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Published on: June 9, 2019
Application of metabolomics to preeclampsia diagnosis
1a Department of Basic Medical Sciences , Neyshabur University of Medical Sciences , Neyshabur , Iran.
Insights
Metabolomics shows promise for diagnosing preeclampsia, a pregnancy disorder causing maternal and infant deaths. This review highlights lipids as key biomarkers, suggesting metabolomic tools could improve early detection and understanding of preeclampsia.
Area of Science:
- Obstetrics and Gynecology
- Biochemistry
- Genomics
Background:
- Preeclampsia (PE) is a serious pregnancy complication characterized by hypertension and proteinuria, leading to significant maternal and neonatal mortality.
- Despite extensive research, the exact pathogenesis of preeclampsia remains unclear, hindering effective early prediction and diagnosis.
- Current methods for predicting and diagnosing preeclampsia have not seen substantial improvements, necessitating novel approaches.
Purpose of the Study:
- To conduct a comprehensive review of metabolomic studies focused on identifying predictive and diagnostic biomarkers for preeclampsia.
- To assess the potential of metabolomics as a clinical tool for early detection and improved understanding of preeclampsia.
Main Methods:
- A systematic literature search was performed across four major electronic databases (PubMed/Medline, Web of Science, Sciencedirect, Scopus) up to March 2018.
- Included studies focused on human subjects and utilized metabolomic approaches for preeclampsia research.
- Twenty-one relevant articles employing diverse biological specimens and analytical platforms were selected for this review.
Main Results:
- Metabolite profiling demonstrated potential in diagnosing preeclampsia and differentiating its subtypes.
- Lipids and their associated metabolites were the most frequently identified biomarkers across the reviewed studies.
- While not yet in routine clinical use, metabolomic biomarkers show significant promise.
Conclusions:
- Metabolomics holds considerable potential for development into a valuable clinical tool for preeclampsia diagnosis.
- Further research and validation of metabolomic biomarkers could significantly enhance early detection and management of preeclampsia.
- Metabolomic approaches may contribute to a deeper understanding of the underlying mechanisms driving preeclampsia.
Abstract:
Preeclampsia is a multifactorial disorder defined by hypertension and increased urinary protein excretion during pregnancy. It is a significant cause of maternal and neonatal deaths worldwide. Despite various research efforts to clarify pathogenies of preeclampsia and predict this disease before beginning of symptoms, the pathogenesis of preeclampsia is unclear. Early prediction and diagnosis of women at risk of preeclampsia has not markedly improved. Therefore, the objective of this study was to perform a review on metabolomic articles assessing predictive and diagnostic biomarkers of preeclampsia. Four electronic databases including PubMed/Medline, Web of Science, Sciencedirect, and Scopus were searched to identify studies of preeclampsia in humans using metabolomics from inception to March 2018. Twenty-one articles in a variety of biological specimens and analytical platforms were included in the present review. Metabolite profiles may assist in the diagnosis of preeclampsia and discrimination of its subtypes. Lipids and their related metabolites were the most generally detected metabolites. Although metabolomic biomarkers of preeclampsia are not routinely used, this review suggests that metabolomics has the potential to be developed into a clinical tool for preeclampsia diagnosis and could contribute to an improved understanding of disease mechanisms.
Abbreviations:
PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.
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