Summary of study ST001642

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 PR001050. The data can be accessed directly via it's Project DOI: 10.21228/M8ZX18 This work is supported by NIH grant, U2C- DK119886.

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Study IDST001642
Study TitleLipidomics in high-risk subjects for the identification of integrated biomarker signatures of type 1 diabetes
Study SummaryWe present the lipidome of plasma collected from high-risk type 1 diabetes subjects. The methyl tert-butyl ether (MTBE) method was used for lipid extraction, followed by high performance liquid chromatography (HPLC) tandem mass spectrometry (LC-MS/MS) using a Q Exactive Orbitrap mass spectrometer and an Accela 600 HPLC. Lipid species were identified and quantified by analyzing the raw files in LipidSearch 4.2. Further analysis was conducted using Graphpad Prism and Ingenuity Pathway Analysis (IPA).
Institute
University of Miami
Last NameBhattacharya
First NameSanjoy
Address1638 NW 10th Avenue, Room 706-A, Miami, FL 33136
Emailsbhattacharya@med.miami.edu
Phone305-482-4103
Submit Date2021-01-06
Raw Data AvailableYes
Raw Data File Type(s).raw
Analysis Type DetailLC-MS
Release Date2021-01-25
Release Version1
Sanjoy Bhattacharya Sanjoy Bhattacharya
https://dx.doi.org/10.21228/M8ZX18
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001050
Project DOI:doi: 10.21228/M8ZX18
Project Title:Parallel multi-omics in high-risk subjects for the identification of integrated biomarker signatures of 4 type 1 diabetes
Project Summary:Biomarkers are of paramount importance for early disease detection and are particularly valuable in type 1 diabetes (T1D) to prevent significant β cell loss before the onset of clinical symptoms. Thus far, single-omics studies have failed to identify such T1D biomarkers. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. Blood from human subjects at high risk for T1D (and healthy controls; n=4 each) were subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to the controls.
Institute:University of Miami
Last Name:Bhattacharya
First Name:Sanjoy
Address:1638 NW 10th Avenue, Room 706-A, Miami, FL 33136
Email:sbhattacharya@med.miami.edu
Phone:305-482-4103
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