Urinary metabolic variation analysis during pregnancy and application in Gestational Diabetes Mellitus and spontaneous abortion biomarker discovery

Pregnancy is associated with the onset of many adaptation processes that are likely to change over the course of gestation. Understanding normal metabolites’ variation with pregnancy progression is crucial for gaining insights of the key nutrients for normal fetal growth, and for comparative researc...

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Veröffentlicht in:Scientific reports 2019-02, Vol.9 (1), p.2605-2605, Article 2605
Hauptverfasser: Liu, Xiaoyan, Wang, Xiangqing, Sun, Haidan, Guo, Zhengguang, Liu, Xiang, Yuan, Tao, Fu, Yong, Tang, Xiaoyue, Li, Jing, Sun, Wei, Zhao, Weigang
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Sprache:eng
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Zusammenfassung:Pregnancy is associated with the onset of many adaptation processes that are likely to change over the course of gestation. Understanding normal metabolites’ variation with pregnancy progression is crucial for gaining insights of the key nutrients for normal fetal growth, and for comparative research of pregnancy-related complications. This work presents liquid chromatography-mass spectrum-based urine metabolomics study of 50 health pregnant women at three time points during pregnancy. The influence of maternal physiological factors, including age, BMI, parity and gravity to urine metabolome was explored. Additionally, urine metabolomics was applied for early prediction of two pregnancy complications, gestational diabetes mellitus and spontaneous abortion. Our results suggested that during normal pregnancy progression, pathways of steroid hormone biosynthesis and tyrosine metabolism were significantly regulated. BMI is a factor that should be considered during cross-section analysis. Application analysis discovered potential biomarkers for GDM in the first trimester with AUC of 0.89, and potential biomarkers for SA in the first trimester with AUC of 0.90. In conclusion, our study indicated that urine metabolome could reflect variations during pregnancy progression, and has potential value for pregnancy complications early prediction. The clinical trial number for this study is NCT03246295.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-39259-2