The glucose area under the profiles obtained with continuous glucose monitoring system relationships with HbA(lc) in pediatric type 1 diabetic patients

Glucose fluctuation promotes cardiomyocyte apoptosis by triggering endoplasmic reticulum (ER) stress signaling pathway in vivo and in vitro

Glucose fluctuation is more harmful than sustained hyperglycemia, but the effect on cardiomyocyte apoptosis have not yet been clarified. In this study, we aim to identify the effect of glucose fluctuation on cardiomyocyte apoptosis and explore the underlying mechanism. Sprague-Dawley rats were intraperitoneally injected with streptozotocin (STZ) and divided into three groups: controlled diabetic group (C-STZ); uncontrolled diabetic group (U-STZ) and glucose fluctuated diabetic group (GF-STZ). After twelve weeks, echocardiography, Hematoxylin-eosin (HE) staining, and Masson staining were adopted to assess the cardiac function and pathological changes. TUNEL staining was used to detect apoptotic cells. Expressions of apoptosis-related proteins and key molecules in the endoplasmic reticulum (ER) stress pathway were determined via western blots. Further, primary cardiomyocytes incubated in different glucose conditions were treated with the inhibitor of ER stress to explore the causative role of ER stress in glucose fluctuation-induced cardiomyocyte apoptosis. In vivo, we demonstrated that glucose fluctuation promoted cardiomyocyte apoptosis, and were more harmful to cardiomyocytes than sustained hyperglycemia. Moreover, glucose fluctuation significantly triggered ER stress signaling pathway. In vitro, primary cardiomyocyte apoptosis induced by glucose fluctuation and the activation of ER stress were significantly attenuated by 4-PBA, which is an ER stress inhibitor. Above all, glucose fluctuation can promote cardiomyocyte apoptosis through triggering the ER stress signaling pathway in diabetic rats and in primary cardiomyocytes.

Influence of Glucose Fluctuation on Peripheral Nerve Damage in Streptozotocin-Induced Diabetic Rats

BACKGROUND

It is unclear whether glycemic variability (GV) is a risk factor for diabetic peripheral neuropathy (DPN), and whether control of GV is beneficial for DPN. The purpose of this study was to investigate the effect of GV on peripheral nerve damage by inducing glucose fluctuation in streptozotocin-induced diabetic rats.

METHODS

Rats were divided into four groups: normal (normal glucose group [NOR]), diabetes without treatment (sustained severe hyperglycemia group; diabetes mellitus [DM]), diabetes+once daily insulin glargine (stable hyperglycemia group; DM+LAN), and diabetes+once daily insulin glargine with twice daily insulin glulisine (unstable glucose fluctuation group; DM+Lantus [LAN]+Apidra [API]). We measured anti-oxidant enzyme levels and behavioral responses against tactile, thermal, and pressure stimuli in the plasma of rats. We also performed a quantitative comparison of cutaneous and sciatic nerves according to glucose fluctuation.

RESULTS

At week 24, intraepidermal nerve fiber density was less reduced in the insulin-administered groups compared to the DM group (P<0.05); however, a significant difference was not observed between the DM+LAN and DM+LAN+API groups irrespective of glucose fluctuation (P>0.05; 16.2±1.6, 12.4±2.0, 14.3±0.9, and 13.9±0.6 for NOR, DM, DM+LAN, and DM+LAN+API, respectively). The DM group exhibited significantly decreased glutathione levels compared to the insulin-administered groups (2.64±0.10 μmol/mL, DM+LAN; 1.93±0.0 μmol/mL, DM+LAN+API vs. 1.25±0.04 μmol/mL, DM; P<0.05).

CONCLUSION

Our study suggests that glucose control itself is more important than glucose fluctuation in the prevention of peripheral nerve damage, and intra-day glucose fluctuation has a limited effect on the progression of peripheral neuropathy in rats with diabetes.

Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring

BACKGROUND

Glucose monitoring is vital for glycemic control in patients with diabetes mellitus (DM). Continuous glucose monitoring (CGM) measures whole-day glucose levels. Hemoglobin A1c (HbA1c) is a vital outcome predictor in patients with DM.

METHODS

This study investigated the relationship between HbA1c and CGM, which remained unclear hitherto. Data of patients with DM (n = 91) who received CGM and HbA1c testing (1-3 months before and after CGM) were retrospectively analyzed. Diurnal and nocturnal glucose, highest CGM data (10%, 25%, and 50%), mean amplitude of glycemic excursions (MAGE), percent coefficient of variation (%CV), and continuous overlapping net glycemic action were compared with HbA1c values before and after CGM.

RESULTS

The CGM results were significantly correlated with HbA1c values measured 1 (r = 0.69) and 2 (r = 0.39) months after CGM and 1 month (r = 0.35) before CGM. However, glucose levels recorded in CGM did not correlate with the HbA1c values 3 months after and 2-3 months before CGM. MAGE and %CV were strongly correlated with HbA1c values 1 and 2 months after CGM, respectively. Diurnal blood glucose levels were significantly correlated with HbA1c values 1-2 months before and 1 month after CGM. The nocturnal blood glucose levels were significantly correlated with HbA1c values 1-3 months before and 1-2 months after CGM.

CONCLUSIONS

CGM can predict HbA1c values within 1 month after CGM in patients with DM.

Relationship between glucose control, glycemic variability, and oxidative stress in children with type 1 diabetes

INTRODUCTION

Few studies assessing the relationship between oxidative stress and glycemic variability in children with type 1 diabetes mellitus (T1DM) are available, and most of them reported no significant results.

OBJECTIVE

To assess the relationship between glucose control, glycemic variability, and oxidative stress as measured by urinary excretion of 8-iso-prostanglandin F2-alpha (8-iso-PGF2α) in children with T1DM.

MATERIALS AND METHODS

A cross-sectional study including 25 children with T1DM. Participants were evaluated during five days in two different situations: 1st phase during a summer camp, and 2nd phase in their everyday life at home. The following data were collected in each study phase:. - Six capillary blood glucose measurements per day. Mean blood glucose (MBG) levels and glucose variability parameters, including standard deviation, coefficient of variation, and mean amplitude of glycemic excursions (MAGE), were calculated. - Capillary HbA1c level. - 24-h urine sample to measure 8-iso-PGF2α.

RESULTS

There were no statistically significant differences in urinary 8-iso-PGF2α levels (142±37 vs. 172±61pg/mg creatinine) and glucose control and glycemic variability parameters between both phases. In the 2nd phase, statistically significant correlations were found between urinary 8-iso-PGF2α and HbA1c levels (r=0.53), MBG (r=0.72), standard deviation (r=0.49), and MAGE (r=0.42). No significant correlations between glucose control, glycemic variability and urinary 8-iso-PGF2α excretion were found in the 1st phase.

CONCLUSIONS

A significant correlation was found between glycemic variability and HbA1c level and urinary 8-iso-PGF2α excretion in a group of children with T1DM during their daily lives. Additional studies are needed to confirm this finding and to explore its long-term impact on health.

Translating the HbA1c assay into estimated average glucose values in children and adolescents with type 1 diabetes mellitus

OBJECTIVE

The A1c assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per decilitre or millimoles per litter) as well as by using continuous glucose monitoring systems (CGMS). We found a mathematical relationship between A1c and average glucose (AG) levels measured by CGMS over 5 days and determined the correlation between the variable CGMS parameters and HbA1c in 50 children with type 1 diabetes mellitus (DM-1) on MDI therapy.

RESEARCH DESIGN AND METHODS

A total of 50 diabetic children randomly selected from a cohort of children with DM-1 were included in the analyses. A1c levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 5 days recorded by CGMS. AG was calculated by combining weighted results from 5 days of continuous glucose monitoring performed before measuring HbA1c, with 3-5 point daily self-monitoring of capillary (fingerstick) glucose.

RESULTS

Linear regression analysis between the A1c and AG values provided the tightest correlations HbA1c=0.0494 MG- 2E-14, R2=0.90, P<0.0001), allowing calculation of an estimated average glucose (eAG) for A1c values.

CONCLUSIONS

Our study showed a linear relationship between HbA1C and AG values measured by CGMS for 5 days before HbA1c measurement. The AG can be easily calculated using a formula derived from linear regression analysis of HbA1c data obtained in our diabetic children.

Glycated Hemoglobin (HbA1c): Clinical Applications of a Mathematical Concept

Background and purpose:

Glycated hemoglobin (HbA1c) reflects the cumulative glucose exposure of erythrocytes over a preceding time frame proportional to erythrocyte survival. HbA1c is thus an areal function of the glucose-time curve, an educationally useful concept to aid teaching and clinical judgment.

Methods:

An ordinary differential equation is formulated as a parsimonious model of HbA1c. The integrated form yields HbA1c as an area-under-the-curve (AUC) of a glucose-time profile. The rate constant of the HbA1c model is then derived using the validated regression equation in the ADAG study that links mean blood glucose and HbA1c with a very high degree of goodness-of-fit.

Results:

This model has didactic utility to enable patients, biomedical students and clinicians to appreciate how HbA1c may be conceptually inferred from discrete blood glucose values using continuous glucose monitoring system (CGMS) or self-monitored blood glucose (SMBG) glucometer readings as shown in the examples. It can be appreciated how hypoglycemia can occur with rapid HbA1c decline despite poor glycemic control.

Conclusions:

Being independent of laboratory assay pitfalls, computed ‘virtual’ HbA1c serves as an invaluable internal consistency cross-check against laboratory-measured HbA1c discordant with SMBG readings suggestive of inaccurate/fraudulent glucometer records or hematologic disorders including thalassemia and hemoglobinopathy. This model could be implemented within portable glucometers, CGMS devices and even smartphone apps for deriving tentative ‘virtual’ HbA1c from serial glucose readings as an adjunct to measured HbA1c. Such predicted ‘virtual’ HbA1c readily accessible via glucometers may serve as feedback to modify behavior and empower diabetic patients to achieve better glycemic control.

Glycemic Variability: How Do We Measure It and Why Is It Important?

Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus (DM); however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications. Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability (GV) includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes. Modern diabetes management modalities, including glucagon-related peptide-1-based therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV. However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications.

Is type 2 diabetes really resolved after laparoscopic sleeve gastrectomy? Glucose variability studied by continuous glucose monitoring

The study was carried out on type 2 diabetic obese patients who underwent laparoscopic sleeve gastrectomy (LSG). Patients underwent regular glycemic controls throughout 3 years and all patients were defined cured from diabetes according to conventional criteria defined as normalization of fasting glucose levels and glycated hemoglobin in absence of antidiabetic therapy. After 3 years of follow-up, Continuous Glucose Monitoring (CGM) was performed in each patient to better clarify the remission of diabetes. In this study, we found that the diabetes resolution after LSG occurred in 40% of patients; in the other 60%, even if they showed a normal fasting glycemia and A1c, patients spent a lot of time in hyperglycemia. During the oral glucose tolerance test (OGTT), we found that 2 h postload glucose determinations revealed overt diabetes only in a small group of patients and might be insufficient to exclude the diagnosis of diabetes in the other patients who spent a lot of time in hyperglycemia, even if they showed a normal glycemia (<140 mg/dL) at 120 minutes OGTT. These interesting data could help clinicians to better individualize patients in which diabetes is not resolved and who could need more attention in order to prevent chronic complications of diabetes.

Diet and glycaemia: the markers and their meaning. A report of the Unilever Nutrition Workshop

Consumption of carbohydrate-containing foods leads to transient postprandial rises in blood glucose concentrations that vary between food types. Higher postprandial glycaemic exposures have particularly been implicated in the development of chronic cardiometabolic diseases. Reducing such diet-related exposures may be beneficial not only for diabetic patients but also for the general population. A variety of markers have been used to track different aspects of glycaemic exposures, with most of the relevant knowledge derived from diabetic patients. The assessment of glycaemic exposures among the non-diabetic population may require other, more sensitive markers. The present report summarises key messages of presentations and related discussions from a workshop organised by Unilever intended to consider currently applied markers of glycaemic exposure. The particular focus of the meeting was to identify the potential applicability of glycaemic exposure markers for studying dietary effects in the non-diabetic population. Workshop participants concluded that markers of glycaemic exposures are sparsely used in intervention studies among non-diabetic populations. Continuous glucose monitoring remains the optimal approach to directly assess glycaemic exposure. Markers of glycaemic exposure such as glycated Hb, fructosamine, glycated albumin, 1,5-anhydroglucitol and advanced glycation end products can be preferred dependent on the aspect of interest (period of exposure and glucose variability). For all the markers of glycaemia, the responsiveness to interventions will probably be smaller among the non-diabetic than among the diabetic population. Further validation and acceptance of existing glycaemic exposure markers applied among the non-diabetic population would aid food innovation and better design of dietary interventions targeting glycaemic exposure.

[Glycemic variability and oxidative stress in children, with type 1 diabetes attending a summer camp]

OBJECTIVE

To assess glycemic variability, oxidative stress and their relationship in children and adolescents with type 1 diabetes (T1DM) attending a summer camp.

PATIENTS AND METHOD

Cross-sectional study that included 54 children and adolescents with T1DM aged 7-16, attending a 7 day summer camp. Sociodemographic information, clinical data, and blood glucose values measured using an Accu-Chek Nano® glucose meter were recorded. Glucose variability markers (standard deviation [SD], low blood glucose index [LBGI], high blood glucose index [HBGI], mean amplitude of glycemic excursions [MAGE] and mean of daily differences [MODD]) were calculated. Oxidative stress was assessed by the measurement of 8-iso-prostaglandin F2 alpha (PGF2α) in a 24-hour urine sample collected at the end of the camp in 14 children.

RESULTS

The Median SD, MAGE and MODD indexes were in the high range (61, 131 and 58 mg/dl, respectively), LBGI in the moderate range (3.3), and HBGI in the low range (4.5). The mean HbA1c was 7.6% and the median urinary excretion rate of 8-iso-PGF2α was 864.39 pg/mg creatinine. The Spearman correlation coefficients between markers of glycemic variability (SD, HBGI, MAGE, MODD) were significant. Non-significant correlations were found between markers of glycemic variability and urinary 8-iso-PGF2α.

CONCLUSIONS

High glycemic variability was observed in children and adolescents attending a summer camp. However, no correlations were found between markers of glycemic variability and oxidative stress measured by urinary 8-iso-PGF2α. Further studies are needed to address the relationship between oxidative stress and glycemic variability in children with T1DM.

Glucose variability: An emerging target for the treatment of diabetes mellitus

Alterations in glucose metabolism in individuals with diabetes have been considered for many years, as they appear at first glance, i.e., simply as hyperglycemia, and its surrogate marker, glycated hemoglobin (HbA1c), used both to estimate the risk of developing diabetic complications and to define the targets and measure the efficacy of diabetes treatments. However, over time diabetes-related glycemic alterations have been considered in more complex terms, by attempting to identify the role of fasting glycemia, postprandial glycemia and hypoglycemia in the overall assessment of the disease. This set of evaluations has led to the concept of glucose variability. Although intuitively easy to understand, it cannot be equally simply translated into terms of definition, measuring, prognostic and therapeutic impact. The literature available on glucose variability is extensive yet confused, with the only common element being the need to find out more on the subject. The purpose of this manuscript is not only to review the most recent evidence on glucose variability, but also to help the reader to better understand the available measurement options, and how the various definitions can differently be related with the development of diabetic complications. Finally, we provide how new and old drugs can impact on glucose variability.

Continuous Glucose Monitoring Systems: A Review

There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.

Detection of glycemic abnormalities in adolescents with beta thalassemia using continuous glucose monitoring and oral glucose tolerance in adolescents and young adults with β-thalassemia major: Pilot study

BACKGROUND

Both insulin deficiency and resistance are reported in patients with β-thalassemia major (BTM). The use of continuous blood glucose monitoring (CGM), among the different methods for early detection of glycemic abnormalities, has not been studied thoroughly in these adolescents.

MATERIALS AND METHODS

To assess the oralglucose tolerance (OGT) and 72-h continuous glucose concentration by the continuous glucose monitoring system (CGMS) and calculate homeostatic model assessment (HOMA), and the quantitative insulin sensitivity check index (QUICKI) was conducted in 16 adolescents with BTM who were receiving regular blood transfusions every 2-4 weeks and iron-chelation therapy since early childhood.

RESULTS

SIXTEEN ADOLESCENTS WITH BTM (AGE: 19.75 ± 3 years) were investigated. Using OGTT, (25%) had impaired fasting blood (plasma) glucose concentration (BG) (>5.6 mmol/L). 2-h after the glucose load, one of them had BG = 16.2 mmol/L (diabetic) and two had impaired glucose tolerance (IGT) (BG > 7.8 and <11.1 mmol/L). Monitoring the maximum (postprandial) BG using CGMS,4 adolescents were diagnosed with diabetes (25%) (BG >11.1 mmol/L) and 9 with IGT (56%). HOMA and QUICKI revealed levels <2.6 (1.6 ± 0.8) and >0.33 (0.36 ± 0.03), respectively, ruling out significant insulin resistance in these adolescents. There was a significant negative correlation between the β-cell function (B%) on one hand and the fasting and the 2-h BG (r=-0.6, and - 0.48, P < 0.01, respectively) on the other hand. Neither fasting serum insulin nor c-peptide concentrations were correlated with fasting BG or ferritin levels. The average and maximum blood glucose levels during CGM were significantly correlated with the fasting BG (r = 0.68 and 0.39, respectively, with P < 0.01) and with the BG at 2-hour after oral glucose intake (r = 0.87 and 0.86 respectively, with P < 0.001). Ferritin concentrations were correlated with the fasting BG and the 2-h blood glucose levels in the OGTT (r = 0.52, and r = 0.43, respectively, P < 0.01) as well as with the average BG recorded by CGM (r = 0.75, P < 0.01).

CONCLUSION

CGM has proven to be superior to OGTT for the diagnosis of glycemic abnormalities in adolescents with BTM. Defective β-cell function rather than insulin resistance appeared to be the cause for these abnormalities.

Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©)

BACKGROUND

Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation.

METHODS

We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states.

RESULTS

Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones.

CONCLUSIONS

By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.

Comparison between sensor-augmented insulin therapy with continuous subcutaneous insulin infusion or multiple daily injections in everyday life: 3-day analysis of glucose patterns and sensor accuracy in children

BACKGROUND

Sensor-augmented continuous subcutaneous insulin infusion (CSII) therapy is superior to CSII therapy alone, but little is known on the effectiveness of sensor-augmented multiple daily injections (MDI) therapy.

METHODS

We compared during everyday life mean glucose control and several variability indexes recorded for 3 days by a real-time glucose sensor (Medtronic, Northridge, CA) in two groups of children treated with either CSII or MDI. Fifty-five consecutive subjects were examined: 17 receiving CSII and 38 receiving MDI basal-bolus therapy (age range, 7-22 years). All subjects wore the sensor for 4 days, and 3 days were used for statistical analysis. Mean glucose and SD, coefficient of variation (CV), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), continuous overall net glycemic action (CONGA) at 2 and 4 h, blood glucose (BG) rate, area under the curve (AUC) above 180 mg/dL and below 70 mg/dL, Low BG Index (LBGI), and High BG Index (HBGI) were calculated.

RESULTS

Patients receiving CSII administered more daily boluses than patients receiving MDI (5.2±1.5 vs. 3.2±0.3, respectively; P=0.001). Mean glucose was lower in the CSII group. AUC above 180 mg/dL and HBGI were higher in the MDI group. CV, CONGA at 2 h, CONGA at 2 h during the day, and HBGI were worse in the MDI group, whereas MODD, LBGI, BG rate, and MAGE were similar. A positive correlation (r=0.95; P<0.05) was found between the paired sensor-meter values. For the glucose values <70 mg/dL, sensitivity was 40%, and specificity was 99%.

CONCLUSIONS

In our pediatric patients during everyday life sensor-augmented CSII therapy seemed more effective than sensor-augmented MDI therapy, in terms both of glucose mean values and of intraday variability. Mild hypoglycemic episodes and indexes of low BG values were similar in the two groups, although the latter results may be inaccurate because of low sensor sensitivity at low glucose value.

Patients' evaluation of nocturnal hypoglycaemia with GlucoDay continuous glucose monitoring in paediatric patients

A study was conducted to evaluate the accuracy of GlucoDay (A. Menarini Diagnostics) during 48 h of continuous glucose monitoring (CGMS) in type 1 diabetic adolescents and use this novel approach to assess otherwise ignored nocturnal hypoglycaemias, in relationship to intermediate-acting insulin administration timing. Twenty type 1 diabetic adolescents with poor metabolic control were selected from our out-patient department. Equal doses of intermediate insulin were administered at 19:00 and at 22:00 of the first and second night of the study, respectively. Correlation coefficient between GlucoDay and standard glucometer was 0.94; 98.3% of data fall in the A + B area of Error Grid Analysis and 1.7% in the D area. The mean error was 13.9% overall and 16.4% with blood glucose values (BGV) <75 mg/dl. The accuracy, ±15 mg/dl, was 82% for BGV <75 mg/dl and 74% for BGV >75 mg/dl. The CGMS discovered nocturnal hypoglycaemia (NH) in 12/18 patients, but no severe hypoglycaemia. During the first night, 8 asymptomatic NH episodes were found with BGV <60 mg/dl and 12 with BGV <80. During the second night, 4 asymptomatic NH episodes with BGV <60 mg/dl and 5 with BGV <80 were found. Furthermore, during the second night, the mean duration of BGV <126 mg/dl was lower than in the first night. GlucoDay is a reliable device for CGMS in paediatric patients and able to determine asymptomatic NH. Bedtime insulin injections provided safer glycaemic profiles and a lower percentage of hypoglycaemic events, representing a safer insulin administration scheme.

Assessing glycaemic variability with continuous glucose monitoring system before and after continuous subcutaneous insulin infusion in people with Type 1 diabetes

We evaluated glycaemic variability with continuous glucose monitoring system in 31 people with Type 1 diabetes mellitus using multiple daily injections initially and after switching to continuous subcutaneous insulin infusion. There was a significant improvement in HbA1c, mean glucose, standard deviation of mean glucose and in hyperglycaemic excursions with CSII.

'Glycaemic variability': a new therapeutic challenge in diabetes and the critical care setting

Much attention has been paid recently to the possibility that oscillating glucose may superimpose on glycated haemoglobin (HbA(1c)) in determining the risk for diabetes complications. Furthermore, recent evidence suggests that glucose variability, particularly if accompanied by frequent hypoglycaemic episodes, may adversely alter the prognosis of acutely ill patients. In vitro and animal studies confirm that oscillating glucose is more dangerous than stable constant high glucose, particularly in activating the pathways involved in the pathogenesis of diabetes complications. The production of free radicals, accompanied by an insufficient increase in intracellular antioxidant defences, seems to account for this phenomenon. In humans, studies also confirm that fluctuating glucose levels produce an increase in free radicals as well as endothelial dysfunction, and that these changes are greater than those produced by stable high glucose. Avoiding glucose fluctuations in diabetic patients and in critically ill patients seems to be an emerging therapeutic challenge.

Glycaemic impact of patient-led use of sensor-guided pump therapy in type 1 diabetes: a randomised controlled trial

AIMS/HYPOTHESIS

The objective of this study was to assess the impact of patient-led sensor-guided pump management on glycaemic control, and compare the effect with that of standard insulin pump therapy.

METHODS

An open multicentre parallel randomised controlled trial was conducted at five tertiary diabetes centres. Participants aged 13.0-40.0 years with well-controlled type 1 diabetes were randomised 1:1 to either study group for 3 months. Randomisation was carried out using a central computer-generated schedule. Participants in the intervention group used sensor-guided pump management; no instructive guidelines in interpreting real-time data were provided ('patient-led' use). Participants in the control group continued their original insulin pump regimen. Continuous glucose monitoring (CGM) and HbA(1c) level were used to assess outcomes. The primary outcome was the difference in the proportion of time in the target glycaemic range during the 3 month study period (derived from CGM, target range 4-10 mmol/l). Secondary outcomes were difference in HbA(1c), time in hypoglycaemic (< or =3.9 mmol/l) and hyperglycaemic (> or =10.1 mmol/l) ranges and glycaemic variability.

RESULTS

Sixty-two participants were recruited and randomised; 5/31 and 2/31 withdrew from intervention and control groups, respectively, leaving 26/31 and 29/31 for the intention-to-treat analyses. When adjusted for baseline values, the mean end-of-study HbA(1c) was 0.43% lower in the intervention group compared with the control group (95% CI 0.19 to 0.75%; p = 0.009). No difference was observed in CGM-derived time in target (measured difference 1.72; 95% CI -5.37 to 8.81), hypoglycaemic (0.54; 95% CI -3.48 to 4.55) or hyperglycaemic (-2.18; 95% CI -10.0 to 5.69) range or in glycaemic variability (-0.29; 95% CI -0.34 to 0.28). Within the intervention group, HbA(1c) was 0.51% lower in participants with sensor use > or =70% compared with participants with sensor use <70% (95% CI -0.98 to -0.04, p = 0.04). Five episodes of device malfunction occurred.

CONCLUSIONS/INTERPRETATION

Individuals established on insulin pump therapy can employ sensor-guided pump management to improve glycaemic control. An apparent dose-dependent effect of sensor usage was noted; however, frequent use of this technology (> or =70%) was not universally acceptable.

TRIAL REGISTRATION

ACTRN12606000049572

The correlation of hemoglobin A1c to blood glucose

The understanding that hemoglobin A1c (HbA1c) represents the average blood glucose level of patients over the previous 120 days underlies the current management of diabetes. Even in making such a statement, we speak of "average blood glucose" as though "blood glucose" were itself a simple idea. When we consider all the blood glucose forms-arterial versus venous versus capillary, whole blood versus serum versus fluoride-preserved plasma, fasting versus nonfasting-we can start to see that this is not a simple issue. Nevertheless, it seems as though HbA1c correlates to any single glucose measurement. Having more than one measurement and taking those measurements in the preceding month improves the correlation further. In particular, by having glucose measurements that reflect both the relatively lower overnight glucose levels and measurements that reflect the postprandial peaks improves not only our ability to manage diabetes patients, but also our understanding of how HbA1c levels are determined. Modern continuous glucose monitoring (CGM) devices may take thousands of glucose results over a week. Several studies have shown that CGM glucose averages account for the vast proportion of the variation of HbA1c. The ability to relate HbA1c to average glucose may become a popular method for reporting HbA1c, eliminating current concerns regarding differences in HbA1c standardization. Hemoglobin A1c expressed as an average glucose may be more understandable to patients and improve not only their understanding, but also their ability to improve their diabetes management.

A review of current evidence with continuous glucose monitoring in patients with diabetes

Devices that measure glucose on a near-continuous basis may provide a better insight into glycemic profiles, allowing patients with diabetes to make therapeutic adjustments to improve metabolic control, thereby reducing the risk of diabetic complications. Motivated and technologically adept patients with brittle diabetes, hypoglycemia unawareness, diabetic pregnancy, or who use pumps might benefit.Current evidence of continuous glucose monitoring (CGM) on health outcome in patients with diabetes is critically reviewed. No data are available on chronic complications or mortality. Therefore, surrogate endpoints need to be investigated, particularly HbA1c, number of hypo- and hyperglycemic episodes, time within normal, high, or low glucose concentrations, glycemic variability, and quality of life.Randomized controlled trials (RCTs) using CGM in a retrospective way did not show metabolic improvement. In contrast, most RCTs applying real-time CGM showed a decrease in HbA1c, reduced glycemic variability, and a diminished number and length of hypo- and hyperglycemic events. Using accurate, real-time CGM devices improves quality of life by reducing the fear of unexpected hypoglycemic events. These beneficial effects were observed despite the fact that in most studies no clear treatment algorithm based on CGM results was provided to the patients. However, most trials were too short in duration, with a variable use of CGM, and were performed in small study samples.In conclusion, real-time CGM systems can improve metabolic control, reduce hypoglycemic episodes, and improve quality of life. Whether this holds true for longer time periods and in the majority of patients remains to be proven. In the long term, CGM might help to reduce chronic diabetes complications and perhaps also mortality, thereby reducing health care costs.

Frequency of HbA1c discordance in estimating blood glucose control

PURPOSE OF REVIEW

HbA1c is a trusted standard for monitoring glycemic control and predicting complications. However, there are emerging issues complicating the interpretation of HbA1c that the clinician caring for patients with diabetes needs to consider.

RECENT FINDINGS

There is increasing evidence of the potential for mismatches between HbA1c and other measures of glycemia which require some caution in HbA1c interpretation. We have attempted to characterize the nature of these discordances by developing the concept of the 'glycation gap' in which differences in protein glycation occurring in the intracellular space (HbA1c) versus in the extracellular space (measured as fructosamine) are compared. We have evidence that HbA1c results discordant from other measures of glycemic control may be secondary to previously unappreciated physiological variables, including erythrocyte lifespan, in hematologically normal individuals. We relate these findings to a number of HbA1c interpretation issues important for diabetes care: factors affecting hemoglobin glycation, relationship of HbA1c to glycemia, standardization of the HbA1c assay, and HbA1c relation to complications.

SUMMARY

There are an increasing number of clinical circumstances in which there are nuances to HbA1c interpretation such that standard norms for assessment of glycemic control or complication risk may need to be modified.

How to assess and compare the accuracy of continuous glucose monitors?

Continuous glucose monitors may be valuable tools for improving glycemic control and avoiding hypoglycemia in patients with diabetes. To this goal, sensor readings must adequately reflect the actual blood glucose, emphasizing the need for solid accuracy assessment methods for continuous glucose sensor readings. Analysis of continuous glucose data is challenging, and despite many efforts there still is no all-embracing method to overcome the obstacles in the assessment of continuous data. In this review we disclose the weaknesses of currently available methods and propose a guideline for sensor accuracy assessment and comparison. For accuracy assessment it is best to first plot the sensor readings against the reference values and draw a line of identity, visualizing the degree of agreement. Thereafter data pairs should be given in a Bland-Altman plot to detect a possible relationship between the difference and the mean. The next step is to calculate the absolute relative difference over all paired readings together and per glucose range. A possible lag time between the measurements of both methods can be detected by combined curve fitting. Finally, sensitivity and positive predictive value for detecting hypoglycemia are important indicators of the sensors' performance. For comparing the accuracy between different glucose sensors it is best to use indirect comparison against blood glucose, rather than direct comparison methods, since none of the current glucose sensors is accurate enough to be considered the reference value.

Continuous glucose monitoring and diabetes health outcomes: a critical appraisal

This paper presents a critical review of continuous glucose monitoring studies that address accuracy, utility in assessing glucose variability, detection of hypoglycemia for improved metabolic control or reduced glucose variability, impact on quality of life, reduced fear of hypoglycemia, and applicability in helping patients to overcome their reluctance to intensify insulin therapy. This analysis can be used to delineate the evidence that is still missing when considering continuous glucose monitoring as an established tool in clinical practice and for reimbursement of the system and services.

Relationship of A1C to glucose concentrations in children with type 1 diabetes: assessments by high-frequency glucose determinations by sensors

OBJECTIVE

Despite the standing of A1C as the most validated and widely used measure for average glycemic control over time, the relationship between A1C and glucose concentrations is not completely understood. The purpose of this Diabetes Research in Children Network (DirecNet) study was to use continuous glucose monitoring data to examine the relationship between A1C and glucose in type 1 diabetes.

RESEARCH DESIGN AND METHODS

Forty-eight youth enrolled in studies of the Navigator continuous glucose monitor were encouraged to wear the Navigator sensor at home continuously. A1C was measured at baseline, at 3 months, and at 6 months. Sensor glucose data were directly transmitted via the Internet, assuring that essentially all glucose values were analyzed.

RESULTS

Subjects had a median of 112 h/week of Navigator data in the first 3 months and 115 h/week in the second 3 months. The slope of mean glucose over the previous 3 months versus A1C was only 18 mg/dl per 1.0% A1C. Individually, there was substantial variation in the relationship between mean glucose and A1C. A1C was not associated with glucose lability after controlling for mean glucose. Measures of an individual's rate of glycation were moderately correlated at the 3- and 6-month visits.

CONCLUSIONS

As the chemistry of glycation would predict, we found no evidence to contradict the simple hypothesis that A1C directly reflects mean glucose over time. There is, however, substantial variability in individual mean glucose concentrations for a given A1C. Transforming reliable A1C values into calculated mean glucose values would, when applied to an individual, introduce substantial error.

How much do forgotten insulin injections matter to hemoglobin a1c in people with diabetes? A simulation study

BACKGROUND

Forgotten or omitted insulin injections are an important contributing factor to poor glycemic control in people with type 1 diabetes. This study uses mathematical modeling and examines the impact on hemoglobin A1c (HbA1c) levels if insulin injections are forgotten. The simulation concerns people with type 1 diabetes on intensive insulin therapy.

METHODS

Five sets of blood glucose profiles with and without a forgotten injection were obtained. The difference to HbA1c was calculated using an HbA1c estimator on the profiles and was multiplied by the frequency of forgotten events. A frequency of 2.1 forgotten injections per week was found in the literature.

RESULTS

Calculations showed that forgetting 2.1 meal-related injections per week would lead to an increase in HbA1c of at least 0.3-0.4% points, and similarly 0.2-0.3% points related to forgotten injections of the long-acting insulin. In case of even more pronounced nonadherence (e.g., if 39% of all injections are forgotten) there is a possible increase of HbA1c of 1.8% points.

CONCLUSIONS

The magnitude of the possible improvement in HbA1c agrees well with other studies in the relation between adherence and HbA1c levels. The estimated numbers suggest that missing injections are an important reason for suboptimal treatment.

Continuous Glucose Monitoring System in children with type 1 diabetes mellitus: a systematic review and meta-analysis

AIMS/HYPOTHESIS

We investigated the potential effects of the Continuous Glucose Monitoring System (CGMS), as compared with self-monitoring of blood glucose, on glycaemic control in children with type 1 diabetes.

METHODS

The following electronic databases were searched throughout June 2007: MEDLINE, EMBASE and The Cochrane Library. Additional references were obtained from reviewed articles. Only randomised controlled trials were included.

RESULTS

We included five trials involving 131 type 1 diabetic patients in the study. Combined data from all trials showed that the CGMS did not significantly reduce HbA1c levels compared with control groups. The pooled weighted mean difference was -0.02% (95% CI -0.29 to 0.25) with a fixed model and remained insignificant in the random effect model. Sensitivity analysis determined that the findings were stable. There was a trend towards a longer time under the CGMS curve for glucose <3.89 mmol/l in the CGMS group compared with the control group (mean difference 49.00 min, 95% CI -18.00 to 116.00). The CGMS significantly increased the number of insulin dose changes per patient per month for those managed with CGMS compared with the control groups (mean difference 6.3 changes, 95% CI 2.88-9.72).

CONCLUSIONS/INTERPRETATION

The Continuous Glucose Monitoring System is not better than self-monitoring of blood glucose with regard to improvement of metabolic control among type 1 diabetic children. However, due to the small number of participants and methodological limitations of the studies included, findings of this meta-analysis should be interpreted with caution.

Continuous glucose monitoring system in the screening of early glucose derangements in children and adolescents with cystic fibrosis

BACKGROUND

In cystic fibrosis (CF), diabetes mellitus (DM) is associated with progression of pulmonary disease and nutritional impairment.

AIM

To compare oral glucose tolerance test (OGTT) and continuous glucose monitoring system (CGMS) in patients with CF with early glucose derangements.

PATIENTS AND METHODS

Thirty-two patients with CF (5-20 years) with intermediate glucose values > 7.7 mmol/l during OGTT received a CGMS registration. Patients were classified into those with normal glucose tolerance (NGT), impaired glucose tolerance (IGT) and DM, according to glucose values at 120 min of OGTT and during CGMS. Furthermore BMI z-scores, forced expiratory volume in 1 second (FEV1%), number of respiratory infections/year, enzyme supplementation, and HbA1c were evaluated.

RESULTS

OGTT and CGMS derangements were in agreement in 43.7% of the patients. BMI z-scores, FEV1%, number of respiratory infections/ year, enzyme supplementation, and HbA1c did not differ among the three groups. HbA1c, correlated positively with 120 min OGTT (r = 0.34; p = 0.059), CGMS area (r = 0.35; p = 0.048) and the number of respiratory infections, and negatively with FEV1%.

CONCLUSIONS

Intermediate glucose values during OGTT should be considered as a screening test in patients with CF. CGMS can be useful in studying the early occurrence of glucose derangements in selected patients.

1,5-anhydroglucitol (GlycoMark) as a marker of short-term glycemic control and glycemic excursions

1,5-anhydroglucitol (1,5-AG) is a validated marker of short-term glycemic control. It is a metabolically inert polyol that competes with glucose for reabsorption in the kidneys. Otherwise stable levels of 1,5-AG are rapidly depleted as blood glucose levels exceed the renal threshold for glucosuria. 1,5-AG more accurately predicts rapid changes in glycemia than hemoglobin A1C (A1C) or fructosamine. It is also more tightly associated with glucose fluctuations and postprandial glucose. Thus, 1,5-AG may offer complementary information to A1C. This review will summarize the limitations of current methods of assessing glycemic control, assess the data to support 1,5-AG as a glycemic marker and highlight the scenarios by which 1,5-AG may fill the gap in assessing glycemic control.

Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

AIMS/HYPOTHESIS

Glucose fluctuations may help predict diabetic complications. We evaluated the relation between glucose variability and oxidative stress in patients with type 1 diabetes.

METHODS

Continuous glucose monitors were inserted subcutaneously in 25 patients. During the measurement, patients collected two 24 h urine samples, while 24 healthy controls collected one 24 h urine sample for determination of 15(S)-8-iso-prostaglandin F2alpha(PGF2alpha) using HPLC tandem mass spectrometry. Mean of the daily differences (MODD), mean amplitude of glycaemic excursions (MAGE) and continuous overlapping net glycaemic action calculated with n hour time-intervals (CONGA-n) were calculated as markers for glucose variability and correlation with 15(S)-8-iso-PGF2alpha excretion was calculated.

RESULTS

Median [interquartile range (IQR)] urinary 15(S)-8-iso-PGF2alpha was higher in patients than healthy controls: 161 (140-217) pg/mg creatinine vs 118 (101-146) pg/mg creatinine (p = 0.001). Median (IQR) MODD was 3.7 (3.2-5.0) mmol/l, MAGE 7.6 (6.4-9.0) mmol/l and CONGA-1 2.3 (2.1-2.8) mmol/l. Univariate regression did not reveal an association for MODD (r2 = 0.01), MAGE (0.08) or CONGA-1 (0.07) with 15(S)-8-iso-PGF2alpha excretion, nor was an association revealed when corrected for HbA1c, age, sex and smoking. Spearman correlation coefficients (r) between 15(S)-8-iso-PGF2alpha excretion and MODD, MAGE and CONGA-1 were non-significant: -0.112, -0.381 and -0.177.

CONCLUSIONS/INTERPRETATION

We report that there is no relationship between glucose variability and urinary 15(S)-8-iso-PGF2alpha. We also confirm that patients with type 1 diabetes have higher levels of urinary 15(S)-8-iso-PGF2alpha than healthy controls, suggesting that in addition to glucose variability, other factors favouring oxidative stress may exist. We did not see a relation between high glucose variability and elevated levels of oxidative stress in patients with type 1 diabetes.

Restoring euglycemia in the basal state using continuous glucose monitoring in subjects with type 1 diabetes mellitus

BACKGROUND

Our objective was to use continuous glucose monitoring to derive the optimal basal insulin infusion rates in adults with type 1 diabetes and using continuous subcutaneous insulin infusion (CSII) pumps.

METHODS

In an effort to mimic euglycemia during the basal state, we used a standard protocol to adjust basal insulin infusion rates in 16 subjects with type 1 diabetes mellitus who were using CSII pumps. All subjects wore Continuous Glucose Monitoring System sensors (CGMS), Medtronic Minimed, Northridge, CA) in order to obtain around-the-clock tracings of their glucose measurements. Subjects were asked to skip meals periodically in order to optimize basal insulin infusion rates, defined as the basal infusion rates that maintained glucose levels in the range of 65-120 mg/dL during the fasting state or between meals.

RESULTS

In order to demonstrate improved glycemic control, with blunting of glucose excursion, we compared the baseline CGMS area under the curve (AUC) to the AUC obtained after optimizing the basal insulin dosages. We analyzed the curves by determining the AUC for glucose excursions above 110 mg/dL. The AUC for glucose excursions above 110 mg/dL was significantly smaller after optimization (19 +/- 13 mg/dL.day) compared to the baseline AUC (50 +/- 31 mg/dL.day) (P < 0.001).

CONCLUSIONS

Using both a standard protocol for initial basal insulin infusion rates and CGMS curves to optimize basal infusion rates, one can improve glycemia in subjects with type 1 diabetes using CSII.

Continuous subcutaneous glucose monitoring shows a close correlation between mean glucose and time spent in hyperglycemia and hemoglobin A1c

BACKGROUND

The Diabetes Control and Complications Trial and United Kingdom Prospective Diabetes Study highlighted hemoglobin A1c (HbA1c) as the main predictor of diabetic complications. Currently, diabetes is managed by frequent capillary spot glucose measurements, but continuous monitoring systems may have the capacity of improving diabetic control. The SCGM 1 system is microdialysis based and allows for monitoring of changes in interstitial fluid glucose levels every minute. The aim of this study was to evaluate the correlation between HbA1c and short-term glucose excursions in patients with type 1 diabetes.

MATERIAL AND METHODS

We investigated 91 patients with type 1 diabetes (mean +/- standard deviation (SD); age 34 +/- 10 years, body mass index 24.2 +/- 4.1 kg/m2) with a duration of diabetes of 17 +/- 11 years for 4.8 +/- 0.4 days. The average HbA1c was 7.9 +/- 1.4%. From the monitoring profiles we determined individual mean glucose, the SD of glucose, and the relative time spent in hyperglycemia and hypoglycemia calculated as the area under the curve (AUC) with arbitrary cutoffs of 180 and 70 mg/dl, respectively.

RESULTS

Mean glucose, SD glucose, and hyperglycemic and hypoglycemic AUC all correlated with HbA1c, but with decreasing statistical power. In multiple linear regression analysis, mean glucose was the sole independent variable (r = 0.626, p < 0.0001). A close correlation between HbA1c and various measures of short-term hyperglycemic values was observed. There was a close correlation between mean glucose and SD glucose, pointing toward increased variability with increasing mean glucose.

CONCLUSIONS

Mean glucose generated after short-term continuous monitoring is the main predictor of HbA1c and reveals increased lability of glucose with increasing mean glucose and HbA1c.

Relevance of casual undetected hyperglycemia among high-risk individuals for developing diabetes

To assess the relevance of unrecognized hyperglycemia among high-risk subjects for developing diabetes a cross-sectional study was carried out. Subjects aged 40-75 years with (high-risk group) and without (control group) history of impaired glucose metabolism underwent a 2h-oral glucose tolerance test (OGTT). All individuals with diabetes diagnostic criteria and all controls with glucose abnormalities at OGTT were excluded. An individualized 48-h continuous glucose monitoring (CGM) calibrated by fasting plasma glucose was performed. The microdialysis-based biosensor recordings were computerized in order to identify continuous glucose profiles. Of the 121 monitored subjects, 104 were finally analyzed (56.7% female, 57.8 years, BMI=29.2, A1C=4.9%, HOMA index=2.5). Glucose profiles corresponded to 31 controls (29.8%), 32 high-risk individuals with normal OGTT (30.8%) and 41 (39.4%) with hyperglycemia at OGTT. The recordings defined as hyperglycemia (fasting >or=6.1 mmol/l, non-fasting >or=7.8 mmol/l) appeared during an average of 1.4h, 4.9h and 7.6h (3.9%, 13.9% and 19% of the CGM time), respectively. The highest percentage of impaired CGM registers corresponded to the fasting period. Nevertheless, the longest duration corresponded to the non-fasting period. The CGM evidenced a relevant degree of casual undetected hyperglycaemia among high-risk individuals.

Glycaemic instability is an underestimated problem in Type II diabetes

The aim of the present study was to assess the level of glycaemic control by the measurement of 24 h blood glucose profiles and standard blood analyses under identical nutritional and physical activity conditions in patients with Type II diabetes and healthy normoglycaemic controls. A total of 11 male patients with Type II diabetes and 11 healthy matched controls participated in a 24 h CGMS (continuous subcutaneous glucose-monitoring system) assessment trial under strictly standardized dietary and physical activity conditions. In addition, fasting plasma glucose, insulin and HbA(1c) (glycated haemoglobin) concentrations were measured, and an OGTT (oral glucose tolerance test) was performed to calculate indices of whole-body insulin sensitivity, oral glucose tolerance and/or glycaemic control. In the healthy control group, hyperglycaemia (blood glucose concentration >10 mmol/l) was hardly present (2+/-1% or 0.4+/-0.2/24 h). However, in the patients with Type II diabetes, hyperglycaemia was experienced for as much as 55+/-7% of the time (13+/-2 h over 24 h) while using the same standardized diet. Breakfast-related hyperglycaemia contributed most (46+/-7%; P<0.01 as determined by ANOVA) to the total amount of hyperglycaemia and postprandial glycaemic instability. In the diabetes patients, blood HbA(1c) content correlated well with the duration of hyperglycaemia and the postprandial glucose responses (P<0.05). In conclusion, CGMS determinations show that standard measurements of glycaemic control underestimate the amount of hyperglycaemia prevalent during real-life conditions in Type II diabetes. Given the macro- and micro-vascular damage caused by postprandial hyperglycaemia, CGMS provides an excellent tool to evaluate alternative therapeutic strategies to reduce hyperglycaemic blood glucose excursions.

Recent advances in continuous glucose monitoring: biocompatibility of glucose sensors for implantation in subcutis

Tight glycemic control slows or prevents the development of short- and long-term complications of diabetes mellitus. Continuous glucose measurements provide improved glycemic control and potentially prevent these diabetic complications. Glucose sensors, especially implantable devices, offer an alternative to classical self-monitored blood glucose levels and have shown promising glucose-sensing properties. However, the ultimate goal of implementing the glucose sensor as the glucose-sensing part of a closed loop system (artificial pancreas) is still years ahead because of malfunctions of the implanted sensor. The malfunction is partly a consequence of the subcutaneous inflammatory reaction caused by the implanted sensor. In order to improve sensor measurements and thereby close the loop, it is crucial to understand what happens at the tissue-sensor interface.

Hyperglycemia and externalizing behavior in children with type 1 diabetes

OBJECTIVE

Ancedotally, parents report behavioral changes in their diabetic children who have fluctuating blood glucose levels. This study aimed to test associations between intercurrent glycemia and child behavior in an ambulant setting.

RESEARCH DESIGN AND METHODS

Prepubertal children attending the Royal Children's Hospital, Melbourne, Australia, with type 1 diabetes received glycemic assessment and simultaneous behavioral assessment on two occasions 6 months apart. Subjects wore a continuous glucose monitor over a 72-h period, and parents completed the Behavior Assessment System for Children at the two study time points.

RESULTS

There was a high correlation between intra-individual externalizing and internalizing behavior scores (r = 0.88, P < 0.001 and r = 0.81, P < 0.001, respectively) at the two time points. Mean blood glucose (MBG) was significantly associated with the mean externalizing behavior score (beta = 1.7 [95% CI 0.6-2.8], adjusted r(2) = 0.088). Percentage of time in the normal (r = -0.2 [-0.3 to -0.5], adjusted r(2) = 0.068) and high (r = 0.2 [0.07-0.3], adjusted r(2) = 0.089) glycemic ranges were significantly associated with the mean externalizing behavior score. For every 5% increase in time in the normal glycemic range, there was a decrease in the externalizing behavior score of 1.0, and for every 5% increase in time in the high glycemic range there was an increase in the externalizing behavior score of 1.0. There was no significant association between MBG and the mean internalizing behavior score.

CONCLUSIONS

Externalizing behaviors were associated with intercurrent glycemic status. These findings underscore the importance of understanding the mechanisms of this association and how it might impact ultimate diabetes outcomes.

Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes

BACKGROUND

The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms.

METHODS

Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G(rms)) and the Clarke error grid analysis were used to evaluate the system efficacy.

RESULTS

Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G(rms), and clinical acceptability by the Clarke error grid analysis, in both clinical data series.

CONCLUSIONS

Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

Nocturnal hypoglycaemias in type 1 diabetic patients: what can we learn with continuous glucose monitoring?

AIM

In type 1 diabetic patients (T1DM), nocturnal hypoglycaemias (NH) are a serious complication of T1DM treatment; self-monitoring of blood glucose (SMBG) is recommended to detect them. However, the majority of NH remains undetected on an occasional SMBG done during the night. An alternative strategy is the Continuous glucose monitoring (CGMS), which retrospectively shows the glycaemic profile. The aims of this retrospective study were to evaluate the true incidence of NH in T1DM, the best SMBG time to predict NH, the relationship between morning hyperglycaemia and NH (Somogyi phenomenon) and the utility of CGMS to reduce NH.

METHODS

Eighty-eight T1DM who underwent a CGMS exam were included. Indications for CGMS evaluation, hypoglycaemias and correlation with morning hyperglycaemias were recorded. The efficiency of CGMS to reduce the suspected NH was evaluated after 6-9 months.

RESULTS

The prevalence of NH was 67% (32% of them unsuspected). A measured hypoglycaemia at bedtime (22-24 h) had a sensitivity of 37% to detect NH (OR=2.37, P=0.001), while a single measure < or =4 mmol/l at 3-hour had a sensitivity of 43% (OR=4.60, P<0.001). NH were not associated with morning hyperglycaemias but with morning hypoglycaemias (OR=3.95, P<0.001). After 6-9 months, suspicions of NH decreased from 60 to 14% (P<0.001).

CONCLUSION

NH were highly prevalent and often undetected. SMBG at bedtime, which detected hypoglycaemia had sensitivity almost equal to that of 3-hour and should be preferred because it is easier to perform. Somogyi phenomenon was not observed. CGMS is useful to reduce the risk of NH in 75% of patients.

Assessment of the relationship between glucose and A1c using kinetic modeling

Treatment goals for diabetic patients are directed towards lowering A1c values by controlling blood glucose concentrations (BGC), making it important to understand the relationship between the two parameters. Because findings from clinical trials about the relationship between BGC and A1c values show a profound variability around the obtained regression lines, they are difficult to apply to individual patients. Therefore, a model was developed and applied based on the kinetics of HbA1c formation and removal. It takes the instability of A1c and loss of hemoglobin into consideration. Data from clinical studies and hypothetical scenarios were used to test the model and to describe the relationship between A1c and BGC. A close agreement between experimental and calculated data was obtained in steady-state and non-steady-state conditions. Aside the erythrocyte life span, the chemical instability of A1c appears to affect A1c levels markedly and their changes due to therapy. A threefold increase in BGC over 30 days prior to A1c measurement can cause an increase in A1c value of about 120% as compared with 4% when it occurs 4 months prior to A1c measurement. Profound daily fluctuations in BGC result in minor changes in A1c. In conclusion, A1c provides information about a patient's glycemia, mainly over the past 2 months, and may not reflect well daily blood glucose fluctuations. This model might be suitable to identify individual differences in glycation rates.

Continuous glucose monitoring-guided insulin adjustment in children and adolescents on near-physiological insulin regimens: a randomized controlled trial

OBJECTIVE

This randomized controlled trial assesses the effect on glycemic control of continuous glucose monitoring system (CGMS)-guided insulin therapy adjustment in young people with type 1 diabetes on intensive diabetes treatment regimens with continuous subcutaneous insulin infusion (CSII) or glargine.

RESEARCH DESIGN AND METHODS

Pediatric subjects were recruited if they had an HbA(1c) (A1C) <10% and had been on CSII or glargine for at least 3 months. Thirty-six subjects were randomized to insulin adjustment on the basis of 72 h of CGMS every 3 weeks or intermittent self-monitoring of blood glucose (SMBG) for 3 months. A1C and fructosamine were measured at baseline and 6 and 12 weeks. Follow-up A1C was measured at 6 months. Mean baseline A1C was 8.2% (n = 19) in the CGMS group and 7.9% (n = 17) in the control group.

RESULTS

There was a significant improvement in A1C from baseline values in both groups, but there was no difference in the degree of improvement in A1C at 12 weeks between the CGMS (-0.4% [95% CI -0.7 to -0.1]) and the control group (-0.4% [-0.8 to 0.2]). In the CGMS group, improved A1C was at the cost of increased duration of hypoglycemia.

CONCLUSIONS

CGMS is no more useful than intermittent fingerstick SMBG and frequent review in improving diabetes control in reasonably well-controlled patients on near-physiological insulin regimens when used in an outpatient clinic setting.

Registro continuo de glucosa en sujetos con alto riesgo de diabetes tipo 2

BACKGROUND AND OBJECTIVE

To assess the relevance of unrecognised hyperglycaemia among high-risk subjects for developing type 2 diabetes.

SUBJECTS AND METHOD

Cross-sectional multicentre study in primary care. Subjects aged 40 to 75 years with previous evidence of impaired glucose metabolism were screened according to the World Health Organization rules by means of an oral glucose tolerance test (OGTT). Those with diabetes diagnostic criteria were excluded. An individualized 48-h ambulatory continuous glucose monitoring (CGM) calibrated by plasma values (Glucoday) was performed. Data from biosensor activity were computerized in order to identify hyperglycaemic records.

RESULTS

50 out of 64 screened subjects were included and evaluated; there were 30 female (60%). Mean age was 59 years; body mass index, 30.9; HbA1c, 5.3%, and HOMA, 2.8. Regarding the CGM results, 9 whole registers (18%) were rejected because of technical problems and 41 continuous profiles (72%) were analysed, corresponding to 17 (41.5%) subjects with normal OGTT and 24 (58.5%) with non-diabetic hyperglycaemia on OGTT. Mean CGM time with hyperglycaemia (fasting > or = 6.1 mmol/l or non-fasting > or = 7.8 mmol/l) accounted for 5.8 h, a 17.2% of the effective register per subject (33.8 h). Both groups matched the World Health Organization diabetes diagnostic interval (fasting > or = 7 mmol/l or non-fasting > or = 11.1 mmol/l): 1.1 h of effective register (3.6%) and 1.2 h (3.3%), respectively.

CONCLUSIONS

The CGM evidenced a high degree of unrecognised hyperglycaemia among high-risk individuals. Therefore, casual plasma glucose measurements should be recommended to achieve early screening.

1,5-anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes

OBJECTIVE

Postprandial hyperglycemia is often inadequately assessed in diabetes management. Serum 1,5-anhydroglucitol (1,5-AG) drops as serum glucose rises above the renal threshold for glucose and has been proposed as a marker for postprandial hyperglycemia. The objective of this study is to demonstrate the relationship between 1,5-AG and postprandial hyperglycemia, as assessed by the continuous glucose monitoring system (CGMS) in suboptimally controlled patients with diabetes.

RESEARCH DESIGN AND METHODS

Patients with type 1 or type 2 diabetes and an HbA(1c) (A1C) between 6.5 and 8% with stable glycemic control were recruited from two sites. A CGMS monitor was worn for two consecutive 72-h periods. Mean glucose, mean postmeal maximum glucose (MPMG), and area under the curve for glucose above 180 mg/dl (AUC-180), were compared with 1,5-AG, fructosamine (FA), and A1C at baseline, day 4, and day 7.

RESULTS

1,5-AG varied considerably between patients (6.5 +/- 3.2 mug/ml [means +/- SD]) despite similar A1C (7.3 +/- 0.5%). Mean 1,5-AG (r = -0.45, P = 0.006) correlated with AUC-180 more robustly than A1C (r = 0.33, P = 0.057) or FA (r = 0.38, P = 0.88). MPMG correlated more strongly with 1,5-AG (r = -0.54, P = 0.004) than with A1C (r = 0.40, P = 0.03) or FA (r = 0.32, P = 0.07).

CONCLUSIONS

1,5-AG reflects glycemic excursions, often in the postprandial state, more robustly than A1C or FA. 1,5-AG may be useful as a complementary marker to A1C to assess glycemic control in moderately controlled patients with diabetes.

Value and limitations of the Continuous Glucose Monitoring System in the management of type 1 diabetes

The CGMS (Continuous Glucose Monitoring System) is a portable device allowing continuous measuring of glucose. It provides recordings of at least 72 h, during which 288 measures/day are performed. Results are visualised in the form of a set of curves, illustrating the variations in blood glucose levels over time. The quality of the records has often been questioned by several authors. Some of the system's physiologically related limitations can be explained by the less than perfect coincidence of variations in glucose levels observed in the interstitial tissue, where CGMS measurings are done, and in the blood, where calibrations are performed. Other limitations, such as defects in accuracy or in reproducibility of tracings or premature curtailments of recordings, are due to technical weaknesses which were considerably improved during the past few years, particularly with regard to the quality of the electrodes providing a more stable signal over time. In clinical practice, CGMS is a tool for investigating the glycaemic patterns of diabetic patients in conjonction with SMBG. It allows the identification of overlooked hyper- or hypoglycaemia. Generally well accepted, it is a usefull tool to analyse the nocturnal period, or any situation where glucose checks are rare. The visual nature of its results provides a facilitating support in the discussion between the patient and the care-provider during consultations or educational sessions. CGMS utilisation was proposed for guiding treatment adjustment. At present, it is still difficult to state with certainty that this tool allows effective improvement in the metabolic control of patients with type 1 diabetes, in view of the paucity of controlled studies showing an impact on HbA1c values or on the frequency of hypoglycaemia, even if such a tendency emerges from most non-controlled intervention trials.

Is immunity in diabetic patients influencing the susceptibility to infections? Immunoglobulins, complement and phagocytic function in children and adolescents with type 1 diabetes mellitus

OBJECTIVE

Previous reports suggest an increased susceptibility of diabetes patients to infections, but little information is available on possible underlying immunologic dysfunctions. The aim of this study was to evaluate humoral factors in pediatric patients with type 1 diabetes mellitus.

METHODS

There were 66 diabetic patients (39 males:27 females; 5-17 yr) classified into two groups according to levels of glycohemoglobin (limit 9%): Group C - controlled (n = 33) and Group UC - uncontrolled (n = 33). We evaluated five patients in C and six in UC who reported previous infections. Immunologic analysis included measurement of plasma concentrations of immunoglobulins (Ig), C3, and C4 levels (turbidimetry); functional hemolytic assays for complement evaluation (CPH for classical and APH for alternative pathways), quantification of C4 isotypes C4A and C4B (ELISA), phagocytosis assays, measurement of bactericidal activity against Staphylococcus aureus, as well as tests of fungicidal capacity for Candida albicans.

RESULTS

The UC Group had higher mean age, received higher insulin doses, and had higher concentrations of glycohemoglobin than the C Group. No significant differences in duration of the disease or nutritional conditions were detected between the groups. Lower IgA values in C (10/33) and lower IgG levels in UC (23/33) were detected, and there were inverse relationship with HbA1c values. Analysis of CPH, APH, C3, and C4 showed normal levels in both groups and no statistical correlation with the HbA1c. However, 9/33 children of the UC Group had decreased C3 values. C4B levels were below the normal range in 8/20 and correlated with higher HbA1c. Both phagocytic assays for S. aureus and Candida albicans were within normal limits.

CONCLUSIONS

Low IgG concentrations and to some degree reduction in C4B levels were related to impaired metabolic control. No strong link between the immunological alterations was found in diabetic patients and the occurrence of infections.

Glycemic characteristics in continuously monitored patients with type 1 and type 2 diabetes: normative values

OBJECTIVE

The purpose of this study was to generate normative values for periods of euglycemia as well as for daily patterns of glycemic excursions in patients with type 1 and type 2 diabetes monitored continuously for a maximum period of 21 days and blinded to glucose levels.

RESEARCH DESIGN AND METHODS

This was a multicenter, prospective observational study in which 101 consecutive patients with type 1 (n = 60) or type 2 (n = 41) diabetes underwent blinded continuous glucose monitoring. Serial glucose measurements were divided into periods of euglycemia (70-180 mg/dl), hyperglycemia (>180 mg/dl), and hypoglycemia (<70 mg/dl). The proportions of time patients were hypoglycemic, euglycemic, and hyperglycemic and the total areas under the curves (AUCs) were determined.

RESULTS

During the observation period the 101 subjects contributed an average 287 +/- 132 h of continuous glucose values. Subjects remained in the euglycemic range for approximately 63% of the total day, were hypoglycemic 8%, and were hyperglycemic 29%. Hypoglycemia was more prevalent nocturnally (11 vs. 7%) and hyperglycemia diurnally (31 vs. 25%). Compared with subjects with type 2 diabetes, type 1 diabetic subjects had more frequent hypoglycemic episodes per day (2.1 vs. 1.0; P < 0.001) that were of longer duration (1.1 vs. 0.7 h; P < 0.0001), reflecting a greater number of hours per day in the hypoglycemic range (2.3 vs. 1.0 h; P < 0.0001). The mean hypoglycemic AUC values were >150% higher for type 1 compared with type 2 diabetic subjects (41 vs. 16, respectively; P < 0.0001).

CONCLUSIONS

These normative data will assist in study and sample size planning for future investigations of continuous glucose monitoring and allow for qualitative comparisons with trials of therapeutic interventions aimed at reducing the occurrence of glycemic excursions.