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

Jose G. Cecatti; Renato T. Souza; Karolina Sulek; Maria L. Costa; Louise C. Kenny; Lesley M. McCowan; Rodolfo C. Pacagnella; Silas G. Villas-Boas; Jussara Mayrink; Renato Passini; Kleber G. Franchini; Philip N. Baker; Mary A. Parpinelli; Iracema M. Calderon; Bianca F. Cassettari; Janete Vetorazzi; Lucia Pfitscher; Edilberto P Rocha Filho; Débora F. Leite; Francisco E. Feitosa; Carolina L. Costa e Silva; Lucilla Poston; Jenny E. Myers; Nigel A B Simpson; James J. Walker; Gus A. Dekker; Claire T. Roberts

doi:10.1186/s12884-016-1006-9

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

Jose G. Cecatti^*, Renato T. Souza, Karolina Sulek, Maria L. Costa, Louise C. Kenny, Lesley M. McCowan, Rodolfo C. Pacagnella, Silas G. Villas-Boas, Jussara Mayrink, Renato Passini, Kleber G. Franchini, Philip N. Baker, Mary A. Parpinelli, Iracema M. Calderon, Bianca F. Cassettari, Janete Vetorazzi, Lucia Pfitscher, Edilberto P Rocha Filho, Débora F. Leite, Francisco E. FeitosaCarolina L. Costa e Silva, Lucilla Poston, Jenny E. Myers, Nigel A B Simpson, James J. Walker, Gus A. Dekker, Claire T. Roberts

^*Corresponding author for this work

Women & Children's Health

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

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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.

Original language	English
Article number	212
Journal	BMC Pregnancy and Childbirth
Volume	16
Issue number	1
DOIs	https://doi.org/10.1186/s12884-016-1006-9
Publication status	Published - 8 Aug 2016

Keywords

Biological biomarker
Mass spectrometry
Metabolomics
Prediction
Spontaneous preterm birth

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Use of metabolomics for the identification_CECATTI_Accepted 4 Aug2016_GOLD VoRFinal published version, 785 KBLicence: CC BY

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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., Baker, P. N., Parpinelli, M. A., Calderon, I. M., Cassettari, B. F., Vetorazzi, J., Pfitscher, L., Filho, E. P. R., Leite, D. F., ... Roberts, C. T. (2016). Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA. BMC Pregnancy and Childbirth, 16(1), Article 212. https://doi.org/10.1186/s12884-016-1006-9

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title = "Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA",

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.",

keywords = "Biological biomarker, Mass spectrometry, Metabolomics, Prediction, Spontaneous preterm birth",

author = "Cecatti, {Jose G.} and Souza, {Renato T.} and Karolina Sulek and Costa, {Maria L.} and Kenny, {Louise C.} and McCowan, {Lesley M.} and Pacagnella, {Rodolfo C.} and Villas-Boas, {Silas G.} and Jussara Mayrink and Renato Passini and Franchini, {Kleber G.} and Baker, {Philip N.} and Parpinelli, {Mary A.} and Calderon, {Iracema M.} and Cassettari, {Bianca F.} and Janete Vetorazzi and Lucia Pfitscher and Filho, {Edilberto P Rocha} and Leite, {D{\'e}bora F.} and Feitosa, {Francisco E.} and {Costa e Silva}, {Carolina L.} and Lucilla Poston and Myers, {Jenny E.} and Simpson, {Nigel A B} and Walker, {James J.} and Dekker, {Gus A.} and Roberts, {Claire T.}",

year = "2016",

month = aug,

day = "8",

doi = "10.1186/s12884-016-1006-9",

language = "English",

volume = "16",

journal = "BMC Pregnancy and Childbirth",

issn = "1471-2393",

publisher = "BioMed Central",

number = "1",

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Cecatti, JG, Souza, RT, Sulek, K, Costa, ML, Kenny, LC, McCowan, LM, Pacagnella, RC, Villas-Boas, SG, Mayrink, J, Passini, R, Franchini, KG, Baker, PN, Parpinelli, MA, Calderon, IM, Cassettari, BF, Vetorazzi, J, Pfitscher, L, Filho, EPR, Leite, DF, Feitosa, FE, Costa e Silva, CL, Poston, L, Myers, JE, Simpson, NAB, Walker, JJ, Dekker, GA & Roberts, CT 2016, 'Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA', BMC Pregnancy and Childbirth, vol. 16, no. 1, 212. https://doi.org/10.1186/s12884-016-1006-9

TY - JOUR

T1 - Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth

T2 - Preterm SAMBA

AU - Cecatti, Jose G.

AU - Souza, Renato T.

AU - Sulek, Karolina

AU - Costa, Maria L.

AU - Kenny, Louise C.

AU - McCowan, Lesley M.

AU - Pacagnella, Rodolfo C.

AU - Villas-Boas, Silas G.

AU - Mayrink, Jussara

AU - Passini, Renato

AU - Franchini, Kleber G.

AU - Baker, Philip N.

AU - Parpinelli, Mary A.

AU - Calderon, Iracema M.

AU - Cassettari, Bianca F.

AU - Vetorazzi, Janete

AU - Pfitscher, Lucia

AU - Filho, Edilberto P Rocha

AU - Leite, Débora F.

AU - Feitosa, Francisco E.

AU - Costa e Silva, Carolina L.

AU - Poston, Lucilla

AU - Myers, Jenny E.

AU - Simpson, Nigel A B

AU - Walker, James J.

AU - Dekker, Gus A.

AU - Roberts, Claire T.

PY - 2016/8/8

Y1 - 2016/8/8

N2 - 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.

AB - 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.

KW - Biological biomarker

KW - Mass spectrometry

KW - Metabolomics

KW - Prediction

KW - Spontaneous preterm birth

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U2 - 10.1186/s12884-016-1006-9

DO - 10.1186/s12884-016-1006-9

M3 - Article

AN - SCOPUS:84981273829

SN - 1471-2393

VL - 16

JO - BMC Pregnancy and Childbirth

JF - BMC Pregnancy and Childbirth

IS - 1

M1 - 212

ER -

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

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Keywords

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