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探究最优血清生物标志物预测子痫前期发生及病情程度

Exploration of optimal serum biomarkers for predicting the occurrence and severity of preeclampsia
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摘要 目的探讨预测子痫前期发生及病情程度的最优血清生物标志物指标。方法选取30例子痫前期孕妇为试验组,同期90例正常妊娠孕妇为对照组。收集研究对象的一般资料,孕10~14周采集空腹静脉血,检测血清胎盘生长因子(PLGF)、可溶性血管内皮生长因子受体-1(sFlt-1)、晚期蛋白氧化产物(AOPPs)、丙二醛(MDA)、总抗氧化能力(T-AOC)水平,分析预测子痫前期的最优血清生物标志物。结果试验组血清PLGF、T-AOC水平低于对照组,sFlt-1、AOPPs、MDA水平高于对照组(P<0.05);sFlt-1是预测子痫前期的最优指标,敏感度为90.24%,95%CI(0.901,0.946),各项指标联合检测可提高诊断效能。结论针对子痫前期,血清生物标志物可早期预测疾病发生及评价病情严重程度,其中最优指标为sFlt-1,而联合指标预测效果更佳。 Objective To explore the optimal biomarker indicators for predicting the occurrence and severity of preeclampsia.Methods 30 pre eclampsia pregnant women were selected as the experimental group,and 90 normal pregnant women during the same period were selected as the control group.Collect general information of the research subjects,collect fasting venous blood from 10~14 weeks of pregnancy,detect serum levels of placental growth factor(PLGF),soluble vascular endothelial growth factor receptor-1(sFlt-1),advanced protein oxidation products(AOPPs),malondialdehyde(MDA),and total antioxidant capacity(T-AOC),and analyze the optimal serum biomarkers for predicting preeclampsia.Results The serum levels of PLGF and T-AOC in the experimental group were lower than those in the control group,while the levels of sFlt-1,AOPPs,and MDA were higher than those in the control group(P<0.05);SFlt-1 is the optimal indicator for predicting preeclampsia,with a sensitivity of 90.24%and a 95%CI(0.901,0.946).Combined detection of various indicators can improve diagnostic effciency.Conclusion For preeclampsia,serum biomarkers can predict disease occurrence and evaluate the severity of the condition in the early stage,with sFlt-1 being the optimal indicator,and the combined indicator has a better predictive effect.
作者 丁少容 Ding Shaorong(MICU,Maoming Maternal and Child Health Hospital,Maoming 525000,China)
出处 《实用妇科内分泌电子杂志》 2024年第17期4-7,共4页 Electronic Journal of Practical Gynecological Endocrinology
基金 茂名市科技计划项目(编号210424084550376)。
关键词 子痫前期 血清生物标志物 病情程度 预测效果 Preeclampsia Biomarkers Degree of illness Prediction effect
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