This study investigates the causal relationship between 108 traits and gestational diabetes mellitus (GDM) using a two-sample Mendelian randomization (MR) analysis. The study utilized genome-wide association study (GWAS) data from the UK Biobank and FinnGen to identify single nucleotide polymorphisms (SNPs) associated with the traits and GDM, respectively. The MR analysis employed the inverse-variance weighted (IVW) method, weighted median technique, and MR-Egger approach to assess the causal associations. The results indicated that 24 genetically predicted risk factors were significantly associated with GDM, including measures of body size, biomarkers, physical measures, and lifestyle factors. These findings provide new insights into the prevention and mitigation of GDM risks. The study highlights the importance of considering genetic variants as instrumental variables to reduce confounding influences and establish a causal link between traits and GDM.This study investigates the causal relationship between 108 traits and gestational diabetes mellitus (GDM) using a two-sample Mendelian randomization (MR) analysis. The study utilized genome-wide association study (GWAS) data from the UK Biobank and FinnGen to identify single nucleotide polymorphisms (SNPs) associated with the traits and GDM, respectively. The MR analysis employed the inverse-variance weighted (IVW) method, weighted median technique, and MR-Egger approach to assess the causal associations. The results indicated that 24 genetically predicted risk factors were significantly associated with GDM, including measures of body size, biomarkers, physical measures, and lifestyle factors. These findings provide new insights into the prevention and mitigation of GDM risks. The study highlights the importance of considering genetic variants as instrumental variables to reduce confounding influences and establish a causal link between traits and GDM.