TyG Index: Reliable Tool for Early Detection of Gestational Diabetes in the Second Trimester, study reveals
China: The triglyceride-glucose (TyG) index effectively identifies the onset of gestational diabetes mellitus (GDM) in the second trimester, consistent with previous studies, a recent study has shown. TyG index incorporation into routine maternal health assessments has important practical implications.
"The TyG index is an important early screening tool that can be integrated into routine obstetric assessments. It enables clinicians to effectively evaluate the risk of diabetes in pregnant women during examinations, supporting early and proactive interventions for high-risk pregnancies," the researchers wrote in Lipids in Health and Disease.
Hong Ding, Department of Public Health, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China, and colleagues aimed to investigate the association between the TyG index in early pregnancy and GDM development in the second trimester.
The primary objectives were to assess the predictive potential of the TyG index for gestational diabetes mellitus (GDM), identify the optimal threshold value for GDM assessment, and compare the predictive performance of the TyG index alone versus its combination with maternal age and pre-pregnancy body mass index. Additionally, the study examined the relationship between the TyG index in early pregnancy and the risk of other pregnancy-related complications (PRCs), including placental abruption and gestational hypertension.
For this purpose, the researchers conducted a prospective cohort study recruiting 1,624 pregnant women who underwent early pregnancy antenatal counseling and comprehensive assessments with continuous monitoring until delivery. For TyG index calculation, health indicators such as maternal triglycerides and fasting plasma glucose were assessed in early pregnancy (before 14 weeks of gestation).
TyG index's predictive power for GDM evaluation in Chinese pregnant women was determined using multifactorial logistic regression. Subgroup analyses were performed to evaluate the efficacy of the TyG index in predicting pregnancy-related complications (PRCs) using receiver operating characteristic (ROC) curve analysis and restricted cubic splines, with the optimal cutoff value determined.
The study led to the following findings:
· Logistic regression analyses revealed a 2.10-fold increase in the GDM risk for every 1-unit increase in the TyG index after adjusting for covariates.
· The highest GDM risk was observed in the group with the highest TyG index compared with the lowest quintile group (odds ratios: 3.25).
· Subgroup analyses indicated that exceeding the recommended range of gestational weight gain and an increased GDM risk were significantly associated.
· Regarding predictive performance, the TyG index exhibited the highest area under the curve (AUC) value in the ROC curve for GDM (AUC: 0.641).
· The optimal cutoff value was 8.890, with both sensitivity and specificity of 0.617.
· Combining the TyG index, maternal age, and pre-pregnancy body mass index proved to be a superior predictor of GDM than the TyG index alone (AUC: 0.672 versus 0.641).
· After adjusting for multiple factors, the analyses indicated that the TyG index was associated with an increased risk of gestational hypertension.
· No significant association was noted between the TyG index and the risk of preeclampsia, placental abruption, intrauterine distress, or premature rupture of membranes.
"Early identification of high-risk groups allows healthcare providers to implement timely interventions, such as more frequent monitoring for high-risk pregnant women, along with personalized nutritional counseling and health education. These strategies can help prevent or mitigate potential complications for both mothers and infants, ultimately improving overall health outcomes for both," the researchers concluded.
Reference:
Guo, Y., Lu, J., Bahani, M. et al. Triglyceride-glucose index in early pregnancy predicts the risk of gestational diabetes: a prospective cohort study. Lipids Health Dis 23, 87 (2024). https://doi.org/10.1186/s12944-024-02076-2
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