ArticleIndian journal of endocrinology and metabolism
Prediction Models for Gestational Diabetes Mellitus: Diagnostic Utility of Clinical and Biochemical Markers.
Article in Indian journal of endocrinology and metabolism. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Accuracy of clinical risk factor-based models as a screening test for detecting gestational diabetes mellitus in a low-resource setting.Archives of endocrinology and metabolism · 2026Article
- Prevalence of gestational diabetes using IADPSG criteria in North India: A cross-sectional study.Bioinformation · 2025Article
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7 authors.
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Abstract
Introduction: The use of oral glucose tolerance test (OGTT) is limited by an inconvenient procedure and poor reproducibility. This study aimed to develop and evaluate prediction models for gestational diabetes mellitus (GDM) diagnosis based on clinical and biochemical parameters. Methods: An observational cross-sectional study was conducted among pregnant women aged 20-40 years in their second trimester (14-28 weeks) in Puducherry, South India, from May 2018 to March 2023. GDM was diagnosed according to the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria. Results: The study included 234 normoglycemic women and 115 GDM women. Model 1 comprised the biomarkers: fasting plasma insulin, serum sex hormone binding globulin (SHBG), homeostatic model assessment of insulin resistance (HOMA IR), and triglyceride glycemic index (TyG index) (area under the curve (AUC): 0.8870; 95% confidence interval (CI): 0.8440-0.9299; sensitivity: 80.87%; specificity: 86.32%); Model 2 incorporated the following clinical parameters: age, body mass index (BMI), gravida, parity, waist circumference, hip circumference, systolic and diastolic blood pressure, family history of GDM, family history of type 2 diabetes mellitus (T2DM), and family history of hypertension (AUC: 0.6846; 95% CI: 0.6269-0.7422; sensitivity: 90.43%; specificity: 31.6%); and Model 3 combined Models 1 and 2 (AUC: 0.9194; 95% CI: 0.8855-0.9531; sensitivity: 80.8%; specificity: 89.74%). Conclusion: The predictive models highlighted in the study serve as effective screening tools for GDM and may help overcome the limitations of OGTT, particularly in settings where procedural challenges exist.
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