Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction.

Objective: We aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases. Methods: We conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM. A total of 11 single- nucleotide polymorphisms (SNPs), including those in HKDC1 (rs10762264), GCK (rs1799884), MTNR1B (rs10830963 and rs1387153), TCF7L2 (rs7903146 and rs12255372), KCNJ11 (rs5219), IGF2BP2 (rs4402960), IRS1 (rs1801278), FTO (rs9939609), and CDKAL1 (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations. Results: Two variants in MTNR1B (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 - 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 - 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 - 0.764). Conclusion: Among 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in MTNR1B in Russian women. However, these variants showed limited value in the identification of GDM cases.

Effect of gene-lifestyle interaction on gestational diabetes risk.

We hypothesized that the association of certain lifestyle parameters with gestational diabetes mellitus (GDM) risk would depend on susceptibility loci. In total, 278 Russian women with GDM and 179 controls completed questionnaires about lifestyle habits (food consumption, physical activity and smoking). GDM was diagnosed according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Maternal blood was sampled for genotyping single-nucleotide polymorphisms (SNPs) in MTNR1B (rs10830963 and rs1387153), GCK (rs1799884), KCNJ11 (rs5219), IGF2BP2 (rs4402960), TCF7L2 (rs7903146 and rs12255372), CDKAL1 (rs7754840), IRS1 (rs1801278) and FTO (rs9939609). Binary logistic regression revealed an interaction effect of sausage intake and the number of risk alleles of two SNPs (rs10830963 in MTNR1B and rs1799884 in GCK) on GDM risk (P < 0.001). Among women without risk alleles of these two SNPs, sausage consumption was positively associated with GDM risk (P trend = 0.045). This difference was not revealed in women carrying 1 or more risk alleles. The risk of GDM increased as the number of risk alles increased in participants with low and moderate sausage consumption (P trend <0.001 and 0.006, respectively), while the risk of GDM in women with high sausage consumption remained relatively high, independent of the number of risk alleles. These findings indicate that the association of sausage consumption with GDM risk can be determined based on the number of risk alleles of rs10830963 in MTNR1B and rs1799884 in GCK.