Short- and long-term effects of real-time continuous glucose monitoring in patients with type 2 diabetes

Abstract

Objective: To determine whether short-time, real-time continuous glucose monitoring (RT-CGM) has long-term salutary glycemic effects in patients with type 2 diabetes who are not on prandial insulin.

Research design and methods: This was a randomized controlled trial of 100 adults with type 2 diabetes who were not on prandial insulin. This study compared the effects of 12 weeks of intermittent RT-CGM with self-monitoring of blood glucose (SMBG) on glycemic control over a 40-week follow-up period. Subjects received diabetes care from their regular provider without therapeutic intervention from the study team.

Results: There was a significant difference in A1C at the end of the 3-month active intervention that was sustained during the follow-up period. The mean, unadjusted A1C decreased by 1.0, 1.2, 0.8, and 0.8% in the RT-CGM group vs. 0.5, 0.5, 0.5, and 0.2% in the SMBG group at 12, 24, 38, and 52 weeks, respectively (P = 0.04). There was a significantly greater decline in A1C over the course of the study for the RT-CGM group than for the SMBG group, after adjusting for covariates (P < 0.0001). The subjects who used RT-CGM per protocol (≥48 days) improved the most (P < 0.0001). The improvement in the RT-CGM group occurred without a greater intensification of medication compared with those in the SMBG group.

Conclusions: Subjects with type 2 diabetes not on prandial insulin who used RT-CGM intermittently for 12 weeks significantly improved glycemic control at 12 weeks and sustained the improvement without RT-CGM during the 40-week follow-up period, compared with those who used only SMBG.

Figure 1

Mean A1C change from baseline by treatment group. Change equals later A1C minus baseline A1C. This figure shows the raw mean changes and SEMs. A separate multilevel model of the actual A1C values, with a transformation of the time variable to reflect the deceleration of change over time (1/time², with time defined as 1–5), showed that the decline in A1C over the course of the study differed between the groups net of other factors known to cause A1C change: age, sex, diabetes therapy, and initiation of insulin during the study. Specifically, the results of a multilevel model found that the decline for the SMBG group was 0.51% (P = 0.002) and the decline for the RT-CGM group was 1.16% (P < 0.0001). These estimates must be multiplied by 1/time² to obtain the change in A1C, which occurred at each time point.

Figure 2

Mean A1C change from baseline per subject adherence to the study protocol, within the treatment groups. Change equals later A1C minus baseline A1C. This figure shows the raw mean changes and SEMs. In the RT-CGM group, 16 subjects wore the technology <48 days and 34 wore it ≥48 days. In the SMBG group, 9 subjects tested less than one time per day and 41 tested one or more times per day. A: The line for the SMBG group is indicated as a reference only; these participants were not included in the multilevel model. B: The line for the RT-CGM group also is indicated as a reference only. Two separate multilevel models of the actual A1C values, with a transformation of the time variable to reflect the deceleration of change over time (1/time²), showed that the decline in A1C over the course of the study differed between the usage groups net of other factors known to cause A1C change: age, sex, diabetes therapy, and initiation of insulin during the study. Specifically, the results of multilevel models found that the decline for the group that took part in RT-CGM for <48 days was 0.76% (P = 0.008), the decline for the group that took part in RT-CGM for ≥48 days was 1.31% (P < 0.0001), the decline for the group that performed SMBG less than one time per day was 0.18% (P < 0.58), and the decline for the group that performed SMBG one or more times per day 0.67% (P < 0.001). These estimates must be multiplied by 1/time² to obtain the change in A1C that occurred at each time point.