Heterogeneity of responses to real-time continuous glucose monitoring (RT-CGM) in patients with type 2 diabetes and its implications for application

Stephanie J Fonda, Sara J Salkind, M Susan Walker, Mary Chellappa, Nicole Ehrhardt, Robert A Vigersky, Stephanie J Fonda, Sara J Salkind, M Susan Walker, Mary Chellappa, Nicole Ehrhardt, Robert A Vigersky

Abstract

Objective: To characterize glucose response patterns of people who wore a real-time continuous glucose monitor (RT-CGM) as an intervention to improve glycemic control. Participants had type 2 diabetes, were not taking prandial insulin, and interpreted the RT-CGM data independently.

Research design and methods: Data were from the first 12 weeks of a 52-week, prospective, randomized trial comparing RT-CGM (n = 50) with self-monitoring of blood glucose (n = 50). RT-CGM was used in 8 of the first 12 weeks. A1C was collected at baseline and quarterly. This analysis included 45 participants who wore the RT-CGM ≥4 weeks. Analyses examined the RT-CGM data for common response patterns-a novel approach in this area of research. It then used multilevel models for longitudinal data, regression, and nonparametric methods to compare the patterns of A1C, mean glucose, glycemic variability, and views per day of the RT-CGM device.

Results: There were five patterns. For four patterns, mean glucose was lower than expected as of the first RT-CGM cycle of use given participants' baseline A1C. We named them favorable response but with high and variable glucose (n = 7); tight control (n = 14); worsening glycemia (n = 6); and incremental improvement (n = 11). The fifth was no response (n = 7). A1C, mean glucose, glycemic variability, and views per day differed across patterns at baseline and longitudinally.

Conclusions: The patterns identified suggest that targeting people with higher starting A1Cs, using it short-term (e.g., 2 weeks), and monitoring for worsening glycemia that might be the result of burnout may be the best approach to using RT-CGM in people with type 2 diabetes not taking prandial insulin.

Figures

Figure 1
Figure 1
Examples of main response patterns observed with RT-CGM.(A high-quality color representation of this figure is available in the online issue.)
Figure 2
Figure 2
Change in A1C as of 12 weeks by baseline A1C for each response pattern. Figure is a scatterplot of each response patterns’ change in A1C and baseline A1C overlaid with a prediction plot to show the trends. To minimize the text in the figure, we assigned the patterns arbitrary numbers and the numbers are shown at the end of each line in the figure. The lines for each pattern start and end at their minimum and maximum data points in the scatterplot.
Figure 3
Figure 3
Discrete views of the RT-CGM display per day first and last available cycle, by response pattern.

References

    1. Ehrhardt NM, Chellappa M, Walker MS, Fonda SJ, Vigersky RA. The effect of real-time continuous glucose monitoring on glycemic control in patients with type 2 diabetes mellitus. J Diabetes Sci Tech 2011;5:668–675
    1. Vigersky RA, Fonda SJ, Chellappa M, Walker MS, Ehrhardt NM. Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes. Diabetes Care 2012;35:32–38
    1. Garg S, Zisser H, Schwartz S, et al. Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial. Diabetes Care 2006;29:44–50
    1. Bailey TS, Zisser HC, Garg SK. Reduction in hemoglobin A1C with real-time continuous glucose monitoring: results from a 12-week observational study. Diabetes Technol Ther 2007;9:203–210
    1. Yoo HJ, An HG, Park SY, et al. Use of a real time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes. Diabetes Res Clin Pract 2008;82:73–79
    1. Raz I, Wilson PW, Strojek K, et al. Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: the HEART2D trial. Diabetes Care 2009;32:381–386
    1. Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH. Glucose variability; does it matter? Endocr Rev 2010;31:171–182
    1. Monnier L, Mas E, Ginet C, et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 2006;295:1681–1687
    1. Ceriello A, Esposito K, Piconi L, et al. Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 2008;57:1349–1354
    1. Su G, Mi S, Tao H, et al. Association of glycemic variability and the presence and severity of coronary artery disease in patients with type 2 diabetes. Cardiovasc Diabetol 2011;10:19.
    1. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes 2005;54:1615–1625
    1. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res 2010;107:1058–1070
    1. Baghurst PA, Rodbard D, Cameron FJ. The minimum frequency of glucose measurements from which glycemic variation can be consistently assessed. J Diabetes Sci Tech 2010;4:1382–1385
    1. Rodbard D. Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control. Diabetes Technol Ther 2009;11(Suppl. 1):S55–S67
    1. Kohnert KD, Vogt L, Salzsieder E. Advances in understanding glucose variability and the role of continuous glucose monitoring. Eur Endocrinol 2011;6:53–56
    1. Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther 2011;13:921–928
    1. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfield D, Heine RJ, A1c-Derived Average Glucose Study Group Translating the A1c assay into estimated average glucose values. Diabetes Care 2008;31:1473–1478
    1. Clipp EC, Pavalko EK, Elder GH. Trajectories of health: in concept and empirical pattern. Behav Health Aging 1992;2:159–179
    1. Tamborlane WV, Beck RW, Bode BW, et al. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008;359:1464–1476
    1. Beck RW, Hirsch IB, Laffel L, et al. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group The effect of continuous glucose monitoring in well-controlled type 1 diabetes. Diabetes Care 2009;32:1378–1383
    1. Deiss D, Bolinder J, Riveline JP, et al. Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabetes Care 2006;29:2730–2732
    1. Zick R, Petersen B, Richter M, Haug C, SAFIR Study Group Comparison of continuous blood glucose measurement with conventional documentation of hypoglycemia in patients with Type 2 diabetes on multiple daily insulin injection therapy. Diabetes Technol Ther 2007;9:483–492
    1. Buckingham B, Caswell K, Wilson DM. Real-time continuous glucose monitoring. Curr Opin Endocrinol Diabetes Obes 2007;14:288–295
    1. Wagner J, Tennen H, Wolpert H. Continuous glucose monitoring: a review for behavioral researchers. Psychosom Med 2012;74:356–365
    1. Allen NA, Jacelon CS, Chipkin SR. Feasibility and acceptability of continuous glucose monitoring and accelerometer technology in exercising individuals with type 2 diabetes. J Clin Nurs 2009;18:373–383
    1. Volpp KG. Paying people to lose weight and stop smoking. LDI Issue Brief 2009;14:1–4
    1. Volpp KG, Asch DA, Galvin R, Loewenstein G. Redesigning employee health incentives—lessons from behavioral economics. N Engl J Med 2011;365:388–390
    1. Parkin CG, Hinnen D, Campbell RK, Geil P, Tetrick DL, Polonsky WH. Effective use of paired testing in type 2 diabetes: practical applications in clinical practice. Diabetes Educ 2009;35:915–927
    1. Polonsky WH, Fisher L, Schikman CH, et al. Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study. Diabetes Care 2011;34:262–267
    1. Durán A, Martín P, Runkle I, et al. Benefits of self-monitoring blood glucose in the management of new-onset Type 2 diabetes mellitus: the St Carlos Study, a prospective randomized clinic-based interventional study with parallel groups. J Diabetes 2010;2:203–211
    1. Franciosi M, Lucisano G, Pellegrini F, et al. ROSES Study Group ROSES: role of self-monitoring of blood glucose and intensive education in patients with Type 2 diabetes not receiving insulin. A pilot randomized clinical trial. Diabet Med 2011;28:789–796

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe