Related Articles

Abstract

Background

Young children with type 1 diabetes are vulnerable to glycemic excursion. Continuous glucose monitoring (CGM), combined with variability statistics, can offer a richer and more complete picture of glycemic variability in young children. In particular, we present data for the Average Daily Risk Range (ADRR) and compare ADRR scores calculated using CGM versus self-monitoring of blood glucose (SMBG) data for young children.

Methods

CGM and SMBG data from 48 young children with type 1 diabetes (mean age, 5.1 years) were used to calculate two separate ADRR scores, using SMBG data (ADRRs) and CGM data (ADRRc), for each child. Additionally, we calculated mean amplitude of glycemic excursion (MAGE) scores for children to examine the concurrent validity of the ADRRs and ADRRc.

Results

Young children's mean ADRRc score was significantly greater than their ADRRs score (55±12 and 46±11, respectively; P<0.001). In addition, 74% of the time the children's ADRRc score reflected greater variability risk than their ADRRs score. Examining the concurrent validity, children's ADRRc scores correlated positively with MAGE scores calculated using their CGM and SMBG data, whereas their ADRRs scores only correlated with MAGE scores calculated using SMBG.

Conclusions

ADRR scores generated for young children with type 1 diabetes demonstrate a high risk for glucose variability, but ADRR scores generated from CGM data may provide a more sensitive measure of variability than ADRR scores generated from SMBG. In young children with type 1 diabetes, ADRR scores calculated from CGM data may be superior to scores calculated from SMBG for measuring risk of excursion.

Introduction

To examine the efficacy of sitagliptin and miglitol when added to ongoing insulin treatment in a patient with type 2 diabetes who had undergone partial gastrectomy.

Methods

Continuous glucose monitoring (CGM) was performed and either sitagliptin or miglitol, or both, were added to fixed-dose insulin therapy. Blood was drawn at 0, 30, 60, and 120 min after breakfast, and C-peptide, glucagon, glucagon-like peptide (GLP)-1, and glucose-dependent insulinotropic peptide (GIP) were measured.

Results

CGM showed that compared to insulin alone, the addition of either sitagliptin or miglitol, or both, to insulin achieved better glucose control. Compared to insulin alone, early postprandial increments in plasma C-peptide levels and suppressed glucagon levels were observed when sitagliptin was added. Glucagon suppression was even more prominent when both sitagliptin and miglitol were added. Compared to insulin alone, GLP-1 levels were higher during the early postprandial stage when sitagliptin or miglitol was added and even higher when both were added. GIP levels decreased when sitagliptin or miglitol, or both, were added to insulin therapy.

Conclusion

The authors showed that the addition of sitagliptin or miglitol, or both, was effective in this insulin-treated patient with diabetes who had undergone gastrectomy.

Background

Fluctuations in blood glucose level cause endothelial dysfunction and play a critical role in onset and/or progression of atherosclerosis. We hypothesized that fluctuation in blood glucose levels correlate with vascular endothelial dysfunction and that this relationship can be assessed using common bedside medical devices.

Methods

Fluctuations in blood glucose levels were measured over 24 hours by continuous glucose monitoring (CGM) on admission day 2 in 57 patients with type 2 diabetes mellitus. The reactive hyperemia index (RHI), an index of vascular endothelial function, was measured using peripheral arterial tonometry (EndoPAT) on admission day 3.

Results

The natural logarithmic-scaled RHI (L_RHI) correlated with SD (r=−0.504; P<0.001), the mean amplitude of glycemic excursions (MAGE) (r=−0.571; P<0.001), mean postprandial glucose excursion (MPPGE) (r=−0.411; P=0.001) and percentage of time ≥200 mg/dl (r=−0.292; P=0.028). In 12 patients with hypoglycemia, L_RHI also correlated with the percentage of time at hypoglycemia (r=−0.589; P=0.044). L_RHI did not correlate with HbA1c or fasting plasma glucose levels. Furthermore, L_RHI did not correlate with LDL cholesterol, HDL cholesterol, and triglyceride levels or with systolic and diastolic blood pressures. Finally, multivariate analysis identified MAGE as the only significant determinant of L_RHI.

Conclusions

Fluctuations in blood glucose levels play a significant role in vascular endothelial dysfunction in type 2 diabetes.

Trial registration

UMIN000007581

Background

Glucose fluctuations including robust postprandial hyperglycemia are a risk for promoting atherosclerosis and diabetic complications. The α-glucosidase inhibitors and the dipeptidyl peptidase-4 (DPP-4) inhibitors have been found to effectively decrease postprandial hyperglycemia independently. Therefore, glycemic control with the combination of these drugs is warranted.

Methods

Continuous glucose monitoring (CGM) was performed for 3 patients with type 2 diabetes and 1 control subject from the beginning to the end of the study. Medications were not administered to any of the subjects on the first day of the study. From the second day to the end of study (days 2-5), the subjects received miglitol (150 mg per day) and on days 4 and 5, sitagliptin (50 mg per day) was added to the treatment regimen. On the first, third, and fifth days of the study, blood was drawn at 0, 30, 60, 120, 180, and 240 min after breakfast for measurements of serum insulin, 1,5-anhydroglucitol (1,5-AG), plasma glucagon, glucagon-like peptide-1 (GLP-1), and glucose-dependent insulinotropic peptide (GIP).

Results

Measurements of CGM and 1,5-AG levels showed that miglitol attenuated the escalation and fluctuation of glucose levels, and this was even more pronounced with the combination of miglitol and sitagliptin. The patterns of insulin secretion and glucagon secretion with miglitol alone or with a combination of miglitol and sitagliptin were various in the study subjects. Miglitol alone enhanced the release of GLP-1 in 1 patient with type 2 diabetes and the control subject, whereas the combination of miglitol and sitagliptin increased GLP-1 levels to varying degrees in all the subjects. Except for 1 subject, none of the subjects showed any change in GIP levels after the addition of sitagliptin, compared to the administration of miglitol alone.

Conclusions

In conclusion, CGM measurements revealed that a combination of the α-GI miglitol and the DPP-4 inhibitor sitagliptin effectively reduced postprandial glucose fluctuation and stabilized blood glucose levels. Completely different response patterns of insulin, glucagon, GLP-1, and GIP were observed among the study subjects with either medication alone or in combination, suggesting that individual hormone-dependent glycemic responses to the α-GI and DPP-4 inhibitors are complicated and multifactorial.

Objective

Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control.

Method

Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70–175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison.

Results

Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE.

Conclusion

We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.

Objective

To assess the accuracy of the Continuous Glucose Monitoring System, CGMS™ (“CGMS”) in children and adolescents with type 1 diabetes when compared with reference serum glucose levels during spontaneous fluctuations in glucose levels over 24 hours and during acute hyper- and hypoglycemia.

Research Design and Methods

Ninety-one subjects with type 1 diabetes (3.5 to 17.7 years old) wore 1 or 2 CGMSs while blood samples were obtained for serum glucose determinations (made at a central laboratory) hourly during the day, every 30 minutes overnight, and every 5 minutes during meal-induced hyperglycemia and insulin-induced hypoglycemia tests, resulting in 6,778 CGMS reference glucose pairs. CGMS function was assessed on each of the three days of sensor life.

Results

The median relative absolute difference (RAD) between the CGMS and reference values was 18% (25th, 75th percentiles= 8%, 34%). Similar results were obtained on each of the three days of sensor life. Accuracy was worse during hypoglycemia than during hyperglycemia. Modified sensors that first became available in November 2002 were more accurate than were the original sensors (median RAD= 11% vs. 19%) and had better precision (r= 0.92 vs. r= 0.77) during time periods in which two CGMSs were simultaneously used.

Conclusions

The CGMS sensors that have been in clinical use until recently are often inaccurate in quantifying glucose values in children with T1DM. However, recent modifications to the sensor have resulted in substantially better accuracy and reliability. This improved function, if confirmed by additional data, may enhance the clinical utility of the CGMS.

OBJECTIVE

The widespread clinical application of continuous glucose monitoring (CGM) is limited by the lack of generally accepted reference values. This multicenter study aims to establish preliminary normal reference values for CGM parameters in a sample of healthy Chinese subjects.

RESEARCH DESIGN AND METHODS

A total of 434 healthy individuals with normal glucose regulation completed a 3-day period of glucose monitoring using a CGM system. The 24-h mean blood glucose (24-h MBG) and the percentage of time that subjects' blood glucose levels were ≥140 mg/dl (PT140) and ≤70 mg/dl (PT70) within 24 h were analyzed.

RESULTS

There was excellent compliance of finger stick blood glucose values with CGM measurements for subjects. Among the 434 subjects, the daily blood glucose varied from 76.9 ± 11.3 to 144.2 ± 23.2 mg/dl. The 24-h MBG, PT140, and PT70 were 104 ± 10 mg/dl, 4.1 ± 5.8%, and 2.4 ± 5.3%, respectively. As for these parameters, no significant differences were found between men and women. The 95th percentile values were adopted as the upper limits of CGM parameters, which revealed 119 mg/dl (6.6 mmol/l) for 24-h MBG, 17.1% for PT140, and 11.7% for PT70.

CONCLUSIONS

We recommend a 24-h MBG value <119 mg/dl, PT140 <17% (4 h), and PT70 <12% (3 h) as normal ranges for the Chinese population.

Introduction:

Type 2 diabetes is increasing in prevalence worldwide and is a leading cause of morbidity and mortality, mainly due to the development of complications. Vildagliptin is an inhibitor of dipeptidyl peptidase 4 (DPP-4), a new class of oral antidiabetic agents.

Aims:

To evaluate the role of vildagliptin in the management of type 2 diabetes.

Evidence review:

Clear evidence shows that vildagliptin improves glycemic control (measured by glycosylated hemoglobin and blood glucose levels) more than placebo in adults with type 2 diabetes, either as monotherapy or in combination with metformin. Vildagliptin is as effective as pioglitazone and rosiglitazone, and slightly less effective than metformin, although better tolerated. Further glycemic control is achieved when adding vildagliptin to metformin, pioglitazone, or glimepride. There is evidence that vildagliptin improves beta-cell function and insulin sensitivity. Vildagliptin does not appear to be associated with weight gain or with a higher risk of hypoglycemia than placebo or other commonly used oral antidiabetic agents. Economic evidence is currently lacking.

Place in therapy:

Vildagliptin improves glycemic control with little if any weight gain or hypoglycemia in adult patients with type 2 diabetes when given alone or in combination with metformin, thiazolidinediones, or sulfonylureas. Since many diabetic patients require combination therapy, the complementary mechanism of action of vildagliptin and other commonly prescribed antidiabetic drugs represents an important new therapeutic option in diabetes management.

Background

In glycemic control, postprandial glycemia may be important to monitor and optimize as it reveals glycemic control quality, and postprandial hyperglycemia partly predicts late diabetic complications. Self-monitoring of blood glucose (SMBG) may be an appropriate technology to use, but recommendations on measurement time are crucial.

Method

We retrospectively analyzed interindividual and intraindividual variations in postprandial glycemic peak time. Continuous glucose monitoring (CGM) and carbohydrate intake were collected in 22 patients with type 1 diabetes mellitus. Meals were identified from carbohydrate intake data. For each meal, peak time was identified as time from meal to CGM zenith within 40–150 min after meal start. Interindividual (one-way Anova) and intraindividual (intraclass correlation coefficient) variation was calculated.

Results

Nineteen patients were included with sufficient meal data quality. Mean peak time was 87 ± 29 min. Mean peak time differed significantly between patients (p = 0.02). Intraclass correlation coefficient was 0.29.

Conclusions

Significant interindividual and intraindividual variations exist in postprandial glycemia peak time, thus hindering simple and general advice regarding postprandial SMBG for detection of maximum values.

Self-monitoring blood glucose (SMBG) devices or glucose meters currently provide a means for patients to manage insulin dosing through intermittent monitoring of blood glucose levels several times a day, but newer continuous glucose monitor devices (CGM) offer the potential of real-time glucose monitoring with less pain and lower cost. Unlike SMBG devices that sample glucose levels in circulating blood, CGM samples from the interstitial fluid. CGM devices generate not only a single level, but through averaging with past glucose results, can predict future trends. While consensus guidelines exist for evaluating the agreement of SMBG devices to other glucose and laboratory methods, no guidelines currently exists for CGM devices. Standards are needed to define the performance of CGM devices, both in terms of spot accuracy and trend information, as well as the level of performance required for clinical management. The Diabetes Technology Society (DTS) has been in close communication on the development of CGM guidelines with the Food and Drug Administration (FDA) and the Clinical and Laboratory Standards Institute (CLSI). Development of standards for CGM devices if adopted by the FDA would set minimum requirements of performance for manufacturers to meet in producing CGM devices and define common terminology for the display and clinical utilization of CGM data. It is expected that CGM performance standards will advance over time as technology improves and consumer demands change. The development of CGM standards will also play an important role in accelerating the development of an artificial pancreas, which relies on CGM technology.

Abstract

Background

Continuous glucose monitoring (CGM) has the potential to provide useful data for behavioral interventions targeting non–insulin-using, sedentary individuals with type 2 diabetes mellitus (T2DM). The aims of this study were to describe CGM in terms of (1) feasibility and acceptability and (2) dietary- and exercise-teaching events.

Methods

Cross-sectional data were analyzed from 27 non–insulin-using adults with T2DM who wore CGM for 72 h as part of a larger study on using CGM for exercise counseling in this population. Feasibility data included accuracy of entering daily self-monitored blood glucose (SMBG) readings and events (e.g., meals, exercise), sensor failures, alarms, optimal accuracy of glucose data, and download failures. Acceptability data included CGM satisfaction and wearing difficulties. Dietary- and exercise-teaching events were identified from CGM and activity monitor data.

Results

CGM graphs showed 141 dietary- and 71 exercise-teaching events. About half the participants (52%) reported difficulty remembering to enter events into CGM monitors, but most (82%) kept an accurate paper log of events. Insufficient SMBG entries resulted in 32 CGM graphs with “use clinical judgment” warnings. Eighty-three percent of missed SMBG entries were from 18 participants 55–77 years old. Missing correlation coefficients resulted from glucose concentrations varying <100 mg/dL. A majority of participants (n ≈ 19) were willing to wear CGM again despite reporting minor discomfort at sensor site and with wearing the monitor.

Conclusions

CGM data provided several teaching opportunities in non–insulin-using adults with T2DM. Overall, CGM was acceptable and feasible. Some identified problems may be eliminated by newer technology.

Background

Glucose variability is one of components of the dysglycemia in diabetes and may play an important role in development of diabetic vascular complications. The objective of this study was to assess the relationship between glycemic variability determined by a continuous glucose monitoring (CGM) system and the presence and severity of coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM).

Methods

In 344 T2DM patients with chest pain, coronary angiography revealed CAD (coronary stenosis ≥ 50% luminal diameter narrowing) in 252 patients and 92 patients without CAD. Gensini score was used to assess the severity of CAD. All participants' CGM parameters and biochemical characteristics were measured at baseline.

Results

Diabetic patients with CAD were older, and more were male and cigarette smokers compared with the controls. Levels of the mean amplitude of glycemic excursions (MAGE) (3.7 ± 1.4 mmol/L vs. 3.2 ± 1.2 mmol/L, p < 0.001), postprandial glucose excursion (PPGE) (3.9 ± 1.6 mmol/L vs. 3.6 ± 1.4 mmol/L, p = 0.036), serum high-sensitive C-reactive protein (hs-CRP) (10.7 ± 12.4 mg/L vs. 5.8 ± 6.7 mg/L, p < 0.001) and creatinine (Cr) (87 ± 23 mmol/L vs. 77 ± 14 mmol/L, p < 0.001) were significantly higher in patients with CAD than in patients without CAD. Gensini score closely correlated with age, MAGE, PPGE, hemoglobin A1c (HbA1c), hs-CRP and total cholesterol (TC). Multivariate analysis indicated that age (p < 0.001), MAGE (p < 0.001), serum levels of HbA1c (p = 0.022) and hs-CRP (p = 0.005) were independent determinants for Gensini score. Logistic regression analysis revealed that MAGE ≥ 3.4 mmol/L was an independent predictor for CAD. The area under the receiver-operating characteristic curve for MAGE (0.618, p = 0.001) was superior to that for HbA1c (0.554, p = 0.129).

Conclusions

The intraday glycemic variability is associated with the presence and severity of CAD in patients with T2DM. Effects of glycemic excursions on vascular complications should not be neglected in diabetes.

Continuous glucose monitoring (CGM) could drive a paradigm shift in diabetes care, but realization of this promise awaits a complementary shift in the way CGM data is used. The most exciting use for CGM is as the input for automated, closed-loop glucose control. Although first generation CGM devices leave much room for improvement, closed-loop control does not have to wait. Algorithms should target blood glucose levels above the normal range for safety in the setting of imperfect CGM measurements. If the mean glucose under closed-loop control is sufficiently close to the chosen target, hemoglobin A1c goals could be met while minimizing risk of hypoglycemia. CGM may also improve the care of intensive care unit patients treated with intensive insulin therapy and the large numbers of diabetic patients in general hospital wards.

The dysglycemia of diabetes includes two components: (1) sustained chronic hyperglycemia that exerts its effects through both excessive protein glycation and activation of oxidative stress and (2) acute glucose fluctuations. Glycemic variability seems to have more deleterious effects than sustained hyperglycemia in the development of diabetic complications as both upward (postprandial glucose increments) and downward (interprandial glucose decrements) changes activate the oxidative stress. For instance, the urinary excretion rate of 8-iso-PGF2α, a reliable marker of oxidative stress, was found to be strongly, positively correlated (r = 0.86, p < .001) with glycemic variability assessed from the mean amplitude of glycemic excursions (MAGE) as estimated by continuous glucose monitoring systems (CGMS). These observations therefore raise the question of whether we have the appropriate tools for assessing glycemic variability in clinical practice. From a statistical point of view, the standard deviation (SD) around the mean glucose value appears as the “gold standard.” By contrast, the MAGE index is probably more appropriate for selecting the major glucose swings that are calculated as the arithmetic mean of differences between consecutive peaks and nadirs, provided that the differences be greater than the SD around the mean values. Furthermore, calculating the MAGE index requires continuous glucose monitoring, which has the advantage to detect all isolated upward and downward acute glucose fluctuations. In conclusion, the increasing use of CGMSs will certainly promote better assessment and management of 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.

Background:

Real-time, personal continuous glucose monitoring (CGM) is a validated technology that can help patients improve glycemic control. Blinded CGM is a promising technology for obtaining retrospective data in clinical research where the quantity and quality of blood glucose information is important. This study was designed to investigate the use of novel procedures to enhance data capture from blinded CGM.

Methods:

Following a 4-week run-in, 46 patients with type 1 diabetes were randomized to one of two prandial insulins for a 12-week treatment period, after which they were crossed over to the alternate treatment for 12 weeks. Continuous glucose monitoring was implemented at the end of run-in (practice only) and during the last 2 weeks of each treatment period. Eighty percent of 288 possible daily glucose values were required for at least three days. Continuous glucose monitoring was extended for an additional week if these criteria were not met, and patients were allowed to insert sensors at home when necessary. Continuous glucose monitoring results were compared to reference eight-point self-monitoring of blood glucose (SMBG).

Results:

Higher than expected sensor failure rate was approximately 25%. During run-in, 12 of 45 attempted profiles failed adequacy criteria. However, treatment periods had only 1 of 82 attempted profiles considered inadequate (6 cases required an additional week of CGM). Using SMBG as reference, 93.7% of 777 CGM values were in Clarke error grid zones A+B.

Conclusions:

With appropriate training, adequate practice, and opportunity to repeat blinded CGM as needed, nearly 100% of attempted profiles can be obtained successfully.

Background

Although incretin therapy is clinically available in patients with type 2 diabetes undergoing hemodialysis, no study has yet examined whether incretin therapy is capable of maintaining glycemic control in this group of patients when switched from insulin therapy. In this study, we examined the efficacy of incretin therapy in patients with insulin-treated type 2 diabetes undergoing hemodialysis.

Methods

Ten type 2 diabetic patients undergoing hemodialysis received daily 0.3 mg liraglutide, 50 mg vildagliptin, and 6.25 mg alogliptin switched from insulin therapy on both the day of hemodialysis and the non-hemodialysis day. Blood glucose level was monitored by continuous glucose monitoring. After blood glucose control by insulin, patients were treated with three types of incretin therapy in a randomized crossover manner, with continuous glucose monitoring performed for each treatment.

Results

During treatment with incretin therapies, severe hyperglycemia and ketosis were not observed in any patients. Maximum blood glucose and mean blood glucose on the day of hemodialysis were significantly lower after treatment with liraglutide compared with treatment with alogliptin (p < 0.05), but not with vildagliptin. The standard deviation value, a marker of glucose fluctuation, on the non-hemodialysis day was significantly lower after treatment with liraglutide compared with treatment with insulin and alogliptin (p < 0.05), but not with vildagliptin. Furthermore, the duration of hyperglycemia was significantly shorter after treatment with liraglutide on both the hemodialysis and non-hemodialysis days compared with treatment with alogliptin (p < 0.05), but not with vildagliptin.

Conclusions

The data presented here suggest that patients with type 2 diabetes undergoing hemodialysis and insulin therapy could be treated with incretin therapy in some cases.

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG). Alarm evaluation in the Clinical and Laboratory Standards Institute (CLSI) guideline for CGM does not specifically address devices that employ both CGM and SMBG. In this report, an alarm evaluation method is proposed for these devices.

Method

The proposed method builds on the CLSI method using data from an in-clinic study of subjects with type 1 diabetes. CGM was used to detect glycemic events, and SMBG was used to determine treatment. To optimize detection of a single glucose level, such as 70 mg/dl, a range of alarm threshold settings was evaluated. The alarm characterization provides a choice of alarm settings that trade off detection and false alarms. Detection of a range of high glucose levels was similarly evaluated.

Results

Using low glucose alarms, detection of 70 mg/dl within 30 minutes increased from 64 to 97% as alarm settings increased from 70 to100 mg/dl, and alarms that did not require treatment (SMBG >85 mg/dl) increased from 18 to 52%. Using high glucose alarms, detection of 180 mg/dl within 30 minutes increased from 87 to 96% as alarm settings decreased from 180 to 165 mg/dl, and alarms that did not require treatment (SMBG <180 mg/dl) increased from 24 to 42%.

Conclusion

The proposed alarm evaluation method provides information for choosing appropriate alarm thresholds and reflects the clinical utility of CGM alarms.

Objective:

This study aimed to compare the glucose-lowering effect and glycemic variability of insulin glargine with those of insulin detemir.

Material and methods:

This was an open-label, single-center, randomized, two-way crossover study in patients with diabetes on basal-bolus insulin therapy, with neutral protamine Hagedorn (NPH) insulin as basal insulin. Patients switched from NPH insulin to a course either of insulin glargine followed by insulin detemir, or insulin detemir followed by insulin glargine, continuing the same dose of the prior bolus of insulin. To evaluate the glucose-lowering effect, daily glycemic profiles were recorded for 72 hours by continuous glucose monitoring (CGM) in an outpatient setting. The mean amplitude of glycemic excursions, standard deviation (SD), and the mean of daily difference (MODD) were used to assess intraday and day-to-day glycemic variability.

Results:

Eleven patients were enrolled and nine completed the study. Mean blood glucose calculated from CGM values was significantly lower with insulin glargine compared with insulin detemir (9.6 ± 2.4 mmol/L versus 10.4 ± 2.8 mmol/L, P = 0.038). The SD was significantly lower with insulin glargine versus insulin detemir (2.5 ± 0.9 mmol/L vs 3.5 ± 1.6 mmol/L, P = 0.011). The MODD value was significantly lower with insulin glargine than with insulin detemir (2.2 ± 1.1 mmol/L vs 3.6 ± 1.7 mmol/L, P = 0.011). There was no significant difference between the two insulin analogs in terms of hypoglycemia.

Conclusion:

This study suggests that insulin glargine leads to more effective and more stable glycemic control than the same dose of insulin detemir.

OBJECTIVE—Postprandial glycemic excursions may contribute to the development of diabetes-related complications. Meals of high and low glycemic index (GI) have distinct effects on postprandial glycemia (PPG). Insulin pump therapy offers the potential to tailor insulin delivery to meal composition; however, optimal bolus types for meals of different glycemic loads have not been defined. We sought to compare the impact of GI combined with varying prandial bolus types on PPG.

RESEARCH DESIGN AND METHODS—An open crossover study examining the effects of four different meal and bolus-type combinations on 3-h PPG (measured by continuous glucose-monitoring system [CGMS]) was conducted. A total of 20 young people aged 8–18 years with type 1 diabetes using insulin-pump therapy participated. Meals had equal macronutrient, energy, and fiber content and differed only in GI (low vs. high). Participants consumed meals of the same GI on consecutive days and were randomized to receive either a standard (100%) or a dual-wave (DW) (50:50% over 2 h) bolus each day. CGMS data from 10 healthy control participants established the target response to each meal.

RESULTS—A DW bolus before low-GI meals decreased PPG area under the curve (AUC) by up to 47% (P = 0.004) and lowered the risk of hypoglycemia for the same premeal glucose (P = 0.005) compared with standard bolus. High-GI meals resulted in significant upward PPG excursions with greater AUC (P = 0.45), regardless of bolus type.

CONCLUSIONS—These data support the use of a DW bolus with low GI meals to optimize PPG in patients with type 1 diabetes using insulin pump therapy.

Background

The objective of this study was to test the hypothesis that maternal blood glucose excursions correlate with deviation from optimized birth weight.

Methods

Patients were recruited for 3-day continuous glucose monitoring (CGM) plus self-blood glucose monitoring followed by routine diabetes screening at 26-28 weeks gestation. Patients and caregivers were blinded to CGM results. The magnitude and duration of blood glucose (BG) excursions were measured as a “glycemia index.” A customized birth weight centile was calculated.

Results

Twenty-three patients consented, 21 completed the study: 5 diabetic and 16 nondiabetic individuals. The duration of CGM was 72 (±7.2) hours, and each patient performed self-BG monitoring ≥3 times per day. All diabetic and 10 nondiabetic patients had several measured BG excursions above 130 mg/dl. A positive correlation was observed between birth weight centile and glycemia index above 130 (p < 0.03); the trend persisted for nondiabetic patients alone (p < 0.05). No significant correlation was noted between birth weight centile and average 3-day CGM values, 3-day fasting BG, average 3-day self-BG monitoring values, or diabetes screening BG value.

Conclusions

The glycemia index has a better correlation with birth weight centile than BG measured by conventional methods in a mixed diabetic and nondiabetic population. Fetal exposure to maternal blood glucose excursions correlates positively with fetal growth, even in nondiabetic patients with apparently normal glucose tolerance.

Background

Current bolus insulin dosing recommendations are based on retrospective studies of patients with Type 1 diabetes in whom the glucose control was not intensely established. Using continuous glucose monitoring (CGM), we prospectively studied these recommendations in patients treated with continuous subcutaneous insulin infusion.

Methods

Thirty subjects were studied over a mean of two weeks of continuous glucose monitoring with near daily insulin adjustments. First a basal glucose goal was achieved of <5% of values <70 mg/dL and <20%>, 170mg/dL. Then bolus dosing factors; Insulin to Carbohydrate Ratio (g of meal carbohydrates/unit of insulin, ICR) and Correction Factor (mg/dL fall in blood glucose/unit of insulin, CF); were established for each meal time to a goal of ± 20% of premeal glucose (ICR) or 80-120 mg/dL (CF) by the fourth post bolus hour.

Results

All treatment goals were achieved in each subject. Modification of formulas from ICR = 450/Total Daily Dose (TDD) to ICR = (217/TDD) + 3 and from CF = 1700/TDD to CF = (1076/TDD) + 12 more closely matched observed results than published formulas. There was no significant difference in each factor with time of day. There was a highly significant relationship between ICR and CF, ICR*4.44 = CF (r = 0.9, p < 0.0005), total basal dose (TBD) and TDD.

Conclusions

Current formulas need to be modified to provide higher insulin bolus doses. The interrelationships between ICR, CF, TBD and TDD suggest that any change in one may require a change in the others.

Objective

This study presents data on the use of continuous glucose monitoring (CGM) in young children with type 1 diabetes mellitus (T1DM). CGM provides moment-to-moment tracking of glucose concentrations and measures of intra- and interday variability which are particularly salient measures in young children with T1DM.

Methods

Thirty-one children (M age = 5.0 years) with T1DM wore the Medtronic Minimed CGM for a mean of 66.8 hours. The CGM was inserted in diabetes clinics and parents were provided brief training.

Results

Few difficulties were experienced and families cited the acceptability of CGM. Participants' CGM data are compared to self-monitoring blood glucose (SMBG) data as well as data from older children with T1DM to illustrate differences in methodology and variability present in this population. CGM data is used to calculate glucose variabilty, which is found to be related to diabetes variables such as history of hypoglycemic seizures.

Conclusions

CGM is an acceptable research tool for obtaining glucose data in young children with T1DM and has been used previously in older children and adults. CGM may be particularly useful in young children who often experience more glucose variability. Data obtained via CGM is richer and more detailed than traditional SMBG data and allows for analyses to link blood glucose with behavior.

Real-time continuous glucose monitoring (RT-CGM) devices provide detailed information on glucose patterns and trends, and alarms that alert the patient to both hyper- and hypoglycemia. This technology can dramatically improve the day-to-day management of patients with diabetes and promises to be a major advance in diabetes care. The safe and effective use of RT-CGM in diabetes management rests on an understanding of several physiological as well as technological issues. This article outlines the key issues that should be addressed in the training curriculum for patients starting on RT-CGM: (1) physiologic lag between interstitial and blood glucose levels and the implications for device calibration, and interpretation and use of data in diabetes management; (2) practical considerations with the use of sensor alarms and caveats in the setting of alarm thresholds; and (3) potential risk for hypoglycemia related to excessive postprandial bolusing by RT-CGM users, and the practical implications for patient training.

Scant data exists on glucose profile variability in healthy individuals. Twenty-nine healthy subjects without diabetes (86% male; mean age, 38 years) were measured by a CGM system and under real-life conditions. The median percentage of time spent on the blood glucose >7.8 mmol/L for 24 hrs was greater than 10% in both NFG and IFG groups. When subjects were divided into either NFG group (i.e., FPG levels of <5.6 mmol/L; n = 22) or IFG group (FPG levels of 5.6–6.9 mmol/L; n = 7), all CGM indicators investigated but GRADE scores, including glucose variability measures, monitoring excursions, hyperglycemia, hypoglycemia, and 24-hour AUC, did not differ significantly between the two groups. GRADE score and its euglycemia% were significantly different between the two groups. Among various CGM indicators, GRADE score may be a sensitive indicator to discriminate glucose profiles between subjects with NFG and those with IFG.