Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Introduction Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition. Objectives To s...

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Veröffentlicht in:Metabolomics 2017-07, Vol.13 (7), p.1, Article 86
Hauptverfasser: Kelly, Rachel S., Giorgio, Rachel T., Chawes, Bo L., Palacios, Natalia I., Gray, Kathryn J., Mirzakhani, Hooman, Wu, Ann, Blighe, Kevin, Weiss, Scott T., Lasky-Su, Jessica
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Sprache:eng
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Zusammenfassung:Introduction Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition. Objectives To summarize the published literature on the use of metabolomics as a tool to study preeclampsia. Methods PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint. Results Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers. Conclusion Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.
ISSN:1573-3882
1573-3890
DOI:10.1007/s11306-017-1225-8