Illuminating diabetes via multi-omics: Unraveling disease mechanisms and advancing personalized therapy

Abstract

Diabetes mellitus (DM) comprises distinct subtypes-including type 1 DM, type 2 DM, and gestational DM - all characterized by chronic hyperglycemia and substantial morbidity. Conventional diagnostic and therapeutic strategies often fall short in addressing the complex, multifactorial nature of DM. This review explores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches. We consolidated genomic, transcriptomic, proteomic, metabolomic, and microbiomic data from major databases and peer-reviewed publications (2015-2025), with an emphasis on clinical relevance. Multi-omics investigations have identified convergent molecular networks underlying β-cell dysfunction, insulin resistance, and diabetic complications. The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis. Novel biomarkers facilitate early detection of DM and its complications, while single-cell multi-omics and machine learning further refine risk stratification. By dissecting DM heterogeneity more precisely, multi-omics integration enables targeted interventions and preventive strategies. Future efforts should focus on data harmonization, ethical considerations, and real-world validation to fully leverage multi-omics in addressing the global DM burden.

Keywords: Biomarker discovery; Diabetes mellitus; Genomics; Metabolomics; Multi-omics; Personalized therapy; Precision medicine; Proteomics; Transcriptomics.

Figure 1

Overview of metabolomics approaches, analytical platforms, applications, and challenges. Samples (biofluids, tissues, cells) are collected from individuals with distinct diabetes subtypes (type 1 diabetes mellitus, type 2 diabetes mellitus, gestational diabetes mellitus) and related complications (e.g., diabetic kidney disease, diabetic retinopathy). Researchers can choose targeted vs untargeted metabolomics strategies depending on the objectives (biomarker discovery, mechanism elucidation). Key analytical platforms include liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance, each capturing unique metabolite classes. The figure also highlights the main challenges-such as data complexity, sample variability, and metabolite identification-along with the resulting potential applications in diagnosis, risk stratification, and therapeutic monitoring. LC-MS: Liquid chromatography-mass spectrometry; GC-MS: Gas chromatography-mass spectrometry; NMR: Nuclear magnetic resonance.

Figure 2

Multi-omics data integration and machine learning in disease research. This schematic illustrates how multi-layered omics data-genomic (e.g., genome-wide association study), transcriptomic (gene expression), proteomic (protein abundance/modifications), metabolomic (small-molecule metabolites), and microbiomic-are consolidated into a unified analysis pipeline. Machine learning models (e.g., neural networks, random forests, or autoencoders) can uncover hidden patterns, classify diabetes subtypes, and predict disease progression or therapy response. The figure underscores how single-cell/spatial omics approaches enhance resolution by identifying rare cell populations, while explainable artificial intelligence techniques clarify the basis of predictive models, ultimately guiding personalized interventions. AI: Artificial intelligence; XAI: Explainable artificial intelligence; ML: Machine learning.