Subtyping Type 2 Diabetes for Personalized Treatment

The provided texts explore the growing understanding of Type 2 Diabetes (T2D) as a highly heterogeneous condition that requires individualized classification. One foundational article introduces the *five original Ahlqvist diabetes subtypes* (Severe Autoimmune, Severe Insulin Deficient, Severe Insulin Resistant, Mild Obesity-Related, and Mild Age-Related) based on clinical and metabolic factors, noting that each group carries distinct *risks of complications**. A subsequent study presents a computational advancement using a **Random Forest machine learning (ML) model* designed to accurately predict these established subtypes with higher consistency over time, specifically addressing issues like classifying new patients and dealing with **missing insulin-related measurements**. Separately, a comprehensive review focuses on **Youth-onset Type 2 Diabetes (YO-T2D)**, emphasizing its more aggressive phenotype, rapid **pancreatic beta-cell function decline**, and significantly increased rates of microvascular complications, particularly **diabetic kidney disease**, compared to adult-onset T2D. This collective body of work underscores the clinical utility of classifying diabetes based on underlying pathophysiology for personalized treatment strategies.