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.

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Study IDST001491
Study TitleGlobal Urine Metabolic Profiling to Predict Gestational Age in Term and Preterm Pregnancies
Study SummaryAssessment 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 NameContrepois
First NameKevin
Address300 Pasteur Dr
Emailkcontrep@stanford.edu
Phone6506664538
Submit Date2020-09-27
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2022-05-16
Release Version1
Kevin Contrepois Kevin Contrepois
https://dx.doi.org/10.21228/M88H6T
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001009
Project DOI:doi: 10.21228/M88H6T
Project Title:Untargeted urine metabolomics to predict gestational age in term and preterm pregnancies
Project Summary:Multi-site collection of urine early in pregnancy (8-19 weeks) and untargeted LC-MS metabolomics to predict gestational age in term and preterm pregnancies
Institute:Stanford University
Department:Genetics
Last Name:Contrepois
First Name:Kevin
Address:300 Pasteur Dr, ALWAY bldg M302, STANFORD, California, 94305, USA
Email:kcontrep@stanford.edu
Phone:6507239914

Subject:

Subject ID:SU001565
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Female

Factors:

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

mb_sample_id local_sample_id Site GA_delivery GA_sampling
SA12570859Bangladesh 24 17
SA125709162Bangladesh 24 17
SA12571089Bangladesh 29 13
SA125711126Bangladesh 29 13
SA125712146Bangladesh 29 15
SA12571381Bangladesh 29 15
SA12571445Bangladesh 29 15
SA12571520Bangladesh 29 15
SA12571622Bangladesh 30 13
SA12571747Bangladesh 30 13
SA12571814Bangladesh 31 11
SA12571976Bangladesh 31 11
SA12572061Bangladesh 31 15
SA125721163Bangladesh 31 15
SA125722106Bangladesh 31 16
SA125723122Bangladesh 31 16
SA12572470Bangladesh 31 8
SA125725172Bangladesh 31 8
SA125726165Bangladesh 33 17
SA12572712Bangladesh 33 17
SA125728153Bangladesh 41 11
SA12572992Bangladesh 41 11
SA125730114Bangladesh 41 13
SA12573150Bangladesh 41 13
SA12573278Bangladesh 41 13
SA125733105Bangladesh 41 13
SA12573495Bangladesh 41 15
SA125735137Bangladesh 41 15
SA125736112Bangladesh 41 16
SA125737159Bangladesh 41 16
SA12573842Bangladesh 41 16
SA12573926Bangladesh 41 16
SA12574019Bangladesh 41 16
SA12574148Bangladesh 41 16
SA125742102Bangladesh 41 17
SA12574329Bangladesh 41 17
SA12574473Bangladesh 41 18
SA125745169Bangladesh 41 18
SA12574674Bangladesh 41 8
SA125747135Bangladesh 41 8
SA125688118Bangladesh_GAPPS 29 12
SA125689113Bangladesh_GAPPS 32 11
SA125690158Bangladesh_GAPPS 33 12
SA12569117Bangladesh_GAPPS 33 13
SA1256924Bangladesh_GAPPS 33 13
SA12569339Bangladesh_GAPPS 33 15
SA12569430Bangladesh_GAPPS 34 11
SA12569596Bangladesh_GAPPS 35 11
SA125696100Bangladesh_GAPPS 36 11
SA12569758Bangladesh_GAPPS 36 11
SA12569894Bangladesh_GAPPS 39 13
SA12569923Bangladesh_GAPPS 39 19
SA12570064Bangladesh_GAPPS 40 11
SA12570188Bangladesh_GAPPS 40 11
SA125702157Bangladesh_GAPPS 40 12
SA12570362Bangladesh_GAPPS 40 12
SA125704154Bangladesh_GAPPS 40 12
SA12570543Bangladesh_GAPPS 40 12
SA125706132Bangladesh_GAPPS 40 12
SA125707143Bangladesh_GAPPS 40 12
SA1257487Pakistan 28 9
SA125749140Pakistan 28 9
SA12575080Pakistan 32 17
SA12575167Pakistan 32 17
SA12575269Pakistan 32 8
SA12575355Pakistan 32 8
SA12575466Pakistan 32 9
SA125755155Pakistan 32 9
SA12575682Pakistan 33 10
SA125757160Pakistan 33 10
SA12575818Pakistan 33 12
SA12575933Pakistan 33 12
SA12576011Pakistan 33 16
SA125761147Pakistan 33 16
SA12576237Pakistan 33 17
SA12576315Pakistan 33 17
SA125764170Pakistan 33 18
SA125765161Pakistan 33 18
SA12576641Pakistan 33 18
SA125767109Pakistan 33 18
SA125768107Pakistan 39 10
SA12576952Pakistan 39 10
SA12577097Pakistan 39 12
SA125771119Pakistan 39 12
SA12577253Pakistan 39 17
SA12577331Pakistan 39 17
SA125774144Pakistan 39 18
SA12577572Pakistan 39 18
SA125776104Pakistan 39 8
SA125777167Pakistan 39 8
SA12577834Pakistan 39 9
SA125779120Pakistan 39 9
SA12578083Pakistan 39 9
SA125781166Pakistan 39 9
SA125782164Pakistan 40 16
SA1257839Pakistan 40 16
SA125784124Pakistan 40 17
SA125785134Pakistan 40 17
SA125786139Pakistan 40 18
SA125787127Pakistan 40 18
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Collection:

Collection ID:CO001560
Collection Summary:The study comprises a single urine sample for each participant (n = 99) that was collected at a prenatal visit after ultrasound confirmed a gestation < 20 weeks. Ultrasound imaging was performed by trained sonologists in compliance with standard-of-care. All study sites employed a uniform method of GA assessment, urine collection and handling. Urine samples were aliquoted and frozen at -80°C within 2 hours. Deidentified urine aliquots were shipped on dry ice from each biorepository to Stanford University as a single batch and under continuous temperature monitoring. Urine samples from 20 healthy pregnancies collected between 8 and 19 weeks of gestation at the Lucile Packard Children’s Hospital at Stanford University, served as the validation cohort.
Sample Type:Urine
Storage Conditions:-80℃

Treatment:

Treatment ID:TR001580
Treatment Summary:There was no treatment.

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.

Combined analysis:

Analysis ID AN002470 AN002471 AN002472 AN002473
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 SeQuant ZIC-HILIC (100 x 2.1mm,3.5um) SeQuant ZIC-HILIC (100 x 2.1mm,3.5um) Hypersil GOLD (150 x 2.1mm,1.9um) Hypersil GOLD (150 x 2.1mm,1.9um)
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 HF hybrid Orbitrap Thermo Q Exactive HF hybrid Orbitrap
Ion Mode POSITIVE NEGATIVE POSITIVE NEGATIVE
Units MS count MS Counts MS Counts MS Counts

Chromatography:

Chromatography ID:CH001810
Chromatography Summary:HILIC experiments were performed using a ZIC-HILIC column 2.1x100 mm, 3.5μm, 200Å (Merck Millipore) and mobile phase solvents consisting of 10mM ammonium acetate in 50/50 acetonitrile/water (A) and 10 mM ammonium acetate in 95/5 acetonitrile/water (B).(Contrepois et al., 2015)
Instrument Name:Thermo Dionex Ultimate 3000 RS
Column Name:SeQuant ZIC-HILIC (100 x 2.1mm,3.5um)
Column Temperature:40
Flow Rate:0.5 ml/min
Solvent A:95% acetonitrile/5% water; 10 mM ammonium acetate
Solvent B:95% acetonitrile/5% water; 10 mM ammonium acetate
Chromatography Type:HILIC
  
Chromatography ID:CH001811
Chromatography Summary:RPLC experiments were performed using a Hypersil GOLD column 2.1 x 150 mm, 1.9 µm, 175Å (Thermo Scientific) and mobile phase solvents consisting of 0.06% acetic acid in water (A) and 0.06% acetic acid in methanol (B). (Contrepois et al., 2015)
Chromatography Comments:Hypersil GOLD column 2.1 x 150 mm, 1.9 µm, 175Å (Thermo Scientific)
Instrument Name:Thermo Dionex Ultimate 3000 RS
Column Name:Hypersil GOLD (150 x 2.1mm,1.9um)
Column Temperature:60
Flow Rate:0.6 ml/min
Solvent A:100% water; 0.06% acetic acid
Solvent B:100% methanol; 0.06% acetic acid
Chromatography Type:Reversed phase

MS:

MS ID:MS002290
Analysis ID:AN002470
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not 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 were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification.
Ion Mode:POSITIVE
Capillary Temperature:375C
Capillary Voltage:3.4kV
Collision Energy:25 & 35 NCE
Collision Gas:N2
Dry Gas Temp:310C
  
MS ID:MS002291
Analysis ID:AN002471
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not 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 were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification.
Ion Mode:NEGATIVE
Capillary Temperature:375C
Capillary Voltage:3.4kV
Collision Energy:25 & 35 NCE
Collision Gas:N2
Dry Gas Temp:310C
  
MS ID:MS002292
Analysis ID:AN002472
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not 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 were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification.
Ion Mode:POSITIVE
Capillary Temperature:375C
Capillary Voltage:3.4kV
Collision Energy:25 & 50 NCE
Collision Gas:N2
Dry Gas Temp:310C
  
MS ID:MS002293
Analysis ID:AN002473
Instrument Name:Thermo Q Exactive HF hybrid Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Data processing. Data from each mode were independently analyzed using Progenesis QI software (v2.3) (Nonlinear Dynamics). Metabolic features from blanks and that did not 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 were corrected by applying locally estimated scatterplot smoothing local regression (LOESS) on pooled samples injected repetitively along the batches (span = 0.75). Data were acquired in four batches for HILIC and RPLC modes. Dilution effects were corrected using probabilistic quotient normalization (PQN) (Rosen Vollmar et al., 2019). Missing values were imputed by drawing from a random distribution of low values in the corresponding sample. Data from each mode were then merged, producing a dataset containing 6,630 metabolic features. Metabolite abundances were reported as spectral counts. Metabolic feature annotation. Peak annotation was first performed by matching experimental m/z, retention time and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases including HMDB (http://www.hmdb.ca/), MoNA (http://mona.fiehnlab.ucdavis.edu/) and MassBank (http://www.massbank.jp/) using the R package ‘MetID’ (v0.2.0) (Shen et al., 2019). Briefly, metabolic feature tables from Progenesis QI were matched to fragmentation spectra with a m/z and a retention time window of ± 15 ppm and ± 30 s (HILIC) and ± 20 s (RPLC), respectively. When multiple MS/MS spectra match a single metabolic feature, all matched MS/MS spectra were used for the identification. Next, MS1 and MS2 pairs were searched against public databases and a similarity score was calculated using the forward dot–product algorithm which considers both fragments and intensities (Stein and Scott, 1994). Metabolites were reported if the similarity score was above 0.4. Spectra from metabolic features of interest important in random forest models (see below) were further investigated manually to confirm identification.
Ion Mode:NEGATIVE
Capillary Temperature:375C
Capillary Voltage:3.4kV
Collision Energy:25 & 50 NCE
Collision Gas:N2
Dry Gas Temp:310C
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