Horm Metab Res 2019; 51(10): 655-660
DOI: 10.1055/a-0945-0328
Endocrine Care
© Georg Thieme Verlag KG Stuttgart · New York

Common Type 2 Diabetes Genetic Risk Variants Improve the Prediction of Gestational Diabetes

Violetta Dziedziejko
1   Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, Szczecin, Poland
Krzysztof Safranow
1   Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, Szczecin, Poland
Maciej Tarnowski
2   Department of Physiology, Pomeranian Medical University, Szczecin, Poland
Andrzej Pawlik
2   Department of Physiology, Pomeranian Medical University, Szczecin, Poland
› Author Affiliations
Further Information

Publication History

received 22 November 2018

accepted 21 May 2019

Publication Date:
12 July 2019 (online)


Gestational diabetes mellitus (GDM) is a carbohydrate intolerance that occurs in women during pregnancy. The aims of this study were to develop a model to predict the risk of GDM development using common clinical parameters and selected genetic polymorphisms and to analyse the performance of the model using receiver operator characteristic (ROC) curves. ROC analysis was used to examine whether the evaluation of genetic polymorphisms may enhance the accuracy of GDM prediction in comparison to using common clinical risk factors only. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance. The diagnosis of GDM was based on a 75 g oral glucose tolerance test at 24–28 weeks gestation. The difference between the AUC of ROC curves for the model 1 including only age and BMI and the model 2 also including 8 genetic polymorphisms was highly significant (p=0.0001) in favour of model 2 (0.090±0.023). Moreover, the additional use of 8 genetic polymorphisms may increase both the sensitivity and specificity of GDM prediction by 10%. The results of this study indicate that the use of 8 genetic polymorphisms associated with carbohydrate and lipid metabolism and type 2 diabetes [PTGS2 (COX2) rs6681231, FADS1 rs174550, HNF1B rs4430796, ADIPOQ rs266729, IL18 rs187238, CCL2 rs1024611, HHEX rs5015480 and CDKN2A/2B rs10811661] together with clinical risk factors (BMI and age) may significantly improve the prediction of GDM.

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