Advertisement

Diabetologia

pp 1–11 | Cite as

Positioning time in range in diabetes management

  • Andrew AdvaniEmail author
Review

Abstract

Recent upswings in the use of continuous glucose monitoring (CGM) technologies have given people with diabetes and healthcare professionals unprecedented access to a range of new indicators of glucose control. Some of these metrics are useful research tools and others have been welcomed by patient groups for providing insights into the quality of glucose control not captured by conventional laboratory testing. Among the latter, time in range (TIR) is an intuitive metric that denotes the proportion of time that a person’s glucose level is within a desired target range (usually 3.9–10.0 mmol/l [3.5–7.8 mmol/l in pregnancy]). For individuals choosing to use CGM technology, TIR is now often part of the expected conversation between patient and healthcare professional, and consensus recommendations have recently been produced to facilitate the adoption of standardised TIR targets. At a regulatory level, emerging evidence linking TIR to risk of complications may see TIR being more widely accepted as a valid endpoint in future clinical trials. However, given the skewed distribution of possible glucose values outside of the target range, TIR (on its own) is a poor indicator of the frequency or severity of hypoglycaemia. Here, the state-of-the-art linking TIR with complications risk in diabetes and the inverse association between TIR and HbA1c are reviewed. Moreover, the importance of including the amount and severity of time below range (TBR) in any discussions around TIR and, by inference, time above range (TAR) is discussed. This review also summarises recent guidance in setting ‘time in ranges’ goals for individuals with diabetes who wish to make use of these metrics. For most people with type 1 or type 2 diabetes, a TIR >70%, a TBR <3.9 mmol/l of <4%, and a TBR <3.0 mmol/l of <1% are recommended targets, with less stringent targets for older or high-risk individuals and for those under 25 years of age. As always though, glycaemic targets should be individualised and rarely is that more applicable than in the personal use of CGM and the data it provides.

Keywords

CGM Diabetes complications HbA1c Hyperglycaemia Hypoglycaemia Review Time below range Time in range 

Abbreviations

CGM

Continuous glucose monitoring (or continuous glucose monitor)

CSII

Continuous subcutaneous insulin infusion

DIAMOND

Multiple Daily Injections and Continuous Glucose Monitoring in Diabetes (study)

GMI

Glucose management indicator

isCGM

Intermittently scanned continuous glucose monitor

MARD

Mean absolute relative difference

PRO

Patient-reported outcome

rtCGM

Real-time continuous glucose monitor

SMBG

Self-monitoring of blood glucose

TAR

Time above range

TBR

Time below range

TIR

Time in range

Notes

Contribution statement

The author researched and wrote the article.

Funding

AA is a recipient of a Diabetes Investigator Award from Diabetes Canada. Research in the Advani Lab is supported by grants from the Canadian Institutes of Health Research, the RDV Foundation, the Heart and Stroke Foundation of Canada and the Kidney Foundation of Canada.

Duality of interest

AA has received research support through his institution from Boehringer Ingelheim and AstraZeneca and an unrestricted educational grant from Eli Lilly and has served on advisory boards for Dexcom, Abbott, Eli Lilly/Boehringer Ingelheim and Novo Nordisk.

Supplementary material

125_2019_5027_MOESM1_ESM.pptx (640 kb)
Slideset of figures (PPTX 640 kb)

References

  1. 1.
    The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329(14):977–986.  https://doi.org/10.1056/NEJM199309303291401 CrossRefGoogle Scholar
  2. 2.
    Runge AS, Kennedy L, Brown AS et al (2018) Does time-in-range matter? Perspectives from people with diabetes on the success of current therapies and the drivers of improved outcomes. Clin Diabetes 36(2):112–119.  https://doi.org/10.2337/cd17-0094 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Beck RW, Bergenstal RM, Riddlesworth TD et al (2018) Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care 42(3):400–405.  https://doi.org/10.2337/dc18-1444 CrossRefPubMedGoogle Scholar
  4. 4.
    Lu J, Ma X, Zhou J et al (2018) Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care 41(11):2370–2376.  https://doi.org/10.2337/dc18-1131 CrossRefPubMedGoogle Scholar
  5. 5.
    Feig DS, Donovan LE, Corcoy R et al (2017) Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial. Lancet 390(10110):2347–2359.  https://doi.org/10.1016/S0140-6736(17)32400-5 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kristensen K, Ogge LE, Sengpiel V et al (2019) Continuous glucose monitoring in pregnant women with type 1 diabetes: an observational cohort study of 186 pregnancies. Diabetologia 62(7):1143–1153.  https://doi.org/10.1007/s00125-019-4850-0 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Rodbard D (2017) Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther 19(Suppl 3):S25–S37.  https://doi.org/10.1089/dia.2017.0035 CrossRefPubMedGoogle Scholar
  8. 8.
    Foster NC, Beck RW, Miller KM et al (2019) State of type 1 diabetes management and outcomes from the T1D Exchange in 2016-2018. Diabetes Technol Ther 21(2):66–72.  https://doi.org/10.1089/dia.2018.0384 CrossRefPubMedGoogle Scholar
  9. 9.
    Wright LA, Hirsch IB (2017) Metrics beyond hemoglobin A1C in diabetes management: time in range, hypoglycemia, and other parameters. Diabetes Technol Ther 19(Suppl 2):S16–S26.  https://doi.org/10.1089/dia.2017.0029 CrossRefPubMedGoogle Scholar
  10. 10.
    Petrie JR, Peters AL, Bergenstal RM, Holl RW, Fleming GA, Heinemann L (2017) Improving the clinical value and utility of CGM systems: issues and recommendations: a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group. Diabetologia 60(12):2319–2328.  https://doi.org/10.1007/s00125-017-4463-4 CrossRefPubMedGoogle Scholar
  11. 11.
    Danne T, Nimri R, Battelino T et al (2017) International consensus on use of continuous glucose monitoring. Diabetes Care 40(12):1631–1640.  https://doi.org/10.2337/dc17-1600 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Beck RW, Riddlesworth TD, Ruedy K et al (2017) Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: a randomized trial. Ann Intern Med 167(6):365–374.  https://doi.org/10.7326/M16-2855 CrossRefPubMedGoogle Scholar
  13. 13.
    Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM (2012) Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care 35(1):32–38.  https://doi.org/10.2337/dc11-1438 CrossRefPubMedGoogle Scholar
  14. 14.
    Bolinder J, Antuna R, Geelhoed-Duijvestijn P, Kroger J, Weitgasser R (2016) Novel glucose-sensing technology and hypoglycaemia in type 1 diabetes: a multicentre, non-masked, randomised controlled trial. Lancet 388(10057):2254–2263.  https://doi.org/10.1016/S0140-6736(16)31535-5 CrossRefPubMedGoogle Scholar
  15. 15.
    Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G (2017) Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial. Diabetes Ther 8(1):55–73.  https://doi.org/10.1007/s13300-016-0223-6 CrossRefPubMedGoogle Scholar
  16. 16.
    Bergenstal RM, Beck RW, Close KL et al (2018) Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care 41(11):2275–2280.  https://doi.org/10.2337/dc18-1581 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Battelino T, Danne T, Bergenstal RM et al (2019) Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care 42(8):1593–1603.  https://doi.org/10.2337/dci19-0028 CrossRefPubMedGoogle Scholar
  18. 18.
    Agiostratidou G, Anhalt H, Ball D et al (2017) Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange. Diabetes Care 40(12):1622–1630.  https://doi.org/10.2337/dc17-1624 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Rhee MK, Ho YL, Raghavan S et al (2019) Random plasma glucose predicts the diagnosis of diabetes. PLoS One 14(7):e0219964.  https://doi.org/10.1371/journal.pone.0219964 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    American Diabetes Association (2019) 6. Glycemic targets: standards of medical care in diabetes-2019. Diabetes Care 42(Suppl 1):S61–S70.  https://doi.org/10.2337/dc19-S006 CrossRefGoogle Scholar
  21. 21.
    The International Hypoglycaemia Study Group (2017) Glucose concentrations of less than 3.0 mmol/l (54 mg/dl) should be reported in clinical trials: a joint position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 60(1):3–6.  https://doi.org/10.1007/s00125-016-4146-6 CrossRefGoogle Scholar
  22. 22.
    American Diabetes Association (2019) 14. Management of diabetes in pregnancy: standards of medical care in diabetes-2019. Diabetes Care 42(Suppl 1):S165–S172.  https://doi.org/10.2337/dc19-S014 CrossRefGoogle Scholar
  23. 23.
    Beyond A1c Working Group (2018) Need for regulatory change to incorporate beyond A1C glycemic metrics. Diabetes Care 41(6):e92–e94.  https://doi.org/10.2337/dci18-0010 CrossRefGoogle Scholar
  24. 24.
    Kovatchev BP (2017) Metrics for glycaemic control - from HbA1c to continuous glucose monitoring. Nat Rev Endocrinol 13(7):425–436.  https://doi.org/10.1038/nrendo.2017.3 CrossRefPubMedGoogle Scholar
  25. 25.
    diaTribe Learn (2017) CGM and time-in-range: what do diabetes experts think about goals? Available from https://diatribe.org/cgm-and-time-range-what-do-diabetes-experts-think-about-goals. Accessed 1 April 2019
  26. 26.
    Vigersky RA, McMahon C (2019) The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technol Ther 21(2):81–85.  https://doi.org/10.1089/dia.2018.0310 CrossRefPubMedGoogle Scholar
  27. 27.
    Hirsch IB, Welsh JB, Calhoun P, Puhr S, Walker TC, Price DA (2019) Associations between HbA1c and continuous glucose monitoring-derived glycaemic variables. Diabet Med.  https://doi.org/10.1111/dme.14065
  28. 28.
    Cryer PE (2014) Glycemic goals in diabetes: trade-off between glycemic control and iatrogenic hypoglycemia. Diabetes 63(7):2188–2195.  https://doi.org/10.2337/db14-0059 CrossRefPubMedGoogle Scholar
  29. 29.
    Bergenstal RM (2015) Glycemic variability and diabetes complications: does it matter? Simply put, there are better glycemic markers! Diabetes Care 38(8):1615–1621.  https://doi.org/10.2337/dc15-0099 CrossRefPubMedGoogle Scholar
  30. 30.
    Rodbard D (2009) Display of glucose distributions by date, time of day, and day of week: new and improved methods. J Diabetes Sci Technol 3(6):1388–1394.  https://doi.org/10.1177/193229680900300619 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bergenstal RM, Ahmann AJ, Bailey T et al (2013) Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the ambulatory glucose profile (AGP). Diabetes Technol Ther 15(3):198–211.  https://doi.org/10.1089/dia.2013.0051 CrossRefPubMedGoogle Scholar
  32. 32.
    Beck RW, Riddlesworth T, Ruedy K et al (2017) Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial. JAMA 317(4):371–378.  https://doi.org/10.1001/jama.2016.19975 CrossRefPubMedGoogle Scholar
  33. 33.
    Messer LH, Forlenza GP, Sherr JL et al (2018) Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system. Diabetes Care 41(4):789–796.  https://doi.org/10.2337/dc17-1682 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Bergenstal RM, Garg S, Weinzimer SA et al (2016) Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes. JAMA 316(13):1407–1408.  https://doi.org/10.1001/jama.2016.11708 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Stone MP, Agrawal P, Chen X et al (2018) Retrospective analysis of 3-month real-world glucose data after the MiniMed 670G system commercial launch. Diabetes Technol Ther 20(10):689–692.  https://doi.org/10.1089/dia.2018.0202 CrossRefPubMedGoogle Scholar
  36. 36.
    Beck RW, Bergenstal RM, Cheng P et al (2019) The relationships between time in range, hyperglycemia metrics, and HbA1c. J Diabetes Sci Technol 13(4):614–626.  https://doi.org/10.1177/1932296818822496 CrossRefPubMedGoogle Scholar
  37. 37.
    Petersson J, Akesson K, Sundberg F, Sarnblad S (2019) Translating glycated hemoglobin A1c into time spent in glucose target range: a multicenter study. Pediatr Diabetes 20(3):339–344.  https://doi.org/10.1111/pedi.12817 CrossRefPubMedGoogle Scholar
  38. 38.
    Lind M, Polonsky W, Hirsch IB et al (2017) 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 317(4):379–387.  https://doi.org/10.1001/jama.2016.19976 CrossRefPubMedGoogle Scholar
  39. 39.
    Olafsdottir AF, Polonsky W, Bolinder J et al (2018) A randomized clinical trial of the effect of continuous glucose monitoring on nocturnal hypoglycemia, daytime hypoglycemia, glycemic variability, and hypoglycemia confidence in persons with type 1 diabetes treated with multiple daily insulin injections (GOLD-3). Diabetes Technol Ther 20(4):274–284.  https://doi.org/10.1089/dia.2017.0363 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G (2017) Use of flash glucose-sensing technology for 12 months as a replacement for blood glucose monitoring in insulin-treated type 2 diabetes. Diabetes Ther 8(3):573–586.  https://doi.org/10.1007/s13300-017-0255-6 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Murphy HR, Rayman G, Duffield K et al (2007) Changes in the glycemic profiles of women with type 1 and type 2 diabetes during pregnancy. Diabetes Care 30(11):2785–2791.  https://doi.org/10.2337/dc07-0500 CrossRefPubMedGoogle Scholar
  42. 42.
    Murphy HR (2019) Continuous glucose monitoring targets in type 1 diabetes pregnancy: every 5% time in range matters. Diabetologia 62(7):1123–1128.  https://doi.org/10.1007/s00125-019-4904-3 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Vos FE, Schollum JB, Coulter CV, Manning PJ, Duffull SB, Walker RJ (2012) Assessment of markers of glycaemic control in diabetic patients with chronic kidney disease using continuous glucose monitoring. Nephrology 17(2):182–188.  https://doi.org/10.1111/j.1440-1797.2011.01517.x CrossRefPubMedGoogle Scholar
  44. 44.
    Bergenstal RM, Gal RL, Connor CG et al (2017) Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels. Ann Intern Med 167(2):95–102.  https://doi.org/10.7326/M16-2596 CrossRefPubMedGoogle Scholar
  45. 45.
    Cohen RM, Franco RS, Smith EP, Higgins JM (2019) When HbA1c and blood glucose do not match: how much is determined by race, by genetics, by differences in mean red blood cell age? J Clin Endocrinol Metab 104(3):707–710.  https://doi.org/10.1210/jc.2018-02409 CrossRefPubMedGoogle Scholar
  46. 46.
    Kovatchev B, Cobelli C (2016) Glucose variability: timing, risk analysis, and relationship to hypoglycemia in diabetes. Diabetes Care 39(4):502–510.  https://doi.org/10.2337/dc15-2035 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Hirsch IB (2015) Glycemic variability and diabetes complications: does it matter? Of course it does! Diabetes Care 38(8):1610–1614.  https://doi.org/10.2337/dc14-2898 CrossRefPubMedGoogle Scholar
  48. 48.
    Lachin JM, Bebu I, Bergenstal RM et al (2017) Association of glycemic variability in type 1 diabetes with progression of microvascular outcomes in the Diabetes Control and Complications Trial. Diabetes Care 40(6):777–783.  https://doi.org/10.2337/dc16-2426 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Monnier L, Colette C, Wojtusciszyn A et al (2017) Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care 40(7):832–838.  https://doi.org/10.2337/dc16-1769 CrossRefPubMedGoogle Scholar
  50. 50.
    Cision PR Newswire (2017) Global CGM market is forecast to cross more than US$ 4 billion by 2024. Available from www.prnewswire.com/news-releases/global-cgm-market-is-forecast-to-cross-more-than-us-4-billion-by-2024-300564996.html. Accessed 3 June 2019
  51. 51.
    Tanenbaum ML, Hanes SJ, Miller KM, Naranjo D, Bensen R, Hood KK (2017) Diabetes device use in adults with type 1 diabetes: barriers to uptake and potential intervention targets. Diabetes Care 40(2):181–187.  https://doi.org/10.2337/dc16-1536 CrossRefPubMedGoogle Scholar
  52. 52.
    Brahimi N, Potier L, Mohammedi K (2017) Cutaneous adverse events related to FreeStyle Libre device. Lancet 389(10077):1396.  https://doi.org/10.1016/S0140-6736(17)30896-6 CrossRefPubMedGoogle Scholar
  53. 53.
    Aerts O, Herman A, Bruze M, Goossens A, Mowitz M (2017) FreeStyle Libre: contact irritation versus contact allergy. Lancet 390(10103):1644.  https://doi.org/10.1016/S0140-6736(17)32142-6 CrossRefPubMedGoogle Scholar
  54. 54.
    Herman A, Aerts O, Baeck M et al (2017) Allergic contact dermatitis caused by isobornyl acrylate in Freestyle(R) Libre, a newly introduced glucose sensor. Contact Dermatitis 77(6):367–373.  https://doi.org/10.1111/cod.12866 CrossRefPubMedGoogle Scholar
  55. 55.
    Ajjan RA, Cummings MH, Jennings P, Leelarathna L, Rayman G, Wilmot EG (2018) Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: implications for clinical practice. Diab Vasc Dis Res 15(3):175–184.  https://doi.org/10.1177/1479164118756240 CrossRefPubMedGoogle Scholar
  56. 56.
    Kovatchev BP, Patek SD, Ortiz EA, Breton MD (2015) Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther 17(3):177–186.  https://doi.org/10.1089/dia.2014.0272 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Aberer F, Hajnsek M, Rumpler M et al (2017) Evaluation of subcutaneous glucose monitoring systems under routine environmental conditions in patients with type 1 diabetes. Diabetes Obes Metab 19(7):1051–1055.  https://doi.org/10.1111/dom.12907 CrossRefPubMedGoogle Scholar
  58. 58.
    Pickup JC, Freeman SC, Sutton AJ (2011) Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data. BMJ 343(jul07 1):d3805.  https://doi.org/10.1136/bmj.d3805 CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Vigersky RA, Shin J, Jiang B, Siegmund T, McMahon C, Thomas A (2018) The comprehensive glucose pentagon: a glucose-centric composite metric for assessing glycemic control in persons with diabetes. J Diabetes Sci Technol 12(1):114–123.  https://doi.org/10.1177/1932296817718561 CrossRefPubMedGoogle Scholar
  60. 60.
    Hempe JM, Liu S, Myers L, McCarter RJ, Buse JB, Fonseca V (2015) The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial. Diabetes Care 38(6):1067–1074.  https://doi.org/10.2337/dc14-1844 CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Lachin JM, Genuth S, Nathan DM, Rutledge BN (2007) The hemoglobin glycation index is not an independent predictor of the risk of microvascular complications in the Diabetes Control and Complications Trial. Diabetes 56(7):1913–1921.  https://doi.org/10.2337/db07-0028 CrossRefPubMedGoogle Scholar
  62. 62.
    Peyser TA, Balo AK, Buckingham BA, Hirsch IB, Garcia A (2018) Glycemic variability percentage: a novel method for assessing glycemic variability from continuous glucose monitor data. Diabetes Technol Ther 20(1):6–16.  https://doi.org/10.1089/dia.2017.0187 CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Rodbard D (2018) Metrics to evaluate quality of glycemic control: comparison of time in target, hypoglycemic, and hyperglycemic ranges with “risk indices”. Diabetes Technol Ther 20(5):325–334.  https://doi.org/10.1089/dia.2017.0416 CrossRefPubMedGoogle Scholar
  64. 64.
    Trefis Team, Great Speculations (2018) How much can Abbottʼs price gain if FreeStyle Libre gets 30% of the blood glucose monitoring market? Available from www.forbes.com/sites/greatspeculations/2018/12/20/how-much-can-abbotts-price-gain-if-freestyle-libre-gets-30-of-the-blood-glucose-monitoring-market/. Accessed 18 February 2019

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Keenan Research Centre for Biomedical Science and Li Ka Shing Knowledge InstituteSt Michael’s HospitalTorontoCanada

Personalised recommendations