Machine Learning, Multi-Omics, and Digital Twin Systems for the Treatment and Management of Diabetes

Review article

Abstract

Diabetes mellitus is regarded as a primary driver of global morbidity and mortality, characterized by profound metabolic heterogeneity that eludes conventional, standardized treatment protocols. Traditional care models, heavily anchored on periodic glycated hemoglobin measurements, fail to capture the highly dynamic and multi-faceted nature of the disease, leading to delayed therapeutic adjustments and sub-optimal long-term outcomes. Modern advancements at the intersection of computational intelligence, multi-omics, and digital health technologies are driving a fundamental reorganization of metabolic disease management. Machine learning models now allow the accurate forecasting of acute glycaemic excursions, personalizing therapeutic interventions before adverse events occur by utilizing high-frequency data streams from continuous glucose monitors, wearable biosensors, and clinical registries. Simultaneously, genomic, transcriptomic, proteomic, and metabolomic profiling have unveiled distinct biological subphenotypes, enabling the targeted matching of pharmacotherapies to individual metabolic signatures. This systems-level approach is further operationalized through automated closed-loop insulin delivery networks and digital twin system that replicate human metabolic physiology in silico. The unification of these technologies shifts metabolic medicine from a historical, reactive discipline into a continuous, predictive, and highly individualised science. The convergence of computational biology, real-time sensing, and multi-omic stratification provides a robust framework to mitigate microvascular and macrovascular complications, optimize therapeutic adherence, and establish a scalable model for personalized endocrinology worldwide.