Background: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.Method: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).Results: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.Conclusion: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

Acetaminophen (APAP) can cause erroneously high readings in real-time continuous glucose monitoring (rtCGM) systems. APAP-associated bias in an investigational rtCGM system (G6) was evaluated by taking the difference in glucose measurements between rtCGM and YSI from 1 hour before to 6 hours after a 1-g oral APAP dose in 66 subjects with type 1 or type 2 diabetes. The interference effect was defined as the average post-dose (30-90 minutes) bias minus the average baseline bias for each subject. The clinically meaningful interference effect was defined as 10 mg/dL. The G6 system's overall mean (±SD) interference effect was 3.1 ± 4.8 mg/dL (one-sided upper 95% CI = 4.1 mg/dL), significantly lower than 10 mg/dL. The G6 system's resistance to APAP interference should provide reassurance to those using the drug.

Background: We evaluated the accuracy and safety of a seventh generation (G7) Dexcom continuous glucose monitor (CGM) during 10.5 days of use in adults with diabetes. Methods: Adults with either type 1 or type 2 diabetes (on intensive insulin therapy or not) participated at 12 investigational sites in the United States. In-clinic visits were conducted on days 1 or 2, 4 or 7, and on the second half of day 10 or the first half of day 11 for frequent comparisons with comparator blood glucose measurements obtained with the YSI 2300 Stat Plus glucose analyzer. Participants wore sensors concurrently on the upper arm and abdomen. Accuracy evaluation included the proportion of CGM values within 15% of comparator glucose levels >100 mg/dL or within 15 mg/dL of comparator levels ≤100 mg/dL (%15/15), along with the %20/20 and %30/30 agreement rates. The mean absolute relative difference (MARD) between temporally matched CGM and comparator values was also calculated. Results: Data from 316 participants (619 sensors, 77,774 matched pairs) were analyzed. For arm- and abdomen-placed sensors, overall MARDs were 8.2% and 9.1%, respectively. Overall %15/15, %20/20, and %30/30 agreement rates were 89.6%, 95.3%, and 98.8% for arm-placed sensors and were 85.5%, 93.2%, and 98.1% for abdomen-placed sensors. Across days of wear, glucose concentration ranges, and rates of change, %20/20 agreement rates varied by no more than 9% from the overall %20/20. No serious adverse events were reported. Conclusions: The G7 CGM provides accurate glucose readings with single-digit MARD with arm or abdomen placement in adults with diabetes. NCT04794478

Background: The purpose of this study was to evaluate the accuracy and efficacy of Dexcom G4 Platinum CGM System. Methods: Seventy-two subjects enrolled at 4 US centers; 61% were male; 83% had T1DM and17% had T2DM. Subjects wore at least 1 system for up to 7 days. Subjects participated in a total of 36 hours in the clinic to contribute YSI reference glucose measurements with venous blood draws every 15 minutes on study Day 1, Day 4, and Day 7. Results:The overall mean absolute relative difference (ARD) versus YSI was 13% with a median of 10%. Precision ARD was 9% ± 4% between 2 sensors with a 7% coefficient of variation. The mean ARD versus SMBG was 14% with a median of 11%. One hundred two (94%) sensors lasted 7 days and the systems displayed 97% of their expected glucose readings in average. The time spent in low CGM readings during nighttime hours decreased from the first night use to the 6th night (P < .001) with a small difference in average CGM glucose from 147 ± 40 mg/dL to 166 ± 62 mg/dL. There were no serious adverse events or infectious complications reported. Conclusions:The study showed the Dexcom G4 Platinum CGM System is one of the most accurate CGMs. The significant reduction in nocturnal time spent in a hypoglycemic state observed during this study suggests that a longer term study of CGM use, especially nocturnal use, could be beneficial for patients with hypoglycemia unawareness.

Background: The potential clinical benefits of continuous glucose monitoring (CGM) have been recognized for many years, but CGM is used by a small fraction of patients with diabetes. One obstacle to greater use of the technology is the lack of simplified tools for assessing glycemic control from CGM data without complicated visual displays of data. Methods: We developed a simple new metric, the personal glycemic state (PGS), to assess glycemic control solely from continuous glucose monitoring data. PGS is a composite index that assesses four domains of glycemic control: mean glucose, glycemic variability, time in range and frequency and severity of hypoglycemia. The metric was applied to data from six clinical studies for the G4 Platinum continuous glucose monitoring system (Dexcom, San Diego, CA). The PGS was also applied to data from a study of artificial pancreas comparing results from open loop and closed loop in adolescents and in adults. Results: The new metric for glycemic control, PGS, was able to characterize the quality of glycemic control in a wide range of study subjects with various mean glucose, minimal, moderate, and excessive glycemic variability and subjects on open loop versus closed loop control. Conclusion: A new composite metric for the assessment of glycemic control based on CGM data has been defined for use in assessing glycemic control in clinical practice and research settings. The new metric may help rapidly identify problems in glycemic control and may assist with optimizing diabetes therapy during timeconstrained physician office visits.