Unmasking heterogeneity in type 2 diabetes: the clinical relevance of phenotyping in the era of precision medicine.

Abstract

Despite its widespread use in clinical practice, the traditional dichotomous classification of diabetes into type 1 and type 2 fails to capture the marked heterogeneity observed in real-world patients, particularly those with type 2 diabetes mellitus (T2DM). The increasing recognition of the complex interplay between insulin resistance, beta-cell dysfunction, autoimmunity, and genetic predisposition has led to the development of phenotypic classification systems that aim to individualize care beyond glycemic targets. Ahlqvist et al. made a major contribution to this field by identifying five clinically meaningful clusters of adult-onset diabetes using routine clinical variables. These clusters differ in their metabolic profiles, complication risks, and therapeutic needs, offering a pragmatic starting point for personalized diabetology. Their clinical relevance has been further explored and validated by follow-up studies that include detailed metabolic phenotyping, cardiac imaging, and genetic analyses. Nevertheless, enthusiasm for cluster-based models must be tempered by critical appraisal. Evidence from large trials suggests that continuous clinical features may better predict disease progression and treatment response than static cluster assignments. Furthermore, these models have yet to be integrated into clinical guidelines or electronic decision-support systems. This Perspective argues for a multidimensional and dynamic approach to diabetes phenotyping, combining clinical, biochemical, imaging, and genetic data to reflect the evolving nature of the disease. Such a framework could enable more precise stratification and intervention, moving toward truly personalized diabetes care. Integrating these models into real-world settings represents the next frontier in precision diabetology.