A method for assessing quality of control from glucose profiles

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

BACKGROUND

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

METHODS

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

RESULTS

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

CONCLUSIONS

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters

We review clinical instances in which A1C should not be used and reflect on the use of other glucose metrics that can be used, in substitution of or in combination with A1C and SMBG, to tailor an individualized approach that will result in better outcomes and patient empowerment.

Continuous glucose monitoring in patients with type 2 diabetes treated with glucagon-like peptide-1 receptor agonist dulaglutide in combination with prandial insulin lispro: an AWARD-4 substudy

AIM

To conduct a substudy, using 24-hour continuous glucose monitoring (CGM), of the AWARD-4 trial, which was designed to compare insulin + glucagon-like peptide-1 receptor agonist treatment with an insulin-only regimen.

METHODS

The AWARD-4 trial randomized 884 conventional insulin regimen-treated patients to dulaglutide 1.5 mg, dulaglutide 0.75 mg and glargine, all in combination with prandial insulin lispro. The CGM substudy included 144 patients inserted with a Medtronic CGMS iPro CGM device to enable 3-day glucose monitoring. CGM sessions were completed at weeks 0, 13, 26 and 52. CGM measures included mean 24-hour glucose, percentage time in target glucose ranges, hyper- and hypoglycaemia and glucose variability. The primary objective was treatment comparison for percentage time spent with CGM glucose values in the 3.9-7.8 mmol/L range after 26 weeks.

RESULTS

At week 26, mean CGM values decreased in all treatment groups (change from baseline -2.8 ± 0.3, -2.4 ± 0.3 and -2.5 ± 0.3 mmol/L for dulaglutide 1.5 mg, dulaglutide 0.75 mg and glargine, respectively); between-group differences were not statistically significant. Treatment groups were similar for percentage time in the 3.9-7.8 mmol/L range. Percentage time in the 3.9-10.0 mmol/L range was greater for dulaglutide 1.5 mg than for glargine (p < 0.05). Dulaglutide and glargine were associated with decreased glucose variability for all CGM variability indices. The overall within-patient standard deviation (s.d.) was significantly reduced with dulaglutide 1.5 mg versus glargine (p < 0.05). At week 52, there were no significant differences among the groups with regard to measures of normoglycaemia or near-normoglycaemia and for the overall within-patient s.d. Treatment with glargine was associated with greater increases in percentage time spent with glucose values ≤3.9 mmol/L, with statistically significant differences between the groups at 52 weeks (p < 0.05).

CONCLUSIONS

In combination with prandial lispro, treatment with dulaglutide and glargine resulted in similar proportions of glucose values in the normoglycaemic range, but dulaglutide provided an improved balance between the proportion of values within the near-normoglycaemia range and values within the hypoglycaemic range.

Glucose Variability: Comparison of Different Indices During Continuous Glucose Monitoring in Diabetic Patients

BACKGROUND

Glucose variability has been suspected to be a major factor of diabetic complications. Several indices have been proposed for measuring glucose variability, but their interest remains discussed. Our aim was to compare different indices.

METHODS

Glucose variability was studied in 150 insulin-treated diabetic patients (46% men, 42% type 1 diabetes, age 52 ± 11 years) using a continuous glucose monitoring system (668 ± 564 glucose values; mean glucose value 173 ± 38 mg/dL). Results from the mean, the median, different indices (SD, MAGE, MAG, glucose fluctuation index (GFI), and percentages of low [<60 mg/dL] and high [>180 mg/dL] glucose values), and ratios (CV = SD/m, MAGE/m, MAG/m, and GCF = GFI/m) were compared using Pearson linear correlations and a multivariate principal component analysis (PCA).

RESULTS

CV, MAGE/m (ns), GCF and GFI (P < .05), MAG and MAG/m (P < .01) were not strongly correlated with the mean. The percentage of high glucose values was mainly correlated with indices. The percentage of low glucose values was mainly correlated with ratios. PCA showed 3 main axes; the first was associated with descriptive data (mean, SD, CV, MAGE, MAGE/m, and percentage of high glucose values); the second with ratios MAG/m and GCF and with the percentage of low glucose values; and the third with MAG, GFI, and the percentage of high glucose values.

CONCLUSIONS

Indices and ratios provide complementary pieces of information associated with high and low glucose values, respectively. The pairs MAG+MAG/m and GFI+GCF appear to be the most reliable markers of glucose variability in diabetic patients.

1,5-Anhydro-D-Glucitol Could Reflect Hypoglycemia Risk in Patients with Type 2 Diabetes Receiving Insulin Therapy

BACKGROUND

The identification of a marker for hypoglycemia could help patients achieve strict glucose control with a lower risk of hypoglycemia. 1,5-Anhydro-D-glucitol (1,5-AG) reflects postprandial hyperglycemia in patients with well-controlled diabetes, which contributes to glycemic variability. Because glycemic variability is related to hypoglycemia, we aimed to evaluate the value of 1,5-AG as a marker of hypoglycemia.

METHODS

We enrolled 18 adults with type 2 diabetes mellitus (T2DM) receiving insulin therapy and assessed the occurrence of hypoglycemia within a 3-month period. We measured 1,5-AG level, performed a survey to score the severity of hypoglycemia, and applied a continuous glucose monitoring system (CGMS).

RESULTS

1,5-AG was significantly lower in the high hypoglycemia-score group compared to the low-score group. Additionally, the duration of insulin treatment was significantly longer in the high-score group. Subsequent analyses were adjusted by the duration of insulin treatment and mean blood glucose, which was closely associated with both 1,5-AG level and hypoglycemia risk. In adjusted correlation analyses, 1,5-AG was negatively correlated with hypoglycemia score, area under the curve at 80 mg/dL, and low blood glucose index during CGMS (P=0.068, P=0.033, and P=0.060, respectively).

CONCLUSION

1,5-AG level was negatively associated with hypoglycemia score determined by recall and with documented hypoglycemia after adjusting for mean glucose and duration of insulin treatment. As a result, this level could be a marker of the risk of hypoglycemia in patients with well-controlled T2DM receiving insulin therapy.

Comparison of glycemic control and variability in patients with type 2 and posttransplantation diabetes mellitus

AIM

Posttransplantation diabetes mellitus (PTDM) is a common complication after renal transplantation leading to increased cardiovascular morbidity and mortality. In subjects with type 2 diabetes (T2DM) increased glycemic variability and poor glycemic control have been associated with cardiovascular complications. We therefore aimed at determining glycemic variability and glycemic control in subjects with PTDM in comparison to T2DM subjects.

METHODS

In this observational study we analyzed 10 transplanted subjects without diabetes (Control), 10 transplanted subjects with PTDM, and 8 non-transplanted T2DM subjects using Continuous Glucose Monitoring (CGM). Several indices of glycemic control quality and variability were computed.

RESULTS

Many indices of both glycemic control quality and variability were different between control and PTDM subjects, with worse values in PTDM. The indices of glycemic control, such as glucose mean, GRADE and M-value, were similar in PTDM and T2DM, but some indices of glycemic variability, that is CONGA, lability index and shape index, showed a markedly higher (i.e., worse) value in T2DM than in PTDM (P value range: 0.001-0.035).

CONCLUSIONS

Although PTDM and T2DM subjects showed similar glycemic control quality, glycemic variability was significantly higher in T2DM. These data underscore potential important pathophysiological differences between T2DM and PTDM indicating that increased glycemic variability may not be a key factor for the excess cardiovascular mortality in patients with PTDM.

Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis

BACKGROUND

Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D).

METHODS

A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance.

RESULTS

A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D.

CONCLUSIONS

Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.

Analysis of Continuous Glucose Monitoring in Pregnant Women With Diabetes: Distinct Temporal Patterns of Glucose Associated With Large-for-Gestational-Age Infants

OBJECTIVE

Continuous glucose monitoring (CGM) is increasingly used to assess glucose control in diabetes. The objective was to examine how analysis of glucose data might improve our understanding of the role temporal glucose variation has on large-for-gestational-age (LGA) infants born to women with diabetes.

RESEARCH DESIGN AND METHODS

Functional data analysis (FDA) was applied to 1.68 million glucose measurements from 759 measurement episodes, obtained from two previously published randomized controlled trials of CGM in pregnant women with diabetes. A total of 117 women with type 1 diabetes (n = 89) and type 2 diabetes (n = 28) who used repeated CGM during pregnancy were recruited from secondary care multidisciplinary obstetric clinics for diabetes in the U.K. and Denmark. LGA was defined as birth weight ≥90th percentile adjusted for sex and gestational age.

RESULTS

A total of 54 of 117 (46%) women developed LGA. LGA was associated with lower mean glucose (7.0 vs. 7.1 mmol/L; P < 0.01) in trimester 1, with higher mean glucose in trimester 2 (7.0 vs. 6.7 mmol/L; P < 0.001) and trimester 3 (6.5 vs. 6.4 mmol/L; P < 0.01). FDA showed that glucose was significantly lower midmorning (0900-1100 h) and early evening (1900-2130 h) in trimester 1, significantly higher early morning (0330-0630 h) and throughout the afternoon (1130-1700 h) in trimester 2, and significantly higher during the evening (2030-2330 h) in trimester 3 in women whose infants were LGA.

CONCLUSIONS

FDA of CGM data identified specific times of day that maternal glucose excursions were associated with LGA. It highlights trimester-specific differences, allowing treatment to be targeted to gestational glucose patterns.

Reversal of Hypoglycemia Unawareness with a Single-donor, Marginal Dose Allogeneic Islet Transplantation in Korea: A Case Report

Pancreatic islet transplantation is a physiologically advantageous and minimally invasive procedure for the treatment of type 1 diabetes mellitus. Here, we describe the first reported case of successful allogeneic islet transplantation alone, using single-donor, marginal-dose islets in a Korean patient. A 59-yr-old patient with type 1 diabetes mellitus, who suffered from recurrent severe hypoglycemia, received 4,163 islet equivalents/kg from a single brain-death donor. Isolated islets were infused intraportally without any complications. The immunosuppressive regimen was based on the Edmonton protocol, but the maintenance dosage was reduced because of mucositis and leukopenia. Although insulin independence was not achieved, the patient showed stabilized blood glucose concentration, reduced insulin dosage and reversal of hypoglycemic unawareness, even with marginal dose of islets and reduced immunosuppressant. Islet transplantation may successfully improve endogenous insulin production and glycemic stability in subjects with type 1 diabetes mellitus.

Glycemic Variability Is Associated with Frequency of Blood Glucose Testing and Bolus: Post Hoc Analysis Results from the ProAct Study

INTRODUCTION

The ProAct study has shown that a pump switch to the Accu-Chek(®) Combo system (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) in type 1 diabetes patients results in stable glycemic control with significant improvements in glycated hemoglobin (HbA1c) in patients with unsatisfactory baseline HbA1c and shorter pump usage time.

PATIENTS AND METHODS

In this post hoc analysis of the ProAct database, we investigated the glycemic control and glycemic variability at baseline by determination of several established parameters and scores (HbA1c, hypoglycemia frequency, J-score, Hypoglycemia and Hyperglycemia Indexes, and Index of Glycemic Control) in participants with different daily bolus and blood glucose measurement frequencies (less than four day, four or five per day, and more than five per day, in both cases). The data were derived from up to 299 patients (172 females, 127 males; age [mean±SD], 39.4±15.2 years; pump treatment duration, 7.0±5.2 years).

RESULTS

Participants with frequent glucose readings had better glycemic control than those with few readings (more than five readings per day vs. less than four readings per day: HbA1c, 7.2±1.1% vs. 8.0±0.9%; mean daily blood glucose, 151±22 mg/dL vs. 176±30 mg/dL; percentage of readings per month >300 mg/dL, 10±4% vs. 14±5%; percentage of readings in target range [80-180 mg/dL], 59% vs. 48% [P<0.05 in all cases]) and had a lower glycemic variability (J-score, 49±13 vs. 71±25 [P<0.05]; Hyperglycemia Index, 0.9±0.5 vs. 1.9±1.2 [P<0.05]; Index of Glycemic Control, 1.9±0.8 vs. 3.1±1.6 [P<0.05]; Hypoglycemia Index, 0.9±0.8 vs. 1.2±1.3 [not significant]). Frequent self-monitoring of blood glucose was associated with a higher number of bolus applications (6.1±2.2 boluses/day vs. 4.5±2.0 boluses/day [P<0.05]). Therefore, a similar but less pronounced effect on glycemic variability in favor of more daily bolus applications was observed (more than five vs. less than four bolues per day: J-score, 57±17 vs. 63±25 [not significant]; Hypoglycemia Index, 1.0±1.0 vs. 1.5±1.4 [P<0.05]; Hyperglycemia Index, 1.3±0.6 vs. 1.6±1.1 [not significant]; Index of Glycemic Control, 2.3±1.1 vs. 3.1±1.7 [P<0.05]).

CONCLUSIONS

Pump users who perform frequent daily glucose readings have a better glycemic control with lower glycemic variability.

Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.

Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose

There is need for readily understandable graphical displays of glucose data to facilitate interpretation by clinicians and researchers. (1) Display of the percentage of glucose values above a specified threshold for hyperglycemia (%High) versus percentage of glucose values below a specified threshold for hypoglycemia (%Low). If all glucose values fell within the target range, then all data points would fall at the origin. (2) After an intervention, one can plot the change in percentage of glucose values above a specified threshold for hyperglycemia versus the change in percentage of glucose values below a specified threshold defining hypoglycemia: The quadrants of this graph correspond to (a) increased risk of both hyper- and hypoglycemia, (b) decreased hyperglycemia but increased risk of hypoglycemia, (c) decreases in both hypo- and hyperglycemia, and (d) decreased hypoglycemia but increased hyperglycemia. (3) A 2-dimensional triangular graph can be used for simultaneous display of %High, %Low, and percentage in target range. (4) Display of risk of hyper- versus risk of hypoglycemia based on both frequency and severity of departures from the target range can be used. (5) Graphs (1) and (4) can also be presented using percentile scores relative to a reference population. (6) It is also useful to analyze %Hypoglycemia or risk of hypoglycemia versus mean glucose. These methods are illustrated with examples from representative cases and shown to be feasible, practical, and informative. These new types of graphical displays can facilitate rapid analysis of risks of hypo- and hypoglycemia simultaneously and responses to therapeutic interventions for individuals or in clinical trials.

Four-Point Preprandial Self-Monitoring of Blood Glucose for the Assessment of Glycemic Control and Variability in Patients with Type 2 Diabetes Treated with Insulin and Vildagliptin

The study explored the utility of four-point preprandial glucose self-monitoring to calculate several indices of glycemic control and variability in a study adding the DPP-4 inhibitor vildagliptin to ongoing insulin therapy. This analysis utilized data from a double-blind, randomized, placebo-controlled crossover study in 29 patients with type 2 diabetes treated with vildagliptin or placebo on top of stable insulin dose. During two 4-week treatment periods, self-monitoring of plasma glucose was undertaken at 4 occasions every day. Glucose values were used to assess several indices of glycemic control quality, such as glucose mean, GRADE, M-VALUE, hypoglycemia and hyperglycemia index, and indices of glycemic variability, such as standard deviation, CONGA, J-INDEX, and MAGE. We found that vildagliptin improved the glycemic condition compared to placebo: mean glycemic levels, and both GRADE and M-VALUE, were reduced by vildagliptin (P < 0.01). Indices also showed that vildagliptin reduced glycemia without increasing the risk for hypoglycemia. Almost all indices of glycemic variability showed an improvement of the glycemic condition with vildagliptin (P < 0.02), though more marked differences were shown by the more complex indices. In conclusion, the study shows that four-sample preprandial glucose self-monitoring is sufficient to yield information on the vildagliptin effects on glycemic control and variability.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis

BACKGROUND

Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.

MATERIALS AND METHODS

A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.

RESULTS

SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.

CONCLUSIONS

The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Glycaemic variability in diabetes: a tool for assessing the quality of glycaemic control and the risk of complications

The routine approach to evaluating the effectiveness of diabetes treatment based on the level of glycated haemoglobin (HbA. 1c) accounts for the average glucose level but does not consider the scope and frequency of its fluctuations. The development of computational methods to analyse glycaemic oscillations has made it possible to propose the concept of glycaemic variability (GV). The interest in research focused on GV increased dramatically after continuous glucose monitoring (CGM) technology was introduced, which provided the opportunity to study in detail the temporal structure of blood glucose curves. Numerous methods for assessing GV proposed over the past five decades characterize glycaemic fluctuations as functions of concentration and time and estimate the risks of hypoglycaemia and hyperglycaemia. Accumulating evidence indicates that GV may serve as a significant predictor of diabetic complications. Prospective studies demonstrate that certain GV parameters have independent significance for predicting diabetic retinopathy, nephropathy and cardiovascular diseases. There is evidence that GV correlates with the severity of atherosclerotic vascular lesions and cardiovascular outcomes in diabetic patients. The mechanisms underlying the relationship between GV and vascular complications are being intensively studied, and recent data show that the effect of GV on vascular walls may be mediated by oxidative stress, chronic inflammation and endothelial dysfunction. Average blood glucose levels and GV are considered independent predictors of hypoglycaemia. Increased GV is associated with impaired hormonal response to hypoglycaemia and is a long-term predictor of hypoglycaemia unawareness. These data allow us to conclude that computational methods for analysing GV in patients with diabetes may serve as a promising tool for personalized assessment of glycaemic control and the risk of vascular complications and hypoglycaemia. Thus, the reduction of GV can be regarded as one of the therapeutic targets to treat diabetes.

Sparse Principal Component Analysis for the parsimonious description of glucose variability in diabetes

Abnormal glucose variability (GV) is considered to be a risk factor for the development of diabetes complications. For its quantification from continuous glucose monitoring (CGM) data, tens of different indices have been proposed in the literature, but the information carried by them is highly redundant. In the present work, the Sparse Principal Component Analysis (SPCA) technique is used to select, from a wide pool of GV metrics, a smaller subset of indices that preserves the majority of the total original variance, providing a parsimonious but still comprehensive description of GV. In detail, SPCA is applied to a set of 25 literature GV indices evaluated on CGM time-series collected in 17 type 1 (T1D) and 13 type 2 (T2D) diabetic subjects. Results show that the 10 GV indices selected by SPCA preserve more than the 75% of the variance of the original set of 25 indices, both in T1D and T2D. Moreover, 6 indices of the parsimonious set are shared by T1D and T2D.

Vascular function and glucose variability improve transiently following initiation of continuous subcutaneous insulin infusion in children with type 1 diabetes

OBJECTIVE

The effect of continuous subcutaneous insulin infusion (CSII) and glucose variability on vascular health in type 1 diabetes (T1D) is not known. We aimed to determine whether initiation of CSII improves vascular function and reduces glucose variability, independent of changes in HbA1c.

METHODS

Twenty-two children with T1D (12.5 ± 2.9 yr) were reviewed immediately prior, 3 wk, and 12 months after initiation of CSII. Vascular function [flow-mediated dilatation (FMD), glyceryl trinitrate-mediated dilatation (GTN)], glucose variability [mean of daily differences (MODD), mean amplitude of glycaemic excursions (MAGE) and continuous overlapping net glycaemic action (CONGA)], and clinical and biochemical data were measured at each visit. Results for the first two visits were compared to a previously studied cohort of 31 children with T1D who remained on multiple daily injections (MDI).

RESULTS

FMD, GTN, blood pressure, HbA1c, fructosamine, and glucose variability significantly improved 3 wk after CSII commencement (all p < 0.05), but there was no change in the MDI control group. At 3 wk, vascular function related to glucose variability [(FMD: MODD, r = -0.62, p = 0.002) and (GTN: MAGE, r = -0.59, p = 0.004; CONGA-4, r = -0.51, p = 0.01; MODD, r = -0.62, p = 0.002)] but not to blood pressure, HbA1c, or fructosamine. At 12 months, FMD, GTN, blood pressure, and glucose variability returned to baseline levels, while HbA1c deteriorated. Carotid intima media thickness was unchanged over 12 months.

CONCLUSIONS

Initiation of CSII rapidly improves vascular function in association with decreased glucose variability; however, the effects are not sustained with deterioration of metabolic control and glucose variability.

Standardization versus customization of glucose reporting

Bergenstal et al. (Diabetes Technol Ther 2013;15:198-211) described an important approach toward standardization of reporting and analysis of continuous glucose monitoring and self-monitoring of blood glucose (SMBG) data. The ambulatory glucose profile (AGP), a composite display of glucose by time of day that superimposes data from multiple days, is perhaps the most informative and useful of the many graphical approaches to display glucose data. However, the AGP has limitations; some variations are desirable and useful. Synchronization with respect to meals, traditionally used in glucose profiles for SMBG data, can improve characterization of postprandial glucose excursions. Several other types of graphical display are available, and recently developed ones can augment the information provided by the AGP. There is a need to standardize the parameters describing glycemic variability and cross-validate the available computer programs that calculate glycemic variability. Clinical decision support software can identify and prioritize clinical problems, make recommendations for modifications of therapy, and explain its justification for those recommendations. The goal of standardization is challenging in view of the diversity of clinical situations and of computing and display platforms and software. Standardization is desirable but must be done in a manner that permits flexibility and fosters innovation.

Glucose variability

The proposed contribution of glucose variability to the development of the complications of diabetes beyond that of glycemic exposure is supported by reports that oxidative stress, the putative mediator of such complications, is greater for intermittent as opposed to sustained hyperglycemia. Variability of glycemia in ambulatory conditions defined as the deviation from steady state is a phenomenon of normal physiology. Comprehensive recording of glycemia is required for the generation of any measurement of glucose variability. To avoid distortion of variability to that of glycemic exposure, its calculation should be devoid of a time component.

Hypo- and hyperglycemia in relation to the mean, standard deviation, coefficient of variation, and nature of the glucose distribution

AIMS

We describe a new approach to estimate the risks of hypo- and hyperglycemia based on the mean and SD of the glucose distribution using optional transformations of the glucose scale to achieve a more nearly symmetrical and Gaussian distribution, if necessary. We examine the correlation of risks of hypo- and hyperglycemia calculated using different glucose thresholds and the relationships of these risks to the mean glucose, SD, and percentage coefficient of variation (%CV).

MATERIALS AND METHODS

Using representative continuous glucose monitoring datasets, one can predict the risk of glucose values above or below any arbitrary threshold if the glucose distribution is Gaussian or can be transformed to be Gaussian. Symmetry and gaussianness can be tested objectively and used to optimize the transformation.

RESULTS

The method performs well with excellent correlation of predicted and observed risks of hypo- or hyperglycemia for individual subjects by time of day or for a specified range of dates. One can compare observed and calculated risks of hypo- and hyperglycemia for a series of thresholds considering their uncertainties. Thresholds such as 80 mg/dL can be used as surrogates for thresholds such as 50 mg/dL. We observe a high correlation of risk of hypoglycemia with %CV and illustrate the theoretical basis for that relationship.

CONCLUSIONS

One can estimate the historical risks of hypo- and hyperglycemia by time of day, date, day of the week, or range of dates, using any specified thresholds. Risks of hypoglycemia with one threshold (e.g., 80 mg/dL) can be used as an effective surrogate marker for hypoglycemia at other thresholds (e.g., 50 mg/dL). These estimates of risk can be useful in research studies and in the clinical care of patients with diabetes.

Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives

AIMS

The aim of this review is to focus on intra-day glucose variability (GV), specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in the non-diabetic population.

DATA SYNTHESIS

The term GV has been used in the literature to express many different concepts; in the present review, we focus our attention on intra-day GV. In particular, we try to assess whether GV provides additional information on glycemic control beyond HbA1c, since GV seems to be incompletely expressed by HbA1c, particularly in patients with good metabolic control. Many different indexes have been proposed to measure GV, however at the moment no "gold standard" procedure is available. Evidence in vitro, in experimental settings and in animal studies, shows that fluctuating glucose levels display a more deleterious effect than constantly high glucose exposure. However, these findings are not completely reproducible in human settings. Moreover, the relationship between GV and cardiovascular events is still controversial.

CONCLUSIONS

The term GV should be reserved to indicate intra-day variability and different indexes of GV should be used, depending on the metabolic profile of the population studied and the specific issue to be investigated. Self glucose monitoring or continuous glucose monitoring should be used for assessing glucose variability.

Use of telemedicine in subjects with type 1 diabetes equipped with an insulin pump and real-time continuous glucose monitoring

We evaluated a telemedicine system in patients with type 1 diabetes who had optimized treatment with an insulin pump and a real-time continuous glucose monitoring system. We conducted a prospective, one-year study of 15 subjects. Three medical visits took place: pre-baseline, baseline and at 6 months. Each month the subjects transmitted information from the glucose meter, glucose sensor and insulin pump. We adjusted the treatment and returned the information by email. We evaluated psychological and metabolic variables, including HbA(1c), hypoglycaemia, hyperglycaemia and glucose variability. At baseline the mean age of the subjects was 40 years and the mean duration of diabetes was 22 years. There was a significant reduction in HbA(1c) (7.50 to 6.97%) at 6 months, a significant increase in the number of self-monitoring blood glucose checks per day (5.2 to 6.2), and significant improvements in variability: MODD, mean of daily difference (67 to 53) and MAGE, mean amplitude of glycaemic excursions (136 to 102). There were significant improvements in quality of life (92 to 87), satisfaction with the treatment (34 to 32) and less fear of hypoglycaemia (36 to 32). Adult subjects with type 1 diabetes on treatment with a continuous insulin infusion system and a real time glucose sensor and who have acceptable metabolic control and optimized treatment can benefit from the addition of a telemetry system to their usual outpatient follow-up.

Hypoglycemia, but not glucose variability, relates to vascular function in children with type 1 diabetes

BACKGROUND

Chronic sustained hyperglycemia unequivocally predicts vascular disease in diabetes. However, the vascular risk of glucose variability, including hypoglycemia, is uncertain. Vascular dysfunction is present in children with type 1 diabetes and is a critical precursor of atherosclerosis. We aimed to evaluate the relationship between glucose variability and vascular function in children with type 1 diabetes.

SUBJECTS AND METHODS

Fifty-two type 1 diabetes subjects (14 [SD 2.7] years old, 25 males) had continuous glucose monitoring that included 48 h of data used to evaluate glucose variability (mean amplitude of glycemic excursions [MAGE] and other measurements) and hypoglycemia indices (glycemic risk assessment diabetes equation [GRADE] hypoglycemia, Low Blood Glucose Index [LBGI], and observed duration of hypoglycemia). Children with type 1 diabetes and 50 age- and gender-matched controls had assessments of vascular function (flow-mediated dilatation [FMD] and glyceryl trinitrate-mediated dilatation [GTN]).

RESULTS

Children with type 1 diabetes had lower FMD and GTN than controls (P=0.02 and P<0.001, respectively). GRADE hypoglycemia and LBGI were inversely related to FMD (r=-0.36, P=0.009 and r=-0.302, P=0.03, respectively) but did not relate to GTN. GRADE hypoglycemia was independently related to FMD (regression coefficient=-0.25±0.09, P=0.006). MAGE and other measurements of glucose variability measurements did not relate to FMD or GTN.

CONCLUSIONS

Hypoglycemia, but not glucose variability, during continuous glucose monitoring relates to impaired vascular endothelial function in children with type 1 diabetes. Hypoglycemia may be an additional risk factor for early cardiovascular disease, but the effect of glucose variability, independent of glycosylated hemoglobin, on vascular function remains uncertain.

Prediction of the risk to develop diabetes-related late complications by means of the glucose pentagon model: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring study

BACKGROUND

By taking parameters into account that describe the variability of continuously monitored glucose and long-term metabolic control [hemoglobin A1c (HbA1c)], the glucose pentagon model (GPM) allows characterization of the glucose profile of individual patients with diabetes in a graphical format. A glycemic risk parameter (GRP) derived from this model might allow a better prognosis of the risk to develop diabetes-related complications than the HbA1c.

METHODS

To evaluate this hypothesis, we analyzed a subset of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring (CGM) study. The values of the different parameters that are integrated in the GPM were extracted automatically from CGM profiles registered before and after 6 months by means of the Medtronic CGM system in 108 patients.

RESULTS

In these patients, the significant reduction in HbA1c from 7.4% to 7.0% was accompanied by a reduction in glycemia from 164 to 156 mg/dl, standard deviation from 61 to 57 mg/dl, area under the curve >160 mg/dl 29.2 to 23.1, and time per day >160 mg/dl 634 to 576 min. This led to a subsequent reduction in GRP from 3.3 to 2.7; this decrease by 18.2% was significantly larger than that in HbA1c by 8.6% (p < .001). Changes in individual GPMs/GRPs support this observation. They also show the impact of high glycemic variability on GPM/GRP.

CONCLUSIONS

Our analysis of data of a study with a considerable sample size and study duration showed that the GPM is not only helpful for rapid assessment of individual glycemic profiles and how therapeutic interventions influence these, but also appears to provide a better prognosis of the risk to develop late complications than the HbA1c per se. However, it is also clear that a true validation of such a model requires performance of a long-term study in a large number of patients with diabetes.

Glycemic Profiles of Healthy Individuals with Low Fasting Plasma Glucose and HbA1c

Scant data exists on glucose profile variability in healthy individuals. Twenty-nine healthy subjects without diabetes (86% male; mean age, 38 years) were measured by a CGM system and under real-life conditions. The median percentage of time spent on the blood glucose >7.8 mmol/L for 24 hrs was greater than 10% in both NFG and IFG groups. When subjects were divided into either NFG group (i.e., FPG levels of <5.6 mmol/L; n = 22) or IFG group (FPG levels of 5.6-6.9 mmol/L; n = 7), all CGM indicators investigated but GRADE scores, including glucose variability measures, monitoring excursions, hyperglycemia, hypoglycemia, and 24-hour AUC, did not differ significantly between the two groups. GRADE score and its euglycemia% were significantly different between the two groups. Among various CGM indicators, GRADE score may be a sensitive indicator to discriminate glucose profiles between subjects with NFG and those with IFG.

Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©)

BACKGROUND

Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation.

METHODS

We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states.

RESULTS

Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones.

CONCLUSIONS

By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.

Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

BACKGROUND

Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability.

METHODS

Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG).

RESULTS

Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2.

CONCLUSIONS

We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Clinical interpretation of indices of quality of glycemic control and glycemic variability

The practicing physician is faced with the task of interpreting>2 dozen indices of quality of glycemic control and glycemic variability. It would be desirable to have reference data from relevant patient populations (eg, patients with the same type of diabetes, duration of diabetes, therapeutic regimen, or glycated hemoglobin [HbA1c] levels). The physician can then select the appropriate reference set for interpretation of results for each patient. Institutions and clinics may wish to develop their own reference data. Results can be interpreted as excellent, good, fair, or poor, corresponding with quartiles of their distributions. Each index of glycemic control and variability can be given a numerical score in terms of its percentile within the selected reference population. One can then compute the mean and standard deviation of the percentile scores to obtain an integrated measure of the quality of glycemic control or variability. We calculated quartiles for measures of quality of glycemic control and variability. One can use the percent coefficient of variation (%CV) with criteria that apply irrespective of the HbA1c level as a general rule for interpretation of glycemic variability. For example, a %CV<33.5% can be regarded as excellent, a %CV between 33.5% to 36.8% as good, a %CV between 36.8% to 40.6% as fair, and a %CV>40.6% as poor. A graphical display can be used to make more accurate assessments for narrow HbA1c ranges, as the percentiles of the %CV can change systematically with HbA1c level or with mean glucose level.

Cluster analysis of self-monitoring blood glucose assessments in clinical islet cell transplantation for type 1 diabetes

OBJECTIVE

Cluster analysis was performed on the results of self-monitoring of blood glucose (SMBG) to discriminate islet graft function after islet cell transplantation (ICT) in patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Eleven islet recipients were included in this study. The patients visited our clinic monthly after ICT and provided blood samples for fasting C-peptide (n = 270), which were used to evaluate islet graft function. They also provided their SMBG data through an automatic data collection system. The SMBG data for 3 days immediately before each clinic visit were evaluated using the following assessments: M value, mean amplitude of glycemic excursions, J index, index of glycemic control, average daily risk range, and glycemic risk assessment diabetes equation. The cluster analysis was performed for both SMBG assessments and samples. Multivariate logistic regression analysis was used to evaluate the clusters of SMBG for assessing islet graft function.

RESULTS

Analysis for SMBG assessments revealed five types of clusters, which showed similar patterns according to functional or dysfunctional islet graft phase. Two clusters, the euglycemia cluster (P < 0.001) and the hypoglycemia cluster (P = 0.001), were significant factors in the logistic model for islet graft function. The SMBG clusters had significant correlations with clinical graft indexes (P < 0.001).

CONCLUSIONS

Cluster analysis of SMBG data as part of an automated data quality system could allow discrimination of islet graft dysfunction after ICT. This approach should be considered for islet recipients.

A dynamic risk measure from continuous glucose monitoring data

BACKGROUND

The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics.

METHODS

A new dynamic risk (DR) is proposed to explicitly consider the rate of change of glucose as a threat factor for the patient (e.g., risk levels in hypoglycemia and hyperglycemia are amplified in the presence of a decreasing and increasing glucose trend, respectively). The practical calculation of DR is made possible by the use of a regularized deconvolution algorithm that is able to deal with noise in CGM data and with the ill-conditioning of the time-derivative calculation, even in online applications.

RESULTS

Results on simulated and real data show that DR can be effectively computed and fruitfully used in real time (e.g., to generate early warnings of hypo-/hyperglycemic threshold crossings). Further applications of DR in the quantification of the efficiency of glucose control are also suggested.

CONCLUSIONS

Exploiting the information on glucose trends empowers the strength of risk measures in interpreting CGM time-series.

Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

BACKGROUND

Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability.

METHODS

Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG).

RESULTS

Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2.

CONCLUSIONS

We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Evaluating rate of change as an index of glycemic variability, using continuous glucose monitoring data

BACKGROUND

There is no consensus as to the best method to assess glycemic variability from continuous glucose monitoring (CGM) data. Rate of change has been suggested as a preferred method of assessing glycemic variability, but this assertion has not been validated.

METHODS

Forty-eight hours of CGM data were analyzed from 22 subjects (seven controls and 15 with type 1 diabetes) purposively sampled to reflect a range of glycemic variability. SD, mean amplitude of glycemic excursion, continuous overall net glycemic action, SD of rate of change (SDRC), and average absolute rate of change (AARC) were calculated and correlated with a clinical assessment of variability. SDRC and AARC were recalculated following a data smoothing process involving aggregation.

RESULTS

SDRC calculated from non-aggregated glucose readings gives a weaker correlation (r = 0.66) with the clinical assessment of variability than the correlations obtained by other indices (r = 0.90-0.96). Following a process of data aggregation, to exclude clinically insignificant fluctuations of blood glucose, we demonstrated that 60 min was the optimal aggregation period. The correlation between clinical assessment of variability and SDRC, 60-min aggregated, is 0.93, which is comparable to correlations shown by other established indices. Similar results are obtained for AARC.

CONCLUSIONS

Rate of change calculated after appropriate data aggregation is a valid index of glycemic variability. Optimal data aggregation is achieved by aggregating into 1-h blocks.

Use of continuous glucose monitoring in subjects with type 1 diabetes on multiple daily injections versus continuous subcutaneous insulin infusion therapy: a prospective 6-month study

OBJECTIVE

To compare use of continuous glucose monitoring in subjects with type 1 diabetes on multiple daily injection (MDI) therapy versus continuous subcutaneous insulin infusion (CSII) therapy for 6 months.

RESEARCH DESIGN AND METHODS

Sixty type 1 diabetic adults with similar baseline characteristics, using either MDI (n = 30) or CSII (n = 30) therapy, were enrolled in this 6-month prospective study. Subjects were instructed to wear the DexCom SevenPLUS continuous glucose monitor at all times throughout the study. All subjects were initially blinded from the continuous glucose monitoring (CGM) glucose data. After 4 weeks of blinded CGM use, the CGM was unblinded, making glucose data available to the patient. The CGM remained in the unblinded state for the remainder of the study (20 weeks). Clinic visits occurred every 4 weeks, at which time A1C values were collected and CGM data were downloaded.

RESULTS

Mean baseline (± SD) A1C was 7.61 (± 0.76) and 7.63 (± 0.68) for CSII and MDI, respectively (P > 0.05). Without any significant therapy change, A1C decrease at 12 weeks was similar in both groups (P = 0.03). When compared with the blinded phase, unblinded use of CGM was associated with similar but significant reductions in glycemic control and variability parameters. In addition, both therapy groups had similar changes in mean glucose and glucose variability indexes at 3 and 6 months (ITT analysis, P > 0.05). Predefined per protocol analysis (sensor use at least 6 days/week) showed greater improvement in time spent in target range glycemia, 3.9-10.0 mmol/L (70-180 mg/dL), in the CSII group.

CONCLUSIONS

We conclude that CGM provides similar benefits in glucose control for patients using MDI or CSII therapy.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.

Translation of personalized decision support into routine diabetes care

OBJECTIVE

The aim of this study was to evaluate the impact of personalized decision support (PDS) on metabolic control in people with diabetes and cardiovascular disease.

RESEARCH DESIGN AND METHODS

The German health insurance fund BKK TAUNUS offers to its insured people with diabetes and cardiovascular disease the possibility to participate in the Diabetiva® program, which includes PDS. Personalized decision support is generated by the expert system KADIS® using self-control data and continuous glucose monitoring (CGM) as its data source. The physician of the participating person receives the PDS once a year, decides about use or nonuse, and reports his/her decision in a questionnaire. Metabolic control of participants treated by use or nonuse of PDS for one year and receiving CGM twice was analyzed in a retrospective observational study. The primary outcome was hemoglobin A1c (HbA1c); secondary outcomes were mean sensor glucose (MSG), glucose variability, and hypoglycemia.

RESULTS

A total of 323 subjects received CGM twice, 289 had complete data sets, 97% (280/289) were type 2 diabetes patients, and 74% (214/289) were treated using PDS, resulting in a decrease in HbA1c [7.10±1.06 to 6.73±0.82%; p<.01; change in HbA1ct0-t12 months -0.37 (95% confidence interval -0.46 to -0.28)] and MSG (7.7±1.6 versus 7.4±1.2 mmol/liter; p=.003) within one year. Glucose variability was also reduced, as indicated by lower high blood glucose index (p=.001), Glycemic Risk Assessment Diabetes Equation (p=.009), and time of hyper-glycemia (p=.003). Low blood glucose index and time spent in hypoglycemia were not affected. In contrast, nonuse of PDS (75/289) resulted in increased HbA1c (p<.001). Diabetiva outcome was strongly related to baseline HbA1c (HbA1ct0; p<.01) and use of PDS (p<.01). Acceptance of PDS was dependent on HbA1ct0 (p=.049).

CONCLUSIONS

Personalized decision support has potential to improve metabolic outcome in routine diabetes care.

Improved quality of glycemic control and reduced glycemic variability with use of continuous glucose monitoring

OBJECTIVE

We evaluated effects of unmasking of continuous display of continuous glucose monitoring (CGM) on quality of glycemic control and glycemic variability.

METHODS

We reanalyzed CGM data from 85 patients using a 7-day glucose sensor. Glucose values were "masked" during the first week but "unmasked" during the next 2 weeks. We evaluated 48 criteria for quality of glycemic control, including mean glucose, SD, percentage of values within-, above- or below- specified ranges, Schlichtkrull's M(100) index, mean amplitude of glycemic excursion (MAGE), mean of daily differences (MODD), the J index, "Index of Glycemic Control" (IGC), Hyperglycemia Index, Hypoglycemia Index, High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI), average daily risk range (ADRR), GRADE scores, and CONGA(n). We calculated SD values between daily means, between days-within time points, within days, between time points (for the average glucose profile for several days), and within series for time segments of arbitrary length.

RESULTS

Unmasking CGM displays resulted in rapid, highly statistically significant improvement in 29 indices, including percentage within, percentage above, and percentage below target range, mean glucose, SD, SD of daily means, MODD, M(100), IGC, GRADE, HBGI, and J index. Both hyperglycemia and hypoglycemia improved during the first week after unmasking; further improvement in hypoglycemia was seen during the following week. Results obtained using multiple criteria were consistent and highly correlated.

CONCLUSIONS

Continuous access to display of CGM sensors dramatically improved 29 indices of glycemic control and glycemic variability.

Responses to continuous glucose monitoring in subjects with type 1 diabetes using continuous subcutaneous insulin infusion or multiple daily injections

OBJECTIVE

We compared changes in response to unmasking of continuous glucose monitoring (CGM) in subjects with type 1 diabetes who use multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII).

RESEARCH DESIGN AND METHODS

Use of real-time CGM (DexCom [San Diego, CA] SEVEN was studied in 38 subjects using CSII and 26 using MDI. CGM output was masked during Week 1 and unmasked during Weeks 2 and 3. We evaluated changes in 16 criteria for quality of glycemic control and eight criteria for glycemic variability.

RESULTS

All 24 criteria showed highly statistically significant improvement when considered simultaneously (P < 0.000001). For subjects using CSII, 18 of 24 criteria improved significantly (nominal P < 0.05); for subjects using MDI, 16 of 24 criteria improved significantly (P < 0.05). Twelve of the comparisons remained significant (P < 0.05) after applying the overconservative Bonferroni correction for multiple comparisons. The percentage of glucose values within the range 80-140 mg/dL increased by 19% and 17% relative to their control values (Week 1) for subjects using MDI and CSII, respectively. Mean glucose, overall SD (SD(T)), SD between daily means (SD(dm)), mean amplitude of glycemic excursion (MAGE), and mean of daily differences (MODD) improved significantly. Responses to CGM display were not significantly different between the MDI and CSII subject groups for any of the 24 criteria considered individually or in groups of eight, 16, or 24.

CONCLUSION

CGM has similar effectiveness in subjects with type 1 diabetes using either CSII or MDI.

A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia

OBJECTIVE

It would be desirable to improve the ability of physicians and patients to identify hypoglycemic episodes when viewing displays of glucose by date, time of day, or day of the week.

RESEARCH DESIGN AND METHODS

A logarithmic scale is utilized for display of glucose versus date and time of day using a range of 40 to 400 mg/dl. Several plausible alternatives are considered for transformation of the glucose data.

RESULT

Use of a semilogarithmic plot triples the percentage of the vertical axis allocated to hypoglycemia (e.g., 40-80 mg/dl) from 10% to 30.1% while compressing the hyperglycemic region. The log scale improves the symmetry of the glucose distribution. Transformations were evaluated corresponding to the Schlichtkrull M(100) value, the high blood glucose index/low blood glucose index of Kovatchev and associates, an index of glycemic control developed by the present author, and the GRADE score of Hill and coworkers. Results are similar for all four transformations. This approach is applicable both to self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM). Based on preliminary results, it is proposed that the log transform could potentially facilitate analysis of glucose patterns and may facilitate rapid and consistent detection and appreciation of the severity and consistency of hypoglycemic episodes, even in the presence of complex overlapping patterns commonly observed in both SMBG and CGM glucose profiles.

CONCLUSION

Display of glucose on a logarithmic scale can potentially improve the accuracy of analysis and interpretation of popular methods for graphic display of glucose values. Device manufacturers should consider including options for semilogarithmic display of glucose on SMBG meters, CGM sensors, and software for retrospective analyses of glucose data.

The assessment of glycemic variability and its impact on diabetes-related complications: an overview

There is a growing body of evidence that the sole use of hemoglobin A1c is insufficient to adequately reflect the metabolic situation of patients with diabetes mellitus. The risk of developing diabetes-related complications apparently not only depends on the long-term stability of glucose values, but also on the presence or occurrence of short-term glycemic peaks and nadirs lasting for minutes or hours during a day. This leads to the phenomenon of glycemic variability. This article reviews the existing evidence for the clinical relevance of short-term glucose variations and the currently available different means of measuring glycemic variability.

New and improved methods to characterize glycemic variability using continuous glucose monitoring

BACKGROUND

Glycemic variability is a possible risk factor for development of complications from diabetes. Numerous methods have been used to characterize glycemic variability.

METHODS

We propose several new methods to characterize glycemic variability. We evaluated these methods empirically and theoretically and compared them with previous methods.

RESULTS

We describe (1) extension and generalization of the mean amplitude of glycemic excursion (MAGE), i.e., "within day variability," (2) extension and generalization of the mean of daily differences (MODD), i.e., the "between day-within time points variability," (3) "between daily means variability," (4) "between time points variability" of the glucose profile averaged over several days, (5) "within series variability" for a time segment of any arbitrary length, (6) new measures of the stability of the daily glycemic patterns, (7) new types of graphical displays, including within day variability, between day-within time points variability, and between daily means variability versus total variability, and between daily means variability versus within day variability, and (8) new methods to evaluate whether within series and between day-within time points variability fluctuate systematically by time of day. We examined the new measures in relation to previous measures of glycemic variability using correlation analysis on a clinical dataset for 85 subjects. MAGE, MODD, and continuous overall net glycemic action (CONGA(n)) are directly proportional to total standard deviation (SD). MAGE is highly correlated with both total SD and within day variability but weakly correlated with measures of between day variability. MODD is highly correlated with between day-within time points variability and total SD but weakly correlated with measures of within day variability.

CONCLUSIONS

We provide a systematic, logical framework to characterize multiple aspects of glycemic variability and have implemented a simple, practical computing format. This approach can help clinical researchers and clinicians identify the major sources of variability for any given patient and monitor responses to interventions.

Reducing glycaemic variability in type 1 diabetes self-management with a continuous glucose monitoring system based on wired enzyme technology

AIMS/HYPOTHESIS

This study was designed to investigate the use and impact of a continuous glucose monitoring system (the FreeStyle Navigator) under home-use conditions in the self-management of type 1 diabetes.

METHODS

A 20 day masked phase, when real-time data and alarms were not available, was compared with a subsequent 40 day unmasked phase for a number of specified measures of glycaemic variability. HbA(1c) (measured by DCA 2000) and a hypoglycaemia fear survey were recorded at the start and end of the study.

RESULTS

The study included 48 patients with type 1 diabetes (mean age 35.7 +/- 10.9, range 18-61 years; diabetes duration 17.0 +/- 9.5 years). Two patients did not complete the study for personal reasons. Comparing masked (all 20 days) and unmasked (last 20 days) phases, the following reductions were seen: time outside euglycaemia from 11.0 to 9.5 h/day (p = 0.002); glucose SD from 3.5 to 3.2 mmol/l (p < 0.001); hyperglycaemic time (>10.0 mmol/l) from 10.3 to 8.9 h/day (p = 0.0035); mean amplitude of glycaemic excursions (peak to nadir) down by 10% (p < 0.001); high blood glucose index down by 18% (p = 0.0014); and glycaemic risk assessment diabetes equation score down by 12% (p = 0.0013). Hypoglycaemic time (<3.9 mmol/l) decreased from 0.70 to 0.64 h/day without statistical significance (p > 0.05). Mean HbA(1c) fell from 7.6 +/- 1.1% at baseline to 7.1 +/- 1.1% (p < 0.001). In the hypoglycaemia fear survey, the patients tended to take less snacks at night-time after wearing the sensor.

CONCLUSIONS/INTERPRETATION

Home use of a continuous glucose monitoring system has a positive effect on the self-management of diabetes. Thus, continuous glucose monitoring may be a useful tool to decrease glycaemic variability.

Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus

AIM

To ascertain if those with diabetes (and their carers) ascribe a similar level of risk to blood glucose control as healthcare professionals.

METHODS

We used a structured questionnaire to ask fifty healthcare professionals how 'dangerous' a given blood glucose value was. Their answers were modelled to produce an algorithm of assessed risk. To examine if patients (and their carers) would apportion a similar level of risk to that of healthcare professionals, the same questionnaire was issued to fifty children and adolescents with Type 1 diabetes. For patients under 8 years old the carers completed the questionnaires (n = 23). Both patient and carers together completed the questionnaire for those aged 8-11 years (n = 15) and patients over the age of 11 years completed the questionnaire themselves (n = 12). The median results and interquartile range of the assessed level of risk, as determined by the two groups, were compared using a generalized linear model.

RESULTS

A significant difference (P < 0.0001) was identified between the median risk assessments of the two groups. The zero level of assessed risk was upward shifted in the patient group by 0.8 mmol/l and indicated the patients' view of risk increased.

CONCLUSIONS

Patients with Type 1 diabetes (and their carers) evaluate the risk from blood glucose values differently from healthcare professionals. The euglycaemic state (zero ascribed risk) that patients chose was 0.8 mmol/l greater than that of healthcare professionals, indicating, perhaps, hypoglycaemia avoidance, a more pragmatic approach or less exposure to current trends in glycaemic control.

Arguments for and against the role of glucose variability in the development of diabetes complications

There is now unequivocal evidence that improving glycemic control in both type 1 and type 2 diabetes reduces the likelihood of developing the micro- and macrovascular complications of the disease. However, it is still unclear whether a patient with very variable glucose is at any different a risk of these problems than someone who has the same mean glucose but much more stable glycemia. This article reviews the evidence that exists to both support and refute the claim that increased glucose variability should be regarded as an independent risk factor for the development of diabetic vascular disease.

Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control

There are a large number of measures of glycemic variability, including standard deviation (SD), percentage coefficient of variation (%CV), interquartile range (IQR), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), and continuous overlapping net glycemic action over an n-hour period (CONGA(n)). These are all highly correlated with the overall or "total" SD, SD(T). SD(T) is composed of several components corresponding to within-day variability, between-day variability (between daily means and between days-within specified time points), and the interaction of these sources of variability. We identify several subtypes of SD; each is highly correlated with SD(T). Variability may also depend on time of day. Numerous measures of quality of glycemic control have been proposed, including a weighted average of glucose values (M)(e.g., M(100) is M at 100 mg/dL), a measure of quality of glycemic control based on mean and SD (J), the Glycemic Risk Assessment Diabetes Equation (GRADE), the Index of Glycemic Control (IGC), the High Blood Glucose Index (HBGI), the Low Blood Glucose Index (LBGI), the Average Daily Risk Range (ADRR), and percentage of glucose values within specified ranges. These methods usually but not always give consistent results: they can differ widely in terms of their ability to detect responses to therapeutic interventions. Based on review of the advantages and limitations of these measures and on extensive experience in the application of these methods, we outline a systematic approach to the interpretation of continuous glucose monitoring data for use by clinical researchers and clinicians to evaluate the quality of glycemic control, glucose variability including within- and between-day variability, the day-to-day stability of glycemic patterns, and changes in response to therapy.

The "glucose pentagon": assessing glycemic control of patients with diabetes mellitus by a model integrating different parameters from glucose profiles

Measuring the hemoglobin A(1c) (HbA(1c)) is the standard-of-care method to assess long-term glycemic control of patients with diabetes, describing the average glycemic level. However, the HbA(1c) does not reflect acute fluctuations in glucose levels. Variability of glycemia probably has an impact on the development of diabetes-related late complications. A novel model presented in this article combines different summary measures derived from continuously recorded glucose profiles (including parameters describing glycemic variability) and the HbA(1c). The five parameters taking into account are the axes of a "glucose pentagon." Connecting the values of these parameters provided an enclosed area of a given size. For a patient with diabetes, these parameters and the connected area describe how his or her glycemia was during the monitoring period. The area of the glucose pentagon for a patient with diabetes, divided by the standard area of healthy subjects, yields a non-dimensional characteristic value defined as the glycemic risk parameter. It is assume that this risk parameter provides a more meaningful overall description of metabolic control than the HbA(1c) alone. In addition, it might also allow a better assessment of a patient's risk for developing diabetes-related late complications in comparison to the HbA(1c) alone. Of critical importance is, of course, that the clinical relevance of the glucose pentagon is verified in adequate long-term clinical studies.

Glycemic risk factors of diabetic vascular complications: the role of glycemic variability

Achieving adequate targets relatively to all risk factors is considered a standard of care for patients with diabetes mellitus. To date, current guidelines underline the importance of the 'glucose triad' (post-prandial glucose, fasting plasma glucose and glycated haemoglobin) as the three glycemic factors that should be controlled in diabetes care; however, several literature data show that optimizing glycemic control needs achieving a control of glycemic variations. The objective of the present work is reviewing biological and clinical data supporting the role of glycemic variability, its measurement and relationship with the three other well-known glycemic risk factors and evidencing the areas that need further investigation. At last, we propose a simple model that summarizes the 'glucose triad' plus the 'new' risk factor glycemic variability (the 'Pyramid of the Risk').