检验医学 ›› 2020, Vol. 35 ›› Issue (2): 100-107.DOI: 10.3969/j.issn.1673-8640.2020.02.002

• 临床应用研究?论著 • 上一篇    下一篇

非靶向脂质组学在子痫前期预测中的应用

曹正1, 董莹1, 赵晟隆2, 王晶1, 张春红1, 陈陆1, 刘京瑞1, 沈敏2, 翟燕红1, 刘晓巍3()   

  1. 1. 首都医科大学附属北京妇产医院检验科,北京 100026
    2. 美康生物参考实验室,浙江 宁波 315104
    3. 首都医科大学附属北京妇产医院围产医学部,北京 100026
  • 收稿日期:2019-05-20 出版日期:2020-02-29 发布日期:2020-03-22
  • 作者简介:null

    作者简介:曹正,男,1982年生,博士,副主任技师,主要从事临床质谱应用及疾病标志物研究。

  • 基金资助:
    北京市医院管理局青年人才培养青苗计划(QML20171401)

Utility of non-targeted lipidomics in predicting preeclampsia

CAO Zheng1, DONG Ying1, ZHAO Shenglong2, WANG Jing1, ZHANG Chunhong1, CHEN Lu1, LIU Jingrui1, SHEN Min2, ZHAI Yanhong1, LIU Xiaowei3()   

  1. 1. Department of Clinical Laboratory,Beijing Obstetrics and Gynecology Hospital,Capital Medical University,Beijing 100026,China
    2. Reference Laboratory,Medical System Biotechnology Co.,Ltd.,Ningbo 315104,Zhejiang,China
    3. Department of Obstetrics,Beijing Obstetrics and Gynecology Hospital,Capital Medical University,Beijing 100026,China
  • Received:2019-05-20 Online:2020-02-29 Published:2020-03-22

摘要:

目的 采用非靶向脂质组学寻找能早期预测子痫前期的生物标志物。方法 选取子痫前期高危孕妇,根据后期是否发生子痫前期分为子痫前期组(33例)和无子痫前期组(对照组,33例)。采用超高效液相色谱(UPLC)-飞行时间质谱(TOF)联用技术对所有对象的血清样本进行脂质组学检测。采用差异倍数、非参数Wilcoxon秩和检验、主成分分析(PCA)和偏最小二乘判别(PLS-DA)筛选生物标志物,并对定性的生物标志物进行代谢通路和富集分析。采用受试者工作特征(ROC)曲线评价筛选出的代谢物对子痫前期的诊断价值。结果 共筛选出42种生物标志物,其中甘氨胆酸、二十二碳五烯酸和磷脂酰肌醇(PI)(16:1(9Z)/16:0)水平在子痫前期组中明显升高,胆固醇酯(CE)[22:4(7Z,10Z,13Z,16Z)]、二酰甘油(DG)[18:0/20:1(11Z)/0:0]水平明显降低。含2种以上生物标志物的代谢通路分别为鞘脂类代谢途径、甘油磷脂代谢途径和初级胆汁酸生物合成途径,主要参与α-亚麻酸和亚麻酸代谢、缩醛磷脂合成、长链饱和脂肪酸的线粒体β氧化、鞘脂代谢及胆汁酸生物合成。ROC曲线分析结果显示,CE[22:4(7Z,10Z,13Z,16Z)]、DG[18:0/20:1(11Z)/0:0]、二十二碳五烯酸、PI[16:1(9Z)/16:0]及甘氨胆酸诊断子痫前期的曲线下面积分别为0.723、0.715、0.678、0.669和0.660。结论 非靶向脂质组学筛选出的生物标志物能有效区分子痫前期患者和无子痫前期孕妇。甘油磷脂代谢、初级胆汁酸代谢、α-亚麻酸和亚麻酸代谢、胆碱代谢与子痫前期发病有关。

关键词: 非靶向脂质组学, 子痫前期, 质谱

Abstract:

Objective Using non-targeted lipidomics to identify biomarkers for the early prediction of preeclampsia. Methods Pregnant women identified with high risk preeclampsia factors were enrolled and classified into preeclampsia group(33 cases) and non-preeclampsia group(control group,33 cases) according to whether or not they developed preeclampsia in subsequent follow-up period. Serum samples from all the subjects were analyzed for lipidomics by ultra performance liquid chromatography(UPLC)/quadrupole time-of-flight(TOF) mass spectrometry. Univariate statistical analysis with fold change value,Wilcoxon rank sum test of nonparametric test,principal component analysis(PCA) and partial least square discriminant analysis(PLS-DA) were used for screening metabolites. Differential metabolites were analyzed for metabolic pathways and the degree of enrichment. Receiver operating characteristic(ROC)curve was used to assess the value of screened metabolites in the diagnosis of preeclampsia. Results A total of 42 differential metabolites were recognized,and glycocholic acid,docosapentaenoic acid and phosphatidylinositol(PI)[16:1(9Z)/16:0] were increased,while cholesteryl ester(CE)[22:4(7Z,10Z,13Z,16Z)] and diacylglycerol(DG)[18:0/20:1(11Z)/ 0:0] were decreased in preeclampsia group. The metabolic pathways containing more than 2 differential metabolites were sphingolipid metabolism,glycerolipid metabolism and primary bile acid biosynthesis. Differential metabolites were mainly involved in alpha-linolenic acid and linolenic acid metabolism,plasmalogen synthesis,mitochondrial oxidation of long-chain saturated fatty acids,sphingolipid metabolism and bile acid biosynthesis. ROC curve analysis showed that the areas under ROC curves of CE [22:4(7Z,10Z,13Z,16Z)],DG [18:0/20:1(11Z)/0:0],docosapentaenoic acid,PI [16:1(9Z)/16:0] and glycocholic acid were 0.723,0.715,0.678,0.669 and 0.660,respectively. Conclusions Lipidomics can effectively distinguish preeclampsia or not. Glycerophospholipid metabolism,primary bile acid metabolism,alpha-linolenic acid and linolenic acid metabolism and choline metabolism may contribute to preeclampsia pathogenesis.

Key words: Non-targeted lipidomics, Preeclampsia, Mass spectrometry

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