2283-P: A Multiomics Atlas of Multisystem Complications in Type 2 Diabetes Reveals Molecular Signatures and Improves Risk Prediction

Introduction and Objective: Type 2 diabetes (T2D) causes multisystem complications, but comprehensive multi-omics analyses across diverse clinical outcomes remain limited. We aimed to comprehensively construct the proteomic and metabolomic atlas of major T2D outcomes and to identify predictive panels that balance performance and cost-effectiveness. Methods: This study integrates UK Biobank proteomic and metabolomic data and combines cross-sectional and longitudinal analyses to construct an atlas of plasma proteins and metabolites associated with major T2D-related clinical outcomes. We developed LASSO-based predictive models, benchmarked them against SCORE2-Diabetes, and evaluated performance using Harrell’s C-index, net reclassification improvement, and integrated discrimination improvement. Results: The study identified molecular signals that consistently exhibited positive or negative associations across multiple T2D outcomes, revealing shared biological pathways. We also uncovered heterogeneous and outcome-specific molecules across diseases. Cross-sectional and longitudinal analyses enabled the identification of state markers and early predictive biomarkers. Furthermore, protein-based models substantially outperform clinical models, and combined models achieve the best performance with consistent improvements in reclassification metrics (median delta C-index = 0.117; range: 0.060-0.213), whereas metabolites provide only modest incremental gains. Evaluation across varying selection thresholds identifies a simplified panel of 200 proteins that maintains robust predictive performance. Conclusion: This large-scale multi-omics study systematically constructs the molecular atlas of T2D complications, providing new insights into disease biology and potential therapeutic targets. It further defines the predictive value of proteomic and metabolomic profiles and proposes a clinically feasible and practical framework for predicting the risk of T2D complications. Disclosure H. Zhang: None. Funding This work was supported by the following funding: Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0509100; 2023ZD0509102), the Program for Innovative Research Team of The First Affiliated Hospital of University of Science and Technology of China (CXGG02), Emergency Scientific and Technological Research Project on Novel Coronavirus Infection (BJ9100000005), Anhui Provincial Clinical Medical Research and Translational Special Project (202204295107020026), National Natural Science Foundation of China (Grant No. 82300904), National Natural Science Foundation of China (Grant No. 82100857), Anhui Provincial Department of Education University Research Project (2023AH051765).