Background

The importance of monitoring blood glucose cannot be overstated given the increasing population of diabetics worldwide and the associated costs [1]. However, due to the painful finger-stick blood sampling process, several studies report that more than half of the patients do not follow daily self-monitoring recommendations [2]. Perhaps a better alternative is an in-vivo continuous blood glucose monitoring while also providing a convenient, and non-intrusive measurement technology to patients. Such continuous monitoring systems allow subjects to prepare the diabetes management strategy and prevent the long-term complications of diseases, such as kidney problems, strokes, heart failure, and ocular disease.

Over the past decades, many methods have been proposed. Among them, optical spectroscopic methods have attracted great attention, namely Near-Infrared Spectroscopy (NIR) and Raman Spectroscopy (RS), given their simplicity and practicality. While NIR appears promising, researchers have found that NIR suffers from low signal-to-noise ratio [3]. Specifically, due to the broad NIR absorption features of glucose, it often confounds with the absorption of other chromophores (proteins, lipids, collagen) in tissue, and thus subsequently reduces the signal-to-noise ratio of actual blood glucose. In addition, other confounding factors such as changes in temperature or contact pressure can easily dominate weak glucose signals. On the other hand, due to the extremely weak Raman scattering signal of water, sharper spectral features, and few spectral overlaps, Raman spectroscopy (RS) is particularly suitable for detecting biological analytes such as blood glucose [4]. RS is often used to identify the chemical composition of tissue by analyzing how visible or near-infrared light is inelastically scattered, as it encounters different kinds of molecules.

Nevertheless, although RS appears promising, the level of this technology is still far from perfection: the key challenges remain to be that glucose-specific peaks in Raman spectra are very weak which are further subdued by strong skin and tissue autofluorescence, which make it difficult to make good prediction models [5, 6]. Another challenge lies on frequent calibration which can often be thrown off by movement of the subject or changes in environmental conditions [7]. The key direction forward is to find a good way to extract these weak glucose-specific peaks and isolate artifacts, likely through advanced signal processing and machine learning methods (e.g., identification of energy wavelengths) or through effective data extraction methods (e.g., choosing suitable body locations).

In conclusion, this project seeks to utilize Raman spectroscopy for developing a non-invasive continuing method for blood glucose monitoring. In the long term, this project envisions the development of a portable and/or wearable monitoring system that could offer continuous glucose measurements. Nevertheless, not to mislead any overpromises, fundamental studies of this technology are needed before this Raman-based system can be used. The strategy of this WP is to build upon the strong past work, so we can quickly create our own prototype, and at the same time, carry out any required fundamental/applied studies to reach our long-term goal.

References

  1. Ogurtsova, K., da Rocha Fernandes, J.D., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N.H., Cavan, D., Shaw, J.E. and Makaroff, L.E. (2017). IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes research and clinical practice, 128, 40-50.
  2. Patton, S. R. (2015). Adherence to glycemic monitoring in diabetes. Journal of diabetes science and technology, 9(3), 668-675.
  3. Yadav, J., Rani, A., Singh, V., & Murari, B. M. (2015). Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy. Biomedical signal processing and control, 18, 214-227.
  4. Pandey, R., Paidi, S. K., Valdez, T. A., Zhang, C., Spegazzini, N., Dasari, R. R., & Barman, I. (2017). Noninvasive monitoring of blood glucose with raman spectroscopy. Accounts of chemical research, 50(2), 264-272.
  5. Barman, I., Kong, C. R., Singh, G. P., & Dasari, R. R. (2011). Effect of photobleaching on calibration model development in biological Raman spectroscopy. Journal of biomedical optics, 16(1), 011004.
  6. Zhao, J., Lui, H., McLean, D. I., & Zeng, H. (2007). Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Applied spectroscopy, 61(11), 1225-1232.
  7. Singh, S.P., Mukherjee, S., Galindo, L.H., So, P.T., Dasari, R.R., Khan, U.Z., Kannan, R., Upendran, A. and Kang, J.W. (2018). Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing. Analytical and bioanalytical chemistry, 410(25), 6469-6475.
  8. Li, N., Zang, H., Sun, H., Jiao, X., Wang, K., Liu, T. C. Y., & Meng, Y. (2019). A noninvasive accurate measurement of blood glucose levels with raman spectroscopy of blood in microvessels. Molecules, 24(8).
  9. Kang, J.W., Park, Y.S., Chang, H., Lee, W., Singh, S.P., Choi, W., Galindo, L.H., Dasari, R.R., Nam, S.H., Park, J. and So, P.T. (2020). Direct observation of glucose fingerprint using in vivo Raman spectroscopy. Science Advances, 6(4).