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

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.