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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

Introduction Glycemic control in participants with insulin-treated diabetes remains challenging. We assessed safety and efficacy of new flash glucose-sensing technology to replace self-monitoring of blood glucose (SMBG). Methods This open-label randomized controlled study (ClinicalTrials.gov, NCT02082184) enrolled adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers. Following 2 weeks of blinded sensor wear, 2:1 (intervention/control) randomization (centrally, using biased-coin minimization dependant on study center and insulin administration) was to control (SMBG) or intervention (glucose-sensing technology). Participants and investigators were not masked to group allocation. Primary outcome was difference in HbA1c at 6 months in the full analysis set. Prespecified secondary outcomes included time in hypoglycemia, effect of age, and patient satisfaction. Results Participants (n = 224) were randomized (149 intervention, 75 controls). At 6 months, there was no difference in the change in HbA1c between intervention and controls: −3.1 ± 0.75 mmol/mol, [−0.29 ± 0.07% (mean ± SE)] and −3.4 ± 1.04 mmol/mol (−0.31 ± 0.09%) respectively; p = 0.8222. A difference was detected in participants aged <65 years [−5.7 ± 0.96 mmol/mol (−0.53 ± 0.09%) and −2.2 ± 1.31 mmol/mol (−0.20 ± 0.12%), respectively; p = 0.0301]. Time in hypoglycemia <3.9 mmol/L (70 mg/dL) reduced by 0.47 ± 0.13 h/day [mean ± SE (p = 0.0006)], and <3.1 mmol/L (55 mg/dL) reduced by 0.22 ± 0.07 h/day (p = 0.0014) for intervention participants compared with controls; reductions of 43% and 53%, respectively. SMBG frequency, similar at baseline, decreased in intervention participants from 3.8 ± 1.4 tests/day (mean ± SD) to 0.3 ± 0.7, remaining unchanged in controls. Treatment satisfaction was higher in intervention compared with controls (DTSQ 13.1 ± 0.50 (mean ± SE) and 9.0 ± 0.72, respectively; p < 0.0001). No serious adverse events or severe hypoglycemic events were reported related to sensor data use. Forty-two serious events [16 (10.7%) intervention participants, 12 (16.0%) controls] were not device-related. Six intervention participants reported nine adverse events for sensor-wear reactions (two severe, six moderate, one mild). Conclusion Flash glucose-sensing technology use in type 2 diabetes with intensive insulin therapy results in no difference in HbA1c change and reduced hypoglycemia, thus offering a safe, effective replacement for SMBG. Trial registration ClinicalTrials.gov identifier: NCT02082184. Funding Abbott Diabetes Care. Electronic supplementary material The online version of this article (doi:10.1007/s13300-016-0223-6) contains supplementary material, which is available to authorized users.


INTRODUCTION
The number of people with diabetes is increasing globally with 90% having type 2 diabetes, a fifth of whom are on insulin treatment. A significant proportion of adults with insulin-treated type 2 diabetes are less than 65 years of age and frequently have poor glycemic control [1,2].
Improving glycemia reduces the risk of diabetes complications and is a key management objective [3]. However, intensification of insulin therapy increases the risk of hypoglycemia [4] which is associated with adverse clinical outcome [5], impacts on quality of life [6], and increases treatment costs secondary to hospital admissions, ambulance call-outs, and clinic attendance [7]. Glycated hemoglobin (HbA1c), the gold standard for assessment of glycemic control, is unable to reflect hypoglycemic risk or indicate glucose variability, which recent reports suggest are associated with inferior clinical outcome [8,9]. Detection of hypoglycemia or glucose variability can be difficult with self-monitoring of blood glucose which is usually the main method used for self-management and adjusting insulin therapy.
For participants on intensive insulin therapy, four or more blood glucose tests are required daily to safely and effectively adjust insulin doses. This is not always achieved because of the pain and inconvenience associated with this method of glucose testing [10,11]. A tool that can support a more comprehensive assessment of glycemia is  [12].
The aim of our study was to assess the role of this new category of glucose-sensing technology on glycemic control in individuals with type 2 diabetes using intensive insulin therapy or continuous subcutaneous insulin infusion (CSII).

Study Design and Participants
We conducted this 6-

Randomization and Masking
Participants were centrally randomized in a 2:1 ratio to sensor-based flash glucose monitoring (intervention group) or to self-monitoring of blood glucose (control group) by an interactive web response system (IWRS) using biased-coin minimization, with study center and insulin administration as prognostic factors. The intention of a 2:1 randomization ratio was to ensure a sufficient number of participants in the intervention arm to complete an additional 6-month, open-access study phase. Participants, investigators, and study staff were not masked to group allocation.

Procedures
Following consent, screening, and enrollment, all participants wore a system locked into masked mode for the 14-day baseline period and were asked to scan their sensor every 8 h.
Sensor glucose measurements were blinded (not visible) to participants and investigators during this phase. Glucose management was supported by continuation of their current regimen for blood glucose monitoring using the strip-port built into the reader and compatible test strips (Abbott Diabetes Care, Witney, Oxon, UK).
Participants were asked to record blood glucose levels in a glucose diary and to log other events (e.g., severe hypoglycemia) in an event diary. Participants with sensor data for at least 50% of the blinded wear or at least 650 individual sensor readings (only two subjects did not meet this criterion and withdrew) were centrally randomized to intervention or control  [14], Diabetes Quality of Life (DQoL) [15], and Diabetes Treatment Satisfaction (DTSQs and DTSQc) [16].
Safety endpoints incorporated all adverse events including severe hypoglycemia (requiring third-party assistance) [13], hypoglycemic events [20], sensor insertion or sensor wear-related symptoms, diabetic ketoacidosis or hyperosmolar hyperglycemic state episodes, and cardiac events.

Statistical Analysis
This study was powered at 90% to detect a difference of 3.8 mmol/mol (0.35%) in HbA1c between the intervention and control group at 6 months with a 5% significance level as per guidance of the Food and Drug Administration [21] and assuming SD for the change of 0.65 [22]. The intervention group was double the size of the control group resulting in a sample size of 210 participants allowing for a dropout rate of 20% post-randomization. Missing values for the primary endpoint were imputed using the last observation carried forward (LOCF) approach. For the sensor data derived secondary endpoints, if less than 72 h of sensor results were available from the final 14-day sensor wear (days 194-208), the last 72 h of available recorded results were used. Analysis of covariance was used to adjust for chance imbalances in baseline measurements between the treatment groups [23], adjusted means were then used to compare differences between the groups for the 6-month endpoints.
Glycemic control and variability results, BMI/weight, and total daily dose of insulin were compared between treatment groups using analysis of covariance of the differences between post-baseline and baseline values with study center and baseline measurement as covariates.
Changes in questionnaire responses were considered using analysis of covariance on baseline values and study center to compare scores from intervention with control group participants. Confidence intervals were calculated for the group least-square mean of each measure and the difference between group least-square means.
Results presented here are for the full analysis set, which included all randomized participants since there were no pregnancies.

Role of the Funding Source
The sponsor designed the study protocol in collaboration with the principal investigator in each country and provided all study materials.
The sponsor was involved in collecting data and reporting results, but was not involved in the authors' interpretation or text writing. The sponsor also gave approval to submit for publication. The corresponding author had full access to all the data in the study and, together with all authors, had final responsibility for the decision to submit for publication.

RESULTS
We baseline characteristics are summarized in Table 1, the full analysis set included 224 randomized participants, and there were no significant differences between groups.     Fig. 3). During the treatment phase (day 15 onwards) average sensor-scanning frequency was 8.3 ± 4.4 (mean ± SD) times/day (median 6.8), i.e., double the frequency of blood glucose testing (Fig. 3). There was no significant difference in the number of scans performed by those \65 years and C65 years of age [8.1 ± 4.6 (median 6.8) and 8.5 ± 4.1 (median 6.9), respectively, p = 0.6627].
There was no correlation between frequency of sensor scanning and reduced time in hypoglycemia or change to HbA1c. Device use for the intervention group (n = 138) was 88.7 ± 9.2% (defined as the percentage of data collected, assuming continuous device wear for 6 months).
Self-monitoring of blood glucose frequency for control participants was 3.9 ± 1.5 test/day (median 3.9) at baseline and this rate was maintained until study end [3.8 ± 1.9 (median 3.9), Fig. 3 There were no changes in body weight (p = 0.2496) or BMI (p = 0.2668) from baseline for either group.
Total treatment satisfaction score for DTSQ (status versus change) was significantly improved for intervention group participants  None of the severe hypoglycemic episodes [13] or hypoglycemic adverse events were associated with the device.
Three participants (one intervention, two controls) experienced an adverse event leading to withdrawal from the study; none were associated with the device.
Six (4.0%) intervention participants reported nine device-related adverse events (two severe, six moderate, and one mild). These were sensor-adhesive reactions, primarily treated with topical preparations. All were resolved at study exit.
There were no reported events of diabetic ketoacidosis or hyperosmolar hyperglycemic state. Seven cardiac events were reported for four (2.7%) intervention and three (4.0%) control participants (none were considered to be related to study procedures or the device).
Anticipated symptoms refer to those typically expected using a sensor device and equate to symptoms normally experienced with blood glucose finger-stick testing, e.g., pain, bleeding, bruising. There were 158 anticipated sensor insertion site symptoms observed for 41 (27.5%) intervention and 9 (12.0%) control participants. These symptoms were primarily (63%) due to the sensor adhesive (erythema, itching, and rash) and resolved without medical intervention. Adverse events and anticipated symptoms associated with the insertion of the sensor and sensor wear are summarized in Table 3 and Supplementary Material p. 12.

DISCUSSION
This European study is the first to investigate the use of flash sensor-based glucose technology as a replacement for standard self-monitoring of blood glucose in individuals with type 2 diabetes treated with intensive insulin therapy.
Whilst the primary endpoint was not achieved (no difference in HbA1c change between the groups at 6 months), the secondary endpoints demonstrate a number of interesting findings for further consideration including use of the There is a paucity of data on continuous glucose monitoring (CGM) use in type 2 diabetes and, to our knowledge, no recent randomised, controlled studies in this population using intensive insulin therapy.
Available data for CGM use in those using oral glucose-lowering medication or basal insulin with higher baseline HbA1c values indicate they are more likely to show benefit with a reduction in this clinical marker [24][25][26]. However, hypoglycaemia was not an endpoint in these studies, and exposure to hypoglycaemic risk is much less in treatment regimens excluding prandial insulin. Reductions in hypoglycaemic markers generally require de-escalation of glucose-lowering therapy [27] with less stringent glucose targets [28]. In the intervention group, HbA1c level improved with significantly reduced exposure to hypoglycaemia.  [29]. Given the association of hypoglycemia with adverse clinical outcome, including enhanced risk of cardiovascular events, increased hospital admissions, and reduced survival [5,7], these results for multiple hypoglycemia-related secondary endpoints highlight the effectiveness and safety of this technology and its potential for improving glycemic control. Detection of In addition to benefiting from less time in hypoglycemia compared with the control group, intervention participants showed improvement in glucose variability [30] and LBGI, a specific risk marker for hypoglycemia [31]. These findings can be partially explained by the documented association between hypoglycemia and glucose variability [30,31]. The reduction in hypoglycemic exposure in the intervention group may offer additional clinical benefits [8,9]. A significant improvement in HbA1c was detected in those younger than 65 years. and perceived control over diabetes [33].

Reductions in hypoglycemia in
Our study results support those of a recent randomized control trial comparing use of this technology with blood glucose testing in adults with well-controlled type 1 diabetes, which also demonstrated superior reduction in hypoglycemia without deterioration of HbA1c and improved treatment satisfaction [34].