Summary of Study ST002112

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 PR001338. The data can be accessed directly via it's Project DOI: 10.21228/M8RX2S 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 IDST002112
Study TitleGlobal, distinctive and personal changes in molecular and microbial profiles induced by specific fibers in humans (Untargeted)
Study SummaryDietary fibers act through the microbiome and improve cardiovascular health, metabolic disorders and cancer prevention. To understand health benefits of dietary fiber supplementation we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multi-omic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel and clinical measurements on healthy and insulin resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation, it provides insights into mechanisms behind fiber induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.
Institute
Stanford University
Last NameLancaster
First NameSamuel
Address240 Pasteur Dr, BMI bldg 4400, Stanford California, 94305
Emailslancast@stanford.edu
Phone6126004033
Submit Date2022-05-06
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2023-03-13
Release Version1
Samuel Lancaster Samuel Lancaster
https://dx.doi.org/10.21228/M8RX2S
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Combined analysis:

Analysis ID AN004115 AN004116 AN004117 AN004118
Analysis type MS MS MS MS
Chromatography type HILIC HILIC Reversed phase Reversed phase
Chromatography system Thermo Vanquish Thermo Vanquish 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) Agilent Zorbax SBaq (2.1 x 50 mm, 1.8 um) Agilent Zorbax SBaq (2.1 x 50 mm, 1.8 um)
MS Type ESI ESI ESI ESI
MS instrument type Orbitrap Orbitrap Orbitrap Orbitrap
MS instrument name Thermo Q Exactive Plus Orbitrap Thermo Q Exactive Plus Orbitrap Thermo Q Exactive Orbitrap Thermo Q Exactive Orbitrap
Ion Mode POSITIVE NEGATIVE POSITIVE NEGATIVE
Units Area Area Area Area

MS:

MS ID:MS003862
Analysis ID:AN004115
Instrument Name:Thermo Q Exactive Plus Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Raw data were imported into Progenesis QI 2.3 software (Water, Milford, MA, USA) to align and quantify chromatographic peaks. Data from all 4 acquisition modes (HILIC positive, HILIC negative, RPLC positive, RPLC negative) were processed independently. Using in house R code, we 1) removed noise, 2) imputed data and 3) adjusted for MS drift with time using the LOESS normalization method on pooled QCs injected every 10 injections in the sequence. We used MetID and our MS/MS data to identify 12740 metabolites with confidence levels ranging from 1-3, where 1 matches MS/MS, retention time and m/z from standards on our platform (843 metabolites), 2 has MS/MS and m/z matches from a database (395 metabolites), and 3 matches the m/z of a database (11,502).
Ion Mode:POSITIVE
  
MS ID:MS003863
Analysis ID:AN004116
Instrument Name:Thermo Q Exactive Plus Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Raw data were imported into Progenesis QI 2.3 software (Water, Milford, MA, USA) to align and quantify chromatographic peaks. Data from all 4 acquisition modes (HILIC positive, HILIC negative, RPLC positive, RPLC negative) were processed independently. Using in house R code, we 1) removed noise, 2) imputed data and 3) adjusted for MS drift with time using the LOESS normalization method on pooled QCs injected every 10 injections in the sequence. We used MetID and our MS/MS data to identify 12740 metabolites with confidence levels ranging from 1-3, where 1 matches MS/MS, retention time and m/z from standards on our platform (843 metabolites), 2 has MS/MS and m/z matches from a database (395 metabolites), and 3 matches the m/z of a database (11,502).
Ion Mode:NEGATIVE
  
MS ID:MS003864
Analysis ID:AN004117
Instrument Name:Thermo Q Exactive Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Raw data were imported into Progenesis QI 2.3 software (Water, Milford, MA, USA) to align and quantify chromatographic peaks. Data from all 4 acquisition modes (HILIC positive, HILIC negative, RPLC positive, RPLC negative) were processed independently. Using in house R code, we 1) removed noise, 2) imputed data and 3) adjusted for MS drift with time using the LOESS normalization method on pooled QCs injected every 10 injections in the sequence. We used MetID and our MS/MS data to identify 12740 metabolites with confidence levels ranging from 1-3, where 1 matches MS/MS, retention time and m/z from standards on our platform (843 metabolites), 2 has MS/MS and m/z matches from a database (395 metabolites), and 3 matches the m/z of a database (11,502).
Ion Mode:POSITIVE
  
MS ID:MS003865
Analysis ID:AN004118
Instrument Name:Thermo Q Exactive Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Raw data were imported into Progenesis QI 2.3 software (Water, Milford, MA, USA) to align and quantify chromatographic peaks. Data from all 4 acquisition modes (HILIC positive, HILIC negative, RPLC positive, RPLC negative) were processed independently. Using in house R code, we 1) removed noise, 2) imputed data and 3) adjusted for MS drift with time using the LOESS normalization method on pooled QCs injected every 10 injections in the sequence. We used MetID and our MS/MS data to identify 12740 metabolites with confidence levels ranging from 1-3, where 1 matches MS/MS, retention time and m/z from standards on our platform (843 metabolites), 2 has MS/MS and m/z matches from a database (395 metabolites), and 3 matches the m/z of a database (11,502).
Ion Mode:NEGATIVE
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