Summary of Study ST001827

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 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 ID	ST001827
Study Title	The pregnancy metabolome from a multi-ethnic pregnancy cohort
Study Summary	The 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
Institute	Icahn School of Medicine at Mount Sinai
Last Name	Wright
First Name	Rosalind J
Address	5 E.98st FL 10th floor
Email	rosalind.wright@mssm.edu
Phone	(212) 241-5287
Submit Date	2021-06-10
Analysis Type Detail	LC-MS
Release Date	2021-06-28
Release Version	1

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Project:

Project ID:	PR001154
Project DOI:	doi: 10.21228/M8J97M
Project Title:	The pregnancy metabolome from a multi-ethnic pregnancy cohort
Project Type:	cohort study
Project Summary:	The 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.
Institute:	Icahn School of Medicine at Mount Sinai
Last Name:	Wright
First Name:	Rosalind J
Address:	5 E 98st FL 10th floor
Email:	rosalind.wright@mssm.edu
Phone:	(212) 241-5287
Funding Source:	R01 HL095606, R01 HL114396, UG3 OD023337, P30 ES023515, UL1 TR001433
Contributors:	Robert O Wright, Elena Colicino, Megan M Niedzwiecki

Subject:

Subject ID:	SU001904
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606
Species Group:	Mammals

Factors:

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

mb_sample_id	local_sample_id	gestage_serum
SA169371	id_358	11
SA169372	id_292	14
SA169373	id_378	14
SA169374	id_367	16
SA169375	id_343	18
SA169376	id_217	18
SA169377	id_317	19
SA169378	id_127	19
SA169379	id_374	20
SA169380	id_379	21
SA169381	id_366	21
SA169382	id_329	22
SA169383	id_237	22
SA169384	id_356	22
SA169385	id_312	23
SA169386	id_164	23
SA169387	id_324	23
SA169388	id_15	23
SA169389	id_204	24
SA169390	id_198	24
SA169391	id_355	24
SA169392	id_261	24
SA169393	id_112	24
SA169394	id_285	24
SA169395	id_136	24
SA169396	id_107	24
SA169397	id_360	24
SA169398	id_196	24
SA169399	id_140	24
SA169400	id_197	24
SA169401	id_69	24
SA169402	id_405	24
SA169403	id_225	24
SA169404	id_210	24
SA169405	id_243	24
SA169406	id_247	24
SA169407	id_305	25
SA169408	id_270	25
SA169409	id_157	25
SA169410	id_331	25
SA169411	id_271	25
SA169412	id_181	25
SA169413	id_273	25
SA169414	id_66	25
SA169415	id_185	25
SA169416	id_389	25
SA169417	id_91	25
SA169418	id_90	25
SA169419	id_172	25
SA169420	id_171	25
SA169421	id_94	25
SA169422	id_173	25
SA169423	id_175	25
SA169424	id_170	25
SA169425	id_83	25
SA169426	id_178	25
SA169427	id_165	25
SA169428	id_325	25
SA169429	id_156	25
SA169430	id_53	25
SA169431	id_293	25
SA169432	id_129	25
SA169433	id_124	25
SA169434	id_200	25
SA169435	id_280	25
SA169436	id_56	25
SA169437	id_216	25
SA169438	id_145	25
SA169439	id_283	25
SA169440	id_28	25
SA169441	id_150	25
SA169442	id_229	25
SA169443	id_294	25
SA169444	id_351	25
SA169445	id_166	26
SA169446	id_235	26
SA169447	id_2	26
SA169448	id_137	26
SA169449	id_142	26
SA169450	id_286	26
SA169451	id_289	26
SA169452	id_97	26
SA169453	id_397	26
SA169454	id_226	26
SA169455	id_303	26
SA169456	id_279	26
SA169457	id_274	26
SA169458	id_123	26
SA169459	id_281	26
SA169460	id_307	26
SA169461	id_153	26
SA169462	id_163	26
SA169463	id_308	26
SA169464	id_85	26
SA169465	id_341	26
SA169466	id_60	26
SA169467	id_190	26
SA169468	id_259	26
SA169469	id_260	26
SA169470	id_188	26

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Collection:

Collection ID:	CO001897
Collection Summary:	At the time of metabolomics profiling (March 2018), 843 women had delivered a live born infant. The analytic sample includes a random subset of 410 mother-child pairs with maternal metabolomics measured during pregnancy (week of serum collection: 25th, 50th, 75th percentiles: 26, 29, 33 weeks).
Sample Type:	Blood (plasma)

Treatment:

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.

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.

Sample Preparation:

Sampleprep ID:	SP001910
Sampleprep Summary:	Following receipt, samples were inventoried and immediately stored at -80℃. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80℃ until processed.

Sampleprep ID:

SP001910

Sampleprep Summary:

Following receipt, samples were inventoried and immediately stored at -80℃. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80℃ until processed.

Combined analysis:

Analysis ID	AN002963
Analysis type	MS
Chromatography type	HILIC
Chromatography system	Thermo Scientific Q-Exactive
Column	SeQuant ZIC-HILIC (150 x 4.6mm,3.5um)
MS Type	ESI
MS instrument type	Orbitrap
MS instrument name	Thermo Q Exactive Orbitrap
Ion Mode	UNSPECIFIED
Units	pmoles/l

Chromatography:

Chromatography ID:	CH002196
Instrument Name:	Thermo Scientific Q-Exactive
Column Name:	SeQuant ZIC-HILIC (150 x 4.6mm,3.5um)
Chromatography Type:	HILIC

MS:

MS ID:	MS002753
Analysis ID:	AN002963
Instrument Name:	Thermo Q Exactive Orbitrap
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	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.
Ion Mode:	UNSPECIFIED