Summary of Study ST001890

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 PR001190. The data can be accessed directly via it's Project DOI: 10.21228/M8WD84 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.

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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.
Institute
Stanford University
Last NameContrepois
First NameKevin
Address300 Pasteur Dr
Emailkcontrep@stanford.edu
Phone6506664538
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
https://dx.doi.org/10.21228/M8WD84
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:


Combined analysis:

Analysis ID AN003066 AN003067 AN003068 AN003069
Analysis type MS MS MS MS
Chromatography type HILIC HILIC Reversed phase Reversed phase
Chromatography system Thermo Dionex Ultimate 3000 RS Thermo Dionex Ultimate 3000 RS Thermo Dionex Ultimate 3000 RS Thermo Dionex Ultimate 3000 RS
Column EMD Millipore ZIC-HILIC (100 x 2.1 mm, 3.5 um) EMD Millipore ZIC-HILIC (100 x 2.1 mm, 3.5 um) Agilent Zorbax SBaq (50 x 2.1 mm, 1.7 μm) Agilent Zorbax SBaq (50 x 2.1 mm, 1.7 μm)
MS Type ESI ESI ESI ESI
MS instrument type Orbitrap Orbitrap Orbitrap Orbitrap
MS instrument name Thermo Q Exactive HF hybrid Orbitrap Thermo Q Exactive HF hybrid Orbitrap Thermo Q Exactive Orbitrap Thermo Q Exactive Orbitrap
Ion Mode POSITIVE NEGATIVE POSITIVE NEGATIVE
Units MS count MS count MS count MS count

MS:

MS ID:MS002853
Analysis ID:AN003066
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data were acquired on a Thermo Q Exactive HF mass spectrometer for HILIC and a Thermo Q Exactive mass spectrometer for RPLC operated in full MS scan mode. MS/MS data were acquired on quality control samples (QC) consisting of an equimolar mixture of all samples in the study. Data from each mode were independently processed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t show sufficient linearity upon dilution in QC samples (r<0.6) were discarded. Only metabolic features present in >2/3 of the samples were kept for further analysis. Inter- and intra-batch variations was were corrected using the LOESS (locally estimated scatterplot smoothing Local Regression) normalization method on QC injected repetitively along the batches (span = 0.75). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample.
Ion Mode:POSITIVE
  
MS ID:MS002854
Analysis ID:AN003067
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data were acquired on a Thermo Q Exactive HF mass spectrometer for HILIC and a Thermo Q Exactive mass spectrometer for RPLC operated in full MS scan mode. MS/MS data were acquired on quality control samples (QC) consisting of an equimolar mixture of all samples in the study. Data from each mode were independently processed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t show sufficient linearity upon dilution in QC samples (r<0.6) were discarded. Only metabolic features present in >2/3 of the samples were kept for further analysis. Inter- and intra-batch variations was were corrected using the LOESS (locally estimated scatterplot smoothing Local Regression) normalization method on QC injected repetitively along the batches (span = 0.75). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample.
Ion Mode:NEGATIVE
  
MS ID:MS002855
Analysis ID:AN003068
Instrument Name:Thermo Q Exactive Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data were acquired on a Thermo Q Exactive HF mass spectrometer for HILIC and a Thermo Q Exactive mass spectrometer for RPLC operated in full MS scan mode. MS/MS data were acquired on quality control samples (QC) consisting of an equimolar mixture of all samples in the study. Data from each mode were independently processed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t show sufficient linearity upon dilution in QC samples (r<0.6) were discarded. Only metabolic features present in >2/3 of the samples were kept for further analysis. Inter- and intra-batch variations was were corrected using the LOESS (locally estimated scatterplot smoothing Local Regression) normalization method on QC injected repetitively along the batches (span = 0.75). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample.
Ion Mode:POSITIVE
  
MS ID:MS002856
Analysis ID:AN003069
Instrument Name:Thermo Q Exactive Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data were acquired on a Thermo Q Exactive HF mass spectrometer for HILIC and a Thermo Q Exactive mass spectrometer for RPLC operated in full MS scan mode. MS/MS data were acquired on quality control samples (QC) consisting of an equimolar mixture of all samples in the study. Data from each mode were independently processed using Progenesis QI software (v2.3) (Nonlinear Dynamics, Durham, NC). Metabolic features from blanks and those that didn’t show sufficient linearity upon dilution in QC samples (r<0.6) were discarded. Only metabolic features present in >2/3 of the samples were kept for further analysis. Inter- and intra-batch variations was were corrected using the LOESS (locally estimated scatterplot smoothing Local Regression) normalization method on QC injected repetitively along the batches (span = 0.75). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample.
Ion Mode:NEGATIVE
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