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

AimsTo estimate the national incidence rate and trend of type 1 diabetes (T1DM) in Germany from 1999 to 2008 and the national prevalence in 2008 in the age group 0–14 years.MethodsData were taken from a nationwide registry for incident cases of T1DM in the ages 0–4 years and 3 regional registries (North-Rhine-Westphalia, Baden-Wuerttemberg and Saxony) for incident cases of T1DM in the ages 0–14 years covering 41% of the child population in Germany. The degree of ascertainment was ≥ 97% in all registries. Incident and prevalent cases were grouped by region, sex, age (0–4, 5–9, 10–14 years), and, for incident data, additionally by two 5-year periods (1999–2003, 2004–2008). Poisson regression models were fitted to the data to derive national estimates of incidence rate trends and prevalence in the age groups 5–9, 10–14 and 0–14 years. We used direct age-standardization.ResultsThe estimated national incidence rate in 0-14-year-olds increased significantly by 18.1% (95%CI: 11.6–25.0%, p<0.001) from 1999–2003 to 2004–2008, independent of sex, corresponding to an average annual increase of 3.4% (95%-CI: 2.2–4.6%). The overall incidence rate was estimated at 22.9 per 100,000 person-years and we identified a within-country west-east-gradient previously unknown. The national prevalence in the ages 0–14 years on 31/12/2008 was estimated to be 148.1 per 100,000 persons.ConclusionsThe national incidence rate of childhood T1DM in Germany is higher than in many other countries around the world. Importantly, the estimated trend of the incidence rate confirms the international data of a global increase of T1DM incidences.

No abstract

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.

BackgroundContinuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic ‘weak points’. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.MethodsFifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.ResultsWe identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the ‘Q-Score’). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of ‘very good’, ‘good’, ‘satisfactory’, ‘fair’, and ‘poor’ metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0–5.9, good; 6.0–8.4, satisfactory; 8.5–11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as ‘low’, ‘moderate’ and ‘high’.ConclusionsThe Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12902-015-0019-0) contains supplementary material, which is available to authorized users.

No abstract

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 ϭ 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 Ϯ 0.67%), mean change in A1C decreased in the CGMS/KADIS group during the follow-up (Ϫ0.34 Ϯ 0.49%; P Ͻ 0.01). MSG levels were not affected in the CGMS group (7.75 Ϯ 1.33 vs. 8.45 Ϯ 2.46 mmol/l) but declined in the CGMS/KADIS group (8.43 Ϯ 1.33 vs. 7.59 Ϯ 1.47 mmol/l; P Ͻ 0.05). Net KADIS effect (Ϫ0.60 [95% CI Ϫ0.96 to Ϫ 0.25%]; P Ͻ 0.01) was associated with reduced duration of hyperglycemia (4.6 vs. 1.0 h/day; P Ͻ 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.