Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the Multisensor system

Non-invasive continuous glucose monitoring (NI-CGM) sensors are still at an early stage of development, but, in the near future, they could become particularly appealing in diabetes management. Solianis Monitoring AG (Zurich, Switzerland) has proposed an approach for NI-CGM based on a multi-sensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to estimate glucose levels from the 150 channels directly measured through the Multisensor. A static multivariate linear regression model (with order and parameters common to the entire population of subjects) was proposed for such a scope (Caduff et al., Biosens Bioelectron 26:3794–3800, 2011). The aim of this work is to evaluate the accuracy in the estimation of glucose levels and trends that the NI-CGM Multisensor platform can achieve by exploiting different techniques for model identification, namely, ordinary least squares, subset variable selection, partial least squares and least absolute shrinkage and selection operator (LASSO). Data collected in human beings monitored for a total of 45 study days were used for model identification and model test. Several metrics of standard use in the diabetes scientific community to measure point and clinical accuracy of glucose sensors were used to assess the models. Results indicate that the LASSO technique is superior to the others shrinking many channel weights to zero thus leading to smoother glucose profiles and resulting in a more robust model to possible artifacts in the Multisensor data. Although, as expected, the performance of the NI-CGM system with the LASSO model is not yet comparable with that of enzyme-based needle glucose sensors, glucose trends are satisfactorily estimated. Considering the non-invasive nature of the multi-sensor platform, this result can have an immediate impact in the current clinical practice, e.g., to integrate sparse self-monitoring of blood glucose data with an indication of the glucose trend to aid the diabetic patient in dealing with, or even preventing in the short time scale, the threats of critical events such as hypoglycaemia.

[1]  B. Bequette.  A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. , 2005, Diabetes technology & therapeutics.

[2]  Pier Giorgio Fabietti,et al.  Control oriented model of insulin and glucose dynamics in type 1 diabetics , 2006, Medical and Biological Engineering and Computing.

[3]  Giovanni Sparacino,et al.  Glucose Concentration can be Predicted Ahead in Time From Continuous Glucose Monitoring Sensor Time-Series , 2007, IEEE Transactions on Biomedical Engineering.

[4]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[5]  Giovanni Sparacino,et al.  “Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues , 2010, Sensors.

[6]  S. D. Jong.  SIMPLS: an alternative approach to partial least squares regression , 1993 .

[7]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[8]  A. Maran,et al.  Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. , 2007, Diabetes research and clinical practice.

[9]  Massimiliano Barolo,et al.  Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch , 2011, Medical & Biological Engineering & Computing.

[10]  Giovanni Sparacino,et al.  Diabetes: Models, Signals, and Control , 2009 .

[11]  Yu Feldman,et al.  Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy. , 2006, Biosensors & bioelectronics.

[12]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[13]  Lutz Heinemann,et al.  Comparison of the Numerical and Clinical Accuracy of Four Continuous Glucose Monitors , 2008, Diabetes Care.

[14]  Andreas Caduff,et al.  Data Processing for Noninvasive Continuous Glucose Monitoring with a Multisensor Device , 2011, Journal of diabetes science and technology.

[15]  D. Cox,et al.  Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. , 2004, Diabetes care.

[16]  Darine Zambrano,et al.  A robust sliding mode controller with internal model for closed-loop artificial pancreas , 2010, Medical & Biological Engineering & Computing.

[17]  B W Bode,et al.  Advanced Technologies and Treatments for Diabetes Continuous glucose monitoring , 2010 .

[18]  Francesco Di Nardo,et al.  Dynamics of insulin action in hypertension: assessment from minimal model interpretation of intravenous glucose tolerance test data , 2011, Medical & Biological Engineering & Computing.

[19]  Lutz Heinemann,et al.  Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin. , 2009, Biosensors & bioelectronics.

[20]  R. Esenaliev,et al.  Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects. , 2002, Diabetes care.

[21]  Giuseppe De Nicolao,et al.  Model predictive control of glucose concentration in type I diabetic patients: An in silico trial , 2009, Biomed. Signal Process. Control..

[22]  Claudio Cobelli,et al.  Assessment of beta-cell function in humans, simultaneously with insulin sensitivity and hepatic extraction, from intravenous and oral glucose tests. , 2007, American journal of physiology. Endocrinology and metabolism.

[23]  D. Cox,et al.  Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose , 1987, Diabetes Care.

[24]  Giuseppe De Nicolao,et al.  Enhancing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach , 2012, IEEE Transactions on Biomedical Engineering.

[25]  Roman Hovorka,et al.  The future of continuous glucose monitoring: closed loop. , 2008, Current diabetes reviews.

[26]  G. W. Small,et al.  Noninvasive glucose sensing. , 2005, Analytical chemistry.

[27]  Pavel Zakharov,et al.  Cutaneous blood perfusion as a perturbing factor for noninvasive glucose monitoring. , 2010, Diabetes technology & therapeutics.

[28]  R. Bergenstal,et al.  Detection of hypoglycemia with continuous interstitial and traditional blood glucose monitoring using the FreeStyle Navigator Continuous Glucose Monitoring System. , 2009, Diabetes technology & therapeutics.

[29]  D. Cox,et al.  Symmetrization of the Blood Glucose Measurement Scale and Its Applications , 1997, Diabetes Care.

[30]  Pavel Zakharov,et al.  Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation. , 2011, Biosensors & bioelectronics.

[31]  G. Mcgarraugh.  The chemistry of commercial continuous glucose monitors. , 2009, Diabetes technology & therapeutics.

[32]  C. Cobelli,et al.  Artificial Pancreas: Past, Present, Future , 2011, Diabetes.

[33]  G. Hicks,et al.  The Enzyme Electrode , 1967, Nature.

[34]  Giovanni Sparacino,et al.  A dynamic risk measure from continuous glucose monitoring data. , 2011, Diabetes technology & therapeutics.

[35]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.