Metabolomics for Prediction of Hypertension in Pregnancy: a Systematic Review and Meta-analysis Protocol

Overview

Journal BMJ Open

Specialty General Medicine

Date 2020 Dec 30

PMID 33376166

Citations 1

Authors

Jussara Mayrink

Debora Farias Batista Leite

Maria Laura Costa

Jose Guilherme Cecatti

Affiliations

Soon will be listed here.

Abstract

Introduction: Hypertension is a very important cause of maternal morbidity and mortality worldwide, despite efforts on prevention. The lack of a tool to provide effective and early prediction of hypertension for a high-risk group may contribute to improving maternal and fetal outcomes. Metabolomics has figured out as a promised technology to contribute to the improvement of hypertension in pregnancy prediction.

Methods And Analysis: Our primary outcome is hypertensive disorders of pregnancy. A detailed systematic literature search will be performed in electronic databases PubMed, EMBASE, Scopus, Web of Science, Latin America and Caribbean Health Sciences Literature, Scientific Electronic Library Online, Health Technology Assessment and Database of Abstracts of Reviews of Effects using controlled terms 'pre-eclampsia', 'hypertensive disorders', 'metabolomics' and 'prediction' (and their variations). Studies from the latest 20 years will be included, except case reports, reviews, cross-sectional studies, letter to editors, expert opinions, commentaries papers or non-human research. If possible, we will perform a meta-analysis. Two peer-reviewers will independently perform the search and in cases of discordance, a third reviewer will be consulted.

Ethics And Dissemination: As a systematic review, ethics approval is not required. The results of this review will present the current use and performance of metabolomics for predicting gestational hypertension. Such data could potentially guide future studies and interventions to improve existing prediction models.

Prospero Registration Number: CRD42018097409.

Citing Articles

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

Mayrink J, Leite D, Nobrega G, Costa M, Cecatti J BMJ Open. 2022; 12(4):e054697.

PMID: 35470187 PMC: 9039389. DOI: 10.1136/bmjopen-2021-054697.

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