Respective Contributions of Glycemic Variability and Mean Daily Glucose as Predictors of Hypoglycemia in Type 1 Diabetes: Are They Equivalent?

Time With Rapid Change of Glucose

BACKGROUND

A variety of metrics are used to describe glycemic variation, some of which may be difficult to comprehend or require complex strategies for smoothing of the glucose curve. We aimed to describe a new metric named time with rapid change of glucose (TRC), which is presented as percentage of time, similar to time above range (TAR), time in range (TIR), and time below range (TBR).

METHOD

We downloaded glucose data for 90 days from 159 persons with type 1 diabetes using the Abbott Freestyle Libre version 1. We defined TRC as the proportion of time (%) with an absolute rate of change of glucose > 1.5 mmol/L/15 minutes (1.8mg/dL/min) corresponding to a minimum rate of change for glucose in the 3.9-10.0 mmol/L (70-180 mg/dL) range within 1 hour. TRC is related to the other glucose variability metrics: CV within day (CVw) and mean amplitude of glycemic excursion (MAGE).

RESULTS

The more than 1.27 million glucose rates were t-location scale distributed with SD 0.91 mmol/L/15 min (1.1 mg/dL/15 min). The median TRC was 6.9% (IQR 4.5%-9.5%). The proportion of TRC with positive slope was 3.9% (2.6%-5.3%) and significantly higher than the proportion with negative slope 2.8% (1.5%-4.4%) P < .001. TRC correlated with CVw and MAGE (Spearman's correlation coefficient .56 and .65, respectively, P < .001).

CONCLUSION

TRC is proposed as an easily perceived metric to compare the performance of hybrid or fully automated closed-loop insulin delivery systems to obtain glucose homeostasis.

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.

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study

AIMS

Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV.

METHODS

We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models.

RESULTS

A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV.

CONCLUSIONS

An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes

AIMS

To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic β-cell function of subjects with type 2 diabetes mellitus (T2DM).

METHODS

A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic β-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables.

RESULTS

Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of β-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects.

CONCLUSION

Our study suggests that impaired pancreatic β-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

Key indices of glycaemic variability for application in diabetes clinical practice

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

Automated Insulin Delivery for Hypoglycemia Avoidance and Glucose Counterregulation in Long-Standing Type 1 Diabetes with Hypoglycemia Unawareness

Objective: Automated insulin delivery (AID) may benefit individuals with long-standing type 1 diabetes where frequent exposure to hypoglycemia impairs counterregulatory responses. This study assessed the effect of 18 months AID on hypoglycemia avoidance and glucose counterregulatory responses to insulin-induced hypoglycemia in long-standing type 1 diabetes complicated by impaired awareness of hypoglycemia. Methods: Ten participants mean ± standard deviation age 49 ± 16 and diabetes duration 34 ± 16 years were initiated on AID. Continuous glucose monitoring was paired with actigraphy to assess awake- and sleep-associated hypoglycemia exposure every 3 months. Hyperinsulinemic hypoglycemic clamp experiments were performed at baseline, 6, and 18 months postintervention. Hypoglycemia exposure was reduced by 3 months, especially during sleep, with effects sustained through 18 months (P ≤ 0.001) together with reduced glucose variability (P < 0.01). Results: Hypoglycemia awareness and severity scores improved (P < 0.01) with severe hypoglycemia events reduced from median (interquartile range) 3 (3-10) at baseline to 0 (0-1) events/person·year postintervention (P = 0.005). During the hypoglycemic clamp experiments, no change was seen in the endogenous glucose production (EGP) response, however, peripheral glucose utilization during hypoglycemia was reduced following intervention [pre: 4.6 ± 0.4, 6 months: 3.8 ± 0.5, 18 months: 3.4 ± 0.3 mg/(kg·min), P < 0.05]. There were increases over time in pancreatic polypeptide (Pre:62 ± 29, 6 months:127 ± 44, 18 months:176 ± 58 pmol/L, P < 0.01), epinephrine (Pre: 199 ± 53, 6 months: 332 ± 91, 18 months: 386 ± 95 pg/mL, P = 0.001), and autonomic symptom (Pre: 6 ± 2, 6 months: 6 ± 2, 18 months: 10 ± 2, P < 0.05) responses. Conclusions: AID led to a sustained reduction of hypoglycemia exposure. EGP in response to insulin-induced hypoglycemia remained defective, however, partial recovery of glucose counterregulation was evidenced by a reduction in peripheral glucose utilization likely mediated by increased epinephrine secretion and, together with improved autonomic symptoms, may contribute to the observed clinical reduction in hypoglycemia.

Hypoglycemia and glycemic variability of people with type 1 diabetes with lower and higher physical activity loads in free-living conditions using continuous subcutaneous insulin infusion with predictive low-glucose suspend system

INTRODUCTION

Maintaining glycemic control during and after physical activity (PA) is a major challenge in type 1 diabetes (T1D). This study compared the glycemic variability and exercise-related diabetic management strategies of adults with T1D achieving higher and lower PA loads in nighttime-daytime and active- sedentary behavior hours in free-living conditions.

RESEARCH DESIGN AND METHODS

Active adults (n=28) with T1D (ages: 35±10 years; diabetes duration: 21±11 years; body mass index: 24.8±3.4 kg/m²; glycated hemoglobin A1c: 6.9±0.6%) on continuous subcutaneous insulin delivery system with predictive low glucose suspend system and glucose monitoring, performed different types, duration and intensity of PA under free-living conditions, tracked by accelerometer over 14 days. Participants were equally divided into lower load (LL) and higher load (HL) by median of daily counts per minute (61122). Glycemic variability was studied monitoring predefined time in glycemic ranges (time in range (TIR), time above range (TAR) and time below range (TBR)), coefficient of variation (CV) and mean amplitude of glycemic excursions (MAGE). Parameters were studied in defined hours timeframes (nighttime-daytime and active-sedentary behavior). Self-reported diabetes management strategies were analysed during and post-PA.

RESULTS

Higher glycemic variability (CV) was observed in sedentary hours compared with active hours in the LL group (p≤0.05). HL group showed an increment in glycemic variability (MAGE) during nighttime versus daytime (p≤0.05). There were no differences in TIR and TAR across all timeframes between HL and LL groups. The HL group had significantly more TBR during night hours than the LL group (p≤0.05). Both groups showed TBR above recommended values. All participants used fewer post-PA management strategies than during PA (p≤0.05).

CONCLUSION

Active people with T1D are able to maintain glycemic variability, TIR and TAR within recommended values regardless of PA loads. However, the high prevalence of TBR and the less use of post-PA management strategies highlights the potential need to increase awareness on actions to avoid glycemic excursions and hypoglycemia after exercise completion.

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study

BACKGROUND

The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.

METHOD

The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.

RESULTS

The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.

CONCLUSIONS

The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Pathophysiology and management of hypoglycemia in diabetes

In the century since the discovery of insulin, diabetes has changed from an early death sentence to a manageable chronic disease. This change in longevity and duration of diabetes coupled with significant advances in therapeutic options for patients has fundamentally changed the landscape of diabetes management, particularly in patients with type 1 diabetes mellitus. However, hypoglycemia remains a major barrier to achieving optimal glycemic control. Current understanding of the mechanisms of hypoglycemia has expanded to include not only counter-regulatory hormonal responses but also direct changes in brain glucose, fuel sensing, and utilization, as well as changes in neural networks that modulate behavior, mood, and cognition. Different strategies to prevent and treat hypoglycemia have been developed, including educational strategies, new insulin formulations, delivery devices, novel technologies, and pharmacologic targets. This review article will discuss current literature contributing to our understanding of the myriad of factors that lead to the development of clinically meaningful hypoglycemia and review established and novel therapies for the prevention and treatment of hypoglycemia.

Diabetes Technology Meeting 2021

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Impact of short-term glycemic variability on risk of all-cause mortality in type 2 diabetes patients with well-controlled glucose profile by continuous glucose monitoring: A prospective cohort study

AIMS

To investigate the association between short-term glycemic variability (GV) and all-cause mortality in type 2 diabetes with well-controlled glucose profile by continuous glucose monitoring (CGM).

METHODS

In this prospective study, 1839 diabetes patients who reached percentage of time in the target glucose range of 3.9-10 mmol/L > 70%, percentage of time above range of 10 mmol/L < 25% and percentage of time below range of 3.9 mmol/L < 4% on CGM were enrolled and were classified into five groups by coefficient of variation for glucose (%CV) level: ≤20%, 20-25%, 25-30%, 30-35%, and > 35%. Cox proportional hazard models were used to estimate hazard ratios (HRs) of all-cause mortality risk associated with the different %CV categories.

RESULTS

At baseline, participants had mean age of 60.9 years and mean HbA_1c of 7.3% (56 mmol/mol). A total of 165 deaths were identified during a median follow-up of 6.9 years. In multivariate Cox regression analysis, HRs associated with %CV categories were 1.00, 1.16 (95% CI 0.78-1.73), 1.38 (95% CI 0.89-2.15), 1.33 (95% CI 0.77-2.29) and 2.26 (95% CI 1.13-4.52) for all-cause mortality.

CONCLUSIONS

Greater %CV was associated with increased risk for all-cause mortality even among patients with seemingly well-controlled glucose status.

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

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

Continuous Glucose Monitoring metrics in the Assessment of Glycemia in Moderate-to-Advanced Chronic Kidney Disease (CKD) in Diabetes

Introduction

Glycated hemoglobin A1c (HbA1c) has reduced reliability in advanced chronic kidney disease (CKD) owing to factors influencing red cell turnover. Recent guidelines support the use of continuous glucose monitoring (CGM) in glycemic assessment in these patients. We evaluated relationships between HbA1c and CGM metrics of average glycemia and glucose variability (GV) in moderate-to-advanced CKD.

Methods

There were a total of 90 patients with diabetes in CKD stages G3b (n = 33), G4 (n = 43), and G5 (nondialysis) (n = 14) (age [mean ± SD] 65.4 ± 9.0 years, estimated glomerular filtration rate [eGFR] 26.1 ± 9.6 ml/min per 1.73 m², and HbA1c 7.4 ± 0.8%). CGM metrics were estimated from blinded CGM (Medtronic Ipro2 with Enlite sensor) and compared with HbA1c in the same week.

Results

Correlations between glucose management indicator (GMI) and HbA1c attenuated with advancing CKD (G3b [r = 0.68, P < 0.0001], G4 [r = 0.52, P < 0.001], G5 [r = 0.22, P = 0.44], P = 0.01 for CKD stage). In G3b and G4, HbA1c correlated significantly with time-in-range (TIR) (3.9–10.0 mmol/l) (r = −0.55 and r = −0.54, respectively) and % time > 13.9 mmol/l (r = 0.53 and r = 0.44, respectively), but not in G5. HbA1c showed no correlation with % time <3.0 mmol/l (r = −0.045, P = 0.67) or % coefficient of variation (CV) (r = −0.05, P = 0.64) in any CKD stage. Only eGFR was a significant determinant of bias for the difference between GMI and HbA1c (difference −0.28%, 95% CI [−0.52 to −0.03] per 15 ml/min per 1.73 m² decrement, P = 0.03).

Conclusion

CGM-derived indices might serve as an adjunct to HbA1c monitoring to guide glycemic management, especially in those with eGFR <30 ml/min per 1.73 m². Time in hypoglycemia and glycemic variability are relevant glycemic targets for optimization not reflected by HbA1c.

Glycaemic management in diabetes: old and new approaches

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

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.

Glycaemic variabilities: Key questions in pursuit of clarity

After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.

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.

Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes

AIMS/INTRODUCTION

To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients.

MATERIALS AND METHODS

This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non-insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9 ) and above (10.0 mmol/L) the target glucose range (3.9-10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia.

RESULTS

Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9-10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut-off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories.

CONCLUSIONS

A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal-setting of %CV.

Beyond A1C-Standardization of Continuous Glucose Monitoring Reporting: Why It Is Needed and How It Continues to Evolve

Continuous glucose monitoring (CGM) systems are becoming part of standard care for type 1 diabetes, and their use is increasing for type 2 diabetes. Consensus has been reached on standardized metrics for reporting CGM data, with time in range of 70-180 mg/dL and time below 54 mg/dL recognized as the key metrics of focus for diabetes management. The ambulatory glucose profile report has emerged as the standard for visualization of CGM data and will continue to evolve to incorporate other elements such as insulin, food, and exercise data to support glycemic management.

Clinical Factors Associated with High Glycemic Variability Defined by Coefficient of Variation in Patients with Type 2 Diabetes

Background

High glycemic Variability (HGV) has become a stronger predictor of hypoglycemia. However, clinical factors associate with HGV still are unknown.

Objective

To determine clinical variables that were associated with a coefficient of variation (CV) above 36% evaluated by continuous glucose monitoring (CGM) in a group of patients with diabetes mellitus.

Methods

A cohort of patients with type 2 diabetes (T2D) was evaluated. Demographic variables, HbA1c, glomerular filtration rate (GFR) and treatment regimen were assessed. A bivariate analysis was performed, to evaluate the association between the outcome variable (CV> 36%) and each of the independent variables. A multivariate model was constructed to evaluate associations after controlling for confounding variables.

Results

CGM data from 274 patients were analyzed. CV> 36% was present in 56 patients (20.4%). In the bivariate analysis, demographic and clinical variables were included, such as time since diagnosis, hypoglycemia history, A1c, GFR and treatment established. In the multivariate analysis, GFR <45 mL/min (OR 2.81; CI 1.27,6.23; p:0.01), A1c > 9% (OR 2.81; CI 1.05,7.51; p:0.04) and hypoglycemia history (OR 2.09; CI 1.02,4.32; p:0.04) were associated with HGV. Treatment with iDPP4 (OR 0.39; CI 0.19,0.82; p:0.01) and AGLP1 (OR 0.08; CI 0.01,0.68; p:0.02) was inversely associated with GV.

Conclusion

Clinical variables such as GFR <45 mL/min, HbA1C>9% and a history of hypoglycemia are associated with a high GV. Our data suggest that the use of technology and treatments able to reduce glycemic variability could be useful in this population to reduce the risk of hypoglycemia and to improve glycemic control.

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.

Dapagliflozin as an Adjunct Therapy to Insulin in Patients with Type 1 Diabetes Mellitus: Efficacy and Safety of this Combination

The prevalence of type 1 diabetes (T1D) is increasing worldwide. T1D reduces life expectancy due to complications including cardiovascular disease. Sodium-glucose co-transporter (SGLT) inhibitors are a new class of drugs developed to treat type 2 diabetes (T2D), and now they can be used as an adjunct to insulin in T1D. In clinical trials, they have been shown to improve glycaemic control and decrease body weight without the risk of increased hypoglycaemia and with a reduction in insulin dose. Four SGLT2 inhibitors have been approved in Europe for the treatment of T2D, while only dapagliflozin and sotagliflozin, a dual SGLT1 and SGLT2 inhibitor approved in 2019, have been approved for the treatment of T1D. Both can be used as an adjunct therapy in combination with insulin in adults with a body mass index (BMI) of ≥27 kg/m², inadequately controlled with insulin. In Europe, dapagliflozin is the only currently available SGLT2 inhibitor indcated as adjunct therapy for patients with T1D. The subgroup of patients with a BMI of ≥27 kg/m² from the DEPICT-1 and -2 trials (Dapagliflozin Evaluation in Patients with Inadequately Controlled Type 1 diabetes) showed similar reduction in hyperglycaemia and body weight but no significant increased risk of diabetic ketoacidosis (DKA) than the overall trial population. The risk of DKA has been shown to increase in patients with T1D treated with adjunct therapy with SGLT2 inhibitors, and studies on sotagliflozin and empagliflozin have suggested a dose response. Thus, it is important to educate patients and doctors how to recognize symptoms of upcoming DKA and mitigate it. An independent DKA education programme has recently been developed to instruct patients with T1D being treated with SGLT inhibitor therapies with and without insulin pumps to prevent, identify and treat DKA. Despite these considerations, clinical trials support the use of SGLT2 inhibitors in the management of T1D. The benefits and potential risks of dapagliflozin as an adjunct therapy to insulin in adults with T1D should be considered in each individual case. Here we discuss the efficacy and safety of dapagliflozin as adjunct therapy in patients with T1D.

Hundert Jahre – Insulin bleibt aktuell und notwendig

In der Behandlung des Typ-1-Diabetes ist die Therapie mit Insulin auch 100 Jahre nach seiner Entdeckung weiterhin eine lebensnotwendige Therapie. Der pharmakologische Fortschritt hat die Behandlung erheblich erleichtert und nähert sich der physiologischen Insulin-Sekretion zunehmend an. In der Behandlung des Typ-2-Diabetes hingegen ist die Insulin-Therapie bei den meisten Patienten zunächst nicht notwendig. Lebensstil-Interventionen und moderne Nicht-Insulin Antidiabetika können häufig zu einer lang andauernden Kontrolle der Erkrankung führen. Die Heterogenität des Typ-2-Diabetes führt jedoch dazu, dass einige Patienten früh von einer Insulin-Therapie profitieren. Auch beim Typ-2-Diabetes können moderne Insulin Präparate die Insulin-Behandlung deutlich erleichtern, auch in Kombination mit anderen Antidiabetika. Einleitung und Begleitung einer Insulin-Therapie gehören somit weiterhin zu den Kernaufgaben der Diabetologie.

Glucose variability and diabetes complications: Risk factor or biomarker? Can we disentangle the "Gordian Knot"?

« Variability in glucose homoeostasis » is a better description than « glycaemic variability » as it encompasses two categories of dysglycaemic disorders: i) the short-term daily glucose fluctuations and ii) long-term weekly, monthly or quarterly changes in either HbA1c, fasting or postprandial plasma glucose. Presently, the relationship between the "variability in glucose homoeostasis" and diabetes complications has never been fully clarified because studies are either observational or limited to retrospective analysis of trials not primarily designed to address this issue. Despite the absence of definitive evidence from randomized controlled trials (RCTs), it is most likely that acute and long-term glucose homoeostasis "cycling", akin to weight and blood pressure "cycling" in obese and hypertensive individuals, are additional risk factors for diabetes complications in the presence of sustained ambient hyperglycaemia. As hypoglycaemic events are strongly associated with short- and long-term glucose variability, two relevant messages can be formulated. Firstly, due consideration should be given to avoid within-day glucose fluctuations in excess of 36% (coefficient of variation) at least for minimizing the inconvenience and dangers associated with hypoglycaemia. Secondly, it seems appropriate to consider that variability in glucose homoeostasis is not only associated with cardiovascular events but is also a causative risk factor via hypoglycaemic episodes as intermediary step. Untangling the" Gordian Knot", to provide confirmation about the impact of variability in glucose homoeostasis and diabetes complications remains a daunting prospect.

Dapagliflozin as an Adjunct Therapy to Insulin in Patients with Type 1 Diabetes Mellitus: Efficacy and Safety of this Combination

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Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

BACKGROUND AND AIMS

Despite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.

METHOD

ology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use.

RESULTS

188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1-10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19-1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79-2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66-0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58-0.95; p:0.017).

CONCLUSION

In spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.

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.

Application of medium-term metrics for assessing glucose homoeostasis: Usefulness, strengths and weaknesses

This review aims to address the issue of whether or not the newer metrics, developed for continuous glucose monitoring [real-time CGM (rtCGM), intermittently scanned CGM (isCGM)], enhance assessment of the "glucose tetrad": Ambient hyperglycaemia, short-term glycaemic variability, postprandial glucose excursions and hypoglycaemia. The ever-increasing number of metrics offered with rtCGM and isCGM includes intermediate-term indicators referred to as "time in range" (TIR), the time spent in the range of 70-180mg/dL (TIR 70-180); time spent above the range of 180mg/dL (TAR>180); and time spent below the range of 70mg/dL or 54mg/dL (TBR<70 or TBR<54). The former two values are strongly correlated with HbA1c levels and can therefore serve as short- or medium-term markers of ambient hyperglycaemia, depending on whether glucose sensors are worn over periods of several days or weeks, respectively, whereas the latter indices (TBR<70 or<54) are more relevant for capturing hypoglycaemic events and quantifying their magnitude and duration, in contrast to random spot testing with self-monitoring of blood glucose. Nevertheless, although analyses of 24h glucose profiles by CGM provide a highly valuable method for quantifying postprandial glucose excursions and short-term glycaemic variability, neither of these factors can be fully represented by such TIR metrics. Thus, other metrics are clearly needed for more comprehensive assessment of glucose homoeostasis.

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

Innumerable studies have suggested "the lower, the better" for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.