AUTHOR=Xiong Liling , Tang Mi , Liu Hong , Cai Jianghui , Jin Ying , Huang Cheng , Xing Shasha , Yang Xiao
TITLE=LC-MS/MS untargeted lipidomics uncovers placenta lipid signatures from intrahepatic cholestasis of pregnancy
JOURNAL=Frontiers in Physiology
VOLUME=15
YEAR=2024
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2024.1276722
DOI=10.3389/fphys.2024.1276722
ISSN=1664-042X
ABSTRACT=Aims: Intrahepatic cholestasis of pregnancy (ICP) stands as the predominant liver disorder affecting pregnant women, with a prevalence ranging from 0.2% to 15.6%. While ICP is known to heighten the chances of perinatal mortality and morbidity, its pathogenesis remains elusive, and therapeutic options are limited. The objective of this study was to explore the characteristic lipid signature in placentas collected from normal pregnancies and those with mild and severe intrahepatic cholestasis of pregnancy. This research aims to clarify the pathogenesis and identify lipid biomarker for ICP through LC-MS/MS based lipidomic analysis.
Methods and materials: Placenta samples were collected from 30 normal pregnancy women and 30 mild and severe ICP women respectively. Women with normal pregnancy and ICP were recruit from April 2021 to July 2022 in Chengdu, China. And LC-MS/MS based lipidomic analysis was used to explore the characteristic placental lipids in mild and severe ICP.
Results: Fourty-four lipids were differentially expressed both in mild and severe ICP placenta. The pathway analysis revealed these lipids are mainly enriched in glycerophospholipid metabolism and autophagy pathway. Weighted correlation network analysis (WGCNA) identified the correlation network module of lipids highly related to ICP. Using multiple logistic regression analysis, we identified three and four combined metabolites that had an area under receiver operating characteristic curves (AUC) ≥ 0.90.
Conclusion: Our results systematically revealed the lipid signature in mild and severe ICP placenta. The results may provide new insight into the treatment and early prediction of ICP.