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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.

In experimental rabbits, cysteine injected intravenously in a dose of 1000 mg/kg temporarily bound zinc in β cells and prevented the formation of chelate zinc complexes in response to subsequent injection of diabetogenic zinc-binding substances that induce cell destruction. Injection of cysteine to animals was associated with a sharply negative reaction to zinc in β cells, which attests to blockade of zinc ions. Injection of cysteine few minutes after dithizone and formation of zinc-dithizone complex was followed by displacement of dithizone from the complex and prevented the development of diabetes in most animals. The most plausible mechanism of preventive effect of cysteine is the formation of 2:1 zinc-cysteine complex in β cells with possible fixation of Zn atom between sulfur atoms from SH groups of two cysteine molecules.

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.

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 (mmol 2 /l 2 ): 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 mmol 2 /l 2 ), 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.

Aims: The dynamical structure of glucose fluctuation has largely been disregarded in the contemporary management of diabetes., Methods: In a retrospective study of patients with diabetes, we evaluated the relationship between glucose dynamics, antihyperglycemic therapy, glucose variability, and glucose exposure, while taking into account potential determinants of the complexity index. We used multiscale entropy (MSE) analysis of continuous glucose monitoring data from 131 subjects with type 1 (n = 18), type 2 diabetes (n = 102), and 11 nondiabetic control subjects. We compared the MSE complexity index derived from the glucose time series among the treatment groups, after adjusting for sex, age, diabetes duration, body mass index, and carbohydrate intake., Results: In type 2 diabetic patients who were on a diet or insulin regimen with/without oral agents, the MSE index was significantly lower than in nondiabetic subjects but was lowest in the type 1 diabetes group (p < 0.001). The decline in the MSE complexity across the treatment groups correlated with increasing glucose variability and glucose exposure. Statistically, significant correlations existed between higher MSE complexity indices and better glycemic control. In multivariate regression analysis, the antidiabetic therapy was the most powerful predictor of the MSE (β = -0.940 ± 0.242, R 2  = 0.306, p < 0.001), whereas the potential confounders failed to contribute., Conclusions: The loss of dynamical complexity in glucose homeostasis correlates more closely with therapy modalities and glucose variability than with clinical measures of glycemia. Thus, targeting the glucoregulatory system by adequate therapeutic interventions may protect against progressive worsening of diabetes control.

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.

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.

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.

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 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 β-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 β-cells are particularly susceptible to damage imposed by oxidative stress. Evidence from experiments, using isolated pancreatic islets or β-cell lines, has linked intermittent high glucose, which mimicks GV under diabetic conditions, to significant impairment of β-cell function. Several clinical studies reported a close association between GV and β-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 β-cell function correlated with reduction of GV. The purpose of the present review is to discuss the detrimental effects of GV and associations with β-cell function as well as upcoming therapeutic strategies directed towards minimising glucose excursions, improving β-cell recovery and preventing progressive β-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.

The model-based Karlsburg Diabetes Management System (KADIS®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients., (Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.)

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.

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.

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., (© 2010 Diabetes Technology Society.)

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., (2010 Diabetes Technology Society.)

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.

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.

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.

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.

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.

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.

Background: Diabetic patients have increased prevalence of peripheral arterial disease (PAD). It is not clearly shown whether the prognostic factors are identical in relation to the type of diabetes. This study was done to compare the associations of PAD with risk factors and with micro- and macrovascular complications of inpatients with type 1 and type 2 diabetes., Methods: In a retrospective cross-sectional study 1087 patients with type 1 diabetes and 1060 patients with type 2 diabetes were examined. PAD was diagnosed when ankle-brachial-pressure-index (ABI) was < 1.0. In cases with incompressible arteries (mediasclerosis) pulse wave forms were analyzed. Multivariate logistic regression analysis was applied to evaluate the impact of different variables on PAD risk, after adjusting for different variables separately., Results: In both types of diabetes (type 1 vs. type 2) PAD risk (odds ratio; OR) was increased in the presence of coronary heart disease (OR 9.3 vs. 3.5), diabetic nephropathy (OR 3.0 vs. 2.8), neuropathy (OR 7.9 vs. 1.8), foot ulceration (OR 8.9 vs. 5.5), increased daily insulin requirement > 0.6 mu/kg b.w. (OR 5.2 vs. 2.9), diabetes duration of 20-29 years (OR 28.9) and > 30 years (OR 51.1) in type 1 diabetes, and diabetes duration of 10-19 years (OR 3.8) and > 20 years (OR 4.3) in type 2 diabetes. In type 2 diabetes, PAD risk was associated with microalbuminuria (OR 2.1), macroalbuminuria (OR 3.3), background retinopathy (OR 1.9), proliferative retinopathy (OR 2.8), increased triglycerides (TG) (OR 1.7) and decreased HDL-cholesterol (HDL-C > 0.90 mmol/l: OR 0.49)., Conclusions: PAD risk factors and micro- and macrovascular comorbidity are very similar in type 1 and type 2 diabetes.

To study whether normalization of hyperglycemia improves islet function in long-standing type 2 diabetes, hyperglycemic CHIG/Han subline of the genetic type 2 diabetic Chinese hamster (>15 mmol/l: n=23) were either treated with insulin implants (liberating 1 U/day) or vehicle for two weeks. Islets were isolated and incubated for 3 h in the presence of 10 mmol/l glucose with or without 0.1 mmol/l 3-isobutyl-1-methylxanthine (IBMX). Specimens were also taken for immunocytochemical analysis of insulin cells. Glucose-stimulated insulin secretion was reduced by 83% in the vehicle-treated diabetic hamsters compared to non-diabetic controls (p<0.001). This impairment was not improved by the two-week insulin treatment. IBMX potentiated glucose-stimulated insulin secretion; this effect was markedly reduced in vehicle-treated diabetics compared to controls (p<0.001). In fact, the linear relation between IBMX-potentiated and glucose-stimulated insulin secretion in controls was absent in islets from diabetic animals. The two week insulin treatment normalized this relation, although still the total insulin secretory response to IBMX and glucose was lower than in controls. Furthermore, the islet insulin content was significantly increased by the 2 week normalization of glucose and, finally, the severe degranulation and lowering of insulin staining in islet beta cells in diabetic animals were markedly improved by insulin treatment. The results suggest that two-weeks of normalization of glycemia in long-standing type 2 diabetes in non-obese Chinese hamster improves beta cell signaling induced by the cyclic AMP pathway in conjunction with improved islet insulin content and beta cell morphology.

A decrease in total magnesium content is not a direct proof of a decreased magnesium ion concentration. It could reflect a phosphate alteration or an ATP metabolism disorder. Plasma phosphate levels are lower in spontaneously hypertensive rats (SHRs) than in Wistar-Kyoto (WKY) rats, and defects in membrane regulation or mitochondrial ATP synthase occur. Only sparse data exist concerning cellular magnesium and phosphate concentrations in hypertensive cells. In aortic smooth muscle cells from 10 SHRs of the Münster strain and 10 age-matched normotensive WKY rats, the intracellular phosphate and magnesium content was measured by electron probe X-ray microanalysis (Camscan CS 24 apparatus, Cambridge, U.K.). The Mg++ content was 0.90+/-0.15 g/kg dry weight in SHRs versus 1.15+/-0.10 g/kg dry weight in WKY rats (p<0.05). Vascular smooth muscle phosphate content was 23.6+/-0.79 g/kg dry weight in WKY rats versus 15.81+/-1.22 g/kg dryweight in SHRs (p<0.01). In seven animals, erythrocytic ATP content was 180.2+/-102 in SHRs vs. 432+/-72 micromol/L cells in WKY rats (p< 0.01). The Na+/K+-ATPase activity was significantly decreased in hypertensive animals as compared to controls (6.49+/-2.3 vs. 12.64+/-2.9 nmol inorganic phosphate/mg protein/min (p< 0.01)). Aortic smooth muscle cells from SHRs are characterized by markedly lowered cellular phosphate and magnesium concentrations and an altered ATP metabolism, possibly due to a membrane defect or a magnesium deficit in hypertensive cells.

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 microg/min) or microalbuminuric (20-200 microg/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.

It is known that the liver is a favourable site for implantation of pancreatic islets since the grafted islets remain metabolically intact and provide long-term normoglycemia in diabetic animals. However, the long-term effects exerted by the grafted tissue on the host organ are not well defined. We therefore investigated by light and electron microscopy the effects of syngeneic islets on the host organ after intraportal transplantation into the liver of streptozotocin (STZ)-induced diabetic LEW.1W rats. In addition, tissue sections of graft-bearing liver were stained by enzyme histochemical methods for beta-hydroxybutyrate dehydrogenase (HBDH) and glucose-6-phosphatase (G6Pase). At 12 weeks after transplantation, the changes seen in the hepatocytes surrounding the grafted islets were hyperproliferation and accumulation of glycogen. Hepatocytes adjacent to the implanted islets displayed increased HBDH activity, whereas G6Pase activity was variable, either decreased or increased. Increased HBDH activity was also observed in the periportal region and in liver cells extending to the central veins. The results demonstrate that intraportal islet grafts, in addition to normalizing glucose homeostasis, exert remarkable effects on the liver parenchyma of experimentally diabetic recipient rats.

To investigate pancreatic histopathology in relation to islet function in type 2 diabetes, pancreatic tissue was examined at disease onset and after death in the genetically diabetic Chinese hamster CHIG/Han subline. The pancreatic islets displayed vacuolation, intraislet fibrosis, variable stages of degranulation, and beta-cell necrosis, but no insulitis. These lesions were associated with changes in immunostaining of major islet peptides, impaired glucose-stimulated (10 mM) insulin secretion, reduced islet insulin content, and the severity of hyperglycemia. The exocrine pancreas was characterized by peri- and intrapancreatic fat and mononuclear cell infiltration. Biopsy of the pancreas had a marked effect on plasma glucose such that at 2 weeks after excision, in 9 and 18% of the severely hyperglycemic hamsters, plasma glucose levels decreased to <7.2 and 15 mM, respectively, and 45% of the mildly hyperglycemic hamsters became transiently normoglycemic (<7.2 mM). The results showed that insulitis is not involved in beta-cell failure of diabetic CHIG/Han hamsters. Vacuolation of islets was the most prominent lesion associated with a functional islet abnormality and development of hyperglycemia. Attenuation of hyperglycemia after biopsy suggests that the pancreas, in type 2 diabetes, maintains the ability to respond to impairment with amelioration of the diabetic state.

This study examined the relationship between islet neurohormonal characteristics and the defective glucose-stimulated insulin secretion in genetic type 2 diabetic Chinese hamsters. Two different sublines were studied: diabetes-prone CHIG hamsters and control CHIA hamsters. The CHIG hamsters were divided into three subgroups, depending on severity of hyperglycemia. Compared to normoglycemic CHIG hamsters and control CHIA hamsters, severely hyperglycemic CHIG hamsters (glucose > 15 mmol/l) showed marked glucose intolerance during i.p. glucose tolerance test and 75% impairment of glucose-stimulated insulin secretion from isolated islets. Mildly hyperglycemic CHIG animals (glucose 7.2-15 mmol/l) showed only moderate glucose intolerance and a 60% impairment of glucose-stimulated insulin secretion from the islets. Immunostaining for neuropeptide Y and tyrosine hydroxylase (markers for adrenergic nerves) and for vasoactive intestinal peptide (marker for cholinergic nerves) revealed significant reduction in immunostaining of islets in the severely but not in the mildly hyperglycemic animals, compared to control CHIA hamsters. The study therefore provides evidence that in this model of type 2 diabetes in Chinese hamsters, severe hyperglycemia is accompanied not only by marked glucose intolerance and islet dysfunction but also by reduced islet innervation. This suggests that islet neuronal alterations may contribute to islet dysfunction in severe but not in mild diabetes.

This study evaluates glucose metabolism and insulin secretion in children with Ullrich-Turner syndrome (UTS), chronic renal failure (CRF) and kidney transplantation (KTx) with rh GH therapy using an intravenous glucose infusion test. Before treatment, glucose AUC was significantly increased in all patient groups when compared to normal controls. Both the early and second phases of insulin secretion were not altered. During treatment, elevated glucose AUC showed a further increase in patients with KTx but not in patients with CRF or UTS. Both the early and second insulin secretion phases rose significantly in UTS and were transiently elevated after 6 and 12 months of therapy in patients with CRF and KTx. We conclude that growth hormone therapy aggravates alteration of glucose metabolism in patients with KTx and not in children with CRF and UTS. Progressive hyperinsulinemia occurred only in patients with UTS.

Syngeneic islets were transplanted into the liver of streptozotocin (STZ)-induced diabetic LEW.1W rats, and the expression of the glucose transporter isoform GLUT 2, an essential component of the glucose-sensing mechanism of the pancreatic beta-cell, was determined in the grafted islet tissue. Graft-bearing liver was obtained 12, 36, and 60 weeks after transplantation, and tissue sections were immunoperoxidase stained for GLUT 2 and major islet peptides. Islet cell aggregates of different sizes were found in the portal tract and in juxtaposition to the hepatocytes. At all time points, beta-cells in the grafts displayed GLUT 2 expression comparable to that of islets in nondiabetic rats. Islet cells containing immunoreactive insulin and islet amyloid polypeptide were plentiful, while those staining positive for glucagon and somatostatin were scarce in these grafts. The results show that beta-cells in islets engrafted in the liver, although initially exposed to chronic hyperglycemia, have the capability of stably expressing GLUT 2 over long-term periods.

Cytoplasmic islet cell antibodies, glutamic acid decarboxylase autoantibodies, spontaneous insulin autoantibodies, and insulin-induced antibodies were analyzed in a 1-year follow-up study of 12 newly diagnosed patients with insulin-dependent diabetes mellitus aged 14 +/- 2 years (range 7-20 years) who had been initially treated with either multiple injections of insulin alone (control group) or, in addition, anti-CD4 monoclonal antibody/prednisolone (treatment group). Despite individual variations in islet cell antibody titers, there were no significant differences in the prevalence or changes in the mean titers between the two groups. Glutamic acid decarboxylase autoantibodies remained almost unchanged, but correlated with levels of islet cell antibodies. While at initiation of treatment only 50% of the patients from both groups had spontaneous insulin autoantibodies, all patients developed insulin-induced antibodies upon conventional insulin therapy during the course of follow-up. This was not related to islet cell antibody or glutamic acid decarboxylase antibody levels. The insulin requirement was markedly reduced through the period of follow-up, but did not significantly differ between the two groups. A correlation between islet cell antibody levels and insulin requirement was observed in the control group but not in the treatment group. Plasma levels of the antibodies were not associated with changes in stimulated C-peptide or hemoglobin A1 concentrations. Activated T-lymphocytes persisted in both groups of patients, but their mean levels were not significantly different. The reason for the absence of statistically significant differences between treatment and control groups could be due to the small number of patients in the study. In conclusion, short-term immune intervention with anti-CD4 monoclonal antibody in addition to insulin therapy did not suppress autoimmune reactions towards the beta cells.

The CHIG/Han subline of the Chinese hamster develops noninsulin-dependent diabetes mellitus characterized by hyperinsulinemia and different degrees of glucose intolerance. To study whether these abnormalities could affect transmembrane cation transport activity, we determined membrane ATPase activity and ATP concentrations in red blood cells of diabetes-resistant CHIA and diabetes-susceptible CHIG sublines of the Chinese hamster. Mg(2+)-ATPase activity was increased in red blood cell membranes of diabetic hamsters compared with that of nondiabetic CHIG and the diabetes-resistant CHIA animals and correlated with plasma triglyceride and cholesterol levels. Ca(2+)-ATPase and Na+/K+ATPase activity were not significantly different between diabetic and nondiabetic hamsters, but for the Na+/K(+)-ATPase, Km was decreased and the Vmax value increased in membrane preparations from severely diabetic hamsters. Both ATP and ADP content were lower in erythrocytes from diabetic than nondiabetic hamsters. Independently of the levels of glycemia, AMP concentrations were higher in CHIG than in CHIA hamsters. While ATP/AMP ratios were found to be decreased in erythrocytes from diabetes-susceptible CHIG hamsters compared to the diabetes-resistant CHIA animals, they were significantly correlated with the levels of glycemia. Furthermore, the relationship between blood glucose levels and kidney weight in hamsters of the diabetes-susceptible CHIG subline was such, that severely hyperglycemic animals displayed the greatest increase in kidney wet weight. These results indicate that the progressive metabolic deterioration in the development of noninsulin-dependent diabetes is associated with significant changes in the activity and kinetic parameters of cellular ATPases which could probably indicate early membrane alterations which may eventually result in the late microangiopathic complications of diabetes.

Two- and three-color cytofluorimetric techniques were used to study the expression patterns of the activation antigen HLA-DR on peripheral blood immunoregulatory T-cells from 25 patients with newly diagnosed insulin-dependent diabetes mellitus (IDDM) and 14 age- and sex-matched control subjects. The mean percentage of total activated (CD3+HLA-DR+) T-cells was significantly elevated in the IDDM group compared with the control group (P < 0.001). In control subjects, basal activation of CD4+ and CD8+ lymphocytes accounted for the low percentage levels of activated T-cells. In contrast, the majority of IDDM patients showed an unbalanced activation of CD4+ and CD8+ lymphocytes with predominant activation of the CD8+ lymphocyte subset. The composition of the activated T-cell fraction was dependent on the composition of the total (activated + nonactivated) T-cell population, as indicated by the positive correlation between the CD4+/CD8+ T-cell ratios in these two cell populations (r = 0.714; P < 0.001). Excessive activation of CD8+ T-cells was attributable to similar increases in the proportions of CD8+ CD45RA+HLA-DR+ (naive) and CD8+CD45RA-HLA-DR+ (memory) cells. Analysis of the CD11b-defined subsets revealed predominant activation of CD8+ CD11b- (cytotoxic) T-cells; CD8+ CD16+ HLA-DR+ natural killer cells were unchanged. The distribution of HLA-DR+ cells among subsets of CD4+ T-cells differed from the pattern in the CD8+ population in that selective activation of CD4+ CD45RA- (memory, helper-inducer) cells accounted for the small increase in activated CD4+ cells.(ABSTRACT TRUNCATED AT 250 WORDS)