Association Between Weight-Adjusted Waist Index and All-Cause and Cardiovascular Mortality in Hypertensive Patients: NHANES 2001–2018

Association Between Weight-Adjusted Waist Index and All-Cause and Cardiovascular Mortality in Hypertensive Patients: NHANES 2001–2018 | 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 Association Between Weight-Adjusted Waist Index and All-Cause and Cardiovascular Mortality in Hypertensive Patients: NHANES 2001–2018 Jiechun YAO, Chun Lin, Qingbo Xu, Guode Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5730258/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 With the global prevalence of hypertension increasing rapidly and its inadequate treatment, this study aimed to investigate the association between weight-adjusted waist index (WWI) and mortality risks among hypertensive patients. Methods Using data from NHANES 2001–2018, we conducted a prospective cohort study of 12,146 hypertensive patients with a median follow-up of 123 months. WWI was calculated as waist circumference (cm) divided by the square root of body weight (kg). Primary outcomes were all-cause and cardiovascular disease (CVD) mortality. Analyses were adjusted for demographic characteristics, metabolic parameters, and lifestyle factors. Results During follow-up, 3,470 all-cause deaths and 940 CVD deaths occurred. After full adjustment, each unit increase in WWI was associated with higher risks of all-cause mortality (HR = 1.17, 95% CI: 1.09–1.26) and CVD mortality (HR = 1.22, 95% CI: 1.08–1.38). Compared to the lowest WWI quartile, the highest quartile showed significantly increased risks for all-cause mortality (HR = 1.34, 95% CI: 1.17–1.54) and CVD mortality (HR = 1.69, 95% CI: 1.30–2.20). Conclusion Higher WWI was independently associated with increased risks of all-cause and CVD mortality among hypertensive patients, suggesting its potential value as a simple prognostic indicator. Weight-adjusted Waist Index Hypertension Cardiovascular Mortality All-cause Mortality NHANES Figures Figure 1 Figure 2 Introduction Hypertension has emerged as a critical global public health challenge, with epidemiological trends that demand urgent attention 1 . The global adult hypertension prevalence rapidly increased from 25.9% in 2000 to 31.1% in 2010 2 . The prevalence of hypertension among Chinese adults increased from 25.2% in 2012 to 27.9% in 2015, according to the National Health Commission of China 3 . More alarmingly, the World Health Organization reports that approximately 80% of hypertension patients remain inadequately treated 4 . Cardiovascular disease, as the primary cause of hypertension-related mortality, has become a major global health threat, with approximately 7.08 million deaths from cerebrovascular diseases in 2020, predominantly cardiovascular mortality 5 . Weight-adjusted Waist Index (WWI) represents an innovative anthropometric indicator that assesses obesity degree by standardizing the ratio of waist circumference to weight, demonstrating unique value in disease risk prediction 6,7 . Compared to traditional Body Mass Index (BMI), WWI exhibits superior prognostic predictive capabilities across multiple clinical populations. Recent studies have confirmed WWI's significant risk prediction potential in type 2 diabetes 8 , fatty liver disease, and asthma patients 10 . Research consistently indicates that as WWI values increase, the risk of cardiovascular disease development and mortality show a significant upward trend 8,11 . Although previous studies have explored the predictive value of WWI, its role in hypertensive patients remains unclear. Most studies have focused on the general population, and it has not been investigated whether WWI could serve as a predictor of mortality in hypertensive patients, who inherently have a higher risk of cardiovascular events 12 . Furthermore, the nonlinear relationship between WWI and mortality has not been thoroughly studied, with previous research suggesting that both high and low WWI values may be associated with increased mortality 13 . This study aims to investigate the association between weight-adjusted waist index (WWI) and all-cause mortality and cardiovascular disease mortality among hypertensive patients. Using data from the National Health and Nutrition Examination Survey (NHANES) 2001–2018, we will analyze whether WWI can serve as an independent predictor of mortality risk in the hypertensive population. Methods Study population This study used NHANES data from 2001–2018, covering nine survey cycles. NHANES annually selects about 5,000 participants from 15 U.S. geographic regions using multi-stage probability sampling. The survey includes interviews and physical examinations, with participants providing written informed consent under NCHS Ethics Review Board approval. The database contains comprehensive health and nutritional data, available at www.cdc.gov/nchs/nhanes/ . From 101,316 NHANES (2001–2018) participants, 12,146 were included in the final analysis after excluding those with missing hypertension data (n = 87,463), WWI data (n = 1,602), and mortality data (n = 105), as shown in Fig. 1 . Measurement of WWI Weight-adjusted Waist Index (WWI) was calculated as waist circumference (cm) divided by the square root of body weight (kg). Trained personnel measured participants wearing examination gowns, using electronic scales for weight and measuring tape at the midaxillary line above the right knee for waist circumference 14 . WWI served as the exposure variable in this study. Assessment of All-Cause Mortality and Cardiovascular Mortality Mortality assessment utilized the NHANES public mortality database (updated through December 31, 2019), matched with the National Death Index through NCHS. Deaths were classified according to ICD-10, with cardiovascular deaths defined by codes I00-I09, I11, I13, and I20-I51. Participants without death records during follow-up were considered survivors 15 . Covariates We assessed participants' health status based on the following questionnaires and indicators: Hypertension was defined as taking antihypertensive medications, being diagnosed with hypertension, or having three consecutive systolic blood pressure measurements ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg 16 . Diabetes was defined as taking glucose-lowering medications or being diagnosed with diabetes, having glycated hemoglobin A1c levels ≥ 6.5%, fasting blood glucose ≥ 126 mg/dL, or 2-hour blood glucose ≥ 200 mg/dL 17 . CAD diagnosis was determined through self-reported physician diagnosis obtained during personal interviews using standardized medical condition questionnaires. Participants were asked: "Has a doctor or other health professional ever told you that you have coronary heart disease?" A "yes" response to this question was considered indicative of having coronary heart disease 18 . Demographic factors were considered as potential confounders, including gender, age, race/ethnicity (categorized as Mexican American, Other Hispanic, Non-Hispanic Black, Non-Hispanic White, and Other Race including Multi-Racial), poverty income ratio, and education level (categorized as Less than 9th grade, 9-11th grade, High School Graduate, Some College or AA degree, and College Graduate or above).Lifestyle factors, such as smoking status (categorized as never smokers, current smokers, and former smokers), alcohol consumption (categorized as drinkers and non-drinkers), and anthropometric measurements (height, body mass index (BMI, kg/m²) calculated as weight in kilograms divided by height in meters squared, and waist circumference (WC)) were also considered. Additionally, the study included various blood markers such as triglycerides (TG) (mmol/L), total cholesterol (TC) (mmol/L), high-density lipoprotein cholesterol (HDL-C) (mmol/L), low-density lipoprotein cholesterol (LDL-C) (mmol/L), serum creatinine (umol/L), fasting blood glucose (mg/dL), and glycated hemoglobin (HbA1C (%)). Statistical analysis All statistical analyses were conducted according to CDC guidelines ( https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx ). Each NHANES participant was assigned a sample weight 19 . Accordingly, we considered significant variance and implemented recommended weighting methods. Continuous data were reported as means with 95% confidence intervals (CI), while discrete data were summarized by frequency of occurrence and respective percentages. Weighted χ2 tests (for categorical variables) or weighted linear regression models (for continuous variables) were employed to calculate differences between WWI quartile groups. Additionally, linear trends across WWI quartiles were evaluated using median values within each quartile as continuous variables. To investigate the relationship between WWI and mortality, univariate and multivariate Cox proportional hazards regression analyses were initially conducted. Three models were constructed: Model 1 with no adjustments; Model 2 adjusted for age, gender, and race; and Model 3, building upon Model 2, incorporated additional variables including education level, marital status, smoking habits, alcohol consumption, and various blood indicators. Restricted cubic spline (RCS) models were then applied for Cox proportional hazards regression to assess potential linear relationships between WWI and mortality. When non-linearity was detected, we first calculated inflection points using recursive algorithms, then constructed two-piecewise Cox proportional hazards models on either side of the inflection point 20 . Multiple imputation methods were employed to handle missing values, and stratified analyses were conducted for significant covariates, including age (< 65 or ≥ 65 years), gender, smoking, alcohol consumption, coronary heart disease, and diabetes. Statistical analyses were performed using R software version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) and Empower Stats (X&Y Solutions Inc., Boston, MA). Two-tailed P values less than 0.05 were considered statistically significant. Results Baseline characteristics of study participants As shown in Table 1, among 12,146 participants stratified by WWI quartiles, significant demographic and clinical differences were observed. Mean age increased from 49.2 years in Q1 to 63.6 years in Q4 (P < 0.0001), with female proportion rising from 42.5–67.5% (P < 0.0001). College graduates decreased from 30.1% in Q1 to 16.3% in Q4 (P < 0.0001). BMI increased from 27.3 to 34.1 kg/m² across quartiles (P < 0.0001). Metabolic parameters showed significant trends, with fasting glucose rising from 104.5 to 122.3 mg/dL and HbA1C from 5.5–6.2% (both P < 0.0001). Current smoking rates decreased (23.9–15.1%), while former smoking increased (24.2–36.3%, P < 0.0001). The prevalence of type 2 diabetes and coronary artery disease increased significantly from Q1 to Q4 (9.0% vs 40.5% and 4.2% vs 10.3%, respectively, both P < 0.0001). Table 1 Baseline characteristics of study population according to weight-adjusted-waist index tertiles, Weighted. Characteristics Weight-adjusted waist index (cm/√kg) P-value Q1 (8.5–10.8) N = 3036 Q2 (10.8–11.4) N = 3037 Q3 (11.4–11.9) N = 3036 Q4 (11.9–15.7) N = 3037 PIR 3.3 (3.2 ,3.4) 3.1 (3.0 ,3.2) 2.9 (2.8 ,2.9) 2.5 (2.4 ,2.6) < 0.0001 Age (years) 49.2 (48.4 ,50.0) 55.9 (55.2 ,56.6) 59.7 (59.0 ,60.4) 63.6 (62.9 ,64.3) < 0.0001 BMI (kg/m 2 ) 27.3 (27.0 ,27.6) 30.1 (29.9 ,30.4) 32.1 (31.8 ,32.5) 34.1 (33.7 ,34.5) < 0.0001 TG(mmol/L) 1.6 (1.5 ,1.7) 1.8 (1.7 ,1.9) 1.8 (1.7 ,1.9) 2.0 (1.8 ,2.1) < 0.0001 LDL_C(mmol/L) 3.0 (2.9 ,3.0) 3.1 (3.0 ,3.1) 3.0 (3.0 ,3.1) 2.9 (2.9 ,3.0) 0.0001 SCr (mg/dL) 85.7 (83.8 ,87.6) 83.8 (82.6 ,85.0) 84.4 (83.0 ,85.7) 84.3 (82.7 ,86.0) 0.3347 TC(mmol/L) 5.2 (5.1 ,5.3) 5.2 (5.2 ,5.3) 5.2 (5.2 ,5.3) 5.1 (5.1 ,5.2) 0.0067 FBG(mg/dL) 104.5 (102.8 ,106.3) 112.2 (110.3 ,114.1) 116.9 (114.6 ,119.2) 122.3 (119.5 ,125.2) < 0.0001 HbA1C (%) 5.5 (5.5 ,5.6) 5.7 (5.7 ,5.8) 5.9 (5.9 ,6.0) 6.2 (6.1 ,6.3) < 0.0001 HDL(mmol/L) 1.4 (1.4 ,1.5) 1.3 (1.3 ,1.4) 1.3 (1.3 ,1.3) 1.3 (1.3 ,1.3) < 0.0001 WC(cm) 92.7 (92.2 ,93.3) 102.9 (102.4 ,103.5) 109.1 (108.4 ,109.8) 116.2 (115.4 ,117.0) < 0.0001 WEIGHT(kg) 81.3 (80.4 ,82.2) 87.3 (86.4 ,88.2) 89.9 (88.7 ,91.1) 90.9 (89.6 ,92.1) < 0.0001 Gender, % < 0.0001 Male 1781 (57.5 ) 1662 (53.6) 1461 (46.2) 1047 (32.5) Female 1255(42.5) 1375 (46.4) 1575 (53.8) 1990 (67.5) Educational leve, % < 0.0001 Less than 9th grade 200 (3.4) 342 (6.2) 503 (10.1) 702 (13.2) 9-11th grade (Includes 12th grade with no diploma) 431 (10.6) 510 (12.9) 494 (13.2) 557 (16.5) High school graduate/GED or equivalent 726 (24.2) 745 (26.7) 794 (28.4) 727 (27.5 ) Some college or AA degree 888 (31.6) 861 (31.1) 796 (30.4) 678 (26.6) College graduate or above 698 (30.1) 554 (23.1) 431 (17.8) 359 (16.3) Race,% < 0.0001 Mexican American 225 (3.2) 393 (5.5) 466 (5.8) 544 (6.5) Other Hispanic 144 (3.1) 164 (3.1) 230 (4.7 ) 258 (5.0) Non-Hispanic White 1311 (69.2) 1439 (71.9) 1489 (72.0 ) 1597 (74.1) Non-Hispanic Black 1170 (19.0) 840 (13.8 ) 690 (12.3) 494 (9.2) Other Race 186 (5.5) 201 (5.7) 161 (5.1 ) 144 (5.2) Alcohol, % 0.0056 NO 1729 (88.6) 1562 (90.7) 1483 (92.2) 1222 (92.7 ) Yes 218 (11.4 ) 187 (9.3) 143 (7.8 ) 106 (7.3) Smoking, % < 0.0001 Non-smoker 1490 (51.9 ) 1480 (47.4 ) 1441 (46.7) 1495 (48.6) Current smoker 743 (23.9) 553 (18.4) 524 (18.4) 451 (15.1 ) Former smoker 713 (24.2 ) 983 (34.2 ) 1056 (34.9 ) 1083 (36.3 ) CAD, % < 0.0001 NO 2787 (95.8) 2753 (92.7) 2725 (92.2 ) 2684 (89.7) Yes 147 (4.2) 240 (7.3) 268 (7.8 ) 313 (10.3 ) T2DM, % < 0.0001 NO 2652 (91.0) 2310 (82.1) 2081 (72.9) 1743 (59.5) Yes 384 (9.0) 727 (17.9 ) 955 (27.1 ) 1294 (40.5 ) Note Continuous data: the mean with 95% confidence intervals, P value was calculated by weighted linear regression model. Categorical data: the number of occurrences with respective percentages, P value was calculated by weighted χ2 test. Abbreviations: PIR,Ratio of family income to poverty;BMI,body mass index;TG,Triglycerides;LDL-C,low-density lipoprotein cholesterol;Scr,Creatinine;TC,Total cholesterol;FBG,Fasting blood glucose;HbA1C,Hemoglobin A1c;CAD, coronary atherosclerotic heart disease; T2DM,diabetess;WC,waist circumference;HDL, high-density lipoprotein-cholesterol. Association of WWI with All-Cause and CVD Mortality As summarized in Table 2, during a median follow-up of 123 months, 3,470 (28.6%) all-cause deaths and 940 (7.7%) CVD deaths were documented. After comprehensive adjustment for confounders (Model 3), elevated WWI was independently associated with increased risks of both all-cause mortality (HR = 1.17, 95% CI: 1.09–1.26, P < 0.0001) and CVD mortality (HR = 1.22, 95% CI: 1.08–1.38, P = 0.0013). Compared with the lowest quartile, participants in the highest WWI quartile showed significantly higher risks of all-cause mortality (HR = 1.34, 95% CI: 1.17–1.54, P < 0.0001) and CVD mortality (HR = 1.69, 95% CI: 1.30–2.20, P < 0.0001), with significant linear trends across quartiles (both P for trend < 0.01). These associations remained significant after adjusting for established risk factors. Table 2 Association of weight-adjusted waist index (cm/√kg) With All-Cause and CVD Mortality. Events(%) Model 1 Model 2 Model 3 HR (95%CI) a P-value HR (95%CI) P-value HR (95%CI) P-value All-cause mortality WWI 3470 (28.6) 1.87 (1.76, 1.99) <0.0001 1.30 (1.21, 1.39) <0.0001 1.17(1.09, 1.26) <0.0001 WWI quartile Q1 529 (17.4) Reference Reference Reference Q2 739 (24.3) 1.63 (1.43, 1.85) <0.0001 1.08 (0.95, 1.23) 0.2321 1.03(0.90, 1.16) 0.7002 Q3 994 (32.7) 2.55 (2.25, 2.88) <0.0001 1.34 (1.17, 1.53) <0.0001 1.19(1.04, 1.37) 0.0103 Q4 1208 (39.8) 3.66 (3.24, 4.14) <0.0001 1.61 (1.40, 1.85) <0.0001 1.34(1.17,1.54) <0.0001 P for trend <0.0001 <0.0001 <0.0001 CVD mortality WWI 940(7.7) 1.98 (1.79, 2.19) <0.0001 1.38 (1.23, 1.55) <0.0001 1.22 (1.08, 1.38) 0.0013 WWI quartile Q1 128 (4.2) Reference Reference Reference Q2 216 (7.1) 2.33 (1.76, 3.08) <0.0001 1.52 (1.15, 2.00) 0.0032 1.46(1.09, 1.94) 0.0101 Q3 266 (8.8) 3.27 (2.53, 4.24) <0.0001 1.67 (1.25, 2.24) 0.0006 1.49(1.11, 2.00) 0.0074 Q4 330 (10.9) 4.84 (3.82, 6.14) <0.0001 2.09 (1.60, 2.72) <0.0001 1.69(1.30,2.20) <0.0001 P for trend <0.0001 <0.0001 0.0016 Model 1: no covariates were adjusted. Model 2: age, gender, and race were adjusted. Model 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% ;survey-weighted percentage;HR, hazard ratio.Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) Nonlinear association of WWI with All-Cause and CVD Mortality Figure 2 illustrates the nonlinear associations between WWI and mortality outcomes using restricted cubic spline analyses. After full adjustment, the smooth curve fitting revealed a non-linear association with all-cause mortality, while the relationship with cardiovascular disease mortality was inconclusive. Two-piecewise Cox proportional hazards models were used to examine threshold effects (Table 3). The model better described the relationship with all-cause mortality (P for log-likelihood ratio test = 0.011) but not with cardiovascular disease mortality (P = 0.592). For all-cause mortality, an inflection point was identified at WWI = 11. Below this threshold, WWI showed no significant association with mortality (HR = 0.95, 95% CI: 0.82–1.10, P = 0.4533); however, above WWI = 11, each unit increase was associated with 20% higher mortality risk (HR = 1.20, 95% CI: 1.13–1.28, P < 0.0001). Model 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% . Table 3 Threshold Effect Analysis of weight-adjusted waist index on All-Cause and CVD Mortality Model 1 Model 2 Model 3 HR (95%CI) a P-value HR (95%CI) a P-value HR (95%CI) a P-value All-cause mortality WWI b 1.87 (1.76, 1.99) <0.0001 1.30 (1.21, 1.39) <0.0001 1.17(1.09, 1.26) <0.0001 Inflection point c 11 11 11 <11 1.92 (1.66, 2.22) 11 1.60 (1.51, 1.69) <0.0001 1.33 (1.25, 1.41) <0.0001 1.20 (1.13, 1.28) <0.0001 P for log likelihood ratio test 0.036 0.002 0.011 CVD mortality WWI 1.98 (1.79, 2.19) <0.0001 1.38 (1.23, 1.55) <0.0001 1.22 (1.08, 1.38) 0.0013 Inflection point 11 11 11 <12.05 2.22 (1.66, 2.97) 12.05 1.58 (1.42, 1.76) <0.0001 1.33 (1.18, 1.50) <0.0001 1.17 (1.03, 1.32) 0.0137 P for log likelihood ratio test 0.052 0.336 0.592 Model 1: no covariates were adjusted. Model 2: age, gender, and race were adjusted. Model 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% ;survey-weighted percentage;HR, hazard ratio. a Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) b Fitting model by standard Cox proportional hazards model. c Fitting model by 2-piecewise Cox proportional hazards model. Stratified analyses of the associations between WWI and all-cause mortality and CVD mortality As shown in Table 4, stratified analyses revealed notable heterogeneity in WWI-mortality associations across subgroups. The association was stronger in males (HR = 1.34, 95% CI: 1.76–1.99, P = 0.0296), with the highest WWI quartile showing doubled risk (HR = 1.90, 95% CI: 1.24–2.91, P = 0.0031), while females showed no significant association (P = 0.4588). Younger participants (< 65 years) demonstrated stronger associations (HR = 1.70, 95% CI: 1.30–2.23, P = 0.0001), particularly in the highest WWI quartile (HR = 3.87, 95% CI: 2.40–6.25, P < 0.0001), compared to modest associations in older participants (HR = 1.28, 95% CI: 1.06–1.54, P = 0.0096). Former smokers (HR = 1.25, 95% CI: 1.00-1.55, P = 0.0488) and current smokers in the highest WWI quartile (HR = 2.30, 95% CI: 1.16–4.55, P = 0.0171) showed significant associations. The relationship was more evident in participants without CAD (HR = 1.23, 95% CI: 1.03–1.46, P = 0.0250) and non-diabetic individuals (HR = 1.21, 95% CI: 1.00-1.46, P = 0.0511). all-cause mortality CVD mortality HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Q1 Q4 Q1 Q4 Gender Male 1.34 (1.76, 1.99) 0.0296 ref 1.90(1.24,2.91) 0.0031 1.40(0.86, 2.27) 0.1775 ref 2.27(1.03,5.02) 0.0421 Female 1.08 (0.88, 1.34) 0.4588 ref 1.22(0.74, 2.03) 0.4416 1.36(0.93, 1.99) 0.1085 ref 1.85(0.62, 5.57) 0.2730 Age <65 1.70 (1.30, 2.23) 0.0001 ref 3.87(2.40, 6.25) =65 1.28 (1.06, 1.54) 0.0096 ref 1.44 (0.96,2.14) 0.0747 1.40(1.00, 1.97) 0.0503 ref 1.59(0.88,2.88) 0.1272 Smoking Non-smoker 1.16 (0.88, 1.52) 0.2913 ref 1.65(0.85, 3.20) 0.1420 1.15(0.65, 2.04) 0.6206 ref 1.13(0.34, 3.76) 0.8446 Current smoker 1.27 (0.85, 1.89) 0.2438 ref 2.30(1.16, 4.55) 0.0171 1.04(0.50, 2.16) 0.9175 ref 2.19(0.47, 10.28) 0.3190 Former smoker 1.25 (1.00, 1.55) 0.0488 ref 1.42(0.90, 2.23) 0.1279 1.96(1.09 3.53) 0.0242 ref 3.42(1.12, 10.43) 0.0309 Alcohol NO 1.17 (0.98, 1.40) 0.0858 ref 1.41(1.00, 2.01) 0.0532 1.33(0.93, 1.89) 0.1171 ref 1.86(0.97, 3.56) 0.0626 Yes 1.23 (0.67, 2.29) 0.5027 ref 2.17 (0.57,8.29) 0.2557 2.11(1.05, 4.22) 0.0353 ref 8.57 (0.17,422.06) 0.2799 CAD NO 1.23 (1.03, 1.46) 0.0250 ref 1.74(1.23, 2.45) 0.0014 1.48(0.97, 2.24) 0.0665 ref 2.56(1.22, 5.39) 0.0129 Yes 1.02 (0.69, 1.50) 0.9353 ref 0.74(0.36, 1.55) 0.4275 0.94(0.52, 1.69) 0.8284 ref 0.74(0.23, 2.44) 0.6227 T2DM NO 1.21 (1.00, 1.46) 0.0511 ref 1.65(1.13, 2.41) 0.0099 1.33(0.96, 1.84) 0.0893 ref 2.17(1.14, 4.15) 0.0189 Yes 1.15 (0.83, 1.61) 0.3968 ref 1.10(0.67, 1.83) 0.6999 1.41(0.63, 3.19) 0.4038 ref 1.50(0.43, 5.21) 0.5196 Table 4 Stratified analyses of the associations between WWI and mortality outcomes (all-cause and cardiovascular disease mortality(CVD)) Note:CAD, coronary atherosclerotic heart disease; T2DM,diabetess;HR, Hazard ratio; CI, confidence interval; Ref, reference. All covariates from model 3 were adjusted.Model 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% .Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Discussion In this study based on NHANES 2001–2018 database, we investigated the association between weight-adjusted waist index (WWI) and the risk of all-cause and cardiovascular mortality among hypertensive patients. This large-scale prospective cohort study included 12,146 hypertensive patients with a median follow-up of 123 months. Our findings demonstrated significant positive associations between WWI and both all-cause and cardiovascular mortality. After comprehensive adjustment for confounding factors, each unit increase in WWI was associated with a 17% higher risk of all-cause mortality (HR = 1.17, 95% CI: 1.09–1.26) and a 22% higher risk of cardiovascular mortality (HR = 1.22, 95% CI: 1.08–1.38). Notably, compared with the lowest WWI quartile, participants in the highest quartile showed a 34% increased risk of all-cause mortality (HR = 1.34, 95% CI: 1.17–1.54) and a 69% increased risk of cardiovascular mortality (HR = 1.69, 95% CI: 1.30–2.20). Our study found significant associations between weight-adjusted waist index (WWI) and both all-cause and cardiovascular mortality risks in hypertensive patients. This finding is consistent with Han et al.'s study on the general US adult population, which similarly found that WWI could predict all-cause and cardiovascular mortality risks (HR = 1.17, 95% CI: 1.09–1.26) 11 . Additionally, Fang et al.'s cross-sectional study confirmed the significant association between WWI and cardiovascular disease risk 12 . Our study has unique advantages: first, it included 12,146 hypertensive patients from the NHANES 2001–2018 database with a follow-up period of 123 months, significantly longer than previous studies; second, we focused specifically on hypertensive patients, differing from Wang et al.'s study that only examined US adults aged 60 and above 16 . In stratified analyses, we found that the association between WWI and mortality risk varied across different subgroups, particularly pronounced among males and individuals under 65 years. This echoes Chen et al.'s findings in the Chinese elderly population 6 . Mechanistically, WWI may influence hypertensive patient outcomes through multiple pathways. As Zhou et al. noted, regional differences and hypertension management levels can affect disease outcomes 1 . Furthermore, Liu et al.'s research suggests that body mass index may mediate the relationship between dietary behavior and hypertension 3 , indicating potential nutritional metabolic mechanisms.Recently, Fang et al. 12 conducted a cross-sectional study in United States adults and demonstrated that elevated WWI was significantly associated with increased cardiovascular disease risk, particularly showing a strong correlation with abdominal aortic calcification (AAC). Won et al. 21 further revealed a significant association between insulin resistance and arterial stiffness (β = 0.267, 95% CI: 0.198–0.336, p < 0.001), which persisted after adjusting for traditional cardiovascular risk factors. These findings suggest that WWI may increase cardiovascular disease risk through pathophysiological processes involving vascular calcification and arterial stiffness, providing novel insights into the biological basis of WWI as a cardiovascular risk predictor. This study has important clinical implications. WWI provides a novel risk stratification tool for hypertensive patients, showing stronger predictive power than BMI, especially in males and those under 65 years. Based on NHANES 2001–2018 data (n = 12,146) with median 123-month follow-up, the findings suggest incorporating WWI into routine clinical assessments, particularly monitoring patients with WWI > 11. The identified non-linear relationship offers precise reference values for risk assessment. Future research should investigate WWI's interactions with other cardiovascular risk factors and population-specific intervention thresholds. This study's strengths include: utilizing the large-scale NHANES 2001–2018 database (n = 12,146, median follow-up 123 months); employing comprehensive statistical analyses including Cox models and restricted cubic spline analysis, which revealed a WWI threshold of 11; conducting detailed stratification by age, gender, and smoking status to evaluate WWI's predictive performance; and using multiple imputation for missing data while adjusting for various confounding factors to ensure reliability. Study limitations include: findings based on NHANES may not generalize to non-US populations; potential selection bias from excluding cases with missing data; observational design only establishes associations, not causation; possible unmeasured confounders despite multiple adjustments, including dietary habits, physical activity, and medication adherence; lack of antihypertensive medication details; and absence of dynamic WWI measurements preventing analysis of WWI changes' impact. Conclusion In this NHANES 2001–2018 cohort study, we first evaluated the association between WWI and mortality risks in hypertensive patients. Results showed a non-linear relationship, with each WWI unit above 11 increasing all-cause mortality risk by 20%. The highest WWI quartile showed 34% higher all-cause mortality and 69% higher cardiovascular mortality than the lowest quartile, particularly in males and those under 65. These findings support WWI as a novel risk stratification tool in clinical practice. Future studies should investigate WWI's interactions with other risk factors and population-specific thresholds. Declarations Acknowledgements We acknowledge the NHANES participants and staff for their contributions. Data availability The NHANES data used for this analysis can be found at https://www.cdc.gov/nchs/nhanes. Author contributions J.C.Y. conceived and designed the study, and wrote the manuscript. C.L. analyzed the data. Q.B.X. took the quality control of data. G.D.L. critically revised the manuscript. All authors read and approved the final manuscript. Funding None Competing interests The authors declare no competing interests References Zhou, B., Perel, P., Mensah, G. A. & Ezzati, M., Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. NAT REV CARDIOL 18 785 (2021). Maksimov, S. A. et al. , Regional living conditions and the prevalence, awareness, treatment, control of hypertension at the individual level in Russia. BMC PUBLIC HEALTH 22 202 (2022). Liu, B. et al. , Body Mass Index Mediates the Relationship between the Frequency of Eating Away from Home and Hypertension in Rural Adults: A Large-Scale Cross-Sectional Study. NUTRIENTS 14 (2022). World Health Organization. A Global Brief on Hypertension. 2013; 10.1000/global-hypertension-brief-2013. Seedat, Y. K., Fixed drug combination in hypertension and hyperlipidaemia in the developing world. CARDIOVASC J AFR 19 124 (2008). Chen, Z. et al. , Association of changes in waist circumference, waist-to-height ratio and weight-adjusted-waist index with multimorbidity among older Chinese adults: results from the Chinese longitudinal healthy longevity survey (CLHLS). BMC PUBLIC HEALTH 24 318 (2024). Park, Y., Kim, N. H., Kwon, T. Y. & Kim, S. G., A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep 8 16753 (2018). Zheng, D. et al. , Association between the weight-adjusted waist index and the odds of type 2 diabetes mellitus in United States adults: a cross-sectional study. FRONT ENDOCRINOL 14 (2024). Shen, Y., Wu, Y., Fu, M., Zhu, K. & Wang, J., Association between weight-adjusted-waist index with hepatic steatosis and liver fibrosis: a nationally representative cross-sectional study from NHANES 2017 to 2020. Front Endocrinol (Lausanne) 14 1159055 (2023). Yu, L. et al. , Association of weight-adjusted-waist index with asthma prevalence and the age of first asthma onset in United States adults. FRONT ENDOCRINOL 14 (2023). Han, Y. et al. , The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in general US adults. CLINICS 78 100248 (2023). Fang, H., Xie, F., Li, K., Li, M. & Wu, Y., Association between weight-adjusted-waist index and risk of cardiovascular diseases in United States adults: a cross-sectional study. BMC Cardiovasc Disord 23 435 (2023). Li, Q. et al. , Association of weight-adjusted-waist index with incident hypertension: The Rural Chinese Cohort Study. Nutr Metab Cardiovasc Dis 30 1732 (2020). Tao, J., Zhang, Y., Tan, C. & Tan, W., Associations between weight-adjusted waist index and fractures: a population-based study. J ORTHOP SURG RES 18 290 (2023). Liu, C., Liang, D., Xiao, K. & Xie, L., Association between the triglyceride-glucose index and all-cause and CVD mortality in the young population with diabetes. CARDIOVASC DIABETOL 23 171 (2024). Wang, J. et al. , The relationship between obesity associated weight-adjusted waist index and the prevalence of hypertension in US adults aged ≥60 years: a brief report. Front Public Health 11 1210669 (2023). Standards of medical care in diabetes--2013. DIABETES CARE 36 Suppl 1 S11 (2013). Zhang, Q., Xiao, S., Jiao, X. & Shen, Y., The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. CARDIOVASC DIABETOL 22 279 (2023). Johnson, C. L. et al. , National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital Health Stat 2 1 (2013). Yu, X., Cao, L. & Yu, X., Elevated cord serum manganese level is associated with a neonatal high ponderal index. ENVIRON RES 121 79 (2013). Won, K. B. et al. , Relationship of insulin resistance estimated by triglyceride glucose index to arterial stiffness. LIPIDS HEALTH DIS 17 268 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5730258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":398860998,"identity":"0f152f55-af5b-4d59-a013-5acaa3b9a14e","order_by":0,"name":"Jiechun YAO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACfvnnxz98qJCQY2NvIFKLZENOGuOMMzbG/DwHiNRi0JBgxszblpYoOSOBWC0MB9Iezmw7nGBw8/HGGww1NtEEtZgzNh43+HDucJ7B7bRiC4ZjabkNhLRYNjMkSM4oO1xscDvHTIKx4TBhLQbHGAykedgOJ264eYZYLWcYzKR5gN6fOYOHSC2SM3iSDSGBDPRLAjF+4ZdgP/gAEpWHN974UGNDWAuKIyUSSFEO0UKqjlEwCkbBKBgZAACD7kPLGKVPWQAAAABJRU5ErkJggg==","orcid":"","institution":"Maoming People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jiechun","middleName":"","lastName":"YAO","suffix":""},{"id":398861001,"identity":"6766d61b-e2f5-4dd1-95f2-81ffad365dae","order_by":1,"name":"Chun Lin","email":"","orcid":"","institution":"Maoming People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Lin","suffix":""},{"id":398861003,"identity":"e4cabb04-54ba-4acd-bbbc-cdc254ef6a9c","order_by":2,"name":"Qingbo Xu","email":"","orcid":"","institution":"Maoming People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingbo","middleName":"","lastName":"Xu","suffix":""},{"id":398861004,"identity":"8f56ddb0-5683-4061-a6c5-32b650c05420","order_by":3,"name":"Guode Li","email":"","orcid":"","institution":"Maoming People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guode","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-29 13:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5730258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5730258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73316608,"identity":"f9037381-89f0-4555-a0bc-5aedcf137952","added_by":"auto","created_at":"2025-01-08 20:07:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":580598,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants’ selection. WWI, weight-adjusted waist circumference index.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5730258/v1/291acd4586e647170b14e9d2.png"},{"id":73316611,"identity":"86af4f34-b8e4-4ed5-bb85-e196cb1ca44b","added_by":"auto","created_at":"2025-01-08 20:07:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":756297,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic spline regression analyses for the association between weight-adjusted waist circumference index (WWI) and the risk of all-cause mortality(A) and CVD(B) . All covariates from model 3 were adjusted.\u003c/p\u003e\n\u003cp\u003eModel 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. \u0026nbsp;PIR,\u003ca href=\"https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.htm#INDFMPIR\"\u003eRatio of family income to poverty\u003c/a\u003e;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% .\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5730258/v1/ba9130f9f4d9be1d183affd5.png"},{"id":90481420,"identity":"41ef868a-7005-4907-97bd-99a97951e3b5","added_by":"auto","created_at":"2025-09-03 08:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2566874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5730258/v1/ac9ba0b7-9483-4dba-95e6-30f05ea5e09d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Weight-Adjusted Waist Index and All-Cause and Cardiovascular Mortality in Hypertensive Patients: NHANES 2001–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension has emerged as a critical global public health challenge, with epidemiological trends that demand urgent attention\u003csup\u003e1\u003c/sup\u003e. The global adult hypertension prevalence rapidly increased from 25.9% in 2000 to 31.1% in 2010\u003csup\u003e2\u003c/sup\u003e. The prevalence of hypertension among Chinese adults increased from 25.2% in 2012 to 27.9% in 2015, according to the National Health Commission of China\u003csup\u003e3\u003c/sup\u003e. More alarmingly, the World Health Organization reports that approximately 80% of hypertension patients remain inadequately treated\u003csup\u003e4\u003c/sup\u003e. Cardiovascular disease, as the primary cause of hypertension-related mortality, has become a major global health threat, with approximately 7.08\u0026nbsp;million deaths from cerebrovascular diseases in 2020, predominantly cardiovascular mortality\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWeight-adjusted Waist Index (WWI) represents an innovative anthropometric indicator that assesses obesity degree by standardizing the ratio of waist circumference to weight, demonstrating unique value in disease risk prediction\u003csup\u003e6,7\u003c/sup\u003e. Compared to traditional Body Mass Index (BMI), WWI exhibits superior prognostic predictive capabilities across multiple clinical populations. Recent studies have confirmed WWI's significant risk prediction potential in type 2 diabetes\u003csup\u003e8\u003c/sup\u003e, fatty liver disease, and asthma patients\u003csup\u003e10\u003c/sup\u003e. Research consistently indicates that as WWI values increase, the risk of cardiovascular disease development and mortality show a significant upward trend\u003csup\u003e8,11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough previous studies have explored the predictive value of WWI, its role in hypertensive patients remains unclear. Most studies have focused on the general population, and it has not been investigated whether WWI could serve as a predictor of mortality in hypertensive patients, who inherently have a higher risk of cardiovascular events\u003csup\u003e12\u003c/sup\u003e. Furthermore, the nonlinear relationship between WWI and mortality has not been thoroughly studied, with previous research suggesting that both high and low WWI values may be associated with increased mortality \u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the association between weight-adjusted waist index (WWI) and all-cause mortality and cardiovascular disease mortality among hypertensive patients. Using data from the National Health and Nutrition Examination Survey (NHANES) 2001\u0026ndash;2018, we will analyze whether WWI can serve as an independent predictor of mortality risk in the hypertensive population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThis study used NHANES data from 2001\u0026ndash;2018, covering nine survey cycles. NHANES annually selects about 5,000 participants from 15 U.S. geographic regions using multi-stage probability sampling. The survey includes interviews and physical examinations, with participants providing written informed consent under NCHS Ethics Review Board approval. The database contains comprehensive health and nutritional data, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFrom 101,316 NHANES (2001\u0026ndash;2018) participants, 12,146 were included in the final analysis after excluding those with missing hypertension data (n\u0026thinsp;=\u0026thinsp;87,463), WWI data (n\u0026thinsp;=\u0026thinsp;1,602), and mortality data (n\u0026thinsp;=\u0026thinsp;105), as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeasurement of WWI\u003c/h3\u003e\n\u003cp\u003eWeight-adjusted Waist Index (WWI) was calculated as waist circumference (cm) divided by the square root of body weight (kg). Trained personnel measured participants wearing examination gowns, using electronic scales for weight and measuring tape at the midaxillary line above the right knee for waist circumference\u003csup\u003e14\u003c/sup\u003e. WWI served as the exposure variable in this study.\u003c/p\u003e\n\u003ch3\u003eAssessment of All-Cause Mortality and Cardiovascular Mortality\u003c/h3\u003e\n\u003cp\u003eMortality assessment utilized the NHANES public mortality database (updated through December 31, 2019), matched with the National Death Index through NCHS. Deaths were classified according to ICD-10, with cardiovascular deaths defined by codes I00-I09, I11, I13, and I20-I51. Participants without death records during follow-up were considered survivors\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eWe assessed participants\u0026apos; health status based on the following questionnaires and indicators: Hypertension was defined as taking antihypertensive medications, being diagnosed with hypertension, or having three consecutive systolic blood pressure measurements\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg\u003csup\u003e16\u003c/sup\u003e. Diabetes was defined as taking glucose-lowering medications or being diagnosed with diabetes, having glycated hemoglobin A1c levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, or 2-hour blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL\u003csup\u003e17\u003c/sup\u003e. CAD diagnosis was determined through self-reported physician diagnosis obtained during personal interviews using standardized medical condition questionnaires. Participants were asked: \u0026quot;Has a doctor or other health professional ever told you that you have coronary heart disease?\u0026quot; A \u0026quot;yes\u0026quot; response to this question was considered indicative of having coronary heart disease\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDemographic factors were considered as potential confounders, including gender, age, race/ethnicity (categorized as Mexican American, Other Hispanic, Non-Hispanic Black, Non-Hispanic White, and Other Race including Multi-Racial), poverty income ratio, and education level (categorized as Less than 9th grade, 9-11th grade, High School Graduate, Some College or AA degree, and College Graduate or above).Lifestyle factors, such as smoking status (categorized as never smokers, current smokers, and former smokers), alcohol consumption (categorized as drinkers and non-drinkers), and anthropometric measurements (height, body mass index (BMI, kg/m\u0026sup2;) calculated as weight in kilograms divided by height in meters squared, and waist circumference (WC)) were also considered.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study included various blood markers such as triglycerides (TG) (mmol/L), total cholesterol (TC) (mmol/L), high-density lipoprotein cholesterol (HDL-C) (mmol/L), low-density lipoprotein cholesterol (LDL-C) (mmol/L), serum creatinine (umol/L), fasting blood glucose (mg/dL), and glycated hemoglobin (HbA1C (%)).\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted according to CDC guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx\u003c/span\u003e\u003c/span\u003e). Each NHANES participant was assigned a sample weight \u003csup\u003e19\u003c/sup\u003e. Accordingly, we considered significant variance and implemented recommended weighting methods. Continuous data were reported as means with 95% confidence intervals (CI), while discrete data were summarized by frequency of occurrence and respective percentages. Weighted \u0026chi;2 tests (for categorical variables) or weighted linear regression models (for continuous variables) were employed to calculate differences between WWI quartile groups. Additionally, linear trends across WWI quartiles were evaluated using median values within each quartile as continuous variables. To investigate the relationship between WWI and mortality, univariate and multivariate Cox proportional hazards regression analyses were initially conducted. Three models were constructed: Model 1 with no adjustments; Model 2 adjusted for age, gender, and race; and Model 3, building upon Model 2, incorporated additional variables including education level, marital status, smoking habits, alcohol consumption, and various blood indicators. Restricted cubic spline (RCS) models were then applied for Cox proportional hazards regression to assess potential linear relationships between WWI and mortality. When non-linearity was detected, we first calculated inflection points using recursive algorithms, then constructed two-piecewise Cox proportional hazards models on either side of the inflection point\u003csup\u003e20\u003c/sup\u003e. Multiple imputation methods were employed to handle missing values, and stratified analyses were conducted for significant covariates, including age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), gender, smoking, alcohol consumption, coronary heart disease, and diabetes.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were performed using R software version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) and Empower Stats (X\u0026amp;Y Solutions Inc., Boston, MA). Two-tailed P values less than 0.05 were considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;1, among 12,146 participants stratified by WWI quartiles, significant demographic and clinical differences were observed. Mean age increased from 49.2 years in Q1 to 63.6 years in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with female proportion rising from 42.5\u0026ndash;67.5% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). College graduates decreased from 30.1% in Q1 to 16.3% in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). BMI increased from 27.3 to 34.1 kg/m\u0026sup2; across quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Metabolic parameters showed significant trends, with fasting glucose rising from 104.5 to 122.3 mg/dL and HbA1C from 5.5\u0026ndash;6.2% (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Current smoking rates decreased (23.9\u0026ndash;15.1%), while former smoking increased (24.2\u0026ndash;36.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The prevalence of type 2 diabetes and coronary artery disease increased significantly from Q1 to Q4 (9.0% vs 40.5% and 4.2% vs 10.3%, respectively, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of study population according to weight-adjusted-waist index tertiles, Weighted.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWeight-adjusted waist index (cm/\u0026radic;kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (8.5\u0026ndash;10.8)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3036\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (10.8\u0026ndash;11.4)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3037\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (11.4\u0026ndash;11.9)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3036\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4 (11.9\u0026ndash;15.7)\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3037\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3 (3.2 ,3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1 (3.0 ,3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9 (2.8 ,2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5 (2.4 ,2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.2 (48.4 ,50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.9 (55.2 ,56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.7 (59.0 ,60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6 (62.9 ,64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3 (27.0 ,27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1 (29.9 ,30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.1 (31.8 ,32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.1 (33.7 ,34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6 (1.5 ,1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.7 ,1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.7 ,1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0 (1.8 ,2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL_C(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0 (2.9 ,3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1 (3.0 ,3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0 (3.0 ,3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9 (2.9 ,3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCr (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.7 (83.8 ,87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.8 (82.6 ,85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.4 (83.0 ,85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.3 (82.7 ,86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2 (5.1 ,5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2 (5.2 ,5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2 (5.2 ,5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1 (5.1 ,5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.5 (102.8 ,106.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.2 (110.3 ,114.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116.9 (114.6 ,119.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.3 (119.5 ,125.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5 (5.5 ,5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.7 (5.7 ,5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9 (5.9 ,6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.2 (6.1 ,6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4 (1.4 ,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3 (1.3 ,1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3 (1.3 ,1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3 (1.3 ,1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.7 (92.2 ,93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.9 (102.4 ,103.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.1 (108.4 ,109.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116.2 (115.4 ,117.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWEIGHT(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.3 (80.4 ,82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.3 (86.4 ,88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.9 (88.7 ,91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.9 (89.6 ,92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1781 (57.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1662 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1461 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1047 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1255(42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1375 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1575 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1990 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational leve, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e503 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e702 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-11th grade (Includes 12th grade with no diploma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e510 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e494 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e557 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e726 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e745 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e794 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e727 (27.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e888 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e861 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e796 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e678 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e698 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e393 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e466 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e544 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (4.7 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1311 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1439 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1489 (72.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1597 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1170 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840 (13.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e690 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e494 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161 (5.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1729 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1562 (90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1483 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1222 (92.7 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218 (11.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (7.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1490 (51.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1480 (47.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1441 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1495 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e743 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e553 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e524 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e451 (15.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e713 (24.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e983 (34.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1056 (34.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1083 (36.3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAD, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2787 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2753 (92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2725 (92.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2684 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268 (7.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313 (10.3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2DM, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2652 (91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2310 (82.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2081 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1743 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e727 (17.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e955 (27.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1294 (40.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eContinuous data: the mean with 95% confidence intervals, P value was calculated by weighted linear regression model. Categorical data: the number of occurrences with respective percentages, P value was calculated by weighted \u0026chi;2 test. Abbreviations: PIR,Ratio of family income to poverty;BMI,body mass index;TG,Triglycerides;LDL-C,low-density lipoprotein cholesterol;Scr,Creatinine;TC,Total cholesterol;FBG,Fasting blood glucose;HbA1C,Hemoglobin A1c;CAD, coronary atherosclerotic heart disease; T2DM,diabetess;WC,waist circumference;HDL, high-density lipoprotein-cholesterol.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of WWI with All-Cause and CVD Mortality\u003c/h3\u003e\n\u003cp\u003eAs summarized in Table\u0026nbsp;2, during a median follow-up of 123 months, 3,470 (28.6%) all-cause deaths and 940 (7.7%) CVD deaths were documented. After comprehensive adjustment for confounders (Model 3), elevated WWI was independently associated with increased risks of both all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.09\u0026ndash;1.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.08\u0026ndash;1.38, P\u0026thinsp;=\u0026thinsp;0.0013). Compared with the lowest quartile, participants in the highest WWI quartile showed significantly higher risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.17\u0026ndash;1.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.30\u0026ndash;2.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with significant linear trends across quartiles (both P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These associations remained significant after adjusting for established risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Association of weight-adjusted waist index (cm/\u0026radic;kg) \u0026nbsp;With All-Cause and CVD Mortality.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003csup\u003ea\u003c/sup\u003e P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI) P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI) P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3470 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.87 (1.76, 1.99) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.30 (1.21, 1.39) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.17(1.09, 1.26) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWI quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e529 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e739 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.63 \u0026nbsp;(1.43, 1.85) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.08 (0.95, 1.23) \u0026nbsp;0.2321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.03(0.90, 1.16) \u0026nbsp;0.7002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e994 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e2.55 \u0026nbsp;(2.25, 2.88) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.34 (1.17, 1.53) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.19(1.04, 1.37) \u0026nbsp;0.0103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1208 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e3.66 \u0026nbsp;(3.24, 4.14) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.61 (1.40, 1.85) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.34(1.17,1.54) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e940(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.98 (1.79, 2.19) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.38 (1.23, 1.55) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.22 (1.08, 1.38) \u0026nbsp;0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWI quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e128 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e216 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e2.33 \u0026nbsp;(1.76, 3.08) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.52 (1.15, 2.00) \u0026nbsp;0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.46(1.09, 1.94) \u0026nbsp;0.0101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e266 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e3.27 \u0026nbsp;(2.53, 4.24) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.67 (1.25, 2.24) \u0026nbsp;0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.49(1.11, 2.00) \u0026nbsp;0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e330 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e4.84 \u0026nbsp;(3.82, 6.14) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.09 (1.60, 2.72) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.69(1.30,2.20) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: no covariates were adjusted.\u003c/p\u003e\n\u003cp\u003eModel 2: age, gender, and race were adjusted.\u003c/p\u003e\n\u003cp\u003eModel 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted.\u003c/p\u003e\n\u003cp\u003ePIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% ;survey-weighted percentage;HR, hazard ratio.Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs)\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eNonlinear association of WWI with All-Cause and CVD Mortality\u003c/h2\u003e\n \u003cp\u003eFigure\u0026nbsp;2 illustrates the nonlinear associations between WWI and mortality outcomes using restricted cubic spline analyses. After full adjustment, the smooth curve fitting revealed a non-linear association with all-cause mortality, while the relationship with cardiovascular disease mortality was inconclusive. Two-piecewise Cox proportional hazards models were used to examine threshold effects (Table\u0026nbsp;3). The model better described the relationship with all-cause mortality (P for log-likelihood ratio test\u0026thinsp;=\u0026thinsp;0.011) but not with cardiovascular disease mortality (P\u0026thinsp;=\u0026thinsp;0.592). For all-cause mortality, an inflection point was identified at WWI\u0026thinsp;=\u0026thinsp;11. Below this threshold, WWI showed no significant association with mortality (HR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.82\u0026ndash;1.10, P\u0026thinsp;=\u0026thinsp;0.4533); however, above WWI\u0026thinsp;=\u0026thinsp;11, each unit increase was associated with 20% higher mortality risk (HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.13\u0026ndash;1.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003cp\u003eModel 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% .\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eThreshold Effect Analysis of weight-adjusted waist index on All-Cause and CVD Mortality\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"505\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003csup\u003ea\u003c/sup\u003e P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003csup\u003ea\u003c/sup\u003e P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003csup\u003ea\u003c/sup\u003e P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eWWI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.87 (1.76, 1.99) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.30 (1.21, 1.39) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.17(1.09, 1.26) \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eInflection point\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.92 (1.66, 2.22) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.99 (0.86, 1.15) 0.9153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.95 (0.82, 1.10) 0.4533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026gt;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.60 (1.51, 1.69) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.33 (1.25, 1.41) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.20 (1.13, 1.28) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eP for log likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.98 (1.79, 2.19) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.38 (1.23, 1.55) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.22 (1.08, 1.38) 0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2.22 (1.66, 2.97) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.12 (0.83, 1.50) 0.4624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.06 (0.78, 1.43) 0.7144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026gt;12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.58 (1.42, 1.76) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.33 (1.18, 1.50) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.17 (1.03, 1.32) 0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eP for log likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eModel 1: no covariates were adjusted.\u003c/p\u003e\n \u003cp\u003eModel 2: age, gender, and race were adjusted.\u003c/p\u003e\n \u003cp\u003eModel 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted.\u003c/p\u003e\n \u003cp\u003ePIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% ;survey-weighted percentage;HR, hazard ratio.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs)\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eFitting model by standard Cox proportional hazards model.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eFitting model by 2-piecewise Cox proportional hazards model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eStratified analyses of the associations between WWI and all-cause mortality and CVD mortality\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;4, stratified analyses revealed notable heterogeneity in WWI-mortality associations across subgroups. The association was stronger in males (HR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.76\u0026ndash;1.99, P\u0026thinsp;=\u0026thinsp;0.0296), with the highest WWI quartile showing doubled risk (HR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.24\u0026ndash;2.91, P\u0026thinsp;=\u0026thinsp;0.0031), while females showed no significant association (P\u0026thinsp;=\u0026thinsp;0.4588). Younger participants (\u0026lt;\u0026thinsp;65 years) demonstrated stronger associations (HR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.30\u0026ndash;2.23, P\u0026thinsp;=\u0026thinsp;0.0001), particularly in the highest WWI quartile (HR\u0026thinsp;=\u0026thinsp;3.87, 95% CI: 2.40\u0026ndash;6.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), compared to modest associations in older participants (HR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.06\u0026ndash;1.54, P\u0026thinsp;=\u0026thinsp;0.0096). Former smokers (HR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 1.00-1.55, P\u0026thinsp;=\u0026thinsp;0.0488) and current smokers in the highest WWI quartile (HR\u0026thinsp;=\u0026thinsp;2.30, 95% CI: 1.16\u0026ndash;4.55, P\u0026thinsp;=\u0026thinsp;0.0171) showed significant associations. The relationship was more evident in participants without CAD (HR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.03\u0026ndash;1.46, P\u0026thinsp;=\u0026thinsp;0.0250) and non-diabetic individuals (HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.00-1.46, P\u0026thinsp;=\u0026thinsp;0.0511).\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 237px;\"\u003e\n \u003cp\u003eCVD mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.34 (1.76, 1.99)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.0296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.90(1.24,2.91)\u003c/p\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.40(0.86, 2.27)\u003c/p\u003e\n \u003cp\u003e0.1775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.27(1.03,5.02)\u003c/p\u003e\n \u003cp\u003e0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.08 (0.88, 1.34)\u003c/p\u003e\n \u003cp\u003e0.4588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.22(0.74, 2.03)\u003c/p\u003e\n \u003cp\u003e0.4416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.36(0.93, 1.99)\u003c/p\u003e\n \u003cp\u003e0.1085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.85(0.62, 5.57)\u003c/p\u003e\n \u003cp\u003e0.2730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.70 (1.30, 2.23)\u003c/p\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.87(2.40, 6.25)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.23(1.29, 3.84)\u003c/p\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e6.26(1.98, 19.78)\u003c/p\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt; =65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.28 (1.06, 1.54)\u003c/p\u003e\n \u003cp\u003e0.0096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.44 (0.96,2.14)\u003c/p\u003e\n \u003cp\u003e0.0747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.40(1.00, 1.97)\u003c/p\u003e\n \u003cp\u003e0.0503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.59(0.88,2.88)\u003c/p\u003e\n \u003cp\u003e0.1272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.16 (0.88, 1.52)\u003c/p\u003e\n \u003cp\u003e0.2913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.65(0.85, 3.20)\u003c/p\u003e\n \u003cp\u003e0.1420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.15(0.65, 2.04)\u003c/p\u003e\n \u003cp\u003e0.6206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.13(0.34, 3.76)\u003c/p\u003e\n \u003cp\u003e0.8446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.27 (0.85, 1.89)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.2438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.30(1.16, 4.55)\u003c/p\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.04(0.50, 2.16)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.9175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.19(0.47, 10.28)\u003c/p\u003e\n \u003cp\u003e0.3190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.25 (1.00, 1.55)\u003c/p\u003e\n \u003cp\u003e0.0488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.42(0.90, 2.23)\u003c/p\u003e\n \u003cp\u003e0.1279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.96(1.09 3.53)\u003c/p\u003e\n \u003cp\u003e0.0242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.42(1.12, 10.43)\u003c/p\u003e\n \u003cp\u003e0.0309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.17 (0.98, 1.40)\u003c/p\u003e\n \u003cp\u003e0.0858\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.41(1.00, 2.01)\u003c/p\u003e\n \u003cp\u003e0.0532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.33(0.93, 1.89)\u003c/p\u003e\n \u003cp\u003e0.1171\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.86(0.97, 3.56)\u003c/p\u003e\n \u003cp\u003e0.0626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.23 (0.67, 2.29)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.17 (0.57,8.29)\u003c/p\u003e\n \u003cp\u003e0.2557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.11(1.05, 4.22)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e8.57 (0.17,422.06)\u003c/p\u003e\n \u003cp\u003e0.2799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.23 (1.03, 1.46)\u003c/p\u003e\n \u003cp\u003e0.0250\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.74(1.23, 2.45)\u003c/p\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.48(0.97, 2.24)\u003c/p\u003e\n \u003cp\u003e0.0665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.56(1.22, 5.39)\u003c/p\u003e\n \u003cp\u003e0.0129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.02 (0.69, 1.50)\u003c/p\u003e\n \u003cp\u003e0.9353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.74(0.36, 1.55)\u003c/p\u003e\n \u003cp\u003e0.4275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.94(0.52, 1.69)\u003c/p\u003e\n \u003cp\u003e0.8284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.74(0.23, 2.44)\u003c/p\u003e\n \u003cp\u003e0.6227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eT2DM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.21 (1.00, 1.46)\u003c/p\u003e\n \u003cp\u003e0.0511\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.65(1.13, 2.41)\u003c/p\u003e\n \u003cp\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.33(0.96, 1.84)\u003c/p\u003e\n \u003cp\u003e0.0893\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.17(1.14, 4.15)\u003c/p\u003e\n \u003cp\u003e0.0189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.15 (0.83, 1.61)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10(0.67, 1.83)\u003c/p\u003e\n \u003cp\u003e0.6999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.41(0.63, 3.19)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.50(0.43, 5.21)\u003c/p\u003e\n \u003cp\u003e0.5196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Stratified analyses of the associations between WWI and mortality outcomes (all-cause and cardiovascular disease mortality(CVD))\u003c/p\u003e\n \u003cp\u003eNote:CAD, coronary atherosclerotic heart disease; T2DM,diabetess;HR, Hazard ratio; CI, confidence interval; Ref, reference. All covariates from model 3 were adjusted.Model 3: age, gender, race,TG,LDL_C,SCr,smooking, Alcohol,FBG HBA1C,CAD,Education level,Hypertension and PIR were adjusted. PIR,Ratio of family income to poverty;TG,Triglycerides;LDL-C, low-density lipoprotein cholesterol;SCr,Creatinine ;FBG,Fasting blood glucose;CAD, coronary atherosclerotic heart disease;HbA1C,Hemoglobin A1c.% .Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study based on NHANES 2001\u0026ndash;2018 database, we investigated the association between weight-adjusted waist index (WWI) and the risk of all-cause and cardiovascular mortality among hypertensive patients. This large-scale prospective cohort study included 12,146 hypertensive patients with a median follow-up of 123 months. Our findings demonstrated significant positive associations between WWI and both all-cause and cardiovascular mortality. After comprehensive adjustment for confounding factors, each unit increase in WWI was associated with a 17% higher risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.09\u0026ndash;1.26) and a 22% higher risk of cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.08\u0026ndash;1.38). Notably, compared with the lowest WWI quartile, participants in the highest quartile showed a 34% increased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.17\u0026ndash;1.54) and a 69% increased risk of cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.30\u0026ndash;2.20).\u003c/p\u003e \u003cp\u003eOur study found significant associations between weight-adjusted waist index (WWI) and both all-cause and cardiovascular mortality risks in hypertensive patients. This finding is consistent with Han et al.'s study on the general US adult population, which similarly found that WWI could predict all-cause and cardiovascular mortality risks (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.09\u0026ndash;1.26)\u003csup\u003e11\u003c/sup\u003e. Additionally, Fang et al.'s cross-sectional study confirmed the significant association between WWI and cardiovascular disease risk\u003csup\u003e12\u003c/sup\u003e. Our study has unique advantages: first, it included 12,146 hypertensive patients from the NHANES 2001\u0026ndash;2018 database with a follow-up period of 123 months, significantly longer than previous studies; second, we focused specifically on hypertensive patients, differing from Wang et al.'s study that only examined US adults aged 60 and above\u003csup\u003e16\u003c/sup\u003e. In stratified analyses, we found that the association between WWI and mortality risk varied across different subgroups, particularly pronounced among males and individuals under 65 years. This echoes Chen et al.'s findings in the Chinese elderly population\u003csup\u003e6\u003c/sup\u003e. Mechanistically, WWI may influence hypertensive patient outcomes through multiple pathways. As Zhou et al. noted, regional differences and hypertension management levels can affect disease outcomes\u003csup\u003e1\u003c/sup\u003e. Furthermore, Liu et al.'s research suggests that body mass index may mediate the relationship between dietary behavior and hypertension\u003csup\u003e3\u003c/sup\u003e, indicating potential nutritional metabolic mechanisms.Recently, Fang et al.\u003csup\u003e12\u003c/sup\u003e conducted a cross-sectional study in United States adults and demonstrated that elevated WWI was significantly associated with increased cardiovascular disease risk, particularly showing a strong correlation with abdominal aortic calcification (AAC). Won et al.\u003csup\u003e21\u003c/sup\u003e further revealed a significant association between insulin resistance and arterial stiffness (β\u0026thinsp;=\u0026thinsp;0.267, 95% CI: 0.198\u0026ndash;0.336, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which persisted after adjusting for traditional cardiovascular risk factors. These findings suggest that WWI may increase cardiovascular disease risk through pathophysiological processes involving vascular calcification and arterial stiffness, providing novel insights into the biological basis of WWI as a cardiovascular risk predictor.\u003c/p\u003e \u003cp\u003eThis study has important clinical implications. WWI provides a novel risk stratification tool for hypertensive patients, showing stronger predictive power than BMI, especially in males and those under 65 years. Based on NHANES 2001\u0026ndash;2018 data (n\u0026thinsp;=\u0026thinsp;12,146) with median 123-month follow-up, the findings suggest incorporating WWI into routine clinical assessments, particularly monitoring patients with WWI\u0026thinsp;\u0026gt;\u0026thinsp;11. The identified non-linear relationship offers precise reference values for risk assessment. Future research should investigate WWI's interactions with other cardiovascular risk factors and population-specific intervention thresholds.\u003c/p\u003e \u003cp\u003eThis study's strengths include: utilizing the large-scale NHANES 2001\u0026ndash;2018 database (n\u0026thinsp;=\u0026thinsp;12,146, median follow-up 123 months); employing comprehensive statistical analyses including Cox models and restricted cubic spline analysis, which revealed a WWI threshold of 11; conducting detailed stratification by age, gender, and smoking status to evaluate WWI's predictive performance; and using multiple imputation for missing data while adjusting for various confounding factors to ensure reliability.\u003c/p\u003e \u003cp\u003eStudy limitations include: findings based on NHANES may not generalize to non-US populations; potential selection bias from excluding cases with missing data; observational design only establishes associations, not causation; possible unmeasured confounders despite multiple adjustments, including dietary habits, physical activity, and medication adherence; lack of antihypertensive medication details; and absence of dynamic WWI measurements preventing analysis of WWI changes' impact.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this NHANES 2001\u0026ndash;2018 cohort study, we first evaluated the association between WWI and mortality risks in hypertensive patients. Results showed a non-linear relationship, with each WWI unit above 11 increasing all-cause mortality risk by 20%. The highest WWI quartile showed 34% higher all-cause mortality and 69% higher cardiovascular mortality than the lowest quartile, particularly in males and those under 65. These findings support WWI as a novel risk stratification tool in clinical practice. Future studies should investigate WWI's interactions with other risk factors and population-specific thresholds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the NHANES participants and staff for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES data used for this analysis can be found at https://www.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.C.Y. conceived and designed the study, and wrote the manuscript. C.L. analyzed the data. Q.B.X. took the quality control of data. G.D.L. critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou, B., Perel, P., Mensah, G. A. \u0026amp; Ezzati, M., Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. \u003cem\u003eNAT REV CARDIOL\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e 785 (2021).\u003c/li\u003e\n\u003cli\u003eMaksimov, S. 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K., Fixed drug combination in hypertension and hyperlipidaemia in the developing world. \u003cem\u003eCARDIOVASC J AFR\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e 124 (2008).\u003c/li\u003e\n\u003cli\u003eChen, Z.\u003cem\u003e et al.\u003c/em\u003e, Association of changes in waist circumference, waist-to-height ratio and weight-adjusted-waist index with multimorbidity among older Chinese adults: results from the Chinese longitudinal healthy longevity survey (CLHLS). \u003cem\u003eBMC PUBLIC HEALTH\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e 318 (2024).\u003c/li\u003e\n\u003cli\u003ePark, Y., Kim, N. H., Kwon, T. Y. \u0026amp; Kim, S. G., A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e 16753 (2018).\u003c/li\u003e\n\u003cli\u003eZheng, D.\u003cem\u003e et al.\u003c/em\u003e, Association between the weight-adjusted waist index and the odds of type 2 diabetes mellitus in United States adults: a cross-sectional study. \u003cem\u003eFRONT ENDOCRINOL\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2024).\u003c/li\u003e\n\u003cli\u003eShen, Y., Wu, Y., Fu, M., Zhu, K. \u0026amp; Wang, J., Association between weight-adjusted-waist index with hepatic steatosis and liver fibrosis: a nationally representative cross-sectional study from NHANES 2017 to 2020. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e 1159055 (2023).\u003c/li\u003e\n\u003cli\u003eYu, L.\u003cem\u003e et al.\u003c/em\u003e, Association of weight-adjusted-waist index with asthma prevalence and the age of first asthma onset in United States adults. \u003cem\u003eFRONT ENDOCRINOL\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eHan, Y.\u003cem\u003e et al.\u003c/em\u003e, The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in general US adults. \u003cem\u003eCLINICS\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e 100248 (2023).\u003c/li\u003e\n\u003cli\u003eFang, H., Xie, F., Li, K., Li, M. \u0026amp; Wu, Y., Association between weight-adjusted-waist index and risk of cardiovascular diseases in United States adults: a cross-sectional study. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e 435 (2023).\u003c/li\u003e\n\u003cli\u003eLi, Q.\u003cem\u003e et al.\u003c/em\u003e, Association of weight-adjusted-waist index with incident hypertension: The Rural Chinese Cohort Study. \u003cem\u003eNutr Metab Cardiovasc Dis\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e 1732 (2020).\u003c/li\u003e\n\u003cli\u003eTao, J., Zhang, Y., Tan, C. \u0026amp; Tan, W., Associations between weight-adjusted waist index and fractures: a population-based study. \u003cem\u003eJ ORTHOP SURG RES\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e 290 (2023).\u003c/li\u003e\n\u003cli\u003eLiu, C., Liang, D., Xiao, K. \u0026amp; Xie, L., Association between the triglyceride-glucose index and all-cause and CVD mortality in the young population with diabetes. \u003cem\u003eCARDIOVASC DIABETOL\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e 171 (2024).\u003c/li\u003e\n\u003cli\u003eWang, J.\u003cem\u003e et al.\u003c/em\u003e, The relationship between obesity associated weight-adjusted waist index and the prevalence of hypertension in US adults aged \u0026ge;60\u0026thinsp;years: a brief report. \u003cem\u003eFront Public Health\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e 1210669 (2023).\u003c/li\u003e\n\u003cli\u003eStandards of medical care in diabetes--2013. \u003cem\u003eDIABETES CARE\u003c/em\u003e \u003cstrong\u003e36 Suppl 1\u003c/strong\u003e S11 (2013).\u003c/li\u003e\n\u003cli\u003eZhang, Q., Xiao, S., Jiao, X. \u0026amp; Shen, Y., The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. \u003cem\u003eCARDIOVASC DIABETOL\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e 279 (2023).\u003c/li\u003e\n\u003cli\u003eJohnson, C. L.\u003cem\u003e et al.\u003c/em\u003e, National health and nutrition examination survey: analytic guidelines, 1999-2010. \u003cem\u003eVital Health Stat 2\u003c/em\u003e 1 (2013).\u003c/li\u003e\n\u003cli\u003eYu, X., Cao, L. \u0026amp; Yu, X., Elevated cord serum manganese level is associated with a neonatal high ponderal index. \u003cem\u003eENVIRON RES\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e 79 (2013).\u003c/li\u003e\n\u003cli\u003eWon, K. B.\u003cem\u003e et al.\u003c/em\u003e, Relationship of insulin resistance estimated by triglyceride glucose index to arterial stiffness. \u003cem\u003eLIPIDS HEALTH DIS\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e 268 (2018).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"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":"Weight-adjusted Waist Index, Hypertension, Cardiovascular Mortality, All-cause Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5730258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5730258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the global prevalence of hypertension increasing rapidly and its inadequate treatment, this study aimed to investigate the association between weight-adjusted waist index (WWI) and mortality risks among hypertensive patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from NHANES 2001\u0026ndash;2018, we conducted a prospective cohort study of 12,146 hypertensive patients with a median follow-up of 123 months. WWI was calculated as waist circumference (cm) divided by the square root of body weight (kg). Primary outcomes were all-cause and cardiovascular disease (CVD) mortality. Analyses were adjusted for demographic characteristics, metabolic parameters, and lifestyle factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring follow-up, 3,470 all-cause deaths and 940 CVD deaths occurred. After full adjustment, each unit increase in WWI was associated with higher risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.09\u0026ndash;1.26) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.08\u0026ndash;1.38). Compared to the lowest WWI quartile, the highest quartile showed significantly increased risks for all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.17\u0026ndash;1.54) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.30\u0026ndash;2.20).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigher WWI was independently associated with increased risks of all-cause and CVD mortality among hypertensive patients, suggesting its potential value as a simple prognostic indicator.\u003c/p\u003e","manuscriptTitle":"Association Between Weight-Adjusted Waist Index and All-Cause and Cardiovascular Mortality in Hypertensive Patients: NHANES 2001–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 20:07:32","doi":"10.21203/rs.3.rs-5730258/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":"8935b780-9a48-47b7-9d3c-51de0cec054c","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T08:09:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 20:07:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5730258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5730258","identity":"rs-5730258","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}