Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. HbA1c Changes
3.1.1. Freestyle Libre Device
3.1.2. Dexcom Device
3.2. Sensor-Based Metrics
3.3. Demographic Predictors of Time Spent in the Target Glucose Range (3.9 to 10 mmol/L)
4. Discussion
4.1. Main Findings
4.2. Comparison with Other Studies—FSL Device
4.3. Comparison with Other Studies—Dexcom Device
4.4. Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Acknowledgments
Conflicts of Interest
References
- Daneman, D. Type 1 diabetes. Lancet 2006, 367, 847–858. [Google Scholar] [CrossRef]
- Patterson, C.C.; Karuranga, S.; Salpea, P.; Saeedi, P.; Dahlquist, G.; Soltesz, G.; Ogle, G.D. Worldwide estimates of incidence, prevalence and mortality of type 1 diabetes in children and adolescents: Results from the International Diabetes Federation Diabetes Atlas, 9th ed. Diabetes Res. Clin. Pract. 2019, 157, 107842. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Murata, T.; Tsuzaki, K.; Yoshioka, F.; Okada, H.; Kishi, J.; Yamada, K.; Sakane, N. The relationship between the frequency of self-monitoring of blood glucose and glycemic control in patients with type 1 diabetes mellitus on continuous subcutaneous insulin infusion or multiple daily injections. J. Diabetes Investig. 2015, 6, 687–691. [Google Scholar] [CrossRef] [PubMed]
- NHS Digital. National Diabetes Audit Reports. Available online: https://digital.nhs.uk/data-and-information/publications/statistical/national-diabetes-audit (accessed on 1 October 2021).
- Vincze, G.; Barner, J.C.; Lopez, D. Factors associated with adherence to self-monitoring of blood glucose among persons with diabetes. Diabetes Educ. 2004, 30, 112–125. [Google Scholar] [CrossRef]
- Avari, P.; Reddy, M.; Oliver, N. Is it possible to constantly and accurately monitor blood sugar levels, in people with Type 1 diabetes, with a discrete device (non-invasive or invasive)? Diabetes Med. 2020, 37, 532–544. [Google Scholar] [CrossRef]
- Leelarathna, L.; Wilmot, E.G. Flash forward: A review of flash glucose monitoring. Diabetes Med. 2018, 35, 472–482. [Google Scholar] [CrossRef] [Green Version]
- Wadwa, R.P.; Laffel, L.M.; Shah, V.N.; Garg, S.K. Accuracy of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring System During 10 Days of Use in Youth and Adults with Diabetes. Diabetes Technol. Ther. 2018, 20, 395–402. [Google Scholar] [CrossRef] [Green Version]
- Shah, V.N.; Laffel, L.M.; Wadwa, R.P.; Garg, S.K. Performance of a Factory-Calibrated Real-Time Continuous Glucose Monitoring System Utilising an Automated Sensor Applicator. Diabetes Technol. Ther. 2018, 20, 428–433. [Google Scholar] [CrossRef] [Green Version]
- Battelino, T.; Danne, T.; Bergenstal, R.M.; Amiel, S.A.; Beck, R.; Biester, T.; Bosi, E.; Buckingham, B.A.; Cefalu, W.T.; Close, K.L.; et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations from the International Consensus on Time in Range. Diabetes Care 2019, 42, 1593–1603. [Google Scholar] [CrossRef] [Green Version]
- Beck, R.W.; Bergenstal, R.M.; Riddlesworth, T.D.; Kollman, C.; Li, Z.; Brown, A.S.; Close, K.L. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care 2019, 42, 400–405. [Google Scholar] [CrossRef] [Green Version]
- Bergenstal, R.M.; Beck, R.W.; Close, K.L.; Grunberger, G.; Sacks, D.B.; Kowalski, A.; Brown, A.S.; Heinemann, L.; Aleppo, G.; Ryan, D.B.; et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018, 41, 2275–2280. [Google Scholar] [CrossRef] [Green Version]
- Available online: https://www.gov.uk/government/statistics/English-indices-of-deprivation-2019 (accessed on 1 October 2021).
- Available online: https://imd-by-postcode.opendatacommunities.org/imd/2019 (accessed on 1 October 2021).
- Deshmukh, H.; Wilmot, E.G.; Gregory, R.; Barnes, D.; Narendran, P.; Saunders, S.; Furlong, N.; Kamaruddin, S.; Banatwalla, R.; Herring, R.; et al. Effect of Flash Glucose Monitoring on Glycemic Control, Hypoglycemia, Diabetes-Related Distress, and Resource Utilization in the Association of British Clinical Diabetologists (ABCD) Nationwide Audit. Diabetes Care 2020, 43, 2153–2160. [Google Scholar] [CrossRef]
- Evans, M.; Welsh, Z.; Ells, S.; Seibold, A. The Impact of Flash Glucose Monitoring on Glycaemic Control as Measured by HbA1c: A Meta-analysis of Clinical Trials and Real-World Observational Studies. Diabetes Ther. 2020, 11, 83–95. [Google Scholar] [CrossRef] [Green Version]
- Fokkert, M.; van Dijk, P.; Edens, M.; Barents, E.; Mollema, J.; Slingerland, R.; Gans, R.; Bilo, H. Improved well-being and decreased disease burden after 1-year use of flash glucose monitoring (FLARE-NL4). BMJ Open Diabetes Res. Care 2019, 7, e000809. [Google Scholar] [CrossRef] [Green Version]
- Beck, R.W.; Riddlesworth, T.; Ruedy, K.; Ahmann, A.; Bergenstal, R.; Haller, S.; Kollman, C.; Kruger, D.; McGill, J.B.; Polonsky, W.; et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Adults with Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial. JAMA 2017, 317, 371–378. [Google Scholar] [CrossRef]
- Lind, M.; Polonsky, W.; Hirsch, I.B.; Heise, T.; Bolinder, J.; Dahlqvist, S.; Schwarz, E.; Ólafsdóttir, A.F.; Frid, A.; Wedel, H.; et al. Continuous Glucose Monitoring vs Conventional Therapy for Glycemic Control in Adults with Type 1 Diabetes Treated with Multiple Daily Insulin Injections: The GOLD Randomized Clinical Trial. JAMA 2017, 17, 379–387. [Google Scholar] [CrossRef]
- Laffel, L.M.; Kanapka, L.G.; Beck, R.W.; Bergamo, K.; Clements, M.A.; Criego, A.; DeSalvo, D.J.; Goland, R.; Hood, K.; Liljenquist, D.; et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Adolescents and Young Adults with Type 1 Diabetes: A Randomised Clinical Trial. JAMA 2020, 323, 2388–2396. [Google Scholar] [CrossRef]
- Thabit, H.; Prabhu, J.N.; Mubita, W.; Fullwood, C.; Azmi, S.; Urwin, A.; Doughty, I.; Leelarathna, L. Use of Factory-Calibrated Real-time Continuous Glucose Monitoring Improves Time in Target and HbA. Diabetes Care 2020, 43, 2537–2543. [Google Scholar] [CrossRef]
- Pratley, R.E.; Kanapka, L.G.; Rickels, M.R.; Ahmann, A.; Aleppo, G.; Beck, R.; Bhargava, A.; Bode, B.W.; Carlson, A.; Chaytor, N.S.; et al. Effect of Continuous Glucose Monitoring on Hypoglycemia in Older Adults with Type 1 Diabetes: A Randomised Clinical Trial. JAMA 2020, 323, 2397–2406. [Google Scholar] [CrossRef]
- Heinemann, L.; Freckmann, G.; Ehrmann, D.; Faber-Heinemann, G.; Guerra, S.; Waldenmaier, D.; Hermanns, N. Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): A multicentre, randomised controlled trial. Lancet 2018, 391, 1367–1377. [Google Scholar] [CrossRef]
- van der Linden, J.; Welsh, J.B.; Walker, T.C. Sustainable Use of a Real-Time Continuous Glucose Monitoring System from 2018 to 2020. Diabetes Technol. Ther. 2021, 23, 508–511. [Google Scholar] [CrossRef]
- Akturk, H.K.; Dowd, R.; Shankar, K.; Derdzinski, M. Real-World Evidence and Glycemic Improvement Using Dexcom G6 Features. Diabetes Technol. Ther. 2021, 23, S21–S26. [Google Scholar] [CrossRef]
Sensor Used | Freestyle Libre (n = 591) | Dexcom G6 (n = 198) |
---|---|---|
Data (n/% or median (IQR)) | ||
n (%) | 591 (74.9) | 198 (25.1) |
Females | 280 (47.4) | 126 (63.6) |
Age, years * | 40 (30,51) | 38 (30,51) |
Diabetes duration, years * | 22 (13, 32) | 23 (15, 33) |
Ethnicity, n (%) | ||
White | 384 (64.9) | 153 (77.3) |
Black | 20 (3.4) | 3 (1.5) |
Asian | 33 (5.6) | 9 (4.5) |
Other | 9 (1.5) | 7 (3.5) |
Not Specified | 144 (24.4) | 26 (13.1) |
Diabetes therapy, n (%) | ||
CSII | 292 (49.4) | 125 (63.1) |
MDI | 292 (49.4) | 57 (28.8) |
Not Specified | 7 (1.2) | 16 (8.1) |
Multiple Deprivation Index ** | ||
1–5 | 323 (54.7) | 102 (51.5) |
6–10 | 264 (44.7) | 95 (48) |
Pre HbA1c | Post HbA1c | Change HbA1c (Pre-Post A1c) | p Value * | |
---|---|---|---|---|
Freestyle Libre (Generation 1) | ||||
All (n = 336) | 61.0 (54.0, 71.0) | 57.0 (49.0, 65.8) | 3.5 (−3.0, 11.0) | <0.001 |
Baseline HbA1c (mmol/mol) | ||||
<59.0 (n = 139) | 52.0 (47.0, 56.0) | 52.0 (47.0, 57.0) | 0.0 (−5.0, 4.0) | 0.503 |
59.0–68.9 (n = 95) | 62.0 (60.5, 65.0) | 59.0 (54.0, 66.0) | 4.0 (−3.5, 9.0) | 0.002 |
≥69.0 (n = 102) | 76.0 (71.4, 89.0) | 65.0 (53.4, 73.0) | 12.3 (3.9, 25.0) | <0.001 |
Dexcom systems (G5/G6) | ||||
All (n = 130) | 60.0 (50.0, 70.0) | 58.8 (50.3, 66.8) | 2.5 (−2.5, 7.5) | 0.002 |
Baseline HbA1c (mmol/mol) | ||||
<59.0 (n = 56) | 50.0 (43.9, 55.5) | 49.5 (43.0, 54.0) | −0.8 (−2.9, 3.9) | 0.938 |
59.0-68.9 (n = 36) | 63.8 (61.5, 65.5) | 59.0 (55.0, 62.6) | 4.8 (0.4, 9.0) | 0.001 |
≥69.0 (n = 38) | 78.3 (71.4, 92.9) | 72.3 (64.9, 92.4) | 4.8 (−2.6, 10.9) | 0.01 |
Sensor Used | Freestyle Libre (n = 591) | Dexcom G6 (n = 177) |
---|---|---|
Data (median (IQR)) | ||
% Activity | 85.0 (54.0, 97.0) | 94.5 (85.7, 97.4) |
Duration of sensor use (Months) | 21.6 (10.1, 29.6) | 24.4 (10.4, 38.1) |
Average Glucose levels (mmol/L) | 9.5 (8.2, 11.1) | 9.4 (8.1, 10.8) |
GMI (mmol/mol) | 57.4 (51.3, 64.9) | 56.9 (50.8, 63.5) |
CV (%) | 37.6 (33.7, 42.5) | 36.1 (33.2, 40.0) |
% In very low range (<3.0 mmol/L) | 0.0 (0.0, 1.0) | 0.3 (0.1, 1.0) |
% In low range (3.0 to 3.8mmol/L) | 3.0 (1.0, 5.0) | 1.6 (0.7, 3.3) |
% In target range (3.9 to 10.0 mmol/L) | 55.0 (41.0, 68.0) | 58.8 (42.7, 73.5) |
% In high range (10.1 to 13.9 mmol/L) | 25.0 (19.0, 30.0) | 23.8 (17.4, 29.4) |
% In very high range (>13.9 mmol/L) | 12.0 (5.0, 25.0) | 11.5 (3.95, 21.6) |
Freestyle Libre (n = 591) | Dexcom G6 (n = 177) | |||
---|---|---|---|---|
Median % time in range (IQR) | p-value * | Median % time in range (IQR) | p-value * | |
Gender | ||||
Male | 57 (42, 71) | 0.017 | 64 (47, 76) | 0.076 |
Female | 53 (41, 66) | 56 (42, 71) | ||
Multiple Deprivation Index | ||||
1–5 | 52 (38, 66) | <0.0005 | 57 (37, 72) | 0.096 |
6–10 | 59 (46, 70) | 60 (47, 74) | ||
Diabetes Therapy | ||||
CSII | 58 (44, 68) | 0.124 | 59 (47, 71) | 0.613 |
MDI | 53 (39, 68) | 60 (39, 77) | ||
Age Group | ||||
≤30 | 50 (36, 64) | 0.003 | 56 (42, 73) | 0.451 |
>30 | 56 (43, 70) | 59 (43, 74) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lee, K.; Gunasinghe, S.; Chapman, A.; Findlow, L.A.; Hyland, J.; Ohol, S.; Urwin, A.; Rutter, M.K.; Schofield, J.; Thabit, H.; et al. Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre. Biosensors 2021, 11, 457. https://doi.org/10.3390/bios11110457
Lee K, Gunasinghe S, Chapman A, Findlow LA, Hyland J, Ohol S, Urwin A, Rutter MK, Schofield J, Thabit H, et al. Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre. Biosensors. 2021; 11(11):457. https://doi.org/10.3390/bios11110457
Chicago/Turabian StyleLee, Kyuhan, Shakthi Gunasinghe, Alyson Chapman, Lynne A. Findlow, Jody Hyland, Sheetal Ohol, Andrea Urwin, Martin K. Rutter, Jonathan Schofield, Hood Thabit, and et al. 2021. "Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre" Biosensors 11, no. 11: 457. https://doi.org/10.3390/bios11110457
APA StyleLee, K., Gunasinghe, S., Chapman, A., Findlow, L. A., Hyland, J., Ohol, S., Urwin, A., Rutter, M. K., Schofield, J., Thabit, H., & Leelarathna, L. (2021). Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre. Biosensors, 11(11), 457. https://doi.org/10.3390/bios11110457