• Diego Yacamán Méndez, Minhao Zhou, Ylva Trolle Lagerros, Donaji V Gómez Velasco, Per Tynelius, Hrafnhildur Gudjonsdottir, Ponce de LeonAntonioACenter for Epidemiology and Community Medicine (CES), Stockholm Health Care Services, Stockholm, Sweden., Katarina Eeg-Olofsson, Claes-Göran Östenson, Boel Brynedal, Carlos A Aguilar Salinas, David Ebbevi, and Anton Lager.
• Department of Global Public Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden. diego.yacaman.mendez@ki.se.
• Bmc Med. 2022 Oct 18; 20 (1): 356.

BackgroundThe prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes.MethodsData on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes.ResultsSix risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years.ConclusionsPhenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.© 2022. The Author(s).

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