Cargando…

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

OBJECTIVE: To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy. DESIGN: Systematic review of observational studies. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA: An electronic literature search was performed in June 2019 and February 2022. Two researchers indep...

Descripción completa

Detalles Bibliográficos
Autores principales: Mayrink, Jussara, Leite, Debora F, Nobrega, Guilherme M, Costa, Maria Laura, Cecatti, Jose Guilherme
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039389/
https://www.ncbi.nlm.nih.gov/pubmed/35470187
http://dx.doi.org/10.1136/bmjopen-2021-054697
_version_ 1784694117401362432
author Mayrink, Jussara
Leite, Debora F
Nobrega, Guilherme M
Costa, Maria Laura
Cecatti, Jose Guilherme
author_facet Mayrink, Jussara
Leite, Debora F
Nobrega, Guilherme M
Costa, Maria Laura
Cecatti, Jose Guilherme
author_sort Mayrink, Jussara
collection PubMed
description OBJECTIVE: To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy. DESIGN: Systematic review of observational studies. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA: An electronic literature search was performed in June 2019 and February 2022. Two researchers independently selected studies published between 1998 and 2022 on metabolomic techniques applied to predict the condition; subsequently, they extracted data and performed quality assessment. Discrepancies were dealt with a third reviewer. The primary outcome was pre-eclampsia. Cohort or case–control studies were eligible when maternal samples were taken before diagnosis of the hypertensive disorder. STUDY APPRAISAL AND SYNTHESIS METHODS: Data on study design, maternal characteristics, how hypertension was diagnosed, metabolomics details and metabolites, and accuracy were independently extracted by two authors. RESULTS: Among 4613 initially identified studies on metabolomics, 68 were read in full text and 32 articles were included. Studies were excluded due to duplicated data, study design or lack of identification of metabolites. Metabolomics was applied mainly in the second trimester; the most common technique was liquid-chromatography coupled to mass spectrometry. Among the 122 different metabolites found, there were 23 amino acids and 21 fatty acids. Most of the metabolites were involved with ammonia recycling; amino acid metabolism; arachidonic acid metabolism; lipid transport, metabolism and peroxidation; fatty acid metabolism; cell signalling; galactose metabolism; nucleotide sugars metabolism; lactose degradation; and glycerolipid metabolism. Only citrate was a common metabolite for prediction of early-onset and late-onset pre-eclampsia. Vitamin D was the only metabolite in common for pre-eclampsia and gestational hypertension prediction. Meta-analysis was not performed due to lack of appropriate standardised data. CONCLUSIONS AND IMPLICATIONS: Metabolite signatures may contribute to further insights into the pathogenesis of pre-eclampsia and support screening tests. Nevertheless, it is mandatory to validate such methods in larger studies with a heterogeneous population to ascertain the potential for their use in clinical practice. PROSPERO REGISTRATION NUMBER: CRD42018097409.
format Online
Article
Text
id pubmed-9039389
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-90393892022-05-06 Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review Mayrink, Jussara Leite, Debora F Nobrega, Guilherme M Costa, Maria Laura Cecatti, Jose Guilherme BMJ Open Obstetrics and Gynaecology OBJECTIVE: To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy. DESIGN: Systematic review of observational studies. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA: An electronic literature search was performed in June 2019 and February 2022. Two researchers independently selected studies published between 1998 and 2022 on metabolomic techniques applied to predict the condition; subsequently, they extracted data and performed quality assessment. Discrepancies were dealt with a third reviewer. The primary outcome was pre-eclampsia. Cohort or case–control studies were eligible when maternal samples were taken before diagnosis of the hypertensive disorder. STUDY APPRAISAL AND SYNTHESIS METHODS: Data on study design, maternal characteristics, how hypertension was diagnosed, metabolomics details and metabolites, and accuracy were independently extracted by two authors. RESULTS: Among 4613 initially identified studies on metabolomics, 68 were read in full text and 32 articles were included. Studies were excluded due to duplicated data, study design or lack of identification of metabolites. Metabolomics was applied mainly in the second trimester; the most common technique was liquid-chromatography coupled to mass spectrometry. Among the 122 different metabolites found, there were 23 amino acids and 21 fatty acids. Most of the metabolites were involved with ammonia recycling; amino acid metabolism; arachidonic acid metabolism; lipid transport, metabolism and peroxidation; fatty acid metabolism; cell signalling; galactose metabolism; nucleotide sugars metabolism; lactose degradation; and glycerolipid metabolism. Only citrate was a common metabolite for prediction of early-onset and late-onset pre-eclampsia. Vitamin D was the only metabolite in common for pre-eclampsia and gestational hypertension prediction. Meta-analysis was not performed due to lack of appropriate standardised data. CONCLUSIONS AND IMPLICATIONS: Metabolite signatures may contribute to further insights into the pathogenesis of pre-eclampsia and support screening tests. Nevertheless, it is mandatory to validate such methods in larger studies with a heterogeneous population to ascertain the potential for their use in clinical practice. PROSPERO REGISTRATION NUMBER: CRD42018097409. BMJ Publishing Group 2022-04-25 /pmc/articles/PMC9039389/ /pubmed/35470187 http://dx.doi.org/10.1136/bmjopen-2021-054697 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Obstetrics and Gynaecology
Mayrink, Jussara
Leite, Debora F
Nobrega, Guilherme M
Costa, Maria Laura
Cecatti, Jose Guilherme
Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title_full Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title_fullStr Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title_full_unstemmed Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title_short Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
title_sort prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
topic Obstetrics and Gynaecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039389/
https://www.ncbi.nlm.nih.gov/pubmed/35470187
http://dx.doi.org/10.1136/bmjopen-2021-054697
work_keys_str_mv AT mayrinkjussara predictionofpregnancyrelatedhypertensivedisordersusingmetabolomicsasystematicreview
AT leitedeboraf predictionofpregnancyrelatedhypertensivedisordersusingmetabolomicsasystematicreview
AT nobregaguilhermem predictionofpregnancyrelatedhypertensivedisordersusingmetabolomicsasystematicreview
AT costamarialaura predictionofpregnancyrelatedhypertensivedisordersusingmetabolomicsasystematicreview
AT cecattijoseguilherme predictionofpregnancyrelatedhypertensivedisordersusingmetabolomicsasystematicreview