中国妇女妊娠期糖尿病的产前筛查生物标志物与母体特征之间的关系

Yu-Ting Lu, Chie-Pein Chen, Fang-Ju Sun, Yi-Yung Chen, Liang-Kai Wang, Chen-Yu Chen
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引用次数: 0

摘要

妊娠期糖尿病(GDM)会对产妇和新生儿造成不良后果。预测妊娠糖尿病的高危人群并进行早期干预可以减少妊娠糖尿病的发生。我们对在2019年1月至2021年5月期间接受第一胎非整倍体和子痫前期筛查的单胎孕妇进行了一项回顾性队列研究。研究收集了第一胎产前筛查生物标志物,包括妊娠相关血浆蛋白A(PAPP-A)、游离β-人绒毛膜促性腺激素和胎盘生长因子(PLGF),并结合孕产妇特征,分析其与GDM的关系。采用接收器操作特征(ROC)曲线和逻辑回归分析来评估与 GDM 相关的变量。GDM组的PAPP-A(5.01 vs. 5.73 IU/L,P < 0.001)和PLGF(39.88 vs. 41.81 pg/mL,P = 0.044)明显低于非GDM组。综合母体特征和生物标志物的 ROC 曲线下面积为 0.73(95% 置信区间 [CI] 0.68-0.79,P < 0.001)。预测 GDM 的公式如下P = 1/[1+exp(-8.148 + 0.057 x 年龄 + 0.011 x 孕前体重指数 + 1.752 x 既往 GDM 史 + 0.95 x 既往子痫前期史 + 0.756 x 糖尿病家族史 + 0.025 x 慢性高血压 + 0.036 x 平均动脉压 - 0.09 x PAPP-A - 0.001 x PLGF)]。逻辑回归分析显示,较高的孕前体重指数(调整后的几率比 [aOR] 1.03,95% CI 1.01 - 1.06,P = 0.012)、既往 GDM 史(aOR 9.97,95% CI 3.92 - 25.37,P <0.001)、糖尿病家族史(aOR 2.36,95% CI 1.39 - 4.02,P = 0.001)、较高的平均动脉压(aOR 1.17,95% CI 1.07 - 1.27,P <0.001)和较低的 PAPP-A 水平(aOR 0.91,95% CI 0.83 - 1.00,P = 0.040)与 GDM 的发生独立相关。Hosmer-Lemeshow检验表明,该模型具有极佳的区分能力(chi-square = 3.089,df = 8,P = 0.929)。将第一胎生物标志物与母体特征相结合,对预测 GDM 的风险很有价值。
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Associations between first-trimester screening biomarkers and maternal characteristics with gestational diabetes mellitus in Chinese women
Gestational diabetes mellitus (GDM) can result in adverse maternal and neonatal outcomes. Predicting those at high risk of GDM and early interventions can reduce the development of GDM. The aim of this study was to examine the associations between first-trimester prenatal screening biomarkers and maternal characteristics in relation to GDM in Chinese women.We conducted a retrospective cohort study of singleton pregnant women who received first-trimester aneuploidy and preeclampsia screening between January 2019 and May 2021. First-trimester prenatal screening biomarkers, including pregnancy-associated plasma protein A (PAPP-A), free beta-human chorionic gonadotropin, and placental growth factor (PLGF), along with maternal characteristics, were collected for analysis in relation to GDM. Receiver operating characteristic (ROC) curve and logistic regression analyses were used to evaluate variables associated with GDM.Of the 1452 pregnant women enrolled, 96 developed GDM. PAPP-A (5.01 vs. 5.73 IU/L, P < 0.001) and PLGF (39.88 vs. 41.81 pg/mL, P = 0.044) were significantly lower in the GDM group than in the non-GDM group. The area under the ROC curve of combined maternal characteristics and biomarkers was 0.73 (95% confidence interval [CI] 0.68–0.79, P < 0.001). The formula for predicting GDM was as follows: P = 1/[1 + exp (-8.148 + 0.057 x age + 0.011 x pregestational body mass index + 1.752 x previous GDM history + 0.95 x previous preeclampsia history + 0.756 x family history of diabetes + 0.025 x chronic hypertension + 0.036 x mean arterial pressure - 0.09 x PAPP-A - 0.001 x PLGF)]. Logistic regression analysis revealed that higher pregestational body mass index (adjusted odds ratio [aOR] 1.03, 95% CI 1.01 - 1.06, P = 0.012), previous GDM history (aOR 9.97, 95% CI 3.92 - 25.37, P < 0.001), family history of diabetes (aOR 2.36, 95% CI 1.39 - 4.02, P = 0.001), higher mean arterial pressure (aOR 1.17, 95% CI 1.07 - 1.27, P < 0.001), and lower PAPP-A level (aOR 0.91, 95% CI 0.83 - 1.00, P = 0.040) were independently associated with the development of GDM. The Hosmer-Lemeshow test demonstrated that the model exhibited an excellent discrimination ability (chi-square = 3.089, df = 8, P = 0.929).Downregulation of first-trimester PAPP-A and PLGF was associated with the development of GDM. Combining first-trimester biomarkers with maternal characteristics could be valuable for predicting the risk of GDM.
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