Characterisation of type 2 diabetes subgroups and their association with ethnicity and clinical outcomes: a UK real-world data study using the East London Database

Abstract

Background Subgroups of type 2 diabetes (T2DM) have been well characterised in experimental studies. It is unclear, however, whether the same approaches can be used to characterise T2DM subgroups in UK primary care populations and their associations with clinical outcomes. Aim To derive T2DM subgroups using primary care data from a multi-ethnic population, evaluate associations with glycaemic control, treatment initiation, and vascular outcomes, and to understand how these vary by ethnicity. Design and setting An observational cohort study in the East London Primary Care Database from 2008 to 2018. Method Latent-class analysis using age, sex, glycated haemoglobin, and body mass index at diagnosis was used to derive T2DM subgroups in White, South Asian, and Black groups. Time to treatment initiation and vascular outcomes were estimated using multivariable Cox-proportional hazards regression. Results In total, 31 931 adults with T2DM were included: 47% South Asian (n = 14 884), 26% White (n = 8154), 20% Black (n = 6423). Two previously described subgroups were replicated, 'mild agerelated diabetes' (MARD) and 'mild obesity-related diabetes' (MOD), and a third was characterised 'severe hyperglycaemic diabetes' (SHD). Compared with MARD, SHD had the poorest long-term glycaemic control, fastest initiation of antidiabetic treatment (hazard ratio [HR] 2.02, 95% confidence interval [CI] = 1.76 to 2.32), and highest risk of microvascular complications (HR 1.38, 95% CI = 1.28 to 1.49). MOD had the highest risk of macrovascular complications (HR 1.50, 95% CI = 1.23 to 1.82). Subgroup differences in treatment initiation were most pronounced for the White group, and vascular complications for the Black group. Conclusion Clinically useful T2DM subgroups, identified at diagnosis, can be generated in routine real- world multi-ethnic populations, and may offer a pragmatic means to develop stratified primary care pathways and improve healthcare resource allocation.