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Preeclampsia is a pregnancy specific syndrome characterized by hypertension and proteinuria after 20 weeks of gestation. To reveal the relationship between lipids and preeclampsia, we conduct lipidomic profiling of maternal serums of 44 severe preeclamptic and 20 healthy pregnancies from a multi-ethnic cohort in Hawaii. Correlation network analysis shows that oxidized phospholipids (OxPLs) have increased inter-correlations and connections in preeclampsia, while other lipids, including triacylglycerols (TAGs), have reduced network correlations and connections. Thirty-one lipid species from various lipid classes demonstrate predominantly reductions and causal relationships with preeclampsia. They include phosphatidylglycerol (PG), TAG, diacylglycerol (DAG), phosphatidylcholine (PC), cholesterol esters (CE), phosphatidylethanolamine (PE), sphingomyelin (SM), ceramides (Cer-NS), hexosyl ceramides (HexCer-NS), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), and free fatty acid (FFA). Many of these lipids are also selected as important features by a linear discriminant analysis (LDA) classifier with high predictive accuracy (F-1 statistic 0.941 and balanced accuracy 0.88), indicating their potential to serve as biomarkers for severe preeclampsia. Our study supports the hypothesis of a phospholipid (PL) centered, dysregulated lipidomic metabolic atlas. That is, severe preeclampsia may be originated from hypoxia, which induces the accumulation of OxPLs through oxidative stress whereas reduces many other lipids (eg. reduced PCs, TAGs and ceramides). These molecular changes coherently lead to dysregulated biological functions, such as insulin signaling and inflammation/infections. Moreover, the lipid changes may also be responsible for the comorbidity between preeclampsia and gestational diabetes, a clinically known risk factor for preeclampsia.

Preeclampsia is a medical condition affecting 5–10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level and studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using the network analysis on the biomarkers of all literatures reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.

Preeclampsia is a pregnancy-specific syndrome characterized by hypertension and proteinuria after 20 weeks of gestation. However, it is not well understood what lipids are involved in the development of this condition, and even less is known how these lipids mediate its formation. To reveal the relationship between lipids and preeclampsia, we conducted lipidomic profiling of maternal sera of 44 severe preeclamptic and 20 healthy pregnant women from a multiethnic cohort in Hawaii. Correlation network analysis showed that oxidized phospholipids have increased intercorrelations and connections in preeclampsia, whereas other lipids, including triacylglycerols, have reduced network correlations and connections. A total of 10 lipid species demonstrate significant changes uniquely associated with preeclampsia but not any other clinical confounders. These species are from the lipid classes of lysophosphatidylcholines, phosphatidylcholines (PCs), cholesteryl esters, phosphatidylethanolamines, lysophosphatidylethanolamines, and ceramides. A random forest classifier built on these lipids shows highly accurate and specific prediction (F1 statistic = 0.94; balanced accuracy = 0.88) of severe preeclampsia, demonstrating their potential as biomarkers for this condition. These lipid species are enriched in dysregulated biological pathways, including insulin signaling, immune response, and phospholipid metabolism. Moreover, causality inference shows that various PCs and lysophosphatidylcholines mediate severe preeclampsia through PC 35:1e. Our results suggest that the lipidome may play a role in the pathogenesis and serve as biomarkers of severe preeclampsia.