First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus

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Resumé

BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.

METHODS: A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m2) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.

RESULTS: Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.

CONCLUSIONS: Current selective screening strategies can potentially be improved by addition of protein predictors.

OriginalsprogEngelsk
Artikelnummere0214457
TidsskriftPLOS ONE
Vol/bind14
Udgave nummer3
Antal sider13
ISSN1932-6203
DOI
StatusUdgivet - 27. mar. 2019

Fingeraftryk

gestational diabetes
Gestational Diabetes
First Pregnancy Trimester
Medical problems
proteomics
Vitronectin
risk factors
Mothers
screening
Proteins
proteins
Screening
Serum
pregnancy complications
pre-eclampsia
cesarean section
Pregnancy Complications
Serum Amyloid P-Component
amyloid
Tandem Mass Spectrometry

Citer dette

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title = "First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus",
abstract = "BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.METHODS: A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m2) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.RESULTS: Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.CONCLUSIONS: Current selective screening strategies can potentially be improved by addition of protein predictors.",
author = "Tina Ravnsborg and Sarah Svaneklink and Andersen, {Lise Lotte T} and Larsen, {Martin R} and Jensen, {Dorte M} and Martin Overgaard",
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First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus. / Ravnsborg, Tina; Svaneklink, Sarah; Andersen, Lise Lotte T; Larsen, Martin R; Jensen, Dorte M; Overgaard, Martin.

I: PLOS ONE, Bind 14, Nr. 3, e0214457, 27.03.2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus

AU - Ravnsborg, Tina

AU - Svaneklink, Sarah

AU - Andersen, Lise Lotte T

AU - Larsen, Martin R

AU - Jensen, Dorte M

AU - Overgaard, Martin

PY - 2019/3/27

Y1 - 2019/3/27

N2 - BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.METHODS: A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m2) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.RESULTS: Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.CONCLUSIONS: Current selective screening strategies can potentially be improved by addition of protein predictors.

AB - BACKGROUND: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.METHODS: A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m2) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.RESULTS: Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.CONCLUSIONS: Current selective screening strategies can potentially be improved by addition of protein predictors.

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