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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported quantitatively in the abstract; described only as 'strong linear relationship' — no correlation coefficient or regression parameters provided

• Primary Outcome: Correlation between tear uric acid (TUA) measured by smart contact lens and serum uric acid (SUA) by conventional blood testing, described qualitatively as demonstrating 'a strong linear relationship'

• Nnt Or Sensitivity: Analytical performance metrics (sensitivity, specificity, limit of detection, mean absolute error of digital twin predictions) not reported in the abstract; lag time profiling described qualitatively as individualised but not quantified

• Confidence Interval: Not reported

Clinical Application

Currently not feasible for clinical implementation. The device is a research prototype requiring further analytical validation, regulatory approval, and manufacturing scale-up. Contact lens wear is contraindicated in a significant proportion of patients (dry eye disease, corneal pathology, contact lens intolerance). The digital twin framework requires individual calibration data from an initial lens-wearing period, adding complexity. Cost-effectiveness has not been assessed. Gout affects approximately 1.5–2% of the Australian adult population and is disproportionately prevalent in Indigenous Australians, Torres Strait Islander peoples, and Pacific Islander communities. Current management relies on serum urate monitoring (target <0.36 mmol/L on PBS-subsidised allopurinol or febuxostat). The RACGP recommends regular SUA monitoring during urate-lowering therapy titration. A validated noninvasive monitoring device could theoretically improve adherence and monitoring frequency in remote and underserved communities, but the current technology is far from TGA registration or PBS listing. No Australian regulatory pathway has been initiated. The study population (Korean urban cohort) may not reflect the genetic, dietary, and comorbidity profile of high-risk Australian subgroups. Potentially applicable to adults with hyperuricemia, gout, or those requiring metabolic monitoring where frequent blood testing is burdensome. Most relevant to patients with recurrent gout, those initiating urate-lowering therapy, or individuals with dietary-driven hyperuricemia. Not yet applicable in clinical practice.

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.

References

1. 1.Song, H., Hong, Y.-M., Kim, D., Seo, H., Park, W., Paek, J., Lee, D., Cho, S. K., Ahn, S. S., Kim, J., & Park, J.-U. (2026). Smart contact lens-trained digital twin for device-free personalized uric acid prediction. Science Advances. PubMed ID: 42341126. https://doi.org/10.1007/978-1-4615-0717-8_124