Summary of Study ST001690
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 PR001086. The data can be accessed directly via it's Project DOI: 10.21228/M8B123 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.
Study ID | ST001690 |
Study Title | Untargeted metabolomic analysis of human blood samples via qualitative GC-MS for T1D biomarker identification |
Study Type | Qualitative GC-MS biomarker identification |
Study Summary | 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. The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of immune cells, particularly, CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. Parallel multi-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and helped identify an associated integrated biomarker signature, which could ultimately facilitate the classification of T1D progressors from non-progressors. |
Institute | Duke University |
Department | Duke Molecular Physiology Institute, School of Medicine |
Laboratory | Metabolomics |
Last Name | Bain |
First Name | James |
Address | 300 N Duke St, Durham, NC, 27701, USA |
james.bain@duke.edu | |
Phone | 919 479 2320 |
Submit Date | 2021-02-09 |
Total Subjects | 9 |
Raw Data Available | Yes |
Raw Data File Type(s) | d |
Analysis Type Detail | GC-MS |
Release Date | 2021-06-10 |
Release Version | 1 |
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Project:
Project ID: | PR001086 |
Project DOI: | doi: 10.21228/M8B123 |
Project Title: | Parallel multi-omics in high-risk subjects for the identification of integrated biomarker signatures of type 1 diabetes |
Project Type: | Untargeted metabolomics: MS qualitative analysis |
Project Summary: | MS qualitative analysis of human blood samples to identify early-identification biomarkers for type 1 diabetes |
Institute: | Duke University |
Department: | Duke Molecular Physiology Institute, School of Medicine |
Laboratory: | Metabolomics |
Last Name: | Bain |
First Name: | James |
Address: | 300 N Duke Street, Durham, NC, 27701, USA |
Email: | james.bain@duke.edu |
Phone: | 919 479 2320 |
Funding Source: | NIH, NIDDK |
Project Comments: | This data represents the untargeted metabolomic analysis of the samples |
Contributors: | O. Alcazar, L.F. Hernandez, E.S. Nakayasu, C. Ansong, C.D. Nicora, C. Ansong, M.J. Muehlbauer, J.R. Bain, C.J. Myer, S.J. Bhattacharya, P. Buchwald, and M.H. Abdulreda |