Monitoring of Diabetes Mellitus Using the Flash Glucose Monitoring System: The Owners' Point of View

The flash glucose monitoring system (FGMS) has recently become one of the most common monitoring methods in dogs and cats with diabetes mellitus. The aim of this study was to evaluate the impact of FGMS on the quality of life of diabetic pet owners (DPOs). Fifty DPOs were asked to answer a 30-question survey. More than 80% of DPOs considered FGMS easier to use and less stressful and painful for the animal compared to blood glucose curves (BGCs). Overall, 92% of DPOs reported that their pet had better diabetes control since using FGMS. The most challenging aspects of using the FGMS were ensuring proper sensor fixation during the wearing period (47%), preventing premature detachment (40%), and purchasing the sensor (34%). Moreover, 36% of DPOs reported that the device cost was difficult to afford in the long term. Comparing dogs and cats, a significantly higher number of dogs' owners found the FGMS to be well-tolerated (79% vs. 40%), less invasive than BGCs (79% vs. 43%), and easier to maintain in situ (76% vs. 43%). In conclusion, FGMS is considered by DPOs to be easy to use and less stressful compared to BGCs, while enabling better glycemic control. Nevertheless, the costs related to its long-term use might be difficult to sustain.

Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology

BACKGROUND

Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time.

METHODS

The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model.

RESULTS

A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B.

CONCLUSIONS

This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.

Delayed Eating Schedule Raises Mean Glucose Levels in Young Adult Males: a Randomized Controlled Cross-Over Trial

BACKGROUND

Misalignment of meals to the biological clock may cause adverse effects on glucose metabolism. However, the effects of repeated different eating schedules (early compared with late) on glucose concentration throughout the day are poorly understood.

OBJECTIVES

We examined the effects of different eating schedules on the 24-h glucose response using a continuous glucose monitor (CGM).

METHODS

Eight young adult males (age, 20.9 ± 3.4 y; body mass index: 21.3 ± 1.8 kg/m²) each followed 2 different eating schedules (early [08:30, 13:30, and 19:30] and late [12:00, 17:00, and 23:00]) in random order. These diet interventions were conducted for 8 d, with an experimental period of 3 d and 2 nights (from dinner on day 7) after 7 d of free living. The 3 meals in each intervention were nutritionally equivalent (55% carbohydrate, 15% protein, and 30% fat). The 24-h mean interstitial glucose concentration on day 8 was obtained under controlled conditions using the CGM (primary outcome). These concentrations were compared among the following 3 schedules using Dunnett's test, with the early eating schedule as reference (1 compared with 2 and 1 compared with 3): 1) early eating schedule (control), 2) late eating schedule according to the clock time (08:00 on day 8 to 08:00 on day 9), and 3) late eating schedule according to the time elapsed since the first meal for 24 h.

RESULTS

The 24-h mean ± SD interstitial glucose concentrations when participants followed the late eating schedule were higher than those when they followed the early eating schedule in terms of clock time (91.2 ± 2.9 compared with 99.2 ± 4.6 mg/dL, P = 0.003) and time elapsed (91.2 ± 2.9 compared with 98.3 ± 3.8 mg/dL, P < 0.001).

CONCLUSIONS

A late eating schedule increases the mean 24-h interstitial glucose concentration in young adult males. This insight will have useful implications in determining meal timings, especially for those with conditions such as diabetes.

Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System

This study reports a clinical evaluation of AiDEX CGM system featuring a 14-day sensor, real-time glucose monitoring and factory-calibration. A multicenter, prospective, masked clinical study was conducted at with a total of 120 participants. Each participant wore 4 studied sensors and had one in-clinic visit for venous blood reference tests. 40 out of the 120 participants wore additional Abbott Libre sensors and performed at least 7 capillary BG tests daily for additional reference and comparison. Continuous glucose error grid analysis (CG-EGA) showed that AiDEX and Abbott Libre had good agreement with venous blood glucose, with 98.69% and 98.96% accurate readings, respectively. Overall MARD of AiDEX CGM systems was 9.08% when compared to venous blood reference and 10.1% when compared to finger capillary BG reference.

Efficacy and safety of degludec U100 versus glargine U300 for the early postoperative management of patients with type 2 diabetes mellitus undergoing coronary artery bypass graft surgery: A non-inferiority randomized trial

AIMS

To compare the efficacy and safety of degludec U100 versus glargine U300 for the early postoperative management of patients with type 2 diabetes mellitus (T2D) undergoing coronary artery bypass graft (CABG) surgery.

METHODS

A total of 239 patients were randomly assigned (1:1) to receive a basal-bolus regimen in the early postoperative period using degludec U100 (n = 122) or glargine U300 (n = 117) as basal and glulisine before meals. The primary outcome was mean differences between groups in their daily BG concentrations. The major safety outcome was the occurrence of hypoglycemia.

RESULTS

There were no differences in mean daily BG concentrations (157 vs. 162 mg/dl), mean percentage of readings within target BG of 70-180 mg/dl (74% vs. 73%), daily basal insulin dose (19 vs. 21 units/day), length of stay (median [IQR]: 9 vs. 9 days), or hospital complications (21.3% vs. 21.4%) between treatment groups. There were no differences in the proportion of patients with BG <70 mg/dl (15.6% vs. 23.1%) or <54 mg/dl (1.6% vs. 4.3%) between degludec-100 and glargine-300 groups.

CONCLUSIONS

Treatment with degludec U100 is as effective and safe as glargine U300 for the early postoperative hospital management of patients with T2D undergoing CABG.

Insulin delivery patterns required to maintain postprandial euglycemia in type 1 diabetes following consumption of traditional Egyptian Ramadan Iftar meal using insulin pump therapy: A randomized crossover trial

OBJECTIVES

During Ramadan, traditional Egyptian Iftar meals have large amounts of high-glycemic index carbohydrate and fat. The efficacy of different bolus regimens on optimizing post prandial glucose (PPG) excursion following this Iftar meal was assessed.

METHODS

A randomized controlled trial evaluating 4-h PPG measured by continuous glucose-monitoring was conducted. A total of 25 youth with T1DM using insulin pumps were given the same Iftar meal (fat [45 g], protein [28 g], CHO [95 g]) on seven consecutive days. Insulin to carbohydrate ratio (ICR) was individualized, and all boluses were given upfront 20 min before Iftar. Participants were randomized to receive a standard bolus and six different split boluses delivered over 4 h in the following splits: dual wave (DW) 50/50; DW 50/50 with 20% increment (120% ICR); DW60/40; DW 60/40 with 20% increment; DW 70/30 and DW 70/30 with 20% increment.

RESULTS

Standard bolus and split 70/30 with 20% increment resulted in significantly lower early glucose excursions (120 min) with mean excursions of less than 40 mg/dL (2.2 mmol/L) compared to other conditions (p < 0.01). The split 70/30 with 20% increment significantly optimized late PPG excursion (240 min) in comparison to standard bolus (p < 0.01), as well as resulting in a significantly lower post meal glucose area under the curve compared with all other conditions (p < 0.01), with no late hypoglycemia.

CONCLUSION

To achieve physiologic PPG profile in traditional Iftar meal, a DW bolus with 20% increment given 20 min preprandial as split bolus 70/30 over 4 h, optimized both early and delayed PPG excursions.

Improving the Bias of Comparator Methods in Analytical Performance Assessments Through Recalibration

BACKGROUND

In analytical performance studies, the choice of comparator method plays an important role, as studies have shown that there exist relevant systematic differences (bias) between laboratory analyzers. The feasibility of retrospective recalibration of measurement results through comparison with methods or materials of higher metrological order to minimize bias was therefore assessed.

METHOD

Existing data from performance studies of continuous and blood glucose monitoring systems were retrospectively analyzed. Comparison with a higher-order method was performed for two different data sets. In both cases, subject samples were measured, and a subset was also measured on a higher-order method. Recalibration based on higher-order materials (standard reference material [SRM]) was conducted for two different data sets containing results from SRM and subject samples. Linear regression analysis was performed for each device separately. Resulting equations were applied to the respective complete data set of subject samples. Bias between devices in a data set across all subject samples was assessed before and after recalibration.

RESULTS

Bias between devices was reduced from -3.6% to +0.6% in one data set and from +11.0% to +0.3% in the other by recalibration based on higher-order method. Using higher-order materials, bias was also reduced by recalibration, but mixed results were found: Bias was reduced from -3.1% to -0.1% in one data set and from -4.3% to -2.7% in the other.

CONCLUSIONS

Recalibration did lead to a decrease in bias and thus can reduce the impact of the choice of comparator method. The procedure should be verified in a prospectively designed setting.

Relationship of glycaemic derangement using continuous glucose monitoring system with sleep pattern among children with type 1 diabetes

BACKGROUND

Glycaemic derangement has been linked to sleep disruption. However, the impact of glycaemic derangement on sleep pattern among children with type 1 diabetes (C-T1D) remains unraveled.

AIM

To assess the effect of nocturnal hyperglycaemia and clinically significant (CS) hypoglycaemia on sleep pattern among C-T1D.

METHODOLOGY

Thirty C-T1D were compared to 30 age and sex matched healthy siblings. Patients having other organ disease that might cause sleep disorders or on medications causing sleep disturbance were excluded. History included diabetes-duration, type and dose of insulin therapy, chronic diabetic-complications, and manifestations of sleep disorders. Epworth Sleepiness Scale-Child Adolescent was used. Continuous glucose monitoring system (CGMS) and overnight polysomnography were done and analysed.

RESULTS

C-T1D had significantly lower sleep efficiency and significantly higher arousal index (AI), periodic limb movement index and apnoea-hypopnoea index compared to controls. Moreover, they had significantly longer sleep-onset latency, light sleep percentage, and shorter rapid eye movement percentage than controls. According to nocturnal CGMS readings, 15 C-T1D had nocturnal hyperglycaemia (50%), six experienced CS hypoglycaemia (20%), two had level-1 hypoglycaemia (6.7%), and seven were within the normoglycaemic range (23.3%). C-T1D experiencing nocturnal CS hypoglycaemia had significantly higher stage 3 sleep (P = 0.004) than controls. On the other hand, C- T1D experiencing nocturnal hyperglycaemia had significantly higher sleep onset latency (P = 0.013), light sleep percentage (P < 0.001), and AI (P < 0.001) than controls. Nocturnal CS hypoglycaemia was positively correlated to deep sleep duration, while hyperglycaemia was correlated to number of awakenings, sleep-onset latency, and light sleep duration.

CONCLUSION

In children with T1D CS hypoglycaemia is associated with sleep deepening, while hyperglycaemia is associated with increased light sleep, sleep onset latency.

Accuracy of a flash glucose monitoring system in cats and determination of the time lag between blood glucose and interstitial glucose concentrations

Background

The FreeStyle Libre (Abbott Laboratories) is a flash glucose monitoring system (FGMS) that measures interstitial glucose concentration (IG). The system is factory‐calibrated, easy to use, inexpensive, and could be useful for monitoring diabetic cats.

Objectives

To evaluate the analytical and clinical accuracy of the FGMS in cats and establish the lag‐time between IG and blood glucose concentration (BG).

Animals

Twenty client‐owned diabetic cats and 7 purpose‐bred healthy cats.

Methods

Prospective study. Blood glucose concentration was measured using a portable glucose meter validated for use in cats that served as a reference method for IG, as measured by FGMS. In diabetic cats, data were collected for sensor wearing time with different methods of application and accuracy across glycemic ranges. Accuracy was determined by fulfillment of ISO15197:2013 criteria. In healthy cats, lag‐time between IG and BG was established after IV administration of exogenous glucose.

Results

Good agreement between IG and BG was obtained (r = .93). Analytical accuracy was not achieved, whereas clinical accuracy was demonstrated with 100% of the results in zones A + B of the Parkes consensus error grid analysis. In the immediate 30 minutes after an IV bolus of glucose, when BG was increasing rapidly (approximately 2%/min), IG increased slowly, resulting in a difference of as much as 579 mg/dL, and no positive correlation between BG and IG was found.

Conclusions and Clinical Importance

The FGMS did not fulfill ISO requirements but is sufficiently accurate for glucose monitoring in cats, while considering the lag between IG and BG during periods of rapid changes in BG.

The Influence of Skin Thickness on Flash Glucose Monitoring System Accuracy in Dogs with Diabetes Mellitus

Simple Summary

A flash glucose monitoring system (FGMS) has been validated for use in dogs with diabetes mellitus and diabetic ketoacidosis. It continuously measures the glucose in the interstitial fluid through a small filament (5 mm long) inserted under the skin. Interstitial glucose concentrations are reportedly comparable to whole blood glucose concentrations. However, several factors can influence the performance of interstitial sensors, including the proportion of interstitial fluid in a tissue. The influence of skin thickness on flash glucose monitoring system accuracy has not been investigated in previous studies; therefore, the aim of this study was to evaluate whether FGMS accuracy is affected by skin thickness. On the basis of our results, skin thickness seems to affect FGMS measurements; the mean bias was significantly inversely correlated (p = 0.02; r = −0.6) with the mean skin thickness, and clinical accuracy according to ISO 15197:2013 criteria was observed only in dogs with skin thickness > 5 mm, with 99% of the results falling in zone A + B of the Parkes consensus error grid analysis. In dogs with thin skin (<5 mm), the clinical accuracy was low, and the results should be interpreted with caution.

Abstract

A flash glucose monitoring system (FGMS) has been validated for use in diabetic dogs. However, it is unknown whether skin thickness affects FGMS measurements. The aim of this study was to evaluate whether FGMS accuracy is affected by skin thickness. Fourteen client-owned diabetic dogs on insulin treatment were prospectively enrolled in the study. The dogs were divided into two groups according to their ultrasound-measured skin thickness: dogs with skin thickness < 5 mm (Group 1) and dogs with skin thickness > 5 mm (Group 2). On days 1, 7 and 14, glucose curves were obtained simultaneously using the FGMS and a validated portable blood glucose meter. Paired measurements were used to calculate the mean bias and to determine accuracy according to ISO 15197:2013 criteria. The mean bias was significantly inversely correlated (p = 0.02; r = −0.6) with the mean skin thickness. Clinical accuracy was observed only in Group 2, with 99% of the results in zone A + B of the Parkes consensus error grid analysis. In conclusion, skin thickness seems to affect FGMS measurements, and the device is accurate in dogs with thicker skin (>5 mm); in dogs with thin skin (<5 mm), the clinical accuracy is low, and the results should be interpreted with caution.

CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review

Glycemic variability (GV) appears today as an integral component of glucose homeostasis for the management of type 2 diabetes (T2D). This review aims at investigating the use and relevance of GV parameters in interventional and observational studies for glucose control management in T2D. It will first focus on the relationships between GV parameters measured by continuous glucose monitoring system (CGMS) and glycemic control and T2D-associated complications markers. The second part will be dedicated to the analysis of GV parameters from CGMS as outcomes in interventional studies (pharmacological, nutritional, physical activity) aimed at improving glycemic control in patients with T2D. From 243 articles first identified, 63 articles were included (27 for the first part and 38 for the second part). For both analyses, the majority of the identified studies were pharmacological. Lifestyle studies (including nutritional and physical activity-based studies, N-AP) were poorly represented. Concerning the relationships of GV parameters with those for glycemic control and T2D related-complications, the standard deviation (SD), the coefficient of variation (CV), the mean blood glucose (MBG), and the mean amplitude of the glycemic excursions (MAGEs) were the most studied, showing strong relationships, in particular with HbA1c. Regarding the use and relevance of GV as an outcome in interventional studies, in pharmacological ones, SD, MAGE, MBG, and time in range (TIR) were the GV parameters used as main criteria in most studies, showing significant improvement after intervention, in parallel or not with glycemic control parameters' (HbA1c, FBG, and PPBG) improvement. In N-AP studies, the same results were observed for SD, MAGE, and TIR. Despite the small number of N-AP studies addressing both GV and glycemic control parameters compared to pharmacological ones, N-AP studies have shown promising results on GV parameters and would require more in-depth work. Evaluating CGMS-GV parameters as outcomes in interventional studies may provide a more integrative dimension of glucose control than the standard postprandial follow-up. GV appears to be a key component of T2D dysglycemia, and some parameters such as MAGE, SD, or TIR could be used routinely in addition to classical markers of glycemic control such as HbA1c, fasting, or postprandial glycemia.

Evaluating Glycemic Control During Basalin or Lantus Administration in Adults With Controlled Type 2 Diabetes Mellitus Using Continuous Glucose Monitoring

AIM

This study aims at evaluating glycemic control during Basalin or Lantus administration in adults with controlled type 2 diabetes mellitus using continuous glucose monitoring system (CGM).

METHODS

47 patients with well-controlled T2DM using both Basalin and oral hypoglycemic drugs were recruited. CGM were applied from day 1 to day 3 with the unchanged dose of Basalin and then removed from day 4. A washout was performed with Lantus at the same dose as Basalin from day 4 to day 10. Then patients were continued to install the CGM under Lantus administration from day 11 to day 13. Variables of CGM, such as the area under the curve (AUC) for both hyperglycemia and hypoglycemia, 24h mean blood glucose (24h MBG), 24h standard deviation of blood glucose (24h SDBG), 24h mean amplitude of glycemic excursion (24h MAGE), PT (percentage of time), and time in range (TIR), were calculated and compared between Basalin group and Lantus group.

RESULTS

The group of Lantus showed lower 24h MBG (p<0.01), 24h MAGE (p<0.05), and lower 24h SDBG (p<0.01) than the Basalin group. Lantus-treated patients had a lower PT and AUC when the cut-off point for blood glucose was 10 mmol/L (p<0.05) and 13.9 mmol/L (p<0.05), respectively. In this study, no patient developed symptomatic hypoglycemia, few hypoglycemia was observed and there was no difference of hypoglycemia between the two groups.

CONCLUSION

In patients with well-controlled T2DM who were treated with insulin glargine, Lantus group showed lower MBG, GV, and lower PT (BG > 10.0 mmol/L, BG > 13.9 mmol/L) than Basalin group. In summary, for T2DM population with HbA1c ≤ 7%, Lantus may be a better choice compared with Basalin.

Review of the Long-Term Implantable Senseonics Continuous Glucose Monitoring System and Other Continuous Glucose Monitoring Systems

The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.

Treatment Effects of Short-Term Continuous Positive Airway Pressure on Blood Glucose Control in Type 2 Diabetic Patients with Obstructive Sleep Apnea Syndrome

Purpose

The study aimed at assessing glucose control measured with a continuous glucose monitoring system (CGMS) before and after short-term continuous positive airway pressure (CPAP).

Materials and Methods

Twenty-four type 2 diabetic patients (T2DM) with Obstructive sleep apnea syndrome (OSAS) (mean age 55.0 ± 9.0 years; BMI 29.5 ± 5.2 kg/m²) were admitted and kept under diet control for 2 days, then underwent 2 overnight polysomnographies: a diagnostic study and one with CPAP titration. Then they were treated by CPAP during sleep for the following three nights. Participants were divided into subgroup D (only diet control) and subgroup M (with DM medication). CGMS was utilized over the last five days. Glucose control was also assessed with plasma insulin and a clinical measure of insulin resistance (HOMA-IR) index.

Results

The mean (±SD) apnea-hypopnea index (AHI) at diagnostic polysomnography was 51.2 ± 22.4 (range 10-88) events/h. CPAP treatment in the subjects with OSAS resulted in the index of oxygenation desaturations being reduced from 33.3 ± 20.1 to 1.1 ± 1.6 (P =0.00). CGMS showed mean 24-hours glucose values significantly lower after CPAP treatment than at baseline in both subgroups (7.97±1.31 vs 7.52±0.94, P=0.033 in subgroup D; and 7.72±1.51 vs 7.17±1.21, P=0.05 in subgroup M), as the fasting plasma insulin levels and HOMA-IR were also decreased significantly after CPAP treatment (13.0 ± 7.5μU/mL vs 10.8 ± 5.4μU/mL, P=0.044; and 4.2 ± 2.2 vs 3.1±1.7, P=0.003, respectively). Standard deviation (SD) and mean amplitude of glucose excursions (MAGE) were also decreased in the subgroup D (1.91 ± 1.10 vs 1.61 ± 1.20, P=0.014; 1.26 ± 1.13 vs 1.01 ± 0.98, P=0.008, respectively) only.

Conclusion

Short-term CPAP treatment in OSAS with type 2 diabetic patients is accompanied by a decrease in blood glucose level and improved insulin sensitivity. Glucose variability was reduced but only in the patients with diet control.

Glycerol Potentiates the Effects of Glucose in Promoting Glucose Recovery During Hypoglycemia: From Basic to Clinical Investigations and Their Therapeutic Application

We compared the effect of oral glucose versus oral glucose combined with glycerol (glucose + glycerol) in promoting glucose recovery during hypoglycemia. These studies were carried out in two series of experiments. In the first series of experiments, 16 overnight fasted rats received an intraperitoneal injection of lispro insulin (1 IU/kg), and 25 min later, they received oral water (control), glucose (0.25 g/kg), glycerol (2.5 g/kg), or glucose (0.25 g/kg) + glycerol (2.5 g/kg). In the second series of experiments on 164 eligible type 1 diabetic (T1D) patients, 30 individuals with a history of hypoglycemia were recruited. Five volunteers did not meet the inclusion criteria and two subjects were excluded after starting the clinical investigation; 23 patients concluded the study. All patients with symptoms of hypoglycemia ingested oral glucose (15 g) or glucose (15 g) + glycerol (9.45 g). To treat hypoglycemia in T1D patients, preparations containing glucose alone or glucose + glycerol were used alternately (2 weeks/2 weeks) in a double-blind crossover scheme. Throughout the clinical research (4 weeks), glucose concentrations were assessed with a continuous glucose monitoring device and the results after the use of glucose alone or glucose + glycerol preparations were compared. Oral glucose combined with glycerol was more effective in promoting glucose recovery in comparison with glucose alone, not only in rats but also in T1D patients. Taken together, our experimental and clinical investigations reported the best performance of oral administration of glucose + glycerol in comparison with isolated glucose.

The efficacy and safety of insulin pump therapy with predictive low glucose suspend feature in decreasing hypoglycemia in children with type 1 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Automated insulin delivery with predictive low glucose suspend (PLGS) feature has the potential to reduce risk of hypoglycemia in patients with type 1 diabetes mellitus (T1DM). We aim to systematically synthesize the evidence on the efficacy and safety of PLGS in children and adolescents with T1DM.

METHODS

We performed a systematic search through Ovid/MEDLINE, Ovid/Embase, and other search engines. We included randomized controlled trials (RCTs) evaluating the effect of sensor augmented pump (SAP) with PLGS feature compared to SAP or insulin pump therapy without SAP in decreasing hypoglycemia in children and adolescents aged 2 to 18 years with T1DM, with at least 2 weeks of follow-up. Two reviewers independently selected studies, extracted data, and evaluated the risk of bias (ROB).

RESULTS

Five RCTs with total sample size of 493 children aged 6 to 18 years met the inclusion criteria. The overall ROB of included studies was low. There is high quality evidence that PLGS is superior to SAP in decreasing time spent in hypoglycemia (sensor glucose [SG] <3.9 mmol/L [<70 mg/dL]/24 h) and nocturnal hypoglycemia (SG <3.9 mmol [<70 mg/dL]/L/night) with an absolute mean difference of 17.4 min/d (95% CI: -19.2, -15.5) and 26.3 min/night (95% CI: -35.5, -16.7), respectively, without increasing percentage of time spent in hyperglycemia or episodes of diabetic ketoacidosis (DKA). There was insufficient evidence for the impact of PLGS on health related quality of life (HRQL).

CONCLUSIONS

PLGS is superior to SAP in decreasing daytime and nocturnal hypoglycemia without increasing the risk of DKA or hyperglycemia. Future studies should address the impact of PLGS on children younger than 6-years-old and HRQL.

Design and Validation of a Diet Rich in Slowly Digestible Starch for Type 2 Diabetic Patients for Significant Improvement in Glycemic Profile

This study aimed at designing a-diet high in slowly digestible starch (SDS) by carefully selecting high-SDS starchy products and to validate its implementation, acceptance, and impact on the postprandial glycemic response in patients with type 2 diabetes (T2D). Starchy products were screened and classified as being either high (high-SDS) or low (low-SDS) in SDS (in vitro SDS method). A randomized controlled cross-over pilot study was performed: Eight patients with T2D consumed randomly a high-SDS or a low-SDS diet for one week each, while their glycemic profile was monitored for 6 days. Based on 250 food product SDS analyses and dietary recommendations for patients with T2D, the high-SDS and low-SDS diets were designed. The high-SDS diet significantly increased SDS intake and the SDS/carbohydrates proportion compared to the low-SDS diet (61.6 vs. 11.6 g/day and 30% vs. 6%; p < 0.0001, respectively). Increasing the SDS/carbohydrate proportion to 50% of the meal was significantly correlated with a 12% decrease in tAUC0-120 min and a 14% decrease in the glycemic peak value (p < 0.001 for both). A high-SDS diet can be easily designed by carefully selecting commercial starchy products and providing relevant recommendations for T2D to improve their glycemic profile.

Glycemic Control Improvement in Italian Children and Adolescents With Type 1 Diabetes Followed Through Telemedicine During Lockdown Due to the COVID-19 Pandemic

BACKGROUND/OBJECTIVE

To minimize the wide spread of coronavirus disease (COVID-19) pandemic, Italy was placed in an almost complete lockdown state that forced people to "stay at home". Aim of this study was to evaluate the effects of lockdown on glycemic control in children and adolescents with type 1 diabetes (T1D) followed through telemedicine.

SUBJECTS/METHODS

This observational study involved patients with T1D using the real-time continuous glucose monitoring (CGM) Dexcom G6^®. Ambulatory glucose profile data from the 3-months before schools closure (November 26, 2019-February 23, 2020; T0) and from the 3-months of consecutive lockdown (February 24-May 18, 2020; T1) were compared.

RESULTS

Sixty-two children and adolescents (11.1 ± 4.37 years, 50% males) with T1D (median time disease 3.67 years) were enrolled in the study. Insulin total daily dose was unchanged, while time spent on physical activities was decreased (p<0.0001). Despite the lack of statistical significance, median value of the glucose management indicator decreased from 7.4% to 7.25%. Glucose standard deviation (p<0.0001) and coefficient of variation (p=0.001) improved across the study. Median time in range increased from 60.5% to 63.5% (p=0.008), time above range decreased from 37.3% to 34.1% (p=0.048), and time below range decreased from 1.85% to 1.45% (p=0.001).

CONCLUSIONS

Overall, in our children and adolescents with T1D glycemic control improved during lockdown. Despite patients were confined to their homes and limited to exercise, our data suggest that the use of real-time CGM, the continuous parental management, and the telemedicine can display beneficial effects on T1D care.

Effect of skipping breakfast for 6 days on energy metabolism and diurnal rhythm of blood glucose in young healthy Japanese males

BACKGROUND

Skipping breakfast has become a common trend that may lead to obesity and type 2 diabetes. Previous studies, which imposed a single incidence of breakfast skipping, did not observe any decrease in 24-h energy expenditure. Furthermore, the effects of breakfast skipping on diurnal blood glucose profiles over 24 h are contradictory.

OBJECTIVE

The aim of this study was to clarify the influence of 6 consecutive days of breakfast skipping and sedentary behavior on energy metabolism and glycemic control.

METHODS

Ten young men participated in 2 trials (with or without breakfast) that lasted for 6 consecutive days, and the 2 trials were conducted 1 wk apart with a repeated-measures design. During the meal intervention, each subject's blood glucose was measured using the continuous glucose monitoring system. If breakfast was skipped, subjects ate large meals at lunch and dinner such that the 24-h energy intake was identical to that of the 3-meal condition. At 2200 on the fifth day, the subjects entered a room-sized respiratory chamber, where they remained for 33 h, and were instructed to carry out sedentary behavior.

RESULTS

The glucose levels were similar between the 2 meal conditions during the first 5 d of meal intervention, but the blood glucose at 2300 was higher in the breakfast-skipping condition than in the 3-meal condition. Breakfast skipping elevated postprandial glycemic response after lunch on the first day of meal intervention. On the sixth day, there were no significant differences in 24-h energy expenditure and substrate oxidation. When subjects remained in a metabolic chamber, the level of physical activity significantly decreased, glycemic stability slightly deteriorated, and mean blood glucose over 24 h was higher in the breakfast-skipping trial than in the 3-meal trial.

CONCLUSIONS

Sedentary lifestyle and repeated breakfast skipping caused abnormal glucose fluctuations, whereas 24-h energy metabolism remained unaffected. Clinical Trial Registry: This trial was registered at http://www.umin.ac.jp/english/ as UMIN000032346.

A Review of Continuous Glucose Monitoring Data Interpretation in the Age of Automated Insulin Delivery

Using a continuous glucose monitor (CGM) improves glycemic control in patients with type 1 diabetes. The ambulatory glucose profile (AGP) has been recommended as a standard method for reporting CGM data. However, in recently developed automated insulin delivery (AID) systems, a standard format for reporting data has not yet been developed. Instead, reports are specific to each system being used. Currently, the only FDA approved AID system is a hybrid closed-loop insulin pump. In these systems, the patient is still required to announce a meal, respond to alerts, and keep the system in automated insulin delivery. The integrated pump and sensor information provides insights into how the system is performing, and how to make changes to tunable parameters, such as carbohydrate to insulin ratios. The reports also offer a window into human behavior related to performing diabetes tasks, responding to alarms, reasons for exiting HCL, and how glycemic goals are being met. This article reviews the pump and CGM data provided by several of the current closed-loop systems with a focus on systems that are currently approved in the United States (MiniMed™ 670G, Tandem Basal:IQ) and those used by patients using do-it-yourself systems. A step-wise approach to reviewing the nuances of these systems is provided. The comparison may reinforce the importance of the continued need for streamlining a standard report for providers to be able to interpret the CGM data of these systems.

Prevalence of Hypoglycaemia among Insulin-Treated Pregnant Women with Diabetes Who Achieved Tight Glycaemic Control

Objectives

To determine the prevalence of hypoglycaemia using continuous glucose monitoring system (CGMS) among insulin-treated pregnant women with diabetes whose glycosylated haemoglobin (HbA1c) were <6.0% and identify the risk factors associated with hypoglycaemia occurrence.

Methodology

We conducted a cross-sectional study using 6-days CGMS to detect the prevalence of hypoglycaemia in 31 insulin-treated pregnant women with diabetes who achieved HbA1c <6.0%. Patients were required to log-keep their self-monitoring blood glucose (SMBG) readings and hypoglycaemia events.

Results

Eight women experienced confirmed hypoglycaemia with additional seven experienced relative hypoglycaemia, giving rise to prevalence rate of 45.2% (one had both confirmed and relative hypoglycaemia). Nine relative hypoglycaemia and 17 confirmed hypoglycaemic events were recorded. Sixteen (94%) out of 17 confirmed hypoglycaemia events recorded by CGMS were asymptomatic and were missed despite performing regular SMBG. Nocturnal hypoglycaemia events were recorded in seven women. Univariable analysis did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.

Conclusion

Insulin-treated pregnant women with diabetes who achieved HbA1c <6.0% were associated with high prevalence of hypoglycaemia. Asymptomatic hypoglycaemia is common in our cohort and frequently missed despite regular SMBG. Present study did not identify any association between conventional risk factors and hypoglycaemia events in our cohort.

Continuous glucose monitoring system can improve the quality of glucose control and glucose variability compared with point-of-care measurement in critically ill patients: A randomized controlled trial

BACKGROUND

The purpose of this study was to determine whether subcutaneous continuous glucose monitoring systems (CGMS) could improve glucose management in critically ill patients compared with frequent and conventional point-of-care (POC) glucose measurements.

METHODS

A total of 144 patients with an expected length of stay in the ICU of at least 72 hours and with an admission glucose or two random glucose values of >10.0 mmol/L within 24 hours after admission, were randomly assigned to the CGMS group (n = 74) or the conventional group (C group, n = 70). Both groups used the same insulin algorithm to reach the same glucose target range (8.0-10.0 mmol/L).

RESULTS

Time in range (TIR, 8.0-10.0 mmol/L), which is our primary outcome measure, was higher in the CGMS group than in the C group (51.5% vs. 29.0%, P < .001). Glucose variability (coefficient of variation, CV; standard deviation, SD; glucose lability index, and GLI) was improved by CGMS (all P < .05). Mean glucose level (MGL) (9.6 vs. 10.3 mmol/L, P = .156) and the proportion of patients with hypoglycemia did not differ between CGMS (5.4%) and C (5.7%) (P = 1.000). However, duration of hypoglycemia was reduced in the CGMS group (15 vs. 28 minutes, P = .032). Clinical outcomes were similar between groups except for the fewer usage of CRRT and lower peak plasma urea nitrogen level in the CGMS group.

CONCLUSION

The use of CGMS, compared with POC glucose measurement, could improve the TIR, GV and duration of hypoglycemia.

New insights from continuous glucose monitoring into the route to diabetes

AIM

Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.

MATERIAL AND METHODS

Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow-up was 32 months (6-72 mo), and there were 17 new cases of T2DM.

RESULTS

In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes-defining criteria and the 2 CGMS-derived variables, only TU100 and HbA_1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54-0.88; P < .01) odds ratio of developing T2DM. With cut-off points of 0.5 for TU100 and 5.7% for HbA_1c , the test "TU < 0.5 and HbA_1c  > 5.7%" had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development.

CONCLUSIONS

Continuous glucose monitoring system allows for a better T2DM risk-development categorization than fasting glucose and HbA_1c in a high-risk population. Continuous glucose monitoring system-derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA_1c categorization. These differences may correlate with distinct pathophysiological mechanisms.

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.

Postprandial walking reduces glucose levels in women with gestational diabetes mellitus

The purpose of this study was to investigate blood glucose changes, as measured by a continuous glucose monitoring system, that occur in women with gestational diabetes mellitus (GDM) following an acute bout of moderate-intensity walking after consuming a high-carbohydrate/low-fat meal. This study found that moderate-intensity walking induced greater postprandial glucose control compared with sedentary activity and it appears that moderate-intensity activity may be used to reduce postprandial glucose levels in women with GDM.

Insulin-induced autoimmune syndrome: A case report

Insulin autoimmune syndrome (IAS) is a disease characterized by hyperinsulinemic hypoglycemia associated with autoantibodies against endogenous insulin. A 56-year-old man was admitted to Ningbo First Hospital for the treatment of spontaneous hypoglycemia. He was found to have elevated fasting insulin level (>1,000 mIU/l) and presence of insulin autoantibodies, and after appropriate workup, was diagnosed with IAS. After treating with prednisone for 2 months, his insulin level started decreasing. In patients with repeated hypoglycemia, IAS should be considered in the differential diagnosis. Prednisone may be effective for the treatment of hypoglycemia in patients with IAS.

Re-evaluation of glycated hemoglobin and glycated albumin with continuous glucose monitoring system as markers of glycemia in patients with liver cirrhosis

Liver cirrhosis (LC) is frequently accompanied by glucose intolerance. The present study was designed to determine whether glycated hemoglobin A1c (HbA1c) and glycated albumin (GA) were predictive markers of glycemia, as determined by a continuous glucose monitoring system (CGMS), in patients with LC. A total of 30 patients with LC, including 3, 19, 5, 2 and 1 with LC due to hepatitis B virus, hepatitis C virus, non-alcoholic steatohepatitis, alcohol and unknown causes, respectively, were assessed by CGMS. The average, maximum and minimum blood glucose (BG) levels were measured by CGMS, and correlated with HbA1c and GA. The average, maximum and minimum BG in these individuals were 142±38.7, 209.3±65.7 and 85.1±25.4 mg/dl, respectively. HbA1c was significantly correlated with average BG (r=0.447, P=0.015) and maximum BG (r=0.523, P=0.004). In addition, GA was significantly correlated with average BG (r=0.687, P<0.001) and maximum BG (r=0.648, P<0.001). Neither HbA1c nor GA was significantly correlated with minimum BG. Correlation analysis yielded formulas by which HbA1c and GA were predictive of average BG in individuals with LC: Average BG=19.2 × HbA1c (%) + 36.5 and average BG=6.6 × GA (%) + 13.0, respectively. In conclusion, HbA1c and GA showed significant correlations with average and maximum BG, as determined by CGMS. The derived formulas allow for estimates of average BG based on HbA1c and GA, and may contribute to the control of glycemia in patients with LC.

Diabetes Educators' Intended and Reported Use of Common Diabetes-Related Technologies: Discrepancies and Dissonance

BACKGROUND

Technology provides adjuvant and/or alternative approaches to care and may promote self-care, communication, and engagement with health care services. Common recent technologies for diabetes include continuous subcutaneous insulin infusions (insulin pumps), continuous glucose monitoring systems, smartphone and tablet applications, and telehealth (video conferencing). This study reports Australian diabetes educators' intentions and reported professional use of these technologies for people with type 1 diabetes, and factors predictive of this.

METHODS

An anonymous, web-based questionnaire based on the technology acceptance model was distributed to members of the Australian Diabetes Educators Association through their electronic newsletter. Exploratory factor analysis revealed a 5-factor solution comprising confidence and competence, improving clinical practice, preparation (intentions and training), ease of use, and subjective norms. Logistic regression analyses identified factors predicting intention and use of technology.

RESULTS

Respondents (n = 228) had high intentions to use technology. The majority reported using continuous subcutaneous insulin infusions, continuous glucose monitoring systems, and applications with patients, but usage was occasional. Confidence and competence independently predicted both intentions and use of all 4 technologies. Preparation (intentions and training) independently predicted use of each technology also.

CONCLUSIONS

Discrepancies and dissonance appear between diabetes educators' intentions and behavior (intentions to use and reported technology use). Intentions were higher than current use, which was relatively low and not likely to provide significant support to people with type 1 diabetes for disease management, communication, and engagement with health care services. Continuing education and experiential learning may be key in supporting diabetes educators to align their intentions with their practice.

Diabetes Educators: Perceived Experiences, Supports and Barriers to Use of Common Diabetes-Related Technologies

BACKGROUND

Various technologies are commonly used to support type 1 diabetes management (continuous subcutaneous insulin infusion therapy, continuous glucose monitoring systems, smartphone and tablet applications, and video conferencing) and may foster self-care, communication, and engagement with health care services. Diabetes educators are key professional supporters of this patient group, and ideally positioned to promote and support technology use. The aim of this study was to examine diabetes educators' perceived experiences, supports, and barriers to use of common diabetes-related technologies for people with type 1 diabetes.

METHODS

This qualitative ethnographic study recruited across metropolitan, regional and rural areas of Australia using purposive sampling of Australian Diabetes Educators Association members. Data were collected by semistructured telephone interviews and analyzed using thematic analysis.

RESULTS

Participants (n = 31) overwhelmingly indicated that overall the use of technology in the care of patients with type 1 diabetes was burdensome for them. They identified 3 themes involving common diabetes-related technologies: access to technology, available support, and technological advances. Overall, these themes demonstrated that while care was usually well intentioned it was more often fragmented and inconsistent. Most often care was provided by a small number of diabetes educators who had technology expertise.

CONCLUSIONS

To realize the potential benefits of these relatively new but common diabetes technologies, many diabetes educators need to attain and retain the skills required to deliver this essential component of care. Furthermore, policy and strategy review is required, with reconfiguration of services to better support care delivery.

Accuracy of a real-time continuous glucose monitoring system in children with septic shock: A pilot study

Aims:

The aim of this prospective, observational study was to determine the accuracy of a real-time continuous glucose monitoring system (CGMS) in children with septic shock.

Subjects and Methods:

Children aged 30 days to 18 years admitted to the Pediatric Intensive Care Unit with septic shock were included. A real-time CGMS sensor was used to obtain interstitial glucose readings. CGMS readings were compared statistically with simultaneous laboratory blood glucose (BG).

Results:

Nineteen children were included, and 235 pairs of BG-CGMS readings were obtained. BG and CGMS had a correlation coefficient of 0.61 (P < 0.001) and a median relative absolute difference of 17.29%. On Clarke's error grid analysis, 222 (94.5%) readings were in the clinically acceptable zones (A and B). When BG was < 70, 70–180, and > 180 mg/dL, 44%, 100%, and 76.9% readings were in zones A and B, respectively (P < 0.001). The accuracy of CGMS was not affected by the presence of edema, acidosis, vasopressors, steroids, or renal replacement therapy. On receiver operating characteristics curve analysis, a CGMS reading <97 mg/dL predicted hypoglycemia (sensitivity 85.2%, specificity 75%, area under the curve [AUC] =0.85). A reading > 141 mg/dL predicted hyperglycemia (sensitivity 84.6%, specificity 89.6%, AUC = 0.87).

Conclusion:

CGMS provides a fairly, accurate estimate of BG in children with septic shock. It is unaffected by a variety of clinical variables. The accuracy over extremes of blood sugar may be a concern. We recommend larger studies to evaluate its use for the early detection of hypoglycemia and hyperglycemia.

Different training status may alter the continuous blood glucose kinetics in self-paced endurance running

The main purpose of the systemic energy metabolism is to provide a source of energy, mainly glucose, for the brain; therefore, blood glucose levels would be expected to correlate with exercise performance. The individual training status may also affect the blood glucose levels. The aim of the present study was to assess the association between blood glucose levels and running velocity during prolonged running in athletes with different training statuses. Two female college athletes, a triathlete and a tennis player, ran a course that was 247.4 m in circumference for 5 h while wearing a continuous glucose monitoring system. Blood was obtained at time-points of -1, 1, 3 and 5 h. The athletes had free access to food and fluids throughout the run. The athletes ran at almost the same pace without a sudden decrease in pace. The blood glucose levels increased and remained high in the triathlete, whereas the tennis player remained hypoglycemic throughout the run. Carbohydrate ingestion did not affect the blood glucose levels. The magnitude of hormonal changes, e.g. insulin, adrenaline and cortisol, was greater in the tennis player. The blood glucose concentration did not correlate with the running velocity or the carbohydrate ingestion; however, a discrepancy in blood glucose transition was observed between the triathlete and the tennis player, indicating a possible association between the adaptation to endurance exercise and the blood glucose kinetics during prolonged running.

Vascular Glucose Sensor Symposium: Continuous Glucose Monitoring Systems (CGMS) for Hospitalized and Ambulatory Patients at Risk for Hyperglycemia, Hypoglycemia, and Glycemic Variability

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, length of stay, and cost in a variety of critical care and non-critical care patient populations in the hospital. The results from prospective randomized clinical trials designed to determine the risks and benefits of intensive insulin therapy and tight glycemic control have been confusing; and at times conflicting. The limitations of point-of-care blood glucose (BG) monitoring in the hospital highlight the great clinical need for an automated real-time continuous glucose monitoring system (CGMS) that can accurately measure the concentration of glucose every few minutes. Automation and standardization of the glucose measurement process have the potential to significantly improve BG control, clinical outcome, safety and cost.

Glucose-lowering effect and glycaemic variability of insulin glargine, insulin detemir and insulin lispro protamine in people with type 1 diabetes

AIM

To compare, using a continuous glucose monitoring (CGM) system, the effect on glycaemic variability of insulin glargine, detemir and lispro protamine.

METHODS

A total of 49 white people with type 1 diabetes, not well controlled by three times daily insulin lispro, taken for at least 2 months before study and on a stable dose, were enrolled. The study participants were randomized to add insulin glargine, detemir or lispro protamine, once daily, in the evening. We used a CGM system, the iPro Digital Recorder (Medtronic MiniMed, Northridge, CA, USA) for 1 week. Glycaemic control was assessed according to mean blood glucose values, the area under the glucose curve above 3.9 mmol/l (AUC(>3.9)) or above 10.0 mmol/l (AUC(>10.0)), and the percentage of time spent with glucose values >3.9 or >10.0 mmol/l. Intraday glycaemic variability was assessed using standard deviation (s.d.) values, the mean amplitude of glycaemic excursions and continuous overlapping of net glycaemic action. Day-to-day glycaemic variability was assessed using the mean of daily differences.

RESULTS

The s.d. was found to be significantly lower with insulin lispro protamine and glargine compared with insulin detemir. AUC(>3.9) was higher and AUC(>10.0) was lower with insulin lispro protamine and glargine compared with detemir. The mean amplitude of glycaemic excursions and continuous overlapping net glycaemic action values were lower with insulin lispro protamine and glargine compared with detemir. In addition, the mean of daily differences was significantly lower with insulin lispro protamine and glargine compared with detemir. Fewer hypoglycaemic events were recorded during the night-time with insulin lispro protamine compared with glargine and detemir.

CONCLUSIONS

The results suggest that insulin lispro protamine and glargine are more effective than detemir in reducing glycaemic variability and improving glycaemic control in people with type 1 diabetes. Insulin lispro protamine seems to lead to fewer hypoglycaemic events than other insulin regimens.

Hypoglycemia in Type 2 Diabetes--More Common Than You Think: A Continuous Glucose Monitoring Study

BACKGROUND

Hypoglycemia is often the limiting factor for intensive glucose control in diabetes management, however its actual prevalence in type 2 diabetes (T2DM) is not well documented.

METHODOLOGY

A total of 108 patients with T2DM wore a continuous glucose monitoring system (CGMS) for 5 days. Rates and patterns of hypoglycemia and glycemic variability (GV) were calculated. Patient and medication factors were correlated with rates, timing, and severity of hypoglycemia.

RESULTS

Of the patients, 49.1% had at least 1 hypoglycemic episode (mean 1.74 episodes/patient/ 5 days of CGMS) and 75% of those patients experienced at least 1 asymptomatic hypoglycemic episode. There was no significant difference in the frequency of daytime versus nocturnal hypoglycemia. Hypoglycemia was more frequent in individuals on insulin (alone or in combination) (P = .02) and those on oral hypoglycemic agents (P < .001) compared to noninsulin secretagogues. CGMS analysis resulted in treatment modifications in 64% of the patients. T2DM patients on insulin exhibited higher glycemic variability (GV) scores (2.3 ± 0.6) as compared to those on oral medications (1.8 ± 0.7, P = .017).

CONCLUSIONS

CGMS can provide rich data that show glucose excursions in diabetes patients throughout the day. Consequently, unwarranted onset of hypo- and hyperglycemic events can be detected, intervened, and prevented by using CGMS. Hypoglycemia was frequently unrecognized by the patients in this study (75%), which increases their potential risk of significant adverse events. Incorporation of CGMS into the routine management of T2DM would increase the detection and self-awareness of hypoglycemia resulting in safer and potentially better overall control.

Full neurological recovery after extreme hypoglycemia during intensive insulin therapy: a case report

Since 2000, there has been an ongoing debate regarding tightness of glycemic control in critically ill patients. An increased risk of hypoglycemia is observed in patients treated with an intensive insulin protocol targeting "normoglycemia," probably accounting for a reduction of the overall benefit. Hypoglycemia is associated with neurological side effects and is found to be an independent predictor of mortality in most trials; however, long-term sequelae are rare if glucose is administered early. We describe a case of prolonged, extreme hypoglycemia in a critically ill patient treated according to an intensive insulin protocol who recovered without any neurological deficit at discharge.

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

BACKGROUND

The purpose of this prospective open-label trial was (1) to assess the influence of oral antidiabetic drugs (OAD) on the glycemic index (GI), glucose response curves (GRCs), daily mean plasma glucose (MPG) and (2) to compare the GI of foods in persons with OAD-treated type 2 diabetes mellitus (T2DM) with the respective GI in healthy persons (HP).

METHODS

Tested foods containing 50 g of carbohydrates were eaten for breakfast and dinner after 10 and 4 h of fasting, respectively. Glycemic index, GRC, and MPG were obtained using the CGMS System Gold (CGMS). In T2DM patients [n = 16; age (mean +/- standard error) 56.0 +/- 2.25 years], foods were tested four times: tests 1, 2, and 3 were performed within one week in which placebo was introduced on day 2, and test 4 was carried out five weeks after reintroduction of OAD. Glycemic indexes, GRC, and MPG from tests 1, 2, 3, and 4 were compared. In a control group of 20 HP (age 24.4 +/- 0.71 years), the mean GIs were calculated as the mean from 20 subject-related GIs.

RESULTS

In T2DM patients, subject-related assessment of GIs, GRC, and MPG distinguished persons with and without OAD effect. Nevertheless, the group-related GIs and the MPG on days 2, 8, and 39 showed no significant difference. There was no significant difference between the GIs in OAD-treated T2DM patients (test 4) versus HP (except in apple baby food). Glucose response curves were significantly larger in T2DM patients (test 4) versus HP.

CONCLUSIONS

Determination of GRC and subject-related GI using the CGMS appears to be a potential means for the evaluation of efficacy of OAD treatment. Further studies are underway.

Professional continuous glucose monitoring in clinical practice 2010

Professional continuous glucose monitoring (PCGM) is a 3-5 day test done to evaluate diabetes control. The PCGM test uses interstitial glucose measurements done every 5 min with a glucose-oxidase-impregnated membrane. The PCGM test evaluates glucose control retrospectively with the glucose results being unknown to the patient until the results are downloaded after the testing period. The PCGM test allows the practitioner and patient to evaluate the effect of diet, physical activity, medications, and lifestyle events on glucose control during the 24 h period. Developing a PCGM program at a medical office involves understanding reimbursement issues and having trained staff and a process in place to initiate the test and download and interpret the data.

The potential use of radio frequency identification devices for active monitoring of blood glucose levels

Imagine a diabetes patient receiving a text message on his mobile phone warning him that his blood glucose level is too low or a patient's mobile phone calling an emergency number when the patient goes into diabetic shock.Both scenarios depend on automatic, continuous monitoring of blood glucose levels and transmission of that information to a phone. The development of advanced biological sensors and integration with passive radio frequency identification technologies are the key to this. These hold the promise of being able to free patients from finger stick sampling or externally worn devices while providing continuous blood glucose monitoring that allows patients to manage their health more actively. To achieve this promise, however, a number of technical issues need to be addressed.

Group of signs: a new method to evaluate glycemic variability

BACKGROUND

Glycemic variability is an important parameter used to resolve potential clinical problems in diabetic patients. It is known that glycemic variability generates oxidative stress and potentially contributes to the development of macro- and microvascular complications in diabetes. By controlling glycemic variability, it is possible to reduce these complications and to set the therapy for all patients with diabetes. The aims of this study were to (1) propose a new standardized, objective, and flexible approach to measure glycemic variability by a continuous glucose monitoring system (CGMS)-the group of signs (GOS) method; (2) compare the correlation between mean amplitude of glucose excursion (MAGE), a well-known index of glycemic variability calculated by the physician and the MAGE defined with the GOS method, in order to validate the GOS; and (3) suggest new indexes of glycemic variability.

METHODS

We tested the GOS algorithm on data collected by a CGMS every 5 minutes for 24 hours on 50 patients. Consequently, for 8 patients we calculated and compared the physician's MAGE in the standard way and by the GOS method.

RESULTS

Comparison between the two methods has shown high correlations, from a minimum correlation of 86% to a maximum of 98%, with p values <0.01 (Pearson test).

CONCLUSIONS

Preliminary data suggest that the proposed algorithm is a valid, efficient, and reliable method able to calculate the standard MAGE on CGMS data systematically and to create other alternative glycemic variability indexes.

Electromagnetic environmental effects testing of medical devices including those used for the treatment of diabetes

BACKGROUND

Electromagnetic emissions from technologies that surround us can produce interference with implanted and externally worn medical devices. Electromagnetic environmental effects (E3) testing of medical devices at the Georgia Tech Research Institute (GTRI) began almost four decades ago and continues to incorporate new devices and new sources of electromagnetic emissions as they are developed and become available. The GTRI Medical Device Test Center provides real-world exposure fields to identify interactions and help manufacturers prevent disruptions from the environments in which their devices must function.

METHODS

Typically, the medical device is mounted in or on a torso simulator containing a saline solution that simulates the electrical characteristics of the body. The torso simulator and the device under test are then moved through the fields generated by production security and logistical system technologies using a computer-controlled positioning system. These tests are conducted with different orientations of the medical device to the electromagnetic source, simulating the way in which device wearers interact with these systems in representative situations.

RESULTS

Particular E3 test results measured on specific devices in the GTRI Medical Device Test Center are proprietary; however, the results of tests to date with current medical devices used for the treatment of diabetes have been encouraging. These devices have included implantable and externally worn insulin infusion pumps and continuous glucose monitoring systems from different manufacturers.

CONCLUSION

Since E3 tests of diabetes treatment devices to date in the test center have centered on devices from only a few of the many current manufacturers, further testing is warranted. In addition, increased functionality, which is being added to existing devices, will create new possibilities for interference in the future.

Evaluation of the VIA Blood Chemistry Monitor for Glucose in Healthy and Diabetic Volunteers

BACKGROUND

Manual methods of blood glucose monitoring are labor-intensive, costly, prone to error, and expose the caregiver to blood. The VIA(R) blood chemistry monitor for glucose can automatically measure plasma glucose (PG) every 5 minutes for 72 hours using blood sampled from a peripheral vein/artery or a central vein.

METHODS

VIA performance was evaluated in eight normal and five type 1 diabetic (T1DM) subjects in 15 separate experiments. The VIA device was connected to a peripheral vein and reported a PG value every 5 minutes during each 510-minute experiment. Blood samples were collected manually every 10 minutes and assayed using a HemoCue(R) beta-glucose analyzer (HC). Whole blood HC measurements were corrected to PG values. Paired HC/VIA measurements (n = 717) were analyzed.

RESULTS

Mean PG was 90 +/- 14 and 96 +/- 12 mg/dl in normal subjects and 194 +/- 64 and 173 +/- 48 mg/dl in T1DM subject as measured by the HC and VIA, respectively. Clark error grid analysis revealed 86% points in zone A, 11% points in zone B, and 2% points in zone D. Linear regression analysis yielded the following equation: VIA = 0.732 x HC + 30.5 (r(2) = 0.954). Residual analysis revealed a glucose-dependent bias between the HC and the VIA. VIA data were transformed using the linear regression equation to correct for bias. After the correction, the mean absolute relative difference between the VIA and the HC was less than 10%, and 99.6% of data were in zones A and B. The VIA was able to sample blood automatically every 5 minutes for more than 8 hours in the laboratory setting. On average, the VIA reported glucose values for 94% of the samples it attempted to obtain.

CONCLUSIONS

This study demonstrated that the VIA blood chemistry monitor for glucose can reliably sample blood frequently for a prolonged period of time safely and effectively in diabetic and nondiabetic volunteers. Agreement between the two devices was the closest at normal glucose concentrations. After correcting for a glucose-dependent bias between the devices, the MARD was consistently less than 10% for all glucose ranges.

Impact of islet transplantation on glycemic control as evidenced by a continuous glucose monitoring system

BACKGROUND

This study evaluated the effects of islet allotransplantation (ITx) on metabolic control utilizing a continuous glucose monitoring system (CGMS) and assessed its effectiveness as an indicator and predictor of graft dysfunction (GD).

METHODS

Glycemic control was assessed in 25 patients with type 1 diabetes mellitus (T1DM); 12 ITx recipients and 13 controls. Mean interstitial glucose, standard deviation (SD), glucose variability, and percentage of time in hyperglycemia (%GT >140 mg/dl), hypoglycemia (%GT <54 mg/dl), and normoglycemia (%GT 54-140 mg/dl) were measured in 72-hour time periods from CGMS recordings in the control group at baseline and in the ITx group at 3, 6, 9, 12, 15, and 18 months after ITx completion and were analyzed as predictors and indicators of GD. Hemoglobin A1c (HbA1c), 90-minute glucose after a mixed meal tolerance test, fasting C-peptide/glucose ratio, and insulin requirements were followed.

RESULTS

Compared to the control group, the percentage of time in hypoglycemia was significantly lower in the ITx group at all time points; time in normoglycemia was increased at all times except at 15 months; and time in hyperglycemia was significantly lower at 6, 9, 12, and 18 months. Mean glucose and glucose variability were significantly lower in the ITx group at all times except at 3 and 15 months, whereas HbA1c and 90-minute glucose were significantly lower in the ITx group at all time points. Mean glucose, SD, glucose variability, and %GT >140 mg/dl were significant as indicators but not as predictors of GD.

CONCLUSIONS

The CGMS demonstrated the benefits of ITx in T1DM, with improvements in glycemic control apparent up to 18 months after transplant. CGMS measures were found to be indicators of GD.

Numerical simulation of the effect of rate of change of glucose on measurement error of continuous glucose monitors

BACKGROUND

A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg*dl(-1)*min(-1) (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg*dl(-1)*min(-1) (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone.

METHOD

Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy.

RESULTS

The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose.

CONCLUSIONS

The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.