Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta-analysis of randomised controlled trials.

AIMS: We aimed to conduct a systematic review and meta-analysis of randomised controlled clinical trials (RCTs) assessing separately and together the effect of the three distinct categories of continuous glucose monitoring (CGM) systems (adjunctive, non-adjunctive and intermittently-scanned CGM [isCGM]), compared with traditional capillary glucose monitoring, on HbA1c and CGM metrics. METHODS: PubMed, Web of Science, Scopus and Cochrane Central register of clinical trials were searched. Inclusion criteria were as follows: randomised controlled trials; participants with type 1 diabetes of any age and insulin regimen; investigating CGM and isCGM compared with traditional capillary glucose monitoring; and reporting glycaemic outcomes of HbA1c and/or time-in-range (TIR). Glycaemic outcomes were extracted post-intervention and expressed as mean differences and 95%CIs between treatment and comparator groups. Results were pooled using a random-effects meta-analysis. Risk of bias was assessed using the Cochrane Rob2 tool. RESULTS: This systematic review was conducted between January and April 2021; it included 22 RCTs (15 adjunctive, 5 non-adjunctive, and 2 isCGM)). The overall analysis of the pooled three categories showed a statistically significant absolute improvement in HbA1c percentage points (mean difference (95% CI): -0.22% [-0.31 to -0.14], I2  = 79%) for intervention compared with comparator and was strongest for adjunctive CGM (-0.26% [-0.36, -0.16]). Overall TIR (absolute change) increased by 5.4% (3.5 to 7.2), I2  = 71% for CGM intervention compared with comparator and was strongest with non-adjunctive CGM (6.0% [2.3, 9.7]). CONCLUSIONS: For individuals with T1D, use of CGM was beneficial for impacting glycaemic outcomes including HbA1c, TIR and time-below-range (TBR). Glycaemic improvement appeared greater for TIR for newer non-adjunctive CGM technology.

Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability.

BACKGROUND: Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. METHODS: We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. RESULTS: The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. CONCLUSION: We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.

Impact on Glycemia Risk Index and other metrics in type 1 adult patients switching to Advanced Hybrid Closed-Loop systems: a one-year real-life experience.

BACKGROUND: Advanced Hybrid Closed-Loop system (AHCL) has profoundly changed type 1 diabetes therapy. This study primarily aimed to assess the impact on Glycemia Risk Index (GRI) and other continuous glucose monitoring (CGM) metrics when switching from one of four insulin strategies to AHCL in type 1 adult patients. METHODS: A single-center, retrospective pre/post observational study; 198 patients (age 44.4 ± 12.7 years, 115 females/83 males, diabetes duration 24.7 ± 11.6 years, HbA1c 7.4 ± 1%), treated with different insulin therapies (MDI, CSII, SAP with PLGS, HCL) were assessed before and after switching to an AHCL (MiniMed 780G, Diabeloop Roche, Tandem Control-IQ) at 1, 3, 6, and 12 months. Mixed-effects multivariable regression models were used to estimate the mean pre/post variations at different time points, adjusted for potential confounders. RESULTS: A month after the switch, there was an improvement in CGM metrics and HbA1c for all patients: GRI -10.7, GMI -0.27%, CV -2.1%, TAR>250 -3.7%, TAR180-250 -5.6%, TIR + 9.7%, HbA1c -0.54% (all p < 0.001). This improvement was maintained throughout the observational period (at 3, 6, and 12 months, with all p-values < 0.001). When improvements across the 780, Diabeloop, and Tandem CIQ devices were compared: Diabeloop demonstrated significantly better performance in terms of GRI, GMI, CV, TAR>250 at T1 (for all p < 0.01); 780 recorded highest average decrease in TAR180-250 (p = 0.020), while Tandem achieved the most significant reduction in TBR54-69 (p = 0.004). CONCLUSIONS: Adopting an AHCL leads to a rapid and sustained improvement in GRI and other parameters of metabolic control for up to a year, regardless of prior insulin therapies, baseline conditions or brands.

Clinical Outcomes with MiniMedTM 780G Advanced Hybrid Closed-Loop Therapy in 2- to 6-Year-Old Children with Type 1 Diabetes.

Advanced hybrid closed-loop (AHCL) therapy with the Medtronic MiniMed™ 780G system improves glycemia; however, the clinical outcomes in younger children remain less established. This pilot study aimed to explore the continuous glucose monitoring (CGM) metrics in very young children on AHCL. Children between 2 and 7 years of age and on insulin pump therapy were recruited. A 2-week phase in manual mode was followed by a 6-week AHCL phase. CGM metrics were analyzed to review glycemic outcomes. Out of 11 participants enrolled [mean (standard deviation [SD]) age 5.3 (0.8) years], 10 completed the study. Time in closed loop was 96.7 (3.9)%. In AHCL, participants had a mean (SD) time in range of 72.6 (7.4)% and spent 3.0 (1.74)% and 0.63 (0.46)% in time <70 and <54 mg/dL, respectively. AHCL is a feasible option for management of young children with type 1 diabetes.

Time in Range Is Associated with Incident Diabetic Retinopathy in Adults with Type 1 Diabetes: A Longitudinal Study.

Abstract Objective: To evaluate the association between continuous glucose monitoring (CGM)-based time in various ranges and the subsequent development of diabetic retinopathy (incident DR) in adults with type 1 diabetes. Methods: Between June 2018 and March 2022, adults with type 1 diabetes with incident DR or no retinopathy (control) were identified. CGM data were collected retrospectively for up to 7 years before the date of eye examination defining incident DR or control. Associations between incident DR and CGM metrics were evaluated using logistic regression models. Results: This analysis included 71 adults with incident DR (mean age 27 years, 52% females, and mean diabetes duration 15 years) and 92 adults without DR (mean age 38 years, 48% females, and mean diabetes duration 20 years). Adjusting for age, diabetes duration, and CGM type, each 0.5% increase in glycated hemoglobin (HbA1c), 10 mg/dL increase in mean glucose, 5% decrease in time in target range 70-180 mg/dL (TIR), 5% decrease in time in tight target range 70-140 mg/dL (TITR), and 5% increase in time above 180 mg/dL (TAR) were associated with 24%, 22%, 18%, 28%, and 20% increase in odds of incident DR, respectively. Spearman correlations of TIR, TITR, TAR, and mean glucose with each other were all ≥0.97. Conclusion: Similar to HbA1c, TIR, TITR, TAR, and mean glucose were associated with increased risk for incident DR in adults with type 1 diabetes. These CGM metrics are highly correlated indicating that they provide similar information on glycemic control and diabetic retinopathy risk.

Safety, Metabolic and Psychological Outcomes of Medtronic MiniMed 780G™ in Children, Adolescents and Young Adults: A Systematic Review.

The MiniMed™ 780G is a second-generation automated insulin delivery system that implements a modified proportional-integral-derivative algorithm with some features of an MD-Logic artificial pancreas algorithm. The system may deliver automatic correction boluses up to every 5 min, and it allows the user to choose between three glucose target setpoints (100, 110 and 120 mg/dL). We aimed to review the current evidence on this device in children, adolescents, and young adults living with type 1 diabetes. We screened 783 papers, but only 31 manuscripts were included in this review. Data on metabolic outcomes show that this system is safe as regards severe hypoglycaemia and diabetic ketoacidosis. The glycated haemoglobin may drop to levels about 7%, with CGM reports showing a time in range of 75-80%. The time above range and the time below range are within the recommended target in most of the subjects. Few studies evaluated the psychological outcomes. This system seems to be more effective than the first-generation automated insulin delivery systems. The MiniMed™ 780G has been associated with an improvement in sleep quality in subjects living with diabetes and their caregivers, along with an improvement in treatment satisfaction. Psychological distress is as reduced as the glucose control is improved. We also discuss some case reports describing particular situations in clinical practice. Finally, we think that data show that this system is a further step towards the improvement of the treatment of diabetes as concerns both metabolic and psychological outcomes.

Real-Life Achievements of MiniMed 780G Advanced Closed-Loop System in Youth with Type 1 Diabetes: AWeSoMe Study Group Multicenter Prospective Trial.

Background: We assessed real-life glycemic outcomes and predictors of composite measures of optimal glycemic control in children and adolescents with type 1 diabetes (T1D) during their initial 12 months of the MiniMed™ 780G use. Methods: This prospective observational multicenter study collected demographic, clinical, and 2-week 780G system data at five time points. Optimal glycemic control was defined as a composite glycemic control (CGC) score requiring the attainment of four recommended continuous glucose monitoring (CGM) targets, as well as the glycemia risk index (GRI) of hypoglycemia and hyperglycemia and composite CGM index (COGI). Outcome measures included longitudinal changes in multiple glycemic parameters and CGC, GRI, and COGI scores, as well as predictors of these optimal measures. Results: The cohort included 93 children, 43% girls, with a median age of 15.1 years (interquartile range [IQR] 12.9,17.0). A longitudinal analysis adjusted for age and socioeconomic index yielded a significant improvement in glycemic control for the entire cohort (ptime < 0.001) after the transition to 780G. The mean hemoglobin A1c (HbA1c) (SE) was 8.65% (0.12) at baseline and dropped by >1% after 1 year to 7.54% (0.14) (ptime < 0.001). Optimal glycemic control measures improved at 12 months post 780G; CGC improved by 5.6-fold (P < 0.001) and was attained by 24% of the participants, the GRI score improved by 10-fold (P = 0.009) and was achieved by 10% of them, and the COGI improved by 7.6-fold (P < 0.001) and was attained by 20% of them. Lower baseline HbA1c levels and increased adherence to Advanced Hybrid Closed-Loop (AHCL) usage were predictors of achieving optimal glycemic control. Conclusions: The AHCL 780G system enhances glycemic control in children and adolescents with T1D, demonstrating improvements in HbA1c and CGM metrics, albeit most participants did not achieve optimal glycemic control. This highlights yet ongoing challenges in diabetes management, emphasizing the need for continued proactive efforts on the part of health care professionals, youth, and caregivers.

Impact of continuous glucose monitoring on glycemic control and its derived metrics in type 1 diabetes: a longitudinal study.

Aim: We explored the effectiveness of continuous glucose monitoring for 1 year on glycated A1c reduction in adults with type 1 diabetes mellitus. Methods: We included type 1 diabetes mellitus adults who were either new continuous glucose monitoring users (N = 155) or non-users who were under standard care (N = 384). Glycated A1c was measured at baseline and 3, 6, 9, and 12 months. Individuals with (N = 155) or without continuous glucose monitoring use (N = 310) were matched 1:2 by propensity score. We used the linear mixed models to identify the quantitative reduction in repeated measures of glycated A1c. Results: The change in glycated A1c from baseline to 12 months was -0.5% ± 1.0% for the continuous glucose monitoring user group (N = 155, P < 0.001) and -0.01% ± 1.0% for the non-user group (N = 310, P = 0.816), with a significant difference between the two groups (P = 0.003). Changes in glycated A1c were significant at 3, 6, 9, and 12 months compared with those at baseline in patients using continuous glucose monitoring (P < 0.001), and the changes differed significantly between the groups (P < 0.001). A linear mixed model showed an adjusted treatment group difference in mean reduction in glycated A1c of -0.11% (95% confidence interval, -0.16 to -0.06) each three months. In the continuous glucose monitoring user group, those who achieved more than 70% of time in range significantly increased from 3 months (37.4%) to 12 months (48.2%) (P < 0.001). Conclusion: In this longitudinal study of type 1 diabetes mellitus adults, the use of continuous glucose monitoring for 1 year showed a significant reduction in glycated A1c in real-world practice.

Limitations of Reporting Time Below Range as a Percentage.

Continuous glucose monitoring (CGM)-based metrics such as time in range and time below range (TBR) and their clinical targets are recommended along with glycated hemoglobin (A1c) to optimize diabetes care. CGM metrics are easy to understand by people with diabetes and have been widely accepted among health care providers. TBR, that is, time spent below a certain glucose level (e.g., <70 mg/dL and <54 mg/dL), is expressed as a percentage. This brief report draws attention to some limitations of TBR when expressed as an integer percentage and highlights problems introduced by rounding-off of numerical values for TBR on ambulatory glucose profile and other reports from CGM and automated insulin delivery systems that may be misleading or incorrectly interpreted. The goal of this article is to generate more discussion around reporting of TBR in ways that can be clinically impactful for people living with diabetes, health care providers, regulatory agencies, and payers to stratify and predict severe hypoglycemic events.

Optimal Sampling Duration for Continuous Glucose Monitoring for the Estimation of Glycemia Risk Index.

Objective: To evaluate optimal continuous glucose monitoring (CGM) sampling duration to estimate glycemia risk index (GRI). Methods: Up to 90 days of CGM data from 225 nonpregnant adults with type 1 diabetes (median age 40 years, 60% females, and 20 years of diabetes duration) and not using hybrid closed-loop system were collected. The association between GRI from various sampling periods and GRI using all 90 days of data was determined using the squared value of the Pearson correlation coefficient (R2). Results: With increasing duration of the CGM sampling period, there was higher correlation with the 90-day GRI: R2 of 0.79 for 7 days, R2 of 0.88 for 14 days, and R2 of 0.93 for 30 days. In a sensitivity analysis, correlation (r) or correlation coefficient (R2) for CGM sampling period for GRI estimation was not different among participants with time <70 mg/dL of <4% and participants with time <70 mg/dL of >4%. Conclusion: Though 14 days of CGM sampling duration is optimal for estimation of GRI, 7 days of CGM data may be enough to estimate GRI to monitor change in quality of glycemia with intervention.

Relationship Between Daytime Versus Nighttime Continuous Glucose Monitoring Metrics with A1C in Adults with Type 1 Diabetes.

Objective: To evaluate influence of daytime versus nighttime continuous glucose monitoring (CGM)-based metrics on A1C in adults with type 1 diabetes (T1D). Research Design and Methods: CGM data from 407 adults with T1D (age 39 ± 15 years, diabetes duration 20 ± 12 years, A1C 7.3% ± 1.4% and 53% female) from two studies were included in this analysis. The association between daytime (6 AM-10.59 PM) and nighttime (11 PM-5.59 AM) CGM variables such as mean glucose, time in range (TIR; 70-180 mg/dL), time in tight target range (TTIR; 70-140 mg/dL), and time above range (TAR >180 mg/dL) was examined within five A1C categories (<7%, 7%-7.9%, 8%-8.9%, 9%-9.9%, and ≥10%). Results: Although mean glucose was increasing with higher A1C, there was no statistical difference in mean glucose between daytime versus nighttime within five A1C groups (143.2 ± 22.7 vs. 143.6 ± 25.0 for A1C <7%, 171.4 ± 17.3 vs. 175.3 ± 28.8 for A1C 7.0%-7.9%, 193.4 ± 19.4 vs. 195.3 ± 29.5 for A1C 8.0%-8.9%, 214.9 ± 28.8 vs. 219.7 ± 36.1 for A1C 9.0%-9.9% and 244.0 ± 39.0 vs. 239.9 ± 50.9 for A1C ≥10%, P > 0.05). Similarly, there was no difference between various CGM metrics by daytime versus nighttime within five A1C groups. Differences between five A1C groups' daytime versus nighttime mean glucose, TIR, TTIR, and TAR were also not statistically significant (all P > 0.05) Conclusion: Daytime versus nighttime glycemic control has similar influence on A1C in adults with T1D.

Clinically Similar Clusters of Daily Continuous Glucose Monitoring Profiles: Tracking the Progression of Glycemic Control Over Time.

Background: The adoption of continuous glucose monitoring (CGM) results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically similar clusters (CSCs), such that the TIR metrics of daily CGM profiles within a cluster are homogeneous. Methods: CSC definition and validation used 204,710 daily CGM profiles in health, and types 1 and 2 diabetes (T1D and T2D) on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of CSCs. Results: The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC, which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares ≥0.83, e.g., correlation ≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of daily changes in a person's glycemic control over time. Conclusion: Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, for example, tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression. Clinical Trial Registration: N/A-not a clinical trial.

Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes.

Background: In recent years, continuous glucose monitor (CGM) use is increasing in people without diabetes to promote healthy lifestyle. CGM metrics such as glucose management indicator (GMI), a statistical formula to estimate glycated hemoglobin (HbA1c) from sensor glucose, is commonly used to approximate HbA1c. This study was aimed to evaluate discordance between GMI and HbA1c in people without diabetes. Methods: Children and nonpregnant adults (age ≥6 years) without diabetes (laboratory HbA1c <5.7% and negative islet antibodies) were invited to participate in a multicenter prospective study aimed to evaluate glycemic profiles in nondiabetic individuals. Each participant wore a blinded Dexcom G6 for up to 10 days. GMI was calculated from mean sensor glucose and discordance between GMI and HbA1c was analyzed. Results: Of 201 screened participants, 153 participants (mean age 31.2 ± 21.0 years, 66.0% female, HbA1c 5.1% ± 0.3%) were included in the analysis. Mean GMI was 0.59% higher than laboratory HbA1c in participants without diabetes. The discordance between GMI and HbA1c of 0.4% or greater was 71% in participants without diabetes compared with 39% in the original GMI development cohort. Conclusion: GMI does not accurately estimate HbA1c in healthy people without diabetes. Clinical trial registration number is: NCT00717977.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data.

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Glycaemic control in the paediatric and young adult population with type 1 diabetes following a single telehealth visit - what have we learned from the COVID-19 lockdown?

AIMS: Children with chronic diseases were unable to receive their usual care during COVID-19 lockdown. We assessed the feasibility and impact of telehealth visits on the time-in-range (TIR) of paediatric individuals with type 1 diabetes (T1D). METHODS: An observational multicentre real-life study. Patients scheduled for an in-clinic visit during the lockdown were offered to participate in a telehealth visit. Sociodemographic, clinical, continuous glucose monitor and pump data were recorded 2 weeks prior and 2 weeks after telehealth visit. The primary endpoint was change in relative-TIR, i.e. change in TIR divided by the percent of possible change (∆TIR/(100-TIRbefore)*100). RESULTS: The study group comprised 195 individuals with T1D (47.7% males), mean±SD age 14.6 ± 5.3 years, and diabetes duration 6.0 ± 4.6 years. Telehealth was accomplished with 121 patients and their parents (62.0%); 74 (38.0%) did not transfer complete data. Mean TIR was significantly higher for the two-week period after the telehealth visit than for the two-week period prior the visit (62.9 ± 16.0, p < 0.001 vs. 59.0 ± 17.2); the improvement in relative-TIR was 5.7±26.1%. Initial higher mean glucose level, lower TIR, less time spent at <54 mg/dl range, longer time spent at 180-250 mg/dl range, higher daily insulin dose, and single-parent household were associated with improved relative-TIR. Multiple regression logistic analysis demonstrated only initial lower TIR and single-parent household were significant, odds ratio: -0.506, (95%CI -0.99,-0.023), p=0.04 and 13.82, (95%CI 0.621, 27.016), p=0.04, respectively. CONCLUSIONS: Paediatric and young adult patients with T1D benefited from a telehealth visit during COVID-19. However, this modality is not yet suitable for a considerable proportion of patients.

Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics.

BACKGROUND: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. METHOD: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. RESULTS: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). CONCLUSION: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Longitudinal Analysis of Real-World Performance of an Implantable Continuous Glucose Sensor over Multiple Sensor Insertion and Removal Cycles.

The Eversense® Continuous Glucose Monitoring (CGM) System, with the first long-term, implantable glucose sensor, has been commercially available in Europe and South Africa since 2016 for adults with diabetes. The performance of the sensor over multiple, sequential 90- or 180-day cycles from either real-world experience or clinical studies has not been previously published. The Eversense Data Management System (DMS) was used to evaluate the accuracy of General Data Protection Regulation (GDPR)-compliant sensor glucose (SG) values against self-monitoring of blood glucose (SMBG) from June 2016 through August 2019 among patients with at least four sensor cycles from European and South African health care practices. Mean SG and associated measures of variability, glucose management indicator (GMI), and percent and time in various hypoglycemic, euglycemic, and hyperglycemic ranges were calculated for the 24-h time period over each cycle. In addition, transmitter wear time was evaluated across each sensor wear cycle. Among the 945 users included in the analysis, the mean absolute relative difference (standard deviation [SD]) using 152,206, 174,645, 206,024, and 172,587 calibration matched pairs against SMBG was 11.9% (3.6%), 11.5% (4.0%), 11.8% (4.7%), and 11.5% (4.1%) during the first four sensor cycles, respectively. Mean values of the CGM metrics over the first sensor cycle were 156.5 mg/dL for SG, 54.7 mg/dL for SD, 0.35 for coefficient of variation, and 7.04% for GMI. Percent SG at different glycemic ranges was as follows: <54 mg/dL was 1.1% (16 min), <70 mg/dL was 4.6% (66 min), ≥70-180 mg/dL (time in range) was 64.5% (929 min), >180-250 mg/dL was 22.8% (328 min), and >250 mg/dL was 8.1% (117 min). The median transmitter wear time over the first cycle was 83.2%. CGM metrics and wear time were similar over the subsequent three cycles. This real-world evaluation of adult patients with diabetes using the Eversense CGM System in the home setting demonstrated that the implantable sensor provides consistent stable accuracy and CGM metrics over multiple, sequential sensor cycles with no indication of degradation of sensor performance.

Lessons learned from the continuous glucose monitoring metrics in pediatric patients with type 1 diabetes under COVID-19 lockdown.

AIMS: Billions of people have been under lockdown in an attempt to prevent COVID-19 spread. Lifestyle changes during lockdown could lead to deterioration of glycemic control in type 1 diabetes (T1D). We aimed to assess the impact of COVID-19 lockdown on the glycemic control of pediatric patients with T1D. METHODS: This observational real-life study from the AWeSoMe Group assessed continuous glucose monitoring (CGM) metrics of 102 T1D patients (52.9% males, mean age 11.2 ± 3.8 years, mean diabetes duration 4.2 ± 3.8 years) who used  Dexcom G5. The data were accessed without any interface between patients, caregivers, and the diabetes team. Study variables from CGM metrics were: mean glucose level, time-in-range (TIR, 70-180 mg/dL; 3.9-10 mmol/L), hypoglycemia (< 54 mg/dL; < 3 mmol/L), hyperglycemia (> 250 mg/dL; > 13.3 mmol/L), coefficient of variation (CV), and time CGM active before and during lockdown. Delta-variable = lockdown variable minus before-lockdown variable. RESULTS: The mean TIR was 60.9 ± 14.3% before lockdown, with no significant change during lockdown (delta-TIR was 0.9 ± 7.9%). TIR during lockdown was significantly correlated with TIR before lockdown (r = 0.855, P < 0.001). Patients with improved TIR (delta-TIR > 3%) were significantly older than patients with stable or worse TIR (P = 0.028). Children aged < 10 years had a significantly higher CV before lockdown and during lockdown than children aged ≥ 10 years (P = 0.02 and P = 0.005, respectively). Among children aged < 10 years, a multiple linear regression model revealed associations of age and lower socioeconomic cluster with delta-TIR (F = 4.416, P = 0.019) and with delta-mean glucose (F = 4.459, P = 0.018). CONCLUSIONS: CGM metrics in pediatric patients with T1D were relatively stable during a nationwide lockdown. Intervention plans should focus on younger patients with lower socioeconomic position.