Elucidating the heterogeneity of prediabetes through subphenotyping with a two-dimensional tree structure.

Abstract

Prediabetes, an intermediate stage of developing diabetes, exhibits considerable phenotypic heterogeneity. Here, we apply the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm to explore prediabetes heterogeneity in 55,777 participants from the China Cardiometabolic Disease and Cancer Cohort (4C) study. Based on 12 clinically available variables, we identify four distinct phenotypes and observe differential risks of type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD). Phenotype 4, characterized by hyperglycemia, insulin resistance, obesity, elevated triglycerides, and liver enzymes, has the highest T2DM risk, while phenotype 3, predominantly driven by obesity, insulin resistance, hyperglycemia, and dyslipidemia, has the highest CKD risk. Phenotypes 3 and 4 show higher CVD risk, with distinct distributions of CVD subtypes. These findings are validated in the external cohort SN_2009-2021, and a user-friendly online tool is provided for individual risk prediction. Overall, our study elucidates the intricate dynamics of prediabetes progression, aiding in personalized management for prediabetes care.