- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03842683
CGM Precision and Glycaemic Variability
Are Todays Continuous Glucose Monitoring Precise and Can They be Used to Reveal and Reduce Glycaemic Variability?
Study Overview
Detailed Description
The following study is an exploratory investigation of continuous glucose monitoring based on data from a completed Novo Nordisk A/S clinical trial. Please refer to ClinicalTrials.gov Identifier: NCT02825251.
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. 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, or even as a component in the holy grail of diabetes technology; the artificial pancreas.
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). 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.
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.
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:
- Test of PG vs IG and effect on clinical research. [analysis of differences]
- Correlation between PG values at bedtime and nocturnal hypoglycaemic events [correlation analyses]
- Effect of main evening meal and meal-time dose on nocturnal hypoglycaemic events [correlation analyses]
- Prediction of PG-confirmed hypoglycaemic events with CGM, dose and meal data as input [machine learning]
- The optimal dose and meal distribution and least CGM variability / eHbA1c [machine learning]
- 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.
Study Type
Enrollment (Actual)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
As copied from the original clinical trial ClinicalTrials.gov Identifier: NCT02825251
Inclusion Criteria:
- Male or female, age at least 18 years at the time of signing the informed consent
- Diagnosed with T1DM (Type 1 Diabetes Mellitus) (based on clinical judgement and/or supported by laboratory analysis as per local guidelines) equal or above 1 year prior to the day of screening
- Using the same Medtronic pump (Minimed 530G (551/751), Paradigm Veo (554/754), Paradigm Revel (523/723), Paradigm (522/722)) for CSII in a basal-bolus regimen with a rapid acting insulin analogue for at least six months prior to screening and willing to stay on the same pump model throughout the trial (if the model is changed the change should not exceed 7 consecutive days.)
- HbA1c (glycosylated haemoglobin) 7.0-9.0% (53-75 mmol/mol) as assessed by central laboratory at screening
- Body mass index (BMI) below or equal to 35.0 kg/m^2 at screening
- Ability and willingness to take at least 3 daily meal-time insulin bolus infusions every day throughout the trial
Exclusion Criteria:
- Any of the following: myocardial infarction, stroke, hospitalization for unstable angina or transient ischaemic attack within the past 180 days prior to the day of screening
- Planned coronary, carotid or peripheral artery revascularisation known on the day of screening
- History of hospitalization for ketoacidosis below or equal to 180 days prior to the day of screening
- Treatment with any medication for the indication of diabetes or obesity other than stated in the inclusion criteria in a period of 90 days before screening
- Any condition which, in the opinion of the Investigator, might jeopardise a Subject's safety or compliance with the protocol
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Optimal Time Shift of Continuous Glucose Monitoring Measurements
Time Frame: 16 weeks
|
Continuous glucose monitoring (CGM) measurements are delayed compared to blood glucose. The CGM signal is time-shifted -1 minute at a time and the mean absolute difference between CGM and blood glucose measurements are calculated at each step. The lowest mean absolute difference depicts the optimal time shift in minutes. The resultant mean absolute relative difference is provided as outcome. Publication reference: https://doi.org/10.1177/1932296819848721 |
16 weeks
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Area Under the Receiver Operating Characteristics Curve of the Hypoglycemia Prediction
Time Frame: 16 weeks
|
Area under the receiver operating characteristics curve (ROC-AUC) is a measure of the prediction capabilities of a prediction algorithm. Each point of the curve gives a sensitivity and a specificity of the prediction. Publication reference: https://doi.org/10.1177/1932296819868727 |
16 weeks
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Peter Vestergaard, PhD, Steno Diabetes Center North Denmark
Publications and helpful links
General Publications
- Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Norgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017 Dec;40(12):1631-1640. doi: 10.2337/dc17-1600.
- Rodbard D. Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes. Diabetes Technol Ther. 2017 Jun;19(S3):S25-S37. doi: 10.1089/dia.2017.0035.
- Jensen MH, Christensen TF, Tarnow L, Seto E, Dencker Johansen M, Hejlesen OK. Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes. Diabetes Technol Ther. 2013 Jul;15(7):538-43. doi: 10.1089/dia.2013.0069. Epub 2013 Apr 30.
- Jensen MH, Christensen TF, Tarnow L, Mahmoudi Z, Johansen MD, Hejlesen OK. Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection. J Diabetes Sci Technol. 2013 Jan 1;7(1):135-43. doi: 10.1177/193229681300700116.
- El-Khatib FH, Balliro C, Hillard MA, Magyar KL, Ekhlaspour L, Sinha M, Mondesir D, Esmaeili A, Hartigan C, Thompson MJ, Malkani S, Lock JP, Harlan DM, Clinton P, Frank E, Wilson DM, DeSalvo D, Norlander L, Ly T, Buckingham BA, Diner J, Dezube M, Young LA, Goley A, Kirkman MS, Buse JB, Zheng H, Selagamsetty RR, Damiano ER, Russell SJ. Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial. Lancet. 2017 Jan 28;389(10067):369-380. doi: 10.1016/S0140-6736(16)32567-3. Epub 2016 Dec 20. Erratum In: Lancet. 2017 Jan 28;389(10067):368. Lancet. 2017 Feb 4;389(10068):e2.
- Rebrin K, Sheppard NF Jr, Steil GM. Use of subcutaneous interstitial fluid glucose to estimate blood glucose: revisiting delay and sensor offset. J Diabetes Sci Technol. 2010 Sep 1;4(5):1087-98. doi: 10.1177/193229681000400507.
- Kovatchev BP, Patek SD, Ortiz EA, Breton MD. Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther. 2015 Mar;17(3):177-86. doi: 10.1089/dia.2014.0272. Epub 2014 Dec 1.
Study record dates
Study Major Dates
Study Start (ACTUAL)
Primary Completion (ACTUAL)
Study Completion (ACTUAL)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- sdcn1802
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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