<![CDATA[Metabolic disorders have been linked to ovarian cancer risk, but the causality and subtype specificity remain unclear. This study applied a computational genetics approach using two-sample Mendelian randomization (MR) to identify potential causal effects of metabolic traits on distinct ovarian cancer histotypes. Genome-wide association summary statistics for adult female body mass index (BMI), fasting glucose, glycated hemoglobin (HbA1c), LDL, and HDL cholesterol were obtained from the IEU OpenGWAS database. Summary-level data for ovarian cancer subtypes (endometrioid, mucinous, clear cell) were analyzed as outcomes. MR analyses were conducted to assess causal relationships using inverse variance weighted, MR-Egger, weighted median, and weighted mode methods, with sensitivity tests for pleiotropy and heterogeneity. Higher BMI was causally associated with increased risk of endometrioid ovarian cancer (MR-Egger OR = 5.56, p = 0.0009; IVW OR = 1.65, p = 0.0056). Elevated fasting glucose increased the risk of mucinous ovarian cancer (OR = 2.10, p = 0.035), and higher HbA1c showed a positive association (OR = 1.32, p = 0.015). LDL cholesterol was modestly associated with mucinous ovarian cancer (OR = 1.25, p = 0.041). Interestingly, higher HDL cholesterol was also linked to increased risk of endometrioid (OR ? 1.27, p = 0.035) and clear cell ovarian cancers (OR = 1.20, p = 0.040). Our analysis revealed significant findings, highlighting distinct metabolic pathways contributing to ovarian cancer subtypes. These results utilize genetic epidemiology and computational biology approaches in uncovering mechanistic links between metabolism and oncogenesis, supporting future precision prevention strategies.]]>
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