Klaus-Dieter Kohnert

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.

Maculopathy in patients with diabetes mellitus type 1 and type 2: associations with risk factors

AIM To examine possible relation between diabetic maculopathy and various risk factors for diabetic complications in patients with diabetes mellitus type 1 and type 2. METHODS Cross sectional study of two cohorts of diabetic patients, comprising 1796 patients with type 1 diabetes (mean age 47 years, mean duration of diabetes 24 years) and 1563 patients with type 2 diabetes (mean age 62 years, mean duration of diabetes 16 years). Retinopathy levels (R0–RV) and maculopathy were assessed by fluorescence angiography and fundus photography and binocular biomicroscopy. Diabetic neuropathy was assessed by means of computer assisted electrocardiography and by thermal and vibratory sensory examination. Patients were classified as normoalbuminuric (<20 μg/min) or microalbuminuric (20–200 μg/min) according to their albumin excretion rates measured in urine collected overnight. Using univariate analyses, the effects of selected patient characteristics on the presence of maculopathy were evaluated. Multiple logistic regression analyses were performed to determine independent effects of risk variables on diabetic maculopathy. RESULTS Background retinopathy (RII) was found to be present in 28% of type 1 diabetic patients and in 38% of type 2 diabetic patients. The prevalence of maculopathy in these patients was remarkably high (42% in type 1 and 53% in type 2 diabetic patients). Patients with maculopathy had significantly impaired visual acuity. Multiple logistic correlation analysis revealed that in both types of diabetes maculopathy exhibited independent associations with duration of diabetes and with neuropathy (p <0.01); in type 1 diabetic patients there were significant associations with age at diabetes onset, serum triglyceride and total cholesterol levels (p <0.05); in type 2 diabetes with serum creatinine levels and with hypertension (p <0.05). CONCLUSIONS Irrespective of the type of diabetes, diabetic patients with long standing diabetes have a high risk for the development of diabetic maculopathy. Diabetic maculopathy is closely associated with diabetic nephropathy and neuropathy and with several atherosclerotic risk factors which suggests that these factors might have an important role in the pathogenesis of maculopathy. However, prospective trials are necessary to evaluate the predictive value of such factors. The findings of the present cross sectional study reinforce the arguments of previous studies by others for tight control of hypertension and hyperglycaemia.

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.

Relationships between glucose variability and conventional measures of glycemic control in continuously monitored patients with type 2 diabetes.

Given the importance of glucose variability in the development of diabetic complications, the present study used continuous glucose monitoring (CGM) to determine various indices of glucose variability and to investigate their relationships with conventional measures of chronic sustained hyperglycemia. We examined 53 women and 61 men, aged 36-79 years afflicted with type 2 diabetes for 1-24 years. The following indices of glycemic variability were computed from CGM data sets: mean amplitude of glycemic excursions (MAGE), CGM glucose range, interquartile range (IQR), SD-score, and average daily risk range (ADRR). CGM measurements and self-monitored blood glucose (SMBG) records were used to calculate mean CGM sensor glucose and mean SMBG, respectively. In simple correlation analysis, the indices of glucose variability showed weak correlations with HbA1c: MAGE (r=0.27, p <0.01), CGM glucose range (r=0.21, p <0.05), IQR (r=0.31, p <0.01), SD-score (r=0.34, p<0.001), and ADRR (r=0.24, p<0.05). These indices were found to differ at identical HbA1c among several patients, as reflected by diurnal excursions of different frequency and magnitude. With the exception of ADRR, stronger correlations were found between mean SMBG and the other variability indices (r=0.51-0.63, p<0.01 for all). CGM provides various indices of glycemic variability not captured by conventional measures of glycemic control. Detection of the location and the magnitude of glucose fluctuations by CGM should aid in optimal treatment of glycemic disorders in type 2 diabetes.

The metabolic syndrome in patients with type 1 diabetes mellitus. Associations with cardiovascular risk factors and cardiovascular morbidity

Hintergrund und Ziel:Nephropathie und Hypertonus steigern bekanntlich die kardiovaskuläre Morbidität von Patienten mit Typ-1-Diabetes. Es war das Ziel dieser Studie zu untersuchen, ob bei Typ-1-Diabetikern auch mit der Entwicklung von klinischen Merkmalen eines metabolischen Syndroms (MS) eine erhöhte kardiovaskuläre Morbidität assoziiert ist.Methodik:In diese retrospektive klinische Querschnittsstudie wurden 1 241 Patienten mit Typ-1-Diabetes eingeschlossen, welche vom 01.01.2002 bis 31.12.2003 in der Diabetesklinik Karlsburg stationär aufgenommen worden waren. Als Kriterien eines MS wurden berücksichtigt: Nüchterntriglyzeride (TG), High-Density-Lipoprotein-Cholesterin (HDL-C), Bodymass- Index (BMI), Tagesinsulinbedarf (IE/kg Körpergewicht [KG]), erhöhter Blutdruck > 130/85 mmHg einschließlich eines manifesten Hypertonus. Bewertet wurden die höchsten Quintilen für jedes der fünf Merkmale: TG 2,9 ± 3,6 mmol/l, HDL-C 1,48 ± 0,46 mmol/l, BMI 29,1 ± 4,98 kg/m2 Körpergröße, Tagesinsulinbedarf 0,71 ± 0,23 IE/kg KG, systolischer Blutdruck 130 ± 12,3 mmHg. Als MS wurde das Vorliegen von mindestens drei der genannten fünf Merkmale definiert. Unter 1 241 Typ- 1-Diabetikern (651 Männer, 590 Frauen) hatten 226 Patienten (129 Männer, 97 Frauen) drei bis fünf Merkmale eines MS. Die Präsentation der Daten erfolgt als Mittelwerte ± SD bzw. als n (%). Die Signifikanzprüfung erfolgte mittels t-Test oder χ2- Test. Die multiple Regressionsanalyse für das Risiko eines MS berücksichtigte die folgenden Variablen: Alter, Diabetesdauer, Geschlecht, glykiertes Hämoglobin (HbA1c), aktuelles Rauchen, Neuropathie, Albuminexkretionsrate (AER), Alkoholgenuss, Retinopathie, periphere arterielle Verschlusskrankheit (PAVK), koronare Herzkrankheit (KHK), TG, HDL-C, Low-Density-Lipoprotein- Cholesterin (LDL-C), Cholesterin, Hypertonus, BMI, Insulinbedarf/kg KG und das Fußsyndrom. Nach Adjustierung zum Alter erfolgte der separate Einschluss der Variablen in das mathematische Modell. Das Risiko eines MS wurde nach Abtrennung der Einschlusskriterien eines MS bewertet.Ergebnisse:Typ-1-Diabetiker mit MS haben ein höheres Alter (46 vs. 36 Jahre; p < 0,01) und eine längere Diabetesdauer (19 vs. 16 Jahre; p < 0,01). Das unabhängige Risiko eines MS (Odds- Ratios) stieg mit dem Alter (40–59 Jahre; 4,21; p < 0,01) und dem HbA1c (1,41; p < 0,01) und war mit PAVK (2,28; p < 0,01), KHK (2,19; p < 0,01) und einem Fußsyndrom (4,17; p < 0,01) assoziiert. Es bestanden keine Beziehungen zur Typ-2-Diabetesheredität ersten und zweiten Grades.Schlussfolgerung:Typ-1-Diabetes mit den klinischen Merkmalen eines MS zeigt eine erhöhte kardiovaskuläre Morbidität. Das Risiko eines MS steigt mit dem Alter und dem HbA1c. Lebensstilfaktoren, wie Gewichtsentwicklung und körperliche Aktivität, dürften damit einen Stellenwert für die Entwicklung eines MS auch bei Patienten mit Typ-1-Diabetes besitzen.Background and Purpose:Type 1 diabetes is known to be associated with increased cardiovascular disease in the presence of nephropathy and hypertension. It was the aim of the present study to elucidate whether or not clinical findings of metabolic syndrome (MS) are further increasing cardiovascular morbidity among type 1 diabetics.Methods:In the present cross-sectional study, 1,241 type 1 diabetics were included. These patients attended the Diabetes Clinic Karlsburg, Germany, from February 1, 2002 to December 31, 2003. The presence of the following findings was taken into consideration as clinical features of MS in type 1 diabetes: fasting triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), body mass index (BMI), daily insulin requirement/kg body weight (b.w.), increased blood pressure > 130/85 mmHg, including overt arterial hypertension. In each of the five categories the highest quintile in each sample was assessed: TG 2.9 ± 3.6 mmol/l, HDL-C 1.48 ± 0.46 mmol/l, BMI 29.1 ± 4.98 kg/m2 height, insulin requirement 0.71 ± 0.23 IU/kg b.w., systolic blood pressure 130 ± 12.3 mmHg. MS was defined as the presence of at least three categories. Among 1,241 type 1 diabetics (651 men, 590 women), 226 patients (129 men, 97 women) fulfilled the criteria of MS. The risk of MS was assessed by multiple regression analysis. Risk variables were: age, diabetes duration, sex, glycated hemoglobin (HbA1c), actual smoking, neuropathy, albumin excretion rate (AER), regular alcohol consumption, retinopathy, peripheral vascular disease (PVD), coronary heart disease (CHD), TGs, HDL-C, low-density lipoprotein cholesterol (LDL-C), cholesterol, blood pressure increase, BMI, increased insulin requirement, and foot syndrome. After adjusting for age, the variables were separately included into the mathematical model. The risk of MS was assessed after excluding the variables defining MS.Results:Type 1 diabetics with MS were characterized by higher age (46 vs. 36 years; p < 0.01), and longer diabetes duration (19 vs. 16 years; p < 0.01). The risk of MS was independently associated (odds ratios) with higher age (40–59 years; 4.21; p < 0.01), increased HbA1c (1.41; p < 0.01), PVD (2.28; p < 0.01), CHD (2.19; p < 0.01), and the foot syndrome (4.17; p < 0.01). There were no significant associations of MS with type 2 diabetes heredity (first and second degree).Conclusion:Patients with type 1 diabetes and the presence of findings of MS are suffering from increased cardiovascular morbidity. The risk of MS increases with the age and HbA1c. Life style factors such as weight gain and muscular inactivity seem to have an influence on the pathogenesis of MS in type 1 diabetes, thereby increasing cardiovascular morbidity.

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.