Subtypes of Type 2 Diabetes and Incident Cardiovascular Disease Risk: UK Biobank and All of Us Cohorts

Objective: To characterize and validate the subtypes of type 2 diabetes (T2D) using a novel clustering algorithm and to further assess their associations with the risk of incident cardiovascular disease (CVD) events.Methods: Unsupervised k-means clustering based on glycated hemoglobin level, age at onset of T2D, body mass index, and estimated glomerular filtration rate was conducted among participants with T2D from the UK Biobank (March 13, 2006, to October 1, 2010) and replicated in the All of Us cohort (May 30, 2017, to April 1, 2021).Results: Five distinct T2D clusters were identified in the UK Biobank and validated in the All of Us cohort, characterizing the phenotypically heterogeneous subtypes. With a median follow-up of 11.69 years for patients with T2D in the UK Biobank, risks of incident CVD events varied considerably between the clusters after adjustment for potential confounders and multiple testing (all P<.001). With cluster 1 characterized by early onset of T2D and mild abnormalities of other variables as the reference, patients in cluster 5 characterized by poor renal function had the highest risk of CVD events (hazard ratio [95% CI], 1.72 [1.45 to 2.03], 2.41 [1.93 to 3.02], and 1.62 [1.35 to 1.94] for composite CVD event, CVD mortality, and CVD incidence, respectively; all P<.001), followed by cluster 4 characterized by poor glycemic control and cluster 3 characterized by severe obesity. No consistently significant difference was found between cluster 2 characterized by late onset of T2D and cluster 1.Conclusion: Our study, using a novel clustering algorithm to identify robust subtypes of T2D, found heterogeneous associations with incident CVD risk among patients with diabetes.& COPY; 2023 THE AUTHORS. Published by Elsevier Inc on behalf of Mayo Foundation for Medical Education and Research. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) & BULL; Mayo Clin Proc. 2023;98(8):1192-1204

Automated summary

Using a novel clustering algorithm, this study characterized and validated subtypes of type 2 diabetes (T2D) and assessed their associations with incident cardiovascular disease (CVD) risk. Five distinct T2D clusters were identified, with significantly different risks of CVD events. Patients with poor renal function had the highest risk of CVD events, followed by those with poor glycemic control and severe obesity.