Flash glucose monitoring improves glycemia in higher risk patients: a longitudinal, observational study under real-life settings

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study

BACKGROUND

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

OBJECTIVE

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

METHODS

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

RESULTS

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

CONCLUSIONS

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes

Background and Objectives: The purpose of this study is to determine the optimal number of scans per day required for attaining good glycemic regulation. Materials and Methods: The association of scanning frequency and glucometrics was analyzed according to bins of scanning frequency and bins of time in range (TIR) in the Croatian population of type 1 diabetes (T1DM) patients. Results: Intermittently scanned continuous glucose monitoring (isCGM) Libre users in Croatia performed on average 13 ± 7.4 scans per day. According to bins of scanning frequency, bin 5 with 11.2 ± 02 daily scans was sufficient for achieving meaningful improvements in glycemic regulation, while decreasing severe hypoglycemia required an increasing number of scans up to bin 10 (31 ± 0.9), yet with no effect on TIR improvement. When data were analyzed according to bins of TIR, an average of 16.3 ± 10.5 scans daily was associated with a TIR of 94.09 ± 3.49% and a coefficient of variation (CV) of 22.97 ± 4.94%. Improvement was shown between each successive bin of TIR but, of notice, the number of scans performed per day was 16.3 ± 10.5 according to TIR-based analysis and 31.9 ± 13.5 in bin 10 according to scan frequency analysis. Conclusions: In conclusion, an optimal average number of scans per day is 16.3 in order to achieve glucose stability and to minimize the burden associated with over-scanning.

Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control

AIM

To evaluate real-life changes of glycemic parameters among flash glucose monitoring (FLASH) users who do not meet glycemic targets.

METHODS

De-identified data were obtained between 2014 and 2021 from patients using FLASH uninterrupted for a 24-week period. Glycemic parameters during first and last sensor use were examined in four identifiable groups: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) on basal-bolus insulin, T2DM on basal insulin, and T2DM without insulin treatment. Within each group, subgroup analyses were performed in persons with initial suboptimal glycemic regulation (time in range (TIR; 3.9-10 mmol/L) <70%, time above range (TAR; >10 mmol/L) >25%, or time below range (TBR; <3.9 mmol/L) >4%).

RESULTS

Data were obtained from 1,909 persons with T1DM and 1,813 persons with T2DM (1,499 basal-bolus insulin, 189 basal insulin, and 125 non-insulin users). In most of the performed analyses, both overall and in the various subgroups, significant improvements were observed in virtually all predefined primary (TIR) and secondary endpoints (eHbA1c, TAR, TBR and glucose variability).

CONCLUSIONS

24-weeks FLASH use in real life by persons with T1DM and T2DM with suboptimal glycemic regulation is associated with improvement of glycemic parameters, irrespective of pre-use regulation or treatment modality.

Time above range for predicting the development of type 2 diabetes

Aim

To investigate the prognostic value of time range metrics, as measured by continuous glucose monitoring, with respect to the development of type 2 diabetes (T2D).

Research design and methods

A total of 499 persons without diabetes from the general population were followed-up for 5 years. Time range metrics were measured at the start and medical records were checked over the period study.

Results

Twenty-two subjects (8.3 per 1,000 person-years) developed T2D. After adjusting for age, gender, family history of diabetes, body mass index and glycated hemoglobin concentration, multivariate analysis revealed 'time above range' (TAR, i.e., with a plasma glucose concentration of >140 mg/dL) to be significantly associated with a greater risk (OR = 1.06, CI 1.01-1.11) of developing diabetes (AUC = 0.94, Brier = 0.035).

Conclusions

Time above range provides additional information to that offered by glycated hemoglobin to identify patients at a higher risk of developing type 2 diabetes in a population-based study.

The Frequency of Intermittently Scanned Glucose and Diurnal Variation of Glycemic Metrics

BACKGROUND

The relation between the frequency of intermittently scanned continuous glucose monitoring (isCGM) and diurnal variation of time in range (TIR) and time below range (TBR) is unknown.

METHOD

A total of 163 persons with type 1 diabetes who used isCGM had glucose data for 60 days downloaded. Mean TIR and median TBR were calculated for 15-minute periods and presented for daytime and nighttime. The values for tertiles of scanning frequency were compared.

RESULTS

The 1st tertile (n = 53) of the population scanned <10 times; the 2nd tertile (n = 56) 10-13 times, and the 3rd tertile (n = 54) >13 per 24 hours. TIR (%, mean ± (SD)) increased significantly from the 1st to the 3rd scan tertile both during the day (43.8 ± 14.8, 52.0 ± 12.3, 62.1 ± 12.8) and the night (44.5 ± 17.3, 52.3 ± 18.5, 64.0 ± 13.9; P < .0001). In contrast, TBR (median, (IQR)) was not significantly associated with scan tertiles during daytime (3.5% (1.1-7.8), 4.4% (1.8-6.1), 3.5% (2.1-6.1); P = .85) or nighttime (3.8% (1.4-13.7), 5.0% (1.6-9.6), 5.7% (3.6-10.9); P = .24). In a multiple regression model, a 50% increase in 24-hour scanning frequency was associated with a 7.8 percentage point increase in TIR (95% CI, 5.6-10.0).

CONCLUSIONS

Increased scanning frequency was associated with a higher TIR both during daytime and nighttime with no change in TBR.

Frequency of flash glucose monitoring and glucose metrics: real-world observational data from Saudi Arabia

BACKGROUND

This real-world data study analyzed glucose metrics from FreeStyle Libre^® flash glucose monitoring in relation to scanning frequency, time in range (TIR) and estimated A1c (eA1c) in Saudi Arabia.

METHODS

Anonymized reader data were analyzed according to scanning frequency quartiles, eA1c categories (<7%,≥7%‒≤9% or>9%) and TIR categories (<50%,≥50%‒≤70% or>70%). Sensors, grouped by reader, were required to have≥120 h of operation. Differences in scanning frequency, eA1c, TIR, time in hypoglycemia and hyperglycemia, and glucose variability (standard deviation [SD] and coefficient of variation [CV]) were analyzed between groups.

RESULTS

6097 readers, 35,747 sensors, and 40 million automatic glucose measurements were analyzed. Patients in the highest scanning frequency quartile (Q4, mean 32.0 scans/day) had lower eA1c (8.47%), greater TIR (46.4%) and lower glucose variation (SD 75.0 mg/dL, CV 38.2%) compared to the lowest quartile (Q1, mean 5.2 scans/day; eA1c 9.77%, TIR 32.8%, SD 94.9 mg/dL, CV 41.3%). Lower eA1c and higher TIR were associated with greater scanning frequency, lower glucose variability and less time in hyperglycemia.

CONCLUSIONS

Higher scanning frequency in flash glucose users from Saudi Arabia is associated with lower eA1c, higher TIR, lower glucose variability and less time in hypoglycemia or hyperglycemia.

Efficacy of Flash Glucose Monitoring in Type 1 and Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Objective

Flash glucose monitoring (FlashGM) is a sensor-based technology that displays glucose readings and trends to people with diabetes. In this meta-analysis, we assessed the effect of FlashGM on glycaemic outcomes including HbA1c, time in range, frequency of hypoglycaemic episodes and time in hypo/hyperglycaemia compared to self-monitoring of blood glucose, using data from randomised controlled trials.

Methods

A systematic search was conducted on MEDLINE, EMBASE and CENTRAL for articles published between 2014 and 2021. We selected randomised controlled trials comparing flash glucose monitoring to self-monitoring of blood glucose that reported change in HbA1c and at least one other glycaemic outcome in adults with type 1 or type 2 diabetes. Two independent reviewers extracted data from each study using a piloted form. Meta-analyses using a random-effects model was conducted to obtain a pooled estimate of the treatment effect. Heterogeneity was assessed using forest plots and the I2 statistic.

Results

We identified 5 randomised controlled trials lasting 10 – 24 weeks and involving 719 participants. Flash glucose monitoring did not lead to a significant reduction in HbA1c. However, it resulted in increased time in range (mean difference 1.16 hr, 95% CI 0.13 to 2.19, I2 = 71.7%) and decreased frequency of hypoglycaemic episodes (mean difference -0.28 episodes per 24 hours, 95% CI -0.53 to -0.04, I2 = 71.4%).

Conclusions

Flash glucose monitoring did not lead to a significant reduction in HbA1c compared to self-monitoring of blood glucose, however, it improved glycaemic management through increased time in range and decreased frequency of hypoglycaemic episodes.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO (CRD42020165688).

Randomised controlled trial of Advanced Hybrid Closed Loop in an Adult Population with Type 1 Diabetes (ADAPT): study protocol and rationale

INTRODUCTION

For many people with type 1 diabetes who struggle to achieve glycaemic control with multiple daily injections of insulin (MDI) plus self-monitoring of blood glucose, MDI plus intermittently scanned continuous glucose monitoring (IS-CGM) or real-time continuous glucose monitoring (RT-CGM), or insulin administration using insulin pump therapy represent optimised care in many regions. Through technological advances an advanced hybrid closed loop (AHCL) system has been developed; studies of incremental effects relative to MDI plus IS-CGM are lacking.

METHODS AND ANALYSIS

The Advanced Hybrid Closed Loop study in Adult Population with Type 1 Diabetes (ADAPT) study is a multinational, prospective, open-label, confirmatory and exploratory randomised controlled trial to examine outcomes with the MiniMed 670G version 4.0 AHCL system (with an equivalent algorithm and commercialised as the MiniMed 780G system, referred to as AHCL) relative to MDI plus IS-CGM in adults with baseline HbA1c≥8.0%. An exploratory cohort will compare AHCL with MDI plus RT-CGM. The study will be conducted in approximately 124 adults on MDI plus either IS-CGM or RT-CGM for at least 3 months prior to screening. The primary endpoint will be the difference in mean HbA1c change from baseline to 6 months between the AHCL and the MDI plus IS-CGM arms. Secondary endpoints will include proportion of time spent in hypoglycaemic, euglycaemic and hyperglycaemic ranges.

ETHICS AND DISSEMINATION

The ADAPT study will be conducted in accordance with the requirements of the Declaration of Helsinki and local laws and regulations, and has been approved by ethics committees. The trial will provide valuable information on the incremental benefits that may be provided by AHCL for patients failing to achieve glycaemic targets on MDI plus IS-CGM or RT-CGM and form a basis for health economic evaluations to support market access.

TRIAL REGISTRATION NUMBER

NCT04235504; Pre-results.

Glucose Awareness to Motivate and Enable Solutions (GAMES) in diabetes mellitus using flash glucose monitoring: A clinical programme

AIMS

This real-world observational clinical programme evaluated short and medium-term effects of intermittent flash glucose monitoring on HbA1c, glycaemic variability and lifestyle behavioural changes.

METHODS

Two first-generation Libre flash glucose monitoring sensors were provided 3-4 months apart with a food, activity diary, user evaluation survey and treatment modification after each sensor wear. T-tests were used to compare glucose variables within each sensor (week 1 vs. week 2) and between sensors (1st sensor vs. 2nd sensor). EasyGV software was used to calculate glycaemic variability.

RESULTS

From 42 type 1 diabetes and 120 type 2 diabetes participants, there was no statistically significant change in mean HbA1c for participants with type 1 diabetes at 3-4 months after the 1st sensor but there was a statistically significant HbA1c reduction for participants with type 2 diabetes [-4 mmol/mol (-0.4%), p = 0.008], despite no statistically significant differences in carbohydrate intake, exercise frequency and duration. Greater reduction was seen in those with baseline HbA1c> 86 mmol/mol (10%) in both type 1 [-12 mmol/mol (-1.1%), p = 0.009] and type 2 diabetes [-11 mmol/mol (-1.0%), p = 0.001). Both type 1 and type 2 diabetes showed improvements in Glucose Management Indicator and percentage time-above-range when comparing week 1 versus week 2 of the same sensor. Higher scan frequency resulted in improved glycaemic parameters and certain measures of glycaemic variability. The majority of participants (85%) agreed that flash glucose monitoring is a useful device but only 60% were keen to use it for daily monitoring.

CONCLUSION

Constant feedback from flash glucose monitoring improves glycaemic parameters within the first week of wear. Intermittent use 3-4 months apart resulted in greater improvements for those with higher baseline HbA1c.

Factors influencing the effectiveness of using flash glucose monitoring on glycemic control for type 1 diabetes in Saudi Arabia

BACKGROUND

In 2017, 35000 Saudi children and adolescents were living with a type 1 diabetes (T1D) diagnosis. Diabetic complications are minimized upon strengthened glycemic regulation. The annual cost of treating diabetic patients with complications was four-fold higher than for patients without complications. The use of flash glucose monitoring (FGM) enables better diabetes treatment and thereby improves glycemic control. Understanding the factors that affect effectiveness of FGM will help enhance the device’s use and management of hospital resources, resulting in improved outcomes.

AIM

To investigate factors that affect effectiveness of the FGM system for glycated hemoglobin (HbA1c) levels/glycemic control among T1D patients.

METHODS

A retrospective empirical analysis of T1D patient records from King Abdul-Aziz University Hospital and Prince Sultan Military Medical City was performed. T1D patients who began FGM between 2017 and 2019 were included.

RESULTS

The data included 195 T1D patients (70 males and 125 females) with a mean age of 23.6 ± 8.1 years. Among them, 152 patients used multiple daily injection and 43 used an insulin pump. The difference in HbA1c level from baseline and after using FGM was -0.60 ± 2.10, with a maximum of 4.70 and a minimum of -6.30. There was a statistically significant negative correlation between the independent variables (age, duration of diabetes, level of engagement) and HbA1c. The group with the highest HbA1c mean (9.85) was 18-years-old, while the group with the lowest HbA1c mean (7.87) was 45-years-old. Patients with a low level of engagement (less than six scans per day) had the highest HbA1c mean (9.84), whereas those with a high level of engagement (more than eight scans per day) had the lowest HbA1c mean (8.33).

CONCLUSION

With proper education, FGM can help people with uncontrolled T1D over the age of 18 years to control their glucose level.

Intermittently Scanned Continuous Glucose Monitoring Data of Polish Patients from Real-Life Conditions: More Scanning and Better Glycemic Control Compared to Worldwide Data

Background: Randomized trials and observational studies have shown that the use of FreeStyle Libre^® intermittently scanned continuous glucose monitoring system (isCGMS) is associated with improved glycemic indices and quality of life. Materials and Methods: In this retrospective, real-world data analysis, we described country-specific glucometrics among isCGMS users from Poland and compared them with international data. The analyzed time period for the Polish data ranged between August 2016 and August 2020, and the analyzed time period for the international data ranged from September 2014 to August 2020. Results: Data from the Polish population were collected from 10,679 readers and 92,627 sensors with 113 million automatically recorded glucose readings. The worldwide database included information from 981,876 readers and 11,179,229 sensors with 13.1 billion glucose readings. On average, the users of isCGMS from Poland performed substantially more scans/day (21.2 ± 14.2 vs. 13.2 ± 10.7), achieved lower eHbA1c (7.0% ± 1.2% vs. 7.5% ± 1.5%), and spent more time-in-range (TIR) (64.2% ± 17.3% vs. 58.1% ± 20.3%) and less time-above-range (TAR) (29.7% ± 18.0% vs. 36.6% ± 21.3%) (P < 0.0001 for all comparisons). Moreover, they were more likely to achieve TIR >70% (36.3% vs. 28.8%), but spent more time-below-range (TBR) (4.7% vs. 3.6%). Our results confirmed that analyzed glucometrics improve as the scan rate frequency increases. However, at a similar scanning frequency to the comparative group, users from Poland achieved lower eHbA1c, higher TIR, and lower TAR, but higher TBR. Conclusions: We report more scanning and better glycemic control in isCGMS users in Poland than worldwide. The cause of this observation remains unknown. Our data also show that in real-life practice, a large number of patients may be willing to perform scanning more frequently than it is usually assumed.

Glycemic Variability in Type 1 Diabetes Mellitus Saudis Using Ambulatory Glucose Profile

Background

Glucose variability (GV) is a common and challenging clinical entity in the management of people with type 1 diabetes (T1DM). The magnitude of GV in Saudi people with T1DM was not addressed before. Therefore, we aimed to study GV in a consecutive cohort of Saudis with T1DM.

Methods

We prospectively assessed interstitial glucose using FreeStyle^® Libre flash glucose monitoring in people with TIDM who attended follow-up in the diabetes clinics at King Fahad Medical City between March and June 2017. Glycemia profile, standard deviation (SD), coefficient of variation (CV), mean of daily differences (MODD), and mean amplitude of glucose excursion (MAGE) were measured using the standard equations over a period of 2 weeks.

Results

Fifty T1DM subjects (20 males) with mean age 20.2 ± 6.1 years and mean fortnight glucose 192 ± 42.3 mg/dl were included. The mean SD of 2-week glucose readings was 100.4 ± 36.3 mg/dl and CV was 52.1% ± 13%. Higher levels of glucose excursions were also observed. MODD and MAGE were recorded as 104.5 ± 51.7 and 189 ± 54.9 mg/dl, respectively which is 2 to 4 times higher than the international standards. Higher MODD and MAGE were observed on weekends compared to weekdays (111.3 ± 62.1 vs 98.6 ± 56.2 mg/dl and 196.4 ± 64.6 vs 181.7 ± 52.4 mg/dl, respectively; P ⩽ .001).

Conclusion

Higher degree of glycemic variability was observed in this cohort of TIDM Saudis. Weekends were associated with higher glucose swings than weekdays. More studies are needed to explore these findings further.

A review of flash glucose monitoring in type 2 diabetes

BACKGROUND

Continuous glucose monitoring systems are increasingly being adopted as an alternative to self-monitoring of blood glucose (SMBG) by persons with diabetes mellitus receiving insulin therapy.

MAIN BODY

The FreeStyle Libre flash glucose monitoring system (Abbott Diabetes Care, Witney, United Kingdom) consists of a factory-calibrated sensor worn on the back of the arm which measures glucose levels in the interstitial fluid every minute and stores the reading automatically every 15 min. Swiping the reader device over the sensor retrieves stored data and displays current interstitial glucose levels, a glucose trend arrow, and a graph of glucose readings over the preceding 8 h. In patients with type 2 diabetes (T2D) receiving insulin therapy, pivotal efficacy data were provided by the 6-month REPLACE randomized controlled trial (RCT) and 6-month extension study. Compared to SMBG, the flash system significantly reduced the time spent in hypoglycemia and frequency of hypoglycemic events, although no significant change was observed in glycosylated hemoglobin (HbA1c) levels. Subsequent RCTs and real-world chart review studies have since shown that flash glucose monitoring significantly reduces HbA1c from baseline. Real-world studies in both type 1 diabetes or T2D populations also showed that flash glucose monitoring improved glycemic control. Higher (versus lower) scanning frequency was associated with significantly greater reductions in HbA1c and significant improvements in other measures such as time spent in hypoglycemia, time spent in hyperglycemia, and time in range. Additional benefits associated with flash glucose monitoring versus SMBG include reductions in acute diabetes events, all-cause hospitalizations and hospitalized ketoacidosis episodes; improved well-being and decreased disease burden; and greater treatment satisfaction.

CONCLUSION

T2D patients who use flash glucose monitoring might expect to achieve significant improvement in HbA1c and glycemic parameters and several associated benefits.

Analysis of Glycemic Improvement in Hemodialysis Patients Based on Time in Range, Assessed by Flash Glucose Monitoring

INTRODUCTION

This study aimed to investigate the application value of "time in ranges (TIRs)" in dialysis patients with diabetes and summarize the experience of optimizing glycemic control by flash glucose monitoring (FGM) system.

METHODS

In this monocentric 2-week pilot study, FGM was applied for 14 days in 57 type 2 diabetes mellitus medically stable patients under maintenance hemodialysis to determine their glycemic improvement. The diagnostic value of TIR versus HbA1c in detecting glucose fluctuations and levels was evaluated using receiver operating characteristic analysis.

RESULTS

Average glucose exhibited stronger association with TIR (r = -0.785, p < 0.001) than HbA1c (r = 0.644, p < 0.001), and mean amplitude of glycemic excursion (MAGE) had the same conclusion (r = -0.568, p < 0.001 for TIR vs. r = 0.423, p = 0.016 for HbA1c). TIR exhibited a higher area under curve than HbA1c in detecting significant derangements in glucose fluctuation, using a 14-day average FGM-derived coefficient of variation >36% as the reference standard (difference between areas: 0.237; 95% CI 0.092-0.383, p = 0.001). We found a significant improvement in TIR (58.38 ± 19.42 vs. 46.45 ± 24.42 mmol/L, p < 0.001) and a significant decline in MAGE (median 5.64 vs.7.42 mmol/L, p < 0.001) compared to the baseline without deterioration of time spent in hypoglycemia.

CONCLUSION

TIR seems to be feasible and clinically useful for AGP analysis in dialysis patients with diabetes, and FGM can be used to improve glycemic control.

Intermittently Scanned Glucose Values for Continuous Monitoring: Cross-Sectional Analysis of Glycemic Control and Hypoglycemia in 1809 Children and Adolescents with Type 1 Diabetes

Background and Objective: Intermittent scanning continuous glucose monitoring (iscCGM) is increasingly used for glycemic monitoring in diabetes care. In this cross-sectional real-world analysis, iscCGM data were compared to traditional parameters of glycemic control in young people with type 1 diabetes. Methods: Using the DPV registry, most recent data from children and adolescents aged <18 years with uploaded iscCGM sensor profiles with at least 14 days of data and a > 50% completeness were evaluated using recommended parameters of sensor metrics. Analysis was performed stratified by age group, glycemic control, and type of therapy; data were taken from DPV data pool in February 2020. Results: Glucose sensor profiles and clinical data from 1809 individuals (mean age 13.4 years, 53% male, and mean diabetes duration 5.02 years) were evaluated in this study. More than 50% of this population (n = 965) reached the current German treatment target of hemoglobin A1c (HbA1c) <7.5%. In this target, the mean scanning frequency was higher than in groups with HbA1c >7.5 or >8.0% (12.0 vs. 10.2 vs 7.6 times per day). The group of preschool children had the highest frequency of scanning (16.6 vs. 13.3 times per day in school kids and 7.9 in adolescents), the lowest HbA1c level, and the lowest risk for hypoglycemia (low blood glucose index 0.8 vs. 1.0 vs 1.2). Conclusion: Real-world data will help to determine the value of iscCGM to improve clinical and patient-related outcomes in pediatric diabetology. Not only the use of a device but also the intensity of use seems to have a high and direct impact on glycemic control.

Relationship Between Estimated Glycosylated Hemoglobin Using Flash Glucose Monitoring and Actual Measured Glycosylated Hemoglobin in a Chinese Population

INTRODUCTION

The aim of this study was to investigate the relationship between actual measured glycated hemoglobin (HbA1c) and estimated glycated hemoglobin (EA1c) in the flash glucose monitoring (FGM) system in Chinese patients with type 2 diabetes.

METHODS

This study was conducted in Nanjing First Hospital. Each patient used FGM twice in a 3-month period (during the first 14 days immediately after baseline and during a second 14-day period from days 76 to 90 after baseline). HbA1c measurements were made using a high-performance liquid chromatography assay before the start of the first FGM period (baseline) and at the end of the second FGM period.

RESULTS

A total of 74 patients (35 men; mean age ± standard deviation [SD] 67.6 ± 5.2 years) were enrolled in the study. The mean (± SD) duration of diabetes was 11.9 ± 7.8 months. The first and second HbA1c measurements were both higher than the EA1c (both p < 0.001). Mean glucose (MG) gradually decreased over time and was the lowest on day 14. Linear regression showed that only HbA1c at baseline affected the gap between HbA1c and EA1c (β = 0.319, p = 0.01) when the educational level, age, gender, duration of diabetes, body mass index, HbA1c at baseline, and number of scans daily were included as independent variables. The best model for calculating EA1c was EA1c% = MG mmol/L × 0.669 - 0.213 × 8th MG + 3.351 when MG > 9.7 mmol/L, and EA1c % = (MG mmol/L + 2.590)/1.590 when MG ≤ 9.7 mmol/L. The correlation coefficient for EA1c and HbA1c in this model (model 7) is higher than that reported the original model in the FGM system 1 (0.955 vs. 0.822, respectively; p < 0.001).

CONCLUSIONS

The EA1c used by FreeStyle Libre™ is lower than the actual measured HbA1c. Improvement in the glucose levels during FGM in these patients may contribute to the lowering of EA1c.

TRIAL REGISTRATION

The study is registered with ClinicalTrials.gov, number NCT03785301.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Efficacy and safety of flash glucose monitoring in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis

INTRODUCTION

Flash glucose monitoring (FGM) is a factory-calibrated sensor-based technology for the measurement of interstitial glucose. We performed a systematic review and meta-analysis to assess its efficacy and safety in patients with type 1 and type 2 diabetes.

RESEARCH DESIGN AND METHODS

PubMed, CENTRAL, Scopus and Web of Science were searched in July 2019. Twelve studies with a follow-up longer than 8 weeks, evaluating 2173 patients on prandial insulin, multiple daily insulin injections or continuous subcutaneous insulin infusion were included. The following data were extracted: HbA1c, time in range, time above 180 mg/dL, time below 70 mg/dL, frequency of hypoglycemic events, number of self-monitoring of blood glucose (SMBG) measurements, total daily insulin dose, patient-reported outcomes, adverse events, and discontinuation rate. A comparison with SMBG was conducted.

RESULTS

FGM use was associated with a reduction in HbA1c (-0.26% (-3 mmol/mol); p=0.002) from baseline to the last available follow-up, which correlated with HbA1c levels at baseline (-0.4% (-4 mmol/mol) for each 1.0% (11 mmol/mol) of HbA1c above 7.2% (55 mmol/mol)). Also, a decrease in time below 70 mg/dL was found (-0.60 hours/day; p=0.04). Favorable findings in patient-reported outcomes and no device-related serious adverse events were reported. When compared with SMBG, FGM was characterized by no statistically different change in HbA1c (p=0.09), with lower number of SMBG measurements per day (-3.76 n/day; p<0.001) and risk of discontinuation (relative risk=0.42; p=0.001). A limited number of studies, with a heterogeneous design and usually with a short-term follow-up and without specific training, were found.

CONCLUSIONS

The present review provides evidence for the use of FGM as an effective strategy for the management of diabetes.

Exercise improves metformin 72-h glucose control by reducing the frequency of hyperglycemic peaks

PURPOSE

To determine the separated and combined effects of metformin and exercise on insulin sensitivity and free-living glycemic control in overweight individuals with prediabetes/type 2 diabetes (T2DM).

METHODS

We recruited 16 adults with BMI of 32.7 ± 4.3 kg m^-2 and insulin resistance (HOMA-IR 3.2 ± 0.4) under chronic metformin treatment (1234 ± 465 g day^-1) enrolled in a high-intensity interval training (HIIT) program. Participants underwent four 72-h experimental trials in a random-counterbalanced order: (1) maintaining their habitual metformin treatment (MET); (2) replacing metformin treatment by placebo (CON); (3) placebo plus two HIIT sessions (EX + CON), and (4) metformin plus two HIIT sessions (MET + EX). We used intermittently scanned continuous glucose monitoring (isCGM) during 72 h in every trial to obtain interstitial fluid glucose area under the curve (IFG_AUC) and the percentage of measurements over 180 mg dL^-1 (% IFG_PEAKS). Insulin sensitivity was assessed on the last day of each trial with HOMA-IR index and calculated insulin sensitivity (C_SI) from intravenous glucose tolerance test.

RESULTS

IFG_AUC was lower in MET + EX and MET than in CON (P = 0.011 and P = 0.025, respectively). In addition, IFG_AUC was lower in MET + EX than in EX + CON (P = 0.044). %IFG_PEAKS were only lower in MET + EX in relation to CON (P = 0.028). HOMA-IR and C_SI were higher in CON in comparison with MET + EX (P = 0.011 and P = 0.022, respectively) and MET (P = 0.006 and P < 0.001, respectively). IFG_AUC showed a significant correlation with HOMA-IR.

CONCLUSION

Intense aerobic exercise in patients with diabetes and prediabetes under metformin treatment reduces free-living 72-h blood hyperglycemic peaks. This may help to prevent the development of cardiovascular complications associated with diabetes.

Flash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain

OBJECTIVE

Observations in real-world settings support and extend findings demonstrated in randomized controlled trials that show flash glucose monitoring improves glycemic control. In this study, Spain-specific relationships between testing frequency and glycemic parameters were investigated under real-world settings.

RESEARCH DESIGN AND METHODS

Deidentified glucose and user scanning data were analyzed and readers were rank ordered into 20 equal sized groups by daily scan frequency. Glucose parameters were calculated for each group: estimated HbA1c, time below range (<70 and ≤54 mg/dL), within range (70-180 mg/dL), and above range (>180 mg/dL). Glycemic variability (GV) metrics were described and data obtained from sensors in Spain and worldwide were compared.

RESULTS

Spanish users (n=22 949) collected 37.1 million glucose scans, 250 million automatically recorded glucose readings, and checked glucose values via a mean of 13 scans/day. Estimated HbA1c, time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and GV metrics were significantly lower in the highest compared with lowest scan rate group (39.6 to 3.9 scans/day). Time-in-range was higher for the highest versus lowest scan rate group at 15.6 vs 11.5 hours/day, respectively. GV metrics correlated positively with time below 70 mg/dL, at or below 54 mg/dL, above 180 mg/dL, and negatively with time-in-range. The relationship between glucose metrics and scan rate was similar in Spain and worldwide. However, time in hypoglycemia in Spain was higher in the groups with lower scan rates.

CONCLUSIONS

As seen in clinical trials, flash glucose monitoring in real-world settings allows frequent glucose checks. High scan rates are associated with the favorable glycemic markers of increased time-in-range and reduced time in hyperglycemia and hypoglycemia, and GV. The same trends, with unique nuances, are observed in both Spanish and global data.

Real-world flash glucose monitoring in Brazil: can sensors make a difference in diabetes management in developing countries?

BACKGROUND

New technologies are changing diabetes treatment and contributing better outcomes in developed countries. To our knowledge, no previous studies have investigated the comparative effect of sensor-based monitoring on glycemic markers in developing countries like Brazil. The present study aims to evaluate the use of intermittent Continuous Glucose Measurements (iCGM) in a developing country, Brazil, regarding (i) frequency of glucose scans, (ii) its association with glycemic markers and (iii) comparison with these findings to those observed in global population data.

METHODS

Glucose results were de-identified and uploaded to a dedicated database when Freestyle Libre™ readers were connected to an internet-ready computer. Data between September 2014 and Dec 2018, comprising 688,640 readers and 7,329,052 sensors worldwide, were analysed (including 17,691 readers and 147,166 sensors from Brazil). Scan rate per reader was determined and each reader was sorted into 20 equally-sized rank ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated A1c, time above, below and within range identified as 70-180 mg/dL.

RESULTS

In Brazil, reader users performed an average of 14 scans per day, while around the world, reader users performed an average of 12 scans per day (p < 0.01). In Brazil dataset, those in the lowest and in the highest groups scanned on average 3.6 and 43.1 times per day had an estimated A1c of 7.56% (59 mmol/mol) and 6.71% (50 mmol/mol), respectively (p < 0.01). Worldwide, the lowest group and the highest groups scanned 3.4 times/day and 37.8 times/day and had an eA1c of 8.14% (65 mmol/mol) and 6.70% (50 mmol/mol), respectively (p < 0.01). For the scan groups in both populations, the time spent above 180 mg/dL decreased as the scan frequency increased. In both Brazil and around the world, as scan frequency increased, time in range (TIR) increased. In Brazil, TIR increased from 14.15 to 16.62 h/day (p < 0.01). Worldwide, TIR increased from 12.06 to 16.97 h/day (p < 0.01).

CONCLUSIONS

We conclude that Brazilian users have a high frequency of scans, more frequent than global data. Similarly to the world findings, increased scan frequency is associated with better glycemic control.

Flash Glucose Monitoring Technology Impact on Diabetes Self-Care Behavior

Individuals with diabetes play a significant role in the control of their condition by participating in their own care. Self-monitoring of blood glucose is of particular importance in maintaining adequate glycemic control but when obtained using traditional fingerstick methods, is often limited with by cost, fear of needles or pain and inconvenience. Flash glucose monitoring is an innovative technology available to address these barriers and help people with diabetes better manage their blood glucose levels. Data demonstrating increased frequency in glucose monitoring, patient perspectives related to self-care behaviors, and implications for practice and future research are described.