Fei Chen, Yang Zhang, Weihao Wang, Ge Li, Jian Zhang, Peiheng Zhang, Jingcui Guo, Wuxiang Xie, Feifei Zhang, Ying Gao
Previous studies have identified heterogeneity among prediabetes subgroups using clinical characteristics; however, biological and metabolic heterogeneity remains insufficiently captured. This study examined whether data-driven clustering based on metabolomic biomarkers could define distinct prediabetes subtypes with differential type 2 diabetes, cardiovascular disease, and chronic kidney disease risk. Using 16 metabolomic biomarkers, we identify three metabolically distinct clusters showing progressively higher risks of incident type 2 diabetes, cardiovascular disease, and chronic kidney disease. Differential diet-cluster associations across clusters were obtained, and Mendelian randomization supported potential causal roles for several metabolomic biomarkers. Metabolomics-based stratification may improve risk prevention and enable cluster-specific dietary interventions in prediabetes.