Prediction of gestational age using urinary metabolites in term and preterm pregnancies.

Kévin Contrepois ; Songjie Chen ; Mohammad S Ghaemi ; Ronald J Wong ; Fyezah Jehan ; Sunil Sazawal ; Abdullah H Baqui ; Jeffrey SA Stringer ; Anisur Rahman ; Muhammad I Nisar ; +40 more... Usha Dhingra ; Rasheda Khanam ; Muhammad Ilyas ; Arup Dutta ; Usma Mehmood ; Saikat Deb ; Aneeta Hotwani ; Said M Ali ; Sayedur Rahman ; Ambreen Nizar ; Shaali M Ame ; Sajid Muhammad ; Aishwarya Chauhan ; Waqasuddin Khan ; Rubhana Raqib ; Sayan Das ; Salahuddin Ahmed ; Tarik Hasan ; Javairia Khalid ; Mohammed H Juma ; Nabidul H Chowdhury ; Furqan Kabir ; Fahad Aftab ; Abdul Quaiyum ; Alexander Manu ; Sachiyo Yoshida ; Rajiv Bahl ; Jesmin Pervin ; Joan T Price ; Monjur Rahman ; Margaret P Kasaro ; James A Litch ; Patrick Musonda ; Bellington Vwalika ; Alliance for Maternal and Newborn Health Improvement (AMANHI) ; Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) ; Gary Shaw ; David K Stevenson ; Nima Aghaeepour ; Michael P Snyder ; (2022) Prediction of gestational age using urinary metabolites in term and preterm pregnancies. Scientific reports, 12 (1). 8033-. ISSN 2045-2322 DOI: 10.1038/s41598-022-11866-6
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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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