Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/60506 |
Resumo: | 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. |
id |
UFMG_8496e7bedd87cd930e4ba4897b3c68c8 |
---|---|
oai_identifier_str |
oai:repositorio.ufmg.br:1843/60506 |
network_acronym_str |
UFMG |
network_name_str |
Repositório Institucional da UFMG |
repository_id_str |
|
spelling |
2023-11-06T20:15:41Z2023-11-06T20:15:41Z202212e05469711910.1136/bmjopen-2021-05469720446055http://hdl.handle.net/1843/60506Objective: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.engUniversidade Federal de Minas GeraisUFMGBrasilMED - DEPARTAMENTO DE GINECOLOGIA OBSTETRÍCIABMJ OpenPregnancyHypertensionMetabolomicsPregnancyhypertensionMetabolomicsPrediction of pregnancy-related hypertensive disorders using metabolomics: a systematic reviewinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://pubmed.ncbi.nlm.nih.gov/35470187/Jussara de Souza Mayrink NovaisDebora f LeiteGuilherme m NobregaMaria Laura CostaJose Guilherme Cecattiapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/60506/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALPrediction of pregnancy-related pdfa.pdfPrediction of pregnancy-related pdfa.pdfapplication/pdf1021957https://repositorio.ufmg.br/bitstream/1843/60506/2/Prediction%20of%20pregnancy-related%20pdfa.pdf8cc2231a54199a823a4146d065eb155eMD521843/605062023-11-06 20:43:28.898oai:repositorio.ufmg.br:1843/60506Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-11-06T23:43:28Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review |
title |
Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review |
spellingShingle |
Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review Jussara de Souza Mayrink Novais Pregnancy hypertension Metabolomics Pregnancy Hypertension Metabolomics |
title_short |
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_sort |
Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review |
author |
Jussara de Souza Mayrink Novais |
author_facet |
Jussara de Souza Mayrink Novais Debora f Leite Guilherme m Nobrega Maria Laura Costa Jose Guilherme Cecatti |
author_role |
author |
author2 |
Debora f Leite Guilherme m Nobrega Maria Laura Costa Jose Guilherme Cecatti |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Jussara de Souza Mayrink Novais Debora f Leite Guilherme m Nobrega Maria Laura Costa Jose Guilherme Cecatti |
dc.subject.por.fl_str_mv |
Pregnancy hypertension Metabolomics |
topic |
Pregnancy hypertension Metabolomics Pregnancy Hypertension Metabolomics |
dc.subject.other.pt_BR.fl_str_mv |
Pregnancy Hypertension Metabolomics |
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. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2023-11-06T20:15:41Z |
dc.date.available.fl_str_mv |
2023-11-06T20:15:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/60506 |
dc.identifier.doi.pt_BR.fl_str_mv |
10.1136/bmjopen-2021-054697 |
dc.identifier.issn.pt_BR.fl_str_mv |
20446055 |
identifier_str_mv |
10.1136/bmjopen-2021-054697 20446055 |
url |
http://hdl.handle.net/1843/60506 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.none.fl_str_mv |
BMJ Open |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
MED - DEPARTAMENTO DE GINECOLOGIA OBSTETRÍCIA |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
instname_str |
Universidade Federal de Minas Gerais (UFMG) |
instacron_str |
UFMG |
institution |
UFMG |
reponame_str |
Repositório Institucional da UFMG |
collection |
Repositório Institucional da UFMG |
bitstream.url.fl_str_mv |
https://repositorio.ufmg.br/bitstream/1843/60506/1/License.txt https://repositorio.ufmg.br/bitstream/1843/60506/2/Prediction%20of%20pregnancy-related%20pdfa.pdf |
bitstream.checksum.fl_str_mv |
fa505098d172de0bc8864fc1287ffe22 8cc2231a54199a823a4146d065eb155e |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG) |
repository.mail.fl_str_mv |
|
_version_ |
1803589241102401536 |