Smart contact lens-trained digital twin for device-free personalized uric acid prediction.

Abstract

Tears contain valuable biomarkers and offer potential for noninvasive disease monitoring. However, the lack of correlation analysis between serum uric acid (SUA) and tear uric acid (TUA) has limited their clinical application in personalized medicine. Here, we present a wireless smart contact lens capable of real-time, noninvasive monitoring of TUA as an alternative to blood-based UA testing. We validated the device through correlation analysis in rabbits and human participants, including individuals with hyperuricemia and gout. Daily-life monitoring enabled personalized characterization of TUA fluctuations in response to food intake and physical activity, together with individualized lag time profiling. A strong linear relationship allowed development of a regression model to derive estimated SUA from TUA. Building on these temporal profiles, lifestyle-informed digital twins were constructed to predict daily uric acid dynamics without continuous lens wear. This digital twin-enabled, device-free framework provides a practical route toward unobtrusive and personalized metabolic health monitoring.