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Combined triple screening and BMI models for gestational diabetes prediction
Naziye Gürkan1, Sakine Merve Aydın2, Mevlüde Alpaslan3
1İstanbul Aydın University Faculty of Medicine, Department of Obstetrics and Gynecology, İstanbul, Türkiye.
Objective:
This study investigated whether routinely used second-trimester triple screening markers (alpha-fetoprotein, human chorionic gonadotropin, and unconjugated estriol) could help identify pregnancies at increased risk of gestational diabetes mellitus. In addition, several hematological inflammation indices were evaluated as potential adjunctive predictors.
Materials And Methods:
Medical records of 405 singleton pregnancies were retrospectively reviewed. All participants underwent triple screening during gestational weeks 15-20 and subsequently underwent oral glucose tolerance testing during gestational weeks 24-28. Patients were categorized, according to oral glucose tolerance test findings, as either having gestational diabetes or as normoglycemic controls. Receiver operating characteristic analyses were performed to assess both the isolated and combined predictive capacities of measurements of alpha-fetoprotein, human chorionic gonadotropin, and unconjugated estriol. The impact of maternal body mass index on model performance was also analyzed.
Results:
Alpha-fetoprotein, human chorionic gonadotropin, and unconjugated estriol levels showed poor discriminatory ability for identifying pregnancies complicated by gestational diabetes, both individually and in combined models. However, adding maternal body mass index to the combined biomarker model substantially improved predictive accuracy (area under the curve=0.809; p<0.001). None of the evaluated inflammatory indices demonstrated significant predictive performance for gestational diabetes mellitus.
Conclusion:
Routine second-trimester triple screening markers appear to have limited standalone value for predicting gestational diabetes. Nevertheless, incorporating maternal body mass index considerably enhances model performance and may improve clinical risk stratification.
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