The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study.
<h4>Background</h4>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.<h4>Methods</h4>A case...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0181468 |
id |
doaj-47dbf15c4c6b4dcf9e81b0c058769e1b |
---|---|
record_format |
Article |
spelling |
doaj-47dbf15c4c6b4dcf9e81b0c058769e1b2021-03-04T11:27:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01127e018146810.1371/journal.pone.0181468The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study.Offer ErezRoberto RomeroEli MaymonPiya ChaemsaithongBogdan DonePercy PacoraBogdan PanaitescuTinnakorn ChaiworapongsaSonia S HassanAdi L Tarca<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.https://doi.org/10.1371/journal.pone.0181468 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Offer Erez Roberto Romero Eli Maymon Piya Chaemsaithong Bogdan Done Percy Pacora Bogdan Panaitescu Tinnakorn Chaiworapongsa Sonia S Hassan Adi L Tarca |
spellingShingle |
Offer Erez Roberto Romero Eli Maymon Piya Chaemsaithong Bogdan Done Percy Pacora Bogdan Panaitescu Tinnakorn Chaiworapongsa Sonia S Hassan Adi L Tarca The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. PLoS ONE |
author_facet |
Offer Erez Roberto Romero Eli Maymon Piya Chaemsaithong Bogdan Done Percy Pacora Bogdan Panaitescu Tinnakorn Chaiworapongsa Sonia S Hassan Adi L Tarca |
author_sort |
Offer Erez |
title |
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. |
title_short |
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. |
title_full |
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. |
title_fullStr |
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. |
title_full_unstemmed |
The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. |
title_sort |
prediction of late-onset preeclampsia: results from a longitudinal proteomics study. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2017-01-01 |
description |
<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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. |
url |
https://doi.org/10.1371/journal.pone.0181468 |
work_keys_str_mv |
AT offererez thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT robertoromero thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT elimaymon thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT piyachaemsaithong thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT bogdandone thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT percypacora thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT bogdanpanaitescu thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT tinnakornchaiworapongsa thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT soniashassan thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT adiltarca thepredictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT offererez predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT robertoromero predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT elimaymon predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT piyachaemsaithong predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT bogdandone predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT percypacora predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT bogdanpanaitescu predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT tinnakornchaiworapongsa predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT soniashassan predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy AT adiltarca predictionoflateonsetpreeclampsiaresultsfromalongitudinalproteomicsstudy |
_version_ |
1714803544023891968 |