Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution

Transitioning from Self-Monitoring of Blood Glucose to Continuous Glucose Monitoring in Combination with a mHealth App Improves Glycemic Control in People with Type 1 and Type 2 Diabetes

Introduction: Integrating mobile health (mHealth) apps into daily diabetes management allows users to monitor and track their health data, creating a comprehensive system for managing daily diabetes activities and generating valuable real-world data. This analysis investigates the impact of transitioning from traditional self-monitoring of blood glucose (SMBG) to real-time continuous glucose monitoring (rtCGM), alongside the use of a mHealth app, on users' glycemic control. Methods: Data were collected from 1271 diabetes type 1 and type 2 users of the mySugr® app who made a minimum of 50 SMBG logs 1 month before transitioning to rtCGM and then used rtCGM for at least 6 months. The mean and coefficient of variation of glucose, along with the proportions of glycemic measurements in and out of range, were compared between baseline and 1, 2, 3, and 6 months of rtCGM use. A mixed-effects linear regression model was built to quantify the specific effects of transitioning to a rtCGM sensor in different subsamples. A novel validation analysis ensured that the aggregated metrics from SMBG and rtCGM were comparable. Results: Transitioning to a rtCGM sensor significantly improved glycemic control in the entire cohort, particularly among new users of the mySugr app. Additionally, the sustainability of the change in glucose in the entire cohort was confirmed throughout the observation period. People with type 1 and type 2 diabetes exhibited distinct variations, with type 1 experiencing a greater reduction in glycemic variance, while type 2 displayed a relatively larger decrease in monthly averages.

How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions

Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.

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.

Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index

BACKGROUND

Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited.

METHODS

A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]).

RESULTS

GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference -22.8 [-27.2, -18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = -.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both).

CONCLUSIONS

Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.

Intra-pancreatic fat is associated with continuous glucose monitoring metrics

AIM

To investigate the relationship of fat in the pancreas with time spent in different glycaemic ranges.

METHODS

Abdominal magnetic resonance imaging at 3.0 Tesla was used to quantify fat in the pancreas as both continuous [i.e. intra-pancreatic fat deposition (IPFD)] and binary (i.e. fatty change of the pancreas vs. normal pancreas) variables. Dexcom G6 devices were used to collect continuous glucose monitoring data every 5 min over a continuous 7-day period. Time above range (TAR), time in range (TIR) and time below range were computed. Statistical models were built to adjust for age, sex, body composition, and other covariates in linear regression analysis and analysis of covariance.

RESULTS

In total, 38 individuals were studied. IPFD was significantly associated with TAR (p = .036) and TIR (p = .042) after adjustment for covariates. For every 1% increase in IPFD, there was a 0.3 unit increase in TAR and a decrease in TIR. Individuals with fatty change of the pancreas, when compared with those with normal pancreas, had significantly higher TAR (p = .034) and lower TIR (p = .047) after adjustment for covariates. Neither IPFD (p = .805) nor fatty change of the pancreas (p = .555) was associated with time below range after adjustment for covariates.

CONCLUSION

Increased fat in the pancreas is associated with excessive glycaemic variability. Fatty change of the pancreas may contribute to heightening the risk of cardiovascular diseases.

Optimal Coefficient of Variance Threshold to Minimize Hypoglycemia Risk in Individuals with Well-Controlled Type 1 Diabetes Mellitus

BACKGRUOUND

This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target.

METHODS

Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0.

RESULTS

Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≥1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≤135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≤135 mg/dL. In profiles with MG ≤135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/ dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1.

CONCLUSION

In well-controlled T1DM with MG ≤135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia.

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.

Risk factors and the CCTA application in patients with vulnerable coronary plaque in type 2 diabetes: a retrospective study

BACKGROUND

Diabetes is an independent risk factor for cardiovascular disease. The purpose of this study was to identify the risk factors for vulnerable coronary plaques (VCPs), which are associated with adverse cardiovascular events, and to determine the value of coronary CT angiography (CCTA) in patients with type 2 diabetes mellitus (T2DM) and VCPs.

METHODS

Ninety-eight T2DM patients who underwent CCTA and intravascular ultrasound (IVUS) were retrospectively included and analyzed. The patients were grouped and analyzed according to the presence or absence of VCPs.

RESULTS

Among the patients with T2DM, time in range [TIR {the percentage of time blood glucose levels were in the target range}] (OR = 0.93, 95% CI = 0.89-0.96; P < 0.001) and the high-density lipoprotein-cholesterol (HDL-C) concentration (OR = 0.24, 95% CI = 0.09-0.63; P = 0.04) were correlated with a lower risk of VCP, but the triglycerides (TG) concentration was correlated with a higher risk of VCP (OR = 1.79, 95% CI = 1.01-3.18; P = 0.045). The area under the receiver operator characteristic curve (AUC) of TIR, and HDL-C and TG concentrations were 0.76, 0.73, and 0.65, respectively. The combined predicted AUC of TIR, and HDL-C and TG concentrations was 0.83 (P < 0.05). The CCTA sensitivity, specificity, false-negative, and false-positive values for the diagnosis of VCP were 95.74%, 94.12%, 4.26%, and 5.88%, respectively. The identification of VCP by CCTA was positively correlated with IVUS (intraclass correlation coefficient [ICC] = 0.90).

CONCLUSIONS

The TIR and HDL-C concentration are related with lower risk of VCP and the TG concentration was related with higher risk of VCP in patients with T2DM. In clinical practice, TIR, HDL-C and TG need special attention in patients with T2DM. The ability of CCTA to identify VCP is highly related to IVUS findings.

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

OBJECTIVE

To evaluate the impact of glucose variability on the relationship between the GRI and other glycemic metrics in a cohort of pediatric and adult patients with type 1 diabetes (T1D) using intermittent scanning continuous glucose monitoring (isCGM).

METHODS

We performed a cross-sectional study of 202 patients with T1D under intensive insulin treatment (25.2% CSII) using isCGM. Clinical, metabolic, and glycemic metrics were collected, and the GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. The correlation between the GRI and other classical glycometrics in relation to the coefficient of variation (CV) was evaluated.

RESULTS

A total of 202 patients were included (53% male; 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution (TIR 59.0 ± 17.0%; CV 39.8 ± 8.0%; GMI 7.3 ± 1.1%). The mean GRI was 54.0 ± 23.3 with a CHypo and CHyper component of 5.7 ± 4.8 and 23.4 ± 14.3, respectively. A strong negative correlation was observed between the GRI and TIR (R = -0.917; R2 = 0.840; p < 0.001), showing differences when dividing patients with low glycemic variability (CV < 36%) (R = -0.974; R2 = 0.948; p < 0.001) compared to those with greater CV instability (≥36%) (R = -0.885; R2 = 0.784; p < 0.001). The relationship of GRI with its two components was strongly positive with CHyper (R = 0.801; R2 = 0.641; p < 0.001) and moderately positive with CHypo (R = 0.398; R2 = 0.158; p < 0.001). When the GRI was evaluated with the rest of the classic glycemic metrics, a strong positive correlation was observed with HbA1c (R = 0.617; R2 = 0.380; p < 0.001), mean glucose (R = 0.677; R2 = 0.458; p < 0.001), glucose standard deviation (R = 0.778; R2 = 0.605; p < 0.001), TAR > 250 (R = 0.801; R2 = 0.641; p < 0.001), and TBR < 54 (R = 0.481; R2 = 0.231; p < 0.001).

CONCLUSIONS

The GRI correlated significantly with all the glycemic metrics analyzed, especially with the TIR. Glycemic variability (GV) significantly affected the correlation of the GRI with other parameters and should be taken into consideration.

Association of cumulative blood glucose load with cardiovascular risk and all-cause mortality

BACKGROUND

Incorporation both the magnitude and duration of exposure to elevated fasting blood glucose (FBG) into a single risk parameter (cumulative FBG load) for future diseases is intuitively appealing, although a data-based demonstration of the utility of this metric is not available. This study aimed to investigate the associations with cumulative FBG load with the risk of cardiovascular diseases (CVD) and all-cause mortality in the general population.

METHODS

This prospective cohort study included 41,728 participants who were free of CVD and underwent four health examinations from 2006 to 2012. Cumulative FBG load during 2006-2012 was calculated as the area under curve for FBG values ≥ 5.6 mmol/L divided by the total area curve. We also compared the predicting value cumulative FBG load with other FBG metrics.

RESULTS

During a median follow-up of 6.75 years, we identified 2323 cases of CVD and 1724 cases of all-cause mortality. Per 1-standard deviation increase in cumulative FBG load was associated with a 16 % higher risk of CVD (hazard ratio [HR]: 1.16; 95 % confidence interval [CI], 1.13-1.20) and 20 % higher risk of all-cause mortality (HR, 1.20; 95 % CI, 1.16-1.25). For the prediction of cardiovascular outcomes and all-cause mortality, cumulative FBG load outperformed FBG time-in-target, visit-to-visit FBG variability, and mean FBG in terms of C-statistics and reclassification indexes.

CONCLUSIONS

Cumulative FBG load may provide a better prediction of cardiovascular outcomes compared with other FBG metrics in the general population. These findings emphasized the important role of cumulative FBG load in assessing cardiovascular and mortality risk.

A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas

BACKGROUND AND OBJECTIVES

Complete identification of the glucose dynamics for a patient generally requires prior clinical procedures and several measurements for the patient. However, these steps may not be always feasible. To address this limitation, we propose a practical approach integrating learning-based model predictive control (MPC), adaptive basal and bolus injections, and suspension with minimal requirements of prior knowledge of the patient.

METHODS

The glucose dynamic system matrices were periodically updated using only input values, without any pretrained models. The optimal insulin dose was calculated based on a learning-based MPC algorithm. Meal detection and estimation modules were also introduced. The basal and bolus insulin injections were fine-tuned using the performance of glucose control from the previous day. To validate the proposed method, evaluations with 20 virtual patients from a type 1 diabetes metabolic simulator were employed.

RESULTS

Time-in-range (TIR) and time-below-range (TBR) were 90.8% (84.1% - 95.6%) and 0.3% (0% - 0.8%), as represented by the median, first (Q1), and third quartiles (Q3), respectively, when meal intakes were fully announced. When one out of three meal intake announcements was missing, TIR and TBR were 85.2% (75.0% - 88.9%) and 0.9% (0.4% - 1.1%), respectively.

CONCLUSIONS

The proposed approach obviates the need for prior tests from patients and shows effective regulation of blood glucose levels. From the perspective of practical implementation in clinical environments, to deal with minimal prior information of the patient, our study demonstrates how essential clinical knowledge and learning-based modules can be integrated into a control framework for an artificial pancreas.

Research progress in the application of time in range: more than a percentage

ABSTRACT

Glucose monitoring is an important part of medical care in diabetes mellitus, which not only helps assess glycemic control and treatment safety, but also assists with treatment adjustment. With the development of continuous glucose monitoring (CGM), the use of CGM has increased rapidly. With the wealth of glucose data produced by CGM, new metrics are greatly needed to optimally evaluate glucose status and guide the treatment. One of the parameters that CGM provides, time in range (TIR), has been recognized as a key metric by the international consensus. Before the adoption of TIR in clinical practice, several issues including the minimum length of CGM use, the setting of the target range, and individualized TIR goals are summarized. Additionally, we discussed the mounting evidence supporting the association between TIR and diabetes-related outcomes. As a novel glucose metric, it is of interest to compare TIR with other conventional glucose markers such as glycated hemoglobin A1c. It is anticipated that the use of TIR may provide further information on the quality of glucose control and lead to improved diabetes management.

Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy

AIM

To evaluate continuous glucose monitoring (CGM) metrics for use as alternatives to glycated haemoglobin (HbA1c) to evaluate therapeutic efficacy.

METHODS

We re-analysed correlations among CGM metrics from studies involving 545 people with type 1 diabetes (T1D), 5910 people with type 2 diabetes (T2D) and 98 people with T1D during pregnancy and the postpartum period.

RESULTS

Three CGM metrics, interstitial fluid Mean Glucose level, proportion of time above range (%TAR) and proportion of time in range (%TIR), were correlated with HbA1c and provided metrics that can be used to evaluate therapeutic efficacy. Mean Glucose showed the highest correlation with %TAR (r = 0.98 in T1D, 0.97 in T2D) but weaker correlations with %TIR (r = -0.92 in T1D, -0.83 in T2D) or with HbA1c (r = 0.78 in T1D). %TAR and %TIR were highly correlated (r = -0.96 in T1D, -0.91 in T2D). After 6 months of use of real-time CGM by people with T1D, changes in Mean Glucose level were more highly correlated with changes in %TAR (r = 0.95) than with changes in %TIR (r = -0.85) or with changes in HbA1c level (r = 0.52). These metrics can be combined with metrics of hypoglycaemia and/or glycaemic variability to provide a more comprehensive assessment of overall quality of glycaemic control.

CONCLUSION

The CGM metrics %TAR and %TIR show much higher correlations with Mean Glucose than with HbA1c and provide sensitive indicators of efficacy. Mean glucose may be the best metric and shows consistently higher correlations with %TAR than with %TIR.

An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes

Aim: To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70-180 mg/dL, time below range (TBR), <70 mg/dL, time above range (TAR), >180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population. Methods: We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (n = 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID). Results: Correlations between baseline TIR and TAR were strong (r = -0.966; P < 0.0001), weak for TBR (r = 0.363; P < 0.0001), and glucose CV (r = 0.037; P = 0.687) while moderate between CV and TBR (r = 0.726; P < 0.0001). Associations were similar for participants aged >60 years (n = 15) versus younger subjects. Correlations of changes in (Δ) TIR with ΔTAR over 26 weeks were strong (r = -0.945; P < 0.001) and correlations for ΔTBR were weak (r = 0.025; P = 0.802). ΔCV did not significantly correlate with ΔTAR (r = -0.064; P = 0.526) but did with ΔTBR (r = 0.770; P = <0.001). Conclusions: Changes in TIR are not associated with changes in TBR. Thus, we recommend that for older AID users whilst TBR targets should be prioritized to reduce hypoglycemia-related risk, TBR should be addressed independently of TIR. Clinical Trial Registratrion number: (ACTRN12617000520336).

Making sense of glucose sensors in end-stage kidney disease: A review

Diabetes mellitus remains the leading cause of end-stage kidney disease worldwide. Inadequate glucose monitoring has been identified as one of the gaps in care for hemodialysis patients with diabetes, and lack of reliable methods to assess glycemia has contributed to uncertainty regarding the benefit of glycemic control in these individuals. Hemoglobin A1c, the standard metric to evaluate glycemic control, is inaccurate in patients with kidney failure, and does not capture the full range of glucose values for patients with diabetes. Recent advances in continuous glucose monitoring have established this technology as the new gold standard for glucose management in diabetes. Glucose fluctuations are uniquely challenging in patients dependent on intermittent hemodialysis, and lead to clinically significant glycemic variability. This review evaluates continuous glucose monitoring technology, its validity in the setting of kidney failure, and interpretation of glucose monitoring results for the nephrologist. Continuous glucose monitoring targets for patients on dialysis have yet to be established. While continuous glucose monitoring provides a more complete picture of the glycemic profile than hemoglobin A1c and can mitigate high-risk hypoglycemia and hyperglycemia in the context of the hemodialysis procedure itself, whether the technology can improve clinical outcomes merits further investigation.

Flash Glucose Monitoring System facilitates sustainable improvements in glycemic control in patients with type 1 diabetes: A 12-month follow-up study in real life

BACKGROUND AND AIMS

Examine the glycemic control on Type 1 Diabetes (T1D) wearing the Flash Glucose Monitoring (FGM) system for a one-year period of time.

METHODS

This prospective study done using 187 patients with T1D (14-40yrs) who self-tested their glucose levels by FGM. Continuous glucose monitoring (CGM) metrics were gathered i.e., Glucose Variability (GV) (%), mean Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR), and average duration of hypoglycemic events at the 3, 6, and 12 month time periods.

RESULTS

At 6th, 9th and 12th months, for values of GV, % in target, TAR and %>250 mg/dL, no significant changes (p > 0.05) were noted compared to 3 months. However, significant changes from the baseline were evident for the values of the mean glucose level at the 3rd (p = 0.028), 9th (p = 0.048) and 12th months (p = 0.022). When the mean glucose value at 3 months was compared to the same at 6, 9, and 12 month period, no significant changes (p > 0.05) were seen. When compared with baseline values, low glucose events at 3 months (p = 0.028), 6 months (p = 0.048), 9 months (p = 0.022) and 12 months (p = 0.038) showed significant changes. However, the percentage below 70 mg/dL (barring the value at 12 months, p = 0.046), no significant changes were observed. The HbA1c revealed significant drop in 3, 6, 9 and 12 months compared to baseline values.

CONCLUSION

Significant improvement was noted in CGM metrics when patients switched from conventional finger pricking method over to FGM system, and the effect was observed during the entire study period.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control.

METHODS

This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured.

RESULTS

At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters.

CONCLUSIONS

DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

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

AIMS

We aimed to conduct a systematic review and meta-analysis of randomised controlled clinical trials (RCTs) assessing separately and together the effect of the three distinct categories of continuous glucose monitoring (CGM) systems (adjunctive, non-adjunctive and intermittently-scanned CGM [isCGM]), compared with traditional capillary glucose monitoring, on HbA1c and CGM metrics.

METHODS

PubMed, Web of Science, Scopus and Cochrane Central register of clinical trials were searched. Inclusion criteria were as follows: randomised controlled trials; participants with type 1 diabetes of any age and insulin regimen; investigating CGM and isCGM compared with traditional capillary glucose monitoring; and reporting glycaemic outcomes of HbA1c and/or time-in-range (TIR). Glycaemic outcomes were extracted post-intervention and expressed as mean differences and 95%CIs between treatment and comparator groups. Results were pooled using a random-effects meta-analysis. Risk of bias was assessed using the Cochrane Rob2 tool.

RESULTS

This systematic review was conducted between January and April 2021; it included 22 RCTs (15 adjunctive, 5 non-adjunctive, and 2 isCGM)). The overall analysis of the pooled three categories showed a statistically significant absolute improvement in HbA1c percentage points (mean difference (95% CI): -0.22% [-0.31 to -0.14], I²  = 79%) for intervention compared with comparator and was strongest for adjunctive CGM (-0.26% [-0.36, -0.16]). Overall TIR (absolute change) increased by 5.4% (3.5 to 7.2), I²  = 71% for CGM intervention compared with comparator and was strongest with non-adjunctive CGM (6.0% [2.3, 9.7]).

CONCLUSIONS

For individuals with T1D, use of CGM was beneficial for impacting glycaemic outcomes including HbA1c, TIR and time-below-range (TBR). Glycaemic improvement appeared greater for TIR for newer non-adjunctive CGM technology.

Expertenaustausch zum Einsatz von kontinuierlichem Glukosemonitoring (CGM) im Diabetesmanagement: Eine aktuelle Bestandsaufnahme und Blick in die Zukunft

CGM mit Darstellung der aktuellen Glukosewerte (rtCGM) ist aktuell einer der wichtigsten diagnostischen Optionen in der Diabetologie. Es ermöglicht eine umfangreiche und unmittelbare Unterstützung und Erleichterung des Diabetesmanagements, besonders wenn eine Insulintherapie angewendet wird. Weiterhin stellt rtCGM den notwendigen Systempartner für die Steuerung der automatisierten Insulinabgabe in AID-Systemen dar. In Verbindung mit Smart-Pens unterstützt ein rtCGM die korrekte Durchführung des Insulinmanagements und erinnert an Bolusinjektionen.

RtCGM-Daten sind heute das Fundament des personalisierten Datenmanagements und Alltagscoachings und stellen die Basis der Digitalisierung und telemedizinischen Intervention dar. Die Möglichkeit der interoperablen Nutzung ist aus therapeutischer Sicht eine zentrale Eigenschaft eines rtCGMs und kann zur Erweiterung der Indikationen, unabhängig von Diabetestyp oder Therapieform führen. Dies könnte auch den vorübergehenden oder intermittierenden Einsatz bei Menschen mit Typ-2-Diabetes ohne Insulinbehandlung betreffen. Kürzlich veröffentlichte internationale Leitlinien, z.B. der Amerikanischen Gesellschaft für klinische Endokrinologie (AACE) fordern auf der Basis umfangreicher Evidenz, dass die Glukosemessung mit einem rtCGM für alle Menschen mit Diabetes nutzbar und verfügbar sein sollte. Bereits in der Phase gestörter Glukosetoleranz kann ein rtCGM-System als Alltagscoaching oder Biofeedback bei Einbettung in ein Gesamtbehandlungskonzept unterstützen, mit dem Ziel aktiver und fundierter Handlungen des Anwenders im Diabetesalltag.

Die Vielfalt der Nutzungsoptionen und die immer schnelleren technischen Innovationszyklen von rtCGM-Systemen wurden mit Blick auf aktuelle Anforderungen und die notwendigen Strukturanpassungen des Gesundheitssystems von einer rtCGM-erfahrenen Expertengruppe diskutiert. Ziel war es, konkrete Lücken in der Versorgungsstruktur sowie potenzielle Handlungsfelder in der Diabetologie zu identifizierten und mögliche Indikationserweiterungen für den Einsatz von rtCGM darzustellen. Dieses, sowie die Erkenntnisse und Schlussfolgerungen der Diskussionen werden in diesem Artikel dargestellt.

Time in Range Assessed by Capillary Blood Glucose in Relation to Insulin Sensitivity and β-Cell Function in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in China

AIMS

This study investigated the association of capillary blood glucose (CBG)-assessed time in range (TIR) (3.9-10.0 mmol/L) with insulin sensitivity and islet β-cell function (BCF).

MATERIALS AND METHODS

We recruited 455 patients with type 2 diabetes mellitus. Seven-point glucose-profile data (pre- and 120-min post-main meals, bedtime) were collected over three consecutive days. Plasma glucose and serum insulin concentrations were measured at 0, 60, and 120 min after a 100-g standard steamed bread meal test. The homeostasis model assessment of insulin resistance (HOMA-IR) and Matsuda index were computed to evaluate insulin resistance (IR). HOMA of β-cell function (HOMA-β) and the area under the curve between insulin and blood glucose (IAUC_0-120 /GAUC_0-120 ) were used to estimate BCF.

RESULTS

TIR was positively correlated with 60- and 120-min insulin values, IAUC_0-120 , the Matsuda index, HOMA-β, and IAUC_0-120 /GAUC_0-120 (r_s : 0.154, 0.129, 0.137, 0.194, 0.341, and 0.334, respectively; P <0.05) but inversely correlated with HOMA-IR (r_s : -0.239, P <0.001). After adjusting for confounders, multinomial multiple logistic regression analysis revealed that the odds ratios (ORs) of achieving the target TIR (>70%) increased by 12% (95% confidence interval [CI]: 3-21%), 7% (95% CI: 1-14%), 10% (95% CI: 5-16%), and 45% (95% CI: 25-68%) for each 10-mIU/L increase in 60- and 120-min insulin value, 10-unit increase in HOMA-β, and unit increase in IAUC_0-120 /GAUC_0-120 , respectively (P <0.05). Nevertheless, the OR decreased by 10% (95% CI: 1-18%) for each unit increase in HOMA-IR (P <0.05).

CONCLUSIONS

IR and BCF are related to CBG-assessed TIR.

Effects of Teneligliptin on HbA1c levels, Continuous Glucose Monitoring-Derived Time in Range and Glycemic Variability in Elderly Patients with T2DM (TEDDY Study)

BACKGROUND

To evaluate the effects of teneligliptin on glycosylated hemoglobin (HbA1c) levels, continuous glucose monitoring (CGM)-derived time in range, and glycemic variability in elderly type 2 diabetes mellitus patients.

METHODS

This randomized, double-blinded, placebo-controlled study was conducted in eight centers in Korea (clinical trial registration number: NCT03508323). Sixty-five participants aged ≥65 years, who were treatment-naïve or had been treated with stable doses of metformin, were randomized at a 1:1 ratio to receive 20 mg of teneligliptin (n=35) or placebo (n=30) for 12 weeks. The main endpoints were the changes in HbA1c levels from baseline to week 12, CGM metrics-derived time in range, and glycemic variability.

RESULTS

After 12 weeks, a significant reduction (by 0.84%) in HbA1c levels was observed in the teneligliptin group compared to that in the placebo group (by 0.08%), with a between-group least squares mean difference of -0.76% (95% confidence interval [CI], -1.08 to -0.44). The coefficient of variation, standard deviation, and mean amplitude of glycemic excursion significantly decreased in participants treated with teneligliptin as compared to those in the placebo group. Teneligliptin treatment significantly decreased the time spent above 180 or 250 mg/dL, respectively, without increasing the time spent below 70 mg/dL. The mean percentage of time for which glucose levels remained in the 70 to 180 mg/dL time in range (TIR70-180) at week 12 was 82.0%±16.0% in the teneligliptin group, and placebo-adjusted change in TIR70-180 from baseline was 13.3% (95% CI, 6.0 to 20.6).

CONCLUSION

Teneligliptin effectively reduced HbA1c levels, time spent above the target range, and glycemic variability, without increasing hypoglycemia in our study population.

Pitfalls of intermittent continuous glucose monitoring in patients with steroid diabetes

Only a few studies of continuous glucose monitoring (CGM) in patients with steroid diabetes have been published. Therefore, we investigated all patients with type 2 diabetes (n = 121) and steroid diabetes (n = 40) who used the FreeStyle Libre Pro^® device (Abbott Japan) at Gunma University Hospital between 2017 and 2019. Glycated hemoglobin (HbA1c), mean sensor glucose (SG), and glucose management indicator values were similar in both groups. However, the indices for glycemic variabilities, expressed as standard deviations and percent coefficients of variation, were higher in patients with steroid diabetes than in those with type 2 diabetes. The associations between HbA1c, mean SG, and time in range (TIR) when glucose values were 70-180, <70, or >180 mg/dL were assessed using Pearson's product-moment correlation coefficient, which demonstrated good correlations in both patient groups. However, patients with steroid diabetes had a higher SG and lower TIR than did counterparts with type 2 diabetes who had similar HbA1c levels. To examine the effect of prednisolone on CGM data, we divided patients with steroid diabetes into 2 subgroups according to prednisolone dose (≤5 and >5 mg), and found that the dose of this steroid impacted the associations between HbA1c and CGM data, mean SG, and TIR. In summary, our data highlight the importance of cautiously interpreting CGM data and the optimal HbA1c level in patients with steroid diabetes to prevent diabetes-related complications. Further analyses using other CGM devices are necessary to further validate our findings.

The Optimal "Time in Range" and "Time below Range" are Difficult to Coordinate in Patients with Type 1 Diabetes

Achieving the optimal glucose level time in range (TIR), as recently proposed by the "International Consensus on Time in Range," is challenging. We retrospectively analyzed data from 192 patients, including 58 with type 1 diabetes, using the FreeStyle Libre Pro system. This device was used by physicians for continuous glucose monitoring (CGM) and for making therapeutic decisions based on unbiased data, as the patients were blinded to their blood glucose levels during monitoring. The desired 70% TIR among patients with type 2 diabetes corresponded to an HbA1c of 7.7%. Importantly, however, a 70% TIR for patients with type 1 diabetes corresponded to an HbA1c of 6.9%, which diverged markedly from the HbA1c of 7.9% that corresponded to the desired 4% time below range (TBR). Moreover, these dissociations were observed more in patients with type 1 diabetes with a higher % coefficient of variation (> 36%). Hence, while the TIR is strongly correlated with HbA1c, it is difficult to coordinate with the TBR in Japanese patients with type 1 diabetes. As these metrics (which are critical indicators in clinical practice) are rapidly gaining popularity globally, including in Japan, our data strongly support the cautious use of new CGM metrics such as TIR and TBR/time above range, and emphasize the importance of individualized treatment in achieving the optimal TIR and TBR, especially in patients with type 1 diabetes.

Early Glycemic Control Assessment Based on Consensus CGM Metrics. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:1269-1275. [PMID: 34891517 DOI: 10.1109/embc46164.2021.9631015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.

Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated.

Effectiveness of the freestyle libre 2 flash glucose monitoring system on diabetes-self-management practices and glycemic parameters among patients with type 1 diabetes using insulin pump

AIMS

To determine the effectiveness of Freestyle Libre 2 (FSL2) on diabetes-self-management (DSM) practices and glycemic parameters among patients with type 1 diabetes (T1D) using insulin pump.

METHODS

This prospective study was performed among 47 patients with T1D (13-21yrs) who self-tested their glucose levels by the conventional finger-prick method using blood glucose meters (BGM). Data related to glycemic profile i.e., mean time in range (TIR), mean time above range (TAR) mean time below range (TBR), mean glucose level, hemoglobin A1c (HbA1c), total daily dose of insulin (TDDI), frequency of glucose monitoring and DSM responses were collected at baseline and 12 weeks.

RESULTS

The mean TIR was 59.8 ± 12.6%, TAR 32.7 ± 11.6%, TBR 7.5 ± 4.3%, mean glycemic variability, standard deviation 63.2 ± 12.5 mg/dL, and the coefficient of variation 41.3 ± 11.4% at 12 weeks. At baseline, the HbA1c level was 8.3%, and at 12 weeks, it dropped to 7.9% (p = 0.064). Baseline glucose monitoring frequency through BGM was 2.4/day; however, after the patients employed the FSL2, a higher degree of frequency of glucose monitoring was evident at 12 weeks as 8.2/day (p < 0.001). Significant improvements were observed in all the DSM subscales at 12 weeks.

CONCLUSION

Using FSL2 was found to raise the patients' DSM levels and improved metabolic control.

Greater daily glucose variability and lower time in range assessed with continuous glucose monitoring are associated with greater aortic stiffness: The Maastricht Study

AIMS

CVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis.

METHODS

We included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SD_CGM] and CGM-assessed CV [CV_CGM]) and time in range (TIR_CGM) with carotid-femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima-media thickness, ankle-brachial index and circumferential wall stress via multiple linear regression.

RESULTS

Higher SD_CGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SD_CGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSG_CGM), SD_CGM and MSG_CGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [-0.018, 0.167], p = 0.160; and 0.059 [-0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CV_CGM (B [95% CI] per 10% CV_CGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIR_CGM (B [95% CI] per 10% TIR_CGM: -0.145 m/s [-0.252, -0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures.

CONCLUSIONS

Our findings show that greater daily glucose variability and lower TIR_CGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIR_CGM to prevent CVD.

The relationship between glycosylated hemoglobin, time-in-range and glycemic variability in type 1 diabetes patients under flash glucose monitoring

OBJECTIVES

Flash glucose monitoring in patients with type 1 diabetes provides new glucometric data that allow for the assessment of glycemic control beyond HbA_1c. The objective of the study was to evaluate the relationship between HbA_1c, time-in-range (TIR) and glycemic variability in a cohort of paediatric and adult patients with type 1 diabetes and treatment with flash glucose monitoring.

MATERIAL AND METHODS

This was a cross-sectional study in 195 patients with type 1 diabetes (42.6% females, 70 paediatric, 26.2% continuous subcutaneous insulin infusion, 28.7% coefficient of variation [CV]≤36%) in intensive treatment and flash glucose monitoring. Clinical, analytical and glucometric data were evaluated.

RESULTS

The relationship between the TIR and HbA_1c showed a strong negative linear correlation (R=-0.746; R²=0.557; P<.001), modified in those patients with CV≤36% (R=-0.852; R²=0.836) compared to CV>36% (R=-0.703; R²=0.551). A similar correlation was found when evaluating the TIR and the Glucose Management Indicator (R=-0.846; R²=0.715; P<.001); in patients with CV≤36% (R=-0.980; R²=0.960) versus CV>36% (R=-0.837; R²=0.701); P<.001. Both correlations remained stable in the paediatric population (R=-0.724; R²=0.525; P<.001) and adults (R=-0.706; R²=0.498; P<.001) and by type of treatment: multiple doses of insulin (R=-0.747; R²=0.558; P<.001) and continuous subcutaneous insulin infusion (R=-0.711; R²=0.506; P<.001). In a multiple regression analysis evaluating HbA_1c as dependent variable, the only parameters that maintained statistical significance were the TIR (β=-0,031; P<.001), CV (β=0.843; P<.05) and TIR-CV interaction (β=-0.017; P<.01).

CONCLUSIONS

The glycemic variability defined by the CV modifies the relationship between the TIR and HbA_1c/Glucose Management Indicator and should be taken into account when individualising TIR targets, regardless of age or the type of treatment used.

Time-in-range for monitoring glucose control: Is it time for a change?

The HbA1c value has been the gold standard for evaluating glucose control for decades. However, it has limitations such as the lack of information on glycemic variability or the risk of hypoglycemia. The increasing use of continuous glucose monitoring has provided patients and healthcare professionals with a range of useful metrics for the management of diabetes. Among them, Time in Range (TIR) is a simple and intuitive metric that gives information regarding the quality of glucose control. It is defined as the time spent in an individual's target glucose range. TIR is strongly correlated with HbA1c, and it has been linked to the risk of developing microvascular and macrovascular complications. The International Consensus on Time in Range has recently set targets for different diabetes populations. For the majority of people with type 1 or type 2 diabetes, a TIR (70-180 mg/dL or 3.9-10.0 mmol/L) of >70%, a time below range (TBR) <70 mg/dL (<3.9 mmol/L) of <4% and a TBR <54 (<3.0 mmol/L) of <1% are recommended. In this review, we address the latest evidence for the use of TIR as an essential parameter in the management of diabetes.

Characteristics of Continuous Glucose Monitoring Metrics in Persons with Type 1 and Type 2 Diabetes Treated with Multiple Daily Insulin Injections

Background: Although guidelines advocate similar continuous glucose monitoring (CGM) targets for insulin-treated persons with type 1 diabetes (T1D) and type 2 diabetes (T2D), it is unclear how these persons differ with respect to hypoglycemia, glucose variability, and other CGM metrics in clinical practice. Methods: We used data from 2 multicenter randomized-controlled trials (GOLD and MDI-Liraglutide) where 161 persons with T1D and 124 persons with T2D treated with multiple daily injections were included and monitored with masked CGM. Results: Persons from both cohorts had similar mean glucose levels, 10.9 mmol/L (196 mg/dL) in persons with T1D and 10.8 mmol/L (194 mg/dL) in persons with T2D. Time in hypoglycemia (<3.9 mmol/L [70 mg/dL]) was 5.1% and 1.0% for persons with T1D and T2D, respectively (P < 0.001). Corresponding estimates for the standard deviations of mean glucose levels were 4.4 mmol/L (79 mg/dL) versus 3.0 (54 mg/dL) (P < 0.001), for coefficient of variation 41% versus 28% (P < 0.001), and for time in range 38.2% versus 45.3%, respectively (P = 0.004). Mean C-peptide levels were 0.05 nmol/L and 0.67 nmol/L (P < 0.001) for persons with T1D and T2D, respectively. Conclusions: Persons with T1D compared with persons with T2D treated with multiple daily insulin injections spend considerably more time in hypoglycemia, have higher glucose variability, and less "time in range." This needs to be taken into account in daily clinical care and in recommended targets for CGM metrics.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Standardized Hybrid Closed-Loop System Reporting

The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.

The Ambulatory Glucose Profile: Opportunities for Enhancement

The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.

Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment

AIMS/INTRODUCTION

To identify the coefficient of variation (CV) threshold for unstable glucose variability (GV) and hypoglycemia, and to characterize a patient population with unstable GV and hypoglycemia.

MATERIALS AND METHODS

This was an observational study that enrolled 284 Japanese outpatients with type 2 diabetes who underwent continuous glucose monitoring. The C-peptide index (CPI = [(fasting serum C-peptide) / (plasma glucose)] × 100) was used as a marker of endogenous insulin secretion. The CV threshold between stable and unstable GV was defined as the upper limit of the CV distribution in the subgroup of patients who did not receive insulin nor insulin secretagogues (relatively stable GV subgroup, n = 104). The optimal CV range corresponding to time below target range ≥4% was determined for all patients using receiver operating characteristic curve analysis. Various characteristics of patients with unstable GV and hypoglycemia were extracted using multivariate logistic regression analysis.

RESULTS

The upper limit of the CV in the relatively stable GV subgroup was 40. The optimal CV range corresponding to time below target range ≥4% was also defined as CV ≥40 (area under the curve 0.85) for all patients. The CPI was an independent risk for CV ≥40 (odds ratio 0.17, 95% confidence interval 0.04-0.50, P < 0.01). The optimal cut-off point for CPI to predict a CV cut-off value of 40 was equivalent to 0.81 (area under the curve 0.80).

CONCLUSIONS

A CV of 40 discriminates unstable GV and hypoglycemia from stable GV in Japanese outpatients with type 2 diabetes. Impaired insulin secretion might affect the stability of GV.

Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring

The contribution of endogenous insulin secretion to glycemic variability (GV) may differ between patients with impaired insulin secretion and those with preserved secretion. Our objective was to determine the linearity of the relationship between fasting C-peptide (CPR) as a marker of endogenous insulin secretion and GV in type 2 diabetes (T2DM), regardless of the type of antidiabetic treatment. We conducted a prospective observational study using continuous glucose monitoring obtained from 284 Japanese outpatients with T2DM with various HbA1c values and antidiabetic treatment. We constructed a prediction curve of base-line CPR versus coefficient of variation (CV) and identified the clinical factors associated with CV using multiple regression analysis. Fasting CPR showed a significant negative log-linear relationship with CV (P < 0.0001), and the latter being strikingly high in the low-CPR group. The multiple regression analysis showed that low CPR was an independent predictor of high CV (P < 0.0001). The significant correlations were sustained in both patients with/without insulin treatment. The contribution of endogenous insulin secretion to GV depends on the extent of insulin secretion impairment. Fasting CPR may represent a useful indicator of GV instability in T2DM.

Hemoglobin A1c modifies the association between triglyceride and time in hypoglycemia determined by flash glucose monitoring in adults with type 1 diabetes: implications for individualized therapy and decision-making

Background

We aimed to investigate the associations of flash glucose monitoring (FGM)-derived metrics with lipid profiles and identify potential modifiers of these associations among adults with type 1 diabetes (T1D).

Methods

A cross-sectional study was conducted among 108 Chinese adults with T1D who used FGM for 14 consecutive days. The relationship between FGM-derived metrics and lipid variables and potential modifiers were identified using interaction and subgroup analysis.

Results

Serum triglyceride level inversely correlated with time below range (glucose <3.9 mmol/L) and time in range (glucose 3.9-10.0 mmol/L) and positively correlated with time above range (glucose >10.0 mmol/L) (Spearman's r=-0.34, -0.25, 0.34, respectively, all P<0.01). Additionally, triglyceride levels had positive correlation with absolute measures of glycemic variability (GV) but not with the coefficient of variation for glucose (Spearman's r=0.12, P>0.05), a relative measure. Multivariate linear regression analysis adjusting for potential confounders including gender, age, disease duration, body mass index (BMI), daily insulin dose, fasting C-peptide, and dyslipidemia medication use showed that higher triglyceride level independently predicted decrease in time below range and time in range and increase in time above range (all P<0.01). Furthermore, interaction analysis found that the interaction between HbA1c and triglyceride was significant in the time below range (P for interaction =0.034). The association between triglyceride and time below range differed substantially after stratification by HbA1c, which was significant in those with HbA1c <7.0% whereas inconsequential among those with HbA1c ≥7.0%. In those with HbA1c <7.0% (n=44), the area under receiver operating characteristic curve of triglyceride predicting achievement of targets of time below range (<4%) was 0.856 (95% confidence interval 0.688-1.000, P=0.042) with an optimal cutoff value of 0.50 mmol/L (sensitivity 100%, specificity 66.7%, positive predictive value 94.4%).

Conclusions

In adults with T1D, HbA1c may be a potential modifier of the association between triglyceride and time below range, suggesting it might be necessary for those with HbA1c <7.0% accompanied by lower triglyceride levels to set a less intensive glycemic target to minimize risk of hypoglycemia.

HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis

Background: There can be marked discordance between laboratory and estimated (using the glucose management indicator [GMI]) glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM). This may cause errors in diabetes management. This study evaluates discordance between laboratory and CGM-estimated HbA1c (eA1C). Methods: We performed a retrospective review of patients with diabetes who use CGM. The patients were seen at the University of Washington (UW) Diabetes Care Center from 2012 to 2019. We used UW's Institute of Translational Health Sciences to extract eligible encounters from the electronic medical record. We required that patients use CGM and that HbA1c and sensor data be obtained fewer than 4 weeks apart. There were no exclusion criteria. We calculated HbA1c-GMI discordance for each subject and assessed for any impact of comorbidities. We defined HbA1c-GMI discordance as absolute difference between laboratory and eA1C. Results: This study included 641 separate office encounters. Ninety-one percent of patients had type 1 diabetes. Most patients had diabetes for greater than 20 years. The mean duration of CGM wear was 24.5 ± 8 days. Only 11% of patients had HbA1c-GMI discordance <0.1%, but 50% and 22% had differences ≥0.5% and ≥1%. There was increased discordance with advanced chronic kidney disease (estimated glomerular filtration rate <60). Discussion: We found substantial discordance between laboratory and eA1C in a real-world setting. Clinicians need be aware that HbA1c may not as accurately reflect mean glucose as previously appreciated.

Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

OBJECTIVE

Although elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19.

RESEARCH DESIGN AND METHODS

Glucose levels were assessed through intermittently scanned continuous glucose monitoring in 35 patients for an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to the intensive care unit, need for mechanical ventilation, or morbidity with critical illness.

RESULTS

TARs using thresholds from 160 to 200 mg/dL were significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR (70-160 mg/dL), but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively.

CONCLUSIONS

Patients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels >160 mg/dL and <70 mg/dL and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.

Impact of Switching from Intermittently Scanned to Real-Time Continuous Glucose Monitoring Systems in a Type 1 Diabetes Patient French Cohort: An Observational Study of Clinical Practices

Aim: Assess the impact of switching from intermittently scanned (FreeStyle Libre [FSL]) to real-time (Dexcom G4 platinum [DG4]) continuous glucose monitoring systems on glycemia control in type 1 diabetes (T1D) patients with high risk of hypoglycemia and/or elevated glycated hemoglobin (HbA1c). Methods: We conducted an observational study in 18 T1D adults with poor glycemic control on FSL. Ambulatory glucose profile data were collected during the last 3 months of FSL use before inclusion (M0 period), during the first 3 months (M3 period) and the last 3 months (M6 period) of DG4 use. Data were then expressed as 24-h averages. Biological HbA1c was measured for all three periods. Patients were their own-controls and statistics were performed using paired t-test or Wilcoxon for matched-pairs. Results: The switch to DG4 at M3 resulted in a higher time-in-range (TIR) 70-180 mg/dL (median [Q1;Q3], 53.1 [44.5;67.3] vs. 41.5 [28.5;62.0], P = 0.0008), and a lower time-below-range <70 mg/dL (TBR mean ± standard deviation (SD), 5.4 ± 3.7 vs. 10.9 ± 7.1, P = 0.0009) and in the glucose % coefficient of variation (%CV mean ± SD, 40.1 vs. 46.9, P = 0.0001). Mean (SD) changes were +10.3 (8.0) percentage points for TIR, -5.5 (5.8) percentage points for TBR, and -6.8 (5.8) percentage points for %CV. These results were confirmed at the M6 period. Conclusions: Switching from FSL to DG4 appears to provide a beneficial therapeutic option without changing insulin delivery systems, regardless of the origin of the patient's initial glycemic issue.

The association between hypoglycemia and glycemic variability in elderly patients with type 2 diabetes: a prospective observational study

BACKGROUND

This study aimed to explore predictive factors of time below target glucose range (TBR) ≥ 1% among patients' characteristics and glycemic variability (GV) indices using continuous glucose monitoring data in elderly patients with type 2 diabetes.

METHODS

We conducted a prospective observational study on 179 (71 female) Japanese outpatients with type 2 diabetes aged ≥ 65 years. The characteristics of the participants with TBR ≥ 1% were evaluated by multivariate logistic regression analysis. Receiver-operating characteristic (ROC) curve analyses of GV indices, comprising coefficient of variation (CV), standard deviation, and mean amplitude of glycemic excursions, were performed to identify the optimal index for the identification of patients with TBR ≥ 1%.

RESULTS

In the multivariate logistic regression analysis, none of the clinical characteristics, including HbA1c and C-peptide index, were independent markers for TBR ≥ 1%, while all three GV indices showed significant associations with TBR ≥ 1%. Among the three GV indices, CV showed the best performance based on the area under the curve in the ROC curve analyses.

CONCLUSIONS

Among elderly patients with type 2 diabetes, CV reflected TBR ≥ 1% most appropriately among the GV indices examined. Trial registration UMIN-CTR: UMIN000029993. Registered 16 November 2017.

Management of glucose profile throughout strict COVID-19 lockdown by patients with type 1 diabetes prone to hypoglycaemia using sensor-augmented pump

AIMS

Spain has been one of the worst affected countries by the COVID-19 pandemic. A very strict lockdown at home was imposed with a tough restriction of mobility. We aimed to evaluate the impact of this exceptional scenario on glucose profile of patients with type 1 diabetes (T1D) prone to hypoglycaemia using sensor-augmented pump (SAP).

METHODS

Patients with T1D prone to hypoglycaemia using SAP (640G Medtronic-Minimed^®) for at least 6 months under the funding of a National Health Service were included in an observational, retrospective study. Data were collected in two periods: pre-lockdown (PL), February 23rd-March 7th and within lockdown (WL), April 1st to 14th 2020. The primary outcome was the difference in the proportion of time in target glucose range of 70-180 mg/dL (TIR). Additional glucometric data and total daily insulin were also analysed.

RESULTS

Fifty-nine patients were included: 33 women, age 46.17 ± 13.0 years and disease duration of 30.2 ± 12.0 years. TIR 70-180 mg/dL (67.6 ± 11.8 vs. 69.8 ± 12.0%), time > 180 (28.1 ± 13.6 vs. 25.5 ± 13.1%), time > 250 (6.9 ± 6.1 vs. 5.1 ± 4.8) and estimated HbA_1c (6.94 ± 0.8 vs. 6.75 ± 0.7%) significantly improved (PL vs. WL, respectively, p < 0.05). Time in hypoglycaemia, coefficient of variation, sensor usage and total daily insulin dose remained unchanged.

CONCLUSIONS

Lockdown conditions imposed by the COVID-19 pandemic may be managed successfully in terms of glycaemia control by population with DT1 prone to hypoglycaemia using SAP. The strict daily routine at home could probably explain the improvement in the time in glycemic target without increasing the time hypoglycaemia.

Time in Range in Relation to All-Cause and Cardiovascular Mortality in Patients With Type 2 Diabetes: A Prospective Cohort Study

OBJECTIVE

There is growing evidence linking time in range (TIR), an emerging metric for assessing glycemic control, to diabetes-related outcomes. We aimed to investigate the association between TIR and mortality in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

A total of 6,225 adult patients with type 2 diabetes were included from January 2005 to December 2015 from a single center in Shanghai, China. TIR was measured with continuous glucose monitoring at baseline, and the participants were stratified into four groups by TIR: >85%, 71-85%, 51-70%, and ≤50%. Cox proportional hazards regression models were used to estimate the association between different levels of TIR and the risks of all-cause and cardiovascular disease (CVD) mortality.

RESULTS

The mean age of the participants was 61.7 years at baseline. During a median follow-up of 6.9 years, 838 deaths were identified, 287 of which were due to CVD. The multivariable-adjusted hazard ratios associated with different levels of TIR (>85% [reference group], 71-85%, 51-70%, and ≤50%) were 1.00, 1.23 (95% CI 0.98-1.55), 1.30 (95% CI 1.04-1.63), and 1.83 (95% CI 1.48-2.28) for all-cause mortality (P for trend <0.001) and 1.00, 1.35 (95% CI 0.90-2.04), 1.47 (95% CI 0.99-2.19), and 1.85 (95% CI 1.25-2.72) for CVD mortality (P for trend = 0.015), respectively.

CONCLUSIONS

The current study indicated an association of lower TIR with an increased risk of all-cause and CVD mortality among patients with type 2 diabetes, supporting the validity of TIR as a surrogate marker of long-term adverse clinical outcomes.

Association of Body Fat Percentage with Time in Range Generated by Continuous Glucose Monitoring during Continuous Subcutaneous Insulin Infusion Therapy in Type 2 Diabetes

BACKGROUND

Obesity is a crucial risk factor associated with type 2 diabetes mellitus (T2DM). Excessive accumulation of body fat may affect the glycemia control in T2DM. This study investigated the relationship between body fat percentage and time in range (TIR) assessed by continuous glucose monitoring (CGM) during short-term continuous subcutaneous insulin infusion (CSII) therapy in T2DM patients.

METHOD

A total of 85 T2DM patients were recruited in this cross-sectional study. All participants underwent 72 h CGM period during short-term CSII therapy. TIR was defined as the percentage of time spent within the target glucose range of 3.9-10.0 mmol/L. Body composition was measured using bioelectrical impedance analysis (BIA) and overfat was defined as an amount of body fat of at least 25% of total body mass for men or at least 30% for women. Multiple linear regression models were used to evaluate the independent association of body fat percentage with TIR after adjusting for confounding factors.

RESULTS

Compared with normal fat T2DM patients, individual with a higher body fat percentage exhibited lower levels of TIR (P = 0.004) and higher 72 h mean blood glucose (72 h MBG) (P = 0.001) during short-term CSII treatment. The prevalence of overfat assessed by body fat percentage decreased with the ascending TIR tertiles (P < 0.05). Multiple linear regression analysis indicated that body fat percentage was significantly associated with TIR independent of age, gender, diabetes duration, HbA1c, and BMI (P = 0.043).

CONCLUSIONS

Body fat percentage was significantly associated with TIR in T2DM during short-term CSII therapy. Reduction of body fat may be an important therapeutic target to improve glycemic control in high body fat T2DM patients, who may benefit less from intensive insulin treatment.

Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control

Glycosylated hemoglobin (HbA1c) has been the sole surrogate marker for assessing diabetic complications. However, consistently reported limitations of HbA1c are that it lacks detailed information on short-term glycemic control and can be easily interfered with by various clinical conditions such as anemia, pregnancy, or liver disease. Thus, HbA1c alone may not represent the real glycemic status of a patient. The advancement of continuous glucose monitoring (CGM) has enabled both patients and healthcare providers to monitor glucose trends for a whole single day, which is not possible with HbA1c. This has allowed for the development of core metrics such as time spent in time in range (TIR), hyperglycemia, or hypoglycemia, and glycemic variability. Among the 10 core metrics, TIR is reported to represent overall glycemic control better than HbA1c alone. Moreover, various evidence supports TIR as a predictive marker of diabetes complications as well as HbA1c, as the inverse relationship between HbA1c and TIR reveals. However, there are more complex relationships between HbA1c, TIR, and other CGM metrics. This article provides information about 10 core metrics with particular focus on TIR and the relationships between the CGM metrics for comprehensive understanding of glycemic status using CGM.

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

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