Characterization and Subgrouping of Type 2 Diabetes

Abstract

Among patients with type 2 diabetes, there exists considerable clinical heterogeneity, with variable mechanisms leading to disease onset. These differences may impact treatment responses and the course of the disease. Subgrouping diabetes aims to foster precision diagnostics to allow precision treatment. It is designed to better understand the diversity/heterogeneity within type 2 diabetes and to provide tailored therapies for specific subgroups of patients. There are two subgrouping approaches. The first is a “simple” method of categorizing patients based on specific clinical factors, which is practical but has not consistently improved clinical outcomes. The “complex” approach utilizes machine learning and considers various clinical and/or genomic data, demonstrating reproducible subgroups and associations with clinical outcomes. Nonetheless, whether these efforts translate into significantly superior outcomes compared to current standard treatments remains to be conclusively established and should be addressed in further research.