Integration of omics technologies for the identification of predictive biomarkers in type 2 diabetes: a comprehensive analysis of recent literature

Abstract

Background: Omics technologies, such as genomics, proteomics, metabolomics, and Transcriptomics are being used for identifying biomarkers. These biomarkers are unraveling the molecular mechanisms underlying type 2 diabetes mellitus (T2DM), which can help to promote more personalized treatment strategies and advance our understanding of disease pathogenesis. Omics approaches enable the examination of genetic, protein, metabolic, and gene expression profiles more comprehensively while offering insights into T2DM risk, progression, and potential therapeutic targets. Methods: This review follows a systematic methodology, aimed at evaluating omics technology’s role in diabetes research. Utilizing literature searches, we got an initial pool of 257 studies with a rigorous selection process and narrowed the selection to 10 high-quality studies. Our methodology approach ensured the inclusion of relevant, peer-reviewed articles that contribute significantly to understanding the application of omics technologies in predicting biomarkers for type 2 diabetes. Results: The systematic review identifies ten high-quality studies illuminating substantial omics technology’s role in advancing our understanding of type 2 diabetes (T2D). Collectively, these studies demonstrate how genomics, proteomics, metagenomics, metabolomics, and Transcriptomics have uncovered novel biomarkers and molecular pathways for T2D. Our findings underscore all the omics potentials specifically for developing predictive biomarkers, enhancing diagnostics, and tailoring personalized treatment strategies. Genetic variations, metabolic alterations, and protein and RNA expression profiles were highlighted as key areas where omics technologies offer insights into the pathophysiology and management of T2D.