AI-Enabled Prediction of Cardiometabolic Disease in Type 2 Diabetes: Integrating Multiomics, Mechanistic Modeling and Precision Medicine

Artificial intelligence (AI) creates an opportunity to move personalized diabetes medicine from classification of established risk factors toward prospective prediction of disease trajectories. This transition is particularly relevant to type 2 diabetes, in which heterogeneous metabolic, inflammatory, vascular, renal, hepatic, and myocardial mechanisms can produce substantially different risks despite similar conventional clinical phenotypes. Current markers including HbA1c, fasting glucose, blood pressure, estimated glomerular filtration rate, albuminuria, and standard lipid measures remain essential but incompletely capture early mechanistic risk, treatment-response heterogeneity, and progression toward cardiometabolic complications. Multiomic technologies, i.e., genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle profiling, provide complementary views of inherited liability, regulatory state, pathway activation, metabolic flux, and inter-organ signaling. However, multiomic translation is limited by high dimensionality, temporal variability, tissue specificity, assay heterogeneity, and difficulty distinguishing causal drivers from downstream correlates. Mechanistic artificial intelligence (AI) offers a potential approach for integrating these data into biologically interpretable models capable of disease-trajectory prediction, endotype classification, and hypothesis-driven therapeutic stratification. Operon(TM), GATC Health's internally operated AI-driven scientific computing platform, is considered as an illustrative architecture for integrating heterogeneous biological data, systems-level inference, and scenario-based simulation. Importantly, such platforms should be distinguished from validated clinical decision systems; disease-specific applications require external and prospective validation before clinical utility can be established. This review proposes a framework for AI-enabled prediction of cardiometabolic disease in diabetes that integrates longitudinal clinical phenotyping, multiomics, mechanistic endotyping, and intervention mapping, while defining the validation, equity, and implementation requirements necessary for translation into precision medicine.