Application Value of Predictive Model Based on Maternal Coagulation Function and Glycolipid Metabolism Indicators in Early Diagnosis of Gestational Diabetes Mellitus - Zheng, Ying; Hou, Weiwei; Xiao, Jing; Huang, Hongling; Quan, Wenqiang; Chen, Yu (Frontiers in Public Health, 2022)
Objective To investigate whether first-trimester fasting plasma glucose (FPG), blood coagulation function and lipid metabolism could predict GDM risk. Methods From October 2020 to May 2021, a total of 584 pregnant women who took prenatal care in Shanghai Jiaotong University Affiliated Sixth People's Hospital were chosen as the observation subjects. The clinical information and serum samples of all pregnant women were collected at 10-13 weeks of gestation and the blood coagulation function, fasting blood glucose and lipid profiles of the pregnant women were detected. A 75g oral glucose tolerance test was performed up to 24-28 weeks of gestation. 142 pregnant women with GDM and 442 pregnant women without GDM were detected. Results Compared with non-GDM group, age, pre-pregnancy BMI, FPG, FIB, D-dimer, FPG, TC, TG, LDL-C, sdLDL-C, APOB and APOE in GDM group were significantly higher than those in non-GDM group, while PT, INR, APTT and TT were significantly lower than those in non-GDM group. Univariate logistic regression analysis was used to explore the risk factors of GDM. Gestational age, pre-pregnancy BMI, FPG, PT, INR, APTT, FIB, TT, D-Dimer, TC, TG, LDL C, sdLDL-C, APOB and APOE were all independent predictors of GDM. Multivariatelogistic regression showed that pre-pregnancy BMI, FPG, APTT, TT, TG, LDL-C, sdLDL-C and APOB were risk factors for GDM. The AUC of the established GDM risk prediction model was 0.892 (0.858-0.927), and the sensitivity and specificity were 80.71% and 86.85%, respectively; which were greater than that of pre-pregnancy BMI , FPG, APTT, TT,TG, LDL-C, sdLDL-C, APOB alone, and the difffference was statistically signifificant(P<0.05). Conclusions FPG, APTT, TT, TG, LDL-C,sdLDL-C, APOB and pre-pregnancy BMI in early pregnancy has important clinical value for the prediction of GDM, We combined these laboratory indicators and established a GDM risk prediction model, which is conducive to the early identification, intervention and treatment of GDM, so as to reduce the morbidity of maternal and infant complications.