Differences for Percentage Times in Glycemic Range Between Continuous Glucose Monitoring and Capillary Blood Glucose Monitoring in Adults with Type 1 Diabetes: Analysis of the REPLACE-BG Dataset

Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes

Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).

Assessing the feasibility of time in tight range (TITR) targets with advanced hybrid closed loop (AHCL) use in children and adolescents: A single-centre real-world study

AIMS

Time in Tight Range (TITR) is a novel glycaemic metric in monitoring type 1 diabetes (T1D) management. The aim of this study was to assess the attainability of the TITR target in children and adolescents using the advanced hybrid closed loop (AHCL).

METHODS

The 2128-day CGM data from 56 children and adolescents with T1D using AHCL (Minimed-780G) were analysed. Time in Range (TIR) (3.9-10 mmol/L), TITR (3.9-7.7 mmol/L), and other glycaemic parameters were separately analysed in terms of whole day, daytime (06.00-23:59), and nighttime (00.00-05.59) results. The participants were divided into two groups by autocorrection rate where Group 1 had a rate of <30% and Group 2 had a rate of ≥30.

RESULTS

All glycaemic parameters indicated a better glycaemic outcome in the nighttime with higher TIR and TITR values compared with daytime (for TIR 87.5 ± 9.5% vs. 78.8 ± 8%, p < 0.001, and TITR 68.2 ± 13.5% vs. 57.5 ± 8.8%, p < 0.001). The rates of TITR >50% and >60% were 87% and 52%, respectively. When those with TITR >60% (n: 29) and those without (n: 27) were evaluated in terms of hypoglycaemia, no statistically significant difference was found in time below range (TBR) 3-3.9 mmol/L (0.3% vs. 2.1%, p: 0.084) and TBR < 3 mmol/L (0.47% vs. 0.3%, p: 0.298). Group 1 had a significantly higher TIR and TITR compared to Group 2 (82.6 ± 6.1% vs. 75.6 ± 8.6%, p: 0.008 and 62.1 ± 7.5% vs. 53.8 ± 7.5%, p: 0.002, respectively).

CONCLUSIONS

Most children and adolescents on AHCL achieved the 50% target for TITR whereas more than half achieved the >60% target. A target of >50% for TITR seems realistic in children with T1D using AHCL.

Determination of Time in Range Associated With HbA1c ≤7% in a Prospective Cohort of Patients With Type 1 Diabetes Using Continuous Glucose Monitoring for Three Months

INTRODUCTION

Finding a goal of time in range (%TIR) that defines good glycemic control is necessary. Previous retrospective studies suggest good concordance between HbA1c ≤7% with a TIR >70%; however, the studies that included the largest number of patients used blood glucose measurement data with a follow-up time of less than 90 days. This study defined the TIR value that best discriminates HbA1c ≤7%.

METHODS

We performed a prospective study of diagnostic tests based on a cohort of patients with type 1 diabetes (T1D) treated with a hybrid closed loop (HCL) followed for three months. The ability of %TIR to distinguish patients with HbA1c ≤7% was evaluated through receiver operating characteristic curve analysis. We determined the %TIR cutoff point with the best operating characteristics.

RESULTS

A total of 118 patients were included (58.1% women, 47% overweight or obese, and 33% with high glycemic variability). A moderate negative correlation (R = -.54, P < .001) was found between %TIR and HbA1c. The discrimination ability was moderate, with an area under the curve of 0.7485 (95% confidence interval = 0.6608-0.8363). The cutoff point that best predicted HbA1c ≤7% was %TIR ≥75.5 (sensitivity 70%, specificity 67%). The findings were similar among those with a coefficient of variation (CV%) ≥36%.

CONCLUSIONS

Our data suggest that the %TIR adequately identifies patients with HbA1c ≤7%. A target of TIR ≥75%, rather than the currently recommended TIR ≥70%, may be a more suitable value for optimal glycemic control.

6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality

Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric that gives greater weight to hypoglycemia than to hyperglycemia and to extreme hypo/hyperglycemia over less extreme hypo/hyperglycemia. This study aimed at validating the effectiveness of GRI and at comparing it with time in range (TIR) in assessing glycemic quality in clinical practice. Methods: A total of 524 ninety-day CGM tracings of 194 insulin-treated adults with diabetes were included in the analysis. GRI was assessed according to standard metrics in ambulatory glucose profiles. Both cross-sectional and longitudinal analyses were performed to compare the GRI and TIR. Results: The GRI was strongly correlated not only with TIR (r = -0.974), but also with the coefficient of variation (r = 0.683). To identify whether the GRI differed by hypoglycemia even with a similar TIR, CGM tracings were grouped according to TIR (50% to <60%, 60% to <70%, 70% to <80%, and ≥80%). In each TIR group, the GRI increased as time below range (TBR)<70 mg/dL increased (P < 0.001 for all TIR groups). In longitudinal analysis, as TBR<70 mg/dL improved, the GRI improved significantly (P = 0.003) whereas TIR did not (P = 0.704). Both GRI and TIR improved as time above range (TAR)>180 mg/dL improved (P < 0.001 for both). The longitudinal change was easily identifiable on a GRI grid. Conclusions: The GRI is a useful tool for assessing glycemic quality in clinical practice and reflects hypoglycemia better than does TIR.

Real-World User and Clinician Perspective and Experience with MiniMed™ 780G Advanced Hybrid Closed Loop System

INTRODUCTION

The advanced hybrid closed loop (AHCL) MiniMed™ 780G system changes basal insulin delivery every 5 min and auto bolus in response to sensor glucose values. We assessed the performance of the AHCL system in real-world settings for individuals with type 1 diabetes (T1DM) as well as user and clinician perspectives and satisfaction.

METHODS

We held two peer group discussions: one having adults with T1DM/parents of children and adolescents with T1DM to understand their experiences with the AHCL system and another with healthcare providers (HCPs). Responses from the discussions were analyzed and categorized into themes by two independent researchers, with any inconsistencies resolved by consensus. We also analyzed data from the system uploaded to CareLink personal software. Glycemic outcomes, including time in range (TIR), time below range (TBR), time above range (TAR), mean sensor glucose (SG) levels, glucose management indicator (GMI), sensor use, and percentage of time spent in AHCL, were determined.

RESULTS

The peer group discussions revealed numerous key themes and issues for each group, such as the significance of setting reasonable expectations, carbohydrate counting and bolus dosing, technical difficulties, and overall user experience. The users (n = 25; T1DM; 17 female; age 13.8 ± 7.49 years; A1C 6.54 ± 0.45%; duration of diabetes 6 ± 6.78 years) were very satisfied with the system. Most users experienced consistent blood glucose values with very few hypoglycemic episodes. However, there were a few limitations reported, such as hyperglycemic episodes caused by inaccuracies in carb counting, issues with sensor connectivity, and cannula blockages or kinking for those using insulin Fiasp. Users achieved a mean GMI of 6.4 ± 0.26%, TIR of 83.0 ± 8.12%, TBR (54-70 mg/dL) of 2.0 ± 0.81%, TBR* (< 54 mg/dL) of 0%. All of the users achieved a TIR of > 70%.

CONCLUSION

The use of the AHCL system in T1DM resulted in robust glycemic control, minimizing hypoglycemia. Providing training to both users and HCPs can help them use the system effectively.

Analysis of real-world capillary blood glucose data to help reduce HbA1c and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation

AIMS

To examine real-world capillary blood glucose (CBG) data according to HbA_1c to define proportions of CBG readings at different HbA_1c levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management.

METHODS

A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA_1c . The proportions of results in different CBG ranges and associations with HbA_1c were evaluated. Patterns in readings following episodes of hyperglycaemia and hypoglycaemia were examined, using glucose to next glucose reading table (G2G).

RESULTS

CBG readings in the target range (3.9-10 mmol/L) increase by ~10% across each CBG quartile (31% in the highest versus 63% in the lowest quartile, p < 0.05). The novel G2G table helps the treatment-based interpretation of data. Hypoglycaemia is often preceded by hyperglycaemia, and vice-versa, and is twice as likely in the highest HbA_1c quartile. Re-testing within 30 min of hypoglycaemia is associated with less hypoglycaemia, 1.6% versus 7.2%, p < 0.001, and also reduces subsequent hyperglycaemia and further hypoglycaemia in the proceeding 24 h. The coefficient of variation, but not standard deviation, is highly associated with hypoglycaemia, r = 0.71, and a CV ≤ 36% equates to 3.3% of CBG readings in the hypoglycaemic range.

CONCLUSIONS

HbA_1c <58 mmol/mol (7.5%) is achievable even when only ~60% of CBG readings are between 3.9-10 mmol/L. Examining readings subsequent to out-of-range readings suggests useful behaviours which people with type 1 diabetes could be supported to adhere to, both in a clinic and structured education programmes, thereby decreasing the risk of hypoglycaemia whilst also reducing hyperglycaemia and improving HbA_1c .

6. Glycemic Targets: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Association of Time in Range with Endothelial Injury in Patients with Type 2 Diabetes Treated with Exenatide

INTRODUCTION

We aimed to investigate whether treatment with exenatide could increase time in range (TIR) and decrease glycemic variability, and to evaluate the association between TIR and endothelial injury in patients with type 2 diabetes mellitus (T2DM).

METHODS

Two-hundred patients with T2DM treated with exenatide for 16 weeks were included in this study. Seven-point fingerstick blood glucose was used to evaluate derived TIR and glycemic variability. The serum levels of soluble endothelial cell protein C receptor (sEPCR) and von Willebrand factor (vWF) were measured. Ninety-three patients having the data of endothelial injury markers were categorized as derived TIR > 70% or ≤ 70% after the treatment and the association between TIR and endothelial injury were evaluated.

RESULTS

Treatment with exenatide for 16 weeks resulted in a significant reduction in fasting blood glucose, postprandial 2 h blood glucose, and glycated hemoglobin A1c (HbA1c) levels in patients with T2DM. Compared with baseline, derived TIR value was significantly increased [85.7 (57.1, 100.0) % vs. 42.9 (14.9, 71.4) %, P < 0.001], and the parameters of glycemic variability were remarkably decreased after the treatment. After the treatment, serum sEPCR level was significantly decreased from baseline in patients with TIR > 70% [74.5 (32.8, 122.5) ng/mL vs. 96.9 (48.5, 150.9) ng/mL, P = 0.006] but not in those with TIR ≤ 70%; serum vWF level was remarkably decreased in patients with TIR > 70% [from 1166.2 (848.1, 1335.5) mIU/mL to 907.4 (674.3, 1335.1) mIU/mL, P = 0.001] while this effect was modest in those with TIR ≤ 70%.

CONCLUSIONS

Treatment with exenatide increases TIR and decreases glycemic variability in patients with T2DM. Moreover, the amelioration of endothelial injury is more pronounced in patients with TIR > 70% after the treatment.

TRIAL REGISTRATION

ChiCTR-IPR-15006558 (registered, 27 May 2015).

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.

6. Glycemic Targets: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Optimizing type 1 diabetes after multiple daily injections and capillary blood monitoring: Pump or sensor first? A meta-analysis using pooled differences in outcome measures

AIMS

Most people living with type 1 diabetes self-manage using multiple daily injection (MDI) insulin regimens and self-monitoring of blood glucose (SMBG). Continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) are adjuncts to education and support self-management optimization. The aim of this systematic review and meta-analysis was to assess which first-line technology is most effective.

METHODS

Electronic databases (MEDLINE, EMBASE and WEB OF SCIENCE) were systematically searched from 1999 to September 2020. Randomized controlled trials comparing either CSII with MDI or CGM with SMBG in adults with type 1 diabetes were included. Data were extracted in duplicate by two reviewers, and were analysed to assess individual and overall treatment effect measures (PROSPERO registration: CRD42020149915).

RESULTS

Glycated haemoglobin was significantly reduced for CGM when compared with SMBG [Cohen's d - 0.62 (95% CI -0.79 to -0.45)] and for CSII when compared with MDI [Cohen's d - 0.44 (95% CI -0.67 to -0.22)]. Rates of severe hypoglycaemia were significantly reduced with CGM compared with SMBG, but did not change for CSII when compared with MDI. Episodes of diabetic ketoacidosis were more likely to occur with CSII than MDI. Both CSII and CGM reduced glucose standard deviation, compared with MDI and SMBG respectively.

CONCLUSIONS

Both CGM and CSII remain impactful interventions compared with SMBG and MDI but in adults with type 1 diabetes and in the contexts in which they have been studied, CGM might have a greater positive impact on glycaemic variability and severe hypoglycaemia than CSII, when added to MDI and SMBG. A head-to-head study, including patient reported outcomes, is required to explore these findings further.

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

BACKGROUND

International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system.

METHOD

A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics.

RESULTS

Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%).

CONCLUSION

Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Fasting and postprandial plasma glucose contribution to glycated haemoglobin and time in range in people with type 2 diabetes on basal and bolus insulin therapy: Results from a pooled analysis of insulin lispro clinical trials

AIMS

To investigate the interrelations between glycaemic metrics of fasting plasma glucose (FPG), postprandial glucose (PPG), glycated haemoglobin (HbA1c), and percentage of time in target range 3.9 to 10.0 mmol/L (%TIR) in patients on insulin therapy.

MATERIALS AND METHODS

A pooled analysis was conducted using datasets extracted from an integrated database of insulin lispro clinical trials (Eli Lilly and Company). Studies in patients with type 2 diabetes on basal-bolus or basal-plus insulin therapy, and with ≥7-point self-monitored blood glucose profiles were included in the analysis. A multivariate regression model was used to quantify the contribution of FPG and PPG change to the change in HbA1c and %TIR. In addition, a linear regression model was used to describe the relationship between %TIR and HbA1c.

RESULTS

Five studies encompassing 1572 patients met the criteria for inclusion. On average, a 1-mmol/L change in FPG was associated with 2.7 mmol/mol (0.25%) change in HbA1c (range 2.0 to 2.8 mmol/mol [0.18%-0.26%]; all P <0.0001), and a 1-mmol/L change in PPG with 1.8 mmol/mol (0.16%) change in HbA1c (range 1.2 to 2.1 mmol/mol [0.11%-0.19%]; all P <0.01). Furthermore, a 1-mmol/L reduction in FPG and PPG was associated with an increase in TIR of 6.5% (range 5.8%-9.2%) and 5.3% (range 4.1%-8.7%), respectively, all P <0.0001. A decrease in HbA1c of 10.9 mmol/mol (1%) corresponded with an increase in TIR of 8.3%, on average.

CONCLUSIONS

In patients with type 2 diabetes on basal-bolus or basal-plus insulin therapy, management of both FPG and PPG is important for achievement of HbA1c and TIR goals.

Time in range: A best practice guide for UK diabetes healthcare professionals in the context of the COVID-19 global pandemic

The emergence of continuous glucose monitoring has driven improvements in glycaemic control and quality of life for people with diabetes. Recent changes in access to continuous glucose monitoring systems within UK health services have increased the number of people able to benefit from these technologies. The COVID-19 pandemic has created an opportunity for diabetes healthcare professionals to use continuous glucose monitoring technology to remotely deliver diabetes services to support people with diabetes. This opportunity can be maximized with improved application and interpretation of continuous glucose monitoring-generated data. Amongst the diverse measures of glycaemic control, time in range is considered to be of high value in routine clinical care because it is actionable and is visibly responsive to changes in diabetes management. Importantly, it is also been linked to the risk of developing complications associated with diabetes and can be understood by people with diabetes and healthcare professionals alike. The 2019 International Consensus on Time in Range has established a series of target glucose ranges and recommendations for time spent within these ranges that is consistent with optimal glycaemic control. The recommendations cover people with type 1 or type 2 diabetes, with separate targets indicated for elderly people or those at higher risk from hypoglycaemia, as well as for women with type 1 diabetes during pregnancy. The aim of this best practice guide was to clarify the intent and purpose of these international consensus recommendations and to provide practical insights into their implementation in UK diabetes care.

6. Glycemic Targets: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes

BACKGROUND AND AIMS

Self-monitoring blood glucose (SMBG) remains a widespread tool to monitor blood glucose. The development of diabetes management systems (DMS) allows SMBG to provide additional information as time spent in target range (TIR). This study evaluates the association between HbA1c and TIR, evaluated through DMS, over 2 months, and 2 weeks.

METHODS AND RESULTS

Type 1 (T1D) and Type 2 (T2D) insulin-treated patients with diabetes were enrolled. We used the term PIR (Points in Range) instead of TIR, since SMBG provides point-in-time glucose values rather than a continuous trend over time. PIR was calculated in 2-month and 2-week time ranges before available HbA1c measurement. One-hundred ninety-seven patients with T1D and 36 with T2D were recruited. HbA1c and PIR were inversely associated (2 months: R -0.72, 2 weeks R -0.70; p < 0.0001) in all subjects. The relationship did not change when T1D and T2D patients were analyzed separately. For every 10% change of PIR, there was a change of HbA1c by 0.4%.

CONCLUSIONS

Our study, for the first time, demonstrates a significant correlation between HbA1c and PIR calculated by DMS. DMS offers additional information useful in disease management of patients with T1D and T2D performing SMBG.

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.