Systems Bioinformatics Applied to Elucidating the Progression from Prediabetes to Type 2 Diabetes

Introduction: The transition from prediabetes to type 2 diabetes mellitus (T2DM) involves complex molecular mechanisms that remain incompletely understood. Systems bioinformatics approaches offer unprecedented opportunities to elucidate the heterogeneous pathogenic pathways underlying disease progression through integrated analysis of multi-omics data. Objective: This narrative review synthesizes evidence on systems biology investigations of prediabetes progression, emphasizing network-based integration strategies and their implications for precision medicine. Methods: A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Embase databases from January 2015 to December 2024. Studies employing systems biology approaches, multi-omics integration, and network-based analyses investigating prediabetes-to-T2DM progression were included. Evidence was synthesized thematically according to omics technologies, network methodologies, biological pathways, and predictive biomarkers. Results: Genome-wide association studies have identified over 700 T2DM-associated loci, revealing distinct mechanistic clusters related to β-cell dysfunction, insulin resistance, and metabolic dysregulation. Single-cell transcriptomics demonstrates β-cell dedifferentiation and immune dysregulation during disease progression. Proteomics and metabolomics profiling identified novel circulating biomarkers, including branched-chain amino acids, inflammatory proteins, and lipid signatures. Weighted gene co-expression network analysis and protein-protein interaction networks revealed hub genes and dysregulated pathways amenable to therapeutic targeting. Multi-omics integration delineated molecular endotypes within clinically-defined prediabetes, each characterized by distinct dominant pathogenic mechanisms. Conclusions: Network-based multi-omics integration elucidates prediabetes heterogeneity and supports precision medicine frameworks, enabling personalized interventions based on individual molecular profiles rather than glycemic thresholds alone.