Noninvasive glucose monitoring: a novel approach

Abstract

Background: The main concern in noninvasive (NI) glucose measurement is achieving high accuracy readings, although no blood (or other fluid) is involved in the process. Using methods based on different physical properties of a measured object can ensure the independence of each of the readings and therefore improve the validity of the end result. By using a combination of (three) independent technologies-ultrasonic, electromagnetic, and thermal-GlucoTrack presents a unique approach for a real-time, truly NI blood glucose spot measurement.

Methods: Clinical trials were performed in two stages. Stage 1 was an initial method validation and performance verification of the device. In this stage, 50 type 1 and 2 diabetic patients, as well as healthy subjects, were evaluated with GlucoTrack against Ascensia Elite (Bayer). In the second stage, 85 additional diabetic subjects were evaluated in half and full daytime sessions using a GlucoTrack comparison with HemoCue (Glucose 201+).

Results: A total of 135 subjects were tested during the trial period, producing 793 data pairs. Using Clarke error grid analysis, 92% of the readings fell in the clinically acceptable zones A and B, with 50% in the A zone. Mean and median relative absolute differences were 29.9 and 19.9%, respectively.

Conclusions: Integrating several modalities for NI assessment of glucose level enables more accurate readings, while a possible aberration in one modality is bypassed by the others. The present generation of GlucoTrack gives promising results; however, further improvement of the accuracy of the device is needed.

Figure 1.

Approximated profile of diurnal variations (pooled data^–) in serum constituents of healthy individuals expressed as a relative change in percentage from the fasting state. Meals are taken around 8:30, 13:30, and 18:30, denoting breakfast, lunch, and dinner, respectively.

Figure 2.

GlucoTrack conceptual block diagram.

Figure 3.

(A) The MU with PEC connected; (B) PEC attached to an earlobe during measurement.

Figure 4.

Approximated equivalent circuit of the epidermis and underlying tissue. E_sc, C_e, R_e, R_m, C_m, and R_ext denote skin surface potential, epidermal capacitance, epidermal resistance, tissue cellular membrane capacitance, tissue cellular membrane resistance, and extracellular resistance, respectively.

Figure 5.

Data collected as a confirmation for the concept of using combined technologies. (A) Raw glucose readings per each technology [(•) thermal, (•) electroconductivity, and (•) ultrasonic channels with mean ARD values of 23.2%, 19.9%, and 22.1%, respectively]. (B) Final results after algorithm activation (mean ARD 15.8%).

Figure 6.

Example of correlation of separate results per technology vs combined result against invasive glucose reference of an 80-year-old type 2 diabetic male.

Figure 7.

Examples of long-term calibration validity: (·) 30-year-old healthy male and (·) 49-year-old type 2 diabetic male; readings were taken over 7 and 3 months, correspondingly, using a single calibration process per each individual.

Figure 8.

Pooled CEG analysis for both stages of the clinical trial: A, 50%, and B, 42%.