Prediction of gestational age using urinary metabolites in term and preterm pregnancies
Abstract 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 tha...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-11866-6 |
| _version_ | 1818548265246785536 |
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| author | Kévin Contrepois Songjie Chen Mohammad S. Ghaemi Ronald J. Wong The Alliance for Maternal and Newborn Health Improvement (AMANHI) The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) Gary Shaw David K. Stevenson Nima Aghaeepour Michael P. Snyder |
| author_facet | Kévin Contrepois Songjie Chen Mohammad S. Ghaemi Ronald J. Wong The Alliance for Maternal and Newborn Health Improvement (AMANHI) The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) Gary Shaw David K. Stevenson Nima Aghaeepour Michael P. Snyder |
| author_sort | Kévin Contrepois |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T08:17:39Z |
| format | Article |
| id | doaj.art-e1e0213ab36143bcb8f2e51065371941 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T08:17:39Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e1e0213ab36143bcb8f2e510653719412022-12-22T00:31:30ZengNature PortfolioScientific Reports2045-23222022-05-0112111110.1038/s41598-022-11866-6Prediction of gestational age using urinary metabolites in term and preterm pregnanciesKévin Contrepois0Songjie Chen1Mohammad S. Ghaemi2Ronald J. Wong3The Alliance for Maternal and Newborn Health Improvement (AMANHI)The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS)Gary Shaw4David K. Stevenson5Nima Aghaeepour6Michael P. Snyder7Department of Genetics, Stanford University School of MedicineDepartment of Genetics, Stanford University School of MedicineDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of MedicineDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of MedicineDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of MedicineDepartment of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of MedicineDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of MedicineDepartment of Genetics, Stanford University School of MedicineAbstract 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.https://doi.org/10.1038/s41598-022-11866-6 |
| spellingShingle | Kévin Contrepois Songjie Chen Mohammad S. Ghaemi Ronald J. Wong The Alliance for Maternal and Newborn Health Improvement (AMANHI) The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) Gary Shaw David K. Stevenson Nima Aghaeepour Michael P. Snyder Prediction of gestational age using urinary metabolites in term and preterm pregnancies Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-11866-6 |
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