Weight-Adjusted Waist Index and Mortality Among Older Adults: Findings from NHANES 1999–2018

Weight-Adjusted Waist Index and Mortality Among Older Adults: Findings from 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 Article Weight-Adjusted Waist Index and Mortality Among Older Adults: Findings from NHANES 1999–2018 Huai Huang, Yongtian Zheng, Yijiang Li, Yinuo Bi, Jianfan Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6987746/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The weight-adjusted waist index (WWI) is a new anthropometric measure that reflects central obesity in relation to body weight. Previous studies have examined the relationship between WWI and all-cause mortality in populations with hypertension; however, evidence specifically pertaining to the elderly remains lacking. This study aims to examine the relationship between WWI and all-cause as well as cardiovascular mortality in older adults. Methods We analyzed 16,242 adults aged ≥ 60 years from the 1999–2018 National Health and Nutrition Examination Survey (NHANES). WWI was calculated from baseline examination measurements of weight and waist circumference. The association between WWI and mortality outcomes was analyzed utilizing the Kaplan–Meier survival modeling, Cox regression analysis, smooth curve fitting analysis, threshold effect analysis, and subgroup analysis. Stratification factors for subgroups included demographics (age, sex, race/ethnicity, marital status, education, poverty-income ratio) and health factors (smoking, alcohol use, physical activity, hypertension, and diabetes). Results The mean age of participants was 70.39 ± 7.32 years, with a gender distribution of 49.87% male and 50.13% female. The mean WWI was 11.51 ± 0.74 cm/√kg. There were 5,779 all-cause deaths and 1,907 cardiovascular disease (CVD) deaths. Kaplan–Meier analysis indicated significant differences in all-cause and CVD mortality across WWI categories. After adjusting for covariates, WWI was positively associated with all-cause mortality (HR = 1.072, 95%CI: 1.03, 1.11, p = 0.0006) and CVD mortality (HR = 1.09, 95%CI :1.02, 1.17, p = 0.015).These associations remained significant after full adjustment. Threshold analysis revealed an inflection point for all-cause mortality at 11.24 cm/√kg, above this threshold, the risk significantly increased (HR = 1.14, 95% CI: 1.08–1.21, p < 0.0001). For CVD mortality, the inflection point was determined to be 12.74 cm/√kg. Conclusion Among older adults in the U.S., a higher WWI is associated with increased risks of all-cause and cardiovascular mortality. This relationship is non-linear, showing minimal risk variation at lower WWI values but a marked increase in mortality risk above approximately 11cm/√kg. WWI is a significant indicator of mortality, even after adjusting for various demographic, socioeconomic, lifestyle, and health factors. Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Weight-adjusted Waist Index Mortality NHANES Cardiovascular Disease Older Adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity represents a significant and escalating public health concern, serving as a substantial risk factor for multiple chronic diseases, including cardiovascular disease (CVD), type 2 diabetes, and hypertension(1, 2). This issue is particularly acute among older adults, a demographic in which obesity is correlated with increased frailty, diminished physical function, and a higher mortality rate(3, 4). The intricacies of the relationship between obesity and mortality within this group are complex and often yield paradoxical results; findings may vary widely based on the metrics used to assess obesity(3, 5). Traditionally, body mass index (BMI) has been the primary anthropometric measure utilized to gauge obesity-related health risks. However, BMI has significant limitations, as it fails to adequately represent fat distribution—particularly central obesity—which is more closely associated with negative cardiometabolic outcomes and heightened mortality risk (6, 7). Central obesity, defined by excessive fat accumulation around the waist, has emerged as a critical predictor of cardiometabolic risk when compared to overall obesity measures such as BMI (8). This has led to the development of the weight-adjusted waist index (WWI), a novel anthropometric measure designed to address the shortcomings of traditional indices. WWI is calculated by dividing waist circumference by the square root of body weight, thereby effectively capturing central obesity while adjusting for overall body weight (9). The WWI not only offers a more precise differentiation between fat and lean muscle mass but has also shown potential in predicting cardiovascular and all-cause mortality across various populations(10). Previous studies, including those that analyzed nationally representative datasets such as the National Health and Nutrition Examination Survey (NHANES), confirm that elevated WWI is independently linked to increased risks of all-cause and cardiovascular mortality, particularly in individuals living with type 2 diabetes (11). Moreover, research by Zheng et al. has demonstrated that WWI serves as a predictive marker for mortality in hypertensive patients, emphasizing its utility beyond conventional obesity assessments (12). The relationship between WWI and mortality in older adults remains to be further explored. This study aims to examining the association between WWI and all-cause and cardiovascular mortality in older adults using the NHANES dataset. Methods Study Population This study conducted a comprehensive analysis utilizing data from the NHANES spanning the years 1999 to 2018. NHANES is an ongoing series of cross-sectional surveys designed with a multistage stratified probability sampling methodology to assess the health and nutritional status of the non-institutionalized civilian population in the United States. Participants engage in detailed interviews and undergo standardized physical examinations at mobile examination centers. The NHANES study was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent. For this analysis, we aggregated data from ten NHANES cycles (1999–2000 to 2017–2018) to establish a robust sample size of older adults. Among the total of 101,316 participants, we specifically identified individuals aged 60 years and older at the time of examination (N=19,087). Participants with incomplete data on waist circumference or weight—both vital for calculating the Waist-to-Weight Index (WWI)—were excluded (N=16,262). Additionally, those without mortality follow-up data were also removed from the analysis (N=16,242). Consequently, the final analytical sample comprised 16,242 older adults aged between 60 and 85 years, all with complete datasets.(Figure. 1) Exposure: Weight-Adjusted Waist Index (WWI) The weight-adjusted waist index (WWI) is computed by dividing waist circumference (cm) by the square root of body weight (kg), expressed in cm/√kg.(13) During the baseline examination of the NHANES, trained technicians performed standardized measurements of waist circumference and body weight. Waist circumference was assessed to the nearest 0.1 cm at the level of the iliac crest with the participant standing upright and exhaling gently. Body weight was recorded to the nearest 0.1 kg using calibrated digital scales while participants wore light clothing. These measurements facilitated the calculation of the WWI for each individual(13, 14). Outcome Ascertainment: Mortality The outcomes of interest were all-cause mortality and cardiovascular-specific mortality. Mortality status and cause of death were determined by NCHS through probabilistic record linkage of NHANES participants with the National Death Index (NDI) (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm). Follow-up time was calculated from the NHANES exam date until date of death or end of follow-up, whichever came first. For decedents, the cause of death was classified by NCHS based on the underlying cause listed on the death certificate, using International Classification of Diseases, 10th Revision (ICD-10) codes. We defined cardiovascular mortality as any death with an underlying cause in the range of ICD-10 I00–I78 (diseases of the heart and blood vessels, including ischemic heart disease, cerebrovascular disease, heart failure, etc.). All other causes (including cancer, respiratory disease, etc.) were counted as non-cardiovascular deaths for the purpose of secondary analysis, but our primary analysis focused on all-cause mortality (death from any cause)(15). In the analytic cohort of 16,242 older adults, a total of 5,779 deaths from all causes occurred during follow-up, of which approximately 1,750 (30.3%) were attributed to cardiovascular causes (e.g., heart disease or stroke). The median follow-up time among survivors was 92 months, and the maximum follow-up time was 20 years for those enrolled in 1999–2000. Covariates We included a range of covariates measured at baseline that could act as confounders or covariates in the association between adiposity and mortality.The demographic covariates included age (years, as a continuous variable), sex (male/female), and race/ethnicity, categorized as Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, or Other race. Socioeconomic factors comprised marital status (married/cohabiting, widowed, divorced/separated, or never married), educational attainment (less than high school, high school graduate/equivalent, or more than high school), and the poverty-to-income ratio (PIR). Health behaviors were assessed through smoking status (current, former, or never smoker) and alcohol consumption (classified as non-drinker, moderate drinker, or heavy drinker; moderate drinking is defined as ≤1 drink/day for women and ≤2 for men, with heavy drinking exceeding these levels). Physical activity was evaluated via a questionnaire assessing total metabolic equivalent (MET)-minutes per week, categorized into four levels: sedentary (0 MET-min/week), low, moderate, and high, according to quartiles. Additionally, we adjusted for clinical conditions at baseline, such as hypertension (yes/no, based on self-reported diagnosis or measured blood pressure ≥140/90 mmHg) and diabetes mellitus (yes/no, identified by self-reported diagnosis or relevant glucose metrics). Statistical Analysis Participants were classified into three groups based on their World War I (WWI) tertiles: T1 (lowest tertile), T2 (middle tertile), and T3 (highest tertile). Baseline characteristics were summarized according to the type of variable: means ± standard deviations for normally distributed continuous variables, medians (interquartile ranges) for non-normally distributed continuous variables, and percentages for categorical variables. Differences among tertiles were evaluated using ANOVA for continuous variables, while chi-square tests were employed for categorical variables. The Kaplan-Meier survival analysis was performed using an unadjusted model to investigate variations in all-cause and cardiovascular disease (CVD) mortality across the three WWI subgroups, with hazard ratios (HRs) and p-values calculated via the Log-Rank Test. To examine the relationship between WWI and mortality, we performed Cox proportional hazards regression analyses incorporating three sequentially adjusted models. Model 1 (unadjusted) did not include any covariate adjustments. Model 2 (minimally adjusted) accounted for age, sex, and race/ethnicity. Model 3 (fully adjusted) further controlled for marital status, educational attainment, poverty income ratio (PIR), smoking status, alcohol consumption, physical activity, hypertension, and diabetes. Hazard ratios and 95% confidence intervals (CIs) were calculated with the lowest tertile serving as the reference group. The proportional hazards assumption was verified with Schoenfeld residuals, revealing no significant violations. To assess potential non-linear associations between WWI and mortality, we conducted two supplementary analyses. Initially, a generalized additive model (GAM) with smoothing splines was utilized to visualize the dose-response relationship between continuous WWI and mortality risk. In instances where non-linearity became apparent, a threshold effect analysis using a two-piecewise linear model was conducted to identify potential inflection points. The optimal threshold was established through recursive analysis, and a log-likelihood ratio test was performed to compare the threshold model against the linear model(11, 16). Finally, sensitivity analyses were conducted, employing subgroup analysis to assess the consistency of results across different strata. Subgroups were categorized based on socioeconomic and lifestyle factors, including age, sex, race/ethnicity, educational background, smoking status, marital status, PIR, alcohol use, total physical activity (MET/week), hypertension, and diabetes. The two-sided alpha level was set at 0.05. All the statistical analyses were performed using the EmpowerStats (www.empowerstats.com, X&Y solutions, Inc. Boston MA) and R software version 3.6.1 (http://www.r-project.org). Results Participant Characteristics A total of 16,242 older adults aged 60 years and above (mean age: 70.39 ± 7.32 years) were included in this study, comprising 8,100 males (49.87%) and 8,142 females (50.13%). The mean Weight-Adjusted Waist Index (WWI) for all participants was 11.51 ± 0.74 cm/√kg. Baseline characteristics stratified by WWI tertiles are summarized in Table 1.Compared to the lowest WWI tertile (T1), individuals in the highest tertile (T3) were significantly older and demonstrated progressively greater body weight and waist circumference (all P < 0.001). The proportion of females was significantly higher in T3 (P < 0.001). Ethnic composition varied markedly across tertiles (P < 0.001), with Non-Hispanic Black participants predominating in T1 and Mexican American individuals being more prevalent in T3.Significant socioeconomic differences were observed among WWI tertiles: participants in T1 had higher educational attainment, whereas those in T3 exhibited lower poverty income ratios (both P < 0.001). Marital status distributions also differed significantly (P < 0.001), with a greater proportion of married individuals in T1 compared to T3.Health-related behaviors varied considerably across tertiles. Alcohol consumption was more frequent among individuals in T3 (P < 0.001), whereas physical activity levels were highest in T1 (P < 0.001). Conversely, the prevalence of current smoking was significantly greater in T1 than in T3 (P < 0.001).A positive gradient across WWI tertiles was observed for the prevalence of hypertension, diabetes mellitus, impaired fasting glycaemia (IFG), and impaired glucose tolerance (IGT) (all P < 0.001 for trend). Notably, both all-cause and cardiovascular mortality rates were significantly elevated in T3 compared to T1 and T2 (all P < 0.001).Kaplan-Meier survival analysis using an unadjusted model (Figure 2) revealed significant divergence in cumulative survival probabilities among WWI tertiles for both all-cause and cardiovascular mortality (P < 0.001). Participants in the highest tertile (T3) exhibited the lowest survival probability, indicating a substantially increased risk of mortality relative to the lower tertiles. Table 1 Basic characteristics of participants by weight-adjusted-waist index tertiles. Total (N=16242) WWI P-value Variables T1 (N=5414) T2 (N=5414) T3 (N=5414) WWI 11.51 ± 0.74 10.71 ± 0.39 11.51 ± 0.17 12.31 ± 0.41 <0.001 Age (years) 70.39 ± 7.32 68.93 ± 7.23 70.32 ± 7.15 71.94 ± 7.28 <0.001 Weight(kg) 78.79 ± 18.71 74.95 ± 17.08 79.95 ± 18.12 81.48 ± 20.17 <0.001 Waist(cm) 101.58 ± 14.39 92.24 ± 11.59 102.25 ± 11.63 110.24 ± 13.74 <0.001 Sex (%） <0.001 Male 8100 (49.87%) 3051 (56.35%) 2974 (54.93%) 2075 (38.33%) Female 8142 (50.13%) 2363 (43.65%) 2440 (45.07%) 3339 (61.67%) Ethnicity (%） <0.001 Non-Hispanic White 8299 (51.10%) 2721 (50.26%) 2753 (50.85%) 2825 (52.18%) Non-Hispanic Black 3184 (19.60%) 1499 (27.69%) 987 (18.23%) 698 (12.89%) Mexican American 2439 (15.02%) 520 (9.60%) 882 (16.29%) 1037 (19.15%) Other Hispanic 1253 (7.71%) 306 (5.65%) 428 (7.91%) 519 (9.59%) Other Race 1067 (6.57%) 368 (6.80%) 364 (6.72%) 335 (6.19%) Education level (%） <0.001 Below high school 3073 (18.96%) 674 (12.47%) 986 (18.25%) 1413 (26.15%) High school 6282 (38.76%) 2023 (37.44%) 2113 (39.12%) 2146 (39.72%) Above high school 6853 (42.28%) 2706 (50.08%) 2303 (42.63%) 1844 (34.13%) Smoking status (%） <0.001 never 7834 (48.29%) 2613 (48.34%) 2512 (46.44%) 2709 (50.08%) Former 6307 (38.88%) 1977 (36.58%) 2241 (41.43%) 2089 (38.62%) now 2082 (12.83%) 815 (15.08%) 656 (12.13%) 611 (11.30%) MARITAL recoded (%） <0.001 Married/Living with Partner 9471 (58.31%) 3323 (61.38%) 3373 (62.30%) 2775 (51.26%) Widowed/Divorced/Separated 5882 (36.21%) 1777 (32.82%) 1769 (32.67%) 2336 (43.15%) Never married 742 (4.57%) 254 (4.69%) 226 (4.17%) 262 (4.84%) Missing 147 (0.91%) 60 (1.11%) 46 (0.85%) 41 (0.76%) Poverty income ratio (%） <0.001 Poor 2507 (15.44%) 643 (11.88%) 798 (14.74%) 1066 (19.69%) Nearly poor 4459 (27.45%) 1266 (23.38%) 1539 (28.43%) 1654 (30.55%) Middle income 4151 (25.56%) 1436 (26.52%) 1413 (26.10%) 1302 (24.05%) High income 3479 (21.42%) 1525 (28.17%) 1163 (21.48%) 791 (14.61%) Missing 1646 (10.13%) 544 (10.05%) 501 (9.25%) 601 (11.10%) Alcohol use recoded (%） <0.001 Never 2681 (16.51%) 755 (13.95%) 803 (14.83%) 1123 (20.74%) Former 4040 (24.87%) 1233 (22.77%) 1329 (24.55%) 1478 (27.30%) Mild 5709 (35.15%) 2196 (40.56%) 1944 (35.91%) 1569 (28.98%) Moderate 1417 (8.72%) 515 (9.51%) 495 (9.14%) 407 (7.52%) Heavy 1111 (6.84%) 364 (6.72%) 442 (8.16%) 305 (5.63%) Missing 1284 (7.91%) 351 (6.48%) 401 (7.41%) 532 (9.83%) Total physical activity (MET/week) (%） <0.001 =600 6493 (39.98%) 2559 (47.27%) 2227 (41.13%) 1707 (31.53%) Missing 6020 (37.06%) 1526 (28.19%) 1906 (35.21%) 2588 (47.80%) Hypertension (%） <0.001 No 4779 (29.43%) 1987 (36.71%) 1569 (28.98%) 1223 (22.59%) Yes 11461 (70.57%) 3426 (63.29%) 3845 (71.02%) 4190 (77.41%) Diabetes Mellitus (%） <0.001 No 9663 (59.49%) 3841 (70.95%) 3164 (58.44%) 2658 (49.09%) Diabetes Mellitus 5050 (31.09%) 1082 (19.99%) 1714 (31.66%) 2254 (41.63%) IFG(Impaired Fasting Glycaemia) 966 (5.95%) 299 (5.52%) 356 (6.58%) 311 (5.74%) IGT(Impaired Glucose Tolerance) 563 (3.47%) 192 (3.55%) 180 (3.32%) 191 (3.53%) All-cause mortality (%） <0.001 survival 10463 (64.42%) 3699 (68.32%) 3504 (64.72%) 3260 (60.21%) non-survival 5779 (35.58%) 1715 (31.68%) 1910 (35.28%) 2154 (39.79%) Cardiovascular mortality (%） <0.001 survival 14335 (88.26%) 4887 (90.27%) 4775 (88.20%) 4673 (86.31%) non-survival 1907 (11.74%) 527 (9.73%) 639 (11.80%) 741 (13.69%) Follow-up time (months) 100.16 ± 61.88 108.46 ± 63.81 100.60 ± 61.68 91.41 ± 58.90 <0.001 WWI Tertiles and Mortality Table 2 illustrates the association between tertiles of the Weight-Adjusted Waist Index (WWI) and all-cause mortality among older adults. In the unadjusted model (Model 1), WWI was significantly positively associated with mortality risk. Specifically, participants in the highest WWI tertile (T3) exhibited a hazard ratio (HR) of 1.59 (95% CI: 1.49–1.69; P < 0.0001) compared to those in the lowest tertile (T1). After adjusting for demographic variables, including age, sex, and ethnicity (Model 2), the association attenuated but remained statistically significant (T3 HR = 1.37; 95% CI: 1.28–1.46; P < 0.0001). Further adjustment for socioeconomic, behavioral, and clinical covariates—such as education, smoking status, income, alcohol consumption, physical activity, hypertension, and diabetes (Model 3)—continued to demonstrate a significant, albeit reduced, association (T3 HR = 1.09; 95% CI: 1.02–1.17; P = 0.015). To further explore this relationship, a dose-response analysis was performed (Figure 3 and Table 3). Nonparametric smoothing curve fitting revealed a nonlinear association between WWI and mortality risk. Standard linear regression analysis indicated that each unit increase in WWI corresponded to a 7% increase in mortality risk (HR = 1.07; 95% CI: 1.03–1.11; P = 0.0006). Segmented regression identified a threshold effect at a WWI value of 11.24 (log-likelihood ratio test, P < 0.003). Below this inflection point, no statistically significant association was observed (HR = 0.94; 95% CI: 0.86–1.03; P = 0.196). Conversely, above the threshold, each additional unit increase in WWI was associated with a 14% elevated risk of all-cause mortality (HR = 1.14; 95% CI: 1.08–1.21; P < 0.0001).These findings suggest that WWI, as a measure of central adiposity, is independently associated with increased all-cause mortality risk in older adults. Table 2. HR (95% CI) for outcomes across WWI tertiles under three models. Model 1 HR (95% CI) P Model 2 HR (95% CI) P Model 3 HR (95% CI) P All-cause mortality Weight-Waist Index 1.32 (1.27, 1.37) <0.0001 1.23 (1.18, 1.27) <0.0001 1.07 (1.03, 1.11) 0.0006 Categories T1 1 1 1 T2 1.23 (1.16, 1.32) <0.0001 1.13 (1.05, 1.20) 0.0004 1.02 (0.95, 1.09) 0.6574 T3 1.59 (1.49, 1.69) <0.0001 1.37 (1.28, 1.46) <0.0001 1.09 (1.02, 1.17) 0.015 P for trend <0.0001 <0.0001 0.0134 Cardiovascular mortality WWI tertile 1.41 (1.33, 1.50) <0.0001 1.30 (1.22, 1.39) <0.0001 1.12 (1.04, 1.19) 0.0018 Categories T1 1 1 1 T2 1.34 (1.20, 1.51) <0.0001 1.21 (1.08, 1.36) 0.0012 1.07 (0.95, 1.21) 0.2427 T3 1.78 (1.59, 1.99) <0.0001 1.51 (1.34, 1.69) <0.0001 1.16 (1.03, 1.30) 0.0168 P for trend <0.0001 <0.0001 0.0163 Abbreviations: HR, hazard ratio; CI, confidence interval Model 1: no adjustments Model 2:Age (years); Sex; Ethnicity Model 3: adjusted for Age (years); Sex; Ethnicity; Education level; Smoking status; MARITAL recoded; Poverty income ratio; Alcohol use recoded; Total physical activity(MET/week); Hypertension; Diabetes Mellitus Table 3.Threshold effect analysis. Adjusted for the variables listed in model 3. All-cause mortality Cardiovascular mortality Linear model 1.07 (1.03, 1.11) 0.0006 1.12 (1.04, 1.19) 0.0018 Two-piecewise linear model Inflection point 11.24 12.74 Inflection point 1.14 (1.08, 1.21) <0.0001 1.44 (1.02, 2.02) 0.0355 P for Log-likelihood ratio 0.003 0.155 WWI and Cardiovascular Mortality We next evaluated the relationship between WWI and cardiovascular mortality. The findings for cardiovascular mortality paralleled those observed for all-cause mortality. In the unadjusted model, the highest WWI tertile (T3) demonstrated significantly elevated cardiovascular mortality risk (HR = 1.78; 95% CI: 1.59-1.99; P < 0.0001). Adjustment for demographic covariates (Model 2) maintained significant risk elevation (HR = 1.51; 95% CI: 1.34-1.69; P < 0.0001). Further adjustment for lifestyle and clinical factors (Model 3) attenuated but preserved statistical significance (HR = 1.16; 95% CI: 1.03-1.30; P = 0.0168). Notably, the middle tertile (T2) showed no significant association in Model 3 (P = 0.243), suggesting a threshold effect. Segmented regression analysis identified an inflection point at WWI=12.74 cm/√kg. Below this threshold, each unit WWI increase corresponded to modest but significant risk elevation (HR = 1.09; 95% CI: 1.01-1.18; P = 0.0220), indicating cardiovascular mortality risk increases even with modest WWI elevations. Above 12.74 cm/√kg, risk escalated substantially (HR = 1.44; 95% CI: 1.02-2.02; P = 0.0355).(Figure 3 and Table 3) Sensitivity and Subgroup Analyses Stratified analyses of all-cause mortality among older adults revealed a significant association between the WW and mortality risk. (Figure 4)In individuals aged 60–65 years, higher WWI was associated with increased mortality risk (HR: 1.33, 95% CI: 1.22–1.45, p < 0.0001). This association was more pronounced in males (HR: 1.47, 95% CI: 1.39–1.55, p < 0.0001) and non-Hispanic whites (HR: 1.45, 95% CI: 1.39–1.52, p < 0.0001). Education level also influenced risk, with participants possessing education beyond high school exhibiting the highest mortality risk (HR: 1.53, 95% CI: 1.44–1.62). Analysis by smoking status indicated elevated risk among former smokers (HR: 1.43, 95% CI: 1.36–1.52) and never smokers (HR: 1.33, 95% CI: 1.26–1.40), whereas no significant association was observed in current smokers (HR: 1.07, 95% CI: 0.98–1.18). Physical activity stratification showed higher risk for individuals with insufficient activity (<600 MET-min/week; HR: 1.37) compared to those with adequate activity (≥600 MET-min/week; HR: 1.27). Although hypertension (HR: 1.28) and diabetes mellitus (HR: 1.21) partially influenced risk, the association between WWI and mortality remained robust. Similar trends were observed in cardiovascular mortality. Significantly elevated risk was noted in the younger elderly (60–65 years; HR: 1.41, 95% CI: 1.19–1.67) and the oldest age group (74–85 years; HR: 1.20, 95% CI: 1.11–1.30), whereas the middle-aged subgroup (66–73 years) showed no significant association (HR: 1.12, 95% CI: 0.99–1.28). Male sex (HR: 1.59, 95% CI: 1.44–1.74, p < 0.0001) and non-Hispanic white ethnicity (HR: 1.59, 95% CI: 1.47–1.72, p < 0.0001) were linked to higher cardiovascular mortality risk. Patterns observed for education and smoking status paralleled those in all-cause mortality, potentially reflecting confounding or competing risks. Discussion In this large cohort study of U.S. older adults (NHANES 1999–2018), we found that the weight-adjusted waist index (WWI) is a significant indicator of mortality. Higher WWI was associated with an increased risk of all-cause death, even after adjusting for numerous demographic, socioeconomic, behavioral, and health-related confounders. The relationship was notably non-linear, with minimal change in mortality risk across lower to mid-range WWI values and a sharp increase in risk once WWI exceeded approximately 11.2 cm/√kg. Specifically, beyond the threshold of WWI ≈11.2, each 1-unit rise in WWI corresponded to a 14% increase in hazard of death. Consistently, older adults in the highest WWI tertile (above roughly 11.2) had about 9% higher mortality than those in the lowest tertile. Although this effect size is moderate, it is clinically relevant considering the high baseline mortality in older populations; a 9% hazard increase could translate to meaningful differences in life expectancy at the population level. We also observed a similar pattern for cardiovascular mortality, suggesting that WWI captures cardiovascular risk factors that contribute to death from heart disease and stroke. To our knowledge, this is one of the first studies focusing on WWI and mortality specifically in older adults in a nationally representative U.S. sample, and our findings reinforce the value of WWI as a potentially important metric of health risk in this age group. The relationship between WWI and mortality underscores its ability to simultaneously capture two critical components: central adiposity, via waist circumference, and muscle-fat balance, by incorporating weight adjustments. This unique dual functionality is particularly pertinent in older adults, who frequently contend with the combined challenges of visceral fat accumulation and sarcopenia, or age-related muscle loss(17, 18). This context of aging complicates the utility of traditional measures like body mass index (BMI), which often fails to accurately reflect health risks associated with increased fat deposition and reduced muscle mass (19, 20). Research indicates that reliance on BMI can produce misleading interpretations, particularly in reflecting mortality risk among older populations, where decreased muscle mass may correlate with lower mortality rates due to the protective factors associated with muscle (21, 22).Contrasting sharply with the "obesity paradox"—the notion that overweight individuals may have lower mortality risks despite higher BMI—WWI appears to circumvent the confounding effects of muscle mass. Evidence suggests that while elevated BMI may suggest risk factors that are not independently representative, WWI provides a more nuanced understanding by effectively distinguishing between healthy weight and unhealthy fat distribution associated with muscular individuals (23, 24). Studies have shown that as muscle mass declines, the inherent protective aspects related to fat mass become increasingly ambiguous, potentially skewing outcomes in mortality assessments based on BMI (25, 26). Therefore, WWI stands out as a more reliable metric that reflects true body composition and visceral fat presence, especially in the aging demographic where muscle maintenance remains critical(27, 28). By accounting for both central fat distribution and preserving the integrity of muscle mass considerations, WWI may provide a more accurate assessment of physical health risks, thereby facilitating improved intervention strategies that are tailored to the physiological realities of aging populations (18-20, 22). In the smoothed curve fitting analysis and the threshold effect analysis, our identified threshold of WWI ≈11.2 cm/√kg for increased mortality risk is remarkably consistent with prior studies. A prospective study in southern China by Ding et al. found that participants with WWI ≥11.2 had significantly higher all-cause and CVD mortality(29). Similarly, Cai et al. studied community-dwelling older adults in Beijing and reported that those in the highest WWI tertile (≥11.25 cm/√kg) had a 166% higher risk of all-cause mortality compared to the lowest tertile (30). The magnitude of the HR in that Chinese study (HR 2.66 for highest vs lowest WWI tertile) is much larger than what we observed (HR ~1.09). This difference could be due to several factors: the Chinese cohort was smaller (n≈1,860, with 339 deaths) leading to less precise but larger point estimates; it had less comprehensive adjustment for confounders (their models adjusted for age, sex, and a limited set of variables); and the baseline characteristics differed (the Chinese sample’s highest WWI group was on average older and perhaps in worse health than ours, given their tertile HRs were quite high). It’s also possible that genetic or lifestyle differences lead to a stronger impact of central obesity in that population. Nonetheless, qualitatively, both studies indicate that high WWI is detrimental. Our findings add that even in a multi-ethnic U.S. population with different prevalence of obesity and comorbidities, WWI remains an important indicator of mortality, though the effect size in fully adjusted models is modest. In another related study, Cao et al. found a U-shaped relationship between WWI and mortality in a national US sample excluding Asian participants, with a nadir WWI risk of 10.46 cm/√kg(31). They found that risk increased both above and below that point, although the increased risk at low WWI was apparent mainly before excluding early follow-up (likely reflecting that low WWI could include very high BMI individuals or anomalies). In our older-focused analysis, we did not observe a statistically significant increase in risk at low WWI – perhaps because very low WWI individuals (who would be heavy-for-waist, possibly indicating high muscle mass or high subcutaneous fat but not visceral fat) are relatively rare among older adults. Our data suggest that within the range typical for seniors, having a WWI below ~9.5 (which would indicate unusually heavy weight relative to waist, possibly implying high muscle or peripheral fat) is uncommon, and we had limited power to detect if there’s a slight uptick in risk there. Overall, our findings align with Cao et al. in that the primary concern is high WWI, not moderate or low WWI. Both studies underscore a threshold around 10.5–11.5 where risk begins to climb – with our analysis pinpointing ~11.2 for ages ≥60.Our findings align with subgroup analyses conducted on specific populations. Zheng et al. reported a significantly higher hazard ratio for mortality in patients with hypertension when compared to our broader cohort of older adults.(12) This discrepancy may be attributed to the inclusion of all older adults in our cohort, rather than exclusively those with hypertension. The presence of concurrent hypertension may potentiate the impact of central obesity on cardiovascular risk, leading to the observed elevated hazard ratios in hypertensive patients. Despite these differences, the consistent direction of the association underscores the increased mortality risk associated with higher WWI, regardless of whether the population includes individuals with chronic conditions or the general older adult population. The association between an increased WWI and heightened mortality risk appears to be mediated through several interconnected biological pathways related to central obesity and body composition.A higher WWI indicates dysfunctional adipose tissue expansion, primarily manifesting as visceral obesity. This condition fosters chronic low-grade inflammation, which is enhanced by the increased production and release of pro-inflammatory cytokines. Such inflammation is pivotal in promoting atherosclerosis by exacerbating endothelial dysfunction, plaque formation, and thrombosis, consequently raising the risk of cardiovascular and cerebrovascular mortality(32-34).The implications of central obesity extend into oxidative stress, which contributes significantly to the escalated risk of cardiovascular-related deaths. Studies have demonstrated that individuals exhibiting elevated WWI levels are prone to higher oxidative stress markers, which are detrimental to cardiovascular health and associated with increased mortality rates (35). Additionally, it is notable that WWI negatively correlates with muscle mass, implicating metabolic dysregulation; the condition of sarcopenia is significantly linked to insulin resistance and a reduction in metabolic resilience(34, 36). The detrimental effects of low muscle mass coupled with high abdominal fat significantly exacerbate health risks due to their intertwined nature.Moreover, WWI shows a strong association with common comorbid conditions such as diabetes, hypertension, and dyslipidemia. These comorbidities are independently linked to elevated mortality risk due to direct organ damage and increased cardiac metabolic load(37, 38). For instance, the coexistence of obesity-related complications such as type 2 diabetes and hypertension has been documented to pose a substantial risk factor for all-cause mortality, further emphasizing the importance of managing central obesity(34, 39).Collectively, these mechanisms—chronic inflammation, oxidative stress, altered body composition, and metabolic dysfunction—explain the relationship between WWI and mortality. Strengths and limitations Despite these strengths and implications, our study has limitations.First, the observational design precludes causal conclusions.Second, WWI and other covariates were measured at baseline only; we did not account for changes in weight or waist over time.Some participants may have lost weight (and thus changed WWI) due to illness during follow-up, which could bias results (though our exclusion of early deaths and adjustment for baseline health factors help mitigate this).Third, our extensive covariate adjustment, while aimed at isolating the WWI effect, may inadvertently adjust away part of the effect if those factors (e.g., diabetes, hypertension) lie on the causal pathway from central obesity to mortality. Fourth, we did not explicitly compare WWI’s predictive power against BMI or other indices in this paper.Fifth, the use of NHANES data across 20 years could introduce some heterogeneity – measurement protocols for waist, slight differences in survey design over time – but NHANES is fairly standardized, and we accounted for survey design to some extent .Finally, though our sample is large and multi-ethnic, it is confined to U.S. older adults.Lifestyle, genetic factors, and obesity patterns differ globally.Our findings might not directly extrapolate to other countries or ethnic groups not well-represented in NHANES. Interestingly, the concordance of the ~11 cm/√kg threshold in both U.S. and Chinese studiespubmed.ncbi.nlm.nih.gov hints at a possible universal biological threshold, but more research would be needed to confirm that. Conclusion Among older adults in the United States, elevated levels of theWWI are significantly associated with increased risks of all-cause and cardiovascular mortality. Those with a high WWI – reflecting a disproportionately large waist relative to their body weight – face higher all-cause and cardiovascular mortality, independent of traditional risk factors. This supports the notion that an unfavorable body composition (excess central adiposity with lower lean mass) is deleterious in aging. Abbreviations NHANES National Health and Nutrition Examination Survey WWI Weight-adjusted waist index BMI Body mass index OR Odds ratio CI Confidence interval ANOVA Analysis of variance GAM Generalized additive model CVD Cardiovascular disease Declarations Authors’ contributions Huai Huang: Conceptualization, Data Curation, Writing ,Review & Editing.Yongtian Zheng:Conceptualization, Data Curation,Writing,Review & Editing.Yijiang Li:Formal Analysis,Methodology,Software,Validation.Yinuo Bi:Data Curation, Validation ,Review & Editing. Jianfan Jian: Data Curation, Validation ,Review & Editing.Xiaosheng Zhu :Conceptualization,Project Administration,Supervision,Review & Editing.Wenyu Jiang :Conceptualization,Project Administration,Supervision,Review & Editing. :Conceptualization,Supervision,Writing-original draft,Writing-review &editing.All authors reviewed the manuscript. Data availability The datasets supporting the findings of this article are available in the NHANES website, https://www.cdc.gov/nchs/nhanes/ .Anyone who would like to request collated data from this study should contact [email protected] • Ethics approval and consent to participate This study used publicly available data from public databases and therefore did not require ethical approval. • Consent for publication All authors contributed to the article and approved the submitted version. •Conflict of interest The authors declare that they have no conflicts of interest. • Funding The authors declare that they have no funding and conflicts of interest related to this research. • Acknowledgments We acknowledge the National Center for Health Statistics for providing access to NHANES data. References Yu S, Wang B, Guo X, Li G, Yang H, Sun Y. Weight-Adjusted-Waist Index Predicts Newly Diagnosed Diabetes in Chinese Rural Adults. Journal of Clinical Medicine. 2023;12(4):1620. 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Aging Clinical and Experimental Research. 2024;36(1). Liu H, Ma Y, Shi L. Higher Weight-Adjusted Waist Index Is Associated With Increased Likelihood of Kidney Stones. Frontiers in endocrinology. 2023;14. Additional Declarations No competing interests reported. Supplementary Files Excel1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 29 Oct, 2025 Editor invited by journal 14 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 01 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":172001,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study sample screening\u003c/p\u003e","description":"","filename":"Figure1jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/e6dd0a1af49217c6d5a64445.jpg"},{"id":96251928,"identity":"2c3ca8c1-0906-4bb0-9da4-d9e204818f3a","added_by":"auto","created_at":"2025-11-19 07:40:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1197156,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis curves for all-cause(A) and Cardiovascular mortality(B).\u003c/p\u003e","description":"","filename":"Figure2jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/e794ee681e5b52b5f8c1b1ef.jpg"},{"id":96250648,"identity":"cc269426-7135-404c-aa44-8c824afb7022","added_by":"auto","created_at":"2025-11-19 07:38:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1316889,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between WWI and mortality outcomes. The red and blue dotted lines represent the estimated values and their corresponding 95% CIs. Adjusted for the variables listed in model 3.\u003c/p\u003e","description":"","filename":"Figure3jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/792b3d65399f26a966683577.jpg"},{"id":96251051,"identity":"19587d3b-70c2-49fb-8856-b49c19781655","added_by":"auto","created_at":"2025-11-19 07:39:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1723934,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Subgroup analyses of WWI and mortality outcomes.\u003c/p\u003e","description":"","filename":"Figure4jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/3afe50e88c63b718c89b1268.jpg"},{"id":96453271,"identity":"e6212f31-43b0-4759-80c6-63f10b90c650","added_by":"auto","created_at":"2025-11-21 09:59:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5369977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/9c532878-e090-4dbc-86e2-d1ff6b1201ee.pdf"},{"id":96156775,"identity":"4b8d08bd-abba-4d20-bd47-6018b93e4bb8","added_by":"auto","created_at":"2025-11-18 08:30:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19607,"visible":true,"origin":"","legend":"","description":"","filename":"Excel1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6987746/v1/423f0117d602ae7f9aaa1fe2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weight-Adjusted Waist Index and Mortality Among Older Adults: Findings from NHANES 1999–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity represents a significant and escalating public health concern, serving as a substantial risk factor for multiple chronic diseases, including cardiovascular disease (CVD), type 2 diabetes, and hypertension(1, 2). This issue is particularly acute among older adults, a demographic in which obesity is correlated with increased frailty, diminished physical function, and a higher mortality rate(3, 4). The intricacies of the relationship between obesity and mortality within this group are complex and often yield paradoxical results; findings may vary widely based on the metrics used to assess obesity(3, 5). Traditionally, body mass index (BMI) has been the primary anthropometric measure utilized to gauge obesity-related health risks. However, BMI has significant limitations, as it fails to adequately represent fat distribution\u0026mdash;particularly central obesity\u0026mdash;which is more closely associated with negative cardiometabolic outcomes and heightened mortality risk (6, 7).\u003c/p\u003e\n\u003cp\u003eCentral obesity, defined by excessive fat accumulation around the waist, has emerged as a critical predictor of cardiometabolic risk when compared to overall obesity measures such as BMI (8). This has led to the development of the weight-adjusted waist index (WWI), a novel anthropometric measure designed to address the shortcomings of traditional indices. WWI is calculated by dividing waist circumference by the square root of body weight, thereby effectively capturing central obesity while adjusting for overall body weight (9). The WWI not only offers a more precise differentiation between fat and lean muscle mass but has also shown potential in predicting cardiovascular and all-cause mortality across various populations(10). Previous studies, including those that analyzed nationally representative datasets such as the National Health and Nutrition Examination Survey (NHANES), confirm that elevated WWI is independently linked to increased risks of all-cause and cardiovascular mortality, particularly in individuals living with type 2 diabetes (11). Moreover, research by Zheng et al. has demonstrated that WWI serves as a predictive marker for mortality in hypertensive patients, emphasizing its utility beyond conventional obesity assessments (12).\u003c/p\u003e\n\u003cp\u003eThe relationship between WWI and mortality in older adults remains to be further explored. This study aims to examining the association between WWI and all-cause and cardiovascular mortality in older adults using the NHANES dataset.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThis study conducted a comprehensive analysis utilizing data from the NHANES spanning the years 1999 to 2018. NHANES is an ongoing series of cross-sectional surveys designed with a multistage stratified probability sampling methodology to assess the health and nutritional status of the non-institutionalized civilian population in the United States. Participants engage in detailed interviews and undergo standardized physical examinations at mobile examination centers. The NHANES study was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor this analysis, we aggregated data from ten NHANES cycles (1999\u0026ndash;2000 to 2017\u0026ndash;2018) to establish a robust sample size of older adults. Among the total of 101,316 participants, we specifically identified individuals aged 60 years and older at the time of examination (N=19,087). Participants with incomplete data on waist circumference or weight\u0026mdash;both vital for calculating the Waist-to-Weight Index (WWI)\u0026mdash;were excluded (N=16,262). Additionally, those without mortality follow-up data were also removed from the analysis (N=16,242). Consequently, the final analytical sample comprised 16,242 older adults aged between 60 and 85 years, all with complete datasets.(Figure. 1)\u003c/p\u003e\n\u003ch3\u003eExposure: Weight-Adjusted Waist Index (WWI)\u003c/h3\u003e\n\u003cp\u003eThe weight-adjusted waist index (WWI) is computed by dividing waist circumference (cm) by the square root of body weight (kg), expressed in\u0026nbsp;cm/\u0026radic;kg.(13) During the baseline examination of the NHANES, trained technicians performed standardized measurements of waist circumference and body weight. Waist circumference was assessed to the nearest 0.1 cm at the level of the iliac crest with the participant standing upright and exhaling gently. Body weight was recorded to the nearest 0.1 kg using calibrated digital scales while participants wore light clothing. These measurements facilitated the calculation of the WWI for each individual(13, 14).\u003c/p\u003e\n\u003ch3\u003eOutcome Ascertainment: Mortality\u003c/h3\u003e\n\u003cp\u003eThe outcomes of interest were all-cause mortality and cardiovascular-specific mortality. Mortality status and cause of death were determined by NCHS through probabilistic record linkage of NHANES participants with the National Death Index (NDI) (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm). Follow-up time was calculated from the NHANES exam date until date of death or end of follow-up, whichever came first. For decedents, the cause of death was classified by NCHS based on the underlying cause listed on the death certificate, using International Classification of Diseases, 10th Revision (ICD-10) codes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe defined cardiovascular mortality as any death with an underlying cause in the range of ICD-10 I00\u0026ndash;I78 (diseases of the heart and blood vessels, including ischemic heart disease, cerebrovascular disease, heart failure, etc.). All other causes (including cancer, respiratory disease, etc.) were counted as non-cardiovascular deaths for the purpose of secondary analysis, but our primary analysis focused on all-cause mortality (death from any cause)(15). In the analytic cohort of 16,242 older adults, a total of 5,779 deaths from all causes occurred during follow-up, of which approximately 1,750 (30.3%) were attributed to cardiovascular causes (e.g., heart disease or stroke). \u0026nbsp; \u0026nbsp;The median follow-up time among survivors was 92 months, and the maximum follow-up time was 20 years for those enrolled in 1999\u0026ndash;2000.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eWe included a range of covariates measured at baseline that could act as confounders or covariates in the association between adiposity and mortality.The demographic covariates included age (years, as a continuous variable), sex (male/female), and race/ethnicity, categorized as Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, or Other race. Socioeconomic factors comprised marital status (married/cohabiting, widowed, divorced/separated, or never married), educational attainment (less than high school, high school graduate/equivalent, or more than high school), and the poverty-to-income ratio (PIR). Health behaviors were assessed through smoking status (current, former, or never smoker) and alcohol consumption (classified as non-drinker, moderate drinker, or heavy drinker; moderate drinking is defined as \u0026le;1 drink/day for women and \u0026le;2 for men, with heavy drinking exceeding these levels). Physical activity was evaluated via a questionnaire assessing total metabolic equivalent (MET)-minutes per week, categorized into four levels: sedentary (0 MET-min/week), low, moderate, and high, according to quartiles. Additionally, we adjusted for clinical conditions at baseline, such as hypertension (yes/no, based on self-reported diagnosis or measured blood pressure \u0026ge;140/90 mmHg) and diabetes mellitus (yes/no, identified by self-reported diagnosis or relevant glucose metrics).\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eParticipants were classified into three groups based on their World War I (WWI) tertiles: T1 (lowest tertile), T2 (middle tertile), and T3 (highest tertile). Baseline characteristics were summarized according to the type of variable: means \u0026plusmn; standard deviations for normally distributed continuous variables, medians (interquartile ranges) for non-normally distributed continuous variables, and percentages for categorical variables. Differences among tertiles were evaluated using ANOVA for continuous variables, while chi-square tests were employed for categorical variables. The Kaplan-Meier survival analysis was performed using an unadjusted model to investigate variations in all-cause and cardiovascular disease (CVD) mortality across the three WWI subgroups, with hazard ratios (HRs) and p-values calculated via the Log-Rank Test.\u003c/p\u003e\n\u003cp\u003eTo examine the relationship between WWI and mortality, we performed Cox proportional hazards regression analyses incorporating three sequentially adjusted models. Model 1 (unadjusted) did not include any covariate adjustments. Model 2 (minimally adjusted) accounted for age, sex, and race/ethnicity. Model 3 (fully adjusted) further controlled for marital status, educational attainment, poverty income ratio (PIR), smoking status, alcohol consumption, physical activity, hypertension, and diabetes. Hazard ratios and 95% confidence intervals (CIs) were calculated with the lowest tertile serving as the reference group. The proportional hazards assumption was verified with Schoenfeld residuals, revealing no significant violations.\u003c/p\u003e\n\u003cp\u003eTo assess potential non-linear associations between WWI and mortality, we conducted two supplementary analyses. Initially, a generalized additive model (GAM) with smoothing splines was utilized to visualize the dose-response relationship between continuous WWI and mortality risk. In instances where non-linearity became apparent, a threshold effect analysis using a two-piecewise linear model was conducted to identify potential inflection points. The optimal threshold was established through recursive analysis, and a log-likelihood ratio test was performed to compare the threshold model against the linear model(11, 16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, sensitivity analyses were conducted, employing subgroup analysis to assess the consistency of results across different strata. Subgroups were categorized based on socioeconomic and lifestyle factors, including age, sex, race/ethnicity, educational background, smoking status, marital status, PIR, alcohol use, total physical activity (MET/week), hypertension, and diabetes.\u003c/p\u003e\n\u003cp\u003eThe two-sided alpha level was set at 0.05. All the statistical analyses were performed using the EmpowerStats (www.empowerstats.com, X\u0026amp;Y solutions, Inc. Boston MA) and R software version 3.6.1 (http://www.r-project.org).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eParticipant Characteristics\u003c/h3\u003e\n\u003cp\u003eA total of 16,242 older adults aged 60 years and above (mean age: 70.39 \u0026plusmn; 7.32 years) were included in this study, comprising 8,100 males (49.87%) and 8,142 females (50.13%). The mean Weight-Adjusted Waist Index (WWI) for all participants was 11.51 \u0026plusmn; 0.74\u0026nbsp;cm/\u0026radic;kg. Baseline characteristics stratified by WWI tertiles are summarized in Table 1.Compared to the lowest WWI tertile (T1), individuals in the highest tertile (T3) were significantly older and demonstrated progressively greater body weight and waist circumference (all P \u0026lt; 0.001). The proportion of females was significantly higher in T3 (P \u0026lt; 0.001). Ethnic composition varied markedly across tertiles (P \u0026lt; 0.001), with Non-Hispanic Black participants predominating in T1 and Mexican American individuals being more prevalent in T3.Significant socioeconomic differences were observed among WWI tertiles: participants in T1 had higher educational attainment, whereas those in T3 exhibited lower poverty income ratios (both P \u0026lt; 0.001). Marital status distributions also differed significantly (P \u0026lt; 0.001), with a greater proportion of married individuals in T1 compared to T3.Health-related behaviors varied considerably across tertiles. Alcohol consumption was more frequent among individuals in T3 (P \u0026lt; 0.001), whereas physical activity levels were highest in T1 (P \u0026lt; 0.001). Conversely, the prevalence of current smoking was significantly greater in T1 than in T3 (P \u0026lt; 0.001).A positive gradient across WWI tertiles was observed for the prevalence of hypertension, diabetes mellitus, impaired fasting glycaemia (IFG), and impaired glucose tolerance (IGT) (all P \u0026lt; 0.001 for trend). Notably, both all-cause and cardiovascular mortality rates were significantly elevated in T3 compared to T1 and T2 (all P \u0026lt; 0.001).Kaplan-Meier survival analysis using an unadjusted model (Figure 2) revealed significant divergence in cumulative survival probabilities among WWI tertiles for both all-cause and cardiovascular mortality (P \u0026lt; 0.001). Participants in the highest tertile (T3) exhibited the lowest survival probability, indicating a substantially increased risk of mortality relative to the lower tertiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u0026nbsp; Basic characteristics of participants by weight-adjusted-waist index tertiles.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N=16242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 193px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eT1 (N=5414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eT2 (N=5414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT3 (N=5414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e11.51 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.71 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e11.51 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.31 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e70.39 \u0026plusmn; 7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e68.93 \u0026plusmn; 7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e70.32 \u0026plusmn; 7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e71.94 \u0026plusmn; 7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eWeight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.79 \u0026plusmn; 18.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e74.95 \u0026plusmn; 17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e79.95 \u0026plusmn; 18.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e81.48 \u0026plusmn; 20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eWaist(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e101.58 \u0026plusmn; 14.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e92.24 \u0026plusmn; 11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e102.25 \u0026plusmn; 11.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e110.24 \u0026plusmn; 13.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSex (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8100 (49.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3051 (56.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2974 (54.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2075 (38.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8142 (50.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2363 (43.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2440 (45.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3339 (61.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eEthnicity (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8299 (51.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2721 (50.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2753 (50.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2825 (52.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3184 (19.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1499 (27.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e987 (18.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e698 (12.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2439 (15.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e520 (9.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e882 (16.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1037 (19.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1253 (7.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e306 (5.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e428 (7.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e519 (9.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1067 (6.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e368 (6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e364 (6.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e335 (6.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eEducation level (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3073 (18.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e674 (12.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e986 (18.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1413 (26.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6282 (38.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2023 (37.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2113 (39.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2146 (39.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6853 (42.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2706 (50.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2303 (42.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1844 (34.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSmoking status (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003enever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e7834 (48.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2613 (48.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2512 (46.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2709 (50.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6307 (38.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1977 (36.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2241 (41.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2089 (38.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003enow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2082 (12.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e815 (15.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e656 (12.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e611 (11.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMARITAL recoded (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMarried/Living with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9471 (58.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3323 (61.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e3373 (62.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2775 (51.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5882 (36.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1777 (32.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1769 (32.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2336 (43.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e742 (4.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e254 (4.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e226 (4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e262 (4.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e147 (0.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e60 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e46 (0.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e41 (0.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003ePoverty income ratio (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2507 (15.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e643 (11.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e798 (14.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1066 (19.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNearly poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4459 (27.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1266 (23.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1539 (28.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1654 (30.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMiddle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4151 (25.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1436 (26.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1413 (26.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1302 (24.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3479 (21.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1525 (28.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1163 (21.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e791 (14.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1646 (10.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e544 (10.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e501 (9.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e601 (11.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eAlcohol use recoded (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2681 (16.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e755 (13.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e803 (14.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1123 (20.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4040 (24.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1233 (22.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1329 (24.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1478 (27.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5709 (35.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2196 (40.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1944 (35.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1569 (28.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1417 (8.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e515 (9.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e495 (9.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e407 (7.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1111 (6.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e364 (6.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e442 (8.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e305 (5.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1284 (7.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e351 (6.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e401 (7.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e532 (9.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eTotal physical activity\u003c/p\u003e\n \u003cp\u003e(MET/week) \u0026nbsp;(%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026lt;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3729 (22.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1329 (24.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1281 (23.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1119 (20.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026gt;=600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6493 (39.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2559 (47.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e2227 (41.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1707 (31.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6020 (37.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1526 (28.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1906 (35.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2588 (47.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eHypertension (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4779 (29.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1987 (36.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1569 (28.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1223 (22.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e11461 (70.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3426 (63.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e3845 (71.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4190 (77.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eDiabetes Mellitus (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9663 (59.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3841 (70.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e3164 (58.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2658 (49.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5050 (31.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1082 (19.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1714 (31.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2254 (41.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eIFG(Impaired Fasting Glycaemia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e966 (5.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e299 (5.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e356 (6.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e311 (5.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eIGT(Impaired Glucose Tolerance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e563 (3.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e192 (3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e180 (3.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e191 (3.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eAll-cause mortality (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003esurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e10463 (64.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3699 (68.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e3504 (64.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3260 (60.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003enon-survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5779 (35.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1715 (31.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1910 (35.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2154 (39.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eCardiovascular mortality (%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003esurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e14335 (88.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4887 (90.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e4775 (88.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4673 (86.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003enon-survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1907 (11.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e527 (9.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e639 (11.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e741 (13.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eFollow-up time (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e100.16 \u0026plusmn; 61.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e108.46 \u0026plusmn; 63.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e100.60 \u0026plusmn; 61.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e91.41 \u0026plusmn; 58.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eWWI Tertiles and Mortality\u003c/h3\u003e\n\u003cp\u003eTable 2 illustrates the association between tertiles of the Weight-Adjusted Waist Index (WWI) and all-cause mortality among older adults. In the unadjusted model (Model 1), WWI was significantly positively associated with mortality risk. Specifically, participants in the highest WWI tertile (T3) exhibited a hazard ratio (HR) of 1.59 (95% CI: 1.49\u0026ndash;1.69; P \u0026lt; 0.0001) compared to those in the lowest tertile (T1). After adjusting for demographic variables, including age, sex, and ethnicity (Model 2), the association attenuated but remained statistically significant (T3 HR = 1.37; 95% CI: 1.28\u0026ndash;1.46; P \u0026lt; 0.0001). Further adjustment for socioeconomic, behavioral, and clinical covariates\u0026mdash;such as education, smoking status, income, alcohol consumption, physical activity, hypertension, and diabetes (Model 3)\u0026mdash;continued to demonstrate a significant, albeit reduced, association (T3 HR = 1.09; 95% CI: 1.02\u0026ndash;1.17; P = 0.015).\u003c/p\u003e\n\u003cp\u003eTo further explore this relationship, a dose-response analysis was performed (Figure 3 and Table 3). Nonparametric smoothing curve fitting revealed a nonlinear association between WWI and mortality risk. Standard linear regression analysis indicated that each unit increase in WWI corresponded to a 7% increase in mortality risk (HR = 1.07; 95% CI: 1.03\u0026ndash;1.11; P = 0.0006). Segmented regression identified a threshold effect at a WWI value of 11.24 (log-likelihood ratio test, P \u0026lt; 0.003). Below this inflection point, no statistically significant association was observed (HR = 0.94; 95% CI: 0.86\u0026ndash;1.03; P = 0.196). Conversely, above the threshold, each additional unit increase in WWI was associated with a 14% elevated risk of all-cause mortality (HR = 1.14; 95% CI: 1.08\u0026ndash;1.21; P \u0026lt; 0.0001).These findings suggest that WWI, as a measure of central adiposity, is independently associated with increased all-cause mortality risk in older adults.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eTable 2. HR (95% CI) for outcomes across WWI tertiles under three models.\u003c/h4\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eModel 1\u003cbr\u003e\u0026nbsp;HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eModel 2\u003cbr\u003e\u0026nbsp;HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eModel 3\u003cbr\u003e\u0026nbsp; HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-cause mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eWeight-Waist Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(1.27, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.18, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e(1.03, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.16, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(1.05, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.95, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.6574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(1.49, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003cp\u003e(1.28, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(1.02, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eWWI tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e(1.33, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003cp\u003e(1.22, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e(1.04, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e(1.20, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(1.08, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e(0.95, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.2427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003cp\u003e(1.59, 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(1.34, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(1.03, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eModel 1: no adjustments\u003c/p\u003e\n\u003cp\u003eModel 2:Age (years); Sex; Ethnicity\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted for Age (years); Sex; Ethnicity; Education level; Smoking status; MARITAL recoded; Poverty income ratio; Alcohol use recoded; Total physical activity(MET/week); Hypertension; Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eTable 3.Threshold effect analysis. Adjusted for the variables listed in model 3.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-cause mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eLinear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e1.07 (1.03, 1.11)\u003c/p\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e1.12 (1.04, 1.19)\u003c/p\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eTwo-piecewise linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt; Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e0.94 (0.86, 1.03)\u003c/p\u003e\n \u003cp\u003e0.1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.18)\u003c/p\u003e\n \u003cp\u003e0.0220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt; Inflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e1.14 (1.08, 1.21)\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e1.44 (1.02, 2.02)\u003c/p\u003e\n \u003cp\u003e0.0355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eP for Log-likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eWWI and Cardiovascular Mortality\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eWe next evaluated the relationship between WWI and cardiovascular mortality. The findings for cardiovascular mortality paralleled those observed for all-cause mortality. In the unadjusted model, the highest WWI tertile (T3) demonstrated significantly elevated cardiovascular mortality risk (HR = 1.78; 95% CI: 1.59-1.99; P \u0026lt; 0.0001). Adjustment for demographic covariates (Model 2) maintained significant risk elevation (HR = 1.51; 95% CI: 1.34-1.69; P \u0026lt; 0.0001). Further adjustment for lifestyle and clinical factors (Model 3) attenuated but preserved statistical significance (HR = 1.16; 95% CI: 1.03-1.30; P = 0.0168). Notably, the middle tertile (T2) showed no significant association in Model 3 (P = 0.243), suggesting a threshold effect. Segmented regression analysis identified an inflection point at WWI=12.74\u0026nbsp;cm/\u0026radic;kg. Below this threshold, each unit WWI increase corresponded to modest but significant risk elevation (HR = 1.09; 95% CI: 1.01-1.18; P = 0.0220), indicating cardiovascular mortality risk increases even with modest WWI elevations. Above 12.74\u0026nbsp;cm/\u0026radic;kg, risk escalated substantially (HR = 1.44; 95% CI: 1.02-2.02; P = 0.0355).(Figure 3 and Table 3)\u003c/p\u003e\n\u003ch3\u003eSensitivity and Subgroup Analyses\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp; Stratified analyses of all-cause mortality among older adults revealed a significant association between the WW and mortality risk. (Figure 4)In individuals aged 60\u0026ndash;65 years, higher WWI was associated with increased mortality risk (HR: 1.33, 95% CI: 1.22\u0026ndash;1.45, p \u0026lt; 0.0001). This association was more pronounced in males (HR: 1.47, 95% CI: 1.39\u0026ndash;1.55, p \u0026lt; 0.0001) and non-Hispanic whites (HR: 1.45, 95% CI: 1.39\u0026ndash;1.52, p \u0026lt; 0.0001). Education level also influenced risk, with participants possessing education beyond high school exhibiting the highest mortality risk (HR: 1.53, 95% CI: 1.44\u0026ndash;1.62). Analysis by smoking status indicated elevated risk among former smokers (HR: 1.43, 95% CI: 1.36\u0026ndash;1.52) and never smokers (HR: 1.33, 95% CI: 1.26\u0026ndash;1.40), whereas no significant association was observed in current smokers (HR: 1.07, 95% CI: 0.98\u0026ndash;1.18). Physical activity stratification showed higher risk for individuals with insufficient activity (\u0026lt;600 MET-min/week; HR: 1.37) compared to those with adequate activity (\u0026ge;600 MET-min/week; HR: 1.27). Although hypertension (HR: 1.28) and diabetes mellitus (HR: 1.21) partially influenced risk, the association between WWI and mortality remained robust.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar trends were observed in cardiovascular mortality. Significantly elevated risk was noted in the younger elderly (60\u0026ndash;65 years; HR: 1.41, 95% CI: 1.19\u0026ndash;1.67) and the oldest age group (74\u0026ndash;85 years; HR: 1.20, 95% CI: 1.11\u0026ndash;1.30), whereas the middle-aged subgroup (66\u0026ndash;73 years) showed no significant association (HR: 1.12, 95% CI: 0.99\u0026ndash;1.28). Male sex (HR: 1.59, 95% CI: 1.44\u0026ndash;1.74, p \u0026lt; 0.0001) and non-Hispanic white ethnicity (HR: 1.59, 95% CI: 1.47\u0026ndash;1.72, p \u0026lt; 0.0001) were linked to higher cardiovascular mortality risk. Patterns observed for education and smoking status paralleled those in all-cause mortality, potentially reflecting confounding or competing risks. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large cohort study of U.S. older adults (NHANES 1999\u0026ndash;2018), we found that the weight-adjusted waist index (WWI) is a significant indicator of mortality. Higher WWI was associated with an increased risk of all-cause death, even after adjusting for numerous demographic, socioeconomic, behavioral, and health-related confounders. The relationship was notably non-linear, with minimal change in mortality risk across lower to mid-range WWI values and a sharp increase in risk once WWI exceeded approximately 11.2\u0026nbsp;cm/\u0026radic;kg. Specifically, beyond the threshold of WWI \u0026asymp;11.2, each 1-unit rise in WWI corresponded to a 14% increase in hazard of death. Consistently, older adults in the highest WWI tertile (above roughly 11.2) had about 9% higher mortality than those in the lowest tertile. Although this effect size is moderate, it is clinically relevant considering the high baseline mortality in older populations; a 9% hazard increase could translate to meaningful differences in life expectancy at the population level. We also observed a similar pattern for cardiovascular mortality, suggesting that WWI captures cardiovascular risk factors that contribute to death from heart disease and stroke. To our knowledge, this is one of the first studies focusing on WWI and mortality specifically in older adults in a nationally representative U.S. sample, and our findings reinforce the value of WWI as a potentially important metric of health risk in this age group.\u003c/p\u003e\n\u003cp\u003eThe relationship between WWI and mortality underscores its ability to simultaneously capture two critical components: central adiposity, via waist circumference, and muscle-fat balance, by incorporating weight adjustments. This unique dual functionality is particularly pertinent in older adults, who frequently contend with the combined challenges of visceral fat accumulation and sarcopenia, or age-related muscle loss(17, 18). This context of aging complicates the utility of traditional measures like body mass index (BMI), which often fails to accurately reflect health risks associated with increased fat deposition and reduced muscle mass (19, 20). Research indicates that reliance on BMI can produce misleading interpretations, particularly in reflecting mortality risk among older populations, where decreased muscle mass may correlate with lower mortality rates due to the protective factors associated with muscle (21, 22).Contrasting sharply with the \u0026quot;obesity paradox\u0026quot;\u0026mdash;the notion that overweight individuals may have lower mortality risks despite higher BMI\u0026mdash;WWI appears to circumvent the confounding effects of muscle mass. Evidence suggests that while elevated BMI may suggest risk factors that are not independently representative, WWI provides a more nuanced understanding by effectively distinguishing between healthy weight and unhealthy fat distribution associated with muscular individuals (23, 24). Studies have shown that as muscle mass declines, the inherent protective aspects related to fat mass become increasingly ambiguous, potentially skewing outcomes in mortality assessments based on BMI (25, 26). Therefore, WWI stands out as a more reliable metric that reflects true body composition and visceral fat presence, especially in the aging demographic where muscle maintenance remains critical(27, 28). By accounting for both central fat distribution and preserving the integrity of muscle mass considerations, WWI may provide a more accurate assessment of physical health risks, thereby facilitating improved intervention strategies that are tailored to the physiological realities of aging populations (18-20, 22).\u003c/p\u003e\n\u003cp\u003eIn the smoothed curve fitting analysis and the threshold effect analysis, our identified threshold of WWI \u0026asymp;11.2\u0026nbsp;cm/\u0026radic;kg\u0026nbsp;for increased mortality risk is remarkably consistent with prior studies. A prospective study in southern China by Ding et al. found that participants with WWI \u0026ge;11.2 had significantly higher all-cause and CVD mortality(29). Similarly, Cai et al. studied community-dwelling older adults in Beijing and reported that those in the highest WWI tertile (\u0026ge;11.25\u0026nbsp;cm/\u0026radic;kg) had a 166% higher risk of all-cause mortality compared to the lowest tertile (30). The magnitude of the HR in that Chinese study (HR 2.66 for highest vs lowest WWI tertile) is much larger than what we observed (HR ~1.09). This difference could be due to several factors: the Chinese cohort was smaller (n\u0026asymp;1,860, with 339 deaths) leading to less precise but larger point estimates; it had less comprehensive adjustment for confounders (their models adjusted for age, sex, and a limited set of variables); and the baseline characteristics differed (the Chinese sample\u0026rsquo;s highest WWI group was on average older and perhaps in worse health than ours, given their tertile HRs were quite high). It\u0026rsquo;s also possible that genetic or lifestyle differences lead to a stronger impact of central obesity in that population. Nonetheless, qualitatively, both studies indicate that high WWI is detrimental. Our findings add that even in a multi-ethnic U.S. population with different prevalence of obesity and comorbidities, WWI remains an important indicator of mortality, though the effect size in fully adjusted models is modest. In another related study, Cao et al. found a U-shaped relationship between WWI and mortality in a national US sample excluding Asian participants, with a nadir WWI risk of 10.46\u0026nbsp;cm/\u0026radic;kg(31). They found that risk increased both above and below that point, although the increased risk at low WWI was apparent mainly before excluding early follow-up (likely reflecting that low WWI could include very high BMI individuals or anomalies). In our older-focused analysis, we did not observe a statistically significant increase in risk at low WWI \u0026ndash; perhaps because very low WWI individuals (who would be heavy-for-waist, possibly indicating high muscle mass or high subcutaneous fat but not visceral fat) are relatively rare among older adults. Our data suggest that within the range typical for seniors, having a WWI below ~9.5 (which would indicate unusually heavy weight relative to waist, possibly implying high muscle or peripheral fat) is uncommon, and we had limited power to detect if there\u0026rsquo;s a slight uptick in risk there. Overall, our findings align with Cao et al. in that the primary concern is high WWI, not moderate or low WWI. Both studies underscore a threshold around 10.5\u0026ndash;11.5 where risk begins to climb \u0026ndash; with our analysis pinpointing ~11.2 for ages \u0026ge;60.Our findings align with subgroup analyses conducted on specific populations. Zheng et al. reported a significantly higher hazard ratio for mortality in patients with hypertension when compared to our broader cohort of older adults.(12) This discrepancy may be attributed to the inclusion of all older adults in our cohort, rather than exclusively those with hypertension. \u0026nbsp;The presence of concurrent hypertension may potentiate the impact of central obesity on cardiovascular risk, leading to the observed elevated hazard ratios in hypertensive patients. Despite these differences, the consistent direction of the association underscores the increased mortality risk associated with higher WWI, regardless of whether the population includes individuals with chronic conditions or the general older adult population.\u003c/p\u003e\n\u003cp\u003eThe association between an increased WWI and heightened mortality risk appears to be mediated through several interconnected biological pathways related to central obesity and body composition.A higher WWI indicates dysfunctional adipose tissue expansion, primarily manifesting as visceral obesity. This condition fosters chronic low-grade inflammation, which is enhanced by the increased production and release of pro-inflammatory cytokines. Such inflammation is pivotal in promoting atherosclerosis by exacerbating endothelial dysfunction, plaque formation, and thrombosis, consequently raising the risk of cardiovascular and cerebrovascular mortality(32-34).The implications of central obesity extend into oxidative stress, which contributes significantly to the escalated risk of cardiovascular-related deaths. Studies have demonstrated that individuals exhibiting elevated WWI levels are prone to higher oxidative stress markers, which are detrimental to cardiovascular health and associated with increased mortality rates (35). Additionally, it is notable that WWI negatively correlates with muscle mass, implicating metabolic dysregulation; the condition of sarcopenia is significantly linked to insulin resistance and a reduction in metabolic resilience(34, 36). The detrimental effects of low muscle mass coupled with high abdominal fat significantly exacerbate health risks due to their intertwined nature.Moreover, WWI shows a strong association with common comorbid conditions such as diabetes, hypertension, and dyslipidemia. These comorbidities are independently linked to elevated mortality risk due to direct organ damage and increased cardiac metabolic load(37, 38). For instance, the coexistence of obesity-related complications such as type 2 diabetes and hypertension has been documented to pose a substantial risk factor for all-cause mortality, further emphasizing the importance of managing central obesity(34, 39).Collectively, these mechanisms\u0026mdash;chronic inflammation, oxidative stress, altered body composition, and metabolic dysfunction\u0026mdash;explain the relationship between WWI and mortality. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eDespite these strengths and implications, our study has limitations.First, the observational design precludes causal conclusions.Second, WWI and other covariates were measured at baseline only; we did not account for changes in weight or waist over time.Some participants may have lost weight (and thus changed WWI) due to illness during follow-up, which could bias results (though our exclusion of early deaths and adjustment for baseline health factors help mitigate this).Third, our extensive covariate adjustment, while aimed at isolating the WWI effect, may inadvertently adjust away part of the effect if those factors (e.g., diabetes, hypertension) lie on the causal pathway from central obesity to mortality. Fourth, we did not explicitly compare WWI\u0026rsquo;s predictive power against BMI or other indices in this paper.Fifth, the use of NHANES data across 20 years could introduce some heterogeneity \u0026ndash; measurement protocols for waist, slight differences in survey design over time \u0026ndash; but NHANES is fairly standardized, and we accounted for survey design to some extent .Finally, though our sample is large and multi-ethnic, it is confined to U.S. older adults.Lifestyle, genetic factors, and obesity patterns differ globally.Our findings might not directly extrapolate to other countries or ethnic groups not well-represented in NHANES. Interestingly, the concordance of the ~11 cm/\u0026radic;kg threshold in both U.S. and Chinese studiespubmed.ncbi.nlm.nih.gov hints at a possible universal biological threshold, but more research would be needed to confirm that. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong older adults in the United States, elevated levels of theWWI are significantly associated with increased risks of all-cause and cardiovascular mortality. Those with a high WWI \u0026ndash; reflecting a disproportionately large waist relative to their body weight \u0026ndash; face higher all-cause and cardiovascular mortality, independent of traditional risk factors. This supports the notion that an unfavorable body composition (excess central adiposity with lower lean mass) is deleterious in aging.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eNHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWWI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeight-adjusted waist index \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOdds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConfidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of variance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized additive model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular disease\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuai Huang: Conceptualization, Data Curation, Writing ,Review \u0026amp; Editing.Yongtian Zheng:Conceptualization, Data Curation,Writing,Review \u0026amp; Editing.Yijiang Li:Formal Analysis,Methodology,Software,Validation.Yinuo Bi:Data Curation, \u0026nbsp;Validation ,Review \u0026amp; Editing. Jianfan Jian: Data Curation, Validation ,Review \u0026amp; Editing.Xiaosheng Zhu :Conceptualization,Project Administration,Supervision,Review \u0026amp; Editing.Wenyu Jiang :Conceptualization,Project Administration,Supervision,Review \u0026amp; Editing. :Conceptualization,Supervision,Writing-original draft,Writing-review \u0026amp;editing.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the findings of this article are available in the NHANES website,\u003cu\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/u\u003e.Anyone who would like to request collated data from this study should contact [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available data from public databases and therefore did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull;Conflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no funding and conflicts of interest related to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the National Center for Health Statistics for providing access to NHANES data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYu S, Wang B, Guo X, Li G, Yang H, Sun Y. Weight-Adjusted-Waist Index Predicts Newly Diagnosed Diabetes in Chinese Rural Adults. Journal of Clinical Medicine. 2023;12(4):1620.\u003c/li\u003e\n\u003cli\u003ePark Y, Kim NH, Kwon TY, Kim SG. A Novel Adiposity Index as an Integrated Predictor of Cardiometabolic Disease Morbidity and Mortality. Scientific Reports. 2018;8(1).\u003c/li\u003e\n\u003cli\u003eZheng Y, Nie Z, Zhang Y, Sun TT. The Weight-Adjusted-Waist Index Predicts All-Cause and Cardiovascular Mortality in Hypertension. Frontiers in Cardiovascular Medicine. 2025;12.\u003c/li\u003e\n\u003cli\u003eJia S, Huo X, Sun L, Yao Y, Chen X. The association between the weight-adjusted-waist index and frailty in US older adults: a cross-sectional study of NHANES 2007-2018. Frontiers in endocrinology. 2024;15:1362194.\u003c/li\u003e\n\u003cli\u003eHuang A, Lin B, Jia Z, Ji X, Chen Y. Correlation Between Weight-Adjusted-Waist Index and Hypertension in the US Population: Based on Data From NHANES 2005\u0026ndash;2018. Frontiers in Cardiovascular Medicine. 2024;11.\u003c/li\u003e\n\u003cli\u003eFang H, Xie F, Li K, Li M, Wu Y. Association Between Weight-Adjusted-Waist Index and Risk of Cardiovascular Diseases in United States Adults: A Cross-Sectional Study. BMC Cardiovascular Disorders. 2023;23(1).\u003c/li\u003e\n\u003cli\u003eAnderson PJ, Critchley J, Chan JCN, Cockram CS, Lee ZSK, Thomas GN, et al. Factor Analysis of the Metabolic Syndrome: Obesity vs Insulin Resistance as the Central Abnormality. International Journal of Obesity. 2001;25(12):1782-8.\u003c/li\u003e\n\u003cli\u003eXia X, Chen S, Tian X, Xu Q, Zhang Y, Zhang X, et al. Roles of general and central adiposity in cardiometabolic multimorbidity: revisiting the obesity paradox using a multistate model. Obesity (Silver Spring, Md). 2024;32(4):810-21.\u003c/li\u003e\n\u003cli\u003eFu H, Liu Z, Yu H, Zhao Y, Gan Y, Chen J, et al. 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A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep. 2018;8(1):16753.\u003c/li\u003e\n\u003cli\u003eWen Z, Li X. Association between weight-adjusted-waist index and female infertility: a population-based study. Frontiers in endocrinology. 2023;14:1175394.\u003c/li\u003e\n\u003cli\u003eZhang Y, Wang F, Tang J, Shen L, He J, Chen Y. Association of triglyceride glucose-related parameters with all-cause mortality and cardiovascular disease in NAFLD patients: NHANES 1999-2018. Cardiovascular diabetology. 2024;23(1):262.\u003c/li\u003e\n\u003cli\u003eLiu M, Wang C, Liu R, Wang Y, Wei B. Association between cardiometabolic index and all-cause and cause-specific mortality among the general population: NHANES 1999-2018. Lipids in health and disease. 2024;23(1):425.\u003c/li\u003e\n\u003cli\u003eLee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. 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Scientific Reports. 2018;8(1).\u003c/li\u003e\n\u003cli\u003eHan SS, Kim KW, Kim KI, Na KY, Chae DW, Kim S, et al. Lean Mass Index: A Better Predictor of Mortality Than Body Mass Index in Elderly Asians. Journal of the American Geriatrics Society. 2010;58(2):312-7.\u003c/li\u003e\n\u003cli\u003eIx JH, Boer IHd, Wassel CL, Criqui MH, Shlipak MG, Whooley MA. Urinary Creatinine Excretion Rate and Mortality in Persons With Coronary Artery Disease. Circulation. 2010;121(11):1295-303.\u003c/li\u003e\n\u003cli\u003eSuthahar N, Zwartkruis VW, Geelhoed B, Withaar C, Meems LMG, Bakker SJL, et al. Associations of Relative Fat Mass and BMI With All‐cause Mortality: Confounding Effect of Muscle Mass. Obesity. 2024;32(3):603-11.\u003c/li\u003e\n\u003cli\u003eHolla J, Leeden Mvd, Knol DL, Roorda LD, Esch Mvd, Voorneman RE, et al. The Association of Body-Mass Index and Depressed Mood With Knee Pain and Activity Limitations in Knee Osteoarthritis: Results From the Amsterdam Osteoarthritis Cohort. BMC Musculoskeletal Disorders. 2013;14(1).\u003c/li\u003e\n\u003cli\u003eBansal N, Hsu Cy, Zhao S, Whooley MA, Ix JH. Relation of Body Mass Index to Urinary Creatinine Excretion Rate in Patients With Coronary Heart Disease. The American Journal of Cardiology. 2011;108(2):179-84.\u003c/li\u003e\n\u003cli\u003eForte GC, Almeida JCd, Silva DTRd, Hennemann MLT, Dalcin PdTR. Association Between Anthropometric Markers and Asthma Control, Quality of Life and Pulmonary Function in Adults With Asthma. Journal of Human Nutrition and Dietetics. 2018;32(1):80-5.\u003c/li\u003e\n\u003cli\u003eSchult OWB, Feinendegen LE, Zaum S, Shreeve WW, Pierson RN. Applications of BMI or BSI: Differences and Revisions According to Age and Height. Journal of Obesity. 2010;2010:1-9.\u003c/li\u003e\n\u003cli\u003eDing C, Shi Y, Li J, Li M, Hu L, Rao J, et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. 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Frontiers in endocrinology. 2023;14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Weight-adjusted Waist Index, Mortality, NHANES, Cardiovascular Disease, Older Adults","lastPublishedDoi":"10.21203/rs.3.rs-6987746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6987746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe weight-adjusted waist index (WWI) is a new anthropometric measure that reflects central obesity in relation to body weight. Previous studies have examined the relationship between WWI and all-cause mortality in populations with hypertension; however, evidence specifically pertaining to the elderly remains lacking. This study aims to examine the relationship between WWI and all-cause as well as cardiovascular mortality in older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed 16,242 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years from the 1999\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES). WWI was calculated from baseline examination measurements of weight and waist circumference. The association between WWI and mortality outcomes was analyzed utilizing the Kaplan\u0026ndash;Meier survival modeling, Cox regression analysis, smooth curve fitting analysis, threshold effect analysis, and subgroup analysis. Stratification factors for subgroups included demographics (age, sex, race/ethnicity, marital status, education, poverty-income ratio) and health factors (smoking, alcohol use, physical activity, hypertension, and diabetes).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean age of participants was 70.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32 years, with a gender distribution of 49.87% male and 50.13% female. The mean WWI was 11.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 cm/\u0026radic;kg. There were 5,779 all-cause deaths and 1,907 cardiovascular disease (CVD) deaths. Kaplan\u0026ndash;Meier analysis indicated significant differences in all-cause and CVD mortality across WWI categories. After adjusting for covariates, WWI was positively associated with all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.072, 95%CI: 1.03, 1.11, p\u0026thinsp;=\u0026thinsp;0.0006) and CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.09, 95%CI :1.02, 1.17, p\u0026thinsp;=\u0026thinsp;0.015).These associations remained significant after full adjustment. Threshold analysis revealed an inflection point for all-cause mortality at 11.24 cm/\u0026radic;kg, above this threshold, the risk significantly increased (HR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.08\u0026ndash;1.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For CVD mortality, the inflection point was determined to be 12.74 cm/\u0026radic;kg.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAmong older adults in the U.S., a higher WWI is associated with increased risks of all-cause and cardiovascular mortality. This relationship is non-linear, showing minimal risk variation at lower WWI values but a marked increase in mortality risk above approximately 11cm/\u0026radic;kg. WWI is a significant indicator of mortality, even after adjusting for various demographic, socioeconomic, lifestyle, and health factors.\u003c/p\u003e","manuscriptTitle":"Weight-Adjusted Waist Index and Mortality Among Older Adults: Findings from NHANES 1999–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:30:49","doi":"10.21203/rs.3.rs-6987746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-05T15:07:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T04:01:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T18:24:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T12:42:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-01T14:06:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cab2b39-8670-4865-a2d4-245313870dac","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58046903,"name":"Health sciences/Cardiology"},{"id":58046904,"name":"Health sciences/Diseases"},{"id":58046905,"name":"Health sciences/Endocrinology"},{"id":58046906,"name":"Health sciences/Health care"},{"id":58046907,"name":"Health sciences/Medical research"},{"id":58046908,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-18T08:30:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:30:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6987746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6987746","identity":"rs-6987746","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}