Sensitivity of Traditional and Risk-Based Glycemic Variability Measures to the Effect of Glucose-Lowering Treatment in Type 2 Diabetes Mellitus

Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM

CONTEXT

There is a challenge to predict treatment effects in patients with type 2 diabetes mellitus (T2DM).

OBJECTIVE

To assess and predict treatment effects in patients with T2DM through time-series analysis of continuous glucose monitoring (CGM) measurements.

METHOD

We extracted and clustered the trend components of CGM measurements to generate representative time-series profiles, which were used as a predictor of treatment effects in groups of patients.

SETTING AND PARTICIPANTS

We recruited 111 outpatients with T2DM at Ningbo City First Hospital, China.

INTERVENTION

The patients underwent CGM measurement for 14 days at the beginning of glucose-lowering treatment.

MAIN OUTCOME MEASURES

Hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) were obtained at the beginning and after 6 months of treatment.

RESULTS

111 patients each had 960 to 1344 CGM measurements for 14 days at 96 measurements per day. The patients were classified into 3 groups according to the profiles of trend components of CGM observed values by time-series clustering method, including decreasing (47 patients), increasing (26 patients), and unchanged (38 patients) profiles. After 6 months of glucose-lowering treatment, FPG declined from 10.2 to 6.8 mmol/L (a decline of 3.4 mmol/L) in the decreasing group, from 8.9 to 9.2 mmol/L (a rise of 0.3 mmol/L) in the increasing group, and from 8.4 to 7.5 mmol/L (a decline of 0.9 mmol/L) in the unchanged group. The changes of HbA1c were 2.3%, 0.2%, and 0.9% for the 3 groups (P < 0.01), respectively.

CONCLUSIONS

Clustering of the trend components of CGM data generates representative CGM profiles that are predictive of 6-month therapeutic effects for T2DM.

Glycemic variability and cardiovascular disease in patients with type 2 diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

Starch digestibility modulation significantly improves glycemic variability in type 2 diabetic subjects: A pilot study

BACKGROUND AND AIMS

In type 2 diabetes (T2D) patients, the reduction of glycemic variability and postprandial glucose excursions is essential to limit diabetes complications, beyond HbA1c level. This study aimed at determining whether increasing the content of Slowly Digestible Starch (SDS) in T2D patients' diet could reduce postprandial hyperglycemia and glycemic variability compared with a conventional low-SDS diet.

METHODS AND RESULTS

For this randomized cross-over pilot study, 8 subjects with T2D consumed a controlled diet for one week, containing starchy products high or low in SDS. Glycemic variability parameters were evaluated using a Continuous Glucose Monitoring System. Glycemic variability was significantly lower during High-SDS diet compared to Low-SDS diet for MAGE (Mean Amplitude of Glycemic Excursions, p < 0.01), SD (Standard Deviation, p < 0.05), and CV (Coefficient of Variation, p < 0.01). The TIR (Time In Range) [140-180 mg/dL[ was significantly higher during High-SDS diet (p < 0.0001) whereas TIRs ≥180 mg/dL were significantly lower during High-SDS diet. Post-meals tAUC (total Area Under the Curve) were significantly lower during High-SDS diet.

CONCLUSION

One week of High-SDS Diet in T2D patients significantly improves glycemic variability and reduces postprandial glycemic excursions. Modulation of starch digestibility in the diet could be used as a simple nutritional tool in T2D patients to improve daily glycemic control. REGISTRATION NUMBER: in clinicaltrials.gov: NCT03289494.

CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters' (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring

INTRODUCTION

Intensive glycemic control minimizes the risks of microvascular complications in diabetes. A1C is a convenient estimate of mean blood glucose, but is not the only marker available. The practical use and limitations of alternative markers and continuous glucose monitors are the focus of this review.

METHODS

PubMed and the Cochrane Library were searched for studies concerning applications or limitations of A1C, fructosamine, glycated albumin, 1,5-anhydroglucitol, skin autofluorescence, and continuous glucose monitoring. Papers reporting on strengths, limitations, or comparisons of these methods were reviewed for inclusion.

RESULTS

A1C reflects three months of glycemic control and is not an ideal marker in all patient populations. Fructosamine and glycated albumin reflect mean blood glucose over three weeks. 1,5-Anhydroglucitol can measure hyperglycemic excursions in days to weeks. Continuous glucose monitors provide immediate feedback for timely intervention to reduce glycemic excursions and can assess glycemic variability. Current barriers to continuous glucose monitor use include inexperience, cost, discomfort, and medication interference.

CONCLUSIONS

Many promising alternative glycemic markers exist. The main limitations for all alternative methods of glycemic monitoring are a lack of standardization for clinically useful cut-offs or guidelines, and a lack of long-term data on their association with complications, particularly in varied patient populations.

Beyond HbA1c : using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision-making

Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user-friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research-related goals and clinical guidance for individuals. Traditionally, HbA_1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA_1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo- and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review presents several clinical scenarios of glycaemic outcomes from CGM data that can be analysed to describe glycaemic variability and its attendant risks of hyperglycaemia and hypoglycaemia, moving towards relevant interpretation of the complex CGM data streams.

Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices"

OBJECTIVE

We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia.

RESEARCH DESIGN AND METHODS

We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups.

RESULTS

There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M₁₀₀, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE_{%Hypoglycemia}), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE_{%Hyperglycemia}), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in M_R, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE_{%Hypoglycemia}, or GRADE_{%Hyperglycemia}.

CONCLUSIONS

Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.

Relationship of Glucose Variability With Glycated Hemoglobin and Daily Mean Glucose: A Post Hoc Analysis of Data From 5 Phase 3 Studies

BACKGROUND

The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG).

METHODS

Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics.

RESULTS

Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group.

CONCLUSIONS

Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.

Lixisenatide reduces glycaemic variability in insulin-treated patients with type 2 diabetes

Chronic hyperglycaemia and glucose variability are associated with the development of chronic diabetes-related complications. We conducted a pooled analysis of patient-level data from three 24-week, randomized, phase III clinical trials to evaluate the impact of lixisenatide (LIXI) on glycaemic variability (GV) vs placebo as add-on to basal insulin. The main outcome GV measures were standard deviation (s.d.), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG) level, area under the curve for fasting glucose (AUC-F), and high (HBGI) and low blood glucose index (LBGI). The change in GV metrics over 24 weeks and relationships among baseline GV, patient characteristics and outcomes were assessed. Data were pooled from 1198 patients (665 LIXI, 533 placebo). Values for s.d., MAG level, MAGE, HBGI, and AUC-F significantly decreased with LIXI vs placebo, while LBGI values were unchanged. Higher baseline GV measures correlated with older age, longer type 2 diabetes duration, lower body mass index, higher baseline glycated/haemogobin, greater reduction in postprandial glucose (PPG) level, and higher rates of symptomatic hypoglycaemia. These data show that LIXI added to basal insulin significantly reduced GV and PPG excursions vs placebo, without increasing the risk of hypoglycaemia (LBGI).