Phenotypic clustering of newly diagnosed type 2 diabetes in a Mediterranean cohort.

Background: Current diagnostic criteria for type 2 diabetes (T2D) capture disease heterogeneity poorly, and do not reliably predict progression, complications, or treatment response. The phenotypic clustering model proposed by Ahlqvist et al. identified five T2D subtypes using six clinical variables at diagnosis, each associated with distinct metabolic profiles and complication risks. Although this framework has been replicated in several cohorts, evidence in Mediterranean populations is lacking.

Methods: We conducted a prospective cohort study in Catalonia (Northeast Spain) including adults with newly diagnosed T2D recruited between March 2022 and January 2026. Using baseline data, we evaluated the Ahlqvist clustering approach. Autoantibody-positive individuals were classified as severe autoimmune diabetes (SAID), and sex-stratified k-means clustering (k = 4) was applied to autoantibody-negative participants. Cluster separation and stability were assessed using principal component analysis and silhouette analyses.

Results: A final total number of 991 individuals with newly diagnosed T2D were included in the analysis. Autoantibodies were present in 67 subjects (6.8%), thereby being classified as SAID. Among the remaining 924 participants, sex-stratified k-means clustering (k = 4) identified clusters with metabolic profiles consistent with the classical subtypes: mild age-related diabetes (MARD, n = 326), severe insulin-resistant diabetes (SIRD, n = 241), mild obesity-related diabetes (MOD, n = 206), and severe insulin-deficient diabetes (SIDD, n = 151). However, cluster separation was modest, and bootstrap stability was limited (Jaccard 0.555-0.718). In an unconstrained analysis, apart from the autoimmune diabetes group, silhouette optimisation identified three clusters as the most internally optimal structure, corresponding broadly to obesity/insulin-resistant (C1, n = 347), insulin-deficient (C2, n = 186), and age-related (C3, n = 391) phenotypes. Stability was substantially higher for the three-cluster solution (Jaccard 0.799-0.863). Concordance between the Ahlqvist and data-driven models was moderate (ARI = 0.473), with MOD individuals distributed across the other clusters.

Conclusions: The Ahlqvist clustering architecture could be approximated in this Mediterranean cohort at diagnosis, but internal stability of the five-cluster solution was limited. In this population, a four-cluster structure showed substantially better internal validity. These findings support the feasibility of phenotypic subclassification of T2D while underscoring the importance of evaluating population-specific cluster structures and their clinical relevance in longitudinal studies.

Trial registration: NCT05333718.