Chagovets Vitaliy, Frankevich Natalia, Starodubtseva Natalia, Tokareva Alisa, Derbentseva Elena, Yuryev Sergey, Kutzenko Anastasia, Sukhikh Gennady, Frankevich Vladimir
National Medical Research Center for Obstetrics, Gynecology and Perinatology Named After Academician Academician V.I. Kulakov of the Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia.
Moscow Center for Advanced Studies, 123592 Moscow, Russia.
Int J Mol Sci. 2025 Jan 28;26(3):1149. doi: 10.3390/ijms26031149.
The prevalence of fetal macrosomia is steadily increasing worldwide, reaching up to 20%. Fetal macrosomia complicates pregnancy and delivery. Current prediction strategies are inaccurate, and most patients with fetal macrosomia go into labor with an "unknown status". The aim of this study was to develop a system for predicting fetal macrosomia based on the lipid profiles of pregnant women's blood serum. In total, 110 patients were included in this study: 30 patients had gestational diabetes mellitus (GDM) and 80 did not. During the observation, blood samples were collected at three time points: in the first trimester (11-13 weeks of pregnancy), in the second trimester (24-26 weeks), and in the third trimester (30-32 weeks). Lipids were detected by flow injection analysis with mass spectrometry. Lipid profiles of pregnant women were discriminated by orthogonal projection on latent structure discriminant analysis (OPLS-DA) in all three trimesters. The developed OPLS-DA models allowed for the prediction of the occurrence of fetal macrosomia during pregnancy. Three sets of models were developed: models independent of GDM status with a sensitivity of 0.85 and specificity of 0.91, models for patients with positive GDM status with a sensitivity of 0.91 and specificity of 0.96, and models for patients with negative GDM status with a sensitivity of 0.93 and specificity of 0.92. Phosphatidylcholines and sphingomyelins were the most important discriminative features. These lipid groups probably play an important role in the pathogenesis of fetal macrosomia and may serve as laboratory markers of this pregnancy complication.
全球范围内，巨大胎儿的患病率正在稳步上升，高达20%。巨大胎儿会使妊娠和分娩复杂化。目前的预测策略并不准确，大多数巨大胎儿患者在分娩时处于“未知状态”。本研究的目的是基于孕妇血清脂质谱开发一种预测巨大胎儿的系统。本研究共纳入110例患者：30例患有妊娠期糖尿病（GDM），80例未患。在观察期间，在三个时间点采集血样：孕早期（妊娠11 - 13周）、孕中期（24 - 26周）和孕晚期（30 - 32周）。通过流动注射分析结合质谱法检测脂质。在所有三个孕期，通过潜在结构判别分析的正交投影（OPLS - DA）对孕妇的脂质谱进行判别。所开发的OPLS - DA模型能够预测孕期巨大胎儿的发生情况。开发了三组模型：不考虑GDM状态的模型，灵敏度为0.85，特异度为0.91；GDM状态为阳性的患者模型，灵敏度为0.91，特异度为0.96；GDM状态为阴性的患者模型，灵敏度为0.93，特异度为0.92。磷脂酰胆碱和鞘磷脂是最重要的判别特征。这些脂质组可能在巨大胎儿的发病机制中起重要作用，并可能作为这种妊娠并发症的实验室标志物。
