EP657 - ECE_2738 - On the road to type 2 diabetes: metabolomic profiling for early detection, risk stratification, and complementary assessment of metabolic trajectories

Abstract

Prediabetes represents a major and growing global health burden, affecting hundreds of millions of individuals worldwide and substantially increasing the risk of progression to type 2 diabetes mellitus (T2D). As metabolic dysfunction precedes overt hyperglycemia by several years, improved characterization of early systemic metabolic alterations is critical for timely risk stratification and prevention. In this study, we analyzed large-scale quantitative metabolomic profiles from more than 8,000 participants of the LIFE Adult Study using high-resolution H-NMR–based metabolomics. Over 250 metabolites and metabolic features per sample were quantified to characterize metabolic states associated with normoglycemia, prediabetes, and manifest T2D. Emphasis was placed on metabolite patterns reflecting insulin resistance, altered lipid and amino acid metabolism, and early disturbances in glucose and energy homeostasis. Integration of metabolomic data with clinical phenotypes enabled the identification of distinct metabolic signatures associated with increased future T2D risk. These signatures extend beyond single biomarkers and capture system-level metabolic dysregulation, supporting metabolomics-based risk stratification as a complementary approach to conventional glycemic markers. The findings underline the potential of metabolomic profiling to improve early detection of prediabetes and to support targeted preventive interventions before irreversible disease progression occurs. Beyond risk assessment, metabolomics-based measures of insulin resistance may also provide a complementary framework for assessing longitudinal metabolic response trajectories in the context of emerging antidiabetic therapies, including incretin-based treatments such as GLP-1 receptor agonists. While GLP-1 therapies exert pleiotropic effects extending beyond insulin resistance—such as glucose-dependent insulin secretion, appetite regulation, gastric emptying, and weight reduction—metabolomic insulin resistance markers may offer additional system-level insight into metabolic adaptation over time. Such approaches are intended to complement, rather than replace, established clinical endpoints including glycemic control, body weight, and treatment tolerability. In summary, large-scale quantitative metabolomics combined with digital analytics provides valuable insights into early metabolic trajectories from health to prediabetes and type 2 diabetes, with implications for prevention, risk stratification, and complementary assessment of metabolic responses.