Identification of metabolomics profiling in maternal urine and hair for gestational diabetes mellitus

Chen Xuyang; Han Dingli; Zhang Hua

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Identification of metabolomics profiling in maternal urine and hair for gestational diabetes mellitus

更新时间：2023-12-22

- Identification of metabolomics profiling in maternal urine and hair for gestational diabetes mellitus
- Journal of Chongqing Medical University Vol. 44, Issue 1, Pages: 12(2019)
- 作者机构：
  
  重庆医科大学附属第一医院妇产科、中国-加拿大-新西兰联合母胎医学实验室,重庆,400016
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019，
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Chen Xuyang, Han Dingli, Zhang Hua. Identification of metabolomics profiling in maternal urine and hair for gestational diabetes mellitus. [J]. Journal of Chongqing Medical University 44(1):12(2019)
DOI：

Chen Xuyang, Han Dingli, Zhang Hua. Identification of metabolomics profiling in maternal urine and hair for gestational diabetes mellitus. [J]. Journal of Chongqing Medical University 44(1):12(2019) DOI：

摘要

目的：研究妊娠期糖尿病（gestational diabetes mellitus，GDM）孕妇的血清和头发的代谢物，并探索GDM的潜在生物标记。方法：49个GDM孕妇纳入病例组和44个孕妇作为对照组，招募于妊娠26~28周。运用气相色谱-质谱联用仪和液相色谱-质谱联用仪来检测孕妇血液及头发标本代谢物。结果：在血清中鉴定出747种代谢物，在头发中鉴定出545种代谢物，其中分别有23种和31种代谢物在GDM组和对照组之间有统计学差异，Amphibine H（P=0.001）、油酰乙醇胺（N-Oleoylethanolamine，P=0.005）、3，4-Methylenepimelic acid（P=0.043）、25-Methyl-21-tritriacontene-1，9，11-triol（P=0.004）等在GDM组中明显升高，胡椒酸（P=0.029）、2，2，9，9-Tetramethyl-undecan-1，10-diol（P=0.017）、刺桐碱（P=0.004）、Docosahexaenoyl Ethanolamide（P=0.021）等在GDM组中明显降低。在这些差异代谢物中，油酰乙醇胺有最好区分GDM与正常孕妇的能力，ROC曲线下面积为0.846（95%CI=0.757~0.920，P=0.005）。结论：代谢组学可以帮助解释促进GDM发展的代谢机制。孕妇头发中发现的代谢物油酰乙醇胺能够区分GDM，提示孕妇头发有作为生物标记物来源的可能。

Abstract

Objective：To investigate the maternal metabolome on serum and hair associated with gestational diabetes mellitus（GDM） and to explore the potential biomarker of GDM. Methods：A total of 49 GDM cases and 44 controls were recruited at 26 to 28 weeks of gestation. The serum and hair metabolomes were analyzed using gas chromatography-mass spectrometry（GC/MS） and liquid chro－matography-mass spectrometry（LC/MS）. Results：Of the 747 metabolites identified in serum and 545 metabolites identified in hair，23 and 31 were significantly different between GDM and control，respectively. For example，Amphibine H（P=0.001），N-Oleoyle－thanolamine（P=0.005），3，4-Methylenepimelic acid（P=0.043），25-Methyl-21-tritriacontene-1，9，11-triol（P=0.004） were increased in GDM. For instance，piperic acid（P=0.029），2，2，9，9-Tetramethyl-undecan-1，10-diol（P=0.017），Lenticin（P=0.004），Docosahex－aenoyl Ethanolamide（P=0.021） were decreased in GDM. And of the all significant metabolites，N-Oleoylethanolamine had the greatest ability to classify GDM status，with area under the receiver operating characteristic curve of 0.846（95%CI=0.757 to 0.920，P=0.005）. Conclusion：Metabolomics can assist with the elucidation of metabolic mechanisms underpinning GDM development. The metabolite（N-Oleoylethanolamine） that could classify GDM status was identified in the hair of participants；highlighting the potential of maternal hair as a source of biomarkers.

关键词

妊娠期糖尿病代谢组学气相色谱-质谱法液相色谱-质谱法

Keywords

gestational diabetes mellitusmetabonomicsgas chromatography-mass spectrometryliquid chromatography-mass spectrometry

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