Lutz Vogt

The Use of a Computer Program to Calculate the Mean Amplitude of Glycemic Excursions

BACKGROUND The mean amplitude of glycemic excursions (MAGE), traditionally estimated with a graphical approach, is often used to characterize glycemic variability. Here, we tested a proposed software program for calculating MAGE. METHODS Development and testing of the software was based on retrospective analyses of 72-h continuous glucose monitoring profile data collected during two different clinical studies involving 474 outpatients (458 with type 2 and 16 with type 1 diabetes) in three cohorts (two type 2 diabetes and one type 1 diabetes), using the CGMS® Gold™ (Medtronic MiniMed, Northridge, CA). Correlation analyses and a Bland-Altman procedure were used to compare the results of MAGE calculations performed using the developed computer program (MAGE(C)) and the original method (MAGE(O)). RESULTS Close linear correlations between MAGE(C) and MAGE(O) were documented in the two type 2 and the type 1 diabetes cohorts (r = 0.954, 0.962, and 0.951, respectively; P < 0.00001 for all), as was the absence of any systematic error between the two calculation methods. Comparison of the two indices revealed no within-group differences but did show differences among the various antihyperglycemic treatments (P < 0.0001). In each of the study cohorts, MAGE(C) correlated strongly with the SD (r = 0.914-0.943), moderately with the mean of daily differences (r = 0.688-0.757), and weakly with glycosylated hemoglobin A1c and mean sensor glucose (r= 0.285 and r = 0.473, respectively). CONCLUSIONS The proposed computerized calculation of MAGE is a practicable method that may provide an efficient tool for assessing glycemic variability.

Utility of different glycemic control metrics for optimizing management of diabetes

The benchmark for assessing quality of long-term glycemic control and adjustment of therapy is currently glycated hemoglobin (HbA1c). Despite its importance as an indicator for the development of diabetic complications, recent studies have revealed that this metric has some limitations; it conveys a rather complex message, which has to be taken into consideration for diabetes screening and treatment. On the basis of recent clinical trials, the relationship between HbA1c and cardiovascular outcomes in long-standing diabetes has been called into question. It becomes obvious that other surrogate and biomarkers are needed to better predict cardiovascular diabetes complications and assess efficiency of therapy. Glycated albumin, fructosamin, and 1,5-anhydroglucitol have received growing interest as alternative markers of glycemic control. In addition to measures of hyperglycemia, advanced glucose monitoring methods became available. An indispensible adjunct to HbA1c in routine diabetes care is self-monitoring of blood glucose. This monitoring method is now widely used, as it provides immediate feedback to patients on short-term changes, involving fasting, preprandial, and postprandial glucose levels. Beyond the traditional metrics, glycemic variability has been identified as a predictor of hypoglycemia, and it might also be implicated in the pathogenesis of vascular diabetes complications. Assessment of glycemic variability is thus important, but exact quantification requires frequently sampled glucose measurements. In order to optimize diabetes treatment, there is a need for both key metrics of glycemic control on a day-to-day basis and for more advanced, user-friendly monitoring methods. In addition to traditional discontinuous glucose testing, continuous glucose sensing has become a useful tool to reveal insufficient glycemic management. This new technology is particularly effective in patients with complicated diabetes and provides the opportunity to characterize glucose dynamics. Several continuous glucose monitoring (CGM) systems, which have shown usefulness in clinical practice, are presently on the market. They can broadly be divided into systems providing retrospective or real-time information on glucose patterns. The widespread clinical application of CGM is still hampered by the lack of generally accepted measures for assessment of glucose profiles and standardized reporting of glucose data. In this article, we will discuss advantages and limitations of various metrics for glycemic control as well as possibilities for evaluation of glucose data with the special focus on glycemic variability and application of CGM to improve individual diabetes management.

Evaluation of the Mean Absolute Glucose Change as a Measure of Glycemic Variability Using Continuous Glucose Monitoring Data

BACKGROUND The mean absolute glucose (MAG) change, originally developed to assess associations between glycemic variability (GV) and intensive care unit mortality, has not yet been validated. We used continuous glucose monitoring (CGM) datasets from patients with diabetes to assess the validity of MAG and to quantify associations with established measures of GV. SUBJECTS AND METHODS Validation was based on retrospective analysis of 72-h CGM data collected during clinical studies involving 815 outpatients (48 with type 1 diabetes and 767 with type 2 diabetes). Measures of GV included SD around the sensor glucose, interquartile range, mean amplitude of glycemic excursions, and the continuous overlapping net glycemic action indices at 1, 3, and 6 h. MAG was calculated using 5-min, 60-min, and seven-point glucose profile sampling intervals; correlations among the variability measures and effects of sampling frequency were assessed. RESULTS Strong linear correlations between MAG change and classical markers of GV were documented (r=0.587-0.809, P<0.001 for all), whereas correlations with both glycosylated hemoglobin and mean sensor glucose were found to be weak (r=0.246 and r=0.378, respectively). The magnitude of MAG change decreased in a nonlinear fashion (P<0.001), as intervals between glucose measurements increased. MAG change, as calculated from 5-min sensor glucose readings, did reflect relatively small differences in glucose fluctuations associated with glycemic treatment modality. CONCLUSIONS MAG change represents a valid GV index if closely spaced sensor glucose measurements are used, but does not provide any advantage over variability indices already used for assessing diabetes control.

Telemedicine-Based KADIS® Combined with CGMS™ Has High Potential for Improving Outpatient Diabetes Care

Background: The Karlsburg Diabetes Management System (KADIS®) was developed over almost two decades by modeling physiological glucose-insulin interactions. When combined with the telemedicine-based communication system TeleDIAB® and a continuous glucose monitoring system (CGMS™), KADIS has the potential to provide effective, evidence-based support to doctors in their daily efforts to optimize glycemic control. Methods: To demonstrate the feasibility of improving diabetes control with the KADIS system, an experimental version of a telemedicine-based diabetes care network was established, and an international, multicenter, pilot study of 44 insulin-treated patients with type 1 and 2 diabetes was performed. Patients were recruited from five outpatient settings where they were treated by general practitioners or diabetologists. Each patient underwent CGMS monitoring under daily life conditions by a mobile monitoring team of the Karlsburg diabetes center at baseline and 3 months following participation in the KADIS advisory system and telemedicine-based diabetes care network. The current metabolic status of each patient was estimated in the form of an individualized “metabolic fingerprint.” The fingerprint characterized glycemic status by KADIS-supported visualization of relationships between the monitored glucose profile and causal endogenous and exogenous factors and enabled evidence-based identification of “weak points” in glycemic control. Using KADIS-based simulations, physician recommendations were generated in the form of patient-centered decision support that enabled elimination of weak points. The analytical outcome was provided in a KADIS report that could be accessed at any time through TeleDIAB. The outcome of KADIS-based support was evaluated by comparing glycosylated hemoglobin (HbAlc) levels and 24-hour glucose profiles before and after the intervention. Results: Application of KADIS-based decision support reduced HbAlc by 0.62% within 3 months. The reduction was strongly related to the level of baseline HbA1c, diabetes type, and outpatient treatment setting. The greatest benefit was obtained in the group with baseline HbAlc levels >9% (1.22% reduction), and the smallest benefit was obtained in the group with baseline HbA1c levels of 6–7% (0.13% reduction). KADIS was more beneficial for patients with type 1 diabetes (0.79% vs 0.48% reduction) and patients treated by general practitioners (1.02% vs 0.26% reduction). Changes in HbA1c levels were paralleled by changes in mean daily 24-hour glucose profiles and fluctuations in daily glucose. Conclusion: Application of KADIS in combination with CGMS and the telemedicine-based communication system TeleDIAB successfully improved outpatient diabetes care and management.

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 HbA1c t0-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 hyperglycemia (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 (HbA1c t0; p < .01) and use of PDS (p < .01). Acceptance of PDS was dependent on HbA1c t0 (p = .049). Conclusions: Personalized decision support has potential to improve metabolic outcome in routine diabetes care.

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

BackgroundContinuous 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.MethodsFifteen 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.ResultsWe 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’.ConclusionsThe 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.

Advances in Understanding Glucose Variability and the Role of Continuous Glucose Monitoring

Consequently, current diabetes management relies mainly on HbA1c to assess quality of treatment and to adjust therapy. Optimal glycaemic control aims to restore levels of HbA1c to as normal as possible to reduce or avoid diabetic complications. However, traditional glucose monitoring has considerable limitations in assessing glycaemic control due to inability to detect fluctuations in glucose levels. Continuous glucose monitoring (CGM) systems allow the detection of the configuration of glucose patterns in several metabolic conditions. Analysis of the CGM patterns revealed that in a portion of patients with diabetes the same HbA1c level may generate unpredictable diurnal glucose fluctuations with different magnitudes. 3 As low glucose levels often offset the high glucose levels, even patients with HbA1c levels within the target range (<6.5%) show remarkable glucose

Declining ß-cell function is associated with the lack of long-range negative correlation in glucose dynamics and increased glycemic variability: A retrospective analysis in patients with type 2 diabetes

Objective To determine whether characteristics of glucose dynamics are reflections of β-cell function or rather of inadequate diabetes control. Materials/methods We analyzed historical liquid meal tolerance test (LMTT) and continuous glucose monitoring (CGM) data, which had been obtained from 56 non-insulin treated type 2 diabetic outpatients during withdrawal of antidiabetic drugs. Computed CGM parameters included detrended fluctuation analysis (DFA)-based indices, autocorrelation function exponent, mean amplitude of glycemic excursions (MAGE), glucose SD, and measures of glycemic exposure. The LMTT-based disposition index (LMTT-DI) calculated from the ratio of the area-under-the-insulin-curve to the area-under-the-glucose-curve and Matsuda index was used to assess relationships among β-cell function, glucose profile complexity, autocorrelation function, and glycemic variability. Results The LMTT-DI was inverse linearly correlated with the short-range α1 and long-range scaling exponent α2 (r = −0.275 and −0.441, respectively, p < 0.01) such that lower glucose complexity was associated with better preserved insulin reserve, but it did not correlate with the autocorrelation decay exponent γ. By contrast, the LMTT-DI was strongly correlated with MAGE and SD (r = 0.625 and 0.646, both p < 0.001), demonstrating a curvilinear relationship between β-cell function and glycemic variability. On stepwise regression analyses, the LMTT-DI emerged as an independent contributor, explaining 20, 38, and 47% (all p < 0.001) of the variance in the long-range DFA scaling exponent, MAGE, and hemoglobin A1C, respectively, whereas insulin sensitivity failed to contribute independently. Conclusions Loss of complexity and increased variability in glucose profiles are associated with declining β-cell reserve and worsening glycemic control.

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (α1, α2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and α exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol2/l2): 21.71 ± 9.82 vs. 7.25 ± 5.92, and α1: 1.94 ± 0.12 vs. 1.75 ± 0.12, α2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol2/l2), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents α1 and α2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (β = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though α1 and MSE failed. In the regression models, including SFE, AFE, and α2 (β = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.

Glycemic Key Metrics and the Risk of Diabetes-Associated Complications

Abstract Prevention of diabetes-associated complications is closely linked to preventing and controlling hyperglycemia. Glycated hemoglobin (HbA1c), a glucose metric and a risk factor for chronic complications, is not reliable under certain clinical conditions, does not capture glyemic variability and glucose dynamics. There is evidence that glycemic variability is an independent predictor variable of hypoglycemia and a potential risk marker for vascular diabetes complications. Despite advanced glucose monitoring methods, monitoring of glucose with blood glucose meters remains indispensible as an adjunct to HbA1c measurements, because it gives direct feedback on short-term changes in glucose levels. Optimized diabetes treatment and prevention or delay of diabetes complications needs both key glucose control metrics on a daily basis, involving fasting, preprandial, and postprandial glucose levels as well as advanced, user-friendly monitoring methods. The broad application of systems for continuous glucose monitoring in clinical settings is partly hampered by lacking measures generally accepted for analysis of glucose profiles and as standards for reporting of glucose data. We performed a literature search, using PubMed and Scopus and included relevant literature published online up to March 1, 2016. In this review, we discuss the importance of several glucose measures for primary and secondary prevention of diabetes complications and possibilities for evaluation of monitored glucose data with special consideration of glycemic variability, glucose dynamics, and the utility of continuous glucose monitoring.