Artificial intelligence is now being used to create “digital twins” — virtual, AI-powered models of an individual’s metabolism — that help people manage chronic conditions like diabetes and obesity without solely relying on expensive drugs. Startup Twin Health combines data from wearable devices such as continuous glucose monitors, blood pressure cuffs, smart scales and activity trackers to build a real-time digital replica of a user’s metabolic function, which then guides personalised health recommendations.
Users feed data into an app that continuously analyses blood sugar, activity, sleep and dietary choices. The AI predicts how specific foods will affect glucose levels and offers tailored suggestions throughout the day — for example, adjusting portion sizes, choosing healthier foods, or recommending movement after meals. Over time, the model adapts to an individual’s preferences while encouraging healthier behaviour changes.
Clinical studies suggest significant outcomes: a trial associated with the Cleveland Clinic found that a majority of participants in the digital twin programme achieved target blood sugar levels with fewer medications and greater weight loss compared with control groups, and many reduced their reliance on glucose-lowering drugs. This points to the potential of AI-driven lifestyle support as a complement or alternative to conventional treatments.
While such systems may feel data-intensive, experts see them as a promising step toward personalised preventive care, empowering users to proactively manage metabolic health and possibly prevent progression to more serious disease. As access grows and technology improves, digital twin-based interventions could become a scalable tool in the fight against diabetes and obesity.
