Summary of Study ST001491
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001009. The data can be accessed directly via it's Project DOI: 10.21228/M88H6T This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001491 |
| Study Title | Global Urine Metabolic Profiling to Predict Gestational Age in Term and Preterm Pregnancies |
| Study Summary | 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. |
| Institute | Stanford University |
| Last Name | Contrepois |
| First Name | Kevin |
| Address | 300 Pasteur Dr |
| kcontrep@stanford.edu | |
| Phone | 6506664538 |
| Submit Date | 2020-09-27 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-05-16 |
| Release Version | 1 |
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Sample Preparation:
| Sampleprep ID: | SP001573 |
| Sampleprep Summary: | Urine aliquots were prepared and analyzed in a random order as previously described (Contrepois et al., 2015). Briefly, frozen urine samples were thawed on ice and centrifuged at 17,000g for 10 min at 4°C. Supernatants (25 µl) were then diluted 1:4 with 75% acetonitrile and 100% water for HILIC- and RPLC-MS experiments, respectively. Each sample was spiked-in with 15 analytical-grade internal standards (IS). Samples for HILIC-MS experiments were further centrifuged at 21,000g for 10 min at 4°C to precipitate proteins. |
