Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA

Show simple item record

dc.contributor.author Cecatti, Jose G.
dc.contributor.author Souza, Renato T.
dc.contributor.author Sulek, Karolina
dc.contributor.author Costa, Maria L.
dc.contributor.author Kenny, Louise C.
dc.contributor.author McCowan, Lesley M. E.
dc.contributor.author Pacagnella, Rodolfo C.
dc.contributor.author Villas-Boas, Silas G.
dc.contributor.author Mayrink, Jussara
dc.contributor.author Passini, Renato
dc.contributor.author Franchini, Kleber G.
dc.contributor.author Baker, Philip N.
dc.date.accessioned 2017-06-21T11:01:23Z
dc.date.available 2017-06-21T11:01:23Z
dc.date.issued 2016-08-08
dc.identifier.citation Cecatti, J. G., Souza, R. T., Sulek, K., Costa, M. L., Kenny, L. C., McCowan, L. M., Pacagnella, R. C., Villas-Boas, S. G., Mayrink, J., Passini, R., Franchini, K. G. and Baker, P. N. (2016) 'Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA', BMC Pregnancy and Childbirth, 16, 212 (9pp). doi: 10.1186/s12884-016-1006-9 en
dc.identifier.volume 16
dc.identifier.startpage 1
dc.identifier.endpage 9
dc.identifier.issn 1471-2393
dc.identifier.uri http://hdl.handle.net/10468/4120
dc.identifier.doi 10.1186/s12884-016-1006-9
dc.description.abstract Background: Spontaneous preterm birth is a complex syndrome with multiple pathways interactions determining its occurrence, including genetic, immunological, physiologic, biochemical and environmental factors. Despite great worldwide efforts in preterm birth prevention, there are no recent effective therapeutic strategies able to decrease spontaneous preterm birth rates or their consequent neonatal morbidity/mortality. The Preterm SAMBA study will associate metabolomics technologies to identify clinical and metabolite predictors for preterm birth. These innovative and unbiased techniques might be a strategic key to advance spontaneous preterm birth prediction. Methods/design: Preterm SAMBA study consists of a discovery phase to identify biophysical and untargeted metabolomics from blood and hair samples associated with preterm birth, plus a validation phase to evaluate the performance of the predictive modelling. The first phase, a case–control study, will randomly select 100 women who had a spontaneous preterm birth (before 37 weeks) and 100 women who had term birth in the Cork Ireland and Auckland New Zealand cohorts within the SCOPE study, an international consortium aimed to identify potential metabolomic predictors using biophysical data and blood samples collected at 20 weeks of gestation. The validation phase will recruit 1150 Brazilian pregnant women from five participant centres and will collect blood and hair samples at 20 weeks of gestation to evaluate the performance of the algorithm model (sensitivity, specificity, predictive values and likelihood ratios) in predicting spontaneous preterm birth (before 34 weeks, with a secondary analysis of delivery before 37 weeks). Discussion: The Preterm SAMBA study intends to step forward on preterm birth prediction using metabolomics techniques, and accurate protocols for sample collection among multi-ethnic populations. The use of metabolomics in medical science research is innovative and promises to provide solutions for disorders with multiple complex underlying determinants such as spontaneous preterm birth. en
dc.description.sponsorship Brazilian National Research Council (Grand Challenges Brazil: Reducing the burden of preterm birth” number 05/2013); Bill and Melinda Gates Foundation (Award 401636/2013-5); Science Foundation Ireland (Program Grant for INFANT (12/RC/2272)); New Enterprise Research Fund; Foundation for Research Science and Technology; Health Research Council (04/198); Evelyn Bond Fund; Auckland District Health Board Charitable Trust; Health Research Board (CSA/2007/2) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher BioMed Central en
dc.relation.uri https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-016-1006-9
dc.rights © 2016, the Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Spontaneous preterm birth en
dc.subject Metabolomics en
dc.subject Prediction en
dc.subject Biological biomarker en
dc.subject Mass spectrometry en
dc.title Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Louise C. Kenny,Irish Centre for Fetal and Neonatal Translational Research (INFANT), Obstetrics and Gynaecology, University College Cork, Cork, Ireland +353-21-490-3000 E-mail: L.Kenny@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Brazilian National Research Council
dc.contributor.funder Bill and Melinda Gates Foundation
dc.contributor.funder Science Foundation Ireland
dc.contributor.funder Enterprise Ireland
dc.contributor.funder Foundation for Research Science and Technology
dc.contributor.funder Health Research Council of New Zealand
dc.contributor.funder Evelyn Bond Fund
dc.contributor.funder Auckland District Health Board Charitable Trust, New Zealand
dc.contributor.funder Health Research Board
dc.description.status Peer reviewed en
dc.identifier.journaltitle BMC Pregnancy and Childbirth en
dc.internal.IRISemailaddress L.Kenny@ucc.ie en
dc.identifier.articleid 212


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2016, the Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Except where otherwise noted, this item's license is described as © 2016, the Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement