Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.

Journal Article (Journal Article)

BACKGROUND: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. METHODS: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was 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 controls. RESULTS: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of 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. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. CONCLUSIONS: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

Full Text

Duke Authors

Cited Authors

  • Alcazar, O; Hernandez, LF; Nakayasu, ES; Nicora, CD; Ansong, C; Muehlbauer, MJ; Bain, JR; Myer, CJ; Bhattacharya, SK; Buchwald, P; Abdulreda, MH

Published Date

  • March 4, 2021

Published In

Volume / Issue

  • 11 / 3

PubMed ID

  • 33806609

Pubmed Central ID

  • PMC7999903

Electronic International Standard Serial Number (EISSN)

  • 2218-273X

Digital Object Identifier (DOI)

  • 10.3390/biom11030383


  • eng

Conference Location

  • Switzerland