ObjectiveTo investigate the diagnostic value of triglyceride (TG) and glycosylated hemoglobin (HbA1c) and their interactions on gestational diabetes mellitus (GDM), so as to provide a basis for future pregnancy monitoring and clinical decision-making. MethodsData of 100 full-term singleton pregnant women who were examined and delivered in the Second People’s Hospital of Wuhu City from January 2020 to January 2023 were retrospectively collected, and they were divided into GDM group (n=33) and non-GDM group (n=67) according to the results of oral glucose tolerance test (OGTT). The general clinical data of the two groups were compared, and the independent risk factors affecting the occurrence of GDM were analyzed using logistic regression analysis. Furthermore, the diagnostic value of the interaction of HbA1c and TG on GDM was analyzed using additive interaction model. A nomogram model to predict the occurrence of GDM was constructed and verified. The effects of HbA1c, TG and their interactions on the occurrence of GDM were analyzed using the receiver operating characteristic curve (ROC). ResultsHbA1c and TG were significantly higher in the GDM group than those in the non-GDM group (P<0.001). History of GDM, family history of diabetes mellitus, body mass index (BMI) before pregnancy, hypertension, TG, frequent consumption of high-calorie food during pregnancy, and HbA1c were the influencing factors for the occurrence of GDM in pregnant women (P<0.001). The nomogram model was constructed according to the seven factors screened by logistic regression analysis, and the average absolute error between the predicted probability by the nomogram model and actual probability of the occurrence of GDM was 0.039. The ROC results showed that the area under the curve (AUC) value of HbA1c was 0.765, the AUC value of TG was 0.833, and the AUC value of the interaction between TG and HbA1c was 0.894, with a statistically significant difference (P<0.05). ConclusionHbA1c and TG are not only influencing factors for GDM, but also their interactions are positively correlated with the occurrence of GDM. The diagnostic value of the two synergistically interacting on GDM is greater than that of them independently on GDM. The nomogram model constructed in this study has good differentiation, accuracy and clinical practicability for predicting the incidence of GDM.