Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review

Leite, Debora Farias Batista; Morillon, Aude-Claire; Melo Júnior, Elias F.; Souza, Renato T.; McCarthy, Fergus P.; Khashan, Ali S.; Baker, Philip; Kenny, Louise C.; Cecatti, Jose Guilherme

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review

dc.contributor.author	Leite, Debora Farias Batista
dc.contributor.author	Morillon, Aude-Claire
dc.contributor.author	Melo Júnior, Elias F.
dc.contributor.author	Souza, Renato T.
dc.contributor.author	McCarthy, Fergus P.
dc.contributor.author	Khashan, Ali S.
dc.contributor.author	Baker, Philip
dc.contributor.author	Kenny, Louise C.
dc.contributor.author	Cecatti, Jose Guilherme
dc.contributor.funder	Coordenação de Aperfeiçoamento de Pessoal de Nível Superior	en
dc.contributor.funder	Science Foundation Ireland	en
dc.contributor.funder	Bill and Melinda Gates Foundation	en
dc.date.accessioned	2019-10-14T21:48:32Z
dc.date.available	2019-10-14T21:48:32Z
dc.date.issued	2019-08-10
dc.description.abstract	Introduction: To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.Objective: To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition. Data sources: Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.Study eligibility criteria: Cohort or nested case–control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile—as a surrogate for fetal growth restriction—by population-based or customised charts.Study appraisal and synthesis methods Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.Results: A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.Conclusions and implications: Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings. PROSPERO registration number: CRD42018089985.	en
dc.description.sponsorship	Brazilian Federal Agency for Support and Evaluation of Graduate Education (88881.134512/2016-01, 88881.134095/2016-01); Brazilian National Research Council (401636/2013-5); Bill and Melinda Gates Foundation (grant OPP1107597; 'Grand Challenges Brazil: Reducing the burden of preterm birth' (number 05/2013))	en
dc.description.status	Peer reviewed	en
dc.description.version	Published Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.articleid	e031238	en
dc.identifier.citation	Leite, D. F. B., Morillon, A.-C., Melo Júnior, E. F., Souza, R. T., McCarthy, F. P., Khashan, A., S., Baker, P., Kenny, L. C. and Cecatti, J. G. (2019) 'Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review', BMJ Open, 9(8), e031238.(14pp.). DOI: 10.1136/bmjopen-2019-031238	en
dc.identifier.doi	10.1136/bmjopen-2019-031238	en
dc.identifier.eissn	2044-6055
dc.identifier.endpage	14	en
dc.identifier.issued	8	en
dc.identifier.journaltitle	BMJ Open	en
dc.identifier.startpage	1	en
dc.identifier.uri	https://hdl.handle.net/10468/8754
dc.identifier.volume	9	en
dc.language.iso	en	en
dc.publisher	BMJ Publishing Group	en
dc.relation.uri	https://bmjopen.bmj.com/content/9/8/e031238
dc.rights	©Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. 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/.	en
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en
dc.subject	Predictive accuracy	en
dc.subject	Metabolomics	en
dc.subject	Small-for-gestational-age (SGA)	en
dc.title	Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review	en
dc.type	Article (peer-reviewed)	en