Evaluate the effects of two dipeptidyl peptidase-IV (DPP-4) inhibitors, sitagliptin and vildagliptin, known to have different efficacy on mean amplitude of glycemic excursions (MAGE), on oxidative stress, and on systemic inflammatory markers in patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS
A prospective, randomized, open-label PROBE design (parallel group with a blinded end point) study was performed in 90 patients with type 2 diabetes inadequately controlled by metformin. The study assigned 45 patients to receive sitagliptin (100 mg once daily; sitagliptin group) and 45 patients to receive vildagliptin (50 mg twice daily; vildagliptin group) for 12 weeks. MAGE, evaluated during 48 h of continuous subcutaneous glucose monitoring, allowed an assessment of daily glucose fluctuations at baseline and after 12 weeks in all patients. Assessment of oxidative stress (nitrotyrosine) and systemic levels of inflammatory markers interleukin (IL)-6 and IL-18 was performed at baseline and after 12 weeks in all patients.
HbA1c, fasting and postprandial glucose, MAGE, and inflammatory and oxidative stress markers were similar between the groups at baseline. After 12 weeks, MAGE (P < 0.01) was lower in the vildagliptin group than in the sitagliptin group. After treatment, HbA1c and postprandial glucose evidenced similar changes between the groups (P = NS). Vildagliptin treatment was associated with a stronger decrease in nitrotyrosine (P < 0.01), IL-6 (P < 0.05), and IL-18 (P < 0.05) than sitagliptin treatment. Nitrotyrosine and IL-6 changes significantly correlated with changes in MAGE but not in fasting glucose and HbA1c.
MAGE reduction is associated with reduction of oxidative stress and markers of systemic inflammation in type 2 diabetic patients. These effects were greater in the vildagliptin group than in the sitagliptin group.
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.
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.
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.
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.
We evaluated the effects of two dipeptidyl peptidase-4 (DPP-4) inhibitors, sitagliptin and vildagliptin, on metabolic parameters in patients with type 2 diabetes mellitus.
A total of 170 type 2 diabetes patients treated with sitagliptin or vildagliptin for more than 24 weeks were selected. The patients were separated into two groups, sitagliptin (100 mg once daily, n=93) and vildagliptin (50 mg twice daily, n=77). We compared the effect of each DPP-4 inhibitor on metabolic parameters, including the fasting plasma glucose (FPG), postprandial glucose (PPG), glycated hemoglobin (HbA1c), and glycated albumin (GA) levels, and lipid parameters at baseline and after 24 weeks of treatment.
The HbA1c, FPG, and GA levels were similar between the two groups at baseline, but the sitagliptin group displayed a higher PPG level (P=0.03). After 24 weeks of treatment, all of the glucose-related parameters were significantly decreased in both groups (P=0.001). The levels of total cholesterol and triglycerides were only reduced in the vildagliptin group (P=0.001), although the sitagliptin group received a larger quantity of statins than the vildagliptin group (P=0.002).The mean change in the glucose- and lipid-related parameters after 24 weeks of treatment were not significantly different between the two groups (P=not significant). Neither sitagliptin nor vildagliptin treatment was associated with a reduction in the high sensitive C-reactive protein level (P=0.714).
Vildagliptin and sitagliptin exert a similar effect on metabolic parameters, but vildagliptin exerts a more potent beneficial effect on lipid parameters.
Diabetes mellitus; DPP-4 inhibitor; Glycated serum albumin; Lipids
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.
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.
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.
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.
C-peptide; Continuous glucose monitoring; Glucagon-like peptide 1; Glucose-dependent insulinotropic peptide; Insulin; Miglitol; Sitagliptin; Type 2 diabetes
The combination therapy of dipeptidyl‐peptidase (DPP)‐4 inhibitor and α‐glucosidase inhibitors (α‐GIs) is highly effective in suppressing postprandial hyperglycemia. The aim of the present study was to compare the effects of voglibose and miglitol on glucose fluctuation, when used in combination with DPP‐4 inhibitor by using continuous glucose monitoring (CGM).
Materials and Methods
In a randomized cross‐over study, 16 patients with type 2 diabetes who presented with postprandial hyperglycemia despite alogliptin (25 mg) were treated with voglibose (0.9 mg) or miglitol (150 mg). We measured standard deviation (SD); mean amplitude of glycemic excursions (MAGE), and mean, minimum and maximum glucose measured by CGM during three phases (alogliptin monotherapy, dual therapy of alogliptin and voglibose, and dual therapy of alogliptin and miglitol). The primary outcome measure was SD between α‐GIs.
SD was significantly improved by the addition of either voglibose (18.9 ± 10.1) or miglitol (19.6 ± 8.2) to alogliptin monotherapy (36.2 ± 8.7). MAGE improved significantly with the addition of either voglibose (57.5 ± 26.1, P < 0.01) or miglitol (64.6 ± 26.2, P < 0.01) to alogliptin monotherapy (101.5 ± 21.5). There was no significant difference in glucose fluctuation between α‐GIs. There were no differences between two groups in mean (132.6 ± 21.4 and 138.7 ± 25.4) and maximum (184.3 ± 48.7 and 191.9 ± 38.3). The minimum glucose under alogliptin plus voglibose (94.9 ± 20.2) was significantly lower than that under alogliptin and miglitol (105.3 ± 21.0).
Glucose fluctuation was improved by the addition of voglibose or miglitol to alogliptin. Glucose fluctuations and postprandial hyperglycemia were similar between α‐GIs. This trial was registered with the University Hospital Medical Information Network (no. UMIN R000010028).
Alpha‐glucosidase inhibitor; Continuous glucose monitoring; Dipeptidyl‐peptidase‐4 inhibitor
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.
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.
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.
Fluctuations in blood glucose levels play a significant role in vascular endothelial dysfunction in type 2 diabetes.
Atherosclerosis; Type 2 diabetes mellitus; Endothelium; Glucose; Continuous glucose monitoring (CGM)
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.
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).
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.
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.
miglitol; sitagliptin; glucagon-like peptide-1 (GLP-1); glucose-dependent insulinotropic peptide (GIP); continuous glucose monitoring (CGM)
Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy.
Subjects and Methods
An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t0) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG0–5h) and variability as the individual coefficient of variation (CV) of AUC-PG0–5h. The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t0 (PGt0), and insulin dose to AUC-PG0–5h and its CV was also investigated.
AUC-PG0–5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5±127.5 vs. 689.2±180.7 mg/dL·h) or 100-g (752.1±237.7 vs. 760.0±263.2 mg/dL·h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PGt0 being the best predictor of AUC-PG0–5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7±15.3% vs. 10.1±12.5%) but independent of the factors studied.
A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.
Continuous glucose monitoring (CGM) gives a unique insight into magnitude and duration of daily glucose fluctuations. Limited data are available on glucose variability (GV) in pregnancy. We aimed to assess GV in healthy pregnant women and cases of type 1 diabetes mellitus or gestational diabetes (GDM) and its possible association with HbA1c. CGM was performed in 50 pregnant women (20 type 1, 20 GDM, and 10 healthy controls) in all three trimesters of pregnancy. We calculated mean amplitude of glycemic excursions (MAGE), standard deviation (SD), interquartile range (IQR), and continuous overlapping net glycemic action (CONGA), as parameters of GV. The high blood glycemic index (HBGI) and low blood glycemic index (LBGI) were also measured as indicators of hyperhypoglycemic risk. Women with type 1 diabetes showed higher GV, with a 2-fold higher risk of hyperglycemic spikes during the day, than healthy pregnant women or GDM ones. GDM women had only slightly higher GV parameters than healthy controls. HbA1c did not correlate with GV indicators in type 1 diabetes or GDM pregnancies. We provided new evidence of the importance of certain GV indicators in pregnant women with GDM or type 1 diabetes and recommended the use of CGM specifically in these populations.
To evaluate the effects of aerobic (AER) or aerobic plus resistance exercise (COMB) sessions on glucose levels and glucose variability in patients with type 2 diabetes. Additionally, we assessed conventional and non-conventional methods to analyze glucose variability derived from multiple measurements performed with continuous glucose monitoring system (CGMS).
Fourteen patients with type 2 diabetes (56±2 years) wore a CGMS during 3 days. Participants randomly performed AER and COMB sessions, both in the morning (24 h after CGMS placement), and at least 7 days apart. Glucose variability was evaluated by glucose standard deviation, glucose variance, mean amplitude of glycemic excursions (MAGE), and glucose coefficient of variation (conventional methods) as well as by spectral and symbolic analysis (non-conventional methods).
Baseline fasting glycemia was 139±05 mg/dL and HbA1c 7.9±0.7%. Glucose levels decreased immediately after AER and COMB protocols by ∼16%, which was sustained for approximately 3 hours. Comparing the two exercise modalities, responses over a 24-h period after the sessions were similar for glucose levels, glucose variance and glucose coefficient of variation. In the symbolic analysis, increases in 0 V pattern (COMB, 67.0±7.1 vs. 76.0±6.3, P = 0.003) and decreases in 1 V pattern (COMB, 29.1±5.3 vs. 21.5±5.1, P = 0.004) were observed only after the COMB session.
Both AER and COMB exercise modalities reduce glucose levels similarly for a short period of time. The use of non-conventional analysis indicates reduction of glucose variability after a single session of combined exercises.
Aerobic training, aerobic-resistance training and glucose profile (CGMS) in type 2 diabetes (CGMS exercise). ClinicalTrials.gov ID: NCT00887094.
This study aimed at evaluating and comparing the performance of a new generation of continuous glucose monitoring (CGM) system versus other CGM systems, under daily lifelike conditions.
A total of 10 subjects (7 female) were enrolled in this study. Each subject wore two Dexcom G4™ CGM systems in parallel for the sensor lifetime specified by the manufacturer (7 days) to allow assessment of sensor-to-sensor precision. Capillary blood glucose (BG) measurements were performed at least once per hour during daytime and once at night. Glucose excursions were induced on two occasions. Performance was assessed by calculating the mean absolute relative difference (MARD) between CGM readings and paired capillary BG readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings.
Overall aggregate MARD was 11.0% (n = 2392). Aggregate MARD for BG <70 mg/dl was 13.7%; for BG between 70 and 180 mg/dl, MARD was 11.4%; and for BG >180 mg/dl, MARD was 8.5%. Aggregate PARD was 7.3%, improving from 11.6% on day 1 to 5.2% on day 7.
The Dexcom G4 CGM system showed good overall MARD compared with results reported for other commercially available CGM systems. In the hypoglycemic range, where CGM performance is often reported to be low, the Dexcom G4 CGM system achieved better MARD than that reported for other CGM systems in the hypoglycemic range. In the hyperglycemic range, the MARD was comparable to that reported for other CGM systems, whereas during induced glucose excursions, the MARD was similar or slightly worse than that reported for other CGM systems. Overall PARD was 7.3%, improving markedly with sensor life time.
accuracy; continuous glucose monitoring; diabetes management; hypoglycemia
Due to industrialization and sedentary life, incidence of type 2 diabetes (DM2) is increasing seriously. Repaglinide is a glucose reducing agent that predominantly reduces post-prandial glucose. Continuous glucose monitoring system (CGMS) monitors blood glucose excursions over a 3-day period. CGMS can be used as a therapeutic and diagnostic instrument in diabetics. There are not enough studies about using CGMS in DM2. The aim of this study was to determine the blood glucose excursions in patients with new onset of DM2. 10 patients with new onset of DM2 were entered to this study. As the first therapeutic management, patients received diabetic diet and moderate exercise for 3-weeks, if they did not achieve blood glucose goal (Fasting blood glucoser (FBG) <120mg/dl, 2-hour postprandial blood glucose (2hpp) <180mg/dl), were considered to undergo 3-days CGMS at baseline and after 4-weeks on Repaglinide (0.5mg three times before meals). Mean excursions of blood glucose were not different at the onset and at the end of treatment (6±4.05 VS 7.6±5.2 episodes, P=0.49). There were also no significant differences between mean duration of hypoglycemic episodes (zero VS 5.1±14.1 hours, P =0.28) and hyperglycemic episodes before and after therapy (7.6±5.2 VS 5.7±4.1, P=0.42), but mean hyperglycemia duration was significantly reduced at the end of therapy (21±26.17 VS 57.7±35.3, P=0.001). Patients experienced a mean of 0.3±0.67 episodes of hypoglycemia after therapy showed no significant difference before it (P =0.19). Mean FBG (with CGMS) was significantly lower after therapy than before it (142.9±54.31 VS 222.9±82.6, P <0.001).
This study showed the usefulness of CGMS not only as a diagnostic but also as an educational and therapeutic tool that in combination with Repaglinide (with the lowest effective dose and duration) can significantly reduce FBG and glycemic excursions in DM2 patients and hypoglycemic events are low.
Repaglinide; Glycemic excursions; Type 2 diabetes; Continuous glucose monitoring system.
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.
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.
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.
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.
blood glucose measurement; continuous glucose monitoring; glycemic control; glycemic variability; machine learning; naive Bayes classifiers
The performance of a continuous glucose monitoring (CGM) system in the early stage of development was assessed in an inpatient setting that simulates daily life conditions of people with diabetes. Performance was evaluated at low glycemic, euglycemic, and high glycemic ranges as well as during phases with rapid glucose excursions.
Each of the 30 participants with type 1 diabetes (15 female, age 47 ± 12 years, hemoglobin A1c 7.7% ± 1.3%) wore two sensors of the prototype system in parallel for 7 days. Capillary blood samples were measured at least 16 times per day (at least 15 times per daytime and at least once per night). On two subsequent study days, glucose excursions were induced. For performance evaluation, the mean absolute relative difference (MARD) between CGM readings and paired capillary blood glucose readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings were calculated.
Overall aggregated MARD was 9.2% and overall aggregated PARD was 7.5%. During induced glucose excursions, MARD was 10.9% and PARD was 7.8%. Lowest MARD (8.5%) and lowest PARD (6.4%) were observed in the high glycemic range (euglycemic range, MARD 9.1% and PARD 7.4%; low glycemic range, MARD 12.3% and PARD 12.4%).
The performance of this prototype CGM system was, particularly in the hypoglycemic range and during phases with rapid glucose fluctuations, better than performance data reported for other commercially available systems. In addition, performance of this prototype sensor was noticeably constant over the whole study period. This prototype system is not yet approved, and performance of this CGM system needs to be further assessed in clinical studies.
accuracy; continuous glucose monitoring systems; hypoglycemia; POCT05-A; precision
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.
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.
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.
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.
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.
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.
Glycemic variability is increasingly recognized as an important issue in diabetes management. However, the lack of normative values may limit its applicability in the clinical setting.
The objective of this study was to establish preliminary normal reference ranges for glycemic variability by analyzing continuous glucose monitoring (CGM) data obtained from healthy Chinese adults.
Three-day CGM data were obtained from 434 healthy adults at 10 academic hospitals throughout China. Glycemic variability was calculated as the 24-hour mean amplitude of glycemic excursions (MAGE) and standard deviations (SD) of blood glucose readings.
434 healthy subjects (male 213, female 221; age 43±14, 20–69 years old; BMI 21.8±1.7 kg/m2, 18.5–24.9 kg/m2) completed the study. MAGE and SD values for the 434 healthy subjects were 1.73 (1.08) mmol/L and 0.75 (0.42) mmol/L [median (interquartile range)], respectively. In both men and women, MAGE and SD tended to increase with age. Neither MAGE nor SD showed a significant difference between men and women. Values for both parameters were non-normally distributed within the population. The 95th percentiles of MAGE and SD were 3.86 and 1.40 mmol/L, respectively. These values were adopted as the upper limits of normal.
MAGE <3.9 mmol/L and SD <1.4 mmol/L are recommended as the normal reference ranges for glycemic variability in Chinese adults. The values established in this study may facilitate the adoption of glycemic variability as a metric of overall glycemic control in diabetes.
glycemic variability; continuous glucose monitoring; reference ranges
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.
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.
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.
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.
blood glucose self-monitoring; continuous glucose sensors; hyperglycemia; postprandial period; type 1 diabetes mellitus
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.
FDA; glucose; guidelines; monitor; standards
Accurate and timely glucose monitoring is essential in intensive care units. Real-time continuous glucose monitoring system (CGMS) has been advocated for many years to improve glycemic management in critically ill patients. In order to determine the effect of calibration time on the accuracy of CGMS, real-time subcutaneous CGMS was used in 18 critically ill patients. CGMS sensor was calibrated with blood glucose measurements by blood gas/glucose analyzer every 12 hours. Venous blood was sampled every 2 to 4 hours, and glucose concentration was measured by standard central laboratory device (CLD) and by blood gas/glucose analyzer. With CLD measurement as reference, relative absolute difference (mean±SD) in CGMS and blood gas/glucose analyzer were 14.4%±12.2% and 6.5%±6.2%, respectively. The percentage of matched points in Clarke error grid zone A was 74.8% in CGMS, and 98.4% in blood gas/glucose analyzer. The relative absolute difference of CGMS obtained within 6 hours after sensor calibration (8.8%±7.2%) was significantly less than that between 6 to 12 hours after calibration (20.1%±13.5%, p<0.0001). The percentage of matched points in Clarke error grid zone A was also significantly higher in data sets within 6 hours after calibration (92.4% versus 57.1%, p<0.0001). In conclusion, real-time subcutaneous CGMS is accurate in glucose monitoring in critically ill patients. CGMS sensor should be calibrated less than 6 hours, no matter what time interval recommended by manufacturer.
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.
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.
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.
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.
We investigated whether continuous glucose monitoring (CGM) levels can accurately assess glycemic control while directing closed-loop insulin delivery.
Subjects and Methods
Data were analyzed retrospectively from 33 subjects with type 1 diabetes who underwent closed-loop and conventional pump therapy on two separate nights. Glycemic control was evaluated by reference plasma glucose and contrasted against three methods based on Navigator (Abbott Diabetes Care, Alameda, CA) CGM levels.
Glucose mean and variability were estimated by unmodified CGM levels with acceptable clinical accuracy. Time when glucose was in target range was overestimated by CGM during closed-loop nights (CGM vs. plasma glucose median [interquartile range], 86% [65–97%] vs. 75% [59–91%]; P=0.04) but not during conventional pump therapy (57% [32–72%] vs. 51% [29–68%]; P=0.82) providing comparable treatment effect (mean [SD], 28% [29%] vs. 23% [21%]; P=0.11). Using the CGM measurement error of 15% derived from plasma glucose–CGM pairs (n=4,254), stochastic interpretation of CGM gave unbiased estimate of time in target during both closed-loop (79% [62–86%] vs. 75% [59–91%]; P=0.24) and conventional pump therapy (54% [33–66%] vs. 51% [29–68%]; P=0.44). Treatment effect (23% [24%] vs. 23% [21%]; P=0.96) and time below target were accurately estimated by stochastic CGM. Recalibrating CGM using reference plasma glucose values taken at the start and end of overnight closed-loop was not superior to stochastic CGM.
CGM is acceptable to estimate glucose mean and variability, but without adjustment it may overestimate benefit of closed-loop. Stochastic CGM provided unbiased estimate of time when glucose is in target and below target and may be acceptable for assessment of closed-loop in the outpatient setting.
Glycemic variability as a marker of endogenous and exogenous factors, and glucose complexity as a marker of endogenous glucose regulation are independent predictors of mortality in critically ill patients. We evaluated the impact of real time continuous glucose monitoring (CGM) on glycemic variability in critically ill patients on intensive insulin therapy (IIT), and investigated glucose complexity - calculated using detrended fluctuation analysis (DFA) - in ICU survivors and non-survivors.
Retrospective analysis were conducted of two prospective, randomized, controlled trials in which 174 critically ill patients either received IIT according to a real-time CGM system (n = 63) or according to an algorithm (n = 111) guided by selective arterial blood glucose measurements with simultaneously blinded CGM for 72 hours. Standard deviation, glucose lability index and mean daily delta glucose as markers of glycemic variability, as well as glucose complexity and mean glucose were calculated.
Glycemic variability measures were comparable between the real time CGM group (n = 63) and the controls (n = 111). Glucose complexity was significantly lower (higher DFA) in ICU non-survivors (n = 36) compared to survivors (n = 138) (DFA: 1.61 (1.46 to 1.68) versus 1.52 (1.44 to 1.58); P = 0.003). Diabetes mellitus was significantly associated with a loss of complexity (diabetic (n = 33) versus non-diabetic patients (n = 141) (DFA: 1.58 (1.48 to 1.65) versus 1.53 (1.44 to 1.59); P = 0.01).
IIT guided by real time CGM did not result in significantly reduced glycemic variability. Loss of glucose complexity was significantly associated with mortality and with the presence of diabetes mellitus.
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).
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.
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).
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.
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.
To evaluate the role of vildagliptin in the management of type 2 diabetes.
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.
dipeptidyl peptidase IV (dipeptidyl peptidase 4) inhibition; glycemic control; LAF 237; type 2 diabetes; vildagliptin