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Omics Integration to Tackle Diabetes and Its Complications

January 1, 2024
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Background

Almost one million research studies about diabetes have been published and the challenge still stands. New directions of using what has been identified and collected previously to predict futuristic research directions will facilitate the establishment of new avenues.  Omics integration in diabetes research revolutionized the practice of one medication or one management care plan for all patients with diabetes. 

This emphasized the importance of navigating the commons and comparing the differences using large-magnitude data from genomics, transcriptomics, proteomics, metabolomics, and pharmaco-genomics.  The number of research projects about diabetes and omics integration increased significantly in the last twenty years and more researchers are interested in this field (Figure 1).

Figure 1. The number of publications about omics integration and diabetes.
Figure 1. The number of publications about omics integration and diabetes.

Designs and protocols

Different omics studies can be designed to leverage the current advanced techniques including (i) NGS or genotyping arrays to identify genetic variants, (ii) RNA sequencing to profile gene expression, (iii) mass spectrometry or protein arrays to profile protein abundance and modifications, and (iv) mass spectrometry or NMR spectroscopy to profile metabolites. The two main clinical study designs that can be adopted are cohort design and cross-sectional design (Figure 2).

Figure 2. Schematic illustration of the basic components of clinical studies using omics integration.
Figure 2. Schematic illustration of the basic components of clinical studies using omics integration.
Selected free full-text articles
  • Sandholm N et al. Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease. Diabetologia. 2022 Sep;65(9):1495-1509. doi: 10.1007/s00125-022-05735-0. Epub 2022 Jun 28. PMID: 35763030; PMCID: PMC9345823. https://pubmed.ncbi.nlm.nih.gov/35763030/

  • Karagiannidis E. et al. Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease. Cardiovasc Diabetol. 2022 May 7;21(1):70. doi: 10.1186/s12933-022-01494-9. PMID: 35525960; PMCID: PMC9077877. https://pubmed.ncbi.nlm.nih.gov/35525960/

  • Jin Q, Ma RCW. Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies. Cells. 2021 Oct 21;10(11):2832. doi: 10.3390/cells10112832. PMID: 34831057; PMCID: PMC8616415. https://pubmed.ncbi.nlm.nih.gov/34831057/

  • Allesøe R. L et al. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol. 2023 Mar;41(3):399-408. doi: 10.1038/s41587-022-01520-x. Epub 2023 Jan 2. Erratum in: Nat Biotechnol. 2023 Jul;41(7):1026. PMID: 36593394; PMCID: PMC10017515. https://pubmed.ncbi.nlm.nih.gov/35763030/

  • Liu J, Liu S, Yu Z, Qiu X, Jiang R, Li W. Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration. J Transl Med. 2022 Dec 16;20(1):604. doi: 10.1186/s12967-022-03826-5. Erratum in: J Transl Med. 2023 Mar 20;21(1):207. PMID: 36527108; PMCID: PMC9756634. https://pubmed.ncbi.nlm.nih.gov/36527108/

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