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

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.

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

“…The implementation of our GDM risk prediction algorithm during preconception care would enable early engagement of women for preventive intervention, compared to existing pregnancy-based GDM risk prediction algorithms [ 14 , 15 ] developed for antenatal care. In another recent study by Wu et al [ 39 ], an early pregnancy prediction model for GDM was developed based on genetic variants (four genetic susceptible single nucleotide polymorphisms (SNPs)) and six basic clinical features (AUC: 0.73). The latter model requires more advanced laboratory testing for SNPs, which may not be routinely available in all standard clinical laboratories.…”

Polygenic Risk Score and Risk Factors for Gestational Diabetes

“…A recent study of Chinese women (475 cases and 487 controls) [27] utilized 4 loci significantly correlated with the incidence of GDM to build PGS. Authors report that genetic risk score was independently associated with GDM and was the most effective predictor with the exception of family history of diabetes.…”

“…Overall, this analysis yielded 120 distinct SNPs. The list of relevant SNPs was further extended based on published GDM studies [25][26][27] resulting in a final set of 174 SNPs considered in the analysis.…”

“…As our goal is to develop a predictive tool to identify at-risk groups for GDM, we combined those women who had only gestational diabetes, and those who have later developed other types of diabetes (see Methods for detailed explanation of selection of cases and control groups). Polygenic risk score was calculated as a weighted sum of a selection of genetic variants, which were derived from the initial set of 174 genetic variants selected from Neale Lab GWAS of UKBB phenotypes http://www.nealelab.is/uk-biobank/ by thresholding pvalue, and previous studies on GDM [25][26][27]. Weights for each variant were learned by utilizing a generalized linear model and logistic regression with added collinearity analysis for the predictor variables.…”

“…To investigate the association of BMI within genetic risk groups with GDM, we divided samples into three groups according to BMI: low (18.5 -25), medium (25)(26)(27)(28)(29)(30), and high >=30 [39]. Furthermore, the PGS was divided into seven levels (i.e., septiles).…”