Skip to main content
Log in

Head-to-head comparison between flash and continuous glucose monitoring systems in outpatients with type 1 diabetes

  • Original Article
  • Published:
Journal of Endocrinological Investigation Aims and scope Submit manuscript

Abstract

Purpose

Continuous glucose monitoring (CGM) is being increasingly used in clinical practice. The flash glucose monitoring (FGM) and CGM are different systems of interstitial glucose recording. We aimed to determine the agreement between the factory-calibrated FGM FreeStyle Libre (FSL) and the gold-standard CGM Dexcom G4 Platinum (DG4P).

Methods

We analyzed data from n = 8 outpatients with type 1 diabetes, who wore the FSL and DG4P for up to 14 days during their habitual life. We aligned FSL and DG4P recordings to obtain paired glucose measures. We calculated correlation coefficients, mean absolute relative difference (MARD), percentages in Clarke error grid areas, time spent in hyperglycaemia, target glycaemia, or hypoglycaemia, as well as glucose variability with both sensors. Comparison with self-monitoring of blood glucose (SMBG) was also performed.

Results

Patients varied in terms of age, diabetes duration, and HbA1c (from 5.9 to 9.6 %). In the pooled analysis of 10,020 paired values, there was a good correlation between FSL and DG4P (r 2 = 0.76; MARD = 18.1 ± 14.8 %) with wide variability among patients. The MARD was significantly higher during days 11–14 than in days 1–10, and during hypoglycaemia (19 %), than in normoglycaemia (16 %) or hyperglycaemia (13 %). Average glucose profiles and MARD versus SMBG were similar between the two sensors. Time spent in normo-, hyper-, or hypoglycaemia, and indexes of glucose variability was similarly estimated by the two sensors.

Conclusions

In outpatients with type 1 diabetes, we found good agreement between the FSL and DG4P. No significant difference was detected in the estimation of clinical diagnostic parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Schutt M, Kern W, Krause U, Busch P, Dapp A, Grziwotz R, Mayer I, Rosenbauer J, Wagner C, Zimmermann A, Kerner W, Holl RW (2006) Is the frequency of self-monitoring of blood glucose related to long-term metabolic control? Multicenter analysis including 24,500 patients from 191 centers in Germany and Austria. Exp Clin Endocrinol Diabetes 114(7):384–388. doi:10.1055/s-2006-924152

    Article  CAS  PubMed  Google Scholar 

  2. Ziegler R, Heidtmann B, Hilgard D, Hofer S, Rosenbauer J, Holl R (2011) Frequency of SMBG correlates with HbA1c and acute complications in children and adolescents with type 1 diabetes. Pediatr Diabetes 12(1):11–17. doi:10.1111/j.1399-5448.2010.00650.x

    Article  PubMed  Google Scholar 

  3. Boland E, Monsod T, Delucia M, Brandt CA, Fernando S, Tamborlane WV (2001) Limitations of conventional methods of self-monitoring of blood glucose: lessons learned from 3 days of continuous glucose sensing in pediatric patients with type 1 diabetes. Diabetes Care 24(11):1858–1862

    Article  CAS  PubMed  Google Scholar 

  4. Liebl A, Henrichs HR, Heinemann L, Freckmann G, Biermann E, Thomas A (2013) Continuous glucose monitoring: evidence and consensus statement for clinical use. J Diabetes Sci Technol 7(2):500–519

    Article  PubMed  PubMed Central  Google Scholar 

  5. Feldman B, Brazg R, Schwartz S, Weinstein R (2003) A continuous glucose sensor based on wired enzyme technology—results from a 3-day trial in patients with type 1 diabetes. Diabetes Technol Ther 5(5):769–779. doi:10.1089/152091503322526978

    Article  CAS  PubMed  Google Scholar 

  6. Hoss U, Budiman ES, Liu H, Christiansen MP (2014) Feasibility of factory calibration for subcutaneous glucose sensors in subjects with diabetes. J Diabetes Sci Technol 8(1):89–94. doi:10.1177/1932296813511747

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bailey T, Bode BW, Christiansen MP, Klaff LJ, Alva S (2015) The performance and usability of a factory-calibrated flash glucose monitoring system. Diabetes Technol Ther 17(11):787–794. doi:10.1089/dia.2014.0378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Damiano ER, McKeon K, El-Khatib FH, Zheng H, Nathan DM, Russell SJ (2014) A comparative effectiveness analysis of three continuous glucose monitors: the Navigator, G4 Platinum, and Enlite. J Diabetes Sci Technol 8(4):699–708. doi:10.1177/1932296814532203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Christiansen M, Bailey T, Watkins E, Liljenquist D, Price D, Nakamura K, Boock R, Peyser T (2013) A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system. Diabetes Technol Ther 15(10):881–888. doi:10.1089/dia.2013.0077

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL (1987) Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10(5):622–628

    Article  CAS  PubMed  Google Scholar 

  11. Schlichtkrull J, Munck O, Jersild M (1965) The M-valve, an index of blood-sugar control in diabetics. Acta Med Scand 177:95–102

    Article  CAS  PubMed  Google Scholar 

  12. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 19(9):644–655

    Article  CAS  PubMed  Google Scholar 

  13. Ryan EA, Shandro T, Green K, Paty BW, Senior PA, Bigam D, Shapiro AM, Vantyghem MC (2004) Assessment of the severity of hypoglycemia and glycemic lability in type 1 diabetic subjects undergoing islet transplantation. Diabetes 53(4):955–962

    Article  CAS  PubMed  Google Scholar 

  14. Kovatchev BP, Otto E, Cox D, Gonder-Frederick L, Clarke W (2006) Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care 29(11):2433–2438. doi:10.2337/dc06-1085

    Article  CAS  PubMed  Google Scholar 

  15. Wojcicki JM (1995) “J”-index. A new proposition of the assessment of current glucose control in diabetic patients. Horm Metab Res 27(1):41–42

    Article  CAS  PubMed  Google Scholar 

  16. Kovatchev BP, Cox DJ, Kumar A, Gonder-Frederick L, Clarke WL (2003) Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data. Diabetes Technol Ther 5(5):817–828. doi:10.1089/152091503322527021

    Article  CAS  PubMed  Google Scholar 

  17. McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ (2005) A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther 7(2):253–263. doi:10.1089/dia.2005.7.253

    Article  CAS  PubMed  Google Scholar 

  18. Molnar GD, Taylor WF, Ho MM (1972) Day-to-day variation of continuously monitored glycaemia: a further measure of diabetic instability. Diabetologia 8(5):342–348

    Article  CAS  PubMed  Google Scholar 

  19. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH (2010) Glucose variability is associated with intensive care unit mortality. Crit Care Med 38(3):838–842. doi:10.1097/CCM.0b013e3181cc4be9

    Article  CAS  PubMed  Google Scholar 

  20. Botev ZI, Grotowski JF, Kroese DP (2010) Kernel density estimation via diffusion. Ann Stat 38(5):2916–2957

    Article  Google Scholar 

  21. Heinemann L, Freckmann G (2015) CGM versus FGM; or, continuous glucose monitoring is not flash glucose monitoring. J Diabetes Sci Technol 9(5):947–950. doi:10.1177/1932296815603528

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ly TT, Breton MD, Keith-Hynes P, De Salvo D, Clinton P, Benassi K, Mize B, Chernavvsky D, Place J, Wilson DM, Kovatchev BP, Buckingham BA (2014) Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp. Diabetes Care 37(8):2310–2316. doi:10.2337/dc14-0147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Scheiner G (2016) CGM retrospective data analysis. Diabetes Technol Ther 18(Suppl 2):S214–S222. doi:10.1089/dia.2015.0281

    Article  PubMed  Google Scholar 

  24. Argento NB, Nakamura K (2016) Glycemic effects of Sglt-2 inhibitor canagliflozin in type 1 diabetes patients using the Dexcom G4 Platinum CGM. Endocr Pract 22(3):315–322. doi:10.4158/EP151016.OR

    Article  PubMed  Google Scholar 

Download references

Funding

Supported by Institutional Grant from the University of Padova.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. P. Fadini.

Ethics declarations

Conflict of interest

AM, AA, and GPF received sponsorship and lecture fees from Abbott and Roche, as well as other manufacturers of glucose sensors. The other authors have nothing to disclose.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments. The study was approved by the Ethical Committee of the University Hospital of Padova (prot. no. 74889).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

B. Bonora and A. Maran have contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bonora, B., Maran, A., Ciciliot, S. et al. Head-to-head comparison between flash and continuous glucose monitoring systems in outpatients with type 1 diabetes. J Endocrinol Invest 39, 1391–1399 (2016). https://doi.org/10.1007/s40618-016-0495-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40618-016-0495-8

Keywords

Navigation