Association of Weight-Adjusted Waist Index with All-Cause and Cardiovascular Mortality in Cardiovascular, Kidney, and Metabolic Syndrome Population: A Cohort Study Based on NHANES 1999–2018

Association of Weight-Adjusted Waist Index with All-Cause and Cardiovascular Mortality in Cardiovascular, Kidney, and Metabolic Syndrome Population: A Cohort Study Based on NHANES 1999–2018 | 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 Research Article Association of Weight-Adjusted Waist Index with All-Cause and Cardiovascular Mortality in Cardiovascular, Kidney, and Metabolic Syndrome Population: A Cohort Study Based on NHANES 1999–2018 Jing Wang, Li Fu, Ting-Ting Wang, Chun-Yu Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620530/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 Weight-adjusted waist index (WWI), an emerging obesity metric integrating body weight and waist circumference, is posited to reflect central obesity more accurately than traditional indices. This study investigated the association between WWI and all-cause and cardiovascular mortality in adults with cancer within the NHANES 1999-2018 cohort, exploring potential nonlinear relationships. Methods A total of 23,981 participants with cardiovascular, kidney, or metabolic syndrome from NHANES 1999–2018 were included, with a mean baseline age of 49.56 (SD 18.0) years. WWI was computed as waist circumference (cm) divided by the square root of weight (kg). Mortality data were obtained through linked National Death Index records until December 31, 2019. Cox proportional hazards regression models and Kaplan-Meier survival curve were used to explore associations between WWI and all-cause mortality (primary outcome) and cardiovascular mortality (secondary outcome). Results During follow-up, 3,622 deaths (15.1%) occurred. WWI showed a significant nonlinear association with all-cause mortality, with a threshold effect at 11.24. Below this inflection point, WWI was not significantly related to mortality (HR=0.98, 95% CI 0.88–1.09, P=0.6948) while above this point, higher WWI was associated with increased risk (HR=1.20, 95% CI 1.11–1.29, P<0.0001). A similar nonlinear relationship was observed for cardiovascular mortality, with threshold at 12.36 (HR below=1.09, 95% CI 0.98–1.21, P=0.0964; HR above=1.45, 95% CI 1.08–1.95, P=0.0137). In fully adjusted models, WWI was significantly associated with all-cause mortality (HR=1.11, 95% CI 1.06–1.17, P<0.0001) and cardiovascular mortality (HR=1.14, 95% CI 1.04–1.25, P=0.0037). Conclusion Elevated WWI is associated with increased all-cause and cardiovascular mortality risk among adults with cardiovascular, kidney, and metabolic syndromes, demonstrating a nonlinear threshold effect. WWI could play a key role in stratifying mortality risk in this high-risk population. Weight-adjusted waist index All-cause mortality Cardiovascular mortality NHANES Metabolic syndrome Cardiovascular disease Kidney disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity and its associated metabolic disturbances pose a significant global public health burden, contributing to elevated morbidity and mortality, especially among individuals with cardiovascular and metabolic diseases [ 17 ]. Although widely used anthropometric measures such as body mass index (BMI) and waist circumference (WC) serve as conventional indicators of obesity, their utility in predicting adverse health outcomes is limited by their inability to account for variations in body composition and fat distribution. The weight-adjusted waist index (WWI), defined as waist circumference divided by the square root of body weight, has emerged as a promising alternative [ 7 ]. By integrating central adiposity with body size adjustments, WWI demonstrates superior performance in assessing cardiometabolic risk compared to traditional indices [ 4 , 21 ]. However, its prognostic significance in high-risk populations—particularly those with cardiovascular-kidney-metabolic (CKM) syndrome—remains underexplored. CKM syndrome represents a complex, multisystem disorder characterized by bidirectional pathophysiological interactions among metabolic dysregulation, chronic kidney disease (CKD), and cardiovascular dysfunction [ 1 , 17 ]. These interconnections amplify the risk of adverse clinical outcomes, including cardiovascular mortality [ 12 ]. Despite growing recognition of WWI’s potential clinical utility, no large-scale epidemiological studies have systematically evaluated its association with mortality in CKM syndrome populations. In particular, the influence of specific metabolic factors on mortality outcomes within CKM patients continues to warrant attention [ 12 , 13 ]. Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018, this study aimed to examine the relationship between WWI and all-cause and cardiovascular mortality in adults with CKM syndrome, with particular attention to potential nonlinear associations [ 8 , 25 ]. Such evaluations are essential, given that previous findings highlight that traditional obesity metrics may not accurately reflect health risks, indicating the need for indices like WWI that more precisely assess central obesity and its implications for health outcomes [ 18 , 22 , 23 ]. Methods Study population and ethics Based on data sourced from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, population-based survey conducted by the National Center for Health Statistics (NCHS), this study adhered to the research protocols approved by the NCHS Research Ethics Review Board. All NHANES participants provided informed consent, with parental/legal guardian consent obtained for those under 16. The datasets analyzed are publicly accessible through the NHANES website ( https://www.cdc.gov/nchs/nhanes/index.htm ). Data were extracted from NHANES surveys conducted from 1999 to 2018, involving 23981 participants identified with cardiovascular, kidney, or metabolic syndrome conditions based on self-reported diagnoses and clinical/laboratory criteria. Participants under 18 years and those missing WWI or mortality data were excluded. A detailed participant selection flowchart illustrates the inclusion and exclusion process. ( Fig. 1 . ) Definition of weight-adjusted-waist index The Weight-Adjusted Waist Index (WWI), calculated as waist circumference divided by the square root of weight, was employed as an exposure variable in this study. This anthropometric index is specifically designed to assess central obesity. "Prior research shows that WWI could be more effective than BMI and waist circumference alone in identifying individuals at higher risk of mortality CKM Syndrome Assessment Chronic Cardiovascular-Kidney-Metabolic (CKM) syndrome is characterized by the concurrent presence of cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic syndrome (MS), either in subclinical or clinical forms. Clinical CVD was defined based on documented histories of myocardial infarction, chronic heart failure, stroke, or coronary heart disease. Subclinical CVD was identified in participants with a calculated 10-year CVD risk of ≥ 20% or those exhibiting high-risk CKD. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and CKD stages were classified according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Metabolic syndrome components comprised overweight or obesity, abdominal obesity, prediabetes, diabetes, hypertension, and dyslipidemia. To address the heterogeneity in disease burden, participants were stratified into five CKM stages as follows: Stage 0 included individuals without any CKM risk factors; Stage 1 comprised those with isolated obesity or prediabetes; Stage 2 included participants with additional components of metabolic syndrome (excluding criteria of Stage 1) or mild to moderate CKD; Stage 3 was defined by the presence of subclinical CVD, as determined by the American Heart Association PREVENT risk equations; and Stage 4 represented individuals with established clinical CVD. Outcomes Participant survival status was determined using the NHANES public-use linked mortality file. Mortality follow-up was censored at December 31, 2019, as the NHANES National Death Index updates its records quadrennially, with the latest available follow-up data extending only through this date. All-cause mortality and cardiovascular disease-specific mortality (classified by ICD-10 codes) were analyzed. Covariates A comprehensive set of covariates were included to control for potential confounding in the association between WWI and mortality outcomes. These covariates were selected based on prior literature. Demographic variables included age (years), sex (male, female), and ethnicity, categorized as Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, and Other Race.Marital status was classified as married or living with partner, widowed/divorced/separated, never married, and missing. Socioeconomic status was assessed by education level, classified into below high school, high school graduate, and above high school, as well as poverty income ratio (PIR), categorized as poor, nearly poor, middle income, high income, and missing. Lifestyle factors included smoking status (never, former, current), alcohol consumption (never, former, mild, moderate, heavy, missing), and total physical activity expressed in metabolic equivalent tasks per week (MET/week), stratified as less than 600, equal to or greater than 600, or missing.Clinical variables comprised the presence of hypertension and diabetes mellitus (no diabetes, diabetes, impaired fasting glucose, impaired glucose tolerance). Additionally, participants were categorized according to CKM syndrome stages (0 through 4) reflecting increasing severity of cardiovascular, kidney, and metabolic disorders. Anthropometric measures included baseline weight (kg), waist circumference (cm), and calculated WWI. Statistical analysis Baseline characteristics were summarized using descriptive statistics. Continuous variables are presented as means ± standard deviations, and categorical variables as frequencies (percentages). Differences in baseline characteristics across Weight-Waist Index (WWI) tertiles (T1, T2, T3) were assessed using one-way ANOVA for continuous variables (e.g., Age, Weight, Waist, Weight-Waist ) and Chi-square tests for categorical variables (e.g.,Sex, Ethnicity, Education level, Smoking status, Marital status, Poverty income ratio, Alcohol use, Total physical activity (MET/week), Hypertension, Diabetes Mellitus, and CKM). A P-value < 0.05 was considered statistically significant. Cox proportional hazards regression models were employed to evaluate the association between WWI and mortality, with WWI analyzed both as continuous and categorical variables. The models were sequentially adjusted for covariates: Model I incorporated basic demographic variables, while Model II further adjusted for lifestyle and clinical confounders. To assess potential non-linear relationships, we performed segmented Cox regression to identify threshold effects of WWI on mortality risk. The optimal breakpoint (K) was determined by maximizing the log-likelihood function. The segmented model was compared to a linear Cox model using likelihood ratio tests [ 2 , 10 , 14 , 16 ]. Survival probabilities for all-cause and cardiovascular mortality across WWI groups were estimated using the Kaplan–Meier method, with the log-rank test evaluating between-group differences. All analyses were conducted using EmpowerStats (X&Y Solutions, Inc., Boston, MA) and R software (version 4.2.0). A two-sided P -value < 0.05 was considered statistically significant. Results Baseline characteristics of the study participants The study population was stratified into tertiles based on the Weight-Waist Index (WWI): low (n = 7994), middle (n = 7993), and high (n = 7994). Significant differences among the tertiles were observed in demographic, anthropometric, lifestyle, socioeconomic, and clinical characteristics (all p-values < 0.001). The mean age increased progressively across the tertiles (39.82 ± 14.97, 50.18 ± 16.65, and 58.69 ± 17.15 years for low, middle, and high WWI groups, respectively). Similarly, weight, waist circumference, and WWI values were significantly higher in the upper tertiles. The proportion of females increased along with WWI tertiles (43.08%, 48.66%, and 63.18%), whereas males decreased. Ethnic distribution varied, with a higher prevalence of Mexican American and Other Hispanic participants in the higher tertiles and a reduced proportion of Non-Hispanic Black individuals. Educational attainment inversely correlated with WWI tertiles, with lower education levels predominating in the high WWI group. Smoking status and alcohol consumption patterns also differed significantly, as did marital status and poverty income ratio, with higher WWI tertiles having greater proportions of widowed/divorced/separated individuals and lower income categories. Physical activity levels, assessed by METs per week, declined across tertiles. Clinically, the prevalence of hypertension, diabetes mellitus, impaired fasting glycemia, impaired glucose tolerance, and chronic kidney disease stages 3 and 4 increased significantly with higher WWI tertiles. Duration of follow-up was inversely related to WWI group. These findings highlight a consistent association between increased WWI and adverse demographic, lifestyle, and metabolic health profiles( Table 1 ). Table 1 Baseline Characteristics Stratified by Weight-Waist Index Tertiles Weight-Waist Index tertile Total Low Middle High P-value P-value* N 7994 7993 7994 Age (years) 49.56 ± 18.02 39.82 ± 14.97 50.18 ± 16.65 58.69 ± 17.15 < 0.001 < 0.001 Weight(kg) 80.84 ± 20.70 74.86 ± 17.27 81.74 ± 19.92 85.92 ± 22.99 < 0.001 < 0.001 Waist(cm) 98.63 ± 15.97 86.91 ± 11.11 98.97 ± 12.13 110.03 ± 15.06 < 0.001 < 0.001 Weight-Waist Index 11.03 ± 0.85 10.10 ± 0.43 11.02 ± 0.21 11.97 ± 0.46 < 0.001 < 0.001 Time (months) 117.43 ± 66.46 129.64 ± 67.58 118.68 ± 65.96 103.98 ± 63.26 < 0.001 < 0.001 Sex < 0.001 - Male 11597 (48.36%) 4550 (56.92%) 4104 (51.34%) 2943 (36.82%) Female 12384 (51.64%) 3444 (43.08%) 3889 (48.66%) 5051 (63.18%) Ethnicity < 0.001 - Non-Hispanic White 10570 (44.08%) 3498 (43.76%) 3453 (43.20%) 3619 (45.27%) Non-Hispanic Black 4947 (20.63%) 2170 (27.15%) 1499 (18.75%) 1278 (15.99%) Mexican American 4246 (17.71%) 946 (11.83%) 1570 (19.64%) 1730 (21.64%) Other Hispanic 2019 (8.42%) 537 (6.72%) 703 (8.80%) 779 (9.74%) Other Race 2199 (9.17%) 843 (10.55%) 768 (9.61%) 588 (7.36%) Education level < 0.001 - Below high school 2905 (12.13%) 451 (5.64%) 906 (11.35%) 1548 (19.40%) High school 9137 (38.15%) 2817 (35.26%) 3113 (39.00%) 3207 (40.19%) Above high school 11910 (49.72%) 4722 (59.10%) 3964 (49.66%) 3224 (40.41%) Smoking status < 0.001 - never 12922 (53.94%) 4510 (56.49%) 4242 (53.10%) 4170 (52.22%) former 6010 (25.09%) 1453 (18.20%) 2126 (26.61%) 2431 (30.44%) now 5026 (20.98%) 2021 (25.31%) 1620 (20.28%) 1385 (17.34%) Marital < 0.001 - Married/Living with Partner 14505 (60.49%) 4580 (57.29%) 5240 (65.56%) 4685 (58.61%) Widowed/Divorced/Separated 5169 (21.55%) 1146 (14.34%) 1605 (20.08%) 2418 (30.25%) Never married 4069 (16.97%) 2180 (27.27%) 1065 (13.32%) 824 (10.31%) Missing 238 (0.99%) 88 (1.10%) 83 (1.04%) 67 (0.84%) Poverty income ratio < 0.001 - Poor 4451 (18.56%) 1316 (16.46%) 1403 (17.55%) 1732 (21.67%) Nearly poor 5819 (24.27%) 1671 (20.90%) 1909 (23.88%) 2239 (28.01%) Middle income 5946 (24.79%) 2042 (25.54%) 1978 (24.75%) 1926 (24.09%) High income 5666 (23.63%) 2326 (29.10%) 2007 (25.11%) 1333 (16.68%) Missing 2099 (8.75%) 639 (7.99%) 696 (8.71%) 764 (9.56%) Alcohol use < 0.001 - Never 3110 (12.97%) 764 (9.56%) 977 (12.22%) 1369 (17.13%) Former 3895 (16.24%) 812 (10.16%) 1280 (16.01%) 1803 (22.55%) Mild 7312 (30.49%) 2673 (33.44%) 2489 (31.14%) 2150 (26.90%) Moderate 3182 (13.27%) 1291 (16.15%) 1055 (13.20%) 836 (10.46%) Heavy 4321 (18.02%) 1794 (22.44%) 1467 (18.35%) 1060 (13.26%) Missing 2161 (9.01%) 660 (8.26%) 725 (9.07%) 776 (9.71%) Total physical activity(MET/week) < 0.001 - =600 11637 (48.53%) 4567 (57.13%) 3953 (49.46%) 3117 (38.99%) Missing 6489 (27.06%) 1366 (17.09%) 2069 (25.89%) 3054 (38.20%) Hypertension < 0.001 - No 13905 (58.00%) 6100 (76.32%) 4672 (58.47%) 3133 (39.20%) Yes 10071 (42.00%) 1893 (23.68%) 3318 (41.53%) 4860 (60.80%) Diabetes Mellitus < 0.001 - No 15448 (66.31%) 6774 (86.00%) 5172 (66.43%) 3502 (45.87%) Diabetes Mellitus 4488 (19.26%) 441 (5.60%) 1345 (17.27%) 2702 (35.39%) IFG(Impaired Fasting Glycaemia) 2084 (8.94%) 402 (5.10%) 812 (10.43%) 870 (11.39%) IGT(Impaired Glucose Tolerance) 1278 (5.49%) 260 (3.30%) 457 (5.87%) 561 (7.35%) CKM < 0.001 - 0 2267 (9.45%) 1835 (22.95%) 383 (4.79%) 49 (0.61%) 1 4456 (18.58%) 2076 (25.97%) 1605 (20.08%) 775 (9.69%) 2 13024 (54.31%) 3652 (45.68%) 4702 (58.83%) 4670 (58.42%) 3 1383 (5.77%) 102 (1.28%) 401 (5.02%) 880 (11.01%) 4 2851 (11.89%) 329 (4.12%) 902 (11.28%) 1620 (20.27%) Data are presented as mean ± standard deviation for continuous variables and number (percentage) for categorical variables. Differences among Weight-Waist Index tertiles were assessed using ANOVA for continuous variables and chi-square tests for categorical variables. P-values represent overall group comparisons; adjusted P-values (P-value*) account for potential confounders. WWI: Weight-Waist Index; IFG: Impaired Fasting Glycaemia; IGT: Impaired Glucose Tolerance; CKM: Chronic Kidney Disease stage (0–4); MET: Metabolic Equivalent of Task. The association between FRI with All-Cause and Cardiovascular Mortality The association between the Weight-Waist Index and mortality outcomes was assessed using two models. In Model I, which assumed a linear relationship, each unit increase in the index was significantly associated with higher risk of all-cause mortality (HR 1.11, 95% CI 1.06–1.17, p < 0.0001) and cardiovascular mortality (HR 1.14, 95% CI 1.04–1.25, p = 0.0037). Model II, incorporating a threshold effect, identified inflection points at 11.24 for all-cause mortality and 12.36 for cardiovascular mortality. Below the threshold, the association was not statistically significant for either outcome (all-cause mortality HR 0.98, 95% CI 0.88–1.09, p = 0.6948; cardiovascular mortality HR 1.09, 95% CI 0.98–1.21, p = 0.0964). Above the threshold, the hazard ratios increased markedly and significantly (all-cause mortality HR 1.20, 95% CI 1.11–1.29, p < 0.0001; cardiovascular mortality HR 1.45, 95% CI 1.08–1.95, p = 0.0137). The difference in effect sizes between the above- and below-threshold segments was statistically significant for all-cause mortality (HR 1.22, 95% CI 1.06–1.41, p = 0.0072) but not for cardiovascular mortality (HR 1.33, 95% CI 0.95–1.87, p = 0.1007). The log-likelihood ratio test supported the threshold model over the linear model for all-cause mortality (p = 0.008), while no significant improvement was observed for cardiovascular mortality (p = 0.112). These results suggest a nonlinear relationship between Weight-Waist Index and mortality risk, with stronger associations observed above specific cutoff points. Table 2 Associations of Weight-Waist Index(WWI)with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models Outcome: All-cause mortality Cardiovascular mortality 模型 I 一条直线效应 1.11 (1.06, 1.17) < 0.0001 1.14 (1.04, 1.25) 0.0037 模型 II 折点(K) 11.24 12.36 K 段效应 2 1.20 (1.11, 1.29) < 0.0001 1.45 (1.08, 1.95) 0.0137 2与1的效应差 1.22 (1.06, 1.41) 0.0072 1.33 (0.95, 1.87) 0.1007 对数似然比检验 0.008 0.112 The model was adjusted for the following variables: age (years), sex, ethnicity, education level, smoking status, marital status, poverty income ratio (PIR), alcohol use, total physical activity (MET/week), hypertension, diabetes mellitus (DM), and chronic kidney disease (CKD). Figure 1 . demonstrates a nonlinear relationship between the Weight-Waist Index (WWI) and the hazard of all-cause mortality. The probability of all-cause mortality remains relatively stable at lower WWI values but begins to increase sharply after approximately a WWI of 11.2. This inflection suggests a threshold effect, consistent with the statistical models, indicating that elevated WWI beyond this point is associated with a significantly increased risk of all-cause mortality. The curve displays a marked upward trajectory at higher WWI levels, highlighting the potential impact of excessive adiposity combined with central fat distribution on mortality risk. Similarly , Fig. 2 . illustrates the relationship between WWI and cardiovascular mortality reveals a threshold pattern. The risk of cardiovascular mortality shows minimal change at lower WWI values but increases notably beyond a WWI near 12.3. This nonlinear association underscores the stronger link between elevated WWI and cardiovascular-specific mortality at higher index values. Although the increase appears pronounced after the threshold, the slope is steeper compared to the all-cause mortality curve, suggesting a more sensitive relationship of cardiovascular outcomes to central adiposity relative to overall mortality. The associations between Weight-Waist Index (WWI) and mortality were evaluated using two multivariable models: Adjust I and Adjust II. In Adjust I, higher WWI was significantly associated with increased risks of all-cause mortality (HR 1.24, 95% CI 1.18–1.30, p < 0.0001) and cardiovascular mortality (HR 1.33, 95% CI 1.23–1.45, p < 0.0001). When categorized into tertiles, participants in the highest WWI tertile had significantly elevated all-cause mortality risk (HR 1.39, 95% CI 1.25–1.54, p < 0.0001) and cardiovascular mortality risk (HR 1.62, 95% CI 1.32–1.97, p < 0.0001) compared to the lowest tertile. After further adjustment in Adjust II, the associations were attenuated but remained statistically significant for all-cause mortality—continuous WWI HR 1.11 (95% CI 1.06–1.17, p < 0.0001) and highest tertile HR 1.12 (95% CI 1.01–1.25, p = 0.0382). However, the associations with cardiovascular mortality in Adjust II were no longer statistically significant (continuous WWI HR 1.14, 95% CI 1.04–1.25, p = 0.0037; highest tertile HR 1.18, 95% CI 0.97–1.45, p = 0.1043). These findings demonstrate that higher WWI levels are associated with increased all-cause mortality, while showing a more limited or non-significant relationship with cardiovascular mortality after full covariate adjustment. Table 4 Associations Between Weight-Waist Index and Mortality Outcomes Assessed by Cox Proportional Hazards Models Exposure Adjust I Adjust II All-cause mortality Weight-Waist Index 1.24 (1.18, 1.30) < 0.0001 1.11 (1.06, 1.17) < 0.0001 Weight-Waist Index tertile Low 1.0 1.0 Middle 1.11 (1.00, 1.24) 0.0467 1.00 (0.90, 1.12) 0.9977 High 1.39 (1.25, 1.54) < 0.0001 1.12 (1.01, 1.25) 0.0382 P for trend < 0.0001 0.0059 Cardiovascular mortality Weight-Waist Index 1.33 (1.23, 1.45) < 0.0001 1.14 (1.04, 1.25) 0.0037 Weight-Waist Index tertile Low 1.0 1.0 Middle 1.31 (1.07, 1.61) 0.0086 1.10 (0.90, 1.35) 0.3663 High 1.62 (1.32, 1.97) < 0.0001 1.18 (0.97, 1.45) 0.1043 P for trend < 0.0001 0.0852 Hazard ratios (HR) and 95% confidence intervals (CI) derived from Cox proportional hazards models assessing the association of Weight-Waist Index (WWI), both as a continuous variable and categorical tertiles, with all-cause and cardiovascular mortality. Adjust I model includes basic demographic covariates; Adjust II further adjusts for additional confounders related to lifestyle and clinical factors. P-values indicate statistical significance of hazard ratios. Survival analysis The Kaplan-Meier survival curves for all-cause and cardiovascular mortality stratified by Weight-Waist Index (WWI) tertiles demonstrate clear differences in cumulative hazard over time. For all-cause mortality( Figure 3 A ), the high WWI tertile group exhibits a consistently higher cumulative hazard compared to the middle and low tertiles, indicating increased mortality risk with higher WWI values. Similarly, the cardiovascular mortality curve༈ Figure 3 B ༉shows that individuals in the high WWI tertile have a greater cumulative hazard than those in the lower tertiles, although the separation between curves appears less pronounced than for all-cause mortality. These survival analyses visually confirm the link between higher WWI and greater mortality risk, aligning with findings from Cox regression analyses and indicating that WWI serves as a reliable marker of mortality outcomes in the studied population. Discussion This large cohort study demonstrates that WWI is non-linearly associated with all-cause and cardiovascular mortality among adults with cardiovascular, kidney, and metabolic syndromes. A threshold effect exists, where only WWI values above 11.24 for all-cause mortality and 12.36 for cardiovascular mortality confer increased risk. The first study investigated the association between WWI and all-cause mortality in American individuals with psoriasis using data from five NHANES cycles (2003–2014). The final sample included 577 participants diagnosed with psoriasis, with mortality outcomes followed through December 2019. Using Cox proportional hazards models and piecewise regression, the study identified a significant nonlinear, L-shaped relationship between WWI and all-cause mortality, with a critical inflection point at 10.50 cm/√kg. Above this threshold, each unit increase in WWI was associated with a 71% higher risk of death. The results remained robust after extensive adjustment for demographic, socioeconomic, and clinical covariates. The study found that higher WWI is strongly associated with increased mortality risk in psoriasis patients, indicating that WWI could serve as a more effective measure than BMI or waist circumference for assessing risk in this population [ 3 ]. The second research focused on a large cohort of 4,463 US cancer survivors from NHANES (1999–2018) to assess the relationship between WWI and both all-cause and cardiovascular mortality, monitored until the end of 2019. Cancer survivors with higher WWI levels demonstrated significantly increased risks of all-cause mortality (HR = 1.13 per unit increase in WWI) and cardiovascular mortality (HR = 1.39), with these associations persisting after adjusting for demographic, lifestyle, and clinical confounders. Notably, the relationship was stronger in male survivors compared to females. The study highlighted the utility of WWI as an accessible, sensitive measure for identifying cancer survivors at elevated risk of mortality [ 11 ]. The third study utilized data from NHANES (1999–2018) and the UK Biobank to explore the association of WWI with all-cause and cardiovascular mortality in individuals diagnosed with type 2 diabetes. The combined analysis included 6,825 NHANES and 31,615 UK Biobank participants. Cox proportional hazards models revealed that participants in the highest WWI quartile had significantly higher risks of all-cause mortality (NHANES HR = 1.73; UKB HR = 1.85) and cardiovascular mortality (NHANES HR = 1.72; UKB HR = 2.12) compared to those in the lowest quartile, controlling for a comprehensive set of covariates. Restricted cubic spline analyses indicated a positive, near-linear association between WWI and mortality risks. These findings indicate that WWI is associated with adverse outcomes in type 2 diabetes, outperforming traditional adiposity measures, and may enhance risk assessment in diabetic populations [ 24 ]. Our study confirms and extends previous research by demonstrating that WWI is non-linearly associated with all-cause and cardiovascular mortality in adults with cardiovascular, kidney, and metabolic syndromes. Consistent with earlier findings [ 20 ], we observed threshold effects at WWI values of 11.24 for all-cause mortality and 12.36 for cardiovascular mortality, beyond which mortality risk significantly increases. This nonlinear pattern aligns with results from studies in more specific populations, such as psoriasis patients, underscoring WWI’s utility as a prognostic measure. Compared to prior studies involving cancer survivors and individuals with type 2 diabetes, our inclusion of a larger, multi-morbid cohort from NHANES (n = 23,981) enhances the generalizability of WWI’s prognostic value across chronic diseases. Differences in threshold values and association strengths may reflect variations in population characteristics, disease processes, and analytic methods. Notably, our use of piecewise Cox models allowed detection of inflection points that were not explicitly reported in previous research. The possible mechanisms of the relationship between WWI and mortality are as follows: First, visceral adipose tissue secretes pro-inflammatory cytokines (e.g., TNF-α, IL-6), which impair insulin signaling pathways, thereby increasing the risk of type 2 diabetes mellitus [ 19 ]. Additionally, visceral fat enhances the release of free fatty acids, contributing to elevated triglycerides and reduced HDL-cholesterol levels, which collectively promote atherosclerosis [ 15 ]. Second, adipose tissue, particularly visceral fat, produces pro-inflammatory mediators (e.g., CRP, TNF-α), inducing low-grade chronic inflammation. This inflammatory state is implicated in the pathogenesis of cardiovascular diseases and cancer [ 9 ]. Third, visceral adiposity disrupts the secretion of adipokines such as leptin and adiponectin, exacerbating metabolic syndrome and elevating cardiovascular risk [ 6 ]. Fourth, excessive visceral fat may exert mechanical compression on vital organs (e.g., liver, kidneys, heart), potentially compromising their function [ 5 ]. Limitations As an observational study, it cannot establish causality, and future prospective studies with larger sample sizes are needed to clarify the causal relationship. Similar to other observational studies, although we adjusted for covariates, residual confounding factors may still exist. Additionally, since the NHANES data were derived from the U.S. population, our findings regarding the association between WWI and mortality are limited to the cardiovascular, kidney, and metabolic syndrome (CKM) population in the United States. Further validation using data from other countries is warranted. The inherent limitations of questionnaire-based surveys (e.g., recall bias and misreporting) may also affect the accuracy of certain self-reported variables. These issues should be addressed in future research. Conclusions In summary, this study demonstrates elevated WWI is associated with increased all-cause and cardiovascular mortality risk in individuals with cardiovascular, kidney, and metabolic syndromes, with nonlinear threshold effects. Further prospective studies are needed to validate optimal WWI cut-off points and elucidate underlying mechanisms governing its nonlinear relationship with mortality. Declarations Author contributions Jing Wang contributed to the study conception and design, as well as the acquisition, analysis, and interpretation of data. Jing Wang also drafted the manuscript. Li Fu and Tingting Wang were responsible for the interpretation of the results. ChunYu Han provided supervision and revised the manuscript. All authors reviewed and approved the final version of the manuscript. As the guarantor of this work, Jing Wang had full access to all study data and takes responsibility for the integrity of the data and the accuracy of the data analysis.The authors reported no conflicts of interest. Funding This work did not receive any financial support. Data Availability The data underlying the results presented in the study are available from“https://www.cdc.gov/nchs/nhanes/”. All relevant data are available without restrictions to ensure the reproducibility of the study. The data used in this study are publicly available from the NHANES database (https://www.cdc.gov/nchs/nhanes/default.aspx) and the National Death Index (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm). Consent for publication All participants agreed to publish. Competing interests The authors declare no competing interests. Conflict of interest The authors declare no conflict of interest regarding the publication of this. Ethics statement The NHANES study protocol was reviewed and approved by the Ethics Review Board (formerly the Institutional Review Board) of the National Center for Health Statistics. Written informed consent was obtained from all participants prior to data collection. Publicly available de-identified datasets were authorized for dissemination through the official NHANES website. The authors assume full responsibility for the content of this publication. References Ärnlöv J, Ingelsson E, Sundström J, Lind L (2010) Impact of Body Mass Index and the Metabolic Syndrome on the Risk of Cardiovascular Disease and Death in Middle-Aged Men. Circulation 121(2):230-236 Cen J, Han Y, Liu Y, Hu H (2022) Evaluated Glomerular Filtration Rate Is Associated With Non-Alcoholic Fatty Liver Disease: A 5-Year Longitudinal Cohort Study in Chinese Non-Obese People. 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Frontiers in Cardiovascular Medicine 9 Ye J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, Li K (2023) Association Between the Weight-Adjusted Waist Index and Stroke: A Cross-Sectional Study. BMC Public Health 23(1) Zhang Q, Zhai Y, Wang J, Han X, Si W, Zhou Y (2025) Association Between Weight-Adjusted Waist Index and Obstructive Sleep Apnea Among Adults in the United States: Data From NHANES (2005–2008 and 2015–2018). Journal of International Medical Research 53(1) Zhang TY, Zhang ZM, Wang XN, Kuang HY, Xu Q, Li HX, Xu CY, Zhao KQ, Zhang C, Hao M (2024) Relationship between weight‐adjusted‐waist index and all‐cause and cardiovascular mortality in individuals with type 2 diabetes. Diabetes, Obesity and Metabolism 26(12):5621-5629 Zhou H, Su H, Gong Y, Chen L, Xu L, Chen GQ, Tong P (2024) The Association Between Weight-Adjusted-Waist Index and Sarcopenia in Adults: A Population-Based Study. Scientific Reports 14(1) Additional Declarations No competing interests reported. Supplementary Files Supplementary.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Abbreviation: NHANES, National Health and Nutrition Examination Survey.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/92739ebc05e5dd0f06a398b6.png"},{"id":93251120,"identity":"2d14067a-ea50-47f4-9059-f2048e43ac13","added_by":"auto","created_at":"2025-10-10 15:46:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. Association Between Weight-Waist Index and Risk of All-Cause Mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/1a3b85d711ca2f344c77b507.png"},{"id":93251117,"identity":"c435595d-e093-4472-926d-629bedb70f73","added_by":"auto","created_at":"2025-10-10 15:46:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. Association Between Weight-Waist Index and Risk of Cardiovascular Mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/6b3c6ef2a932a5b621349df5.png"},{"id":93251130,"identity":"9e7fb9b8-1da9-4602-bb0a-c262273b2053","added_by":"auto","created_at":"2025-10-10 15:46:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309416,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/ab4891f86786e6c375bd4b83.png"},{"id":95491137,"identity":"6806d8d6-9d37-488b-996d-fabdc566e673","added_by":"auto","created_at":"2025-11-10 02:38:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1672294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/bb7d0016-974f-4501-95fc-33cfdb7becdc.pdf"},{"id":93252353,"identity":"10aabd89-5138-4ea4-9be9-864f0b893a10","added_by":"auto","created_at":"2025-10-10 15:54:18","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":110592,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.doc","url":"https://assets-eu.researchsquare.com/files/rs-7620530/v1/ef9aa674a1a3d36a2811c9e5.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Weight-Adjusted Waist Index with All-Cause and Cardiovascular Mortality in Cardiovascular, Kidney, and Metabolic Syndrome Population: A Cohort Study Based on NHANES 1999–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eObesity and its associated metabolic disturbances pose a significant global public health burden, contributing to elevated morbidity and mortality, especially among individuals with cardiovascular and metabolic diseases\u003c/span\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlthough widely used anthropometric measures such as body mass index (BMI) and waist circumference (WC) serve as conventional indicators of obesity, their utility in predicting adverse health outcomes is limited by their inability to account for variations in body composition and fat distribution.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe weight-adjusted waist index (WWI), defined as waist circumference divided by the square root of body weight, has emerged as a promising alternative\u003c/span\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBy integrating central adiposity with body size adjustments, WWI demonstrates superior performance in assessing cardiometabolic risk compared to traditional indices\u003c/span\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, its prognostic significance in high-risk populations\u0026mdash;particularly those with cardiovascular-kidney-metabolic (CKM) syndrome\u0026mdash;remains underexplored.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCKM syndrome represents a complex, multisystem disorder characterized by bidirectional pathophysiological interactions among metabolic dysregulation, chronic kidney disease (CKD), and cardiovascular dysfunction\u003c/span\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThese interconnections amplify the risk of adverse clinical outcomes, including cardiovascular mortality\u003c/span\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDespite growing recognition of WWI\u0026rsquo;s potential clinical utility, no large-scale epidemiological studies have systematically evaluated its association with mortality in CKM syndrome populations. In particular, the influence of specific metabolic factors on mortality outcomes within CKM patients continues to warrant attention\u003c/span\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eUsing data from the National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2018, this study aimed to examine the relationship between WWI and all-cause and cardiovascular mortality in adults with CKM syndrome, with particular attention to potential nonlinear associations\u003c/span\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSuch evaluations are essential, given that previous findings highlight that traditional obesity metrics may not accurately reflect health risks, indicating the need for indices like WWI that more precisely assess central obesity and its implications for health outcomes\u003c/span\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population and ethics\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e Based on data sourced from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, population-based survey conducted by the National Center for Health Statistics (NCHS), this study adhered to the research protocols approved by the NCHS Research Ethics Review Board. All NHANES participants provided informed consent, with parental/legal guardian consent obtained for those under 16. The datasets analyzed are publicly accessible through the NHANES website (\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData were extracted from NHANES surveys conducted from 1999 to 2018, involving 23981 participants identified with cardiovascular, kidney, or metabolic syndrome conditions based on self-reported diagnoses and clinical/laboratory criteria. Participants under 18 years and those missing WWI or mortality data were excluded. A detailed participant selection flowchart illustrates the inclusion and exclusion process. (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eDefinition of weight-adjusted-waist index\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe Weight-Adjusted Waist Index (WWI), calculated as waist circumference divided by the square root of weight, was employed as an exposure variable in this study. This anthropometric index is specifically designed to assess central obesity. \"Prior research shows that WWI could be more effective than BMI and waist circumference alone in identifying individuals at higher risk of mortality\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eCKM Syndrome Assessment\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChronic Cardiovascular-Kidney-Metabolic (CKM) syndrome is characterized by the concurrent presence of cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic syndrome (MS), either in subclinical or clinical forms. Clinical CVD was defined based on documented histories of myocardial infarction, chronic heart failure, stroke, or coronary heart disease. Subclinical CVD was identified in participants with a calculated 10-year CVD risk of \u0026ge;\u0026thinsp;20% or those exhibiting high-risk CKD. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and CKD stages were classified according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Metabolic syndrome components comprised overweight or obesity, abdominal obesity, prediabetes, diabetes, hypertension, and dyslipidemia.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTo address the heterogeneity in disease burden, participants were stratified into five CKM stages as follows: Stage 0 included individuals without any CKM risk factors; Stage 1 comprised those with isolated obesity or prediabetes; Stage 2 included participants with additional components of metabolic syndrome (excluding criteria of Stage 1) or mild to moderate CKD; Stage 3 was defined by the presence of subclinical CVD, as determined by the American Heart Association PREVENT risk equations; and Stage 4 represented individuals with established clinical CVD.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParticipant survival status was determined using the NHANES public-use linked mortality file. Mortality follow-up was censored at December 31, 2019, as the NHANES National Death Index updates its records quadrennially, with the latest available follow-up data extending only through this date. All-cause mortality and cardiovascular disease-specific mortality (classified by ICD-10 codes) were analyzed.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA comprehensive set of covariates were included to control for potential confounding in the association between WWI and mortality outcomes. These covariates were selected based on prior literature. Demographic variables included age (years), sex (male, female), and ethnicity, categorized as Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, and Other Race.Marital status was classified as married or living with partner, widowed/divorced/separated, never married, and missing. Socioeconomic status was assessed by education level, classified into below high school, high school graduate, and above high school, as well as poverty income ratio (PIR), categorized as poor, nearly poor, middle income, high income, and missing. Lifestyle factors included smoking status (never, former, current), alcohol consumption (never, former, mild, moderate, heavy, missing), and total physical activity expressed in metabolic equivalent tasks per week (MET/week), stratified as less than 600, equal to or greater than 600, or missing.Clinical variables comprised the presence of hypertension and diabetes mellitus (no diabetes, diabetes, impaired fasting glucose, impaired glucose tolerance). Additionally, participants were categorized according to CKM syndrome stages (0 through 4) reflecting increasing severity of cardiovascular, kidney, and metabolic disorders. Anthropometric measures included baseline weight (kg), waist circumference (cm), and calculated WWI.\u003c/span\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline characteristics were summarized using descriptive statistics. Continuous variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables as frequencies (percentages). Differences in baseline characteristics across Weight-Waist Index (WWI) tertiles (T1, T2, T3) were assessed using one-way ANOVA for continuous variables (e.g., Age, Weight, Waist, Weight-Waist ) and Chi-square tests for categorical variables (e.g.,Sex, Ethnicity, Education level, Smoking status, Marital status, Poverty income ratio, Alcohol use, Total physical activity (MET/week), Hypertension, Diabetes Mellitus, and CKM). A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCox proportional hazards regression models were employed to evaluate the association between WWI and mortality, with WWI analyzed both as continuous and categorical variables. The models were sequentially adjusted for covariates: Model I incorporated basic demographic variables, while Model II further adjusted for lifestyle and clinical confounders.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTo assess potential non-linear relationships, we performed segmented Cox regression to identify threshold effects of WWI on mortality risk. The optimal breakpoint (K) was determined by maximizing the log-likelihood function. The segmented model was compared to a linear Cox model using likelihood ratio tests\u003c/span\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSurvival probabilities for all-cause and cardiovascular mortality across WWI groups were estimated using the Kaplan\u0026ndash;Meier method, with the log-rank test evaluating between-group differences.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAll analyses were conducted using EmpowerStats (X\u0026amp;Y Solutions, Inc., Boston, MA) and R software (version 4.2.0). A two-sided P -value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eBaseline characteristics of the study participants\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe study population was stratified into tertiles based on the Weight-Waist Index (WWI): low (n\u0026thinsp;=\u0026thinsp;7994), middle (n\u0026thinsp;=\u0026thinsp;7993), and high (n\u0026thinsp;=\u0026thinsp;7994). Significant differences among the tertiles were observed in demographic, anthropometric, lifestyle, socioeconomic, and clinical characteristics (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean age increased progressively across the tertiles (39.82\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97, 50.18\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65, and 58.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.15 years for low, middle, and high WWI groups, respectively). Similarly, weight, waist circumference, and WWI values were significantly higher in the upper tertiles. The proportion of females increased along with WWI tertiles (43.08%, 48.66%, and 63.18%), whereas males decreased. Ethnic distribution varied, with a higher prevalence of Mexican American and Other Hispanic participants in the higher tertiles and a reduced proportion of Non-Hispanic Black individuals. Educational attainment inversely correlated with WWI tertiles, with lower education levels predominating in the high WWI group. Smoking status and alcohol consumption patterns also differed significantly, as did marital status and poverty income ratio, with higher WWI tertiles having greater proportions of widowed/divorced/separated individuals and lower income categories. Physical activity levels, assessed by METs per week, declined across tertiles. Clinically, the prevalence of hypertension, diabetes mellitus, impaired fasting glycemia, impaired glucose tolerance, and chronic kidney disease stages 3 and 4 increased significantly with higher WWI tertiles. Duration of follow-up was inversely related to WWI group. These findings highlight a consistent association between increased WWI and adverse demographic, lifestyle, and metabolic health profiles(\u003c/span\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics Stratified by Weight-Waist Index Tertiles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index tertile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.82\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.18\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight(kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.84\u0026thinsp;\u0026plusmn;\u0026thinsp;20.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.74\u0026thinsp;\u0026plusmn;\u0026thinsp;19.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.92\u0026thinsp;\u0026plusmn;\u0026thinsp;22.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist(cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.63\u0026thinsp;\u0026plusmn;\u0026thinsp;15.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.97\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110.03\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.43\u0026thinsp;\u0026plusmn;\u0026thinsp;66.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.64\u0026thinsp;\u0026plusmn;\u0026thinsp;67.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118.68\u0026thinsp;\u0026plusmn;\u0026thinsp;65.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103.98\u0026thinsp;\u0026plusmn;\u0026thinsp;63.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11597 (48.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4550 (56.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4104 (51.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2943 (36.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12384 (51.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3444 (43.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3889 (48.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5051 (63.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10570 (44.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3498 (43.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3453 (43.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3619 (45.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4947 (20.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2170 (27.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1499 (18.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1278 (15.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4246 (17.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e946 (11.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1570 (19.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1730 (21.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019 (8.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e537 (6.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e703 (8.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e779 (9.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2199 (9.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e843 (10.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e768 (9.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e588 (7.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2905 (12.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451 (5.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e906 (11.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1548 (19.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9137 (38.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2817 (35.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3113 (39.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3207 (40.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11910 (49.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4722 (59.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3964 (49.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3224 (40.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12922 (53.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4510 (56.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4242 (53.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4170 (52.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eformer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6010 (25.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1453 (18.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2126 (26.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2431 (30.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5026 (20.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2021 (25.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1620 (20.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1385 (17.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with Partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14505 (60.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4580 (57.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5240 (65.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4685 (58.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5169 (21.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1146 (14.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1605 (20.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2418 (30.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4069 (16.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2180 (27.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1065 (13.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e824 (10.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238 (0.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (1.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (1.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (0.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty income ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4451 (18.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1316 (16.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1403 (17.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1732 (21.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNearly poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5819 (24.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1671 (20.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1909 (23.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2239 (28.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5946 (24.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2042 (25.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1978 (24.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1926 (24.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5666 (23.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2326 (29.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2007 (25.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1333 (16.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2099 (8.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e639 (7.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e696 (8.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e764 (9.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3110 (12.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e764 (9.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e977 (12.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1369 (17.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3895 (16.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e812 (10.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1280 (16.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1803 (22.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7312 (30.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2673 (33.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2489 (31.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2150 (26.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3182 (13.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1291 (16.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1055 (13.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e836 (10.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4321 (18.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1794 (22.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1467 (18.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1060 (13.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2161 (9.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e660 (8.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e725 (9.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e776 (9.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal physical activity(MET/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5855 (24.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2061 (25.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1971 (24.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1823 (22.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11637 (48.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4567 (57.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3953 (49.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3117 (38.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6489 (27.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1366 (17.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2069 (25.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3054 (38.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13905 (58.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6100 (76.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4672 (58.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3133 (39.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10071 (42.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1893 (23.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3318 (41.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4860 (60.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15448 (66.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6774 (86.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5172 (66.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3502 (45.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4488 (19.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441 (5.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1345 (17.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2702 (35.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFG(Impaired Fasting Glycaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2084 (8.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402 (5.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e812 (10.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e870 (11.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIGT(Impaired Glucose Tolerance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1278 (5.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260 (3.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e457 (5.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e561 (7.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2267 (9.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1835 (22.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e383 (4.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (0.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4456 (18.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2076 (25.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1605 (20.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e775 (9.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13024 (54.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3652 (45.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4702 (58.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4670 (58.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1383 (5.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (1.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e401 (5.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e880 (11.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2851 (11.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e329 (4.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e902 (11.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1620 (20.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and number (percentage) for categorical variables. Differences among Weight-Waist Index tertiles were assessed using ANOVA for continuous variables and chi-square tests for categorical variables. P-values represent overall group comparisons; adjusted P-values (P-value*) account for potential confounders. WWI: Weight-Waist Index; IFG: Impaired Fasting Glycaemia; IGT: Impaired Glucose Tolerance; CKM: Chronic Kidney Disease stage (0\u0026ndash;4); MET: Metabolic Equivalent of Task.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eThe association between FRI with All-Cause and Cardiovascular Mortality\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe association between the Weight-Waist Index and mortality outcomes was assessed using two models. In Model I, which assumed a linear relationship, each unit increase in the index was significantly associated with higher risk of all-cause mortality (HR 1.11, 95% CI 1.06\u0026ndash;1.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and cardiovascular mortality (HR 1.14, 95% CI 1.04\u0026ndash;1.25, p\u0026thinsp;=\u0026thinsp;0.0037). Model II, incorporating a threshold effect, identified inflection points at 11.24 for all-cause mortality and 12.36 for cardiovascular mortality. Below the threshold, the association was not statistically significant for either outcome (all-cause mortality HR 0.98, 95% CI 0.88\u0026ndash;1.09, p\u0026thinsp;=\u0026thinsp;0.6948; cardiovascular mortality HR 1.09, 95% CI 0.98\u0026ndash;1.21, p\u0026thinsp;=\u0026thinsp;0.0964). Above the threshold, the hazard ratios increased markedly and significantly (all-cause mortality HR 1.20, 95% CI 1.11\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; cardiovascular mortality HR 1.45, 95% CI 1.08\u0026ndash;1.95, p\u0026thinsp;=\u0026thinsp;0.0137). The difference in effect sizes between the above- and below-threshold segments was statistically significant for all-cause mortality (HR 1.22, 95% CI 1.06\u0026ndash;1.41, p\u0026thinsp;=\u0026thinsp;0.0072) but not for cardiovascular mortality (HR 1.33, 95% CI 0.95\u0026ndash;1.87, p\u0026thinsp;=\u0026thinsp;0.1007). The log-likelihood ratio test supported the threshold model over the linear model for all-cause mortality (p\u0026thinsp;=\u0026thinsp;0.008), while no significant improvement was observed for cardiovascular mortality (p\u0026thinsp;=\u0026thinsp;0.112). These results suggest a nonlinear relationship between Weight-Waist Index and mortality risk, with stronger associations observed above specific cutoff points.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Weight-Waist Index(WWI)with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome:\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiovascular mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e模型 I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e一条直线效应\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11 (1.06, 1.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14 (1.04, 1.25) 0.0037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e模型 II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e折点(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; K 段效应 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.88, 1.09) 0.6948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.09 (0.98, 1.21) 0.0964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;K 段效应 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20 (1.11, 1.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.08, 1.95) 0.0137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2与1的效应差\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.22 (1.06, 1.41) 0.0072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33 (0.95, 1.87) 0.1007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e对数似然比检验\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe model was adjusted for the following variables: age (years), sex, ethnicity, education level, smoking status, marital status, poverty income ratio (PIR), alcohol use, total physical activity (MET/week), hypertension, diabetes mellitus (DM), and chronic kidney disease (CKD).\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edemonstrates a nonlinear relationship between the Weight-Waist Index (WWI) and the hazard of all-cause mortality. The probability of all-cause mortality remains relatively stable at lower WWI values but begins to increase sharply after approximately a WWI of 11.2. This inflection suggests a threshold effect, consistent with the statistical models, indicating that elevated WWI beyond this point is associated with a significantly increased risk of all-cause mortality. The curve displays a marked upward trajectory at higher WWI levels, highlighting the potential impact of excessive adiposity combined with central fat distribution on mortality risk.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSimilarly\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eillustrates the relationship between WWI and cardiovascular mortality reveals a threshold pattern. The risk of cardiovascular mortality shows minimal change at lower WWI values but increases notably beyond a WWI near 12.3. This nonlinear association underscores the stronger link between elevated WWI and cardiovascular-specific mortality at higher index values. Although the increase appears pronounced after the threshold, the slope is steeper compared to the all-cause mortality curve, suggesting a more sensitive relationship of cardiovascular outcomes to central adiposity relative to overall mortality.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe associations between Weight-Waist Index (WWI) and mortality were evaluated using two multivariable models: Adjust I and Adjust II. In Adjust I, higher WWI was significantly associated with increased risks of all-cause mortality (HR 1.24, 95% CI 1.18\u0026ndash;1.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and cardiovascular mortality (HR 1.33, 95% CI 1.23\u0026ndash;1.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). When categorized into tertiles, participants in the highest WWI tertile had significantly elevated all-cause mortality risk (HR 1.39, 95% CI 1.25\u0026ndash;1.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and cardiovascular mortality risk (HR 1.62, 95% CI 1.32\u0026ndash;1.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to the lowest tertile. After further adjustment in Adjust II, the associations were attenuated but remained statistically significant for all-cause mortality\u0026mdash;continuous WWI HR 1.11 (95% CI 1.06\u0026ndash;1.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and highest tertile HR 1.12 (95% CI 1.01\u0026ndash;1.25, p\u0026thinsp;=\u0026thinsp;0.0382). However, the associations with cardiovascular mortality in Adjust II were no longer statistically significant (continuous WWI HR 1.14, 95% CI 1.04\u0026ndash;1.25, p\u0026thinsp;=\u0026thinsp;0.0037; highest tertile HR 1.18, 95% CI 0.97\u0026ndash;1.45, p\u0026thinsp;=\u0026thinsp;0.1043). These findings demonstrate that higher WWI levels are associated with increased all-cause mortality, while showing a more limited or non-significant relationship with cardiovascular mortality after full covariate adjustment.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations Between Weight-Waist Index and Mortality Outcomes Assessed by Cox Proportional Hazards Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjust I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjust II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24 (1.18, 1.30)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.11 (1.06, 1.17)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11 (1.00, 1.24) 0.0467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.90, 1.12) 0.9977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.39 (1.25, 1.54)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12 (1.01, 1.25) 0.0382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.33 (1.23, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14 (1.04, 1.25) 0.0037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-Waist Index tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.31 (1.07, 1.61) 0.0086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10 (0.90, 1.35) 0.3663\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.62 (1.32, 1.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.18 (0.97, 1.45) 0.1043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHazard ratios (HR) and 95% confidence intervals (CI) derived from Cox proportional hazards models assessing the association of Weight-Waist Index (WWI), both as a continuous variable and categorical tertiles, with all-cause and cardiovascular mortality. Adjust I model includes basic demographic covariates; Adjust II further adjusts for additional confounders related to lifestyle and clinical factors. P-values indicate statistical significance of hazard ratios.\u003c/span\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSurvival analysis\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe Kaplan-Meier survival curves for all-cause and cardiovascular mortality stratified by Weight-Waist Index (WWI) tertiles demonstrate clear differences in cumulative hazard over time. For all-cause mortality(\u003c/span\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e), the high WWI tertile group exhibits a consistently higher cumulative hazard compared to the middle and low tertiles, indicating increased mortality risk with higher WWI values. Similarly, the cardiovascular mortality curve༈\u003c/span\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e༉shows that individuals in the high WWI tertile have a greater cumulative hazard than those in the lower tertiles, although the separation between curves appears less pronounced than for all-cause mortality. These survival analyses visually confirm the link between higher WWI and greater mortality risk, aligning with findings from Cox regression analyses and indicating that WWI serves as a reliable marker of mortality outcomes in the studied population.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThis large cohort study demonstrates that WWI is non-linearly associated with all-cause and cardiovascular mortality among adults with cardiovascular, kidney, and metabolic syndromes. A threshold effect exists, where only WWI values above 11.24 for all-cause mortality and 12.36 for cardiovascular mortality confer increased risk.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe first study investigated the association between WWI and all-cause mortality in American individuals with psoriasis using data from five NHANES cycles (2003\u0026ndash;2014). The final sample included 577 participants diagnosed with psoriasis, with mortality outcomes followed through December 2019. Using Cox proportional hazards models and piecewise regression, the study identified a significant nonlinear, L-shaped relationship between WWI and all-cause mortality, with a critical inflection point at 10.50 cm/\u0026radic;kg. Above this threshold, each unit increase in WWI was associated with a 71% higher risk of death. The results remained robust after extensive adjustment for demographic, socioeconomic, and clinical covariates. The study found that higher WWI is strongly associated with increased mortality risk in psoriasis patients, indicating that WWI could serve as a more effective measure than BMI or waist circumference for assessing risk in this population\u003c/span\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe second research focused on a large cohort of 4,463 US cancer survivors from NHANES (1999\u0026ndash;2018) to assess the relationship between WWI and both all-cause and cardiovascular mortality, monitored until the end of 2019. Cancer survivors with higher WWI levels demonstrated significantly increased risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.13 per unit increase in WWI) and cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.39), with these associations persisting after adjusting for demographic, lifestyle, and clinical confounders. Notably, the relationship was stronger in male survivors compared to females. The study highlighted the utility of WWI as an accessible, sensitive measure for identifying cancer survivors at elevated risk of mortality\u003c/span\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe third study utilized data from NHANES (1999\u0026ndash;2018) and the UK Biobank to explore the association of WWI with all-cause and cardiovascular mortality in individuals diagnosed with type 2 diabetes. The combined analysis included 6,825 NHANES and 31,615 UK Biobank participants. Cox proportional hazards models revealed that participants in the highest WWI quartile had significantly higher risks of all-cause mortality (NHANES HR\u0026thinsp;=\u0026thinsp;1.73; UKB HR\u0026thinsp;=\u0026thinsp;1.85) and cardiovascular mortality (NHANES HR\u0026thinsp;=\u0026thinsp;1.72; UKB HR\u0026thinsp;=\u0026thinsp;2.12) compared to those in the lowest quartile, controlling for a comprehensive set of covariates. Restricted cubic spline analyses indicated a positive, near-linear association between WWI and mortality risks. These findings indicate that WWI is associated with adverse outcomes in type 2 diabetes, outperforming traditional adiposity measures, and may enhance risk assessment in diabetic populations\u003c/span\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOur study confirms and extends previous research by demonstrating that WWI is non-linearly associated with all-cause and cardiovascular mortality in adults with cardiovascular, kidney, and metabolic syndromes. Consistent with earlier findings\u003c/span\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ewe observed threshold effects at WWI values of 11.24 for all-cause mortality and 12.36 for cardiovascular mortality, beyond which mortality risk significantly increases. This nonlinear pattern aligns with results from studies in more specific populations, such as psoriasis patients, underscoring WWI\u0026rsquo;s utility as a prognostic measure. Compared to prior studies involving cancer survivors and individuals with type 2 diabetes, our inclusion of a larger, multi-morbid cohort from NHANES (n\u0026thinsp;=\u0026thinsp;23,981) enhances the generalizability of WWI\u0026rsquo;s prognostic value across chronic diseases. Differences in threshold values and association strengths may reflect variations in population characteristics, disease processes, and analytic methods. Notably, our use of piecewise Cox models allowed detection of inflection points that were not explicitly reported in previous research.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe possible mechanisms of the relationship between WWI and mortality are as follows: First, visceral adipose tissue secretes pro-inflammatory cytokines (e.g., TNF-α, IL-6), which impair insulin signaling pathways, thereby increasing the risk of type 2 diabetes mellitus\u003c/span\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdditionally, visceral fat enhances the release of free fatty acids, contributing to elevated triglycerides and reduced HDL-cholesterol levels, which collectively promote atherosclerosis\u003c/span\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSecond, adipose tissue, particularly visceral fat, produces pro-inflammatory mediators (e.g., CRP, TNF-α), inducing low-grade chronic inflammation. This inflammatory state is implicated in the pathogenesis of cardiovascular diseases and cancer\u003c/span\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThird, visceral adiposity disrupts the secretion of adipokines such as leptin and adiponectin, exacerbating metabolic syndrome and elevating cardiovascular risk\u003c/span\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFourth, excessive visceral fat may exert mechanical compression on vital organs (e.g., liver, kidneys, heart), potentially compromising their function\u003c/span\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAs an observational study, it cannot establish causality, and future prospective studies with larger sample sizes are needed to clarify the causal relationship. Similar to other observational studies, although we adjusted for covariates, residual confounding factors may still exist. Additionally, since the NHANES data were derived from the U.S. population, our findings regarding the association between WWI and mortality are limited to the cardiovascular, kidney, and metabolic syndrome (CKM) population in the United States. Further validation using data from other countries is warranted. The inherent limitations of questionnaire-based surveys (e.g., recall bias and misreporting) may also affect the accuracy of certain self-reported variables. These issues should be addressed in future research.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn summary, this study demonstrates elevated WWI is associated with increased all-cause and cardiovascular mortality risk in individuals with cardiovascular, kidney, and metabolic syndromes, with nonlinear threshold effects. Further prospective studies are needed to validate optimal WWI cut-off points and elucidate underlying mechanisms governing its nonlinear relationship with mortality.\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJing Wang contributed to the study conception and design, as well as the acquisition, analysis, and interpretation of data. Jing Wang also drafted the manuscript. Li Fu and Tingting Wang were responsible for the interpretation of the results. ChunYu Han provided supervision and revised the manuscript. All authors reviewed and approved the final version of the manuscript. As the guarantor of this work, Jing Wang had full access to all study data and takes responsibility for the integrity of the data and the accuracy of the data analysis.The authors reported no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work did not receive any financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying the results presented in the study are available from\u0026ldquo;https://www.cdc.gov/nchs/nhanes/\u0026rdquo;. All relevant data are available without restrictions to ensure the reproducibility of the study.\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the NHANES database (https://www.cdc.gov/nchs/nhanes/default.aspx) and the National Death Index (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants agreed to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest regarding the publication of this.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES study protocol was reviewed and approved by the Ethics Review Board (formerly the Institutional Review Board) of the National Center for Health Statistics. Written informed consent was obtained from all participants prior to data collection. Publicly available de-identified datasets were authorized for dissemination through the official NHANES website.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors assume full responsibility for the content of this publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026Auml;rnl\u0026ouml;v J, Ingelsson E, Sundstr\u0026ouml;m J, Lind L (2010) Impact of Body Mass Index and the Metabolic Syndrome on the Risk of Cardiovascular Disease and Death in Middle-Aged Men. Circulation 121(2):230-236\u003c/li\u003e\n\u003cli\u003eCen J, Han Y, Liu Y, Hu H (2022) Evaluated Glomerular Filtration Rate Is Associated With Non-Alcoholic Fatty Liver Disease: A 5-Year Longitudinal Cohort Study in Chinese Non-Obese People. 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Arteriosclerosis Thrombosis and Vascular Biology 27(5):996-1003\u003c/li\u003e\n\u003cli\u003eGhigliotti G, Barisione C, Garibaldi S, Fabbi P, Brunelli C, Spallarossa P, Altieri P, Rosa G, Spinella G, Palombo D, Arsenescu R, Arsenescu V (2014) Adipose Tissue Immune Response: Novel Triggers and Consequences for Chronic Inflammatory Conditions. Inflammation 37(4):1337-1353\u003c/li\u003e\n\u003cli\u003eKim KJ, Son S, Kim KJ, Kim SG, Kim NH (2023) Weight‐adjusted Waist as an Integrated Index for Fat, Muscle and Bone Health in Adults. Journal of Cachexia Sarcopenia and Muscle 14(5):2196-2203\u003c/li\u003e\n\u003cli\u003eLam BCC, Koh GCH, Chen C, Wong M, Fallows S (2015) Comparison of Body Mass Index (BMI), Body Adiposity Index (BAI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR) and Waist-to-Height Ratio (WHtR) as Predictors of Cardiovascular Disease Risk Factors in an Adult Population in Singapore. Plos One 10(4):e122985\u003c/li\u003e\n\u003cli\u003eLampe L, Zhang R, Beyer F, H\u0026uuml;hn S, Masouleh SK, Preusser S, Bazin PL, Schroeter ML, Villringer A, Witte AV (2019) Visceral Obesity Relates to Deep White Matter Hyperintensities via Inflammation. Annals of Neurology 85(2):194-203\u003c/li\u003e\n\u003cli\u003eLi Q, Han Y, Hu H, Zhuge Y (2022) Gamma-Glutamyl Transferase to High-Density Lipoprotein Cholesterol Ratio Has a Non-Linear Association With Non-Alcoholic Fatty Liver Disease: A Secondary Prospective Cohort Study in Non-Obese Chinese Adults. Frontiers in Medicine 9\u003c/li\u003e\n\u003cli\u003eLi S, Jin J, Zhang W, Cao Y, Qin H, Wang J, Yu J, Wang W (2024) Association of weight-adjusted waist index with all-cause and cause-specific mortality among cancer survivors: a cohort study of the NHANES 1999-2018. Frontiers in Endocrinology 15:1422071\u003c/li\u003e\n\u003cli\u003eLi Y, Gui J, Zhang X, Wang Y, Mei Y, Yang X, Liu H, Guo L, Li J, Lei Y, Li X, Sun L, Liu Y, Yuan T, Wang C, Zhang D, Wei H, Li J, Liu M, Hua Y, Zhang L (2023) Predicting Hypertension by Obesity- And Lipid-Related Indices in Mid-Aged and Elderly Chinese: A Nationwide Cohort Study From the China Health and Retirement Longitudinal Study. BMC Cardiovascular Disorders 23(1)\u003c/li\u003e\n\u003cli\u003eLiu W, Yang X, Zhan T, Huang M, Tian X, Xia T, Huang X (2024) Weight-Adjusted Waist Index Is Positively and Linearly Associated With All-Cause and Cardiovascular Mortality in Metabolic Dysfunction-Associated Steatotic Liver Disease: Findings From NHANES 1999-2018. Frontiers in Endocrinology 15\u003c/li\u003e\n\u003cli\u003eMo Z, Hu H, Du X, Huang Q, Chen P, Lai L, Yu Z (2022) Association of Evaluated Glomerular Filtration Rate and Incident Diabetes Mellitus: A Secondary Retrospective Analysis Based on a Chinese Cohort Study. Frontiers in Medicine 8\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;hman MK, Wright A, Wickenheiser KJ, Luo W, Eitzman DT (2009) Visceral Adipose Tissue and Atherosclerosis. Current Vascular Pharmacology 7(2):169-179\u003c/li\u003e\n\u003cli\u003eQin X, Hu H, Cen J, Wang X, Wan Q, Wei Z (2022) Association Between Urinary Protein-to-Creatinine Ratio and Chronic Kidney Disease Progression: A Secondary Analysis of a Prospective Cohort Study. Frontiers in Medicine 9\u003c/li\u003e\n\u003cli\u003eRossi JLS, Barbalho SM, Ara\u0026uacute;jo RRD, Bechara MD, Sloan KP, Sloan L (2021) Metabolic Syndrome and Cardiovascular Diseases: Going Beyond Traditional Risk Factors. 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Frontiers in Cardiovascular Medicine 9\u003c/li\u003e\n\u003cli\u003eYe J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, Li K (2023) Association Between the Weight-Adjusted Waist Index and Stroke: A Cross-Sectional Study. BMC Public Health 23(1)\u003c/li\u003e\n\u003cli\u003eZhang Q, Zhai Y, Wang J, Han X, Si W, Zhou Y (2025) Association Between Weight-Adjusted Waist Index and Obstructive Sleep Apnea Among Adults in the United States: Data From NHANES (2005\u0026ndash;2008 and 2015\u0026ndash;2018). Journal of International Medical Research 53(1)\u003c/li\u003e\n\u003cli\u003eZhang TY, Zhang ZM, Wang XN, Kuang HY, Xu Q, Li HX, Xu CY, Zhao KQ, Zhang C, Hao M (2024) Relationship between weight‐adjusted‐waist index and all‐cause and cardiovascular mortality in individuals with type 2 diabetes. Diabetes, Obesity and Metabolism 26(12):5621-5629\u003c/li\u003e\n\u003cli\u003eZhou H, Su H, Gong Y, Chen L, Xu L, Chen GQ, Tong P (2024) The Association Between Weight-Adjusted-Waist Index and Sarcopenia in Adults: A Population-Based Study. Scientific Reports 14(1)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Weight-adjusted waist index, All-cause mortality, Cardiovascular mortality, NHANES, Metabolic syndrome, Cardiovascular disease, Kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-7620530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eWeight-adjusted waist index (WWI), an emerging obesity metric integrating body weight and waist circumference, is posited to reflect central obesity more accurately than traditional indices. This study investigated the association between WWI and all-cause and cardiovascular mortality in adults with cancer within the NHANES 1999-2018 cohort, exploring potential nonlinear relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 23,981 participants with cardiovascular, kidney, or metabolic syndrome from NHANES 1999–2018 were included, with a mean baseline age of 49.56 (SD 18.0) years. WWI was computed as waist circumference (cm) divided by the square root of weight (kg). Mortality data were obtained through linked National Death Index records until December 31, 2019. Cox proportional hazards regression models and Kaplan-Meier survival curve were used to explore associations between WWI and all-cause mortality (primary outcome) and cardiovascular mortality (secondary outcome).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003e\u0026nbsp;During follow-up, 3,622 deaths (15.1%) occurred. WWI showed a significant nonlinear association with all-cause mortality, with a threshold effect at 11.24. Below this inflection point, WWI was not significantly related to mortality (HR=0.98, 95% CI 0.88–1.09, P=0.6948) while above this point, higher WWI was associated with increased risk (HR=1.20, 95% CI 1.11–1.29, P\u0026lt;0.0001). A similar nonlinear relationship was observed for cardiovascular mortality, with threshold at 12.36 (HR below=1.09, 95% CI 0.98–1.21, P=0.0964; HR above=1.45, 95% CI 1.08–1.95, P=0.0137). In fully adjusted models, WWI was significantly associated with all-cause mortality (HR=1.11, 95% CI 1.06–1.17, P\u0026lt;0.0001) and cardiovascular mortality (HR=1.14, 95% CI 1.04–1.25, P=0.0037).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eElevated WWI is associated with increased all-cause and cardiovascular mortality risk among adults with cardiovascular, kidney, and metabolic syndromes, demonstrating a nonlinear threshold effect. WWI could play a key role in stratifying mortality risk in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Association of Weight-Adjusted Waist Index with All-Cause and Cardiovascular Mortality in Cardiovascular, Kidney, and Metabolic Syndrome Population: A Cohort Study Based on NHANES 1999–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:46:14","doi":"10.21203/rs.3.rs-7620530/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":"04d6a9a2-9562-4427-bfe9-6f25aac68eb6","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T02:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:46:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620530","identity":"rs-7620530","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}