Hamamatsu Photonics K.K., Hamamatsu, Japan.
Hamamatsu University, School of Medicine, Hamamatsu, Japan.
J Biomed Opt. 2024 Mar;29(3):037001. doi: 10.1117/1.JBO.29.3.037001. Epub 2024 Mar 5.
Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin.
Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy.
The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices.
Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests.
This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.
许多研究人员试图使用近红外 (NIR) 光谱技术无创地估计血糖水平 (BGL)。然而,血糖引起的光吸收变化在近红外区域较弱,并且经常被其他成分(如水和血红蛋白)的干扰所掩盖。
本研究提出了一种指数,该指数不是通过葡萄糖的直接光学吸收来计算,而是根据氧合血红蛋白和去氧血红蛋白信号计算得出,在使用常规可见光谱和近红外光谱时,与 BGL 具有良好的相关性。
该代谢指数基于组织耗氧量,通过分析方法推导得出,并在一系列葡萄糖挑战实验中得到验证和再现。利用市售的智能设备,设计了血糖估计单元原型。
我们的实验结果表明,近红外光谱中氧合血红蛋白和去氧血红蛋白信号之间的相位延迟与传统连续血糖监测仪测量的 BGL 相关。该方法也被证实与基于智能手机摄像头的可见光谱系统配合良好。该方法在总共 19 项口服挑战测试中也表现出了出色的重复性。
本研究证明了使用现有的脉搏血氧仪和智能手表中的光电体积描记传感器进行无创血糖监测的可行性。在糖尿病或不健康个体中评估该方法可能会进一步提高其实用性。