| Téléchargement | - Voir la version finale : Prediction of gestational age using urinary metabolites in term and preterm pregnancies (PDF, 7.7 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1038/s41598-022-11866-6 |
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| Auteur | Rechercher : Contrepois, Kévin; Rechercher : Chen, Songjie; Rechercher : Ghaemi, Mohammad S.1; Rechercher : Wong, Ronald J.; Rechercher : Jehan, Fyezah; Rechercher : Sazawal, Sunil; Rechercher : Baqui, Abdullah H.; Rechercher : Nisar, Muhammad I.; Rechercher : Dhingra, Usha; Rechercher : Khanam, Rasheda; Rechercher : Ilyas, Muhammad; Rechercher : Dutta, Arup; Rechercher : Mehmood, Usma; Rechercher : Deb, Saikat; Rechercher : Hotwani, Aneeta; Rechercher : Ali, Said M.; Rechercher : Rahman, Sayedur; Rechercher : Nizar, Ambreen; Rechercher : Ame, Shaali M.; Rechercher : Muhammad, Sajid; Rechercher : Chauhan, Aishwarya; Rechercher : Khan, Waqasuddin; Rechercher : Raqib, Rubhana; Rechercher : Das, Sayan; Rechercher : Ahmed, Salahuddin; Rechercher : Hasan, Tarik; Rechercher : Khalid, Javairia; Rechercher : Juma, Mohammed H.; Rechercher : Chowdhury, Nabidul H.; Rechercher : Kabir, Furqan; Rechercher : Aftab, Fahad; Rechercher : Quaiyum, Md Abdul; Rechercher : Manu, Alexander; Rechercher : Yoshida, Sachiyo; Rechercher : Bahl, Rajiv; Rechercher : Rahman, Anisur; Rechercher : Pervin, Jesmin; Rechercher : Price, Joan T.; Rechercher : Rahman, Monjur; Rechercher : Kasaro, Margaret P.; Rechercher : Litch, James A.; Rechercher : Musonda, Patrick; Rechercher : Vwalika, Bellington; Rechercher : Stringer, Jeffrey S. A.; Rechercher : Shaw, Gary; Rechercher : Stevenson, David K.; Rechercher : Aghaeepour, Nima; Rechercher : Snyder, Michael P. |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : Bill and Melinda Gates Foundation; Rechercher : National Institutes of Health; Rechercher : National Institute of Allergy and Infectious Diseases |
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| Format | Texte, Article |
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| Résumé | 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. |
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| Date de publication | 2022-05-16 |
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| Maison d’édition | Springer Science and Business Media LLC |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 52c2b4e3-bf14-441c-b0c3-308f432671df |
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| Enregistrement créé | 2022-05-31 |
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| Enregistrement modifié | 2022-05-31 |
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