One Year Real-World Use of the Control-IQ Advanced Hybrid Closed-Loop Technology

Background: The t:slim X2™ insulin pump with Control-IQ^® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect^® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%-96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70-180 mg/dL) was 63.6 (IQR: 49.9%-75.6%) and increased to 73.6% (IQR: 64.4%-81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time <70 mg/dL remained consistent at ∼1% (IQR: 0.5%-1.9%). Conclusion: In this real-world use analysis, Control-IQ technology retained, and to some extent exceeded, the results obtained in randomized controlled trials, showing glycemic improvements in a broad age range of people with different types of diabetes.

Time in Range Is Associated with Carotid Intima-Media Thickness in Type 2 Diabetes

Background: Time in range (TIR) is an emerging metric of glycemic control and is reported to be associated with microvascular complications of diabetes. We sought to investigate the association of TIR obtained from continuous glucose monitoring (CGM) with carotid intima-media thickness (CIMT) as a surrogate marker of cardiovascular disease (CVD). Methods: Data from 2215 patients with type 2 diabetes were cross-sectionally analyzed. TIR of 3.9-10.0 mmol/L was evaluated with CGM. CIMT was measured using high-resolution B-mode ultrasonography and abnormal CIMT was defined as a mean CIMT ≥1.0 mm. Logistic regression models were used to examine the independent association of TIR with CIMT. Results: Compared with patients with normal CIMT, those with abnormal CIMT had significantly lower TIR (P < 0.001). The prevalence of abnormal CIMT progressively decreased across the categories of increasing TIR (P for trend <0.001). In a fully adjusted model controlling for traditional risk factor of CVD, each 10% increase in TIR was associated with 6.4% lower risk of abnormal CIMT. Stratifying the data by sex revealed that TIR was significantly associated with CIMT in males but not in females. In a subset of patients (n = 612) with complete data on diabetic retinopathy and albuminuria, we found that the relationship between TIR and CIMT remained to be significant, regardless of the status of microvascular complications. Conclusions: TIR is associated with CIMT in a large sample of patients with type 2 diabetes, suggesting a link between TIR and macrovascular disease.

A View Beyond HbA1c: Role of Continuous Glucose Monitoring

Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium-glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.

Impact of COVID-19 lockdown on glycemic control in patients with type 1 diabetes

INTRODUCTION

The COVID-19 pandemic has forced governments to take exceptional measures to minimize its spread, imposing lockdown policies. The aim of this study was to evaluate the impact of lockdown on type 1 diabetes (T1D) glycemic control.

MATERIAL AND METHODS

People with T1D using flash glucose monitoring were included. Data from the 14 days before lockdown were compared with data from the last 14 days after 8 weeks of lockdown.

RESULTS

A total of 307 patients were included (age 45.8 ± 12.6 years, 50.2% male, diabetes duration 21.1 ± 12.3 years). Only one patient had COVID-19 infection. Mean glucose decreased from 166.89 ± 29.4 to 158.0 ± 29.0 mg/dL and estimated HbA1c declined from 7.4 ± 1.0 to 7.1 ± 1.0% (54 ± 10.9 vs 57 ± 10.9 mmol/mol; p < 0.001). Time in range increased from 57.8 ± 15.8 to 62.46 ± 16.1%. Time in hyperglycemia > 180 mg/dL and >250 mg/dL decreased from 37.3 ± 1.9% to 32.0 ± 17.1% and from 13.0 ± 11.3 to 10.3 ± 10.6%, respectively; (p < 0.001). Time in hypoglycaemia <70 mg/dL increased from 4.9 ± 4.0% to 5.5 ± 4.4% (p < 0.001). No differences in time <54 mg/dl, coefficient of variation (CV%) or number of scans per day were found.

CONCLUSION

Despite the limitations of lockdown, glycemic control improved in patients with T1D. These results suggest that having more time for self-management may help improve glycemic control in the short term.

Positioning time in range in diabetes management

Recent upswings in the use of continuous glucose monitoring (CGM) technologies have given people with diabetes and healthcare professionals unprecedented access to a range of new indicators of glucose control. Some of these metrics are useful research tools and others have been welcomed by patient groups for providing insights into the quality of glucose control not captured by conventional laboratory testing. Among the latter, time in range (TIR) is an intuitive metric that denotes the proportion of time that a person's glucose level is within a desired target range (usually 3.9-10.0 mmol/l [3.5-7.8 mmol/l in pregnancy]). For individuals choosing to use CGM technology, TIR is now often part of the expected conversation between patient and healthcare professional, and consensus recommendations have recently been produced to facilitate the adoption of standardised TIR targets. At a regulatory level, emerging evidence linking TIR to risk of complications may see TIR being more widely accepted as a valid endpoint in future clinical trials. However, given the skewed distribution of possible glucose values outside of the target range, TIR (on its own) is a poor indicator of the frequency or severity of hypoglycaemia. Here, the state-of-the-art linking TIR with complications risk in diabetes and the inverse association between TIR and HbA_1c are reviewed. Moreover, the importance of including the amount and severity of time below range (TBR) in any discussions around TIR and, by inference, time above range (TAR) is discussed. This review also summarises recent guidance in setting 'time in ranges' goals for individuals with diabetes who wish to make use of these metrics. For most people with type 1 or type 2 diabetes, a TIR >70%, a TBR <3.9 mmol/l of <4%, and a TBR <3.0 mmol/l of <1% are recommended targets, with less stringent targets for older or high-risk individuals and for those under 25 years of age. As always though, glycaemic targets should be individualised and rarely is that more applicable than in the personal use of CGM and the data it provides.

Glycemic Control in Type 1 Diabetes Mellitus During COVID-19 Quarantine and the Role of In-Home Physical Activity

Background: To limit the spread of coronavirus disease 2019 (COVID-19), governments have ordered a series of restrictions that may affect glycemic control in individuals with type 1 diabetes mellitus (T1DM), since physical activity (PA) was not allowed outside home. Methods: We retrospectively evaluated glycemic control of individuals with T1DM using hybrid closed loop (HCL) system in the period before the SARS-CoV-2 outbreak in Italy (February 10-23, 2020-Time 1), when movements were only reduced (February 24-March 8, 2020-Time 2) and during complete lockdown (March 9-22, 2020-Time 3). Information about regular PA (at least 3 h per week) prior and during the quarantine was collected. Results: The study included 13 individuals with a median age of 14.2 years and a good glycemic control at baseline (glucose management indicator of 7%, time in range [TIR] of 68%, time below range [TBR] of 2%). All individuals continued to show good glycemic control throughout the study period. There was an increase in TIR during the study period (+3%) and TIR was significantly higher during Time 3 (72%) than during Time 2 (66%). TBR was significantly lower during Time 3 (1%) both compared with Time 1 and Time 2 (2%). A meaningful variance in TIR at Time 3 between individuals who performed or not PA during quarantine and a significant increase in TIR between Time 2 and Time 3 in individuals both doing PA at baseline and during quarantine was found. At logistic regression, only the presence of PA during quarantine significantly predicted a TIR >70%. Conclusions: Glycemic control of T1DM in adolescents using HCL system did not worsen during the restrictions due to COVID-19 pandemics and further improved in those who continued PA during the quarantine. Maintaining regular PA in a safe home environment is an essential strategy for young individuals with T1DM during the COVID-19 crisis.

Retrospective Analysis of 3-Month Real-World Glucose Data After the MiniMed 670G System Commercial Launch

Real-world data from the first 3141 patients who completed 3 months of SmartGuard™ Auto Mode-enabled MiniMed™ 670G system use during the MiniMed 670G System Commercial Launch are reported. CareLink™ system data uploaded by real-world patients in the Commercial Launch from March 17, 2017 to December 31, 2017 were deidentified and analyzed. Comparisons of overall and night (10:00 PM-07:00 AM) time spent below, within, and above target glucose range (TIR) (70-180 mg/dL) between the baseline Manual Mode and closed-loop Auto Mode periods were made. These were evaluated alongside data from the 124 patients (aged 14-75 years) who completed the 3-month MiniMed 670G system pivotal trial (NCT 2463097), from June 2, 2015 to March 7, 2016. Real-world patients used Auto Mode a median 80.8% of the time (19 h and 24 min of the day). The overall mean of time spent in TIR was 66.0% during baseline Manual Mode versus 73.3% during Auto Mode (P < 0.001); the mean percentage of sensor glucose values <70 mg/dL was 2.7% versus 2.1% (P < 0.001); and that >180 mg/dL was 31.4% versus 24.6% (P < 0.001). The nighttime and early morning (03:00 AM-06:00 AM) TIR during Auto Mode was greater than that during baseline Manual Mode (nighttime: 77.2% vs. 67.4% [P < 0.001], early morning: 70.9% vs. 84.6% [P < 0.001]). Similar differences between Manual Mode and Auto Mode TIR were observed across different age groups. A slight increase in total insulin delivered was also observed. Consistent with improved glycemic control demonstrated in the pivotal trial, analysis of CareLink system data from >3000 real-world patients who completed 3 months of Auto Mode-enabled MiniMed 670G system use demonstrated increased TIR and decreased time below and above TIR compared with baseline. These improved clinical outcomes were observed across a broad age range of patients with type 1 diabetes.

Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen

Background: This observational study investigated whether the connected NovoPen^® 6 could influence insulin regimen management and glycemic control in people with type 1 diabetes (T1D) using a basal-bolus insulin regimen and continuous glucose monitoring in a real-world setting. Methods: Participants from 12 Swedish diabetes clinics downloaded pen data at each visit (final cohort: n = 94). Outcomes included time in range (TIR; sensor glucose 3.9-10.0 mmol/L), time in hyperglycemia (>10 mmol/L), and hypoglycemia (L1: 3.0- <3.9 mmol/L; L2: <3.0 mmol/L). Missed bolus dose (MBD) injections were meals without bolus injection within -15 and +60 min from the start of a meal. Outcomes were compared between the baseline and follow-up periods (≥5 health care professional visits). Data were analyzed from the first 14 days following each visit. For the TIR and total insulin dose analyses (n = 94), a linear mixed model was used, and for the MBD analysis (n = 81), a mixed Poisson model was used. Results: TIR significantly increased (+1.9 [0.8; 3.0]_{95% CI} h/day; P < 0.001) from baseline to follow-up period, with a corresponding reduction in time in hyperglycemia (-1.8 [-3.0; -0.6]_{95% CI} h/day; P = 0.003) and L2 hypoglycemia (-0.3 [-0.6; -0.1]_{95% CI} h/day; P = 0.005), and no change in time in L1 hypoglycemia. MBD injections decreased by 43% over the study (P = 0.002). Change in MBD injections corresponded to a decrease from 25% to 14% based on the assumption that participants had three main meals per day. Conclusions: Our study highlights the potential benefit on glycemic control and dosing behavior when reliable insulin dose data from a connected pen contribute to insulin management in people with T1D.

Closed-Loop Insulin Therapy Improves Glycemic Control in Adolescents and Young Adults: Outcomes from the International Diabetes Closed-Loop Trial

Objective: To assess the efficacy and safety of closed-loop control (CLC) insulin delivery system in adolescents and young adults with type 1 diabetes. Research Design and Methods: Prespecified subanalysis of outcomes in adolescents and young adults aged 14-24 years old with type 1 diabetes in a previously published 6-month multicenter randomized trial. Participants were randomly assigned 2:1 to CLC (Tandem Control-IQ) or sensor augmented pump (SAP, various pumps+Dexcom G6 CGM) and followed for 6 months. Results: Mean age of the 63 participants was 17 years, median type 1 diabetes duration was 7 years, and mean baseline HbA1c was 8.1%. All 63 completed the trial. Time in range (TIR) increased by 13% with CLC versus decreasing by 1% with SAP (adjusted treatment group difference = +13% [+3.1 h/day]; 95% confidence interval [CI] 9-16, P < 0.001), which largely reflected a reduction in time >180 mg/dL (adjusted difference -12% [-2.9 h/day], P < 0.001). Time <70 mg/dL decreased by 1.6% with CLC versus 0.3% with SAP (adjusted difference -0.7% [-10 min/day], 95% CI -1.0% to -0.2%, P = 0.002). CLC use averaged 89% of the time for 6 months. The mean adjusted difference in HbA1c after 6 months was 0.30% in CLC versus SAP (95% CI -0.67 to +0.08, P = 0.13). There was one diabetic ketoacidosis episode in the CLC group. Conclusions: CLC use for 6 months was substantial and associated with improved TIR and reduced hypoglycemia in adolescents and young adults with type 1 diabetes. Thus, CLC has the potential to improve glycemic outcomes in this challenging age group. The clinical trial was registered with ClinicalTrials.gov (NCT03563313).

Time in range: a new parameter to evaluate blood glucose control in patients with diabetes

The International Consensus in Time in Range (TIR) was recently released and defined the concept of the time spent in the target range between 70 and 180 mg/dL while reducing time in hypoglycemia, for patients using Continuous Glucose Monitoring (CGM). TIR was validated as an outcome measures for clinical Trials complementing other components of glycemic control like Blood glucose and HbA1c. The challenge is to implement this practice more widely in countries with a limited health public and private budget as it occurs in Brazil. Could CGM be used intermittently? Could self-monitoring blood glucose obtained at different times of the day, with the amount of data high enough be used? More studies should be done, especially cost-effective studies to help understand the possibility of having sensors and include TIR evaluation in clinical practice nationwide.

Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes

Background: As the use of continuous glucose monitoring (CGM) has increased, time in range (TIR) and other core CGM metrics are now emerging as the core metrics for clinical targets and assessing diabetic complications, beyond HbA1c. This study investigated the association between the CGM-derived TIR, hyperglycemia, hypoglycemia metrics, and albuminuria. Methods: A total of 866 subjects with type 2 diabetes who underwent 3 or 6 days of CGM and had urinary albumin-to-creatinine ratio (ACR) measurements were retrospectively reviewed. CGM metrics were defined according to the most recent international consensus. Albuminuria was defined as one or more of the ACR measurements being >30 mg/g. Results: The overall prevalence of albuminuria was 36.6%. The prevalence of albuminuria was lower in subjects who achieved the target of TIR 70-180 mg/dL, time above range (TAR) >180 mg/dL, and TAR >250 mg/dL, as recommended by international consensus (P < 0.001). Multiple logistic regression analysis revealed that the odds ratio of having albuminuria was 0.94 (95% confidence interval: 0.88-0.99, P for trend = 0.04) per 10% increase in TIR of 70-180 mg/dL, after adjusting for multiple factors, including glycemic variability. The results were similar for hyperglycemia metrics (TAR >250 mg/dL and TAR >180 mg/dL). Conclusions: TIR 70-180 mg/dL and hyperglycemia metrics are strongly associated with albuminuria in type 2 diabetes.

Rapid Improvement in Time in Range After the Implementation of an Advanced Hybrid Closed-Loop System in Adolescents and Adults with Type 1 Diabetes

Background: Advanced hybrid closed-loop (AHCL) systems represent the next step of automation intended to maximize normoglycemia in people with type 1 diabetes (T1D). In the AHCL MiniMed 780G system, different algorithm glucose targets for insulin infusion are available and autocorrection boluses are delivered. The aim was to prospectively evaluate the impact of the implementation of this AHCL system in a clinical setting. Materials and Methods: T1D subjects using a sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS) were upgraded to AHCL. Baseline, every 3 days, 2-week and 1-month sensor and pump data were downloaded. Glucose target was set to 100 mg/dL and active insulin time to 2 h for all the subjects. Time in different glucose ranges was compared. Results: Fifty-two T1D subjects were included (age: 43 ± 12 years, 73% females, diabetes duration: 27 ± 11 years, HbA1c: 7.2% ± 0.9%, time in SAP-PLGS: 5 ± 2 years). Time in range (TIR) 70-180 mg/dL increased from 67.3% ± 13.6% at baseline to 79.6% ± 7.9% at 1 month (P = 0.001). Time in hyperglycemia >180 and >250 mg/dL decreased from 29.4% ± 15.1% to 17.3% ± 8.6% and from 6.9% ± 7.8% to 2.5% ± 2.4%, respectively (P = 0.001). No differences in time in hypoglycemia <70 or <54 mg/dL were found. Time in Auto Mode was 97% ± 4%, and autocorrection insulin was 31% ± 14% of bolus insulin. Four hours postprandial glucose was improved from 162 ± 26 mg/dL at baseline to 142 ± 16 mg/dL at 1 month (P = 0.001). No severe hypoglycemia or diabetic ketoacidosis episodes occurred. Conclusion: AHCL systems allow well-controlled T1D patients to rapidly increase their TIR. The most aggressive settings allow optimal outcomes in TIR, without increasing hypoglycemia frequency.

Improving the clinical value and utility of CGM systems: issues and recommendations : A joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionise the use of intensive insulin therapy in diabetes; however, progress towards that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardised format for displaying results and uncertainty on how best to use CGM data to make therapeutic decisions. This scientific statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes.

Time In Range in Children with Type 1 Diabetes Using Treatment Strategies Based on Nonautomated Insulin Delivery Systems in the Real World

Background: Glucose sensors consist of real-time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM). Their clinical use has been widely increasing during the past 5 years. The aim of this study is to evaluate percentage of time in range (TIR) in a large group of children with type 1 diabetes (T1D) using glucose sensors with nonautomated insulin delivery systems, in a real-world setting. Methods: An 11-center cross-sectional study was conducted during January-May 2019. Children with T1D <18 years, all using rtCGM or isCGM for >1 year, treated with multiple daily injections (MDI) or nonautomated insulin pump (IP), were recruited consecutively. Clinical data, HbA1c measurement, and CGM downloaded data were collected by each center and included in a centralized database for the analysis. Glucose metrics of four treatment strategies were analyzed: isCGM-MDI, rtCGM-MDI, isCGM-IP, and rtCGM-IP. Results: Data from 666 children with T1D (51% male and 49% female), median age 12 years, diabetes duration 5 years, were analyzed. An rtCGM was used by 51% of the participants, and a nonautomated IP by 46%. For isCGM-MDI, rtCGM-MDI, isCGM-IP, and rtCGM-IP, the median TIR 70-180 mg/dL values were 49%, 56%, 56%, and 61% (P < 0.001), respectively; HbA1c 7.6%, 7.5%, 7.3%, and 7.3% (P < 0.001), respectively. Use of rtCGM was associated with significant lower time below target range <70 mg/dL and reduced the percentage coefficient of variation of glucose (%CV), independently by the insulin delivery system used. Conclusions: Among nonautomated insulin delivery strategies, simultaneous use of rtCGM and IP was associated with higher percentage of TIR, lower time above range >180 mg/dL and lower HbA1c. If there are no barriers, an upgrade of the treatment strategy with a higher performing technology should be offered to all children who do not achieve blood glucose metrics within the suggested range.

Type 1 diabetes and COVID-19: The "lockdown effect"

AIMS

The aim of this study was to evaluate the effect the lockdown imposed during COVID-19 outbreak on the glycemic control of people with Type 1 diabetes (T1D) using Continuous (CGM) or Flash Glucose Monitoring (FGM).

MATERIALS AND METHODS

We retrospectively analyzed glucose reading obtained by FGM or CGM in T1D subjects. Sensor data from 2 weeks before the lockdown (Period 0, P₀), 2 weeks immediately after the lockdown (period 1, P₁), in mid-lockdown (Period 2, P₂) and immediately after end of lockdown (Period 3, P₃) were analyzed.

RESULTS

The study included 63 T1D patients, (FGM: 52, 82%; CGM:11, 18%). Sensor use (91%) were slightly reduced. Despite this reduction, Time in Range increased in P₁ (62%), P₂ (61%) and P₃ (62%) as compared to P₀ (58%, all p < 0.05 or less) with concomitant reduction in the Time Above Range (P₀: 38%; P₁: 34%, P₂: 34%, P₃: 32%, all p < 0.05 or less vs. P₀). Average glucose and GMI improved achieving statistical difference in P₃ (165 vs. 158 mg/dl, p = 0.040 and 7.2% (55 mmol/mol) vs. 7.0% (53 mmol/mol), p = 0.016) compared to P0. Time Below Range (TBR) and overall glucose variability remained unchanged. Bi-hourly analysis of glucose profile showed an improvement particularly in the early morning hours.

CONCLUSIONS

In T1D subjects with good glycemic control on CGM or FGM, the lockdown had no negative impact. Rather a modest but significant improvement in glycemic control has been recorded, most likely reflecting more regular daily life activities and reduces work-related distress.

Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?

Objective: To bridge the gap between laboratory-measured hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM)-derived time in target range (TIR), introducing TIR-driven estimated A1c (eA1c). Methods: Data from Protocol 1 (training data set) and Protocol 3 (testing data set) of the International Diabetes Closed-Loop Trial were used. Training data included 3 months of CGM recordings from 125 individuals with type 1 diabetes, and HbA1c at 3 months; testing data included 9 months of CGM recordings from 168 individuals, and HbA1c at 3, 6, and 9 months. Hemoglobin glycation was modeled by a first-order differential equation driven by TIR. Three model parameters were estimated in the training data set and fixed thereafter. A fourth parameter was estimated in the testing data set, to individualize the model by calibration with month 3 HbA1c. The accuracy of eA1c was assessed on months 6 and 9 HbA1c. Results: eA1c was tracked for each individual in the testing data set for 6 months after calibration. Mean absolute differences between HbA1c and eA1c 3- and 6-month postcalibration were 0.25% and 0.24%; Pearson's correlation coefficients were 0.93 and 0.93; percentages of eA1c within 10% from reference HbA1c were 97.6% and 96.3%, respectively. Conclusions: HbA1c and TIR are reflections of the same underlying process of glycemic fluctuation. Using a model individualized with one HbA1c measurement, TIR provides an accurate approximation of HbA1c for at least 6 months, reflecting blood glucose fluctuations and nonglycemic biological factors. Thus, eA1c is an intermediate metric that mathematically adjusts a CGM-based assessment of glycemic control to individual glycation rates.

Association of time in range, as assessed by continuous glucose monitoring, with painful diabetic polyneuropathy

AIMS/INTRODUCTION

This study aimed to evaluate the association between time in range (TIR) obtained from continuous glucose monitoring and the prevalence and degree of painful diabetic neuropathy.

MATERIALS AND METHODS

A total of 364 individuals with diabetic peripheral neuropathy were enrolled in this study. Sensor-based flash glucose monitoring systems were used to monitor the participants' glucose levels, and the glycemic variability metrics were calculated, including the TIR, glucose coefficient of variation, standard deviation and the mean amplitude of glycemic excursions. The participants were asked to record any form of pain during the 2 weeks of monitoring, and score the pain every day on a numerical rating scale. Based on the numerical rating scale, the patients were divided into the pain-free group, mild pain group and moderate/severe pain group.

RESULTS

Overall, 51.92% (189/364) of the participants were diagnosed with painful diabetic neuropathy. Compared with the pain-free group, the level of TIR decreased significantly in the mild pain and moderate/severe pain groups (P < 0.05). The prevalence of mild pain and moderate/severe pain decreased with increasing TIR quartiles (all P < 0.05). Multiple linear regression analysis showed that TIR was significantly negatively correlated with the numerical rating scale score after adjustment for glycated hemoglobin, glycemic variability indicators and other risk factors (P < 0.05). Logistic regression analysis showed that a decreasing level of TIR was significantly associated with an increasing risk of any pain and moderate/severe pain (P < 0.05).

CONCLUSIONS

TIR is correlated with painful diabetic neuropathy and is underscored as a valuable clinical evaluation measure.

Health-Related Quality of Life and Treatment Satisfaction in Parents and Children with Type 1 Diabetes Using Closed-Loop Control

Introduction: Hybrid closed-loop systems increase time-in-range (TIR) and reduce glycemic variability. Person-reported outcomes (PROs) are essential to assess the utility of new devices and their impact on quality of life. This article focuses on the PROs for pediatric participants (ages 6-13 years) with type 1 diabetes (T1D) and their parents during a trial using the Tandem Control-IQ system, which was shown to increase TIR and improve other glycemic metrics. Research Design and Methods: One hundred and one children 6 to 13 years old with T1D were randomly assigned to closed-loop control (CLC) or sensor-augmented pump (SAP) in a 16-week randomized clinical trial with extension to 28 weeks during which the SAP group crossed over to CLC. Health-related quality of life and treatment satisfaction measures were obtained from children and their parents at baseline, 16 weeks, and 28 weeks. Results: Neither the children in the CLC group nor their parents had statistically significant changes in PRO outcomes compared with the SAP group at the end of the 16-week randomized controlled trial and the 28-week extension. Parents in the CLC group reported nonsignificant improvements in some PRO scores when compared with the SAP group at 16 weeks, which were sustained at 28 weeks. Sleep scores for parents improved from "poor sleep quality" to "adequate sleep quality" between baseline and 16 weeks, however, the change in scores was not statistically different between groups. Conclusions: Children with T1D who used the Control-IQ system did not experience increased burden compared with those using SAP based on person-reported outcomes from the children and their parents. Clinical Trials Registration: NCT03844789.

Improved Postprandial Glucose with Inhaled Technosphere Insulin Compared with Insulin Aspart in Patients with Type 1 Diabetes on Multiple Daily Injections: The STAT Study

BACKGROUND

The majority of therapies have generally targeted fasting glucose control, and current mealtime insulin therapies have longer time action profiles than that of endogenously secreted insulin. The primary purpose of this study was to assess both glucose time-in-range (TIR: 70-180 mg/dL) and postprandial glucose excursions (PPGE) in 1-4 h using a real-time continuous glucose monitor (CGM) with Technosphere insulin (TI) versus insulin aspart in patients with type 1 diabetes (T1DM) on multiple daily injections (MDI).

RESEARCH DESIGN AND METHODS

This pilot, investigator-led, collaborative, open-label, multicenter, clinical research trial enrolled 60 patients with T1DM with HbA1c levels ≥6.5% and ≤10%. Individuals were randomized to treatment with titrated TI (n = 26) or titrated insulin aspart (n = 34), stratified by baseline HbA1c levels (≤8% or >8%). All were required to wear a real-time CGM throughout the trial. All patients in the TI group were advised to take supplemental inhalations at 1 and 2 h after meals if indicated based on postprandial glucose (PPG) values. The coprimary outcomes were assessed both in the full intent-to-treat population and in those individuals randomized to TI who were compliant with supplemental doses ≥90% of the time (n = 15). The CGM data were analyzed using linear regression models.

RESULTS

Overall, those treated with TI versus aspart achieved comparable TIR, but less time spent in hypoglycemia (<60 and <50 mg/dL, both P < 0.05). In the TI-compliant group (n = 15), TIR was significantly greater (62.5% ± 2.6% vs. 53.8% ± 1.7%, P = 0.009) and time in hyperglycemia >180 mg/dL was lower (34.2% ± 2.7% vs. 41.0% ± 1.7%, P = 0.045) as compared with the aspart group. PPG was also significantly lower in the TI cohort at 60 and 90 min postmeal, and PPGE were lower in the TI-compliant group as compared with the aspart group over 1-4-h postmeal (P < 0.05). In addition, there was weight gain in the aspart group compared with weight loss in the TI group (P = 0.006) despite higher prandial TI insulin dose.

CONCLUSIONS

We conclude that using TI appropriately at mealtimes with supplemental dosing improves prandial glucose (TIR and 1-4 h) control without any increase in time in hypoglycemia or weight gain in patients with T1DM on MDI. The study results support a larger study using a treat-to-target design to confirm these findings. Clinical trial reg. no. NCT03143816, clinicaltrials.gov .

More Time in Glucose Range During Exercise Days than Sedentary Days in Adults Living with Type 1 Diabetes

Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15-68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70-180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time <70 mg/dL higher (9.3% ± 11.0% vs. 7.1% ± 9.1%, P = 0.04) on exercise days compared with sedentary days. Glucose variability and % time <54 mg/dL did not differ significantly between exercise and sedentary days. No significant differences in glucose control by exercise type were observed. Conclusion: Participants had lower 24-h mean glucose levels and a greater time in range on exercise days compared with sedentary days, with mode of exercise affecting glycemia similarly. In summary, this study offers data supporting frequency of exercise as a method of facilitating glucose control but does not suggest an effect for mode of exercise.

In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Objective: Safety data on Do-It-Yourself Artificial Pancreas Systems are missing. The most widespread in Europe is the AndroidAPS implementation of the OpenAPS algorithm. We used the UVA/Padova Type 1 Diabetes Simulator to in silico test safety and efficacy of this algorithm in different scenarios. Methods: We tested five configurations of the AndroidAPS algorithm differing in aggressiveness and patient's interaction with the system. All configurations were tested with insulin sensitivity variation of ±30%. The most promising configurations were tested in real-life scenarios: over- and underestimated bolus by 50%, bolus delivered 15 min before meal, and late bolus delivered 15 min after meal. Continuous Glucose Monitoring (CGM) time in ranges (TIRs) metrics were used to assess the glycemic control. Results: In silico testing showed that open-source closed-loop system AndroidAPS works effectively and safely. The best results were reached if AndroidAPS algorithm worked with microboluses and when half of calculated bolus was issued (mean glycemia 131 mg/dL, SD 27 mg/dL, TIR 91%, time between 54 and 70 mg/dL <1%, and low blood glucose index even <1). The meal bolus over- and underestimation as well as late bolus did not affect the TIR and, importantly, the time between 54 and 70 mg/dL. Conclusion: In silico testing proved that AndroidAPS implementation of the OpenAPS algorithm is safe and effective, and it showed a great potential to be tested in prospective home setting study.

Use of Real-Time Continuous Glucose Monitoring Improves Glycemic Control and Other Clinical Outcomes in Type 2 Diabetes Patients Treated with Less Intensive Therapy

Objective: Use of real-time continuous glucose monitoring (rtCGM) has been shown to improve glycemic control in patients with type 2 diabetes (T2D) who are treated with intensive insulin therapy. However, most T2D patients are denied coverage for rtCGM due to failure to meet payer eligibility requirements: treatment with ≥3 insulin injections (or pump) and history of 4 × /day blood glucose testing. We investigated the relevance of these criteria to successful rtCGM use. Methods: This 6-month, prospective, interventional, single-arm study assessed the clinical effects of use rtCGM in patients with T2D treated with basal insulin only or noninsulin therapy. Primary outcomes were changes in HbA1c, average glucose, glycemic variability (% coefficient of variation), and percent of time in range (%TIR), below range (%TBR) and above range (%TAR). Results: Thirty-eight patients were included in the analysis (10.1% ± 1.8% HbA1c, 54.7 ± 10.2 years, 35.6 ± 6.4 body mass index). At 6 months, we observed reductions in HbA1c (-3.0% ± 1.3%, P < 0.001) and average glucose (-23.6 ± 38.8, P < 0.001). %TIR increased 15.2 ± 22.3, from 57.0 ± 29.9 to 72.2 ± 23.6, P < 0.001, with all patients maintaining %TBR targets (<4% at 70 mg/dL, <1% at <54 mg/dL). No changes in glycemic variability were observed. The greatest improvements in %TIR and %TAR were seen in patients treated with ≤1 medication. Conclusions: rtCGM use was associated with significant glycemic improvements in T2D patients treated with basal insulin only or noninsulin therapy. Given the growing body of evidence supporting rtCGM use in this population, insurance eligibility criteria should be modified to expand rtCGM use by T2D patients treated with less intensive therapies.

Improved Glycemia with Hybrid Closed-Loop Versus Continuous Subcutaneous Insulin Infusion Therapy: Results from a Randomized Controlled Trial

Objective: To evaluate safety and effectiveness of MiniMed™ 670G hybrid closed loop (HCL) in comparison with continuous subcutaneous insulin infusion (CSII) therapy for 6 months in persons with type 1 diabetes (T1D). Methods: Adults (aged 18-80 years), adolescents, and children (aged 2-17 years) with T1D who were using CSII therapy were enrolled and randomized (1:1) to 6 months of HCL intervention (n = 151, mean age of 39.9 ± 19.8 years) or CSII without continuous glucose monitoring (n = 151, 35.7 ± 18.4 years). Primary effectiveness endpoints included change in A1C for Group 1 (baseline A1C >8.0%), from baseline to the end of study, and difference in the end of study percentage of time spent below 70 mg/dL (%TBR <70 mg/dL) for Group 2 (baseline A1C ≤8.0%), to show superiority of HCL intervention versus control. Secondary effectiveness endpoints were change in A1C and %TBR <70 mg/dL for Group 2 and Group 1, respectively, to show noninferiority of HCL intervention versus control. Primary safety endpoints were rates of severe hypoglycemia and diabetic ketoacidosis (DKA). Results: Change in A1C and difference in %TBR <70 mg/dL for the overall group were significantly improved, in favor of HCL intervention. In addition, a significant mean (95% confidence interval) change in A1C was observed for both Group 1 (-0.8% [-1.1% to -0.4%], P < 0.0001) and Group 2 (-0.3% [-0.5% to -0.1%], P < 0.0001), in favor of HCL intervention. The same was observed for difference in %TBR <70 mg/dL for Group 1 (-2.2% [-3.6% to -0.9%]) and Group 2 (-4.9% [-6.3% to -3.6%]) (P < 0.0001 for both). There was one DKA event during run-in and six severe hypoglycemic events: two during run-in and four during study (HCL: n = 0 and CSII: n = 4 [6.08 per 100 patient-years]). Conclusions: This RCT demonstrates that the MiniMed 670G HCL safely and significantly improved A1C and %TBR <70 mg/dL compared with CSII control in persons with T1D, irrespective of baseline A1C level.

Glycemic variability modifies the relationship between time in range and hemoglobin A1c estimated from continuous glucose monitoring: A preliminary study

AIMS

Although there is a linear relationship between time in range (TIR) and hemoglobin A1c (HbA1c), a great variability of calculated TIR values for a given HbA1c, and vice versa, has been reported. Whether glycemic variability accounts for part of this variability remains to be investigated.

METHODS

The data of continuous glucose monitoring (CGM) from 2559 patients with type 2 diabetes was analyzed. Glycemic variability was assessed by glucose coefficient of variation (CV), and estimated HbA1C (eHbA1c) was calculated from mean sensor glucose.

RESULTS

A strong correlation between TIR and eHbA1c (r = -0.908) was observed. The slopes of regression lines fitted to TIR values as a function of eHbA1c differed significantly for individuals with varying degrees of CV, especially when patients were stratified as stable (CV < 36%) or unstable (CV ≥ 36%) glucose levels. For patients in the high- or low-range of eHbA1c, there was a high variability of TIR values according to CV.

CONCLUSIONS

Glycemic variability significantly mediates the relationship between TIR and eHbA1c, and should be taken into consideration when setting an individualized target of TIR.

Glycemic Outcomes in Baseline Hemoglobin A1C Subgroups in the International Diabetes Closed-Loop Trial

Using a closed-loop system significantly improves time in range (TIR) 70-180 mg/dL in patients with type 1 diabetes (T1D). In a 6-month RCT, 112 subjects were randomly assigned to closed-loop control (Tandem Control-IQ) after obtaining 2 weeks of baseline Continuous glucose monitoring (CGM) data from sensor-augmented pump therapy. We compared glycemic outcomes from baseline to end of study among subgroups classified by baseline HbA1c levels. All HbA1c subgroups showed an improvement in TIR due to reduction of both hyperglycemia and hypoglycemia. Those with HbA1c <6.5% improved mostly by reducing nocturnal hypoglycemia due to the automated basal insulin adjustments. Those with HbA1c ≥8.5% improved mostly by reducing daytime and nocturnal hyperglycemia due to both automated basal insulin adjustments and correction boluses during the day. There does not appear to be any reason to exclude individuals with T1D from automated insulin delivery based on their HbA1c. Clinical Trial Identifier: NCT03563313.