Summary of Study ST001890

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001190. The data can be accessed directly via it's Project DOI: 10.21228/M8WD84 This work is supported by NIH grant, U2C- DK119886.


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.

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Study IDST001890
Study TitleMultiomics Longitudinal Modeling of Preeclamptic Pregnancies (part II)
Study SummaryPreeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC=0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC=0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.
Stanford University
Last NameContrepois
First NameKevin
Address300 Pasteur Dr
Submit Date2021-07-26
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2022-11-23
Release Version1
Kevin Contrepois Kevin Contrepois application/zip

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Project ID:PR001190
Project DOI:doi: 10.21228/M8WD84
Project Title:Preeclampsia and plasma metabolomics
Project Summary:Longitudinal untargeted plasma metabolomics of pregnant women with preeclampsia
Institute:Stanford University
Last Name:Contrepois
First Name:Kevin
Address:300 Pasteur Dr