Noninvasive Tissue Glucose Prediction in Patients with Type 1 Diabetes

Background: Pain-free assessment of the glucose information required for treatment monitoring is one of the most desired unmet medical needs in insulin treated patients with type 1 diabetes. TensorTip CoG, is a non-invasive tissue glucose monitoring device (NI-CoG) with an additional built-in invasive glucose meter (Inv-CoG), which operates based on optical methods in the near-infrared and visible wavelength range. Here we report about the results obtained from type 1 patients in the course of two clinical studies.

Methods: The data from 6 patients with type 1 diabetes (3 male/3 female, age: 43±16 years, HbA1c: 8.0±0.5%), who had participated in a standard meal study experiment, and the results from 29 type 1 patients, who had participated in a system accuracy evaluation in accordance with ISO15197:2015 (13 male/16 female, age: 42±14 years, HbA1c: 7.5±0.5%) were combined for this analysis.

Results: The meal study patients (n = 66 data points) and the ISO study patients (n = 29) showed good agreement of the NI-CoG predicted tissue glucose with the capillary reference method (YSI Stat2300 plus). Mean absolute relative difference (values >100 mg/dL) was calculated to be 13.3% (mean absolute difference for values<100 mg/dL: 16 mg/dL). The consensus error grid (version for type 1 diabetes) revealed 78% of the data points to be in zone A (B: 18%, C: 4%). Zone C values were only observed in patients without proper device calibration over the entire measurement range of the device.

Conclusions: The results of this new non-invasive glucose prediction device are encouraging for routine use in patients with type type 1 diabetes. This non-invasive technology has reached an accuracy level for glucose prediction that is comparable to the results published for needle sensors working for flash or continuous glucose monitoring.