Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

Abstract

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Figure 1

Schematic of Raman spectroscopy-based transcutaneous blood glucose detection. Illustration of glucose molecules in bloodstream and interstitial fluid, and the generation of Raman spectrum from noninvasive interrogation of the fingertip.

Figure 2

Toward noninvasive blood glucose monitoring. (A) Schematic of transdermal glucose monitoring using injectable glucose responsive fluorescent hydrogel microbeads. (Reprinted with permission from ref . Copyright 2010 National Academy of Sciences.) (B) Enzyme-based optical glucose sensor realized on a single-walled nanotube platform. Photograph shows a 200 μm × 1 cm capillary containing the SWNT solution. Corresponding fluorescence intensity map across capillary is shown alongside the reaction dynamics. (Adapted with permission from ref . Copyright 2005 Nature Publishing Group.) (C) Schematic of experimental setup used for SERS based in vivo glucose monitoring in a rat bearing surgically implanted sensor. (D) Fabrication of sensor by deposition of silver through a mask of self-assembled nanospheres and their functionalization by successive emersions in ethanolic solutions of decanethiol and mercaptohexanol. (E) Atomic force micrograph of the fabricated structure. (F) Position of localized surface plasmon resonance following functionalization. ((C–F) Reprinted with permission from ref . Copyright 2006 American Chemical Society.)

Figure 3

Instrumentation for noninvasive near-infrared spectroscopic probing of human fingertip. (A) Fiber probe based portable Raman spectroscopic system developed for transcutaneous glucose monitoring in backscattering configuration. (B) Optical fiber probe showing arrangement of excitation and emission fibers relative to the other optical components. (C) Portable Raman system in transmission mode. (D) CHC used for enhancing light collection, as viewed from the output aperture. (E) Side view of the Raman spectroscopy setup in transmission configuration showing the position of CHC relative to excitation and collection fibers. (F) and (G) Active tissue modulation interface incorporated into measurement device. ((C–E) Reprinted with permission from ref . Copyright 2011 AIP Publishing LLC. (F, G) Reprinted with permission from ref . Copyright 2011 AIP Publishing LLC.)

Figure 4

Multivariate data analysis. (A) Illustration of Raman spectral data set (intensity, wavelength) acquired as a function of time. The hyperspectral stack is analyzed to reveal the compositional contributors and their temporal changes. (B) The central idea of implicit calibration is to obtain the regression vector (b) from the recorded spectra (S) and glucose concentrations (c) in the calibration samples. The regression vector can then be used prospectively to predict glucose concentration using only the acquired spectrum from a test sample.

Figure 5

(A) Effects of turbidity on sampling volume as observed from simulations with tissue of different scattering coefficients. (B) Representation of the similar photon-tissue interactions for diffusely reflected and Raman scattered photons of the same wavelength. (C) Illustration of glucose diffusion between the blood and ISF compartments (mass transfer coefficient: k_M) with a smaller amount of cellular uptake (k_U). (D) Physiological lag between the glucose in the two compartments leads to a lack of one-to-one correspondence during rapid changes (rise/fall) in glucose concentrations.