The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (P_interaction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902–0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

Introduction: Gestational diabetes (GDM) is one of the common complications of female pregnancy, which seriously affects the health of mothers and their offspring. So far, the etiology has not been fully clarified.Methods: A case-control study was conducted to clarify the relationship between Erb-b2 receptor tyrosine kinase 4 (ERBB4) functional tag genetic variants (rs1595064, rs1595065, rs1595066 and rs6719645) and the risk of GDM. Associations between variants and GDM risk were evaluated with the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). Subsequently, the false-positive reporting probability (FPRP), multi-factor dimension reduction (MDR) and bioinformatics analysis were adopted to confirm the significant associations. A nomogram model was constructed to predict the risk of GDM.Results: Association analysis demonstrated that rs1595066 TT genotype performed a protective effect on GDM risk among all subjects (TT vs. CC: adjusted OR = 0.60, 95% CI = 0.38 - 0.94, P = 0.026; TT vs. CC/CT: adjusted OR = 0.61, 95% CI = 0.40 - 0.95, P = 0.027). Meanwhile, stratified analysis showed that rs1595066 TT can also reduce the GDM risk in age > 30.09 years old, pre-pregnancy BMI > 22.23 Kg/m², SBP ≤ 110.08 mmHg, etc subgroups. Interactions between rs1595066 and DBP (P_interaction = 0.01), FPG (P_interaction < 0.001) and HbA1c (P_interaction < 0.001) were detected. The FPRP analysis confirmed that association between rs1595066 and GDM risk in subjects of FPG < 4.79 mmol/L (P = 0.199) is true. The MDR analysis showed that rs1595066 was the best single locus model while the 4-loci model was the best multiple factors model to predict GDM risk. Functional prediction revealed that rs1595066 may disturb the stability of miRNA-mRNA binding. The predictive nomogram model has a well consistence and acceptable discriminative ability with a diagnosed AUC of 0.813.Discussion: ERBB4 variants can change an individual’s susceptibility to GDM via the interaction of gene-gene, gene-environment and changes in the regulatory effects of miRNAs on ERBB4 expression.