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The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study
BACKGROUND: 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. METHODS: A case-control longitudinal study was condu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524331/ https://www.ncbi.nlm.nih.gov/pubmed/28738067 http://dx.doi.org/10.1371/journal.pone.0181468 |
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author | Erez, Offer Romero, Roberto Maymon, Eli Chaemsaithong, Piya Done, Bogdan Pacora, Percy Panaitescu, Bogdan Chaiworapongsa, Tinnakorn Hassan, Sonia S. Tarca, Adi L. |
author_facet | Erez, Offer Romero, Roberto Maymon, Eli Chaemsaithong, Piya Done, Bogdan Pacora, Percy Panaitescu, Bogdan Chaiworapongsa, Tinnakorn Hassan, Sonia S. Tarca, Adi L. |
author_sort | Erez, Offer |
collection | PubMed |
description | BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-5524331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55243312017-08-07 The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study Erez, Offer Romero, Roberto Maymon, Eli Chaemsaithong, Piya Done, Bogdan Pacora, Percy Panaitescu, Bogdan Chaiworapongsa, Tinnakorn Hassan, Sonia S. Tarca, Adi L. PLoS One Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Public Library of Science 2017-07-24 /pmc/articles/PMC5524331/ /pubmed/28738067 http://dx.doi.org/10.1371/journal.pone.0181468 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Erez, Offer Romero, Roberto Maymon, Eli Chaemsaithong, Piya Done, Bogdan Pacora, Percy Panaitescu, Bogdan Chaiworapongsa, Tinnakorn Hassan, Sonia S. Tarca, Adi L. The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524331/ https://www.ncbi.nlm.nih.gov/pubmed/28738067 http://dx.doi.org/10.1371/journal.pone.0181468 |
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