Global, Regional, and National Burden of Ovarian Cancer Attributable to High Body-Mass Index Among Women Aged 55 Years and Older: A GBD 2021 Analysis

BACKGROUND: High body-mass index (BMI) defined in the Global Burden of Disease Study as BMI above the theoretical minimum risk exposure level, is an established modifiable risk factor for ovarian cancer, particularly among older women. However, a comprehensive assessment of the global disease burden attributable to high BMI, including its spatiotemporal evolution and underlying drivers, remains lacking, particularly for older women. METHODS: Using data from the Global Burden of Disease Study 2021, we analyzed disability-adjusted life years (DALYs) and deaths from ovarian cancer attributable to high BMI among women aged ≥55 years from 1990 to 2021. We examined temporal trends, geographic disparities by Socio-Demographic Index (SDI), region, and nation, and performed decomposition analyses to identify contributions from population growth, aging, and epidemiological changes. RESULTS: Globally, the attributable burden increased from 1990 to 2021, with population growth as the main driver. of rising absolute DALYs (86.72) and deaths (85.17). Marked socioeconomic disparities were observed. While high-SDI regions had the highest age-standardized rates in 2021, they showed declining trends., whereas low-middle and low-SDI regions experienced the most rapid increases. Regionally, the fastest increases occurred in South Asia, Southeast Asia, and East Asia, while nationally the largest rises were observed in Timor-Leste, Viet Nam, and Bangladesh. In these settings, epidemiological changes were the dominant contributor to increasing burden. CONCLUSIONS: The global burden of ovarian cancer due to high BMI is rising and unequally distributed across SDI regions. While population growth drives the overall increase, escalating obesity prevalence is the key driver in rapidly developing regions. Targeted obesity prevention, improved early detection, and strengthened health-care systems should be prioritized, particularly in high-growth regions. Future studies should improve data quality in low-resource settings and explore cost-effective interventions.