Association between the Weight-to-Waist Ratio and All-Cause Mortality in Cancer Patients A Cox Proportional-Hazards Regression Analysis of Cohort Data

Association between the Weight-to-Waist Ratio and All-Cause Mortality in Cancer Patients A Cox Proportional-Hazards Regression Analysis of Cohort Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between the Weight-to-Waist Ratio and All-Cause Mortality in Cancer Patients A Cox Proportional-Hazards Regression Analysis of Cohort Data Guangzhi Wan, Jiannan Shang, Jingzhi Guan, Chunyan Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927009/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective This study aimed to investigate the association between weight-to-waist ratio (WWR) and all-cause mortality risk in cancer patients, clarify whether it is an independent prognostic factor for cancer patients, and analyze its potential interaction effects. Methods A cohort study design was adopted. We screened data from 18,469 baseline participants and finally included 834 cancer patients with complete baseline data (331 deceased cases and 503 surviving cases). The Wilcoxon rank-sum test and Pearson chi-square test were used to compare baseline characteristics between the deceased and surviving groups. A Cox proportional-hazards regression model was employed to analyze the association between WWR and mortality risk, and a multifactorial-adjusted model was constructed to control for confounders. The dose-response relationship was analyzed by quartile grouping, heterogeneity was evaluated through subgroup analysis, and a non-linear relationship was tested using restricted cubic spline (RCS) analysis. Results The median WWR in the deceased group was 0.79 (IQR: 0.72–0.87), which was significantly lower than that in the surviving group (0.84, IQR: 0.77–0.90) (P < 0.001). After adjusting for demographic characteristics, lifestyle factors, and comorbidities (Model 3), WWR was significantly inversely associated with all-cause mortality risk (HR = 0.06, 95% CI: 0.02–0.24, P < 0.001). Multivariate-adjusted dose-response analysis showed that mortality risk decreased stepwise with increasing WWR quartiles, reaching a plateau in the highest quartile (Q4) (Q2 vs. Q1: HR = 0.70, P = 0.023; Q3 vs. Q1: HR = 0.57, P = 0.001; Q4 vs. Q1: HR = 0.57, P = 0.003). This indicates that a higher WWR is a protective factor against all-cause mortality in cancer patients, with a dose-response relationship. Subgroup analysis suggested potential interaction effects between WWR and urbanization level (p for interaction = 0.014) and history of heart disease (p for interaction = 0.084). RCS analysis indicated a linear association between WWR and mortality risk (p for overall association < 0.001, p for non-linearity = 0.077). Conclusion WWR is an independent protective factor for all-cause mortality in cancer patients, showing a dose-response relationship. Improving WWR by optimizing the balance between body weight and waist circumference may provide a novel intervention strategy for improving cancer prognosis. weight-to-waist ratio (WWR) cancer all-cause mortality risk Cox proportional-hazards model dose-response relationship body fat distribution interaction effect Figures Figure 1 Figure 2 Figure 3 Introduction In recent years, the independent impact of abnormal body fat distribution on the prognosis of cancer patients has been multidimensionally confirmed in multiple fields, including cancer epidemiology and cancer metabolism[ 1 , 2 ]. The relationship between obesity and cancer is complex, with its effects exhibiting significant heterogeneity across different cancer types and different measurement indicators. A 2022 Mendelian randomization study provided moderate evidence for a causal association between visceral adipose tissue (VAT) and the risk of specific cancers[ 3 ]. This study, using genetic instrumental variables to control for confounders effectively, found that a genetically predicted increase in visceral fat volume significantly elevated the risk of pancreatic cancer and lung squamous cell carcinoma, and this effect was independent of body mass index (BMI). This suggests that visceral fat, rather than general obesity alone, may be a key pathogenic factor driving the progression of certain cancers. Concurrently, a 2024 Meta-Analysis including 73 cohort studies revealed another aspect of the relationship between body fat and cancer from a prognostic standpoint[ 4 ]. This analysis found that a high BMI or obesity was a significant risk factor for overall survival in breast cancer patients. However, in patients with certain gastrointestinal cancers, such as gastric cancer and colorectal cancer, and others, a higher BMI was conversely associated with better survival prognosis, exhibiting the "obesity paradox". These studies collectively indicate that fat distribution (e.g., visceral fat) and general obesity indicators (e.g., BMI) have differential effects on the onset and progression of different cancer types[ 5 , 6 ]. Although the association between body fat-related parameters and cancer prognosis has been preliminarily established, the independent prognostic value of WWR—a novel body fat assessment indicator integrating body weight and central obesity—remains not sufficiently supported by systematic clinical research in cancer patients[ 7 ]. Current limited exploratory studies suggest that WWR may have potential superior discriminatory ability for cancer prognosis compared with traditional body fat indicators; however, its dose-response relationship with all-cause mortality risk in cancer patients, independent predictive value, and underlying mechanisms remain unclear. Based on this research gap, the present study intends to systematically explore the patterns of association between WWR and long-term survival outcomes in cancer patients by leveraging large-sample prospective cancer cohort data, via multifactorial Cox proportional-hazards regression models and restricted cubic spline analysis. This work seeks to provide a novel clinically translatable biomarker for prognostic stratification and screening of metabolic intervention targets in cancer patients. Participants and Methods 1.1 Study Participants This study adopted a prospective cohort design. Data from baseline participants in the 2006 baseline wave of the HRS database were abstracted. The flowchart of participant selection is illustrated in Fig. 1. The initial baseline population comprised 18,469 participants. After sequentially excluding participants with prevalent malignant cancer at baseline (n = 2,562), those who did not develop malignant tumors during the follow-up period (n = 12,726), those lost to follow-up (n = 1,187), those with missing weight-to-waist ratio (WWR) data (n = 1,157), and those with missing smoking status data (n = 3), respectively, a final cohort of 834 malignant tumor patients was included for statistical analysis. During the follow-up period, the primary study outcome was all-cause mortality. The occurrence of mortality events and the corresponding follow-up time were accurately confirmed through official records from the database. 1.2 Definition of Key Variables 1.2.1 Primary Exposure The primary exposure was Weight-to-Waist Ratio (WWR), calculated as body weight (kg)/waist circumference (cm). All data were accurately obtained via standardized measurements at baseline. Continuous variables for this variable were presented as median (interquartile range, IQR). For dose-response relationship analysis, participants were categorized into four WWR quartile groups (Q1–Q4) based on WWR, with Q1 (the lowest WWR group) serving as the reference group. 1.2.2 Primary Outcome The primary outcome was all-cause mortality among cancer patients. The occurrence, timing, and outcome determination of mortality events were confirmed via official follow-up records from the HRS database. 1.2.3 Covariates Covariates included demographic characteristics (age, sex, race, educational attainment, level of urbanization), lifestyle factors (smoking status, alcohol consumption), and comorbidities (hypertension, type 2 diabetes, chronic obstructive pulmonary disease [COPD], organic heart disease, rheumatoid arthritis / osteoarthritis). The definitions and diagnostic criteria for all covariates were based on previously published relevant clinical studies. 1.3 Statistical Analysis Data analysis was conducted with R software (version 4.3.3). A two-sided P < 0.05 was considered statistically significant for all comparisons. In the comparison of baseline characteristics, continuous variables were compared via the Wilcoxon rank-sum test, and categorical variables via the Pearson chi-square test, between the deceased and surviving groups.To explore the association between WWR and mortality risk among cancer patients, three hierarchical Cox proportional hazards regression models were constructed: Model 1 (crude model, WWR only); Model 2 (adjusted for WWR, demographic characteristics, and lifestyle factors); and Model 3 (further adjusted for comorbidities on the basis of Model 2). The dose-response relationship was assessed by categorizing participants into four WWR quartile groups (Q1–Q4), with Q1 (the lowest WWR group) as the reference group. Subgroup analyses were performed stratified by clinical features, including age, sex, race, and others, to assess heterogeneity and potential interaction effects in the association between WWR and mortality risk across different subgroups. Potential nonlinearity in the association between WWR and mortality risk among cancer patients was tested using a restricted cubic spline (RCS) model with four knots (5th, 35th, 65th, and 95th percentiles). Results 2.1 Baseline Characteristics Among the 834 malignant tumor patients finally included in this study, the median age was 66.00 years (IQR: 59.00–72.00), and 426 patients (51.1%) were male. Significant differences were observed between the deceased and surviving groups in all indicators including WWR, age, sex, smoking status, alcohol consumption, educational attainment, hypertension, chronic obstructive pulmonary disease (COPD), organic heart disease, arthritis, and urbanization level (all P 0.05) (Table 1 ). Table 1 Comparison of baseline clinical characteristics between the deceased and surviving groups among 834 malignant tumor patients. Characteristic Overall, N = 834 0, N = 503 1, N = 331 p-value 1 WWR, Median (IQR) 0.82 (0.75, 0.88) 0.84 (0.77, 0.90) 0.79 (0.72, 0.87) < 0.001 Age, Median (IQR) 66.00 (59.00, 72.00) 63.00 (57.00, 68.00) 72.00 (65.00, 77.00) < 0.001 Gender, n (%) 426.0 (51.1%) 238.0 (47.3%) 188.0 (56.8%) 0.007 Race, n (%) 0.786 1 693.0 (83.1%) 416.0 (82.7%) 277.0 (83.7%) 2 114.0 (13.7%) 69.0 (13.7%) 45.0 (13.6%) 3 27.0 (3.2%) 18.0 (3.6%) 9.0 (2.7%) Smoke, n (%) 518.0 (62.1%) 297.0 (59.0%) 221.0 (66.8%) 0.025 Drink, n (%) 473.0 (56.7%) 306.0 (60.8%) 167.0 (50.5%) 0.003 Education, n (%) < 0.001 1 132.0 (15.8%) 49.0 (9.7%) 83.0 (25.1%) 2 483.0 (57.9%) 295.0 (58.6%) 188.0 (56.8%) 3 219.0 (26.3%) 159.0 (31.6%) 60.0 (18.1%) Hypertension, n (%) 459.0 (55.0%) 261.0 (51.9%) 198.0 (59.8%) 0.024 Diabetes, n (%) 139.0 (16.7%) 77.0 (15.3%) 62.0 (18.7%) 0.194 COPD, n (%) 66.0 (7.9%) 26.0 (5.2%) 40.0 (12.1%) < 0.001 Heart.Disease, n (%) 171.0 (20.5%) 69.0 (13.7%) 102.0 (30.8%) < 0.001 Arthritis, n (%) 452.0 (54.2%) 251.0 (49.9%) 201.0 (60.7%) 0.002 Urbanization.level, n (%) 0.039 1 398.0 (47.7%) 258.0 (51.3%) 140.0 (42.3%) 2 173.0 (20.7%) 97.0 (19.3%) 76.0 (23.0%) 3 263.0 (31.5%) 148.0 (29.4%) 115.0 (34.7%) 1 Wilcoxon rank sum test; Pearson's Chi-squared test 2.2 Cox Proportional Hazards Regression Analysis Cox proportional hazards regression analysis revealed that the association between WWR and mortality risk in malignant tumor patients was statistically significant in all adjusted models. WWR was significantly and inversely associated with mortality risk in malignant tumor patients in Model 1 (HR = 0.02, 95% CI: 0.01–0.07, P < 0.001). After adjusting for demographic characteristics and lifestyle factors in Model 2, WWR remained significantly and inversely associated with mortality risk in malignant tumor patients (HR = 0.09, 95% CI: 0.02–0.34, P < 0.001). In Model 3, after further adjusting for comorbidities on the basis of Model 2, WWR remained an independent protective factor for mortality risk in malignant tumor patients (HR = 0.06, 95% CI: 0.02–0.24, P < 0.001). Furthermore, age (per 1-year increase, HR = 1.07, P < 0.001), male sex (reference: female sex, HR = 1.66, P < 0.001), alcohol consumption (HR = 0.68, P = 0.001), and organic heart disease (HR = 1.57, P < 0.001) were also independent prognostic factors for mortality risk in malignant tumor patients (Table 2 ). Table 2 Cox Proportional Hazards Regression Analysis of the Association between WWR and Mortality Risk among Malignant Tumor Patients. Group Characteristic N Event N HR 1 95% CI 1 p-value Model 1 WWR 834 331 0.02 0.01, 0.07 < 0.001 Model 2 WWR 834 331 0.09 0.02, 0.34 < 0.001 Model 3 WWR 834 331 0.06 0.02, 0.24 < 0.001 1 HR = Hazard Ratio, CI = Confidence Interval Note WWR = weight-to-waist ratio; COPD = chronic obstructive pulmonary disease; ¹HR = hazard ratio, CI = confidence interval (95%); Model 1: unadjusted model; Model 2: adjusted for age, sex, smoking status, alcohol consumption, educational attainment, as well as urbanization level; Model 3: adjusted for all variables in Model 2 plus additional variables: hypertension, type 2 diabetes, chronic obstructive pulmonary disease, organic heart disease, and arthritis. 2.3 Dose-Response Relationship Between WWR Quartiles and Mortality Risk Model 3 (fully adjusted multivariate model) revealed a significant dose-response relationship between WWR levels and mortality risk in malignant tumor patients, with mortality risk decreasing progressively as WWR levels increased. Mortality risk was significantly reduced in the Q2 group compared with the Q1 group (the lowest WWR group) (HR = 0.70, 95% CI: 0.52–0.95, P = 0.023). Mortality risk was further reduced in the Q3 group (HR = 0.57, 95% CI: 0.41–0.80, P = 0.001). However, mortality risk in the Q4 group (the highest WWR group) was comparable to that in the Q3 group, with no further reduction (HR = 0.57, 95% CI: 0.39–0.83, P = 0.003) (Table 3 ). Table 3 Analysis Results of the Dose-Response Relationship Between WWR Quartiles and Mortality Risk Among Malignant Tumor Patients. Group Characteristic N Event N HR 1 95% CI 1 p-value Model 1 WWR_q 834 331 Q1 209 — — Q2 208 0.59 0.45, 0.79 < 0.001 Q3 208 0.49 0.36, 0.65 < 0.001 Q4 209 0.43 0.31, 0.58 < 0.001 Model 2 WWR_q 834 331 Q1 209 — — Q2 208 0.73 0.54, 0.99 0.042 Q3 208 0.62 0.45, 0.86 0.004 Q4 209 0.61 0.43, 0.88 0.008 Age 834 331 1.07 1.06, 1.09 < 0.001 Gender 834 331 1.62 1.26, 2.08 < 0.001 Smoke 834 331 1.16 0.91, 1.49 0.223 Drink 834 331 0.65 0.52, 0.82 < 0.001 Education 834 331 0.76 0.64, 0.90 0.001 Urbanization.level 834 331 1.15 1.01, 1.30 0.031 Model 3 WWR_q 834 331 Q1 209 — — Q2 208 0.70 0.52, 0.95 0.023 Q3 208 0.57 0.41, 0.80 0.001 Q4 209 0.57 0.39, 0.83 0.003 Age 834 331 1.07 1.06, 1.09 < 0.001 Gender 834 331 1.60 1.23, 2.08 < 0.001 Smoke 834 331 1.13 0.88, 1.45 0.338 Drink 834 331 0.67 0.53, 0.85 < 0.001 Education 834 331 0.77 0.65, 0.92 0.004 Urbanization.level 834 331 1.14 1.01, 1.30 0.037 Hypertension 834 331 1.17 0.93, 1.48 0.190 Diabetes 834 331 1.07 0.80, 1.43 0.660 COPD 834 331 1.40 0.99, 1.98 0.055 Heart.Disease 834 331 1.56 1.22, 1.99 < 0.001 1 HR = Hazard Ratio, CI = Confidence Interval Note WWR = weight-to-waist ratio; WWR_q = WWR quartiles; COPD = chronic obstructive pulmonary disease; Q1 = the lowest WWR quartile group (reference group); ¹HR = hazard ratio, CI = confidence interval (95%); Model 1: unadjusted model; Model 2: adjusted for age, sex, smoking status, alcohol consumption, educational attainment, as well as urbanization level; Model 3: adjusted for all variables in Model 2 plus additional variables: hypertension, type 2 diabetes, chronic obstructive pulmonary disease, organic heart disease, and arthritis. 2.4 Subgroup Analysis Results Subgroup analyses showed a significant inverse association between WWR and mortality risk in the overall study population of malignant tumor patients (HR = 0.020, 95% CI: 0.010–0.070). Stratified analyses showed that a significant interaction existed between urbanization level and WWR in terms of mortality risk (Pinteraction = 0.014). The interaction between organic heart disease and WWR marginally approached statistical significance (Pinteraction = 0.084). No significant interactions between WWR and other clinical characteristics, such as age, sex, and smoking status, were detected in terms of mortality risk (all Pinteraction > 0.05) (Fig. 2 ). 2.5 Results of the Non-Linearity Test The results of restricted cubic spline (RCS) analysis showed a statistically significant overall association between WWR and mortality risk in malignant tumor patients (P < 0.001). The further non-linearity test yielded P = 0.077, suggesting that the association between WWR and mortality risk was predominantly linear in nature, with no statistically significant non-linear relationship detected (Fig. 3 ). Discussion This large prospective cohort study, using multivariate Cox proportional hazards regression, dose-response analysis, subgroup analysis, and restricted cubic spline (RCS) non-linearity testing, provides the first evidence in oncology that weight-to-waist ratio (WWR) is an independent protective factor for mortality among patients with malignant tumors. A linear inverse association and a clear dose-response pattern were observed, and these findings remained consistent after adjustment for demographic characteristics, lifestyle factors, and comorbidities. Our results carry important clinical implications for prognostic evaluation and intervention strategies in cancer patients. As a novel composite adiposity indicator integrating body weight and central obesity, WWR shows clear advantages in cancer prognosis assessment. It overcomes the limitations of BMI, which reflects overall weight but ignores fat distribution, and also addresses the shortcoming of waist circumference alone, which does not consider baseline weight or the balance between fat mass and lean mass. Thus, WWR can more accurately reflect the body fat distribution and metabolic status of cancer patients [ 8 , 9 ].The protective effect of WWR observed in this study may be attributed to the balanced combination of weight and waist circumference, indirectly reflecting a more favorable body fat profile (e.g., lower visceral fat proportion). This is consistent with the findings of a 2025 Mendelian randomization study in the International Journal of Epidemiology, which demonstrated that larger waist circumference was strongly associated with an increased risk of colorectal cancer mortality. The underlying mechanism involves pro-inflammatory cytokines secreted by visceral fat, including tumor necrosis factor-α and interleukin-6, which promote tumor cell proliferation and inhibit apoptosis [10,11]. A higher WWR indicates a more appropriate body weight for a given waist circumference, or a slimmer waist for a given body weight, corresponding to lower visceral adiposity and a milder inflammatory microenvironment, thereby reducing the mortality risk in cancer patients. From the perspective of cancer metabolism, the association between WWR and cancer prognosis may be mediated through the insulin-like growth factor-1 (IGF-1) signaling pathway and chronic inflammatory pathways, which is consistent with and complementary to classic findings in cancer metabolism [ 12 ]. A 2023 study in Cancer & Metabolismreported that obesity (including abnormal fat distribution) significantly increases the risk of breast cancer recurrence by elevating IGF-1 levels and activating a chronic inflammatory microenvironment (HR = 1.44 for BMI-defined obesity, HR = 1.31 for waist circumference-defined obesity) [ 13 – 15 ]. In the present study, the reduced mortality risk associated with elevated WWR essentially reflects lower IGF-1 levels and milder chronic inflammation, which effectively inhibit tumor cell proliferation and metastasis and thereby improve long-term survival. Although we did not perform stratified analyses by cancer type, our findings suggest that future studies should further explore the prognostic value of WWR in specific cancer types, including gastrointestinal, breast, and lung cancers. From a clinical perspective, our subgroup analysis provides important evidence for the precise prognostic evaluation of cancer patients with different clinical characteristics. The significant interaction between urbanization level and WWR (P for interaction = 0.014) and the marginal interaction between organic heart disease and WWR (P for interaction = 0.084) carry important clinical implications. Regions with a higher urbanization level usually have better medical resources but also more prevalent unhealthy lifestyles, such as physical inactivity and high-fat/high-sugar diets, which may attenuate the protective effect of WWR. Cancer patients with comorbid heart disease often present with more severe metabolic disorders and poorer cardiovascular compensatory capacity, which may offset part of the protective effect of WWR achieved by improving body fat distribution. These findings suggest that, in clinical practice, for cancer patients living in highly urbanized areas or with comorbid heart disease, WWR optimization should be combined with lifestyle interventions and cardiovascular management to maximize its prognostic benefit. As the first prospective cohort study in oncology to systematically explore the association between WWR and mortality in cancer patients, this study has several major strengths. First, based on a large sample and rigorous adjustment for confounders, we used multiple statistical methods to verify the robustness of our results, supporting a high level of evidence. Second, by integrating recent advances in oncology and obesity research, we provided mechanistic insights into the association between WWR and cancer prognosis, enhancing the scientific depth of this study. Nevertheless, several limitations should be acknowledged. First, WWR was only measured at baseline without dynamic monitoring, which precluded evaluation of the longitudinal association between changes in WWR during treatment and prognosis. Second, the lack of stratification by cancer type and stage prevented us from determining the prognostic value of WWR across different cancer subtypes and stages. Third, as an observational cohort study, we cannot establish a causal relationship between WWR and cancer mortality; future randomized controlled trials are warranted to verify whether interventions targeting WWR can improve cancer prognosis. Conclusion This comprehensive analysis based on a prospective cohort confirms that weight-to-waist ratio (WWR) is significantly and linearly inversely associated with mortality risk in patients with malignant tumors and serves as an independent protective factor with a clear dose-response relationship: mortality risk decreases progressively with increasing WWR until reaching a plateau. In clinical cancer practice, WWR can be incorporated into prognostic evaluation systems as a novel adiposity-related biomarker. Meanwhile, clinical interventions that optimize the balance between weight and waist circumference (e.g., maintaining healthy body weight while controlling waist circumference and reducing visceral fat accumulation) to increase WWR may provide a new metabolic target and strategy for improving cancer prognosis. Furthermore, for cancer patients living in highly urbanized areas or with comorbid heart disease, personalized WWR intervention strategies combined with lifestyle modifications and comorbidity management are needed to maximize its protective prognostic effect. Future oncology studies should conduct prospective cohort studies with dynamic monitoring of WWR to clarify the association between longitudinal changes in WWR and cancer prognosis; perform stratified analyses by cancer type and stage to define subtype-specific prognostic values; and carry out randomized controlled trials to verify whether dietary, exercise, or other interventions targeting WWR can improve survival outcomes. These efforts will provide more robust evidence for the translational application of WWR in cancer prognostic evaluation and clinical intervention. Declarations 5 Clinical trial number: not applicable. 6 Funding ： None. 7 Ethics approval and consent to participate This study is a secondary analysis of de-identified, publicly available cohort data from one longitudinal study. HRS was approved by the University of Michigan Institutional Review Board; all respondents provided written informed consent. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiannan","middleName":"","lastName":"Shang","suffix":""},{"id":596865136,"identity":"7e00443f-3467-449c-b838-6da667509ca7","order_by":2,"name":"Jingzhi Guan","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingzhi","middleName":"","lastName":"Guan","suffix":""},{"id":596865137,"identity":"b8ffe694-6638-442e-a9d8-78cb884cfb1a","order_by":3,"name":"Chunyan Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHACNgjF3nzwwYcKGzk29vYDRGrhOZZsOONMmjEfz5kEIrVI+KgJc7YcSpwn4WCAV71u++Fnj3l3HJY3l+BhY2ZsOJDeJsGQwPCjYhtOLWZn0syNec8cNtw5u/fY48Idd3LbpBsPMPacuY1by4EcNmnetsOMG+6cSzeeeeZZbpvMgQRmxjY8Ws6/AWux33AjxwzESGeTSDDAr+UGxJZEmJYEIrQ8M5Oc25aevOEMJJAN24CBfBCvX84nP5N422Ztu+E4JCrl5dvbDz74UYFbCxQ0o3IPEFIPBHVEqBkFo2AUjIIRCwD3BmI9nO9/IAAAAABJRU5ErkJggg==","orcid":"","institution":"Heze City Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-02-20 14:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103733417,"identity":"557127a8-f764-43e5-86aa-3475c2cac910","added_by":"auto","created_at":"2026-03-02 09:28:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56597,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study participant selection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8927009/v1/9449de4d32188a06f5f5a6b5.png"},{"id":103733283,"identity":"a4a02ac5-450b-414a-badb-841e361a55f4","added_by":"auto","created_at":"2026-03-02 09:27:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94236,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of the Association Between WWR and Mortality Risk Among Malignant Tumor Patients.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8927009/v1/88b714b9a668a0aef824af0a.png"},{"id":103733329,"identity":"56b8b7aa-3e77-4014-bf6c-a5110c367316","added_by":"auto","created_at":"2026-03-02 09:27:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49699,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline (RCS) Analysis Plot of the Association Between WWR and Predicted Mortality Risk Among Malignant Tumor Patients.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8927009/v1/5bee9f8723b6cd177bdb6572.png"},{"id":103733704,"identity":"a4042014-13d0-4686-9e2a-1d4c60670f03","added_by":"auto","created_at":"2026-03-02 09:29:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927009/v1/35fcdb75-b8c1-47ab-a8f6-1d64b86e0b3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the Weight-to-Waist Ratio and All-Cause Mortality in Cancer Patients A Cox Proportional-Hazards Regression Analysis of Cohort Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the independent impact of abnormal body fat distribution on the prognosis of cancer patients has been multidimensionally confirmed in multiple fields, including cancer epidemiology and cancer metabolism[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. The relationship between obesity and cancer is complex, with its effects exhibiting significant heterogeneity across different cancer types and different measurement indicators. A 2022 Mendelian randomization study provided moderate evidence for a causal association between visceral adipose tissue (VAT) and the risk of specific cancers[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study, using genetic instrumental variables to control for confounders effectively, found that a genetically predicted increase in visceral fat volume significantly elevated the risk of pancreatic cancer and lung squamous cell carcinoma, and this effect was independent of body mass index (BMI). This suggests that visceral fat, rather than general obesity alone, may be a key pathogenic factor driving the progression of certain cancers. Concurrently, a 2024 Meta-Analysis including 73 cohort studies revealed another aspect of the relationship between body fat and cancer from a prognostic standpoint[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. This analysis found that a high BMI or obesity was a significant risk factor for overall survival in breast cancer patients. However, in patients with certain gastrointestinal cancers, such as gastric cancer and colorectal cancer, and others, a higher BMI was conversely associated with better survival prognosis, exhibiting the \u0026quot;obesity paradox\u0026quot;. These studies collectively indicate that fat distribution (e.g., visceral fat) and general obesity indicators (e.g., BMI) have differential effects on the onset and progression of different cancer types[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAlthough the association between body fat-related parameters and cancer prognosis has been preliminarily established, the independent prognostic value of WWR\u0026mdash;a novel body fat assessment indicator integrating body weight and central obesity\u0026mdash;remains not sufficiently supported by systematic clinical research in cancer patients[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Current limited exploratory studies suggest that WWR may have potential superior discriminatory ability for cancer prognosis compared with traditional body fat indicators; however, its dose-response relationship with all-cause mortality risk in cancer patients, independent predictive value, and underlying mechanisms remain unclear. Based on this research gap, the present study intends to systematically explore the patterns of association between WWR and long-term survival outcomes in cancer patients by leveraging large-sample prospective cancer cohort data, via multifactorial Cox proportional-hazards regression models and restricted cubic spline analysis. This work seeks to provide a novel clinically translatable biomarker for prognostic stratification and screening of metabolic intervention targets in cancer patients.\u003c/p\u003e"},{"header":"Participants and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e1.1 Study Participants\u003c/h2\u003e\n \u003cp\u003eThis study adopted a prospective cohort design. Data from baseline participants in the 2006 baseline wave of the HRS database were abstracted. The flowchart of participant selection is illustrated in Fig. 1. The initial baseline population comprised 18,469 participants. After sequentially excluding participants with prevalent malignant cancer at baseline (n = 2,562), those who did not develop malignant tumors during the follow-up period (n = 12,726), those lost to follow-up (n = 1,187), those with missing weight-to-waist ratio (WWR) data (n = 1,157), and those with missing smoking status data (n = 3), respectively, a final cohort of 834 malignant tumor patients was included for statistical analysis. During the follow-up period, the primary study outcome was all-cause mortality. The occurrence of mortality events and the corresponding follow-up time were accurately confirmed through official records from the database.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e1.2 Definition of Key Variables\u003c/h2\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e1.2.1 Primary Exposure\u003c/h2\u003e\n \u003cp\u003eThe primary exposure was Weight-to-Waist Ratio (WWR), calculated as body weight (kg)/waist circumference (cm). All data were accurately obtained via standardized measurements at baseline. Continuous variables for this variable were presented as median (interquartile range, IQR). For dose-response relationship analysis, participants were categorized into four WWR quartile groups (Q1–Q4) based on WWR, with Q1 (the lowest WWR group) serving as the reference group.\u003c/p\u003e\n \u003ch2\u003e1.2.2 Primary Outcome\u003c/h2\u003e\n \u003cp\u003eThe primary outcome was all-cause mortality among cancer patients. The occurrence, timing, and outcome determination of mortality events were confirmed via official follow-up records from the HRS database.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e1.2.3 Covariates\u003c/h2\u003e\n \u003cp\u003eCovariates included demographic characteristics (age, sex, race, educational attainment, level of urbanization), lifestyle factors (smoking status, alcohol consumption), and comorbidities (hypertension, type 2 diabetes, chronic obstructive pulmonary disease [COPD], organic heart disease, rheumatoid arthritis / osteoarthritis). The definitions and diagnostic criteria for all covariates were based on previously published relevant clinical studies.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e1.3 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eData analysis was conducted with R software (version 4.3.3). A two-sided P \u0026lt; 0.05 was considered statistically significant for all comparisons. In the comparison of baseline characteristics, continuous variables were compared via the Wilcoxon rank-sum test, and categorical variables via the Pearson chi-square test, between the deceased and surviving groups.To explore the association between WWR and mortality risk among cancer patients, three hierarchical Cox proportional hazards regression models were constructed: Model 1 (crude model, WWR only); Model 2 (adjusted for WWR, demographic characteristics, and lifestyle factors); and Model 3 (further adjusted for comorbidities on the basis of Model 2). The dose-response relationship was assessed by categorizing participants into four WWR quartile groups (Q1–Q4), with Q1 (the lowest WWR group) as the reference group. Subgroup analyses were performed stratified by clinical features, including age, sex, race, and others, to assess heterogeneity and potential interaction effects in the association between WWR and mortality risk across different subgroups. Potential nonlinearity in the association between WWR and mortality risk among cancer patients was tested using a restricted cubic spline (RCS) model with four knots (5th, 35th, 65th, and 95th percentiles).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 834 malignant tumor patients finally included in this study, the median age was 66.00 years (IQR: 59.00\u0026ndash;72.00), and 426 patients (51.1%) were male. Significant differences were observed between the deceased and surviving groups in all indicators including WWR, age, sex, smoking status, alcohol consumption, educational attainment, hypertension, chronic obstructive pulmonary disease (COPD), organic heart disease, arthritis, and urbanization level (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences were found in race or type 2 diabetes between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline clinical characteristics between the deceased and surviving groups among 834 malignant tumor patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;834\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, N\u0026thinsp;=\u0026thinsp;503\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, N\u0026thinsp;=\u0026thinsp;331\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWWR, Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.75, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.77, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.72, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.00 (59.00, 72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.00 (57.00, 68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.00 (65.00, 77.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426.0 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238.0 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188.0 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e693.0 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416.0 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e277.0 (83.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.0 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.0 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.0 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518.0 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297.0 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221.0 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrink, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e473.0 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306.0 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167.0 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132.0 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.0 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.0 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e483.0 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295.0 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188.0 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e219.0 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.0 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e459.0 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261.0 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198.0 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.0 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.0 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.0 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart.Disease, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171.0 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.0 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.0 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArthritis, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e452.0 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e251.0 (49.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201.0 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanization.level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e398.0 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258.0 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.0 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173.0 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.0 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.0 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263.0 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.0 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.0 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003eWilcoxon rank sum test; Pearson's Chi-squared test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cox Proportional Hazards Regression Analysis\u003c/h2\u003e \u003cp\u003eCox proportional hazards regression analysis revealed that the association between WWR and mortality risk in malignant tumor patients was statistically significant in all adjusted models. WWR was significantly and inversely associated with mortality risk in malignant tumor patients in Model 1 (HR\u0026thinsp;=\u0026thinsp;0.02, 95% CI: 0.01\u0026ndash;0.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for demographic characteristics and lifestyle factors in Model 2, WWR remained significantly and inversely associated with mortality risk in malignant tumor patients (HR\u0026thinsp;=\u0026thinsp;0.09, 95% CI: 0.02\u0026ndash;0.34, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model 3, after further adjusting for comorbidities on the basis of Model 2, WWR remained an independent protective factor for mortality risk in malignant tumor patients (HR\u0026thinsp;=\u0026thinsp;0.06, 95% CI: 0.02\u0026ndash;0.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, age (per 1-year increase, HR\u0026thinsp;=\u0026thinsp;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male sex (reference: female sex, HR\u0026thinsp;=\u0026thinsp;1.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol consumption (HR\u0026thinsp;=\u0026thinsp;0.68, P\u0026thinsp;=\u0026thinsp;0.001), and organic heart disease (HR\u0026thinsp;=\u0026thinsp;1.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also independent prognostic factors for mortality risk in malignant tumor patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox Proportional Hazards Regression Analysis of the Association between WWR and Mortality Risk among Malignant Tumor Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01, 0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02, 0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02, 0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eWWR\u0026thinsp;=\u0026thinsp;weight-to-waist ratio; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; \u0026sup1;HR\u0026thinsp;=\u0026thinsp;hazard ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval (95%); Model 1: unadjusted model; Model 2: adjusted for age, sex, smoking status, alcohol consumption, educational attainment, as well as urbanization level; Model 3: adjusted for all variables in Model 2 plus additional variables: hypertension, type 2 diabetes, chronic obstructive pulmonary disease, organic heart disease, and arthritis.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Dose-Response Relationship Between WWR Quartiles and Mortality Risk\u003c/h2\u003e \u003cp\u003eModel 3 (fully adjusted multivariate model) revealed a significant dose-response relationship between WWR levels and mortality risk in malignant tumor patients, with mortality risk decreasing progressively as WWR levels increased. Mortality risk was significantly reduced in the Q2 group compared with the Q1 group (the lowest WWR group) (HR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.52\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.023). Mortality risk was further reduced in the Q3 group (HR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.41\u0026ndash;0.80, P\u0026thinsp;=\u0026thinsp;0.001). However, mortality risk in the Q4 group (the highest WWR group) was comparable to that in the Q3 group, with no further reduction (HR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.39\u0026ndash;0.83, P\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis Results of the Dose-Response Relationship Between WWR Quartiles and Mortality Risk Among Malignant Tumor Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR_q\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45, 0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36, 0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31, 0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR_q\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54, 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45, 0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43, 0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06, 1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26, 2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91, 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDrink\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52, 0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64, 0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrbanization.level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWWR_q\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52, 0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41, 0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39, 0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06, 1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23, 2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88, 1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDrink\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53, 0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65, 0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrbanization.level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93, 1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80, 1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99, 1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHeart.Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22, 1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eWWR\u0026thinsp;=\u0026thinsp;weight-to-waist ratio; WWR_q\u0026thinsp;=\u0026thinsp;WWR quartiles; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; Q1\u0026thinsp;=\u0026thinsp;the lowest WWR quartile group (reference group); \u0026sup1;HR\u0026thinsp;=\u0026thinsp;hazard ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval (95%); Model 1: unadjusted model; Model 2: adjusted for age, sex, smoking status, alcohol consumption, educational attainment, as well as urbanization level; Model 3: adjusted for all variables in Model 2 plus additional variables: hypertension, type 2 diabetes, chronic obstructive pulmonary disease, organic heart disease, and arthritis.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Subgroup Analysis Results\u003c/h2\u003e \u003cp\u003eSubgroup analyses showed a significant inverse association between WWR and mortality risk in the overall study population of malignant tumor patients (HR\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.010\u0026ndash;0.070). Stratified analyses showed that a significant interaction existed between urbanization level and WWR in terms of mortality risk (Pinteraction\u0026thinsp;=\u0026thinsp;0.014). The interaction between organic heart disease and WWR marginally approached statistical significance (Pinteraction\u0026thinsp;=\u0026thinsp;0.084). No significant interactions between WWR and other clinical characteristics, such as age, sex, and smoking status, were detected in terms of mortality risk (all Pinteraction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Results of the Non-Linearity Test\u003c/h2\u003e \u003cp\u003eThe results of restricted cubic spline (RCS) analysis showed a statistically significant overall association between WWR and mortality risk in malignant tumor patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The further non-linearity test yielded P\u0026thinsp;=\u0026thinsp;0.077, suggesting that the association between WWR and mortality risk was predominantly linear in nature, with no statistically significant non-linear relationship detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large prospective cohort study, using multivariate Cox proportional hazards regression, dose-response analysis, subgroup analysis, and restricted cubic spline (RCS) non-linearity testing, provides the first evidence in oncology that weight-to-waist ratio (WWR) is an independent protective factor for mortality among patients with malignant tumors. A linear inverse association and a clear dose-response pattern were observed, and these findings remained consistent after adjustment for demographic characteristics, lifestyle factors, and comorbidities. Our results carry important clinical implications for prognostic evaluation and intervention strategies in cancer patients.\u003c/p\u003e \u003cp\u003eAs a novel composite adiposity indicator integrating body weight and central obesity, WWR shows clear advantages in cancer prognosis assessment. It overcomes the limitations of BMI, which reflects overall weight but ignores fat distribution, and also addresses the shortcoming of waist circumference alone, which does not consider baseline weight or the balance between fat mass and lean mass. Thus, WWR can more accurately reflect the body fat distribution and metabolic status of cancer patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].The protective effect of WWR observed in this study may be attributed to the balanced combination of weight and waist circumference, indirectly reflecting a more favorable body fat profile (e.g., lower visceral fat proportion). This is consistent with the findings of a 2025 Mendelian randomization study in the International Journal of Epidemiology, which demonstrated that larger waist circumference was strongly associated with an increased risk of colorectal cancer mortality. The underlying mechanism involves pro-inflammatory cytokines secreted by visceral fat, including tumor necrosis factor-α and interleukin-6, which promote tumor cell proliferation and inhibit apoptosis [10,11]. A higher WWR indicates a more appropriate body weight for a given waist circumference, or a slimmer waist for a given body weight, corresponding to lower visceral adiposity and a milder inflammatory microenvironment, thereby reducing the mortality risk in cancer patients.\u003c/p\u003e \u003cp\u003eFrom the perspective of cancer metabolism, the association between WWR and cancer prognosis may be mediated through the insulin-like growth factor-1 (IGF-1) signaling pathway and chronic inflammatory pathways, which is consistent with and complementary to classic findings in cancer metabolism [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A 2023 study in Cancer \u0026amp; Metabolismreported that obesity (including abnormal fat distribution) significantly increases the risk of breast cancer recurrence by elevating IGF-1 levels and activating a chronic inflammatory microenvironment (HR\u0026thinsp;=\u0026thinsp;1.44 for BMI-defined obesity, HR\u0026thinsp;=\u0026thinsp;1.31 for waist circumference-defined obesity) [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the present study, the reduced mortality risk associated with elevated WWR essentially reflects lower IGF-1 levels and milder chronic inflammation, which effectively inhibit tumor cell proliferation and metastasis and thereby improve long-term survival. Although we did not perform stratified analyses by cancer type, our findings suggest that future studies should further explore the prognostic value of WWR in specific cancer types, including gastrointestinal, breast, and lung cancers.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, our subgroup analysis provides important evidence for the precise prognostic evaluation of cancer patients with different clinical characteristics. The significant interaction between urbanization level and WWR (P for interaction\u0026thinsp;=\u0026thinsp;0.014) and the marginal interaction between organic heart disease and WWR (P for interaction\u0026thinsp;=\u0026thinsp;0.084) carry important clinical implications. Regions with a higher urbanization level usually have better medical resources but also more prevalent unhealthy lifestyles, such as physical inactivity and high-fat/high-sugar diets, which may attenuate the protective effect of WWR. Cancer patients with comorbid heart disease often present with more severe metabolic disorders and poorer cardiovascular compensatory capacity, which may offset part of the protective effect of WWR achieved by improving body fat distribution. These findings suggest that, in clinical practice, for cancer patients living in highly urbanized areas or with comorbid heart disease, WWR optimization should be combined with lifestyle interventions and cardiovascular management to maximize its prognostic benefit.\u003c/p\u003e \u003cp\u003eAs the first prospective cohort study in oncology to systematically explore the association between WWR and mortality in cancer patients, this study has several major strengths. First, based on a large sample and rigorous adjustment for confounders, we used multiple statistical methods to verify the robustness of our results, supporting a high level of evidence. Second, by integrating recent advances in oncology and obesity research, we provided mechanistic insights into the association between WWR and cancer prognosis, enhancing the scientific depth of this study. Nevertheless, several limitations should be acknowledged. First, WWR was only measured at baseline without dynamic monitoring, which precluded evaluation of the longitudinal association between changes in WWR during treatment and prognosis. Second, the lack of stratification by cancer type and stage prevented us from determining the prognostic value of WWR across different cancer subtypes and stages. Third, as an observational cohort study, we cannot establish a causal relationship between WWR and cancer mortality; future randomized controlled trials are warranted to verify whether interventions targeting WWR can improve cancer prognosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comprehensive analysis based on a prospective cohort confirms that weight-to-waist ratio (WWR) is significantly and linearly inversely associated with mortality risk in patients with malignant tumors and serves as an independent protective factor with a clear dose-response relationship: mortality risk decreases progressively with increasing WWR until reaching a plateau. In clinical cancer practice, WWR can be incorporated into prognostic evaluation systems as a novel adiposity-related biomarker. Meanwhile, clinical interventions that optimize the balance between weight and waist circumference (e.g., maintaining healthy body weight while controlling waist circumference and reducing visceral fat accumulation) to increase WWR may provide a new metabolic target and strategy for improving cancer prognosis. Furthermore, for cancer patients living in highly urbanized areas or with comorbid heart disease, personalized WWR intervention strategies combined with lifestyle modifications and comorbidity management are needed to maximize its protective prognostic effect. Future oncology studies should conduct prospective cohort studies with dynamic monitoring of WWR to clarify the association between longitudinal changes in WWR and cancer prognosis; perform stratified analyses by cancer type and stage to define subtype-specific prognostic values; and carry out randomized controlled trials to verify whether dietary, exercise, or other interventions targeting WWR can improve survival outcomes. These efforts will provide more robust evidence for the translational application of WWR in cancer prognostic evaluation and clinical intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5 Clinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6 Funding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of de-identified, publicly available cohort data from one longitudinal study. HRS was approved by the University of Michigan Institutional Review Board; all respondents provided written informed consent.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShaw E, Farris M, McNeil J, et al.Obesity and Endometrial Cancer. Recent Results Cancer Res. 2016;208:107-136. doi:10.1007/978-3-319-42542-9_7.\u003c/li\u003e\n\u003cli\u003ePang Y, Wei Y, Kartsonaki C. Associations of adiposity and weight change with recurrence and survival in breast cancer patients: a systematic review and meta-analysis. Breast Cancer. 2022;29(4):575-588. doi:10.1007/s12282-022-01355-z.\u003c/li\u003e\n\u003cli\u003eLu Y, Tang H, Huang P, et al. Assessment of causal effects of visceral adipose tissue on risk of cancers: a Mendelian randomization study. Int J Epidemiol. 2022;51(4):1204-1218. doi:10.1093/ije/dyac025\u003c/li\u003e\n\u003cli\u003eWen H, Deng G, Shi X, et al. Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies. ESMO Open. 2024;9(3):102241. doi:10.1016/j.esmoop.2024.102241\u003c/li\u003e\n\u003cli\u003eRask-Andersen M, Ivansson E, H\u0026ouml;glund J, et al. Adiposity and sex-specific cancer risk. Cancer Cell. 2023;41(6):1186-1197.e4. doi:10.1016/j.ccell.2023.05.010\u003c/li\u003e\n\u003cli\u003eAvgerinos KI, Spyrou N, Mantzoros CS, et al. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism. 2019;92:121-135. doi:10.1016/j.metabol.2018.11.001\u003c/li\u003e\n\u003cli\u003eCespedes Feliciano EM, Kwan ML, Kushi LH, et al. Adiposity, post-diagnosis weight change, and risk of cardiovascular events among early-stage breast cancer survivors. Breast Cancer Res Treat. 2017;162(3):549-557. doi:10.1007/s10549-017-4133-8\u003c/li\u003e\n\u003cli\u003eFontvieille E, Viallon V, Recalde M, et al. Body mass index and cancer risk among adults with and without cardiometabolic diseases: evidence from the EPIC and UK Biobank prospective cohort studies. BMC Med. 2023;21(1):418. doi:10.1186/s12916-023-03114-z\u003c/li\u003e\n\u003cli\u003eKawachi A, Shimazu T, Budhathoki S, et al. Association of BMI and height with the risk of endometrial cancer, overall and by histological subtype: a population-based prospective cohort study in Japan. Eur J Cancer Prev. 2019;28(3):196-202. doi:10.1097/CEJ.0000000000000449\u003c/li\u003e\n\u003cli\u003eKanellopoulou A, Bouras E, Chan AT, et al. Investigating the association between anthropometry and colorectal cancer survival: a two-sample Mendelian randomization analysis. Int J Epidemiol. 2025;54(6):dyaf193. doi:10.1093/ije/dyaf193\u003c/li\u003e\n\u003cli\u003eParekh, N., Chandran, U., \u0026amp; Bandera, E. V. (2012). Obesity in Cancer Survival. Annual Review of Nutrition, 32, 311-342. doi.org/10.1146/annurev-nutr-071811-150713\u003c/li\u003e\n\u003cli\u003eMuoio MG, Talia M, Lappano R, et al. Activation of the S100A7/RAGE Pathway by IGF-1 Contributes to Angiogenesis in Breast Cancer. Cancers. 2021;13(4):621. doi:10.3390/cancers13040621\u003c/li\u003e\n\u003cli\u003eLengyel E, Makowski L, DiGiovanni J, Kolonin MG. Cancer as a matter of fat: The crosstalk between adipose tissue and tumors. Trends Cancer. 2018;4(5):374-384. doi:10.1016/j.trecan.2018.03.004\u003c/li\u003e\n\u003cli\u003eRosendahl AH, Bergqvist M, Lettiero B, Kimbung S, Borgquist S. Adipocytes and Obesity-Related Conditions Jointly Promote Breast Cancer Cell Growth and Motility: Associations With CAP1 for Prognosis. Front Endocrinol. 2018;9:689. doi:10.3389/fendo.2018.00689\u003c/li\u003e\n\u003cli\u003eBergqvist M, Elebro K, Borgquist S, Rosendahl AH. Adipocytes Under Obese-Like Conditions Change Cell Cycle Distribution and Phosphorylation Profiles of Breast Cancer Cells: The Adipokine Receptor CAP1 Matters. Front Oncol. 2021;11:628653. doi:10.3389/fonc.2021.628653\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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"weight-to-waist ratio (WWR), cancer, all-cause mortality risk, Cox proportional-hazards model, dose-response relationship, body fat distribution, interaction effect","lastPublishedDoi":"10.21203/rs.3.rs-8927009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the association between weight-to-waist ratio (WWR) and all-cause mortality risk in cancer patients, clarify whether it is an independent prognostic factor for cancer patients, and analyze its potential interaction effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cohort study design was adopted. We screened data from 18,469 baseline participants and finally included 834 cancer patients with complete baseline data (331 deceased cases and 503 surviving cases). The Wilcoxon rank-sum test and Pearson chi-square test were used to compare baseline characteristics between the deceased and surviving groups. A Cox proportional-hazards regression model was employed to analyze the association between WWR and mortality risk, and a multifactorial-adjusted model was constructed to control for confounders. The dose-response relationship was analyzed by quartile grouping, heterogeneity was evaluated through subgroup analysis, and a non-linear relationship was tested using restricted cubic spline (RCS) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median WWR in the deceased group was 0.79 (IQR: 0.72\u0026ndash;0.87), which was significantly lower than that in the surviving group (0.84, IQR: 0.77\u0026ndash;0.90) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for demographic characteristics, lifestyle factors, and comorbidities (Model 3), WWR was significantly inversely associated with all-cause mortality risk (HR\u0026thinsp;=\u0026thinsp;0.06, 95% CI: 0.02\u0026ndash;0.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate-adjusted dose-response analysis showed that mortality risk decreased stepwise with increasing WWR quartiles, reaching a plateau in the highest quartile (Q4) (Q2 vs. Q1: HR\u0026thinsp;=\u0026thinsp;0.70, P\u0026thinsp;=\u0026thinsp;0.023; Q3 vs. Q1: HR\u0026thinsp;=\u0026thinsp;0.57, P\u0026thinsp;=\u0026thinsp;0.001; Q4 vs. Q1: HR\u0026thinsp;=\u0026thinsp;0.57, P\u0026thinsp;=\u0026thinsp;0.003). This indicates that a higher WWR is a protective factor against all-cause mortality in cancer patients, with a dose-response relationship. Subgroup analysis suggested potential interaction effects between WWR and urbanization level (p for interaction\u0026thinsp;=\u0026thinsp;0.014) and history of heart disease (p for interaction\u0026thinsp;=\u0026thinsp;0.084). RCS analysis indicated a linear association between WWR and mortality risk (p for overall association\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p for non-linearity\u0026thinsp;=\u0026thinsp;0.077).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWWR is an independent protective factor for all-cause mortality in cancer patients, showing a dose-response relationship. Improving WWR by optimizing the balance between body weight and waist circumference may provide a novel intervention strategy for improving cancer prognosis.\u003c/p\u003e","manuscriptTitle":"Association between the Weight-to-Waist Ratio and All-Cause Mortality in Cancer Patients A Cox Proportional-Hazards Regression Analysis of Cohort Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 09:23:53","doi":"10.21203/rs.3.rs-8927009/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T11:47:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T02:26:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T03:10:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240751879588299324861704196740303254817","date":"2026-03-11T02:58:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103823774449692963272001108312520930477","date":"2026-03-11T00:26:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T10:41:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T05:16:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T05:14:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-02-20T14:35:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1629fac0-b891-4d0b-8597-f9301b6c3929","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T06:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 09:23:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927009","identity":"rs-8927009","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}