Are Todays Continuous Glucose Monitoring Precise and Can They be Used to Reveal and Reduce Glycaemic Variability?
Conditions: Diabetes Type 1
Sponsor: Peter Vestergaard

Title:  
Are Todays Continuous Glucose Monitoring Precise and Can 
They be Used to Reveal and Reduce Glycaemic Variability? 
 
NCT:  
NCT03842683 
 
Date:  
August 31th 2018  

Mandatory elements to be included in access requests 
Data Sharing Agreement 
Please refer to the Data Sharing Agreement 
Potential conflicts of interest outside the funding of the proposed research 
Please refer to the Data Sharing Agreement 
Funding of the proposed research 
None 
Research team information 
Morten Hasselstrøm Jensen, Senior Researcher, Steno Diabetes Center Nordjylland 
Professor Peter Vestergaard, Head of Research at Steno Diabetes Center Nordjylland 
Professor Ole Hejlesen, Aalborg University 
Lise Tarnow, Head of Steno Diabetes Center Zealand 
 
Abbreviated CV’s for all team members are attached. 
 
Research proposal information 
Title of the proposed research 
 
Are todays Continuous Glucose Monitoring precise and can they be used to reveal and reduce 
glycaemic variability? 
 
Scientific rationale and relevance of the proposed research describing the objectives and the 
hypotheses corresponding to the objectives of the research.  
 
Continuous Glucose Monitoring (CGM) provides an interstitial glucose reading every 5 minutes 
and is thus a powerful and important tool to identify glycaemic variability in people with 
diabetes. [1] CGM is valuable for people with diabetes to understand their glucose metabolism 
and it has the potential to be used for detection and prediction of glycaemic excursions, such 
as, the potentially fatal and inevitable events of hypoglycaemia, [2] [3] or even as a 
component in the holy grail of diabetes technology; the artificial pancreas. [4] 
 
However, CGM has been criticised for being inaccurate and unreliable, amongst others, due to 
the physiological and a device-related delay between plasma glucose (PG) and interstitial 
glucose (IG). [5] [6] Nevertheless, CGM keeps on being popular and in February 2017 an 
international consensus was established at the Advanced Technologies & Treatments for 
Diabetes (ATTD) congress that even considers CGM data as a valuable and meaningful end 
point to be used in clinical trials of new drugs and devices for diabetes treatment where 
accuracy is of high importance. [7]  
 
The above mentioned use cases entail that the CGM data are accurate. Therefore, the first part 
of this research proposal is to investigate whether the newest state-of-the-art CGM devices 
used in Novo Nordisk trials are in fact accurate. Based on these results, it is investigated to 
which degree glycaemic variability can be revealed.    

 
Short lay summary of the proposed research intended for public disclosure 
 
Use of devices for continuous monitoring of the blood sugar is valuable for people with 
diabetes to understand their disease and to help prevent low blood sugar. Furthermore, 
continuous monitoring should be used in drug development to evaluate efficacy and safety. 
However, the devices have been criticised for being too inaccurate. This investigation sought to 
reveal the inaccuracies of current devices and to assess the subsequent usability related to the 
mentioned use cases.  
 
Research methodology and data (brief description of the intended method for the use of the 
requested data and a detailed description of the different data sources of the data that is 
intended to be included) 
 
To investigate the accuracy of CGM, mean absolute relative difference (MARD) will be 
calculated and presented and the impact of the delay assessed by time shifting CGM 
measurements. Furthermore, correlation analyses, between for example, PG and first 
derivative of IG, will be performed to try to understand when CGM devices tend to measure 
inaccurate. Lastly, machine learning and/or deep learning approaches will be utilised to reveal 
glycaemic patterns and to detect/predict outcomes, such as, hypoglycaemia. 
 
Different glycaemic variability investigations will be undertaken: 
x 
Test of PG vs IG and effect on clinical research. [analysis of differences] 
x 
Correlation between PG values at bedtime and nocturnal hypoglycaemic events [correlation 
analyses] 
x 
Effect of main evening meal and meal-time dose on nocturnal hypoglycaemic events 
[correlation analyses] 
x 
Prediction of PG-confirmed hypoglycaemic events with CGM, dose and meal data as input 
[machine learning] 
x 
The optimal dose and meal distribution and least CGM variability / eHbA1c [machine 
learning] 
x 
Algorithm to suggest optimal dosing in relation to glycaemic variability [machine learning] 
 
Requested data are demographic, CGM, meal, dose and hypoglycaemia data from the following 
trial. The analyses are independent of treatment and therefore the treatment arm can be 
blinded. 
 
NN1218-3854 
 
Statistical analysis plan (include a description of the endpoints and their time points and the 
planned statistical analyses including the analysis set, the analysis models (with factors and 
covariates), any transformation of data, plans for handling of missing data, plans for 
addressing multiplicity issues and plans for sensitivity analyses) 
 
Please refer to the Data Sharing Agreement 
 
Publication plan (list the planned number of abstracts and manuscripts (i.e. 'working title of 
these') and the scientific congresses under consideration for presentation of the research 
findings. Include the anticipated timelines) 
 
Please refer to the Data Sharing Agreement 
 
 
 

References 
[1] D. Rodbard, »Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating 
Improved Glycemic Outcomes,« Diabetes Technology & Therapeutics, årg. 19, nr. S3, 
2017.  
[2] M. H. Jensen, T. F. Christensen, L. Tarnow, E. Seto, M. Dencker Johansen og O. K. 
Hejlesen, »Real-Time Hypoglycemia Detection from Continuous Glucose Monitoring Data of 
Subjects with Type 1 Diabetes,« Diabetes Technology & Therapeutics, 2013.  
[3] M. Jensen, T. Christensen, L. Tarnow, Z. Mahmoudi, M. Johansen og O. Hejlesen, 
»Professional continuous glucose monitoring in subjects with type 1 diabetes: 
Retrospective hypoglycemia detection,« Journal of Diabetes Science and Technology, årg. 
7, nr. 1, 2013.  
[4] F. H. El-Khatib, C. Balliro, M. A. Hillard, K. L. Magyar, L. Ekhlaspour, M. Sinha, D. Mondesir, 
A. Esmaeili, C. Hartigan, M. J. Thompson, S. Malkani, J. P. Lock, D. M. Harlan, P. Clinton, 
E. Frank, D. M. Wilson, D. DeSalvo, L. Norlander, T. Ly, B. A. Buckingham, J. Diner, M. 
Dezube, L. A. Young, A. Goley, M. S. Kirkman, J. B. Buse, H. Zheng, R. R. Selagamsetty, E. 
R. Damiano og S. J. Russell, »Home use of a bihormonal bionic pancreas versus insulin 
pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial,« 
The Lancet, 2017.  
[5] K. Rebrin, N. F. Sheppard og G. M. Steil, »Use of Subcutaneous Interstitial Fluid Glucose to 
Estimate Blood Glucose: Revisiting Delay and Sensor Offset,« 2010. 
[6] B. P. Kovatchev, S. D. Patek, E. A. Ortiz og M. D. Breton, »Assessing Sensor Accuracy for 
Non-Adjunct Use of Continuous Glucose Monitoring,« Diabetes Technology & Therapeutics, 
2015.  
[7] T. Danne, R. Nimri, T. Battelino, R. M. Bergenstal, K. L. Close, J. H. DeVries, S. Garg, L. 
Heinemann, I. Hirsch, S. A. Amiel, R. Beck, E. Bosi, B. Buckingham, C. Cobelli, E. Dassau, 
F. J. Doyle, S. Heller, R. Hovorka, W. Jia, T. Jones, O. Kordonouri, B. Kovatchev, A. 
Kowalski, L. Laffel, D. Maahs, H. R. Murphy, K. Nørgaard, C. G. Parkin, E. Renard, B. 
Saboo, M. Scharf, W. V. Tamborlane, S. A. Weinzimer og M. Phillip, »International 
consensus on use of continuous glucose monitoring,« Diabetes Care, 2017.