Data-driven subclassification of type 2 diabetes in Mexico: a conceptual framework to improve diabetes care

Type 2 diabetes (T2D) is a heterogeneous disease and a major public health concern in low- and middle-income countries (LMICs). To address this heterogeneity, several subgroup classification frameworks have been proposed. Among them, the most widely used is the data-driven classification proposed by Ahlqvist et al., which includes severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). However, reproducing this framework is limited by the need for specialized measurements of beta-cell function and insulin resistance, which are not routinely available in most clinical or epidemiological settings in LMICs. To overcome these barriers, we derived a classification algorithm to reproduce these diabetes subgroups. Here, we present a four-step roadmap in which this framework may help characterize T2D heterogeneity across Mexico, potentially guide pathophysiology-oriented treatment strategies, support public health monitoring, and enhance our understanding of T2D heterogeneity to improve diabetes care in our country.