Summary of Study ST001827

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 PR001154. The data can be accessed directly via it's Project DOI: 10.21228/M8J97M This work is supported by NIH grant, U2C- DK119886.


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Study IDST001827
Study TitleThe pregnancy metabolome from a multi-ethnic pregnancy cohort
Study SummaryThe PRogramming of Intergenerational Stress Mechanisms (PRISM) study is an urban, ethnically diverse pregnancy cohort that was designed to study a range of chemical and non-chemical stressors in relation to maternal health, pregnancy outcomes, and child development. Pregnant women were enrolled from Boston and New York City hospitals and affiliated prenatal clinics beginning in 2011. Eligibility criteria included English or Spanish-speaking, over 18 years of age at enrollment, and singleton pregnancy. Exclusion criteria included HIV+ status or self-reported drinking ≥7 alcoholic drinks per week before pregnancy or any alcohol after pregnancy recognition
Icahn School of Medicine at Mount Sinai
Last NameWright
First NameRosalind J
Address5 E.98st FL 10th floor
Phone(212) 241-5287
Submit Date2021-06-10
Analysis Type DetailLC-MS
Release Date2021-06-28
Release Version1
Rosalind J Wright Rosalind J Wright application/zip

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Treatment ID:TR001917
Treatment Summary:Maternal blood was collected by venipuncture (mean ± standard deviation (SD): 29.6 ± 4.90 weeks) and serum aliquots were stored at −80℃ until assayed. Untargeted metabolomics analysis was conducted on 100µl of serum at Metabolon, Inc (Durham, NC, USA) with ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). The method utilized an ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds; the extract compound was gradient eluted from a C18 column using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was analyzed with the prior approach but it was chromatographically optimized for more hydrophobic compounds and operated at an overall higher organic content. A third aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range between methods covered 70-1000 m/z. Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Peaks were quantified using area-under-the-curve. Batch adjustment to correct variation resulting from instrument inter-day tuning differences was performed for each compound in run-day blocks by dividing by the median of the values for the experimental samples for each instrument run day, then multiplying these values by the original median. In one serum sample with a lower volume (65µl instead of 80µl), metabolite intensities were scaled accounting for the volume of serum available, under the assumption that metabolite signal intensities scale linearly with the sample volume. We normalized all metabolomic data using first the natural base for log-scaling, thus removing skewness of the data. We then used a Pareto scaling approach, which incorporates a scaling factor equal to the square root of the standard deviation of individual metabolites so that larger fold changes were scaled more than smaller fold changes (Grace and Hudson, 2016). A total of 1,110 biochemicals were detected across all four assays. Potential sample outliers were examined using principal component analysis (PCA), though none were identified. Final data were presented as normalized levels to facilitate both linear and non-linear analyses and to harmonize all variables on a common scale.