Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

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  • Anders Pors
  • Kaspar G. Rasmussen
  • Rune Inglev
  • Nina Jendrike
  • Amalie Philipps
  • Ajenthen G. Ranjan
  • Vibe Vestergaard
  • Jan E. Henriksen
  • Nørgaard, Kirsten
  • Guido Freckmann
  • Karl D. Hepp
  • Michael C. Gerstenberg
  • Anders Weber

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Original languageEnglish
JournalACS Sensors
Volume8
Issue number3
Pages (from-to)1272-1279
Number of pages8
ISSN2379-3694
DOIs
Publication statusPublished - 2023

Bibliographical note

Funding Information:
The authors wish to acknowledge the support from the colleagues at RSP Systems, Steno Diabetes Center in Odense and Copenhagen, and the Institute for Diabetes Technology in Ulm. The staff at KLIFO is recognized for the support in execution of the trial.

Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.

    Research areas

  • calibration stability, diabetes, in vivo Raman spectroscopy, multivariate data analysis, non-invasive glucose monitoring, portable sensor, tissue diagnostics

ID: 362330061