Almazov National Medical Research Centre, Saint Petersburg, Russia.
Department of Internal Diseases and Endocrinology, St. Petersburg Pavlov State Medical University, Saint Petersburg, Russia.
Front Endocrinol (Lausanne). 2021 Apr 19;12:628582. doi: 10.3389/fendo.2021.628582. eCollection 2021.
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.
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 (rs10762264), (rs1799884), (rs10830963 and rs1387153), (rs7903146 and rs12255372), (rs5219), (rs4402960), (rs1801278), (rs9939609), and (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.
Two variants in (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).
Among 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in in Russian women. However, these variants showed limited value in the identification of GDM cases.
我们旨在探讨常见遗传风险变异与俄罗斯女性妊娠期糖尿病（GDM）风险之间的关联，并评估其在 GDM 病例识别中的效用。
我们进行了一项病例对照研究，纳入了 1142 名孕妇（688 例 GDM 病例和 454 例对照），这些孕妇均在阿尔马佐夫国家医学研究中心登记。采用国际糖尿病与妊娠研究组标准诊断 GDM。共检测了 11 个单核苷酸多态性（SNP），包括 （rs10762264）、 （rs1799884）、 （rs10830963 和 rs1387153）、 （rs7903146 和 rs12255372）、 （rs5219）、 （rs4402960）、 （rs1801278）、 （rs9939609）和 （rs7754840）。采用 Taqman 检测方法对 SNP 进行基因分型。使用逻辑回归模型计算比值比（OR）及其置信区间（CI）。使用 6 个 SNP 计算简单计数遗传风险评分（GRS）。使用临床协变量、本研究中与 GDM 显著相关的 SNP、GRS 及其组合，计算预测 GDM 风险的逻辑回归模型的受试者工作特征曲线下面积（c 统计量）。
（rs1387153 和 rs10830963）中的两个变异与 GDM 风险增加显著相关。在调整年龄、孕前 BMI、动脉高血压、既往 GDM、糖耐量受损、多囊卵巢综合征、糖尿病家族史和产次后，这种关联仍然显著（P=0.001 和 P<0.001）。在相互调节后，rs1387153 对 GDM 易感性的影响减弱，而 rs10830963 的影响仍然显著（P=0.004）。临床变量可预测 GDM（c 统计量 0.712，95%CI：0.675-0.749），将 GRS 添加到模型中可适度提高预测准确性（0.719，95%CI 0.682-0.755），仅添加 rs10830963 可进一步提高预测准确性（0.729，95%CI 0.693-0.764）。
在与其他人群的 T2D 和/或 GDM 相关的 11 个 SNP 中，我们在俄罗斯女性中证实了 （rs10830963）与 GDM 显著相关。然而，这些变异在 GDM 病例的识别中显示出有限的价值。
