AI-Driven Digital Twin Medicine for Precision Diabetes Care

Hsiu-Hsi ChenTaiwan Speaker AI-Driven Digital Twin Medicine for Precision Diabetes CareDiabetes mellitus is one of the most rapidly growing global health challenges, affecting nearly 589 million adults worldwide and projected to reach a prevalence of approximately 13% by 2050. Early detection and timely intervention are therefore critical to preventing long-term microvascular and macrovascular complications. However, type 2 diabetes typically evolves through a prolonged asymptomatic phase, creating a substantial window for population-based screening and preventive strategies. Using data from the Changhua Community-based Integrated Screening (CHCIS) program in Taiwan, we quantified the natural history of diabetes progression and developed personalized risk prediction models. In this population, prediabetes prevalence reached approximately 18.6% and type 2 diabetes prevalence 10.6%. Continuous-time Markov modeling characterized the dynamic metabolic trajectory from normoglycemia to prediabetes, asymptomatic diabetes, and symptomatic disease, with estimated transition rates of 0.0328, 0.1764, and 0.0988 per person-year, respectively, highlighting a prolonged subclinical phase during which targeted screening and intervention may substantially alter disease progression. Building on this natural history framework, multistate personalized risk prediction models incorporating demographic, metabolic, and lifestyle factors—including age, body mass index, blood pressure, triglycerides, liver enzymes, smoking status, and family history of diabetes—were developed to estimate individualized progression risks. The machine learning with Random forest was adopted to identify important personalized features for predictive performance across disease stages and provided empirical support for targeted screening and precision prevention strategies. To further refine individualized prediction, genetic risk markers identified in prospective cohort studies were incorporated into multivariate risk scores. Variants in genes involved in glucose metabolism and β-cell function contributed to pathway-specific risk scores for transitions from normal glucose tolerance to impaired fasting glucose and from prediabetes to diabetes. Integrating clinical and genetic risk factors enabled personalized kinetic trajectories for disease progression and intervention strategies. Emerging evidence further suggests that integrating multi-omics data—including metabolomics, proteomics, epigenomics, and microbiome profiles—may enhance AI-based personalized risk prediction by capturing dynamic biological states and gene–environment interactions beyond static genetic susceptibility. In parallel, continuous glucose monitoring (CGM) technologies provide high-resolution physiological signals reflecting real-time glycemic dynamics, lifestyle responses, and treatment effects. Integrating population screening data, multiscale risk prediction, genetic and multi-omics susceptibility profiles, and real-time CGM signals creates the foundation for AI-driven digital twin medicine, in which a dynamic virtual representation of each individual can simulate metabolic trajectories and evaluate alternative interventions before they occur in reality. Ultimately, the convergence of population screening, multiscale risk prediction, multi-omics profiling, and continuous metabolic monitoring within AI-driven digital twin frameworks may redefine diabetes care—shifting healthcare systems from reactive disease treatment toward proactive precision prevention and lifelong metabolic health management. Keywords: Diabetes mellitus; Digital twin medicine; Continuous glucose monitoring; Multistate Markov model; Precision medicine; Genetic risk prediction; Population screening; Artificial intelligence