Eckhard Salzsieder

Glycemic Variability Correlates Strongly With Postprandialβ-Cell Dysfunction in a Segment of Type 2 Diabetic Patients Using Oral Hypoglycemic Agents

OBJECTIVE Glucose fluctuations trigger activation of oxidative stress, a main mechanism leading to secondary diabetes complications. We evaluated the relationship between glycemic variability and β-cell dysfunction. RESEARCH DESIGN AND METHODS We conducted a cross-sectional study in 59 patients with type 2 diabetes (aged 64.2 ± 8.6 years, A1C 6.5 ± 1.0%, and BMI 29.8 ± 3.8 kg/m2[mean ± SD]) using either oral hypoglycemic agents (OHAs) (n = 34) or diet alone (nonusers). As a measure of glycemic variability, the mean amplitude of glycemic excursions (MAGE) was computed from continuous glucose monitoring data recorded over 3 consecutive days. The relationships between MAGE, β-cell function, and clinical parameters were assessed by including postprandial β-cell function (PBCF) and basal β-cell function (BBCF) obtained by a model-based method from plasma C-peptide and plasma glucose during a mixed-meal test as well as homeostasis model assessment of insulin sensitivity, clinical factors, carbohydrate intake, and type of OHA. RESULTS MAGE was nonlinearly correlated with PBCF (r = 0.54, P < 0.001) and with BBCF (r = 0.31, P = 0.025) in OHA users but failed to correlate with these parameters in nonusers (PBCF P = 0.21 and BBCF P = 0.07). The stepwise multiple regression analysis demonstrated that PBCF and OHA combination treatment were independent contributors to MAGE (R2 = 0.50, P < 0.010), whereas insulin sensitivity, carbohydrate intake, and nonglycemic parameters failed to contribute. CONCLUSIONS PBCF appears to be an important target to reduce glucose fluctuations in OHA-treated type 2 diabetes.

Computer-aided systems in the management of type I diabetes: the application of a model-based strategy

One approach of improving metabolic control in type I diabetic patients is the application of computer-aided procedures aimed at supporting the decision on optimal therapeutic regimens. To accomplish this, a complex strategy was developed which in an individual patient permits (1) the evaluation of metabolic data by means of statistical and graphical methods, and (2) the prediction of the outcome in feedback and in non-feedback-controlled insulin therapy. The latter is realized by means of simulation, employing a structured model of the glucose-insulin control system where the model parameters can either be identified individually or be taken at random. The practical applicability was validated in C-peptide-negative type I diabetic patients who were on intensified insulin injection therapy. The comparison between theoretical predictions and daily glycaemic profiles measured by the patients under ambulatory conditions showed close correspondence which justifies the application of this method as a clinical decision support.

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.

The Karlsburg Diabetes Management System: translation from research to eHealth application.

Background: Several telemedicine-based eHealth programs exist, but patient-focused personalized decision support (PDS) is usually lacking. We evaluated the acceptance, efficiency, and cost-effectiveness of telemedicine-assisted PDS in routine outpatient diabetes care. Methods: Data are derived from the Diabetiva® program of the German health insurance company BKK TAUNUS. Diabetiva offers telemedicine-based outpatient health care in combination with PDS generated by the Karlsburg Diabetes Management System, KADIS®. This retrospective analysis is based on data from the first year of running KADIS-based PDS in routine diabetes care. Participants were insured persons diagnosed with diabetes and cardiovascular diseases. For final analysis, patients were grouped retrospectively as users or nonusers according to physician acceptance or not (based on questionnaires) of the KADIS-based PDS. Results: A total of 538 patients participated for more than one year in the Diabetiva program. Of these patients, 289 had complete data sets (two continuous glucose monitoring measurements, two or more hemoglobin A1c (HbA1c) values, and a signed questionnaire) and were included in the final data analysis. Of the physicians, 74% accepted KADIS-based PDS, a rate that was clearly related to HbA1c at the beginning of the observation. If KADIS-based PDS was accepted, HbA1c decreased by 0.4% (7.1% to 6.7%). In contrast, rejection of KADIS-based PDS resulted in an HbA1c increase of 0.5% (6.8% to 7.3%). The insurance company revealed an annual cost reduction of about 900 € per participant in the Diabetiva program. Conclusions: KADIS-based PDS in combination with telemedicine has high potential to improve the outcome of routine outpatient diabetes care.

Morphology of Pancreatic Islets: A Time Course of Pre-diabetes in Zucker Fatty Rats

The Zucker fatty rat (fa/fa; ZR) is considered as a model for pre-diabetes, as characterised by a genetic defect in the leptin receptor, which results in hyperphagia, insulin resistance, hyperinsulinaemia, hyperlipoproteinaemia, and obesity. These animals become glucose intolerant but do not develop type 2 diabetes. As a consequence of increased adiposity and insulin resistance, the endocrine pancreas of ZR undergoes adaptive and compensatory changes. Measurements of the time course of the pathological changes by the histological analysis of the pancreatic islet in combination with metabolic parameters are an effective way to reveal disease progression. A loss in glucose tolerance occurs in ZR by 10 weeks of age and progressively worsens by 19 weeks of age. This process is accompanied by impaired islet histology, changes of beta-cell mass, and impaired islet function. The early expression of insulin resistance and glucose intolerance in ZR results in morphological and functional changes of pancreatic islets despite their capability to maintain normoglycaemia.

Experimental validation of a glucose-insulin control model to simulate patterns in glucose turnover

To verify a structured model of the glucose-insulin system, metabolic measurements were compared with model-based simulations in insulin-dependent diabetic dogs which had been previously identified in terms of model parameters. Glycaemia, glucosuria, plasma insulin, and the rates of appearance Ra and disappearance Rd of glucose (kinetics of double-labelled glucose, evaluated according to Steeles equation in its non-steady-state version) were observed under the following conditions, starting from normoglycaemia during glucose-controlled insulin infusion (GCII): (I) insulin withdrawal, (II) insulin withdrawal and glucose infusion, (III) constant i.v. infusion of glucose and insulin, (IV) glucose infusion during GCII. After fitting the patterns of glycaemia, simulations of the other state variables were accomplished, employing the individual model parameters, the preset experimental inputs, and the GCII control constants (test IV only). Under nearly all conditions, correspondence was excellent between measured and simulated data. There were, however, the following exceptions: incomplete representation by the model of kinetics in glucose utilisation after interruption of insulin supply, overestimation of glucosuria by the model in the presence of insulin. It is concluded that the model provides a reasonable representation of metabolic processes which are of importance in the treatment of insulin-dependent diabetes mellitus and that it might thus appropriately simulate the outcome of metabolic regimens.

Mixed graphical models for simultaneous model identification and control applied to glucose–insulin metabolism

In this paper a method for model identification of biological systems described by stochastic linear differential equations using a new computational technique for statistical Bayesian inference, namely mixed graphical models in the sense of Lauritzen and Wermuth, is presented. The model is identified in terms of biological model parameters and noise parameters. This non-linear estimation problem is solved by means of an exact inference algorithm. The parameter estimates are given as a-posteriori distributions which can be interpreted as fuzzy possibility distributions. For model-based simulations of the underlying biological system the model parameters are represented as uncertain parameters with the distributions obtained from the estimation procedure. We apply the presented methods to a model for the glucose-insulin metabolism: the Karlsburg model for type I diabetes.

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.