BACKGROUND: The relationship between Metabolic Syndrome (MetS) and ovarian dysfunction has been widely reported in observational studies, yet it remains not fully understood. This study employs genetic prediction methods and utilizes summary data from genome-wide association studies (GWAS) to investigate this causal link. METHODS: We employed a bidirectional two-sample Mendelian Randomization (MR) analysis utilizing MetS and ovarian dysfunction summary data from GWAS. Inverse variance weighted (IVW) was employed as the primary MR method, supplemented by Weighted Median, Weighted Mode, and MR-Egger methods. The robustness of the results was further assessed through sensitivity analyses including MR-Egger regression, MR-PRESSO, Cochran's Q, and leave-one-out test. RESULTS: Our MR analysis identified a causal relationship between genetically determined insulin resistance (OR = 0.26, 95% CI: 0.08-0.89, P = 0.03), waist circumference (OR = 2.14, 95% CI: 1.45-3.15, P <
0.001), BMI (OR = 2.1, 95% CI: 1.56-2.83, P <
0.001) and ovarian dysfunction. Conversely, reverse MR analysis confirmed causal effects of ovarian dysfunction on metabolic syndrome (OR = 0.98, 95% CI: 0.97-0.99, P <
0.001) and waist circumference (OR = 0.99, 95% CI: 0.98-0.99, P = 0.02). The results of MR-Egger regression test indicated that the whole analysis was not affected by horizontal pleiotropy. Additionally, the MR-PRESSO test identified outliers in SNPs, but after removal of outliers, results remained unchanged. CONCLUSION: This study reveals a bidirectional causal connection between metabolic syndrome and ovarian dysfunction via genetic prediction methods. These findings are crucial for advancing our understanding of the interactions between these conditions and developing strategies for prevention and treatment.