Genetic Susceptibility, Mendelian Randomization, and Nomogram Model Construction of Gestational Diabetes Mellitus

Abstract Context Gestational diabetes mellitus (GDM) is a pregnancy-complicated disease that poses a risk to maternal and infant health. However, the etiology of the disease has been not yet elucidated. Objective To detect the genetic susceptibility and construct a nomogram model with significantly associated polymorphisms and key clinical indicators for early prediction of GDM. Methods Eleven functional single nucleotide polymorphisms (SNPs) screened by genome-wide association study were genotyped in 554 GDM cases and 641 healthy controls. Functional analysis of GDM positively associated SNPs, multivariate mendelian randomization (MVMR), and a GDM early predictive nomogram model construction were performed. Result rs1965211, rs3760675, and rs7814359 were significantly associated with genetic susceptibility to GDM after adjusting age and prepregnancy body mass index (pre-BMI). It seems that GDM-associated SNPs have effects on regulating target gene transcription factor binding, posttranscriptional splicing, and translation product structure. Besides, rs3760675 can be expression quantitative trait loci and increase the XAB2 mRNA expression level (P = .047). The MVMR analysis showed that the increase of clinical variables of BMI, hemoglobin A1c (HbA1c), and fasting plasma glucose (FPG) had significant causal effects on GDM (BMI-ORMVMR = 1.52, HbA1c-ORMVMR = 1.32, FPG-ORMVMR = 1.78), P < .05. A nomogram model constructed with pre-BMI, FPG, HbA1c, and genotypes of rs1965211, rs3760675, and rs7814359 showed an area under the receiver operating characteristic curve of 0.824. Conclusion Functional polymorphisms can change women's susceptibility to GDM and the predictive nomogram model based on genetic and environmental factors can effectively distinguish individuals with different GDM risks in early stages of pregnancy.