Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12-12

AUTHORS

Rachel S. Kelly, Damien C. Croteau-Chonka, Amber Dahlin, Hooman Mirzakhani, Ann C. Wu, Emily S. Wan, Michael J. McGeachie, Weiliang Qiu, Joanne E. Sordillo, Amal Al-Garawi, Kathryn J. Gray, Thomas F. McElrath, Vincent J. Carey, Clary B. Clish, Augusto A. Litonjua, Scott T. Weiss, Jessica A. Lasky-Su

ABSTRACT

IntroductionPreeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive.ObjectivesThis study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms.MethodsMetabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods.ResultsIn total, 72 (0.9%) metabolite features were associated (p < 0.01) with preeclampsia after adjustment for maternal age, race, and gestational age. These features had moderate to good discriminatory ability; in ROC curve analyses a summary score based on these features displayed an area under the curve (AUC) of 0.794 (95%CI 0.700, 0.888). This profile retained the ability to distinguish preeclamptic from healthy pregnancies in the third trimester [AUC: 0.762 (95% CI 0.663, 0.860)]. Additionally, metabolite set enrichment analysis identified common pathways, including glycerophospholipid metabolism, at the two time-points. Integration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system.ConclusionsThese findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required. More... »

PAGES

7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-016-1149-8

DOI

http://dx.doi.org/10.1007/s11306-016-1149-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015767923

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28596717


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12 schema:description IntroductionPreeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive.ObjectivesThis study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms.MethodsMetabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods.ResultsIn total, 72 (0.9%) metabolite features were associated (p < 0.01) with preeclampsia after adjustment for maternal age, race, and gestational age. These features had moderate to good discriminatory ability; in ROC curve analyses a summary score based on these features displayed an area under the curve (AUC) of 0.794 (95%CI 0.700, 0.888). This profile retained the ability to distinguish preeclamptic from healthy pregnancies in the third trimester [AUC: 0.762 (95% CI 0.663, 0.860)]. Additionally, metabolite set enrichment analysis identified common pathways, including glycerophospholipid metabolism, at the two time-points. Integration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system.ConclusionsThese findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.
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19 IntroductionPreeclampsia
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119 schema:name Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia
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