Analysis We excluded participants with blood potassium levels 5.5 mmol/L to assess the stability of the results. Consistent with the results of the initial analysis. This indicates that there is a notable positive relationship between BAR and CVD. Importantly, this correlation persists regardless of whether BAR is considered as a continuous variable, or as a categorical variable. Such consistent outcomes reinforce the reliability of the association between BAR and CVD( Table 5 ). 3.6 Receiver operating characteristic curve analysis ROC curves were generated for BAR, BUN, and albumin levels to evaluate their effectiveness in predicting CVD. As illustrated in Fig. 4 , the AUC for BAR was 0.685, outperforming that for both BUN (AUC = 0.669) and albumin (AUC = 0.613). Therefore, BAR demonstrated a notable predictive advantage. Discussion Timely and early identification of CVD is the responsibility of clinicians. In this study, we found a correlation between the BAR and CVD risk. This outcome was anticipated because the BAR functions as a composite measure to evaluate BUN in conjunction with albumin. Earlier research has indicated that increased BUN levels and low albumin levels serve as separate risk factors for CVD [ 8 , 16 – 18 ] . In contrast to earlier studies, BAR compensates for the absence of a correlation when considering albumin or BUN individually. BUN is an important hematological indicator for monitoring kidney function, but it is less sensitive than the glomerular filtration rate (GFR) and creatinine. Moreover, BUN is influenced by several factors, including age, protein intake, and metabolic profiles. BUN has been identified as a strong predictor of cardiovascular disease in several studies and may even exceed GFR and serum creatinine; however, the underlying mechanisms remain unclear [ 19 – 21 ] . In previous analyses of all-cause mortality [ 22 , 23 ] , BUN levels were significantly higher in in-hospital decedents than in survivors. Thus, BUN levels may reflect disease severity. In addition, in a study by Sullivan [ 24 ] , BUN demonstrated some predictive value for disease prognosis. A retrospective cohort study carried out in China by Chen et al. [ 25 ] established a significant correlation between elevated BUN levels and in-hospital mortality rates among patients experiencing acute exacerbations of chronic obstructive pulmonary disease (COPD). The findings from this research suggest that monitoring BUN levels could serve as a crucial factor in assessing the severity of exacerbations and potentially predicting the clinical outcomes for these patients during their hospital stay. Albumin is one of the most important nutritional indicators in the body, and previous studies have suggested that hypoalbuminemia is associated with the prognosis of several cardiovascular diseases [ 26 , 27 ] . A cohort study found that serum albumin levels were strongly associated with the risk of cardiovascular complications and death in patients with chronic kidney disease (CKD) [ 28 ] . The risk of cardiovascular complications was significantly increased when serum albumin levels were < 3.4 g/dL. Serum albumin levels may be influenced by inflammation and nutritional status, and lower serum albumin levels may reflect a systemic inflammatory response or malnutrition, which may indirectly increase the risk of cardiovascular disease [ 29 ] . Research on heart failure indicates that while hypoalbuminemia may not be a direct result of heart failure, chronic heart failure over an extended period is often associated with complications, such as infections, malnutrition, liver impairment, and renal disease, which can exacerbate the loss of albumin and disturb the body’s fluid equilibrium [ 30 ] . Heart failure is a condition of relative hypoperfusion in organs due to overload of the heart, and the occurrence of hypoalbuminemia can lead to additional fluid loss within the circulatory system, resulting in a detrimental cycle that negatively affects prognosis. Previous studies [ 31 , 32 ] have established a significant association between hepatic and renal impairment and cardiovascular disease, indicating that these conditions frequently coexist among patients with cardiac issues. Specifically, mortality rates have been found to correlate strongly with the MELD-XI score, which is derived from the blood creatinine and bilirubin levels. Additionally, markers such as BUN and albumin serve as clinical indicators of liver and kidney dysfunction. As a result, there appear to be parallels between the BAR and MELD-XI scores regarding the assessment of patient health status. A recent study [ 7 ] revealed that elevated levels of the BAR score function as an independent risk factor for both in-hospital mortality and mortality within a 90-day period among critically ill patients diagnosed with chronic heart failure. This finding underscores the potential significance of BAR as a clinical marker, suggesting that it may effectively reflect circulating blood volume in these patients. An increase in the BAR values signifies a relative deficiency in circulating blood volume, leading to the conclusion that further investigation is warranted. Future research should focus on clarifying the role of BAR in evaluating a patient’s effective circulating blood volume as well as its potential utility in informing volume management strategies for individuals with CVD. Several studies have reported the predictive value of BAR for respiratory diseases [ 3 , 4 , 14 ] . However, this novel indicator has not been studied in cardiovascular diseases. In our cross-sectional study, we observed a U-shaped relationship between the BAR and the risk of developing CVD. Interestingly, we found a gradual decrease in the risk of CVD at BAR values 2.49. This result suggests that there is a threshold effect of the BAR as a predictor of CVD risk, which is consistent with the clinical situation of CVD. When BAR was analyzed as a continuous variable, there was a 6% increase in the risk of CVD occurrence for each 1-unit increase in BAR value. When analyzed in subgroups, there was an increase in the risk of occurrence in groups with higher BAR values. These results suggest that higher BAR values imply higher serum BUN levels and lower albumin levels in patients with CVD. Several factors may explain the association between BAR levels and CVD. First, the inflammatory response plays a crucial role in CVD patients, accelerating the process of protein hydrolysis; because of the low albumin level in patients, the BUN level is elevated [ 33 ] . Second, in patients suffering from CVD, there is activation of the renin-angiotensin-aldosterone system (RAAS) along with the sympathetic nervous system as a result of diminished cardiac function [ 34 , 35 ] . This activation leads to stimulation by angiotensin and adrenergic signals, resulting in renal vasoconstriction along with a reduction in glomerular filtration rate and renal blood flow. Consequently, this mechanism increases urea reabsorption, which in turn raises BUN levels. In addition, in our study, there was a difference in AUC between BAR and BUN and albumin (AUC BAR : 0.685, AUC Alb : 0.613, AUC BUN : 0.669). Therefore, we suggest that the BAR may be a convenient predictor in patients with CVD. However, this needs to be confirmed by further prospective cohort studies, as this study was only cross-sectional, and there was no direct causal relationship. BAR is associated with the risk of developing CVD. We suggest that the BAR values should be emphasized in clinical practice. It is crucial to quickly and accurately identify patients with CVD and take appropriate interventions to improve prognosis. Our study has several limitations. First, given its cross-sectional design, a causal relationship between BAR and CVD risk could not be established. It is important to recognize the observational nature of this study and interpret the results with caution. Future prospective clinical trials on CVD interventions will be critical to discerning the causal nature of the observed association. Second, reliance on self-reported CVD questionnaires introduces a potential memory bias. Third, despite the use of regression modelling and stratified analyses, we cannot completely rule out the influence of unmeasured confounders on the results of observed associations. Fourth, the existing results originate from a study involving adults in the United States, and additional investigations are required to assess their applicability to different populations. Fifth, in the present study, we did not analyze the effect of the therapeutic intervention on BUN and albumin levels in participants with CVD, which may have affected the results. Conclusions In this cross-sectional investigation, we observed a U-shaped association between BAR and the likelihood of developing CVD. Additional prospective cohort studies are required to confirm this association. Declarations Ethics statement The data for this study were obtained from public databases and did not require additional ethical approval. Author contributions Wei Chen: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Xinghong Zhou: Investigation, Project administration, Resources, Supervision, Visualization, Writing – original draft. Bailing Zhang: Investigation, Project administration, Resources, Supervision, Visualization, Writing – original draft. Yue Wu: Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Funding No funding. Acknowledgments We are grateful to Dr Liu Jie of the PLA General Hospital for his help, especially in statistical knowledge and overall conceptualization. Conflict of interest All authors declare no conflict interests. References Kumar M, Patil S, Godoy L, et al. Demand Ischemia as a Predictor of Mortality in Older Patients With Delirium. Front Cardiovasc Med. 2022. 9: 917252. Giuliani A, Montesanto A, Matacchione G, et al. 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Eur J Intern Med. 2018. 52: 8-12. Arques S, Ambrosi P. Human serum albumin in the clinical syndrome of heart failure. J Card Fail. 2011. 17(6): 451-8. Huang F, Fan J, Wan X, et al. The association between blood albumin level and cardiovascular complications and mortality risk in ICU patients with CKD. BMC Cardiovasc Disord. 2022. 22(1): 322. Wang Z, Zhang L, Li S, et al. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database. BMC Infect Dis. 2022. 22(1): 629. Eckart A, Struja T, Kutz A, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med. 2020. 133(6): 713-722.e7. Biegus J, Demissei B, Postmus D, et al. Hepatorenal dysfunction identifies high-risk patients with acute heart failure: insights from the RELAX-AHF trial. ESC Heart Fail. 2019. 6(6): 1188-1198. Biegus J, Zymliński R, Sokolski M, et al. Impaired hepato-renal function defined by the MELD XI score as prognosticator in acute heart failure. Eur J Heart Fail. 2016. 18(12): 1518-1521. Perry AS, Dooley EE, Master H, Spartano NL, Brittain EL, Pettee Gabriel K. Physical Activity Over the Lifecourse and Cardiovascular Disease. Circ Res. 2023. 132(12): 1725-1740. Michel-Flutot P, Mansart A, Fayssoil A, Vinit S. Effects of C2 hemisection on respiratory and cardiovascular functions in rats. Neural Regen Res. 2023. 18(2): 428-433. Savarese G, Stolfo D, Sinagra G, Lund LH. Heart failure with mid-range or mildly reduced ejection fraction. Nat Rev Cardiol. 2022. 19(2): 100-116. Tables Table 1. Baseline characteristics of participants. Variables Total Q1 (3.90) P -value Participants 19770 4917 4885 4970 4998 Age(y) 49.5 ± 17.6 40.9 ± 14.9 45.1 ± 16.2 50.5 ± 16.5 61.1 ± 15.7 < 0.001 Gender < 0.001 Male 9546 (48.3) 1947 (39.6) 2267 (46.4) 2611 (52.5) 2721 (54.4) Female 10224 (51.7) 2970 (60.4) 2618 (53.6) 2359 (47.5) 2277 (45.6) Race < 0.001 Mexican American 2709 (13.7) 652 (13.3) 675 (13.8) 692 (13.9) 690 (13.8) Non-Hispanic White 7405 (37.5) 1619 (32.9) 1682 (34.4) 1862 (37.5) 2242 (44.9) Non-Hispanic Black 4345 (22.0) 1338 (27.2) 1110 (22.7) 992 (20.0) 905 (18.1) Others 5311 (26.9) 1308 (26.6) 1418 (29.0) 1424 (28.7) 1161 (23.2) Education level(y) < 0.001 12 15478 (78.3) 3880 (78.9) 3905 (79.9) 3931 (79.1) 3762 (75.3) PIR 2.5 ± 1.6 2.3 ± 1.5 2.5 ± 1.6 2.6 ± 1.6 2.6 ± 1.5 < 0.001 BMI 29.3 ± 7.1 28.7 ± 7.3 29.0 ± 6.9 29.7 ± 7.0 30.0 ± 7.2 < 0.001 Hypertension < 0.001 No 12555 (63.5) 3692 (75.1) 3477 (71.2) 3144 (63.3) 2242 (44.9) Yes 7215 (36.5) 1225 (24.9) 1408 (28.8) 1826 (36.7) 2756 (55.1) Hyperlipidemia < 0.001 No 12890 (65.2) 3759 (76.4) 3393 (69.5) 3133 (63) 2605 (52.1) Yes 6880 (34.8) 1158 (23.6) 1492 (30.5) 1837 (37) 2393 (47.9) DM < 0.001 No 16542 (83.7) 4431 (90.1) 4313 (88.3) 4180 (84.1) 3618 (72.4) Yes 2710 (13.7) 389 (7.9) 451 (9.2) 650 (13.1) 1220 (24.4) Borderline 518 ( 2.6) 97 (2.0) 121 (2.5) 140 (2.8) 160 (3.2) Smoking status < 0.001 Current 3790 (19.2) 1350 (27.5) 976 (20.0) 803 (16.2) 661 (13.2) Former 4614 (23.3) 790 (16.1) 973 (19.9) 1235 (24.8) 1616 (32.3) Never 11366 (57.5) 2777 (56.5) 2936 (60.1) 2932 (59.0) 2721 (54.4) Physical activity < 0.001 Vigorous 4618 (23.4) 1279 (26.0) 1245 (25.5) 1187 (23.9) 907 (18.1) Moderate 5093 (25.8) 1270 (25.8) 1277 (26.1) 1278 (25.7) 1268 (25.4) Sedentary 10059 (50.9) 2368 (48.2) 2363 (48.4) 2505 (50.4) 2823 (56.5) Drinking < 0.001 No 5966 (30.2) 1606 (32.7) 1465 (30.0) 1404 (28.2) 1491 (29.8) Yes 13804 (69.8) 3311 (67.3) 3420 (70.0) 3566 (71.8) 3507 (70.2) Cr (umol/L) 79.6 ± 40.8 68.9 ± 16.2 72.9 ± 16.7 77.0 ± 20.0 99.4 ± 71.5 < 0.001 Glucose (mmol/L) 5.8 ± 2.3 5.5 ± 2.0 5.6 ± 2.0 5.8 ± 2.1 6.3 ± 2.8 < 0.001 Table 1. Continued Variables Total Q1 (3.90) P-value K (mmol/L) 4.0 ± 0.4 3.9 ± 0.3 4.0 ± 0.3 4.0 ± 0.3 4.1 ± 0.4 < 0.001 UA (umol/L) 322.9 ± 86.1 300.1 ± 79.3 314.4 ± 80.0 324.8 ± 81.7 351.9 ± 94.0 < 0.001 RBC (m/μL) 4.7 ± 0.5 4.6 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.6 ± 0.6 < 0.001 WBC (K/μL) 7.3 ± 3.7 7.3 ± 2.3 7.2 ± 2.2 7.2 ± 2.1 7.4 ± 6.4 0.001 Hemoglobin (g/dL) 13.9 ± 1.5 13.9 ± 1.6 14.0 ± 1.5 14.1 ± 1.4 13.7 ± 1.6 < 0.001 Platelet (K/μL) 238.3 ± 61.7 246.6 ± 62.9 242.5 ± 61.1 236.5 ± 59.2 227.7 ± 62.0 < 0.001 Heart failure < 0.001 No 19118 (96.7) 4848 (98.6) 4817 (98.6) 4840 (97.4) 4613 (92.3) Yes 652 ( 3.3) 69 (1.4) 68 (1.4) 130 (2.6) 385 (7.7) CHD < 0.001 No 18983 (96.0) 4842 (98.5) 4790 (98.1) 4800 (96.6) 4551 (91.1) Yes 787 ( 4.0) 75 (1.5) 95 (1.9) 170 (3.4) 447 (8.9) Angina < 0.001 No 19298 (97.6) 4848 (98.6) 4824 (98.8) 4862 (97.8) 4764 (95.3) Yes 472 ( 2.4) 69 (1.4) 61 (1.2) 108 (2.2) 234 (4.7) Heart attack < 0.001 No 18955 (95.9) 4808 (97.8) 4791 (98.1) 4795 (96.5) 4561 (91.3) Yes 815 ( 4.1) 109 (2.2) 94 (1.9) 175 (3.5) 437 (8.7) Stroke < 0.001 No 19036 (96.3) 4809 (97.8) 4775 (97.7) 4810 (96.8) 4642 (92.9) Yes 734 ( 3.7) 108 (2.2) 110 (2.3) 160 (3.2) 356 (7.1) CVD < 0.001 No 17685 (89.5) 4643 (94.4) 4591 (94) 4511 (90.8) 3940 (78.8) Yes 2085 (10.5) 274 (5.6) 294 (6) 459 (9.2) 1058 (21.2) PIR, poverty-to-income ratio; DM, diabetes mellitus; Cr, creatinine; K, potassium; UA, uric acid; BMI, body mass index ; RBC, red blood cell; WBC, white blood cell; CHD, coronary heart disease; CVD, cardiovascular disease. Table 2. Association of covariates and CVD risk. Variable OR_95CI P -value Variable OR_95CI P -value Age(y) 1.07 (1.07~1.08) <0.001 Smoking status Gender Current 1 (reference) Male 1 (reference) Former 1.50 (1.33~1.70) <0.001 Female 0.72 (0.65~0.79) <0.001 Never 0.57 (0.51~0.64) <0.001 Race Physical activity Mexican American 1 (reference) Vigorous 1 (reference) Non-Hispanic White 2.17 (1.84~2.55) <0.001 Moderate 3.02 (2.52~3.61) <0.001 Non-Hispanic Black 1.73 (1.45~2.06) <0.001 Sedentary 4.27 (3.62~5.03) <0.001 Others 1.07 (0.89~1.28) 0.489 Drinking Education level(y) No 1 (reference) <9 1 (reference) Yes 1.08 (0.97~1.19) 0.155 9-12 0.86 (0.72~1.02) 0.081 Cr (umol/L) 1.01 (1.01~1.01) 12 0.58 (0.50~0.66) <0.001 Glucose (mmol/L) 1.13 (1.11~1.15) <0.001 PIR 0.84 (0.82~0.87) <0.001 K (mmol/L) 2.54 (2.25~2.86) <0.001 BMI 1.03 (1.02~1.03) <0.001 UA (umol/L) 1.00 (1.00~1.00) <0.001 Hypertension RBC (m/μL) 0.57 (0.52~0.63) <0.001 No 1 (reference) WBC (K/μL) 1.02 (1.01~1.04) 0.007 Yes 6.30 (5.68~6.99) <0.001 Hemoglobin (g/dL) 0.88 (0.86~0.91) <0.001 Hyperlipidemia Platelet (K/μL) 0.99 (0.99~1.00) <0.001 No 1 (reference) Yes 3.81 (3.46~4.18) <0.001 DM No 1 (reference) Yes 4.52 (4.08~5.00) <0.001 Borderline 2.34 (1.84~2.98) <0.001 PIR, poverty-to-income ratio; DM, diabetes mellitus; Cr, creatinine; K, potassium; UA, uric acid; BMI, body mass index ; RBC, red blood cell; WBC, white blood cell; CHD, coronary heart disease; CVD, cardiovascular disease. Table 3. Association between BAR and CVD. Quartiles OR (95% CI) Crude p-Value Model1 p-Value Model2 p-Value Model3 p-Value BAR 1.44 (1.41~1.48) <0.001 1.15 (1.12~1.18) <0.001 1.11 (1.08~1.14) <0.001 1.06 (1.02~1.09) 0.003 BAR(category) Q1(<2.44) 0.92 (0.78~1.09) 0.345 1.26 (1.05~1.5) 0.012 1.19 (0.99~1.43) 0.069 1.21 (1.01~1.46) 0.041 Q2(2.44-3.08) 1(Ref.) 1(Ref.) 1(Ref.) 1(Ref.) Q3(3.08-3.90) 1.59 (1.36~1.85) 3.90) 4.19 (3.66~4.8) <0.001 1.69 (1.46~1.96) <0.001 1.53 (1.31~1.78) <0.001 1.31 (1.11~1.53) 0.001 P for trend 1.76 (1.68~1.84) <0.001 1.18 (1.13~1.24) <0.001 1.15 (1.09~1.21) <0.001 1.08 (1.03~1.14) 0.003 Model 1 was adjusted for age, gender, race, education level, PIR, BMI. Model 2 was adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking. Model 3 was adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking, Cr, glucose, K, UA, RBC, WBC, hemoglobin, Platelet. Table 4. Threshold effect analysis of the relationship of BAR with CVD. BAR Adjusted Model ＜2.49 0.619 (0.439~0.872) 0.0061 ＞2.49 1.082 (1.032~1.135) 0.0012 Log-likelihood ratio test <0.001 Table 5. Association between BAR and CVD in participants with extreme potassium was not include. (3.5mmol/L<K+ <5.5mmol/L，18693). Quartiles OR (95% CI) Crude p-Value Model1 p-Value Model2 p-Value Model3 p-Value BAR 1.45 (1.41~1.49) <0.001 1.15 (1.11~1.18) <0.001 1.11 (1.08~1.14) <0.001 1.05 (1.01~1.09) 0.012 BAR(category) Q1(<2.44) 0.91 (0.77~1.09) 0.312 1.24 (1.03~1.49) 0.024 1.17 (0.96~1.41) 0.114 1.19 (0.98~1.44) 0.078 Q2(2.44-3.08) 1(Ref.) 1(Ref.) 1(Ref.) 1(Ref.) Q3(3.08-3.90) 1.58 (1.35~1.84) 3.90) 4.19 (3.64~4.82) <0.001 1.65 (1.41~1.92) <0.001 1.48 (1.26~1.73) <0.001 1.26 (1.07~1.49) 0.006 P for trend 1.76 (1.68~1.84) <0.001 1.17 (1.12~1.23) <0.001 1.14 (1.08~1.19) <0.001 1.07 (1.01~1.13) 0.014 Model 1 was adjusted for age, gender, race, education level, PIR, BMI. Model 2 was adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking. Model 3 was adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking, Cr, glucose, K, UA, RBC, WBC, hemoglobin, Platelet. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6204802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445854883,"identity":"42b262e8-d15e-4604-a184-222c031dbf32","order_by":0,"name":"Wei Chen","email":"","orcid":"","institution":"People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":445854884,"identity":"0e0026f2-f18b-4320-985b-d4eefb66a62e","order_by":1,"name":"Xinghong Zhou","email":"","orcid":"","institution":"People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Xinghong","middleName":"","lastName":"Zhou","suffix":""},{"id":445854885,"identity":"2b7eee00-9d5a-48f4-893d-7c5a4be5fab5","order_by":2,"name":"Bailing Zhang","email":"","orcid":"","institution":"People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Bailing","middleName":"","lastName":"Zhang","suffix":""},{"id":445854886,"identity":"f2e95a2a-ad81-42f3-b2e3-6681f98da9d1","order_by":3,"name":"Yue Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNvbm4z8kftjw2Lc3JD5IqKghrIWP51iChGVPmowBz4HHBg/OHCOsRU4ix0Cigu2wjYFE4jPJhy3MRDgMaIvBDZ40HnOJ5LSKxAY2Bv727gRCfjmQOMPChsey51najcQdMgwSZ85uIGjLYQmgLQzHc4BazrAxGEjkEtAikWPY/IftMA/DgfxvBYltzERpMWaQAGoxOJGQxkCcFp5jaQySPWk8kj0HkiUSzhzjIegX+fbmYwzAqLTnZ29I/PijokaOv70XvxYMwEOa8lEwCkbBKBgFWAEAe7VLO45bTF8AAAAASUVORK5CYII=","orcid":"","institution":"People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture","correspondingAuthor":true,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-11 15:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6204802/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6204802/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81933794,"identity":"2a8a91e3-7969-49af-86ea-3793247859c4","added_by":"auto","created_at":"2025-05-05 05:41:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427080,"visible":true,"origin":"","legend":"\u003cp\u003eThe study’s flow diagram.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6204802/v1/0662424446156263fa713abe.jpg"},{"id":81935134,"identity":"f452e9d8-cbe6-4c1f-a665-8b4e0218837c","added_by":"auto","created_at":"2025-05-05 06:00:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":693939,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between BAR and CVD. Only 99% of the data is shown. CVD, cardiovascular disease; BAR, blood urea nitrogen to serum albumin ratio.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6204802/v1/fc35adaebf6147f917fb13cc.jpg"},{"id":81933792,"identity":"d3800cba-e32d-4fa5-92e2-0083d45e588c","added_by":"auto","created_at":"2025-05-05 05:41:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1355222,"visible":true,"origin":"","legend":"\u003cp\u003eEffect size of BAR on the presence of CVD in the age, gender, BMI, hypertension, DM, smoking status and drinking subgroup. OR, odds ratio; CI, confidence interval; CVD, cardiovascular disease; BAR, blood urea nitrogen to serum albumin ratio; BMI, body mass index; DM, diabetes mellitus.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6204802/v1/d06266f6f51153c9ef717fe4.jpg"},{"id":81933793,"identity":"bd2a1326-dbec-4d27-84f5-cc0572c617f0","added_by":"auto","created_at":"2025-05-05 05:41:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":589526,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic (ROC) curve. AUC: Area Under The Curve.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6204802/v1/977ddddcd3e5b2f326b460d8.jpg"},{"id":92359337,"identity":"ac68f512-2be4-4496-8182-9346e4292519","added_by":"auto","created_at":"2025-09-28 16:01:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4318581,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6204802/v1/b48ecf43-dbf1-4fca-b712-8e34b69aed43.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Blood Urea Nitrogen-to-Serum Albumin Ratio and Cardiovascular Disease Risk: A NHANES Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a primary factor contributing to mortality and disability globally, with its prevalence increasing, especially in the United States\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Over the past decade, global CVD deaths have increased by 12.5%, accounting for nearly one-third of global deaths\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The exact causes of CVD are complex and involve interactions between dietary, nutritional, genetic, and environmental factors. Although significant advances have been made in the early detection and management of CVD in recent years, there remains a need to explore new biomarkers to improve the effectiveness of risk assessment and early intervention.\u003c/p\u003e \u003cp\u003eThe blood urea nitrogen to serum albumin ratio, commonly referred to as BAR, has emerged as a novel prognostic biomarker that has garnered attention in recent years. This innovative ratio brings together two significant predictors: blood urea nitrogen (BUN) and serum albumin levels. By integrating these two critical components, BAR offers a more comprehensive tool for assessing patient prognosis, highlighting its potential utility in clinical settings. BUN reflects renal function and protein metabolism status, whereas albumin is an important indicator for assessing nutritional status and liver function. In recent years, it has been found that this biomarker may be important in assessing a patient's systemic status, as well as being a good predictor of mortality in patients with acute kidney injury, pneumonia, and acute pulmonary embolism\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. While BUN is not as sensitive to renal insufficiency as serum creatinine, earlier research has validated that a rise in BUN correlates with a worse prognosis in individuals suffering from heart failure\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Furthermore, hypoalbuminemia, which is believed to be mainly the result of cachexia, renal and hepatic insufficiency, and inflammation, has now been identified as an independent risk factor for various cardiovascular conditions\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile previous studies have examined the connection between cardiovascular health and both BUN and albumin levels separately, investigations focusing on BAR are still quite sparse. Therefore, this study will systematically assess the correlation between BAR and CVD using the 2011\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES) database to identify new biomarkers for the early identification of CVD and provide a valuable reference for clinical practice.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population:\u003c/h2\u003e \u003cp\u003eThis study utilized information from the NHANES covering four cycles: 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2018. The NHANES is a comprehensive and nationally representative survey that meticulously collects a wide array of health and nutritional data from a diverse sample of the American adult population. The Research Ethics Review Board of the Centers for Disease Control and Prevention (CDC) granted ethical approval for the NHANES study. To safeguard participants\u0026rsquo; rights, the NHANES requires informed written consent. The datasets from NHANES, utilized in our research, are available to the public on the official NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study exclusively focused on adult participants aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years. At the outset, those without serum BUN or albumin measurements were excluded from the sample (n\u0026thinsp;=\u0026thinsp;14,472). Subsequently, 4,406 individuals were dismissed due to missing data concerning CVD, and an additional 508 participants were excluded because of incomplete covariate information. Ultimately, 19,770 individuals were included in the final analysis (Fig.\u0026nbsp;1). This research followed the protocols established by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study variables and Outcomes\u003c/h2\u003e \u003cp\u003eBAR (mg/g) was calculated by dividing the initial BUN (mg/dl) by the serum albumin (g/dl), with both values sourced directly from laboratory data files \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The main outcome variable was the diagnosis of CVD (either present or absent). Participants were evaluated for the occurrence of CVD by inquiring, \u0026ldquo;Has a healthcare provider or doctor ever told you that you have angina, experienced a heart attack, suffered a stroke, or been diagnosed with coronary heart disease?\u0026rdquo; during the Medical Conditions Questionnaire.\u003c/p\u003e \u003cp\u003eData on covariates were gathered using a combination of questionnaires, physical examinations, and laboratory tests. The demographic data collected included age, gender, race, education level, poverty income ratio (PIR), and body mass index (BMI). Hypertension is recognized when the systolic blood pressure reads\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or the diastolic pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or if there is an existing documented diagnosis of the condition. The assessment of previous health conditions, such as diabetes mellitus (DM) and hyperlipidemia, relied on responses provided in the questionnaire regarding whether the individual had previously disclosed these conditions to a healthcare provider\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In alignment with the definitions established in previous literature\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, individuals' smoking status was divided into three categories: current smokers, former smokers, and those who have never smoked. Physical activity levels were categorized into three distinct types: sedentary, moderately active, and vigorously active. To ascertain alcohol consumption, participants were asked a straightforward question: \u0026ldquo;Have you had at least 12 alcohol drinks in your lifetime?\u0026rdquo; Additionally, the laboratory measurements comprised the counts of white blood cells (WBC), red blood cells (RBC), platelets, hemoglobin concentrations, as well as levels of creatinine (Cr), BUN, albumin, blood glucose, uric acid, and serum potassium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThis paper provides a comprehensive secondary review of datasets that are publicly accessible for research purposes. To ascertain whether the continuous variables within these datasets adhered to a normal distribution, the authors employed the Shapiro-Wilk statistical test. For the categorical variables, the findings are represented using proportions, expressed as percentages (%), while the continuous variables are summarized through their mean values along with standard deviations (SD), where it is deemed appropriate. A variety of statistical methodologies were implemented to assess differences between the various groups included in the study. These methodologies encompassed one-way analyses of variance, which were utilized for data that were normally distributed; Kruskal-Wallis tests, which were employed for data exhibiting skewed distributions; and chi-square tests, which were applied to evaluate categorical variables. Furthermore, logistic regression modeling was carried out to calculate the OR and the corresponding 95 percent confidence intervals (95% CIs) related to the associations with BAR and CVD. The selection of confounding variables for this analysis was methodically based on factors of clinical interest as well as insights drawn from prior scientific literature\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. We constructed three models: Model 1 adjusted for age, gender, race, education level, PIR, and BMI. Model 2 was additionally adjusted for hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking. Model 3 was additionally adjusted for Cr, glucose, K, UA, RBC, WBC, hemoglobin, and platelets.\u003c/p\u003e \u003cp\u003eFurthermore, we conducted restricted cubic spline (RCS) regressions with four sections at the 5th, 35th, 65th, and 95th percentiles of BAR. This was done to evaluate the linear relationship and examine the dose-response curves linking BAR to CVD, taking into account the adjustments made for the variables in Model 3. Following the adjustments in Model 3, a smoothed two-segment logistic regression model was utilized to investigate the threshold of the association between BAR and CVD. To identify inflection points, we employed likelihood ratio tests and self-supporting weight sampling techniques.\u003c/p\u003e \u003cp\u003eIn addition, subgroup analyses regarding the relationship between BAR and CVD were conducted, which included variables such as age (\u0026lt;\u0026thinsp;65 vs. \u0026gt;65 years), gender, BMI (\u0026lt;\u0026thinsp;25 vs. \u0026ge;25 Kg/m2), hypertension, diabetes mellitus, smoking status (current vs. former vs. never), and alcohol consumption (No vs. Yes). Multivariate logistic regression was employed to assess the heterogeneity present among the various subgroups involved in the study. In addition, likelihood ratio tests were conducted to examine the interactions between these subgroups and BAR factors. To further enhance the validity and reliability of our results, participants who exhibited extreme blood potassium levels\u0026mdash;defined as those below 3.5 mmol/L or above 5.5 mmol/L\u0026mdash;were excluded from the sensitivity analyses. This careful exclusion was implemented to reinforce the robustness of our findings. Furthermore, ROC curves were generated to evaluate the effectiveness of BAR in predicting the risk of CVD. These curves provide a graphical representation of the diagnostic performance, allowing for a more thorough understanding of BAR's predictive capability in relation to CVD outcomes.\u003c/p\u003e \u003cp\u003eThe sample size was exclusively based on existing data, implying that no preliminary statistical power assessments were available to inform this procedure. Analyses were conducted using the R statistical software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and Free Statistics software version 2.1\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, established through a two-tailed test and demonstrated statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study population characteristics\u003c/h2\u003e \u003cp\u003eThe study included 19,770 participants who were selected based on a set of specific inclusion and exclusion criteria. The detailed screening process used in this study is illustrated in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. To facilitate the analysis, the participants were categorized into four distinct groups according to the quartiles of the BAR (Q1: \u0026lt;2.44; Q2:2.44–3.08; Q3:3.08–3.90; Q4: \u0026gt;3.90); the baseline characteristics of these four groups are presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e, providing an overview of the demographic and health-related factors associated with each quartile. Participants with higher BAR were typically older; had fewer years of education; higher proportions of non-Hispanic whites; higher BMI; higher prevalence of hypertension, hyperlipidemia, and diabetes; lower proportions of current smokers and sedentary individuals; and higher proportions of alcohol drinkers. RBC, hemoglobin, and platelet counts were lower than those in the group with lower BAR, whereas WBC, UA, Cr, blood glucose, and potassium levels were higher than those in the group with lower BAR. The incidence of CVD increased progressively with increasing BAR (5.6% vs. 6% vs. 9.2% vs. 21.2%, p \u0026lt; 0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Factors associated with CVD\u003c/h2\u003e \u003cp\u003eIn the univariate analysis, every covariate showed a correlation with CVD, and age, race, hypertension, hyperlipidemia, DM, drinking, Cr, blood glucose, potassium, and WBC count were positively associated with myocardial infarction (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between BAR and CVD\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e presents the OR along with their corresponding 95% CIs that are associated with CVD in relation to the BAR. In the analysis where BAR was treated as a continuous variable, a statistically significant correlation was established between BAR and the occurrence of CVD in the unadjusted model, revealing an OR of 1.44 with a 95% CI ranging from 1.41 to 1.48, and a p-value of less than 0.001. Further examination involved multiple regression models that incorporated stepwise adjustments for various covariates. In Model 1, which accounted for factors such as age, gender, race, education level, PIR, and BMI, the resulting OR was 1.15, with a 95% CI of 1.12 to 1.18 and a p-value of less than 0.001, indicating a meaningful relationship between these demographic factors and CVD risk in relation to BAR. Model 2 enhanced this analysis by including additional variables, specifically hypertension, hyperlipidemia, DM, smoking status, physical activity, and alcohol consumption. This model produced an OR of 1.11, with a 95% CI of 1.08 to 1.14 and a p-value of less than 0.001, suggesting that these lifestyle and health factors also significantly contributed to the likelihood of developing CVD. Finally, Model 3 expanded the scope of analysis even further by integrating laboratory values such as Cr, glucose, K+, uric acid, RBC, WBC, hemoglobin, and platelet. This comprehensive model yielded an OR of 1.06 (95% CI :1.02–1.09, p = 0.003). This finding indicates that for every 1 unit increase in BAR, there is a 6% increase in the likelihood of developing CVD, highlighting the continued relevance of BAR as a significant predictor of cardiovascular risk even after accounting for various health and demographic factors.\u003c/p\u003e \u003cp\u003eMoreover, when regarded as a categorical variable, the association between BAR and the risk of CVD reflected the trends identified in the continuous analyses. The subjects were classified into four categories based on the quartiles of BAR. The positive relationship between BAR and CVD risk continued to be significant following thorough adjustments. In comparison to Q2, the OR was 1.21 (95% CI: 1.01–1.46, p = 0.041) for Q1, 1.09 (95% CI: 0.92–1.29, p = 0.311) for Q3, and 1.31 (95% CI: 1.11–1.53, p = 0.001) for Q4 (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;2, Using RCS with combined adjustment for potential confounding variables, we found a U-shaped curve (p \u003csub\u003efor non−linear\u003c/sub\u003e \u0026lt; 0.001) for the association between BAR and CVD. We also performed a threshold analysis, the OR for the occurrence of CVD was 0.619 (95% CI: 0.439–0.872, p = 0.0061) at BAR \u0026lt; 2.49 (cut-off value), and the OR for the occurrence of CVD was 1.082 (1.032–1.135, p = 0.0012) at BAR \u0026gt; 2.49. This means that for BAR \u0026lt; 2.49, the risk of CVD was reduced by 38.1% for each unit increase in the BAR. When BAR \u0026gt; 2.49, the risk of CVD increased by 8.2% for each unit increase in BAR(\u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analyses outcomes\u003c/h2\u003e \u003cp\u003eTo determine if there was a significant interaction effect among subgroups, we conducted stratified subgroup analyses. After stratification by age, gender, BMI, hypertension, DM, smoking habits, and alcohol consumption, no notable interactions were identified within any of the subgroups (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eWe excluded participants with blood potassium levels \u0026lt; 3.5 mmol/L and \u0026gt; 5.5 mmol/L to assess the stability of the results. Consistent with the results of the initial analysis. This indicates that there is a notable positive relationship between BAR and CVD. Importantly, this correlation persists regardless of whether BAR is considered as a continuous variable, or as a categorical variable. Such consistent outcomes reinforce the reliability of the association between BAR and CVD(\u003cb\u003eTable\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Receiver operating characteristic curve analysis\u003c/h2\u003e \u003cp\u003eROC curves were generated for BAR, BUN, and albumin levels to evaluate their effectiveness in predicting CVD. As illustrated in \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e, the AUC for BAR was 0.685, outperforming that for both BUN (AUC = 0.669) and albumin (AUC = 0.613). Therefore, BAR demonstrated a notable predictive advantage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTimely and early identification of CVD is the responsibility of clinicians. In this study, we found a correlation between the BAR and CVD risk. This outcome was anticipated because the BAR functions as a composite measure to evaluate BUN in conjunction with albumin. Earlier research has indicated that increased BUN levels and low albumin levels serve as separate risk factors for CVD\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In contrast to earlier studies, BAR compensates for the absence of a correlation when considering albumin or BUN individually.\u003c/p\u003e\u003cp\u003eBUN is an important hematological indicator for monitoring kidney function, but it is less sensitive than the glomerular filtration rate (GFR) and creatinine. Moreover, BUN is influenced by several factors, including age, protein intake, and metabolic profiles. BUN has been identified as a strong predictor of cardiovascular disease in several studies and may even exceed GFR and serum creatinine; however, the underlying mechanisms remain unclear\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In previous analyses of all-cause mortality\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, BUN levels were significantly higher in in-hospital decedents than in survivors. Thus, BUN levels may reflect disease severity. In addition, in a study by Sullivan\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, BUN demonstrated some predictive value for disease prognosis. A retrospective cohort study carried out in China by Chen et al. \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e established a significant correlation between elevated BUN levels and in-hospital mortality rates among patients experiencing acute exacerbations of chronic obstructive pulmonary disease (COPD). The findings from this research suggest that monitoring BUN levels could serve as a crucial factor in assessing the severity of exacerbations and potentially predicting the clinical outcomes for these patients during their hospital stay.\u003c/p\u003e\u003cp\u003eAlbumin is one of the most important nutritional indicators in the body, and previous studies have suggested that hypoalbuminemia is associated with the prognosis of several cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A cohort study found that serum albumin levels were strongly associated with the risk of cardiovascular complications and death in patients with chronic kidney disease (CKD)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The risk of cardiovascular complications was significantly increased when serum albumin levels were \u0026lt; 3.4 g/dL. Serum albumin levels may be influenced by inflammation and nutritional status, and lower serum albumin levels may reflect a systemic inflammatory response or malnutrition, which may indirectly increase the risk of cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Research on heart failure indicates that while hypoalbuminemia may not be a direct result of heart failure, chronic heart failure over an extended period is often associated with complications, such as infections, malnutrition, liver impairment, and renal disease, which can exacerbate the loss of albumin and disturb the body’s fluid equilibrium\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Heart failure is a condition of relative hypoperfusion in organs due to overload of the heart, and the occurrence of hypoalbuminemia can lead to additional fluid loss within the circulatory system, resulting in a detrimental cycle that negatively affects prognosis.\u003c/p\u003e\u003cp\u003ePrevious studies\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e have established a significant association between hepatic and renal impairment and cardiovascular disease, indicating that these conditions frequently coexist among patients with cardiac issues. Specifically, mortality rates have been found to correlate strongly with the MELD-XI score, which is derived from the blood creatinine and bilirubin levels. Additionally, markers such as BUN and albumin serve as clinical indicators of liver and kidney dysfunction. As a result, there appear to be parallels between the BAR and MELD-XI scores regarding the assessment of patient health status. A recent study\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e revealed that elevated levels of the BAR score function as an independent risk factor for both in-hospital mortality and mortality within a 90-day period among critically ill patients diagnosed with chronic heart failure. This finding underscores the potential significance of BAR as a clinical marker, suggesting that it may effectively reflect circulating blood volume in these patients. An increase in the BAR values signifies a relative deficiency in circulating blood volume, leading to the conclusion that further investigation is warranted. Future research should focus on clarifying the role of BAR in evaluating a patient’s effective circulating blood volume as well as its potential utility in informing volume management strategies for individuals with CVD.\u003c/p\u003e\u003cp\u003eSeveral studies have reported the predictive value of BAR for respiratory diseases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, this novel indicator has not been studied in cardiovascular diseases. In our cross-sectional study, we observed a U-shaped relationship between the BAR and the risk of developing CVD. Interestingly, we found a gradual decrease in the risk of CVD at BAR values \u0026lt; 2.49 and a gradual increase when BAR \u0026gt; 2.49. This result suggests that there is a threshold effect of the BAR as a predictor of CVD risk, which is consistent with the clinical situation of CVD. When BAR was analyzed as a continuous variable, there was a 6% increase in the risk of CVD occurrence for each 1-unit increase in BAR value. When analyzed in subgroups, there was an increase in the risk of occurrence in groups with higher BAR values. These results suggest that higher BAR values imply higher serum BUN levels and lower albumin levels in patients with CVD. Several factors may explain the association between BAR levels and CVD. First, the inflammatory response plays a crucial role in CVD patients, accelerating the process of protein hydrolysis; because of the low albumin level in patients, the BUN level is elevated\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Second, in patients suffering from CVD, there is activation of the renin-angiotensin-aldosterone system (RAAS) along with the sympathetic nervous system as a result of diminished cardiac function\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. This activation leads to stimulation by angiotensin and adrenergic signals, resulting in renal vasoconstriction along with a reduction in glomerular filtration rate and renal blood flow. Consequently, this mechanism increases urea reabsorption, which in turn raises BUN levels. In addition, in our study, there was a difference in AUC between BAR and BUN and albumin (AUC\u003csub\u003eBAR\u003c/sub\u003e: 0.685, AUC\u003csub\u003eAlb\u003c/sub\u003e: 0.613, AUC\u003csub\u003eBUN\u003c/sub\u003e: 0.669). Therefore, we suggest that the BAR may be a convenient predictor in patients with CVD. However, this needs to be confirmed by further prospective cohort studies, as this study was only cross-sectional, and there was no direct causal relationship. BAR is associated with the risk of developing CVD. We suggest that the BAR values should be emphasized in clinical practice. It is crucial to quickly and accurately identify patients with CVD and take appropriate interventions to improve prognosis.\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, given its cross-sectional design, a causal relationship between BAR and CVD risk could not be established. It is important to recognize the observational nature of this study and interpret the results with caution. Future prospective clinical trials on CVD interventions will be critical to discerning the causal nature of the observed association. Second, reliance on self-reported CVD questionnaires introduces a potential memory bias. Third, despite the use of regression modelling and stratified analyses, we cannot completely rule out the influence of unmeasured confounders on the results of observed associations. Fourth, the existing results originate from a study involving adults in the United States, and additional investigations are required to assess their applicability to different populations. Fifth, in the present study, we did not analyze the effect of the therapeutic intervention on BUN and albumin levels in participants with CVD, which may have affected the results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this cross-sectional investigation, we observed a U-shaped association between BAR and the likelihood of developing CVD. Additional prospective cohort studies are required to confirm this association.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from public databases and did not require additional ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Chen: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXinghong Zhou: Investigation, Project administration, Resources, Supervision, Visualization, Writing \u0026ndash; original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBailing Zhang: Investigation, Project administration, Resources, Supervision, Visualization, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eYue Wu: Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Dr Liu Jie of the PLA General Hospital for his help, especially in statistical knowledge and overall conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKumar M, Patil S, Godoy L, et al. 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Integrated bioinformatic analysis reveals immune molecular markers and potential drugs for diabetic cardiomyopathy. Front Endocrinol (Lausanne). 2022. 13: 933635.\u003c/li\u003e\n\u003cli\u003eWu Z, Yu S, Kang X, et al. Association of visceral adiposity index with incident nephropathy and retinopathy: a cohort study in the diabetic population. Cardiovasc Diabetol. 2022. 21(1): 32.\u003c/li\u003e\n\u003cli\u003eShi Y, Duan H, Liu J, et al. Blood urea nitrogen to serum albumin ratio is associated with all-cause mortality in patients with AKI: a cohort study. Front Nutr. 2024. 11: 1353956.\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;tzinger F, Kunz M, Lauder L, B\u0026ouml;hm M, Mahfoud F. Arterial Hypertension-clinical trials update 2023. Hypertens Res. 2023. 46(9): 2159-2167.\u003c/li\u003e\n\u003cli\u003eLiu H, Wang D, Wu F, Dong Z, Yu S. Association between inflammatory potential of diet and self-reported severe headache or migraine: A cross-sectional study of the National Health and Nutrition Examination Survey. Nutrition. 2023. 113: 112098.\u003c/li\u003e\n\u003cli\u003eZeng Z, Ke X, Gong S, et al. Blood urea nitrogen to serum albumin ratio: a good predictor of in-hospital and 90-day all-cause mortality in patients with acute exacerbations of chronic obstructive pulmonary disease. BMC Pulm Med. 2022. 22(1): 476.\u003c/li\u003e\n\u003cli\u003eChen Y, Tang H, Luo N, et al. Association between flavonoid intake and rheumatoid arthritis among US adults. J Nutr Biochem. 2024. 131: 109673.\u003c/li\u003e\n\u003cli\u003eLiu M, Chan CP, Yan BP, et al. Albumin levels predict survival in patients with heart failure and preserved ejection fraction. Eur J Heart Fail. 2012. 14(1): 39-44.\u003c/li\u003e\n\u003cli\u003eChien SC, Chen CY, Lin CF, Yeh HI. Critical appraisal of the role of serum albumin in cardiovascular disease. Biomark Res. 2017. 5: 31.\u003c/li\u003e\n\u003cli\u003eFilippatos G, Rossi J, Lloyd-Jones DM, et al. Prognostic value of blood urea nitrogen in patients hospitalized with worsening heart failure: insights from the Acute and Chronic Therapeutic Impact of a Vasopressin Antagonist in Chronic Heart Failure (ACTIV in CHF) study. J Card Fail. 2007. 13(5): 360-4.\u003c/li\u003e\n\u003cli\u003eKhoury J, Bahouth F, Stabholz Y, et al. Blood urea nitrogen variation upon admission and at discharge in patients with heart failure. ESC Heart Fail. 2019. 6(4): 809-816.\u003c/li\u003e\n\u003cli\u003evan Veldhuisen DJ, Ruilope LM, Maisel AS, Damman K. Biomarkers of renal injury and function: diagnostic, prognostic and therapeutic implications in heart failure. Eur Heart J. 2016. 37(33): 2577-85.\u003c/li\u003e\n\u003cli\u003eMeekers E, Dauw J, Martens P, et al. Renal function and decongestion with acetazolamide in acute decompensated heart failure: the ADVOR trial. Eur Heart J. 2023. 44(37): 3672-3682.\u003c/li\u003e\n\u003cli\u003eHu G, Wu Y, Zhou Y, et al. Prognostic role of D-dimer for in-hospital and 1-year mortality in exacerbations of COPD. Int J Chron Obstruct Pulmon Dis. 2016. 11: 2729-2736.\u003c/li\u003e\n\u003cli\u003eYu X, Zhu GP, Cai TF, Zheng JY. Establishment of risk prediction model and risk score for in-hospital mortality in patients with AECOPD. Clin Respir J. 2020. 14(11): 1090-1098.\u003c/li\u003e\n\u003cli\u003eSullivan DH, Sullivan SC, Bopp MM, Roberson PK, Lensing SY. BUN as an Independent Predictor of Post-Hospital-Discharge Mortality among Older Veterans. J Nutr Health Aging. 2018. 22(7): 759-765.\u003c/li\u003e\n\u003cli\u003eChen L, Chen L, Zheng H, Wu S, Wang S. The association of blood urea nitrogen levels upon emergency admission with mortality in acute exacerbation of chronic obstructive pulmonary disease. Chron Respir Dis. 2021. 18: 14799731211060051.\u003c/li\u003e\n\u003cli\u003eArques S. Human serum albumin in cardiovascular diseases. Eur J Intern Med. 2018. 52: 8-12.\u003c/li\u003e\n\u003cli\u003eArques S, Ambrosi P. Human serum albumin in the clinical syndrome of heart failure. J Card Fail. 2011. 17(6): 451-8.\u003c/li\u003e\n\u003cli\u003eHuang F, Fan J, Wan X, et al. The association between blood albumin level and cardiovascular complications and mortality risk in ICU patients with CKD. BMC Cardiovasc Disord. 2022. 22(1): 322.\u003c/li\u003e\n\u003cli\u003eWang Z, Zhang L, Li S, et al. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database. BMC Infect Dis. 2022. 22(1): 629.\u003c/li\u003e\n\u003cli\u003eEckart A, Struja T, Kutz A, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med. 2020. 133(6): 713-722.e7.\u003c/li\u003e\n\u003cli\u003eBiegus J, Demissei B, Postmus D, et al. Hepatorenal dysfunction identifies high-risk patients with acute heart failure: insights from the RELAX-AHF trial. ESC Heart Fail. 2019. 6(6): 1188-1198.\u003c/li\u003e\n\u003cli\u003eBiegus J, Zymliński R, Sokolski M, et al. Impaired hepato-renal function defined by the MELD XI score as prognosticator in acute heart failure. Eur J Heart Fail. 2016. 18(12): 1518-1521.\u003c/li\u003e\n\u003cli\u003ePerry AS, Dooley EE, Master H, Spartano NL, Brittain EL, Pettee Gabriel K. Physical Activity Over the Lifecourse and Cardiovascular Disease. Circ Res. 2023. 132(12): 1725-1740.\u003c/li\u003e\n\u003cli\u003eMichel-Flutot P, Mansart A, Fayssoil A, Vinit S. Effects of C2 hemisection on respiratory and cardiovascular functions in rats. Neural Regen Res. 2023. 18(2): 428-433.\u003c/li\u003e\n\u003cli\u003eSavarese G, Stolfo D, Sinagra G, Lund LH. Heart failure with mid-range or mildly reduced ejection fraction. Nat Rev Cardiol. 2022. 19(2): 100-116.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. \u0026nbsp;Baseline characteristics of participants.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eQ2 (2.44-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eQ3 (3.08-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eQ4 (\u0026gt;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e49.5 \u0026plusmn; 17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e40.9 \u0026plusmn; 14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e45.1 \u0026plusmn; 16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e50.5 \u0026plusmn; 16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e61.1 \u0026plusmn; 15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9546 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1947 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2267 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2611 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2721 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10224 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2970 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2618 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2359 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2277 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2709 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e652 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e675 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e692 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e690 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7405 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1619 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1682 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1862 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2242 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4345 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1338 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1110 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e992 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e905 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5311 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1308 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1418 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1424 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1161 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level(y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026lt;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1829 ( 9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e316 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e406 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e494 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e613 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2463 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e721 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e574 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e545 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e623 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e15478 (78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3880 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3905 (79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3931 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e3762 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29.3 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e28.7 \u0026plusmn; 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e29.0 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e29.7 \u0026plusmn; 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e30.0 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12555 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3692 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3477 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3144 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2242 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7215 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1225 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1408 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1826 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2756 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12890 (65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3759 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3393 (69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3133 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2605 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e6880 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1158 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1492 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1837 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2393 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e16542 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4431 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4313 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4180 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e3618 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2710 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e389 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e451 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e650 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1220 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e518 ( 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e97 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e121 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e140 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e160 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3790 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1350 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e976 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e803 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e661 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4614 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e790 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e973 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1235 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1616 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e11366 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2777 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2936 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2932 (59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2721 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4618 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1279 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1245 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1187 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e907 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5093 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1270 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1277 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1278 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1268 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10059 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2368 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2363 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2505 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2823 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5966 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1606 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1465 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1404 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1491 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e13804 (69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3311 (67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3420 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3566 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e3507 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr (umol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e79.6 \u0026plusmn; 40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e68.9 \u0026plusmn; 16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e72.9 \u0026plusmn; 16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e77.0 \u0026plusmn; 20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e99.4 \u0026plusmn; 71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e5.6 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e6.3 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1. \u0026nbsp;Continued\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eQ1 (\u0026lt;2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eQ2 (2.44-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eQ3 (3.08-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eQ4 (\u0026gt;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA (umol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e322.9 \u0026plusmn; 86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e300.1 \u0026plusmn; 79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e314.4 \u0026plusmn; 80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e324.8 \u0026plusmn; 81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e351.9 \u0026plusmn; 94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBC (m/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC (K/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e13.9 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e13.9 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e14.0 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e14.1 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e13.7 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet (K/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e238.3 \u0026plusmn; 61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e246.6 \u0026plusmn; 62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e242.5 \u0026plusmn; 61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e236.5 \u0026plusmn; 59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e227.7 \u0026plusmn; 62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19118 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4848 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4817 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4840 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4613 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e652 ( 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e69 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e68 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e130 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e385 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e18983 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4842 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4790 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4800 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4551 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e787 ( 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e75 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e95 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e170 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e447 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAngina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19298 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4848 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4824 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4862 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4764 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e472 ( 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e69 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e61 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e108 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e234 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart attack\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e18955 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4808 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4791 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4795 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4561 (91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e815 ( 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e109 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e94 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e175 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e437 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19036 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4809 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4775 (97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4810 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4642 (92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e734 ( 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e108 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e110 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e160 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e356 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e17685 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4643 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4591 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4511 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e3940 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2085 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e274 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e294 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e459 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1058 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIR, poverty-to-income ratio; DM, diabetes mellitus; Cr, creatinine; K, potassium; UA, uric acid; BMI, body mass index ; RBC, red blood cell; WBC, white blood cell; CHD, coronary heart disease; CVD, cardiovascular disease.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. \u0026nbsp;Association of covariates and CVD risk.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"712\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOR_95CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eOR_95CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.07 (1.07~1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.50 (1.33~1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.72 (0.65~0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.57 (0.51~0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e2.17 (1.84~2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e3.02 (2.52~3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.73 (1.45~2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e4.27 (3.62~5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.07 (0.89~1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level(y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026lt;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.08 (0.97~1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.86 (0.72~1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr (umol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.01 (1.01~1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.58 (0.50~0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.13 (1.11~1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.84 (0.82~0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2.54 (2.25~2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.03 (1.02~1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA (umol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.00 (1.00~1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBC (m/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.57 (0.52~0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC (K/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.02 (1.01~1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e6.30 (5.68~6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.88 (0.86~0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet (K/\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.99 (0.99~1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e3.81 (3.46~4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e4.52 (4.08~5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e2.34 (1.84~2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePIR, poverty-to-income ratio; DM, diabetes mellitus; Cr, creatinine; K, potassium; UA, uric acid; BMI, body mass index ; RBC, red blood cell; WBC, white blood cell; CHD, coronary heart disease; CVD, cardiovascular disease.\u003c/p\u003e\n\u003cp\u003eTable 3. Association between BAR and CVD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 626px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.44 (1.41~1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.15 (1.12~1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.11 (1.08~1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.06 (1.02~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBAR(category)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.92 (0.78~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.26 (1.05~1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.19 (0.99~1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.21 (1.01~1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2(2.44-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3(3.08-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.59 (1.36~1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.15 (0.97~1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.11 (0.94~1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.09 (0.92~1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.19 (3.66~4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.69 (1.46~1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.53 (1.31~1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.31 (1.11~1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.76 (1.68~1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.18 (1.13~1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.15 (1.09~1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.08 (1.03~1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e was adjusted for age, gender, race, education level, PIR, BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u0026nbsp;\u003c/strong\u003ewas adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u0026nbsp;\u003c/strong\u003ewas adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking, Cr, glucose, K, UA, RBC, WBC, hemoglobin, Platelet.\u003c/p\u003e\n\u003cp\u003eTable 4. Threshold effect analysis of the relationship of BAR with CVD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 395px;\"\u003e\n \u003cp\u003eAdjusted Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e＜2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e0.619 (0.439~0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e＞2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1.082 (1.032~1.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eLog-likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Association between BAR and CVD in participants with extreme potassium was not include. (3.5mmol/L\u0026lt;K+ \u0026lt;5.5mmol/L，18693).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"719\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 626px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.45 (1.41~1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.15 (1.11~1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.11 (1.08~1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.05 (1.01~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBAR(category)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.91 (0.77~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.24 (1.03~1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.17 (0.96~1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.19 (0.98~1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2(2.44-3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3(3.08-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.58 (1.35~1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.12 (0.95~1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.08 (0.91~1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.05 (0.89~1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.19 (3.64~4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.65 (1.41~1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.48 (1.26~1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.26 (1.07~1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.76 (1.68~1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.17 (1.12~1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.14 (1.08~1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.07 (1.01~1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e was adjusted for age, gender, race, education level, PIR, BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u0026nbsp;\u003c/strong\u003ewas adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u0026nbsp;\u003c/strong\u003ewas adjusted for age, gender, race, education level, PIR, BMI, hypertension, hyperlipidemia, DM, smoking status, physical activity, drinking, Cr, glucose, K, UA, RBC, WBC, hemoglobin, Platelet.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Association, blood urea nitrogen (BUN), albumin, National Health and Nutrition Examination Survey (NHANES), cardiovascular disease (CVD)","lastPublishedDoi":"10.21203/rs.3.rs-6204802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6204802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCardiovascular disease (CVD) ranks among the top causes of mortality worldwide, particularly impacting the US. The Blood Urea Nitrogen-to-Serum Albumin Ratio (BAR) has recently been identified as a promising biomarker that integrates indicators of both renal and nutritional health. However, the association between BAR and CVD has not been thoroughly explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized cross-sectional data from individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years who participated in the NHANES from 2011\u0026ndash;2018. To evaluate the stability of the findings, cubic spline models with restricted parameters along with logistic regression were employed, and both subgroup analyses and Receiver Operating Characteristic (ROC) curve analyses were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 19770 participants, 10.5% (2085/19770) were diagnosed with CVD. When BAR was analyzed as a continuous variable, the full model-adjusted OR was 1.06 (95% CI: 1.02\u0026thinsp;~\u0026thinsp;1.09, p\u0026thinsp;=\u0026thinsp;0.003). When compared with Q2, the OR values for Q1, Q3, and Q4 groups were 1.21 (95% CI: 1.01\u0026thinsp;~\u0026thinsp;1.46), 1.09 (95% CI: 0.92\u0026thinsp;~\u0026thinsp;1.29), and 1.31 (95% CI: 1.11\u0026thinsp;~\u0026thinsp;1.53), respectively. The correlation between BAR and CVD showed a U-shaped curve (p \u003csub\u003efor non\u0026minus;linear\u003c/sub\u003e \u0026lt; 0.001). The threshold analysis resulted in a 2.49. Subgroup analyses did not reveal any significant interactions with BAR across the different subgroups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The ROC curve demonstrated an area under the curve (AUC) of 0.685 (95% CI: 0.672\u0026ndash;0.698), indicating BAR's ability to predict CVD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBAR is a potential predictor of CVD risk with a U-shaped association. Further prospective studies are required to validate our findings.\u003c/p\u003e","manuscriptTitle":"Association between Blood Urea Nitrogen-to-Serum Albumin Ratio and Cardiovascular Disease Risk: A NHANES Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 05:33:14","doi":"10.21203/rs.3.rs-6204802/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e60f021e-aa7e-4dd5-9e1c-3d98f140ead4","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-28T15:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 05:33:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6204802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6204802","identity":"rs-6204802","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}