Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose and insulin measurements. We aimed to identify type 2 diabetes subtypes in clinical settings using electronic health records and study their epidemiology. We identified 727,076 adults (≥18 years) with newly diagnosed type 2 diabetes from Epic Cosmos research platform data across all 50 states and the District of Columbia between 2012 and 2023. Classification models developed in cohort studies were applied to study the sociodemographic distribution of subtypes. Cox proportional hazards regression models, adjusted for age and sex, were used to assess the rates of microvascular complications (retinopathy, neuropathy and nephropathy) and macrovascular complications (severe atherosclerotic cardiovascular disease ASCVD, other ASCVD and heart failure). Among newly diagnosed individuals (mean age 64.4 years SD 13.3, 52% female), 21.6% were classified as having severe insulin-deficient diabetes (SIDD), 23.8% were classified as having mild obesity-related diabetes (MOD), 40.9% were classified as having mild age-related diabetes and 13.7% were classified as having the mixed subtype. Compared with those classified as having MOD, individuals classified as having SIDD had higher HRs for retinopathy (HR 2.83; 95% CI 2.73, 2.93), neuropathy (HR 1.57; 95% CI 1.54, 1.60), nephropathy (HR 1.34; 95% CI 1.32, 1.37), severe ASCVD (HR 1.49; 95% CI 1.46, 1.53), other ASCVD (HR 1.23; 95% CI 1.21, 1.25) and heart failure (HR 1.17; 95% CI 1.15, 1.20). SIDD and MOD were more prevalent among Hispanics (28.4% and 30.1%, respectively) and non-Hispanic Black people (25.5% and 30.0%, respectively) compared with non-Hispanic White people (20.1% and 21.6%, respectively), and were also more prevalent in the District of Columbia and Utah, respectively, compared with the rest of the country. Individuals with different type 2 diabetes subtypes, identified through electronic health records, differ in terms of their risk of vascular complications. These findings support leveraging routine electronic health record data to improve the characterisation of patient heterogeneity at the time of diabetes diagnosis.
Li et al. (Sat,) studied this question.