Literature DB >> 29965778

Application of metabolomics to preeclampsia diagnosis.

B Fatemeh Nobakht M Gh1.   

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.

Entities:  

Keywords:  Preeclampsia; diagnosis; mass spectrometry; metabolomics; nuclear magnetic resonance spectroscopy

Mesh:

Substances:

Year:  2018        PMID: 29965778     DOI: 10.1080/19396368.2018.1482968

Source DB:  PubMed          Journal:  Syst Biol Reprod Med        ISSN: 1939-6368            Impact factor:   3.061


  12 in total

1.  A review of omics approaches to study preeclampsia.

Authors:  Paula A Benny; Fadhl M Alakwaa; Ryan J Schlueter; Cameron B Lassiter; Lana X Garmire
Journal:  Placenta       Date:  2020-01-22       Impact factor: 3.481

2.  Maternal Diet and the Serum Metabolome in Pregnancy: Robust Dietary Biomarkers Generalizable to a Multiethnic Birth Cohort.

Authors:  Russell J de Souza; Meera Shanmuganathan; Amel Lamri; Stephanie A Atkinson; Allan Becker; Dipika Desai; Milan Gupta; Piush J Mandhane; Theo J Moraes; Katherine M Morrison; Padmaja Subbarao; Koon K Teo; Stuart E Turvey; Natalie C Williams; Philip Britz-McKibbin; Sonia S Anand
Journal:  Curr Dev Nutr       Date:  2020-09-02

3.  Metabolomic Markers for Predicting Preeclampsia in the First Trimester of Pregnancy: A Retrospective Study.

Authors:  Ekaterina V Ilgisonis; Raisa Shalina; Nigyar Kasum-Zade; Kristina G Burkova; Oxana P Trifonova; Dmitry L Maslov; Anna L Kaysheva; Sergey S Markin
Journal:  Molecules       Date:  2022-04-12       Impact factor: 4.927

4.  Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.

Authors:  Shilong Li; Zichen Wang; Luciana A Vieira; Amanda B Zheutlin; Boshu Ru; Emilio Schadt; Pei Wang; Alan B Copperman; Joanne L Stone; Susan J Gross; Yu-Han Kao; Yan Kwan Lau; Siobhan M Dolan; Eric E Schadt; Li Li
Journal:  NPJ Digit Med       Date:  2022-06-06

5.  The Relationship between Angiogenic Factors and Energy Metabolism in Preeclampsia.

Authors:  Alejandra Abascal-Saiz; Marta Duque-Alcorta; Victoria Fioravantti; Eugenia Antolín; Eva Fuente-Luelmo; María Haro; María P Ramos-Álvarez; Germán Perdomo; José L Bartha
Journal:  Nutrients       Date:  2022-05-23       Impact factor: 6.706

Review 6.  Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction.

Authors:  J Mayrink; M L Costa; J G Cecatti
Journal:  ScientificWorldJournal       Date:  2018-12-06

Review 7.  Identification of Biomarkers for Preeclampsia Based on Metabolomics.

Authors:  Mengxin Yao; Yue Xiao; Zhuoqiao Yang; Wenxin Ge; Fei Liang; Haoyue Teng; Yingjie Gu; Jieyun Yin
Journal:  Clin Epidemiol       Date:  2022-03-19       Impact factor: 4.790

8.  Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review.

Authors:  Jussara Mayrink; Debora F Leite; Guilherme M Nobrega; Maria Laura Costa; Jose Guilherme Cecatti
Journal:  BMJ Open       Date:  2022-04-25       Impact factor: 3.006

9.  Proteomics and Metabolomics Profiling of Platelets and Plasma Mediators of Thrombo-Inflammation in Gestational Hypertension and Preeclampsia.

Authors:  Luiz Gustavo N de Almeida; Daniel Young; Lorraine Chow; Joshua Nicholas; Adrienne Lee; Man-Chiu Poon; Antoine Dufour; Ejaife O Agbani
Journal:  Cells       Date:  2022-04-07       Impact factor: 7.666

10.  Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia.

Authors:  Seung Mi Lee; Yujin Kang; Eun Mi Lee; Young Mi Jung; Subeen Hong; Soo Jin Park; Chan-Wook Park; Errol R Norwitz; Do Yup Lee; Joong Shin Park
Journal:  Sci Rep       Date:  2020-09-30       Impact factor: 4.379

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