Summary of Study ST003630

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 PR002243. The data can be accessed directly via it's Project DOI: 10.21228/M8PR8Q 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 IDST003630
Study TitleIndividual glycemic responses to carbohydrates vary and reflect underlying metabolic physiology (Lipidomics)
Study SummaryWe measured PPGRs using continuous glucose monitoring (CGM) in 55 well-phenotyped participants challenged with seven different carbohydrates administered in replicate under standardized conditions. Plasma sample were collected at baseline visit for metabolomics. The ClinicalTrials.gov registration identifier is NCT03919877.
Institute
Stanford University
Last NameMichael
First NameSnyder
Address300 Pasteur Drive, M-344A Stanford, California 94305
Emailmpsnyder@stanford.edu
Phone(650) 723-4668
Submit Date2024-11-18
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2025-01-12
Release Version1
Snyder Michael Snyder Michael
https://dx.doi.org/10.21228/M8PR8Q
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR002243
Project DOI:doi: 10.21228/M8PR8Q
Project Title:Individual glycemic responses to carbohydrates vary and reflect underlying metabolic physiology
Project Summary:Elevated postprandial glycemic responses (PPGRs) are associated with type 2 diabetes and cardiovascular disease. PPGRs to the same foods have been shown to vary between individuals, but the systematic characterization of the underlying physiologic and molecular basis is lacking. We measured PPGRs using continuous glucose monitoring (CGM) in 55 well-phenotyped participants challenged with seven different carbohydrates administered in replicate under standardized conditions. We also measured the effects of preloading a rice meal with fiber, protein, or fat (“mitigators”). To examine the physiologic and molecular basis for inter-individual PPGR differences, we performed gold-standard metabolic tests and multi-omics profiling. We discovered: 1. Postprandial glycemic responses (PPGRs) to different standardized carbohydrate meals vary between individuals. 2. Individuals’ PPGRs are associated with their metabolic phenotypes, including insulin resistance. 3. Individual’s PPGRs can be reduced in magnitude and delayed by premeal mitigators which is associated with their metabolic phenotypes. 4. Individuals can be stratified by their PPGRs to different carbohydrate meals, and PPGR subtypes have distinct metabolic profiles and multi-omics patterns. 5. Individuals’ metabolic phenotype can be inferred from both food-specific PPGRs and baseline omics.
Institute:Stanford University
Department:Genetics
Laboratory:Michael P. Snyder
Last Name:Snyder
First Name:Michael
Address:300 Pasteur Drive, M-344A Stanford, California 94305
Email:mpsnyder@stanford.edu
Phone:(650) 723-4668
Funding Source:NIH

Subject:

Subject ID:SU003760
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Male and female

Factors:

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

mb_sample_id local_sample_id Sample source Time
SA393523XB21_4plasma Baseline
SA393524XB107_5plasma Baseline
SA393525XB95_3plasma Baseline
SA393526XB94_2plasma Baseline
SA393527XB115_1plasma Baseline
SA393528XB107_4plasma Baseline
SA393529XB79_6plasma Baseline
SA393530XB14_3plasma Baseline
SA393531XB76_1plasma Baseline
SA393532XB20_5plasma Baseline
SA393533XB65_1plasma Baseline
SA393534XB21_5plasma Baseline
SA393535XB6_2plasma Baseline
SA393536XB115_2plasma Baseline
SA393537XB38_5plasma Baseline
SA393538XB21_3plasma Baseline
SA393539XB1_3plasma Baseline
SA393540XB59_3plasma Baseline
SA393541XB111_3plasma Baseline
SA393542XB100_4plasma Baseline
SA393543XB111_2plasma Baseline
SA393544XB107_3plasma Baseline
SA393545XB22_1plasma Baseline
SA393546XB33_4plasma Baseline
SA393547XB114_1plasma Baseline
SA393548XB59_4plasma Baseline
SA393549XB45_2plasma Baseline
SA393550XB97_1plasma Baseline
SA393551XB97_3plasma Baseline
SA393552XB25_2plasma Baseline
SA393553XB115_4plasma Baseline
SA393554XB68_5plasma Baseline
SA393555XB22_2plasma Baseline
SA393556XB20_6plasma Baseline
SA393557XB89_4plasma Baseline
SA393558XB94_3plasma Baseline
SA393559XB114_3plasma Baseline
SA393560XB79_7plasma Baseline
SA393561XB112_2plasma Baseline
SA393562XB25_1plasma Baseline
SA393563XB114_2plasma Baseline
SA393564XB100_5plasma Baseline
SA393565XB65_2plasma Baseline
SA393566XB1_5plasma Baseline
SA393567XB43_3plasma Baseline
SA393568XB115_3plasma Baseline
SA393569XB1_4plasma Baseline
SA393570XB2_2plasma Baseline
SA393571XB97_2plasma Baseline
SA393572XB43_2plasma Baseline
SA393573XB43_1plasma Baseline
SA393574XB68_4plasma Baseline
SA393575XB6_3plasma Baseline
SA393576XB89_3plasma Baseline
SA393577XB20_4plasma Baseline
SA393578XB59_1plasma Baseline
SA393579XB45_1plasma Baseline
SA393580XB38_1plasma Baseline
SA393581XB79_3plasma Baseline
SA393582XB54_1plasma Baseline
SA393583XB79_2plasma Baseline
SA393584XB20_2plasma Baseline
SA393585XB111_1plasma Baseline
SA393586XB59_2plasma Baseline
SA393587XB95_2plasma Baseline
SA393588XB32_1plasma Baseline
SA393589XB44_1plasma Baseline
SA393590XB20_1plasma Baseline
SA393591XB24_1plasma Baseline
SA393592XB79_1plasma Baseline
SA393593XB1_1plasma Baseline
SA393594XB101_1plasma Baseline
SA393595XB70_3plasma Baseline
SA393596XB14_2plasma Baseline
SA393597XB95_1plasma Baseline
SA393598XB18_1plasma Baseline
SA393599XB100_2plasma Baseline
SA393600XB70_2plasma Baseline
SA393601XB70_1plasma Baseline
SA393602XB68_1plasma Baseline
SA393603XB2_1plasma Baseline
SA393604XB14_1plasma Baseline
SA393605XB100_1plasma Baseline
SA393606XB89_1plasma Baseline
SA393607XB68_2plasma Baseline
SA393608XB1_2plasma Baseline
SA393609XB100_3plasma Baseline
SA393610XB79_5plasma Baseline
SA393611XB38_4plasma Baseline
SA393612XB94_1plasma Baseline
SA393613XB20_3plasma Baseline
SA393614XB68_3plasma Baseline
SA393615XB34_1plasma Baseline
SA393616XB6_1plasma Baseline
SA393617XB107_2plasma Baseline
SA393618XB62_1plasma Baseline
SA393619XB21_2plasma Baseline
SA393620XB38_3plasma Baseline
SA393621XB33_3plasma Baseline
SA393622XB21_1plasma Baseline
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Collection:

Collection ID:CO003753
Collection Summary:Participants underwent evaluations, screening tests, and metabolic tests at the CTRU after an overnight fast. During the screening and the omics visits, stool, urine, peripheral blood mononuclear cells (PBMC), plasma, and serum samples were collected. Some individuals had multiple omics visits to monitor omics changes throughout the study.
Sample Type:Blood (plasma)
Storage Conditions:-80℃

Treatment:

Treatment ID:TR003769
Treatment Summary:Baseline sample no treatment.
Human Fasting:Yes

Sample Preparation:

Sampleprep ID:SP003767
Sampleprep Summary:Metabolites and complex lipids were extracted using a biphasic separation with cold methyl tert-butyl ether (MTBE), methanol, and water in the deep well plate format. Briefly, 1 mL of ice-cold MTBE and 260 μL methanol was added to 40 μL of the plasma spiked-in with 40 µL deuterated lipid internal standards (Sciex, cat# 5040156, lot# LPISTDKIT-103). The samples were then agitated at 4°C for 30 minutes. After the addition of 250 μL of ice-cold water, the samples were vortexed for 1 minute and centrifuged at 3,800 g for five minutes at 4°C. The upper organic phase contained the lipids, the lower aqueous phase contained the metabolites, and the proteins were precipitated at the bottom of the well. For quality control, three reference plasma samples (40 µL plasma) as well as one control sample lacking any sample were processed in parallel per plate. For lipidomics, 700 µL of the organic phase was dried down under a stream of nitrogen and resolubilized in 200 µL of methanol for storage at -20°C until analysis. On the day of the analysis, samples were dried down, resuspended in 300 µL of 10 mM ammonium acetate in 90:10 methanol:toluene and centrifuged at 3,800 g for five minutes at 4°C.
Sampleprep Protocol Filename:lipidomics_process.pdf
Extract Storage:-80℃

Combined analysis:

Analysis ID AN005963
Analysis type MS
Chromatography type None (Direct infusion)
Chromatography system none
Column none
MS Type ESI
MS instrument type QTRAP
MS instrument name 5500 QTRAP MS System
Ion Mode UNSPECIFIED
Units nmol/g

Chromatography:

Chromatography ID:CH004531
Chromatography Summary:Check the protocol for details
Instrument Name:none
Column Name:none
Column Temperature:NA
Flow Gradient:NA
Flow Rate:NA
Internal Standard:Yes
Solvent A:none
Solvent B:none
Chromatography Type:None (Direct infusion)

MS:

MS ID:MS005678
Analysis ID:AN005963
Instrument Name:5500 QTRAP MS System
Instrument Type:QTRAP
MS Type:ESI
MS Comments:Complex lipids were quantified using a targeted MS-based approach using the Lipidyzer Platform. Lipid extracts were analyzed using the Lipidyzer platform that comprises a 5500 QTRAP system equipped with a SelexION differential mobility spectrometry (DMS) interface (Sciex) and a high flow LC-30AD solvent delivery unit (Shimazdu, Columbia, MD). Briefly, lipid molecular species were identified and quantified using multiple reaction monitoring (MRM) and positive/negative ionization switching. Two acquisition methods were employed covering 13 lipid classes; method 1 had SelexION voltages turned on while method 2 had SelexION voltages turned off. Data quality was ensured by i) tuning the DMS compensation voltages using a set of lipid standards (cat# 5040141, Sciex) after each cleaning, more than 24 hours of idling or three days of consecutive use, ii) performing a quick system suitability test (QSST) (cat# 5040407, Sciex) before each batch to ensure acceptable limit of detection for each lipid class, and iii) triplicate injection of lipids extracted from a reference plasma sample (cat# 4386703, Sciex) at the beginning of the batch. Lipidyzer data were reported by the Lipidomics Workflow Manager (LWM, v1.0.5.0) software which calculates concentrations for each detected lipid as the average intensity of the analyte MRM divided by the average intensity of the most structurally similar internal standard (IS) MRM multiplied by its concentration. Lipids detected in less than 2/3 of the samples were discarded and missing values were imputed by drawing from a random distribution of low values class-wise in the corresponding sample. Lipid abundances were reported as concentrations in nmol/g.
Ion Mode:UNSPECIFIED
Analysis Protocol File:lipidomics_process.pdf
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