Identification of distinct trajectories in preclinical type 2 diabetes and their associations with outcomes

Type 2 Diabetes Mellitus (T2DM) is increasingly prevalent and significantly impacts patients lives. However, the phenotypic and genetic heterogeneity of the preclinical stage of T2DM, along with the subsequent effects on various clinical outcomes, remain unclear, impeding progress in disease screening and prevention. To address this gap, we employed a robust machine learning algorithm (Subtype and Stage Inference, SuStaIn) with cross-sectional clinical data from the UK Biobank (20,305 preclinical-T2DM participants and 20,305 controls) to identify underlying subtypes and their progression trajectories for preclinical-T2DM. Our analysis revealed one subtype distinguished by elevated circulating leptin levels and decreased leptin receptor levels, coupled with increased BMI, diminished lipid metabolism, and heightened susceptibility to psychiatric conditions such as anxiety disorder, depression disorder, and bipolar disorder. Conversely, individuals in the second subtype manifested typical abnormalities in glucose metabolism, including rising glucose and HbA1c levels, with observed correlations with neurodegenerative disorders. Over ten-year follow-up observations of these individuals reveal differential deterioration in brain and heart organs, and statistically significant difference in disease risk and clinical outcomes between the two subtypes. Our findings indicate a heterogenous pathobiological basis underlying the progression of preclinical-T2DM, with clinical implications for understanding human health from a multiorgan perspective, and improving disease risk screening, prediction, and prevention efforts.