Diabetes Type 1NCT03842683
CGM Precision and Glycaemic Variability
An observational study of Diabetes Type 1, run by Peter Vestergaard.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetes Type 1
- What is being tested?
- CGM
- How many people
- 472 participants
- Where
- No sites listed yet
- Study dates
- 6 Jun 2016to 20 Jun 2017
- Phase
- Observational
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Adults with type 1 diabetes
Who may be able to join
- 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
Who cannot join
- 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
About this study
Official title: Are Todays Continuous Glucose Monitoring Precise and Can They be Used to Reveal and Reduce Glycaemic Variability?
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.
Read the 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.
- Conditions
- Diabetes Type 1
- Being tested
- CGM
- Medical subject headings
- Diabetes Mellitus, Type 1
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CGMOther
This study seeks to assess CGM accuracy and develop prediction models for hypoglycemia detection and no intervention is therefore applied.
What is being measured
The primary outcome is the main question the study is designed to answer.
Optimal Time Shift of Continuous Glucose Monitoring Measurements
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
Measured: 16 weeks
Secondary outcomes (1)
Area Under the Receiver Operating Characteristics Curve of the Hypoglycemia Prediction
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
Measured: 16 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Feb 2019
- Study start6 Jun 2016
- Primary completion20 Jun 2017
- Study completion20 Jun 2017
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Diabetes Type 1Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 472 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peter Vestergaard
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Peter Vestergaard, Aalborg University Hospital
- Investigators
- Peter Vestergaard, PhD, Principal investigator, Steno Diabetes Center North Denmark
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03842683
- Sponsor's protocol ID
- sdcn1802
- Status last verified
- Oct 2019
- First submitted
- 13 Feb 2019
- Last updated
- 20 Nov 2019
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 28585879Jensen 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.
PubMed 23631608Jensen 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.
PubMed 23439169El-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.
PubMed 28007348Rebrin 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.
PubMed 20920428Kovatchev 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.
PubMed 25436913Danne 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.
PubMed 29162583
Study at a glance
From the registry record, last updated by the sponsor on 20 Nov 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Are Todays Continuous Glucose Monitoring Precise and Can They be Used to Reveal and Reduce Glycaemic Variability?
- ClinicalTrials.gov ID
- NCT03842683
- Acronym / sponsor ID
- sdcn1802
- Conditions
- Diabetes Type 1
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 472 participants (actual)
- Lead sponsor
- Peter VestergaardUniversity, hospital or other
- Study start
- 6 Jun 2016 (actual)
- Primary completion
- 20 Jun 2017 (actual)
- Study completion
- 20 Jun 2017 (actual)
- First posted
- 15 Feb 2019 (actual)
- Results
- Posted 20 Nov 2019
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Adults with type 1 diabetes
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CGMThis study seeks to assess CGM accuracy and develop prediction models for hypoglycemia detection and no intervention is therefore applied. | Other | – |
Primary outcome
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
1 secondary outcome
Area Under the Receiver Operating Characteristics Curve of the Hypoglycemia Prediction
Time frame: 16 weeks
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Nov 2019.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 28585879Rodbard 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.
- Background: PubMed 23631608Jensen 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.
- Background: PubMed 23439169Jensen 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.
- Background: PubMed 28007348El-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.
- Background: PubMed 20920428Rebrin 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.
- Background: PubMed 25436913Kovatchev 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.
- Background: PubMed 29162583Danne 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Peter Vestergaard, PhD principal investigator
Steno Diabetes Center North Denmark
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Type 1, run by Peter Vestergaard.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Nov 2019. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 20 Nov 2019). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Optimal Time Shift of Continuous Glucose Monitoring Measurements (time frame: 16 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03842683, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Nov 2019.