University of Helsinki and Turunmaa District Hospital, Gynaecological Outpatient Clinic, Hospital District of Southwest Finland, Kaskenkatu 13, 20700, Turku, Finland.
Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, P.O. Box 140, FI-00029, Helsinki, Haartmaninkatu 2, Finland.
BMC Pregnancy Childbirth. 2018 Jul 3;18(1):279. doi: 10.1186/s12884-018-1908-9.
The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort.
We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models.
We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity.
Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts.
International Standard Randomised Controlled Trial number ISRCTN14030412 , Date of registration 6/09/2007, retrospectively registered.
孕早期人绒毛膜促性腺激素（hCG-h）与总人绒毛膜促性腺激素的比例（%hCG-h）是预测早发型子痫前期的有前途的生物标志物。我们想评估临床危险因素、平均动脉压（MAP）、%hCG-h、hCGβ、妊娠相关血浆蛋白 A（PAPP-A）、胎盘生长因子（PlGF）和子宫动脉平均搏动指数（Uta-PI）在预测子痫前期（PE）及其早发型、晚发型、严重和非严重 PE 亚组中的表现高危队列中的作用。
我们对前瞻性收集的子痫前期和宫内生长受限预测与预防（PREDO）队列中的 257 名高危女性进行了亚组研究。多变量逻辑回归用于构建预测模型。第一个模型包括背景变量和 MAP。此外，在第二个模型中加入了生物标志物，在第三个模型中加入了平均 Uta-PI。在每个步骤中都包含了能改善模型拟合的所有变量。所有模型的曲线下面积（AUC）都进行了测定。
我们发现血清 PlGF 浓度较低与早发型 PE 相关，而 %hCG-h 较低与晚发型 PE 相关。严重 PE 患者血清 PlGF 水平较低，hCGβ 水平较高，而非严重 PE 患者 %hCG-h 和血清 PAPP-A 水平较低。通过使用多变量回归分析，第三个模型对所有 PE 的最佳预测：AUC 为 0.66，90%特异性时的敏感性为 36%。第三个模型对晚发型、严重和非严重 PE 的预测准确率也最高：AUC 为 0.66，敏感性为 32%，AUC 为 0.65，敏感性为 24%，AUC 为 0.60，敏感性为 22%，特异性均为 90%。早发型 PE 的最佳预测是使用第二个模型：AUC 为 0.68，特异性为 90%时的敏感性为 20%。
尽管多变量模型不符合作为临床有用的筛查工具的要求，但我们的结果表明，PE 危险因素女性的生物标志物谱根据 PE 的亚型而不同。PE 的异质性导致难以在高危人群中找到用于预测早孕 PE 的新的、有临床意义的生物标志物。
国际标准随机对照试验编号 ISRCTN8345055 ，注册日期 2007 年 9 月 6 日，回溯注册。
