In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens

Wonjung Park, Hunkyu Seo, Jeongho Kim, Yeon-Mi Hong, Hayoung Song, Byung Jun Joo, Sumin Kim, Enji Kim, Che-Gyem Yae, Jeonghyun Kim, Jonghwa Jin, Joohee Kim (), Yong-ho Lee (), Jayoung Kim (), Hong Kyun Kim () and Jang-Ung Park ()
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Wonjung Park: Yonsei University
Hunkyu Seo: Yonsei University
Jeongho Kim: Kyungpook National University
Yeon-Mi Hong: Yonsei University
Hayoung Song: Yonsei University
Byung Jun Joo: Yonsei University
Sumin Kim: Yonsei University
Enji Kim: Yonsei University
Che-Gyem Yae: Kyungpook National University School of Medicine
Jeonghyun Kim: Kwangwoon University
Jonghwa Jin: Kyungpook National University Hospital
Joohee Kim: Biomedical Research Division Korea Institute of Science and Technology
Yong-ho Lee: Yonsei University College of Medicine
Jayoung Kim: Yonsei University College of Medicine
Hong Kyun Kim: Kyungpook National University
Jang-Ung Park: Yonsei University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Tears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called ‘personalized lag time’. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation.

Date: 2024
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DOI: 10.1038/s41467-024-47123-9

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