Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
Publication: Diabetes Technology & Therapeutics
Volume 25, Issue Number 12
Abstract
Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric that gives greater weight to hypoglycemia than to hyperglycemia and to extreme hypo/hyperglycemia over less extreme hypo/hyperglycemia. This study aimed at validating the effectiveness of GRI and at comparing it with time in range (TIR) in assessing glycemic quality in clinical practice.
Methods: A total of 524 ninety-day CGM tracings of 194 insulin-treated adults with diabetes were included in the analysis. GRI was assessed according to standard metrics in ambulatory glucose profiles. Both cross-sectional and longitudinal analyses were performed to compare the GRI and TIR.
Results: The GRI was strongly correlated not only with TIR (r = −0.974), but also with the coefficient of variation (r = 0.683). To identify whether the GRI differed by hypoglycemia even with a similar TIR, CGM tracings were grouped according to TIR (50% to <60%, 60% to <70%, 70% to <80%, and ≥80%). In each TIR group, the GRI increased as time below range (TBR)<70 mg/dL increased (P < 0.001 for all TIR groups). In longitudinal analysis, as TBR<70 mg/dL improved, the GRI improved significantly (P = 0.003) whereas TIR did not (P = 0.704). Both GRI and TIR improved as time above range (TAR)>180 mg/dL improved (P < 0.001 for both). The longitudinal change was easily identifiable on a GRI grid.
Conclusions: The GRI is a useful tool for assessing glycemic quality in clinical practice and reflects hypoglycemia better than does TIR.
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References
1. Klonoff DC, Wang J, Rodbard D, et al. A glycemia risk index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings. J Diabetes Sci Technol 2023;17(5):1226–1242.
2. Perez-Guzman MC, Shang T, Zhang JY, et al. Continuous glucose monitoring in the hospital. Endocrinol Metab (Seoul) 2021;36:240–255.
3. Yoo JH, Kim JH. Time in range from continuous glucose monitoring: A novel metric for glycemic control. Diabetes Metab J 2020;44:828–839.
4. ElSayed NA, Aleppo G, Aroda VR, et al. Glycemic targets: Standards of care in diabetes-2023. Diabetes Care 2023;46:S97–S110.
5. Yoo JH, Kim JH. Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: Improvement in glycemic control. Diabetes Metab J 2023;47:27–41.
6. Barnett MP, Bangalore S. Cardiovascular risk factors: It's time to focus on variability! J Lipid Atheroscler 2020;9:255–267.
7. Kompala T, Wong J, Neinstein A. Diabetes specialists value CGM despite challenges in prescribing and data review process. J Diabetes Sci Technol 2023;17(5):1265–1273.
8. Hoogendoorn CJ, Hernandez R, Schneider S, et al. Glycemic risk index profiles and predictors among diverse adults with type 1 diabetes. J Diabetes Sci Technol 2023. [Epub ahead of print];
9. Rodbard D. Quality of glycemic control: Assessment using relationships between metrics for safety and efficacy. Diabetes Technol Ther 2021;23:692–704.
10. Moscardó V, Herrero P, Reddy M, et al. Assessment of glucose control metrics by discriminant ratio. Diabetes Technol Ther 2020;22:719–726.
11. Jung HS. Clinical implications of glucose variability: Chronic complications of diabetes. Endocrinol Metab (Seoul) 2015;30:167–174.
12. Vigersky RA, Shin J, Jiang B, et al. The comprehensive glucose pentagon: A glucose-centric composite metric for assessing glycemic control in persons with diabetes. J Diabetes Sci Technol 2018;12:114–123.
13. Hill NR, Hindmarsh PC, Stevens RJ, et al. A method for assessing quality of control from glucose profiles. Diabet Med 2007;24:753–758.
14. Augstein P, Heinke P, Vogt L, et al. Q-Score: Development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies. BMC Endocr Disord 2015;15:22.
15. Yoo JH, Kim JY, Kim JH. Association between continuous glucose monitoring-derived glycemia risk index and albuminuria in type 2 diabetes. Diabetes Technol Ther 2023. [Epub ahead of print];
16. Wang Y, Lu J, Ni J, et al. Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study. Diabetes Obes Metab 2023;25:2457–2463.
17. Rodbard D. Metrics to evaluate quality of glycemic control: Comparison of time in target, hypoglycemic, and hyperglycemic ranges with “Risk Indices”. Diabetes Technol Ther 2018;20:325–334.
18. Leelarathna L, Thabit H, Wilinska ME, et al. Evaluating glucose control with a novel composite continuous glucose monitoring index. J Diabetes Sci Technol 2020;14:277–283.
19. Clarke W, Kovatchev B. Statistical tools to analyze continuous glucose monitor data. Diabetes Technol Ther 2009;11(Suppl 1):S45–S54.
20. Avari P, Uduku C, George D, et al. Differences for percentage times in glycemic range between continuous glucose monitoring and capillary blood glucose monitoring in adults with type 1 diabetes: Analysis of the REPLACE-BG dataset. Diabetes Technol Ther 2020;22:222–227.
21. Vigersky RA, McMahon C. The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technol Ther 2019;21:81–85.
22. Yoo JH, Choi MS, Ahn J, et al. Association between continuous glucose monitoring-derived time in range, other core metrics, and albuminuria in type 2 diabetes. Diabetes Technol Ther 2020;22:768–776.
23. Mayeda L, Katz R, Ahmad I, et al. Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease. BMJ Open Diabetes Res Care 2020;8:e000991.
24. Lu J, Ma X, Shen Y, et al. Time in range is associated with carotid intima-media thickness in type 2 diabetes. Diabetes Technol Ther 2020;22:72–78.
25. Karakus KE, Shah VN, Klonoff D, et al. Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes. Diabetes Obes Metab 2023. [Epub ahead of print];
26. Benhamou PY, Adenis A, Tourki Y, et al. Efficacy of a hybrid closed-loop solution in patients with excessive time in hypoglycaemia: A post hoc analysis of trials with DBLG1 system. J Diabetes Sci Technol 2022;19322968221128565. [Epub ahead of print];
27. Rodbard D. Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose. J Diabetes Sci Technol 2015;9:56–62.
28. Link M, Kamecke U, Waldenmaier D, et al. Comparative accuracy analysis of a real-time and an intermittent-scanning continuous glucose monitoring system. J Diabetes Sci Technol 2021;15:287–293.
29. Beck RW, Riddlesworth TD, Ruedy K, et al. Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: A randomized trial. Ann Intern Med 2017;167:365–374.
30. Aleppo G, Ruedy KJ, Riddlesworth TD, et al. REPLACE-BG: A randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes. Diabetes Care 2017;40:538–545.
31. Brown SA, Kovatchev BP, Raghinaru D, et al. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. N Engl J Med 2019;381:1707–1717.
32. Beck RW, Riddlesworth T, Ruedy K, 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.
Information & Authors
Information
Published In
Diabetes Technology & Therapeutics
Volume 25 • Issue Number 12 • December 2023
Pages: 883 - 892
PubMed: 37668665
Copyright
Copyright 2023, Mary Ann Liebert, Inc., publishers.
History
Published in print: December 2023
Published online: 23 November 2023
Published ahead of print: 25 October 2023
Published ahead of production: 5 September 2023
Authors
Authors' Contributions
J.Y.K., J.H.Y., and J.H.K. contributed to the design of the study. J.Y.K., J.H.Y., and J.H.K. conducted data collection. J.Y.K. conducted the analyses. J.Y.K., J.H.Y., and J.H.K. interpreted the results. J.Y.K. wrote the initial draft of the manuscript, with revisions by all authors. The final manuscript was approved by all authors. J.H.Y., J.Y.K., and J.H.K. are the guarantors of this work.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
This work was supported by the Korean Endocrine Society Big Data Research Fund 2022 and a grant from the Korean Medical Device Development Fund funded by the Korea government (Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, Ministry of Food and Drug Safety) (Grant No. RS-2022-00141116). The funders played no role in the design and conduct of the study, analysis, interpretation of data, review, or approval of the manuscript.
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