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Early prediction and longitudinal modeling of preeclampsia from multiomics
Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pre...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768681/ https://www.ncbi.nlm.nih.gov/pubmed/36569558 http://dx.doi.org/10.1016/j.patter.2022.100655 |
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author | Marić, Ivana Contrepois, Kévin Moufarrej, Mira N. Stelzer, Ina A. Feyaerts, Dorien Han, Xiaoyuan Tang, Andy Stanley, Natalie Wong, Ronald J. Traber, Gavin M. Ellenberger, Mathew Chang, Alan L. Fallahzadeh, Ramin Nassar, Huda Becker, Martin Xenochristou, Maria Espinosa, Camilo De Francesco, Davide Ghaemi, Mohammad S. Costello, Elizabeth K. Culos, Anthony Ling, Xuefeng B. Sylvester, Karl G. Darmstadt, Gary L. Winn, Virginia D. Shaw, Gary M. Relman, David A. Quake, Stephen R. Angst, Martin S. Snyder, Michael P. Stevenson, David K. Gaudilliere, Brice Aghaeepour, Nima |
author_facet | Marić, Ivana Contrepois, Kévin Moufarrej, Mira N. Stelzer, Ina A. Feyaerts, Dorien Han, Xiaoyuan Tang, Andy Stanley, Natalie Wong, Ronald J. Traber, Gavin M. Ellenberger, Mathew Chang, Alan L. Fallahzadeh, Ramin Nassar, Huda Becker, Martin Xenochristou, Maria Espinosa, Camilo De Francesco, Davide Ghaemi, Mohammad S. Costello, Elizabeth K. Culos, Anthony Ling, Xuefeng B. Sylvester, Karl G. Darmstadt, Gary L. Winn, Virginia D. Shaw, Gary M. Relman, David A. Quake, Stephen R. Angst, Martin S. Snyder, Michael P. Stevenson, David K. Gaudilliere, Brice Aghaeepour, Nima |
author_sort | Marić, Ivana |
collection | PubMed |
description | Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia. |
format | Online Article Text |
id | pubmed-9768681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97686812022-12-22 Early prediction and longitudinal modeling of preeclampsia from multiomics Marić, Ivana Contrepois, Kévin Moufarrej, Mira N. Stelzer, Ina A. Feyaerts, Dorien Han, Xiaoyuan Tang, Andy Stanley, Natalie Wong, Ronald J. Traber, Gavin M. Ellenberger, Mathew Chang, Alan L. Fallahzadeh, Ramin Nassar, Huda Becker, Martin Xenochristou, Maria Espinosa, Camilo De Francesco, Davide Ghaemi, Mohammad S. Costello, Elizabeth K. Culos, Anthony Ling, Xuefeng B. Sylvester, Karl G. Darmstadt, Gary L. Winn, Virginia D. Shaw, Gary M. Relman, David A. Quake, Stephen R. Angst, Martin S. Snyder, Michael P. Stevenson, David K. Gaudilliere, Brice Aghaeepour, Nima Patterns (N Y) Article Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia. Elsevier 2022-12-09 /pmc/articles/PMC9768681/ /pubmed/36569558 http://dx.doi.org/10.1016/j.patter.2022.100655 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Marić, Ivana Contrepois, Kévin Moufarrej, Mira N. Stelzer, Ina A. Feyaerts, Dorien Han, Xiaoyuan Tang, Andy Stanley, Natalie Wong, Ronald J. Traber, Gavin M. Ellenberger, Mathew Chang, Alan L. Fallahzadeh, Ramin Nassar, Huda Becker, Martin Xenochristou, Maria Espinosa, Camilo De Francesco, Davide Ghaemi, Mohammad S. Costello, Elizabeth K. Culos, Anthony Ling, Xuefeng B. Sylvester, Karl G. Darmstadt, Gary L. Winn, Virginia D. Shaw, Gary M. Relman, David A. Quake, Stephen R. Angst, Martin S. Snyder, Michael P. Stevenson, David K. Gaudilliere, Brice Aghaeepour, Nima Early prediction and longitudinal modeling of preeclampsia from multiomics |
title | Early prediction and longitudinal modeling of preeclampsia from multiomics |
title_full | Early prediction and longitudinal modeling of preeclampsia from multiomics |
title_fullStr | Early prediction and longitudinal modeling of preeclampsia from multiomics |
title_full_unstemmed | Early prediction and longitudinal modeling of preeclampsia from multiomics |
title_short | Early prediction and longitudinal modeling of preeclampsia from multiomics |
title_sort | early prediction and longitudinal modeling of preeclampsia from multiomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768681/ https://www.ncbi.nlm.nih.gov/pubmed/36569558 http://dx.doi.org/10.1016/j.patter.2022.100655 |
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