Summary of Study ST001430
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 PR000918. The data can be accessed directly via it's Project DOI: 10.21228/M81H58 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 | ST001430 |
Study Title | Metabolic dynamics and prediction og gestational ange and time to delivery in pregant women |
Study Summary | Metabolism during pregnancy is a constantly changing yet precisely programmed process, the failure of which may have devastating consequences for the fetus. To capture in high resolution the sequence of metabolic events underlying the normal human pregnancy, we carried out an untargeted metabolome investigation on 784 weekly blood samples collected from 30 Danish pregnant women. The study revealed extensive metabolome alterations over the course of normal pregnancy: of 9,651 detected metabolic features, 4,995 were significantly changed (FDR < 0.05). Many metabolic changes were timed precisely according to pregnancy progression so that the overall metabolic profile demonstrated a highly choreographed pattern. Using machine-learning methods, we were able to build a linear models with five metabolites (four steroids and one phospholipid) that predicts gestational age with high accuracy (Pearson correlation coefficient, R = 0.95). |
Institute | Stanford University |
Laboratory | Snyder lab |
Last Name | Liang |
First Name | Liang |
Address | Alway M339, 300 Pasteur Drive, Palo Alto, California, 94305, USA |
liangtro@stanford.edu | |
Phone | +1 8167852490 |
Submit Date | 2019-08-30 |
Raw Data Available | Yes |
Raw Data File Type(s) | mzXML |
Analysis Type Detail | LC-MS |
Release Date | 2020-07-24 |
Release Version | 1 |
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Project:
Project ID: | PR000918 |
Project DOI: | doi: 10.21228/M81H58 |
Project Title: | Metabolic dynamics and prediction of gestational age and time to delivery in pregnant women |
Project Summary: | Metabolism during pregnancy is a constantly changing yet precisely programmed process, the failure of which may have devastating consequences for the fetus. To capture in high resolution the sequence of metabolic events underlying the normal human pregnancy, we carried out an untargeted metabolome investigation on 784 weekly blood samples (3 outlier samples are removed) collected from 30 Danish pregnant women. The study revealed extensive metabolome alterations over the course of normal pregnancy: of 9,651 detected metabolic features, 4,995 were significantly changed (FDR < 0.05). Many metabolic changes were timed precisely according to pregnancy progression so that the overall metabolic profile demonstrated a highly choreographed pattern. Using machine-learning methods, we were able to build a linear models with five metabolites (four steroids and one phospholipid) that predicts gestational age with high accuracy (Pearson correlation coefficient, R = 0.95). |
Institute: | Stanford University |
Last Name: | Liang |
First Name: | Liang |
Address: | Alway M339, 300 Pasteur Drive, Palo Alto, California, 94305, USA |
Email: | liangtro@stanford.edu |
Phone: | 8167852490 |
Publications: | https://doi.org/10.1016/j.cell.2020.05.002 |