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<p>Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder characterized by insulin resistance, pancreatic β-cell dysfunction, and chronic low-grade inflammation. In recent years, advances in biomarker research have significantly improved our understanding of the pathogenesis, progression, and personalized management of T2DM. This narrative review explores the role of diverse biomarker categories, including proteomic, metabolomic, epigenetic, immune-inflammatory, gut microbiota (GM)-derived, and exosome-based markers, in early diagnosis, risk stratification, and therapeutic monitoring. Proteomic markers such as adipokines, hepatokines, and cytokines provide insight into metabolic dysfunction and inflammatory status. Metabolomic profiling reveals key signatures of mitochondrial impairment and insulin resistance through altered levels of amino acids, lipids, and short-chain fatty acids (SCFAs). Epigenetic modifications, including DNA methylation and histone acetylation, offer dynamic and potentially reversible indicators of β-cell stress and glycemic control. Furthermore, GM-derived metabolites and specific microbial taxa serve as novel, modifiable markers of metabolic health, while circulating exosomes encapsulate microRNAs (miRNAs) and proteins that reflect and influence disease states. Immune and inflammatory markers, including Monocyte Chemoattractant Protein-1 (MCP-1), Interleukin-6 (IL-6), Tumor Necrosis Factor alpha (TNF-α), the neutrophil-to-lymphocyte ratio, and the pyrin domain containing 3 (NLRP3) inflammasome, further clarify the link between innate immunity and metabolic dysfunction. The integration of artificial intelligence (AI) and machine learning (ML) models with biomarker data has enabled highly accurate prediction tools and decision support systems. Personalized treatment strategies, guided by biomarker panels and pharmacogenetic insights, are reshaping diabetes mellitus (DM) care toward precision medicine. Collectively, these emerging biomarkers and digital tools hold transformative potential for improving diagnosis, monitoring, and individualized therapy in T2DM.</p>

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Unexplored biomarkers in type 2 diabetes mellitus: unlocking new frontiers in diagnosis and treatment

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Roney Miah,
  • Ahmad Mahfuz Gazali,
  • Sumy Akter,
  • Mohd Fadhlizil Fasihi Mohd Aluwi

摘要

Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder characterized by insulin resistance, pancreatic β-cell dysfunction, and chronic low-grade inflammation. In recent years, advances in biomarker research have significantly improved our understanding of the pathogenesis, progression, and personalized management of T2DM. This narrative review explores the role of diverse biomarker categories, including proteomic, metabolomic, epigenetic, immune-inflammatory, gut microbiota (GM)-derived, and exosome-based markers, in early diagnosis, risk stratification, and therapeutic monitoring. Proteomic markers such as adipokines, hepatokines, and cytokines provide insight into metabolic dysfunction and inflammatory status. Metabolomic profiling reveals key signatures of mitochondrial impairment and insulin resistance through altered levels of amino acids, lipids, and short-chain fatty acids (SCFAs). Epigenetic modifications, including DNA methylation and histone acetylation, offer dynamic and potentially reversible indicators of β-cell stress and glycemic control. Furthermore, GM-derived metabolites and specific microbial taxa serve as novel, modifiable markers of metabolic health, while circulating exosomes encapsulate microRNAs (miRNAs) and proteins that reflect and influence disease states. Immune and inflammatory markers, including Monocyte Chemoattractant Protein-1 (MCP-1), Interleukin-6 (IL-6), Tumor Necrosis Factor alpha (TNF-α), the neutrophil-to-lymphocyte ratio, and the pyrin domain containing 3 (NLRP3) inflammasome, further clarify the link between innate immunity and metabolic dysfunction. The integration of artificial intelligence (AI) and machine learning (ML) models with biomarker data has enabled highly accurate prediction tools and decision support systems. Personalized treatment strategies, guided by biomarker panels and pharmacogenetic insights, are reshaping diabetes mellitus (DM) care toward precision medicine. Collectively, these emerging biomarkers and digital tools hold transformative potential for improving diagnosis, monitoring, and individualized therapy in T2DM.