孕前体质量指数、孕早期空腹血糖及血脂水平在预测妊娠期糖尿病中的临床价值

Clinical value of pre-pregnant body mass index,fasting plasma glucose and blood lipids levels during first trimester in predicting gestational diabetes mellitus

目的探讨孕前体质量指数(BMI)、孕早期FBG和血脂水平对妊娠期糖尿病(GDM)的预测价值。方法回顾性分析1 480例孕妇的临床资料,根据OGTT结果分为GDM组152例和对照组1 328例,比较两组孕前BMI、孕早期(孕7~15周)FBG及血脂水平等指标,分析影响GDM发生的危险因素,采用受试者工作特征(ROC)曲线评价相关指标对GDM的预测效能。结果年龄、孕前BMI、孕次、孕早期FBG及TG水平是GDM的独立危险因素(均P<0.05)。ROC曲线分析结果显示,孕前BMI、孕早期FBG、TG预测GDM的曲线下面积分别为0.811、0.792、0.729(均P<0.05),敏感度分别为82.53%、80.26%、81.72%,特异度为68.48%、71.09%、70.38%。结论年龄大、孕次多、孕前BMI高、孕早期FBG及TG水平增高的孕妇发生GDM的风险增高,其中孕前BMI、孕早期FBG水平对GDM均有较好的预测效能。

Objective To explore the value of pre-pregnant body mass index（ BMI）,FBG and blood lipids levels during the first trimester in predicting gestational diabetes mellitus（ GDM）. Methods The clinical data of 1 480 pregnant women were retrospectively analyzed. The subjects were divided into GDM group（ n = 152） and control group（ n = 1 328） according to the result of OGTT. The pre-pregnant BMI,FBG and blood lipids levels during the first trimester（ 7-15 weeks of pregnancy） were compared between the two groups. The risk factors for the incidence of GDM were analyzed. Receiver operating characteristic（ ROC） curve was used to evaluate the efficiency of relevant indices for predicting GDM. Results Age,pre-pregnant BMI,pregnant frequency,FBG and TG levels during the first trimester were the independent risk factors for GDM（ all P〈0. 05）. The result of ROC curve analysis indicated that the areas under the curve for pre-pregnant BMI,FPG and TG during the first trimester in predicting GDM were 0. 811,0. 792 and 0. 729 respectively（ all P〈0. 05）,the sensitivities were 82. 53%,80. 26% and 81. 72% respectively,and the specificities were 68. 48%,71. 09% and 70. 38% respectively. Conclusion The risk of GDM is higher in the pregnant women who are older or have more pregnant frequency,increased pre-pregnant BMI,elevated FBG or TG level during the first trimester. Pre-pregnant BMI or FPG level during the first trimester achieves better efficiency in predicting GDM.