Neutrophil-lymphocyte ratio predicts all-cause and cardiovascular mortality in a pan- vascular disease population: a nationally representative study

Neutrophil-lymphocyte ratio predicts all-cause and cardiovascular mortality in a pan- vascular disease population: a nationally representative 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 Article Neutrophil-lymphocyte ratio predicts all-cause and cardiovascular mortality in a pan- vascular disease population: a nationally representative study Xueyuan Yang, Lei Chen, Hong Xiao, Kui Li, Changlong Yang, Jiafei Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5336184/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 A novel medical specialty, pan-vascular medicine, has recently emerged for atherosclerosis treatment. Its objective is the integrated management of pan-vascular diseases, including coronary artery cerebrovascular, and peripheral artery diseases. This study aimed to examine the correlation between neutrophil-lymphocyte ratio (NLR) and mortality in a population with pan-vascular disease, to assess its predictive value. Methods This retrospective cohort study comprised 1,767 individuals with pan-vascular disease sourced from the NHANES database (2001–2016). Study endpoints were all-cause and cardiovascular mortality. The relationship among NLR, all-cause mortality, and cardiovascular mortality was examined in a population with a broad range of vascular diseases. Weighted Cox regression analyses and restricted cubic spline (RCS) analyses were conducted. Discrepancies in survival rates between the three groups classified according to NLR were investigated using Kaplan-Meier survival analysis. Prognostic accuracy of the NLR model for mortality in the pan-vascular disease population was evaluated using time-dependent receiver operating characteristic curves and calibration curves. Results The median follow-up period for this study was 90 months, during which a total of 832 patients died, including 269 who died of cardiovascular causes. Neutrophil-to-lymphocyte ratio (NLR) was an independent predictor of all-cause mortality [hazard ratio (HR) = 1.13, 95% confidence interval (CI) (1.07–1.20), p < 0.001] and cardiovascular mortality [HR = 1.14, 95% CI (1.05–1.24), p = 0.001] in individuals with pan-vascular disease. RCS analysis indicated a linear association between NLR and all-cause mortality (p-value for nonlinearity = 0.108) and cardiovascular mortality (p-value for nonlinearity = 0.149) in the population with pan-vascular disease. Risk of all-cause mortality and cardiovascular mortality was elevated among individuals with higher levels of NLR. NLR model exhibited favorable predictive efficacy for all-cause mortality and cardiovascular mortality in the pan-vascular disease population. Furthermore, the calibration curve illustrated the high predictive accuracy of the NLR model for all-cause mortality in the pan-vascular disease population at 3 and 5 years. Conclusions NLR is an independent risk factor for cardiovascular mortality and all-cause mortality in pan-vascular disease populations, and is linearly associated with all-cause mortality and cardiovascular mortality in pan-vascular disease populations and has some predictive value. Health sciences/Medical research/Biomarkers Health sciences/Medical research/Epidemiology Health sciences/Medical research/Translational research neutrophil-lymphocyte ratio pan-vascular disease all-cause mortality cardiovascular mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Atherosclerosis is a common and prevalent disease worldwide and is a major mortality threat to the population. Atherosclerotic diseases, represented by coronary atherosclerosis and stroke, have become the number one public health threat [ 1 ]. It is worth noting that due to the specialization of medical disciplines, atherosclerotic diseases in different parts of the body are classified into different disciplines for management, which results in systemic vascular diseases not being effectively assessed and treated. In order to change this status quo, a new discipline called ‘pan-vascular medicine’ has emerged, which is dedicated to the comprehensive management of systemic vascular diseases. Pan-vascular diseases are mainly manifested as coronary artery disease, cerebrovascular disease, peripheral artery disease, and other vascular diseases [ 2 ]. Given the significant public health problems caused by pan-vascular disease, identification of populations with poor prognosis for pan-vascular disease by a number of inexpensive indicators and early medical intervention are important for improving survival time in such populations. Atherosclerosis is an accumulation of fatty, fibrous tissue in the vessel wall, and activation of inflammatory pathways is an important pathogenesis of this disease [ 1 , 3 ]. The neutrophil-lymphocyte ratio (NLR) is a straightforward and accessible marker of systemic inflammation. It can be employed as an early warning indicator of conditions such as atherosclerosis and stress. Current research indicates that NLR is significantly correlated with cardiovascular and peripheral vascular disease [ 4 – 7 ]. Furthermore, NLR has demonstrated considerable prognostic value in high-risk groups with cardiovascular and cerebrovascular conditions, including hypertension, diabetes mellitus, stroke, and coronary heart disease [ 8 – 11 ]. However, current studies have focused only on single-vessel disease, and comprehensive pan-vascular disease population assessments are lacking. In conclusion, the present study aims to evaluate the relationship between neutrophil-to-lymphocyte ratio (NLR) and all-cause mortality, as well as cardiovascular mortality, in a population with a broad range of vascular diseases. Additionally, the study will assess the predictive value of NLR for mortality in this population. Methods Study population The National Health and Nutrition Examination Survey (NHANES) is an epidemiological survey of the nutritional and health status of populations in the United States. Prior to participation, each individual signed an informed consent form that was ethically approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). The NHANES survey employs a representative sample of approximately 10,000 individuals from a representative sample of approximately 30 of the approximately 3,000 U.S. counties surveyed. This nationwide, complex, multistage sample is conducted every two years (one cycle). Participants‘ demographic data, socio-economic information, clinical examination data, and the prevalence of selected diseases are collected through standardized questionnaires and standardized physical examinations, and patients’ blood test indicators are measured through a mobile examination Centre, all of which are subject to strict quality control. This study was a retrospective cohort study that included a pan-vascular disease population from 8 cycles of the NHANES study (2001–2016). Combined with the pan-vascular disease definition, i.e., the main manifestations are coronary artery disease, cerebrovascular disease and peripheral artery disease [ 2 ]. The following groups were included in the study: (1) Individuals who were informed by a physician or health professional of the presence of cardiovascular disease, including but not limited to coronary heart disease, angina/angina pectoris, and myocardial infarction; (2) Individuals who were informed by a physician or health professional of the presence of stroke; (3) Individuals who were informed of the presence of peripheral arterial disease, as determined by measurement of ankle-brachial index (ABI), defined as ABI < 0.90 on either side [ 12 ]. The following groups were excluded from the study: (1) Individuals with incomplete data on fasting weights; (2) Individuals with incomplete data on education levels; (3) Individuals with incomplete data on poverty income ratio. A total of 1,767 individuals were ultimately included (Fig. 1 ), of whom 1,234 had comorbid cardiovascular disease, 611 had comorbid stroke, and 171 had comorbid peripheral arterial disease. State of survival The primary outcomes of this study were all-cause mortality and cardiovascular mortality. The data for these outcomes were obtained from the National Center for Health Statistics website ( https://www.cdc.gov/nchs/data-linkage/mortality-public.htm ). The cardiovascular mortality data were collected through follow-up of participants in the NHANES study until the end of the study period on 31 December 2019. Study inclusion variables The following variables were included in this study: Demographics Data: sex, age, race, education, Poverty income ratio (PIR). Among the races are Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race. Educational attainment was classified into the following categories: some college, graduated high school, 9th-11th grade, or less than 9th grade. PIR is classified as ≤ 1, 1 3.0 [ 13 ]. Questionnaire data: Smoking, congestive heart failure, asthma, chronic bronchitis, liver disease, diabetes, hypertension, heart attack, angina/angina pectoris, coronary heart disease, stroke. Hypertension was defined as a definitive diagnosis of high blood pressure by a doctor or health professional, or taking prescription medication for high blood pressure [ 14 ]. Diabetes mellitus is defined as a definitive diagnosis of diabetes mellitus by a doctor or health professional or taking oral glucose-lowering medication or insulin [ 8 ]. The use of cigarettes in quantities of more than 100 is defined as smoking [ 14 ]. The remaining co-morbidities were defined as the presence of a relevant disease communicated by a doctor or health professional. Examination Data: BMI, ABI. eGFR was calculated using the CKD-EPI formula developed by the Chronic Kidney Disease Epidemiology Collaboration, which standardizes race, sex, and age to more accurately assess kidney function [ 15 ]. Laboratory data: Fasting blood glucose (FPG), glycated hemoglobin (HbA1c), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), creatinine (Scr), uric acid (Uric Acid), red blood cell count (RBC), platelet count (PLT), neutrophil count (Neutrophil), lymphocyte count (Lymphocyte), wherein NLR is the calculation of neutrophil/lymphocyte [ 16 ]. NLR was grouped by the three-quartile method into Low level NLR(0.04 < NLR ≤ 1.83, n = 583), Medium level NLR (1.83 < NLR ≤ 2.71, n = 596), High level NLR (2.71 < NLR, n = 588) three groups. Statistical analyses Continuous variables are expressed as mean and standard deviation or median and interquartile range, depending on whether they followed a normal distribution, and categorical variables are expressed as percentages. The variables were divided into 3 groups, low, medium, or high NLR according to NLR tertiles. Between-group differences were compared using ANOVA or rank sum test depending on whether the variables followed a normal distribution and categorical variables were compared using chi-squared analysis. The association of NLR with all-cause mortality and cardiovascular mortality in a pan-vascular disease population was assessed using weighted Cox regression, with model 1 being a one-factor weighted Cox regression model. Models 2 and 3 were multifactorial weighted Cox regression models, with covariates adjusted for the correlation of the variables with the clinical outcomes before modeling, the same covariates were selected for both outcomes and covariances between the included variables were detected by the variance inflation factor, with all covariates having a variance inflation factor of less than 5, and there was no significant multicollinearity. Model 2 was adjusted for sex, age, race, PIR, and education level, and model 3 was adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes. In addition, to assess differences in survival between the three NLR groups, Kaplan-Meier survival analyses and log-rank tests were performed. Restricted cubic spline (RCS) analyses assessed the potential non-linear relationship between all-cause mortality and cardiovascular mortality in the NLR and pan-vascular disease populations, with variables from model 3 included in the RCS assessment. The predictive value of NLR for 1-, 3- and 5-year all-cause mortality and cardiovascular mortality in a pan-vascular disease population was assessed using time-dependent receiver operating characteristic (time-dependent ROC) curves, with variables adjusted for model 3 included in the time-dependent ROC assessment. The agreement between the NLR predicted and actual values of all-cause mortality and cardiovascular mortality for the pan-vascular disease population at 1, 3, and 5 years was assessed using the calibration curve incorporating the variables in model 3. Subgroup and interaction analyses stratified by age, diabetes status, and sex were performed to assess the effect of NLR on all-cause and cardiovascular mortality in different subgroups. In this study, to avoid bias due to missing data, the data were interpolated using the Multiple Interpolation of Chained Equations (MICE) method. All analyses were performed using R software (version 4.3.1), and two-sided p-values of less than 0.05 were considered statistically significant. The analyses also took into account the NHANES complex sample weight, where the sample weight was the 2-year MEC weight of the fasting sub-sample divided by 8. Results Baseline characteristics The study included 1,767 eligible patients with pan-vascular disease. Significant differences were observed between the three NLR groups in Age, TC, HDL-C, LDL-C, TG, FPG, Scr, Uric acid, RBC, eGFR, Neutrophil, Lymphocyte, Sex, Race, family income-poverty ratio, heart failure, chronic bronchitis, and Smoking were statistically different (all p < 0.05). The High-level NLR group was older, had higher creatinine levels, included more males than females, and had more patients who smoked (Table 1 ). Table 1 Baseline information table based on NLR tertile grouping Variables Total n = 1,767 Low-level NLR (0.04 < NLR ≤ 1.83) n = 583 Medium-level NLR (1.83 < NLR ≤ 2.71) n = 596 High-level NLR (2.71 < NLR) n = 588 P -value Age, years 69(60,78) 66(57,74) 69(59,78) 72(64,80) < 0.001 BMI, kg/m2 28.59(25.20,32.92) 28.93(25.30,33.00) 28.86(25.50,33.20) 28.12(24.62,32.58) 0.062 TC,mmol/L 4.63(3.90,5.43) 4.78(4.11,5.54) 4.59(3.92,5.49) 4.53(3.72,5.30) < 0.001 HDL-C,mmol/L 1.24(1.03,1.53) 1.29(1.09,1.58) 1.22(1.03,1.50) 1.22(1.03,1.51) 0.003 LDL-C,mmol/L 2.56(1.97,3.28) 2.69(2.10,3.35) 2.53(1.96,3.31) 2.47(1.89,3.13) 0.001 TG,mmol/L 1.37(0.96,2.03) 1.37(0.94,2.00) 1.45(1.00,2.21) 1.25(0.92,1.89) 0.001 FPG, mg/dL 107.00(97.00,123.65) 106.00(96.00,122.45) 106.00(97.00,122.00) 108.10(98.20,126.05) 0.046 HbA1c,% 5.8(5.4,6.3) 5.8(5.4,6.3) 5.7(5.4,6.3) 5.8(5.4,6.3) 0.864 Scr, mg/dL 1.00(0.80,1.20) 0.94(0.78,1.12) 0.98(0.80,1.20) 1.03(0.88,1.29) < 0.001 Uric acid,µmol/L 350.90(291.50,413.40) 345.00(285.50,404.50) 350.90(291.50,416.40) 356.90(297.40,416.40) 0.042 RBC,× 10 9 /L 4.59(4.23,4.96) 4.56(4.20,4.89) 4.65(4.30,4.99) 4.58(4.18,4.96) 0.003 PLT,× 10 9 /L 224(186,271) 220(184,264) 226(188,274) 225(185,273) 0.286 eGFR,mL/min/1.73m 2 73.29(56.50,89.90) 79.00(61.77,95.33) 73.59(58.30,90.45) 66.74(51.61,84.82) < 0.001 Neutrophil, × 10 9 /L 4.10(3.20,5.20) 3.20(2.50,3.90) 4.10(3.40,4.90) 5.20(4.20,6.32) < 0.001 Lymphocyte, × 10 9 /L 1.80(1.40,2.30) 2.30(1.90,2.80) 1.80(1.50,2.20) 1.40(1.10,1.70) < 0.001 NLR 2.26(1.64,3.08) 1.43(1.16,1.63) 2.25(2.00,2.47) 3.59(3.09,4.54) < 0.001 Sex,% < 0.001 Male 1007(57.0) 281(48.2) 344(57.7) 382(65.0) Female 760(43.0) 302(51.8) 252(42.3) 206(35.0) Race,% < 0.001 Mexican American 193(10.9) 63(10.8) 65(10.9) 65(11.1) Other Hispanic 114(6.5) 46(7.9) 43(7.2) 25(4.3) Non-Hispanic White 1047(59.3) 265(45.5) 382(64.1) 400(68.0) Non-Hispanic Black 329(18.6) 174(29.8) 85(14.3) 70(11.9) Other Race 84(4.8) 35(6.0) 21(3.5) 28(4.8) Education levels,% 0.758 Less than 9th grade 313(17.7) 106(18.2) 109(18.3) 98(16.7) 9–11th grade 304(17.2) 104(17.8) 101(16.9) 99(16.8) High school graduate 423(23.9) 128(22.0) 153(25.7) 142(24.1) Some college 727(41.1) 245(42.0) 233(39.1) 249(42.3) Family income-poverty ratio,% 0.011 ≤ 1.0 400(22.6) 158(27.1) 129(21.6) 113(19.2) 1.0–3.0 872(49.3) 268(46.0) 310(52.0) 294(50.0) > 3.0 495(28.0) 157(26.9) 157(26.3) 181(30.8) Heart failure,% 0.001 No 1409(79.7) 475(81.5) 494(82.9) 440(74.8) Yes 358(20.3) 108(18.5) 102(17.1) 148(25.2) Asthma,% 0.055 No 1452(82.2) 461(79.1) 501(84.1) 490(83.3) Yes 315(17.8) 122(20.9) 95(15.9) 98(16.7) Chronic bronchitis,% 0.018 No 1549(87.7) 520(89.2) 532(89.3) 497(84.5) Yes 218(12.3) 63(10.8) 64(10.7) 91(15.5) Liver condition,% 0.419 No 1654(93.6) 541(92.8) 564(94.6) 549(93.4) Yes 113(6.4) 42(7.2) 32(5.4) 39(6.6) Smoking,% 0.003 No 690(39.0) 257(44.1) 231(38.8) 202(34.4) Yes 1077(61.0) 326(55.9) 365(61.2) 386(65.6) Hypertension,% 0.161 No 518(29.3) 162(27.8) 192(32.2) 164(27.9) Yes 1249(70.7) 421(72.2) 404(67.8) 424(72.1) Diabetes,% 0.674 No 1265(71.6) 425(72.9) 421(70.6) 419(71.3) Yes 502(28.4) 158(27.1) 175(29.4) 169(28.7) Association of NLR with all-cause mortality as well as cardiovascular mortality in a population with pan-vascular disease During follow-up of 1,767 patients, with a median follow-up of 90 months and an interquartile range of follow-up time (52,136), a total of 832 deaths occurred, of which 269 were due to cardiac causes. Table 2 shows the relationship between the NLR and all-cause mortality as well as cardiovascular mortality in the population with all vascular diseases. One-way weighted Cox regression (model 1) indicated that NLR was a risk factor for all-cause mortality in the pan-vascular disease population [HR = 1.24, 95% CI (1.17–1.32), p < 0.001], and after adjustment for covariates, model 2 [HR = 1. 17, 95% CI (1.11–1.23), p < 0.001], model 3 [HR = 1.13, 95% CI (1.07–1.20), p < 0.001] indicated that NLR was an independent risk factor for all-cause mortality in the pan-vascular disease population. In addition, one-way weighted Cox regression (model 1) also indicated that NLR was a risk factor for cardiovascular mortality in the pan-vascular disease population [HR = 1.25, 95% CI (1.16–1.35), p < 0.001], and after adjustment for covariates, model 2 [HR = 1. 18, 95% CI (1.09–1.27), p < 0.001], Model 3 [HR = 1.14, 95% CI (1.05–1.24), p = 0.001] indicated that NLR was independently associated with an increased risk of cardiovascular mortality in the pan-vascular disease population. Restricted cubic spline analyses showed that NLR was linearly associated with all-cause mortality (Fig. 2 a) (p for nonlinear = 0.108) and cardiovascular mortality (Fig. 2 b) (p for nonlinear = 0.149) in the pan-vascular disease population. Table 2 Weighted Cox regression analyses of all-cause mortality as well as cardiovascular mortality in NLR and pan-vascular disease populations Model 1 Model 2 Model 3 HR (95% CI) p HR (95% CI) p HR (95% CI) p All-cause mortality 1.24(1.17–1.32) < 0.001 1.17(1.11–1.23) < 0.001 1.13(1.07–1.20) < 0.001 Low-level NLR Ref Ref Ref Medium-level NLR 1.10(0.88–1.37) 0.392 0.99(0.81–1.20) 0.917 1.01(0.80–1.26) 0.958 High-level NLR 1.98(1.58–2.50) < 0.001 1.57(1.27–1.93) < 0.001 1.51(1.22–1.87) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cardiovascular mortality 1.25(1.16–1.35) < 0.001 1.18(1.09–1.27) < 0.001 1.14(1.05–1.24) 0.001 Low-level NLR Ref Ref Ref Medium-level NLR 1.02(0.69–1.50) 0.925 0.94(0.63–1.39) 0.747 0.92(0.60–1.42) 0.706 High-level NLR 2.13(1.41–3.21) < 0.001 1.67(1.10–2.52) 0.015 1.59(1.06–2.39) 0.024 P for trend < 0.001 < 0.001 < 0.001 Model 1: Not adjusted Model 2: Adjusted for sex, age, ethnicity, PIR, education level Model 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, Hypertension, diabetes mellitus Kaplan-Meier survival analysis of all-cause mortality and cardiovascular mortality in a population with pan-vascular disease based on the tertile classification of the NLR Figure 3 a shows a Kaplan-Meier survival analysis of NLR versus all-cause mortality in the overall vascular disease population based on NLR tertile groupings, with a significantly higher risk of all-cause mortality in the overall vascular disease population with higher NLR values (p < 0.001). Figure 3 b shows the relationship between NLR and cardiovascular mortality in pan-vascular disease populations based on NLR tertile subgroups, with a higher risk of cardiovascular death in such populations as NLR levels increase (p < 0.001). NLR time-dependent ROC for the prediction of all-cause mortality and cardiovascular mortality in a population pan-vascular disease Figure 4 a shows the time-dependent ROC of the NLR model for predicting all-cause mortality in the pan-vascular disease population. The area under the curve of the NLR model for predicting 1-year, 3-year and 5-year all-cause mortality was 0.802 (95% Cl = 0. 748-0.856), 0.781 (95% Cl = 0.750–0.811) and 0.773 (95% Cl = 0.746–0.799), respectively, the model showed good predictive value for all-cause mortality in the pan-vascular disease population, with the best predictive performance for 1-year all-cause mortality. Figure 4 b shows the time-dependent ROC of the NLR model for predicting cardiovascular mortality in the pan-vascular disease population. The area under the curve of the NLR model for predicting cardiovascular mortality at 1 year, 3 years, and 5 years were 0.809 (95% CI = 0.713–0.906), 0.800 (95% CI = 0.748–0.852), 0.797 (95% CI = 0.756–0.839), the model also showed good predictive value for cardiovascular mortality in the pan-vascular disease population, with the best predictive performance for 1-year cardiovascular mortality. In addition, the time-dependent ROC of the model without NLR for predicting all-cause mortality and cardiovascular mortality in the pan-vascular disease population was further assessed (Additional file 1: Supplementary Fig. 1). The model without NLR was not as good as the model with NLR for predicting all-cause mortality and cardiovascular mortality in the pan-vascular disease population at 1, 3, and 5 years. Calibration curve of the NLR for the prediction of all-cause mortality and cardiovascular mortality in a population with pan-vascular disease Figure 5 a shows the calibration curve of the NLR model for predicting all-cause mortality in the pan-vascular disease population. The predicted and actual values of the NLR model for predicting all-cause mortality in the pan-vascular disease population at 3 and 5 years were in good agreement, and the accuracy of the prediction model was good. Notably, the NLR model predicted 1-year, 3-year, and 5-year cardiovascular mortality in the pan-vascular disease population with low predictive accuracy and poor predictive accuracy(Fig. 5 b). In addition, the calibration curve for predicting all-cause mortality and cardiovascular mortality in the population with pan-vascular lesions was tested for the model without NLR (Additional file 2: Supplementary Fig. 2). Association of the NLR with total mortality and mortality due to cardiovascular disease in different subgroups of the population Table 3 shows weighted Cox regression analyses of NLR and all-cause mortality in pan-vascular disease populations grouped by sex, age, and diabetes status, and the analyses in subgroups of age, sex, and diabetes status yielded results that were more consistent with those of the overall population, with NLR remaining an independent risk factor for all-cause mortality in these populations. In addition, an interaction was observed in the age subgroup (p = 0.044). Table 3 Weighted Cox regression analyses of NLR and all-cause mortality in different subgroups. Model 1 Model 2 Model 3 HR (95% CI) p HR (95% CI) p HR (95% CI) p p interaction Sex 0.404 Male 1.28(1.19–1.38) < 0.001 1.19(1.11–1.27) < 0.001 1.16(1.08–1.25) < 0.001 Female 1.23(1.15–1.31) < 0.001 1.16(1.10–1.23) < 0.001 1.14(1.08–1.21) < 0.001 Diabetes 0.330 Yes 1.27(1.17–1.38) < 0.001 1.23(1.14–1.33) < 0.001 1.21(1.11–1.32) < 0.001 No 1.24(1.16–1.32) < 0.001 1.15(1.09–1.22) < 0.001 1.12(1.05–1.19) =60 1.19(1.13–1.25) < 0.001 1.20(1.14–1.26) < 0.001 1.15(1.09–1.22) < 0.001 < 60 1.52(1.15–2.02) 0.003 1.39(1.04–1.85) 0.024 1.34(0.98–1.85) 0.070 Model 1: Not adjusted Model 2: adjusted for sex, age, race, PIR, education level, but not subgroup variables Model 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes, but not subgroup variables Table 4 shows the weighted Cox regression analyses of NLR and cardiovascular mortality in pan-vascular disease populations in different subgroups, and in analyses of men, non-diabetic, and age greater than or equal to 60 years subgroups NLR remained an independent risk factor for cardiovascular mortality in these populations. However, an independent risk effect of NLR on cardiovascular mortality was not observed in the age < 60 years, diabetes mellitus, and female populations. In addition, an interaction was found in the sex subgroup (p = 0.005). Table 4 Weighted Cox regression analyses of NLR and cardiovascular mortality in different subgroups of the population Model 1 Model 2 Model 3 HR (95% CI) p HR (95% CI) p HR (95% CI) p p interaction Sex 0.005 Male 1.40(1.27–1.54) < 0.001 1.30(1.19–1.42) < 0.001 1.28(1.13–1.44) < 0.001 Female 1.17(1.05–1.30) 0.004 1.08(0.96–1.22) 0.177 1.07(0.95–1.21) 0.242 Diabetes 0.935 Yes 1.22(1.04–1.43) 0.017 1.17(0.99–1.38) 0.071 1.18(0.99–1.41) 0.071 No 1.27(1.17–1.38) < 0.001 1.18(1.09–1.28) =60 1.23(1.14–1.32) < 0.001 1.22(1.14–1.32) < 0.001 1.18(1.08–1.28) < 0.001 < 60 1.25(0.74–2.13) 0.409 1.16(0.72–1.85) 0.541 1.09(0.65–1.84) 0.738 Model 1: Not adjusted Model 2: adjusted for sex, age, race, PIR, education level, but not subgroup variables Model 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes, but not subgroup variables Discussion This study examined the association of NLR with all-cause mortality and cardiovascular mortality in a pan-vascular disease population, and here are some of our key findings: (1) NLR is an independent risk factor for cardiovascular mortality and all-cause mortality in all vascular disease populations; and shows a dose-response relationship; (2) The risk of all-cause mortality and cardiovascular death in the pan-vascular disease population was significantly higher with increasing NLR levels; (3) The NLR has some predictive value for all-cause mortality and cardiovascular mortality at 1, 3, and 5 years in pan-vascular disease populations. Inflammation has now been shown to promote the development of atherosclerosis by decades of epidemiological studies and large clinical trials [ 17 , 18 ]. Neutrophil, as a key immune cell, plays an important role in the development of atherosclerosis, mediating plaque formation and further deterioration [ 19 ]. Whereas the lymphocyte, as an immunomodulatory cell, has a physiological role in promoting the resolution of inflammation and has a protective effect on arterial plaque formation [ 20 , 21 ]. Thus, these two types of cells embody the body's pro-inflammatory and anti-inflammatory homeostasis. Currently, the NLR calculated from neutrophils and lymphocytes is considered an inexpensive and efficient indicator of systemic inflammation [ 22 ]. In our study, NLR was observed to be an independent risk factor for all-cause mortality and cardiovascular mortality in a pan-vascular disease population. Previous studies have shown that NLR is associated with an increased risk of death in patients with coronary artery disease and in patients with acute coronary syndromes. Gaggini et al. [ 23 ] included 1,460 patients diagnosed with coronary artery disease in a retrospective cohort study with the endpoint of all-cause mortality and a median follow-up period of 26 months, and found a higher mortality rate in patients with coronary artery disease with an elevated NLR. Pruc et al. [ 24 ] included 90 studies related to NLR in patients with acute coronary syndromes, encompassing 45,990 patients, in a meta-analysis that showed higher NLR was associated with short-term adverse cardiovascular events in acute coronary syndromes. Elevated pre-procedure NLR has also been shown to be a risk factor for adverse outcomes in patients with coronary artery disease undergoing percutaneous coronary intervention [ 25 ]. These studies suggest that NLR has an important prognostic value in cardiac vascular lesions. In addition, NLR has received attention in patients with stroke and peripheral vascular lesions. Wang [ 26 ] et al. conducted a study on ischemic stroke, which consisted of a retrospective cohort study and meta-analysis, the retrospective cohort study included 808 patients with ischemic stroke and the study outcomes were the incidence of stroke complications as well as the 3-month functional outcome, which affirmed that higher NLR was associated with an increase in the incidence of adverse complications as well as the 3-month mortality rate in patients with ischemic stroke. Erturk et al. [ 7 ] conducted a retrospective cohort study of 593 patients diagnosed with peripheral arterial occlusive disease with the endpoint of cardiovascular mortality and a median follow-up period of 20 months and demonstrated that elevated NLR increased cardiovascular mortality in such patients. In the present study, we further combined pan-vascular disease patients with cardiovascular disease, stroke and peripheral arterial disease, and in the whole pan-vascular disease population, NLR remained an independent risk factor for all-cause mortality and cardiovascular mortality in these populations, obtaining results consistent with the above studies, which also showed the important prognostic value of NLR in the pan-vascular disease population. In addition, in the survival analyses of the pan-vascular disease population, mortality was significantly higher in the higher NLR group, both in terms of all-cause mortality and cardiovascular mortality. Second, the models including NLR showed better predictive performance for mortality in both time-dependent ROC analyses, suggesting that NLR is an effective indicator for early identification of people at high risk of pan-vascular disease and may be used as an early indication to influence medical decisions. Of note, a significant independent risk effect of NLR on cardiovascular mortality in the pan-vascular disease population was not observed in the subgroup analyses in the age < 60 years, diabetes mellitus, and female populations, which may be explained to some extent by the following reasons: (1) Differences in hormone levels between males and females, experimental studies have demonstrated that estrogens and androgens have a significant impact on the biological processes of atherosclerotic cardiovascular disease [ 27 ], and female estrogen reduces cardiovascular disease by inhibiting inflammatory responses and improving vascular endothelial function [ 28 , 29 ]. (2) As age increases, the process of vascular disease gradually accelerates, and aging individuals are exposed to more cardiovascular risk factors, such as hypertension, hyperglycemia, obesity, and hyperlipidemia [ 30 ]. (3) Insufficient sample size resulted in statistically insignificant results. (4) The prognostic value of NLR in these populations is not significant. This study has some advantages as follows, firstly, it is the first time to explore the relationship between NLR and all-cause mortality and cardiovascular mortality in a pan-vascular disease population, and secondly, the NHANES database has a rigorous data quality control system, strong representativeness, scientific and reasonable survey sampling, and a high degree of reliability of the data source, which makes the study conclusions more objective. However, there are some limitations. First, because the NHANES database only included ABI as an indicator from 2001 to 2004 between 2001 and 2016, making the inclusion of the population with peripheral arterial disease in this study small (171), the study may have been under-representative of the pan-vascular disease population. Second, because cross-sectional measurements of the NLR do not reflect its change over time, it is not clear whether the NLR remained consistent over the follow-up period. Future longitudinal studies should be conducted based on the availability of repeated data measurements. In addition, due to the limitations of the NHANES database disease collection, this study did not exclude patients with hematological or immune disorders, which can affect the NLR values causing some bias. Lastly, the NHANES database only collects data from studies in the US population, so the extrapolation of this study’s conclusions is limited. Therefore, more multi-center, large-sample longitudinal studies should be conducted in the future to further address these issues. Conclusions NLR is an independent risk factor for all-cause mortality and cardiovascular mortality in pan-vascular disease populations. NLR is linearly associated with and has some predictive value for all-cause mortality and cardiovascular mortality in pan-vascular disease populations. NLR, as an inexpensive and easily available biomarker, is expected to serve as a potential predictor of prognosis in pan-vascular disease populations in the future. Abbreviations ABI, Ankle-Brachial Index BMI, Body Mass Index CI, Confidence Interval CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration HbA1c, Glycosylated Hemoglobin HDL-C, High-Density Lipoprotein Cholesterol HR, Hazard Ratio LDL-C, Low-Density Lipoprotein Cholesterol NLR, Neutrophil-Lymphocyte Ratio NHANES, National Health and Nutrition Examination Survey NHCS, National Center for Health Statistics PIR, Poverty Income Ratio PLT, Platelet Count RBC, Red Blood Cell Count RCS, Restricted Cubic Spline ROC, Receiver Operating Characteristic TC, Total Cholesterol TG, Triglyceride Scr, Creatinine Declarations Ethics approval and consent to participate All participants in this study signed an informed consent form and ethical approval was obtained from the Ethics Review Board of the National Centre for Health Statistics (NCHS). Consent for publication Not applicable. Availability of data and materials All data for this study are available on the National Center for Health Statistics website. (https://www.cdc.gov/nchs/nhanes/index.htm) Competing interests All authors have no competing interests. Funding This work was supported by a grant from the Medical Science and Technology Innovation Training Programme for Guizhou University Students (ZYDC202302112). Authors' contributions XXY designed the study, analyzed the data, and wrote the manuscript, KL performed quality control and validation of the data, CLY, LC, HX and JFL interpreted the results of the data analyses, and YM reviewed and finally revised the manuscript. Acknowledgments We thank Professor Jun Liu, School of Public Health, Zunyi Medical University, for providing statistical review of this study and all participants who contributed to the NHANES study. References Libby, P. et al. Atherosclerosis. Nat. reviews Disease primers . 5 (1), 56 (2019). Zhou, X., Yu, L., Zhao, Y. & Ge, J. Panvascular medicine: an emerging discipline focusing on atherosclerotic diseases. Eur. Heart J. 43 (43), 4528–4531 (2022). Trtica Majnarić, L., Guljaš, S., Bosnić, Z., Šerić, V. & Wittlinger, T. Neutrophil-to-Lymphocyte Ratio as a Cardiovascular Risk Marker May Be Less Efficient in Women Than in Men. Biomolecules 11 (4). (2021). Guo, X. et al. Neutrophil:lymphocyte ratio is positively related to type 2 diabetes in a large-scale adult population: a Tianjin Chronic Low-Grade Systemic Inflammation and Health cohort study. Eur. J. Endocrinol. 173 (2), 217–225 (2015). Corriere, T. et al. Neutrophil-to-Lymphocyte Ratio is a strong predictor of atherosclerotic carotid plaques in older adults. Nutr. metabolism Cardiovasc. diseases: NMCD . 28 (1), 23–27 (2018). Kaya, H., Ertaş, F. & Soydinç, M. S. Association between neutrophil to lymphocyte ratio and severity of coronary artery disease. Clin. Appl. thrombosis/hemostasis: official J. Int. Acad. Clin. Appl. Thrombosis/Hemostasis . 20 (2), 221 (2014). Erturk, M. et al. Predictive value of elevated neutrophil to lymphocyte ratio for long-term cardiovascular mortality in peripheral arterial occlusive disease. J. Cardiol. 64 (5), 371–376 (2014). Zhang, X. et al. The neutrophil-to-lymphocyte ratio is associated with all-cause and cardiovascular mortality among individuals with hypertension. Cardiovasc. Diabetol. 23 (1), 117 (2024). Dong, G. et al. The neutrophil-lymphocyte ratio as a risk factor for all-cause and cardiovascular mortality among individuals with diabetes: evidence from the NHANES 2003–2016. Cardiovasc. Diabetol. 22 (1), 267 (2023). Alpua, M. et al. First Admission Neutrophil-Lymphocyte Ratio May Indicate Acute Prognosis of Ischemic Stroke. Rambam Maimonides Med. J. 12 (3). (2021). Arbel, Y. et al. Neutrophil/lymphocyte ratio is related to the severity of coronary artery disease and clinical outcome in patients undergoing angiography. Atherosclerosis . 225 (2), 456–460 (2012). Hicks, C. W. et al. Glycated albumin and HbA1c as markers of lower extremity disease inUS adults with and without diabetes. Diabetes Res. Clin. Pract. 184 , 109212 (2022). Liu, K. et al. Association of serum 25-hydroxyvitamin D concentrations with all-cause and cause-specific mortality among individuals with gout and hyperuricemia. Nutr. J. 23 (1), 89 (2024). Hou, X. Z. et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study. Cardiovasc. Diabetol. 23 (1), 86 (2024). Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150 (9), 604–612 (2009). Misirlioglu, N. F. et al. The Relationship between Neutrophil-Lymphocyte Ratios with Nutritional Status, Risk of Nutritional Indices, Prognostic Nutritional Indices and Morbidity in Patients with Ischemic Stroke. Nutrients 16 (8). (2024). Libby, P., Nahrendorf, M. & Swirski, F. K. Leukocytes Link Local and Systemic Inflammation in Ischemic Cardiovascular Disease: An Expanded Cardiovascular Continuum. J. Am. Coll. Cardiol. 67 (9), 1091–1103 (2016). Weber, C., Habenicht, A. J. R. & von Hundelshausen, P. Novel mechanisms and therapeutic targets in atherosclerosis: inflammation and beyond. Eur. Heart J. 44 (29), 2672–2681 (2023). Zernecke, A. et al. Protective role of CXC receptor 4/CXC ligand 12 unveils the importance of neutrophils in atherosclerosis. Circul. Res. 102 (2), 209–217 (2008). Rajakariar, R. et al. Novel biphasic role for lymphocytes revealed during resolving inflammation. Blood . 111 (8), 4184–4192 (2008). Adamstein, N. H. et al. The neutrophil-lymphocyte ratio and incident atherosclerotic events: analyses from five contemporary randomized trials. Eur. Heart J. 42 (9), 896–903 (2021). Zahorec, R. Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl. Lek. Listy . 122 (7), 474–488 (2021). Gaggini, M. et al. FIB-4 Index and Neutrophil-to-Lymphocyte-Ratio as Death Predictor in Coronary Artery Disease Patients. Biomedicines 11 (1). (2022). Pruc, M. et al. Diagnostic and prognostic performance of the neutrophil-to-lymphocyte ratio in acute coronary syndromes: A meta-analysis of 90 studies including 45 990 patients. Kardiologia polska . 82 (3), 276–284 (2024). Wada, H. et al. Pre-procedural neutrophil-to-lymphocyte ratio and long-term cardiac outcomes after percutaneous coronary intervention for stable coronary artery disease. Atherosclerosis . 265 , 35–40 (2017). Wang, L. et al. Neutrophil to lymphocyte ratio predicts poor outcomes after acute ischemic stroke: A cohort study and systematic review. J. Neurol. Sci. 406 , 116445 (2019). Vaura, F., Palmu, J., Aittokallio, J., Kauko, A. & Niiranen, T. Genetic, Molecular, and Cellular Determinants of Sex-Specific Cardiovascular Traits. Circul. Res. 130 (4), 611–631 (2022). Alvarez, A., Hermenegildo, C., Issekutz, A. C., Esplugues, J. V. & Sanz, M. J. Estrogens inhibit angiotensin II-induced leukocyte-endothelial cell interactions in vivo via rapid endothelial nitric oxide synthase and cyclooxygenase activation. Circul. Res. 91 (12), 1142–1150 (2002). Sukovich, D. A. et al. Expression of interleukin-6 in atherosclerotic lesions of male ApoE-knockout mice: inhibition by 17beta-estradiol. Arterioscler. Thromb. Vasc. Biol. 18 (9), 1498–1505 (1998). Zhao, D., Wang, Y., Wong, N. D. & Wang, J. Impact of Aging on Cardiovascular Diseases: From Chronological Observation to Biological Insights: JACC Family Series. JACC Asia . 4 (5), 345–358 (2024). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx File name: Additional file 1; Supplementary figure 1 File format: word Title of data: An NLR-free ROC model for predicting mortality over time in pan-vascular disease populations Additionalfile2.docx File name: Additional file 2; Supplementary figure 2 File format: word Title of data: NLR-free model calibration curves for predicting mortality in pan-vascular disease populations Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5336184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":383399213,"identity":"496420be-c954-4868-91d8-892d322c07b8","order_by":0,"name":"Xueyuan Yang","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueyuan","middleName":"","lastName":"Yang","suffix":""},{"id":383399215,"identity":"ad7b44aa-f8b1-41db-adde-b6b9df9b8bc0","order_by":1,"name":"Lei Chen","email":"","orcid":"","institution":"Zunyi Medical 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07:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5336184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5336184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71511679,"identity":"6875bf5e-79aa-4fed-ae6c-569a080d04a8","added_by":"auto","created_at":"2024-12-16 10:28:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart for inclusion of study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/468bf4347a9eaec412a3b2da.jpeg"},{"id":71513319,"identity":"9241155b-639f-489d-9b5c-7f248bfd54a8","added_by":"auto","created_at":"2024-12-16 10:44:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analyses of NLR and all-cause and cardiovascular mortalities in a pan-vascular disease population.\u003c/strong\u003e a: All-cause mortality b: Cardiovascular mortality, Models adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/0eeaf09f9c584bd7a8dc1533.jpeg"},{"id":71511682,"identity":"5932cae1-cc65-4708-8098-aeb309cfa9e4","added_by":"auto","created_at":"2024-12-16 10:28:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analyses of all-cause and cardiovascular mortalities in NLR and pan-vascular disease populations\u003c/strong\u003e. a: All-cause mortality b: Cardiovascular mortality.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/12c7c2772228e234ce823ccd.jpeg"},{"id":71512108,"identity":"610c0acb-d760-437f-b200-61c3a279922e","added_by":"auto","created_at":"2024-12-16 10:36:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent ROC for NLR prediction of all-cause and cardiovascular mortalities in a pan-vascular disease population.\u003c/strong\u003e a: All-cause mortality b: Cardiovascular mortality, with models adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/309817fc4bad71b5dcad9df9.jpeg"},{"id":71511680,"identity":"59b53d0e-1ba7-4d58-8a13-5843a0e74c05","added_by":"auto","created_at":"2024-12-16 10:28:51","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":184865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve for NLR prediction of all-cause and cardiovascular mortalities in a pan-vascular disease population.\u003c/strong\u003e a: All-cause mortality b: Cardiovascular mortality, model adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes mellitus\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/74102f929b57b7e434250cab.jpeg"},{"id":75988150,"identity":"7c136d6f-66fd-4cb0-9da4-ebfa8e0b5baa","added_by":"auto","created_at":"2025-02-11 08:39:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2628925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/f8fc0740-207e-4758-ada1-e3efdacec57c.pdf"},{"id":71512109,"identity":"eb5bb07b-a57b-444c-83b7-15108c0c84b5","added_by":"auto","created_at":"2024-12-16 10:36:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":168075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: \u003c/strong\u003eAdditional \u0026nbsp;file 1; Supplementary figure 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format: \u003c/strong\u003eword\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e An NLR-free ROC model for predicting mortality over time in pan-vascular\u0026nbsp;disease populations\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/b67738814cee5e60ecf7e445.docx"},{"id":71511684,"identity":"a6392830-35f9-42ce-b4df-617f889383cc","added_by":"auto","created_at":"2024-12-16 10:28:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":404554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: \u003c/strong\u003eAdditional file 2; Supplementary figure 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format: \u003c/strong\u003eword\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle of data:\u003c/strong\u003e NLR-free model calibration curves for predicting mortality in pan-vascular disease populations\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5336184/v1/f6e9c96e17b69c8d559f91da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neutrophil-lymphocyte ratio predicts all-cause and cardiovascular mortality in a pan- vascular disease population: a nationally representative study","fulltext":[{"header":"Background","content":"\u003cp\u003eAtherosclerosis is a common and prevalent disease worldwide and is a major mortality threat to the population. Atherosclerotic diseases, represented by coronary atherosclerosis and stroke, have become the number one public health threat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is worth noting that due to the specialization of medical disciplines, atherosclerotic diseases in different parts of the body are classified into different disciplines for management, which results in systemic vascular diseases not being effectively assessed and treated. In order to change this status quo, a new discipline called \u0026lsquo;pan-vascular medicine\u0026rsquo; has emerged, which is dedicated to the comprehensive management of systemic vascular diseases. Pan-vascular diseases are mainly manifested as coronary artery disease, cerebrovascular disease, peripheral artery disease, and other vascular diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given the significant public health problems caused by pan-vascular disease, identification of populations with poor prognosis for pan-vascular disease by a number of inexpensive indicators and early medical intervention are important for improving survival time in such populations.\u003c/p\u003e \u003cp\u003eAtherosclerosis is an accumulation of fatty, fibrous tissue in the vessel wall, and activation of inflammatory pathways is an important pathogenesis of this disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The neutrophil-lymphocyte ratio (NLR) is a straightforward and accessible marker of systemic inflammation. It can be employed as an early warning indicator of conditions such as atherosclerosis and stress. Current research indicates that NLR is significantly correlated with cardiovascular and peripheral vascular disease [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, NLR has demonstrated considerable prognostic value in high-risk groups with cardiovascular and cerebrovascular conditions, including hypertension, diabetes mellitus, stroke, and coronary heart disease [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, current studies have focused only on single-vessel disease, and comprehensive pan-vascular disease population assessments are lacking. In conclusion, the present study aims to evaluate the relationship between neutrophil-to-lymphocyte ratio (NLR) and all-cause mortality, as well as cardiovascular mortality, in a population with a broad range of vascular diseases. Additionally, the study will assess the predictive value of NLR for mortality in this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is an epidemiological survey of the nutritional and health status of populations in the United States. Prior to participation, each individual signed an informed consent form that was ethically approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). The NHANES survey employs a representative sample of approximately 10,000 individuals from a representative sample of approximately 30 of the approximately 3,000 U.S. counties surveyed. This nationwide, complex, multistage sample is conducted every two years (one cycle). Participants\u0026lsquo; demographic data, socio-economic information, clinical examination data, and the prevalence of selected diseases are collected through standardized questionnaires and standardized physical examinations, and patients\u0026rsquo; blood test indicators are measured through a mobile examination Centre, all of which are subject to strict quality control. This study was a retrospective cohort study that included a pan-vascular disease population from 8 cycles of the NHANES study (2001\u0026ndash;2016). Combined with the pan-vascular disease definition, i.e., the main manifestations are coronary artery disease, cerebrovascular disease and peripheral artery disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The following groups were included in the study: (1) Individuals who were informed by a physician or health professional of the presence of cardiovascular disease, including but not limited to coronary heart disease, angina/angina pectoris, and myocardial infarction; (2) Individuals who were informed by a physician or health professional of the presence of stroke; (3) Individuals who were informed of the presence of peripheral arterial disease, as determined by measurement of ankle-brachial index (ABI), defined as ABI\u0026thinsp;\u0026lt;\u0026thinsp;0.90 on either side [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The following groups were excluded from the study: (1) Individuals with incomplete data on fasting weights; (2) Individuals with incomplete data on education levels; (3) Individuals with incomplete data on poverty income ratio. A total of 1,767 individuals were ultimately included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of whom 1,234 had comorbid cardiovascular disease, 611 had comorbid stroke, and 171 had comorbid peripheral arterial disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eState of survival\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes of this study were all-cause mortality and cardiovascular mortality. The data for these outcomes were obtained from the National Center for Health Statistics website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/data-linkage/mortality-public.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The cardiovascular mortality data were collected through follow-up of participants in the NHANES study until the end of the study period on 31 December 2019.\u003c/p\u003e\n\u003ch3\u003eStudy inclusion variables\u003c/h3\u003e\n\u003cp\u003eThe following variables were included in this study: Demographics Data: sex, age, race, education, Poverty income ratio (PIR). Among the races are Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race. Educational attainment was classified into the following categories: some college, graduated high school, 9th-11th grade, or less than 9th grade. PIR is classified as \u0026le;\u0026thinsp;1, 1\u0026thinsp;\u0026lt;\u0026thinsp;PIR\u0026thinsp;\u0026le;\u0026thinsp;3, and PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.0 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Questionnaire data: Smoking, congestive heart failure, asthma, chronic bronchitis, liver disease, diabetes, hypertension, heart attack, angina/angina pectoris, coronary heart disease, stroke. Hypertension was defined as a definitive diagnosis of high blood pressure by a doctor or health professional, or taking prescription medication for high blood pressure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Diabetes mellitus is defined as a definitive diagnosis of diabetes mellitus by a doctor or health professional or taking oral glucose-lowering medication or insulin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The use of cigarettes in quantities of more than 100 is defined as smoking [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The remaining co-morbidities were defined as the presence of a relevant disease communicated by a doctor or health professional. Examination Data: BMI, ABI. eGFR was calculated using the CKD-EPI formula developed by the Chronic Kidney Disease Epidemiology Collaboration, which standardizes race, sex, and age to more accurately assess kidney function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Laboratory data: Fasting blood glucose (FPG), glycated hemoglobin (HbA1c), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), creatinine (Scr), uric acid (Uric Acid), red blood cell count (RBC), platelet count (PLT), neutrophil count (Neutrophil), lymphocyte count (Lymphocyte), wherein NLR is the calculation of neutrophil/lymphocyte [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. NLR was grouped by the three-quartile method into Low level NLR(0.04\u0026thinsp;\u0026lt;\u0026thinsp;NLR\u0026thinsp;\u0026le;\u0026thinsp;1.83, n\u0026thinsp;=\u0026thinsp;583), Medium level NLR (1.83\u0026thinsp;\u0026lt;\u0026thinsp;NLR\u0026thinsp;\u0026le;\u0026thinsp;2.71, n\u0026thinsp;=\u0026thinsp;596), High level NLR\u003c/p\u003e \u003cp\u003e(2.71\u0026thinsp;\u0026lt;\u0026thinsp;NLR, n\u0026thinsp;=\u0026thinsp;588) three groups.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eContinuous variables are expressed as mean and standard deviation or median and interquartile range, depending on whether they followed a normal distribution, and categorical variables are expressed as percentages. The variables were divided into 3 groups, low, medium, or high NLR according to NLR tertiles. Between-group differences were compared using ANOVA or rank sum test depending on whether the variables followed a normal distribution and categorical variables were compared using chi-squared analysis. The association of NLR with all-cause mortality and cardiovascular mortality in a pan-vascular disease population was assessed using weighted Cox regression, with model 1 being a one-factor weighted Cox regression model. Models 2 and 3 were multifactorial weighted Cox regression models, with covariates adjusted for the correlation of the variables with the clinical outcomes before modeling, the same covariates were selected for both outcomes and covariances between the included variables were detected by the variance inflation factor, with all covariates having a variance inflation factor of less than 5, and there was no significant multicollinearity. Model 2 was adjusted for sex, age, race, PIR, and education level, and model 3 was adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes. In addition, to assess differences in survival between the three NLR groups, Kaplan-Meier survival analyses and log-rank tests were performed. Restricted cubic spline (RCS) analyses assessed the potential non-linear relationship between all-cause mortality and cardiovascular mortality in the NLR and pan-vascular disease populations, with variables from model 3 included in the RCS assessment. The predictive value of NLR for 1-, 3- and 5-year all-cause mortality and cardiovascular mortality in a pan-vascular disease population was assessed using time-dependent receiver operating characteristic (time-dependent ROC) curves, with variables adjusted for model 3 included in the time-dependent ROC assessment. The agreement between the NLR predicted and actual values of all-cause mortality and cardiovascular mortality for the pan-vascular disease population at 1, 3, and 5 years was assessed using the calibration curve incorporating the variables in model 3. Subgroup and interaction analyses stratified by age, diabetes status, and sex were performed to assess the effect of NLR on all-cause and cardiovascular mortality in different subgroups. In this study, to avoid bias due to missing data, the data were interpolated using the Multiple Interpolation of Chained Equations (MICE) method. All analyses were performed using R software (version 4.3.1), and two-sided p-values of less than 0.05 were considered statistically significant. The analyses also took into account the NHANES complex sample weight, where the sample weight was the 2-year MEC weight of the fasting sub-sample divided by 8.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe study included 1,767 eligible patients with pan-vascular disease. Significant differences were observed between the three NLR groups in Age, TC, HDL-C, LDL-C, TG, FPG, Scr, Uric acid, RBC, eGFR, Neutrophil, Lymphocyte, Sex, Race, family income-poverty ratio, heart failure, chronic bronchitis, and Smoking were statistically different (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The High-level NLR group was older, had higher creatinine levels, included more males than females, and had more patients who smoked (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline information table based on NLR tertile grouping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal n\u0026thinsp;=\u0026thinsp;1,767\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-level NLR (0.04\u0026thinsp;\u0026lt;\u0026thinsp;NLR\u0026thinsp;\u0026le;\u0026thinsp;1.83)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;583\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium-level NLR (1.83\u0026thinsp;\u0026lt;\u0026thinsp;NLR\u0026thinsp;\u0026le;\u0026thinsp;2.71)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;596\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-level NLR (2.71\u0026thinsp;\u0026lt;\u0026thinsp;NLR)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;588\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(60,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(57,74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(59,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(64,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.59(25.20,32.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.93(25.30,33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.86(25.50,33.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.12(24.62,32.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.63(3.90,5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78(4.11,5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.59(3.92,5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.53(3.72,5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(1.03,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29(1.09,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22(1.03,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22(1.03,1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56(1.97,3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.69(2.10,3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53(1.96,3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47(1.89,3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37(0.96,2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37(0.94,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45(1.00,2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25(0.92,1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.00(97.00,123.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.00(96.00,122.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.00(97.00,122.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.10(98.20,126.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8(5.4,6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8(5.4,6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7(5.4,6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8(5.4,6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.80,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94(0.78,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98(0.80,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03(0.88,1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid,\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350.90(291.50,413.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e345.00(285.50,404.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350.90(291.50,416.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e356.90(297.40,416.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC,\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.59(4.23,4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56(4.20,4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65(4.30,4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.58(4.18,4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT,\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224(186,271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220(184,264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226(188,274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225(185,273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR,mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.29(56.50,89.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.00(61.77,95.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.59(58.30,90.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.74(51.61,84.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil,\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10(3.20,5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.20(2.50,3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.10(3.40,4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.20(4.20,6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte,\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80(1.40,2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30(1.90,2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80(1.50,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40(1.10,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.26(1.64,3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43(1.16,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25(2.00,2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59(3.09,4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1007(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e382(65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e760(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1047(59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382(64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e400(68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation levels,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e727(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245(42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233(39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income-poverty ratio,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400(22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e294(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157(26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1409(79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475(81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494(82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e440(74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1452(82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461(79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e501(84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e490(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic bronchitis,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1549(87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520(89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532(89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e497(84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver condition,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1654(93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541(92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e564(94.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e549(93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e690(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1077(61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365(61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e386(65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518(29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164(27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1249(70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404(67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e424(72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1265(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421(70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e419(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e502(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of NLR with all-cause mortality as well as cardiovascular mortality in a population with pan-vascular disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring follow-up of 1,767 patients, with a median follow-up of 90 months and an interquartile range of follow-up time (52,136), a total of 832 deaths occurred, of which 269 were due to cardiac causes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the relationship between the NLR and all-cause mortality as well as cardiovascular mortality in the population with all vascular diseases. One-way weighted Cox regression (model 1) indicated that NLR was a risk factor for all-cause mortality in the pan-vascular disease population [HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI (1.17\u0026ndash;1.32), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and after adjustment for covariates, model 2 [HR\u0026thinsp;=\u0026thinsp;1. 17, 95% CI (1.11\u0026ndash;1.23), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], model 3 [HR\u0026thinsp;=\u0026thinsp;1.13, 95% CI (1.07\u0026ndash;1.20), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] indicated that NLR was an independent risk factor for all-cause mortality in the pan-vascular disease population. In addition, one-way weighted Cox regression (model 1) also indicated that NLR was a risk factor for cardiovascular mortality in the pan-vascular disease population [HR\u0026thinsp;=\u0026thinsp;1.25, 95% CI (1.16\u0026ndash;1.35), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and after adjustment for covariates, model 2 [HR\u0026thinsp;=\u0026thinsp;1. 18, 95% CI (1.09\u0026ndash;1.27), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], Model 3 [HR\u0026thinsp;=\u0026thinsp;1.14, 95% CI (1.05\u0026ndash;1.24), p\u0026thinsp;=\u0026thinsp;0.001] indicated that NLR was independently associated with an increased risk of cardiovascular mortality in the pan-vascular disease population.\u003c/p\u003e \u003cp\u003eRestricted cubic spline analyses showed that NLR was linearly associated with all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) (p for nonlinear\u0026thinsp;=\u0026thinsp;0.108) and cardiovascular mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) (p for nonlinear\u0026thinsp;=\u0026thinsp;0.149) in the pan-vascular disease population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted Cox regression analyses of all-cause mortality as well as cardiovascular mortality in NLR and pan-vascular disease populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(1.17\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17(1.11\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13(1.07\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10(0.88\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.81\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.80\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98(1.58\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57(1.27\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51(1.22\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25(1.16\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18(1.09\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14(1.05\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(0.69\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.63\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92(0.60\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-level NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13(1.41\u0026ndash;3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67(1.10\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59(1.06\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: Not adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2: Adjusted for sex, age, ethnicity, PIR, education level\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, Hypertension, diabetes mellitus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKaplan-Meier survival analysis of all-cause mortality and cardiovascular mortality in a population with pan-vascular disease based on the tertile classification of the NLR\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows a Kaplan-Meier survival analysis of NLR versus all-cause mortality in the overall vascular disease population based on NLR tertile groupings, with a significantly higher risk of all-cause mortality in the overall vascular disease population with higher NLR values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the relationship between NLR and cardiovascular mortality in pan-vascular disease populations based on NLR tertile subgroups, with a higher risk of cardiovascular death in such populations as NLR levels increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNLR time-dependent ROC for the prediction of all-cause mortality and cardiovascular mortality in a population pan-vascular disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea shows the time-dependent ROC of the NLR model for predicting all-cause mortality in the pan-vascular disease population. The area under the curve of the NLR model for predicting 1-year, 3-year and 5-year all-cause mortality was 0.802 (95% Cl\u0026thinsp;=\u0026thinsp;0. 748-0.856), 0.781 (95% Cl\u0026thinsp;=\u0026thinsp;0.750\u0026ndash;0.811) and 0.773 (95% Cl\u0026thinsp;=\u0026thinsp;0.746\u0026ndash;0.799), respectively, the model showed good predictive value for all-cause mortality in the pan-vascular disease population, with the best predictive performance for 1-year all-cause mortality. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb shows the time-dependent ROC of the NLR model for predicting cardiovascular mortality in the pan-vascular disease population. The area under the curve of the NLR model for predicting cardiovascular mortality at 1 year, 3 years, and 5 years were 0.809 (95% CI\u0026thinsp;=\u0026thinsp;0.713\u0026ndash;0.906), 0.800 (95% CI\u0026thinsp;=\u0026thinsp;0.748\u0026ndash;0.852), 0.797 (95% CI\u0026thinsp;=\u0026thinsp;0.756\u0026ndash;0.839), the model also showed good predictive value for cardiovascular mortality in the pan-vascular disease population, with the best predictive performance for 1-year cardiovascular mortality. In addition, the time-dependent ROC of the model without NLR for predicting all-cause mortality and cardiovascular mortality in the pan-vascular disease population was further assessed (Additional file 1: Supplementary Fig.\u0026nbsp;1). The model without NLR was not as good as the model with NLR for predicting all-cause mortality and cardiovascular mortality in the pan-vascular disease population at 1, 3, and 5 years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCalibration curve of the NLR for the prediction of all-cause mortality and cardiovascular mortality in a population with pan-vascular disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea shows the calibration curve of the NLR model for predicting all-cause mortality in the pan-vascular disease population. The predicted and actual values of the NLR model for predicting all-cause mortality in the pan-vascular disease population at 3 and 5 years were in good agreement, and the accuracy of the prediction model was good. Notably, the NLR model predicted 1-year, 3-year, and 5-year cardiovascular mortality in the pan-vascular disease population with low predictive accuracy and poor predictive accuracy(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In addition, the calibration curve for predicting all-cause mortality and cardiovascular mortality in the population with pan-vascular lesions was tested for the model without NLR (Additional file 2: Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of the NLR with total mortality and mortality due to cardiovascular disease in different subgroups of the population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows weighted Cox regression analyses of NLR and all-cause mortality in pan-vascular disease populations grouped by sex, age, and diabetes status, and the analyses in subgroups of age, sex, and diabetes status yielded results that were more consistent with those of the overall population, with NLR remaining an independent risk factor for all-cause mortality in these populations. In addition, an interaction was observed in the age subgroup (p\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted Cox regression analyses of NLR and all-cause mortality in different subgroups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28(1.19\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19(1.11\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16(1.08\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23(1.15\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16(1.10\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.14(1.08\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27(1.17\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23(1.14\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21(1.11\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24(1.16\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(1.09\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12(1.05\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19(1.13\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20(1.14\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.15(1.09\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52(1.15\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39(1.04\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.34(0.98\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1: Not adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 2: adjusted for sex, age, race, PIR, education level, but not subgroup variables\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes, but not subgroup variables\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the weighted Cox regression analyses of NLR and cardiovascular mortality in pan-vascular disease populations in different subgroups, and in analyses of men, non-diabetic, and age greater than or equal to 60 years subgroups NLR remained an independent risk factor for cardiovascular mortality in these populations. However, an independent risk effect of NLR on cardiovascular mortality was not observed in the age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, diabetes mellitus, and female populations. In addition, an interaction was found in the sex subgroup (p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted Cox regression analyses of NLR and cardiovascular mortality in different subgroups of the population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40(1.27\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30(1.19\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.28(1.13\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.05\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08(0.96\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07(0.95\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22(1.04\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17(0.99\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18(0.99\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27(1.17\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18(1.09\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.14(1.04\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23(1.14\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22(1.14\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18(1.08\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25(0.74\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.72\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09(0.65\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1: Not adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 2: adjusted for sex, age, race, PIR, education level, but not subgroup variables\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 3: Adjusted for sex, age, race, PIR, education level, BMI, LDL-C, TG, HbA1c, Scr, Uric acid, congestive heart failure, asthma, chronic bronchitis, liver condition, smoking status, hypertension, diabetes, but not subgroup variables\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the association of NLR with all-cause mortality and cardiovascular mortality in a pan-vascular disease population, and here are some of our key findings: (1) NLR is an independent risk factor for cardiovascular mortality and all-cause mortality in all vascular disease populations; and shows a dose-response relationship; (2) The risk of all-cause mortality and cardiovascular death in the pan-vascular disease population was significantly higher with increasing NLR levels; (3) The NLR has some predictive value for all-cause mortality and cardiovascular mortality at 1, 3, and 5 years in pan-vascular disease populations.\u003c/p\u003e \u003cp\u003eInflammation has now been shown to promote the development of atherosclerosis by decades of epidemiological studies and large clinical trials [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Neutrophil, as a key immune cell, plays an important role in the development of atherosclerosis, mediating plaque formation and further deterioration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Whereas the lymphocyte, as an immunomodulatory cell, has a physiological role in promoting the resolution of inflammation and has a protective effect on arterial plaque formation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, these two types of cells embody the body's pro-inflammatory and anti-inflammatory homeostasis. Currently, the NLR calculated from neutrophils and lymphocytes is considered an inexpensive and efficient indicator of systemic inflammation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, NLR was observed to be an independent risk factor for all-cause mortality and cardiovascular mortality in a pan-vascular disease population. Previous studies have shown that NLR is associated with an increased risk of death in patients with coronary artery disease and in patients with acute coronary syndromes. Gaggini et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] included 1,460 patients diagnosed with coronary artery disease in a retrospective cohort study with the endpoint of all-cause mortality and a median follow-up period of 26 months, and found a higher mortality rate in patients with coronary artery disease with an elevated NLR. Pruc et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] included 90 studies related to NLR in patients with acute coronary syndromes, encompassing 45,990 patients, in a meta-analysis that showed higher NLR was associated with short-term adverse cardiovascular events in acute coronary syndromes. Elevated pre-procedure NLR has also been shown to be a risk factor for adverse outcomes in patients with coronary artery disease undergoing percutaneous coronary intervention [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These studies suggest that NLR has an important prognostic value in cardiac vascular lesions. In addition, NLR has received attention in patients with stroke and peripheral vascular lesions. Wang [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] et al. conducted a study on ischemic stroke, which consisted of a retrospective cohort study and meta-analysis, the retrospective cohort study included 808 patients with ischemic stroke and the study outcomes were the incidence of stroke complications as well as the 3-month functional outcome, which affirmed that higher NLR was associated with an increase in the incidence of adverse complications as well as the 3-month mortality rate in patients with ischemic stroke. Erturk et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] conducted a retrospective cohort study of 593 patients diagnosed with peripheral arterial occlusive disease with the endpoint of cardiovascular mortality and a median follow-up period of 20 months and demonstrated that elevated NLR increased cardiovascular mortality in such patients. In the present study, we further combined pan-vascular disease patients with cardiovascular disease, stroke and peripheral arterial disease, and in the whole pan-vascular disease population, NLR remained an independent risk factor for all-cause mortality and cardiovascular mortality in these populations, obtaining results consistent with the above studies, which also showed the important prognostic value of NLR in the pan-vascular disease population. In addition, in the survival analyses of the pan-vascular disease population, mortality was significantly higher in the higher NLR group, both in terms of all-cause mortality and cardiovascular mortality. Second, the models including NLR showed better predictive performance for mortality in both time-dependent ROC analyses, suggesting that NLR is an effective indicator for early identification of people at high risk of pan-vascular disease and may be used as an early indication to influence medical decisions. Of note, a significant independent risk effect of NLR on cardiovascular mortality in the pan-vascular disease population was not observed in the subgroup analyses in the age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, diabetes mellitus, and female populations, which may be explained to some extent by the following reasons: (1) Differences in hormone levels between males and females, experimental studies have demonstrated that estrogens and androgens have a significant impact on the biological processes of atherosclerotic cardiovascular disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and female estrogen reduces cardiovascular disease by inhibiting inflammatory responses and improving vascular endothelial function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. (2) As age increases, the process of vascular disease gradually accelerates, and aging individuals are exposed to more cardiovascular risk factors, such as hypertension, hyperglycemia, obesity, and hyperlipidemia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. (3) Insufficient sample size resulted in statistically insignificant results. (4) The prognostic value of NLR in these populations is not significant.\u003c/p\u003e \u003cp\u003eThis study has some advantages as follows, firstly, it is the first time to explore the relationship between NLR and all-cause mortality and cardiovascular mortality in a pan-vascular disease population, and secondly, the NHANES database has a rigorous data quality control system, strong representativeness, scientific and reasonable survey sampling, and a high degree of reliability of the data source, which makes the study conclusions more objective. However, there are some limitations. First, because the NHANES database only included ABI as an indicator from 2001 to 2004 between 2001 and 2016, making the inclusion of the population with peripheral arterial disease in this study small (171), the study may have been under-representative of the pan-vascular disease population. Second, because cross-sectional measurements of the NLR do not reflect its change over time, it is not clear whether the NLR remained consistent over the follow-up period. Future longitudinal studies should be conducted based on the availability of repeated data measurements. In addition, due to the limitations of the NHANES database disease collection, this study did not exclude patients with hematological or immune disorders, which can affect the NLR values causing some bias. Lastly, the NHANES database only collects data from studies in the US population, so the extrapolation of this study\u0026rsquo;s conclusions is limited. Therefore, more multi-center, large-sample longitudinal studies should be conducted in the future to further address these issues.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNLR is an independent risk factor for all-cause mortality and cardiovascular mortality in pan-vascular disease populations. NLR is linearly associated with and has some predictive value for all-cause mortality and cardiovascular mortality in pan-vascular disease populations. NLR, as an inexpensive and easily available biomarker, is expected to serve as a potential predictor of prognosis in pan-vascular disease populations in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABI, Ankle-Brachial Index\u003c/p\u003e\n\u003cp\u003eBMI, Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI, Confidence Interval\u003c/p\u003e\n\u003cp\u003eCKD-EPI, Chronic Kidney Disease Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003eHbA1c, Glycosylated Hemoglobin\u003c/p\u003e\n\u003cp\u003eHDL-C, High-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eHR, Hazard Ratio\u003c/p\u003e\n\u003cp\u003eLDL-C, Low-Density Lipoprotein Cholesterol NLR, Neutrophil-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eNHANES, National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNHCS, National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003ePIR, Poverty Income Ratio\u003c/p\u003e\n\u003cp\u003ePLT, Platelet Count\u003c/p\u003e\n\u003cp\u003eRBC, Red Blood Cell Count\u003c/p\u003e\n\u003cp\u003eRCS, Restricted Cubic Spline\u003c/p\u003e\n\u003cp\u003eROC, Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eTC, Total Cholesterol\u003c/p\u003e\n\u003cp\u003eTG, Triglyceride\u003c/p\u003e\n\u003cp\u003eScr, Creatinine\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in this study signed an informed consent form and ethical approval was obtained from the Ethics Review Board of the National Centre for Health Statistics (NCHS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data for this study are available on the National Center for Health Statistics website. (https://www.cdc.gov/nchs/nhanes/index.htm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Medical Science and Technology Innovation Training Programme for Guizhou University Students (ZYDC202302112).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXXY designed the study, analyzed the data, and wrote the manuscript, KL performed quality control and validation of the data, CLY, LC, HX and JFL interpreted the results of the data analyses, and YM reviewed and finally revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Jun Liu, School of Public Health, Zunyi Medical University, for providing statistical review of this study and all participants who contributed to the NHANES study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLibby, P. et al. 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Diagnostic and prognostic performance of the neutrophil-to-lymphocyte ratio in acute coronary syndromes: A meta-analysis of 90 studies including 45 990 patients. \u003cem\u003eKardiologia polska\u003c/em\u003e. \u003cb\u003e82\u003c/b\u003e (3), 276\u0026ndash;284 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWada, H. et al. Pre-procedural neutrophil-to-lymphocyte ratio and long-term cardiac outcomes after percutaneous coronary intervention for stable coronary artery disease. \u003cem\u003eAtherosclerosis\u003c/em\u003e. \u003cb\u003e265\u003c/b\u003e, 35\u0026ndash;40 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L. et al. Neutrophil to lymphocyte ratio predicts poor outcomes after acute ischemic stroke: A cohort study and systematic review. \u003cem\u003eJ. Neurol. 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Expression of interleukin-6 in atherosclerotic lesions of male ApoE-knockout mice: inhibition by 17beta-estradiol. \u003cem\u003eArterioscler. Thromb. Vasc. Biol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (9), 1498\u0026ndash;1505 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, D., Wang, Y., Wong, N. D. \u0026amp; Wang, J. Impact of Aging on Cardiovascular Diseases: From Chronological Observation to Biological Insights: JACC Family Series. \u003cem\u003eJACC Asia\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e (5), 345\u0026ndash;358 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"neutrophil-lymphocyte ratio, pan-vascular disease, all-cause mortality, cardiovascular mortality","lastPublishedDoi":"10.21203/rs.3.rs-5336184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5336184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA novel medical specialty, pan-vascular medicine, has recently emerged for atherosclerosis treatment. Its objective is the integrated management of pan-vascular diseases, including coronary artery cerebrovascular, and peripheral artery diseases. This study aimed to examine the correlation between neutrophil-lymphocyte ratio (NLR) and mortality in a population with pan-vascular disease, to assess its predictive value.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study comprised 1,767 individuals with pan-vascular disease sourced from the NHANES database (2001\u0026ndash;2016). Study endpoints were all-cause and cardiovascular mortality. The relationship among NLR, all-cause mortality, and cardiovascular mortality was examined in a population with a broad range of vascular diseases. Weighted Cox regression analyses and restricted cubic spline (RCS) analyses were conducted. Discrepancies in survival rates between the three groups classified according to NLR were investigated using Kaplan-Meier survival analysis. Prognostic accuracy of the NLR model for mortality in the pan-vascular disease population was evaluated using time-dependent receiver operating characteristic curves and calibration curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median follow-up period for this study was 90 months, during which a total of 832 patients died, including 269 who died of cardiovascular causes. Neutrophil-to-lymphocyte ratio (NLR) was an independent predictor of all-cause mortality [hazard ratio (HR)\u0026thinsp;=\u0026thinsp;1.13, 95% confidence interval (CI) (1.07\u0026ndash;1.20), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and cardiovascular mortality [HR\u0026thinsp;=\u0026thinsp;1.14, 95% CI (1.05\u0026ndash;1.24), p\u0026thinsp;=\u0026thinsp;0.001] in individuals with pan-vascular disease. RCS analysis indicated a linear association between NLR and all-cause mortality (p-value for nonlinearity\u0026thinsp;=\u0026thinsp;0.108) and cardiovascular mortality (p-value for nonlinearity\u0026thinsp;=\u0026thinsp;0.149) in the population with pan-vascular disease. Risk of all-cause mortality and cardiovascular mortality was elevated among individuals with higher levels of NLR. NLR model exhibited favorable predictive efficacy for all-cause mortality and cardiovascular mortality in the pan-vascular disease population. Furthermore, the calibration curve illustrated the high predictive accuracy of the NLR model for all-cause mortality in the pan-vascular disease population at 3 and 5 years.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNLR is an independent risk factor for cardiovascular mortality and all-cause mortality in pan-vascular disease populations, and is linearly associated with all-cause mortality and cardiovascular mortality in pan-vascular disease populations and has some predictive value.\u003c/p\u003e","manuscriptTitle":"Neutrophil-lymphocyte ratio predicts all-cause and cardiovascular mortality in a pan- vascular disease population: a nationally representative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 10:28:46","doi":"10.21203/rs.3.rs-5336184/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":"1c898316-e6b8-4f49-a317-32ed7f23c677","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40839543,"name":"Health sciences/Medical research/Biomarkers"},{"id":40839544,"name":"Health sciences/Medical research/Epidemiology"},{"id":40839545,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2025-06-10T10:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 10:28:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5336184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5336184","identity":"rs-5336184","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}