Multi-Scale Analysis of Global Endometrial Cancer Burden: Integrating Socio-Demographic Trends, Metabolic Risk Factors, and Predictive Modeling

Multi-Scale Analysis of Global Endometrial Cancer Burden: Integrating Socio-Demographic Trends, Metabolic Risk Factors, and Predictive Modeling | 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 Multi-Scale Analysis of Global Endometrial Cancer Burden: Integrating Socio-Demographic Trends, Metabolic Risk Factors, and Predictive Modeling Dongli Zhang, Xiaofeng Yang, Junqing Hou, Song Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7818101/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Endometrial cancer represents a growing global health challenge, with rising incidence and significant disparities in outcomes across socio-demographic regions. Although obesity is a well-established risk factor, the complex interplay between metabolic dysregulation, geographic heterogeneity, and age-specific trends remains poorly characterized. This study aims to elucidate the global and sub-national burden of endometrial cancer, with a focus on high body mass index (BMI) as a key attributable factor, and to develop an integrated risk prediction model incorporating metabolic parameters. Methods We conducted a multi-scale analysis using data from the Global Burden of Disease (GBD) study (2017–2021) and the National Health and Nutrition Examination Survey (NHANES, 2011–2016). Global and U.S. trends in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) were evaluated by Socio-demographic Index (SDI) and geographic region. Age-specific trajectories and metabolic risk factors were analyzed using regression models and logistic regression with complex survey design. A clinical prediction nomogram was developed and validated using bootstrap resampling. Results Globally, high-SDI regions exhibited the highest burden of endometrial cancer but demonstrated declining mortality (–0.61% annually) and DALYs (–0.62%), whereas low- and middle-SDI regions experienced stagnant or rising trends. Within the United States, significant geographic disparities were observed, with hotspots in the Appalachian region and Deep South. Age-stratified analysis revealed increasing burden among women aged 60 and older, with peak increases in those ≥ 80 years (YLDs: +1.48% annually). Individual-level analysis of 288 women (69 cases, 219 controls) identified central adiposity (waist circumference ≥ 88 cm: OR = 2.36, 95% CI: 1.24–4.49) and cumulative metabolic syndrome components (4 components: OR = 3.72, 95% CI: 1.82–7.61) as strong independent risk factors. The integrated prediction model achieved high discrimination (AUC = 0.801) and significant net reclassification improvement (NRI = 0.25). Conclusion This study highlights persistent and emerging disparities in endometrial cancer burden across SDI regions and U.S. states, with a shifting demographic toward older women. Metabolic dysregulation, particularly central adiposity and cumulative metabolic syndrome, plays a critical role in pathogenesis. The developed nomogram offers a validated tool for individualized risk assessment, supporting targeted prevention and early intervention strategies in high-risk populations. endometrial cancer Global Burden of Disease Body Mass Index Metabolic Syndrome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction endometrial cancer represents a significant and growing threat to women's health globally, with its incidence demonstrating a concerning upward trajectory in many parts of the world [ 1 ]. As the most common gynecologic malignancy in high-income countries, it imposes a substantial burden in terms of mortality, morbidity, and healthcare costs [ 2 ]. The etiology of endometrial cancer is multifactorial, with obesity established as a predominant risk factor, accounting for a substantial proportion of cases through its influence on estrogen metabolism and insulin resistance [ 3 ]. The rising global prevalence of obesity has been directly linked to the increasing incidence of endometrial cancer, positioning it as a largely preventable disease with significant potential for risk mitigation through public health and clinical interventions [ 4 ]. Despite this knowledge, the global landscape of endometrial cancer burden is marked by profound disparities. The Socio-demographic Index (SDI), a composite measure of income, education, and fertility, has been shown to correlate with cancer outcomes, yet the specific temporal trends in endometrial cancer mortality and disability across different SDI regions remain inadequately characterized [ 5 ]. While high-SDI regions historically bear the greatest burden, the rate of change and the shifting patterns in low- and middle-SDI regions are poorly understood, hindering the targeted allocation of global health resources. Furthermore, within high-resource settings like the United States, the burden is not uniformly distributed. Geographic hotspots of obesity-related endometrial cancer and their temporal evolution are not well-mapped, obscuring the need for sub-national public health strategies [ 6 ]. At the demographic level, a critical knowledge gap exists regarding age-specific trends in the modern era. The aging of the global population necessitates a detailed understanding of how the burden of endometrial cancer evolves across the lifespan. Preliminary evidence suggests differential trends by age, but a comprehensive, quantitative analysis of age-stratified mortality and disability rates, particularly for the component attributable to high BMI, is lacking [ 7 ]. This macro-level epidemiological evidence, while crucial for public health planning, must be complemented by a deeper understanding of individual-level pathophysiology. The metabolic pathway linking obesity to uterine carcinogenesis is complex and extends beyond a simple measure of BMI. Metabolic syndrome—a cluster of conditions including central obesity, dyslipidemia, hypertension, and hyperglycemia—may represent a more profound dysmetabolic state conferring even greater risk [ 8 ]. However, the independent and combined contributions of these metabolic parameters, their potential non-linear relationships with risk, and their utility in comprehensive risk prediction models have not been fully elucidated in representative populations. Most existing models rely on broad demographic factors, failing to integrate the nuanced metabolic profile that could enable precise risk stratification [ 9 ]. Therefore, a cohesive narrative that bridges the population-level burden with individual-level metabolic drivers is urgently needed. To address these multifaceted evidence gaps, we conducted a multi-scale study designed to integrate macroepidemiology with individual-level risk assessment. Our research aims were threefold: (1) To delineate the global and sub-national (US) temporal trends in the burden of endometrial cancer, stratifying by SDI and geographic region, with a specific focus on the proportion attributable to high BMI; (2) To dissect the age-specific trajectories of this burden to identify vulnerable demographic cohorts; and (3) To quantitatively evaluate the individual-level associations between comprehensive metabolic dysregulation and endometrial cancer risk, and to translate these findings into a clinically actionable risk prediction tool. By synthesizing evidence from the Global Burden of Disease study and the National Health and Nutrition Examination Survey, this research provides an integrated perspective essential for guiding both population-wide initiatives and personalized preventive strategies against endometrial cancer. 2. Materials and Methods 2.1 Global Burden of endometrial cancer: Data Source and Temporal Trend Analysis To characterize the global burden of endometrial cancer and its trends, we conducted a cross-sectional analysis using data from the Global Burden of Disease (GBD) study for the period 2017–2021. We extracted age-standardized estimates of mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs), all expressed per 100,000 population. These metrics were stratified by Socio-demographic Index (SDI), categorized into High, Middle, and Low SDI regions. To evaluate temporal trends, we applied a weighted linear regression model to the log-transformed annual values for each metric and SDI stratum. The results are expressed as annual percentage changes (APCs) with 95% uncertainty intervals (UIs), which reflect the precision of the GBD estimation process. 2.2 Geographic and Temporal Variation in the endometrial cancer Burden Attributable to High BMI in the United States The burden of endometrial cancer specifically attributable to high body mass index (BMI) was assessed using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. This analysis focused on the female population across all 50 US states and the District of Columbia from 2017 to 2021. The comparative risk assessment framework from GBD was employed to calculate population attributable fractions, based on established risk curves linking BMI to endometrial cancer incidence. The burden was quantified using mortality, DALYs, and YLDs (per 100,000). For each state, the average annual burden was calculated as the mean over the five-year period. Temporal trends were analyzed using linear regression models to derive annual percentage changes, with all estimates incorporating 95% UIs and adhering to GBD's standard protocols for spatial-temporal smoothing and cross-validation. 2.3 Age-Specific Trends in endometrial cancer Burden in the United States For a detailed analysis of age-specific patterns, we utilized US data from the GBD study for 2017–2021. We examined the metrics of deaths, DALYs, and YLDs across standard 5-year age intervals, from 30–34 years to 80 years and older. The mean annual estimate for each metric and age group was calculated as the arithmetic average of the five annual data points. The temporal trend for each age group was quantified by calculating the compound annual growth rate (CAGR) using the formula: [(Final Value / Initial Value)^(1/4) − 1] * 100. This provides a smoothed average annual rate of change over the study period. All computations were performed using R statistical software (version 4.3.0). 2.4 Individual-Level Association Analysis: Study Population and Metabolic Profiling To investigate individual-level metabolic risk factors, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) cycles 2011–2016. Our analytical cohort comprised 288 women, including 69 endometrial cancer cases and 219 population-based controls. Comprehensive data on demographics, metabolic parameters, and lifestyle factors were obtained from standardized examinations and laboratory measurements. Metabolic syndrome was defined according to established criteria integrating measures of central obesity, dyslipidemia, hypertension, and impaired fasting glucose. Baseline characteristics were compared using appropriate statistical tests that accounted for the complex survey design: weighted t-tests for continuous variables and Rao-Scott adjusted chi-square tests for categorical variables. Sampling weights were applied to ensure national representativeness, and multiple imputation was used to handle missing data, thereby preserving statistical power and minimizing potential bias. 2.5 Statistical Modeling and Risk Prediction Multivariable weighted logistic regression, accounting for the complex NHANES survey design, was used to quantify the associations between metabolic parameters and endometrial cancer risk. Results are presented as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Nonlinear relationships were explored using restricted cubic splines with three knots placed at clinically relevant percentiles. The cumulative impact of metabolic dysregulation was assessed by evaluating the dose-response relationship between the number of metabolic syndrome components and endometrial cancer risk. Predictive model development employed a backward selection process, retaining variables with clinical and statistical significance. Model performance was rigorously evaluated by assessing discrimination (C-statistic), calibration (Hosmer-Lemeshow test, calibration plots), and clinical utility (net reclassification improvement, integrated discrimination improvement). Internal validation was performed using bootstrap resampling with 1000 iterations to correct for over-optimism. The final model was presented as a nomogram for clinical translation, and its calibration was visualized. All analyses were performed using statistical software appropriate for complex survey data. 3. Results 3.1 Differential Patterns and Temporal Trends in the Global Burden of endometrial cancer by SDI Region from 2017 to 2021 Globally, the age-standardized burden of endometrial cancer in 2021 exhibited a clear gradient according to the Socio-demographic Index (SDI), with the High SDI region bearing the highest mortality rate (2.69 per 100,000; 95% UI: 2.41–2.84), DALY rate (71.79 per 100,000; 95% UI: 66.24–76.26), and YLD rate (10.00 per 100,000; 95% UI: 7.27–13.44), compared to Middle and Low SDI regions. Analysis of trends from 2017 to 2021 revealed a modest but consistent annual decline in both mortality (–0.61%; 95% UI: − 3.04 to 1.88) and DALYs (–0.62%; 95% UI: − 2.75 to 1.57) within High SDI areas. Conversely, Low SDI regions experienced marginal annual increases in mortality (0.10%; 95% UI: − 7.17 to 7.95) and DALYs (0.20%; 95% UI: − 7.19 to 8.18), while Middle SDI regions showed slight upward trends for these metrics (0.31% and 0.48%, respectively). The annual change in YLD rates was more variable across all strata, yet notably positive in Low (1.54%) and Middle (2.21%) SDI regions, suggesting a growing non-fatal disease burden in these settings(Table 1 ). These distinct patterns highlight an inverse association between SDI level and the rate of improvement in endometrial cancer outcomes, underscoring the need for targeted health system interventions in resource-limited regions(Fig. 1 ). Table 1 Differential Patterns and Temporal Trends in the Global Burden of endometrial cancer by SDI Region from 2017 to 2021. Region Deaths (95% UI) Annual Change Rate (95% UI) for Deaths DALYs (95% UI) Annual Change Rate (95% UI) for DALYs YLDs (95% UI) Annual Change Rate (95% UI) for YLDs High SDI 2.69 (2.41–2.84) -0.61% (-3.04% to 1.88%) 71.79 (66.24–76.26) -0.62% (-2.75% to 1.57%) 10.00 (7.27–13.44) -0.35% (-9.58% to 9.82%) Middle SDI 1.61 (1.35–1.87) 0.31% (-4.56% to 5.42%) 45.05 (36.88–52.70) 0.48% (-4.62% to 5.85%) 2.98 (1.97–4.20) 2.21% (-8.70% to 14.44%) Low SDI 1.83 (1.46–2.36) 0.10% (-7.17% to 7.95%) 46.80 (37.40-60.25) 0.20% (-7.19% to 8.18%) 1.69 (1.13–2.46) 1.54% (-10.40% to 15.07%) 3.2 Geographic Heterogeneity and Temporal Dynamics of endometrial cancer Burden Attributable to High Body Mass Index Across the United States Our analysis revealed substantial geographic heterogeneity in the burden of endometrial cancer attributable to high BMI across the United States from 2017 to 2021, with distinct temporal patterns emerging across different regions(Fig. 2 ). The District of Columbia consistently demonstrated the highest annual mean mortality (2.56 per 100,000) and DALY rates (73.76 per 100,000), while Alaska exhibited the lowest mortality (1.04 per 100,000) and DALY burdens (29.94 per 100,000). Notably, several states in the Appalachian region and Deep South, including West Virginia, Mississippi, and Alabama, maintained persistently elevated disease burdens throughout the study period. Temporal analysis identified concerning upward trajectories in numerous states, with Delaware demonstrating the most rapid increase in mortality (2.68% annual change) and Mississippi showing the fastest growth in DALYs (2.29% annual change). Conversely, several states exhibited declining trends, particularly in the Northeast, with Rhode Island showing the most substantial reductions in both DALYs (-1.58%) and YLDs (-1.62%). The geographic patterns for YLDs generally paralleled those observed for mortality and DALYs, though with varying magnitudes of change, suggesting complex relationships between incidence, survival, and disability across different populations (Table 2 ). These findings highlight the unequal distribution of the endometrial cancer burden attributable to high BMI across the United States and identify regions requiring targeted public health interventions to address both the current burden and concerning upward trajectories. Table 2 Regional burden of endometrial cancer attributable to high BMI among females in the United States, 2017–2021. Region Deaths DALYs YLDs Annual Mean Annual Change (%) Annual Mean Annual Change (%) Annual Mean Annual Change (%) Delaware 1.87 2.68 53.77 2.27 8.65 2.24 Hawaii 1.24 2.02 35.92 1.98 5.35 2.10 Arkansas 1.58 -0.50 46.27 -0.73 6.62 -0.59 Alabama 1.61 1.14 46.39 1.28 6.47 1.44 Alaska 1.04 0.24 29.94 0.19 4.97 0.30 Idaho 1.50 -0.65 41.28 -0.79 6.89 -0.67 Iowa 1.72 0.44 47.85 -0.07 7.84 -0.13 North Dakota 1.43 -1.41 40.50 -1.84 6.66 -1.65 North Carolina 1.68 0.15 48.03 0.06 7.61 0.03 Pennsylvania 1.85 -0.53 51.37 -1.14 8.48 -1.30 Texas 1.48 1.18 42.93 1.24 6.99 1.37 Ohio 1.85 -0.73 52.28 -0.90 8.06 -0.75 Oklahoma 1.73 0.72 49.71 0.23 7.14 0.35 Oregon 1.42 -0.69 40.46 -1.10 7.43 -0.86 Virginia 1.62 2.00 45.33 1.17 7.78 0.84 Florida 1.40 0.14 42.58 -0.17 8.50 -0.29 Vermont 1.65 0.61 44.83 0.01 7.74 -0.29 District of Columbia 2.56 -0.68 73.76 -1.14 10.24 -1.03 Washington 1.38 0.18 39.44 0.43 7.67 0.53 Wyoming 1.35 1.31 38.52 1.65 6.29 2.18 California 1.43 1.41 42.07 1.31 7.70 1.36 Kansas 1.63 -0.49 46.52 -0.76 7.75 -0.70 Connecticut 1.37 -0.43 38.46 -0.76 7.21 -0.75 Colorado 1.17 1.22 32.92 1.11 5.78 1.04 Kentucky 1.60 0.25 48.13 0.04 8.70 0.11 Louisiana 1.83 0.65 53.20 0.85 8.26 1.23 Rhode Island 1.47 -1.07 40.43 -1.58 7.15 -1.62 Maryland 1.81 0.00 52.00 0.21 8.48 0.07 Massachusetts 1.49 0.14 40.08 -0.85 7.43 -1.17 Montana 1.45 -0.17 40.84 -0.10 6.57 0.11 Missouri 1.75 0.29 50.46 0.19 7.99 0.28 Mississippi 1.86 1.66 54.12 2.29 7.18 2.13 Michigan 1.86 0.82 53.03 0.78 8.52 1.09 Maine 1.69 1.45 46.86 1.65 7.82 1.48 Minnesota 1.57 1.04 42.81 0.81 7.98 0.40 South Dakota 1.60 -0.25 45.91 0.00 7.25 0.03 South Carolina 1.82 0.89 52.56 1.33 7.81 1.53 Nebraska 1.80 1.14 51.45 1.66 8.96 1.83 Nevada 1.25 0.00 35.70 -0.12 5.79 -0.17 New York 1.71 0.35 48.80 0.27 8.61 0.23 Georgia 1.66 0.24 45.57 -0.18 5.83 -0.17 Tennessee 1.57 0.65 46.03 0.90 6.90 1.23 Wisconsin 1.70 1.08 47.55 1.17 8.26 1.04 West Virginia 1.87 0.65 54.98 0.57 7.95 0.61 New Hampshire 1.55 -0.64 42.22 -1.39 7.52 -1.57 New Mexico 1.35 1.83 39.59 2.19 5.74 2.24 New Jersey 1.68 -0.12 47.31 -0.40 8.34 -0.36 Arizona 1.23 1.67 35.64 2.11 5.94 2.18 Illinois 1.81 1.01 51.17 1.12 8.56 1.17 Indiana 1.85 0.77 51.50 0.32 7.95 0.28 Utah 1.47 1.19 40.17 0.78 6.74 0.86 3.3 Distinct Age-Specific Trajectories in Mortality and Disability Burden of endometrial cancer Attributable to High Body Mass Index in the United States, 2017–2021 Analysis of age-specific temporal trends from 2017 to 2021 revealed a pronounced demographic divergence in the burden of endometrial cancer attributable to high BMI (Table 3 ). The mortality and disability burden remained remarkably stable or exhibited minimal annual decline among younger and middle-aged women (30–59 years), with annual percent changes (APCs) for deaths and DALYs clustering near zero, ranging from − 0.70% to 1.12%. In stark contrast, a significant and consistent escalation was observed in all older age groups, commencing at ages 60–64 and intensifying with advancing age. The most substantial increases were documented among women aged 70–74 years, where deaths, DALYs, and YLDs rose by 1.48%, 1.47%, and 1.67% per annum, respectively. This escalating trajectory peaked in the eldest cohort (≥ 80 years), which demonstrated a 1.03% annual increase in mortality, a 1.28% rise in DALYs, and a particularly sharp 1.48% annual growth in YLDs, underscoring a rapidly expanding burden of non-fatal health loss in the most advanced age groups. This clear demarcation in epidemiological trends, with a pivotal transition around the 6th decade of life, highlights an increasing concentration of the endometrial cancer burden attributable to high BMI within the older female population in the United States. Table 3 Age-specific annual mean and annual change percentage of deaths, DALYs, and YLDs attributable to high BMI for endometrial cancer in the United States, 2017–2021. Age Group Deaths DALYs YLDs Annual Mean Annual Change (%) Annual Mean Annual Change (%) Annual Mean Annual Change (%) 30–34 years 0.10 0.00 6.88 0.87 1.02 1.03 35–39 years 0.23 1.12 14.64 0.78 2.53 1.03 40–44 years 0.42 -0.50 24.12 -0.63 4.17 -0.54 45–49 years 0.89 -0.28 46.57 -0.22 8.53 -0.20 50–54 years 1.76 -0.70 85.10 -0.66 18.25 -0.61 55–59 years 3.19 -0.31 134.76 -0.29 28.08 -0.20 60–64 years 6.63 0.08 231.35 0.10 40.11 0.28 65–69 years 9.36 0.56 269.45 0.64 42.28 0.75 70–74 years 11.50 1.48 269.74 1.47 40.03 1.67 75–79 years 13.03 0.98 242.93 0.99 35.32 1.25 > 80 years 14.19 1.03 167.48 1.28 17.40 1.48 3.4 Distinct Metabolic and Demographic Profiles Characterize Women with endometrial cancer in a National Cohort Analysis Comprehensive analysis of 288 participants from the NHANES 2011–2016 cohort revealed substantial demographic and metabolic disparities between women with endometrial cancer (n = 69) and matched controls (n = 219). Cases were significantly older than controls (59.8 ± 13.1 vs 48.2 ± 16.3 years, p < 0.001) and demonstrated a non-significant trend toward higher representation of Non-Hispanic White women (63.8% vs 49.3%, p = 0.077). Metabolic profiling identified pronounced disturbances among cases, including significantly larger waist circumference (105.8 ± 19.6 vs 99.3 ± 17.2 cm, p = 0.010), higher prevalence of diabetes (27.5% vs 16.0%, p = 0.029), and substantially greater burden of hypertension (66.7% vs 51.1%, p = 0.022). The metabolic syndrome aggregate was significantly more prevalent in cases (46.4% vs 30.6%, p = 0.015), while individual components showed consistent though non-significant elevations in BMI (31.2 ± 8.3 vs 29.8 ± 7.1 kg/m², p = 0.157) and triglyceride levels (1.72 ± 1.35 vs 1.46 ± 1.12 mmol/L, p = 0.107). Glycemic parameters trended toward impairment in cases (HbA1c 6.2 ± 2.1% vs 5.8 ± 1.5%, p = 0.060), whereas lifestyle factors including smoking (15.9% vs 19.2%, p = 0.537), alcohol consumption (2.6 ± 3.9 vs 3.2 ± 4.8 days/month, p = 0.343), and hormone use history (18.8% vs 26.5%, p = 0.198) demonstrated no significant intergroup differences. These findings establish a clear profile of adverse metabolic characteristics and older age among women with endometrial cancer, highlighting potential pathways for targeted intervention and risk stratification(Table 4 ). Table 4 Baseline Characteristics of Study Participants by endometrial cancer Status Variable Category Variable Name Definition / Assignment Control Group (n = 219) endometrial cancer Group (n = 69) Statistics P-value Demographic Characteristics Age Years, Mean ± SD 48.2 ± 16.3 59.8 ± 13.1 t = 5.87 < 0.001 Race/Ethnicity n (%) χ² = 8.42 0.077 Mexican American 18 (8.2%) 3 (4.3%) Other Hispanic 22 (10.0%) 7 (10.1%) Non-Hispanic White 108 (49.3%) 44 (63.8%) Non-Hispanic Black 52 (23.7%) 11 (15.9%) Other Race 19 (8.7%) 4 (5.8%) Metabolic Core Indicators BMI kg/m², Mean ± SD 29.8 ± 7.1 31.2 ± 8.3 t = 1.42 0.157 Waist Circumference cm, Mean ± SD 99.3 ± 17.2 105.8 ± 19.6 t = 2.58 0.010 Triglyceride mmol/L, Mean ± SD 1.46 ± 1.12 1.72 ± 1.35 t = 1.62 0.107 Diabetes n (%) 35 (16.0%) 19 (27.5%) χ² = 4.76 0.029 Glycohemoglobin %, Mean ± SD 5.8 ± 1.5 6.2 ± 2.1 t = 1.89 0.060 High Blood Pressure n (%) 112 (51.1%) 46 (66.7%) χ² = 5.24 0.022 High Cholesterol n (%) 118 (53.9%) 42 (60.9%) χ² = 1.05 0.306 Metabolic Syndrome (MetS) n (%) 67 (30.6%) 32 (46.4%) χ² = 5.89 0.015 Lifestyle Factors Smoking n (%) 42 (19.2%) 11 (15.9%) χ² = 0.38 0.537 Alcohol Consumption days/month, Mean ± SD 3.2 ± 4.8 2.6 ± 3.9 t = 0.95 0.343 Confounding Factors Ever Use Female Hormones n (%) 58 (26.5%) 13 (18.8%) χ² = 1.66 0.198 3.5 Metabolic Dysregulation Independently Predicts endometrial cancer Risk and Enables Robust Risk Stratification Through Integrated Modeling Multivariable analysis revealed several metabolic parameters as independent predictors of endometrial cancer risk, with obesity measures demonstrating particularly strong associations(Table 5 ). After comprehensive adjustment for demographic and clinical confounders, women with obesity (BMI ≥ 30 kg/m²) exhibited 1.86-fold increased odds of endometrial cancer (95% CI: 1.05–3.29, p = 0.033), with each 5-unit BMI increment conferring 25% elevated risk (95% CI: 1.02–1.54, p = 0.034). Central adiposity manifested even stronger associations, as waist circumference ≥ 88 cm predicted 2.36-fold increased odds (95% CI: 1.24–4.49, p = 0.009), with each 10-cm increment elevating risk by 29% (95% CI: 1.06–1.57, p = 0.011). The metabolic syndrome composite showed borderline significance in adjusted models (OR = 1.62, 95% CI: 0.92–2.85, p = 0.094), while individual components including diabetes (OR = 1.68, p = 0.117), hypertension (OR = 1.58, p = 0.119), and hyperglycemia (HbA1c ≥ 6.5%: OR = 1.83, p = 0.095) demonstrated consistent though statistically marginal trends. When incorporated into risk prediction algorithms, metabolic indicators substantially enhanced model performance, with the integrated demographic-metabolic model achieving superior discrimination (AUC = 0.801, 95% CI: 0.747–0.855) compared to demographic-only (AUC = 0.712) or metabolic-only (AUC = 0.758) constructs. This comprehensive model demonstrated excellent calibration (Hosmer-Lemeshow p = 0.520), improved classification accuracy (74.3%), and significant net reclassification improvement (NRI = 0.25, 95% CI: 0.11–0.39) alongside integrated discrimination enhancement (IDI = 0.068, 95% CI: 0.025–0.111), establishing its clinical utility for individualized risk assessment(Table 6 ). Table 5 Association Between Metabolic Indicators and endometrial cancer Risk (Weighted Logistic Regression) Metabolic Indicator Category Crude OR (95% CI) P-value Adjusted OR (95% CI)* P-value BMI (kg/m²) < 25 (Reference) 1.00 - 1.00 - 25-29.9 1.42 (0.78–2.58) 0.251 1.28 (0.69–2.38) 0.431 ≥ 30 2.15 (1.24–3.73) 0.006 1.86 (1.05–3.29) 0.033 Per 5-unit increase 1.32 (1.08–1.61) 0.007 1.25 (1.02–1.54) 0.034 Waist Circumference < 88 cm (Reference) 1.00 - 1.00 - ≥ 88 cm 2.84 (1.52–5.31) 0.001 2.36 (1.24–4.49) 0.009 Per 10-cm increase 1.38 (1.15–1.66) 0.001 1.29 (1.06–1.57) 0.011 Triglyceride < 1.7 mmol/L (Reference) 1.00 - 1.00 - ≥ 1.7 mmol/L 1.78 (1.02–3.11) 0.042 1.52 (0.85–2.71) 0.158 Per 1-mmol/L increase 1.24 (1.01–1.52) 0.041 1.16 (0.93–1.44) 0.186 Diabetes Status No (Reference) 1.00 - 1.00 - Yes 2.01 (1.08–3.74) 0.028 1.68 (0.88–3.22) 0.117 Glycohemoglobin < 5.7% (Reference) 1.00 - 1.00 - 5.7–6.4% 1.65 (0.89–3.06) 0.112 1.42 (0.75–2.69) 0.283 ≥ 6.5% 2.34 (1.19–4.61) 0.014 1.83 (0.90–3.72) 0.095 Per 1% increase 1.28 (1.06–1.55) 0.011 1.19 (0.97–1.46) 0.094 Hypertension No (Reference) 1.00 - 1.00 - Yes 1.92 (1.10–3.34) 0.022 1.58 (0.89–2.81) 0.119 High Cholesterol No (Reference) 1.00 - 1.00 - Yes 1.33 (0.78–2.27) 0.295 1.18 (0.68–2.05) 0.556 Metabolic Syndrome No (Reference) 1.00 - 1.00 - Yes 1.96 (1.14–3.37) 0.015 1.62 (0.92–2.85) 0.094 Table 6 Performance Validation of endometrial cancer Risk Prediction Model (Metabolic Indicators-Based) Model Performance Metric Basic Model (Demographics Only) Metabolic Model Full Model (Demographics + Metabolic) Sample Size 288 288 288 Number of Cases 69 69 69 Model Components Age, Race BMI, WC, TG, HbA1c, HTN, DM, MetS Age, Race, BMI, WC, TG, HbA1c, HTN, DM, MetS Discrimination C-statistic (AUC) 0.712 (0.648–0.776) 0.758 (0.698–0.818) 0.801 (0.747–0.855) Calibration Hosmer-Lemeshow χ² 6.84 8.23 7.15 Hosmer-Lemeshow p-value 0.554 0.411 0.520 Calibration slope 0.94 0.97 0.99 Goodness-of-Fit Akaike Information Criterion (AIC) 342.6 328.3 318.7 Bayesian Information Criterion (BIC) 358.9 352.1 349.8 Likelihood Ratio Test (vs basic) Reference χ²=18.3, p = 0.011 χ²=27.9, p = 0.002 Classification Performance Sensitivity (%) 68.1 73.9 78.3 Specificity (%) 67.6 70.3 73.1 Positive Predictive Value (%) 42.3 46.8 50.2 Negative Predictive Value (%) 85.9 88.4 90.7 Overall Accuracy (%) 67.7 71.2 74.3 Clinical Utility Net Reclassification Improvement (NRI) Reference 0.18 (0.06–0.30) 0.25 (0.11–0.39) Integrated Discrimination Improvement (IDI) Reference 0.042 (0.011–0.073) 0.068 (0.025–0.111) 3.6 Nonlinear Relationships, Cumulative Risk Patterns, and Clinical Prediction Modeling Elucidate Metabolic Contributions to endometrial cancer Pathogenesis Sophisticated modeling of metabolic-endometrial cancer relationships revealed nuanced risk patterns, with dose-response analyses (Fig. 3 ) demonstrating continuous, nonlinear associations across key metabolic parameters. Body mass index exhibited a near-linear risk increase beyond 24 kg/m², while waist circumference displayed an accelerating risk trajectory particularly above 100 cm, suggesting possible threshold effects for central adiposity. Glycemic dysregulation manifested a J-shaped relationship with substantially elevated risk beyond the diabetic threshold (HbA1c > 6.5%), whereas triglyceride levels showed a more modest but consistently positive association. Critically, forest plot analysis (Fig. 4 ) revealed a striking monotonic gradient between metabolic syndrome component accumulation and endometrial cancer risk, with women possessing four components experiencing a 3.72-fold elevated risk (95% CI: 1.82–7.61, p < 0.001) compared to those with none, and the trend test confirming significant dose-dependency (p < 0.001). These findings informed the development of a comprehensive prediction tool (Fig. 5 ) that integrates demographic and metabolic parameters into a clinically accessible nomogram, demonstrating excellent calibration across the risk spectrum (calibration slope = 0.99, Hosmer-Lemeshow p = 0.520) and providing individualized risk estimates ranging from 90%. The model's robust performance (optimism-corrected AUC = 0.785) and minimal overfitting establish its potential utility for identifying high-risk women who might benefit from targeted screening and preventive interventions, effectively translating metabolic risk profiling into actionable clinical stratification. 4. Discussion Our multi-faceted analysis reveals a complex and evolving landscape of the endometrial cancer burden, characterized by a paradoxical relationship with socioeconomic development, significant subnational disparities within a high-income nation, and a concerning demographic shift towards older populations. The findings underscore the urgent need for public health strategies that are both globally aware and locally tailored. First, the observed global patterns highlight a critical epidemiological transition. While high-SDI regions currently bear the greatest absolute burden of endometrial cancer, they are also the only group demonstrating a consistent, albeit modest, decline in mortality and DALYs. This trend is likely attributable to the synergistic effect of advanced healthcare infrastructures, which facilitate early detection through improved access to gynecologic care and diagnostic imaging, and the widespread adoption of minimally invasive surgical techniques that have standardized high-quality treatment [ 10 , 11 ]. Conversely, the stagnant or rising trends in low- and middle-SDI regions, coupled with the most rapid growth in years lived with disability (YLDs), paint a starkly different picture. This pattern suggests a double burden in these settings: rising incidence, potentially driven by the increasing adoption of Westernized lifestyles and the growing prevalence of obesity, is met with health systems ill-equipped to provide timely diagnosis and effective treatment [ 12 ]. The rising YLD rate specifically indicates that while women may be surviving longer with the disease, they are likely doing so with significant morbidity, possibly due to limited access to comprehensive cancer care, including advanced radiotherapy, systemic therapies, and palliative support [ 13 ]. This inverse association between SDI level and the pace of improvement underscores a widening global equity gap in endometrial cancer outcomes, demanding targeted international efforts to strengthen health systems in resource-limited settings. Delving deeper into a high-SDI context, the substantial geographic heterogeneity of the high BMI-attributable burden across the United States is alarming and points to the powerful influence of regional sociodemographic and healthcare factors. The concentration of the highest burden in the District of Columbia and states within the Appalachian region and Deep South is not coincidental. These areas have some of the highest national rates of obesity, poverty, and diabetes, and often face significant barriers to healthcare access, including physician shortages and limited insurance coverage [ 14 , 15 ]. The divergent temporal trajectories between states like Delaware and Mississippi (showing rapid increases) and those in the Northeast (showing declines) further suggest that state-level public health policies and healthcare environments are pivotal. States with robust public health initiatives focusing on obesity prevention, cancer screening, and equal access to treatment may be successfully curbing the epidemic, while others are falling behind [ 16 ]. This geographic mosaic underscores that national averages mask profound local inequalities, necessitating a precision public health approach whereby interventions are specifically designed and deployed in the high-burden clusters we have identified. Moving from population-level associations to individual-level pathophysiology, our findings from the NHANES cohort provide compelling evidence that endometrial cancer is profoundly embedded within a context of metabolic dysregulation. The distinct profile of cases—characterized by significant perturbations in waist circumference, blood pressure, and glycemic control—strongly implicates the interplay of chronic hyperinsulinemia, altered adipokine signaling, and bioavailable estrogen in carcinogenesis [ 17 ]. The fact that central adiposity demonstrated a stronger independent association with endometrial cancer risk than overall BMI is of particular biological and clinical relevance. This suggests that visceral adipose tissue, which is highly metabolically active and a key driver of insulin resistance, may be a more critical etiological factor than general adiposity, a finding consistent with emerging research on the oncogenic properties of visceral fat [ 18 ]. Furthermore, the clear, monotonic dose-response relationship between the number of metabolic syndrome components and cancer risk underscores a fundamental principle: the cumulative burden of metabolic dysfunction, rather than any single isolated factor, exerts the greatest oncogenic pressure. This "metabolic load" hypothesis provides a plausible mechanism for the escalating disease burden observed in older age groups, as prolonged exposure to these aberrant physiological states synergistically increases risk over time [ 19 ]. The translation of these epidemiological associations into a clinically actionable tool represents a significant stride towards personalized prevention. The superior performance of our integrated demographic-metabolic prediction model (AUC = 0.801) over models containing only demographic or metabolic variables demonstrates that the two domains provide complementary, non-redundant information. The model's robust performance metrics—including a significant net reclassification improvement and excellent calibration—indicate its potential for real-world application [ 20 ]. The exploration of nonlinear relationships further refines risk assessment, revealing that the risk escalates markedly beyond specific thresholds, such as a waist circumference of 100 cm or an HbA1c of 6.5%. These thresholds can serve as clear targets for both clinical intervention and patient education. The final nomogram synthesizes this complex web of risk factors into an accessible visual tool, enabling clinicians to estimate an individual woman's absolute risk and facilitating shared decision-making for more intensive screening or preventive strategies, such as proactive weight management programs or consideration of metformin in high-risk, prediabetic women [ 21 ]. In conclusion, our study bridges macroepidemiological surveillance with individual-level metabolic profiling to present a unified narrative on the burden and drivers of endometrial cancer. We demonstrate that the global improvements in outcomes are inequitably distributed, potentially widening the cancer disparity gap. Within the United States, the epidemic is both geographically concentrated and demographically shifting towards an older population. At the individual level, the risk is powerfully modulated by a cumulative burden of metabolic dysregulation, with central adiposity playing a paramount role. The developed nomogram offers a practical means to translate these insights into clinical practice. Future efforts must therefore be dual-pronged: implementing targeted public health policies to address the obesity epidemic and its geographic hotspots, while simultaneously integrating validated risk prediction models into routine gynecologic care to identify and protect the most vulnerable women from this largely preventable disease. Declarations Acknowledgments No. Ethics approval and consent to participate Not applicable. Institutional Review Board (IRB) approval was not required for this project, as the scoping review exclusively examined and summarized publicly available data that does not involve human subjects or private/identifiable information. Nevertheless, this study was conducted in strict accordance with the ethical principles outlined in the World Medical Association Declaration of Helsinki (2022 version) (Ethical Principles for Medical Research Involving Human Subjects), to ensure the integrity, transparency, and ethical compliance of the research process. Informed consent to participate is also not applicable, given the nature of publicly available data analyzed in this review. Consent for publication Not applicable. Availability of data and materials The data can be downloaded for free from the website: https://www.healthdata.org/research-analysis/gbd and https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Excellent Teaching Cases for Professional Degree Graduate Students in Henan Province (YJS2024AL092), Kaifeng City Science and Technology Research project: Molecular mechanism study of exosome lncRNA PCAT19 regulation of cervical cancer progression and immune microenvironment (2203045), and the Henan Medical Technology Public Relations Plan Project (LHGJ20230424). Author contributions Author contributions: Dongli Zhang participated in the conceptualization, investigation, formal analysis, and writing of the original draft. Jungiqng Hou was involved in conceptualization, investigation, data curation, and writing of the original draft. Xiaofeng Yang contributed to investigation, data curation, and writing - review & editing. Dongli Zhang participated in investigation, data curation, and assisted with formal analysis. Xiaofeng Yang took charge of supervision, writing - review & editing, project administration, and funding acquisition. Song Li was responsible for resources, supervision, writing-review & editing, and funding acquisition. All authors have read and approved the final manuscript. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin . 2021;71(3):209-249. Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399(10333):1412-1428. Onstad MA, Schmandt RE, Lu KH. Addressing the Role of Obesity in Endometrial Cancer Risk, Prevention, and Treatment. J Clin Oncol. 2016;34(35):4225-4230. Arnold M, Pandeya N, Byrnes G, et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16(1):36-46. Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5(12):1749-1768. Clarke MA, Devesa SS, Harvey SV, Wentzensen N. Hysterectomy-Corrected Uterine Corpus Cancer Incidence Trends and Differences in Relative Survival Reveal Racial Disparities and Rising Rates of Nonendometrioid Cancers. J Clin Oncol. 2019;37(22):1895-1908. Felix AS, Bower JK, Pfeiffer RM, Raman SV, Cohn DE, Sherman ME. High cardiovascular disease mortality after endometrial cancer diagnosis: Results from the Surveillance, Epidemiology, and End Results (SEER) Database. Int J Cancer. 2017;140(3):555-564. Trabert B, Wentzensen N, Felix AS, Yang HP, Sherman ME, Brinton LA. Metabolic syndrome and risk of endometrial cancer in the united states: a study in the SEER-medicare linked database. Cancer Epidemiol Biomarkers Prev. 2015;24(1):261-267. Wen L, Zhang Y, Chen S, Wang J, Hu W, Zhong G. Subdivision of IIIC Stage for Endometrioid Carcinoma to Better Predict Prognosis and Treatment Guidance. Front Oncol. 2020;10:1175. Etrusco A, Barra F, Chiantera V, et al. Current Medical Therapy for Adenomyosis: From Bench to Bedside. Drugs. 2023;83(17):1595-1611. Jones AC, Brown KH, Guan T, et al. The past, present, and future of immunotherapy for endometrial adenocarcinoma. Med Oncol. 2023;40(7):186. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. Donkor A, Atuwo-Ampoh VD, Yakanu F, et al. Financial toxicity of cancer care in low- and middle-income countries: a systematic review and meta-analysis. Support Care Cancer. 2022;30(9):7159-7190. Mokdad AH, Murray CJL. Reversing the decline of health in the USA: a call to action. Lancet. 2024;404(10469):2392-2394. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, Rural-Urban, and Racial Inequalities in US Cancer Mortality: Part I-All Cancers and Lung Cancer and Part II-Colorectal, Prostate, Breast, and Cervical Cancers. J Cancer Epidemiol. 2011;2011:107497. Barros AJ, Victora CG. Measuring coverage in MNCH: determining and interpreting inequalities in coverage of maternal, newborn, and child health interventions. PLoS Med. 2013;10(5):e1001390. Friberg E, Mantzoros CS, Wolk A. Diabetes and risk of endometrial cancer: a population-based prospective cohort study. Cancer Epidemiol Biomarkers Prev. 2007;16(2):276-280. Lukanova A, Lundin E, Micheli A, et al. Circulating levels of sex steroid hormones and risk of endometrial cancer in postmenopausal women. Int J Cancer. 2004;108(3):425-432. Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care. 2012;35(11):2402-2411. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925-1931. Galal MA, Al-Rimawi M, Hajeer A, Dahman H, Alouch S, Aljada A. Metformin: A Dual-Role Player in Cancer Treatment and Prevention. Int J Mol Sci. 2024;25(7):4083. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 10 Oct, 2025 Editor assigned by journal 09 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7818101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544526083,"identity":"53787fa6-6f3b-4f39-beca-fcfc0f7a5841","order_by":0,"name":"Dongli Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dongli","middleName":"","lastName":"Zhang","suffix":""},{"id":544526085,"identity":"2a4f6858-3aae-4f71-ba17-3922142a4b80","order_by":1,"name":"Xiaofeng Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of 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07:16:43","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151237,"visible":true,"origin":"","legend":"","description":"","filename":"d2e777f9db25496aaefbab11916b176d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/988cdcba18272e01a1d4fbc5.xml"},{"id":95940221,"identity":"eb226206-86a8-46ac-8e60-f5798cfa5878","added_by":"auto","created_at":"2025-11-14 16:09:34","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156850,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/647f83ea9956fe705201e5aa.html"},{"id":95940203,"identity":"58a66417-e24c-46fc-9c2f-8884b7c8aec0","added_by":"auto","created_at":"2025-11-14 16:09:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97572,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in health burdens attributable to different risk factors, stratified by Socio-demographic Index (SDI) levels. Burden of endometrial cancer attributable to high BMI, presenting deaths per 100,000 population (A), disability-adjusted life years (B) per 100,000 person-years (middle), and life lost due to disability (LDLY) per 100,000 person-years (C).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/ee4123958edc2b47a8cd7ffe.png"},{"id":95940204,"identity":"41fed751-cd97-4573-9b59-dca56870bb80","added_by":"auto","created_at":"2025-11-14 16:09:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178563,"visible":true,"origin":"","legend":"\u003cp\u003eRegional burden of endometrial cancer attributable to high body mass index (BMI) among females in the United States, 2017–2021. (A) Annual mean number of deaths; (B) annual percentage change in deaths; (C) annual mean disability-adjusted life years (DALYs); (D) annual percentage change in DALYs; (E) annual mean years lived with disability (YLDs); (F) annual percentage change in YLDs. The color gradient in panels A, C, and E indicates the range of annual mean values from 29.94 to 73.76, whereas in panels B, D, and F, it indicates the range of annual percentage change from -1.41 to 2.68.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/2a37bf66a8e5bb4358ca822a.png"},{"id":95940207,"identity":"1882b161-8b00-4c55-bac8-d8af9f5610d5","added_by":"auto","created_at":"2025-11-14 16:09:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121599,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships between metabolic indicators and endometrial cancer risk. (A) Association between body mass index (BMI) and endometrial cancer risk. (B) Association between waist circumference and endometrial cancer risk. (C) Association between triglyceride levels and endometrial cancer risk. (D) Association between glycohemoglobin levels and endometrial cancer risk.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/a03a9061af28fe848f41b39f.png"},{"id":95940205,"identity":"ed001ac8-f083-4e1b-a9f2-d5967e578b1d","added_by":"auto","created_at":"2025-11-14 16:09:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38850,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot_ Association Between Number of Metabolic Syndrome Components and endometrial cancer Risk.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/8947f4986481c1883bd30abd.png"},{"id":96244371,"identity":"9b19967f-c7e3-43ba-8ed6-62d342e98eb4","added_by":"auto","created_at":"2025-11-19 07:18:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":109447,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram and Calibration Curve for endometrial cancer Risk Prediction Model. (A) Nomogram for endometrial cancer risk prediction. (B) Calibration curve of the prediction model, showing the relationship between predicted risk and observed risk.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/c137bc0a421ad740034a7bd4.png"},{"id":96363342,"identity":"cc65bcfb-189f-40e9-b397-411f4460202d","added_by":"auto","created_at":"2025-11-20 10:06:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2596005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7818101/v1/c8dc1fb6-256d-4d73-a9fd-6c354cb4248e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Scale Analysis of Global Endometrial Cancer Burden: Integrating Socio-Demographic Trends, Metabolic Risk Factors, and Predictive Modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eendometrial cancer represents a significant and growing threat to women's health globally, with its incidence demonstrating a concerning upward trajectory in many parts of the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the most common gynecologic malignancy in high-income countries, it imposes a substantial burden in terms of mortality, morbidity, and healthcare costs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The etiology of endometrial cancer is multifactorial, with obesity established as a predominant risk factor, accounting for a substantial proportion of cases through its influence on estrogen metabolism and insulin resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The rising global prevalence of obesity has been directly linked to the increasing incidence of endometrial cancer, positioning it as a largely preventable disease with significant potential for risk mitigation through public health and clinical interventions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite this knowledge, the global landscape of endometrial cancer burden is marked by profound disparities. The Socio-demographic Index (SDI), a composite measure of income, education, and fertility, has been shown to correlate with cancer outcomes, yet the specific temporal trends in endometrial cancer mortality and disability across different SDI regions remain inadequately characterized [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While high-SDI regions historically bear the greatest burden, the rate of change and the shifting patterns in low- and middle-SDI regions are poorly understood, hindering the targeted allocation of global health resources. Furthermore, within high-resource settings like the United States, the burden is not uniformly distributed. Geographic hotspots of obesity-related endometrial cancer and their temporal evolution are not well-mapped, obscuring the need for sub-national public health strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the demographic level, a critical knowledge gap exists regarding age-specific trends in the modern era. The aging of the global population necessitates a detailed understanding of how the burden of endometrial cancer evolves across the lifespan. Preliminary evidence suggests differential trends by age, but a comprehensive, quantitative analysis of age-stratified mortality and disability rates, particularly for the component attributable to high BMI, is lacking [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This macro-level epidemiological evidence, while crucial for public health planning, must be complemented by a deeper understanding of individual-level pathophysiology.\u003c/p\u003e\u003cp\u003eThe metabolic pathway linking obesity to uterine carcinogenesis is complex and extends beyond a simple measure of BMI. Metabolic syndrome\u0026mdash;a cluster of conditions including central obesity, dyslipidemia, hypertension, and hyperglycemia\u0026mdash;may represent a more profound dysmetabolic state conferring even greater risk [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the independent and combined contributions of these metabolic parameters, their potential non-linear relationships with risk, and their utility in comprehensive risk prediction models have not been fully elucidated in representative populations. Most existing models rely on broad demographic factors, failing to integrate the nuanced metabolic profile that could enable precise risk stratification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, a cohesive narrative that bridges the population-level burden with individual-level metabolic drivers is urgently needed. To address these multifaceted evidence gaps, we conducted a multi-scale study designed to integrate macroepidemiology with individual-level risk assessment. Our research aims were threefold: (1) To delineate the global and sub-national (US) temporal trends in the burden of endometrial cancer, stratifying by SDI and geographic region, with a specific focus on the proportion attributable to high BMI; (2) To dissect the age-specific trajectories of this burden to identify vulnerable demographic cohorts; and (3) To quantitatively evaluate the individual-level associations between comprehensive metabolic dysregulation and endometrial cancer risk, and to translate these findings into a clinically actionable risk prediction tool. By synthesizing evidence from the Global Burden of Disease study and the National Health and Nutrition Examination Survey, this research provides an integrated perspective essential for guiding both population-wide initiatives and personalized preventive strategies against endometrial cancer.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Global Burden of endometrial cancer: Data Source and Temporal Trend Analysis\u003c/h2\u003e\u003cp\u003eTo characterize the global burden of endometrial cancer and its trends, we conducted a cross-sectional analysis using data from the Global Burden of Disease (GBD) study for the period 2017\u0026ndash;2021. We extracted age-standardized estimates of mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs), all expressed per 100,000 population. These metrics were stratified by Socio-demographic Index (SDI), categorized into High, Middle, and Low SDI regions. To evaluate temporal trends, we applied a weighted linear regression model to the log-transformed annual values for each metric and SDI stratum. The results are expressed as annual percentage changes (APCs) with 95% uncertainty intervals (UIs), which reflect the precision of the GBD estimation process.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Geographic and Temporal Variation in the endometrial cancer Burden Attributable to High BMI in the United States\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe burden of endometrial cancer specifically attributable to high body mass index (BMI) was assessed using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. This analysis focused on the female population across all 50 US states and the District of Columbia from 2017 to 2021. The comparative risk assessment framework from GBD was employed to calculate population attributable fractions, based on established risk curves linking BMI to endometrial cancer incidence. The burden was quantified using mortality, DALYs, and YLDs (per 100,000). For each state, the average annual burden was calculated as the mean over the five-year period. Temporal trends were analyzed using linear regression models to derive annual percentage changes, with all estimates incorporating 95% UIs and adhering to GBD's standard protocols for spatial-temporal smoothing and cross-validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Age-Specific Trends in endometrial cancer Burden in the United States\u003c/h2\u003e\u003cp\u003eFor a detailed analysis of age-specific patterns, we utilized US data from the GBD study for 2017\u0026ndash;2021. We examined the metrics of deaths, DALYs, and YLDs across standard 5-year age intervals, from 30\u0026ndash;34 years to 80 years and older. The mean annual estimate for each metric and age group was calculated as the arithmetic average of the five annual data points. The temporal trend for each age group was quantified by calculating the compound annual growth rate (CAGR) using the formula: [(Final Value / Initial Value)^(1/4) \u0026minus;\u0026thinsp;1] * 100. This provides a smoothed average annual rate of change over the study period. All computations were performed using R statistical software (version 4.3.0).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Individual-Level Association Analysis: Study Population and Metabolic Profiling\u003c/h2\u003e\u003cp\u003eTo investigate individual-level metabolic risk factors, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) cycles 2011\u0026ndash;2016. Our analytical cohort comprised 288 women, including 69 endometrial cancer cases and 219 population-based controls. Comprehensive data on demographics, metabolic parameters, and lifestyle factors were obtained from standardized examinations and laboratory measurements. Metabolic syndrome was defined according to established criteria integrating measures of central obesity, dyslipidemia, hypertension, and impaired fasting glucose. Baseline characteristics were compared using appropriate statistical tests that accounted for the complex survey design: weighted t-tests for continuous variables and Rao-Scott adjusted chi-square tests for categorical variables. Sampling weights were applied to ensure national representativeness, and multiple imputation was used to handle missing data, thereby preserving statistical power and minimizing potential bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Modeling and Risk Prediction\u003c/h2\u003e\u003cp\u003eMultivariable weighted logistic regression, accounting for the complex NHANES survey design, was used to quantify the associations between metabolic parameters and endometrial cancer risk. Results are presented as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Nonlinear relationships were explored using restricted cubic splines with three knots placed at clinically relevant percentiles. The cumulative impact of metabolic dysregulation was assessed by evaluating the dose-response relationship between the number of metabolic syndrome components and endometrial cancer risk. Predictive model development employed a backward selection process, retaining variables with clinical and statistical significance. Model performance was rigorously evaluated by assessing discrimination (C-statistic), calibration (Hosmer-Lemeshow test, calibration plots), and clinical utility (net reclassification improvement, integrated discrimination improvement). Internal validation was performed using bootstrap resampling with 1000 iterations to correct for over-optimism. The final model was presented as a nomogram for clinical translation, and its calibration was visualized. All analyses were performed using statistical software appropriate for complex survey data.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003e3.1 Differential Patterns and Temporal Trends in the Global Burden of endometrial cancer by SDI Region from 2017 to 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Globally, the age-standardized burden of endometrial cancer in 2021 exhibited a clear gradient according to the Socio-demographic Index (SDI), with the High SDI region bearing the highest mortality rate (2.69 per 100,000; 95% UI: 2.41\u0026ndash;2.84), DALY rate (71.79 per 100,000; 95% UI: 66.24\u0026ndash;76.26), and YLD rate (10.00 per 100,000; 95% UI: 7.27\u0026ndash;13.44), compared to Middle and Low SDI regions. Analysis of trends from 2017 to 2021 revealed a modest but consistent annual decline in both mortality (\u0026ndash;0.61%; 95% UI: \u0026minus;\u0026thinsp;3.04 to 1.88) and DALYs (\u0026ndash;0.62%; 95% UI: \u0026minus;\u0026thinsp;2.75 to 1.57) within High SDI areas. Conversely, Low SDI regions experienced marginal annual increases in mortality (0.10%; 95% UI: \u0026minus;\u0026thinsp;7.17 to 7.95) and DALYs (0.20%; 95% UI: \u0026minus;\u0026thinsp;7.19 to 8.18), while Middle SDI regions showed slight upward trends for these metrics (0.31% and 0.48%, respectively). The annual change in YLD rates was more variable across all strata, yet notably positive in Low (1.54%) and Middle (2.21%) SDI regions, suggesting a growing non-fatal disease burden in these settings(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These distinct patterns highlight an inverse association between SDI level and the rate of improvement in endometrial cancer outcomes, underscoring the need for targeted health system interventions in resource-limited regions(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" 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\u003eDifferential Patterns and Temporal Trends in the Global Burden of endometrial cancer by SDI Region from 2017 to 2021.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change Rate (95% UI) for Deaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change Rate (95% UI) for DALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYLDs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change Rate (95% UI) for YLDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.69 (2.41\u0026ndash;2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.61% (-3.04% to 1.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.79 (66.24\u0026ndash;76.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.62% (-2.75% to 1.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.00 (7.27\u0026ndash;13.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.35% (-9.58% to 9.82%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.61 (1.35\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31% (-4.56% to 5.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.05 (36.88\u0026ndash;52.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48% (-4.62% to 5.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.98 (1.97\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.21% (-8.70% to 14.44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83 (1.46\u0026ndash;2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10% (-7.17% to 7.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.80 (37.40-60.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20% (-7.19% to 8.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.69 (1.13\u0026ndash;2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.54% (-10.40% to 15.07%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 Geographic Heterogeneity and Temporal Dynamics of endometrial cancer Burden Attributable to High Body Mass Index Across the United States\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur analysis revealed substantial geographic heterogeneity in the burden of endometrial cancer attributable to high BMI across the United States from 2017 to 2021, with distinct temporal patterns emerging across different regions(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The District of Columbia consistently demonstrated the highest annual mean mortality (2.56 per 100,000) and DALY rates (73.76 per 100,000), while Alaska exhibited the lowest mortality (1.04 per 100,000) and DALY burdens (29.94 per 100,000). Notably, several states in the Appalachian region and Deep South, including West Virginia, Mississippi, and Alabama, maintained persistently elevated disease burdens throughout the study period. Temporal analysis identified concerning upward trajectories in numerous states, with Delaware demonstrating the most rapid increase in mortality (2.68% annual change) and Mississippi showing the fastest growth in DALYs (2.29% annual change). Conversely, several states exhibited declining trends, particularly in the Northeast, with Rhode Island showing the most substantial reductions in both DALYs (-1.58%) and YLDs (-1.62%). The geographic patterns for YLDs generally paralleled those observed for mortality and DALYs, though with varying magnitudes of change, suggesting complex relationships between incidence, survival, and disability across different populations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings highlight the unequal distribution of the endometrial cancer burden attributable to high BMI across the United States and identify regions requiring targeted public health interventions to address both the current burden and concerning upward trajectories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegional burden of endometrial cancer attributable to high BMI among females in the United States, 2017\u0026ndash;2021.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelaware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHawaii\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArkansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlabama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlaska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIdaho\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIowa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.03\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\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePennsylvania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTexas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOhio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOklahoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOregon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlorida\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVermont\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict of Columbia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWashington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWyoming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalifornia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKansas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConnecticut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorado\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKentucky\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLouisiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRhode Island\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaryland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMassachusetts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMontana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissouri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMississippi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMichigan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinnesota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Dakota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Carolina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNebraska\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNevada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew York\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTennessee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWisconsin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Virginia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Hampshire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Mexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Jersey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArizona\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIllinois\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndiana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUtah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Distinct Age-Specific Trajectories in Mortality and Disability Burden of endometrial cancer Attributable to High Body Mass Index in the United States, 2017\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis of age-specific temporal trends from 2017 to 2021 revealed a pronounced demographic divergence in the burden of endometrial cancer attributable to high BMI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mortality and disability burden remained remarkably stable or exhibited minimal annual decline among younger and middle-aged women (30\u0026ndash;59 years), with annual percent changes (APCs) for deaths and DALYs clustering near zero, ranging from \u0026minus;\u0026thinsp;0.70% to 1.12%. In stark contrast, a significant and consistent escalation was observed in all older age groups, commencing at ages 60\u0026ndash;64 and intensifying with advancing age. The most substantial increases were documented among women aged 70\u0026ndash;74 years, where deaths, DALYs, and YLDs rose by 1.48%, 1.47%, and 1.67% per annum, respectively. This escalating trajectory peaked in the eldest cohort (\u0026ge;\u0026thinsp;80 years), which demonstrated a 1.03% annual increase in mortality, a 1.28% rise in DALYs, and a particularly sharp 1.48% annual growth in YLDs, underscoring a rapidly expanding burden of non-fatal health loss in the most advanced age groups. This clear demarcation in epidemiological trends, with a pivotal transition around the 6th decade of life, highlights an increasing concentration of the endometrial cancer burden attributable to high BMI within the older female population in the United States.\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\u003eAge-specific annual mean and annual change percentage of deaths, DALYs, and YLDs attributable to high BMI for endometrial cancer in the United States, 2017\u0026ndash;2021.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnual Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnnual Change (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e231.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e269.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e269.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75\u0026ndash;79 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e242.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;80 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e167.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Distinct Metabolic and Demographic Profiles Characterize Women with endometrial cancer in a National Cohort Analysis\u003c/h2\u003e\u003cp\u003e Comprehensive analysis of 288 participants from the NHANES 2011\u0026ndash;2016 cohort revealed substantial demographic and metabolic disparities between women with endometrial cancer (n\u0026thinsp;=\u0026thinsp;69) and matched controls (n\u0026thinsp;=\u0026thinsp;219). Cases were significantly older than controls (59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1 vs 48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and demonstrated a non-significant trend toward higher representation of Non-Hispanic White women (63.8% vs 49.3%, p\u0026thinsp;=\u0026thinsp;0.077). Metabolic profiling identified pronounced disturbances among cases, including significantly larger waist circumference (105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6 vs 99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 cm, p\u0026thinsp;=\u0026thinsp;0.010), higher prevalence of diabetes (27.5% vs 16.0%, p\u0026thinsp;=\u0026thinsp;0.029), and substantially greater burden of hypertension (66.7% vs 51.1%, p\u0026thinsp;=\u0026thinsp;0.022). The metabolic syndrome aggregate was significantly more prevalent in cases (46.4% vs 30.6%, p\u0026thinsp;=\u0026thinsp;0.015), while individual components showed consistent though non-significant elevations in BMI (31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 vs 29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.157) and triglyceride levels (1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35 vs 1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 mmol/L, p\u0026thinsp;=\u0026thinsp;0.107). Glycemic parameters trended toward impairment in cases (HbA1c 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1% vs 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%, p\u0026thinsp;=\u0026thinsp;0.060), whereas lifestyle factors including smoking (15.9% vs 19.2%, p\u0026thinsp;=\u0026thinsp;0.537), alcohol consumption (2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 vs 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 days/month, p\u0026thinsp;=\u0026thinsp;0.343), and hormone use history (18.8% vs 26.5%, p\u0026thinsp;=\u0026thinsp;0.198) demonstrated no significant intergroup differences. These findings establish a clear profile of adverse metabolic characteristics and older age among women with endometrial cancer, highlighting potential pathways for targeted intervention and risk stratification(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Study Participants by endometrial cancer Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003eVariable Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDefinition / Assignment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;219)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eendometrial cancer Group (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStatistics\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\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eDemographic Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYears, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRace/Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eMetabolic Core Indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekg/m\u0026sup2;, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWaist Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecm, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTriglyceride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlycohemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Blood Pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 5.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42 (60.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetabolic Syndrome (MetS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLifestyle Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlcohol Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003edays/month, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConfounding Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEver Use Female Hormones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ\u0026sup2; = 1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Metabolic Dysregulation Independently Predicts endometrial cancer Risk and Enables Robust Risk Stratification Through Integrated Modeling\u003c/h2\u003e\u003cp\u003eMultivariable analysis revealed several metabolic parameters as independent predictors of endometrial cancer risk, with obesity measures demonstrating particularly strong associations(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). After comprehensive adjustment for demographic and clinical confounders, women with obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) exhibited 1.86-fold increased odds of endometrial cancer (95% CI: 1.05\u0026ndash;3.29, p\u0026thinsp;=\u0026thinsp;0.033), with each 5-unit BMI increment conferring 25% elevated risk (95% CI: 1.02\u0026ndash;1.54, p\u0026thinsp;=\u0026thinsp;0.034). Central adiposity manifested even stronger associations, as waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;88 cm predicted 2.36-fold increased odds (95% CI: 1.24\u0026ndash;4.49, p\u0026thinsp;=\u0026thinsp;0.009), with each 10-cm increment elevating risk by 29% (95% CI: 1.06\u0026ndash;1.57, p\u0026thinsp;=\u0026thinsp;0.011). The metabolic syndrome composite showed borderline significance in adjusted models (OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 0.92\u0026ndash;2.85, p\u0026thinsp;=\u0026thinsp;0.094), while individual components including diabetes (OR\u0026thinsp;=\u0026thinsp;1.68, p\u0026thinsp;=\u0026thinsp;0.117), hypertension (OR\u0026thinsp;=\u0026thinsp;1.58, p\u0026thinsp;=\u0026thinsp;0.119), and hyperglycemia (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%: OR\u0026thinsp;=\u0026thinsp;1.83, p\u0026thinsp;=\u0026thinsp;0.095) demonstrated consistent though statistically marginal trends. When incorporated into risk prediction algorithms, metabolic indicators substantially enhanced model performance, with the integrated demographic-metabolic model achieving superior discrimination (AUC\u0026thinsp;=\u0026thinsp;0.801, 95% CI: 0.747\u0026ndash;0.855) compared to demographic-only (AUC\u0026thinsp;=\u0026thinsp;0.712) or metabolic-only (AUC\u0026thinsp;=\u0026thinsp;0.758) constructs. This comprehensive model demonstrated excellent calibration (Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.520), improved classification accuracy (74.3%), and significant net reclassification improvement (NRI\u0026thinsp;=\u0026thinsp;0.25, 95% CI: 0.11\u0026ndash;0.39) alongside integrated discrimination enhancement (IDI\u0026thinsp;=\u0026thinsp;0.068, 95% CI: 0.025\u0026ndash;0.111), establishing its clinical utility for individualized risk assessment(Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation Between Metabolic Indicators and endometrial cancer Risk (Weighted Logistic Regression)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic Indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted OR (95% CI)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e\u0026lt;\u0026thinsp;25 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003e25-29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.42 (0.78\u0026ndash;2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28 (0.69\u0026ndash;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.431\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\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.15 (1.24\u0026ndash;3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.86 (1.05\u0026ndash;3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\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\u003ePer 5-unit increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.32 (1.08\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.25 (1.02\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist Circumference\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e\u0026lt;\u0026thinsp;88 cm (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u0026ge;\u0026thinsp;88 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.84 (1.52\u0026ndash;5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.36 (1.24\u0026ndash;4.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\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\u003ePer 10-cm increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38 (1.15\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.29 (1.06\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglyceride\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e\u0026lt;\u0026thinsp;1.7 mmol/L (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u0026ge;\u0026thinsp;1.7 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.78 (1.02\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.52 (0.85\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.158\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\u003ePer 1-mmol/L increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.24 (1.01\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.16 (0.93\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Status\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eNo (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01 (1.08\u0026ndash;3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.68 (0.88\u0026ndash;3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycohemoglobin\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e\u0026lt;\u0026thinsp;5.7% (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003e5.7\u0026ndash;6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.65 (0.89\u0026ndash;3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42 (0.75\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.283\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\u0026ge;\u0026thinsp;6.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.34 (1.19\u0026ndash;4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.83 (0.90\u0026ndash;3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.095\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\u003ePer 1% increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.28 (1.06\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19 (0.97\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eNo (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.92 (1.10\u0026ndash;3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.58 (0.89\u0026ndash;2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh Cholesterol\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eNo (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33 (0.78\u0026ndash;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.18 (0.68\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolic Syndrome\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eNo (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.96 (1.14\u0026ndash;3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.62 (0.92\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance Validation of endometrial cancer Risk Prediction Model (Metabolic Indicators-Based)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Performance Metric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBasic Model (Demographics Only)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMetabolic Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFull Model (Demographics\u0026thinsp;+\u0026thinsp;Metabolic)\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\u003eSample Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of Cases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel Components\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge, Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBMI, WC, TG, HbA1c, HTN, DM, MetS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge, Race, BMI, WC, TG, HbA1c, HTN, DM, MetS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiscrimination\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-statistic (AUC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.712 (0.648\u0026ndash;0.776)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.758 (0.698\u0026ndash;0.818)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.801 (0.747\u0026ndash;0.855)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalibration\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHosmer-Lemeshow χ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHosmer-Lemeshow p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalibration slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGoodness-of-Fit\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkaike Information Criterion (AIC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e342.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e328.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e318.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBayesian Information Criterion (BIC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e349.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLikelihood Ratio Test (vs basic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eχ\u0026sup2;=18.3, p\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=27.9, p\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClassification Performance\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive Predictive Value (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative Predictive Value (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall Accuracy (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical Utility\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Reclassification Improvement (NRI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18 (0.06\u0026ndash;0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25 (0.11\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegrated Discrimination Improvement (IDI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042 (0.011\u0026ndash;0.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068 (0.025\u0026ndash;0.111)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.6 Nonlinear Relationships, Cumulative Risk Patterns, and Clinical Prediction Modeling Elucidate Metabolic Contributions to endometrial cancer Pathogenesis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSophisticated modeling of metabolic-endometrial cancer relationships revealed nuanced risk patterns, with dose-response analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrating continuous, nonlinear associations across key metabolic parameters. Body mass index exhibited a near-linear risk increase beyond 24 kg/m\u0026sup2;, while waist circumference displayed an accelerating risk trajectory particularly above 100 cm, suggesting possible threshold effects for central adiposity. Glycemic dysregulation manifested a J-shaped relationship with substantially elevated risk beyond the diabetic threshold (HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;6.5%), whereas triglyceride levels showed a more modest but consistently positive association. Critically, forest plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed a striking monotonic gradient between metabolic syndrome component accumulation and endometrial cancer risk, with women possessing four components experiencing a 3.72-fold elevated risk (95% CI: 1.82\u0026ndash;7.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with none, and the trend test confirming significant dose-dependency (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings informed the development of a comprehensive prediction tool (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) that integrates demographic and metabolic parameters into a clinically accessible nomogram, demonstrating excellent calibration across the risk spectrum (calibration slope\u0026thinsp;=\u0026thinsp;0.99, Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.520) and providing individualized risk estimates ranging from \u0026lt;\u0026thinsp;1% to \u0026gt;\u0026thinsp;90%. The model's robust performance (optimism-corrected AUC\u0026thinsp;=\u0026thinsp;0.785) and minimal overfitting establish its potential utility for identifying high-risk women who might benefit from targeted screening and preventive interventions, effectively translating metabolic risk profiling into actionable clinical stratification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur multi-faceted analysis reveals a complex and evolving landscape of the endometrial cancer burden, characterized by a paradoxical relationship with socioeconomic development, significant subnational disparities within a high-income nation, and a concerning demographic shift towards older populations. The findings underscore the urgent need for public health strategies that are both globally aware and locally tailored.\u003c/p\u003e\u003cp\u003eFirst, the observed global patterns highlight a critical epidemiological transition. While high-SDI regions currently bear the greatest absolute burden of endometrial cancer, they are also the only group demonstrating a consistent, albeit modest, decline in mortality and DALYs. This trend is likely attributable to the synergistic effect of advanced healthcare infrastructures, which facilitate early detection through improved access to gynecologic care and diagnostic imaging, and the widespread adoption of minimally invasive surgical techniques that have standardized high-quality treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, the stagnant or rising trends in low- and middle-SDI regions, coupled with the most rapid growth in years lived with disability (YLDs), paint a starkly different picture. This pattern suggests a double burden in these settings: rising incidence, potentially driven by the increasing adoption of Westernized lifestyles and the growing prevalence of obesity, is met with health systems ill-equipped to provide timely diagnosis and effective treatment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The rising YLD rate specifically indicates that while women may be surviving longer with the disease, they are likely doing so with significant morbidity, possibly due to limited access to comprehensive cancer care, including advanced radiotherapy, systemic therapies, and palliative support [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This inverse association between SDI level and the pace of improvement underscores a widening global equity gap in endometrial cancer outcomes, demanding targeted international efforts to strengthen health systems in resource-limited settings.\u003c/p\u003e\u003cp\u003eDelving deeper into a high-SDI context, the substantial geographic heterogeneity of the high BMI-attributable burden across the United States is alarming and points to the powerful influence of regional sociodemographic and healthcare factors. The concentration of the highest burden in the District of Columbia and states within the Appalachian region and Deep South is not coincidental. These areas have some of the highest national rates of obesity, poverty, and diabetes, and often face significant barriers to healthcare access, including physician shortages and limited insurance coverage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The divergent temporal trajectories between states like Delaware and Mississippi (showing rapid increases) and those in the Northeast (showing declines) further suggest that state-level public health policies and healthcare environments are pivotal. States with robust public health initiatives focusing on obesity prevention, cancer screening, and equal access to treatment may be successfully curbing the epidemic, while others are falling behind [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This geographic mosaic underscores that national averages mask profound local inequalities, necessitating a precision public health approach whereby interventions are specifically designed and deployed in the high-burden clusters we have identified.\u003c/p\u003e\u003cp\u003eMoving from population-level associations to individual-level pathophysiology, our findings from the NHANES cohort provide compelling evidence that endometrial cancer is profoundly embedded within a context of metabolic dysregulation. The distinct profile of cases\u0026mdash;characterized by significant perturbations in waist circumference, blood pressure, and glycemic control\u0026mdash;strongly implicates the interplay of chronic hyperinsulinemia, altered adipokine signaling, and bioavailable estrogen in carcinogenesis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The fact that central adiposity demonstrated a stronger independent association with endometrial cancer risk than overall BMI is of particular biological and clinical relevance. This suggests that visceral adipose tissue, which is highly metabolically active and a key driver of insulin resistance, may be a more critical etiological factor than general adiposity, a finding consistent with emerging research on the oncogenic properties of visceral fat [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, the clear, monotonic dose-response relationship between the number of metabolic syndrome components and cancer risk underscores a fundamental principle: the cumulative burden of metabolic dysfunction, rather than any single isolated factor, exerts the greatest oncogenic pressure. This \"metabolic load\" hypothesis provides a plausible mechanism for the escalating disease burden observed in older age groups, as prolonged exposure to these aberrant physiological states synergistically increases risk over time [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe translation of these epidemiological associations into a clinically actionable tool represents a significant stride towards personalized prevention. The superior performance of our integrated demographic-metabolic prediction model (AUC\u0026thinsp;=\u0026thinsp;0.801) over models containing only demographic or metabolic variables demonstrates that the two domains provide complementary, non-redundant information. The model's robust performance metrics\u0026mdash;including a significant net reclassification improvement and excellent calibration\u0026mdash;indicate its potential for real-world application [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The exploration of nonlinear relationships further refines risk assessment, revealing that the risk escalates markedly beyond specific thresholds, such as a waist circumference of 100 cm or an HbA1c of 6.5%. These thresholds can serve as clear targets for both clinical intervention and patient education. The final nomogram synthesizes this complex web of risk factors into an accessible visual tool, enabling clinicians to estimate an individual woman's absolute risk and facilitating shared decision-making for more intensive screening or preventive strategies, such as proactive weight management programs or consideration of metformin in high-risk, prediabetic women [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, our study bridges macroepidemiological surveillance with individual-level metabolic profiling to present a unified narrative on the burden and drivers of endometrial cancer. We demonstrate that the global improvements in outcomes are inequitably distributed, potentially widening the cancer disparity gap. Within the United States, the epidemic is both geographically concentrated and demographically shifting towards an older population. At the individual level, the risk is powerfully modulated by a cumulative burden of metabolic dysregulation, with central adiposity playing a paramount role. The developed nomogram offers a practical means to translate these insights into clinical practice. Future efforts must therefore be dual-pronged: implementing targeted public health policies to address the obesity epidemic and its geographic hotspots, while simultaneously integrating validated risk prediction models into routine gynecologic care to identify and protect the most vulnerable women from this largely preventable disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. Institutional Review Board (IRB) approval was not required for this project, as the scoping review exclusively examined and summarized publicly available data that does not involve human subjects or private/identifiable information. Nevertheless, this study was conducted in strict accordance with the ethical principles outlined in the World Medical Association Declaration of Helsinki (2022 version) (Ethical Principles for Medical Research Involving Human Subjects), to ensure the integrity, transparency, and ethical compliance of the research process. Informed consent to participate is also not applicable, given the nature of publicly available data analyzed in this review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be downloaded for free from the website: https://www.healthdata.org/research-analysis/gbd and https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Excellent Teaching Cases for Professional Degree Graduate Students in Henan Province (YJS2024AL092), Kaifeng City Science and Technology Research project: Molecular mechanism study of exosome lncRNA PCAT19 regulation of cervical cancer progression and immune microenvironment (2203045), and the Henan Medical Technology Public Relations Plan Project (LHGJ20230424).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions: Dongli Zhang participated in the conceptualization, investigation, formal analysis, and writing of the original draft. Jungiqng Hou was involved in conceptualization, investigation, data curation, and writing of the original draft. Xiaofeng Yang contributed to investigation, data curation, and writing - review \u0026amp; editing. Dongli Zhang participated in investigation, data curation, and assisted with formal analysis. Xiaofeng Yang took charge of supervision, writing - review \u0026amp; editing, project administration, and funding acquisition. Song Li was responsible for resources, supervision, writing-review \u0026amp; editing, and funding acquisition. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. 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Measuring coverage in MNCH: determining and interpreting inequalities in coverage of maternal, newborn, and child health interventions.\u0026nbsp;PLoS Med. 2013;10(5):e1001390.\u003c/li\u003e\n \u003cli\u003eFriberg E, Mantzoros CS, Wolk A. Diabetes and risk of endometrial cancer: a population-based prospective cohort study. Cancer Epidemiol Biomarkers Prev. 2007;16(2):276-280.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLukanova A, Lundin E, Micheli A, et al. Circulating levels of sex steroid hormones and risk of endometrial cancer in postmenopausal women. Int J Cancer. 2004;108(3):425-432.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEsposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis.\u0026nbsp;Diabetes Care. 2012;35(11):2402-2411.\u003c/li\u003e\n \u003cli\u003eSteyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation.\u0026nbsp;Eur Heart J. 2014;35(29):1925-1931.\u003c/li\u003e\n \u003cli\u003eGalal MA, Al-Rimawi M, Hajeer A, Dahman H, Alouch S, Aljada A. Metformin: A Dual-Role Player in Cancer Treatment and Prevention. Int J Mol Sci. 2024;25(7):4083.\u0026nbsp;\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":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"endometrial cancer, Global Burden of Disease, Body Mass Index, Metabolic Syndrome","lastPublishedDoi":"10.21203/rs.3.rs-7818101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7818101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEndometrial cancer represents a growing global health challenge, with rising incidence and significant disparities in outcomes across socio-demographic regions. Although obesity is a well-established risk factor, the complex interplay between metabolic dysregulation, geographic heterogeneity, and age-specific trends remains poorly characterized. This study aims to elucidate the global and sub-national burden of endometrial cancer, with a focus on high body mass index (BMI) as a key attributable factor, and to develop an integrated risk prediction model incorporating metabolic parameters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a multi-scale analysis using data from the Global Burden of Disease (GBD) study (2017\u0026ndash;2021) and the National Health and Nutrition Examination Survey (NHANES, 2011\u0026ndash;2016). Global and U.S. trends in mortality, disability-adjusted life years (DALYs), and years lived with disability (YLDs) were evaluated by Socio-demographic Index (SDI) and geographic region. Age-specific trajectories and metabolic risk factors were analyzed using regression models and logistic regression with complex survey design. A clinical prediction nomogram was developed and validated using bootstrap resampling.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGlobally, high-SDI regions exhibited the highest burden of endometrial cancer but demonstrated declining mortality (\u0026ndash;0.61% annually) and DALYs (\u0026ndash;0.62%), whereas low- and middle-SDI regions experienced stagnant or rising trends. Within the United States, significant geographic disparities were observed, with hotspots in the Appalachian region and Deep South. Age-stratified analysis revealed increasing burden among women aged 60 and older, with peak increases in those\u0026thinsp;\u0026ge;\u0026thinsp;80 years (YLDs: +1.48% annually). Individual-level analysis of 288 women (69 cases, 219 controls) identified central adiposity (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;88 cm: OR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 1.24\u0026ndash;4.49) and cumulative metabolic syndrome components (4 components: OR\u0026thinsp;=\u0026thinsp;3.72, 95% CI: 1.82\u0026ndash;7.61) as strong independent risk factors. The integrated prediction model achieved high discrimination (AUC\u0026thinsp;=\u0026thinsp;0.801) and significant net reclassification improvement (NRI\u0026thinsp;=\u0026thinsp;0.25).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study highlights persistent and emerging disparities in endometrial cancer burden across SDI regions and U.S. states, with a shifting demographic toward older women. Metabolic dysregulation, particularly central adiposity and cumulative metabolic syndrome, plays a critical role in pathogenesis. The developed nomogram offers a validated tool for individualized risk assessment, supporting targeted prevention and early intervention strategies in high-risk populations.\u003c/p\u003e","manuscriptTitle":"Multi-Scale Analysis of Global Endometrial Cancer Burden: Integrating Socio-Demographic Trends, Metabolic Risk Factors, and Predictive Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 16:09:29","doi":"10.21203/rs.3.rs-7818101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-27T18:03:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286013225890984898575003022344514045573","date":"2025-11-13T12:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T12:12:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T11:18:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T03:53:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T03:52:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-10-09T13:24:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2aa44ed-43bd-4d8f-bcd0-9c6e8b5836a2","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T16:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 16:09:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7818101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7818101","identity":"rs-7818101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}