Italian contributions to the development of continuous glucose monitoring sensors for diabetes management

Sensors (Switzerland)

Volumn 12, Issue 10, 2012, Pages 13753-13780

  Sparacino, Giovanni ^a   Zanon, Mattia ^a   Facchinetti, Andrea ^a   Zecchin, Chiara ^a   Maran, Alberto ^a   Cobelli, Claudio ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Biosensors; Diabetes; Glucose oxidase; Model; Non invasive sensors; Sensor calibration; Signal processing; Time series

Indexed keywords

CONTINUOUS GLUCOSE MONITORING SENSORS; CONTINUOUS GLUCOSEMONITORING (CGM); GLUCOSE CONCENTRATION; INTERNATIONAL RESEARCHES; NATIONAL HEALTH SYSTEM; NON-INVASIVE SENSORS; RETROSPECTIVE ANALYSIS; SENSOR CALIBRATION;

ALGORITHMS; ARTIFICIAL ORGANS; BIOSENSORS; GLUCOSE; GLUCOSE OXIDASE; GLUCOSE SENSORS; MEDICAL PROBLEMS; MODELS; SENSORS; SIGNAL PROCESSING;

TIME SERIES ANALYSIS;

BLOOD; BLOOD GLUCOSE MONITORING; DIABETES MELLITUS; EQUIPMENT; EQUIPMENT DESIGN; GENETIC PROCEDURES; GLUCOSE BLOOD LEVEL; HUMAN; INDUSTRY; ITALY; METHODOLOGY; REVIEW;

BIOSENSING TECHNIQUES; BLOOD GLUCOSE; BLOOD GLUCOSE SELF-MONITORING; DIABETES MELLITUS; EQUIPMENT DESIGN; HUMANS; INDUSTRY; ITALY;

EID: 84868251461     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s121013753     Document Type: Review

References (140)

1. IDF Diabetes Atlas; 5th ed. Available online: http://www.idf.org/diabetesatlas/ (accessed on 4 September 2012).
2. Report Sanità e Salute. Available online: http://www.istat.it/dati/catalogo/20101119_00/PDF/ cap3.pdf (accessed on 4 September 2012).
3. Wild, S.; Roglic, G.; Green, A.; Sicree, R.; King, H. Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care 2004, 27, 1047-1053.
4. Health in the European Union. Trends and Analysis. Available online: http://www.euro.who.int/__data/assets/pdf_file/0003/98391/E93348.pdf (accessed on 4 September 2012).
5. Marchesini, G.; Forlani, G.; Rossi, E.; Berti, A.; De Rosa, M. ARNO Working Group. The direct economic cost of pharmacologically-treated diabetes in Italy-2006. The ARNO observatory. Nutr. Metab. Cardiovasc. Dis. 2011, 21, 339-346.
6. Zimmet, P.; Alberti, K.G.; Shaw, J. Global and societal implications of the diabetes epidemic. Nature 2001, 414, 782-787.
7. Cobelli, C.; Dalla Man, C.; Sparacino, G.; Magni, L.; De Nicolao, G.; Kovatchev, B.P. Diabetes: Models, Signals, and Control. IEEE Rev. Biomed. Eng. 2009, 2, 54-96.
8. Heinemann, L.; Boecker, D. Lancing: quo vadis? J. Diabetes Sci. Technol. 2011, 5, 966-981.
9. Larizza, C.; Bellazzi, R.; Stefanelli, M.; Ferrari, P.; De Cata, P.; Gazzaruso, C.; Fratino, P.; D'Annunzio, G.; Hernando, E.; Gomez, E.J. The M2DM Project-The experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients. Methods Inf. Med. 2006, 45, 79-84.
10. Hirsch, I.B.; Brownlee, M. Should minimal blood glucose variability become the gold standard of glycemic control? J. Diabetes Complicat. 2005, 19, 178-181.
11. Kovatchev, B.P.; Otto, E.; Cox, D.; Gonder-Frederick, L.; Clarke, W.L. Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care 2006, 29, 2433-2438.
12. Magni, P.; Bellazzi, R. A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring. IEEE Trans. Biomed. Eng. 2006, 53, 977-985.
13. Rodbard, D. Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes. J. Diabetes Sci. Technol. 2007, 1, 62-71.
14. McGarraugh, G.V.; Clarke, W.L.; Kovatchev, B.P. Comparison of the clinical information provided by the FreeStyle Navigator continuous interstitial glucose monitor versus traditional blood glucose readings. Diabetes Technol. Ther. 2010, 12, 365-371.
15. Klonoff, D.C. Continuous glucose monitoring: Roadmap for 21st century diabetes therapy. Diabetes Care 2005, 28, 1231-1239.
16. Bode, B.W.; Battelino, T. Continuous glucose monitoring. Int. J. Clin. Pract. Suppl. 2010, 166, 11-15.
17. Torres, I.; Baena, M.G.; Cayon, M.; Ortego-Rojo, J.; Aguilar-Diosdado, M. Use of sensors in the treatment and follow-up of patients with diabetes mellitus. Sensors 2010, 10, 7404-7420.
18. Poscia, A.; Mascini, M.; Moscone, D.; Luzzana, M.; Caramenti, G.; Cremonesi, P.; Valgimigli, F.; Bongiovanni, C.; Varalli, M. A microdialysis technique for continuous subcutaneous glucose monitoring in diabetic patients (Part 1). Biosens. Bioelectron. 2003, 18, 891-898.
19. Tura, A.; Maran, A.; Pacini, G. Non-invasive glucose monitoring: Assessment of technologies and devices according to quantitative criteria. Diabetes Res. Clin. Pract. 2007, 77, 16-40.
20. Vashist, S.K. Non-invasive glucose monitoring technology in diabetes management: A review. Anal. Chim. Acta New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol. Ther. 2009, 11, 551-65.
21. Deiss, D.; Bolinder, J.; Riveline, J.P.; Battelino, T.; Bosi, E.; Tubiana-Rufi, N.; Kerr, D.; Phillip, M. Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabete Care 2006, 29, 2730-2732.
22. Tamborlane, W.V.; Beck, R.W.; Bode, B.W.; Buckingham, B.; Chase, H.P.; Clemons, R.; Fiallo-Scharer, R.; Fox, L.A.; Gilliam, L.K.; Hirsch, I.B.; et al. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N. Engl. J. Med. 2008, 359, 1464-1476.
23. Battelino, T.; Phillip, M.; Bratina, N.; Nimri, R.; Oskarsson, P.; Bolinder, J. Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes. Diabetes Care 2011, 34, 795-800.
24. Bergenstal, R.M.; Tamborlane, W.V.; Ahmann, A.; Buse, J.B.; Dailey, G.; Davis, S.N.; Joyce, C.: Peoples, T.; Perkins, B.A.; Welsh, J.B.; et al. Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes. N. Engl. J. Med. 2010, 363, 311-320.
25. Cengiz, E.; Sherr, J.L.; Weinzimer, S.A.; Tamborlane, W.V. New-generation diabetes management: Glucose sensor-augmented insulin pump therapy. Expert Rev. Med. Devices. 2011, 8, 449-458.
26. Langendam, M.; Luijf, Y.M.; Hooft, L.; Devries, J.H.; Mudde, A.H.; Scholten, R.J. Continuous glucose monitoring systems for type 1 diabetes mellitus. Cochrane Database Syst. Rev. 2012, in press.
27. Bellazzi, R. Telemedicine and diabetes management: Current challenges and future research directions. J. Diabetes Sci. Technol. 2008, 2, 98-104.
28. Capozzi, D.; Lanzola, G. Utilizing information technologies for lifelong monitoring in diabetes patients. J. Diabetes Sci. Technol. 2011, 1, 55-62.
29. Martínez-Sarriegui, I.; García-Sáez, G.; Rigla, M.; Brugués, E.; de Leiva, A.; Gómez, E.J.; Hernando, E.M. How continuous monitoring changes the interaction of patients with a mobile telemedicine system. J. Diabetes Sci. Technol. 2011, 5, 5-12.
30. Rigla, M. Smart telemedicine support for continuous glucose monitoring: The embryo of a future global agent for diabetes care. J. Diabetes Sci. Technol. 2011, 5, 63-67.
31. Sparacino, G.; Facchinetti, A.; Maran, A.; Cobelli, C. Continuous glucose monitoring time series and hypo/hyperglycemia prevention: Requirements, methods, open problems. Curr. Diabetes Rev. 2008, 4, 181-192.
32. Harvey, R.A.; Wang, Y.; Grosman, B.; Percival, M.W.; Bevier, W.; Finan, D.A.; Zisser, H.; Seborg, D.E.; Jovanovic, L.; Doyle, F.J., 3rd; Dassau, E. Quest for the artificial pancreas: Combining technology with treatment. IEEE Eng. Med. Biol. Mag. 2010, 29, 53-62.
33. Hovorka, R.; Allen, J.M.; Elleri, D.; Chassin, L.J.; Harris, J.; Xing, D.; Kollman, C.; Hovorka, T.; Larsen A.M.; Nodale, M.; et al. Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: A phase 2 randomised crossover trial. Lancet 2010, 375, 743-751.
34. Cobelli, C.; Renard, E.; Kovatchev, B. Artificial pancreas: Past, present, future. Diabetes 2011, 60, 2672-2682.
35. Thabit, H.; Hovorka, R. Closed-loop insulin delivery in type 1 diabetes. Endocrinol. Metab. Clin. North. Am. 2012, 41, 105-117.
36. Breton, M.; Farret, A.; Bruttomesso, D.; Anderson, S.; Magni, L.; et al. on behalf of The International Artificial Pancreas Study Group. Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes 2012, 61, 2230-2237.
37. Nimri, R.; Atlas, E.; Ajzensztejn, M.; Miller, S.; Oron, T.; Phillip, M. Feasibility study of automated overnight closed-loop glucose control under md-logic artificial pancreas in patients with type 1 diabetes: The DREAM project. Diabetes Technol. Ther. 2012, 14, 728-735.
38. Medtronic Diabetes. Guardian CGM System. Available online: http://www. medtronicdiabetes.com/products/guardiancgm (accessed on 4 September 2012).
39. Dexcom SEVEN PLUS Wireless Glucose Meter. http://www.dexcom.com/seven-plus (accessed on 4 September 2012).
40. Abbott Diabetes Care. Freestyle Navigator. Available online: http://www.abbott diabetescare.co.uk/your-products/freestyle-navigator (accessed on 4 September 2012).
41. Menarini Diagnostics. Available online: http://www.menarinidiag.co.uk/Products/ continuous_glucose_monitoring/introduction (accessed on 4 September 2012).
42. Jungheim, K.; Wientjes, K.J.; Heinemann, L.; Lodwig, V.; Koschinsky, T.; Schoonen, A.J. Subcutaneous continuous glucose monitoring: Feasibility of a new microdialysis-based glucose sensor system. Diabetes Care 2001, 24, 1696-1697.
43. Ricci, F.; Moscone, D.; Palleschi, G. Ex vivo continuous glucose monitoring with microdialysis technique: The example of GlucoDay. IEEE Sens. J. 2008, 8, 63-70.
44. McGarraugh, G. The chemistry of commercial continuous glucose monitors. Diabetes Technol. Ther. 2009, 11, S17-S24.
45. Girardin, C.M.; Huot, C.; Gonthier, M.; Delvin, E. Continuous glucose monitoring: A review of biochemical perspectives and clinical use in type 1 diabetes. Clin. Biochem. 2009, 42, 136-142.
46. Wang, J. Electrochemical glucose biosensors. Chem. Rev. 2008, 108, 814-825.
47. Ginsberg, H.B. The current environment of CGM technologies. J. Diabetes Sci. Technol. 2007, 1, 117-121.
48. Valgimigli, F.; Lucarelli, F.; Scuffi, C.; Morandi, S.; Sposato, I. Evaluating the clinical accuracy of GlucoMen®Day: A novel microdialysis-based continuous glucose monitor. J. Diabetes Sci. Technol. 2010, 4, 1182-1192.
49. Ricci, F.; Caprio, F.; Poscia, A.; Valgimigli, F.; Messeri, D.; Lepori, E.; Dall'Oglio, G.; Palleschi, G.; Moscone, D. Toward continuous glucose monitoring with planar modified biosensors and microdialysis. Study of temperature, oxygen dependence and in vivo experiment. Biosens. Bioelectron. 2007, 22, 2032-2039.
50. Ricci, F.; Moscone, D.; Tuta, C.S.; Palleschi, G.; Amine, A.; Poscia, A.; Valgimigli, F.; Messeri, D. Novel planar glucose biosensors for continuous monitoring use. Biosens. Bioelectron. 2005, 20, 1993-2000.
51. Lucarelli, F.; Ricci, F.; Caprio, F.; Valgimigli, F.; Scuffi C.; Moscone, D.; Palleschi, G. GlucoMen Day Continuous Glucose Monitoring System: A Screening for Enzymatic and Electrochemical Interferents. J. Diabetes Sci. Technol. 2012, 6, 1172-1181.
52. Clarke, W.L.; Cox, D.; Gonder-Frederick, L.A.; Carter, W.; Pohl, S.L. Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 1987, 10, 622-628.
53. Kovatchev, B.P.; Gonder-Frederick, L.A.; Cox, D.J.; Clarke, W.L. Evaluating the accuracy of continuous glucose-monitoring sensors: Continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. Diabetes Care 2004, 27, 1922-1928.
54. Sparacino, G.; Facchinetti, A.; Cobelli, C. "Smart" continuous glucose monitoring sensors: On-line signal processing issues. Sensors 2010, 10, 6751-6772.
55. Bequette, B.W. Continuous glucose monitoring: Real-time algorithms for calibration, filtering, and alarms. J. Diabetes Sci. Technol. 2010, 4, 404-418.
56. Palerm, C.C.; Willis, J.P.; Desemone, J.; Bequette, B.W. Hypoglycemia prediction and detection using optimal estimation. Diabetes Technol. Ther. 2005, 7, 3-14.
57. Facchinetti, A.; Sparacino, G.; Cobelli, C. An online self-tunable method to denoise CGM sensor data. IEEE Trans. Biomed. Eng. 2010, 57, 634-641.
58. Facchinetti, A.; Sparacino, G.; Cobelli, C. Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring. IEEE Trans. Biomed. Eng. 2011, 58, 2664-2671.
59. Aussedat, B.; Dupire-Angel, M.; Gifford, R.; Klein, J.C.; Wilson, G.S.; Reach, G. Interstitial glucose concentration and glycemia: Implications for continuous subcutaneous glucose monitoring. Am. J. Physiol. Endocrinol. Metab. 2000, 278, E716-E728.
60. Rossetti, P.; Bondia, J.; Vehí, J.; Fanelli, C.G. Estimating plasma glucose from interstitial glucose: The issue of calibration algorithms in commercial continuous glucose monitoring devices. Sensors 2010, 10, 10936-10952.
61. King, C.; Anderson, S.M.; Breton, M.; Clarke, W.L.; Kovatchev, B.P. Modeling of calibration effectiveness and blood-to-interstitial glucose dynamics as potential confounders of the accuracy of continuous glucose sensors during hyperinsulinemic clamp. J. Diabetes Sci. Technol. 2007, 1, 317-322.
62. Knobbe, E.J.; Buckingham, B. The extended Kalman filter for continuous glucose monitoring. Diabetes Technol. Ther. 2005, 7, 15-27.
63. Barceló-Rico, F.; Bondia, J.; Díez, J.L.; Rossetti, P. A multiple local models approach to accuracy improvement in continuous glucose monitoring. Diabetes Technol. Ther. 2012, 14, 74-82.
64. Facchinetti, A.; Sparacino, G.; Cobelli, C. Enhanced accuracy of continuous glucose monitoring by online extended kalman filtering. Diabetes Technol. Ther. 2010, 12, 353-363.
65. Guerra, S.; Facchinetti, A.; Sparacino, G.; De Nicolao, G.; Cobelli, C. Enhancing the accuracy of subcutaneous glucose sensors: A real-time deconvolution-based approach. IEEE Trans. Biomed. Eng. 2012, 59, 1658-1669.
66. Sparacino, G.; Zanderigo, F.; Corazza, S.; Maran, A.; Facchinetti, A.; Cobelli, C. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans. Biomed. Eng. 2007, 54, 931-937.
67. Gani, A.; Gribok, A.V.; Rajaraman, S.; Ward, W.K.; Reifman, J. Predicting subcutaneous glucose concentration in humans: Data-Driven glucose modeling. IEEE Trans. Biomed. Eng. 2009, 56, 246-254.
68. Eren-Oruklu, M.; Cinar, A.; Quinn, L.; Smith, D. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol. Ther. 2009, 11, 243-253.
69. Pérez-Gandía, C.; Facchinetti, A.; Sparacino, G.; Cobelli, C.; Gómez, E.J.; Rigla, M.; De Leiva, A.; Hernando, M.E. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol. Ther. 2010, 12, 81-88.
70. Pappada, S.M.; Cameron, B.D.; Rosman, P.M.; Bourey, R.E.; Papadimos, T.J.; Olorunto, W.; Borst, M.J. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol. Ther. 2011, 13, 135-141.
71. Naumova, V.; Pereverzyev, S.V.; Sivananthan, S. A meta-learning approach to the regularized learning-Case study: Blood glucose prediction. Neural Netw. 2012, 33, 181-193.
72. Dassau, E.; Cameron, F.; Lee, H.; Bequette, B.W.; Zisser, H.; Jovanovic, L.; Chase, H.P.; Wilson, D.M.; Buckingham, B.A.; Doylek, F.J. Real-time hypoglycemia prediction suite using continuous glucose monitoring: A safety net for the artificial pancreas. Diabetes Care 2010, 33, 1249-1254.
73. Buckingham, B.; Chase, H.P.; Dassau, E.; Cobry, E.; Clinton, P.; Gage, V.; Caswell, K.; Wilkinson, J.; Cameron, F.; Lee, H.; Bequette, B.W.; Doyle, F.J. Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care 2010, 33, 1013-1017.
74. Facchinetti, A.; Sparacino, G.; Trifoglio, E.; Cobelli, C. A new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms. Diabetes Technol. Ther. 2011, 13, 111-119.
75. Zanderigo, F.; Sparacino, G.; Kovatchev, B.; Cobelli, C. Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose error-grid analysis. J. Diabetes Sci. Technol. 2007, 1, 645-651.
76. Sivananthan, S.; Naumova, V.; Dalla Man, C.; Facchinetti, A.; Renard, E.; Cobelli, C.; Pereverzyev, S.V. Assessment of blood glucose predictors: The prediction-error grid analysis. Diabetes Technol. Ther. 2011, 13, 787-796.
77. Zecchin, C.; Facchinetti, A.; Sparacino, G.; De Nicolao, G.; Cobelli, C. Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration. IEEE Trans. Biomed. Eng. 2012, 59, 1550-1560.
78. Facchinetti, A.; Sparacino, G.; Guerra, S.; Luijf, Y.M.; DeVries, J.H.; Mader, J.K.; Ellmerer, M.; Benesch, C.; Heinemann, L.; Bruttomesso, D.; Avogaro, A.; Cobelli, C.; On behalf of the AP at home Consortium. Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept. Diabetes Care 2012, in press.
79. PubMed. Available online: http://www.pubmed.com (accessed on 4 September 2012).
80. Maran, A.; Crepaldi, C.; Tiengo, A.; Grassi, G.; Vitali, E.; Pagano, G.; Bistoni, S.; Calabrese, G.; Santeusanio, F.; Leonetti, F.; et al. Continuous subcutaneous glucose monitoring in diabetic patients: A multicenter analysis. Diabetes Care 2002, 25, 347-352.
81. Rossetti, P.; Porcellati, F.; Fanelli, C.G.; Bolli, G.B. Evaluation of the accuracy of a microdialysis-based glucose sensor during insulin-induced hypoglycemia, its recovery, and post-hypoglycemic hyperglycemia in humans. Diabetes Technol. Ther. 2006, 8, 326-337.
82. Wentholt, I.M.E.; Maran, A.; Masurel, N.; Heine, R.J.; Hoekstra, J.B.L.; De Vries, J.H. Nocturnal hypoglycaemia in Type 1 diabetic patients, assessed with continuous glucose monitoring: frequency, duration and associations. Diabetic Med. 2007, 24, 527-532.
83. Meschi, F.; Bonfanti, R.; Rigamonti, A.; Giulio, F.; Battaglino, R.; Viscardi, M.; Poscia, A.; Chiumello, G. Patients' evaluation of nocturnal hypoglycaemia with GlucoDay continuous glucose monitoring in paediatric patients. Acta Diabetol. 2010, 47, 295-300.
84. Maran, A.; Pavan, P.; Bonsembiante, B.; Brugin, E.; Ermolao, A.; Avogaro, A.; Zaccaria, M. Continuous glucose monitoring reveals delayed nocturnal hypoglycemia after intermittent high-intensity exercise in nontrained patients with type 1 diabetes. Diabetes Technol. Ther. 2010, 12, 763-768.
85. Dalfrà, M.G.; Sartore, G.; Di Cianni, G.; Mello, G.; Lencioni, C.; Ottanelli, S.; Sposato, J.; Valgimigli, F.; Scuffi, C.; Scalese, M.; Lapolla A. Glucose variability in diabetic pregnancy. Diabetes Technol. Ther. 2011, 13, 853-859.
86. Di Bartolo, P.; Pellicano, F.; Scaramuzza, A.; Sardu, C.; Casetti, T.; Bosi, E.; Miselli, V.; Brandolini, S.; Fabbri, T.; Meandri, P.; Cannatà, F. Better postprandial glucose stability during continuous subcutaneous infusion with insulin aspart compared with insulin lispro in patients with type 1 diabetes. Diabetes Technol. Ther. 2008, 10, 495-498.
87. Di Flaviani, A.; Picconi, F.; Di Stefano, P.; Giordani, I.; Malandrucco, I.; Maggio, P.; Palazzo, P.; Palazzo, P.; Sgreccia, F.; Peraldo, C.; Farina, F.; Frajese, G.; Frontoni, S. Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care 2011, 34, 1605-1609.
88. Rizzo, M.R.; Barbieri, M.; Marfella, R.; Paolisso, G. Reduction of oxidative stress and inflammation by blunting daily acute glucose fluctuations in patients with type 2 diabetes: Role of dipeptidyl peptidase-IV inhibition. Diabetes Care 2012, in press.
89. Sartore, G.; Chilelli, N.C.; Burlina, S.; Stefano, P.D.; Piarulli, F.; Fedele, D.; Mosca, A.; Lapolla, A. The importance of HbA1c and glucose variability in patients with type 1 and type 2 diabetes: outcome of continuous glucose monitoring (CGM). Acta Diabetol. 2012, in press.
90. DIAdvisor: Personal glucose predictive diabetes advisor. Available online: http://www.diadvisor.eu (accessed on 4 September 2012).
91. Farret, A.; Renard, E.M.; Place, J.; Mindlova, M.; Vavrova, E.; Saudek, F.; Vedovato, M.; Maran, A.; Avogaro, A.; DIAdvisor consortium. Clinical assessment of DIAdvisor device shows high accuracy in glucose prediction at 20-min horizon and a coherence of most advices on therapy in patients with type 1 diabetes. In Proceedings of 48th European Association for the Study of Diabetes Meeting, Berlin, Germany, 1-5 October 2012.
92. Artificial Pancreas (AP) at Home. Available online: http://www.apathome.eu (accessed on 4 September 2012).
93. Magni, L.; Forgione, M.; Toffanin, C.; Dalla Man, C.; Kovatchev, B.; De Nicolao, G.; Cobelli, C. Run-to-run tuning of model predictive control for type 1 diabetes subjects: in silico trial. J. Diabetes Sci. Technol. 2009, 1, 1091-1098.
94. Heinemann, L.; Benesch, C.; DeVries, J. H. AP@home: A novel European approach to bring the artificial pancreas home. J. Diabetes Sci. Technol. 2011, 5, 1363-1372.
95. Renard, E.M.; DeVries, J.H.; Hovorka, R.; Doll, W.; Heinemann, L.; Cobelli, C.; Magni, L.; Farret, A.; Luijf, Y.M.; Leelarathna, L.; Mader, J.K.; Benesch, C.; Bruttomesso, D.; Di Palma, F.; Nodale, M. Time in hypoglycaemia in patients with type 1 diabetes is dramatically reduced when insulin infusion is driven by two closed-loop algorithms in a randomised clinical trial. In Proceedings of 48th European Association for the Study of Diabetes Meeting, Berlin, Germany, 1-5 October 2012.
96. Yamakoshi, Y.; Ogawa, M.; Yamakoshi, T.; Satoh, M.; Nogawa, M.; Tanaka, S.; Tamura, T.; Rolfe, P.; Yamakoshi, K. A new non-invasive method for measuring blood glucose using instantaneous differential near infrared spectrophotometry. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology, Lyon, France, 22-26 August 2007; pp. 2964-2967.
97. Berman, H.L.; Roe, J.N.; Blair, R.N. Glucose measurement utilizing non-invasive assessment methods U.S. Patent 6,522,903, 18 February 2003.
98. Cunningham, D.D.; Stenken J.A. Near-Infrared spectroscopy for non invasive glucose sensing. In In Vivo Glucose Sensing; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2010; Chapter 13.
99. Shen, Y.C.; Davies, A.G.; Linfleld, E.H.; Elsey, T.S.; Taday, P.F.; Arnone, D.D. The use of Fourier-transform infrared spectroscopy for the quantitative determination of glucose concentration in whole blood. Phys. Med. Biol. 2003, 48, 2023-2032.
100. Lipson, J.; Bernhardt, J.; Block, U.; Freeman, W.R.; Hofmeister, R.; Hristakeva, M.; Lenosky, T.; McNamara, R.; Petrasek, D.; Veltkamp, D.; Waydo, S.; C8 MediSensors. Requirements for calibration in noninvasive glucose monitoring by Raman spectroscopy. J. Diabetes. Sci. Technol. 2009, 3, 233-241.
101. Cohen, O.; Fine, I.; Monashkin, E.; Karasik, A. Glucose correlation with light scattering patterns-A novel method for non-invasive glucose measurements. Diabetes Technol. Ther. 2003, 5, 11-17.
102. Gabbay, R.A.; Sivarajah, S. Optical coherence tomography-based continuous noninvasive glucose monitoring in patients with diabetes. Diabetes Technol. Ther. 2008, 10, 188-193.
103. Badugu, R.; Lakowicz, J.R.; Geddes, C.D. A glucose-sensing contact lens: From bench top to patient. Curr. Opin. Biotech. 2005, 16, 100-107.
104. Pirnstill, C.W.; Malik, B.H.; Gresham, V.C.; Coté, G.L. In vivo glucose monitoring using dual-wavelength polarimetry to overcome corneal birefringence in the presence of motion. Diabetes Technol. Ther. 2012, in press.
105. Trombetta, P.; Londoni, V. Diode laser device for the non-invasive measurement of glycaemia. U.S. Patent Appl. 2011/0152647 A1, 23 June 2011.
106. Londoni, V.; Trombetta, P. Diode laser device for the non-invasive measurement of glycaemia. WO Patent. Appl. 2010/013264 A1, 4 February 2010.
107. Yeh, S.J.; Hanna, C.F.; Khalil, O.S. Monitoring blood glucose changes in cutaneous tissue by temperature-modulated localized reflectance measurements. Clin. Chem. 2003, 49, 924-934.
108. Weiss, R.; Yegorchikov, Y.; Shusterman, A.; Raz, I. Non invasive continuous glucose monitoring using photoacoustic technology-Results from the first 62 subjects. J. Diabetes Sci. Technol. 2007, 9, 68-74.
109. Gourzi, M.; Rouane, A.; Guelaz, R.; Alavi, M.S.; McHugh, M.B.; Nadi, M.; Roth, P. Non-invasive glycaemia blood measurements by electromagnetic sensor: Study in static and dynamic blood circulation. J. Med. Eng. Technol. 2005, 29, 22-26.
110. Tura, A.; Sbrignadello, S.; Cianciavicchia, D.; Pacini, G.; Ravazzani, P. A low frequency electromagnetic sensor for indirect measurement of glucose concentration: In vitro experiments in different conductive solutions. Sensors 2010, 10, 5346-5358.
111. Caduff, A.; Hirt, E.; Feldman, Y.; Ali, Z.; Heinemann, L. First human experiments with a novel non-invasive, non-optical continuous glucose monitoring system. Biosens. Bioelectron. 2003, 19, 209-217.
112. Wentholt, I.M.; Hoekstra, J.B.; Zwart, A.; DeVries, J.H. Pendra goes Dutch: Lessons for the CE mark in Europe. Diabetologia 2005, 48, 1055-1058.
113. Tura, A.; Sbrignadello, S.; Barison, S.; Conti, S.; Pacini G. Impedance spectroscopy of solutions at physiological glucose concentrations. Biophys. Chem. 2007, 129, 235-241.
114. Gelao, G.; Marani, R.; Carriero, V.; Perri, A.G. Design of a dielectric spectroscopy sensor for continuous and non-invasive blood glucose monitoring. IJAE&T 2012, 3, 55-64.
115. Caduff, A.; Talary, M.S.; Mueller, M.; Dewarrat, F.; Klisic, J.; Donath, M.; Heinemann, L.; Stahel, W.A. Non-invasive glucose monitoring in patients with Type 1 diabetes: A multisensor system combining sensors for dielectric and optical characterisation of skin. Biosens. Bioelectron. 2009, 24, 2778-2784.
116. Harman-Boehm, I.; Gal, A.; Raykhman, A.M.; Naidis, E.; Mayzel, Y. Noninvasive glucose monitoring: increasing accuracy by combination of multi-technology and multi-sensors. J. Diabetes Sci. Technol. 2010, 4, 583-595.
117. Amaral, C.F.; Brischwein, M.; Wolf, B. Multiparameter techniques for non-invasive measurement of blood glucose. Sens. Actuat. B Chem. 2009, 140, 12-16.
118. Smith, A.; Yang, D.; Delcher, H.; Eppstein, J.; Williams, D.; Wilkes, S. Fluorescein kinetics in interstitial fluid harvested from diabetic skin during fluorescein angiography: implications for glucose monitoring. Diabetes Technol. Ther. 1999, 1, 21-27.
119. Newman, S.P.; Cooke, D.; Casbard, A.; Walker, S.; Meredith, S.; Nunn, A.; Steed, L.; Manca, A.; Sculpher, M.; Barnard, M.; Kerr, D.; Weaver, J.; Ahlquist, J.; Hurel, S.J. A randomised controlled trial to compare minimally invasive glucose monitoring devices with conventional monitoring in the management of insulin-treated diabetes mellitus (MITRE). Health Technol. Assess. 2009, 13, 1-194.
120. Chuang, H.; Trieu, M.; Hurley, J.; Taylor, E.J.; England, M.R.; Nasraway, S.A. Pilot studies of transedrmal continuous glucose measurement in outpatient diabetic and inpatients during and after cardiac surgery. J. Diabetes Sci. Technol. 2008, 2, 595-602.
121. Tura, A. Advances in the development of devices for noninvasive glycemia monitoring: Who will win the race? NT&M 2010, 28, 33-39.
122. Ferrante do Amaral, C.E.; Wolf, B. Current development in non-invasive glucose monitoring. Med. Eng. Phys. 2008, 30, 541-549.
123. Srinivasan, V.; Pamula, V.K.; Pollack, M.G.; Fair, R.B. Clinical diagnostics on human whole blood, plasma, serum, urine, saliva, sweat, and tears on a digital microfluidic platform. In Proceedings of 7th International Conference on Miniaturized Chemical and Biochemical Analysts Systems, Squaw Valley, CA, USA, 5-9 October 2003; pp. 1287-1290.
124. Barman, I.; Kong, C.R.; Singh, G.P.; Dasari, R.R.; Feld, M.S. Accurate spectroscopic calibration for noninvasive glucose monitoring by modeling the physiological glucose dynamics. Anal. Chem. 2010, 82, 6104-6114.
125. Mueller, M.; Talary, M.S.; Falco, L.; De Feo, O.; Stahel, W.A.; Caduff, A. Data processing for noninvasive continuous glucose monitoring with a Multisensor device. J. Diabetes. Sci. Technol. 2011, 5, 694-702.
126. Enejder, A.M.; Scecina, T.G.; Oh, J.; Hunter, M.; Shih, W.C.; Sasic, S.; Horowitz, G.L.; Feld, M.S. Raman spectroscopy for noninvasive glucose measurements. J. Biomed. Opt. 2005, 10, 031114.
127. Arnold, M.A.; Liu, L.; Olesberg, J.T. Selectivity assessment of noninvasive glucose measurements based on analysis of multivariate calibration vectors. J. Diabetes Sci. Technol. 2007, 1, 454-462.
128. Zanon, M.; Sparacino, G.; Facchinetti, A.; Riz, M.; Talary, M.S.; Suri, R.E.; Caduff, A.; Cobelli, C. Non-invasive continuous glucose monitoring: Improved accuracy of point and trend estimates of the Multisensor system. Med. Biol. Eng. Comput. 2012, 50, 1047-1057.
129. Caduff, A.; Mueller, M.; Megej, A.; Dewarrat, F.; Suri, R.E.; Klisic, J.; Donath, M.; Zakharov, P.; Schaub, D.; Stahel, W.A.; Talary, M.S. Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation. Biosens. Bioelectron. 2011, 26, 3794-3800.
130. Caduff, A.; Heinemann, L.; Talary, M.S.; Di Benedetto, G.; Lutz H.U.; Theander, S. A 4-h hyperglycaemic excursion induces rapid and slow changes in major electrolytes in blood in healthy human subjects. Acta Diabetol. 2011, 49, 333-339.
131. Chen, C.S.; Wang, K.K.; Jan, M.Y.; Hsu, W.C.; Li, S.P.; Wang-Lin, Y.Y.; Bau, J.G. Noninvasive blood glucose monitoring using the optical signal of pulsatile microcirculation: A pilot study in subjects with diabetes. J. Diabetes Complicat. 2008, 22, 371-376.
132. Harman-Boehm, I.; Gal, A.; Raykhman, A.M.; Zahn, J.D.; Naidis, E.; Mayzel, Y. Noninvasive glucose monitoring: A novel approach. J. Diabetes Sci. Technol. 2009, 3, 253-260.
133. Amir, O.; Weinstein, D.; Zilberman, S.; Less, M.; Perl-Treves, D.; Primack, H.; Weinstein, A.; Gabis, E.; Fikhte, B.; Karasik, A. Continuous noninvasive glucose monitoring technology based on occlusion spectroscopy. J. Diabetes. Sci. Technol. 2007, 1, 463-469.
134. C8 Medisensor Optical Glucose Monitor. Available online: http://www.c8medisensors.com/ home/ (accessed on 5 September 2012).
135. Bolla, A.M.; Ceriotti, F.; De Terlizzi, C.; Molinari, C.; Perticone, F.; Trombetta, P.; Scavini, M.; Bosi, E. Accuracy of a new laser technology device for non invasive measurement of glucose in man. In Proceedings of 5th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD), Barcelona, Spain, 8-11 February 2012.
136. Guerra, S.; Sparacino, G.; Facchinetti, A.; Schiavon, M.; Dalla Man, C.; Cobelli, C. A dynamic risk measure from continuous glucose monitoring data. Diabetes Technol. Ther. 2011, 13, 843-852.
137. Zecchin, C.; Facchinetti, A.; Manohar, C.; Kudva, Y.C.; Levine, J.A.; Basu, A.; Sparacino, G.; Dalla Man, C.; Cobelli, C. Physical activity measured by PAMS device correlates with first- and second-order glucose concentration derivatives. In Proceedings of 12th Diabetes Technology Meeting, Bethesda, MD, USA, 8-10 November 2012.
138. Facchinetti, A.; Del Favero, S.; Sparacino, G.; Cobelli, C. Detecting failures of the glucose sensor-insulin pump system: Improved overnight safety monitoring for Type-1 diabetes. In Proceedings of 2011 IEEE Annual International Conference on Medicine and Biology Society, Boston, MA, USA, 30 August-3 September 2011; pp. 4947-4950.
139. Zanon, M.; Sparacino, G.; Facchinetti, A.; Talary, M.S.; Caduff, A.; Cobelli, C. Non-invasive continuous glucose monitoring by multisensor system: improved accuracy using an elastic net regression. In Proceedings of 12th Diabetes Technology Meeting, Bethesda, MD, USA, 8-10 November 2012.
140. Cobelli, C.; Renard, E.; Kovatchev, B.P.; Keith-Hynes, P.; Ben Brahim, N.; Place, J.; Del Favero, S.; Breton, M.; Farret, A.; Bruttomesso, D.; Dassau, E.; Zisser, H.; Doyle, F.J., 3rd; Patek, S.D.; Avogaro, A. Pilot studies of wearable outpatient artificial pancreas in type 1 diabetes. Diabetes Care 2012, 35, e65-e67.