An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China

Qi Wu; Yanmin Chen; Menglin Zhou; Mengting Liu; Lixia Zhang; Zhaoxia Liang; Danqing Chen

doi:10.1186/s13098-022-00788-y

An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China

Diabetol Metab Syndr. 2022 Jan 24;14(1):15. doi: 10.1186/s13098-022-00788-y.

Authors

Qi Wu¹, Yanmin Chen¹, Menglin Zhou¹, Mengting Liu¹, Lixia Zhang¹, Zhaoxia Liang^{1

2}, Danqing Chen³

Affiliations

¹ Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
² Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Los Angeles, United States of America.
³ Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China. chendq@zju.edu.cn.

Abstract

Objectives: To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy.

Methods: Four hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model.

Results: We found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, a predictive model of GDM in early pregnancy was constructed, in which genetic risk score was independently associated with GDM (AOR = 2.061, P < 0.001) and was the most effective predictor with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690-0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort.

Conclusions: Based on the genetic variants and clinical characteristics, this study developed and verified the early pregnancy prediction model of GDM. This model can help screen out the population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy.

Keywords: Clinical characteristics; Early pregnancy; Gestational diabetes mellitus; Prediction model; Single nucleotide polymorphism.

Abstract

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