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.

Show all samples  |  Perform analysis on untargeted data  
Download mwTab file (text)   |  Download mwTab file(JSON)   |  Download data files (Contains raw data)
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:

Project:

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
Email:kcontrep@stanford.edu
Phone:6506664538

Subject:

Subject ID:SU001968
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Gestational Age At Sampling Gestational Age At Delivery Preeclampsia
SA175644190240510910 31 1
SA175645190170510810 37 1
SA175646106340510710 38 1
SA175647105580510810 38 1
SA175648105410510810 39 -
SA175649196000510810 39 1
SA175650105020510810 41 -
SA175651106390510910 41 1
SA175652108160511811 30 1
SA175653108360511811 35 1
SA175654108510511811 36 1
SA175655108490511811 37 1
SA175656105610511811 37 1
SA175657108090511811 37 1
SA175658105350511811 38 -
SA175659105280511811 38 -
SA175660105510511811 39 -
SA175661108330511811 39 1
SA175662105150511811 40 -
SA175663107320511811 40 -
SA175664106010511811 40 1
SA175665107220512812 30 1
SA175666107270512812 37 1
SA175667107210512812 39 -
SA175668105470512812 39 -
SA175669107330512812 41 -
SA175670108220513813 33 1
SA175671108030513813 36 1
SA175672108400513813 37 1
SA175673108240513813 37 1
SA175674107360513813 39 1
SA175675107120513813 40 -
SA175676107230513813 40 1
SA175677107130513813 41 -
SA175678108490514814 37 1
SA175679105240514814 38 -
SA175680108330514814 39 1
SA175681108160515815 30 1
SA175682108360515815 35 1
SA175683108510515815 36 1
SA175684105300515815 36 1
SA175685107270515815 37 1
SA175686105580515815 38 1
SA175687105410515815 39 -
SA175688105510515815 39 -
SA175689105150515815 40 -
SA175690105230515815 41 -
SA175691105460515815 41 -
SA175692107330515815 41 -
SA175693101040516816 32 1
SA175694108220516816 33 1
SA175695100560516816 36 1
SA175696108090516816 37 1
SA175697190170516816 37 1
SA175698108240516816 37 1
SA175699108400516816 37 1
SA175700105350516816 38 -
SA175701106340516916 38 1
SA175702105470516816 39 -
SA175703108040516816 39 -
SA175704105380516816 39 -
SA175705101120516816 39 1
SA175706107120516816 40 -
SA175707106010516816 40 1
SA175708106390516916 41 1
SA175709107220517817 30 1
SA175710105050517817 37 -
SA175711105330517817 37 1
SA175712105530517817 37 1
SA175713107360517817 39 1
SA175714196000517917 39 1
SA175715107230517817 40 1
SA175716107130517817 41 -
SA175717108030518818 36 1
SA175718105240518818 38 -
SA175719107210518818 39 -
SA175720105480518818 40 -
SA175721107320518818 40 -
SA175722105030518818 41 -
SA175723108490522822 37 1
SA175724108330522822 39 1
SA175725108160523823 30 1
SA175726101040523823 32 1
SA175727108240523823 37 1
SA175728108360524824 35 1
SA175729108510524824 36 1
SA175730100560524824 36 1
SA175731105530524824 37 1
SA175732108400524824 37 1
SA175733106340524924 38 1
SA175734105470524824 39 -
SA175735107320524824 40 -
SA175736107220525825 30 1
SA175737190170525825 37 1
SA175738105610525825 37 1
SA175739105580525825 38 1
SA175740107360525825 39 1
SA175741196000525925 39 1
SA175742107120525825 40 -
SA175743105150525825 40 -
Showing page 1 of 2     Results:    1  2  Next     Showing results 1 to 100 of 172

Collection:

Collection ID:CO001961
Collection Summary:Urine was collected from pregnant women
Sample Type:Urine

Treatment:

Treatment ID:TR001980
Treatment Summary:N/A

Sample Preparation:

Sampleprep ID:SP001974
Sampleprep Summary:Urine samples were thawed on ice, prepared and analyzed randomly as previously described (Contrepois et al., 2015). Briefly, urine samples were 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 IS.

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 2.1mm,3.5um) EMD Millipore ZIC-HILIC (100 x 2.1mm,3.5um) Agilent Zorbax SBaq (50 x 2.1mm,1.7um) Agilent Zorbax SBaq (50 x 2.1mm,1.7um)
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

Chromatography:

Chromatography ID:CH002270
Chromatography Summary:HILIC experiments were performed using a ZIC-HILIC column 2.1 x 100 mm, 3.5 μm, 200Å (Merck Millipore, Darmstadt, Germany) and mobile phase solvents consisting of 10 mM ammonium acetate in 50/50 acetonitrile/water (A) and 10 mM ammonium acetate in 95/5 acetonitrile/water (B).
Instrument Name:Thermo Dionex Ultimate 3000 RS
Column Name:EMD Millipore ZIC-HILIC (100 x 2.1mm,3.5um)
Solvent A:50% acetonitrile/50% water; 10mM ammonium acetate
Solvent B:95% acetonitrile/5% water; 10mM ammonium acetate
Chromatography Type:HILIC
  
Chromatography ID:CH002271
Chromatography Summary:RPLC experiments were performed using a Zorbax SBaq column 2.1 x 50 mm, 1.7 μm, 100Å (Agilent Technologies, Palo Alto, CA) and mobile phase solvents consisting of 0.06% acetic acid in water (A) and 0.06% acetic acid in methanol (B).
Instrument Name:Thermo Dionex Ultimate 3000 RS
Column Name:Agilent Zorbax SBaq (50 x 2.1mm,1.7um)
Solvent A:100% water; 0.06% acetic acid
Solvent B:100% methanol; 0.06% acetic acid
Chromatography Type:Reversed phase

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
  logo