Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes.

BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control. METHODS: This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured. RESULTS: At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters. CONCLUSIONS: DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Model-Based Tool for Personalized Adjustment of Basal Insulin Supply in Patients With Intensified Conventional Insulin Therapy.

BACKGROUND: The decisive factor in successful intensive insulin therapy is the ability to deliver need-based-adjusted nutrition-independent insulin dosages at the closest possible approximation to the physiological insulin level. Because this basal insulin requirement is strongly influenced by the patient's lifestyle, its subtlety is of great importance. This challenge is very different between patients with type 1 diabetes and those with insulin-dependent type 2 diabetes. Furthermore, it is more difficult to finetune a basal insulin dosage with intensified conventional insulin therapy (ICT), due to delayed insulin delivery, compared to insulin pump therapy, which provides continuous delivery of small doses of exclusively short-acting insulin. In all cases, the goal is to achieve an optimal basal delivery rate. METHOD: We hypothesized that this goal could be achieved with a modeling tool that determined the optimal basal insulin supply based on the patient's anamnestic data and monitored glucose values. This type of modeling tool has been used in health insurance programs in Germany to improve insulin control in patients that receive ICT. RESULTS: Our retrospective data analysis showed that this modeling tool provided a significant improvement in metabolic control, significant reductions in HbA1c and Q scores, and improved time-in-range values, with reduced daily insulin levels. CONCLUSION: The model-based basal rate test could provide additional data of the actual effect of the basal insulin adjustment in intensified insulin treated diabetes to the physician or treatment team.

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 (ß = -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 (ß = -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.

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.

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.

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 ß-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 ß-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 ß-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 ß-cell reserve and worsening glycemic control.

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.

Glycaemic variability and pancreatic ß-cell dysfunction.

The importance of glycaemic variability (GV) as a factor in the pathophysiology of cellular dysfunction and late diabetes complications is currently a matter of debate. However, there is mounting evidence from in vivo and in vitro studies that GV has adverse effects on the cascade of physiological processes that result in chronic ß-cell dysfunctions. Glucose fluctuations more than sustained chronic hyperglycaemia can induce excessive formation of reactive oxygen (ROS) and reactive nitrogen species (RNS), ultimately leading to apoptosis related to oxidative stress. The insulin-secreting ß-cells are particularly susceptible to damage imposed by oxidative stress. Evidence from experiments, using isolated pancreatic islets or ß-cell lines, has linked intermittent high glucose, which mimicks GV under diabetic conditions, to significant impairment of ß-cell function. Several clinical studies reported a close association between GV and ß-cell dysfunction, although the deleterious effects are difficult to demonstrate. Notwithstanding, early therapeutic interventions in patients with type 1 as well as type 2 diabetes, using different strategies of optimising glycaemic control, have shown that favourable outcomes on recovery and maintenance of ß-cell function correlated with reduction of GV. The purpose of the present review is to discuss the detrimental effects of GV and associations with ß-cell function as well as upcoming therapeutic strategies directed towards minimising glucose excursions, improving ß-cell recovery and preventing progressive ß-cell loss. Measuring GV has importance for management of diabetes, because it is the only one component of the dysglycaemia that reflects the degree of dysregulation of glucose homeostasis.

DPP-4 inhibition increases GIP and decreases GLP-1 incretin effects during intravenous glucose tolerance test in Wistar rats.

GIP metabolite [GIP (3-42)] and GLP-1 metabolite [GLP-1 (9-36) amide] have been reported to differ with regard to biological actions. Systemic DPP-4 inhibition can therefore reveal different actions of GIP and GLP-1. In catheter wearing Wistar rats, insulinotropic effects of equipotent doses of GIP (2.0 nmol/kg) and GLP-1 (7-36) amide (4.0 nmol/kg) and vehicle were tested in the absence/presence of DPP-4 inhibition. Blood glucose and insulin were frequently sampled. DPP-4 inhibitor was given at -20 min, the incretin at -5 min and the intravenous glucose tolerance test (0.4 g glucose/kg) commenced at 0 min. G-AUC and I-AUC, insulinogenic index and glucose efflux, were calculated from glucose and insulin curves. Systemic DPP-4 inhibition potentiated the acute GIP incretin effects: I-AUC (115±34 vs. 153±39 ng·min/ml), increased the insulinogenic index (0.74±0.24 vs. 0.99±0.26 ng/mmol), and improved glucose efflux (19.8±3.1 vs. 20.5±5.0 min⁻¹). The GLP-1 incretin effects were diminished: I-AUC (124±18 vs. 106±38 ng·min/ml), the insulinogenic index was decreased (0.70±0.18 vs. 0.50±0.19 ng/mmol), and glucose efflux declined (14.9±3.1 vs. 11.1±3.7 min⁻¹). GLP-1 and GIP differ remarkably in their glucoregulatory actions in healthy rats when DPP-4 is inhibited. These previously unrecognized actions of DPP-4 inhibitors could have implications for future use in humans.

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.

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.

Influence of oral antidiabetic drugs on hyperglycemic response to foods in persons with type 2 diabetes mellitus as assessed by continuous glucose monitoring system: a pilot study.

BACKGROUND: The purpose of this prospective open-label trial was (1) to assess the influence of oral antidiabetic drugs (OAD) on the glycemic index (GI), glucose response curves (GRCs), daily mean plasma glucose (MPG) and (2) to compare the GI of foods in persons with OAD-treated type 2 diabetes mellitus (T2DM) with the respective GI in healthy persons (HP). METHODS: Tested foods containing 50 g of carbohydrates were eaten for breakfast and dinner after 10 and 4 h of fasting, respectively. Glycemic index, GRC, and MPG were obtained using the CGMS System Gold (CGMS). In T2DM patients [n = 16; age (mean +/- standard error) 56.0 +/- 2.25 years], foods were tested four times: tests 1, 2, and 3 were performed within one week in which placebo was introduced on day 2, and test 4 was carried out five weeks after reintroduction of OAD. Glycemic indexes, GRC, and MPG from tests 1, 2, 3, and 4 were compared. In a control group of 20 HP (age 24.4 +/- 0.71 years), the mean GIs were calculated as the mean from 20 subject-related GIs. RESULTS: In T2DM patients, subject-related assessment of GIs, GRC, and MPG distinguished persons with and without OAD effect. Nevertheless, the group-related GIs and the MPG on days 2, 8, and 39 showed no significant difference. There was no significant difference between the GIs in OAD-treated T2DM patients (test 4) versus HP (except in apple baby food). Glucose response curves were significantly larger in T2DM patients (test 4) versus HP. CONCLUSIONS: Determination of GRC and subject-related GI using the CGMS appears to be a potential means for the evaluation of efficacy of OAD treatment. Further studies are underway.

Glycemic variability correlates strongly with postprandial beta-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 beta-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/m(2)[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, beta-cell function, and clinical parameters were assessed by including postprandial beta-cell function (PBCF) and basal beta-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 (R(2) = 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.

Outpatient assessment of Karlsburg Diabetes Management System-based decision support.

OBJECTIVE: We sought to assess the benefit of the Karlsburg Diabetes Management System (KADIS) in conjunction with the continuous glucose monitoring system (CGMS) in an outpatient setting. RESEARCH DESIGN AND METHODS: A multicentric trial was performed in insulin-treated outpatients (n = 49), aged 21-70 years, with a mean diabetes duration of 14.2 years. Subjects were recruited from five outpatient centers and randomized for CGMS- or CGMS/KADIS-based decision support and followed up for 3 months. After two CGMS monitorings, the outcome parameters A1C (%), mean sensor glucose of the CGMS profile (MSG) (mmol/l), and duration of hyperglycemia (h/day) were evaluated. RESULTS: In contrast with the CGMS group (0.27 +/- 0.67%), mean change in A1C decreased in the CGMS/KADIS group during the follow-up (-0.34 +/- 0.49%; P < 0.01). MSG levels were not affected in the CGMS group (7.75 +/- 1.33 vs. 8.45 +/- 2.46 mmol/l) but declined in the CGMS/KADIS group (8.43 +/- 1.33 vs. 7.59 +/- 1.47 mmol/l; P < 0.05). Net KADIS effect (-0.60 [95% CI -0.96 to - 0.25%]; P < 0.01) was associated with reduced duration of hyperglycemia (4.6 vs. 1.0 h/day; P < 0.01) without increasing hypoglycemia. Multiple regression revealed that the A1C outcome was dependent on KADIS-based decision support. Age, sex, physician's specialty, diabetes type, and BMI had no measurable effect. CONCLUSIONS: If physicians were supported by CGMS/KADIS in therapeutic decisions, they achieved better glycemic control for their patients compared with support by CGMS alone. KADIS is a suitable decision support tool for physicians in outpatient diabetes care and has the potential to improve evidence-based management of diabetes.

Chronic hyperglycemia but not glucose variability determines HbA1c levels in well-controlled patients with type 2 diabetes.

To determine the relationships between HbA1c, characteristics of hyperglycemia and glycemic variability in well-controlled type 2 diabetes (HbA1c<7.0%), we studied 63 primary-care patients (36 men and 27 women), aged 34-75 years, with type 2 diabetes for 2-32 years using a continuous glucose monitoring system (CGMS) and standardized meal test (MMT). Duration of hyperglycemia (>8.0 mmol/l), standard deviation score (S.D.-score) and mean amplitude of glycemic excursions (MAGE) were analyzed from CGMS data and postprandial glucose during MMT (PPG(MMT)). Patients were hyperglycemic for 5.7h/day (median), experienced 4.1 hyperglycemic episodes/day, and 78% exceeded PPG levels of 8.0 mmol/l. HbA1c, though associated with the extent of hyperglycemia (r=0.40, p<0.001), failed to correlate with S.D.-score and MAGE. Multiple regression analysis demonstrated that HbA1c was predicted only by fasting glucose (R(2)=0.24, p<0.001) but neither by PPG(MMT), duration of hyperglycemia, S.D.-score nor MAGE. CGMS and meal test provide the tools for complete characterization of glycemia in type 2 diabetes. In well-controlled type 2 diabetes, HbA1c correlates with chronic hyperglycemia but not with glucose variability. Our data suggest that chronic sustained hyperglycemia and glucose fluctuations are two independent components of dysglycemia in 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 (HbA1c) levels and 24-hour glucose profiles before and after the intervention. RESULTS: Application of KADIS-based decision support reduced HbA1c 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 HbA1c 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.

[The metabolic syndrome in patients with type 1 diabetes mellitus. Associations with cardiovascular risk factors and cardiovascular morbidity]. / Metabolisches Syndrom bei Patienten mit Diabetes mellitus Typ 1. Auswirkungen auf das kardiovaskuläre Risikoprofil und die kardiovaskuläre Morbidität.

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/m(2) 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 (HbA(1c)), 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 HbA(1c) (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 HbA(1c). 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.