Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Data Sets

Telemedicine in Improving Glycemic Control Among Children and Adolescents With Type 1 Diabetes Mellitus: Systematic Review and Meta-Analysis

BACKGROUND

Type 1 diabetes mellitus (T1DM) is the most common chronic autoimmune disease among children and adolescents. Telemedicine has been widely used in the field of chronic disease management and can benefit patients with T1DM. However, existing studies lack high-level evidence related to the effectiveness of telemedicine for glycemic control in children and adolescents with T1DM.

OBJECTIVE

This study aims to systematically review the evidence on the effectiveness of telemedicine interventions compared with usual care on glycemic control among children and adolescents with T1DM.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Cochrane Library, Embase, Web of Science (all databases), and CINAHL Complete from database inception to May 2023. We included randomized controlled trials (RCTs) that evaluated the effectiveness of a telemedicine intervention on glycemic control in children and adolescents with T1DM. In total, 2 independent reviewers performed the study selection and data extraction. Study quality was assessed using the Cochrane Risk of Bias 2 tool. Our primary outcome was glycated hemoglobin (HbA1c) levels. Secondary outcomes were quality of life, self-monitoring of blood glucose, the incidence of hypoglycemia, and cost-effectiveness. A random-effects model was used for this meta-analysis.

RESULTS

Overall, 20 RCTs (1704 participants from 12 countries) were included in the meta-analysis. Only 5% (1/20) of the studies were at high risk of bias. Compared to usual care, telemedicine was found to reduce HbA1c levels by 0.22 (95% CI -0.33 to -0.10; P<.001; I2=35%). There was an improvement in self-monitoring of blood glucose (mean difference [MD] 0.54, 95% CI -0.72 to 1.80; P=.40; I2=67.8%) and the incidence of hypoglycemia (MD -0.15, 95% CI -0.57 to 0.27; P=.49; I2=70.7%), although this was not statistically significant. Moreover, telemedicine had no convincing effect on the Diabetes Quality of Life for Youth score (impact of diabetes: P=.59; worries about diabetes: P=.71; satisfaction with diabetes: P=.68), but there was a statistically significant improvement in non-youth-specific quality of life (MD -0.24, 95% CI -0.45 to -0.02; P=.04; I2=0%). Subgroup analyses revealed that the effect of telemedicine on HbA1c levels appeared to be greater in studies involving children (MD -0.41, 95% CI -0.62 to -0.20; P<.001), studies that lasted <6 months (MD -0.32, 95% CI -0.48 to -0.17; P<.001), studies where providers used smartphone apps to communicate with patients (MD -0.37, 95% CI -0.53 to -0.21; P<.001), and studies with medication dose adjustment (MD -0.25, 95% CI -0.37 to -0.12; P<.001).

CONCLUSIONS

Telemedicine can reduce HbA1c levels and improve quality of life in children and adolescents with T1DM. Telemedicine should be regarded as a useful supplement to usual care to control HbA1c levels and a potentially cost-effective mode. Meanwhile, researchers should develop higher-quality RCTs using large samples that focus on hard clinical outcomes, cost-effectiveness, and quality of life.

Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach

BACKGROUND

Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia.

METHODS

Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set.

RESULTS

The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%.

CONCLUSIONS

The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.

Key indices of glycaemic variability for application in diabetes clinical practice

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

Effectiveness of flash glucose monitoring in patients with type 1 diabetes and recurrent hypoglycemia between early and late stages after flash glucose monitoring initiation

AIMS

Evaluate the effectiveness of reimbursed flash glucose monitoring with optional alarms (FGM) in preventing severe hypoglycemia (SH) and reducing hypoglycemia exposure in T1D patients prone to hypoglycemia.

METHODS

Ambispective study in T1D patients treated with multiple daily injections (MDI) and prone to hypoglycemia, initiating reimbursed FGM (FreeStyle Libre 2). The primary outcome was the number of SH events (requiring third party assistance) and main secondary outcomes were time below range < 70 (TBR < 70) and < 54 mg/dL (TBR < 54), impaired awareness of hypoglycemia (IAH) and quality of life (QoL). Logistic regression models were constructed to explore variables associated with success of the intervention.

RESULTS

We included 110 patients (52.7 % women, mean age 47.8 ± 17.0 years). SH events at 1-year follow-up decreased from 0.3 ± 0.6 to 0.03 ± 0.2 (p < 0.001). Significant reductions in patients presenting an SH (26.4 % vs. 2.9 %, p < 0.001) and IAH (47.1 % vs. 25.9 %, p = 0.002) were observed, as well as improvements in QoL. TBR < 70 and TBR < 54 were not significantly reduced. Baseline GMI was inversely associated with a decrease in TBR < 70 [OR 0.37 (0.15-0.93)] and directly with an increase in time in range 70-180 mg/dL [OR 2.10 (1.03-4.28)].

CONCLUSIONS

FGM decreased SH and improved hypoglycemia awareness and QoL. Initial tight glycemic control was associated with a decrease in hypoglycemia, while patients with suboptimal control reduced hyperglycemia.

A Framework for Integrating Continuous Glucose Monitor-Derived Metrics into Economic Evaluations in Type 1 Diabetes

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.

Risk Factor Analysis for Type 2 Diabetes Patients About Hypoglycemia Using Continuous Glucose Monitoring: Results from a Prospective Observational Study

Introduction: To determine the relationship between hypoglycemia and glucose variability in outpatients with type 2 diabetes mellitus (T2DM). Materials and Methods: The study participants were 999 outpatients with T2DM who used the FreeStyle Libre Pro for continuous glucose monitoring (FLP-CGM). Hypoglycemia was defined as glucose level of <3.0 mM, and the frequency of episodes and duration of hypoglycemia were evaluated by comparing patients who did or did not achieve time-below-range <3.0 mM (TBR^<3.0) of <1% of the time. The association of TBR^<3.0 and long% coefficient of variation (%CV) with medications used was examined using multivariate analysis with a proportional odds model. Results: The average TBR^<3.0 was 0.33% (4.75 min). The TBR^<3.0 >1% group comprised 71/999 patients. Patients of the TBR^<3.0 >1% group had lower body mass index, longer disease duration, and poorer renal function. For the TBR^<3.0 >1% group, the predicted cutoff values were 7.19 mM average glucose (AG), and 30.30% for %CV. When AG <7.19 mM and %CV >30.30% were considered as hypoglycemic risk factors, the frequency and duration of hypoglycemia increased as the risk factor values increased. In multivariate analysis, sulfonylurea (SU) use, insulin use, and low blood glucose index correlated significantly with increased length of TBR^<3.0 and %CV, even after adjustment for concomitant diabetes medications. Conclusion: In T2DM, maintaining TBR^<3.0 <1% requires to keep AG >7.2 mM and %CV <30%, in addition to comprehensive management of CGM metrics. Since SU and insulin use is associated with prolonged TBR^<3.0 and increased %CV, their doses should be adjusted to avoid excessive fall in AG and raising %CV.

Impact of flash glucose monitoring on glycemic control varies with the age and residual β-cell function of patients with type 1 diabetes mellitus

AIMS/INTRODUCTION

We aimed to explore the clinical factors associated with glycemic variability (GV) assessed with flash glucose monitoring (FGM), and investigate the impact of FGM on glycemic control among Chinese type 1 diabetes mellitus patients in a real-life clinical setting.

MATERIALS AND METHODS

A total of 171 patients were included. GV was assessed from FGM data. A total of 110 patients wore FGM continuously for 6 months (longitudinal cohort). Hemoglobin A1c (HbA1c), fasting and 2-h postprandial C-peptide, and glucose profiles were collected. Changes in HbA1c and glycemic parameters were assessed during a 6-month FGM period.

RESULTS

Individuals with high residual C-peptide (HRCP; 2-h postprandial C-peptide >200 pmol/L) had less GV than patients with low residual C-peptide ( 2-h postprandial C-peptide ≤200 pmol/L; P < 0.001). In the longitudinal cohort (n = 110), HbA1c and mean glucose decreased, time in range (TIR) increased during the follow-up period (P < 0.05). The 110 patients were further divided into age and residual C-peptide subgroups: (i) HbA1c and mean glucose were reduced significantly only in the subgroup aged ≤14 years during the follow-up period, whereas time below range also increased in this subgroup at 3 months (P = 0.047); and (ii) HbA1c improved in the HRCP subgroup at 3 and 6 months (P < 0.05). The mean glucose decreased and TIR improved significantly in the low residual C-peptide subgroup; however, TIR was still lower and time below range was higher than those of the HRCP subgroup at all time points (P < 0.05).

CONCLUSIONS

HRCP was associated with less GV. FGM wearing significantly reduced HbA1c, especially in pediatric patients and those with HRCP. Additionally, the mean glucose and TIR were also found to improve.

Glycaemic management in diabetes: old and new approaches

HbA_1c is the most used parameter to assess glycaemic control. However, evidence suggests that the concept of hyperglycaemia has profoundly changed and that different facets of hyperglycaemia must be considered. A modern approach to glycaemic control should focus not only on reaching and maintaining optimal HbA_1c concentrations as early as possible, but to also do so by reducing postprandial hyperglycaemia, glycaemic variability, and to extend as much as possible the time in range in near-normoglycaemia. These goals should be achieved while avoiding hypoglycaemia, which, should it occur, should be reverted to normoglycaemia. Modern technology, such as intermittently scanned glucose monitoring and continuous glucose monitoring, together with new drug therapies (eg, ultra-fast insulins, SGLT2 inhibitors, and GLP-1 receptor agonists), could help to change the landscape of glycaemia management based on HbA_1c in favour of a more holistic approach that considers all the different aspects of this commonly oversimplified pathophysiological feature of diabetes.

Real-world performance of the MiniMed™ 670G system in Europe

AIM

To evaluate the real-world performance of the MiniMed 670G system in Europe, in individuals with diabetes.

MATERIALS AND METHODS

Data uploaded from October 2018 to July 2020 by individuals living in Europe were aggregated and retrospectively analysed. The mean glucose management indicator (GMI), percentage of time spent within (TIR), below (TBR) and above (TAR) glycaemic ranges, system use and insulin consumed in users with 10 or more days of sensor glucose data after initial Auto Mode start were determined. Another analysis based on suboptimally (GMI > 8.0%) and well-controlled (GMI < 7.0%) glycaemia pre-Auto Mode initiation was also performed.

RESULTS

Users (N = 14 899) spent a mean of 81.4% of the time in Auto Mode and achieved a mean GMI of 7.0% ± 0.4%, TIR of 72.0% ± 9.7%, TBR less than 3.9 mmol/L of 2.4% ± 2.1% and TAR more than 10 mmol/L of 25.7% ± 10%, after initiating Auto Mode. When compared with pre-Auto Mode initiation, GMI was reduced by 0.3% ± 0.4% and TIR increased by 9.6% ± 9.9% (P < .0001 for both). Significantly improved glycaemic control was observed irrespective of pre-Auto Mode GMI levels of less than 7.0% or of more than 8.0%. While the total daily dose of insulin increased for both groups, a greater increase was observed in the latter, an increase primarily due to increased basal insulin delivery. By contrast, basal insulin decreased slightly in well-controlled users.

CONCLUSIONS

Most MiniMed 670G system users in Europe achieved TIR more than 70% and GMI less than 7% while minimizing hypoglycaemia, in a real-world environment. These international consensus-met outcomes were enabled by automated insulin delivery meeting real-time insulin requirements adapted to each individual user.

Patient-reported outcomes in adults with type 1 diabetes in global real-world clinical practice: The SAGE study

AIMS

To conduct a secondary analysis of the SAGE study to evaluate the association between glycaemic control and patient-reported outcomes (PROs), in adults with type 1 diabetes (T1DM) across different age groups and regions.

MATERIALS AND METHODS

SAGE was a multinational, cross-sectional, observational study in adults with T1DM. Data were collected at a single visit, analysed according to predefined age groups (26-44, 45-64, and ≥65 years), and reported across different regions. PRO questionnaires were applied to assess hypoglycaemia fear (Hypoglycemia Fear Survey-II), diabetes-related distress (Problem Areas In Diabetes questionnaire), insulin treatment satisfaction (Insulin Treatment Satisfaction Questionnaire), and diabetes-specific quality of life (QoL; Audit of Diabetes-Dependent Quality of Life). Multivariable analysis was performed to evaluate the relationship between glycated haemoglobin (HbA1c) target achievement (<7% and individualised targets) with PRO scores.

RESULTS

The PRO scores showed relatively low levels of diabetes-related emotional distress and fear of hypoglycaemia, moderate to high treatment satisfaction, and low diabetes-related impact on QoL. Results were generally comparable across age groups with some regional variability. Achievement of the HbA1c <7% target was associated with less worry about hypoglycaemia, lower diabetes-related emotional distress, higher insulin treatment satisfaction, and higher QoL. Achievement of individualised HbA1c targets was associated with lower diabetes-related emotional distress and higher insulin treatment satisfaction.

CONCLUSIONS

Better glycaemic control was most closely associated with low emotional distress due to diabetes and high patient-reported insulin treatment satisfaction.

Hospitalization for hypoglycaemia in people with diabetes in Denmark, 1997-2017: Time trends in incidence and HbA1c and glucose-lowering drug use before and after hypoglycaemia

Objective

To assess incidence trends of first hospitalization for hypoglycaemia in Denmark and to examine HbA_1c levels and glucose‐lowering drug use before and after hospitalization among individuals with type 1 or type 2 diabetes.

Research Design and Methods

We performed a population‐based study linking diagnosis, prescription and laboratory data. Standardized incidence of first hospitalization for hypoglycaemia in Denmark was assessed for each calendar year 1997–2017. HbA1c and glucose‐lowering drug use was compared with age‐ and sex‐matched diabetes comparisons without hospitalization for hypoglycaemia.

Results

The annual age‐ and sex‐standardized incidence rate of first hospitalization for hypoglycaemia per 100,000 person‐years increased during 1997–2003 (from 17.7 to 30.3 per 100,000 person‐years), remained stable until 2010 (30.4) and gradually declined until 2017 (22.0). During this period, we identified 3,479 people with type 1 diabetes and 15,329 people with type 2 diabetes experiencing first hospitalization for hypoglycaemia. Both diabetes groups experienced a mean HbA1c decrease of ~12%–15% in the months preceding first hospitalization, followed by a gradually increasing HbA1c afterwards. People with type 1 diabetes and hospitalization used similar insulin therapies as those without hospitalization. People with type 2 diabetes and hospitalization more often received insulin (55%) than comparisons (45%), and 45% discontinued insulin or stopped all glucose‐lowering therapy after first hospitalization.

Conclusions

Incidence of hospitalizations for hypoglycaemia has declined by one fourth the last decade in the Danish population. A HbA1c decrease precedes first hospitalization for hypoglycaemia in individuals with diabetes, and profound changes in glucose‐lowering drug therapy for type 2 diabetes occur after hospitalization.

Glycemic Variability, Oxidative Stress, and Impact on Complications Related to Type 2 Diabetes Mellitus

Chronic hyperglycemia is an established risk factor for the development of complications in both type 1 and type 2 diabetes, but glycemic variability has emerged as a possible independent risk factor for diabetes complications, possibly through oxidative stress. In this review, methods to access glycemic variability and oxidative stress, as well as their correlations, are discussed. Non-pharmacological and pharmacological strategies are also debated to achieve better glycemic control, not only by HbA1c target but also with reduced glycemic fluctuations, possibly minimizing the risk of diabetes complications.

Continuous Glucose Monitoring in Adults with Type 1 Diabetes: Real-World Data from the German/Austrian Prospective Diabetes Follow-Up Registry

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70-180 mg/dL] 54% vs. 49%) and hypoglycemic range <70 mg/dL (7% vs. 5%), less time in hyperglycemic range >180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged <30 years. We found no significant differences between genders. Multivariable regression models revealed the highest Time In Range (TIR) and lowest time with sensor glucose >250 mg/dL, CV and daytime-night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

Value of Capillary Glucose Profiles in Assessing Risk of Nocturnal Hypoglycemia in Type 1 Diabetes Based on Continuous Glucose Monitoring

INTRODUCTION

This study aimed to evaluate the occurrence of nocturnal hypoglycemia in type 1 diabetes (T1D) based on continuous glucose monitoring (CGM), and to explore the value of capillary glucose profiles in assessing the risk of nocturnal hypoglycemia. The study also intended to develop a predictive model to identify people with high risk of nocturnal hypoglycemia.

METHODS

A total of 169 participants with T1D received 3 days of blinded CGM; meanwhile, their self-monitoring blood glucose (SMBG) profiles were recorded. Logistic regression analyses were used to evaluate contributory factors of nocturnal hypoglycemia. Potential indicators were estimated using area under receiver operator curve (AUC) analyses.

RESULTS

During the retrospective CGM period, 95 (56.2%) participants with T1D reported 238 events of hypoglycemia, and 69 (29.0%) of these episodes occurred during the nighttime. Increased risk of nocturnal hypoglycemia correlated with lower HbA1c, glycated albumin, and mean blood glucose (OR = 0.790, 0.940, 0.651, respectively; P < 0.05) and higher standard deviation, mean amplitude of glycemic excursions, and low blood glucose index (OR = 1.463, 1.168, 4.035, respectively; P < 0.05) after adjustment for age and duration. Of the daily SMBG profiles, fasting blood glucose (OR = 0.643, P = 0.001) and blood glucose at bedtime (OR = 0.851, P = 0.037) were associated with the occurrence of nocturnal hypoglycemia. The BGn model, which was derived from the variation of capillary glucose, could discriminate individuals with increased risk of nocturnal hypoglycemia (AUC = 0.774).

CONCLUSIONS

Nocturnal hypoglycemia constitutes nearly one-third of hypoglycemic events in people with T1D. Strict glycemic control and great fluctuation of glucose are potential contributory factors. Daily SMBG profiles and the BGn model could help assess the risk of nocturnal hypoglycemia in T1D, which may support further development of preventive strategies.

Prediction of Nocturnal Hypoglycemia From Continuous Glucose Monitoring Data in People With Type 1 Diabetes: A Proof-of-Concept Study

BACKGROUND

Intensive insulin therapy has documented benefits but may also come at the expense of a higher risk of hypoglycemia. Hypoglycemia is associated with higher all-cause mortality and nocturnal hypoglycemia has been associated with the sudden dead-in-bed syndrome. This proof-of-concept study sought to investigate if nocturnal hypoglycemia can be predicted.

METHOD

Continuous glucose monitoring, meal, insulin, and demographics data from 463 people with type 1 diabetes were obtained from a clinical trial. A total of 4721 nights without or with hypoglycemia (429) were available including data from three consecutive days before the night. Thirty-two features were calculated based on these data. Data were split into 20% participants for evaluation and 80% for training. The optimal feature subset was found from forward selection of the 80% participants with linear discriminant analysis as basis for the classifier.

RESULTS

The forward selection resulted in a feature subset of four features. The evaluation resulted in an area under the receiver operating characteristics curve (ROC-AUC) of 0.79 leading to a sensitivity and a specificity of, e.g., 75% and 70%.

CONCLUSIONS

It was possible to predict nocturnal hypoglycemic episodes with a ROC-AUC of 0.79. A warning at bedtime about nocturnal hypoglycemia could be of great help for people with diabetes to enable preventive actions. Further development of the proposed algorithm is needed for implementation in everyday practice.

Continuous Glucose Monitoring in People With Type 1 Diabetes on Multiple-Dose Injection Therapy: The Relationship Between Glycemic Control and Hypoglycemia

OBJECTIVE

The inverse relationship between overall glucose control and hypoglycemia risk is weakened by the use of real-time continuous glucose monitoring (rtCGM). We assess the relationship between glucose control and hypoglycemia in people with type 1 diabetes using multiple-dose injection (MDI) regimens, including those at highest risk of hypoglycemia.

RESEARCH DESIGN AND METHODS

CGM data from the intervention (rtCGM) and control (self-monitored blood glucose [SMBG]) phases of the Multiple Daily Injections and Continuous Glucose Monitoring in Diabetes (DIAMOND) and HypoDE studies were analyzed. The relationship between glucose control (HbA_1c and mean rtCGM glucose levels) and percentage time spent in hypoglycemia was explored for thresholds of 3.9 mmol/L (70 mg/dL) and 3.0 mmol/L (54 mg/dL), and ANOVA across the range of HbA_1c and mean glucose was performed.

RESULTS

A nonlinear relationship between mean glucose and hypoglycemia was identified at baseline, with the steepest relationship seen at lower values of mean glucose. The use of rtCGM reduces the exposure to hypoglycemia at all thresholds and flattens the relationship between overall glucose and hypoglycemia, with the most marked impact at lower values of mean glucose and HbA_1c. Exposure to hypoglycemia varied at all thresholds across the range of overall glucose at baseline, in the SMBG group, and with rtCGM, but the relationships were weaker in the rtCGM group.

CONCLUSIONS

Use of rtCGM can flatten and attenuate the relationship between overall glucose control and hypoglycemia, exerting its greatest impact at lower values of HbA_1c and mean glucose in people with type 1 diabetes using MDI regimens and at highest risk of hypoglycemia.

Cost-effectiveness of Initiating an Insulin Pump in T1D Adults Using Continuous Glucose Monitoring Compared with Multiple Daily Insulin Injections: The DIAMOND Randomized Trial

BACKGROUND

The economic impact of both continuous glucose monitoring (CGM) and insulin pumps (continuous subcutaneous insulin infusion [CSII]) in type 1 diabetes (T1D) have been evaluated separately. However, the cost-effectiveness of adding CSII to existing CGM users has not yet been assessed.

OBJECTIVE

The aim of this study was to evaluate the societal cost-effectiveness of CSII versus continuing multiple daily injections (MDI) in adults with T1D already using CGM.

METHODS

In the second phase of the DIAMOND trial, 75 adults using CGM were randomized to either CGM+CSII or CGM+MDI (control) and surveyed at baseline and 28 weeks. We performed within-trial and lifetime cost-effectiveness analyses (CEAs) and estimated lifetime costs and quality-adjusted life-years (QALYs) via a modified Sheffield T1D model.

RESULTS

Within the trial, the CGM+CSII group had a significant reduction in quality of life from baseline (-0.02 ± 0.05 difference in difference [DiD]) compared with controls. Total per-person 28-week costs were $8,272 (CGM+CSII) versus $5,623 (CGM+MDI); the difference in costs was primarily attributable to pump use ($2,644). Pump users reduced insulin intake (-12.8 units DiD) but increased the use of daily number of test strips (+1.2 DiD). Pump users also increased time with glucose in range of 70 to 180 mg/dL but had a higher HbA1c (+0.13 DiD) and more nonsevere hypoglycemic events. In the lifetime CEA, CGM+CSII would increase total costs by $112,045 DiD, decrease QALYs by 0.71, and decrease life expectancy by 0.48 years.

CONCLUSIONS

Based on this single trial, initiating an insulin pump in adults with T1D already using CGM was associated with higher costs and reduced quality of life. Additional evidence regarding the clinical effects of adopting combinations of new technologies from trials and real-world populations is needed to confirm these findings.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Marked improvement in HbA1c following commencement of flash glucose monitoring in people with type 1 diabetes

AIMS/HYPOTHESIS

Minimal evidence supports the efficacy of flash monitoring in lowering HbA_1c. We sought to assess the impact of introducing flash monitoring in our centre.

METHODS

We undertook a prospective observational study to assess change in HbA_1c in 900 individuals with type 1 diabetes following flash monitoring (comparator group of 518 with no flash monitoring). Secondary outcomes included changes in hypoglycaemia, quality of life, flash monitoring data and hospital admissions.

RESULTS

Those with baseline HbA_1c ≥58 mmol/mol (7.5%) achieved a median -7 mmol/mol (interquartile range [IQR] -13 to -1) (0.6% [-1.2 to -0.1]%) change in HbA_1c (p < 0.001). The percentage achieving HbA_1c <58 mmol/mol rose from 34.2% to 50.9% (p < 0.001). Median follow-up was 245 days (IQR 182 to 330). Individuals not using flash monitoring experienced no change in HbA_1c across a similar timescale (p = 0.508). Higher HbA_1c (p < 0.001), younger age at diagnosis (p = 0.003) and lower social deprivation (p = 0.024) were independently associated with an HbA_1c fall of ≥5 mmol/mol (0.5%). More symptomatic (OR 1.9, p < 0.001) and asymptomatic (OR 1.4, p < 0.001) hypoglycaemia was reported after flash monitoring. Following flash monitoring, regimen-related and emotional components of the diabetes distress scale improved although the proportion with elevated anxiety (OR 1.2, p = 0.028) and depression (OR 2.0, p < 0.001) scores increased. Blood glucose test strip use fell from 3.8 to 0.6 per day (p < 0.001). Diabetic ketoacidosis admissions fell significantly following flash monitoring (p = 0.043).

CONCLUSIONS/INTERPRETATION

Flash monitoring is associated with significant improvements in HbA_1c and fewer diabetic ketoacidosis admissions. Higher rates of hypoglycaemia may relate to greater recognition of hitherto unrecognised events. Impact upon quality of life parameters was mixed but overall treatment satisfaction was overwhelmingly positive.

Glycaemic variability in diabetes: clinical and therapeutic implications

Glycaemic variability is an integral component of glucose homoeostasis. Although it has not yet been definitively confirmed as an independent risk factor for diabetes complications, glycaemic variability can represent the presence of excess glycaemic excursions and, consequently, the risk of hyperglycaemia or hypoglycaemia. Glycaemic variability is currently defined by a large and increasing number of metrics, representing either short-term (within-day and between-day variability) or long-term glycaemic variability, which is usually based on serial measurements of HbA_1c or other measures of glycaemia over a longer period of time. In this Review, we discuss recent evidence examining the association between glycaemic variability and diabetes-related complications, as well as non-pharmacological and pharmacological strategies currently available to address this challenging aspect of diabetes management.

Glucocentric risk factors for macrovascular complications in diabetes: Glucose 'legacy' and 'variability'-what we see, know and try to comprehend

Recognizing the role of dysglycaemia, 'ambient' hyperglycaemia, 'metabolic memory' and glycaemic variability as risk factors for macrovascular diseases is mandatory for effective diabetes management. Chronic hyperglycaemia, also referred to as 'ambient hyperglycaemia', was only fully acknowledged as a risk factor for adverse cardiovascular events when the beneficial effects of intensive glucose-lowering strategies were consolidated in the extended follow-up (> 10 years) of patients included in the United Kingdom Prospective Diabetes Study (UKPDS) and Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study. These studies led to the concept of the glucose-lowering 'legacy effect' (metabolic memory), which depends on the duration and magnitude of glucose-lowering, and is not a 'forever' phenomenon, as demonstrated in the 15-year follow-up of the Veterans Affairs Diabetes Trial (VADT). The relatively weak evidence for linking long- and short-term glycaemic variability to vascular complications in patients with diabetes is mainly due to a reliance on observational and retrospective studies, and the lack of randomized interventional trials. However, hypoglycaemia may play an intermediary role in accentuating the link between glycaemic variability and vascular events.

Risk Factors of Hypoglycemia in Patients with Type 2 Diabetes Mellitus: A Study Based on Continuous Glucose Monitoring

BACKGROUND

The objective of this study was to determine the risk factors of hypoglycemia by evaluating the glycemic profile using continuous glucose monitoring (CGM) in patients with type 2 diabetes mellitus (T2DM).

METHODS

The participants were 294 patients with T2DM who received inpatient diabetes education. The mean blood glucose (MBG), coefficient of variation (CV), mean postprandial glucose excursion, low blood glucose index (LBGI), and percentage of time with blood glucose (BG) at <70 mg/dL were measured on admission using CGM. We predicted the risk of hypoglycemia utilizing transform to Gaussian model. The primary end point was the relationship between CGM parameters and hypoglycemia.

RESULTS

Multivariate logistic regression analysis showed that disease duration, MBG, CV, LBGI, and Predicted% of BG correlated significantly with hypoglycemia. Receiver operating characteristic curve analysis showed that the optimal cutoff points for MBG and CV in predicting hypoglycemia were 152 mg/dL and 22%, respectively. The proportion of patients with hypoglycemia was 0% for the group with no hypoglycemia risk factors, 4.2% for the group with one risk factor, and 36.6% for the group with two risk factors, showing a linear increase across the groups (P < 0.001). LBGI was the best predictor of hypoglycemia; and Predicted% BG <70 mg/dL was very useful as an index to predict hypoglycemia.

CONCLUSIONS

Patients with low MBG levels and large fluctuations in BG were more likely to develop hypoglycemia, suggesting that assessment of these two variables is useful for the prediction of hypoglycemia. To achieve good glycemic control free of hypoglycemia, approaches are needed that do not only lower BG level but also minimize fluctuations in blood and interstitial fluid glucose level.