Noninvasive Photonic-Crystal Material for Sensing Glucose in Tears
Abstract
Most noninvasive (NI) methods for the determination of glucose either detect a small specific glucose signal or measure the effect of glucose on a tissue optical property (1)(2). A recent review identified the three main issues in NI glucose measurements as specificity, compartmentalization of glucose values, and calibration (1). This editorial discusses a photonic crystal method (3), with respect to these issues. Asher’s group have constructed a photonic glucose sensor in the form of thin acrylamide PCCA hydrogel films that contain glucose molecular recognition elements (3)(6)(7). Phenylboronic acid derivatives in the lattice bind glucose, causing a change in charge distribution and a blue shift, −Δλ, in the diffracted light (3). In vitro experiments showed that Δλ responded to changes in glucose concentration with highest sensitivity at glucose concentrations <10 mmol/L (3)(6)(7). The magnitude of the blue shift decreased as a function of glucose concentration when glucose approached 20 mmol/L (3). This made the PCCAs suitable only for detection of the tear-glucose concentration, which is considerably lower than that of blood. The PCCA lattice shrinks as the glucose concentration increases (3) and possibly reaches a lower limit where minimal Δλ is observed. The authors conceive using the polymer film sensor as a contact lens that changes color according to the glucose concentration in tears. Boronic acid fluorophores embedded in a commercial contact lens and immersed in glucose solutions undergo changes in fluorescence intensity and wavelength on binding to glucose (8). The fluorescent contact lens film has the highest sensitivity at low glucose concentrations, which correspond to those in tear fluid. The novelty and the sensitivity of PCCAs at low glucose concentrations are the impetus for this editorial, in which I examine the contact lens construct as a NI glucose testing modality. The detection method depends on interaction between glucose and a specific binding molecule. It will have a specificity advantage over near-infrared absorption and scattering methods, but successful application in vivo is awaited. The dynamic range is limited to the glucose concentration in tears (3). The reversibility of the Δλ change on a reverse in the change in glucose concentration must be demonstrated in animal models and in human volunteers. Current patient care is based on measurement of glucose in venous blood or arterialized venous blood. NI methods attempt to determine glucose in other body fluids, such as in tissue interstitial fluid, eye vitreous fluid, or tears, as substitutes for venous or capillary blood glucose. NI-determined glucose values in any body compartment must track changes in blood glucose without a lag time (9). This may not be the case when changes in blood glucose concentrations are sudden and are of too large a magnitude to allow for equilibration between the vascular compartment and other body compartments (1)(9)(10). Even for blood glucose measurements, there are site-specific rates of increase and decrease in blood glucose values (10). Equilibration between glucose in the blood and in other body fluids is a controversial issue with widely different reported lag times (1). Alexeev et al. (3) propose using photonic contact lens sensors with tears as the body fluid. The relative concentrations and lag times between glucose concentrations in tears and in the vascular system will require detailed studies. Tears are generated in the lachrymal glands, and external stimuli affect the rate of tear generation. There are limited reports on the relationship between tear and blood glucose concentrations. The relationship seems to depend on the sampling method for tears and the extent of eye irritation during sampling (11). Earlier tear-glucose studies were semiquantitative because the investigators used color strips, and they did not include glucose surge experiments. Gassett et al. (12) showed that in an oral glucose tolerance test (OGTT), tear-glucose concentrations tracked blood glucose with a time lag (from graph) of ∼20 min. The mean (SD) tear glucose value for 30 nondiabetic individuals was 0.24 (0.17) mmol/L, and that of blood was 4.4 (1.7) mmol/L (12). Citing earlier studies, Van Haeringen (11) concluded that there was no significant increase in tear-glucose concentrations when blood glucose concentrations were >20 mmol/L, which he considered to indicate that the corneal and conjunctival epithelium acted as a barrier against glucose transfer from tissues into the tear fluid. Tissue fluid, and not the lachrymal gland fluid, contributed to the “tear glucose” after mechanically stimulated methods of collection, making the relationship between tear glucose and blood glucose concentrations similar to that between blood glucose and tissue fluid (11). Daum and Hill (13) reported a mean tear-glucose concentration of 0.42 (0.36) mmol/L for 12 nondiabetic individuals. The tear-glucose concentration general
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Other contact lens-based optical sensors have involved the use of photonic crystal materials in combination with responsive hydrogels or fluorescent dyes for measuring glucose and other target analytes in tears.
“Other contact lens-based optical sensors have involved the use of photonic crystal materials in combination with responsive hydrogels or fluorescent dyes for measuring glucose and other target analytes in tears110-113.”
From Wearable biosensors for healthcare monitoring (2019) · cites this paper as [110]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Polymerized colloidal crystal hydrogel films as intelligent chemical sensing materials
Holtz, J. H., & Asher, S. A. (1997) · Nature · doi:10.1038/39834 · Cited by 2,003 · see citing works
Photonic Crystal Carbohydrate Sensors: Low Ionic Strength Sugar Sensing
Asher, S. A., Alexeev, V. L., Goponenko, A. V., Sharma, A. C., Lednev, I. K., Wilcox, C. S., & Finegold, D. N. (2003) · Journal of the American Chemical Society · doi:10.1021/ja021037h · Cited by 487 · see citing works
Clinical biochemistry of tears
Haeringen, N. J. V. (1981) · Survey of Ophthalmology · doi:10.1016/0039-6257(81)90145-4 · Cited by 445 · see citing works
High Ionic Strength Glucose-Sensing Photonic Crystal
Alexeev, V. L., Sharma, A. C., Goponenko, A. V., Das, S., Lednev, I. K., Wilcox, C. S., Finegold, D. N., & Asher, S. A. (2003) · Analytical Chemistry · doi:10.1021/ac030021m · Cited by 405 · see citing works
Photonic Crystal Glucose-Sensing Material for Noninvasive Monitoring of Glucose in Tear Fluid
Alexeev, V. L., Das, S., Finegold, D. N., & Asher, S. A. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2004.039701 · Cited by 370 · see citing works
Spectroscopic and Clinical Aspects of Noninvasive Glucose Measurements
Khalil, O. S. (1999) · Clinical Chemistry · doi:10.1093/clinchem/45.2.165 · Cited by 331 · see citing works
Self-citationNon-Invasive Glucose Measurement Technologies: An Update from 1999 to the Dawn of the New Millennium
Khalil, O. S. (2004) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2004.6.660 · Cited by 256 · see citing works
Self-citationBadugu, R., Lakowicz, J. R., & Geddes, C. D. (2003) · Analytical Chemistry · doi:10.1021/ac0303721 · Cited by 151 · see citing works
Development of an Intelligent Polymerized Crystalline Colloidal Array Colorimetric Reagent
Reese, C. E., Baltusavich, M. E., Keim, J. P., & Asher, S. A. (2001) · Analytical Chemistry · doi:10.1021/ac010667j · Cited by 95 · see citing works
Tear Glucose Detection of Hyperglycemia
Gasset, A. R., Braverman, L. E., Fleming, M. C., Arky, R. A., & Alter, B. R. (1968) · American Journal of Ophthalmology · doi:10.1016/0002-9394(68)93093-6 · Cited by 68 · see citing works
Koschinsky, T., Jungheim, K., & Heinemann, L. (2003) · Diabetes Technology & Therapeutics · doi:10.1089/152091503322527030 · Cited by 64 · see citing works
DAUM, K. M., & Hill, R. M. (1982) · PubMed · Cited by 56 · see citing works
Estimation of tear glucose level and its role as a prompt indicator of blood sugar level.
Chatterjee, P. R., De, S., Datta, H., Chatterjee, S., Biswas, M., Sarkar, K., & Mandal, L. K. (2003) · PubMed · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Heinemann L, Koschinsky T. Continuous glucose monitoring: an overview of today's technologies and their clinical applications [Review]. Int J Clin Pract Suppl 2002;129:75-9.
Cited by
Sorted by citations
Wearable biosensors for healthcare monitoring
Kim, J., Campbell, A. S., de Ávila, B. E.-F., & Wang, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0045-y · Cited by 3,373 · see citing works
Device integration of electrochemical biosensors
Wu, J., Liu, H., Chen, W., Ma, B., & Ju, H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00032-w · Cited by 931 · see citing works
Non-invasive glucose monitoring technology in diabetes management: A review
Vashist, S. K. (2012) · Analytica Chimica Acta · doi:10.1016/j.aca.2012.03.043 · Cited by 575 · see citing works
Current development in non-invasive glucose monitoring
Ferrante do Amaral, C. E., & Wolf, B. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2007.06.003 · Cited by 269 · see citing works
Recent advances in noninvasive glucose monitoring
Chung, J. W. Y., So, H., Choi, K., & Wong, T. K. S. (2012) · Medical Devices Evidence and Research · doi:10.2147/mder.s28134 · Cited by 221 · see citing works
Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review
Hina, A., & Saadeh, W. (2022) · Sensors · doi:10.3390/s22134855 · Cited by 144 · see citing works
Gušev, M., Poposka, L., Spasevski, G., Kostoska, M., Koteska, B., Simjanoska, M., Ackovska, N., Stojmenski, A., Tasič, J. F., & Trontelj, J. K. (2020) · Journal of Sensors · doi:10.1155/2020/9628281 · Cited by 84 · see citing works
Kandwal, A., Sharma, Y. D., Jasrotia, R., Kit, C. C., Natrayan, L., Sillanpää, M. E. T., Liu, L. W., Igbe, T., Kumari, A., Sharma, R., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37825 · Cited by 44 · see citing works
An Analysis and Design of Photonic Crystal-Based Biochip for Detection of Glycosuria
Sharma, P., & Sharan, P. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2441651 · Cited by 39 · see citing works
Li, Z., Li, G., Yan, W., & Lin, L. (2014) · Infrared Physics & Technology · doi:10.1016/j.infrared.2014.09.040 · Cited by 33 · see citing works
Portable system for the detection of micromolar concentrations of glucose
Kostov, Y. V., Ge, X., Rao, G., & Tolosa, L. M. (2014) · Measurement Science and Technology · doi:10.1088/0957-0233/25/2/025701 · Cited by 18 · see citing works
A novel non-invasive blood glucose monitoring approach using saliva
Malik, S., Gupta, S., Khadgawat, R., & Anand, S. (2015) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices.2015.7091562 · Cited by 17 · see citing works
Waghmare, A., Parizi, F. S., Hoffman, J. S., Wang, Y., Thompson, M. J., & Patel, S. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580855 · Cited by 16 · see citing works
Noninvasive Blood Glucose Measurement
Uwadaira, Y., & Ikehata, A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812019-4.00036-2 · Cited by 14 · see citing works
Stress biomarkers in the tear film
Banbury, L. K. (2009) · Cross Connect (Southern Cross University) · Cited by 11 · see citing works
Low Cost and Flexible Sensor System for Non-Invasive Glucose In-Situ Measurement
Hassan, U., Zulfiqar, M. H., Rahman, M. M. U., & Riaz, K. (2020) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast47879.2020.9044524 · Cited by 11 · see citing works
Photon counting polarimetry measurement towards non-invasive biomedical glucose monitoring
Zain, M. N. M., Musa, A., Hisham, M. H., Laili, A. R., & Yusof, Z. M. (2014) · IEEE International Conference on Photonics · doi:10.1109/icp.2014.7002364 · Cited by 7 · see citing works
Ultra-Narrow Spectral Response of a Hybrid Plasmonic-Grating Sensor
Elshorbagy, M. H., Cuadrado, A., García‐Cámara, B., Vergaz, R., Gomez-Pedrero, J. A., & Alda, J. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2960556 · Cited by 7 · see citing works
Trends from Minimally Invasive to Non-invasive Glucose Measurements
Gušev, M., Poposka, L., Guseva, E., Kostoska, M., Koteska, B., Simjanoska, M., Ackovska, N., & Stojmenski, A. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245402 · Cited by 5 · see citing works
Non-Invasive Monitoring of Diabetes
Khalil, O. S. (2007) · Springer eBooks · doi:10.1007/0-387-33015-1_7 · Cited by 3 · see citing works
Self-citationNon Invasive Blood Glucose Detection along with an Assistive Diabetes Monitoring App
Aswathy, S., & Anusree, L. (2018) · International Conference on Current Trends towards Converging Technologies (ICCTCT) · doi:10.1109/icctct.2018.8551094 · Cited by 3 · see citing works
High-speed dual-wavelength optical polarimetry for glucose sensing
Grunden, D. T., Pirnstill, C. W., & Coté, G. L. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2040239 · Cited by 2 · see citing works
Non-Invasive Approach for Glucose Detection in Urine Quality Using Its Image Analysis
Yudhana, A., Sabila, L. Y., Subrata, A. C., Pratama, H. H., & Akbar, M. S. (2022) · Biomedical engineering · doi:10.5772/intechopen.104791 · Cited by 2 · see citing works
An Assessment of Pain-Free Blood Glucose Level by Noninvasive Methods
Agalya, V., & Sumathi, S. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.13501 · Cited by 2 · see citing works
AL-Khafaji, R. D. M., Pusppanathan, J., Moolla, A., & Phang, F. A. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-97578-3_8 · Cited by 1 · see citing works
Chapter 3 Non-invasive analytics for point-of-care testing of glucose
Vashist, S. K., & Luong, J. H. T. (2016) · CRC Press eBooks · doi:10.1201/9781315366746-4
Qi, Y. (2019) · Radar (Oxford Brookes University) · doi:10.24384/wpqe-fr48
The Clinical Accuracy of Non-Invasive Glucose Monitoring for ex vivo Artificial Pancreas
Levy, M. M., & Levy, Y. M. (2016) · Journal of Emerging Investigators · doi:10.59720/16-008
Hybrid deep learning-based blood glucose level monitoring using PPG signals
Soliman, A. Y., Nor, A. M., Omer, O. A., & Mubarak, A. S. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05368-5
Peng, Y., Huang, X., Zhang, X., Jiang, Y., Zhang, X., Zeng, X., Sun, F., Liu, P., Zhou, J., Luo, Z., et al. (2026) · Interdisciplinary medicine · doi:10.1002/inmd.70132
- Authors
- Khalil, O. S.
- Affiliations
- Abbott (United States) (US)
- Corresponding author
- Khalil, Omar S
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-12-01
- Volume
- 50
- Issue
- 12
- Pages
- 2236-2237
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- OpenAlex
- W2121778355
- Cited by
- 30
- References
- 13
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 2 · 2016: 2 · 2015: 2 · 2014: 4 · 2012: 2
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Clinical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Photonic Crystals and Applications
- Physical Sciences › Chemistry › Organic Chemistry › Polydiacetylene-based materials and applications
- Physical Sciences › Chemical Engineering › Bioengineering › Analytical Chemistry and Sensors
- Keywords
- non-invasive glucose monitoring, photonic crystals, tear glucose, phenylboronic acid, blue shift, calibration, dynamic range, blood glucose monitoring, diabetes monitoring, sensor reversibility
- MeSH terms
- Blood Glucose, Boronic Acids, Contact Lenses, Crystallization, Glucose, Humans, Microspheres, Tears
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review