Expert Recommendations for Using Time-in-Range and Other Continuous Glucose Monitoring Metrics to Achieve Patient-Centered Glycemic Control in People With Diabetes

Proposed Practical Guidelines to Improve Glycaemic Management by Reducing Glycaemic Variability in People with Type 1 Diabetes Mellitus

INTRODUCTION

For decades, glycaemic variability (GV) was ignored in clinical practice because its precise assessment was challenging and there were no specific recommendations to reduce it. However, the current widespread use of continuous glucose monitoring (CGM) systems has changed this situation. Associations between high GV and risk of hypoglycaemia, onset of macro- and microvascular complications and mortality have been described in type 1 diabetes (T1D). It is therefore important to identify the causes of excessive glycaemic excursions and make recommendations for people with T1D to achieve better glycaemic management by minimising GV in both the short term and the long term.

METHODS

To achieve these aims, a panel comprising four endocrinologists, one diabetes nurse educator and one nutritionist worked together to reach a consensus on the detection of triggers of GV and propose clinical guidelines to reduce GV and improve glycaemic management by reducing the risk of hypoglycaemias.

RESULTS AND CONCLUSIONS

In total, four different areas of interest were identified, in which the insufficient education and/or training of people with T1D could lead to higher GV: physical activity; dietary habits; insulin therapy, especially when pump-based systems are not used; and other causes of GV increase. Practical, easy-to-follow recommendations to reduce GV in daily activities were then issued, with the aim of enabling people with T1D to reduce either hypoglycaemia or hyperglycaemia episodes. By doing this, their quality of life may be improved, and progression of chronic complications may be prevented or delayed.

Use of Glucose Monitoring Devices Among Adults with Diabetes in Germany: Results from Nationwide Surveys Conducted in 2017 and 2021/2022

Background: Devices for continuous glucose monitoring (CGM) have been developed to optimize blood glucose control and liberate people with diabetes from finger-prick glucose measurements. Since 2016, the devices have been reimbursed in Germany for people with diabetes receiving insulin therapy, resulting in their increased use among people with type 1 diabetes (T1D) and type 2 diabetes (T2D). We investigated the prevalence of CGM use and its associated factors among German adults with diabetes in 2017 and 2021/2022. Methods: Participants aged 18 years or older with diagnosed diabetes were identified from two nationwide population-based telephone surveys in 2017 (n = 1396) and 2021/2022 (n = 1456). Prevalence and dynamics of CGM use were examined overall and stratified by sociodemographic and diabetes-related characteristics. Factors associated with CGM use were obtained from logistic regression models. Results: The overall prevalence of CGM use was 8.2% in 2017 and 16.6% in 2021/2022. An increase in CGM use was observed across all the subgroups except for those without antidiabetic medications. CGM use increased from 31.1% to 75.4% in adults with T1D, from 6.3% to 13.6% in adults with T2D, and from 14.6% to 36.7% in all insulin users. In both surveys, younger age, insulin use, T1D, and reporting hypoglycemia were associated with CGM use. In addition, in 2017, higher education level and absence of obesity were associated with CGM use, whereas in 2021/2022, participation in the diabetes self-management education program and higher self-assessed quality of diabetes care were associated with CGM use. Conclusion: Among adults with diabetes in Germany, CGM use increased about twofold within 5 years, irrespective of sociodemographic factors. Educational inequality in CGM use diminished over time. The higher self-rated quality of diabetes care associated with the recent use of CGM provides further evidence to support its use among all adults with diabetes in Germany.

Time Below Range and Its Influence on Hypoglycemia Awareness and Severe Hypoglycemia: Insights From the Association of British Clinical Diabetologists Study

OBJECTIVE

This study aimed to explore the relationship between time below range (TBR), impaired awareness of hypoglycemia (IAH), and severe hypoglycemia (SH).

RESEARCH DESIGN AND METHODS

This cross-sectional study analyzed data from individuals with diabetes using continuous glucose monitors (CGMs) in the Association of British Clinical Diabetologists audit. Hypoglycemia awareness was assessed via the Gold score (≥4 denoting IAH), and SH was defined as hypoglycemia requiring third-party assistance. Logistic regression was used to determine the association between TBR percentage (<70 mg/dL; 3.9 mmol/L) at first follow-up and follow-up Gold score and SH incidence. The Youden J index identified optimal TBR percentage cutoffs for detecting IAH and SH.

RESULTS

The study included 15,777 participants, with follow-up TBR and SH data available for 5,029. The median TBR percentage was 4% (interquartile range 2-6.6%), with 42% meeting the recommended TBR of ≤4%. Adjusted for age, sex, and BMI, TBR was significantly associated with SH (P < 0.001) and IAH (P = 0.005). Optimal TBR cutoffs for identifying IAH and SH were 3.35% and 3.95%, yielding negative predictive value (NPV) values of 85% and 97%, respectively.

CONCLUSIONS

Our findings support the international consensus recommending a TBR of <4% in type 1 diabetes, with high NPV values suggesting the utility of TBR in screening for SH.

Time above range and no coefficient of variation is associated with diabetic retinopathy in individuals with type 1 diabetes and glycated hemoglobin within target

AIMS

This study aimed to investigate the association between glucose metrics and diabetic retinopathy in type 1 diabetes (T1D) patients using flash continuous glucose monitoring (FGM) systems, including those maintaining glycated hemoglobin (HbA1c) within the target range.

METHODS

We conducted a cross-sectional study involving 1070 T1D patients utilizing FGM systems. Data on clinical, anthropometric, and socioeconomic characteristics were collected and retinopathy was classified based on international standards.

RESULTS

Patients' mean age was 47.6 ± 15.0 years, with 49.4% of them being females. Within the cohort, 24.8% of patients presented some form of retinopathy. In the analysis involving the entire sample of subjects, male gender (OR = 1.51, p = 0.027), Time Above Range (TAR) > 250 mg/dL (OR = 1.07, p = 0.025), duration of diabetes (OR = 1.09, p < 0.001), smoking (OR = 2.30, p < 0.001), and history of ischemic stroke (OR = 5.59, p = 0.025) were associated with diabetic retinopathy. No association was observed between the coefficient of variation and diabetic retinopathy (p = 0.934). In patients with HbA1c < 7%, the highest quartile of TAR > 250 was independently linked to diabetic retinopathy (OR = 8.32, p = 0.040), in addition to smoking (OR = 2.90, p = 0.031), duration of diabetes (OR = 1.09, p < 0.001), and hypertension (OR = 2.35, p = 0.040).

CONCLUSION

TAR > 250 mg/dL significantly emerges as a modifiable factor associated with diabetic retinopathy, even among those patients maintaining recommended HbA1c levels. Understanding glucose metrics is crucial for tailoring treatment strategies for T1D patients.

Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes

PURPOSE

To investigate the impact of clinical and socio-economic factors on glycaemic control and construct statistical models to predict optimal glycaemic control (OGC) after implementing intermittently scanned continuous glucose monitoring (isCGM) systems.

METHODS

This retrospective study included 1072 type 1 diabetes patients (49.0% female) from three centres using isCGM systems. Clinical data and net income from the census tract were collected for each individual. OGC was defined as time in range > 70%, with time below 70 mg/dL < 4%. The sample was randomly split in two equal parts. Logistic regression models to predict OGC were developed in one of the samples, and the best model was selected using the Akaike information criterion and adjusted for Pearson's and Hosmer-Lemeshow's statistics. Model reliability was assessed via external validation in the second sample and internal validation using bootstrap resampling.

RESULTS

Out of 2314 models explored, the most effective predictor model included annual net income per person, sex, age, diabetes duration, pre-isCGM HbA1c, insulin dose/kg, and the interaction between sex and HbA1c. When applied to the validation cohort, this model demonstrated 72.6% specificity, 67.3% sensitivity, and an area under the curve (AUC) of 0.736. The AUC through bootstrap resampling was 0.756. Overall, the model's validity in the external cohort was 80.4%.

CONCLUSIONS

Clinical and socio-economic factors significantly influence OGC in type 1 diabetes. The application of statistical models offers a reliable means of predicting the likelihood of achieving OGC following isCGM system implementation.

Smart Insulin Pen in Pregnant Women with Type 1 Diabetes: An Encouraging Case Series

Background: The management of type 1 diabetes in pregnancy with new technologies is challenging. Sometimes the complexity of new-generation systems such as "continuous subcutaneous insulin infusion, CSII" and patient or provider preference do not allow their use, so women with type 1 diabetes in pregnancy continue to be treated with subcutaneous multiple-injection insulin therapy using pens. Smart insulin pens are new tools that allow for data collection on insulin dose and time of administration and have additional connectivity features. Objective: To retrospectively describe the use of a smart insulin pen coupled with rt-CGM (InPenTM system) in three pregnancies complicated by type 1 diabetes. Methods: Participants used the InPenTM system in pregnancy and consented to analysis of glycaemic data and pregnancy outcome. Results: An increase in pregnancy specific time-in-range glucose was observed in the three patients related to the duration of insulin action, insulin sensitivity factors, and a pre-set target glucose range for pregnancy. No diabetic ketoacidosis or severe hypoglycaemia occurred. Conclusions: We describe practical considerations in three pregnant patients with type 1 diabetes where the InPenTM system was used with suggestive improvements in the time-in-range.

Association of smoking with the efficacy of continuous glucose monitoring in type1 diabetes: A propensity score-matched cohort study

INTRODUCTION

Smoking affects glycemic control in individuals with type1 diabetes (T1D); however, its impact in the era of continuous glucose monitoring (CGM) has not been thoroughly studied.

MATERIALS AND METHODS

A retrospective cohort study was conducted at two centers, involving 405 T1D patients treated with multiple daily insulin injections and using CGM. The patients were matched using propensity scores based on sociodemographic and clinical characteristics. HbA1c levels were analyzed before and after a 2.2-year follow-up period. The analysis was performed using mixed linear regression and multivariable conditional logistic models.

RESULTS

The sample included 135 smokers and 270 non-smokers, with a mean age of 47.6years, and 50.1% were women. Both groups had a similar baseline HbA1c of 8.0 (1.5%). After follow-up, non-smokers reduced their HbA1c to 7.3 (1.1%), while smokers only reduced it to 7.7 (1.3%), 95%CI [-0,57-0,10]). The proportion of non-smokers achieving HbA1c<7% increased from 25% to 38.1%, 95%CI [0,14-0.36], whereas smokers showed no change (25.9%, 95%CI [-0.13-0.21]). Smoking was independently associated with a higher risk of not achieving HbA1c<7%, despite CGM use (odds ratio 1.89, 95%CI [1.13-3.17].

CONCLUSION

Smoking limits the glycemic control benefits of CGM in individuals with T1D. It is crucial to include smokers in clinical trials and to develop strategies to discourage smoking in this population to maximize the benefits of diabetes technology.

Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study

Objective: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). Patients and Methods: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Results: Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; P < 0.001), CHypo (2.9% [SD 0.3]; P < 0.001), CHyper (6.3% [SD 1.4]; P < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; P < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; P < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; P < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; P = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; P = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; P < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; P < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; P = 0.004). Conclusions: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.

Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review

INTRODUCTION

Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain.

METHODS

The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain.

RESULTS

A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future.

CONCLUSION

By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Exploring the Continuous Glucose Monitoring in Pediatric Diabetes: Current Practices, Innovative Metrics, and Future Implications

Continuous glucose monitoring (CGM) systems, including real-time CGM and intermittently scanned CGM, have revolutionized diabetes management, particularly in children and adolescents with type 1 diabetes (T1D). These systems provide detailed insights into glucose variability and detect asymptomatic and nocturnal hypoglycemia, addressing limitations of traditional self-monitoring blood glucose methods. CGM devices measure interstitial glucose concentrations constantly, enabling proactive therapeutic decisions and optimization of glycemic control through stored data analysis. CGM metrics such as time in range, time below range, and coefficient of variation are crucial for managing T1D, with emerging metrics like time in tight range and glycemia risk index showing potential for enhanced glycemic assessment. Recent advancements suggest the utility of CGM systems in monitoring the early stages of T1D and individuals with obesity complicated by pre-diabetes, highlighting its therapeutic versatility. This review discusses the current CGM systems for T1D during the pediatric age, established and emerging metrics, and future applications, emphasizing the critical role of CGM devices in improving glycemic control and clinical outcomes in children and adolescents with diabetes.

A moderately higher time-in-range threshold improves the prognosis of type 2 diabetes patients complicated with COVID-19

Objective

After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type 2 diabetes(T2D).

Research design and methods

63 T2D patients complicated with COVID-19 were evaluated. Patients were divided into favorable outcome group and adverse outcome group according to whether achieving composite endpoint (a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death). TIR, the time-below-range (TBR) and the time-above-range (TAR) were calculated from intermittently scanned continuous glucose monitoring. Logistic regression analysis and other statistical methods were used to analyze the correlation between glucose variability and prognosis to establish the appropriate reference range of TIR.

Results

TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes.

Conclusion

The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with T2D.

Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?

BACKGROUND

Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes.

METHODS

We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies.

RESULTS

Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes.

CONCLUSIONS

Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AIMS

This study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D).

METHODS

Data were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data.

RESULTS

Logistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60-53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63-61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans.

CONCLUSIONS

The results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Background

Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).

Objective

In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?

Methods

We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.

Results

Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.

Conclusion

Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

Differential correlation between time in range and eGFR or albuminuria in type 2 diabetes

INTRODUCTION

As a CGM-derived indicator, 'time in range' (TIR) is emerging as a key indicator for accurate assessment of glycaemic control. However, there is few report focusing on the correlation of TIR with albumuria and renal fuction. The aim of this work was to investigate whether TIR, as well as nocturnal TIR and hypoglycaemic events is related to the presence and severity of albuminuria and decrease of eGFR in type 2 diabetes.

RESEARCH DESIGN AND METHODS

A total of 823 patients were enrolled in this study. All patients received continuous glucose monitoring, TIR indicating the percentage of time that blood glucose was in the range of 3.9-10.0 mmol/L. The Spearman analysis was applied to analyze the relationship between TIR (or nocturnal TIR) and ACR. Logistic regression was used to explore whether TIR (or nocturnal TIR) is an independent risk factor for albuminuria.

RESULTS

The prevalence of albuminuria decreased with increasing TIR quartiles. Binary logistic regression revealed that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria. Multiple regression analysis found that only nocturnal TIR was obviously related to the severity of albuminuria. In our study, eGFR was significantly associated with the number of hypoglycemic events.

CONCLUSIONS

In T2DM patients, TIR and nocturnal TIR is associated with the presence of albuminuria independent of HbA1c and GV metrics. Nocturnal TIR shows better correlation than TIR. The role of TIR especially nocturnal TIR in the evaluation of diabetes kidney disease should be emphasized.