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Global Urine Metabolic Profiling to Predict Gestational Age 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-resource countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimation of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort (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 that associated with pregnancy progression. We developed a prediction model that predicted GA with high accuracy using the levels of three metabolites (rho = 0.87, .RMSE = 1.58 weeks). These predictions were robust irrespective of whether the pregnancy went to term or ended prematurely. Overall, we demonstrate the feasibility of implementing urine collection for metabolomics analysis in large-scale multi-site studies and we report a predictive model of GA with a potential clinical value.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Urine

SUBMITTER: Kevin Contrepois

PROVIDER: ST001491 | MetabolomicsWorkbench | Sun Sep 27 00:00:00 BST 2020

REPOSITORIES: MetabolomicsWorkbench

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