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The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
by
Chaiworapongsa, Tinnakorn
, Done, Bogdan
, Tarca, Adi L.
, Erez, Offer
, Panaitescu, Bogdan
, Pacora, Percy
, Chaemsaithong, Piya
, Hassan, Sonia S.
, Maymon, Eli
, Romero, Roberto
in
Abundance
/ Age
/ Animal models
/ Aptamers
/ Bioindicators
/ Biological activity
/ Biological markers
/ Biology and Life Sciences
/ Biomarkers
/ Biomarkers - blood
/ Biomarkers - metabolism
/ Case-Control Studies
/ Childrens health
/ Control methods
/ Correlation analysis
/ Diagnosis
/ Diagnostic systems
/ Female
/ Genetic aspects
/ Gestation
/ Gestational Age
/ Growth factors
/ Gynecology
/ Health aspects
/ Humans
/ Identification and classification
/ Longitudinal Studies
/ Mathematical models
/ Matrilysin
/ Matrix metalloproteinase
/ Matrix metalloproteinases
/ Medicine
/ Medicine and Health Sciences
/ Metalloproteinase
/ Obstetrics
/ Patients
/ Performance prediction
/ Physical Sciences
/ Placenta
/ Placenta Growth Factor - blood
/ Placenta Growth Factor - metabolism
/ Pre-eclampsia
/ Pre-Eclampsia - blood
/ Pre-Eclampsia - etiology
/ Pre-Eclampsia - metabolism
/ Prediction models
/ Preeclampsia
/ Pregnancy
/ Pregnancy Proteins - metabolism
/ Prospective Studies
/ Proteases
/ Proteins
/ Proteome - metabolism
/ Proteomics
/ Proteomics - methods
/ Research and Analysis Methods
/ Risk Factors
/ Sensitivity
/ Signal transduction
/ Signal Transduction - physiology
/ Vascular endothelial growth factor
/ Vascular Endothelial Growth Factor A - blood
/ Vascular Endothelial Growth Factor A - metabolism
2017
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The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
by
Chaiworapongsa, Tinnakorn
, Done, Bogdan
, Tarca, Adi L.
, Erez, Offer
, Panaitescu, Bogdan
, Pacora, Percy
, Chaemsaithong, Piya
, Hassan, Sonia S.
, Maymon, Eli
, Romero, Roberto
in
Abundance
/ Age
/ Animal models
/ Aptamers
/ Bioindicators
/ Biological activity
/ Biological markers
/ Biology and Life Sciences
/ Biomarkers
/ Biomarkers - blood
/ Biomarkers - metabolism
/ Case-Control Studies
/ Childrens health
/ Control methods
/ Correlation analysis
/ Diagnosis
/ Diagnostic systems
/ Female
/ Genetic aspects
/ Gestation
/ Gestational Age
/ Growth factors
/ Gynecology
/ Health aspects
/ Humans
/ Identification and classification
/ Longitudinal Studies
/ Mathematical models
/ Matrilysin
/ Matrix metalloproteinase
/ Matrix metalloproteinases
/ Medicine
/ Medicine and Health Sciences
/ Metalloproteinase
/ Obstetrics
/ Patients
/ Performance prediction
/ Physical Sciences
/ Placenta
/ Placenta Growth Factor - blood
/ Placenta Growth Factor - metabolism
/ Pre-eclampsia
/ Pre-Eclampsia - blood
/ Pre-Eclampsia - etiology
/ Pre-Eclampsia - metabolism
/ Prediction models
/ Preeclampsia
/ Pregnancy
/ Pregnancy Proteins - metabolism
/ Prospective Studies
/ Proteases
/ Proteins
/ Proteome - metabolism
/ Proteomics
/ Proteomics - methods
/ Research and Analysis Methods
/ Risk Factors
/ Sensitivity
/ Signal transduction
/ Signal Transduction - physiology
/ Vascular endothelial growth factor
/ Vascular Endothelial Growth Factor A - blood
/ Vascular Endothelial Growth Factor A - metabolism
2017
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The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
by
Chaiworapongsa, Tinnakorn
, Done, Bogdan
, Tarca, Adi L.
, Erez, Offer
, Panaitescu, Bogdan
, Pacora, Percy
, Chaemsaithong, Piya
, Hassan, Sonia S.
, Maymon, Eli
, Romero, Roberto
in
Abundance
/ Age
/ Animal models
/ Aptamers
/ Bioindicators
/ Biological activity
/ Biological markers
/ Biology and Life Sciences
/ Biomarkers
/ Biomarkers - blood
/ Biomarkers - metabolism
/ Case-Control Studies
/ Childrens health
/ Control methods
/ Correlation analysis
/ Diagnosis
/ Diagnostic systems
/ Female
/ Genetic aspects
/ Gestation
/ Gestational Age
/ Growth factors
/ Gynecology
/ Health aspects
/ Humans
/ Identification and classification
/ Longitudinal Studies
/ Mathematical models
/ Matrilysin
/ Matrix metalloproteinase
/ Matrix metalloproteinases
/ Medicine
/ Medicine and Health Sciences
/ Metalloproteinase
/ Obstetrics
/ Patients
/ Performance prediction
/ Physical Sciences
/ Placenta
/ Placenta Growth Factor - blood
/ Placenta Growth Factor - metabolism
/ Pre-eclampsia
/ Pre-Eclampsia - blood
/ Pre-Eclampsia - etiology
/ Pre-Eclampsia - metabolism
/ Prediction models
/ Preeclampsia
/ Pregnancy
/ Pregnancy Proteins - metabolism
/ Prospective Studies
/ Proteases
/ Proteins
/ Proteome - metabolism
/ Proteomics
/ Proteomics - methods
/ Research and Analysis Methods
/ Risk Factors
/ Sensitivity
/ Signal transduction
/ Signal Transduction - physiology
/ Vascular endothelial growth factor
/ Vascular Endothelial Growth Factor A - blood
/ Vascular Endothelial Growth Factor A - metabolism
2017
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The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
Journal Article
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
2017
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Overview
Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.
A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.
1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.
Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Age
/ Aptamers
/ Female
/ Humans
/ Identification and classification
/ Medicine
/ Medicine and Health Sciences
/ Patients
/ Placenta
/ Placenta Growth Factor - blood
/ Placenta Growth Factor - metabolism
/ Pregnancy Proteins - metabolism
/ Proteins
/ Research and Analysis Methods
/ Signal Transduction - physiology
/ Vascular endothelial growth factor
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