Causal Effects of Genetically Determined Metabolites on Risk of Polycystic Ovary Syndrome: A Mendelian Randomization Study

Abstract

Background: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder that is influenced by both genetic and environmental factors. However, the etiology of PCOS remains unclear. Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of genetically determined metabolites (GDMs) on the risk of PCOS. We used summary level data of a genome-wide association study (GWAS) on 486 metabolites (n = 7,824) as exposure and a PCOS GWAS consisting of 4,138 cases and 20,129 controls as the outcome. Both datasets were obtained from publicly published databases. For each metabolite, a genetic instrumental variable was generated to assess the relationship between the metabolite and PCOS. For MR analysis, we primarily used the standard inverse variance weighted (IVW) method, while three additional methods—the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods—were performed as sensitivity analyses. Results: Using genetic variants as predictors, we observed a robust relationship between epiandrosterone sulfate (EPIA-S) and PCOS (PIVW = 0.0186, PMR−Egger = 0.0111; PWeighted−median = 0.0154, and PMR−PRESSO = 0.0290). Similarly, 3-dehydrocarnitine, 4-hydroxyhippurate, hexadecanedioate, and β-hydroxyisovalerate may also have causal effects on PCOS development. Conclusions: We identified metabolites that might have causal effects on PCOS development. Our study emphasizes the role of genetic factors underlying the causal relationships between metabolites and PCOS and provides novel insights through the integration of metabolomics and genomics to better understand the mechanisms involved in human disease pathogenesis.