Disease Subtyping in Type 2 Diabetes: From Clusters to Clinical Translation

Type 2 diabetes (T2D) is a heterogeneous metabolic disorder with diverse etiologies, clinical trajectories, and treatment responses. Traditional management strategies adopt a one-size-fits-all approach, primarily focused on lowering hyperglycemia, but this paradigm overlooks important inter-individual differences that influence outcomes and long-term complications. Recent advances in systems biology and computational analytics have enabled novel approaches to disease subtyping, with the aim of stratifying patients into biologically and clinically meaningful groups. This narrative review synthesizes current evidence on clustering methods applied to T2D subtyping. Hard clustering approaches, such as k-means and hierarchical clustering, have most often been used to define phenotypic subtypes based on clinical traits, including age of onset, body mass index, insulin resistance, and beta (β)-cell function. While these models provide discrete categories that can be easily interpreted, they may oversimplify disease heterogeneity. In contrast, soft clustering techniques, including archetype analysis and latent class models, allow individuals to exhibit characteristics across multiple clusters, reflecting overlapping biological pathways. Genetic clustering has further revealed stable, mechanistically distinct endotypes, with potential to guide treatment selection and refine risk prediction. The clinical translation of T2D subtyping holds promise for tailoring interventions to underlying pathophysiology, improving therapeutic efficacy, and reducing adverse outcomes. However, challenges remain, including validation in diverse populations, cost-effectiveness considerations, and the need for prospective clinical trials stratified by subtype. Subtyping represents an important step toward precision medicine in T2D, with the potential to transform clinical care from uniform management to individualized treatment strategies.