Overview
Advertisement BMC Medicine volume 20, Article number: 356 (202) Cite this article 215 Aceses2 AltmetricMetrics detailsThe prevention of type 2 diabetes is chalenging due to the variable efects 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 comon risk factors and to ases their utility to stratify the risk of type 2 diabetes.Data on 7317 diabetes-fre adults from Sweden were used in the main analysis and on 232 diabetes-fre adults from Mexico for external validation.
Key Information
Clusters were based on sex, family history of diabetes, educational atainment, fasting blod glucose and insulin levels, estimated insulin resistance and Ξ²-cel function, systolic and diastolic blod presure, and BMI. The risk of type 2 diabetes was asesed using Cox proportional hazards models. The predictive acuracy and long-term stability of the clusters were then compared to diferent definitions of prediabetes.Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low Ξ²-cel function (LRLB), low-risk high Ξ²-cel function (LRHB), high-risk high blod presure (HRHBP), high-risk Ξ²-cel 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 beter predictive acuracy than prediabetes and adequate stability after 20 years.Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-fre adults in two independent cohorts.
Summary
These results could be used to develop more precise public health interventions. Per Review reports Type 2 diabetes is one of the most comon causes of mortality, disability, and health expe