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Prediction of gestational age using urinary metabolites in term and preterm pregnancies
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow a...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110694/ https://www.ncbi.nlm.nih.gov/pubmed/35577875 http://dx.doi.org/10.1038/s41598-022-11866-6 |
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author | Contrepois, Kévin Chen, Songjie Ghaemi, Mohammad S. Wong, Ronald J. Jehan, Fyezah Sazawal, Sunil Baqui, Abdullah H. Stringer, Jeffrey S. A. Rahman, Anisur Nisar, Muhammad I. Dhingra, Usha Khanam, Rasheda Ilyas, Muhammad Dutta, Arup Mehmood, Usma Deb, Saikat Hotwani, Aneeta Ali, Said M. Rahman, Sayedur Nizar, Ambreen Ame, Shaali M. Muhammad, Sajid Chauhan, Aishwarya Khan, Waqasuddin Raqib, Rubhana Das, Sayan Ahmed, Salahuddin Hasan, Tarik Khalid, Javairia Juma, Mohammed H. Chowdhury, Nabidul H. Kabir, Furqan Aftab, Fahad Quaiyum, Abdul Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv Pervin, Jesmin Price, Joan T. Rahman, Monjur Kasaro, Margaret P. Litch, James A. Musonda, Patrick Vwalika, Bellington Shaw, Gary Stevenson, David K. Aghaeepour, Nima Snyder, Michael P. |
author_facet | Contrepois, Kévin Chen, Songjie Ghaemi, Mohammad S. Wong, Ronald J. Jehan, Fyezah Sazawal, Sunil Baqui, Abdullah H. Stringer, Jeffrey S. A. Rahman, Anisur Nisar, Muhammad I. Dhingra, Usha Khanam, Rasheda Ilyas, Muhammad Dutta, Arup Mehmood, Usma Deb, Saikat Hotwani, Aneeta Ali, Said M. Rahman, Sayedur Nizar, Ambreen Ame, Shaali M. Muhammad, Sajid Chauhan, Aishwarya Khan, Waqasuddin Raqib, Rubhana Das, Sayan Ahmed, Salahuddin Hasan, Tarik Khalid, Javairia Juma, Mohammed H. Chowdhury, Nabidul H. Kabir, Furqan Aftab, Fahad Quaiyum, Abdul Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv Pervin, Jesmin Price, Joan T. Rahman, Monjur Kasaro, Margaret P. Litch, James A. Musonda, Patrick Vwalika, Bellington Shaw, Gary Stevenson, David K. Aghaeepour, Nima Snyder, Michael P. |
author_sort | Contrepois, Kévin |
collection | PubMed |
description | Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value. |
format | Online Article Text |
id | pubmed-9110694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91106942022-05-18 Prediction of gestational age using urinary metabolites in term and preterm pregnancies Contrepois, Kévin Chen, Songjie Ghaemi, Mohammad S. Wong, Ronald J. Jehan, Fyezah Sazawal, Sunil Baqui, Abdullah H. Stringer, Jeffrey S. A. Rahman, Anisur Nisar, Muhammad I. Dhingra, Usha Khanam, Rasheda Ilyas, Muhammad Dutta, Arup Mehmood, Usma Deb, Saikat Hotwani, Aneeta Ali, Said M. Rahman, Sayedur Nizar, Ambreen Ame, Shaali M. Muhammad, Sajid Chauhan, Aishwarya Khan, Waqasuddin Raqib, Rubhana Das, Sayan Ahmed, Salahuddin Hasan, Tarik Khalid, Javairia Juma, Mohammed H. Chowdhury, Nabidul H. Kabir, Furqan Aftab, Fahad Quaiyum, Abdul Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv Pervin, Jesmin Price, Joan T. Rahman, Monjur Kasaro, Margaret P. Litch, James A. Musonda, Patrick Vwalika, Bellington Shaw, Gary Stevenson, David K. Aghaeepour, Nima Snyder, Michael P. Sci Rep Article Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value. Nature Publishing Group UK 2022-05-16 /pmc/articles/PMC9110694/ /pubmed/35577875 http://dx.doi.org/10.1038/s41598-022-11866-6 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Contrepois, Kévin Chen, Songjie Ghaemi, Mohammad S. Wong, Ronald J. Jehan, Fyezah Sazawal, Sunil Baqui, Abdullah H. Stringer, Jeffrey S. A. Rahman, Anisur Nisar, Muhammad I. Dhingra, Usha Khanam, Rasheda Ilyas, Muhammad Dutta, Arup Mehmood, Usma Deb, Saikat Hotwani, Aneeta Ali, Said M. Rahman, Sayedur Nizar, Ambreen Ame, Shaali M. Muhammad, Sajid Chauhan, Aishwarya Khan, Waqasuddin Raqib, Rubhana Das, Sayan Ahmed, Salahuddin Hasan, Tarik Khalid, Javairia Juma, Mohammed H. Chowdhury, Nabidul H. Kabir, Furqan Aftab, Fahad Quaiyum, Abdul Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv Pervin, Jesmin Price, Joan T. Rahman, Monjur Kasaro, Margaret P. Litch, James A. Musonda, Patrick Vwalika, Bellington Shaw, Gary Stevenson, David K. Aghaeepour, Nima Snyder, Michael P. Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title | Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title_full | Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title_fullStr | Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title_full_unstemmed | Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title_short | Prediction of gestational age using urinary metabolites in term and preterm pregnancies |
title_sort | prediction of gestational age using urinary metabolites in term and preterm pregnancies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110694/ https://www.ncbi.nlm.nih.gov/pubmed/35577875 http://dx.doi.org/10.1038/s41598-022-11866-6 |
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