Publication:

Evaluation of triglyceride/glucose level index as a predictor of gestational diabetes mellitus: a comparative study with haemoglobin A1c

Background Gestational diabetes mellitus (GDM) is a pregnancy complication characterized by chronic hyperglycaemia, which increases the risk of adverse outcomes for both the mother and foetus. Early identification and intervention are crucial for improving pregnancy outcomes. The triglyceride-glucose (TyG) index, calculated using fasting triglyceride and glucose levels, has shown promise as a predictor of insulin resistance and metabolic disturbances. This study aims to evaluate the potential of the TyG index, along with HbA1c, as early biomarkers for GDM prediction.

Methods This prospective study was conducted at GS Medical College & Hospital, Pilkhuwa, Hapur, over 18 months (January 2022 to June 2024), involving 144 pregnant women aged 18-30 years. The study included fasting glucose, triglyceride, and HbA1c measurements, along with the oral glucose tolerance test (OGTT) to diagnose GDM. The TyG index was calculated as [fasting triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Statistical analysis was performed using SPSS version 26.0, with Chi-square, t-tests, and regression models.

Results The study revealed that 11.8% of participants were diagnosed with GDM. The GDM group had significantly higher levels of HbA1c (6.34±0.59%) compared to the non-GDM group (4.32±1.26%) (p<0.0001). The TyG index was also significantly higher in the GDM group (150.4±25.2) compared to the non-GDM group (129.27±20.66) (p = 0.0002). Both markers showed strong associations with GDM, suggesting their utility for early prediction.

Conclusions The study supports the use of HbA1c and the TyG index as effective early biomarkers for GDM. These markers can help identify high-risk women and enable timely intervention to reduce adverse maternal and fetal outcomes. Early screening using these biomarkers should be prioritized, particularly for high-risk populations. Further research is needed to validate their diagnostic utility and integrate them into routine clinical practice.