Parallel multi‐omics in high‐risk subjects for the identification of integrated biomarker signatures of type 1 diabetes

Oscar Alcazar, Luis F. Hernandez, Ernesto S. Nakayasu, Carrie D. Nicora, Charles Ansong, Michael J. Muehlbauer, James R. Bain, Ciara J. Myer, Sanjoy K. Bhattacharya, Peter Buchwald, Midhat H. Abdulreda

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Biomarkers are crucial for detecting early type‐1 diabetes (T1D) and prevent-ing 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 con-trols; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and tran-scriptomics. 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 activa-tion, proliferation, and migration of CD4 T‐lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF‐κB, TGF‐β, VEGF, ara-chidonic acid, and arginase, and inhibition of miRNA Let‐7a‐5p. IPA‐predicted candidate bi-omarkers 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.

Original languageEnglish (US)
Article number383
Pages (from-to)1-25
Number of pages25
Issue number3
StatePublished - Mar 2021


  • Biomarker signature
  • Biomarkers
  • Diagnosis
  • Early prediction
  • Integrated analysis
  • Lipidomics
  • Metabolomics
  • Multi‐omics
  • Network prediction
  • Omics
  • Prognosis
  • Proteomics
  • Signaling pathways
  • Transcriptomics
  • Type 1 diabetes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


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