Introduction: Ultra-processed foods (UPF) have been implicated in chronic disease development, but their association with ovarian cancer—a leading cause of gynecologic cancer mortality—requires systematic evaluation. This review examines evidence linking UPF consumption to ovarian cancer risk. Methods: We systematically reviewed observational studies assessing UPF consumption (using NOVA classification or quantitative methods) and ovarian cancer incidence or mortality in adult women. Nine studies met inclusion criteria. Results: The UK Biobank prospective cohort (197,426 participants; 143 ovarian cancer cases; 9.8 years follow-up) demonstrated significant positive associations between UPF consumption and ovarian cancer incidence (HR 1.19 per 10% increment; 95% CI: 1.08–1.30; p<0.001) and mortality (HR 1.30; 95% CI: 1.13–1.50). Supporting evidence from case-control studies showed preserved foods consumption (>13.5 g/day) was associated with 78% increased odds of epithelial ovarian cancer (OR 1.78; 95% CI: 1.35–2.34), while a "meat and fat" dietary pattern was associated with 2.5-fold increased risk (OR 2.49; 95% CI: 1.75–3.55). Brazilian cross-sectional studies identified UPF consumption among ovarian cancer survivors, particularly those under 40 years. Discussion: The positive association observed in the UK Biobank persisted after adjustment for multiple confounders including age, BMI, reproductive factors, and socioeconomic status. The consistency across different dietary exposures—UPF, preserved foods, and high-fat dietary patterns—suggests that processed and energy-dense foods may contribute to ovarian carcinogenesis through multiple pathways, potentially including inflammation, insulin resistance, and endocrine disruption. BMI adjustment in the Kolahdooz study strengthened the observed association, indicating mechanisms independent of adiposity. Conclusion: Evidence supports a positive association between consumption of ultra-processed and processed foods and increased ovarian cancer risk. Further research should identify specific UPF subgroups and vulnerable populations.
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