Novel subgroups of prediabetes and the associations with outcomes in health professionals: a data–driven cluster analysis

Background: This study examined prediabetic clusters and their associations with type 2 diabetes (T2D) and cardiovascular disease (CVD) using variables from metabolic syndrome, glycemic measures, and blood lipids. Methods: A total of 1,016 prediabetic individuals were classified into four clusters using k-means clustering. Weibull proportional hazards models estimated T2D and CVD risk, and T2D polygenic risk scores (PRS) were analyzed to refine risk within each cluster. Results: Four clusters were identified: the metabolic syndrome prediabetes (MESPD) cluster, characterized by elevated BMI and adverse lipid profiles, had the highest T2D risk (HR 5.86). The mild age-related prediabetes (MARPD) cluster, associated with older age, showed an increased T2D risk. In contrast, the low–risk prediabetes (LORPD) cluster exhibited the lowest risk, suggesting that a reduced metabolic burden may confer greater disease stability. PRS were used to refine risk stratification, with the MESPD cluster showing a significant genetic predisposition to T2D. PRS also enhanced predictive accuracy for the LORPD cluster, providing additional insights into genetic factors. Conclusions: The findings highlight the importance of precision medicine by identifying prediabetic subgroups with varying risks for T2D. Incorporating genetic data, the study improves models and offers insights for future research and interventions to prevent prediabetes progression.