183-OR: Metabolic Signatures of Type 2 Diabetes Subtypes in UK Biobank

Type 2 diabetes (T2D) is a complex disease with heterogeneous pathophysiology. Five distinct subtypes of T2D were previously identified in the UK Biobank and validated in the All of Us cohort through a novel clustering algorithm: Cluster 1-5, characterized by early-onset of T2D, late-onset of T2D, severe obesity, poor glycemic control, and poor renal function, respectively. We aim to establish the subtype-specific signatures of metabolites. Nuclear magnetic resonance spectroscopy-derived metabolomic profiling was undertaken on baseline plasma samples in 12,271 UK Biobank participants with T2D. Among a subset of 2828 participants with metabolomic data available, elastic net regularized regressions with 10-fold cross-validation were applied to identify subtype-specific metabolic signatures. Of the 168 named metabolites used in the analyses, we selected a combination of 39 metabolites most significantly associated with subtypes of T2D (P<0.0001, FDR<0.05). We observed subtype-specific highest levels of creatinine in the impaired renal function characterized Cluster 5 and the lowest level in Cluster 1, featured by early-onset of T2D (mean difference= 2.46 [SD=0.09], P<0.0001). Cluster 4 characterized by poor glycemic control had increased levels of glucose and decreased levels of glutamine, indicating a greater level of hyperglycemia. Individuals in Cluster 3 characterized by severe obesity exhibited elevated levels of inflammation metabolites biomarker (glycoprotein acetyls) and obesity-related amino acids (tyrosine and valine). The plasma unsaturation degree of fatty acid was also lower in Cluster 3 compared to other clusters. Cluster 2 patients had lower levels of glucose and glycoprotein acetyls and higher levels of glutamine, omega-3 fatty acids, and docosahexaenoic acids. Overall, Cluster 1 and 2 had the least metabolic dysregulation among the T2D subtypes. We have identified metabolic signatures for T2D subtypes, revealing subtype-specific metabolic mechanisms for precision interventions.