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Introduction: Phenomapping can be used to identify subgroups of patients with similar risk factor profiles; however, there is no widely accepted clustering method. We compared 3 phenomapping strategies to identify groups of individuals with diabetes mellitus (DM) and high cardiovascular disease (CVD) risk. Methods: Participants with DM and no baseline CVD from the ACCORD trial (N = 6591) were included in this study. Among 45 mixed data variables, we used an informed variable selection method with optimization of the Wald index to select the top 20 covariates associated with survival. Gaussian mixture models (GMM), Latent Class Analysis (LCA), and finite mixture model-based clustering (FMM) were performed to identify mutually exclusive phenogroups. Bayesian information criterion (BIC) and Dunn index were calculated for each method and compared. The primary composite endpoint was cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Results: FMM clustering with 3 phenogroups was the optimal clustering strategy as determined by the lowest BIC and highest Dunn index (Figs A-B). Phenogroup 1 had a higher burden of comorbidities and DM complications; phenogroup 2 were older with intermediate CV comorbidity burden, but lowest complications from DM; and phenogroup 3, the largest group, had a lower prevalence of CV-related comorbidities. In adjusted Cox models, over 4.9 years median follow-up, phenogroup 1 and 2 participants (vs. phenogroup 3) had a significantly higher risk of primary composite endpoint (Fig C). In Cox analysis, when group membership was added to 10 optimal predictors of primary composite endpoint, the C -index improved from 0.68 to 0.70 ( P = 0.02). Similar risk patterns were also observed in an external cohort (Look AHEAD trial, N = 4211) with the highest risk of adverse outcome seen in phenogroup 1 (Fig D). Conclusion: Semi-supervised clustering using FMM efficiently identifies phenogroups of patients with DM with distinct clinical characteristics and CVD risk.