Wu Qi, Chen Yanmin, Zhou Menglin, Liu Mengting, Zhang Lixia, Liang Zhaoxia, Chen Danqing
Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Los Angeles, United States of America.
Diabetol Metab Syndr. 2022 Jan 24;14(1):15. doi: 10.1186/s13098-022-00788-y.
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.
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.
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.
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.
评估基因变异和临床特征对妊娠期糖尿病(GDM)风险的影响,并构建和验证早期妊娠GDM预测模型。
纳入475例GDM患者和487例非GDM患者作为对照,构建早期妊娠GDM预测模型。两组均有完整的基因分型结果和临床资料。将他们随机分为试验队列(70%)和测试队列(30%)。然后,将包括985例孕妇的模型验证队列用于GDM早期妊娠预测模型的外部验证。
我们发现母亲年龄、妊娠次数、产次、BMI和糖尿病家族史与GDM显著相关(OR>1;P<0.001),辅助生殖是GDM的一个关键危险因素(OR=1.553,P=0.055)。MTNR1B rs10830963、C2CD4A/B rs1436953和rs7172432、CMIP rs16955379与GDM的发病率显著相关(AOR>1,P<0.05)。因此,这四个基因易感单核苷酸多态性(SNP)和六个临床特征被纳入早期妊娠GDM预测模型的构建。在试验队列中,构建了早期妊娠GDM预测模型,其中基因风险评分与GDM独立相关(AOR=2.061,P<0.001),并且是除糖尿病家族史外最有效的预测因子。预测模型的ROC-AUC为0.727(95%CI 0.690-0.765),敏感性和特异性分别为69.9%和64.0%。预测能力也在测试队列和验证队列中得到验证。
基于基因变异和临床特征,本研究开发并验证了早期妊娠GDM预测模型。该模型有助于在妊娠早期筛查出GDM高危人群,并在妊娠早期及时对其进行生活方式干预。