Applications of digital twin technology in cardiometabolic disease management: a scoping review

Cardiometabolic diseases are characterized by long disease courses, substantial inter-individual heterogeneity, and complex interactions among multiple risk factors, posing significant challenges to long-term management and precision interventions. Digital twin technology, which constructs individualized digital representations of patients, has demonstrated unique potential in precision disease management and clinical decision support. However, there remains a gap in the systematic synthesis of its applications in cardiometabolic disease management.

This scoping review aimed to systematically outline the core application scenarios of digital twin technology in cardiometabolic disease management, summarize the integrated data sources and key technical approaches used to construct and operate digital twins, identify current technical, ethical, and clinical challenges, and outline future research directions.

A scoping review was conducted on November 29, 2025. Relevant studies on the application of digital twin technology in cardiometabolic disease management were systematically searched, screened, and selected. Data were extracted and synthesized with a focus on application scenarios, data sources, modeling approaches, implementation stages, and reported challenges.

Of the 5596 articles identified, 32 met inclusion criteria after screening. Digital twin applications in cardiometabolic diseases primarily focused on individualized risk prediction and stratification, optimization of treatment and intervention strategies, and clinical decision support. Integrated data sources mainly included physiological and metabolic data, electronic data of clinical documents, medical imaging, and behavioral and lifestyle-related data. Data-driven models were the most commonly used approach, followed by mechanistic models, hybrid models, multi-scale or multi-physics models, and simulation-based optimization methods. Most studies remained at the proof-of-concept or early implementation stage. Major challenges included limited data availability and integration, difficulties in model personalization and validation, ethical and privacy concerns, and insufficient integration into clinical and care workflows.

Digital twin technology shows significant promise for precision management of cardiometabolic diseases. Existing studies indicate that digital twins are primarily applied in individualized risk assessment, optimization of treatment strategies, disease progression simulation, and long-term management support. However, prevailing research remains predominantly data-driven and is not yet fully aligned with sustained disease management and routine care processes. Future efforts should emphasize mechanistic and hybrid modeling approaches, conduct multicenter prospective validations and randomized controlled trials, and promote interoperable and privacy-preserving system deployment to support the sustainable implementation of digital twins in cardiometabolic disease management.