Subtyping of Type 2 Diabetes from a Large Middle Eastern Biobank: Implications for Precision Medicine.

Type 2 diabetes (T2D) can be classified into Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obesity-related Diabetes (MOD), and Mild Age-related Diabetes (MARD). This classification can help in predicting disease complications and determining the best treatment for individuals. However, the applicability of this classification to non-European populations and sensitivity to confounding factors remain unclear. We applied k-means clustering to a large Middle Eastern biobank cohort (Qatar Biobank; QBB, comprising 13,808 individuals; 2,687 with T2D). We evaluated the efficacy of the European cluster coordinates and analyzed the impact of using actual age on clustering outcomes. We examined sex differences, analyzed insulin treatment frequency, investigated the clustering of monogenic diabetes (MD) focusing on maturity-onset diabetes of the young (MODY), and evaluated the prevalence of chronic kidney disease (CKD) and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) among T2D subtypes. We identified the four T2D subtypes within a large Arab cohort. Data-derived centers outperformed European coordinates in classifying T2D. The use of actual age, as opposed to age of diagnosis, impacted MOD and MARD classification. Obesity prevalence was significantly higher in females. However, that did not translate to worse disease severity, as indicated by comparable levels of HbA1C and HOMA2-IR. Insulin was predominantly prescribed for individuals in SIDD and SIRD, who also displayed the highest risk of CKD, followed by MOD. Interestingly, most MODY individuals were clustered within MARD, further highlighting the need for precise classification and tailored interventions. The observed sex differences underscore the importance of tailoring treatment plans for females compared to males. Individuals who are at a higher risk of CKD and MASLD may require closer monitoring and physician oversight. Additionally, in populations without access to genetic testing, likely MODY individuals can be identified within the MARD cluster. These findings strongly support the need for a transition to more personalized, data-driven treatment approaches to minimize diabetes-related complications and improve disease outcomes.