Digital twins in obesity: Emerging applications in prevention, prediction and personalised management

Obesity is a chronic and relapsing disease that now affects more than one billion people worldwide and drives a rising burden of cardiometabolic and other noncommunicable diseases. Conventional care relies on population averages and reactive adjustment, which poorly reflects the metabolic heterogeneity of individuals. The digital twin, a dynamic virtual replica of a person that is continuously updated with real world data, has emerged as a candidate solution for precise and adaptive obesity management. This narrative review synthesises current knowledge on the concept, enabling technologies, clinical applications, challenges, and future directions of digital twins in obesity. A narrative synthesis was conducted using peer reviewed articles, authoritative reports, and seminal computational studies retrieved from PubMed, Scopus, and Google Scholar. Sources were selected for relevance, methodological quality, and contribution to conceptual or clinical understanding rather than through an exhaustive systematic protocol. The final search was conducted in August 2026, and included studies were classified against explicit operational criteria for a digital twin. Evidence indicates that digital twins integrate wearable sensor streams, continuous glucose data, laboratory biomarkers, and behavioural inputs with artificial intelligence and mechanistic metabolic models to individualise nutrition, activity, and pharmacological guidance. Reported outcomes include clinically meaningful weight reduction, improved glycaemia and blood pressure, and reduced medication use. However, the strongest clinical evidence derives predominantly from people with type 2 diabetes, largely within a single commercial digital-twin ecosystem, and direct evidence in primary obesity remains limited; findings from diabetes should therefore be regarded as indirect and extrapolated rather than established in obesity. Persistent barriers involve data quality, validation, interoperability, equity, privacy, and regulation. Digital twins offer a promising, personalised, and preventive approach to obesity care, yet rigorous validation, transparent governance, and equitable access are required before routine clinical adoption.