目的基于临床常规指标建立妊娠糖尿病(GDM)风险评估模型,以便更加有效地防治GDM。方法前瞻性纳入2016年12月-2018年8月在禹州市人民医院建卡的1765例孕妇,收集年龄、孕前体重指数(BMI)、孕期增重、产次、不良孕产史、糖尿病(DM)家族史、血脂和孕早期空腹血糖(FPG)水平等资料,以是否发生GDM分为GDM组(157例)和非GDM组(1608例),行单因素和多因素分析,建立风险预测模型。结果①单因素分析显示:GDM组和非GDM组在年龄、孕前BMI、孕期增重、产次、DM家族史构成比和三酰甘油(TG)、孕早期FPG水平上存在差异(P <0. 05)。②多因素Logistic回归分析显示:年龄、孕前体重、孕期增重、TG和孕早期FPG是预测GDM的独立指标(P <0. 05)。预测模型:PGDM=1/{1+EXP[-(-8. 892+0. 203×年龄(25～34岁)+1. 085×年龄≥35岁-0. 810×孕前偏瘦+0. 992×孕前超重+1. 938×孕前肥胖-0. 740×孕期增重偏低+1. 169×孕期增重超标+0. 643×TG+0. 906×FPG)]}。③模型预测GDM的受试者工作特征曲线下面积(AUC)=0. 824(95%CI:0. 793～0. 856),与随机面积0. 5比较,P=0. 000,以预报概率0. 532(约登指数最大)作为切割点,模型预测GDM的灵敏度、特异度和一致率分别为0. 733、0. 796和79. 04%;当预报概率为0. 5时,灵敏度、特异度和一致率分别为0. 814、0. 656和67. 08%。结论以母体年龄、孕前BMI、孕期增重、TG和孕早期FPG建立的风险预测模型可为GDM的早期预警提供参考。

Objective To establish risk assessment model for gestational diabetes mellitus( GDM) based on conventional clinical index,in order to effectively prevent and control GDM. Methods This prospective study was conducted in 1765 pregnant woman who were registered in Yuzhou People's Hospital from December 2016 to August 2018. The age,pre-pregnancy body mass index( BMI),gestational weight gain,parity,bad history of pregnancy and childbirth,family history of diabetes,plasma lipids,fasting plasma glucose( FPG) of the participants during the first trimester were collected. According to whether or not GDM,the pregnant women were divided into GDM group( n = 157) and non-GDM group( n = 1608). The above-mentioned index were compared between two groups with univariate and multivariate analyses. A risk prediction model of GDM was established. Results ① Univariate analysis indicated that the age,pre-pregnancy BMI,gestational weight gain,parity,family history of diabetes,triglyceride( TG),FPG during the first trimester had statistical difference between GDM group and non-GDM group. ② Multiple logistic regression analysis showed that age,prepregnancy BMI,gestational weight gain,TG,FPG during the first trimester were the independent prediction index of GDM( P < 0. 05).Prediction model was as follows: PGDM= 1/{ 1 + EXP[-(-8. 892 + 0. 203 × gae( 25-34 years) + 1. 085 × age≥35 years-0. 810 ×pre-pregnancy lean + 0. 992 × pre-pregnancy over weight + 1. 938 × pre-pregnancy obesity-0. 740 × insufficient gestational weight gain +1. 169 × excessive gestational weight gain + 0. 643 × TG + 0. 906 × FPG) ]} . ③ The area under the receiver operating characteristic( ROC) curve of mode which were used to predict GDM was 0. 824( 95% CI: 0. 793-0. 856). Compared with the random area( 0. 5),there was statistical difference( P = 0. 000). The cut off point for prediction probability was 0. 532( Youden's index was the biggest),and the sensibility,specificity and accuracy of mode for GDM was 0. 733,0. 796 and 79. 04%,respectively. When the cut off point was0. 5,the sensibility,specificity and accuracy was 0. 814,0. 656 and 67. 08%,respectively. Conclusion The risk prediction model based on the factors such as age,pre-pregnancy BMI,gestational weight gain,TG,and FPG during the first trimester can provide reference for early warning of GDM.