【作者】 苏翠红；

【导师】 李笑天；

【作者基本信息】 复旦大学 ， 妇产科学， 2010， 硕士

【摘要】 第一部分中孕期血清学预测子痫前期发病研究探讨目的：探究中孕早期唐氏综合症筛查指标、PlGF及sFlt-1与子痫前期发病风险的相关性,寻找其与子痫前期发病的的关系,从而为子痫前期的早期预测和发病机制研究提供新的策略。材料方法：本文研究设计为巢式病例—对照研究,选取符合条件的孕14-20周于上海市复旦大学附属妇产科医院建卡产检孕妇,采集完整病史并于孕14-20周进行唐氏筛查时同时留取肘静脉血,检测血清学PlGF、sFlt-1、AFP、HCG、uE3表达情况。结果：1.孕14-20周PlGF、AFP、孕妇初诊BMI的水平和孕妇初诊舒张压的改变与子痫前期发病风险相关。2.将孕妇孕14-20周PlGF、AFP、初诊舒张压及初诊BMI联合作ROC曲线时其ROC曲线面积为0.874,临界值取-854.19时敏感度及特异度分别是65.22%、95.65%,此时约登指数最大为0.61,准确率为80.50%。。结论：孕14-20周联合PlGF、AFP、孕妇初诊BMI的水平和孕妇舒张压可能有益于早期预测子痫前期。第二部分中孕期代谢组学预测子痫前期目的：运用基于高效液相色谱—飞行时间质谱联用技术(HPLC/TOF-MS)通过代谢组学方法分析子痫前期患者血清中内源性代谢物的变化,寻找子痫前期相关预测标志物；在此基础上寻找其与子痫前期发病的的关系,探讨代谢物的改变在子痫前期发病的预测价值。材料方法：本文研究设计为巢式病例—对照研究,选取符合条件的孕14-20周于上海市复旦大学附属妇产科医院建卡产检孕妇,采集完整病史并于孕14-20周进行唐氏筛查时同时留取肘静脉血,采用液相色谱—飞行时间质谱连用技术进行分析,在+ESI及-ESI两种模式下共同检测以提供更全面得生物信息研究血清代谢组学改变,寻找血清小分子标记物。采用主成分分析(PCA)及支持向量机多元数据分析方法分析结果。结果：子痫前期前期患者与正常孕妇相比较,采用主成分分析法发现对照组及子痫前期组小分子代谢物存在教显著差别。通过支持向量机法将十个标志物：+ESI状态下检测到的123Da、128 Da、173 Da、191Da、229Da、286 Da、316Da、481Da、787Da和-ESI状态下检测到的187Da,建立预测模型可以成功预测子痫前期,准确率达100%。结论：代谢组学技术成功鉴别了对照组及子线前期组代谢物的差异性改变,并且为子痫前期的早期预测提供依据。更多还原

【Abstract】 Section 1 Prediction and approach assessing the incidence of preeclampsia of serum markers at the second trimesterOBJECTIVE:To investigate whether the serous concentrations of Down’s syndrome markers, placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) in early pregnancy are altered in women who develope preeclampsia, and identify the factors associated with the increased risk of developing preeclampsia and to assess the predictive value on the preeclampsia of these factors.METHODS:A nested, case-control study was performed within a prospective cohort study of Down syndrome screening attending our hospitals in Shanghai for their prenatal care. We investigated 46 preeclamptic patients. The control group consisted of 46 healthy pregnant women. We reviewed the obstetric records of these women and measured serous levels ofα-fetoprotein (AFP), unconjugated estriol (uE3), total human chorionic gonadotrophin (HCG), sFlt-1 and PlGF of the serum sampled at 14-20 weeks of gestation.RESULTS:1. Changed levels of PlGF, BMI, AFP and diastolic blood pressure at first prenatal visit were associated with risk of preeclampsia.2. Between14-20 weeks, the ROC curve for the combining AFP、sFlt-1、BMI and diastolic blood pressure at first prenatal visit for the group with preeclampsia compared with unaffected subjects yielded a AUC of 0.874 (95%CI0.79-0.93). At 95.65% specificity, the cutoff value was-854.19, and sensitivity was 65.22%, Youden index and accuracy rate were 0.61 and 80.5%.CONCLUSION:During early second trimester, combined PlGF, BMI, AFP and diastolic blood pressure together may provide a helpful method for predicting preeclampsia. Section 2 Prediction and approach related to the incidence of pre-eclampsia of metabonomics at the second trimesterOBJECTIVE: High-performance liquid chromatography-time of flight mass spectrometry based metabolism was used to investigate the differences of serous metabolic patterns between patients with preeclampsia and healthy controls to find tentative diagnostic biomarkers. Based on the findings above, we tried to describe the metabolite changes related to the disease .It will provide a novel technological platform for the mechanism study of preeclampsia and assess the predictive value on the preeclampsia of these biomarkers.METHODS:A nested, case-control study was performed within a prospective cohort study of Down syndrome screening that our hospitals in Shanghai attended for their prenatal care. The serous samples were obtained when they came for Down syndrome screening. We investigated 46 preeclamptic patients. The control group consisted of 46 healthy pregnant women. We reviewed the obstetric records of these women. The serum samples were analyzed with High-performance liquid chromatography-time of flight mass spectrometry and the combination of the +ESI and -ESI scans were used to provide more useful information about the samples .The serum metabolic profiles were obtained. Principal components analysis (PCA) and Support Vector Machine (SVM) were used to compare such metabolic profiles.RESULTS:The metabolite differences in patients with preeclampsia: several tentative diagnostic biomarkers were found. From the support vector machines(SVM) method, ten unique m/z values were identified, the m/z values of markers selected from the +ESI scans were 123 Da,128Da,173 Da,191 Da,229Da,286Da,316Da,481 Da and 787 Da; marker selected from the -ESI scans was 187 Da. 10-fold cross-validation method was conducted by SVM to evaluate the prediction model, accuracy rate was 100%.CONCLUSION:The serous endogenous metabolites of preeclampsia have a high value in predicting preeclampsia.更多还原